Jim also explains why he does not expect a recession or the end of the long-term bull market, but believes investors may need to reduce their concentration in AI and technology stocks as leadership quietly shifts toward the broader market.

Jim Paulsen joins us to explain why weakening economic momentum, tightening financial conditions and extreme AI enthusiasm could set the stage for a 10% to 20% stock market correction. We discuss labor market weakness, the growing divide between technology and the broader economy, fading tech leadership, market complacency, bond yields and the demographic forces that could keep US growth and inflation lower for years.

Jim also explains why he does not expect a recession or the end of the long-term bull market, but believes investors may need to reduce their concentration in AI and technology stocks as leadership quietly shifts toward the broader market.

Main topics covered

• Why Jim expects a 10% to 20% market correction without a recession
• What zero job creation, declining full-time employment and rising unemployment reveal about the labor market
• Why housing starts, real disposable income and GDP forecasts point to weaker economic growth
• How higher Treasury yields, oil prices, a stronger dollar and slower money growth have tightened financial conditions
• Why the economic damage from an oil shock often appears after oil prices peak
• The widening earnings and economic divide between AI investment and the rest of the economy
• What investor positioning, shrinking liquidity and low defensive exposure reveal about market complacency
• Why strong earnings momentum does not eliminate the risk of a market decline
• Evidence that technology, communication services and the Magnificent Seven are losing market leadership
• Why old economy sectors may outperform technology during the next stage of the bull market
• How weak labor force growth could push economic growth, inflation and Treasury yields lower
• Why demographics, immigration and productivity will shape the long-term US economic outlook

Timestamps

00:00 Why Jim Paulsen expects a 10% to 20% market correction
04:32 The labor market weakness investors may be overlooking
08:42 Housing, disposable income and GDP growth are deteriorating
13:03 How tighter financial conditions could slow the economy
17:09 Why oil shocks and the yield curve threaten earnings growth
21:41 Investor complacency and the disconnect between markets and Main Street
25:54 How today’s AI boom differs from the dot-com bubble
30:20 Defensive stocks reach an extreme last seen near major market tops
34:36 Record earnings expectations, momentum and extreme valuations
39:00 Technology, communication services and the Magnificent Seven lose momentum
43:00 The hidden market rotation from new era to old era stocks
47:01 Why Jim expects Treasury yields to fall below 3%
51:43 The demographic forces suppressing growth and inflation
55:45 America’s long-term growth challenge and what could change it

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Jim: You bet. You bet, Justin. It’s good to be here. I always appreciate you guys every month, chatting a little bit.

Justin: It’s early July, and it’s pretty hot in most parts of the country weather-wise, maybe not so hot from a market and economic activity standpoint. I think that’s what we’re gonna work through with you today. We have a number of slides you’ve given up to us graciously that you have put out on Paulsen Perspectives, which is your Substack. We encourage people to go check it out.

And I think today, you know, you’re kind of building on where we left off last month and making the case a little bit more strongly that things kind of don’t look so great under the surface. And so we’re kind of gonna chunk it out in sections here. I think there’s five or six, maybe even more sections that, you know, you’ve isolated that you’re paying a lot of attention to. So to start, I’ll sort of let you paint maybe a little bit broad picture, and then we’ll get into some of the charts here.

Jim: Okay. That sounds good. Yeah. Yeah, I’ve been expecting for some time that we’d have a leadership shift that goes on from kind of new era stocks to other areas of the market, which I’ve been referring to as broad marketplace, and that has occurred here.

But I’m now just, I think since the AI surge from the March 30th low to the June 2nd top has kind of put me over the edge a little bit in terms of I think that thing got kind of frothy. And ever since then it’s been a little, you know, sucking up all the financial market information in the room, and it’s all about AI.

And it feels a little more like dot-com to me, and there’s several things that have creeped up on indicators I watch. So I kind of, you know, a week into June or so, I’ve kind of gone on record as saying I think there’s gonna be a correction here in the next several months. I think we might end up the year about where we have been at its highs. Or maybe we won’t make that up till early next year.

But I kind of, my guess is, is that we’re gonna have a correction somewhere between 10 to 20% that’ll feel ugly and scare people. And I don’t think it’s gonna be a bear market, primarily ‘cause I don’t think we’re gonna have recession. But I do think we’re gonna be worried about growth again, and we’re gonna be worried about a bear market.

And the composition of that, if I had to guess, I’d think, you know, the tech stocks or new era stocks, they go up, they go down more than 20%. But a lot of the rest of it doesn’t go down near as much. Maybe it declines 10 on average, and we have something like a 15-percenter or something. I don’t know. You’re talking about a 50/50 weight between those two parts now really.

You know, it’s interesting, last night I looked at S&P 500 technology sector. It’s already off 10% from its June high. You wouldn’t know that — now I’m not talking about the market is in, but the S&P tech is. And you wouldn’t know that just ‘cause of the excitement of AI. You know, it’s pulled back off that ferocious high, and it’s already at a 10% decline.

So that’s kinda where I’m at, and I’ve got some reasons for that, Justin, of why I’ve kinda come to that conclusion. Again, I mean, I still think we’re in a bull that probably extends into 2030 or a little beyond. And so part of me even questions why I’m messing around with this because who cares if it comes back? But it’s big enough if it goes over 10%, in my mind, I think it makes a little sense to move a little at the margin.

I’m not suggesting anyone sell all their new era stocks. I’m not suggesting that at all. I’m not gonna... No one else could time anything that great to do that. I am thinking, though, they may even wanna go to an underweight to its market position on that and to move to an overweight in some of the broader marketplace, which we could talk a little bit about a little bit later. So that’s kinda where I’m heading.

Justin: Yeah. Okay, okay. Yeah, I think that 10 to 20% pullback, if that’s where we land, you know, certainly starts to get people’s attention as they open up their, you know, monthly statements and see, “Wait a minute, you know, my account’s down that much.” But so let’s step into... I think the first general area was, you know, the economic momentum starting to show some signs of slowing down, so let’s talk about that.

Jim: Yeah. I think on average, you know, we came into this year, there was a lot more concern late last year and really early in this year about the economy, and it’s slowing a lot. Job growth had slowed to practically zero, and there was a lot more concern. Then there was some pickup in the job market, a little pickup in some of the economic reports, and people have really calmed down about that.

I don’t think the slowdown’s over, and I think it’s gonna re-intensify again. There’s still a lot of stuff that really doesn’t look right for people being as calm as we are about the economy, and I thought I’d just run through some of those here in these first charts.

This first chart is just the annual growth in household employment. And actually it’s the average of household and non-farm. So if you put them together, they average out to zero in the last year. And this is even after, you know, the last couple of months reports, where people felt better about the job numbers. I think the payroll numbers are up three-tenths in the last year. The household numbers are down three-tenths in the last year. So they average out at a big goose egg.

Nowhere, nowhere in our history that I’ve been in the business, going back to the early ‘80s, was zero job creation over any 12-month period considered something that was okay and people could feel confident about. And not only that, but confident enough that most people thought the Fed is gonna raise rates in the face of that. And yet that’s where we are. So it doesn’t take a lot. It could maybe, you know, maybe jobs take off. I don’t think so.

If I just look at a couple more, like the next one I’m looking at, looking at the unemployment rate in blue, and the red line is just the part-time employment level. And, you know, they both kinda tell the same story, but they’re both telling a story. And that is they’re doing something you just don’t do in an economic expansion in the last couple of years. You don’t have part-time employment rising, and you don’t have the unemployment rate rising when you’re still in an official economic expansion. It’s just not — it just isn’t, never really been looked at that way at all. Those are things that only occur in the middle of recessions.

If you go to the next one, another part of the jobs market that’s really bad is labor force growth. Now, that’s the blue line in this chart. The annual growth rate in that is negative over the last year on this chart. And I’ve also put on there full-time employment. So full-time jobs are declining in this expansion. And fewer and fewer people are working in the labor force. In the last several months, or just in the last year, you can see that the decline in the labor force has been pretty spectacular, something that generally doesn’t ever happen unless you’re in recession.

But if you didn’t have that decline, imagine where this unemployment rate would be right now. We’ve had zero job growth. If labor force would’ve grown its normal half to 1%, my gosh, the unemployment rate would be not, not in the, you know, the threes, it’d be close to five just over the last year. So to me, I don’t know how sustainable this is, is what I’m getting at. It’s got me more and more frightened that some of that comes home to roost.

If we broaden out a little bit from the jobs market — I guess I got one more of the jobs market on the next chart. Just more recently, you know, we had a pretty lousy jobs report this month in terms of the numbers that came out. It came out much more disappointing than what people were looking for. We had downward revisions for the previous months. Well, one of the reasons is because, kind of shows up in this chart, the red line there is unemployment claims on an inverted scale. They’ve been climbing again. You can see they did go down, that red line on an inverted scale. Claims got improved, but now they’ve been coming back up.

And then if I overlay that with the blue line, which is the monthly ADP report on employment, it too has rolled over and now it’s been coming down. So yeah, we had a little burst there, you know, at the start of this year in job data, but that’s kind of gone away again. And I think sort of fits with a generalized weakness overall.

If I look at the next chart here real quick, this is to broaden out to some other indicators. You know, this is housing starts, and it’s just lousy. And not only is it lousy, it’s just gotten lousier here in the last couple months. It’s fallen quite dramatically. You know, it’s about as bad as it was in the worst of the housing crisis in 2009. There’s just no activity going on there. Does that jive with what we see with, you know, explosive earnings in the economy? No. Does it jive with what I just showed you about how the job market looks? Absolutely, it does.

If you go to the next one, this one is not widely publicized, but it’s real — it’s annual growth of real disposable personal income excluding subsidies or transfer payments made to individuals. It’s deeply into negative territory, declining over the last year. Again, you just don’t see this historically outside of a recession, when you get into this. So there’s no jobs, there’s no real income being generated internally by private market players that aren’t being propped up by subsidies. And I think that’s, to me, that’s gonna come through not only in weak housing spending, but in ultimately weak retail spending and commercial consumption as well.

If you go to the next chart, this came from the Atlanta Federal Reserve GDPNow forecast. This just came out, I think, last night or the night before. It is now down for this quarter to one and a quarter percent. I know that the PCE now forecast for the second quarter just fell below 2% this morning. So we’re not having, you know, robust growth going on here. You can see, earlier, for a little period of time all these numbers looked better, but they’re all kind of rolled over and kind of rolled down on themselves again.

One more comment at the end. I wanna make the point that momentum in the economy, I think, has been weak and is still kinda weak. Yeah, it had a little period to pick up. The Fed felt a little better about things, enough to quit easing and whatnot, but I don’t think a lot has changed there. And I think as I’ll get to the next section here, I think a lot of this is gonna get even worse.

Jack: Yeah, and as we get into the next section, you made the point here that despite the fact that growth is not great, policy has been somewhat contractionary, right?

Jim: Yeah. And I just got a number of different things that I’m looking at on that regard. This one is just a bar chart kinda showing what has happened here since early this year. All of them have some different dates on them, but they’re all tied to really since early this year. A lot of them really just since the war turned out, or the hostilities with Iran.

If you think about it, what the hostilities did was it brought back tightening of economic policies again. It raised yields. It slowed the rate of growth in real money supply. It raised inflation, lowering the real wages and real growth in monetary aggregates over that period of time. We’ve had a reinversion or flattening of the yield curve over this period of time. And believe it or not, while we’re spending on the war, out of the federal government, the net deficit spending over the last 12 months to GDP ratio has actually contracted by about two percentage points, from about 7% to 5% over the last maybe 15 months.

So we have a contraction of fiscal policy. We’ve had the 10-year yield go up, I don’t know, about 70 basis points from its lows below 4%. It’s almost 4.60 this morning. We’ve had the real money supply that was just starting to recover, got almost to 3%. It has come down again to about a quarter of percent year on year, almost going negative again. We had a pretty good flattening in the 10s to 2s yield curve over this period of time. The US dollar, which I’ve not mentioned, is up 5 to 6% in nominal terms, and a little more than that in real terms, since this war broke out.

And my thought about this is a lot of the reason the economy picked up a little bit as we entered this year was these things went the other way. Bond yields came down, money growth was improving, the dollar came down, and so we’ve sort of reversed all that, and with a lag, I think that’s gonna hit a lot of economic information and take sort of the thrust out of the economy that everyone’s sort of betting on.

If you go to the next chart, this is just an attempt to show that these policies are gonna matter. And what I have in here on the red line is a quasi economic policy measure. And I can’t see it real well here with the small type, but it basically made... It’s made up of 50% weighting on the US Treasury yield, a 30% weighting on the real US dollar index, and a 20% weighting on WTI crude oil prices. And my point is, is if I weight those accordingly, when rates go up, when the dollar goes up, when oil goes up, those are all negative contractionary forces, if you will, over the span. So what we’ve done with this war is we retightened policy.

Now, how does it affect the economy? The blue line there is the Citigroup US Economic Surprise Index. Okay? And the surprise index, when it rises, it’s saying that economic reports are coming out better than expected by most people. When does that happen? It happens when the economy’s picking up. It’s starting to accelerate, and it... When it does that, most economic reports are better than people thought, and they have to revise them up ‘cause the things are getting better. But it does the same thing on the downside. When that index falls, it says that reports start coming in less than expected. And to me, that’s a measure of momentum in the economy, is what surprise indices are.

Now, what I did was take that policy variable, the red line, I pushed it forward by three months, and I turned it over, inverted it, and this is the result you see. Three months later, when that policy variable improves, that is, it rises, three months later, the economy picks up. And when it gets worse, when it tightens, three months later, the economy slows down. And you can see what we’re looking for in the coming three months, overall, is a pretty good slowdown in economic momentum. I think some of that’s starting to show up, but more of it will as we move on.

Other things — I’ve referenced this a little bit in the past, but there’s this sense that, you know, we’re past the peak in oil prices. Now we’ll see. We’re back at, at the time of this talk, we’re back firing missiles again, but I think we have seen the peak in oil. And there’s this sense that, you know, we brought this thing to some sort of resolution, and if we get beyond it, we’re gonna be okay. But the reality is, when I look back at every major oil spike that we’ve had since 1970, the damage for the economy and the stock market generally doesn’t show up until oil peaks, and then the damage starts to show up.

And I just show this on the S&P 500. This is a chart of the S&P 500, and I put the dates on there of every major crude oil price spike since 1970, major ones. And in every case, you had additional damage in the stock market. Either it went nowhere or, more frequently, it went down a fair amount once oil prices peaked. Oftentimes, while they were rising, market did fine, and so does the economy. It’s once they reach a peak with a lag time that they start to bite.

And you think about it, when oil first goes up, it goes up fast and dramatically. Takes a while before prices at the pump goes up, then it takes a while before people start to do anything about it and change their behaviors. Takes a while for that energy price spike to go into other prices, other farm prices for other things, and those prices go up, and those behaviors change. And so oftentimes the greatest damage from oil price spikes doesn’t occur when everyone’s focused on them. It occurs after they forget about it, and I think that’s what’s coming now down the pipe.

Another thing I’m concerned about: this chart looks at the annual growth in forward 12-month earnings for the S&P 500, the blue line. You can see there’s strong momentum here going on. Or actually is this forward... Yeah, estimated forward 12-month earnings. But what I’ve laid on top of there, and it’s leading by 12 months, is the 10s to 2s yield curve. And you can see that there’s some impetus for why we’re having a little better economy, a little better earnings, because the yield curve was deeply inverted not that long ago, and then it has steepened back into positive territory here recently.

But what it’s done now is it’s rolled over, really since the end of last year. And I think that’s gonna, with a lag of about 12 months or so, it’s gonna start to impact earnings, which we’re getting pretty close to that window of 12 months later. And I do think that policy tight— This is just one example, just the yield curve. I could show you similar relationships like this with the real money supply, with bond yields as a whole, with fiscal juice, with the dollar. That is, they all have that impact. And I just showed you that oil prices tend to have a lagged impact negative as well. And people are really liking earnings momentum, but I’m saying it and the economy as a whole could be starting to weaken again as we go through the balance of this year.

Jack: This next set of charts gets at something you’ve talked about a lot in the podcast, which is this idea that the bifurcation between old era and new era has gotten very extreme.

Jim: Right. Right. These are just the things. You know, I’m concerned about weak economy, I’m concerned about tightening policy, and I’m also concerned about the bifurcation that’s going on in this. And we’ve talked about this in the past, so I won’t spend a lot of time on it. But this is just taking the S&P 500 and taking the two sectors which make up new era stocks — that’s information technology sector and the communication services sector — and look at their cap-weighted forward 12-month earnings here. This is estimated 12-month forward earnings.

And you can see that the spike that’s occurred here of late, a lot of that recently has been AI, but it’s just been explosive. But the rest of the S&P, the other nine sectors, their market cap forward earnings is the red line. Are they up? Yeah, they’re up a little bit, but boy, lately it’s been pretty flat out there. I mean, they’re not going down, let’s say that, but they’re not exactly going up anywhere close to what people focus on when they look at the S&P earnings going through the roof. It’s mostly all the blue line.

And my point about this: how long can this big of a fundamental divergence or bifurcation sustain itself out there in the economy? How can one part be on fire and most of the rest of it is barely moving? I mean, earnings on that red line are not much higher than they were, what, two years ago? Three years ago? It’s a bifurcation where we’re leaving a lot of damage in the wake of just this one small segment. I just... Maybe it’s sustainable, but I suspect it.

Another way to look at this bifurcation is not only through earnings, if you go to the next slide, but just in real economy itself. I divided up real GDP into two components, that comprised by real new era spending, which I just defined as the real investment spending on information processing equipment and on intellectual property products. That makes up 13% of real GDP. That’s the red line. It has risen by an 8% annualized pace almost in the last six quarters. The other 87% of real GDP, the old era parts, has risen by a little over 1% in the last six quarters. Again, how long can we do that without something breaking? Which then feeds into, you know, the stock market as well. So I’m a little worried about that aspect as well.

Jack: So these next charts get into this idea that we’ve certainly been seeing some optimism in the stock market, and I think you would argue even some degree of complacency, right?

Jim: Yeah. I don’t know whether you call this optimism or complacency. I’m more confident that we’re complacent. It’s debatable how optimistic we are. There’s certainly some sentiment reads that are still sort of midland or, some, you know... Recently I saw where the CNN Fear and Greed was, like, deep fear. So some of those standard measures would not suggest there’s a sheer optimism.

But I do think there’s, at a minimum, complacency. We’ve just gotten so used to this thing. Just keeps going up and, you know, tech stocks always work out and buy the dips. And those mentalities are all real strong, and then we’re really strong now because earnings momentum’s so good. And what I’m starting to see though, if I get away from some of those sentiment reads and just look at behavioral sentiment indicators, behavioral sentiment. And this is one right here.

The blue line here is the S&P 500 index, and the red line is — it is still a sentiment read, but basically what it measures is the exposure that individual investors have to the stock market. This looks at their exposure to stocks less their exposure of cash. And that ratio of how much in stocks less cash is at one of its highest ratios here over this chart. It’s really only been higher at the top of the dot-com, and that was only by a little bit. We’re not at a real conservative asset allocation, you know, among players. This looks more like optimistic players than not.

Another thing I’m looking at is the Morning Consult survey. Every morning a group of investors, consumers are interviewed and, you know, how they’re feeling about things. And a lot of this is tied directly to the stock market. And what I’m doing is comparing that Morning Consult survey, which is the red line, to the S&P 500 there, which is the blue line, just since this bull market began. And you can see, up until really this AI surge in March 30th, at least directionally, these things moved at the hip right together, up and down pretty much throughout this bull market.

But since March 30th, stock market went super higher, Morning Consult’s gone south. And again, I don’t know. I don’t know which one’s right or which one’s gonna win out, but it gives me pause, where before it was always having the support of at least those underlying investor attitudes. That is another bifurcation that disturbs me a little bit.

If I go to this next chart, this is one that’s bothered me for a while and continues to bug me. But I’m looking here at the S&P 500 in blue to the red line records the total level of corporate and household cash in the United States as a percent of GDP, nominal GDP. And you can see really for the last 30 years or so, back to 1990 at least, the stock market’s been hugely tied to liquidity in the economy. When there’s lots of it, goes well. When it dries up, it tends to go bad.

And that really was directly the case until the start of this bull. And it’s really parted company quite a bit since that period of time, where liquidity continues to dry up relative to GDP. And I think that’s gonna be important, particularly now that even tech cash is starting to diminish, as people get into using leverage as opposed to cash in the new era sectors.

You know, you’ve got... Another chart I didn’t throw in here today, but if I look at the ratio of cash holdings to new era spending, that ratio has been rising steadily throughout this bull until recently, where now cash relative to tech spending, new era components of it, is starting to roll over. And again, it’s just something else that’s a warning sign to add on the pile of concern.

Jack: Don’t know what it means, but that Main Street sentiment thing from the previous chart is really interesting to me from the perspective of, like, if we think back to, like, 1999, I would assume this would’ve looked very different, right? I mean, ‘cause we’ve got, like, bubble-like behavior in the markets now. We had bubble-like behavior in the markets then. But I would assume sentiment would’ve been much better than it is now, and I just wonder if you have any thoughts on that and what that means.

Jim: We — the answer is we don’t know ‘cause this doesn’t go back that far. This doesn’t go back, I think it’s something like maybe 2019. I can’t remember exactly the date when they started this. But it’s been very good since they brought it out. And so I don’t know what it was back in dot-com. But I suspect, I suspect there was greater optimism then, at least on Main Street, I think there was.

Because at that time, what’s really different between these two cycles to me is there was not this bifurcation like we see today. Yeah, there was a bifurcation in the stock market, but over that bull, a lot of ex-tech stocks also went up nicely. They just didn’t go up as much. We’ve literally had where most of the market hasn’t done hardly anything, and we have this one sector that’s going through the roof.

Same thing in the economy. Throughout the 1990s, we had, you know, we had 3% job creation a lot of the time. 2.5, 3% job creation. We had 2.5, 3% productivity at times over that period of time. Measured productivity. So we truly had a booming economy that was broad-based and all participatory.

That has not been the case here. So I do think, you know, there’s a divergence. And I think it’s very telling, Jack, when you look at that’s really come about just since that AI surge. This AI surge to me has a lot of different ways that kinda looks like it’s really like a one-off where nothing else really participated in it. It’s this one small part that lit fire and nothing else really did. And it kinda shows up, you know, on this survey as well. They don’t see anything close to the kind of excitement that shows up in that AI move. And you know, if you back out the AI move from the S&P 500, that blue chart looks far different. You know, far different without AI stocks in there boosting that thing up.

So I don’t know exactly what it was, but I suspect there was a lot more optimism. Now, in some sense, that’s a good thing, and I think that’s one of the reasons I don’t expect this thing to end today in a dot-com result right now. I don’t. Because in part, so much of the market, so much of the economy has not participated like it did in the ‘90s and is in great need of liquidation. What we have is a small part that’s probably been even more aggressive than it was in dot-com, in my view. And it does need liquidation, but it’s gonna happen against a lot of the rest stuff that is in pretty decent shape. That’s kinda why I’m thinking we might have a 20-plus in new era and a 10% or less in the rest. Not an all-out collapse across the board.

This chart just looks at how defensiveness has left the marketplace in a big way. And this is — you know, this did happen also in 2000. This looks at the market cap weighting of the S&P 500 defensive sectors as a percent of total market capitalization within the S&P 500. Basically, I just included the, you know, utilities, consumer staples, the healthcare, REITs in that measure, and we’re right back down to where we were at the dot-com top.

Now, what I think is most telling is, as you think about when you enter the stock market today in the S&P 500, you have the defensive parts comprising 16, 17%. At the start of the 1990s, at the bottom of the ‘09 market, you had a defensive weighting that was twice as great as that. Double what it is today. Now, that says not only is there not much downside protection, but you’re also involving yourself in a market which now is gonna have a lot more volatility than it used to have when defensiveness played a bigger role in it, and we have both things that we’re dealing with today. The chance of not much safety jacket until the weighting of these parts get bigger, and secondly, just daily volatility is gonna be much more pronounced because of the lack of defensiveness.

If you look at the next chart, this is another way to look at that defensiveness thing, but it’s quite striking when you look at these dates. And it’s another way of looking at defensiveness. I’ve got back to 1962 here. I used the Kenneth French database, and I looked at the lowest-priced beta quintile stocks to the highest-priced beta quintile stocks, their relative total return performances. And if you think about it, in aggressive markets where people are optimistic, they’re gonna bid up high-priced beta stocks the most relative to the low beta. And when they’re defensive and worried, they’re gonna bid up low-priced beta stocks relative to high. So when this ratio is very high, it says people are being very defensive.

And you can look at the dates at the peaks here. ‘63 was the missile crisis year, you know, at the bottom of the missile crisis market. You got 12/74 at the bottom of the Nifty Fifty. You got 10/90, the real — the commercial real estate crisis on the coast, starting the — before the bull start of the ‘90s. You got ‘02, which is the dot-com bottom. You got 11/08, which was close to the bottom of the great financial crisis. You got 2020 in March, which is the bottom of the pandemic market. You got 12/22, which was right at — close to the bottom of the ‘22 bear market or the start of the current bull market. That’s where this thing peaks, massive defensiveness.

We’re at the opposite end of that extreme. All those days correspond to big market tops, and we’re sitting at one of the lowest ratios there that we’ve ever had, going all the way back to the 1960s. Again, just disturbing to me a little bit. Tells you something that people are piling into and what they’re not involving themselves into.

This also just shows that there’s a lot of excitement here about earnings momentum, and legitimately so. This is the S&P 500 forward twelve-month consensus EPS on a log scale. And you can see earnings are doing just fine by estimates. Now, two points. One is that, while rising earnings momentum is a wonderful thing, and it is. It is until it isn’t. That’s to say, if you’re gonna say, “Earnings momentum is great today, so I don’t have to be worried about anything,” that’s an incorrect statement. If you’re saying, “Earnings momentum is great today, so that’s good for stocks,” that’s a correct statement.

As you can see, all throughout the ‘90s, it was really good and did fine, okay? A lot of these other periods. But when the market does roll over, which is where those arrows are pointed, usually it rolls over when earnings momentum is fantastic. So having fantastic earnings momentum does not mean a bear market is a long ways away, okay? Because almost all of them happen from peak earnings momentum. Doesn’t mean — there’s a lot of times when it doesn’t happen from peak earnings momentum, but a lot of them start from that situation. It’s not like it has to roll over before you get in trouble, and that’s the point I’m making.

Now, what this also says, and we’ll get to that maybe in a couple other charts, there’s a lot of optimism here. Because unlike trailing earnings, estimated earnings are a sentiment measure as much as they are an earnings measure. I mean, if you’re looking, these are brought by analysts, and if your stocks are doing well today, then you just keep raising your target prices, particularly because, you know, you gotta be able to recommend them for another year. And so there’s a bit of a sentiment read that what we have going on here, telling us about a fundamental, and I think that’s always been a problem.

Couple more things on earnings momentum here on the next couple charts. This chart just looks at that estimated forward twelve-month EPS in relation to the trailing ten-year, hundred and twenty-month level of earnings in the past. And just to show you how aggressive this twelve-month earnings number is relative to past estimates in relation to kind of the average of the last ten years, we’re at a record high in that measure. That’s a pretty steep slope to achieve, where not only are earnings great, but they’re really great in relation to what we’ve produced over the last ten years, saying something about sentiment.

I think I have one more in here, too. I’m just looking at this last chart, comparing the dot-com situation, which was the red line in this chart, the last five years of the dot-com compared to — well, actually the last three years of the dot-com compared to the three years of this bull market, and the blue line being this bull market. And you can see that at the end of the dot-com, you had this surge in momentum winning. Momentum stocks took over the marketplace right at the end of that bull market. Well, they’ve done that again here in a big way in the last few months. Again, none of this to me is definitive. It doesn’t tell me that this is over or has to happen. I’m just piling up stuff in my mind that is guiding me more concerned about being a little more cautious.

The last thing is, is valuation, Jack, which — you can look at it a number of different ways, but this has kind of been my favorite way to look at it. I have a lot of problem with valuation measures anyway, this one included, I suppose. But all I’m doing here is looking at the level of the S&P 500 every month compared to its trend line average over since that period, since 1950. And you can see it sort of trades regularly around the zero marker. It goes above and below its trend line over time, which you’d expect. And you can also expect that when it’s really below trend line, it’s a better value than when it’s really above trend line. What going above it is talking about momentum and all that.

But I wasn’t too concerned. Just a few months ago, in late 2025, this thing was about twenty — a little over twenty-three percent to its trend line. High? Yes. But it was in that same range that you saw in the ‘60s, and that persisted for a long time. Traded, you know, twenty percent premium to trend line. It was a bull market that lasts a long time. I was even okay with that. You know, we could persist there.

But what it’s done just in the last six months or so is it’s gone from that kinda premium to a sixty percent premium now above its trend line, just in a matter of six months. And now the only thing that’s ever been higher than that was dot-com. Now, still quite a bit higher, but nonetheless, nothing else is like that. And again, that gives me a little pause that this thing maybe is getting a bit extreme.

Jack: And so we’ve talked about this idea that tech has dominated for a long time. One of the things in this next section you’re showing, though, is tech may be relinquishing that leadership to some degree, right?

Jim: Yeah. I do think, I do think that there are some signs that tech is losing its mojo a bit here. It’s starting to show up. And what is interesting is it’s kinda doing it despite the fact that we had the AI surge, the AI new excitement wave, if you will, that you can see here from March 30th to the June 2nd high.

This is the relative price of the S&P 500 technology index, just since year-end. And what I wanna point out — or not since year-end, go back to 2025. What I wanna point out here is that tech stocks have already had a pretty good pullback relative to the S&P 500 here since the June 2nd high. In fact, if I update this chart, it even has gone to lower levels than shown here.

But what really gets me is tech stocks now have been a market performer relative to the S&P 500 going back to October of last year. They’re not just that always outperforming animal anymore, if you will. They’re also getting extremely volatile. I mean, look at the volatility we’ve had now in the last — I could take this back to ‘04, and actually we’re not that far away from summer of ‘04, where it hasn’t been that much more of an outperformer. But it certainly has been an underperformer since October of 2025, and in the interim, the relative volatility is pretty spectacular.

So one of the reasons people latched on to tech stocks was not only their phenomenal year-in, year-out returns, but also, you know, they were pretty steady, steady Eddie kinda returns. They didn’t have a lot of volatility to them. They’re starting to get lack of both, not greatest relative returns and starting to get more volatile.

But it’s not just the S&P 500 tech. If you go to the next chart, look at some aspects of it. You know, this is the other sector in the S&P which is part of the new era, the communication services sector. It’s just really rolled over and died. It did have that AI excitement, but it not only gave that back, it went far beyond that. You could see that these stocks now have been market performers going back to the start of 2025 over that period of time. This idea of buy and hold tech ‘cause it’s always gonna win is gonna start to wear on people’s portfolio statements, I think, is what I’m getting at.

If I look at the third one, this is the venerable Mag Seven index relative to the S&P 500, and it has really fallen on hard times. It really didn’t get any pop from AI at all, and it’s just fallen off the chart. I mean, how many portfolios owned this or companies in it for so long here during this bull market, and they probably might still be owning them, but they’re, you know, it’s starting to get long in the tooth now, and that’s not what people are used to when they bought technology.

And even AI, in the last chart of these, Jack, that’s also, you know, it’s still up a lot from its March relative move, but it’s starting to give back some of this as well, some of the excitement’s coming out of that as well. I’m just amazed that when you have something as ferocious as this AI, you know, obsession, if you will, wave through the markets, that it really didn’t do much good for new era stocks that have been dominating this throughout the period of time.

So I’m kinda thinking we’ve got new leadership sorta sneakily coming up on people, and it’s starting to get a little more notice, but it’s still not greatly noticed. I think most people still think, “I’m gonna stick with tech because that’s always a win over time.” I don’t even disagree with that necessarily. I think, you know, 5, 10 years probably it’ll be a winner. But that doesn’t mean we couldn’t have some difficult periods of time, and right now every portfolio pretty much probably is over-weighted new era securities. You just can’t help yourself. I mean, I’m probably as guilty of it. You know, even if I haven’t bought any for a while, I probably haven’t sold any of them, ‘cause, you know, they just, they keep doing okay.

And the reality of that is when you don’t sell any of those, you don’t buy anything else, because they’re not doing that well, suddenly your portfolio gets way over-weighted in this one area. And with damage like, which is starting to occur in this sector, more and more people are gonna go, “Well, gee whiz, maybe, maybe I should lighten up a little bit.” And if everyone does that, that’s where you could get a 20% correction in new era securities.

So let’s look at leadership, just what’s happened here. I would argue, and I just did in a piece, that you could say already, and no one’s done this that I’m aware of, but you could say this bull market has been driven by two distinct leaders already in this bull market. Not one, two different leaders. This one shows the performance of new era securities in blue on a relative basis to the S&P 500 from 10/12/2022 when the bull started, up until last October, which was the last high in tech over that period of time. And over that period of time, tech stocks outperformed by 50%, or new era stocks. And while the rest of the S&P 500, the other nine sectors of the S&P 500, it underperformed by 20%. Okay? That was a definite new era led bull market, really warped, no doubt about it.

But look what’s happened since last October in the next chart. If I start both of these again at one, index them at one, their relative price performances, you now have gone from October — so you’re talking what? Eight months, nine months, whatever it is — where this S&P 500 has now been led by broad market plays or the rest of the old era securities within the S&P 500, the green line in that. The nine sectors — if you bought the nine sectors, market cap weighted them, you would be beating the two sectors of new era securities over that period, over that period of time.

I just looked last night — I put out a tweet this morning that I now can go back a full year, as of last night, July 7th. You can go back a full year where the old era parts of the S&P 500 have beat the new era parts for... That’s the first one-year period by a wide margin that’s happened during this bull market. So something different’s definitely occurring.

I’ve got a piece that will be coming out tomorrow if I get off this call and finish it. Just one part of it will show — look at the relative performance of broad market plays in the S&P 5— or broad market plays in the stock market. I take it back to 1999, and from ‘99 up till 2010 or ‘11, it was dominated by broad market plays soundly beating new era securities. However, the last 15 years has been almost the other way around. Yeah, there’s been little whatevers, maybe like we’re seeing now, but as far as the trend line, if I draw a trend line from 2011 down to today, it just never surpassed it. This one has surpassed it the first time in 15 years.

Jack: If we showed this chart to, like, a lot of people, I think many people would be surprised by this. I don’t think many people would realize, like, the behind-the-scenes leadership. I mean, we’re hearing about tech and AI and stuff in the news all the time. I think a lot of people would be surprised at this relative rotation we’re seeing behind the scenes.

Jim: I agree. I think it’s starting to get a little more play, but I think, I think you’re right. I think most people... You know, I know just in discussions with people I talk with that, you know, a lot of different people I respect and manage money. You know, it’s like you have to make a call in here. Are you gonna sit with the AI story? Are you gonna accept it? Are you gonna go with this win over time, or not? And I’m not sure you have to make it that black and white. As I say, I wouldn’t totally dismiss that AI wins over time, but it doesn’t mean you have to be over-weighted the whole time.

You could go, you could still own some and be under-weighted, and there could be a goodly period of time where the broader marketplace continue to win, and I think, I think we might be in one of those periods. And going forward in the balance this year, I still think that’s gonna continue to be the case.

And not only that, I think there’s gonna be some damage that’s gonna be noticeable on those that just sit with, you know, too much of an overweight in that area. And to your point, Jack, I think you are right, and that’s one of the reasons why we maybe haven’t seen as much damage yet as we may, is if people start to recognize that, then people will start to make portfolio moves, at least at the margin. And here we don’t need wholesale selling or buying of anything. If we get everyone to do marginal moves, and I know everyone’s gonna have to move in the same direction, then that could be a pretty big move that’s giving — pulling down new era and giving thrust to broader market or old era plays.

Jack: So in this last set of charts, we’re gonna take a look at some of the economic data behind the scenes. In the first one here, we’re looking at yields and inflation.

Jim: Yeah. Just a couple comments on bond yields, I guess overall. There’s this view — there’s a strong view out there that still exists today that we hit our all-time low in bond yields right before the pandemic, and that, you know, we now have turned the corner. And just like the last time we turned a corner, we’re now headed higher in bond yields. And, you know, there’s a lot of disastrous scenarios out there about, you know, with government debt piling up and with inflation being a problem, that we could go a lot higher in bond yields. I think just the opposite of that.

I think, I think bond yields are gonna return pretty close to where they were. I don’t know if they’ll get all the way back 10-year to 2%, but I think we’re gonna see, you know, sub 3 maybe or something in the next few years. And this is just one example. I got a better one coming after it. But if I look at the bond market here, the 10-year bond yield is the blue line. The red line is just — this is amazingly simple. That’s why it’s... I just took a weighted moving average of CPI inflation rates over the previous 10-year period. Okay? And if I do that, I get the red line. That’s — you weight the current year at 10, two years ago at nine, eight, seven, six, five, four, three, two, one. A long-term moving average of inflation.

Look how close that lines up with where bond yields go over time since, what, 1970 or whatever it is there. That’s a heck of a, heck of a pretty good record of just answering the question, do you think rates are headed higher or headed lower? This thing’s done a darn good job of that.

Now, what I looked at with this yellow, blue, purple, green line going forward is just say estimating what the inflation rate is gonna run in the coming five years. And I got it going from 4% on the yellow to 3% on the turquoise, 2% on the purple, 1% on the green. If we get back to 2% and hold that for five years, you got... You can see on the right, you got a bond yield that’s under 3% on average based on this historic relationship. You’d have to get a lot higher. You’d have to get and sustain inflation rates probably at 5% or more year in, year out before you’re really gonna take the bond market a lot higher than it already is in terms of yield, at least according to this construct.

Now, the real reason is in these last few charts that I’m really concerned about where the economy might be going now in the next five years. And what these get to is what’s going on with — I titled this piece with the demographic dungeon the US is getting sent back to, if you will. Demographics in the United States have worsened considerably in recent years, and that is a huge, huge force on economic activity, on inflation, on yields, on prices in the economy, is the degree of growth in the labor force or the degree of demographic power our economy has. We don’t have it, and right now it’s gonna get worse than it’s been.

And I’m just gonna relate, in all these charts — the first one is being real GDP, the trailing five-year average annualized real GDP growth rate in blue. And what I’ve laid on top of that is the trailing five-year average annualized growth rate in the labor force. Now, again, not perfect here. This is the worst one, by the way. The next get better. But pretty good summation that when labor force growth is doing well, so is economic growth. When labor force growth stalls, so does economic growth. Look what’s happened since 2010. We just died out down here in the demographic dungeon.

Why is that? I mean, we’ve been, we’ve been — we can’t get growth above, much above two percent. It takes — the only time we can do it right here in blue, right here in 2025, was when we had a pandemic, and we juiced it with, like, you know, thirty percent money supply growth and twenty percent deficit spending and zero interest rates. And, you know, we get a good grow — otherwise, we can’t get above two. Why is that? Because our demographic growth is under one percent and getting worse. It’s more like a half a percent. If you can’t get growth back to where it was back here in the ‘60s and ‘70s, we’re stuck in the demographic dungeon of growth.

I sometimes chuckle when I hear people worried about overheated growth. “Oh, man, we gotta raise rates ‘cause, oh, inflation’s getting out of control.” We’re growing at such a slow rate of new labor inputs, I don’t think there’s any way we have overheated growth capabilities. Now, if I put higher productivity on that, maybe we stretch out a little more growth, but we certainly won’t have inflation if that’s the case.

Let’s look at the next two. That’s a forecast for GDP. Oh, I’m sorry. Go back to there real quick, Jack. The — to the GDP chart. The green dotted line here is based on where this red five-year trailing growth in labor force will go over the next five years if it grows at the forecasted rate, I believe, about half percent a year, which is what the consensus is right now. Not mine, but just picked off the consensus out there. And you can see that we’re gonna be down here with a half a percent labor force growth and maybe be doing well to get one and a half to two percent GDP growth over the next five years because of the depressing effect that lack of labor force does to growth in the economy.

The next chart looks at what it does to inflation. This lays that same chart in red, the five-year trailing labor force growth rate, on top of the annual — not five-year, but just the annual year-in, year-out growth rate and the inflation rate, the blue line, CPI inflation rate. We had a good surge in labor force growth right here in 2023 and 2024. Why? Because we killed it off for a while after the pandemic. We took demographics down so badly that then for a few years, we actually had good growth in demographics again as people came back to the labor force. But now we’re stuck with our kinda trend line growth, which again is gonna be five percent, and that’s suggesting an inflation rate, what? Zero to one percent.

I think, I think we don’t have to do much to get there. I don’t think the Fed... I mean, this idea that we need to raise rates right now, what good is that gonna do? Is that gonna bring — open up the Strait of Hormuz? Is it gonna bring peace to the Middle East, which will stop the rise in oil prices? No. I don’t think it’ll do any of that. What we have is a supply side based inflation problem, and raising rates is not gonna improve the supply. It will hurt demand, but we already have relatively weak demand overall. And if demographics keep playing out, it’s gonna get worse.

And so finally, I’ll relate this to bond yields, and this is the closest one. This has got an amazingly close correlation. I’m talking about taking the trailing five-year growth of the US labor force and laying it on the day in, day out bond yield, which is what you have in blue there. Correlation, I can’t remember, something like .8 over this period of time. It’s remarkable how well the last five years does in picking up the ups and downs of bond yields over much of this history. And you can see what the green bar is now suggesting, the green dotted line, for the pressures on bond yields.

So I think, I think if anything, we have a lack of inflation, a weak growth problem coming in the next five years. Now, productivity could help that, but again, if we do get productivity, that probably leads to even further downward pressure on inflation and yields rather than upward pressure. And if you don’t get productivity, you just got a good old sluggish growing economy. Now ultimately, we need immigration in this country, or we need a higher cultural birth rate if we wanna grow faster than two percent stall speed for the rest of our lives. But this kinda lays out the importance of demographics and what they imply about where we might be headed in the next five years.

So there’s a lot of talk about a productivity boom and a wonderful environment. I don’t know, this tells a very different story. Not that it’s curmudgeonly terrible, but it’s very different than what I’m hearing, I guess.

Justin: As we wrap this up, I just wanna make sort of one, I think, overarching comment here. And that is, you know, what our audience is seeing is someone with decades of experience in looking at the markets and economy that has, in my opinion, been right about way more things than he’s been wrong, and presenting a very compelling case for what he’s seeing and, importantly, what you’re looking at. And, you know, markets and economies are complex, and I think this has been an extremely valuable discussion around someone with your level of knowledge and expertise and the things that you look at day to day and over the long term, Jim. So I know this is—

Jim: I appreciate that.

Justin: Our audience is gonna get a tremendous amount of value in this one, for sure.

Jim: Well, I appreciate that. I would say, though, that I have certainly been wrong—

Justin: Oh, of course.

Jim: And I will certainly be wrong again. I’ll miss some things, take into consideration. But I try my damnedest to do as best I can, and I try to react to things when I get convinced that there’s something to react to, a little bit.

And I don’t see the world ending or anything along those lines. I think our biggest problem in this country, I think, is a lack of growth, and I think it’s tied a lot to demographics and low birth rates, and we’re going the same way that Europe and China has before us, and I think we still have a chance to change that. And certainly productivity could help, and technology is exciting that we... It’s better we have than anyone else. But I do think we still face that very serious challenge going forward.

Justin: You know, I had invited Neel Kashkari on the podcast. He said no. He actually got back to me, but I think I might send him this episode just to see what he says.

Jim: Yeah, I’d be interested in what Neel had to say on that. That’d be good. So. All right. Well, I always appreciate you guys having me on every time, so thanks again.

Justin: Absolutely, Jim. Class act. All right. Thank you very much. We’ll see you next month.

• Why the S&P 500 is still in a long-term uptrend but showing short-term momentum loss

• How Katie defines overbought and oversold using the stochastic oscillator

• Why the March monthly MACD sell signal became an unusual whipsaw

• What the QQQs and Nasdaq 100 are saying about technology leadership

• How investors can use stop losses, hedges and moving averages to manage risk

• Why the market has held up despite underperformance in the Magnificent Seven

• The difference between market breadth and market leadership

• Why sector rotation is improving in healthcare, industrials, utilities, insurers and biotech

• How sentiment indicators like the VIX and Fear and Greed Index fit into market timing

• How the Fairlead Tactical Sector ETF uses trend following, sector rotation, Treasuries and gold

• What the charts are saying about emerging markets, developed international stocks and the U.S.

• Why gold has moved from a strong bull market into a more tactical trading environment

Timestamps

00:00 Intro
00:58 Why the S&P 500 is losing short-term momentum
05:04 How overbought conditions can reset without a major decline
08:39 Why whipsaws make confirmation so important
12:02 What the QQQs are saying about technology leadership
16:51 How to manage risk with stop losses and hedges
20:07 Why the market held up despite Mag Seven weakness
23:49 How market breadth differs from market leadership
28:14 What sentiment indicators are saying about investor positioning
32:58 Why the market is in a technical void
36:00 Sector rotation beyond technology and semiconductors
40:54 How the Fairlead Tactical Sector ETF manages drawdowns
46:05 What international stock charts are saying versus the U.S.
50:13 Why markets have been resilient despite geopolitical risk
52:05 What the chart of gold is telling investors now

Don’t let him tell you otherwise. We have one of our favorite technical analysts back with us, Fairlead Strategies’ own Katie Stockton. What’s good, Katie?

Katie: A lot, I think. So far so good for the summer, thanks.

Matt: I like the sound of that, especially post Fourth of July. So straight into the deep end, last quarter we talked about a continued long-term uptrend for the S&P. Wouldn’t we all like that? Nice, easy, quiet summer hanging out by the pool. Sipping—

Katie: Rarely ever, I know. That used to be sort of a guarantee, didn’t it? But not so much anymore.

Matt: Don’t people take vacations? What, did COVID ruin this? So you’re seeing some shorter term potential warning signs. Let’s start here. Let’s get this chart up. What’s going on?

Katie: Yeah, the shorter term, I mean, there’s been a loss of momentum. We’re maintaining right now a neutral bias, and the rationale behind that is simply the loss of momentum. You can see it in the moving averages, like the 20-day moving average, for one.

That’s a very close sort of tight moving average, and normally we might just ignore it, but when you have such a steep trend and then it loses enough steam to have that 20-day roll over, we do pay attention, and it has been an environment where it has been just this really steep up move from the leadership, as you know.

So that puts us on guard for any loss of momentum. The consolidation phase that initiated in June is, you know, neutral. It has a hold right now, and we’re gonna simply watch the boundaries of that consolidation pattern to determine, you know, whether we wanna go more risk on or risk off.

Matt: So this is a place where I think the whole overbought and oversold part becomes really interesting in your work because we achieved overbought. So actually define those terms in your, the way you define them first, and then let’s talk about what we’ve moved through with that momentum loss.

Katie: Yeah, no, it’s a good question because I do think overbought and oversold are terms that are overused, right? They should reflect positive momentum, so overbought is a function of positive momentum, and when it makes a difference, when it has sort of the implications that the word suggests, it’s when you see the downtick in momentum.

So it is measurable, though, both overbought and oversold, we derive from the stochastic oscillator. So for us it’s either above 80% is overbought, below 20% is oversold. That’s how we define it. It might mean different things to different people, but it is measurable, so when technicians who are professionals are talking about it, they usually are referring to an actual indicator. As opposed to just something kind of feeling overbought or oversold.

The better term almost would be overextended, right? One way or the other. So if somebody feels like something’s overextended, that would be maybe a good way to label something. But the overbought condition, you’d be hard-pressed to find that missing from a stock or a security that’s breaking out, right?

So it’s a normal function of a breakout or positive momentum. But when you get that downtick and the stochastic oscillator for us rolls back below 80%, that’s where you have a so-called overbought sell signal, and that’s when you wanna take action, right? In terms of risk management.

Matt: So now let’s talk about short-term versus long-term overbought and oversold, which thank you for saying it. It’s overused. And it’s one of those things that’s used to the point of almost being meaningless so often. So the fact that you actually have some meaning here, talk me through short-term versus long-term in those signals.

Katie: Yeah, for us, I mean, really important to always have the multiple timeframes in mind because we’re talking to people that some look out three to five years in their investing time horizon, whereas others are actually day trading.

So we talk to everyone in there and need to look at multiple timeframes, but we find a lot of information from them, and we start usually with a monthly bar chart and therein a monthly stochastic oscillator to give us that long-term takeaway on that overbought, oversold spectrum. And then we’ll go down to the daily bar chart to get that shorter term view.

So a short-term overbought sell signal would have implications for days, maybe a couple weeks, whereas a long-term one on the monthly chart would of course have implications for months, if not longer.

Matt: I also think it’s interesting to point out that that sell signal doesn’t mean it has to go down to clear. You can clear that signal in a number of ways. You wanna just say this out loud. I think this gets overlooked too.

Katie: Yeah. So you don’t need an oversold condition to, I guess, yeah, remedy the overbought condition. You can see an upturn in the indicator from neutral territory. We like this setup called a stochastic pop, we call it, where the stochastics will come down, but they hook right back up.

It’s like a whipsaw. And it’s a positive whipsaw usually. So there are nuances to it for sure. There’s a few indicators that do have buy, sell, buy, sell kind of behavior. But for the stochastic oscillator, it’s not quite as clean as that maybe. And I would argue that that’s the case for quite a few overbought, oversold type inputs.

We use the DeMark indicators as another one. They too don’t toggle between buy and sell, but rather suggest when the trend might be overextended in either direction, and that can happen multiple times on the buy side or the sell side.

Matt: Okay, let’s jump to MACD because last quarter when we were talking about this, you were saying, “Here’s a MACD sell signal, but we still need other factors for confirmation.” So educational perspective first. Let’s talk about what happened from then to now and what you saw because I don’t think that confirmation ever came.

Katie: Well, so it depends on how you define the confirmation, and we like to see a couple things for that. But to address that signal in particular, what we were looking at was the monthly MACD indicator for the S&P 500, which did confirm or actually log a sell signal or bearish crossover in March.

So it happens right at the end of the month. The closing print determines whether that MACD crosses over or not. If you wanna be more stringent, you might wait for confirmation on a second month, which did not happen because it whipsawed higher in April, which is really very unusual. In fact, I think it’s happened maybe three times in twenty years, so really unusual and unexpected.

And then another way you could look for confirmation would be something like one of those stochastic downturns below 80%. It’s a matter of putting more weight of the evidence in your favor, right? And how much time do you wanna give, you know, the market and its volatility before you say, “Okay, this is a real signal.”

But that March signal proved to be a pretty dramatic whipsaw just happening so quickly. I mean, I’ve seen them, you know, two, three months later, we’ll have a higher reading or, you know, the reverse reading, but really very unusual the very next month to receive that. So, you know, the loss of momentum still kind of shook the market in terms of the corrective phase, and it’s resumed in a way that the momentum compares, I’d say, less favorably than it did in, say, 2025, 2024, and you can see that in the MACD histogram, which is just a derivation of the MACD itself.

So it’s less strong momentum even though we saw the lift to new highs. So it doesn’t minimize, of course, returns associated with that, but it tells us that it’s probably more of a mature uptrend for that reason, and that’s why we just kind of further brace ourselves for any other confirmed sell signals that arise.

Matt: The whipsaw. Let’s just talk about the whipsaw for a second. This is part of why we wanna use these indicators that help smooth stuff out, but it’s also — whipsaws mess with the indicators in a way, too. How do you— Well, you, yeah... how do you process that one, that—

Katie: I mean, I think it’s a matter of waiting for those qualifiers, right?

So with the longer term things it might be hard to wait two months, but the good news is we have shorter term indicators to help sort of set the tone, right? So rather than maybe waiting two months to say, “Well, this is confirmed or not,” you can refer to the shorter term timeframe like a weekly MACD, right?

So the weekly MACD certainly flipped positive in a more timely fashion than we had from the monthly MACD, right? So I think it’s a matter of zooming in on tighter time horizons when it comes to trading and positioning. When it comes to long-term investing, you would be well-served by just waiting for that confirmation, just that additional confirmation, whether it’s one additional month, so two months on a signal, which is kind of similar to what we do on breakouts and breakdowns.

We always wait for two closes on the time horizon in question above or below a certain level. So that double confirmation time and price is really very valuable and will help prevent whipsaws. You’re always gonna have whipsaws, though, with anything, in the same way that you’ll always have knee-jerk moves in response to earnings, right?

So it’s not a function that’s limited to technical analysis, I would say. It certainly crosses disciplines and, you know, it’s just a matter of putting the more probabilities in your favor by having more of these indicators on your side across the different timeframes. So when we got bullish again on the back of that signal was when we had breakouts, when we started to see breakouts from both the bottom up perspective and then ultimately from the S&P 500.

So it’s just a matter of being adaptive, too. Not saying, okay, well, you know, the predictive value is, you know, not such that we can tell you where the S&P 500 will be at year-end. But rather to say that we have a prevailing uptrend, it’s lost some steam, the sentiment is doing this, that creates, you know, this kind of environment.

So it’s more about what’s the current status and how do we wanna be positioned within that context, and how much attention are we paying to risk based on all the inputs that we have. ‘Cause no one knows where the market will be in six months.

Justin: What are you seeing in the QQQs? I think, you know, there’s so much things obviously going on in the headlines with AI and SpaceX, and obviously the stocks in this index, the Nasdaq, has been such a strong performer.

I feel like every time, you know, investors feel like, “Okay, tech is done, it’s gonna roll over,” like another group in the tech market, in the tech sector sorta picks up the slack. But what are you seeing with the recent move in the QQQs? And I guess, you know, how is that in relation to the S&P 500? Like what is the technical showing you?

Katie: They really are almost like in greater focus for our client base than the S&P 500 at times because I think, you know, most people are most interested in technology.

And of course, the mega caps is having been a source of upside leadership that’s been pretty consistent. The triple Q exposure is a bit greater to that than the SPYs, for example. So there’s definitely a hyper-focus on the triple Qs and reasonably so. And we have seen there too a loss of momentum pretty well aligned with what we have from the S&P 500.

You know, there’s the 20-day rolling over, and that’s something that reflects probably a choppier environment for even the summer months is what it would indicate based on history. And so that puts us in that mindset of, okay, we wanna be adherent to stop losses or, you know, more mindful of sell signals, things of that nature.

So we have that, but we only have that short-term indication right now. We don’t have intermediate-term sell signals for either benchmark. We don’t have confirmed sell signals right now on the monthly charts of either benchmark, Nasdaq 100 or S&P 500. So the messaging has been that this is a short-term issue right now.

There are signs of exhaustion that are longer term from the DeMark indicators, but they’re unconfirmed, so kind of like lower conviction. The takeaway for us with that is just that we’re probably into more of like a neutral environment where we’ll see some digestion, but not any kind of big dramatic bearish reversal as is indicated by those gauges right now.

So we have the short-term loss of momentum. That’s pretty obvious at this point, but it started a few weeks ago. And now we’re watching the 50-day moving average and also just the recent highs as the boundaries of the consolidation phase for triple Qs, for SPYs as well. If we were to see the 50-day moving average taken out decisively, that naturally would be a setback.

It is widely followed, but it’s a great gauge of the intermediate-term trend. So that’s where we’d probably start to see some of those weekly indicators roll over in a meaningful way. We don’t have that yet, but we’re kind of mentally prepared for that. And with the breach of the 50-day, I think things would get a little dicier than they are now.

But I think what’s more interesting is your last, you know, point in regards to the relationship between the Nasdaq 100, tech-heavy, mega cap heavy, to the S&P 500. And we’ve seen a bit of a chink in the armor of the outperformance from the triple Qs versus the S&P 500.

Again, short term, it’s not an intermediate term reversal or anything of that nature. Not yet, at least. But we have definitely seen that kind of stalwart leadership from tech, from semis in particular, and from mega caps show some slippage. The mega caps have really started to underperform.

Semiconductors have just really over the past couple weeks started to lose their stronghold, and you’re seeing that most in the triple Qs and their relative strength ratio. So it’s a bit of a risk, I would say, but not a decisive intermediate term reversal at this stage. So something that we’re watching, and also I think it has people thinking about, well, what happens when the AI trade, the confidence behind that, suffers from something, right?

You could argue that Korea is doing that right now, or South Korea. You know, the news there, the pullback there is impacting sentiment around AI, around the semiconductor sector. It’s again, not that meaningful yet in terms of the intermediate term indicators, but it definitely has people thinking about, okay, well, what if this isn’t here to bolster the market?

And I think that’s just a healthy way of thinking, because I’m quite sure a lot of people are uncomfortably concentrated in these areas of the market.

Justin: You may have already spoken to this, but that is, I think, one of the challenges for investors when you’re in a stock or an index that has done so well and support is...

‘Cause I’m looking at the chart here, you know, on the QQQ, support is, like, below 650. This is rough. And it’s, you know, above 700, wherever it is now. So it’s like how do you tactically manage that when you have something that’s done so well, you wanna protect some of those profits? Is it using things like stop losses? Like how do you sort of tactically address that, I guess, in the short run?

Katie: Yeah, I— So I, for sure stop losses and I think that’s one great takeaway from the charts is that you can identify the key levels where historically that ETF or product has held and use that as a stop loss because if that support or demand isn’t there anymore, well, that’s a breakdown of sorts, right?

So that’s a great way to manage risk. And then, you know, just simple trend following, right? By using the signals perhaps from the MACD or from the curvature of the moving averages to say, “Okay, well, risk is heightened here of a pullback.” Some people shouldn’t do any repositioning through a pullback.

So it depends really on the overall financial picture. What’s your time horizon? Are you retired? You know, those type of questions come into play. But simply from a technical perspective, stop losses and, you know, knowing when it makes sense maybe to hedge a certain segment of your exposure. When we tell our clients it’s a good time to be hedged, usually we’re kind of addressing the higher beta segment of the market and, you know, maybe take a small position in an inverse ETF like PSQ to hedge the risk that they carry through that pullback or just to limit the drawdown, if they can think about it that way without having to get the taxable implications of selling their positions that they still love from a fundamental perspective, but also perhaps feel like things are a little overdone.

So I think that it’s either through ETFs that they can achieve that hedge, usually just a partial hedge, or through a stop loss or trend following discipline that really needs to be systematic and that’s something I think we all, you know, sort of don’t do as much as we should do, which is to rely on these tools because they are almost like reporting what’s going on in the market and as much as we let our biases get in the way, the market doesn’t really lie.

So I think it’s a matter of really respecting the tools and to do so regularly. That’s where I think you get the most benefit from using the charts and technical analysis and, you know, we’re all even — even us at Fairlead, you know, it’s our day job here. Personally in investing it’s still very difficult to respect all of the breakdowns and the sell signals that we see because, you know, you just kind of let those biases get into your decision-making.

Justin: You have a chart in here on the Mag Seven, and you have it also versus the S&P 500, like in the lower part. But just, I want you to speak to it, but are you surprised that the market has kinda hung in here given some of the underperformance in the Mag Seven? Like, I remember like, you know, listening to commentators saying, “You know, if the Mag Seven falls apart, the market’s done. The market’s so concentrated.” And yet, you know, these other areas of the market have sort of picked up the slack, and that’s kind of been the case for a lot of the time when the Mag Seven has kinda gone through these rough periods. So what are your thoughts around that?

Katie: Yeah, no, for sure. I mean, it has surprised me because when you look at the ratio of the Mag Seven to the S&P, it almost looks like an eerie, like head and shoulders top.

You know, it’s really quite bearish the way it sets up. Whereas in absolute terms, it looks kind of like more of a general mill or General Mills, right? A general, run-of-the-mill correction. And that is something that usually we would see as an opportunity. And so we had to reconcile, I think, the sort of set up in absolute and relative terms for one, that the pullback, by the way, did follow a pretty substantial rally, and so it’s only a partial retracement in absolute terms.

So it really doesn’t look like any kind of breakdown. And yet you’re right. I mean, it’s just been remarkable how strong or resilient the market has been in light of this underperformance, and it’s been wholly related to the AI trade as it pertains to the semiconductor sector in particular. So it’s really a very sort of narrow story this time around.

It’s not to say it hasn’t happened before with different sectors, maybe like financials stepping in, but this has been more about the semis versus the mega caps. So that becomes more important almost towards market sentiment than the mega caps as before. So, and I think though that where we’re seeing that impact to a degree is in the momentum strength behind those long-term momentum indicators.

So the MACD histogram, you know, on the monthly chart of the S&P, it compares unfavorably, and I think that’s probably part and parcel with this. But that doesn’t mean you’re necessarily losing money, right? On the, you know, if you have broad-based exposure. The catch is that a lot of people do have that heavy mega cap exposure, and they might be what they feel like is, you know, with an underweight position in, you know, the DRAM stocks or something like this, right?

So you’ve gotten, I think, a little bit of a performance chase in that world. So it is— I think it’s remarkable. I think it’s really interesting and, by the time we kind of figure it out and get our heads around it, it probably will change. So I wouldn’t, I wouldn’t, you know, suspect that this is going to be, you know, the lasting setup necessarily.

The market breadth is obviously a huge piece of the puzzle. And leadership and breadth are two really different things. I might have said this on this podcast before, but market breadth is participation, so how many stocks are up on up days? Whereas market leadership is where is the outperformance coming from?

So those are two really different things. So we’ve had especially narrow leadership to the semis in this environment. But the breadth has been pretty decent actually, so that participation has been pretty good. Just doesn’t mean that we’re getting that strong outperformance outside of it, at least up until very recently.

Justin: Does that market breadth indicator, does that take in— ‘Cause I mean, lately, small caps and maybe some value stuff has been holding up a little bit better. Like, how does, like, those types of areas of the market — does it impact breadth less because they’re smaller, or is that not really the case? It’s just overall advance decline counts?

Katie: Yeah. So there’s so many ways you can measure it, honestly. We use the NYSE cumulative advance decline line typically for our running measure of it. And then we’ll also use things like percentage of stocks above their 50-day moving averages, which would be more of an oscillating measure.

And we found that, you know, the cumulative measure is not, like, the best market timing device, but it gives you a sense of a backdrop, right? It tells you that most stocks are still rising. Like, you don’t have some big negative divergence. New highs and market breadth are not necessarily, you know, an action point for the market, but they do affirm what’s already happened for the major indices, right?

So it’s just confirmation as opposed to a breakout that you could really feel like it’s actionable necessarily. It’s like you’d rather see that, of course, than lower highs. It also means that it’s been an easier market environment than others. When others will have weak breadth, it just means your stock selection becomes so much more important.

So while I’d say a lot of people have probably underperformed the S&P 500 in this environment, it doesn’t mean they’re unhappy with their portfolios because they’re probably doing all right. You know, the vast majority of stocks are still going up with the market, even if, you know, that person might feel like they’re underexposed to that leadership segment.

You know, their portfolio is still working for them, right? So it’s an interesting thing to kind of pick apart, but there’s nothing worrisome right now in market breadth, and I think how it’s translating on the sector front right now is really very interesting.

Matt: Well, let’s talk about that for a second because I think breadth as a term is really useful in the framing context of anybody trying to be active. If you’re trying to be active, breadth is a great overlay. What are you seeing at the sector level?

Katie: Yeah. And so — and like breadth also applies to sectors, but so does leadership, right? So when we talk about sector rotation, we’re talking about typically where is the relative performance coming from.

And that has really shifted recently, and it’s almost 100% a function of the technology sector and also triple Qs seeing a little bit of a downtick in that relative performance. Because they have such a huge footprint, both in number of stocks within the market and also their market cap footprint, they — when they start to, you know, underperform, it means that the relative performance looks so much better for almost every other area, right?

So it’s kind of an interesting thing that relative strength can improve for just about everything else when it has been a narrow tech-led environment. And that’s indeed what’s happening. I mean, even the relative strength for the consumer staples sector, which is not a great performer of late, has upticked just as a function of the downtick in tech.

But now what we’ve seen with the ones that have outperformed and have done so more meaningfully than consumer staples, we’re getting breakouts. So we even just today highlighted a breakout for one in the healthcare sector. So we’ve been sort of anticipating this with the relative improvement over time.

And indeed, there’s been a lot of resistance levels cleared by the benchmarks for healthcare, biotech, and in some other segments too. So we’re really very respectful of breakouts when they happen ‘cause that’s the breadth. And then we’re of course, always chasing relative strength as well.

So now we have good breadth. Relative strength has shifted enough that some sectors, industrials would be another one that that’s happened more recently, have emerged and are a source of breakouts. And we love breakouts. We always wanna have that exposure.

Matt: So one place where less so a breakout right now, and I know it oscillates, but I wanna talk about sentiment because sentiment seems like it’s showing how a lot of us are feeling right now when I look at this chart.

Katie: Yeah.

Matt: Even though those stocks are towards their highs, sentiment’s telling a different story on paper.

Katie: Yeah. No, it often does, doesn’t it? You know, it’s funny ‘cause we all know how sentiment is from our conversations and from how we feel. But what’s great is we have these, like, measurable sentiment gauges.

The, a very popular one is the VIX, the CBOE Volatility Index, and we use that as a transactional gauge. It’s looking at basically how hedged, mostly institutions are. And that can be real money or representing real money. So things like that can be so informational. And we also use the Fear and Greed Index, which is like an aggregated sentiment transaction gauge in a way.

So that will tell us what not only the VIX is sort of saying, but the VIX alongside other things like junk bond demand are saying. And right now it is pretty oversold, to go ahead and use that word, but not to the point where we have that turnaround to suggest that it’s done. What we find is that this is another oscillating measure, and when you have a sub 25% reading, that’s the so-called oversold reading.

But it’ll often dip to a deeper level, and it’s that pivot point, and when it comes back above, you know, 20, 25%, that’s where we like to pay attention. So we’re not convinced that we have that decisive turnaround yet. There’s always noise. But certainly sentiment is from that measure getting to sort of an extreme or has gotten to an extreme bearish level that we start paying attention to because those bearish sentiment readings are usually the stuff of major lows.

The catch is they can get much more bearish before they turn, right? So you wanna be not too, too quick to respect the bearish sentiment reading, and make sure that you have those indicators on your side as well that are measuring the trend. So at a very minimum, we tell people to make sure that a bearish sentiment reading gives way to improved short-term momentum.

That can be, you know, watching the daily MACDs if you’re real short term. It could be watching the 20-day moving average for an upturn. So you wanna make sure that that momentum also confirms what’s happening.

Matt: Sentiment regarding where we started the conversation too, because we had sentiment that was on the side of greed without being fully across that line, and now we’ve gone into this consolidation. And I think it helps tell the story of why the consolidation feels so nauseating without crazy lows. But I’m curious, did this line up in this cycle at all for you, just in this consolidation phase since last we spoke?

Katie: It’s funny because we didn’t have an overbought reading, not for many months. So I think that was a little bit surprising. What we find is that in, you know, very strong tapes, you’d expect to have more overbought sell signals in the market internals. But they’re of course like far fewer and farther between than the oversold readings, which do generate much greater extremes.

The way we measure that is through analysis of not just one sentiment gauge, but multiple. So we look for extremes in like several measures before getting excited. So we don’t just look at the fear and greed and say, “That’s our primary gauge.” We also refer to other gauges and what we found is that we had, I think it was only two out of the, say, dozen or so indicators that we track for market internals hit an overbought extreme, which honestly isn’t really that extreme.

So if you’re using that as your primary input, which we are more adherent to momentum gauges, but if you’re simply watching for those extremes, we didn’t have a super extreme environment in terms of people getting overly bullish, right? Nor do we have an especially extreme environment on the other side yet.

So yeah, we haven’t had a lot of huge extremes since last year’s major low after the Q1 corrective phase. That was the last giant like oversold collection of readings that we received. So I pay most attention when you have the collection of extremes that are impacting, let’s say, three or more as a rule, and we just really haven’t had that yet, so kind of interesting.

Matt: I think it’s really interesting because it’s this idea that there’s points in time when multiple indicators line up. And right now, where we are, nothing’s really lined up in a way that gives a strong sense of a new trend’s emerging, a bottom’s in, or we’re about to go lower. We’re sort of in this weird cloud right now.

Katie: Well, yeah, it’s like almost like the void, right? But it’s where the market is most of the time. So when it comes to the market internals, the extremes are that way for a reason, right? You might see a collection of extremes like two to three times a year, I’d say, on average. And this year is below average in logging those types of extremes.

So you have on and off years. So it is really interesting. But we pay most attention when we get those big extreme readings, and I’m quite sure we will get them between now and year-end at some point. But yeah, is it imminent? We can’t say for sure, obviously, and I would say we’d feel more conviction that it’s with this kind of corrective phase if we were to see greater deterioration in our weekly indicators.

Matt: I want to go back to sectors. I want to sort of go through them one at a time if we can, because the difference of 2025 to 2026 thus far—

Katie: Mm-hmm.

Matt: —there’s been some wild swings between sectors, between that rotation from leadership. The energy numbers alone.

Katie: Yeah.

Matt: I mean, that’s a tiny part of the index. It’s a big number on a performance basis. How should we be making sense of this digestion that 2026 has brought us?

Katie: Yeah. I mean, I would just say that with the digestion phase, like in terms of sector rotation, like you — again, you have to kind of go back to reconciling the breadth and the leadership from the market.

So I feel like that, you know, the breadth has been good enough that we have in our own ETF, the Fairlead Tactical Sector ETF, recently maintained a nearly 100% exposure to the equity sectors of the market. So there’s enough working that warrants that kind of even in an equal weight strategy like TACK, holding eight sectors of the market.

And so that kind of for us would — like, we consider to be a more full equity exposure. That’s a function of the breadth of the market. The narrow leadership has been the bigger challenge and where we got the first correction this year and where we’ll probably get the second correction this year, is when the sentiment shifts more meaningfully around the leadership segments of the market.

And we have some perhaps early indications of that, but not again, to the extent that we feel like it’s high conviction. The sectors, if we were to pick them apart sort of one by one, again, most are in uptrends. Most are not in, you know, strong, steep uptrends like we’ve seen from the technology sector.

But now we have some of those breakouts, you know, like the industrials and healthcare and the beleaguered areas of the market, which previously was, you know, financials or REITs or consumer staples, even discretionary. So those areas of the market, in absolute terms, they don’t look too bad, honestly.

You know, the longer-term trends are pretty much intact. If you had checked up on them a year ago, you’d probably have sort of a similar takeaway as a year ago. We’re not seeing breakdowns, not a lot of breakdowns, unless they’re more short-term at times, right? We saw certainly a lot of, not necessarily breakdowns long-term, but overbought downturns after the correction culminated in March.

So that gives you a little bit of that kind of, you know, it shakes your confidence a little bit. But for our ETF, it’s like we only, you know, went down to like an 87.5% position in the equity market and then came right back to the full exposure. So it wasn’t really enough to impact the long-term momentum or long-term uptrends that are still really very widespread on the sector front.

Right now, I last looked at the utility sector, especially electric utilities, and boy, I mean, they’re acting really well, right? Not necessarily a super promising source of relative outperformance, but you can definitely make a case for them, you know, short term and also long term based on their action.

So there’s a lot to do out there. You know, the bottom-up work shows a lot of diversity in the charts, which is another cool takeaway, I think, from the current environment, and that is largely related to the sectors. Energy as another sector has seen like a pretty substantial downdraft, as you’d imagine with the price of crude oil.

But now we have some oversold indications that are getting kind of interesting, some signs of downside exhaustion from the DeMark indicators. So we pay attention when that happens and look for opportunities that become a little higher conviction. So even in areas that have done really poorly, we can make the case perhaps for either countertrend exposure or taking advantage of their corrective phases.

So it’s pretty unique environment in that way that despite that narrow leadership, there’s still been a lot to do, if you have sort of a broad exposure that you’re seeking in your portfolio, just, you know, if you’re a generalist, of which, you know, sometimes I feel like the only questions I get are about technology.

But it is, I think, positive to have kind of a generalist outlook when it comes to the sectors and for your core holdings, especially if you’re most interested in technology. It can create some ballast to a portfolio in a way, you know, if you have other sectors that might be less correlated to the S&P 500.

Matt: Which gets extra interesting, and thanks for bringing it up. Like the utilities performance, the reversal in REITs after all the BREIT drama and things that we saw in the middle of last year. There’s a lot of other moving sectors right now, and even if it’s not showing up in headline ways. At least we can talk about something other than tech.

Katie: Yeah, I know. We actually featured in an article recently the insurers, so the insurance sector, which we’re not getting a lot of questions about it, but boy, it started acting really well, and you started to see some breakouts. So it is a market that I would say is rewarding the breakout.

So when we see a breakout, we’re inclined to believe it. And it’s almost counterintuitive, I think, to sometimes you feel like you’re chasing some of these rallies, but the follow-through has generally been really very good. So, you know, when a stock clears its 200-day moving average, that type of thing, we’re generally seeing good follow-through.

So with breakouts in some of these other sectors or subgroups, it’s been great. I mean, biotech as another example within healthcare, that broke out a few weeks ago, and it’s still going. So you can get a little bit more, I guess, momentum out of the breakouts, which by the nature, they’re relieving the chart of their resistance, right?

So the supply that was there isn’t there anymore, and that’s a positive takeaway.

Justin: Katie, I know, you know, we’ve talked to you a number of times, and we always like to spend some time on the methodology behind TACK. And you kinda hit on it to some extent, but I think it’d be good to sort of hear from you, you know, how the ETF has managed both in these risk-on and risk-off environments.

And, you know, I was just looking at the — on the fund website, you know, you guys benchmark it to the Russell 1000 equal weighted. And, you know, when you look at, like, the risk statistics, things like standard deviation, you know, worst quarter, I mean, max drawdown’s not on there, but, you know, I’m sure it’s way...

It’s like not only has the fund kept close to a long-only strategy, but in periods of stress, you know, the strategy actually has delivered. So just kinda talk to that ‘cause I think we kinda sometimes lose sight of that as investors, like the importance of the downside management too.

Katie: Yeah. No, I appreciate that, Justin. And I agree with you. I mean, I think, like, kinda going back to that comment about ballast, right, to a portfolio that’s tech heavy, I think TACK is a great sort of strategy to that end. And I say that because not only of that equal weight exposure, which kind of inherently provides that ballast, right, but then also the long-term trend following that’s informing it.

So at its core, TACK is like a sector rotation strategy. It evaluates all the sectors using sector SPDR ETFs, and it looks for the best sort of long-term trend following inputs. And if all 11 economic sectors are checking boxes, well, we narrow those to eight, and we do that using a quantitative overlay.

And if there’s only eight, we’ll hold all of eight, and that’s where we stand currently. And if there’s less than eight, well, then that becomes an environment in which we will use other asset classes. So we will shift the bucket that was sort of dedicated to the sector that’s now not showing those long-term bullish characteristics, and we give that bucket to a combination of ETFs representing treasuries, so both short-term and long-term treasuries and gold, so using the gold MiniShares.

So it’s a really pretty unique strategy in that it has that trend following element. It uses asset allocation, but then at its core is sort of an equal weight sector strategy that the end result, I think, is most, I guess, relevant when we have our conversations with investors because we want them to think in terms of what’s our desired outcome.

And I know that’s become like a phrase in the world of ETFs, but we wanna make sure that we are limiting drawdowns consistently through this strategy. And, you know, that’s just as important to us because we believe that especially, you know, as you’re either on the verge of retirement, in retirement or, you know, unfortunately perhaps investing at the start of a bear cycle, which will and does happen, that you have some protection, right?

Some downside protection, and not through some sophisticated option strategy, but through that asset allocation and through also the trend following, which inherently kind of manages risk in and of itself. So that’s the TACK strategy. I think, you know, the low beta profile is something that is desirable, the low correlation to the S&P 500, and even low relative correlation to that benchmark, which has no asset allocation, of course, right?

We’re comparing it to an equal weight index that suffers, you know, bigger drawdowns and has more volatility, right? So it’s almost like the more accurate comparison could be something that’s like a 60/40 type of portfolio, right? But then you have that extra gold element to it, and it doesn’t actually over history tend to run 60/40 exactly.

It obviously fluctuates. It can actually go all the way to full exposure in assets outside of equities. So as much as it’s an equity-focused fund, it can at times hold no equities.

Justin: That’s great. Thank you. Yeah, I think it’s a very interesting strategy to be tucked in, you know, inside investor’s portfolio, particularly for someone that is worried about drawdowns or maybe more specifically for, you know, people that tend to behave badly when market drawdowns happen. And a lot of investors do that, you know?

Katie: Yeah. We all do. And systematic, I think, is, you know, an important takeaway as well, where it’s responding to market moves as opposed to, you know, our discretion. We’re not waking up and saying, “Hmm, you know, what should this look like this month?”

It’s really we’re letting the indicators guide our positioning, which I think is something that’s — again, that risk management is inherent to being more systematic in an approach. So yeah, it is pretty unique.

Justin: Another area I think that investors are or were, and probably still are underexposed to is, you know, international stocks, both developed and emerging markets.

So talk to us about what the charts are telling you about international, in those two areas of international versus the US. I mean, it’s been, I think, a pretty good run since the beginning of ‘25 for both sort of cohorts of these non-US stocks. But what are the charts saying here?

Katie: Well, it’s been pretty interesting because emerging markets have this element of the technology sector dominating the performance of the benchmark.

So we have the South Korean market, as mentioned, had a very strong run-up, now quite a strong pullback, but that’s largely related to just almost like a very small segment of that market, but a very dominant and powerful one in the semiconductor sector. So the emerging market proxies have been pretty, I guess, influenced by that.

Also, I mean, we always would talk about how China dominates these emerging market proxies like EEM. But boy, China has really done poorly of late, and you wouldn’t really see that in those EEM proxies, and that’s exactly how strong Taiwan and these other regions in Asia have done well and contributed to that relative performance.

So it’s a really interesting kind of case study, but it’s almost like you want to maybe focus more on the country level than from a top-down perspective. But the relative trend is favorable longer term emerging markets to US, and that’s something that we sort of started to see inklings of a few months ago, and it’s persisted.

You can see it in the curvature of the moving averages of the ratio and the action, the trend following. And then for EAFE countries, developed global versus US, we’ve seen a pullback. I think you tuned into the fact of the timing of that really came on the back of when the war initiated and then when also we saw that really strong recovery in the US.

I think when the US is working, it certainly is the dominant sort of player, I think in global investment markets. So that underperformance from the EAFE countries, I think it sort of — you could almost translate it as if we’re talking about semis versus our market as like the US is representing semis, right?

So I think that the underperformance is wholly a function of the lesser technology exposure in these other countries. And that when we see more sort of value rotation, which we’re starting to see some hints of, that that’s when the European countries tend to benefit from that in relative terms.

So it really is — it’s based on the sector positioning and the actual price action in absolute terms, and the US is really very influential on those ratios. But I would say the takeaway, honestly, in the ratio itself is sort of neutral, because following this pullback associated with, you know, the conflict and the rebound and what have you, you know, the range is still holding.

So the pullback has brought the ratio into some range support and even still, the long-term shift still seems to be intact, right? So even though there’s a range now, it’s a better picture than it was two years ago for international markets versus US. So it just opens up more doors, I think, to invest in countries where you have a compelling case to do so, either from a macro or fundamental perspective.

Commodities are a huge influence on the country level, as you know as well. So, you know, if you’re constructive on certain metals or on certain energy commodities, well, that might lead you to different markets. But overall, I would say in line to better performance is likely to characterize this year, and that opens up more doors for us.

Matt: It’s fascinating how markets have digested this conflict and this event and how little we see it sort of in that chart itself when we look at it.

Katie: Yeah. It is. It is. And I think it’s, you know, it’s all about sentiment. And so somehow we’ve managed through this environment without losing the sentiment that has boosted the market.

And I’m not talking about the Fear and Greed Index type of sentiment, but the demand, right? And that demand I think has been fueled by the AI trade. And you know, it might be different next time, but I think that that sort of excitement has been exactly why we’ve been able to navigate this challenging environment.

But I do think that the shock in crude oil prices is something that remains a risk even though we’ve seen a big retracement. And I do think that credit spreads, you know, looking somewhat oversold from a technical perspective are also a risk. So these are things that we’ll watch and will keep us, you know, our guards up to some degree, just given exactly what you cite there, Matt, is in terms of it’s surprising to see such a resilient tape in the face of some of the geopolitical risks.

Matt: Another one that’s a surprise, and so far best explanation I’ve seen of this came from Ben Hunt, who shared it on our monthly show, where he basically said trust in the US Central Bank seems to be recovering from a low from when the president was beating up on Powell last year, and that seems to have been righting itself, and maybe that’s the explanation for where I wanna go next, which is the price of gold.

This is another one that, well, we’ve come off the top a little bit. What’s going on with gold?

Katie: It’s been a corrective phase that now shows signs of downside exhaustion. But what we saw last quarter and what remains an issue is a loss of long-term upside momentum that’s meaningful enough to hit our monthly indicators.

So I think we’re now in the mindset of, “Okay, well, gold, you know, more short to intermediate term swings are what we’re going to try to navigate as opposed to that nice strong bull market that we had previously.”

Matt: Katie, people wanna find you on the internet, bug you, get some of this research, where can we send them?

Katie: Of course. Yeah. So we have a website, fairleadstrategies.com, and we encourage people to take a trial of our research. We offer free one-month trials. They can also find me on LinkedIn and on X. It’s @StocktonKatie. And, yeah, we also have the Fairlead Funds website, which is all about TACK, so encourage people to reach out.

Matt: Make sure you check it out. Make sure you head over to Excess Returns on Substack. We’ll have notes, transcripts, all sorts of things on this episode and more. Katie, thanks so much for doing this.

Katie: Thank you, guys. Nice to see you.

Matt: Like, comment, subscribe, all the things below, and we are out.

Main topics covered

• Why evidence-based investing matters during bubble-like markets

• The emotional reality of holding risk assets through painful periods

• How to think about market concentration without jumping straight to bubble calls

• Why global diversification changes the mega-cap dominance story

• What high market valuations mean for financial planning and expected returns

• Why wide valuation spreads may create a better setup for value stocks

• What factor research says about AI capex and corporate investment

• How Longview builds a diversified factor strategy around discount rates

• Why implementation, trading flexibility and scale matter in factor investing

• The small cap premium debate, IPOs, fallen angels and survivorship bias

• Why AI may increase data mining risk in quantitative investing

• How fixed income tax drag can quietly reduce after-tax returns

Timestamps

00:00 Why painful markets create future return premiums
04:00 Market concentration, AI winners and the value of diversification
09:40 How high valuations should influence financial planning
13:12 Why wide valuation spreads matter for value investors
14:01 What factor research says about AI capex
16:20 How Longview’s EBI strategy looks for higher discount rates
18:58 Why Longview starts with the market and then tilts
21:45 Comparing 1999, 2008 and today through expected returns
24:33 Intangible assets, price-to-book and the limits of accounting adjustments
28:32 SpaceX, IPOs and how indexes handle new mega-cap companies
33:21 Why implementation and trading flexibility can affect returns
36:17 Passive flows, price elasticity and market price discovery
39:35 The small cap premium, IPOs and fallen angels
42:21 Are today’s small caps lower quality than history?
46:01 Why AI may not uncover the next great factor premium
48:04 Why fixed income may be the most inefficient part of taxable portfolios
51:29 How LVIG tries to convert bond income into deferred capital appreciation
52:50 The after-tax return opportunity from tax deferral
54:58 Which investors may benefit most from tax-efficient fixed income
56:26 Where to learn more about Matt Zenz and Longview

Matt: Hey, Jack. How you doing?

Jack: Good. It’s great to have you. And I’m kind of excited because you are an evidence-based investor, which is what I’ve tried to be my whole career as well, and I feel like we might be in a world right now where people might need a little bit of that. I don’t know if you think that’s an exaggeration, but I feel like evidence-based investing might be something that’s good for everybody right now.

Matt: Yeah. I mean, personally, in my investment philosophy, I think it’s what everybody should be doing all of the time. But yeah, there are certainly times when maybe people need a reminder or need to dive into it a little bit more. And so with social media now and everybody touting these levered products or the new IPOs that are coming out, and people get real excited about these types of things, sometimes it’s good to kinda go back to basics in terms of what drives returns.

Jack: Yeah, you’re definitely right. I mean, we should always be evidence-based investors. But during these, I don’t, I don’t wanna say bubble, but like bubble-like periods, people tend to be a little more detached maybe from the evidence than they normally are.

Matt: Yeah, exactly. Yeah. They live in the moment and kind of forget about what’s happened in the past.

Jack: I’m just curious before we start. Going through these periods, you’ve looked at evidence through 100 years of history in terms of how things happen. When you go through these periods, do you feel differently in the period than looking at the evidence? It’s something I’ve experienced a lot in my career.

Like when you look at the data and you’re like, “All right, value investing can struggle,” and you’re like, “Oh, look at the chart though. It came right back up.” But then you go through it and you’re like, “This is a disaster.” Or you go through these bubble periods and it’s like, oh, I know if I went through the 1990s, I would’ve been like, “No problem.”

Like I understand the evidence will take over again. But then like you’re in it and you’re like, “AI will change the world forever,” and, you know? I mean, do you see that disconnect between like being in the real period and also like looking at the evidence?

Matt: Yeah. I mean, I try to divorce myself of that as much as possible.

But when we talk to clients and prospects and those types of things, it is almost impossible to divorce yourself from what you’re hearing today to the history. Like, there’s so many people who are just like, “Oh yeah, 2008? Yeah, I would’ve bought back in. Like I rode those returns no problem.” But you know, that 20% drop in the first couple weeks of COVID, like they were freaking out.

Or you know, any type of small drop. And so at the time it always feels worse, but that is ultimately what drives the return. If it didn’t feel bad, if it wasn’t painful to keep holding it, you wouldn’t get rewarded with higher returns. And so it’s that discomfort is what actually drives the return that you get if you’re actually able to stick with it for the long term.

Jack: Yeah. I don’t know if it was Corey Hoffstein, but someone said, “No pain, no premium.” And I think that’s a great saying to think about things like that and how they work over the long term.

Matt: Yeah. Yeah. No, I totally agree with that assessment.

It’s, you get paid for risk, right? And if you’re willing to bear risk— and risks can be defined as feeling uncomfortable, not being able to meet your goals, downside returns. And so that’s... you gotta balance those, and if you’re willing to stick with it, then you’re gonna get rewarded with return.

Jack: So I’m not gonna ask you about the overall market, but I do wanna, like, have you help me put it in context because we are in a period that some people call bubble-like.

We’re in a period where we’re seeing, I don’t know if it’s unprecedented concentration, but we’re certainly seeing high concentration in the major indexes. I’m just wondering, as someone who’s looked at evidence, who’s looked at 100 years, how do you think about this period in general?

Matt: Yeah, I mean, typically I think, like, is it a bubble type question is usually the wrong starting point.

A lot of people, they think about a story like, “Oh, market’s getting more concentrated,” and they hunt for data to support that. And as an evidence-based investor, we try to stay away from that. We try to start with prices. And so right now, what is the market telling you with the prices? It’s saying there are a handful of companies that are extremely valuable.

The market thinks they’re valuable because they think they’re gonna deliver a lot of profits in the future. That might happen. It might not. And for us, we think about market concentration, we just think it really means there’s really an extra value on being diversified. Diversification is especially valuable during these periods of time.

And so we try to encourage people to think and be careful about — just because things have done well recently doesn’t mean they’re gonna continue to do well, but it also doesn’t mean that just because markets are concentrated in a handful of names, that means the market’s gonna do worse. You have to try to remain diversified, and there’s lots of ways to kind of get that diversification out there.

Jack: Yeah, I’ve always wondered if that bubble question is useful because a lot of people like to say, “Are we or are not in a bubble?” But then when you get to the question of like, what do I do about it? It becomes much more challenging to try to figure that part of it out.

Matt: Yeah. I mean, bubbles are super easy to identify in hindsight, right?

But it’s kind of... but really hard to know while you’re in it, right? The reason why these markets are frothy, that these AI companies are doing really well, is because maybe they will deliver on those expectations. Maybe they won’t. For us, it’s not so much about thinking about things in terms of number of companies.

What we think about in terms of is economic engines. So if you look at like the S&P 500, the top 10 names are something like forty percent. But most of those names are in IT companies or AI companies. And so it’s not so much that it’s a number of names thing, it’s what is the economic exposure you’re exposed to.

And if you’re only invested in the S&P 500, your main allocation is really to information technology, IT, in the US. Now, if you’re a global investor, you add US small caps, you add international stocks. You just went from your top 10 holdings being forty percent, cut in half down to about twenty-two, twenty-three percent.

That’s a huge difference. And so when we make portfolios, when we think about investing, we think about it globally, and then that market concentration you see at the top in the US is less important. And I also think we don’t also think about individual names. Like, just think if Nvidia acquired Tesla and Exxon.

That company would be even bigger than it is today. It might be 10% of the market. But the underlying economic exposures are the same as if you held the three companies separately. So just because companies are acquiring other ones or growing, what really matters is what is the underlying economic drivers behind that?

Are there other companies with similar economic drivers? If something bad happens in that piece, that sort of economic structure, will that have a negative impact? And that’s why we wanna be as diversified as possible, invest globally, invest in small caps so that you’re not kinda concentrated in maybe that one IT, AI bet.

Jack: That’s what I like about what you do, is you guys — and correct me if I’m wrong, but you guys kinda start with the market, and then you make adjustments from there. And I think a lot of the value investors, me included, have kinda been shaking our fist at the Mag Seven for, like, these past however many decades while they just continued outperforming and using our ratios and saying that doesn’t make any sense.

But, like, if you start with the market and then adjust from there, you understand, like, these companies, if you look at their fundamentals a decade ago and you look at them now, they’ve gotten a lot better. But that doesn’t say they’re not expensive now, it just means these companies have been growing their businesses a lot, and that partially, at least, represents why they’re such a big part of the market.

Matt: Yeah, exactly. Yeah, they’ve done well. And so they’ve performed well for two reasons. One is they’ve been really profitable, but the reason why their values are so high today is because people expect them to continue to be profitable and continue to have really high profit margins, and so that’s all in the price today.

And investors who invested in those companies 10 years ago got the benefit of that. What we don’t know is what’s gonna happen the next 10 years. If they continue to exceed expectations, then those investors will be very happy and continue to outperform. If they don’t, then they won’t. But ultimately, all of the buzz around those companies is already factored into the price.

And so what you would need if you wanted to continue to hold those is they have to do better than people expect.

Jack: So you don’t see anything in market concentration in and of itself that’s a concern for the market?

Matt: No. And it depends what you mean by concentration. Again, it’s individual names versus sort of sectors.

But not really. There are many markets that have very few names. There are countries with one stock in them, with 10 stocks in them. And those companies have delivered high returns. When we look at the number of names in a particular market or the amount that the top names make up in any particular market, there’s no evidence that says higher concentrated markets tend to have lower returns or higher returns.

What we think is the best course of action is to limit your volatility, which would be as diversified as possible. But concentration itself doesn’t mean higher or lower returns.

Jack: I’m gonna guess you don’t concern yourself with overall market valuation too much. But I am just wondering, like, is that accurate first of all?

Matt: It is, in terms of... so I kind of wear two hats. So I’m the chief investment officer at Longview Research Partners, which you mentioned, which is an asset manager. But on the side we have a sister firm that’s an RIA, where we manage money for individual clients. That’s where this thinking about the valuation of the market matters to us.

And that’s because higher valuations tend to mean over the long term, lower returns. Doesn’t mean negative returns. The market is always priced to deliver a positive return. If it wasn’t, no one would buy it at that price, and the price would come down. And so if it’s always a positive return, I wanna be invested in it.

Now, what it tells me, though, is what I should expect for financial planning. We use it as a financial planning tool. If the market’s at really high valuations, it means returns might be lower than the historical average, and so we need to factor that into financial plans. If valuations are low, it might mean that future returns are higher than they have been in the past.

So it doesn’t impact what we invest in, but it does impact how we plan for the future.

Jack: That actually leads perfectly into my next question because as you think about those expected returns, you have one camp of people that says there’s a long-term average in terms of the market valuation, and we’re obviously way, way above that, and they say we’re gonna revert back to that over time, which would affect those expected returns.

And you have other people who say, “Yes, the market is expensive now, but we would expect valuations over time to go up, for a lot of different reasons.” I mean, the mix of the market right now would be one. We’ve got higher margin businesses leading the market. But what do you think about that?

I mean, do you have any thoughts on this idea of, like, if the market should be reverting back to its long-run average or if things have changed in the world and we’re in a better world and maybe higher valuations are justified in general? Maybe not the ones we have now, but at least in general.

Matt: Yeah. So there is some data around mean reversion, right? So price-to-earnings ratios are really high right now, but there are two ways for price-to-earnings ratios to go back to historical averages. One is prices to go down, right? If you want your P/E ratio to shrink, you have to have prices go down or earnings go up.

And so P/E ratios could shrink without any impact on price. Your return could be the exact same it has historically, but earnings can just go up a lot, and then your P/E ratio comes back down to normal levels. Or prices could go down. And so in different times in the past, different things have happened in terms of sometimes prices go down, sometimes earnings go up.

So ultimately, when it comes to investing, we don’t know what the driver is gonna be. We use valuations kinda, as I mentioned before, in planning. But ultimately, we don’t use it to inform future — in terms of, like, what companies we’re gonna invest in.

But just because the market as a whole might revert from a price-to-earnings perspective doesn’t mean that individual companies all have the same price to earnings. There’s a wide spread in individual companies, and that’s where you can add value when it comes to investing. Even if the market has an average P/E of twenty-five and the historical average is eighteen and we think it’s gonna revert, well, not every company has a P/E of 25.

Some have lower than 18, some have higher than 25. And if you can focus on the companies with lower valuations, that implies higher returns for those individual stocks.

Jack: Yeah, that gets to the idea of value spreads, which we are seeing are still, I mean, maybe not unprecedentedly wide right now, but still pretty wide between the most expensive companies and the cheapest companies.

Matt: Correct. Yes. They’re like something in the eighty-fifth, ninetieth percentile right now, so they’re not as crazy as they were, let’s say, two years ago. But they are still wide. Wide valuation spreads typically mean larger value premiums, which is what we’ve seen over the last year. That’s come true.

But again, you never know. That’s — over many decades, that’s what tends to happen. But over any individual year or any short timeframe, we don’t really know. But we should expect value to do a little bit better than growth, better than it has historically because valuation spreads are so wide.

But things can always get worse, always go in the other direction.

Jack: I wanted to ask you about this AI CapEx, but not from the perspective we’ve been talking about it with a lot of other investors. We’ve been talking about the idea of people who studied railroads and past booms and things like that. But one of the unique things about you is you’ve studied factor investing for a long time, and there’s definitely some interesting research in factor investing about what happens when firms spend a lot of money on CapEx historically. So I’m wondering if you could share that.

Matt: Yeah. So this gets to kind of the investment, right? If you just go back to the valuation equation where your return is the profits that the company has discounted to today. But those profits can either be given back to you as an investor or invested back into the company. And if they’re invested back into the company, they’re not going to you, which can impact your return.

You’re not getting that return. And so what the research has shown is that companies that do a ton of investment tend to have lower returns in the future. Now, what I mean by a lot of investment is we’re talking seventy to 100% growth in assets, meaning they’re like doubling the size of their company because of the amount of investment that they’re doing.

A lot of these AI companies, Google, Microsoft, etc., they are investing a lot into CapEx, into these data centers. But as a percentage of their overall company, it’s actually not that much. And so when we think about these CapEx investments, we do avoid companies that invest a lot, but a lot is actually more than you think.

And so these companies that are doing these data center type things actually aren’t hitting that threshold of investing enough that we would be worried from an expected return perspective. And so there’s really not much that the data says when it comes to these types of companies investing in the CapEx that they’re doing now in data centers, et cetera.

It could work out, it could not. There’s not enough data to say one way or another if that’s something that you should dive into more or avoid.

Jack: That’s such an important point because you have to think about it relative to the size of the companies that are doing it. And people talk about — they throw out massive numbers of this massive amount of CapEx, but we also have to keep in mind these are massive companies doing the massive CapEx.

Matt: Yeah. They’re trillion-dollar companies. And if they do a couple tens of billions of dollars, that’s not a huge percentage of their overall company.

Jack: I wanna talk about how you think about some of these things from an investment standpoint. But first, this is probably a good time to talk about your strategy in general. So with EBI, can you talk about how you manage the portfolio, how your investment strategy works?

Matt: Yeah. Now, for us, you mentioned before we’re factor investors. When most people think about factor investing, they think about the known five-factor model, the known premiums, things like size, value, profitability.

I think we think about things a little bit differently. We think about things in terms of discount rates. So any company, if you wanna know the value of it, what you do is you take what you think the future cash flows are gonna be, and you discount them to today. That discount rate is your expected return.

It’s the dividend discount model, valuation model. And so what we’re trying to do in EBI is simply find the companies with the highest discount rates. Those are the companies that investors are demanding the highest return for. And if you invest a little bit more in those companies, you, on expectation, should have higher returns.

Now, the typical factors that people think about — value, profitability, size — those are great proxies for things that have higher discount rates, higher expected returns. And so we use those as clues to find the companies that have higher discount rates. So for example, you have two companies that have the same price, but one has much higher profits.

Well, that company has a higher expected return. You’re paying the same thing, but you expect to receive more with the higher profits. That must mean those profits are discounted at a higher rate. It means investors are demanding a higher return for that investment. The same can be true on the flip side.

If you have two companies that have the same level of profits, but one’s at a much cheaper price, it must mean that investors are discounting those profits by more. It means the expected return is higher. And so price, future profits — those are things like value, profitability. You can use those metrics to get you to that level.

And so ultimately, that’s what we’re doing in this fund, is doing it in a low-cost, diversified way. We hold thousands of companies from large to small across the US in a low-cost way. But we’re just tilting a little bit more to the ones with higher discount rates. And we’re doing it in a very nimble way.

We keep — we’re not a huge trillion-dollar asset manager, and so that keeps us a lot more nimble, allows us to move much more quickly as prices change every single day.

Jack: Can you talk about the logic for starting with the market and then adjusting? Because I guess one way you could run this is you could say, like, “Give me the 50 companies with the highest expected return,” or something, “I’m just gonna buy those.”

But you’ve decided to start with the market and then make adjustments around that. Can you explain that logic?

Matt: Yeah. So it all depends on how much risk you’re willing to take, right? So there is a single company out there that has the highest expected return. When you look at these metrics, the investors out there are discounting the future cash flows at the highest possible rate, meaning it’s the riskiest company but it has the highest expected return.

You could put all of your money in that one company, but there’s no guarantee, right? Risks happen, and maybe you lose it all. And so then you could say, “Well, I’ll do two companies,” or, “I’ll do five,” or, “I’ll do 50,” or however many you wanna do. And so it’s just a question of how much risk you’re willing to take and what do you want your overall portfolio to look like.

And so we think the market is a great place to start. You start with market cap weights. That’s how much the market values every company. We’re gonna start there. We’re only gonna deviate if we have a good reason. A good reason is we think certain companies the market thinks have a higher expected return, maybe because they’re riskier.

So we’re gonna put a little bit more weight there and a little bit less weight in other companies. And we could have done just 50, but then we’re taking a lot of risk. If those 50 just happen to be a bad draw or you go through a five to 10-year period where the risks materialized and those companies really took a big hit, well, now your whole financial plan could be destroyed.

And so it’s really about balancing risk and reward, and we think within our fund, EBI, we’ve done that. We’ve got the whole market, so our tracking error should be in the 3% to 6% range, and we expect to outperform due to those tilts that we talked about.

Jack: Yeah, and I think behavior would probably be a big part of that as well. Like, I remember, when I was early in my factor career, I decided I’m gonna be a hero, you know. I’m gonna run these 20 stock focused factor models, and then as soon as that goes bad, you realize investors aren’t sticking with those things when things go south on you. So this — I would think this is a much better approach behaviorally.

Matt: No. I mean, we don’t hear it too much now because value’s done well over the last year, but a year ago, for 15 years people are talking about value’s dead. And so that was a 15-year period where value underperformed. If you were only in the 50 most value companies, you may have underperformed by 5%, 6% annually for a decade.

That means you have half the amount of money you otherwise would have. A lot of people just compare themselves to their neighbor. Their neighbor’s buying new cars, getting a new house, and you’re like, “What the heck?” Are you gonna stick with that? I don’t know. A lot of people can’t.

And so you have to see what can you stick with from a behavioral perspective. And so leaning in a little bit, maybe you can stick with it through the bad times, whereas leaning in a lot, maybe you get out and then you never capture the benefit.

Jack: Does that expected return framework allow you to look at an opportunity set at different times? Like for instance, I would bet the opportunity set in 2009 was a lot better than it was in 1999, just because in general there’s more companies that are cheaper or might look more attractive fundamentally. Is that the wrong way to look at it, or does that give you an opportunity to say, like, we have better opportunities one time than another?

Matt: Yeah. So I think there’s two different ways to think about the opportunity. One is the entire market, right? So when the entire market is at super high valuations or super low valuations like 1999 or 2008, that tells you what the opportunity in the market as a whole is.

Again, we always expect the market to have a positive return, but in 1999, maybe you thought the future return is only gonna be 6% annualized. Whereas in 2008 when you’re looking at it, it’s 10% annualized just because of where valuations are today. And so 2008 looked like a better time to get in.

It was also because it was riskier, right? Like getting in at 2008, you’re getting a higher future return because you’re taking a lot of risk. Things could always get worse. We didn’t know what the Fed was gonna do and that kind of stuff was gonna solve the problem. Then that’s kind of the first level.

Then the second level is within the market, what is the spread of things? And so in 2008, kinda everything was depressed, so there wasn’t a large spread between the growthy names and the value names. They all kind of looked similar. And so you didn’t have as much opportunity there from a value versus growth perspective, but you did have that big opportunity in terms of the market as a whole.

Then you look at 1999, the spread between growth companies and value was huge, similar to what it is today. And so while the overall market may not have as big of an opportunity, individual names within the market have more opportunity, and there’s more lower valuation names that you could invest in that have higher expected return.

And we did see that in 2000 to 2004 where value crushed it. We have no idea what’s gonna happen over the next four years. But we’re seeing similarly high market valuations and similarly high valuation spreads to what we saw in 1999. But that’s just an anecdote. That’s one time in history, and we don’t base anything we do off of one period in history.

Jack: But it’s interesting to hear, ‘cause this is a completely different way to look at comparing 1999 to now than most people look at. Most people are looking at what was going on with the tech and the environment and all that, and you can look with actual numbers, with spreads, and kinda say, “Here’s how these two periods compare to each other.”

Matt: Yeah. And people try to compare it like, oh, the internet was this massive technology. Everybody was investing into it. Now we have AI. Those are the same. We tend not to think about it that way. We look at prices and profits. What are the profits of these companies? What are the prices telling us?

What does that mean about the return investors are demanding for different stocks? And that tells us how we should approach investing in that environment.

Jack: Speaking of tech companies that have sort of taken over the market here, how do you think about valuation in the world we live in today? Like, some people argue some of the things like price to book that we’ve used for a long time are no longer really applicable. A lot of these companies have most of their value in intangible assets. Do you think about valuation differently because of the types of companies we have today than maybe in the past?

Matt: Yeah. So we don’t, and the reason is because it’s really hard to do. So if you think about intangibles, a lot of people think the value of companies now is more in intangible assets. So things like, you do a bunch of research, develop a new product, and that has value to it, or you have a patent or things like that.

And there’s two different types of intangibles. There’s externally acquired and internally developed. So if I’m a company and I go and buy another company, whatever market price I pay for that company, you can add up all of the assets and liabilities, and you get this net asset number. Whatever I pay over that, that is the intangible value, right?

Like, I take all the factories, add all those up, all the cash, it’s worth $10, and I buy it for $20. That means there was some intangible value of $10. That goes on my balance sheet now ‘cause I bought that company. There are some firms that want to remove that from the book value, saying, “That’s an intangible asset. We shouldn’t be accounting for that, ‘cause that’s not part of the company, really.”

I think that’s kind of silly. You’re removing information. The market determined what the price was of that intangible asset. It was purchased on market, and that has value, and you should factor that into your valuation of any company.

So that’s one side. Then there’s the internally developed. I’m a company, I’ve done a ton of R&D, developed AI as this great capacity, and that’s why my company is valued so highly. You wanna capture that. Like, what is the value of that intangible asset? Well, it’s really, really hard to determine.

There are a lot of people who have tried in different ways, and there just isn’t anything that’s repeatable or reliable in determining that. So one test you could do is you could say... well, a lot of people try to capitalize R&D, right? Every year you’ve spent a lot of money on R&D for the last decade, and that has value, right?

It’s not just R&D goes out the window. That’s an asset, and we could capitalize that. Every year, whatever you spent in R&D, let’s pretend that’s an asset, make that intangible and add it. And then companies do that, and they get acquired. You can go back and see, okay, they were acquired for some price.

How much was that intangible asset actually worth when the market bought them? And compare it to your research results of capitalizing the R&D. And what you find is there’s no relation. Capitalizing the historical R&D told you nothing about what that intangible value is actually worth in the future. And so I’d love to be able to capture the intangible value.

I just... there hasn’t been a good, repeatable, reliable way to do that. And so we think it’s better just not to touch it than try to do something that creates noise and provides no value.

Jack: Yeah, to your point, I think if we’re gonna figure that out, eventually it’s gonna be like some more advanced methods, because if I wanted to find the value of Google’s brand or Google’s search engine, I couldn’t have adjusted their financial statements in any way that would’ve gotten me to that value.

Matt: Right. I wouldn’t know how to do that, and there are a lot of people trying to do that with different AI models and that kind of stuff. I’m not sure how they would do that through that either, and you have a huge data mining and short sample size problem, in that this hasn’t been around for very long.

The data hasn’t been around for very long. Markets are extremely noisy. And so it’s really hard to draw any conclusions off of five or 10 years of data. You need 100 years of data in 50 countries before you can really say anything that meaningful about what truly drives returns.

Jack: Given that you start with the index in the construction of your portfolio, I thought it’d be interesting to ask you about some of the stuff that’s going on at the index level right now, because we just had a major IPO with SpaceX.

We’ve got some other ones coming, and the indexes have been all over the place trying to figure out what do we do with this? Do we break our rules? Do we add it right away? And I could see two arguments to it. I could see one side, like, this has not typically gone well for the indexes when they’ve added these types of companies.

But on the other side, I could see, like, they’re trying to represent the market, and some of these will be very big positions. I mean, obviously, SpaceX only has a small float right now, but these will be pretty big companies, and you could make the argument you gotta add them. I’m just wondering if you have any thoughts around that.

Matt: Yeah. I mean, indices have — it’s tough. They have a dual mandate, right? So they started out purely as a benchmark. That was what they were meant to do. Let me just give you the performance of the market. And under that framework, they should add it. It’s part of the market. But indices have changed, and in a good way, in that they now are more for investment, and people are actually investing in these indices.

And I think a lot of the reason is because typical active managers have underperformed, and people are better off in just indices than a typical active portfolio. And so now these indices need to think about, well, I need this thing to be investable. I need to make this such that people wanna buy it on the other end.

And so that changes their dynamic. And so different indices are gonna come on different sides of that based on the feedback they get from their end people who subscribe to that index. Some of them are adding it sooner, some of them are not. To your point, it hasn’t fared well, and this is where being flexible, not being so rigid in terms of having to follow an index, allows you to take advantage of these types of things.

So typically, IPOs tend to underperform in their first year. Part of that reason is due to the fact that most companies that IPO tend to have really bad valuation characteristics, right? We already said that valuation matters, the discount rate matters. They tend to have really low discount rates, meaning low expected returns.

And so they tend to have lower expected returns. So we ideally would want to avoid them. That’s why they don’t have great returns in their first year. And so what you want for markets to work is you want price discovery. That often takes some time for markets to understand what this company is, how they want to value it.

When companies have low free float, when they have lockups, that restricts supply, that inhibits price discovery. And so generally, we try to wait until those lockups end around six months before we would add a security to our universe and start investing in it. Indices don’t have to balance different objectives, giving people exposure to the market but then also making it investable, and they’re gonna fall on different sides.

When you have a flexible approach, you can make the decision that’s in the best interest of highest expected return, and in most cases, that’s waiting six months and then adding it to your portfolio just like it would be any other stock. Right now, SpaceX looks really bad from a valuation perspective.

It’s... from a valuation and profitability perspective, it’s not something we would want a lot of weight in.

Jack: To your point too, there’s so many things, like when an IPO comes out, so many mechanical flows that are going on that have nothing to do with the fundamentals of the company. So I can understand the idea of, like, let’s wait this out a little bit. Like, this index is adding it, these people are... Like, let that play out a little bit and then maybe add it after that.

Matt: Yeah. I mean, only 5% of the shares are actually trading right now, and we expect something like thirty — depending on what metrics they hit in terms of price over the next six months — but another like 30% to kind of release itself from the lockup over the next six months.

And so you’re gonna have a lot of people probably selling some of their shares. And so then our view is let’s let those mechanics play out. After that six-month period of time, then we’ll look to adding it to the portfolio.

Jack: Yeah, that’s something people miss a lot too because of these free float adjustments. Like, SpaceX would not, even if it went in the S&P 500 tomorrow, it would not go in nearly at its market cap weight. It would go in way, way, way less than that because there’s only a small float out there right now.

Matt: Correct. But some of these indices are adjusting their float rules, right? I saw—

Jack: that.

Matt: The Nasdaq is doing it, yeah. So if maybe a company only has 5% float, you wanna weight it at 5% of the total company value such that your weight matches what’s actually available. Well, some of these indices aren’t doing that. They’re actually putting the weight at something like three or four times that.

And so that creates a problem because now you’re demanding three to four times the liquidity of this actual name, and that can have some negative consequences in terms of price and that type of stuff. So yeah, you gotta be careful. Implementation is so important, and these indices need to be careful in terms of how they’re doing it.

I mean, they don’t necessarily care, but the end investors in those indices are gonna feel the pain of that.

Jack: Yeah. And on that issue of implementation, this is something we talked to you a lot about last time you were on. That’s one of your things at Longview that’s the most important to you in terms of differentiating yourself from maybe some of the larger providers that have to move massive amounts of money around, is your ability to implement properly.

So I’m just wondering if you have any... First of all, can you explain what you do? And then you probably have some data now since last time we were on in terms of how that’s going. So I’m wondering if you can talk about that.

Matt: Yeah. Yeah. So when you think about this, when we think about comparing any two strategies, things like that, we think of the three Ps: price, premiums, and process.

So price, what are you paying for the strategy? Premiums, what research do you have that you think stock A is better than stock B? And then process, how do you do it day to day? And if you compare an evidence-based factor strategy to an index, an index wins on price, right? They give really low prices.

But typical evidence-based strategies win on premiums and process, right? They’re gonna go after companies with higher expected returns. We expect that to play out in the long run. And then they’re gonna have the flexibility that indices don’t have around process, where you can trade every day. When we compare ourselves to some of the larger evidence-based firms out there, ones that have a trillion or more in assets, we have the same price as them.

We go after the same premiums, right? We’re all looking at the same research. We all define the premiums roughly in the same way. We all pursue the same ones. The difference is in process, how we actually do it. And if you’re a trillion-dollar manager, you might own five to ten percent of every small value company out there.

Well, if prices change every day, if you no longer wanna hold that name, you now need to sell five percent of the outstanding shares. Whereas if you’re a smaller manager, you might need to sell .05% of the outstanding shares. And for us, it takes a day to buy or sell a company. For these larger managers, it takes them a year.

Well, that’s gonna impact returns. If you believe prices mean anything and prices change, and that reflects your future return, you wanna be able to move as quickly as possible, and these larger managers just can’t do that. Now, it’s still better than indexing, but you don’t wanna be trading all of your volume in one day.

But the fact that you have to wait a year to slowly get in or out of a name, that’s ultimately gonna lower and impact returns. And we’ve seen that in our first year. So we’ve been running our fund for about a year and a half, and when we compare ourselves to the large trillion-dollar evidence-based investors out there, we’ve outperformed a similar type portfolio by about two and a half percent over the first year and a half.

That’s meaningful. Again, we’re going after the exact same premiums, define them in the same way. The difference is just in process and how we do it, and we’ve been able to pick up an extra two-plus percent annually so far in the first year and a half, just by caring about implementation.

Jack: On this issue of execution and flows, do you have any thoughts? One of the things we’ve talked about a lot in the podcast — I don’t know if you’re familiar with Mike Green’s work, but this idea that we’ve got a lot of money flowing into passive investing these days. Like, for most 401Ks, that’s the default option. That leads to significant flows into the market, which don’t really care about the fundamentals.

They’re just kinda flowing into the market. And Mike has argued that, in terms of the relative pricing of stocks, that can have an impact because maybe Nvidia’s liquidity doesn’t scale as much as its market cap does. And so maybe there’s this pressure on these biggest stocks over time that’s, like, forcing them up relative to other stocks.

And I’m just wondering, do you have any thoughts on that? I know you’ve looked at implementation a lot. Do you have any thoughts on that, and whether you think that’s accurate?

Matt: Yeah. So I think what Mike talks about is a little bit different than what I think the average person talks about. So the average person thinks, “Okay, money is going into indices. It’s pushing up the biggest companies larger.” Right? That’s just not understanding how indices work, right? Indices market cap weight, meaning the same proportional amount is going into each company, and so they’re not pushing up prices of large companies over small companies anymore.

Now, in fairness to Mike’s argument, he doesn’t say that. He says, “Well, you have to look at price elasticity,” that the same amount of dollars is going in proportionally, but if certain companies have more of an elastic or inelastic price, that’s gonna impact different companies more or less. And I think that may be true.

It’s really hard to know what the elasticity of price is for each individual company. That probably changes throughout time. And so it’s unclear as to, like, on the margin, how large this impact is. I also, as an investor, don’t know what you would necessarily do about that in terms of how you would benefit from that in your processes.

The way I think about it is, people invest... what I care about is price discovery, is information getting into prices, and information does get into prices via indices. Just because you buy an index doesn’t mean you’re not adding value to the price of any individual stock.

Because people use indices in all sorts of ways. I can express my opinion of investing in the US versus international by buying US index funds. I can express my view of just the market in general by shifting money from private equity to global stock, global index funds. I could express my want to do small value funds through a small value index.

And so people can express their opinions all the time through index funds, and that’s what you see. These index funds have tons of volume traded. It’s not from buy and hold investors. It’s from people speculating. And so what we care about, as I mentioned, is price discovery. We think that’s still happening, and you can see that in individual stocks.

We don’t see the correlation between stocks increasing. It’s not like every stock in the S&P 500 moves the same. They all move differently. When news comes out, let’s say a company gets acquired, we see the stock price immediately jump. When earnings announcements come out, we see the stock price change.

And so price discovery is still happening. That’s what investors should care about, is can you still trust the price? We believe we still can because we still see it in markets. And if more of the market is indexed, more of the market is indexed, and that’s not necessarily an issue for us or what we’re trying to do.

Jack: I wanna ask you about something that we’ve probably had the most divergent opinions on in the podcast, which is the small cap premium. We have some people who say there is a small cap premium. We used to have some people say there’s not a small cap premium. Some people say there is one if you adjust it for low quality companies. We’ve had other people who say you wanna use small caps, but you only wanna use it within the other factors.

So, like, value is better in small caps, so you wanna use it that way, but you don’t wanna use it on its own. There’s so many different things. We just had Bridgeway on recently, and they wrote a paper where basically they showed that if you pull out IPOs and if you pull out fallen large caps, the small cap premium returns.

So I’m just wondering, what are your thoughts in general? There seems to be so many opinions on this whole small cap premium.

Matt: Yeah. I’d probably fall in the latter camp. In terms of, again, everything comes down to valuations and discount rates, right? The reason why small cap companies have tended to do better is because there are companies with really attractive discount rates within small caps.

There are also some companies with really unattractive discount rates within small caps. You just tend to see the extremes. And so if you filter out the extremes, the bad extremes, right? So people will talk about that as like the junk names, or maybe it’s the IPO and the fallen names or what have you.

These names that look really bad from a valuation discount rate perspective, then the remaining ones look really good and tend to outperform. When you look at them holistically across the board and you include the bad with the good, you don’t see anything. And so it’s more a function of there are more companies within small caps.

You see more extremes within small caps. And so when you factor out the quote unquote junk, then the ones remaining do better. Now, I would say they’re not doing better because they are small. They’re doing better because they have really good discount rates based on the valuation, based on future profits and the current price.

They just happen to be small, and you see more of them within small caps just because there are more small caps and you see more deviations. Like a massive top 10 company is just not gonna have as extreme valuation ratios as you’re gonna see within small caps.

Jack: Yeah, it was interesting. This is a point Wes Gray made when he came on. He was saying, like, small cap value doesn’t out... value doesn’t outperform more in small caps because they’re small. It outperforms because you can find more value-ness. So in other words, you can find more value within the small cap universe than you can find in other places.

Matt: Exactly. Exactly. And so when you’re a factor investor and you tilt to value, profitability, those types of things, valuation ratios, you tend to tilt to small because that’s where you see the most extreme examples. And I think it’s important to differentiate those two things, because if people just think small is going to have higher returns, it’s not the whole story.

You need — it’s really about the valuation ratio, and that just happens to be in small.

Jack: Do you think there’s anything to this idea that the small cap universe is worse? I mean, people point to two things. One is, I think the number of unprofitable companies in the Russell is near all-time highs, and the other is maybe some of the quality small caps are staying private longer now, so they’re not coming in.

So people argue those two things come together to make a worse small cap universe than we’ve seen in history. Do you think there’s any truth to that?

Matt: Not really, no. So I do hear this argument a lot, and it seems compelling, right? You see the OpenAIs, the Anthropics, the SpaceX, and you’re like, “Man, these companies stayed private this whole time. They’re huge companies. They’ve been incredible investments. If I had just gotten access to it, if they had IPO’d earlier, been a small cap company, I would have gotten it.” And I think that suffers from a couple different cognitive biases. The first is survivorship bias. You only know those companies exist because they were successful.

There’s lots of companies that could have IPO’d as small caps at the same time SpaceX could have that went to zero. Think like WeWork. WeWork was an extremely popular company. Everybody was talking about it. It took a while before it was gonna think about IPO’ing, and it went bankrupt. It ended up not being able to IPO and ended up going bankrupt.

So investors would not have been better off holding a name like that. So that’s one thing. The second is it’s not like the historical small premium that we’ve seen has come only from companies who recently IPO’d. It’s not like small cap companies are all companies that are only five years old, recently IPO’d, and some of them are gonna go to the moon, and that’s how you’re gonna get your return.

There’s always gonna be small companies for good reason. They just have lower values, and that’s just not the reason for the return. So the fact that SpaceX didn’t IPO five years ago, where you could have captured it as part of the small cap premium, isn’t destroying the small cap premium because that’s not where it comes from to begin with.

On top of that, a lot of these private companies are owned by public companies. So if you own Microsoft, you own part of OpenAI. If you own Google and Amazon, you own part of Anthropic. So these companies that you want exposure to, you do have some exposure through these public companies. And so ultimately, no, I don’t think the small cap universe is worse.

Is it different? Yeah, it’s different. We’ve seen small cap premiums in almost every country we’ve looked at. Some of those countries have ten small cap companies, some of them have 100 small cap companies, some of them have 1,000. So I think the decreasing number of names or the unprofitable ones or all of these things are things we’ve seen before in more extremes in other countries, and they don’t give me any worry that the small cap premium is dead or you’re not gonna be able to get the returns in public markets anymore or anything like that.

Jack: Yeah, I think that survivorship bias thing with OpenAI is so important. It was funny. We had Michael Mauboussin on. He was explaining a company to us, and he went through all these amazing characteristics of the company. And when you’re listening to him, you’re thinking, like, it’s Microsoft, it’s Google, it’s whatever it is.

Like, it’s one of these great tech names. And then at the end he’s like, “The company was Enron.” And that explains the whole thing though. When you looked at the stuff with Enron at the time, that could look like some of the great companies, and it ended up being a zero.

Matt: Yeah. And, like, just — there’s a chart out there that has, like, the largest 10 companies by decade in the US, and it’s constantly changing, right? And at the time it always feels like these companies are gonna take over the world, and then 10 years later it’s a different 10 companies. And in hindsight it makes perfect sense as to why these ones are now the biggest versus those.

But you’re always gonna get that rotation. Things are always gonna change. You can’t rely on the — the biggest winners in the past aren’t necessarily the biggest winners in the future. They can go down. Enron’s a great example of that.

Jack: One more before we move on to bonds. I wanna ask you about AI. Do you think, for the types of things you do, is AI gonna be significantly additive? On one hand I could argue, yeah, AI can find all these things, but on the other hand I could argue we’ve researched these factor premiums for a very, very long time.

And AI might just — it might be beat to death to the point that AI is not gonna uncover something that we haven’t found already. Do you have any thoughts on that?

Matt: Yeah. I’m in the latter camp of, like, you’ve had hundreds of academics all trying to get their PhD thesis looking at this historical data for decades, and they’ve found about one meaningful thing a decade, right?

You had the size premium, then value, then profitability, investment. Like, each of these — a good idea comes around basically once a decade in this factor type investing. And on top of that, each subsequent factor that’s been found explains less and less of the return because you already have a lot of it being explained by the prior ones.

And so the amount of value being added on each subsequent thing is lower and lower. And now you throw AI into the mix where it can do tests on millions of different signals, find patterns that may or may not be patterns. It may just happen to have happened that way. And so I think there’s a huge data mining risk there.

I also think, again, markets are super noisy. To say anything with any confidence, you need a lot of data going back historically. A lot of these AI data sets, they’re pulling data from, like, the last five to 10 years and trying to say something meaningful. That’s just not a long enough period of time, right?

Like, if you only looked at the last 10 years in the US, you would say that there’s a growth premium, that I should invest only in growth stocks, that it’s the opposite of the value premium. So you need many market cycles, many decades to really know what the truth is, and I think we’re gonna have a lot of short-term type things with AI that ultimately is gonna probably increase fees for investors, increase turnover, lower returns, and just end up being great for the asset managers who are doing it and terrible for the investors in those funds.

Jack: I wanna shift to fixed income. And fixed income is interesting because I feel like people like you and me are spending the vast majority of our time thinking about equities. Like, a lot of the research is equities, and people may not spend enough time on fixed income. And you’ve been working on fixed income recently, and you’ve called it quietly the most inefficient corner of a client’s portfolio.

So can you talk about that and what you’ve been doing?

Matt: Yeah. It’s always tough talking about fixed income because people view that as, like, a snooze fest, right? Like, equity is exciting, and listeners to this episode are probably like, “All right. I’m done with this episode. Turn it off.” And I’m hoping people stick around for another 10 minutes because this is gonna completely change how you think about fixed income.

So as I mentioned before, we are an RIA as well. We manage individual clients’ money. And fixed income is extremely inefficient, both from an operational perspective and a tax perspective. I’m giving my money to somebody that I want just to stay invested and compound. And what happens is every single year they give me my money back in terms of that fixed income, right?

They’re giving you that income back. And not only that, a slice of it is taken every single year by the government, and that goes to taxes. And it’s at income tax rates, which is the highest possible tax rate. And so this creates huge inefficiencies, both in terms of reinvesting the money, having it compound, but then also a massive tax bill I have to pay.

And so what we’re trying to do is re-engineer how people experience fixed income from a tax perspective. Equities is a lot more seamless. I give my money to a manager, and that money basically stays invested, and it compounds, and when I wanna sell it, I pay the tax. I control when I pay the tax, and it’s much more efficient from that perspective.

We want fixed income to be more like equities in that sense.

Jack: It’s interesting too because the tax drag on a typical bond fund, I would assume, is pretty substantial, right?

Matt: Yeah. It can be huge, especially for people at the highest tax rate, right? So if you think, the US Agg, let’s just say, is — it’s a little over, but let’s just say it’s around 4% right now.

If you’re at the 40% marginal tax rate — most high income people are at that rate — you’re paying 1.6% a year in taxes. So when you look at over 20 years, that lost compounding, that tax drag that you’re suffering ends up being about half the value of that fixed income investment.

So you’re losing half the return to taxes, and so that tax drag is huge. People think about one investment over another saving 10 basis points. Like, “I’m gonna pick this manager because their fee is 10 basis points less than this manager.” Well, we’re talking about an order of magnitude larger than that.

We’re talking about 1.6% a year being lost to taxes in these types of investments. So the drag is huge.

Jack: And it’s interesting ‘cause fees are obviously very, very important in investing, but I feel like a lot of times people pay attention to fees more than they pay attention to taxes. And in a lot of cases, taxes are actually a much more significant fee than fees are.

Matt: Yeah. That wasn’t always the case because fees used to be a lot higher. And as fees have come down, now the tax piece is the monster in the room, the elephant in the room. And yeah, you’re absolutely right. People are still focused on fees, and they’re missing the biggest one, which is taxes.

And that may have partially been because they didn’t think there was anything they could do about it. But now we think there is.

Jack: So could you explain how you’re tackling this problem with LVIG?

Matt: Yeah. So as a starting point, LVIG’s goal is to invest in investment grade US fixed income, so both treasuries, corporate bonds, et cetera, in the roughly US Agg type asset class.

And the idea is, rather than receiving income, we wanna capture the return through NAV appreciation rather than through fixed income. So it’s getting the returns of fixed income but not forced income. And the way we do that is by investing in underlying ETFs. So we invest in low-cost diversified ETFs, and then we rotate between them.

And so around dividend dates for different ETFs, we might sell one and buy a different one. And by avoiding that dividend, we avoid getting that distribution, and we don’t avoid the tax, we just defer the tax. You then control when you pay the tax. It’s when you sell the investment. And in the meantime, all of your money stays invested, all of it continues to compound, and you get that benefit of tax deferral and compounding.

Jack: Have you thought about how much the benefit could be of this? Obviously, it varies based on tax rate and a lot of other factors, but I’m sure you’ve done some research in general to how we could think about that.

Matt: Yeah. So the benefit is huge. And the reason the benefit is huge is because the opportunity is huge, right? We already talked about you’re losing one point six percent every single year to that tax drag. And so the question is, how much of that can we recapture for clients? And so what we’ve found is it’s in the half a percent to one percent range that you can outperform on an after-tax basis. And the reason for that is really a few things.

So the first is that one point six percent you are paying every single year to Uncle Sam — that money stays invested and you earn a return. So you get to earn that money every single year, and every year you do this, that money just keeps on compounding and compounding and compounding, and that return difference just grows and grows and grows.

So that’s just the benefit of deferral. Then the next benefit is the difference in tax rates. So by deferring tax, you’re not avoiding it. It’s just, what am I paying in taxes today versus what may I pay in tax in the future? Similar concept to like a Roth conversion. Should I do a Roth conversion? Well, what is my tax rate today?

What do I expect my tax rate to be in the future? There are lots of reasons why you might expect your tax rate in the future to be lower. You could be in a lower income state. Your income itself could be lower. You could get capital gains treatment. You maybe donate the shares or get a step up at death.

All of these are reasons why you might have a lower tax rate in the future than you do today. And so that one point six percent annual drag, if you’re a forty percent income investor, if, let’s say, in the future when you decide to sell the investment you get twenty percent tax treatment, that ends up being about eighty to ninety basis points a year in annualized after-tax outperformance from that difference in tax rates.

And so this is really about tax deferral, tax control, that you pay the tax when you want to, and depending on what you expect your future tax rates to be, that’ll tell you how big the benefit is.

Jack: I would think this would work for all taxable type investors, but are there certain types of investors where this type of strategy makes the most sense?

Matt: Yeah. What we’ve seen is pretty much every taxable investor, this makes sense. We haven’t really found a case where it doesn’t, as long as you are at something around the twenty percent marginal tax rate, which is something like one hundred K worth of income for a couple married filing jointly.

So it’s most investors this matters. And so if you are a long-term investor, right? The longer you hold this, the bigger the benefit, right? So if you’re trying to buy fixed income for six months, maybe this doesn’t make much sense. But if you’re a long-term investor, a taxable investor at the twenty percent or above tax rate, this strategy makes a lot of sense.

Your after-tax long run returns are a lot higher. And I always try to put my advisor hat on when I think about these types of strategies, and when you reduce your income today, it opens up tons of financial planning opportunities, right? So now you can do more Roth conversions. Maybe you can do capital gain harvesting.

Now asset location becomes easier. Maybe you avoid net investment income taxes because your income is lower. There are so many — or IRMAA surcharges, or so many different financial planning ideas that advisors can then bring to their clients because they have more flexibility because the client’s income is lower, that you are controlling when you pay the tax rather than being forced to pay it every single year.

Jack: Well, Matt, this has been awesome. I really appreciate you coming on. It’s always good, I think, at a time like this to get an evidence-based take. I mean, I was ready to talk about the TAM of Mars, and now I’m kind of at least a little bit back into reality. So thank you. If people wanna find out more about you or Longview, where can they go?

Matt: Yeah. So we have a website, longviewresearchpartners.com. That’s probably the best place. Or you can reach out to me on LinkedIn. We’re somewhat active there as well, posting things, and yeah, that’s probably the best way to reach out.

Jack: Well, Matt, thank you again. I really appreciate you coming on.

Matt: Thanks, Jack. I appreciate it.

Main topics covered

• Why AI CapEx has become one of the biggest drivers of the US economy and stock market

• How the high-end consumer, asset prices and AI spending have created a circular market setup

• Why today’s fixed income market is very different from the zero-rate era

• How bonds can serve as income, ballast and portfolio protection in the current environment

• Why the Fed may care more about inflation expectations than markets expect

• The Fed’s overlooked third mandate and what moderate long-term interest rates mean

• How Kevin Warsh could change the Fed’s approach to forward guidance, inflation and the balance sheet

• Why the business cycle is not dead, even if Fed intervention has lengthened it

• What investors should understand about the national debt, higher rates and inflation

• Why private credit is useful but not automatically better than public credit

• How flexible fixed income investing can find opportunities across credit, securitized markets and capital structures

• Why sentiment, not just fundamentals, drives market prices

Timestamps

00:00 AI CapEx, the stock market and the fragile economic loop
04:03 Why fixed income markets look different after zero rates
08:45 Does the Fed still have investors’ backs?
13:43 Are AI companies using dangerous forms of financing?
18:54 Why starting yields change the stock bond hedge
23:42 The Fed’s overlooked third mandate
29:03 Why inflation expectation stability may drive Fed policy
33:11 How Kevin Warsh may change the Fed regime
38:46 What a smaller Fed balance sheet could mean for asset prices
43:24 The national debt, higher rates and inflation
50:25 Why fixed income should be managed across silos
55:08 The one lesson for the average investor

Jeff: Hi, it’s great to see you and great to be here, so appreciate it.

Justin: You are managing director at Aristotle Pacific and the portfolio manager across several of the firm’s fixed income strategies. Through this role that you currently sit in, and prior roles at PIMCO and Thornburg, you had a front row seat to global fixed income markets through many different regimes, many different credit environments. And today, what we’d like to discuss with you is the current macro environment, Fed policy, credit markets, inflation, and where investors should be looking for opportunities in today’s market. People always say that the fixed income guys are the smartest guys in the room, and much smarter than the equity guys. So we’re— Jack and I are hoping today that some of this intelligence rubs off on both of us.

Jack: It would have to do a lot of work to rub off on us, Justin. We’ve got a lot of work to do.

Justin: You’re right. Well, hopefully Jeff is patient with us as we work through this. You said that the markets are focused on an increasingly narrow set of things that are working and really aren’t appropriately weighting some of the headwinds out there. So can you explain where you’re coming from with that?

Jeff: Yeah. Look, the way I would describe it is we’ve all talked about this K-shaped economy for quite some time. And what we are seeing is the economy is humming along, but it is relatively narrow in the sense that there’s only a few things that are really working, and even those couple of things that are working are very interrelated.

And so what I really mean by that is if we just unpack where GDP is today, if we unpack the reality of higher rates, higher inflation, having pressure on that lower end consumer, that’s old news. That started in 2022 with the rising rates. Now it’s continued to spread, and it’s been made notably worse by war in Iran, increasing oil prices, et cetera. And at least for the moment, we’ve got a temporary reprieve that’s helping to alleviate that.

But really what’s been driving the economy is this incredible AI CapEx expansion. Right? We’ve got 600 some odd billion dollars from only a handful of companies, and for the moment, there’s lots of questions around ultimately where AI goes, its impact on the consumer, its impact on the broader global economy. But in the build-out phase, it’s for real. We need real people. We need real things. We need to be digging in the ground. We need energy. We need copper. We need chips. We need all of these things. And so that is a massive, massive tailwind to the US economy. It’s also been a massive tailwind to equity market returns.

And so the more narrow focus that we’ve been seeing from the consumer is that really the only part of the consumer that’s holding up amongst this massive tailwind is that increasingly high-end consumer. And they’re only holding up because they are the ones that are benefited primarily from asset price appreciation, right? House values have gone up, equity markets have done well, and so that higher end consumer that has a lot of assets continues to spend. They’re ones that are really propelling the economy. That middle and lower income consumer are acting as drags. We’re seeing delinquencies increase. And so when I think about what’s working in the economy, it’s AI CapEx. That ultimately would potentially prove catastrophic, but I think if nothing else, would act dramatically to slow where the economy’s currently heading.

Justin: And how does that sort of view play into or weigh into how you’re looking at sort of the fixed income markets today?

Jeff: Yeah, the way that I would think about it, look, fixed income markets are very different today than what many folks think about the fixed income markets. If we just kind of take out that last 15, 18 years of unfortunate fixed income markets and what we know, that’s the vast majority of many of our investing experiences, right? That was a period of very low interest rates, and it was a period of what I will directly call market manipulation by central banks. Now, for many good reasons, and I’m sure we’ll get into that here later on, or if you want to, but the reality today is that we have emerged from a period of what was below trend inflation, and we have emerged from a period where central banks were trying to push towards price stability by actually trying to create inflation, right?

We were significantly below that 2% inflation level that most central banks around the world define as price stability, and we were struggling to get there. And one of the things they did in that environment was keep rates very, very low to help prop up the economy, to pull demand functions forward, and act as a catalyst to propelling the level of prices up towards price stability. That’s not the environment that we’re in today, right? The environment that we’re in today is we have above trend inflation. Central banks are trying to pull it back down. And so the reason why I wanna start with that as backdrop is because we have to think about what fixed income is and the purpose it serves within investors’ portfolio very differently.

So first, I would say the level of income generation is notably higher than it has been in the past, right? It’s relatively easy for fixed income investors to get five and a half to mid six percent in very high quality fixed income assets today. But then secondly, and most importantly, fixed income is always meant to be ballast in investors’ portfolio. We’re not supposed to be the most interesting folks in the room. I wouldn’t necessarily even say we’re the smartest by any stretch, but we’re definitely not supposed to be the most volatile and the most interesting. And so traditionally what happens is if the economy was to slow, central banks around the world, the US Federal Reserve, would be cutting rates. That pushes the level of prices of fixed income up, and it acts as ballast within the context of a portfolio.

And really that’s tremendously important, and I think that’s how folks should be thinking about it. One is income generation as a yield source as part of their total return equation, but even more importantly, within the context of overall equity and other risk asset allocations, what kind of protection and ballast can fixed income really provide in today’s environment?

Justin: Think that’s a very interesting point, that a lot of times in investing, you see an environment take place, and then you assume that that’s sort of the environment that you’re gonna be in going forward, and that can be such a challenge for investors. You know, whether it’s a fixed income regime or an equity regime, and how that can be very different than what the historical precedent actually is, and has been or will be. So I think that’s a very fair and good point that you bring up.

Jeff: Yeah. Look, I think so many things that all of us have grown up on just simply isn’t the reality, in my opinion, of the world today and as we look forward. And I’ll say one more thing, right? Central banks were created for the very purpose of acting in an independent way to keep us as a global economy, as a US economy from experiencing runaway inflation, right? They were created with the sole purpose, essentially, to act as a brake on, quote, “the reckless fiscal authorities’” fiscal spending. And in general, the way you should think about that is central banks were created to pull inflation down or prevent it from rising rapidly.

And it was an entirely new playbook for all of us when Japan was the first country to really slip into this disinflationary period. And there were a lot of PhDs, Bernanke included, that said, “You know, all you have to do is just throw more money at the system. Anyone can overcome below trend inflation. You’re just not doing enough.” Then he became chairman of the Federal Reserve, and he wasn’t successful at breaking us out of this low inflationary environment. And so really to me, central banks are most effective at doing what they were designed to do, preventing this runaway inflation, and that’s the environment we have today. And we were all along for the ride with all of these various monetary experiments in trying to arrest deflation and disinflationary environments.

Jack: It’s interesting thinking about them as sort of their goal is to prevent runaway inflation, because for that huge period, they didn’t even have to care about runaway inflation. They could basically do whatever they wanted, and it didn’t impact inflation. So it’s almost like they maybe forgot a little bit about the playbook ‘cause it was so long they didn’t have to use it.

Jeff: Well, I think that’s exactly right. But to be fair, it’s not just them, it’s all of us, right? It’s all of us. And I think one of the big things and one of the big questions in markets today, especially as we emerge out of this period where Chairman Powell is now in the history books and we have Chairman Warsh coming in, is how does Warsh think about this? How does Warsh think about the balance sheet? How does Warsh think about quantitative easing? How does Warsh think about the Greenspan put, right? Will there be a Warsh put?

And I think all of us have come to this belief that central banks have our back as investors. That’s what they’re there to do, is provide financial stability. But that’s just not the case, in my opinion, right? I think what central banks are designed to do, the US Fed already is a very unique central bank in this world in the sense that it doesn’t only have one mandate, it has two, or I will actually argue, or I assert very directly, it has three. But if we just focus on those two and just even really that price stability mandate, financial returns, financial assets affect all of our ability to consume, and our ability to consume affects price stability, inflation or lack thereof. And so that’s what central banks care about. That’s what the Fed cares about. They don’t care about financial markets. They only care about financial markets into that price stability equation.

Jack: And we’re gonna talk a lot more about central banks in a minute. But first I wanted to get back to AI CapEx because I was interested to talk to you because we’ve talked to a lot of equity investors about AI CapEx, and they kind of look at it, you know, they’re looking at it from a growth perspective. But I would think a fixed income person is thinking this more from the perspective, like, “I gotta get my money back.” Like it’s a different way that fixed income people would probably look at this. So I’m just wondering if you have any insights on what you’re seeing in terms of this massive AI CapEx from, like, the fixed income side.

Jeff: Well, the first thing that I’ll say is, right, we started off by talking about there are very few things in the US economy that are really truly working. When you think about we’re roughly a $30 trillion US economy, and you think about 600 some odd billion dollars of CapEx coming all online all this year and from only four or so companies, right? That gives you a sense of just when we talk about 3% GDP type growth, that’s essentially it. So it’s very concentrated, but it’s very important. And $600 billion is a whole heck of a lot of money, right? We all know this.

And so the first thing that I would say is when you have that much CapEx, you will take money in any way, shape, or form that you can possibly get it. And so we’re seeing it come from the equity market and equity raise, right? We just came off the SpaceX IPO. But right thereafter, a week and a half later, SpaceX tapped the fixed income markets. And we’ve seen the same thing from Microsoft, from Meta, from Amazon, from all of these companies as they engage in a CapEx build-out.

And you’re right. The old joke is that fixed income investors, we’re always grumpy. We always wake up on the wrong side of the bed, and maybe that’s true or maybe it’s not. But what I would really say is it’s because we care about getting our money back. If AI works incredibly well and profits are even beyond our wildest imaginations, I don’t benefit as a fixed income investor. I get my money back, that’s the upside. I get interest along the way, and the downside is the exact same. I could potentially lose it. So we have to think about it very differently.

Thankfully, almost all of these major companies are incredibly high quality tech companies with very, very strong balance sheets, really. Most of them started with close to no debt on their balance sheets, and they’re just beginning the phase of tapping the bond markets, tapping the debt markets to really raise capital for these AI expansions. All of them have very strong business lines away from AI. And so the way I would think about it is it’s very attractively priced today in the sense that you can get a 6 to 7% type yield depending on where you play on the yield curve and what the quality spectrum is. But from companies that are notably higher quality than where else you might have to look to get that similar yield profile.

The big headwind is we know that markets are tapping the market today, and we know that they’re gonna tap the market tomorrow. And so there’s just this endless supply that continues to come, and that’s keeping yields maybe artificially wide relative to my opinion of the risks of being repaid. Versus almost the entirety of the rest of the fixed income market, it’s the exact opposite of the equation, right? Investors are pricing in almost no risk of recession, of defaults, of any challenges in the macro economy. And I would say the challenge potentially in AI is much more an equity question, but the profile is quite interesting from a fixed income perspective.

Jack: So do you see any of the danger stuff? Like you have some people out there in the news talking about like they’re starting to get into dangerous forms of financing and things like that. Like around the edges, are we seeing any of that yet or is this still pretty solid? ‘Cause to your point, it was from cash flow for a long period of time, which is different than something like fiber back in the day. Like this seemed like it was safer coming out of the gate.

Jeff: Look, I think that’s the million-dollar question. My honest take is at this point we’re not seeing, quote, “dangerous forms of financing.” What we are seeing is the reality that what we have ascribed as a market as just kind of a one-way train up and to the right, increasing forever, overall revenues coming from AI, increasing adoption, is a challenge, right? We haven’t ever experienced any prior technology that has gone in a perfectly straight line.

You know, I will date myself here a little bit in terms of the internet age, right? I started on bulletin board systems, and then I went to something called CompuServe, and then I went to AOL, and then it moved to MySpace, and a million iterations right along the way. And MySpace is still... Or sorry, Facebook is still around, but all the rest of them have moved on, been acquired, failed, whatever, in many of its various forms. And so I just think that we have to keep that in mind as investors.

I don’t think we’re seeing dangerous forms of financing, but we will see increased competition. There will be winners and losers, and there will be competition for our dollars, both from an innovation perspective, but also increasingly from a price perspective, and that’s really the thing that has me scared the most, is there’s a lot of companies that aren’t the best, but there’s a lot of companies in AI that are pretty gosh darn good and charging a whole heck of a lot less. And so I think we just have to think about what that revenue equation is and the multiples that we’re assigning to it. And the same thing on fixed income. We have to make sure that we are focused on those companies that start with just absolutely bulletproof balance sheets, have a very large moat around their AI offerings, and really have the ability to pay us back at the end of the day.

Justin: And that was great. Jeff, was that you on the Raging Bull message board? I don’t know if you remember the Raging Bull.

Jeff: Constraint. Constraint is really my view. There’s opportunities, but there’s always risk, and we just have to remember that just because it’s sunny today doesn’t mean that it won’t be stormy tomorrow.

Jack: Well, it was funny, when we had Cliff Asness on, he admitted he was anonymous on the Yahoo message boards back in the day, making some comments on different things. So it’s a very different world now than it was then.

I wanted to ask you, going back to the idea of inflation being here, like one of the questions that we talk about a lot in the podcast is for many, many years bonds acted as a great hedge for stocks. And now we have some debate around that which we haven’t had in a very, very long time. And I’m just wondering, as like a fixed income person, can you kind of put that in context? Like how you’re thinking about the correlation between bonds and stocks, and maybe bonds as a hedge for stocks like in a more inflationary period.

Jeff: Yeah. Well, I think you nailed it right there on its head in that inflationary type period. And so generally speaking, when the economy is doing well, we have inflation, and stocks are working incredibly well because the economy is doing well. And bonds generally are lackluster because rates are rising to potentially bring down how well that economy is doing. And that actually may be that environment that we have today.

But really the most important point is that is absolutely the environment that we had coming out of the global financial crisis, coming out of US Federal Reserve rates that were pinned at zero, coming out of that period of well below 2% inflation, and coming out of that COVID period where the Fed had to ultimately raise rates to arrest what was a runaway inflation because of the supply shock from supply constraints around the closing due to a global pandemic.

And so that was a tough journey, but it was also a very predictable journey, right? We all knew that after a decade of financial repression, after a decade of zero rates, there was only one direction that rates could go, and that was up, and that’s painful for fixed income. But we’ve taken that pain, we’ve taken that medicine, and so maybe we get a hike or two out of the US Fed, maybe we get a cut or two, but really I think it’s pretty hard to argue that we’re in a pretty comfortable spot. Rates are much closer to, quote, “neutral.” They’re not necessarily stimulative. They’re not necessarily holding back the economy today.

But that puts us in a very different backdrop because when equity markets might not work in a recessionary type period, almost assuredly we will all be consuming less, almost assuredly inflation will be coming down, and the Fed will be doing what the Fed is supposed to do, which is cutting rates. And so that’s really the... All of the pain, all of that lack of negative correlation, what most investors have experienced over the last decade, we have to remind ourselves that’s not normal because it’s not normal that we started with zero rates. It’s not normal that we started with 1% inflation. This is actually the normal time period.

And so what I expect going forward is, again, as equity markets are potentially experiencing stress, given that narrowness of the economy and just given traditionally within the economy, central banks will be cutting rates and fixed income will serve as a tremendously valuable hedge to equity assets. But not only equity assets, but also credit assets, right? We have to think about how we use Treasuries versus maybe other creditors within our fixed income portfolio as providing ballast because it’s the outcome that our clients are after.

Jack: Yeah, that’s such an important point because starting point matters, right? I mean, now we’re starting— our starting point is higher rates and our starting point is higher inflation. Before it was basically zero and zero. So that’s a very different dynamic going forward as you think about bonds as a hedge for stocks, right?

Jeff: That’s exactly it. That starting place matters, and today is very different. It’s very different than what most of us know within the world of fixed income because, look, this hasn’t just been one or two or three years. It’s been a decade and a half, and that’s just the reality. We haven’t seen a recession essentially since the global financial crisis in 2008. But business cycles are healthy. And we will get a business cycle. I feel very confident saying that. Now ask me on timing and, of course, the old adage that predicting the future is easy unless you ask me... Sorry. Predicting what might happen is easy unless you ask me about the future, or however that old quote goes. But today is a very different starting place than where we have been.

Jack: Do you think we’re just... You brought up the business cycle. Do you think we’re in a different scenario with respect to the business cycle? Because you could argue if you look through history, we’re having, like, less recessions than we used to. Some people argue we have maybe more rolling recessions now where they happen in certain areas, but they don’t happen overall. Like, do you think something’s changed significantly with the business cycle versus what we saw in history?

Jeff: I don’t. Look, people ask me all the time what ends this incredible expansion that we’ve had, and my honest answer is I don’t know, and nobody else does either, right? So if anyone tells you they have the crystal ball, they’ve got the playbook, I would question exactly what they know that potentially all the rest of us don’t. But what I will say is my general answer is I think what we get this time is just a regular, boring old business cycle where the Fed has raised rates. We’ve seen inflation move up. That acts as a demand dampener on all of us. That’s exactly what we’re seeing. We’ve already talked about that. And eventually that overexuberance just rolls over, just a very classic business cycle.

I think what we’ve all been conditioned is we look for the canary in the coal mine because what we’ve had is, right, a global financial crisis where the financial system was just in broad meltdown. What we had was a global pandemic. I think what the biggest thing that I would point to is part of the reason why we haven’t had a classic business cycle is because of all of that Federal Reserve intervention and its direct focus on financial markets. And so I think what it’s done is it’s lengthened that business cycle. It’s allowed us to continue into potentially those periods of overexuberance for longer than maybe is even healthy. But it hasn’t killed the business cycle.

And so again, we’re seeing the pressures build. Anyone’s best guess in terms of the timing of eventually when it happens, but one of the things I like to say is all of us focus on fundamentals, right? We as good business analysts, we try and focus on fundamentals, what’s ultimately driving individual companies, how those companies are driving the stock market. But what really drives prices is sentiment. And once sentiment rolls over, it’s tough, right? We’ve all just been conditioned to buy the dip, the belief that tomorrow will be better perhaps than a weak day today. But after a few weeks or even potentially a few months of things just going down and to the right versus up and to the right, our reaction functions become very different, and that’s the business cycle. So we have to focus on sentiment. Sentiment is still very, very strong within credit markets, within equity markets. And no, I don’t think the business cycle’s dead. I think it’s as alive as it ever has been. And we need to focus on the business cycle.

Jack: I think what was great about that answer is you’re talking more about a garden variety, like business cycle recession type thing. You know, so many people are looking at what happened for a long period of time and thinking like it has to end catastrophically, or it has to be 2008 or something like that because we saw that. And it doesn’t seem like— It seems like this is a more reasonable way to look at it than it has to end with crisis and catastrophe.

Jeff: Well, we can all hope that that’s actually the way that it does come to fruition, it does end. But I think another important point is we also have to remind ourselves that global financial crisis in 2008, which was really that last business cycle, was just tough on everybody. But if we go to one prior to that, right, the internet boom and the tech bubble ultimately, it was a little bit different. It was much more dramatic within markets than it was on the Main Street economy. And so if anything, to me, that next business cycle, again, full humbleness and recognizing nobody knows what happens tomorrow, it feels a little bit more like that, where the markets may have a little bit more of a wild ride than just broadly the underlying economy. And it’s, again, back to your point, is it’s because starting place matters. And so I think it’ll be really interesting to watch and fascinating for all of us within the markets.

Jack: I wanna shift to the Fed. And I was watching some of your appearances, and you’ve talked about this idea of a third mandate for the Fed in addition to employment and stable prices. Can you talk about what that is and what that means?

Jeff: It’s something I’m pretty passionate about. Look, I will... I’m gonna go a level further. To me, it’s not even debatable. The Fed does have a third mandate, and so I will point to — a lot of people don’t believe me when I talk about this. And I will say, “Just pull up the actual document that governs what the Federal Reserve is supposed to do.” And so it’s the Federal Reserve Act of 1913, and it basically is the mandate from Congress, and it says that essentially the central bank within the US is supposed to pursue effectively the goals of maximum employment, price stability, and moderate long-term interest rates, right? And so I may not be very smart, but I’m pretty sure I can count to three, and I just counted three.

And so that third mandate, technically, as it’s given by Congress, is moderate long-term interest rates. The question becomes: What the heck are moderate long-term interest rates, and how does the Fed think about them? And so I’ve actually asked a couple former Fed officials, but importantly, Chairman Powell actually got this question in a press conference maybe four or so press conferences ago. It almost never comes up, and you should have seen the smile on my face as it came up, because it’s something I’ve talked about for quite some time. But his answer essentially was as good as any. He said a couple things. What he said ultimately is, “Look, we, the Federal Reserve, don’t know what moderate long-term interest rates are, and they change throughout time. And moderate long-term interest rates are what you get when you successfully balance maximum employment and price stability.” And so that’s very similar to a couple other Fed folks that I’ve talked to.

And on the surface, that may sound not interesting and almost like you could dismiss it outright, but I actually would argue the exact opposite. I think it’s tremendously important, and I think it’s tremendously important because we’ve already talked about a few of these things. The US Fed is one of the most unique central banks in the world. It has, again, let’s just say two mandates. Almost every other central bank has one mandate and only one mandate. That’s price stability, 2% inflation. You get that and nothing else matters. The US Fed, at minimum, has those two mandates, price stability and maximum employment, which are oftentimes two different sides of that teeter-totter, right?

And I think we can look at today’s environment as exactly that. It’s not even controversial that we have inflation that’s above the Fed’s target, and it’s maybe a little controversial of how strong the labor market is. It wasn’t that long ago we were talking about some potential weakness on the labor side. And so on one side of that coin, you could say the Fed should be raising rates. On the other, you could say the Fed should be lowering rates. And what should the Fed be doing?

And what I will say is that third mandate recognizes inherently that the economy changes throughout time. It’s much like a lot of what makes that American system great is the flexibility that the founding folks in this instance, right, the people that put the central bank there, gave them a lot of flexibility to look at today, look at the drivers today and adapt.

And so I will argue that there’s kind of been three primary iterations of that third mandate. The first one was financial stability, and we’ve talked about this. It’s not because the Fed cared about the level of asset prices or the level of stocks. It was because in a time period where they were trying to push inflation up, if they engaged in QE1, QE2, they pulled forward our demand function. They propped up the level of assets within the economy. It made all of us more confident to go out and spend, and that helped keep the level of prices up towards price stability.

Now that evolved. And so in the 2020 time period, and this was before the runaway inflation, I actually think Chairman Powell told us that second iteration became social stability. So we went from financial stability to social stability. And what the Fed told us is they used to be very reactive, right? Sorry, they used to be very proactive. Sorry, let me be very careful. They used to be very proactive. Once overall employment got too strong, they worried that it would bleed into higher asset prices, higher consumption, and so they would proactively raise the level of rates to ensure that that didn’t happen. But because we came out of that period of below-trend inflation, the Fed said, “We have been wrong in how we’ve run monetary policy.” And what we learned is by keeping that expansionary unemployment rate very, very low for a long period of time, it actually compressed the wage gap, and we like that. We want more of that.

And so I think coming out of 2020, the Fed actually focused on social stability. And we saw that, right? We saw low-wage income earners really close that wage gap versus high-income earners. But as we look forward, I think, again, the Fed has redefined that third mandate, and it’s now one of inflation expectation stability. And the reason why I think all of that’s important is because that puts us squarely to where we are today and exactly what we heard from Warsh coming in, which is the Fed is unambiguously committed to 2% inflation. We’ve been too far away from it for way too long, and we’re going to get back.

And so I think that we have to, as investors, remind ourselves that the economy is different today. The way the Fed thinks about the economy is different today. And I really think that third mandate, and if I’m right, inflation expectation stability, that’s where we should be watching to make sure that after five years of missing on the high side, if we keep on going, that’s gonna be a challenge, and the Fed isn’t willing to tolerate that anymore. So I think that’s actually the driver of what they’re looking at today, even more so than those first two mandates.

Jack: I could be way off on this because I am definitely not a fixed income guy, but I’m wondering if, like, the aggressive use of forward guidance also plays into that. Because if I want stability, I probably want to be telling people what I’m doing way in advance and, like, not surprising anybody. I mean, does that make sense?

Jeff: It absolutely makes sense, and I think it— to me, the answer to that question is it depends on which regime you were in. And so, look, there haven’t been many Federal Reserve chair folks that have started in one period and exited in another, right? And Powell was one of those. He took over a Fed with well below trend inflation, and he exited a Fed with well above trend inflation. And I think one of the things he could have done a little bit better was make that transition in recognizing that and shift the Fed policies. Because it’s no different than running a business, right? You run the business in a very different way when things are going incredibly well than when you have to hunker down, cut costs, think about preservation more so than expansion.

And so in today’s environment, all of that forward guidance, I think is much less useful than it was in the past because the primary tool that the Fed has, interest rates were already very, very low, and so you had to rely on all sorts of other things in order to hopefully propel the economy. But I think really the transition today is we can go back to basics. We can understand that, yes, the Fed has one big blunt tool, but it’s incredibly, incredibly effective. The challenge with it is it’s like a lot of modern medicine, right? You take one pill, it might cure this ailment over here, but you might not feel so well along the way to recovery. And I think, again, this Fed just might be willing to take that pain in recognizing that we have to get inflation back under control. That will likely come at some expense of the employment side, and it’s because we as an economy are just so incredibly strong today that we need to rein in that business cycle.

Jack: What you said about other central banks not having the employment mandate, like, got me thinking. Like, I wonder what the consequences are of that for the US versus other central banks. Like, I would guess you would have— if you’re not solely focused on inflation, maybe the US would have more variable inflation because there’s times where I have to deal with higher inflation because of my other mandate. I mean, is that the consequence, probably, of having the dual mandate, is maybe inflation’s more variable and you might have to live with higher inflation at times?

Jeff: I don’t think so. The way that I would describe it is, look, I think it’s quite amazing that they have this dual mandate or even that tri-mandate. I think it’s almost asinine to think about a Fed that always has to do something very mechanically. And we can look at other economies around the world today, right? One of the things the Fed does is they define price stability as core. They want to think about it not in just the sense of there’s been this supply shock of high oil prices, and we need to immediately react. We need to think, step back and say, “Maybe this won’t last forever, and we can focus on kind of the through cycle view.” As for the ECB, it just focuses on that headline inflation number, and they were raising rates. And so I really think what it allows the Fed to do is be a bit more forward-looking, a bit more thoughtful in the responses.

But a challenge, a big challenge with that is in some ways it’s, in markets, easier to have a Fed that has a pure reaction function. We know the equation. If inflation is high, they’re automatically gonna raise rates. We know that. We do the work for them, and we just move on. Because they have hundreds of PhDs on their staff, and they still don’t know what’s gonna happen till tomorrow, and that’s not a fault of the Fed. It’s just the reality of the world that we live in. Nobody knows what’s gonna happen tomorrow. And so I think we need to give them a little bit of grace, but actually I think it makes the US economy way stronger as a result.

Jack: So taking this from the theory into practice, like how do you think about the situation the Fed finds itself in right now? You know, before this whole war, they seemed to be a little bit more focused on the easing side, a little bit more focused on the employment. Now we have seen inflation. The latest meeting they seem to be focusing maybe a little bit in the other direction, although they haven’t made any changes. Like how do you think about what they should be doing and what they will do in this situation?

Jeff: Look, the first thing that I will say is we’ve talked a lot about change, right? The world is always changing. And that’s what makes investing so fascinating, right? It’s what keeps us all excited to wake up, at least myself, every morning, is the world is different. And so the world is different in a lot of ways. It’s very different for this Fed versus the prior Fed. But for us as investors, if nothing else, Warsh is likely to be very different than Powell. And so we’ve had this regime change.

And one of the most important things that I wanna remind kind of everybody and even myself, right? I say it predominantly for myself, is we know coming in, and Warsh told us many times, that he is less of a fan of forward guidance. And so everything that he has told us thus far as Chairman of the Federal Reserve, he is deliberately trying not to give us a lot of insight into what may happen in the future. Now, it’s our jobs to read into that. It’s our jobs to form views and make opinions on where he ultimately heads. But we have to be very humble is that we’re also on that learning journey. So I think where the Fed is today and what maybe the Fed should be doing is exactly at least what I heard from Warsh in that first press conference.

The second thing I would say is everything we know about Warsh, and we’ve got a rich history just given the fact that he’s not new to the Fed, I kind of like to equate it, right, if you go from having friends with kids to having your own kids. Becoming a father, all of a sudden you’re not a dad and then you are a dad, or you’re not a mom and then you are a mom. Or for, you know, other listeners potentially, you’ve got a lot of friends that may be married, but then all of a sudden on that day you become married, the world just changes. And so everything we know about what Warsh thought the Fed should do, we have to re-question because now it’s not what he thinks it should do, it’s he gets to actually decide what they do. And so that might be different as well.

But everything we’ve heard I think is exactly what should be happening. We have to take the environment that we’re in today. We’re now five-plus years in a period of above trend inflation. We have to be asking ourselves a very serious question of no one is worried about runaway inflation today, but at what point after how many years might they be, right? We know it’s not an infinite timeline. But it’s apparently more than five years. And so I think the Fed is saying, to evolve, we have to probably raise rates if we don’t see a direct and obvious path of inflation coming down. And we have to ask ourselves as investors what might lead to that.

So the Fed is gonna have a reprieve. We’re going to see oil prices come down just as we’ve already seen. That will bleed into inflation. So we’ve got a couple of months where inflation will be coming down. But just like the Fed looked through the supply side shock on the way up, they also have to look through that supply side shock on the way down, and I think that’s exactly what the Fed is going to do.

I think with technology, that’s potentially the saving grace. We’ve all talked about and heard about that amazing productivity that might come from AI, but I would ask ourselves: Are we actually seeing it in our own lives? Because yes, it will come through, but I think it probably comes through over decades, right? The rise of the computer did not move to an immediately high productivity environment. It happened over the course of a decade and a half. And so I think that’s exactly what we’re gonna see with AI. I think inflation is probably much stronger at the core level, and the Fed is going to raise rates and be willing to sacrifice or accept the consequences of that on the employment market.

Jack: Do you think there’s any lasting impact from the war on inflation? Like, we’ve heard guests who had both sides of this, some who said, “You know, it’s... Once it comes back down, we’re gonna be okay,” others who say, you know, “Oil’s inputs to other things, so that’s gonna still bleed through for a while. And also, if oil prices come down, you might spur demand again, so you might actually get a little bit more inflation that way.” Like, do you think there’s a lasting impact to this, or do you think it’s mostly behind us?

Jeff: I mean, the way that I think about it is there’s a lasting impact to everything. Everything that we do today, every choice that we make today will ultimately impact how we think about the world tomorrow, and obviously some things will be far more consequential than others. But I think this war, it will have lasting impacts. I’m surprised to see where oil prices have immediately kind of retraced back to almost pre-war levels. The reality is, is we don’t know what the Middle East looks like a year from now. We don’t know that the 60 days is going to last beyond 60 days. Every single day there are still headlines that are very contradictory from both sides, the US or Iran. I think what is the most obvious thing that I could say is we’re not going back to the exact markets and the exact state of the Middle East that we had pre-war. Things will be different. And so in that environment, I absolutely think there are lasting impacts.

But even beyond that, what I would say is, let’s forget about the war. Let’s just say it never happened. That was a period where we already had above-trend inflation. In this last CPI print, we actually had negative goods inflation. We continue to have a challenge with services inflation, and that wasn’t impacted by the war. And so if anything, I think oil’s bleed through to goods prices maybe had an immediate high impact and it will come down, but services are completely unchanged, and we have a services inflation problem, and the Fed knows that.

I can make a very credible case, and if I was to put myself on one side of kind of the three places that you just suggested, I think, look, the economy’s on incredibly strong footing. It was, if all else equal, the uncertainty of the war, the uncertainty around what might happen in geopolitics, the impact of high oil prices, that made us all maybe pull back our demand function a little bit. And so as we pull that off, I worry that the end of the war might actually be slightly inflationary rather than disinflationary, at least over the medium term.

Jack: Just one more on the Fed. I wanted to ask you about Warsh and the balance sheet. And I’m not a, like, a close Fed watcher, so I could be wrong about this, but he believes— I think he believes in a smaller Fed balance sheet. And so I was wondering, like, do you think that has any impact? Like, do you think that’s something that’s gonna be studied for a long time and maybe nothing’s gonna happen in the short term, or do you think there’s any impact to that?

Jeff: Well, I’ll kind of go back to what I said before. We know what Warsh thought about the balance sheet coming into the Fed. It’s yet to be determined what he thinks about the balance sheet as the actual FOMC chair, right? Because the decisions he makes today are very different, right? Everyone has an opinion on what their boss should be doing, but maybe those opinions are a little different when they actually become the boss.

And so, look, you’re 100% right. Coming in, I think Warsh believed that all else equal, we need to think about inflation in kind of two different regimes. You’ve got goods inflation, and you have asset inflation. And what balance sheet has mostly created is asset price inflation, right? So if you prop up, if you bring the level of rates down, you buy Treasuries, you support the mortgage market, et cetera, all else equal, it pushes the level of housing prices up. It impacts the level of stock prices. And so that benefits asset holders, which are broadly that high income part of the population. But what really the Fed is tasked with doing is preventing price instability or creating price stability for the entirety of the population.

And so all else equal, he’s created a task force for that. A lot of people ask me what’s gonna come out of the task force, and the most insightful thing that I honestly can say is that I’ve never been part of a task force or heard of a task force that comes back and says, “You know what? Everything’s great. We’re not gonna do anything.” So we’re going to get something. And really what I think is likely is the balance sheet over time is going to shrink.

Now, I’ve already said it’s not that the Fed cares about financial asset prices directly, but they do care indirectly in the sense of they don’t want a financial catastrophe because that would lead to demand destruction, and it would lead to a price instability challenge when inflation comes down. So Warsh is going to be very mindful of that. I think we will step gradually into that reduction. But I think the direction of the balance sheet is it’s going to play a smaller role in the US economy, a smaller role in the Fed’s arsenal. And if you believe, and certainly I do, that it has been a positive force on asset prices, I think we have to ask ourselves the opposite question: What impact will it have on the way down? And I think all else equal, it’s just another potential challenge that we have to navigate within markets.

Jack: Yeah, that point about talking about what you’re gonna do when you’re in the seat and actually doing it is such a good point. Like, and it applies to so many areas of investing. Like, all of us say, like, “In March of 2009, I’ll be a hugely aggressive buyer.” And then the reality is, like, put us in the situation in March of 2009, like, most people are not a hugely aggressive buyer when they see the world collapsing around them. So it’s just interesting that that’s such an important point.

Jeff: Well, and I’ll go back. What drives prices? Sentiment drives prices. And sentiment can shift very quickly. And it’s actually one of the things that I think makes professional investing very different than personal investing. And I’m obviously an investor and a fixed income investor, but the way I run professional portfolios is different than the way I run my portfolios because sentiment affects me directly, right? My own situation, my happiness or lack thereof.

But I think that kind of gets back to the question of what role does fixed income supposed to serve? And I really will go back to we’ve just been on an incredible journey in the world of equity prices. And so there’s all sorts of anecdotes. I’m certainly not your first guest to talk about high multiples. We are today running essentially at peak margins, and we have to ask ourselves the question of how sustainable is that? Capital has taken from labor for a very long time, ever since the 1950s. And so ultimately the direction that that has to go is it can’t continue on infinitely. It has to continue at some point potentially to re-equilibrate. And at what point does labor take from capital and what is the implication as that potentially happens to financial asset prices?

Jack: I always like when we have things going on in the background. I’ll leave it up to you if you wanna—

Jeff: Re-answer that without the background noise?

Jack: No, that’s totally fine. Okay. That’s all very, very normal for the podcast. Perfect. Yeah, we have all kinds of stuff going on in the background.

Jeff: Perfect.

Jack: Maybe if someone wheeled a baby in, we’d probably go viral, so, like that. Remember that video back in the day? I wanted to ask you about the national debt because we’ve gotten all kinds of different opinions on that. I mean, there’s been many people panicked about the debt for a very long time, and so far we haven’t seen many consequences of it. But we get different opinions. We get some people who say, you know, the impact of the debt ultimately is just maybe higher rates and higher inflation over time, and we get other people who talk about debt crises, you know, potentially in the future. So I’m just wondering from, you’re an expert in fixed income markets, and I’m just wondering, like, what you think about the national debt and what its impact actually is.

Jeff: It’s, look—

Jack: A long question, I know...

Jeff: It’s incredibly difficult. It’s an incredibly difficult question, because we have to think about the impact short-term versus long-term. I think what it creates is long-term, it’s unsustainable. I don’t think anyone would really push back against that. It’s unsustainable, and so eventually it will fix. And the question we have to ask ourselves, does it fix instantaneously in kind of a giant kaboom moment? Probably not.

But there’s a lot of ways out of our debt crisis. I think we have become very accustomed to running overly expansionary fiscal policies. That’s going to have to end. I think we have become accustomed to cutting tax rates on the wealthy, to cutting tax rates on businesses. That’s going to have to end, right? It’s just like when you speak to somebody who maybe you’re friends with and they got in an argument with their spouse. You always have to remind yourself that there’s two sides to that equation, and both sides probably have some validity to them.

And so I think as we look forward, all of these things that have acted as a tailwind to consumption, to markets, to just the broad economy that we have, are going to become less of a tailwind, and eventually they’re going to become a headwind. And that is a tough thing to navigate. And so I don’t think it’s tremendously challenging to fixed income markets in the immediate term. Part of that solution is probably inflation. We’ve already had a while of that, but you can’t have that in perpetuity.

And so I do think it has been acting as a steepener on the yield curve. That is to say it’s introducing higher uncertainty for longer periods of time, and that longer periods of time just means that rates have to be notably higher on that long end. Obviously, we’ve seen a little bit the opposite here just recently as we ascribe more confidence to that inflation question today with Warsh. But it’s having real impacts. And if I was to put it back directly to financial markets, I think really the impact that it’s likely to have is that our rates are likely to stay higher for longer than they would have otherwise, and that’s exactly what we’ve seen.

And so in a period of rising interest rates, a lot of people are looking at floating rate debt as a potential safe haven, and that’s exactly right. But the flip side is, is that floating rate debt also rolls over, which means those companies have a higher interest burden in their balance sheet equation. And it means that that is a potential source of instability for those companies with a high floating rate component. So I’ll look at private credit as potentially one area where that introduces even higher uncertainty. But at the end of the day, I think it’s a much more medium to long-term challenge. But it doesn’t mean that that medium to long-term challenge doesn’t have implications for today. It just means that we’re not investing for this kind of regime change or big kaboom type environment.

Jack: You mentioned private credit, and that’s another one we talk about a lot on the podcast. And again, you’ve got... I’ve asked all these two-sided questions, but you really do have... It’s another one where you’ve got people on both sides. I mean, you’ve got people who, some say private credit is great, and, you know, there’s been a couple of one-off type things that have occurred in there, and other people who think it’s a more systemic problem what’s going on in private credit right now. And what do you think about that?

Jeff: Look, private credit is here to stay. And private credit is not new, to be fair. It’s just been this tremendous flow of capital that has gone into private credit that creates some questions. I think private credit is tremendously valuable in the sense of if you’re a small company and you value having a small cohort or even one potential borrower to work with in periods as your business changes, that’s exactly what the world of private credit is meant to do. If you’re an investor and want to give up liquidity for a slightly enhanced return profile, that’s another reason why private credit might be great.

But at the end of the day, private credit is the same as public credit. It’s credit. What you have to underwrite is the ability and likelihood that you are gonna receive your capital back. And so what we see in private credit is with all of this influx of capital into the private credit area, public markets have roughly 65% of the below investment grade rated double B, which is that highest quality part of the below investment grade. What you have is almost the exact opposite in private credit, and that is to say that your average company borrowing there is just much lower quality. They have much higher leverage. What you have in public credit is you’ve got about four and a half times free cash flow coverage. What you have in private credit is about two and a half times. And so as asset classes, the returns on private credit are higher, but it’s because the risk is higher. These are less quality companies.

And so as I look forward, you know, I think what I would say is we will get a business cycle. That’s true for private credit, it’s true for public credit, it’s true for equities, it’s true for everything. And the way that I would think about private credit today is make sure that whoever you might be investing with, you believe in their ability to navigate that business cycle effectively, because really I think that’s what’s going to determine individual outcomes. And that’s really what we should care about. But private credit is, it’s valuable. It’s here to stay. It serves an important purpose, but it’s not always and everywhere better than public credit and vice versa. It’s just a complement and it serves two different purposes.

Jack: And correct me if I’m wrong, but it would seem like not a great place to index in private credit. It would seem like you’d want someone who knows what’s going on a little bit more than, like, running like an index type strategy. Does that make sense?

Jeff: I think it does. Really what I would say is I think indexing again is also something just different, whether that be in public credit or private credit or in equities or not. If you just want broad beta exposure at this point, the private credit market is probably large enough that there are some effective products that can just give you beta exposure. But if you’re looking for alpha, I think you want active management. You really want a private credit firm, again, with that ability to navigate whatever challenges may come their way.

Because I think the way that I would think about private and public credit is the intensity of investing becomes much higher when markets are less strong. When you’re having to deal with individual situations that, as the world changed, aren’t working out the way that you underwrote them, it just takes more resource, more expertise, more dedicated focus. And so any recently formed private credit shop needs to be thinking about their staffing into the future because eventually they too will have to navigate that credit cycle. And so again, my biggest piece of advice is just make sure that you believe in your manager’s ability to navigate that credit cycle with the resources, expertise, and knowledge because eventually they will have to.

Jack: Just one more for me before I hand it back to Justin. I wanted to ask you about how you think about managing fixed income portfolios, ‘cause in my research, I found this thing that you said: “Fixed income should be managed flexibly rather than silos.” And I’m wondering what... Could you talk about what that means?

Jeff: Yeah. Look, I think every investor has to be honest with themselves, right? We as human beings, we’d like to think that we’re good at everything and we can do everything, but none of us are. And so I think as investors, we really have to be honest with ourselves in saying, “What do I bring to the table? What process have I put in place that I can replicate throughout market cycles to act as grounding to how I perform research and how I build portfolios?”

And so the way that I’ve liked to talk about fixed income markets is a lot of the market is set up for the old world of investing, right? Where there’s just these clear delineations between equity and fixed income, public credit and private credit, investment grade and below investment grade, corporate securities and asset-backed securities. And today, the world is far more complex, and we are focusing on outcomes. We’re just doing things differently, right?

If you look at— We started this conversation with a potential question around AI and CapEx, and so, yes, it’s coming in equity markets. Yes, it’s coming in fixed income markets. Yeah, it’s coming in corporate form, in asset-backed form, in CMBS form. It’s coming in private markets. It’s coming everywhere. Companies these days are tapping markets in all sorts of ways, but we on the asset management side are still very siloed in our thinking, right? You have, again, you have equity investors, fixed income investors, asset-backed investors, corporate investors. And so what I really mean by that and that flexibility is to look across various capital structures and search for relative value.

Because one of the biggest things from my philosophy on fixed income, the most mispriced asset in any given point in the cycle actually isn’t what might go wrong. It’s the market becomes overly exuberant in pricing volatility or the lack thereof, right? It’s how do I capture a much lower volatility investment today where it doesn’t have to cost me in terms of yield and total return today?

And so one of the examples that I like to give is if we said we want to invest in hotels, you could go buy Marriott or Hilton equity. You could go buy corporate bonds. You could buy the timeshare version in asset-backed. You could buy an individual property in a CMBS bond. You could buy a pool of properties from a manager who manages a hotel REIT. And each one of those would be focused on by a different analyst at many firms, right? An equity analyst, a CMBS, an ABS, a corporate analyst, a REIT analyst. But it’s all the same thing. If that hotel operator does well, they all do well, and if that hotel operator doesn’t or we face a recession, they all do relatively poorly.

But the market at any given time will be pricing in the potential upside and downside. But in these periods like we have today, and thankfully for the bulk of the market, things are going okay. The economy’s doing well, and what really that relative value is when you look across those individual silos of fixed income is it allows you to say, “One of those is gonna perform notably better on the downside, but the market’s not pricing that today.” And that’s really what I mean by that flexibility, is looking across the various asset classes of fixed income, because most of the world isn’t equipped to do that. And as a result of that, if you put that process in place, to me, it gives you an edge in thinking about how to protect, which we’ve talked about really is a core tenet of being a successful fixed income investor.

Justin: Jeff, we appreciate your time today. We always like to ask our guests two standard closing questions. The first one is, what’s the one thing you believe about investing that most of your peers would disagree with you with?

Jeff: Oh, gosh. There’s a lot. You know, to me, I will go back to something I said earlier. I think the edge that many people in investing really wanna hang their hat on is a belief that I can underwrite a company or cash flows. I can know the equity story so much better than anyone else. And there’s elements of truth to that, right? There’s no shortcut to the hard work of just being a great analyst and understanding what the potential drivers are. But really what’s going to drive that asset price is sentiment. And so I think maybe if I was to answer that question directly, it would be, say, you have to focus on what do you think you know, what’s priced, what maybe isn’t priced, and how might sentiment intersect with that? It’d be the biggest piece.

If I was to insert maybe one other piece, and I think we’ve kind of already talked about that, is know yourself. Your sentiment will also shift as the world shifts. And so it might be a great investment, but also know your holding period, your ability to hang on in tough times, your ability to continue to add in, and always give yourself room to be more wrong. Because as an investor, you will be wrong at times. And you always wanna have that capacity to step into that.

Justin: That’s great. And then the last one for us is, based on your experience in the markets, what’s the one lesson you would teach your average investor? And maybe that is the lesson, but if there’s something else you’d like to sort of share, that’s great, too.

Jeff: Well, I guess I kinda preempted that next question. I mean, really I think that the biggest thing that I would say is, again, you will be challenged. You always wanna give yourself room to be more wrong. You know, one of my great mentors always used to talk about, he’s bought the bottom in many markets, and I think we all have, right? We’ve all bottom ticked something various. But the challenge is, is you haven’t only bought the bottom. You bought... You liked it here, and then you really liked it here, and then it went here, and you kind of had to be adding along the way. And so I’d say it’s the exact same thing with the move up in rates. You always wanna be able to buy a little bit more as rates move higher because all else equal things are more interesting. So always give yourself room to be more wrong because you never wanna be stopped out on the wrong side of that equation.

Justin: You’ve been a great sport today, Jeff. Thank you very much. We appreciate it.

Jeff: Yeah. Thank you. Always interesting to talk about the world. There’s a lot to talk about, and so I really appreciate it.

Main topics covered

• Why political backlash against AI data centers may become a bigger risk than open source competition

• How Sam Altman, Dario Amodei and AI lab leaders are shaping the public narrative around artificial intelligence

• Why model progress, enterprise AI adoption and compute demand remain central to the AI bull market

• Meb Faber on 250 years of US market history and the power of long-term compounding

• Why expensive US stock valuations can coexist with long-term optimism about America

• How bear markets reset speculative excess and why younger investors may benefit from future declines

• Warren Pies on whether semiconductors are being priced like a less cyclical industry

• Why peak margins and low valuation multiples can be misleading in cyclical businesses

• Kai Wu and Ritavan on how AI changes moats, code, proprietary data and corporate strategy

• The System Gambit framework and why old checklists can fail when the game changes

• How investors should think about US versus international markets across decades and centuries

• Why future diversification may depend on where the next great innovation sandbox emerges

Timestamps

00:00 Intro and weekly lineup
04:00 AI data centers, politics and the PR problem
09:18 Meb Faber on US market history and bear markets
14:44 Are semiconductors still cyclical?
20:56 Kai Wu on code, AI and changing moats
25:57 Ritavan on the System Gambit and the Ottoman Empire
30:28 Meb Faber on US versus international stocks
36:00 America as an innovation sandbox
38:06 Closing thoughts and where to follow Excess Returns

Matt: We’re a relative hairs trade.

Jack: I am—

Matt: This is terrible. Did they actually say that? Is that actually—

Jack: They did. There was something in there. I am, I’m Jack Forehand, otherwise known as the bald guy to the YouTube commenters, and—

Matt: Better than Jack Forehead. Sorry. I’m really sorry. Didn’t mean to—

Jack: I’m, I’m joined by a man who — I don’t think his barber probably could not pick him out of a lineup at this point, I would say. It’s probably been a while since you’ve been there is what it looks like from these comments.

Matt: You know, you look over those thumbnails. We’re a little bit shorter on the collar in the last week, I’m just saying.

Jack: Oh, you did. You did a trim.

Matt: I did a trim. Okay. Did I tell you what my frigging doctor told me on the annual check-in?

Jack: No.

Matt: I walk into my doctor in whatever, the winter early this year, for the annual physical and the whole thing, and he basically does one of these, like, the look at you. Like, this type of deal. And he’s like, “Well, Matt, I can see from your chart you’ve gained some weight, and I can tell you it’s not just because your hair is longer.” It’s like, you bastard. I didn’t come here to get burned by you. Tell me something good.

Jack: That is good stuff.

Matt: All right. Well, that’s for the hair differential crowd. It is. We...

Jack: Yeah, we’ve probably given them enough. It is funny though — whenever I do your YouTube thumbnail, I still got, like, your headshot from your investment advisor thing. And I think people are like, “Is that the person that’s in the video, or is that not the person that’s in the video?”

Matt: That’s all right. I like keeping people on their toes. That’s what we’re aiming for here. That’s what we’re aiming to do.

Jack: So we should probably get into the clips here. As you can see down the side, we’ve got some really good stuff here. You just completed an interview with Warren Pies. Warren is awesome. Like, I like... I always like listening to our podcast, but it’s like I look forward to the editing when I get this, ‘cause I get to see your interviews with Warren before everyone else. So we’ve got Warren, we’ve got Meb Faber, his great book that’s coming out on July 4th about the history of the American economy and markets.

Matt: Great hats and swag too. A lot of great hats and swag with that Meb.

Jack: Yeah, yeah, yeah. You always know it’s coming. He may or may not be sending us a hat. We’ll see, we’ll see how that plays out. Did you hear — so I don’t wanna derail this too much, but did you hear a story about the book and the guy that, that he sent it to all his podcast guests?

Matt: Not only did I see that, I... He said it, and then they started popping up online. Have you seen people sharing the book in delight and surprise?

Jack: Yeah. Well, no, the story is basically he sent it to all the guests on his podcast. And then he noticed that on Amazon there was one for sale when his book’s not out yet.

Matt: Oh, no.

Jack: And basically he’s like, he’s like, “I’m not gonna name names, but it was one of my VC-based guests.” And then so what he did, which is awesome, like exactly what you or I would’ve done — he bought the book back to shame the person for selling it. So like Meb has reacquired his book, which is — that’s just a great story. I had to get that one in.

Matt: That belongs as a footnote to a chapter for next time Toby writes a book. Toby needs to do like aggressive greenmail and buyback strategies with a footnote to the time Meb bought his own book back. I’m putting that on the list, Toby. Add it for the next one.

Jack: So now that people have heard enough from us, let’s get into the clips. AI is probably the topic everybody’s talking about right now. It’s been driving the market. And Warren has an interesting take on AI. So here’s Warren talking about that.

Warren: So we’re in this middle, the — in the midst of really thinking through what I would say is the open source panic. We really wanted to get to the bottom of that. Some of the charts that we’ve seen floating around within more traditional macro. Our read on that, and we can talk about it, is that that’s not really something to worry about. That’s not an existential threat at this moment in time. The data and analytics that people are pulling from, really when you dig into them, aren’t too worrisome, and so that’s not a worry.

I think if you ask me, the biggest concern is regulatory. What happens — I do think that the messaging around data centers — you could call it the horseshoe effect, you could call it whatever you want — or, you know, like you’re seeing both the left and the right start to say, “Why are we building data centers?” And I mean, I’m somewhat sympathetic to that. I mean, every... My son actually asked me that this weekend. He’s 15, and he was like, “Dad, do you think we should be building data centers?” So I mean, this is becoming an issue. It’s becoming something people are really talking about.

And so I think the messaging — and the guys who run the labs are horrible, horrible spokesmen for this, for this world. They’re not the marketers for a reason. Not at all. And so, you know, if our fate, if the AI fate is left in the hands of Dario and Sam, we’re all screwed, in my opinion, just because they give off a vibe of... Their messaging is objectively scary. Like Dario just says, “You know, we’re just gonna lose a lot of jobs and be ready and hand-wring and be nervous.” And Sam is — I think most people find him to be kind of shady and not trustworthy. And so to me, both of those guys are kind of nice figureheads for this problem out here, the regulatory problem.

I don’t think it’s a today issue. I think it looms out in the future, like that 2028 deadline is kind of important. What kind of progress do we make between then and now? What kind of messaging changes between now and then? Those are all important things, so that’s a big deal.

And then just, I think, market sentiment — but this is usually something that’s... ‘Cause market sentiment sounds so general, but we do... There is a large degree of financing when it comes to the labs and their training budgets and things like that. And so if market sentiment was to sour for some reason, then you could see progress stall out. And I think model progress, as we’ve said, is really the lifeblood of the bull market today, is you need to see models improve, and if models improve, you can envision deeper adoption, enterprise adoption that flows into compute demand.

Jack: What I thought was good about this is everybody’s talking about, you know, what percentage of people are using the best tokens versus the lower tokens, or open source versus closed source. And I think what he said is the issue right now. I think this political thing is the issue. And the way that it’s perceived and the data centers — and I know you know this through your work with Ben and Pershing — like, this is exactly what’s going to determine the future of this, and I think sometimes the rest of us are getting lost in maybe all this other minutiae when that is the thing that we need to be worried about.

Matt: This is the story of can we keep selling this? Can we keep selling these prices, these valuations, all this to the American public? Because if they decide to turn on data centers, we have a different problem. And you brought this up — I think this is in our month-end show too — where we’re talking about how efficiency is going to drive what we do with all this high-priced memory, all this high-priced usage.

So you’re seeing this narrow in now on we know it’s expensive, we’re seeing company investment, we’re seeing them chase it, and we’re also seeing this public pushback. This blowback is mounting. It’s on both sides of the aisle. If we end up capped here with how much memory is out there, how much of this stuff is available, it’s going to be all about efficiency. It’s gonna be about mapping that model progress determining everything for where this is going. It probably doesn’t reach ahead and just go hard one way or the other. We’re gonna just settle in on some balance that we’re not predicting right now, but man, when Warren says, “My 15-year-old comes to me and asks me about this,” this is real.

Jack: Yeah, and you can understand both sides of it. Like, AI is going to exacerbate wealth inequality. I think—

Matt: It already has. All technology has.

Jack: It already has. Yeah. It’s gonna keep doing that, but by the same token, AI is going to be a positive thing for your average person. And so those two things, like, sitting out there — like, this one is gonna make people hate data centers, and this one people are gonna realize, “I can do stuff with this.” And, like, just figuring out the balance between that, ‘cause AI is losing the PR battle right now. There’s no doubt about it. Like, your average person does not see the benefits of this. They don’t like the data centers. Like, this is being lost right now. And like he mentioned, Sam, you know, Dario — these are not the people that should be trying to help you win the PR war.

Matt: Sam, Dario, and Elon are the ones we are now banking on winning the PR battle to sell this narrative.

Jack: Right, and they’re trying to, like, get regulatory capture by making fears worse than they are. They’re trying to do all kinds of other things, and, like, they’re not doing a good job as the PR people. But I think Warren’s point is so important. I think this is what’s gonna determine what happens with AI.

Matt: What’s fascinating about what Warren also said here is he sees more of this playing out still 2027, I think he said. Might have been 2028. Still a little bit in the future. That’s different from what we’ve been seeing and what Ben Hunt’s been talking about as this story’s coming home to roost in the midterms. Now, the layover effect might not be that it really starts to affect us until we’re well into next year, but the reality is these stories are through the roof. I’ve been writing about it. I’ve got a couple things on Panoptica about this, too, where it’s this pushback is real. It’s probably louder than most investors think if they’re just looking at the stock charts.

Jack: So let’s bring in the positivity here. 250 years of America, incredible economic growth, incredible performing stock market. Here’s Meb Faber talking about some of that.

Meb: I really wanted to add a postscript to the book and be like, you know, “PS,” end notes like, “Well, just to point out, I don’t wanna mark the top of this cycle by putting out this very patriotic and optimistic book.” You know, look, it’s no secret that we believe that the broad US stock market is very expensive. Doesn’t mean it can’t keep going up. There’s plenty of other great assets around the world — global stocks, REITs, bonds, you know, real assets like TIPS and commodities. But also, other stocks within the US look great. You don’t have to pick market cap weighted, right? But that’s the long history, and I think it’s a feature, not a bug, right?

You know, how many of us look around the last couple years, the last decade, we’re like, “Man, look at this just dumb stuff people are doing with their money.” They’re buying a bunch of NFTs. They’re, you know, buying stocks that trade at 100 times revenue. All these things that which on average you see during these romping, stomping bull markets. You know, bear markets help to clean that out quite a bit, you know? People tend to pull in the reins a bit and stop doing really speculative, dumb stuff when they’ve lost half their money. You know, it’s time to get serious, right? It feels like when you got a mortgage, you got some kids, and, oh, by the way, there’s a recession and you lost your job, on and on. You know, people tend to stop YOLOing a little more when that happens.

But as you mentioned, it’s been a long time, you know. We wrote one of my favorite papers the last few years ago was “The Bear Market in Diversification,” just talking about how special this period has been for US stocks since 2009. And we started to see a shift in the last couple years. We’ve seen a lot of other ideas start to really accelerate and start to perform. So I think there’s nothing to be afraid of bear markets. I think if you’re young, hey, that’s the best thing that happened to you. Get a nice fat 50% decline. Good for you if you’re in your 20s, 30s, even your 40s. If you’re probably 80, 90, you know, maybe not so much. But I think it’s a natural part of it, and it’s the cycle of, you know, the cycle of life.

Jack: What was interesting about this clip is this is the balance between sort of this long term — the US has been amazing, you know, all the innovation, the US economy has been amazing — and valuation. So that’s why I brought this clip in, because all of that, those great things we would expect to continue, at least for the foreseeable future. But you do have this other part of the equation, which is valuations do matter over... I mean, obviously we’re not looking at a 250-year timeframe when we’re investing. So valuations do matter.

Matt: They matter, and they’re coming home. They’re coming home to roost at some point where they’re gonna matter for a chunk of your life. The other part of this that he says that I absolutely love — the planner hat on me just gets all excited — and it’s this idea that your biggest drawdown, he doesn’t say this specifically here, but your biggest drawdown is always in front of you if you’re doing this right. And the problem is when you lay that over your timeline.

So if you’ve been invested since 1980 and you’ve had drawdowns, it sucked a couple times. But if you were, you know, 40 years old in the ‘80s, where you are now, it’s probably a good time to take some risk off the table. And if you’re 20 or you’re 30 or you’re 40 years old now and you’re accumulating assets, you wanna be smart, because when these valuation resets happen, they take time, they clear stuff out, and you get the benefit of it on the other side. What you don’t want is to be 80 years old after this run, you know, and just assuming that this is what you have. I don’t know what even you’re doing with it if you’ve accumulated all this wealth or you’re piling it all up. You have some plan for it. You don’t wanna see that get cut in half. Don’t be dumb about this.

Jack: But my big challenge here, Matt, is how do I come up with a negative YouTube thumbnail for 250 years of prosperity? You know, we gotta get people to click on this thing.

Matt: Well, you say, “Did 250 years of prosperity just end?” Or we have to like—

Jack: Yeah, yeah. I was thinking—

Matt: The bull’s running off the cliff. Like, you know?

Jack: I was thinking like “your children will pay for your prosperity” is a good way to go.

Matt: Oh, there you go.

Jack: Yeah. Yeah. Like, take that angle. Like, you know, take it and reverse it. Like, the price will be paid down the road for all this prosperity. I don’t know. I gotta figure it out. I may just have to. I mean, we’re literally releasing it on July the 4th, so I think I’m gonna have to just go positive with the YouTube thumbnail and not try to dig some negative story out of this. I don’t know.

Matt: I don’t know.

Jack: I’m going full American flag.

Matt: Go full American flag. Yeah. Let’s go all the way. Let’s, um... I mean, you wanna just fly down to the White House, and we’ll record a live banner there that we can put out. What do you think?

Jack: We’ll be, like, taken away by security from the White House, which is probably, probably not what we—

Matt: You wanna climb a skyscraper with a banner? See those people—

Jack: I saw that today, yeah. Did you see the guy — the guy proposed, too. Like, they came down to the lower part, then he proposed, like, in the... So, I mean, this obviously ends badly for them, but it’s quite a show they’re putting on.

Matt: The order of experience I had with this event was seeing it online, and I think it was Tony Greer basically tweeting out, like, “Oh, they just beat me to it. I was gonna do that,” something like that. And then my wife’s reaction was, “Who would do that in this heat?” ‘Cause it’s very hot. And then everybody’s like, “Oh, they got engaged. That’s adorable. I hope security wasn’t too mean to them.” Still don’t know how they got up there. But don’t know how markets got to these valuations either, but here we are. Maybe somebody writes a book about this, too.

Jack: So on to semis. And semis has been the talk of investing right now because the returns have been ridiculous. People are arguing it’s a cyclical industry, it’s not a cyclical industry — what’s going on here? So here’s Warren Pies talking about that.

Warren: And that’s like the big question, and I’m not smart enough to answer it. But I am smart enough to acknowledge that there are some things happening under the surface of the market that suggest the way these things are being priced — suggests that investors are treating them as at least less cyclical than they’ve been historically. And this is a big deal for me in the way the overall market gets valued.

So we came into the year bullish. We’re bullish. We’ve been bullish, and one of the big bear arguments to talk about things that create anxiety has been valuations, and we’ve talked about this at — I don’t know if I talked on this broadcast. I think I might have. But one of the reasons we have not seen overvaluation in this market is that we have massive margin expansion out of non-cyclical pockets of the market. And when you get margin expansion from non-cyclical pockets of the market, you should expect to see multiples expand. And when we adjust for everything, the market has been pretty rational in how it’s done that.

The semi case is maybe the first bit of exuberance that we’re seeing, though. So semis, traditionally the most cyclical tech industry. Therefore, when margins expand — if you look at a historic chart of margins for the semi group in price to sales multiple, when margins hit a cycle peak, price to sales starts going down to a cycle trough. You don’t wanna pay up for peak cycle margins in this cyclical type of group. That tendency has totally broken down. We’ve had margins explode, obviously, out of the semi group. You can see Micron. You can see Nvidia. You can see all these companies where bottlenecks or whatever have exploded margins. And instead of getting like price to sales for the group going way down, you know, to one or two times, price to sales has exploded.

So for us, this is one of those things where how do we factor this into the overall market valuation? ‘Cause semis are like twenty percent of the market now, and it’s not a small factor when you’re trying to do your overall top-down analysis. So they are priced right now — the answer to the question is they are priced right now like they won’t be cyclical. So I think there’s a knee-jerk kind of feeling, because of history and everything I laid out, to fade, say this is a bubble — probably is the beginning of a bubble in some spots for sure, in this very specific part of our market. I don’t think the overall market’s a bubble, but there’s always air pockets of exuberance, and I think this is one.

But again, going back to the ‘90s, I don’t think we’re... This cycle is going to be long. It’s gonna be the longest cycle we’ve ever seen. And so there is... I’m not buying into the sci-fi world where semis are just no longer cyclical, but I’m also not buying the idea that this is close to a cycle top, and that we should be expecting margin growth to slow down anytime soon.

Jack: This is the interesting thing about this — it’s the interesting thing to some degree about all cyclical industries — is typically, and people don’t... It’s counterintuitive to people, but typically in history when margins are expanding and valuations are low, you don’t wanna own. And that, like, that makes no sense to anybody, because they’re like, “Wait a second. Margins are expanding, or margins are good, valuations look really cheap, and you don’t wanna own them.” And that’s because you typically are at the peak at that point. So the question with semis is, is the world changed and that’s not the case anymore? Or is this just a cyclical industry that’s at a cyclical top?

Matt: This whole idea with any cyclical industries — and I think it’s the Molodovsky effect — where it’s basically you wanna pay for them when they’re at really rich, insane PEs, which is usually in the trough, and you wanna sell them when they’re at really attractive low PEs and growth is booming, because it usually means they’re at the peak. And there’s a counter-rational adjustment that you have to make for these companies when you think of them.

The other part that Warren says inside of this interview as it relates to it — and this is the part that I’m scared of, because I’m in the camp that these are still cyclical industries. They just have a different type of growth-oriented moat that’s around them that’s at least temporary. Maybe it matters forever. But it’s just they’ve re-rated higher. I’m giving them that. I’m conceding that, but I still think they’re cyclical to a degree. He talked about the ‘90s. Did you hear the month count stat that he threw out with semis in the ‘90s?

Jack: I did, but I can’t repeat it.

Matt: That’s okay. I’m gonna say I might have this slightly off ‘cause it was... it’s from the interview itself. 63 months is the run in the ‘90s that we go through where we experience this. We’re 30-something months into this, and there were some big declines in the ‘90s. There was some other stuff. It’s not to say the historical analog is perfect. It’s just to say this painting of semis as not being cyclical, of having growth as the moat, of being able to do this — it feels crazy now. We’re about half the duration of we got in a prior bubble with this stuff, and it’s really hard to hold that thought in your head when you’re seeing some of the prices and valuations right now and trying to map this growth rate forward.

Jack: And the next logical progression from semis is obviously the Ottoman Empire. So we’re gonna get this clip from Kai Wu and Ritavan, where we discuss the Ottoman Empire.

Matt: Before you play this clip — not only do we discuss the Ottoman Empire, but what’s fascinating about Ritavan and this new book that he has out is it’s all about corporate strategy in this environment, and, I mean, this goes hand-in-hand especially with the stuff Warren’s talking about here.

Jack: He’s really good. Like, his stuff is really... Like, this is an excellent, excellent episode.

Matt: Yeah...

Jack: It goes beyond investing, which we don’t do that much, but it’s an excellent, excellent episode. It’s really good.

Matt: Listen to the episode, because we take what he learned from his first book called Data Impact, which was all about basically what businesses do in an age of data — almost like a chapter or something about what to do with AI. That became the focal point of all the speeches and other things that he gave when he presented on the book. Turns into this entire book that’s about corporate strategy for companies grappling with this stuff. So yeah. What do you say, Jack? Let’s roll that clip.

Jack: So let’s play the clip, and then I’m gonna punt, and Matt’s gonna explain what’s going on with the Ottoman Empire.

Kai: What I’m thinking about is this, which is the moat — go back to that analogy — the moat is, you know, is dependent on the game you’re playing, right? So in a certain setting, for example, code can be a moat, right? If you’re the only person who possesses the ability to code, then you have a moat, or you as a company have a moat. You know, the obvious analogy today is that, you know, the world has shifted to a world where AI can write code very cheaply and quickly. And so anyone can vibe code or has access to advanced coding tools. So the game has changed, and so perhaps that moat is no longer a moat in the world of AI.

And so I think, you know, as we step back just from this one particular case, you know, you have to be very thoughtful about what game are you playing. In other words, what system, to take your language, Ritavan, is currently the dominant paradigm. And then the question becoming is what is a scarce asset? What are the, you know, the moats in that setting? And I think the context matters a lot. And so I think what’s really interesting about your work is, you know, is it kind of forces you as an investor to think through, you know, how is the world changing and what other systems are available to businesses, you know, both through the technology and other factors.

And then you ask the second order question, which is for each company, as you kind of go through the list, what assets do they possess today? Are they optimized for the system A or system B? Because obviously as an investor, you’re looking to kind of front run change and look for companies that, you know, might be priced based on, you know, the past paradigm where — but other investors in the market might be missing, you know, how their assets can actually be quite valuable in the future, right?

So like, you know, we saw the other day, like, I think it was Getty, right? Their stock rerated because, you know, they made a deal, I think, with one of the AI labs. But the idea just being that, hey, look, proprietary data could be really valuable for training, right? So that’s information that was, of course, available and valuable in the past, but perhaps is even more valuable in a world of AI where it can be used as an input to an AI model. So I think looking for analogies like that, situations like that, could be a helpful, like, framework for investors.

Ritavan: Yeah. Let me actually just jump in with a historical example, because, you know, the moat word obviously comes from fortresses and protecting a, you know, a physical fort. And there is this interesting historical setting where — this is during the Ottoman Empire. There’s an Albanian prince who’s been, you know, taken young, Gjergj Kastrioti, and he’s sort of, you know, trained by the Ottomans, becomes a cavalry commander. They give him a Turkish title, which is Iskender Beg. And then at some point they send him back to run that part of what was then — you know, what is now Albania, but that region back then. And at some point he figures out, “Okay, I think I’d rather fight for my folks,” and, you know, breaks away from the Ottomans.

And, you know, that region and all the regions around what was the Ottoman Empire then really struggled with the Ottomans, because they just had these vast armies, and they were able to marshal these resources, and people could — you know, you couldn’t sort of hold out against that.

And what Skanderbeg then does interestingly is he’s got his main fortress. The Ottomans send a massive army. They lay siege to that fortress. And now comes the paradigm change, because it’s the same fortress, the same armies — everything is the same, right? So if you go by your checklist, you’ve ticked all your boxes, and actually the outcome is very clear, which is the Ottomans eventually, you know, have a successful siege and they take over the fortress.

What Skanderbeg does now is suddenly invert the paradigm, right, in some ways. So he says the fortress was seen to be — like it had to have a big moat, tall walls, et cetera, and the idea was you lock yourself in the fortress, you let the army lay siege, and you try to outlast the siege. Skanderbeg instead leaves a very small garrison inside the fort, gets the hell out before the Ottomans arrive, and hides in nearby forests and hills. The Ottomans now lay siege, and now suddenly the vast Ottoman army that can actually be modularized and moved around, et cetera, on an open battlefield is fixed around the fort, and then he just keeps harassing them night after night after night.

And I think this is exactly my point, which is, if you have a checklist, a checklist is a list of binary items that has been abstracted out from a causal model of how reality is supposed to function. If you change that causal model, right — if you change the rules of the game, if you change the game itself — that checklist per se is useless, because the checklist is just an artifact.

Jack: So Matt, as I said here, I’m gonna let you take the lead on this one ‘cause I don’t have too many insights on the Ottoman Empire, but I will have some stuff, so I’ll let you go first.

Matt: I have very few insights on the Ottoman Empire. I do know that Türkiye beat the USA in the last stage of the group match, and, you know, anybody near the Ottoman Empire is on my, you know, nasty list right now for this stage of the game.

Jack: I didn’t know the Türkiye thing. When did that change? Do we know?

Matt: I did end up looking it up online ‘cause I wanted to know. Same thing with, you know, the Democratic Republic of the Congo, or whatever the DR stands for, that everybody’s calling it DR Congo, and I’m going, “Is that okay to say?” Probably not, but the announcers are all saying it, so I’m going, “Ah, I don’t really know.” It feels wrong. I feel like I shouldn’t be now saying it on YouTube.

But this is the idea from Ritavan’s book, is that you basically move, as industries progress, from playing one game to a next game. And so it’s kinda like I’m playing checkers with you, then I realize, oh, there’s a chess game over there. I’ve gotten more advanced. Now I’m playing chess. Then I realize there’s a Go game over there. Now I leave and I’m playing that. And who you’re competing with, they might still be playing the old game, and you have to be aware of this. So the idea of the system gambit is sometimes you have to sacrifice in the game you’re currently playing to go play at the new game.

And with this AI adoption cycle that he sees us as we’re in right now, he lays out in the book a number of systems using examples, many of them historical, many of them from things like conflicts, where somebody plays a type of move to go, “Oh, I’m not playing the same game that I started off playing right here.” And how do corporations shift that strategy in a dynamic environment like what we’re in right now?

So what’s really cool inside of this piece, and what Kai’s talking about, is how this is that code is the moat idea that Kai’s been talking about, where you start to realize the actual thing you do as a business, what can AI do to make that smarter, and then all of a sudden stop treating your business the way you were treating it before AI. You now have to treat it in a totally different way. I know you can relate to this with all the stuff we’re doing with Excess Returns especially.

Jack: Yeah, it’s interesting. I was just thinking about the castle thing, and I’m obviously gonna analyze this completely wrong, but, like, the idea that basically they were in the castle and the castle was a weakness, and then they got out of the castle and they turned it... They basically flipped it. And I was thinking about that, ‘cause I’ve had to build some investment strategies with AI recently. I had to take some stuff we had and, like, carry it to a different universe. And one of the things I realized in that is, like, I was worried. Like, basically as someone who can construct investment strategies, like, code is just gonna do all that. You’re just gonna say, “Build the investment strategy,” and it’s done.

But that’s not the way it’s worked out at all. Like, it’s worked where code has become — is flipped basically, and has become leverage to make me better at what I’m doing. Because if you just said, like, at the beginning, if you just say to the AI, “Take this thing and regenerate it,” like, there’s all kinds of problems with that. And those are the types of problems you don’t understand unless you’ve been doing this for a really, really long period of time. And so I’ve been able to flip this now where, like, I can add value to it, but I can leverage myself dramatically by the ability for it to take the value and turn it into, like, quick results. So I have no idea if that’s anything to do with the clip, but it’s just something that I’ve been seeing recently that I think is relevant.

Matt: Well, it does, because the gambit in this becomes your knowledge of the thing you were doing previously. You realize to just say, “Hey, help me copy/paste this to a new location,” is actually more problematic than you seeing, “I need to — you to leverage what I understand and what you’re capable of doing before we go to the next place.” Because if I just try to order you on what to do, it’s actually counterproductive to the whole process.

And in the analogy, what he literally says is they’re being invaded, they know they can’t defend the walls of the fortress, so they go and they hide in the hills. They let the fortress be overtaken, or the castle be overtaken, pretty easily without much resistance, but then guerrilla warfare from outside in the woods where now they have an advantage. A lot like the Colonel Blotto problem that Mauboussin writes about. There’s a lot of stuff like this where you just need to pick your battles in the right domain.

But the example you gave is actually kind of perfect, because you’re saying with this new tool set, I need to not... Like, I was playing checkers before, now I’m playing chess. My opponents might not realize that we’ve shifted games. That creates the opportunity. That step function has moved several times, even in the last 18 months here, and it’s pretty wild.

Jack: Yeah. Hopefully we’re doing the same thing with the podcast as well in terms of the way we’re thinking this through.

So let’s go to the last clip here, ‘cause we are running out of time, as we — you and I always go over. This is a really interesting one. This is US versus international. And this is talking about the greatness of the US, and it’s interesting how far this chart just goes back. I think it goes back to 1800. So here’s Meb talking about that.

Meb: Hindsight bias. No. Look, I mean, the reality is you gotta remember, like even at the start of the 20th century, you look at where the US was — I don’t think it was preordained that this is guaranteed to happen. And if you were to, like, if you were to horse race and were to place your bets on countries that, you know, “Hey, this is where I’m putting my money for the next 100 years,” you know, there’s probably a dozen choices that might’ve been interesting.

And even during the 20th century — like this is the crazy part I love talking about — the US didn’t hold the crown, right? You had Japan being the largest stock market. You told someone in Japan in the ‘80s like, “Hey, we’re gonna teleport 50 years in the future, and your stock market’s gonna go from the largest in the world to 5% of the world,” they would be like, “You’re crazy.” And likewise, if you were to tell people — if you were having a pint in a pub in the UK with a bunch of Brits and say, “Hey guys, this Korean War that just ended, you know, they’re gonna chop the country in half, and the bottom half is gonna be bigger than your stock market in 50 years,” they’d be like, “You’ve lost your mind,” right? Like, what? But both of these things have happened.

And so, you know, I think if you look at the long history of markets and countries, you know, it’s tough. It’s tough to pick the right horse. I mean, look, the US has all the tailwinds and has had all of them for a long time. I mean, just you go down the list and check. But it’s not guaranteed that that will always be in place forever. You know, it’s been a special place. It’s had a ton going for it. I think it’s unique. I think it’s the best country in the world, on and on. But, you know, there’s a lot of billions of people around the world that would also like to be where we are, right? It’s still the number one — despite all the, you know, political discourse we read, it’s still by far the number one country that everyone wants to move to, for a lot of reasons.

And so if you look at a lot of the, you know, metrics — number of Nobel laureates, R&D spend, you know, on and on — you know, the US is definitely still numero uno. VC money — it just... Like, some of the stats on VC investing, also things like market cap of companies in the US versus places like Europe, and you’re just like, “Euro bros, what the hell are you guys doing? Like, how are you guys falling so far behind? Like, what is happening?” Like, great that we’re... And maybe we’re wasting it, but everything — energy production, on and on.

And so, that all having been said, you know, as a percentage of world GDP, US is only a quarter. You know, so there is quite a big disconnect between US and, um... But if you were to like go out 50 years and say, “Hey, Matt, do you think the US is gonna be two-thirds world market cap?” I would say no. I would fully expect countries like China and India to, you know, accelerate and get a bigger piece of the pie. Like, how could they not? But, you know, would I expect the US to go from two-thirds world market cap to five like Japan did? No. No, I wouldn’t. So still really bullish over the long term, but you can’t muck it up, you know, that’s for sure. And we’re — who knows? We’ll see.

Jack: This is interesting because, first of all, it just shows how great, like, an economic engine the US has been. The stock market, like everything about the US, the innovation — it just shows the result of all that stuff. And to be honest, I have no idea how you build a chart of that in 1800, so I can’t... You know, what’s in that chart for US, what’s in that chart for international — like, I can’t evaluate that. Whether it’s someone in the comments is gonna be like, “There was only this and this is... You can’t even, like, you can’t evaluate this based on this.” But the idea is the US has done very, very, very well.

And I... This made me think — like, we think about that US versus international in short-term timeframes, and I even mean like decades by that. But it also made me like broaden that out, and he said like it was not preordained that this would happen to the US. And I kinda think about that in the future, and it’s not preordained now that, you know, the next 100-plus years will be dominated by the US. And I just think about like what that means in terms of other countries and what we’re doing and how things are changing. I don’t have any great answers on it, but it just put it in like a context of — you know, most investors think in like a one-year timeframe. Like, quants like us think in a decades timeframe, but this is like a completely different timeframe to think it through.

Matt: It makes me think of Rupert Mitchell and his chart of truth, ‘cause he’s always tacking back to this on the shorter gyrations on this stat.

Jack: This is like the chart of truth since 1800 or something.

Matt: This is the ultimate chart of truth. This is the chart of truths. I can’t wait. I want... Rupert, you’re coming back on. We’re talking about this and we’re talking about that just to straight up—

Jack: The problem is if Rupert’s waiting for that international line to cross the US one in this one, he’s not gonna see a signal for a while, I don’t think.

Matt: Okay. And to your point, this is the second thing here — so I’m in full agreement. Basically from 1800 to 1950, I don’t really know what to do with what even is there. I’m scared to ask. But we touch on this in the interview, in the conversation with Meb on this. There’s kind of this amazing thing that from the advent and the invention of the joint stock companies, what happens is trade and industrialization starts to expand around the globe. America kind of becomes this weird sandbox for all these new entities. And structure’s the same, but America becomes a sandbox for these new entities, new ventures, both as we expand the territory, but then as we expand the global footprint. And of course, that takes us right up to, like, the Bretton Woods stuff. That takes us right up to the world wars and settling global trade, and all the things that have set the scope for globalization post, you know, mid-1940s on.

So what’s interesting — and what I hate that this puts in my head is the damn SpaceX thing, or the damn space and time. And I start to think about, yeah, this might... So this goes, maybe this goes for the rest of our lifetime in whatever gyrations, but the next great advancement on the expanse of this thing is like, well, where else do we look? Where are the... Where are we taking these structures and implying them somewhere? Maybe it’s virtual. Maybe this is where the LLMs and crypto and everything else takes over, and we invent a new space to do this. It’s digital real estate, and we’re all buying skins in whatever Fortnite universe hellscape that I’m gonna find myself in when I’m 80 years old.

Jack: Well, the good news is, first of all, in 250 years, obviously we’re all gonna be alive now due to all these advancements in AI. That seems obvious at this point.

Matt: But 300 years, I’ll be on this show with you. We’ll be talking about the look then.

Jack: Even beyond that, I think we’ll probably be looking at the Earth-Mars chart at that point and talking about, like, the dominance of Earth over Mars or Mars over Earth, you know, and how that relationship’s gonna be. I think it’s gonna be the other way, right?

Matt: That’s the way this is supposed to go. It’s gonna be Mars that’s dominating. Well, yeah, because—

Jack: It’ll be the upstart.

Matt: Yeah, it’s gotta be the upstart, and that’s what... This whole thing has me thinking of where’s the next upstart, and not in the pure VC way. But where’s the new place where we go to innovate on the next round of ideas? Because I think that’s what humans keep doing.

Jack: Can you imagine if international diversification becomes Earth versus Mars?

Matt: I’m like, “This is where this is going.” That’s the direction you’re heading. Like, right?

Jack: With clients. Like—

Matt: This is the poor advisor of the future. Which is why in all the great sci-fi novels it becomes an AI or something else, right? Like in Ender’s Game, it becomes the computer. It’s all series, and inside of that is the investment advisor is running the money separately and whatever. But this is where this goes. All the venture goes into space. It’s very strange. It’s very speculative. The Wild West era, gonna be completely bonkers. New snake oil. Can’t wait.

Jack: So on that note, I’m in a third floor office and didn’t realize that the AC vents are closed.

Matt: Oh, no.

Jack: So the sweat has been increasing dramatically throughout this. I mean, it’s gotta be 100 up here, I think, at this point or something. But I’m gonna let you bring us home, so I’m gonna go jump in a lake or something.

Matt: Please go jump in a cool lake. At least take a cold shower or something. For those of you watching along who have made it through another... I’m sure this was a 30-minute episode by the time we edit the clips in. Clearly we hit our mark we set out. Go to the Substack, get signed up. Can’t stress enough. We’re putting so much cool stuff over there. You wanna get in on it. Wherever you’re watching this, thank you very much. Like, comment, subscribe, all the things below. We’re out.

Main topics covered

* Why America can be viewed as the ultimate venture capital success story
* How joint stock companies, risk-taking and ownership helped shape the U.S. economy
* Why studying 250 years of market history changes how investors think about volatility
* The long-term case for stocks and why the time horizon matters so much
* Why bear markets are a natural part of capitalism and long-term compounding
* How U.S. market dominance happened and why it was not preordained
* Why expensive valuations, low dividend yields and new supply may matter today
* The role of dividends, buybacks, shareholder yield and reinvestment in long-term returns
* Why diversification across global stocks, bonds and real assets can help investors stay invested
* What gold, REITs and foreign stocks teach us about starting points and narratives
* Why early investing, child investment accounts and compounding can change investor behavior
* How creative destruction reshapes sectors, companies and the market leaders of each era
* Why Meb remains optimistic about America while still cautious on parts of the U.S. market

Timestamps

00:00 Why America was not guaranteed to become the market winner
01:15 Meb Faber on writing Investing in America
02:25 America as the ultimate venture capital success story
06:22 How a culture of ownership helped the U.S. stock market compound
09:19 Why studying 250 years of market history matters
12:00 Why ownership is the core investing lesson
15:14 Bear markets, recessions and the danger of recent history
18:16 Why U.S. stocks beat the rest of the world by so much
22:20 Lessons from financial history that surprised Meb
27:05 Why stocks can lose for long periods and bonds can win
30:00 Why investors need to get used to being in a drawdown
33:24 Dividends, buybacks and the importance of reinvestment
37:27 Why gold and REITs beat the S&P 500 after 2000
40:55 How balanced portfolios survive different market regimes
43:03 The power of starting early and letting compounding work
48:16 Why global diversification matters outside the U.S.
50:40 Creative destruction, sector change and market leadership
55:20 Why Meb is still optimistic about investing in America
59:33 Where to find the book, Cambria and Meb online

Meb: What’s up, y’all? Good to be back.

Matt: So this book, let’s be honest. You’re answering the age-old question that I’ve long wondered, which is were Washington and Franklin really just Andreessen Horowitz of America A to Z? It’s a beautiful thing. You happy with this book? You happy with how this came out?

Meb: You know, I learned a lot. This is book number eight, and I haven’t written one in a decade. I had a child, so that’s part of it. And I’ve definitely never done a coffee table book, and I’ve definitely never done a coffee table book on my own. So this is self-published. And we learned a lot, for sure.

We were joking on Twitter, first 10,000 people that buy the book get the exclusive privilege of not having a picture of me anywhere in the book. There’s no, like, author photo about the author because I forgot to put it in. But it was a really fun experience, you know, to show how good I am at marketing.

I don’t think anyone in history has ever published a book on a Saturday, in the summer, on a holiday. It’s like a triple witching of things not to do when publishing a book, and I don’t care. I’m doing it. July 4th, baby. 250-year birthday. Happy birthday, America.

Matt: Take that, marketing lessons. So start me with this one. The US as the ultimate VC success story. I actually think this is a beautiful framing. Take a deep pull on that coffee. Stir the thoughts. Tell me why we’re a VC success story.

Meb: So I’m gonna tell you guys something that I don’t know if I’ve shared widely. There’s actually gonna be two coffee table books. And the first one I wrote—

Matt: How many coffee tables do you think a modern American has?

Meb: The first one I wrote was actually a history of global stock markets back to 1600. ‘Cause the party got started before we even existed as a country. Party got started in Amsterdam and some other places before that perhaps, but really in force in Amsterdam. And then the locus of power shifted to London.

But we got started, you know, 1792 Buttonwood Agreement, which by the way, I learned buttonwood tree is just a sycamore, which is an interesting takeaway. Anyway, a lot of little facts we learned. But, you know, when you’re doing a lot of... One of the most interesting parts about writing this book was, of course, the research.

And so I probably read, skimmed, I don’t know, 30, 50, 70 financial history books that I’d never heard of, which was super fun. Like, everyone knows kind of a lot of the most well-read ones, but there’s a whole right tail of financial history books that were super fun to skim through. But as you start to dig, one of the takeaways was there’s the narrative that we all learned in elementary school, “Hey, America was founded by a lot of immigrants coming over, and they were escaping, you know, religious persecution. They were in search of a better life and freedom.”

And while all that is true, if we know anything about incentives and money, a lot of it was these were funded, not necessarily by the Crown in some cases, but in some cases by joint stock companies. And so you had things like the Virginia Company, chartered 1606. So this was before, you know, the founding of the US. The Jamestown colony, which, you know, may not be the most successful example, but there were so many other examples. You had Plymouth Colony. The Mayflower was financed. You have even, you know, the Puritans. Everyone’s like, “Um, they would never do this.” Yeah, the Massachusetts Bay Colony. And it wasn’t just the Brits. You had the Dutch with New Netherlands, West India Company, and even the Swedes with New Sweden.

But you even had examples, ‘cause I mean, think about it. It’s like when you talk about venture capital, and the phrase actually originated with this concept of they called them merchant adventurers or adventure merchants, which sounds like venture capitalists. And so, talk about the most risky portfolio diversification idea. You finance a ship to go get a bunch of gold in the New World. That sucker sinks, some pirates take it over, right? Like, you lose all your money. That’s a big zero. And if anything the VCs know is you spread your bets. Stock investors, you can’t just invest in one. You gotta invest in 10 or 20.

And then all of a sudden, if you could spread your bets... Well, most people couldn’t finance 10 ships, and so then you started a joint stock company that could finance a bunch of ships, and all of a sudden, this very new, interesting joint stock company idea became a thing. And this thing helped to start the United States.

And so you had some great examples of product market fit where like, “Oh, well, crap, we can’t find any gold here,” but you know what grows really nicely is tobacco and, you know, things like that. You read these stories and it’s a lot of fun, and that was a little bit of the origins. So maybe it was something in the water, something in the DNA, something in the soil that got all these risk-takers to start thinking even from the get-go.

Matt: There’s something about how a successful thing begets this culture of ownership as you break it out in the book. And I love this idea because it feels like that culture of ownership, it’s not that it forms, like you said before, it’s Amsterdam and these other places, but it kinda does reform around America in these early stages with all these different ventures coming together and then finding success, and it feels like that snowballs. Is that a fair statement?

Meb: One of my favorite parts of the book is the quotes, and some of them kinda give you chills. They’re inspiring, you know, very patriotic. Ralph Waldo Emerson has one we put in each decade chapter. He says, “America is the country of the future. It is a country of beginnings, of projects, of vast designs and expectations.” And I just like, this is the optimistic, you know, feeling to that.

And there’s a handful of others. Old Abe Lincoln kinda got it. He’s like, “The prudent penniless beginner in the world labors for wages a while, saves a surplus, and invests it in tools or land.” You know, so it’s already thinking about putting money to work, honest Abe.

But you know, it’s funny. I think about this a lot currently, right? US is two-thirds of world market cap, just unbelievable success story. And if you look at any of the surveys, are Americans... Is it a good idea to start a company? It’s like 90%. Rest of the world, it’s like 10%. What percentage of Americans own stock and how much do they put in stock? Just order of magnitude greater than if you go talk to people in Europe. In addition to not having air conditioning, they generally don’t invest in stocks. It might be real estate, it might be cash. The same with our Japanese friends, same with Aussies, on and on.

And so this wedge, if you know anything about compounding and equities, if presumably the last 400 years or 200 years are similar to the next two or 400, equities that compound at 7, 8, 9, 10% is a lot more than bonds or cash that compound at 3, 4, 5. Or certainly more than money under the mattress, which is a negative three per year on real returns, right? Like, that’s a zero and a negative three. So ignoring the fact that I think the stock market’s wicked expensive right now, that wedge only grows, right? So you... That compounding continues to where the US and people that invest become a bigger part of the pie. Anyway, that’s been going on for 200 years, and I can’t see anything really, really getting in the way of it.

Matt: So the people who start this off, they didn’t have coffee table books. I’m not even sure if they necessarily had traditional coffee tables. They had something like it, I am sure. But they didn’t have the book to say, “Here’s 200 years of stock history. Here’s why this is a good idea.” The people who come after get all the benefit from it. You ask this question in the book, like, why study the past, particularly this far back, knowing the people in the past couldn’t have studied any of this themselves?

Meb: A couple things. You know, part of the book, I, as with everything on Twitter and social media, people are always, “But actually, this one thing, Meb, you know how expensive it would’ve been to buy stocks and, you know, couldn’t buy an index and all these things.” I’m like, look, look, look. It’s meant to be a visualization. It’s meant to be conceptual to understand.

But even Ben Franklin got it, you know? He said, “Money makes money, and the money that makes money makes money.” He understood this compounding and being an owner. And part of my belief is it actually doesn’t matter that much what you invest in, which sounds like a, you know, a true heretic, but it’s the fact that you’re putting money to work, the mindset of being an owner, and then it’s money you’re not spending, which is what we all wanna do, right? You wanna go buy the new Lambo, go on a vacation, on and on.

And so yes, look, how many people invested in 1800? Very few. How many stocks were there? Very few. But those that did were hugely rewarded. And of course there’s differences, right? If you look back in the 19th century, most investors really were investing for income, you know, and bonds were a huge portion of what people did. And when they invested in stocks, it wasn’t for capital appreciation. Ignoring the few sort of South Sea bubble, canal bubble, railroad manias, those periods where things went kinda crazy. Most people are like, “Hey, yeah, I got a nice fat 5% yield. That’s all I care about.” Which is funny today, ‘cause we’re at all-time lows on the dividend yield, which I’m kinda cheering that it crosses below one in the US just to say that I saw it.

I also am kinda cheering for the CAPE ratio for once more in my lifetime to be higher than my age. I think I’m the downside of that slope where, you know, I turn 49 this year. I don’t think that it’s ever gonna happen again. This is my one shot at being able to see a bubble get above my age maybe.

But yeah, it’s meant to be instructive. So when I say that, “Hey, if you put a dollar in stocks in 1800, it’s $200 million today,” I could’ve said, “If you put $100 in stocks, then it would be $20 billion,” but people, like, I feel like would get even angrier at that. But it’s meant to be kinda, “Hey, take away this lesson,” which is long-term compounding is the key lesson to take on this. And long term, youngins, is not hours, weeks, months. It’s years and decades.

Matt: What about looking at stocks? Why’d you focus on stocks? Just because it was easier than focusing on some other weird asset class?

Meb: There’s an interesting takeaway in the beginning. If you think about ownership and if you look at the Forbes top 100, it’s all business people. It’s people who started companies and investors in those companies. Even the top celebrities, athletes, creators nowadays, if you look where did they make their big money, I mean, everything from Jordan to Dre to Dolly Parton, on and on, it’s actually usually not their career, it’s their business, right? Where they made their money.

So this concept has always been around, and being an owner, to me, is a huge unlock, and there’s the easy and hard way to do it. The hard way is starting your own company, and we all know most companies fail. It’s like being an entrepreneur is the hardest thing on the planet. Obviously rewarding when it works, but really tough. Or you can just hire Tim Cook and, you know, all the Warren Buffett and all these top CEOs by buying a share of their company through public stocks.

And so there’s a really interesting idea in the book, and others have talked about this of course, but stock market’s hella volatile on one year, one month, one day. I mean, it went down twenty percent one day before, right? But you start to zoom out, and that’s the entire thesis of this book, was that it zooms in on every decade and it’s like, “Look how crazy this decade was.” And then even the terrible decades like the Great Depression, the ‘30s, on and on, it zooms out and shows the full two hundred-year period, and you can barely find some of these blips on this logarithmic chart for two hundred years.

And so the takeaway is like, hey, if you’re gonna put money to work for long periods, stocks are the place to be. Now, it doesn’t mean that bonds aren’t a part of this. We can get super weird and deep on asset allocation. But one of the takeaways was that if you look at stocks versus bonds, the kink happens at around twenty years in terms of, hey, on a twenty-year rolling basis, the best case outcome for stocks, better than bonds. The worst case outcome, stocks is better than bonds. And looking on an after inflation basis. But even weirder, stocks are less volatile than bonds at the twenty-year time horizon, which is crazy to think about, right? But on average, it’s true. And then obviously the return advantage, it’s like, I think, well over double for stocks on average over bonds for twenty years.

So the hard part, of course, and we have tons of quotes in this. Peter Lynch has a good one. He says, “Stock market has been the best place to be over the last ten years, thirty years, hundred years. But if you need the money in one or two years, probably shouldn’t be buying stocks,” right? But the disconnect of humans on, “Hey, I invest for the long term,” and then what time horizon that they actually base their decisions and execute them, huge mismatch, right? They’re checking the news, worrying about the Fed, thinking about the election, what’s gold doing. Ah, you know. And it creates so many, so many problems as we all know.

Justin: Meb, I’m pretty sure I have this right, but your drawdown chart that you have in there kind of highlights this too, is that, you know, I think bear markets used to come around, huh, well, maybe they will come around more often in the future, who knows? But, you know, historically speaking, you know, the US economy has experienced more frequent recessions, more frequent bear markets. And to your point, a lot of times some of those great returns, right, come after those lost decades, you know, end of ‘08, early ‘09 to, you know, after the, um, well, the Depression was a long period of time before the market recovered.

But the point is, is that, you know, we’re in an interesting time today in that we haven’t really seen a true bear market since ‘08. And so you wonder, you know, what does that, you know, how does that impact future returns for investors today? It’s just an interesting question.

Meb: I really wanted to add a postscript to the book and be like, you know, “PS,” end notes, like, “Well, just to point out, I don’t wanna mark the top of this cycle by putting out this very patriotic and optimistic book.” You know, look, it’s no secret that we believe that the broad US stock market is very expensive. Doesn’t mean it can’t keep going up. There’s plenty of other great assets around the world, global stocks, REITs, bonds, you know, real assets like TIPS and commodities. But also other stocks within the US look great. You don’t have to pick market cap weighted, right?

But that’s the long history, and I think it’s a feature, not a bug, right? You know, how many of us look around the last couple years, the last decade and we’re like, “Man, look at this just dumb stuff people are doing with their money.” They’re buying a bunch of NFTs. They’re, you know, buying stocks that trade at 100 times revenue. All these things that which on average you see during these romping, stomping bull markets.

You know, bear markets help to clean that out quite a bit, you know? People tend to pull in the reins a bit and stop doing really speculative, dumb stuff when they’ve lost half their money. You know, it’s time to get serious, right? It feels like when you got a mortgage, you got some kids, and, oh, by the way, it’s a recession and you lost your job, on and on. You know, people tend to stop YOLOing a little more when that happens.

But as you mentioned, it’s been a long time, you know? We wrote one of my favorite papers the last few years ago was “The Bear Market and Diversification,” just talking about how special this period has been for US stocks since 2009. And we’ve started to see a shift in the last couple years. We’ve seen a lot of other ideas start to really accelerate and start to perform.

So I think there’s nothing to be afraid of bear markets. I think if you’re young, hey, that’s the best thing that happened to you. Get a nice fat 50% decline. Good for you if you’re in your 20s, 30s, even your 40s. If you’re probably 80, 90, you know, maybe not so much. But I think it’s a natural part of it, and it’s the cycle of, you know, the cycle of life.

Justin: Do you think... One of the statistics that kind of blew my mind was, and you hit on this earlier, this long-term compounding excess returns of US over foreign stocks, and I think it’s something like, I don’t know, you have it in the book here. It was, I think—

Matt: The Bryan Taylor chart, where basically a dollar invested in 1800 grows to over $4 million in the US versus rest of the world, $51,000.

Justin: That’s crazy to me. I mean, what do you think? And maybe you hit on it. I mean, what in your opinion, Meb, is the driver there? Is it more risk-taking here? Is it more ownership? Like, what would you accredit that to?

Meb: Hindsight bias. No. Look, I mean, the reality is you gotta remember, like even at the start of the 20th century, you look at where the US was. I don’t think it was preordained that this is guaranteed to happen. And if you were to, like, if you were to horse race and were to place your bets on countries that, you know, “Hey, this is where I’m putting my money for the next 100 years,” you know, there’s probably a dozen choices that might’ve been interesting.

And even during the 20th century, like this is the crazy part I love talking about, the US didn’t hold the crown, right? You had Japan being the largest stock market. You told someone in Japan in the ‘80s, like, “Hey, we’re gonna teleport 50 years in the future, and your stock market’s gonna go from the largest in the world to 5% of the world,” they would be like, “You’re crazy.” And likewise, if you were to tell people, if you were having a pint in a pub in the UK with a bunch of Brits and say, “Hey guys, this Korean War that just ended, you know, they’re gonna chop the country in half, and the bottom half is gonna be bigger than your stock market in 50 years,” they’d be like, “You’ve lost your mind,” right? Like, “What?” But both of these things have happened.

And so, you know, I think if you look at the long history of markets and countries, you know, it’s tough. It’s tough to pick the right horse in the winner. I mean, look, the US has all the tailwinds and has had all of them for a long time. I mean, just you go down the list and check. But it’s not guaranteed that that will always be in place forever. You know? It’s been a special place. It’s had a ton going for it. I think it’s unique. I think it’s the best country in the world, on and on. But, you know, there’s a lot of billions of people around the world that would also like to be where we are, right? It’s still the number one, despite all the, you know, political discourse we read, it’s still by far the number one country that everyone wants to move to for a lot of reasons.

And so if you look at a lot of the, you know, metrics, number of Nobel laureates, R&D spend, you know, on and on, you know, the US is definitely still numero uno. VC money, it just... Like the, some of the stats on VC investing, also things like market cap of companies in the US versus places like Europe, and you’re just like, “Euro bros, what the hell are you guys doing? Like, how are you guys falling so far behind? Like, what is happening?” Like, great that we’re, and maybe we’re wasting it, but everything, energy production, on and on.

And so that all having been said, you know, as a percentage of world GDP, US is only a quarter. You know? So there is quite a big disconnect between US and, um... But if you were to like go out 50 years and say, “Hey Meb, do you think the US is gonna be two-thirds of world market cap?” I would say no. I would fully expect countries like China and India to, you know, accelerate and get a bigger piece of the pie. Like, how could they not? But I would, you know... Would I expect the US to go from two-thirds world market cap to five, like Japan did? No. No, I wouldn’t. So still really bullish over the long term, but you can’t muck it up. You know, that’s for sure. And we’re... Who knows? We’ll see.

Justin: Was there anything... Was there any decade or any... I’m sure you’ve learned a tremendous amount in just studying this history, but, you know, was there any decade that really sort of jumped out at you as being a big surprise in terms of, you know, something you didn’t know about this country and our markets that sort of took you by surprise?

Meb: Oh, there’s so many little nuggets that are just kinda funny. If you look at one... You know, one of the things we talk about, as all this discussion of Greenland was going on last year, and people were pulling their hair out. And it’s like, by the way, do you guys know, like, I don’t know what percentage of the country’s been acquired, but it’s a lot. You know, it’s half maybe. Louisiana Purchase, Alaska, on and on. Which by the way, amazing deals. I think really fantastic.

There’s some fun stats like, you know, we love to tell people, “Do you know that the motto on the US coin was not always ‘In God We Trust.’ In fact, it used to be, ‘Mind your business.’” Which I think is just absolutely hilarious. It was designed by... The Fugio Cent was designed by Benjamin Franklin. And there was actually a second part to the phrase, and I’m trying to remember what it was. Oh, it was, “Time flies, so mind your business.” I just think it’s funnier if you say, “Just mind your business.” But, but—

Matt: And in this case, that meant, like, be mindful of your business.

Meb: Yeah.

Matt: Right?

Meb: Yeah. Yeah. Exactly. But anything about Ben Franklin was super fun. But there’s a lot of... I mean, you know, look, there’s... We don’t teach personal finance in school, and so there’s a lot of kinda common misunderstandings when it comes to investing. And so we tackle a lot of those in the book. We talk about the decline in purchasing power. We talk about how you gotta reinvest your dividends. You can’t spend them.

And look, you know, you gotta remember, like every history around the world, this is... You know, it’s not all roses, and particularly for lots of different groups. I mean, women couldn’t vote. Black people, for very large percentage of the time, had a rough go. Native Americans, I mean, my goodness. On and on. But, you know, overall, the progress certainly... And it gets into all sorts of different things about immigration, on and on.

But the 19th century for me was a century that I knew much less about than, obviously, than the 20th century. And it’s fun to read through some of the speculative times with canals, with railroads, and looking to kinda what’s going on with AI and CapEx spending today, say, “Well, you know, maybe not that different a couple hundred years ago as it would’ve been,” and a lot of rhymes that we have today.

Justin: Yeah, that was one question I was gonna ask you, which is, you know, was there any decades that really look similar to where we are? And the railroad point is a good one. And, you know, sometimes people talk back to the fiber optic build-out during the internet boom and railroads, and how a lot of times the CapEx, you know, the returns didn’t come necessarily from those investments per se to the companies, but they kinda filtered out into the fabric of society more so. So it’ll be interesting to see with AI how that plays out.

Meb: And, you know, we have one chart that shows there’s a big element of luck and just, you know, randomness. And it shows, hey, if you invested during a certain decade, like maybe the ‘80s or the ‘90s or this past decade, amazing. But there’s been plenty of other decades where not so great. You know, the ‘70s, what a hard time to be an investor, particularly on a real basis after inflation. 2000s, you know, stocks negative, right? On and on. So there’s a huge element of us extrapolating our own lived experience, which I think is of course dangerous, ‘cause if anything, there tends to be some mean reversion over time. We’re not just probably gonna do 15% a year for forever.

Matt: I mean, it sure would be nice though. It sure would be nice.

Meb: I just... You know, hey, I’m okay. Like, you know, I would love to see, we joke, you know, the Japanese bubble probably double where we are today. And I said, “I’m okay.” Bubbles are the most fun, man. Money just sloshing around everywhere. Companies giving away free benefits. You know, Uber — it was great when Uber used to be like $5 for an Uber everywhere. Now it’s like 60 bucks. You know, go back to those times of... I love it when the venture capitalists are subsidizing us. Come on.

Matt: So on the drawdown point, you put that one in four years is negative for stocks. Bonds have outpaced stocks over many long windows, including stretches of 40 and 60 years. So not even just these little time periods that you were talking about a minute ago. Stocks go down a lot, and there are some crazy stretches where bonds beat stocks. What’d you learn from looking at those?

Meb: Look, you know, we’re of the mindset that the most important takeaway for any investor is getting to the finish line. And, you know, what is this all for, right? It’s not to just accumulate as much as possible. It’s, “Hey, I’m investing. I wanna maximize my return, of course,” but also, you know, there’s a purpose to this. And so the biggest struggle for most, particularly individuals, but this is also true for institutions, this happens quite a lot if you’ve seen my commentary about CalPERS and Bridgewater and on and on, is that the path matters, and particularly the losses when you’re going through volatile periods.

I think, you know, it’s like the Richter scale in earthquakes where you... every 10% above 20% isn’t like a 10 percentage points worse. It’s like a 10X as painful, you know? So 20% most people can handle, but then 30, 40, 50, people start losing their mind and they behave, you know, the flight response.

So I think by far when people look back over on their lifetime, they’re on their deathbed, no one’s gonna be like, “You know what? Man, if only I’d gotten 9% instead of eight.” But they will look back and be like, you know, 8% instead of zero because you panicked and sold everything or just, you know, did something really dumb, YOLO’d into a bunch of, you know, fart coins or something.

And so in no question in my mind is it totally sensible to diversify across the really three main asset classes: global stocks, global bonds, and global real assets. And I think if you could just close your eyes and put US stocks in a lockbox for 50 years in a trust and wake up in 50 years, like you’ll be fine. No problem there, or 20 years even. But the path where you may lose half in between there is tough ‘cause in a, particularly in a world where we can all look it up every second on, you know, now it’s gonna be all night too. Who knows? I think that’s really hard for people. And so there’s no question that getting some zigs and zags in there across different asset classes is a totally useful and thoughtful way to go about it.

Matt: Maybe best captured, you have the line, “If you’re going to be an investor, get used to being a loser.” Little counterintuitive. Explain that one.

Meb: Well, there’s another quote by Howard Marks too in the book where he’s like, “It’s more important to ensure survival under negative outcomes than it is to guarantee maximum returns under favorable ones.” And so the reality is, you know, any investment, stocks, bonds, is either at an all-time high or it’s in a drawdown. There’s only two states that exist. And the reality is investments spend, I think it’s over two-thirds, three-quarters of the time in some form of drawdown. Now, usually maybe it’s only one, five, 10%, but sometimes it’s a lot more. And I think that’s hard for people, right?

It’s also true when you start to extrapolate to individual stocks. That’s even harder where, you know, two-thirds, Hank Bessembinder, who we feature in the book, you know, his famous paper that stocks outperform T-bills, it’s like two-thirds of stocks underperform an index, about half have zero returns over their lifetime and, you know, what is it, a quarter essentially go to zero. So it’s even harder there.

But that’s just part of the tuition. That’s part of the cost of business. And there’s a lot of quotes from Munger and others where they’re like, “Look, if you can’t handle these downturns, like you shouldn’t be invested.” And or you need to set up the guardrails like having a financial advisor, having some rules in place that won’t, you know, cause you to, in 2009, sell everything, “I can’t take anymore,” and then never get back in. ‘Cause that’s a terminal outcome for a lot of people. That’s really what we’re trying to avoid.

Matt: Inside of those terminal outcomes too, I think, and this is where it’s both tempting and exciting to look at it this way, in decades. So obviously the decade chunks are convenient. Everybody knows what they are, and years ending in zero make it easy to talk about. There’s some pros and cons of splitting it this way. Give me your pros and cons list for this.

Meb: The pro is it starts to help people zoom out for sure. And it does feel like for whatever reason, the decade ends, beginnings act as turning points. I mean, we certainly saw it in the US in ‘99. We saw it in Japan, on and on. And it’s just easy. Like we’re narrative animals. Like we tell stories, we tell stories, we put names on things. The Roaring ‘20s, Nifty ‘50s, Dot-com. What are we gonna call this one? COVID meme stonk, AI, whatever this boom is.

Matt: Brought to you by Meb Faber’s new book.

Meb: Yeah. So I think that that’s a big part of it. You know, the human element certainly, like, is there any reason that stocks trade at a 42 CAPE ratio today versus a five historically at other times? No, it’s just what people are willing to pay, right? Like, that’s the difference. And so, you know, I think the narrative helps us get through it, sustain...

Justin: One of the things that you mentioned earlier is the dividend yield on, take something like the S&P 500. I don’t know, what is it? Sub 2% right now today? But yet—

Meb: It was like 1.05. It’s almost below one. It just set an all-time low.

Justin: Oh, is it? Oh, wow. Almost sub one. That’s crazy. I didn’t realize it was that low. And to the point, you know, I think something like maybe two-thirds, half or two-thirds of the returns historically have come from dividends.

But what that got me thinking of, Meb, is like you kinda, we’re kinda getting off the book here a little bit, but you know, you sort of, one of your very first investment strategies that you put in an ETF wrapper was shareholder yield, which is looking at all the different ways companies can return capital to shareholders. And, you know, on the one hand, you would think a very low yield might be problematic for future returns, but at the same time, I think companies are using a lot of different ways to return that capital to shareholders. So maybe it’s not as bad as it seems on the surface.

Meb: Yeah. You could do— You could run a hypothetical that, let’s say, none of the companies in the stock market pay dividends ever historically. Well, you have the same exact equity line. It just all comes from capital appreciation, right? They retain the earnings. Or you could say they pay out all the earnings, very similar to something like REITs. Again, exact same capital appreciation line, but the huge takeaway on the dividend part is you have to reinvest those dividends.

And we ran some paid surveys, thousands of people, and found out that seventy percent of individuals don’t understand that. Like, they think dividends are free coupon payments very similar to bonds. So if you had to go back to probably the 1800s, that was the reason why I think, you know, people were like, “Oh, no, invest in stocks for this dividend income. They’re paying me. I’m getting an allowance. I’m getting a certain payment.” Like, no, that’s not how it works.

And so for good reasons and bad, I think everyone could certainly take away whatever they want from this current situation. You know, there— Is there no question the US stocks are expensive, so the dividend yield is low? Yes, that’s true. But also, companies have shifted a ton from paying out in dividends over half to buybacks. And so on a total shareholder yield basis, it looks much more reasonable on the US stock market as far as buybacks.

However, 2026, the year that we are currently, all of a sudden, that faucet of buybacks for a lot of big companies, you’re seeing two things happen. One, they are ramping up a ton of CapEx and ramping down buybacks, these hyperscalers. And two, you’re getting something you haven’t had for a very long time, which is big time supply. You know, these three IPOs, one of which is completed, two more on deck, are arguably as big as all the IPOs in the 1990s combined. SpaceX, OpenAI, Anthropic, just because they’re so massive. And so it’s the old school supply and demand. We’re getting a bunch of supply coming to the market. Wouldn’t that be boring, but fitting, that that was what kills the bull market is just old school issuance rather than anything else.

So I, you know, I think it’s hard because there’s a couple things wrapped up in this topic and— But it’s a good example too of, you know, comparing markets over different periods. We have a chart in the book of what sectors look like in 1900 versus what they look like today. Hey, you bought the stock market in 1900, guess what? You’re investing mostly in railroads, right? And today that’s very different. And but it’s also true on things like dividends and taxation and on and on. So you gotta be mindful extrapolating too much from what’s happened in the past. But the consistency, I think the common thread has been there that it pays to be an owner and put your money to work.

Justin: One of the things that surprised me is you have a chart of US stocks, REITs, and gold going back to 2000. And I think if you polled, you know, most investors in the room, most would say hands down the S&P was the best performing asset. But, you know, gold and REITs actually have outdone US stocks. Now, we’re using that 2000 start date, which was sort of like the start of that bear market, so maybe that’s a little unfair. But the point is, is that it’s, you know, 25 years or whatever it is of, you know, US stocks underperforming those other two asset classes, which is also interesting and shows the importance of diversification, I think.

Meb: Sure. Every asset class has its day in the sun and day in the shade over time. And there was a very contentious recent episode on CNBC where Joe Kernen and Jeremy Grantham got into it a bit.

Justin: Oh, I saw that.

Meb: And, you know, Kernen was like, “Oh, you’ve been bearish forever, and you’ve done a huge disservice.” And, you know, Grantham kinda rebuttaled on and on. And despite how much I love US stocks, look, they’re great. If you look at the global market portfolio, US is a percentage of the global portfolio of stocks and bonds globally. If you, you know, partitioned it out, US would be about— US stocks would only be about a third of the total pie, right? ‘Cause it’s roughly half stocks and bonds. Of that, it’s usually half US and foreign. So foreign bonds are actually bigger than US bonds. And then in the stock component, US is bigger. But so even then, you’re only putting a third in US stocks.

But we wrote a paper called “What If You Invested in No US Stocks” as a thought experiment. Like, clearly that would destroy your returns. Like, you— My God, you could never. And it turns out you do just fine, right? Totally, totally reasonable. If you invest in foreign stocks and REITs and gold and bonds and corporate bonds, you know. So there’s no magical one asset that, you know, if you take it out. Leuthold used to write about this. They called it the donut portfolio. You do totally fine over time. Now they zig and zag for sure, but there’s other times when assets, you know, really do well and have a run of one year or an entire decade.

Cliff Asness wrote an old paper on this many moons ago where he looked at, I think, kind of five main asset classes, and he rolled it forward every five years and said, “Well, could you predict what happens the next five years? Like, maybe the things that did terrible did great the next five, or maybe the things that did great kept doing great.” And it’s actually, like, pretty tough, right? It’s pretty, pretty a lot of randomness involved.

And so I think the narrative of, you know, just starting points and ending points certainly makes it obvious. But yeah, if you were to ask most people on the street, “Hey, this century, what’s been the best of those three assets?” Ninety percent of people get it wrong. Ninety-five percent maybe. You’ll get your odd Canadian that’ll say, “Nope, it’s gold.” Right? Or, “Nope, it’s REITs.” But you know, nobody would expect US stocks to be last in that race.

Matt: I like that we pin it on Canadians once in a while, though. I think it’s important to make sure they know.

Meb: Yeah.

Matt: The rise of a 250-year bull market, and how since 2000, both gold and REITs have actually outperformed the S&P 500. So talk to me about balancing that idea. Stocks for the long run and this 250-year bull market in US stocks with the idea — how this stuff works.

Meb: We did an old book, which we hope to update next year ‘cause now it’s a decade old, called Global Asset Allocation, where we looked at a lot of portfolios, how they were weighted, how they performed. And the takeaway was that they all did great, no matter what you invested in really, as long as you had some of the main ingredients: global stocks, global bonds, global real assets.

And you would... Huge dispersion in any year, so the best performer versus the worst performer. So portfolios like 60/40, endowment, risk parity, on and on. The spread in any given year was like 20 percentage points from best to worst. But over the full period, it was within like a percent or two, so very tight coupling over time.

But you would, in some cases, you know, hey, the ‘70s, like you better have had real asset exposure or else the ‘70s was rough. The ‘80s, ‘90s was, you know, hey, bull market and capital assets like stocks and bonds. Those did amazing. And then 2000 decade, oh man, US stocks underperformed everything in the world. Everyone wanted emerging markets and on and on.

So I think this concept of balance, you know, one of the portfolios we tongue-in-cheek kinda profiled in the book was the Talmud portfolio where, you know, it was a riff on something that’s 2,000 years old where it said, “Let every man invest a third in land, a third in business, and keep a third in reserve.” And so we just said, “Hey, that sounds like a third stocks, bonds and cash.” Or, sorry, stocks, REITs or real assets, and bonds, cash. And that’s actually a really hard portfolio to beat. I think that would beat the vast majority of institutions over time.

And so thinking about having, you know, a global portfolio certainly, to me, it’s gonna be less volatile, slower drawdowns, more balance, including those because, you know, all three of those kind of respond in different market environments based on yield curve, based on growth, based on interest rates, on and on. And to me, it’s a lot more balanced.

Matt: Let’s look at the one that’s about the investor who starts at 20, contributes for just six years, and how they end up with about as much as the person who waits until 40 and then contributes for the next 30 years. The financial planner in me cries when I look at this chart, but I know it’s true. What is it?

Meb: I feel like this one, the financial planners get. Like, this is something that is taught if you ever have a personal finance course, this is something. You’re gonna get something like this in almost every one. I think the challenge, though, is, like, beating it into young kids’ heads. You know, it’s like, okay, you get it, but are you actually doing it? And it’s hard, you know. Most of us, you’re in your 20s, you’re in your teens, like, you don’t have any money. And if you do, you wanna spend it by going to the movies with your friends, maybe having some beers, maybe getting a Eurail pass. Like, you’re not thinking about, hey, me at 50, old AF with some kids and worrying about a mortgage. Like, I don’t have sympathy for that guy or empathy or anything. Like, forget about him.

You know, but it is important and it is a huge unlock where, you know, $10,000 at 20, 10% a year, at retirement age is a million. And you don’t have to do anything. Like, it’s the most magical thing in the world. So there’s a lot of ideas wrapped up in there, I think, that would benefit our world. Certainly target date funds, certainly retirement accounts where it’s kinda locked up. I think there’s probably some serious fintech ideas that could be built that would be billion-dollar ideas, where you kinda wrap up an account maybe in a trust or just in a, basically a lockbox and say, like, “Look, you’re in. Too bad. You can’t get out.” You know? And so annuities were something that was kind of like this, but they are so damn expensive that it kinda eats away at the benefits of doing it. But I think there’s some real interesting ideas there. So yeah, listeners—

Justin: Or maybe youngins.

Meb: Get started early.

Justin: Well, maybe the Trump accounts will help with this one. You know?

Meb: I love the idea. I love it so much. You know, I like to call it Invest America, which was the charity behind it. You know, I think it’s unnecessary to politicize it, you know, and name it after our supreme leader, but, you know, that’s Donald, so. But look, God bless that it’s happening. I think it’s the best... I think we’ll look back on it and be like, “What a great decision.”

And I think it’s actually less from the financial part of it. Like, I actually don’t think, hey, you got an 18-year-old that’s now got 10 or 20 grand. Is that gonna be life-changing? Maybe. But more importantly, you feel like you’re part of it, and you start to flex a muscle that instead of being like, “Oh, hey, look, there’s frigging AI data centers in my backyard,” or, “Look at this evil Elon Musk shooting up rockets and catching them with chopsticks,” to all of a sudden being like, “Hey, I’m a shareholder in that. I own part of that.” You know? “I’m cheering for these incredible entrepreneurs and scientists,” on and on. And so I love that part of it. But it also, it flexes the muscle of watching the balance grow. “Hey, I get it. Like, oh, this was 1,000 bucks and now it’s five.” And learning that muscle memory imprint early, I think is gonna be awesome.

So we’re actually donating to... If anyone’s written a book, you know that very rarely are there profits, but hopefully there’s a ton of profits for this book, and we’re gonna donate it to that charity. My hope is that, yeah, all of it’s going to Invest America. You know, my hope is that we’ve been in contact with them so they, um... At some point I’d love to do it. I mean, look, I’m not Michael Dell who plopped down, like, $5 billion. But I think it’d be cool at some point to be able to donate to individual zip codes and say, you know, “Hey, where my dad was born, my mom was born, where I was born,” you know, like a salmon swimming back upriver. Let me give a shout-out to those places that helped raise all of us. I think you can get down to the state level maybe. But also I’m sure I would have a lot more negotiating power if I was donating $10 million or $100 million than whatever this may be, the $10,000, $100,000. But we’ll see.

Maybe it ends up on the New York Times after this podcast, y’all, and everyone buys. By the way, listeners, you can buy bulk. If you buy, wanna buy 50 or 100 for your financial practice or all your clients, you get 33% and 50% discounts on Itasca. So if you Google Itasca Meb Faber, it’s automatically applied at checkout. So you can get half off if you buy 100 books and have that warm, fuzzy feeling knowing that it all goes to the youngins.

Matt: Meb Faber is for the children.

Meb: That’s right.

Matt: That’s the way it goes. Over 20 years, you’ve never lost money in stocks—

Meb: Dude.

Matt: Except if you’re outside of the US market. Yeah, this is the salmon swimming upstream, but in the Slovenian fish farm where the Faber family’s on vacation.

Meb: You gotta remember there’s always outliers, and so one of my favorite books, Triumph of the Optimists, looks at all these individual countries back to 1900. And when you study history, you realize that, look, shite happens. China, Russia, at some point both of them said, “Oh, thank you very much. We’re closing our stock market. Goodbye.” Right? And so all those positions went to zero.

You have other times where the country stock markets just do poorly, and a lot of ex-US countries over the past 10, 15 years, you know, places like Greece. China at one point had no stock returns for like 30 years a couple years ago and got arguably to the most, the cheapest level it’s ever been. I love hearing that as a contrarian value person, but others kind of run away.

But there’s been some stock markets that have been as good or even better than the US over various periods like, I believe like tiny South Africa, Australia, Switzerland are all in the running there, and there’s others on the other end. You know, big takeaway, don’t lose world wars. That makes it tough. Austria had pretty, pretty tough returns, but other ones, even like Japan post their big boom and bubble struggled, and that’s a famous, you know, kind of fintwit meme of talking about Japan example as an outlier. But to me it’s not an outlier, like it’s part of the narrative. Like it’s still a top five global economy.

So I think, you know, there’s plenty of examples of countries that have, you know, the— The path is, has and will be tough. And so the takeaway to me is that in no scenario has investing in an individual country been better than diversifying globally. Switzerland, you could maybe make the example over various periods, but almost universally lower volatility, lower drawdowns, investing in a global portfolio.

Matt: Talk on the sector level, and I love this. I love the chart of 1900 to 2025, where we’re basically all rail and coming out of beautiful coal-powered northeastern Pennsylvania with our transcontinental railroad. I feel this ‘cause I look at this chart and I see the wealth in the area I grew up in just going off the cliff as these industries change.

Meb: Where was that?

Matt: Wilkes-Barre/Scranton area of Pennsylvania, so.

Meb: You know, if you study the long history of markets, of industries, you know, it’s a story of one of the benefits and beauties of capitalism and free markets is that creative destruction, right? Where... And I think it’s a benefit, but in no uncertain terms does it leave gravestones as far as companies, as far as entire towns, cities, states that were set up for one environment and then whoosh. You know, look at something like Texas, very energy-focused. New York, very financial-focused. Midwest, industrials. California, tech. On and on, right? Like, we can understand that. And you have disruptions of companies like the modern era. Everyone gets Blockbuster, or everyone gets things like Kodak, right? Where all of a sudden you got digital cameras and they’re slow to adjust and... But that’s just the nature of markets.

And so this kinda couples with my other favorite chart, which is stocks by decade, and looking at, hey, what are the top stocks today? Everyone knows it’s got a nickname, the Mag Seven, right? Or the Mangoes or whatever it is, but it’s all US tech stock dominance. But even if you go back to 2000, you know, pre-GFC, you had different companies, a bunch of Chinese stocks. You go back to ‘80s, Japan, on and on. And you go back even further and you’re like, “I don’t even know what some of these stocks are.” You know? Maybe my mom was talking about GE or AT&T or Standard Oil, you know, on and on. But the constant certainly is change.

And the intro to the book is even, makes that reference where we were talking about getting into a Waymo, and I was like, imagine telling someone 150 years ago, the, you know, the story, “Hey, I’m getting into this robot-driven car,” and what their responses would be, and we were laughing ‘cause their response would be like, “What’s a car?” Like, they didn’t even have cars, right? Like they’re riding around on horses. So what will it be in 50, 100 years? They’ll be laughing at us and be like, “God, you guys couldn’t even teleport? What the hell?” You know? Like, it’s gonna be fun to watch, but certainly weird as well.

Justin: So just two more for me. One, Meb, where can I get that hat?

Meb: So, Cambria, my... Last year, I’m kind of a procrastinator. I don’t really have a manic personality except when it comes to writing, and I can only write, like, a book... Like, when I write a book it takes like a month. I can’t, like, if you’re like, “Hey, write this book over the next year,” like, I’ll wait 10 months and then do it. So one of the things I was doing to avoid writing the book was designing hats.

And so, like, we’ve probably got 50 different hats for, you know, our ETF— it started with our ETFs. So let’s see, we got EYLD with a nice wave. We got FYLD. We got MFEW, on and on. And then we made some—

Justin: Some designer hats. Love it.

Meb: Well, you know, I Googled custom patch hats, ‘cause those, I wanted to get some really nice custom patch hats, and this will surprise everyone listening, but that led me to a domain, custompatchhats.com. Which is actually based in North Carolina, so shout out to my North Carolina tweeps. But they actually do incredible work.

And so we’ve probably purchased and given away, I don’t know, certainly in the thousands of hats. So if you see a SYLD hat in the wild, we got a giant flag in front of our office which has the... For the listeners who aren’t watching this on YouTube, it’s a romping, stomping bull, but with American flag hide coating. So we’ll send you guys one. But my wife was thrilled to get a Investing in America bikini with these on. It’s a white bikini with various bulls on places.

Justin: Nice.

Meb: We’ve had fun designing some of the schwag for it, for sure.

Justin: And for me, just lastly, I want to ask you, you know, and this might not be sort of a fair question, but would you say after looking at this, these 250 years in America, in this country, are you more or less optimistic about sort of investing in America sort of for the future?

Meb: We did a post, I believe it was during the pandemic. We used to do this kind of every few years, every five years, I need to update it, saying, “Hey, here’s how I invest. You know, here’s how as a CIO and founder of this company, how I invest my own money.” And on the public side, certainly I invest in our own funds. But on the private side, you do a lot of angel investing. And one of the reasons, and I detailed this in a blog post called “Journey to 100X,” but we’ve kind of talked about it the whole way, which is over 10 years now, which is crazy. One of the benefits of angel investing is you get to see the future.

So on our podcast, maybe four or five years ago, we did a series on investing in space startups, ‘cause you started to see all of these companies all of a sudden having massive traction, success, growth, revenue in the space economy, which was formerly only these giant mega companies like a Boeing, McDonnell Douglas, Lockheed. But partially because of SpaceX, you’ve had this huge amount of companies that could start up, and they didn’t need twenty billion of startup capital, they needed five million.

So part of it lets you look around the corner, but part of it is so optimistic, right? You know, public markets are tough. You know, you get a bunch of geopolitical news, you turn on CNBC, it’s just like negative, negative, negative, negative, negative, on and on. And so you’re constantly dealing with stress and anxieties. Is my portfolio gonna go down? What’s the Fed gonna do? On and on. Are stocks expensive? In angel investing, it’s the exact opposite. It’s just like people are delusional founders that all think they’re gonna succeed and change the world doing really cool stuff. So I leaned heavily into that.

In addition, there’s some amazing tax benefits in the US with QSBS rules and regulations originally passed under Obama and expanded under Trump. So, you know, not a political thing. I think it’s one of the most impactful legislations we’ve ever seen in the US as far as funding small, young companies.

I’ve now invested in over four hundred startups, and that sounds crazy to most people. My check sizes tend to be small. But as a quant, there’s something that I get that applies not just to startups, but to public markets too, and really anything in investing is, you know, this long tail of power laws where, you know, some of these companies and stocks, you know, generate a huge portion of the returns. But you gotta own enough of them. And so one way in the public markets, of course, is through indexes. But in the private markets, it’s a little harder to buy an index. You just gotta buy a bunch of them. And so... But it’s been a lot of fun. But to me, that’s like the most optimistic thing in the world because you see these little startups grow up to be a hundred million, a billion, ten billion dollar companies, you know, that are the big giants of the future.

So I think it’s a fun balance. But yeah, I’m super optimistic. There is no disconnect in my mind to be able to say these two things and keep a straight face. I am super optimistic on the long-term prospects of the US economy and stock market, and I am hella bearish on how expensive the S&P 500 market cap weight is. There’s plenty of other stocks, I think, that look fantastic in the US. Value, small, mid, foreign, on and on. So the short term, there may be a little pain at some point, but long term, you know, I’m a romping, stomping optimist in the US.

Matt: Well, we love him.

Meb: There’s a great quote that we end the book on from old J.P. Morgan, and he says, “The man who is a bear on the future of the United States will always go broke.” 1895.

Matt: Hang it in the Louvre, as they say.

Meb: Yeah.

Matt: As they say. Yeah. Where — people wanna get the book, self-publishing it, they wanna order a fine coffee table, where are we sending them to get the book? Say the title one more time, too.

Meb: Investing in America: The Rise of a 250-Year Bull Market. Amazon’s got it. Barnes & Noble should have it. Your local bookstore, maybe. Itasca. And that’s if you particularly wanna buy bulk orders. But you guys can also email me, listeners. If you find yourself in the neighborhood, I’ll sign one for you. And if your local bookstore doesn’t have them, tell them. Say, “Hey, yo, Tattered Cover, buy some copies of Meb’s book.” And, you know, they often will.

Matt: Get them to buy a copy. Maybe even offer them, get them a hat on the side.

Meb: Yeah. There you go.

Matt: Local bookstores love swag just as much as everybody else. Meb, people wanna bug you on the internet, where should we send them?

Meb: My day job is managing ETFs at Cambria Funds. Cambria the company hits a 20-year anniversary this year. My goodness, I don’t feel that old. So cambriafunds.com profiles our 20 ETFs. But you can find most of our free content on the blog at Meb Faber, podcast, The Meb Faber Show. Watch me pick fights on Twitter/X @MebFaber, and any other place good podcasts are found.

Matt: Gotta love it. Meb, thanks so much for coming out. Super excited for this book. You really do wanna check this thing out. Also, keep tabs on the Excess Returns substack. We’re gonna write some more stuff up on this very topic. Thanks for watching. Wherever you’re watching or listening, like, comment, subscribe, all the things below, and we are out.

Main topics covered

• Why the SpaceX IPO became the biggest market story of the month

• How index flows, ETF buying and hedge fund positioning shaped SpaceX trading

• Andy Constan on why future earnings growth may be oversubscribed across AI stocks

• Why AI spending is benefiting semiconductors, memory and chip equipment companies

• The Fab Five companies behind semiconductor capacity and why they matter

• Ben Hunt on Fed credibility, market narratives, gold, the dollar and trust

• Brent Kochuba on options flows, correlation risk and volatility spasms in tech stocks

• Why short-term options volume may signal excess speculation in QQQ and AI stocks

• How SpaceX options trading changed after the first wave of retail excitement

• Eric Pachman on why headline job growth may hide weakness in wages and job quality

• Why crack spreads, refining constraints and oil logistics may matter more for inflation than crude prices alone

• What investors should watch next in AI, semiconductors, memory, innovation and market cycles

Timestamps

00:00 Opening clips from Andy Constan, Ben Hunt, Brent Kochuba and Eric Pachman
01:02 Matt and Jack introduce Last Call and the June market review
03:05 Why SpaceX dominated the month and how the IPO traded after opening
07:33 Andy Constan on Fab Five Freddy eating the semis
10:35 Why future earnings growth may be oversubscribed across the stock market
13:35 How AI compute spending flows through chips, fabs and semiconductor equipment
17:45 Are parts of the semiconductor market showing signs of an earnings bubble?
20:12 Ben Hunt on the Fed credibility chart that surprised him
23:50 Why Fed credibility, Sell America, gold and the dollar are connected
29:48 Brent Kochuba on options flows behind AI stocks, semis and SpaceX
33:36 Why semiconductor volatility may be warning of a short-term reset
38:46 What SpaceX options trading says after the initial surge
42:12 Eric Pachman on jobs, wages and what the Fed may be missing
48:24 Why crack spreads matter for oil, refining, gas prices and inflation
55:28 What to watch next in AI, semiconductors, memory demand and market cycles
59:01 Why efficiency, competition and cyclical thinking matter for AI investors
01:03:02 Matt and Jack close the episode

Jack: It’s going well. My private jet has pretty much been parked this month, Matt. I’ve been at home working so much. You know, the thing we got off into the video, but hasn’t been happening in reality.

Matt: Hasn’t been happening in reality. And the way reality feels, did I just introduce that we’re talking about the last quarter? Is that where my brain is right now? The last month was the last quarter of, like, right?

Jack: It’s all the same thing at this point, isn’t it? I mean, did anything different happen in the last month or the last quarter? I mean, they just seem like the same thing.

Matt: I’ll tell you what happened, and I know we’re gonna get into it in a second here. What happened is a company finally went public that we’ve been waiting forever to go public, and they basically promised in their S-1 that their TAM was space and time.

So I’m gonna blame the last month feeling like the last quarter, if not the last quarter century, on we finally IPO’d space and time. Is that a get out of jail card for this?

Jack: Yeah, I think so. And you know, now my TAM for this is all of YouTube. So, like, cooking shows and stuff, we’re coming for you, Matt. If you’re watching YouTube, you wanna watch us analyze the markets.

Matt: If we are not the Martha Stewart and Snoop Dogg of finance, I don’t know what we are.

Jack: Alton Brown’s got nothing on us, Matt. Besides, like, a better personality and, like, a much larger audience. But other than that, he’s got nothing on us.

Matt: He’s got some good jokes. He’s got some good jokes, and some great techniques. I have definitely stolen multiple techniques from him, and I unabashedly will, yes, send my love to him and Kenji. Don’t forget Kenji. Always, always with the Kenji. Where are we gonna go? We gotta talk actual market stuff. Are you ready to get into this?

Jack: Yeah. We got some awesome guests today. We’ve got Andy Constan on the macro, we’ve got Ben Hunt on the narrative, we got Brent Kochuba on the flows behind the scenes, and we’ve got an interesting look behind the data with Eric Packman. So we got 10 minutes with all of them. We should probably talk less, and let’s get this party started.

Matt: Let’s get this party started. It’s a party in the USA. The World Cup continues. Let us know who you’re rooting for. We’ll know by the end of this one if we’re still US fans or if our hearts have been broken. And that being said, let’s get right into the rearview.

Jack: So we almost have to start with SpaceX, right, Matt? I mean, what you mentioned. I mean, SpaceX was the story of the last month. When we talked last time, we were trying to figure out what’s gonna happen when it actually comes public. Now it actually did come public, and all the stuff we talked about in terms of flows behind the scenes, we got to see how it played out in the real world.

So I think that’s where we gotta start.

Matt: We have to start here. This is the biggest story probably of the year so far, honestly, outside of the Strait of Hormuz. This is the most oxygen-absorbing thing. So it finally happened. It priced, it opened higher, it then proceeded to go up like bananas for the next two days, and then basically fell. Like a rock? Like a misfired rocket? What’s the right analogy here? It’s still in the air.

Here’s the beautiful thing. It’s still above where it opened. We haven’t, as of this recording, you know, on the first here, dipped below where we opened up at. But, you know, it shot way up. I was telling you earlier, the funny thing was right before it IPO’d, I started to talk to people, and they started to tell me about index flows.

And I was having this horrible feeling of this is the shoeshine boy thing. People are telling me about how index flows work, not because I’m an expert, because I know how little I know about this topic. And I spent way too much time talking to Dave Nadig and Adam Butler and Mike Green and people like that about it, and I feel like I know nothing.

And now this person is like, “Well, you know, when the ETF flows hit.” I’m going, “Oh God. Oh God.”

Jack: Yeah.

Matt: Yeah. “Back away.”

Jack: But that is... So it’s interesting, ‘cause the story is the flows, and that’s kinda what we talked about, is that no one cares about the fundamentals of SpaceX right now, or at least that’s not what’s driving it.

But also, the other point, and that gets to what you’re talking about, is you can’t predict the flows. Like, even the Dave Nadigs that we talk to will say, “We know there’s gonna be these big flows,” but everybody’s front-running them. You know, Nasdaq’s, I think, adding it — is it July 7th now? I believe is supposed to be that.

Matt: Yeah, we still got days till the Nasdaq. VTI, all the weird ones that track space as an index, they mostly bought so far, so far as we know. We don’t yet know with a bunch of the private funds. They haven’t publicly reported exactly what they did. Or if they have, it’s, unless you’re a shareholder, you don’t know exactly what they did yet.

So there’s all this information. And the stuff that bought, bought the teeniest, tiniest portion, which is something that David flagged on our Clickbaiter right before.

Jack: Yeah, and this might be shocking to you, Matt, but all these large hedge funds, they know as well that the Nasdaq’s gonna add it on July 7th, and they may be, if there’s anything to be done in terms of making money in front of that, they may be doing it. So — and that’s the whole—

Matt: Are you telling me that the smart money is actually smart sometimes? Is that what you’re trying to tell me?

Jack: It’s not even — it might even be dumb, but the point is so many people are thinking this through in so many different ways. Like, you hear some stuff now in the media about like, “Oh, when the Nasdaq buys, it’s gotta go up.”

Like, people are thinking this through, like, 27 levels beyond that. And it may go up when the Nasdaq buys, but it’s not gonna go up because the Nasdaq’s buying it because everybody’s been trying to game this thing well before it happens.

Matt: So 4D chess isn’t a real game. I think that’s the lesson here, and we’re being reminded this by the giant spike in the price it’s settling in.

Who knows? The market cap adjustments are gonna happen. The ETF flows are gonna happen with their — kind of tricky to understand, but very sensible — slow buying patterns over the next however many months, if not years. And we’re gonna see all these unlocks potentially starting in as soon as what? Late, mid to late August, I think, right?

Jack: That’s right. So we’ll see how it plays out. And I will say, Matt, in general for the world right now, I think there’s probably less 4D chess going on than a lot of people think is going on. We’re always talking about, like we’re always thinking about, “Oh, this person’s thinking 27 levels down and stuff,” and I don’t know that necessarily that’s always what’s happening.

Matt: We’re playing chess on a flat map maybe.

Jack: I’m certainly not doing it myself, by the way. There’s no 4D chess going on over here.

Matt: Excess Returns.

Jack: Like 1D is probably all I got.

Matt: 1D, and 4D chess. Excess Returns. This is, we gotta get a new tagline going. All right. Let’s get into some guests. Who are we leading off with? Andy? Is Andy our lead-off hitter?

Jack: So we’re starting off with Andy. And yeah, Andy had a really interesting tweet that I read that got me wanting to talk to him about this. And you’ll like this. So his thesis is Fab Five Freddy is eating the semis.

Matt: Oh.

Jack: Yeah, for you, Matt, I’m like, I didn’t wanna tell you in advance that was what it was, because I’m like, “This is perfect. This is like right in Matt’s sweet spot.”

Matt: I love this. I love this, and I extra love it. Fab Five Freddy read his own audiobook, so even if you read the actual book, get the audiobook too.

That was a discovery of mine. I’ve been absolutely loving this. It’s been with me on walks, you know, on and off for the last month or so. So let’s get me into some Fab Five Freddy. Let’s go.

Jack: So if you’re like me and you’re like, “What the hell does that even mean?” here’s my conversation with Andy, and you’ll find out.

Andy, welcome back to Last Call.

Andy: Thanks, Jack. Good to see you again.

Jack: I’m excited to have you. You’re doing so much stuff with us these days. I feel like one of these times you’re gonna say, “Jack, no more.”

Andy: It’s coming soon.

Jack: But until you do, I’m gonna get you on as much as I can.

Andy: Sounds good.

Jack: I wanna ask you, you wrote a tweet that said, “Fab Five Freddy is eating the semis.” And I wanna get into that and what that... ‘Cause I didn’t even know what that meant, so I wanna get into what that means. But first I wanna lead into it with what you’ve been talking about in general, because I think that relates to this whole idea.

You’ve been talking about this idea that nominal GDP is kind of a pie, and tech can consume so much of that pie, but it can’t... It can only grow to a certain point. And so I wanted to maybe talk about that pie concept to start, as it relates to tech, and then we’ll get down to how that pie relates to what’s going on inside of AI.

Andy: Sure. I mean, at the high level, the GDP is what we all make and what we all buy. And that’s it. And so all the stuff that we buy, some of it’s gonna be food, some of it’s gonna be gas, some of it’s gonna be housing, some of it’s gonna be clothing, some of it’s gonna be education, some of it’s gonna be tokens, some of it’s gonna be microchips, semiconductors.

Some of it’s going to be laptops. Everything. It all goes in there. And so earnings are a portion of GDP. And so the earnings available to the world are the world. The whole world is the global GDP. And so the question becomes: What does the GDP depend on?

Because things are happening that should grow the GDP more than they normally do. And the reason is, GDP depends on — real GDP depends on people and how much they make of stuff. And so people isn’t really changing much. Slow moving. What they make is changing depending on how productive they are.

And so it’s possible that the AI boom can generate more tools that can help us make more stuff, which makes more GDP. And so the pie can grow, and it is growing. It’s growing pretty rapidly. The other part of the pie is the nominal part, which is inflation adjusting the real GDP, and obviously that can — inflation can occur based on monetary policy tools, supply shocks, which are transitory.

I hate to use that word. Rarely is there a demand shock where suddenly people can’t buy stuff. Maybe the COVID situation was like that, or demand shock where they suddenly want to buy stuff. That rarely happens, but mostly inflation becomes a monetary issue. And so the combination of monetary policy and productivity can cause the pie to grow.

The problem is that everybody needs their share of pie or else their stock prices don’t go up. And so what my thesis is, is that the price of stocks, which is partly their market value and then partly based on their expectations of future earnings growth, that future earnings growth is oversubscribed.

There’s too many people that — too many stocks that expect massive earnings growth, and there’s just not enough pie for all of them to be successful. Now, some of them can really be successful. They can get not only a — the pie can grow, and they can get a massive share of that pie. But all the stocks struggle to get a massive share.

Usually, one company getting a great share takes pie from somebody else. And so that’s the basic thesis. I’ve written a number of Damp Spring reports. We talked about it on other podcasts. That’s the big level idea. But now I think we’re in an interesting period of time where we know who’s getting the spending.

Like it’s clear who’s spending money on stuff that’s causing this tremendous amount of growth, and that is everyone that’s building compute. And so who are those people? Well, they’re neocloud providers that are just trying to get data centers built. And then there are hyperscalers who have their own clouds that they’re trying to build.

And they’re all trying to build compute. And why are they trying to build compute? Because somebody wants to use compute to use AI. And so that’s happening. That compute build is massive. Of course, they don’t necessarily make any money on the compute if they give it away. And they’ve been pretty by and large subsidizing tokens. So it hasn’t been a great return on investment for all these companies that are building all this compute. But they’re paying somebody. They’re paying memory companies, storage companies, Nvidia, AMD, the big chip makers themselves, Intel.

And so those guys are just hoovering in pie. As much pie as they want, they can eat. Their problem is they simply just don’t have the appetite, meaning they don’t have the available stuff to sell. There’s a bottleneck of stuff they wanna sell. And so the winners have been, and you’ve seen that this year, the winners have been the people that are getting the spending, because they’re able to get a very large share of pie.

And those companies that haven’t been winning are the ones that have been doing all the spending. Fab Five Freddy is just the next level of that. So what’s a fab? Well, firstly, Fab Five Freddy was a great rap star. Yo! MTV Raps was his gig. I remember back in the day. It was great. Back in the day.

But the Fab Five has been used as a variety of things. I’m trying to get it to be in common usage for five companies, and those five companies are everybody that makes the things that can make more chips, which is the wafer, the fabrication company’s business. Now, who are fabs?

The biggest one by far is Taiwan Semiconductor. Many orders of magnitude less include Intel, but they don’t have enough capacity. They don’t have enough — they can’t make silicon fast enough for the chip makers to do what they do. So that’s the bottleneck. There are other smaller bottlenecks, and there’s lots of bottlenecks, but the one I’m focused on is the raw chip capacity.

And so what you’ve seen is chip prices going through the roof. All the chip companies have order books that they can’t fill, which is great for those companies, and they’re earning all sorts of money. But what they need is more capacity, more chips to be made. And so the Fab Five companies are the companies that make the stuff that makes the chips.

And so those include Tokyo Electric, which is Tokyo Electron — Electronics? Electrics — which is 8035 on the Nikkei. On the Topix, sorry. And four US, well, not really US companies, four companies. ASML, which makes the big, massive machines that make the clean room equipment. KLA Tencor. Applied Materials.

And Lam Research. And all four of those companies are the ones that are making the stuff that, at this point, people are buying, but it’s nowhere near what they’re buying in terms of chips.

And these companies have gotten hot. Their stocks are by far the ones that are most parabolic nowadays. You look at companies like Micron, you think that’s parabolic. Then you look at these companies that make the equipment that Micron will use — that the foundries will use to make the chips that they will then give to Micron — and you realize that the Fab Five has really been where all of the performance has been lately in the semiconductor indexes.

And so to me, I’m now looking at who’s getting the pie. We already said that the hyperscalers are not eating pie. They’re spending. The chip companies, they’ve been eating pie, but now — their earnings growth has been fantastic, but somehow their multiple isn’t that high. And I think that’s because they’re handing their money, and they will have to hand their money, to this Fab Five group.

And so then the Fab Five group will be the one that really is where all the retained earnings are. And so I think that’s what’s happening in markets right now. There’s still not enough pie for all of them, but we’re right in the midst of a situation where the semis have outperformed the hyperscalers, the people that are buying their stuff.

And now the people that are making the stuff that’ll solve, or address, the chip supply problem are the ones that are ultimately gonna get all the pie. And you’re starting to see that in stock prices.

Jack: So is the idea, like if you look at the Microns of the world, the idea is we’re seeing, I don’t wanna use the word bubble, but like bubble regime-like situations, but more of an earnings type bubble, right?

‘Cause a lot of people get that wrong, you know, when they look at markets. There have been in the past earnings bubbles, which means, you know, the stuff looked relatively cheap, but the earnings were so inflated by some sort of, you know, one-time type thing that there actually was still a bubble.

I mean, do we have characteristics of that in those types of companies?

Andy: Right. I mean, ultimately semiconductors, there are some companies that claim they have — and Nvidia is a good example — claim they have a moat. They take the raw silicon and turn it into something special. And I believe that.

The people that make memory don’t have a moat. All they have is the normal boom-bust, glut versus — what is it when you don’t have anything? — scarcity versus glut sort of cyclicality. And what happens is, and this has happened every single time. Whenever there’s more chip demand than there can be supply, supply rallies, chip companies earn a ton of money.

They spend to increase capacity. The increased capacity results in falling prices. Falling prices result in lower profits, and the whole cycle rolls again and again and again. And that’s been — they say this time is different. But based on what’s happening to Fab Five, I don’t think it’s different. I just think it’s gonna take some time for these companies to increase capacity to meet the bottleneck.

And so you have companies like Micron who trade at what appears to be cheap multiples because their earnings have grown 100%. But they’re ultimately not retaining that earnings because they’re just gonna spend it on CapEx by building their capacity.

Jack: Well, Andy, this has been great. I appreciate you taking the time. Have a happy Fourth.

Andy: You too.

Matt: Our next segment is Why Am I Reading This Now? That means Ben Hunt is joining the fray. We’re gonna talk about what’s been going on in markets, and he has some just really interesting thoughts here on when it gets confusing, what you do with your exposure. Let’s talk to Ben Hunt.

Next up, we’ve got Ben Hunt, Epsilon Theory, Perseant. What’s good, Ben Hunt? How you doing?

Ben: I’m doing great, Matt. How are you doing?

Matt: I’m doing fantastic because this chart almost broke my brain this week. We’re talking about the Fed, trust, credibility.

Ben: Yep.

Matt: Let’s get this up on the screen. Tell me what’s going on here. I think some people are gonna fall out of their chair.

Ben: This is a wild chart, right? Because this is data that, like you just described, why we’re just seeing this now, right? As they say. So we’ve made a lot of improvements in our underlying technology, and so, you know, we read everything in the world, and then we track narratives.

You know, how present is it? Fancy word, semantic density. But just how loud is it? How loud is the narrative? And so what we’ve built now is a way of filtering. So it’s not just central banks in general, but we’re now able to filter down on Fed credibility versus BOJ, Bank of Japan, credibility, versus ECB credibility, versus Bank of England credibility.

We’re able to filter down to these really — we like to call it resolution. We’re able to take much higher resolution snapshots of all of this. So now we’re able to drill down to specific central banks, and this is the Fed. What we’re looking at here is a picture just specifically of the Fed. And what’s crazy is that this picture of the Fed looks nothing like any other central bank.

So it wasn’t really getting picked up in our other, “Oh, what’s happening with central banks around the world?” This Fed picture, this is unique for all central banks. And what this is showing you is, oh my God, I had no idea how bad the lack of credibility narrative for the Fed was at exactly this time last year.

So if you’re looking at that, this is... Look at that bright white line. That’s the net. That’s the credibility reading minus the losing credibility — gaining credibility minus losing credibility. So that white line is really low. This is going back eleven years. I mean, it’s... This is crazy. But the story that the Fed was losing credibility reached basically all-time peaks in that peak of the gray shaded area and the trough of the white line.

That’s June 30th, 2025. And you remember what was happening then. This was at the peak of Trump going after Powell for, you know, “You gotta resign, we’re gonna fire you.” All this, the Fed is just going to become a rubber stamp for Trump. Monetary policy would just, you know, take rates down to zero or whatever it was gonna be.

That is crazy how bad, how bad that is in terms of the narrative. But now look at what’s happened since then. By the way, that peak right there, that is also the peak of repatriation, of Sell America.

Matt: Yeah, all these stories line up—

Ben: Another narrative for the track.

Matt: And I wanna frame this for — the ascent that we’ve been on from this low. This is repatriation. This is the gold story. This is how we’re talking about everything else that the Fed’s now back in another type of a pickle with, but the labor market, inflation, all of that gets thrown through that tumbler in 2025 when that thing’s at its low. So now what’s been going on as we’re moving out of it?

Ben: Well, this is market-driven. So you’re seeing now, so now that line, what you get is that white line, the net solidifying credibility versus losing credibility. Well, on balance, the Fed is all of a sudden highly credible.

And, you know, knock me over with a feather, right? Because I listen to the Warsh press conference and the task forces and all this stuff is like, “Really, dude? Really?” But this is why I think what we do is so important, because everyone, all of us observers of markets, I’m sure other people on this call, I bet a lot of them felt like I did.

Like it was, “Oh, this doesn’t sound very credible,” and, you know, “What’s Warsh doing?” And that doesn’t fill me with confidence. What I’m telling you is both that the losing credibility narrative has gone away from this time a year ago, and the gaining credibility narrative, the dark blue on this map, it’s now positive, right?

It’s now louder than normal. So whatever you think of Warsh and the press conferences, and I personally didn’t think much, what this is showing you is that the Warsh appointment, and kind of going back to general order and Trump not going ballistic against the Fed every chance he gets, is having a real impact.

‘Cause markets want to believe in the Fed. They want the Fed to be large and in charge. And I gotta tell you, it’s working. This is what’s happening here. Very counterintuitive for what I was feeling personally, but these numbers, these... This is really solid, solid data.

Matt: So when we look at this, what’s the translation? How do you use this to help explain both what’s been going on in a portfolio versus where this puts us now? Because again, and I’m with you, different framing than where my brain was processing what’s going on with the Fed.

Ben: Yeah. So I’ll point to the one thing in particular that’s been a puzzler to me, which is why gold has, to use the technical term, sucked, you know, over the last couple of months.

Because, oh, we got a war, we got inflation worries, we got countries wanting to kind of move away from, you know, the US and its overbearing-ness, shall we say. The weakness of gold has been a real, has been a puzzler. The strength of the U.S. dollar has been a puzzler, especially once we got the MOU and the ceasefire signed.

Why is the dollar being so strong? Well, it’s this. This is why the dollar is being so strong and why gold is being so weak. Because the whole idea of de-dollarization, the whole idea of why do you buy gold — gold is an insurance policy against central, against Fed error, right? It’s the inverse. It’s one — my friend Brent Donnelly came up with this. The price of gold is one divided by trust.

And what we’re showing is that trust is going up. So that means the price of gold goes down. That’s what this is telling me. And it’s helped me out a ton because, again, regardless of my own personal feelings, what is clear as day is that the narrative that the Fed’s losing credibility, that narrative’s getting a lot quieter. It was as loud as it’s ever been last summer, and now it’s muted. I mean, it’s just basically average. And the narrative that the Fed is gaining credibility, that’s really climbing sharply here. It’s really climbing sharply.

Matt: One other point from inside of this. You said it, and I wanna call attention to it.

Ben: Yeah.

Matt: Previously, you were pulling the data on all central banks, lumping them together. What made you realize how you wanted to parse this out by central bank, by geography, so that you could find these signal differences?

Ben: No, it wasn’t a realization. We’ve always wanted to do it. It’s just that as we’ve been able to improve our technology, we’re now able to drill down and increase the resolution here. So, you know, we’ve always wanted to do it. Now we can, and it’s really exciting stuff.

Matt: Well, I think that’s a pretty good reason we should remind people they should come check out what’s going on at Perseant.

Ben: Come check it out.

Matt: Ben, anything else you wanna share with the audience? Tell them where to bug you on the internet.

Ben: Well, I’m still Epsilon Theory all over the place, and the company’s name is Perseant. And, anyway, come find me. Check it out. It’s worth it.

Matt: Come find Ben. Lots, lots, lots more of these charts. Ben, thanks for joining us.

Ben: Thanks, Matt.

Jack: And what I always love about what we can do on this show, Matt, is we always can take a look. We can talk about the high level, the macro, the market, all that stuff. We always can take a step back and look behind the scenes, and we’ve got Brent Kochuba talking about what he’s seeing in the options market. We talked about SpaceX, we talked about the semis. Here’s my conversation with Brent.

Brent, welcome to Last Call.

Brent: Thanks, Jack. I’m glad to be back. Surprised I’m invited back, but here I am.

Jack: We’ve invited you every time, so as long as you keep saying yes, you’re gonna keep being on here. Eventually, you’re gonna have enough of me and I’m gonna have to, like, try to figure this out myself or something. No one wants to see my options analysis.

Brent: I’m just riding your coattails. You keep growing, so I keep saying yes, hoping that I pick up some of the breadcrumbs that are left behind.

Jack: Well, I’m excited to talk to you. We’ve got a lot to do in 10 minutes. I know you’ve got your COR1M indicator you wanna look at. We also wanna talk to... I’m interested in just understanding what’s going on with semis, ‘cause we’ve seen a pullback here after, like, a massive run. And then I’m also interested in getting your take on SpaceX and, now that options are trading, what you’ve been seeing behind the scenes. So, but first, I know we’ve got this COR1M, so what are we seeing here?

Brent: Yeah, and all these things tie together. And I think anybody who’s heard our OPEX Effect over the last — even going all the way back to 2024 — would know that when this COR1M metric, which is a CBOE index, it measures what’s called correlation.

And what this essentially is telling us is that single stock implied vols are very high. What does that mean? People are buying tons of options in all the stocks you just mentioned — the AI trade, semis, chips, SpaceX, all that stuff, right? And at the same time, what they do is they sell SPDR vol or S&P vol, right?

So you end up with a breakdown in kind of correlation. And the risk in that is, and this kinda ties into the dispersion trade, that that relationship gets out of whack. And when that relationship gets out of whack, we tend to have what I like to refer to as volatility spasms. And so what I’ve found is if you, as a general rule, look at when COR1M goes below eight, things tend to break.

And so just to put this into context, I’m on this chart here. This is part of our founder’s note this morning to our members. And what you see here is in early June, COR1M went below eight, and then we had one of the biggest one-day Nasdaq volatility events ever. We had a giant seven percent drawdown over the course of, like, one or two days, and then that dip got bought very fast, admittedly, but that was a major volatility event.

And then you can see, we also just recently, last week around June 20th, we had another similar spasm, right? Where the Nasdaq lost five percent in a single day. And now we are back to COR1M being at five. And so we’re heading into a holiday-shortened week here. But to me, this signals that this trade is about to have another spasm.

Now, when I post this on X and things like that, people go, “Oh, Brent, well, the dip just gets bought, so this doesn’t matter.” But at some point, I think you have one of these events that sort of catches, and then we have a little bit more of a sustained drawdown than kind of like a one or two-day drop, right?

And so that is the backdrop. Now, what is driving this relationship? As I mentioned before, it’s people bidding up these AI names, the same names that we’ve all been trading in or trafficking in or pushing higher, et cetera. And that trade is now, I think, getting quite milked. And the reason I say it is because we had Micron earnings last week.

The earnings were very good. The stock didn’t do a whole lot. It was quarter end, you know, yesterday, quarter start today, so a lot of these stocks, AMD, Intel, names like that, were up seven, eight percent yesterday. It was really quite a move. And so I think what we’re setting up here now is for a pretty sharp kind of correction or re-syncing of a lot of volatility relationships that are kind of busted right now, to kind of phrase it that way.

Jack: And I know you have another chart here where we’re gonna be able to look at, like, some of the stuff behind the scenes, and semis have just been — I mean, it’s been crazy. The run that semis had, I’m like shooting myself like, “Why didn’t you buy semis, Jack?” Like, even though I know nothing about semis or tech or anything, but it’s like... I mean, the kinds of runs, you know, even though there’s been somewhat of a pullback, like, the kind of runs companies like Micron have had is like, it’s unfathomable. It’s pretty crazy.

Brent: It really is. And the earnings, I guess, have been justifying that, right? And so I’m not here to say I’m gonna stick a fork in some trade, you know — the fundamental story behind, you know, the semis and what happens one, two, three years from now or even six months from now, right?

I’m talking about in the short term what the problems are. And the short term is that the options market and sort of bullish sentiment, right, has in the short term bid some of this stuff up too much. And what you’re looking at here is our compass view. And so this displays this relationship very well.

Here we have the Qs. And look at the difference between the QQQ IV and the SPDR IV. There’s a huge distance on that chart. QQQ has been pricing in or has been experiencing two percent daily moves, which is pretty wild compared to the S&P, which has been seeing something like one percent moves, right? So the relationship between these two indexes is hugely different.

The other one here is semis, which is obviously a leading sector, and then the DRAM component. If you’ve been looking at the Korean stocks too, like, you know, that market’s been going crazy because of the bid into this memory component, right? And so what we’re seeing here that is interesting is the overall implied vol is very elevated.

This is sort of like crashy, high volatility. In this case, it’s been crashing up, high volatility relationships. But what we’re seeing is the names are moving from the upper right on this chart, which is like very rich call prices, to starting to move to the put side a little bit. So what does that actually mean?

If we’re moving to the put side, it’s because people are starting to sell calls into the richness and high implied vol of these sectors, of these stocks. And I think that makes sense because the implied vol is so high. So you’re starting to see that call skew come in a little bit, which is essentially saying people are starting to monetize the call side a little bit.

To me, that’s the early sign that, okay, I think we’re about to take a little bit of a break here in some of these very rich leading sectors, right? And then this also displays this difference or dispersion between the tech stocks and the non-tech stocks, right, or stuff that’s not related to the AI trade.

Generally, the Qs and the Spiders kind of trade in a similar sector or similar space. Same thing with semis and the DRAM, right? All that stuff is — these are index-type ETFs, equity ETFs, right? These aren’t crazy single stocks. These are baskets of stocks. And usually, they all sort of trade in implied vol terms relatively close to each other, right?

Now, they haven’t over the last few months, and I think that these things all have to sync up a little bit better. What does that mean? Well, they would all land in the same quadrant, for example. And I think that this is kind of a moment here in the next couple of days, where into early next week, into mid-July, we’re gonna see this relationship sort of sync up.

And I suspect when we do our July OPEX check, we’ll be seeing some of these relationships re-sync, and maybe that marks a little bit of a correction in stocks.

Jack: And correct me if I’m wrong, but the interesting part about this is like with DRAM and SMH, when you get these melt-ups, a lot of times what you see is just, the implied volatility just gets too high, right?

Brent: Yeah.

Jack: So it’s not necessarily even people like making a directional bet on where they’re gonna go. It’s just people saying like, “This can’t continue. This is just too expensive.” I mean, is that the right way to look at it?

Brent: Yeah. I think what it is, is the market keeps pricing in larger and larger moves, right? And that was crazy into April and May, but what happened? The earnings were blowouts, right? I mean, so many of those earnings were beating expectations, and the expectations would go up, and then, you know, the semi names would increase their expectations, right, after earnings. And so like look at Micron.

They had incredible earnings, right? And so they’re justifying the price. The issue is the rate of change is unsustainable. Like, how much longer can you keep going up in the Qs at two percent per day? That is not sustainable. It all gets priced in at some point. And now we have a very disjointed market, as you can see here in the volatility space.

Another thing I’ll note on this point, Jack. I just did a study I put on X. Over eighty percent of the QQQ volume now is at expirations that are five days to expiration or less, and over seventy-three percent of SPX volume is in expirations that are five days to expiration or less. So if you’re gonna sit here and tell me that, “Hey, Brent, this is all about the AI trade in the future,” and you think that these names are gonna continue at this torrid pace and there’s so much value there, why is all the volume in five days to expiration or less options?

Like, is that how you wanna express this future that’s gonna be in these memory names? Like, doesn’t make sense, right? So basically what this is, is the bulls need their hand slapped. If you look back in July of 2024, we had a similar setup, right?

We lost ten percent in the S&P over the course of about two, three weeks. Ultimately, the market bottomed in that area, and then we kind of ripped into end of the year. And so I even think if you’re a bull, you wanna see this correction, this consolidation, this sort of reset, a cleaning, you know, clearing of the deck, so to speak.

And then once the relationship sort of re-sync, we’re in a better spot to sort of base and then move back higher. I think that, you know, that’s kind of how I would expect this to all play out over the next couple of weeks.

Jack: Just before we wrap up, I wanted to ask you about SpaceX, because last time we talked, there were not options trading on SpaceX and now there are.

Brent: Yeah.

Jack: And, you know, I was debating. I don’t — I know nothing about options, but I was like, “Is this gonna be one of the biggest players in the options space?” Like, how is this all gonna play out? So I’m just wondering what you’ve seen behind the scenes so far.

Brent: There was like a gamma squeeze or huge options longs for like a day, and the implied vol got so silly that what happened is everyone came in, the big boys came in, and they sold options like crazy.

They sold calls, they sold puts, and that vol came in. The stock went from 130 to 220 back to sort of 150, and now we’re seeing that vol kinda get sucked out, right? And now as we’re speaking, I think today and into the next couple of days, SpaceX is getting added to the Nasdaq. And you and I were joking before, it’s like you don’t see these names move to the Nasdaq or one of these big indices and then continue to go up from there, right?

It’s like the Nasdaq or the index, you know, pension funds of the world tend to buy these at short-term highs because people know it’s gonna go into the index, right? So I think that SpaceX will kinda continue to consolidate here for a little while. It’s blanketed by this positive gamma positioning, so I don’t think you’re gonna see like a 20% drop or anything, you know, kinda crazy like that.

I think it’s gonna be a stock that just sort of cools off a little bit, normalizes some, and then, you know, we’ll see what happens with Elon. And there’s always rumors flying around, right? They’re gonna buy T-Mobile, they’re gonna merge with Tesla, they’re gonna... You know, who knows what happens with xAI, all that sort of stuff.

So I don’t think it’s like dead money for the future. I just think in the next couple of weeks there’s a lot that has to be digested. With July OPEX a lot of this options positioning rolls off, and the vol should really continue to sink there, meaning drop, and then I think that kind of allows the name to sort of reset, stabilize again, kind of like the overall market, and then we’ll see what happens from there.

It could very well become the ultimate meme stock again and start doing crazy moves. I just think that there’s so much big positioning that was short vol based and getting the flows into these indexes that, again, once that flow starts to clear out into July options expiration, maybe after July expiration we’ll start to see, you know, silly upside again. And then maybe—

Jack: How about from an options volume perspective? Like I assume this is not Tesla or Nvidia yet, but is this like a top 10 stock or is this way further down the list?

Brent: It was doing... I mean, it did bananas volume, just absolutely bananas volume, the first couple of days, right? It was outpacing every other stock, single stock.

That volume has come down a lot. And so now it’s acting much more like a, quote-unquote, “normal stock.” Like it’s certainly a big name in the options space, but as you can see here, I mean, it’s early, but this is generally what the ranking looks like, right? SpaceX is top 20, but it’s nowhere near a Tesla or Nvidia type volume, right?

And so, you know, at this time, again, that volume has certainly come in. I think there’s a lot of people who got long these options in the retail space. They got their hands slapped, and so they tend to disappear from that name. But if it starts to meme again, then I think you’d see that volume start to pick back up.

Jack: Well, Brent, this is great. It’s always good to take a look at what’s going on behind the scenes. Thank you for joining us.

Brent: Thank you, Jack. Appreciate it.

Matt: When it comes to looking at the numbers behind the data, Eric Packman, writing at Data for the People, has been doing some amazing work, especially about if you’re trying to think about the Fed, you’re trying to think about wars, you’re trying to think about what’s going on with unemployment and rates right now via inflation, he’s the guy you gotta talk to. So here’s Eric Packman. This is Behind the Data.

Next up, we have Eric Packman, Data for the People. Spoiler, tease, I don’t know what to call it. Next month in July, I’m having Eric back so he can go do a deep dive on all the new tools they’re rolling out. But in the meantime, we’re gonna talk about what’s going on at the Fed. Eric, how’s it going, man?

Eric: It’s great, Matt. Great to be back.

Matt: And when I say we’re gonna talk about what’s going on at the Fed, let me be clear, Eric’s got the best data on what’s going on in both the labor markets and the inflation data sets.

When we wanna peel back the layers of the onion, Eric’s the guy that I go to. Let’s start with labor. Let’s start with jobs. Let’s start with employment. What are you seeing? Talk to me about wages. Talk to me about the upcoming jobs number, or that maybe just came out when this episode’s live.

Eric: Yeah. Yeah, so jobs is an interesting situation because obviously it’s, you know, been a lot stronger than most people have expected. We’ve talked about this before, and about like a year and a half ago, maybe even two years ago, when I was with Bank Creek, we made an interactive drill-down tool where you can kind of explore the different caverns and levels of the BLS’s hierarchy within the non-farm payroll data that comes out, you know, probably yesterday if this comes out on Friday.

And so I’ve been using that, and it’s a free tool on bankcreek.com, and you can see exactly what is going on in the job market. And I mean, I think everybody knows by now, we were talking about this a year and a half ago, but everybody knows by now it’s really all healthcare. But leisure and hospitality has been really strong too, right?

And then we just put out a piece on Data for the People, which I highly recommend people read, because there is information in there that you may wanna know going into 2027 if you are an investor about, like, a potential risk to the job market. But the vast majority of job growth within healthcare and social assistance has been in social assistance, and this is a result of the baby boomer retirement.

So really starting at about 2015, you started to see a surge in jobs and services for the elderly and disabled in the home healthcare aide space, and we broke that down by state to actually show how that looked. And so that’s what you’re seeing right now. I mean, when you look at the chart of services for the elderly, it is literally straight up into the right, no bump for COVID, literally recession-proof, and that is everything you need to know about the demographic situation of this country, and there’s almost nothing that this country can do to stop that.

Demographics are gravity, right? So problem is, is those people make about $35,000 a year, and we created another tool to show you that. So if you go to Data for the People, it was last Friday, I believe, it’s called the Wage Ledger, and actually my first data fellow created this, so I was really proud of him.

He’s a high school student that did this. Basically just took the Occupational Employment Wage Statistics data and put it in an interactive table. It’s a really hard to work with spreadsheet. It crashes Excel. And so it was the simplest thing, but it’s really, really helpful. And what I urge people to do is go on Data for the People, use the tool, sort everything.

You can do it by city, by the way, but if you’re looking at the overall country, sort it by total number of jobs from high to low. 4.3 million home healthcare aides, right? $35,000 a year. Restaurant workers, cooks, cashiers. Where do all the people work? They work in jobs that basically maybe cover the poverty level for a family of four. And so when you—

Matt: And this isn’t just where they work. You were saying this is where the growth is. So when we talk about growth in health—

Eric: This is the occupation, yes. Exactly. So if you go and you look at both of these, pull them up side by side on your screen, you know. You look at them and you’re like, “Where’s the growth coming?”

It’s coming from healthcare, social assistance, a little bit of construction, a lot of leisure and hospitality. But drill down and look where in leisure and hospitality, and then go look at how much these people get paid, and then you start worrying and you’re like, “Oh, wow.” Like, we are growing jobs, and the Fed is overall looking at this number and they’re like, “The job market is resilient,” but we’re creating jobs that pay people $35,000 a year.

That’s not resilience. That’s maybe sustenance and no more than that. And so where’s the disposable income? Where’s the strength? Where’s the growth in the economy? Where are the $150,000, $200,000 jobs that are being displaced by AI? They’re not in professional services anymore. You can look at the dashboard and see that.

Those are going down. Long-term trend going down here for the last, like, couple years. And so this is what I think the Fed is missing, is that they have these crude tools to just look at overall jobs, but they don’t have the capacity to do a weighted average to actually say, like, “Are these jobs actually creating wealth? Are they creating savings? Are they creating any sort of kind of viability to live in America for Americans?” And the answer is no.

And so if they stick with that and they basically keep saying, “Well, you know, the job number is strong,” just because you’re looking at the overall count, that’s gonna put upward pressure on rates, even though if you dig underneath that, what you’ll see is these are very low-pay jobs.

Matt: What’s fascinating inside, and this is where I wanna shift to inflation from here, because as Mike Green and Adam Butler were writing about with the precarity line work, your work diving deeper inside of that, we got to publish some of that on Panoptica as well as Data for the People, is these are the jobs that are being created on the lower leg of the K.

That growth is not keeping up with costs or providing an affordable lifestyle for the people who are retaining jobs or getting the new jobs. And that sets up a problem with inflation. Because as costs rise, it drives a lot of pressure onto those that makes it far more sensitive to this. Take us back for a second.

I actually wanna start with crack spreads. You’ve been writing a lot about this. And this is probably what people would expect from you. But you had a prior work experience that gives you... This is a full circle moment that—

Eric: It really is.

Matt: —you’re talking about crack spreads again. Give that background, then tell me what’s going on there in the oil industry and how it follows through.

Eric: This literally brings me back to my first job out of college, undergrad. So my first job out of college was I worked for ExxonMobil, and I modeled refineries. I built refinery simulation models where you took crude oil coming in, and we literally had molecular models. This was back in like 2002 and everything, so some pretty cool tech back then, before the days of AI and everything.

And you would figure out — like you would break crude oil down into its molecular composition. Crude oil is just — and this is what, like, people in the market do not understand. And if you are participating in the market right now, you better read up on this. Otherwise, you’re dealing with a lot of risk that you don’t understand.

Crude oil represents a class, like fruit is a class of different types of fruit inside of that, but crude oil is even more complex. So think about it. When we at ExxonMobil would define crude oil, it would become a set of thousands of molecules, different types of hydrocarbons. Two carbons, three carbon, four carbon, five carbon, six carbon.

Is it, you know, what type of chain is this? Is it a straight chain? You know, did it have sulfur locked inside of it? Like what type of sulfur was it? Was there nitrogen in it? Like what type of molecule was it? Like that’s how I defined crude oil. So I have a very, very good understanding for that crude oil is not a fungible concept.

And modeled that through refineries so I could actually see how it behaved. And if you made the slightest changes to a crude oil, like even the type of sulfur — it could be in the parts per million level — you made the slightest change, it would blow the specs on the other side, and blow the economics of the refinery, because they’d have to blend it down in order to make spec and ultimately sell it on the market.

So throw into that, now that you understand refining and how complex it is, and that refineries are not just machines that process crude oil. They are purpose-built, specially tuned for specific types of molecular slates, right, that we call crude oil. Now, close down the choke point where 11% of the world’s oil goes through.

Most of it is the type of oil that the world’s refineries are built to process. It’s the medium and heavy oil, which actually creates a lot of the heavier products, jet fuel, diesel, those kind of things, right? Close that down. Wait for, like, several months. Open, and then let everybody reshuffle all the flows.

So think about this as a complex — I mean, you could even look at maps. Like, there are tankers going everywhere. It’s a—

Matt: Logistics problem.

Eric: That’s what it is. Very, very complex logistics problem. It’s a logistics problem. Yeah. Everything is going to the wrong place. All refinery economics are suboptimal.

Everybody’s just trying to get oil, right? So they can get it, refine it, turn it into gas. They don’t care about the economics. Open it back up. The market, you know, celebrates. China doesn’t need as much oil. We got new Iranian oil on. The market is awash in oil. Well, what’s the crack spread doing?

The crack spread does not lie. It is the truth. It is telling you, is the right type of oil making it to the right place? And in the US right now, the crack spread, as of this morning, was $62 per barrel. That is the refiner margin. I take three barrels of crude and I turn it into two barrels of gasoline and one barrel of diesel.

That is approaching all-time high levels from 2022. Normal crack spreads are between $15 and $20. The alarm bells are ringing, the sirens are going off, and everybody is ignoring it right now. And so, look, the powers that be can say we want gas prices to come down as much as they want, but all I’m saying is, like, even if it goes bump in the road and oil prices go back up to 80 or 90, you will see gas prices, at a crack spread of 60 or $70, higher than they have ever been in the past.

We don’t need oil at 150 anymore. When crack spreads are that high, gas prices are so far, and diesel are so far, disconnected from oil. And that’s what we ultimately consume. That’s what people feel, you know. People feel it at the pump. They feel it at the grocery store. And what transports your groceries?

Trucks that use diesel, that basically pay fuel surcharge, and they pass it through to the Krogers of the world and everything. And so, like, all of our prices, the inflation that we feel, is really more dependent on the combination of the oil price and the crack spread, not just the oil price. And I really, really think people are missing that, and I don’t need to tell you, like, how this is going or how it’s gonna end.

All I can tell you is that study for yourself how refineries work. Look up what a crude oil assay is. Understand how complex, you know, a barrel of crude oil is, and how different it can be from one type of crude to the next, and how it messes with refinery economics. And then just understand that we just disrupted the entire supply chain a la COVID.

Some supply chains took years to heal after COVID, and we have no precedent for what just happened. We don’t know what’s gonna happen now. Could crack spreads go to 100? Sure, why not? I mean, I don’t know. It’s never happened before. And so I’m just bracing for the worst as an engineer, because I know when something has never happened in history, to just stay out of the way of the Mack truck that is coming.

And then maybe we get lucky and maybe it all sorts it out, but I’m gonna wait to kinda see how this all sorts out, wait to see those crack spreads come back down to something reasonable like 15 to $20 before saying we dodged the bullet. And then study afterwards why that happened. Or we’re gonna be on the opposite side, where everybody is gonna know what a crack spread is, and that’s gonna be a little scary.

Matt: So if you’re worrying or thinking about employment and inflation, this is why we’re talking to Eric. Eric, if people wanna bug you on the internet, get more of this information, where should we send them?

Eric: Data4thepeople.com, number four, just like this. I am largely off social media, so if you want to hear from me, I write five days a week, and I publish, like, this level of granular work.

This week we’re publishing four brand-new data visualizations. So AI has, you know, quadrupled my productivity or more. So if you want to drill deep into topics that impact the American public, especially the, I’d call it the silent American public, then go to Data for the People, explore our research.

It’s all free. And then please sign up for the distribution list and tell a friend. Anyone that really cares about understanding how this really works — like, all of our growth is organic word of mouth right now.

Matt: All right, friends. I’m telling you that, and watch, Eric’s coming back sometime in July. We’re gonna do a deep dive on a bunch of these new series and tools that he’s rolled out. Thanks, Eric.

Eric: Thank you.

Jack: So Matt, we’ve taken a look back. We’ve talked to some great guests. Now we gotta take a look forward.

And I think — I don’t know what you’re thinking about as we head into next month. I’m thinking more about, like, where the value’s gonna accrue in AI. Like, everything’s kinda been going up. Now people are starting to think about this a little bit more. You’ve seen this a little bit in the semis, with Citrini. He’s been talking about this idea, and I’ll put up the tweet. He was responding to somebody who’s been talking about this idea that there might be a significant breakthrough coming through in, like, the architecture. But the point he was really making is when the price of something is so high, like what’s happened with DRAM, there’s a lot of knock-on effects that go on, and there’s a lot of incentive for people to try to figure out how to use it more efficiently.

So he ended his tweet with, “You need to be an extremely firm believer in Jevons paradox as applies to memory to remain long the OEMs here. Otherwise, it is essentially a bet that’s short innovation out of necessity.” So that just made me think a lot about we’re gonna think a lot more now about what’s going on behind the scenes, all the knock-on effects.

Like, this is not just gonna be an everything goes up thing. It’s gonna be like, let’s think about what’s actually happening.

Matt: It takes money to buy these chips. It takes money to expand your memory, your capacity, and whatever you’re agreeing to do. So we’re seeing equity issuance. We’re seeing debt issuance.

We’re seeing money get raised by corporations to go and continue spending in this space, and we’ll likely see some of that continue for at least the very short term. The bigger question, though, is just how long can it continue where the demand just stays there at this level? Semis are, and they’ve been for my entire career, and comment if you feel otherwise about this, it’s a cyclical industry. It’s like when we talk about—

Jack: It has been, at least.

Matt: Yeah, we gotta talk about it the same way we talk about oil and gas, and we talk about miners, and we talk about these other typically cyclical industries where they go through these waves. And we’re just at a really interesting point right now where we’ve seen a run-up in demand that has rightfully pushed the share prices of these companies much, much, much higher than we’ve seen yet to date.

But it still moves in cycles. And as investors, we have to be aware of what those cycles look like. That doesn’t mean you have to wait for them to drop 30 or 50 or whatever percent you think is due in a cyclical rollover. It just means be aware of — if you think we’ve exited the cyclical nature of these names.

And if you do think they’re cyclical, which I think is what the Citrini piece is arguing here, be mindful of that right now.

Jack: It’s also interesting, like the different levels of demand. So you’ve got your and my demand, that’s consumer demand, but I don’t know about you, but I don’t have any DRAM in the house.

Like I do, but it’s not, I’m not actually doing anything with it. Like I’m actually typing into a keyboard and getting a response. And so, like the demand of the people that are actually buying the DRAM — and this is the thing, like Jeremy Grantham said this thing when he was on, about like, there’s gonna be blood in the streets with AI.

And his point was, a lot of these big companies have kinda had their own, like, islands for a long time. Like, “I had my, you know, area here and you’re not competing with me here.” And like he was arguing that they were monopolies. Whether that’s true or not, they kinda had their own islands, and now they’re competing with each other.

And so this idea that everybody’s gonna squeeze every dollar that they can out of this system right now. So if the price of DRAM is very, very high, everybody’s gonna be figuring out everything they can around how do I use it more efficiently? Maybe someone else will figure out how I can make something better than somebody else is making.

Like, that’s what’s gonna happen now, is the blood in the streets idea, everybody’s gonna get every dime they can. Like I think that’s the way this is gonna work, and it just changes... It’s not an everything goes up together type of thing. It’s like you have to understand behind the scenes what’s going on, which I don’t do since I’m not a tech analyst, but I love listening to it and playing one on TV.

Matt: These things evolve over time. That’s the most important part here, I think, to remember, is that this evolves in real time. I’m gonna make one other tortured World Cup thing, which is we’re watching these teams. You’re starting to see the really good teams play the mediocre to we-just-got-through-to-the-knockout-level teams, and you’re gonna see a lot of this low block or park the bus strategy.

I’m sure you’re familiar with all these terms, Jack. All your in-depth—

Jack: Some of them maybe.

Matt: Okay. Park the bus sounds just like it is. Usually you’re the less good team. You’re either less fast or less capable than who your opponent is. So what you do is you park the bus, meaning you literally put as much of your team packed in front of the goal in a way as possible, and just hope you get lucky on a counterattack.

And the thing about that strategy is, for an attacking team, if it works, the more time that the game goes on, the more frustrated the attacking — the better team — gets, and the more confident the park the bus team gets. And there’s this interesting dramatic tension that unfolds over the course of a full match where the park the bus team, if they are successful in doing that, that confidence will build, and somewhere towards the back portion of the game, you’re gonna see them all of a sudden going like, “All right. Well, we haven’t run that much, and now we can actually cause some real trouble to this far better team who now is just miserable over how they can’t crack this thing.”

When we see these efficiency moves in markets, they play out over time. They play out over quarters, in some cases years, and we start to see we wanna get the best use out of the most efficient areas and be very conscientious of these costs, ‘cause they flow through to the bottom line of these companies.

It feels like we’re at the part of the AI cycle for that to start happening. And maybe we could put a laundry list of Jeremy Grantham blood in the streets calls all in the list, and we can laugh at that or whatever. But the guy’s pointing at something very legitimate here, that this competition is about to change its stripes in 2026, before the end of this year, and that’s gonna be fascinating to watch.

Jack: Well, before I let you take us home, I’ll just say two things on soccer. First of all, Mbappé, that’s ridiculous. Like, I was just watching that France game the other day. It’s like they’re literally just toying with people. They could just do whatever they want. Like, they don’t even have to run that hard.

Like, it’s just like perfect passing. He does whatever he wants. It’s like, you look at it and you’re like, “How is anybody beating that?”

Matt: Olise, Ballon d’Or in the next couple of years. That guy is otherworldly. He takes Mbappé, who is already incredible and deserves all the credit that he’s getting for it, but that Olise, his passes before those goals are just alien, inhuman.

And had Mbappé scored that bicycle kick thing, that would’ve gone down in... We would’ve been watching that clip forever. Forever. That would’ve been the flagship of this World Cup. The man is just crazy. I’m claiming my brother’s French spouse’s family. Like, those once-removed in-laws. I’m like, “I just wanna be French for this tournament,” because, you know, let’s face it. Hopefully the US is advancing, but when it comes down to it, I think France takes this.

Jack: The other thing that I think is interesting, and it applies to everything, is this idea that, like, the more you know the game, the less you work. So, like, you watch France. Like, they don’t even have to run that hard.

Like, they’re putting the ball in the right spot. They... I think people criticize Messi for this at times, but he’s so good that he, like, knows exactly the position to put himself in. And, like, in a lot of ways, like, less work is more, if you know that.

Matt: The best part about Messi is they built the team around that awareness, as opposed to, say, a Portugal, who puts Cristiano right on top.

So they tuck Messi in just a little bit, so he’s not the frontmost player and he’s not even the backmost midfielder, so he can literally just kind of stand around for most of the game and then go, “Oh, I see that,” and be incredibly impactful. That’s smart strategy as you’re getting older, and it’s, yeah, it’s worth noticing how that plays out in lots of different things.

I think there’s a beautiful metaphor in that.

Jack: So now that people have figured out you don’t wanna put me and you on play-by-play and color for the soccer matches, it might be time for you to bring us home.

Matt: Hey, man, we can do at least as good as Alexi Lalas.

Jack: We can talk the whole time. I just don’t know if we’d say anything insightful.

Matt: I will make as many Janet Jackson references as Alexi Lalas. Maybe even more, ‘cause I won’t keep milking the “What have you done for me lately?” joke, although I love it every time he says it and it gets stuck in my head, and that’s my favorite thing about him, and my keychain that I have somewhere back there of Alexi Lalas from 1994, which is a deep point of pride for me.

So go to Excess Returns on Substack. Check out... We have a whole page just for this episode and all the other episodes we do, including our five lessons. Get on that Substack, please, please, please, and wherever you’re listening, like, comment, subscribe, all the things below. We’re out.

Main topics covered

• Which bearish AI arguments actually matter for investors

• Why regulatory risk may be the biggest long-term AI concern

• How data center spending is crowding out housing investment

• Why the Fed may struggle to cool AI-driven investment without hurting the labor market

• What GPU availability says about real-time AI compute demand

• Why open source AI is not yet replacing frontier models

• How token pricing and OpenRouter data help measure AI usage

• Why semiconductor stocks may still be in the middle of a major cycle

• How semis are being valued differently than traditional cyclicals

• Why Fed policy, earnings growth and market multiples are key to the second half of 2026

• What oil positioning and refined product inventories say about macro risk

• Why 3Fourteen remains constructive on equities despite rising overheating risk

Timestamps

00:00 Intro
01:04 Which bearish AI arguments have teeth?
04:00 Why AI regulation is the biggest long-term risk
07:03 Technology spending versus housing investment
11:03 How AI CapEx is showing up in inflation data
13:04 Why the labor market is more fragile than headline jobs data suggests
16:24 Why GPU availability is a cleaner signal than CapEx announcements
21:00 What token pricing and OpenRouter data reveal about AI demand
27:36 How 3Fourteen benchmarks frontier models against open source AI
30:00 Why the semiconductor selloff looked like a buyable dip
34:02 Are semiconductors still cyclical businesses?
38:08 Why Fed tightening could be the thing that ends the bull market
42:15 What the oil shock means now
45:47 Refined product inventories, crack spreads and energy stocks
47:18 Are earnings estimates becoming too optimistic?
50:49 Why the debasement regime still supports equities
54:05 Where to find Warren Pies and 3Fourteen Research

Warren: I’m good. Appreciate that intro. Thank you, Matt.

Matt: The Leo Tolstoy of Twitter threads and LinkedIn posts with some of this stuff. You’re killing me with the content. It’s great.

Warren: I’ll take it. What is it? Every bear market is an allegory or whatever. I don’t know.

Matt: Yeah, you got it. You got it. There you go. Right up there with the family quotes. Yep. See, you’re well-versed. I know you’re well-read. Speaking of well-read though, I’m starting with Fernando. Fernando is not here with us but is in spirit always. He said that being invested in the market right now means constantly fighting against a deluge of bearish arguments about AI. Kind of feels like everything. ‘Cause when you sort through it, which of the bearish AI arguments do you think actually have any teeth?

Warren: Yeah, I mean, I think that’s, when you zoom way out, that’s kind of markets in general, right? It’s just you’re always assessing risks, and that’s how we’re wired, and anything that’s new or novel is gonna be especially fertile ground for concerns to crop up and for bear market stories and risks to crop up.

And so our job, whatever it is, AI, whatever, is to, as researchers, test the theories as they come and try to be objective and level-headed about it. And so that’s what we do often with AI. I’m fortunate to work with Fernando, like you said. He has a background in machine learning, and so this is not something that, like, he had to pick up on the fly. It’s an area of the world he’s been involved in for over a decade. And so it is massively fortuitous for me ‘cause I’m just, like, a dumb macro guy with a background in energy, and so I’m just trying to pick this up on the fly.

And I can understand why there’s so many generalists who are skeptical and worried about this build-out, and it’s turned them off, and it’s made them just nervous and probably caused a lot of frustration because in order to... I mean, tech is now, what, 50% of the market. In order to outperform, if tech’s outperforming and you already feel like tech’s a big overweight, you have to be even more overweight to outperform. And so it’s been a really tough market I’ve seen from a behavioral standpoint over the last couple years.

So yeah, that’s totally the right description, and I think that came from Fernando. I do think that there’s always worries. I’m wired to be worried. I’m always peppering Fernando. That’s our dynamic. He’s more of an optimist, a techno-optimist, and I’m more of, like I said, a traditional macro worrier, and I’m always peppering him with questions and saying, you know, like, “What about this, and what about this, and what about this?” And sometimes things land, and sometimes they don’t.

I think that right now we’re in the midst of really thinking through what I would say is the open source panic. We really wanted to get to the bottom of that. Some of the charts we’ve seen floating around within more traditional macro. Our read on that, and we can talk about it, is that that’s not really something to worry about. That’s not an existential threat at this moment in time. The data and analytics that people are pulling from, really when you dig into them, aren’t too worrisome, and so that’s not a worry.

I think if you ask me, the biggest concern is regulatory. What happens — I do think that the messaging around data centers, you could call it the horseshoe effect, you could call it whatever you want, but you’re seeing both the left and the right start to say, “Why are we building data centers?” And I mean, I’m somewhat sympathetic to that. I mean, my son actually asked me that this weekend. He’s 15, and he was like, “Dad, do you think we should be building data centers?” So, I mean, this is becoming an issue. It’s becoming something people are really talking about. And so I think the messaging — the guys who run the labs are horrible, horrible spokesmen for this world.

Matt: They’re not the marketers for a reason.

Warren: Not at all. And so, you know, if our fate, if the AI fate is left in the hands of Dario and Sam, we’re all screwed, in my opinion, just because they give off a vibe of... Their messaging is objectively scary. Like Dario just says, you know, “We’re just gonna lose a lot of jobs, and be ready, and hand-wring, and be nervous.” And Sam is... I think most people find him to be kind of shady and not trustworthy. And so to me, both of those guys are kind of nice figureheads for this problem out here, the regulatory problem.

I don’t think it’s a today issue. I think it looms out in the future, like that 2028 deadline is kind of important. What kind of progress do we make between then and now? What kind of messaging changes between now and then? Those are all important things, so that’s a big deal.

And then just, I think, market sentiment. But this is usually something that’s... ‘Cause market sentiment sounds so general, but there is a large degree of financing when it comes to the labs and their training budgets and things like that. And so if market sentiment was to sour for some reason, then you could see progress stall out. And I think model progress, as we’ve said, is really the lifeblood of the bull market today, is you need to see models improve, and if models improve, you can envision deeper adoption, enterprise adoption that flows into compute demand. All of that stuff comes together to then ratify the concerns around hyperscaler CapEx. That’s a big part of the market too, and all these things start to flow together synergistically.

But the tip of the spear — and that’s why I think that Mythos model announcement back in early April was really like when we started this generational semi run. To us, that was a big deal. And so that’s where we’re at right now. I don’t see a problem when it comes to market sentiment, but regulatory and then sentiment to help finance this, the continued AI experiment.

Matt: I wanna get back into that in a little bit, the model improvement part. I think that is super, super important. I think tying it back to the news of the last couple of months is really, really relevant with the Mythos part. But first, especially because you got a 15-year-old bringing this up, and we have midterms this year, and whatever else is gonna come, you call this the single best chart to capture the current macro. I wanna get this technology versus housing investment chart up. This is fascinating on another way this is coming home to roost. Unpack what we’re seeing.

Warren: Yeah. So this is the part of the side effect when we talked about the regulatory. Why are people — are people happy with the AI build-out? And I don’t even think this is something that the average person can feel yet, but maybe they’ll, maybe... Or they don’t know it intellectually, maybe they feel it, is that AI is part of the crowding out of some of these traditional consumer sectors.

And so, you know, we as a macro shop, one of the areas which I think we’ll talk about, our source of truth is the residential — at least it usually is — the residential construction payroll gauge. So how many people are employed in residential and housing construction? It’s an important gauge. I mean, usually you see that is the best leading slice of the jobs market before recessions. It slows with a nice lead time. Usually, an eight percent drawdown in that slice of the jobs market happens before each modern recession. So it gives you a good lead and a good signal.

In this cycle, we’ve seen weak housing, and starting to see weak residential construction payroll data. And you’re actually seeing residential fixed investment plateau and then fall since 2022. And you would expect this to be a weak overall economy, a weak consumer. But instead, what we’ve had is, as we went from 2022, 2023, and the AI mega theme took off, we’ve seen IT spend — information processing and equipment spending combined — has overtaken residential fixed investment. So we’re at like $1.5 trillion versus $1.1 trillion in favor of tech spending fixed investment.

So it’s a crowding out. You have housing having to make room for this. It’s taking up real resources in the economy to build out data centers and do all this work. You know, the whole anecdotal, you can’t find a plumber, you can’t find an electrician. Trades are being utilized and maybe even pulled away from the housing sector. And so that drives up cost. It also potentially raises the cost of money, the neutral rate.

So this goes to the Fed dilemma that we’re gonna deal with in the second half of the year. Will the Fed slow inflation, address inflation, and raise rates enough to slow that big tech boom that we’re experiencing? One of our working theories is that probably not, you know, all else equal, because in order to... The most responsive area of the economy to monetary policy is still the housing market and residential fixed investment. And so they would have to do a lot of leaning on that sector of the economy before they even touch this data center build-out. Like, it’s the hyperscaler CapEx going up above a trillion dollars next year and then beyond — twenty-five, fifty, seventy-five basis points of hikes is not gonna change that trajectory. They’re gonna have to do something much more that bleeds into the capital markets and hits the wealth effect and all these other things.

So that’s the dilemma. That’s the K-shape economy, the bifurcated economy. That one chart and that dichotomy between residential fixed investment plateauing and going down and IT spending and tech fixed investment just moonshotting higher, that captures all of these issues. So to me, it’s the number one chart to start framing a macro discussion.

Matt: Drilling on this just for a second because this does make an incredibly important point for the Fed right now and where they are, because hyperscaler CapEx, we’re looking at like two and a half percent of GDP, and a huge chunk of this rise in core PCE is coming from information processing equipment spend. That’s gonna flow through the way we think about inflation and growth.

Warren: Yeah, you’re starting to see the Fed talk inflation. They did inflation ex tariff, inflation ex oil. Now you’re hearing some inflation ex tech spending. I mean, PCE just from, like you said, those two PCE categories, which are small categories, but just exploding—

Matt: Yeah. We never talk about them like this.

Warren: Yeah, I mean, this is weird. We did the math. I think it was like we were adding seven basis points on a month over month basis to core PCE. So I mean, that’s the difference between, like, if you’re at 0.2 versus 0.27, that’s a big difference if you annualize that rate out.

And so yeah, it is in the inflation data, and so the Fed has to take that in, and then they have to decide, do they have the tools to address that, you know? And I’ve had a lot of debates around this, and, like, they do have a dual mandate too, which makes this complicated, you know. So the Fed has to... A lot of people pretend the Fed is like one of the other global central banks where they just have price stability as a mandate. They have price stability and full employment. And so if they were to just address inflation, they could end up killing the labor market, ‘cause it doesn’t look like there’s any real tightness in the labor market or any inflation coming from the labor market. It’s coming from a generational capital spending boom out of the tech sector and out of the fiscal deficits that we’re seeing, which have kind of taken a backseat, but they’re constantly running in the background.

Matt: Zoom in on jobs a little bit, and partly because I so appreciate the way you always frame residential construction and stuff like that here, but also because the K-shaped part of the labor market, that is where we’re looking at. Eric Packman at Data for the People has been highlighting this a lot for, like, a year and a half. The jobs we’re adding are not the $200,000 white collar job. We’re adding nurses, home health aides, things like that in the $30,000, $40,000 a year range. That’s not helping consumption. It probably distorts the way the Fed looks at the labor markets. What do you think about that?

Warren: Yeah, I mean, again, it goes back to really monetary policy is not a surgical instrument. It’s a sledgehammer in a lot of ways. You know, you can’t just surgically stop one area of the economy and hold another one constant. Like, if they wanna raise rates to really impact inflation, they’re going to end up killing a lot of activity, and it’s in these areas that are already weak that’ll suffer the most.

And so like you said, if you go back one year, ex healthcare, education, government jobs, the non-farm payrolls is flat. And so that’s not a sign of a really strong labor market. It’s actually, if you go back in time, that looks like kind of recessionary levels. You’ve had two months in a row and a two-plus percent drawdown now in the residential construction payroll sliver that we look at, which — again, eight percent is like a pre-recession level. But a two percent drawdown in two months in a row, that’s a yellow light flashing in the economy traditionally.

If you look at wage data — if the labor market was really tight, ‘cause a lot of people say, “Hey, you’re looking at the establishment survey. We’ve had a negative population flow because of immigration policy, so the labor force is shrinking. We don’t need to be growing jobs in this type of a backdrop in order to stabilize the labor market.” If that was true, I think you’d be seeing wages stabilize at the very least. But wage growth has stagnated. It keeps making new cycle lows if you like the Atlanta Fed Wage Tracker. And if you dig into that wage tracker, college-educated workers, that’s like the weakest wage growth sliver within that. And you’re also seeing the percentage of workers who are getting no raise go back above pre-pandemic levels. So it’s something like almost fourteen percent of workers getting zero raise in that data set.

So you start putting all this stuff together, I don’t think it’s a... Like, building data centers out takes up a lot of certain resources and certain trades, but it is far from this like economy-wide labor market boom taking place. And it is. It’s crowding out a lot of the more labor-intense areas of the market that we rely on usually. So I don’t see, when I put all that together, even though we’ve had three straight decent non-farm payroll reports, and that’s kind of what the market’s trying to digest, I don’t see a picture of a labor market that’s really reheating yet.

And then we had the JOLTS data come out, and that’s also like bad response rates and things like that. Openings were up, but if you look at the quits rate, it’s down. Like, I don’t know. I don’t think I’m gonna over-index that data set. So when I weigh all the evidence, and I’m open-minded, maybe we’re getting to reheat. I mean, I think we did have a fiscal expansion the first half of the year. We have a big CapEx boom and wealth boom and things that could power this economy. But it’s been a muddle through labor market for a while now, and I think that’s the reality that we’re facing — the Fed’s facing.

Matt: Let’s go back to GPU demand because you keep telling me this, and I believe this. This has been really useful in framing this argument for other people. We’ll get the chart of this up here too. Why is availability basically a cleaner read than CapEx announcements or revenue, and this FAST Index? I wanna talk about what that’s telling you.

Warren: Yeah. So this is something... Going back again, being very fortunate to be working with Fernando, this is something that back in 2023, when the AI story was really taking off and we were doing the back of the envelope calculations on what this meant for the economy and the labor market and white collar displacement — we were pretty big skeptics, to be honest, back then.

And so Fernando started compiling data where he’d go out to the various neoclouds, and on an hour-by-hour basis every day, all these years going back, starting in 2023, we would check how available each GPU was. And we’ve done this starting back with the A100s and going through with the Hoppers and then now with the Blackwells, and we’re testing it always and tracking how frequently we’re able to obtain a GPU from a neocloud. And then we compile that together, and we average it and basically have an index, and it tells us... It gives us a good read on GPU supply and demand, you know. And this is kind of the bleeding edge of the market, is like on-demand GPUs from the neoclouds.

And so that’s what we look at, and again, we initially started collecting the data set from a place, a posture of skepticism, and while we watched this whole thing unfold, it actually converted us into believers, where we could see compute demand just continually growing. Then GPU availability would be trending down, and we could get these cycles, and you would see GPU... And then you started seeing GPU availability collapse for like the H100, and then you would see the rental prices going up about two months after that. We saw that again with Blackwell. We saw Blackwell B200 availability collapse this year, the beginning of this year. I mean, we went through the spring of this year and saw availability for all GPUs go to zero percent, but Blackwell went to zero percent, and then rental rates exploded off the back of that.

So availability is a real time, hour by hour, day by day read on supply and demand dynamics in the GPU rental market. And I think, you know... To me, that’s the kind of analytics that this ecosystem lacks. And so we really pay close attention to it. We’ve built out a lot of different views. We now have foreign availability. We have it by different vintage. And we’ve created an index that’s weighted newer vintage over older vintage.

And again, to me, like, you go through this period of time where... I don’t know if the open source story is necessarily an anti-compute demand story, but there’s always latent fear out there, and what we’ve seen is that just in recent weeks, Blackwell availability crept up maybe, like, two or 3% — back on the floor, unavailable. You can’t get it. There’s a scramble for compute that’s been going on in this market really since the agentic age in the early part of the year, and it has not let up. And our data does not show a sign of that.

If the market starts loosening — you know, people are worried today Meta’s coming in, coming to market, and they’re gonna, like, start renting out their cloud capacity — and, hey, we’ll see that. That will flow into the neoclouds. That will flow into our hour-by-hour, day-by-day data, and we’ll know. We’ll know. Is this actually impairing the story? But again, we’re data-driven, and that’s gonna be the thing that leads us.

Matt: The other thing I love that you’re tracking inside of this, because you’re raising this point, which is we need other ways to track this. It’s kinda like I’m thinking about World Cup tickets and people thinking they can just go to StubHub or SeatGeek or whatever and get them, and they go, “Oh, this isn’t the way this works for my local baseball team or football team. I have to think about this in a whole new light.” This is where we are with this.

I wanna talk about the token maxing thing. You were flagging online basically how it’s fading. Token costs were collapsing as the bill came due. You go to OpenRouter, and you actually measure it. What’s the data show?

Warren: Yeah. Again, this is — I think the way, from the beginning of the conversation, you take in the bearish argument, assess it objectively, and try to weigh its validity. And so the most recent thing that’s been cropping up is two things. And I love the guys over at Silicon Data, you know, and I’m interested in the work they do, and I think they’re doing good work. And I talk to the folks over there pretty frequently. So I’m not denigrating their stuff whatsoever.

But they had this token cost index that came out. It’s, you know, a really interesting series. It purports to basically show the price of tokens. And it had a strong rise through the year and then a collapse. And I started seeing things like dual Y-scale charts of this token cost index versus Mag Seven, and that’s like become this — the macro... The macro answer to everything is dual Y-scale charts of two series that are both going down at the same time, and you’re just like, “Aha, that’s something.” But anyway, it became a very big deal out in the macro world. So that caught our eye, and basically the implication is that the price of tokens are falling, on a global, whatever basis, however they’re doing it. And that implies that this token scramble, the token maxing thing, is falling off.

The other thing we saw was the OpenRouter data. So OpenRouter, platform for routing token consumption and model usage, and what you saw was the chart of open source Chinese models, open weight models, taking massive share on OpenRouter from the frontier models. And so this became another bearish argument.

So again, Fernando, who’s deep in the weeds on this stuff — and if you ever really wanna get deep in the weeds, he would be a great guest. But Fernando went through — and we’ve been collecting OpenRouter data for a while actually, internally — and so went through and looked at the pricing of these tokens, for frontier tokens on OpenRouter and open source token pricing. And it’s true there has been an explosion in open source tokens processed. But you have to put that in context. The total inference month over month tokens processed on OpenRouter is up forty percent month over month. Open source has gone from like forty-five percent to thirty-five percent, or forty-three to thirty-three, something like that, over this month. And so you can back into what’s the growth rate here. It’s probably something, I don’t know, like sixty, sixty-five percent month over month growth in open source tokens on OpenRouter. But still double digit month over month growth in frontier tokens.

And moreover, to go back to the pricing point, we’re seeing pricing for frontier tokens rising on OpenRouter, and we’re seeing open source token pricing falling on OpenRouter. So the signal that I take from that, just as a macro generalist, is that this worry that overall token demand is falling off, it doesn’t wash. This is a small sample of the overall market, and it would not be representative necessarily. It would understate the strength in token demand from the frontier models, if anything, because you’re just not going to OpenRouter to access frontier models typically. You’re going straight to the lab.

So to me, the price rising and growth of tokens processed for the frontier tokens rising tells me that these concerns are way overblown, that in fact, the token scramble continues. This whole usage rate continues. Now, I have no doubt there’s gonna be ebbs and flows in that. The token maxing thing is kind of stupid. Like, it’s a stupid metric to just say the more tokens you use, the better job you’re doing. I mean, there’s obviously gonna be—

Matt: It’s a stupid metric that comes with stupid stories. Right. Like, you say that, then you’re gonna get the thing about some yahoo at work doing some bozo thing. That makes headlines, now everybody makes fun of it. But that’s not the story whenever they do that to you.

Warren: Yeah, and so to me that’s... I get it, but again, maybe that’s happening. I don’t have a strong—

Matt: But tokens aren’t Beanie Babies.

Warren: Right, but maybe— Like, it’s not that. Maybe that’s happening, and if it is, we should see it in the data. Like, in my view, if we had a token maxing craze, and something came over corporate America, and Uber blew through their budget, and all these companies blew through their token budget, and now they’re pulling back aggressively, and the OpenRouter data’s showing they’re all switching to open source models, and the open source models are good enough now to do all the tasks that we really wanted done on the frontier — we would see price for frontier tokens in our data falling. We’d see demand for frontier tokens on OpenRouter falling. You wouldn’t just see the mix shifting, you would see that part of the pie going down.

Instead, the pie’s just growing rapidly. The total pie is growing rapidly, frontier growing, but open source exploding, and that makes sense. I mean, it makes sense to be more efficient with your token usage, and the open source models are getting better. We run our own AI application — it’s a research assistant that Fernando created, and he does his own custom benchmarking, and you can see it in our benchmarking that the open source models have gotten very good, but they’re not good enough. They’re not good enough to displace the frontier right now for our purposes, and if not for us, then probably not for most.

Matt: I’m putting this chart up here. This is the chart of basically those open source models getting closer and closer to the frontier, and basically the threat of the models to where they stand. Do you wanna just explain real quick, because I think this is interesting. You have code correctness, instruction following, summary accuracy, chart quality along the bottom, and how you’re doing that custom benchmarking. Explain this a little.

Warren: Well, I don’t do it. Fernando does it. But we have an app — it’s an AI assistant. It’s taken all of our code and our data and everything, and you can come to it, and I guess this could be a marketing segment too. Like, if you’re interested, you can put your name in and try it out. And this has also been the thing. It was an internal tool to begin with. We call it Caliban. It was an internal tool. We used it to create a large amount of our studies, but it wasn’t good enough to do everything. And then about December or January, when all the models leveled up, we were able to start doing almost all of our stuff. And now I would say we just live in that environment to create charts and studies. I mean, you see our work. It’s like I don’t think anybody’s doing more complex stuff than we are, and it’s all with it, living within that app.

So that’s what it’s doing. We’re giving it to clients. We’re selling it to outsiders, and they’re doing their own custom analysis and creating charts, creating bundles, doing all that stuff. So it’s an analysis and charting tool, and Fernando is in there as the creator of this, benchmarking it across these different dynamics that you just read off and seeing, you know, how good is it?

‘Cause we would love to... One of the few things we’ve learned, and this kinda helps us with the tokenomics understanding, is whenever a new client comes to us, we have to kind of guard the amount of tokens they use, ‘cause people can blow through a budget, and the whole thing becomes uneconomic for us. So we would love it if we could switch everyone to cheap open source Chinese models, you know. But our testing says there’s still a significant gap. You know, even though it looks like it is getting closer, they are getting better. I don’t wanna underplay that. But for our purposes, across those four dimensions, it’s still not good enough. And I think that that experience is extremely instructive and helpful for us in understanding the overall landscape that we’re dealing with here as investors.

Matt: So let’s get into markets a little bit more. You’ve called semis the meta group underneath the AI story. When that leadership started to crack, with semis dropping double digits, handful of sessions, a lot of pain, a lot of red on the tape, you argued that it looked more like a buyable dip rather than a top. I want you to explain what you saw.

Warren: Well, I mean, I think that the number one thing is going back to the availability. The idea is that this is a mega trend. The other factor here is that if you look back to the ‘90s — and I think my hypothesis, we could be wrong, and we’ll see how everything goes — my hypothesis is that this will end up being a bigger cycle for semiconductors than we saw in the late ‘90s, in that ‘95 to 2000 period.

Now let’s talk about kind of the negatives. So the negatives is we run a bubble screen, or what we call a blow-off top, blow-off terminal top screen. We run it for all these markets, and we run it for all the industries within the S&P 500. And semis and construction engineering are the two industries right now that screen positive as a potential blow-off terminal top. This condition, though, first started back in — gosh, I think it was thirty months ago at this point, and so we’ve seen it for a while.

Back in the ‘90s, we first had it in 1995. That was the first time you saw semis register as a terminal blow-off top. And that condition lasted for sixty-three months on and off, but basically, we saw the bull market run for sixty-three months, and I think over the course of that time it 11x’d as a group. And we’re at like 4x right now and tracking exactly where we were in the ‘90s.

The other thing, though, is that back then we saw... Once you get into these periods and you’re in this mega trend, this CapEx explosion, we did see four big pullbacks or bear markets, like twenty percent plus drawdowns, in the semi group. We’ve had two of them so far. So everything lines up well. And it’s like one a year. You’re gonna get one pullback like this a year, one twenty percent drop a year in the group. Be ready for it.

The question you have to ask yourself is: Is this the top? Is this the end of the cycle? And I’m just... I don’t see that in the fundamental data, in the GPU availability data. There’s enough visibility for spend and for CapEx and for everything in the other things that we’ve talked about. And so to me, you have to let technicals guide you in those moments and then have conviction to step in if your fundamentals are in place.

And I think, you know, if something... Again, remember in 2000 there was a couple factors. I mean, there was a pretty hard stop to that bull market because of Y2K, so everything got pulled forward, and we’re not gonna have anything like that this time around. And number two, you had the Fed hiking rates, which we talked about. So if the Fed started hiking rates, and then you saw semis pull back, I would have to do a reevaluation of, is this really the end? Is the Fed gonna break the back of this thing earlier than I expect? But that’s not my forecast, and I think we’re thirty months in, thirty-one months in at this point maybe, and we had a sixty-three-month run back in the ‘90s, and I do think, if anything, this is gonna be a longer and stronger bull market.

Doesn’t mean we’re not gonna... I think there was a thirty-eight percent drop at some point in that late ‘90s period. We could definitely do that at some point. I mean, we go up a hundred percent in three months, and then, you know, you can easily pull back twenty-five percent. But the question is, have we seen the absolute top? I would say no.

Matt: I have to ask this very pointedly about this. Are semis still cyclical as an industry, and we’re just in this phase where temporarily they don’t act like cyclicals ‘cause they grow, grow, grow, grow, grow and then they become cyclical again on the other end, or are they suddenly wide moat businesses?

Warren: Man, that’s like the big question, and I’m not smart enough to answer it. But I am smart enough to acknowledge that there are some things happening under the surface of the market — the way these things are being priced suggests that investors are treating them as at least less cyclical than they’ve been historically. And this is a big deal for me in the way the overall market gets valued.

So we came into the year bullish. We’ve been bullish, and one of the big bear arguments, to talk about things that create anxiety, has been valuations, and we’ve talked about this at... I don’t know if I talked on this broadcast. I think I might have. But one of the reasons we’ve not seen overvaluation in this market is that we have massive margin expansion out of non-cyclical pockets of the market. And when you get margin expansion from non-cyclical pockets of the market, you should expect to see multiples expand. And when we adjust for everything, the market has been pretty rational in how it’s done that.

The semi case is maybe the first bit of exuberance that we’re seeing, though. So semis, traditionally the most cyclical tech industry. Therefore, when margins expand — if you look at a historic chart of margins for the semi group and price to sales multiple, when margins hit a cycle peak, price to sales starts going down to a cycle trough. You don’t wanna pay up for peak cycle margins in this cyclical type of group. That tendency has totally broken down. We’ve had margins explode, obviously, out of the semi group. You can see Micron. You can see Nvidia. You can see all these companies where bottlenecks or whatever have exploded margins. And instead of getting, like, price to sales for the group going way down, you know, to one or two times, price to sales has exploded.

So for us, this is one of those things where how do we factor this into the overall market valuation? ‘Cause semis are, like, twenty percent of the market now, and it’s not a small factor when you’re trying to do your overall top-down analysis. So they are priced right now — the answer to the question is they are priced right now like they won’t be cyclical.

So I think there’s a knee-jerk kind of feeling, because of history and everything I laid out, to fade, say this is a bubble — probably is the beginning of a bubble in some spots, for sure, in this very specific part of our market. I don’t think the overall market’s a bubble, but there’s always air pockets of exuberance, and I think this is one. But again, going back to the ‘90s, this cycle is going to be long. It’s gonna be the longest cycle we’ve ever seen. And so I’m not buying into the sci-fi world where semis are just no longer cyclical, but I’m also not buying the idea that this is close to a cycle top and that we should be expecting margin growth to slow down anytime soon. And so to me, there is no finite answer to that right now. It’s a little bit of both. It’s just a matter of being aware and baking that into your overall market forecast. And valuations are a horrible timing tool, so I’m not gonna time anything off of it. I’m just gonna observe.

Matt: Don’t time anything off it. And if I learned anything from my own childhood in the ‘80s into the ‘90s, it’s that I don’t wanna grow up, and I wanna be a Toys “R” Us kid. And the spoiler is I grew up, and Toys “R” Us went bankrupt after multiple turns in private equity. So this all ends badly one way or another. You’ve got this line about bull markets: they don’t die of old age, they get murdered by the central bank. I know we mentioned it a little bit before, but I think with the backdrop of what you just explained about semis and their contributing to margins and where we are in the cycle here, it feels like Fed mistake is now top of the list for the next couple of years.

Warren: Well, yeah, I mean, we’re off the Iran war, obviously, and the oil spike.

Matt: Are we though? Are we?

Warren: Yeah. I mean, we haven’t talked about it for 40 minutes, so that means we’re off of it. That means we’re off. I think... But what we’re left with is kinda the after effects of that. I do think that the war, we spiked oil prices. We’ve had... Look, I don’t think that the inflation shock we saw coming out of COVID was really a supply shock, but the Fed claimed it as such. So they said, “Brief, transitory inflation” back then. “It was a supply shock. We’ll look through it.” And that didn’t really work out for them.

Then we had tariffs last year. Again, I think that’s more of a valid supply shock. They said, “We need to look through this. There’s issues with the labor market,” and yada, yada, yada. And now, of course, we had a supply-driven spike in the price of oil — Strait of Hormuz closed. And this is your quintessential supply shock, but it’s all these things together have really put the pressure on the Fed.

So now you have the Fed back in a corner where people are starting... They’re worried people are starting to lose faith in them. And so they could end up switching to a more hawkish reaction function. And obviously, you have Warsh in, so there is a change of leadership. And so this is a real risk. I think it’s a time where the Fed is on a true hold. We’ve been in this default cutting position, and I think today is a true hold where the next move is... The next move is actually more likely to be a hike than a cut, but the next move could be either one. They’re on a true hold.

And so that’s a different spot for the equity market. And I think whenever you look at valuation multiples, or when major tops are formed, Fed tightening shows up around these periods. When you think about multiple expansion, you don’t get multiple expansion during tightening cycles, during hiking cycles. You get multiple expansion when the Fed is cutting, and that was the assumption for us coming into the year. You have to take that out.

So earnings up 25% this year, that’s great, but we should expect multiple contraction against that. Earnings up 25% this year, but as we talked about, it’s a more cyclical stream of earnings. It’s semis and energy driving the marginal gain in earnings, so you should expect multiple contractions. So these are kind of things that cut against the bull story, and you have to just weigh that out. It has to be part of your analysis.

And so if the Fed were to actually take the move from a true hold to a hike, I think you cut risk. That would be my... We’ll see what happens then. I don’t wanna prejudge it. I sound like Kevin Warsh. I don’t wanna prejudge it, but, you know, I think that would be your very simple game plan. Our simple rule of thumb we’ve told people, clients, for years is that, hey, if you don’t see a recession in your indicators and the Fed is easing, you can’t be underweight equities, and that’s changing if the Fed gets into a true hold and then potentially a hike cycle. So that’s a big deal to me as a macro guy.

Matt: I could see Waller, Williams, Warsh, and Warren. You guys could all throw up there with Powell and three in the mix.

Warren: WWWP, yeah. No, I do think that’s the big voting bloc. That’s why I don’t think there’s gonna be a hike this year. You have Williams, Warsh, Waller, Powell. That’s the core four. If you read the tea leaves, I think they all believe rates are restrictive and believe that they’re just fine sitting here and holding at three and three-quarters or whatever. And then you have Kashkari, who came out and basically agreed with that view. And so that’s five out of the 12. You really need a clean sweep of the rest to start a hike cycle here, and I don’t think we’re gonna get that. It would really take a lot out of the jobs data to move in that direction, in my opinion, and I don’t think we’re gonna get that. So for now, I’m holding tight that, you know, we’re gonna be okay on the equity side.

Matt: Take me back to oil just for a second, because I wanna talk about is there anything more there? Do we have the lingering effects of an oil shock? Is that a myth? Is there more nuance to this that we’re gonna find out in three months when some piece of supply doesn’t get delivered to the right place and we all freak out again?

Warren: I think that we dodged the big bullet. We’re still releasing SPRs, so the odd feeling is that right now the market’s transitioning from a severe shortage to a bit of a glut, because of all the SPR releases, because China hasn’t returned to the market. And so it’s kind of crazy to say, but if China were to just run back in — ‘cause China killed about 4% of global oil demand just like overnight by stopping their imports and turning on their—

Matt: As they do.

Warren: Yeah, they just do that. They are the swing. We have swing producers, we now have swing consumers. And so China really stress tested their ability to do that. And as long as they don’t come back, I think the market’s pretty over-supplied now. The one thing that’s worked — ‘cause I think every oil forecaster who went through this expected us to have a harder time dealing with the Strait of Hormuz being closed. I did. I’m not gonna run from that.

Matt: You got, you got energy analyst bones in you.

Warren: Certainly did. I mean, it was a scary time. But the thing that’s worked, if you could have stripped away all the scary stories, is technicals and positioning data. And right now the positioning data is back to very stretched. There is about a 40% managed money short position, which in my framework is extreme pessimism and short position. And the best signal for the last 10 years, the single best signal, is positioning in the oil market. So we’re stretched on the short side. If you get a short covering, there’ll be a bid into the price of oil.

Do you wanna buy that and hold that long term in the face of now OPEC starting to overproduce and SPR oil floating around, and your bull case is now, “Oh, we’re gonna buy back SPR and refill it”? That’s not a good bull case. And so I think you’re gonna get a spike here, like back to 85, 90 Brent, whenever we get that short cover, and you gotta wait for it though. And that’s possible, in my view. But the long-term clearing price of oil is not gonna be significantly higher than where we are right now, in my opinion, like seventy-five, eighty. We have raised it. It’s higher than it was before the war, but it doesn’t feel like a macro risk to me. It doesn’t feel like anything we can’t deal with. It won’t be a problem. If we go back into a war and closing the Strait of Hormuz, yeah, I mean, anything’s on the table then. But I just get the sense that the administration has no appetite for that.

Matt: Any... And it’s okay if you don’t have anything on this. It’s just curious, because when I think about other cyclical industries, like I think about the refiners, and I think about where they are right now with where crack spreads have moved to and the whole just shape of this mess and oil as it gets worked out, as the kinks get worked out, basically. Assuming this stops, assuming we revert towards normal with some extra embedded costs, assuming we get some short covering and they’re allowed to normalize out there too. Do we think about some of those other cyclical industries? Are there any possible flare-ups or flare-downs that come out of this for equity markets writ large?

Warren: Not really. I think, I mean, refined product — when people say we need to watch inventories, in my view, we’re really saying you need to watch refined product inventories. You need to watch gasoline inventories and distillate inventories, specifically in the United States. We are at multi-decade lows for both. You know, we did drain inventories aggressively, and we now have refiners that are running full out, like higher than we’ve seen them run at this point in the year, or really any time of the year, in the post-COVID era. And so those refiners are minting money right now. They’re buying cheap oil and selling decently elevated refined product and capturing that spread.

I don’t see it creating any kinda systemic problem. It’s just gonna create more tweets from Trump, where Trump is like, “Why is the price of gasoline not coming down as fast as the price of oil?” And this is just how the markets work and stuff like that. But yeah, I mean, they’re a decent enough play in the energy space for that reason, but don’t see it as anything systemic.

Matt: It’s interesting too, ‘cause it’s still such a tiny part of the broader markets. And actually, let me use this to steer us here. Let’s go back to earnings. Let’s go back to how strong those estimates have been, what you said about multiples before. Let’s get the S&P 500 yearly trailing twelve-month earnings growth chart up. You said that it’s basically front-loaded to match the AI build-out. Where do we think we’re going in a year?

Warren: Right. I mean, we’re probably gonna be in mid double digits for a bit. And my reason for posting that chart is you see this... So everyone’s also looking for this idea that we’re in an earnings bubble. That’s, like, one of the big things that’s going on right now. We’re in an earnings—

Matt: It’s fun to say.

Warren: It is. An earnings bubble. And there is a long-term analyst estimate chart that’s floating around that, you know, I think it was Yardeni who made the chart. And there is a field for long-term analyst estimates, I think it’s in IBES or something, and it’s sparsely populated. Like, if you’re doing, like, annual estimates, you have, like, 90-something analysts who are filling in estimates, and for this series there might be, like, three. I mean, it’s not representative. No one takes it serious, and it shows 25% annual growth in earnings for five years. And I just can’t... You can’t make sense of that number. I mean, I think it’s cap-weighted versus earnings-weighted, and it’s done a lot of different ways.

But the true numbers, the consensus numbers, are what we show in our chart, and it’s much less scary than that. A five-year growth rate, including the 23% we’re growing this year, is more like 15 or 16%. That’s what we should see. We did one where we stripped out this year, ‘cause it is front-loaded like you said, and said, “What’s the growth rate in earnings?” I think it’s about 14 or 15% out through the end of the decade. Strong, but not in this historically crazy world where we can’t hit it without nominal GDP being 10%.

And that’s what I hear a lot of the macro bears saying: “Oh, there’s a big divergence between where this GDP is and the speed limit of the economy and what the analysts have baked in,” and they’re just taking faulty numbers. These aren’t real numbers. 25% five-year growth rate, annualized growth rate over the next five years — that’s not real. That’s not happening. No one’s... If you’re using that to base a whole macro theory off of, you’re not interacting with, like, the reality of what analysts are saying.

Matt: Part of what’s fascinating about this to me, when I look at your five-year projection or the way you’re looking at the projection here, is the composition of markets is gonna change over that period of time. It goes back to that 30-month versus 60-month run-up in the ‘90s and what we see, where that peters out, and then where the potential effects of this land in the rest of the real economy. So when I think — if I’m gonna think out two or three years, how do I think through that?

Warren: Yeah, I mean, I think, to be totally honest, I think looking out two to three years is kind of a fool’s errand. There’s too much stuff that’s gonna happen between now... I can’t think about... Two to three years from now, I can make a prediction, and who knows where we’ll be in three years, if I’ll even be able — you’ll be able to find me to come back and hold my feet to the fire. I think it’s too far out to really have any confidence.

The thing you can say, and I think the thing that people are doing, is: does the three-year out estimate where we are today — does it align with the cone of probabilities that we’re gonna go through? And the argument, when you use something like a twenty-five percent annualized number, is that analysts are seriously removed from the economic reality. But those aren’t real numbers. The real baked-in numbers for like 2028, 2029 is, you know, thirteen percent earnings growth, twelve percent earnings growth. Do we hit that? I don’t know. Is it possible? Do we need some kind of economic regime change to hit that? No, we don’t.

Matt: Speaking of economic regime change, last time we talked about the debasement regime. We talked about why you would not be underweight equities, and that’s proven very correct. I’ll just put it at that. As we think intermediate term, though, do we stay positive on it? That base you just laid out doesn’t sound like a bad path. Doesn’t mean we won’t go up and down, we won’t have drawdowns and some pullbacks, but still feels like you’re saying stay long the equity markets.

Warren: Yeah. So we came in the year and we said we thought H1, first half of the year, is peak Goldilocks. So we saw disinflation continuing in the form of oil, in the form of shelter, in the form of labor, and we saw fiscal expansion from the One Big Beautiful Bill and AI powering — we wrote about this — an earnings explosion. So we thought there’d be an earnings explosion, fiscal expansion, but still disinflation, so it’d be this perfect Goldilocks environment, and the bulk of the returns for the year would come in the first half, and that’s anti-midterm election year cycle. I kinda like looking at those cycle charts and then fading them because they’re so... People overweight them.

So I think we got that, except the Iran war really disturbed the Goldilocks balance. So now it’s about, like we said, sifting through what we’re left with coming into H2. And I think that the Iran war has taken away the Goldilocks environment. The AI build-out that we talked about, and the spending of tech and the crowding out of things and raising inflation a bit in the economy, that’s also overheating. So we are transitioning into the risk of overheating and bringing the Fed in. And so that’s the big risk.

The positive is that earnings — even though we thought there’d be an earnings explosion, I don’t think we expected twenty-five percent earnings growth this year. Earnings is really taking the baton. So when I put all this together, we came into the year with a very aggressive, I’d say pretty aggressive, target on the S&P 500 of 7,850. We came out and just basically said, “We’re holding it steady.” We think that the earnings will be higher than we expected. The Fed will be tighter than we expected. Multiples will contract more than we expected. But because we have earnings so high, we can still land at that 7,850 level. So we came in at 7,850. We held our target steady. It’s not the most rip-roaring bullish thing to say, but I think we’ll be just fine.

That’s a pretty good year. That’s a fifteen percent up year on the back of two twenty percent up years. And I don’t think you have any reason to believe that the top, the secular top, is in until the Fed comes in and starts tightening or the economy starts looking like it’s gonna weaken. It’s very difficult to get a recession, like we said, when hyperscalers are spending three percent of GDP and the fiscal deficit’s at five, six percent. So generally a good backdrop for equities.

Matt: From Warren Pies’ mouth to Leo Tolstoy’s ears. That’s where I’m taking this one. It’s just fine, Leo. It’s just fine. Warren, if people wanna check out the research, they wanna see this new tool, which is amazing — it’s on the website. You wanna check this tool out. It’s really, really cool. Where should we send them to bug you on the internet?

Warren: 3fourteenresearch.com — the number three, spell out fourteen, research dot com. You can find me on Twitter. The name of the AI research assistant is Caliban. So if you google or search for 3Fourteen Research Caliban, you’ll find that landing page. And, you know, we are an institutional shop, so, you know, in general, we service institutional investors. And so put your name in, fill out the information, a member of the team will send you some sample research, and we’ll discuss if it’s a good fit.

Matt: If you’re out there, you’re professionally running money, you’re doing this for yourself, you want some off the beaten path charts, data, and tools, I can’t suggest enough 3Fourteen Research. Super, super, super cool. Warren, thanks so much for joining me today.

Warren: Thank you for having me. Appreciate it.

Matt: You’re watching Excess Returns. Check out the Substack. We’ll have transcripts, posts, all sorts of stuff about this episode going live there in the days ahead. Like, comment, subscribe, all the things below, and we are out.

The System Gambit
https://amzn.to/4b0J32I

Main topics covered

• Why the traditional moat checklist can fail investors

• The three requirements for a true System Gambit

• How investors can evaluate business strategy from the outside

• Why code is not always the moat in the age of AI

• What history can teach investors about asymmetry and leverage

• Why AI adoption is not the same as AI value creation

• The difference between moving fast and understanding the game

• Lessons from Nokia, ASML, Amazon and Walmart

• How intangible investment and J curves can hide long-term value

• Why the best companies build compounding systems competitors cannot copy

• How investors can identify companies changing the game rather than optimizing the old one

Timestamps

00:00 Opening preview and introduction

04:00 The three ingredients of a System Gambit

08:49 Why code is not the moat in AI software

13:00 Skanderbeg and changing the rules of the game

17:00 Good moats, good narratives and asymmetric advantage

22:31 Microscope vs telescope as a lesson for AI

28:35 AI winners, losers and high dispersion markets

32:08 Signal quality, bottlenecks and why AI adoption is not enough

36:00 Nokia, agility and the failure to build a causal model

40:15 Why understanding the game beats speed

44:00 Intangible investment, the J curve and ASML’s hidden edge

49:54 The contrarian AI thesis behind The System Gambit

54:00 How to recognize a real System Gambit

58:27 Amazon, Walmart and multi-paradigm compounding

1:03:00 Prime, FBA and platform leverage

1:07:00 Walmart’s answer to Amazon

1:11:06 Closing thoughts and where to find Ritavan

And our guest today is investor, speaker, author of Data Impact, and now, what we’re really excited to talk about with him today, System Gambit, Ritavan. Welcome to Excess Returns.

Ritavan: Thanks, Matt. Great to be here.

Matt: A long time coming, my friend. So most investors, and we talk to a lot of investors, especially, especially when there’s a quality bias, and let’s be honest, it’s embarrassing to say we like low quality crappy things.

So when we talk about quality, people have a checklist, and one of the most common items on the checklist is, does this company have a moat? Brand, switching costs, network effects. You’ve got this book that says that question is almost besides the point. What’s the test that you’d actually run on a company instead?

Ritavan: Yeah. I mean, about the moat, right? You kind of picture it as if you can sort of measure the thickness of the fort wall or something. I think that’s very often the image, and it makes it sound as if you’re standing in front of a fortress, you need to get out your measuring scale, and then you write down, “Ah, okay, it’s that many meters,” and that’s done.

But it never is like that, right? And so I don’t really like the idea of having the moat as an entry on the checklist, because it’s not something you can just tick off. I think what is more important is if you want something defensible, the focus should be on figuring out if the business you have is a system that compounds in a way and at a rate that some other business you’re comparing it with just cannot.

And I think that is a much more fundamental idea that is independent of, oh, okay, does it have a moat, or is it growing, or whatever it is, right?

Matt: I mean, it’s a perfect explanation of where you’re coming from on this and part of why we wanted to talk about this. This is what sets your concept of this apart. We don’t normally talk about moats like this. I wanna get into the system gambit, and I want you to define it here at the top, because it’s not just this make a sacrifice now for the payoff later.

It’s not that traditional chess gambit definition that jumps out in most of our brains. What are the three conditions that have to be true at the same time for something to actually qualify as a system gambit?

Ritavan: Yeah. So just to take a step back, right? The word gambit is from chess, and in chess, what you do is you sacrifice a pawn, or you sacrifice some form of material, to gain a positional advantage that then allows you to either win the game or dominate the game.

And the system gambit is essentially generalizing that idea to systems and hence to business and investing. And the idea is the following. You are in a system but you wanna cross into a new system, right? So that’s the difference with chess. You’re not in the same game. You’re trying to go in a new game.

To get there, though, you need to sacrifice something in the current system. You want to gain a structural advantage, or you wanna build a structural advantage that then allows you to compound in this new system. And there are three properties that are super important. The first one is a self-improving loop.

That means every iteration of that loop has to structurally get better. If that’s not the case, you have a nice asset, right? You just have a nice asset. You don’t have the compounding. The second important thing is path dependence, meaning if it’s something that anyone else with just more capital can buy on the market, then you don’t really—then you haven’t really executed a system gambit, because it’s something that someone with more capital can acquire later on.

So it has to be something that is built loop iteration by loop iteration, right? That’s the path dependence. And I think if you have these two things already, right, you’re in a very good place. And the third thing, which is super important, is management logic antagonism.

So what that means is you are doing things in a certain way that if anyone else wanted to copy you, they’d have to stop or break what they’re currently doing, right? Once you have these three things, you have executed a system gambit successfully. If any one of these is missing, then it’s not a system gambit.

It’s something else, right? It can either be a good asset, it can be a wild bet, it can be a bunch of things, but for it to be a system gambit, you need all three.

Kai: So the gambit aspect, you’re saying it’s like a chess analogy, sacrifice a pawn to take a queen, but the key difference is that it’s not the same game.

You’re switching game. You’re switching systems.

Ritavan: Correct.

Kai: So is the sacrifice in this case, the cost, is it simply the switching costs of moving from system A to system B, or are there material costs in addition to that friction?

Ritavan: The goal is to be willing to sacrifice into a new system.

And sometimes that requires a whole bunch of things that are non-material, but it could also be material costs, right? But I think the important part is you are choosing across from the system you’re in to a new system. And the sacrifice is often very quantifiable in the existing system, right? So if you’re operating in a certain system, you have metrics to quantify how you are performing in that system.

And in a very large way, the sacrifice is often seeing those metrics collapse, right? Precisely because you’re moving out of that system. That is why it hurts, right? It’s not so much, “Oh, I need to buy this new thing,” or whatever. It’s much more, “I am performing really crap in my existing system.” And I think that is what is very hard to do for people.

Kai: Interesting. So I sit here as, like, a public markets investor. So I’m not inside the walls of a company. I don’t get access to all the kind of private information and stuff under the hood. So from the standpoint of a public market investor who’s looking at companies and stocks, how can I assess which companies are, first of all, attempting to execute this sort of gambit, and second, which ones are positioned to potentially successfully do it, right?

You mentioned these three prerequisites. What sorts of metrics, data, or whatnot would someone like myself look at if I’m trying to determine in real time? ‘Cause obviously we can look at historical examples and say, “Yeah, we know with the benefit of hindsight that company X successfully did this.”

But in real time, how am I supposed to determine which companies are in the midst of executing this transition?

Ritavan: Yeah. So I think looking from the outside, the most important thing you wanna ask yourself is, how hard would this thing be to replicate, right? Because the core idea underlying the system gambit, I just hold it up.

So this is a pre-launch copy, so it’s a bright, bright blue cover, so it wouldn’t be hard to find. But the subtitle is that it’s about finding leverage to unlock compounding value. And the idea of leverage, it’s not financial leverage, obviously. It is leverage in the old sort of Archimedes sense, right?

You want to get the most impact or value with the least amount of effort or pressure you apply. And the way you can try to figure that out or sort of reverse engineer this from the outside is to ask yourself, how much of this is leverage based on asymmetric proprietary strength of the company, right?

How much of this is being done in a way that others cannot just copy?

Matt: I’m pushing this, Ritavan, excuse me for a moment as I point this out to our good friend Kai Wu here on the show with us. Because Kai, last couple of years, like, the code is not the moat for software companies. I’ve heard this from you once or twice—or 30 times. That the underlying tech matters less than whether they’ve got brand, human capital, network effects, whatever around it. Any... As you’re encountering this three-phase net, this system from Ritavan, what does that make you think of? I’m just curious.

Kai: Yeah, I mean, I think what I’m thinking about is this, which is the moat—go back to that analogy—the moat is dependent on the game you’re playing, right?

So in a certain setting, for example, code can be a moat, right? If you’re the only person who possesses the ability to code, then you have a moat, or you as a company have a moat. You know, the obvious analogy today is that the world has shifted to a world where AI can write code very cheaply and quickly.

And so anyone can vibe code or has access to advanced coding tools. So the game has changed, and so perhaps that moat is no longer a moat in the world of AI. And so I think as we step back just from this one particular case, you have to be very thoughtful about what game are you playing.

In other words, what system, to take your language, Ritavan, is currently the dominant paradigm. And then the question becoming is, what is a scarce asset? What are the moats in that setting? And I think the context matters a lot.

And so I think what’s real interesting about your work is it kind of forces you as an investor to think through, you know, how is the world changing and what other systems are available to businesses, both through the technology and other factors. And then you ask the second order question, which is for each company, as you kind of go through the list, what assets do they possess today?

Are they optimized for system A or system B? Because obviously as an investor, you’re looking to kind of front run change and look for companies that might be priced based on the past paradigm, but other investors in the market might be missing how their assets can actually be quite valuable in the future, right?

So like we saw the other day, I think it was Getty, right? Their stock rerated because it was—they made a deal, I think, with one of the AI labs. But the idea just being that, hey, look, proprietary data could be really valuable for training, right? So that’s information that was of course available and valuable in the past, but perhaps is even more valuable in a world of AI where it can be used as an input to an AI model.

So I think looking for analogies like that, situations like that, could be a helpful framework for investors.

Ritavan: Yeah. Let me actually just jump in with a historical example, because the moat word obviously comes from fortresses and protecting a physical fort.

And there is an interesting historical setting where—this is during the Ottoman Empire. There’s an Albanian prince who’s been taken young, Gjergj Kastrioti, and he’s sort of, you know, trained by the Ottomans, becomes a cavalry commander. They give him a Turkish title, which is Iskender Beg.

And then at some point they send him back to run that part of what was then—what is now Albania, but that region back then. And at some point he figures out, “Okay, I think I’d rather fight for my folks,” and breaks away from the Ottomans. And you know, that region and all the regions around what was the Ottoman Empire then really struggled with the Ottomans, because they just had these vast armies, and they were able to marshal these resources, and people couldn’t sort of hold out against that.

And what Skanderbeg then does, interestingly, is—he’s got his main fortress. The Ottomans send a massive army. They lay siege to that fortress. And now comes the paradigm change, because it’s the same fortress, the same armies. Everything is the same, right? So if you go by your checklist, you’ve ticked all your boxes, and actually the outcome is very clear, which is the Ottomans eventually have a successful siege, and they take over the fortress.

What Skanderbeg does now is suddenly invert the paradigm, right, in some ways. So he says the fortress was seen to—like, it had to have a big moat, tall walls, et cetera. And the idea was you lock yourself in the fortress, you let the army lay siege, and you try to outlast the siege. Skanderbeg instead leaves a very small garrison inside the fort, gets the hell out before the Ottomans arrive, and hides in nearby forests and hills.

The Ottomans now lay siege, and now suddenly the vast Ottoman army that can actually be modularized and moved around, et cetera, on an open battlefield is fixed around the fort. And then he just keeps harassing them night after night after night. And I think this is exactly my point, which is if you have a checklist, a checklist is a list of binary items that has been abstracted out from a causal model of how reality is supposed to function.

If you change that causal model, right, if you change the rules of the game, if you change the game itself, that checklist per se is useless, because the checklist is just an artifact. And I think this is sort of the core thrust and contribution of The System Gambit, is stop trying to operate and look for patterns at the level of the artifact, right?

Go down—from first principles—and look at the system you’re operating in, because that is where the opportunities are. And it might not always be visible, right, from the outside what a business is doing, but I think what you can do from the outside is just ask yourself, “If you were in their shoes, what would you do? What do you see?” So do not operate at the checklist level. Go a level down.

And if I go back to how the whole checklist thing exploded and became so popular with investors, I think it’s Mohnish Pabrai who, at least as far as I know, started popularizing it in my world. I’m sure a lot of people were doing that too.

But it’s based on Atul Gawande’s book, right? It’s—I forget which year, somewhere around 2008, 2012, somewhere around there. So I was at the end of high school, and I remember I was excited reading it then too. And what Atul Gawande, who’s an excellent surgeon, essentially observes is, hey, surgeons, pilots, a bunch of people are using checklists successfully, right?

That’s his core thesis. But then you have to ask yourself, what is the causal model on which a pilot operates or an aircraft operates? What’s the causal model on which the human body operates, right? And all of these are based on hard science, more or less, right? So the human body does not change based on someone’s social media post, right?

The physiology of the human body doesn’t change, to a large extent, as far as surgeries are concerned, right? The same with aerodynamics, right? Mood of the passengers does not change, you know, the turbulence outside, et cetera. And so the causal model is what is really the underlying sort of source for that checklist.

And if the causal model is fixed, then it makes sense to rely on checklists. But when you have the opportunity to either change the causal model or see it change, et cetera, then the checklist as an artifact itself should be questioned.

Matt: Break apart good moat versus good narrative. Because I think what’s interesting here is we have the checklists, we have the stuff we can point at, say, “Here’s the data, here’s the Checklist Manifesto version of this thing.”

But then also, sub-fascinating point, Being Mortal, the later book by the same author there, when he talks about grappling with the health of his dad. I think those books belong together, personal opinion. So anyway, good checklist versus the actual narrative for a moat. How do you separate those two things?

Because I think you’re dancing right on the top of it here.

Ritavan: So I think the narrative question is, for me, independent of a specific structural aspect of the system, right? I think you can take anything and try to get the best possible narrative out. So I’d remove the narrative out of the picture for now.

Speaking of the moat, I think the important thing is to ask yourself—like, if we go back to that example I just used with the small Albanian army and the large Ottoman Empire, and I have a bunch of these in the book, right? So I’m trying to use those that are not in the book. The book is just full of examples across history, across the world as much as possible.

But the idea is the following: it’s always the same resources. That’s the same army. One is bigger, one is smaller. It’s the same David-Goliath kind of setting. But the idea is: how do you win despite not having the most resources or the largest size, et cetera? And all through history, in every aspect, whether it’s sport or business or music, whatever, right?

You keep seeing this thing happen, because otherwise, logically, if there were no asymmetry, the largest guy should always win, and it’ll converge to a point where there’s only one large thing, right? So clearly, there is a mechanism in the world that allows you to leverage asymmetry, right? To go into a new system, and then that new system has a structural compounding loop, right?

With self-improvement, with path dependence, and with management logic antagonism that allows you to really diverge, right? So the original system continues in the direction it was going. The new system goes in a different direction, and those two aren’t really compatible, meaning you cannot have this and that at the same time, and that’s why the sacrifice, right? Now, to go back to this Gjergj Kastrioti or Skanderbeg example, Skanderbeg could not fight in the Ottoman paradigm in which he was trained by staying in the fort and defending the fort based on the moat and the walls of the fort and win. He had to sacrifice the fort in the sense of put a much smaller garrison, let the Ottomans lay siege to it.

Yeah, those guys probably had a really bad time inside the fort, the poor guys from the smaller garrison, but it allowed him to win the war, right? So you have to sacrifice something. So to the Ottomans, it looks stupid. They say, “Oh, this guy’s such a loser, he’s run away.” But he doesn’t mind being called a loser short term if at the end he wins the war.

And I think that is the point. Very often, David with the small sling, right, aiming at Goliath looks like a fool, looks like a loser, but he’s precisely using that asymmetry. And that’s sort of—it’s almost like a celebration of thinking about leverage and asymmetry, and this is what The System Gambit is about.

Kai: I’m always a fan of the underdog, so I like that. And I think you make a good point that if it were always the case that the big guy always wins, then we’d have one company, right? So what you’re saying reminds me of the work by—which I’m sure you’re familiar with—of Clay Christensen and The Innovator’s Dilemma.

Like, why is it that the big companies don’t do the gambit themselves? I’m sure there are things holding it back. Can you maybe talk to that a bit?

Ritavan: So I think Clay Christensen describes a very specific kind of situation, right? Where you have a large incumbent with high-end products, and then you have a new entrant with a low-end quality product.

So it’s a very specific description of a similar mechanism, right? What I try to do with the system gambit is to say, okay, Clay Christensen is one example in a concrete sort of market setting. What I try to do is, if we take a step back and think from a first principle systems thinking perspective, what are all possible gambits?

Like, can I think—and I think, Kai, you would resonate—always try to find some kind of eigenvector basis, right? Can I find a bunch of dimensions that are as independent as possible? And obviously, they’re not all orthogonal. They’re all independent, but they’re not all orthogonal. I would have ideally had them orthogonal.

And the idea is: what are these mechanisms, right, that underlie these type of phenomena? And the book has eight such system gambits, right? And you spoke about changing the paradigm, the goals, et cetera. So the first system gambit is actually paradigm change and goal displacement, right? And so each gambit focuses on one mechanism.

That does not mean that you have to execute them independently. In fact, some of them have strong relationships or causality involved. But the idea is I’m giving you eight ways to look for asymmetry and leverage asymmetry. And then obviously, based on your context, based on your setting as an investor or operator or both, you want to ask yourself, what will give me, you know, the highest, sort of the greatest, bang for the buck, right?

That’s the question to ask. So it’s not one of those close-your-eyes, follow and tick three boxes type of thing, because I think that’s just trivial. That’s a farce. That’s not how things work.

Kai: In your book you tell the story of the microscope. And so over two centuries, it generated what you said, quote, “visibility without understanding,” end quote, before anyone really had built the standards around it to interpret what it was that the microscope was showing them.

So talk to me about this metaphor. Like, why is this the right metaphor for thinking about how a lot of companies are now using AI and AI dashboards today?

Ritavan: Yeah. So the microscope is actually a very interesting example, right? It’s a mad exciting technology when it gets sort of invented, right?

So it’s Robert Hooke in what’s England today, and Antonie van Leeuwenhoek in the Netherlands. Around the same time, they come up with pretty different devices, different looking devices that in principle are more or less the same optically. And what happens is now suddenly the entire world—not the entire world, but, let’s say, many people who could afford that—could see microscopic stuff.

And so everyone is looking at these little things jiggling and structured, and they’re drawing and hallucinating, right? I mean, that’s what they’re basically doing, because what happened is you could have the same sample, you could have two people looking at it through the same microscope at two different points in time and see something completely different.

And it’s a bit like with a lot of Gen AI today, like, essentially you and me can send the same prompt to the same model at two different points in time and get two different answers, because these are inherently stochastic systems. But what happened with the microscope was, in fact, that lighting wasn’t standardized, that the preparation of the sample that was being studied wasn’t standardized, et cetera, and those things had to be sort of sorted out.

But even when that happened, the bigger problem was there was no causal understanding of how physiology worked, how human physiology worked. And so even though, over time, you had accurate visuals of stuff, what would you do with them? You know. So the microscope starts out as an exciting technology.

For about a hundred fifty years, you have hallucinations. After about a hundred fifty years to about two hundred years, you have new techniques that come out for standardized lighting, standardized sample prep, et cetera. So at least there is objectivity in what you’re seeing. But there is still no revolution in clinical microbiology and physiology, because there is no scientific model to understand that.

And this went pretty viral when I shared this article. It was before the book came out sometime last year. And a friend of mine who’s a PhD in math, I think from CMU or Duke, I forget, smart guy, et cetera. And I shared it with him, and he just shot back on WhatsApp saying, “Yeah, cool, but what about the telescope dude?”

And I said, “Yeah, true, actually. Let’s dig in,” right? So I hadn’t thought about it. So I said, “Okay, let’s look up what the telescope is.” So again, now from first principles, the microscope and the telescope are essentially the same technology, meaning it’s two refractive lenses in a tube, right? Except unlike Hooke and Leeuwenhoek—Robert Hooke in England—Galileo Galilei points it at the stars.

And within a super short period of time of a few months or years, you have a complete revolution in astronomy, right? Our understanding of how the universe worked as a species just changed overnight. And the question is the same technology in two different fields—let’s just go abstract and abstract it out and call them systems, right?

So you have the biology system on one hand, and you have the astronomy system on the other. The same technology, essentially the same technology, in two different systems, two very different outcomes, right? One stagnates and hallucinates for two hundred—a hundred fifty, two hundred—years. The other one has an instant revolution.

And so that’s—I think the point is the tool is great at whatever level, in the sense of, we’re always obsessed of, “Oh, this model could do that. That model could do something else.” I mean, I’m not saying it’s not interesting or not relevant, right? But that’s not the end of the story, right? So irrespective of what AI or any algorithm or model or technology can do, the question you wanna ask yourself is, what’s the system in which it’s operating, and with what system does it come into contact?

And how does that system need to operate so that you can create value? And now, what’s interesting is astronomy, right, had a causal model, which was the geocentric model, right? So everything is turning around the Earth, et cetera. With the microscope, you have an observation that directly disproves that model.

And now suddenly you have the question saying, “Okay, then what’s going on? If it’s not what we thought.” Nice thing is you had Kepler and Kepler’s mentor, right, Tycho Brahe, noting down observations, et cetera. So you had a large data set. You had a smart guy who fit a model on that data set, with Kepler’s model of the world.

And now suddenly you have these quick iterations between observed reality through the tool that’s empirical, that’s verifiable, and that’s trustable. Meaning Galileo Galilei could look at the moon sitting in Italy, and an astronomer sitting in Paris could do the same thing. They would see more or less the same thing, right?

They’re not seeing two completely different things. And you have everything needed to improve the causal model. And when something does not work in the causal model, you tweak and change, et cetera, until reality and the causal model essentially are as accurate depictions of each other as possible, right?

And microbiology doesn’t have that. And I think that is the point in business, is you wanna ask yourself, irrespective of what the technology can do, in the sense of—or given whatever the technology can do now, let’s not wait and think what’ll happen in ten years, right? Will AI be conscious or whatever other absurd questions people ask themselves.

But ask yourself, given what it can do today, right now, right, what is the system in which it’s operating, and how does it fit into that system, and how do I allow it to compound value? Because that is what you’re really interested in.

Matt: Kai, this makes me think of your—when you’re talking about the high dispersion environment we’re in right now and winners and losers, and to Ritavan’s point, it’s like we’re breaking out in two different directions.

We have this technology. It applies maybe to tech and growth companies in one direction and hard asset and other companies or capital-intensive industries in another direction, creating a winner-loser class in each. I’m just curious, how does that metaphor land with you and what you’re seeing today?

Kai: Yeah, I think that’s an apt description of the K-shaped AI economy, right? We’re seeing AI is driving pretty much all stock returns. I think I saw a chart where it was like 100% of the stock market returns have been driven by AI stocks over some trailing period. But of course it creates winners and losers.

And what we’ve seen historically in these disruptive periods has been this effect, right? Where the market will quickly look around and say, “Hey, what is perceived to be a winner and a loser?” And then shoot first, ask questions later. I think what’s interesting is that when you actually look at how things have played out subsequently, yes, many of the folks who are presumed to be losers actually end up becoming bankrupt, but many actually recover, right?

So I think that’s the piece that people miss. I think going back to what you were talking about with the microscope, it connects to that too. And I think a lot of people, when they think about AI, they’re obsessed with, like, the technology itself. Oh, this is such a cool model. Like, it’s got this many billion parameters.

It scored this rating on this metric, like on these various tests. It’s, like, the best model. But what matters more than the model itself, I think, is often two pieces. So one is, like, the complementary assets, like the harness, they call it, right, around it. Like, why was Claude Code such a breakthrough?

Well, the model was better, but it was more around, like, the instrumentation they gave it access to. Now it’s running on your local computer, on your terminal. It can look at your local files. It can do tool calls. It can run Linux commands, right? So that was really important. And then the second complementary aspect was just the setting, right?

So an AI model, we know what it does. It’s kind of a stochastic parrot, right? It’s a fancy autocomplete. And so in some settings, that’s actually not that useful, right? Like, I wouldn’t use it as my therapist, for example. There’s too much downside there.

But when it comes to code, software development, actually it’s the perfect setting to be using an AI tool, because it’s closed loop. Will it compile? Will it not? It can check itself, and there’s plenty of data obviously to train on. There’s very little, like, implicit knowledge. All the information by definition is codified in your, say, GitHub repo.

And so I think the analogy here, just to bring this back, is it’s not just the model itself. The technology itself is obviously important in any paradigm shift, but it’s what are you building around it? The harnesses, the complementary technologies, and then what setting are you applying it to, whether it’s astronomy or biology.

Sometimes certain fields are ripe for a revolution and others just are not the correct place to use the tool, right? And this goes back to the idea that to a hammer, everything’s a nail. So you have a cool AI model. Great. I’m just gonna use it on everything. That’s actually not the right approach.

And I think for a lot of companies, to bring it back to the business setting, I think that’s the mistake that’s being made. A lot of companies are like, “All right, cool. We have AI now.” Like, it’s time to bump—pump up our stock price and talk about AI disruption and AI transformation. So I’m just gonna spend much, much money on buying AI. But they aren’t thinking enough about what are they actually trying—problems they’re trying to solve, and is this the right tool to solve those problems?

Ritavan: And, Kai, just to pick two words that you said. So it’s really about signal quality at the code bottleneck.

I think that is the question, which is, are you measuring? Are you closing the loop, right, at the right place? And how well are you able to measure that? That is what you want, right? Because a tool in a system—you can get a better tool later on, or the capabilities of the tool might change, but the question is: what is it directed at, right?

And I think that’s again the microscope versus telescope thing. Are you pointing it at, I don’t know, plant tissue, or are you pointing it at the stars, right, or at the moon? And I think that’s really the question. If you’re pointing it at the moon, it’s an objective, independent thing that you cannot change or shape, that everyone else can also point it to.

The lighting is standardized, et cetera. So there’s just very high signal quality at the core bottleneck, and every time you develop a model, you can check and test a model. I mean, a causal model of the universe, you can check and test and get hard feedback of whether you’re right or wrong.

And you can’t do that with a microscope initially. And I think that is—if we just sort of take that lesson to business and ask yourself: yes, okay, you deployed an AI tool successfully, or you adopted an AI tool, because I think the big dashboard KPI now is AI adoption, right? But adoption per se is meaningless, right?

Because just because you’re using something—you could be using a microscope or you could be using a telescope, right? And it’s always the same technology again. So you can drive adoption to a hundred percent, except the telescope is creating value and the microscope isn’t. And so you wanna ask yourself, have you closed the loop?

Do you have high quality signal at where the loop closes, and is that pointed at the core bottleneck? Because that is where you’re gonna capture value. Everything else is meaningless.

Kai: Right. This is kind of the whole pushback, the token maxing phenomenon, which was a brief flash in the pan where—

Ritavan: It’s already gone, yeah.

Kai: You had CEOs—

Ritavan: It’s gone.

Kai: I think that was—

Ritavan: It’s gone.

Kai: ...like a one-month thing. But it was so, so crazy to me. It’s like, the metric should not be how much money are you spending on Anthropic. Like, that seems—

Ritavan: Yeah.

Kai: ...crazy to me.

Ritavan: There’s a bit of Goodhart’s—I think it’s Goodhart’s law. What is it? Like, when a metric becomes the goal, then it stops being a good metric. And I think you have a bit of that too, right? Which is, be very, be very smart about what you pick as the metric, because if you just pick something stupid, you’re gonna get awful results. It’s just logical. There’s no—

Kai: Right. You’re incentivizing waste. Surprise.

Ritavan: Right. Exactly.

Kai: Okay. So our next question. I wanted to go to an analogy between two different historical companies. So first was Nokia, right? So they saw the smartphone coming, they documented it, and then sat on it. They did nothing.

And then on the other hand, you had ASML, right? Which, as we all know, was very successful. So what was the difference in how these organizations were built that led to these widely varying outcomes?

Ritavan: So I think the most important thing is to say, again, I’m looking at this from the outside, right?

So because you ask how do you see it from the outside, and so this whole thing is based on what I saw from the outside, because I was not inside either of these two companies. So let’s start with Nokia, because if you see press releases from the CEO, their kind of strategy documents and investor calls, et cetera, you see their focus is the following, which is, we want to be as close to where things are happening.

We want to log, measure, document as soon as possible what’s happening, and then we will adapt. That’s broadly their pitch, which is the sort of lean, agile, whatever, just quickly change. Like, when things change, you try to change as fast, right? So you try to align your clock speed to change.

And what happens there is then that they’re in Japan, they see what’s happening in Japan—and they see what’s happening later on with the iPhone. Except you can collect the best possible signal, but if you do not have a meaningful causal model of how you, in your system, meaning in your market with your customers, et cetera, et cetera—like, what is the game you’re gonna play with that signal, right?

If you don’t have a causal model of that, if all you’re doing is reacting to someone else’s moves, right? Oh, the Japanese player did this, let me try to copy. Or the iPhone does that, let me try to copy. If that’s all you’re doing, you’re not really playing a game. You’re just—like, you’re aping what others are doing.

That cannot be a winning strategy. And that’s kind of what Nokia did during that phase. And it’s pretty well documented. Like, you have all these interviews with the CEO, and they’re really selling this story of it’s all about speed, it’s all about agility, et cetera. And they were amazing at speed and agility.

Like, if you see the way they’re reacting, commenting, and documenting what’s going on, it’s perfect. The point is you don’t have a causal model, right? It’s like if I see LeBron do something, I copy that. Then I see Mbappé do something, I copy that. What are you playing? Are you playing basketball? Are you playing soccer?

Like, what are you trying to achieve? That’s, I think, the absurdity of agile, right? You can be very agile, very dynamic, very fast-moving, but it’s not going towards any meaningful goal based on some causal model of something, of a game you’re playing, right? Of a system you’re operating in.

ASML, on the other hand, very interesting, because—and from the system gambit angle—because a lot of, like, you see this insane technology, right? Or, like, almost like a portfolio of technologies. Like, every machine is like hundreds of millions, and the most expensive ones around four hundred million or something like that.

And you think, “Ah, okay. These guys, they’re heavy in R&D,” which they are. They’re producing a super advanced machine. Their core capability is manufacturing that machine and then selling it, right? That’s their core business model. I think what’s underappreciated is the fact that these machines are so sophisticated and subtle and sensitive that you can’t just, like, sell it to TSMC or sell it to Samsung or sell it to Intel, and then they just magically start producing.

I mean, you’re not selling a kitchen knife or something, right? It’s just way more sophisticated and sensitive. And so what happens is the real capability that ASML has built is a causal model of how their particular machine in a particular fab operates on a particular design at a particular point in time with a particular set of environmental conditions.

That knowledge, that institutional knowledge, that is what’s valuable, right? And that is unique. And that’s why—I mean, I think the underappreciated thing often in business is there is no easy way to put your understanding of your causal model, of your system, of the game you’re playing—you cannot put that on the balance sheet. Like, there is no clear way to financially map your understanding of the game and the system on a balance sheet or on an income statement or something. And I think that is why a lot of people aren’t incentivized to do it. And that’s unfortunate, because that is where the edge lies.

It’s not in agility, it’s not in speed, it’s not just in moving fast, right? It’s about having an understanding of the game. And you see that in all games, right? I mean, there’s just this recent clip with Messi where he’s barely running, because you wanna be at the right—like, once you understand soccer well, you wanna be at the right place at the right time and do the right thing. It’s not about how much effort you put in. It’s not about how many steps you clock, right? Those are the rookie KPIs, right? And so you can ace the rookie KPIs. But the question is, do you wanna be doing that, right?

And is that going to help you win?

Matt: Be less aping. Be more Albanian-ing, something like that?

Ritavan: No, build a causal model. I think the really big takeaway is, in every field, right? Whatever—if it’s sports, if it’s business, if you’re building an empire, if you’re trying to win a battle, whatever it is you do, right, outside of business—you’re always building a causal model, and the one with the better causal model has a better foundation to do all the other stuff.

And I think this is a very underappreciated thing very often in business, both from an operating and from an investing perspective. And—

Matt: When we’re looking at those companies, though, I have to feel like you’re making that assessment, and back to the Albanian scenario, it looks like you’re losing.

Like, you’re making the investment, you’re deliberately not playing game A because you’re trying to play game B in the new system. And by deliberately not doing that, it’s hard to look at that and discern between successfully playing a new game and playing a failing strategy.

Ritavan: Yeah. And I think there’s a third angle where, if you had one of these short-sighted private equity players sort of acquire ASML, I think you could really cut a whole bunch of stuff and max out on EBIT for three months or for a quarter, perhaps a few quarters, and kill the company, right?

I think that’s very much also a common playbook, is you just—the first thing when you see a golden goose is you slaughter the goose and you sell the flesh, right? That’s the first move that every pathetic operator really does, and it looks amazing for the quarter. The question is what happens after?

And I think the smart move is, can you take a step back and figure out how you can get that goose to lay golden eggs and hopefully hatch some of those eggs to get more golden geese, right? That’s the game. And I think this is, to your question, right, Matt, is do not look at the financial value of what is being sold, right?

Because if I sell the flesh of the golden goose, I might be able to get a much higher payoff than if I just sold one or two eggs from the golden goose, right? The question is, what is driving a certain gain or drop in financial outcomes?

I think that matters so much more. Now, if we just take another example from agriculture or something, if you inherit or you buy a field which has crops on it, right? The first thing you can do is just cut all the crops and sell them right now. But then next season, you’re essentially gonna be broke.

And so the question is much more, what is that compounding loop, right? Have you figured out a causal model of what your field and agriculture in that place can do? How do you max out on that? Where’s the hidden leverage, right? And what is your causal model that allows you to create value with that field, with the resources you have, but by being smart about it and doing things differently and not necessarily first just cutting and selling everything, which would be a typical one-quarter private equity play, or just doing what the farmer beside you is doing.

And I think those are the two defaults where people go to, and those are problematic, because I think there is a whole range of other things that you can do, and it’s just intellectual, I would say, laziness not to explore that.

Kai: Yeah, I mean, I think that’s exactly right, that optimizing for short-term performance—whether as an operator or if you’re an investor looking for companies that have traditional quality metrics. Oh, yeah, we have high and stable EPS, right? Like, that can oftentimes miss the J curve, right? Where—so from my standpoint, intangible investments, this is kinda where a lot of my work focuses—they tend to be interesting because, and underappreciated by many investors, because they have this J curve profile where you spend the money on the R&D if you’re ASML, and it doesn’t actually lead to a payoff in the next, say, year or two.

And in fact, may actually hurt your earnings because you’re now spending resources to invest. And it shows up over the next ten years and perhaps is what allows you to be such a great company. But I think for a lot of investors, they see that, they don’t like that. That’s like a negative attribute where it should really be positive.

And I think what’s interesting about your work is you’re taking that one step further, and you’re saying it’s not just making an investment, it’s making an investment in finding the next—whatever the next paradigm or system is that can generate this compounding. So it’s almost like a super investment.

And it all goes down to the same underlying concept, which is this J curve, or, like, trying to defer, defer the rewards—just the idea of investing.

Ritavan: Yeah. And in many ways, I think with ASML also, you would think it’s a production company, right? They’re producing these machines and selling them.

In many ways, actually, what they’re doing is some form of contracted delivery, right? Basically, they’re guaranteeing your fab—like, if you’re a TSMC or an Intel, they’re gonna guarantee that you would be able to produce this many chips per hour at this level of quality, because it’s their engineers embedded on the ground to make sure their machine can deliver that.

And I think that is something that—I mean, it’s irrespective of how much, let’s say, they get paid in terms of service fee to do that. I think financially, irrespective of that, just the fact that they’ve got boots on the ground and know what’s happening in the fab, that knowledge institutionally is impossible to replicate.

There’s no way—

Kai: And we’re seeing a resurgence in the forward deployed engineer these days, and I feel like that’s the same model, right?

Ritavan: Correct. It’s not just a product. But ASML did it probably fifty, sixty, whatever years before Palantir, and they didn’t spin that good a story.

I think that—I mean, they’re not necessarily dominating the narrative, I think. And that’s kind of the point, is, if you only look at the current financial statement number of something now, that just misses the opportunity that the system gambit, I think, gives you.

Matt: So I think you successfully rejected the business book checklist, which I applaud now.

I mean, I applauded when I read it. That’s part of why I wanted to talk to you about this book, because you gave me so many examples of non-financial history stories that apply strategically to how we think about corporate strategy, how we think about what does ROI look like in an age of AI right now.

I’m just curious, how conscious was that as a choice? Did you know you were gonna go into all these weird historical places when you started the book as analysts? Not—

Ritavan: Yeah, not at all. So I think it started out—like, my previous book, Data Impact, had a six-step framework, and the second step in the framework was the word leverage, right?

And a lot of readers came back to me and said, “You got us excited. You’ve now got us hungry. You served the starter. Now where’s the main course?” And I wasn’t expecting that kind of reaction. But that really kind of irked me. I had this itch then, and I had to scratch, because I felt I did not have a deep enough answer that I was proud of.

Like, what I wrote in Data Impact in the leverage chapter was, hey, focus on your unique proprietary strengths. Focus on your physical, non-digital assets. Focus on your brand, on your reputation, on a whole bunch of things that typical business books also have.

And I said, “Hey, you bring these together, you know, tangible and intangible. You combine them, you leverage them, and you create value.” But I think anyone could have written that. I felt like there wasn’t that unique take on this, and the question was, what is the mechanism?

Because I can have all these assets—like, you can picture two companies who have the same strong brand, who can have the same intangible other assets, who can have the physical footprint of stores or whatever, right? So they can have all of this and yet you can get two very different outcomes over ten years, right?

So the question is, what is the mechanism that allows you to take all of this and leverage it successfully? And from that starting point, I think it was really a journey of just curiosity and looking around and not shutting stuff out. So, like, not digging into and within just the sort of narrow business literature or field.

But to say, “Hey, if something has a certain universal characteristic, like, can I find an underlying mechanism that appears everywhere, right? Broadly.” Then you know that you unlocked something that’s meaningful, something that’s durable, and something that has a certain truth to it, right?

That it’s not just, oh, I kind of overfit these three data points type of thing. Because I think that’s easy to do, and a lot of playbooks and checklists and a lot of that is typically this, and I think you can sell and hustle those type of things short term. But I think if you want to really unlock a hidden structure, you just have to look more widely.

And, you know, one thing just kept leading to another. As I traveled, met people, saw stuff, I just—every time I would take a step back and say, “What is the underlying mechanism here? What is the system? How is value being created or not? And where is the asymmetry? Where is someone able to do more with less resources?”

And then you just keep seeing these patterns all over. And then you just have to kind of order your thoughts, find commonalities in those patterns, extract these principles, and then just package it into a book. And that’s broadly, I think, the work I’ve done.

Matt: The last chapter of this book—and I think this is so important for where we are right now. We’re post the SpaceX IPO. We’re in the middle of whatever’s gonna go on with that.

We’re before Anthropic, OpenAI, whatever these other companies coming public is about to happen. We’re in this boom of money coming into the market to develop these ideas. We’re gonna find out who the real Albanians are. That’s basically what I’m thinking. You still feel like you have a pretty contrarian take.

I’m thinking of the last chapter in the book. Can you lay out that thesis, lay out what you’re seeing right now where we are?

Ritavan: Yes, I think the core point is the following, which is that in many ways, AI is a general purpose technology, but for the first time, it’s a general purpose technology that you can bolt onto whatever your system is, right?

So whatever paradigm you’re operating in. So if you’re an industrial business, then you sort of bolt it onto your industrial paradigm, and you build dashboards, and you try to predict and measure stuff here and there, right? If you’re a digital company, meaning like a software company, then you apply it within that in terms of how can I validate a use case faster, et cetera.

If you’re a platform business, you apply that to increase interactions, to tweak your recommendation algorithms and a bunch of stuff, right? And so the core idea is the following: because the data that you have is generated through your existing business, through your existing system, that data by definition is within-system, within-paradigm data.

If you bolt AI onto that, you’re essentially training within paradigm, within system. And the core idea of the system gambit, and especially the first system gambit, is about paradigm change and goal displacement. That is where there’s opportunity, right? Because if you stay within the same system, you’re again playing the resource game, right?

Who has more resources, and then you know who has more resources. If you want to get asymmetry, you have to see differently. It’s the same system physically perhaps, but your take on that, the paradigm you choose to play in, right, the game you choose to play in, changes the outcomes. And I think the biggest danger with AI and AI adoption today is if you do it in a naive way, if you do it in a way that anyone with a credit card and an API key can copy you, right, then where is your edge?

And more importantly, you’ve just increased your cost without any knowledge or without any understanding of how you’re creating more value. And this within-paradigm optimization is exactly how you optimize yourself to irrelevance. And especially if you’re one of the big, large players, you need to be extra careful, because it’s usually the underdogs that find the leverage and that eat your lunch.

Kai: So then what is the system gambit in this case? You’re saying what it’s not. It’s not stay within this existing paradigm, slightly optimize your business using AI. If it’s not that, what is it?

Ritavan: So it is defensive, right? So if you see someone do that and you feel okay—for some, they have already optimized within paradigm a little bit and, I don’t know, increased EBIT by one percent sustainably—then you can be a follower there, right?

Don’t try to lead that trend. Just see what people are doing, and then you just follow, because essentially you are letting them de-risk whether something works or not. And that’s actually why I call the system anti-gambit. So if you only do this, right, then you’re kind of doing the opposite of what the gambit should be about.

In a system gambit—and again, there are eight system gambits in the book, it goes one by one through each of them—it gives you essentially, like—if we go into fitness and health, I’m not proving anything. That’s why you could always say, “Oh, you’re using mainly examples that work,” and so you’re not really proving the point, et cetera.

But I’m thinking at it much more from a practical perspective. I’m a bit like a fitness trainer giving you a bunch of reps you can do and variations on the exercise. So each gambit, think of that as like a composite muscle group exercise, and I’m basically saying, “Hey, you can do the squat this way.

You can split your legs a bit more. You can squat on one leg, or, like, stretch this leg or whatever.” So I’m giving you a bunch of things in each chapter. You just repeat and get the reps in. And the idea is that you start then pattern matching stuff in a way that AI cannot, because AI is trained on granular data within your paradigm.

And so the idea is I’m giving you a way to concretely, practically look for patterns based on a vast and diverse range of examples across all of the eight chapters. And with that, you can then look at your specific context, right? Your asymmetric proprietary advantage, et cetera, and ask yourself, “Which of these system gambits is for me now relevant, right? And how could I execute it?” Right? Or if you’re an investor, who out there, seeing from the outside, seems to be executing it? Because the thing with the J curve is, right, that’s just a descriptive artifact of what’s going on. The question is who is seeing just a drop in performance, or, like, a value trap, I guess, in the investor jargon, right?

Who is seeing just some kind of drop and it stays there, and who’s going to unlock a compounding loop, right? So who’s building a structural condition that compounds? And then that is the core contribution here, is once you go through the eight system gambits, you have eight concrete things, and you’ve done the reps by the time you’re done with the book, so you start seeing these patterns.

Matt: Kai, is there anything in your work on intangibles where you see this methodology map over some of the historic work that you’ve done?

Kai: Yeah. So a lot of the work I’ve done as a quantitative investor has been trying to understand, like, base rates. So for example, if there is a disruption occurring in e-commerce, and you have all these brick-and-mortar retailers, and some of them are trying to become Walmart and successfully navigate this, but most fail—like, what’s the success rate in that situation?

And then the second question, of course, being less about the fundamentals, more about what’s priced into the market. So as an investor, you pay the stock price that’s available to you at the time. And so if stock prices are down, say in software stocks today, what’s the chance that they recover versus not?

And do we see more dispersion? And historically, the answer would be yes, where many of these companies are zeros and others end up being great generational buys, where you buy a stock at a P/E ratio of eight that goes on to become the next Walmart in its class, right?

So the answer is a few things, historically, right? So obviously it’s the use of the technology itself, right? Are they adapting to the new paradigm? But it goes beyond that. I think, you mentioned actually some examples of, like, proprietary data.

Obviously, AI is only as useful as the data you train it on. It’s garbage in, garbage out. And so to the extent an enterprise and incumbent has a lot of interesting customer data, or whatever is applicable to its domain, that could potentially be a very valuable asset in the age of AI. And you kind of go down the list, and it does turn out that, like, complementary assets have continued to, at least historically, hold value.

And of course, things change, right? Moats, assets, brands may be valuable one day and less valuable in a different context. But on average, shaken across the thousands of stocks over decades, these complementary assets, generally intangible but also tangible as well, have tended to be decent.

I mean, I guess one question I would spin back to you is around—it is—you know, you frame it as kind of binary. There’s gambits and then anti-gambits. Is there a middle category where it’s like, “Hey, look, you’re an industrial business. You have a great business. You’re a market leader in whatever category you happen to compete in, and now AI is here,” right?

Like, maybe it’s fine for them to just say, “We’re gonna take incremental advantages of AI without trying to completely reorient our business.” It’s fine. It is a sustaining innovation. Like, in the internet example I just gave, if you’re a retailer, then yeah, you definitely need to figure out e-commerce, ‘cause if you don’t, you’re cooked.

But there are plenty of businesses that just used email to be a little bit more productive. Is it fine to be in that final category? Like, should everyone be trying to execute a system gambit at the same time, knowing that not everyone will be successful in doing so?

Or is it fine to kind of just not even play that game?

Ritavan: So since we spoke about retail and Walmart, I’ll just lay out a thesis, and I think that’ll answer the question, hopefully—perhaps not with one sentence, but with a lot of context. So if you go back in the financial press, like, about a dozen years ago or so, or two decades or so ago, you had initially the phenomenon that Amazon, like the e-commerce business, was not making any money, right?

And so you have people writing, “Oh, this is one of these dot-com dinosaurs, and I don’t know why they’re not going bust. And it’s not really a business, and what are they doing? They’re selling books now. They’re selling everything under the planet. What nonsense. That’s not a business model,” et cetera.

What Amazon is doing in the background is actually executing the system gambit in multiple paradigms, right? So Jeff Bezos speaks about the flywheel, and I forget who the author was, but there was this idea of the fly—

Matt: Jim Collins.

Ritavan: Jim Collins, exactly. So Jim Collins, I think, held a workshop in the late nineties, and then Jeff Bezos really liked that and said, “Everything we do has to be based on a flywheel.”

And a flywheel is basically compounding, right? So it’s like every loop, every iteration gets better. And so the flywheel is essentially a compounding loop. And what Jeff Bezos did, obviously, is then to say, “Okay, everything we do has that feature,” which means you have to sacrifice short term on the metrics, metrics like profitability, to build that compounding loop.

And so essentially, they executed probably the greatest system gambit of the early two thousands, and in many ways until now. But what they’re doing is operating on three paradigms. So they’re, on one hand, building out their warehousing infrastructure, which is an industrial style business.

Which is you want as much predictability as possible. You’re moving physical inventory around, you need that physical footprint for warehouses. On the other hand, instead of building out the physical footprint, like stores, to sell the stuff, what they do instead is to operate in the digital paradigm, right?

So it’s the Amazon online store, right? So you go and order there. That is driven on learning loops, right? So you want to collect as much data, user data, as possible to understand what users want, when they order what, et cetera. So it allows you to price things dynamically, et cetera. It allows you to offer discounts.

It allows you to recommend products better. And now comes the interesting part. So you’ve built an industrial warehousing, logistics business operating on data and algorithms. You’ve built an online storefront operating on data and algorithms. That in itself is cool, because that’s two flywheels running.

Now comes the cross-system reinforcing feedback loops. Because I understand user behavior really well, I can actually start shaping it, right? So now if I have a product that’s on inventory in my warehouse, I can discount it a little more aggressively, right, to sell that product off and to improve my inventory levels. If I have a shortage of a product, I can jack up prices. And so now, by building a cross-system feedback loop, right? So you’re taking two paradigms in which there is already a feedback loop running, a compounding loop, and now you’re crossing them.

And then comes the platform paradigm, where you—with Fulfilled by Amazon, with FBA. So now you open it to all sellers, right? And so you’re now saying, “I already have a physical footprint. I own the customer on one hand. Now, why should I produce and sell my own stuff? Let me just offer that infrastructure to all these people who want to sell their stuff.”

Except you have leverage on them, because they can’t just leave, right? Once they commit to using your stuff, they are playing the rule on your terms, and you can see what products are selling better. And then with basics, you can essentially put them out of business, which Amazon actually did. Now, let’s leave out the ethics and whatever you think about that.

It’s just worth understanding the insane leverage they unlocked by building within-paradigm compounding loops and then crossing them and, you know, making them cross across paradigms and compound. And with Fulfilled by Amazon, now you understand user behavior. You’re selling someone else’s products.

You’re not limited to your own products, right? So that’s the platform model. You take your existing customers, your existing physical assets, and you open it up to any seller. And then you add Prime, and Prime is the layer that kind of connects all of these, right? So with Prime, you essentially tie all these three things together.

You maintain leverage over everyone. So you maintain leverage over your customers, because you know so much about them, et cetera. Like, if they go to someone else, they won’t get as good recommendations. You have leverage over your sellers. And that is why no player out there, like a digital startup that is super fast at shipping software or at building an online storefront, et cetera, they are not able to really ever attack and break Amazon, because Amazon is a three-paradigm beast.

A traditional business that is a strong logistics business that says, “Okay, I’m gonna knock these guys out of the market,” they can’t do that, because it’s, again, a three-paradigm compounding beast. And I think that is something that’s underappreciated. And I think for the first time, in The System Gambit, I really felt I understood why Amazon is so unbeatable.

And now comes the Walmart part, right? So as long as Walmart was trying to play the Amazon game—so from the early 2000s, late ‘90s, early 2000s to 2010, Amazon is the joker, the loser, the no-profit business. And then people finally caught on to that and realized, “Oh shit, these guys were executing a multi-paradigm system gambit, built this massive compounding machine.”

And what is sacrificing a five percent EBIT for ten years, right? It’s meaningless, right? In terms of the amount of value they built, sacrificing that EBIT for ten years is chicken shit. Once they have that, then suddenly the same narrative, the same people now say, “Oh, now Walmart is dead,” right?

So Walmart’s running successfully. While Amazon was not profitable, Walmart was putting out good profits, and so Walmart was the good guys, Amazon’s gonna go bust. Then they realize, oh no, Amazon has become a beast, now Walmart will go bust. Which is also an absurd story, because anyway.

So Walmart now has these physical stores, right? As long as you say Walmart now has to become the new Amazon, meaning they have to sell everything online, that’s an absurd proposition, because Walmart needs to focus on their asymmetric proprietary advantages, right? What is it that they can leverage?

And obviously what they will leverage will be different from Amazon. And so, over about ten years, Walmart is kind of aping on the outside what Amazon does. “Okay, we will use some digital systems here and there. We’ll get a better CRM. We’ll do this, that”—and these kind of small defensive tweaks, what I call the system anti-gambit.

It’s not a bad thing necessarily. It’s just the rational thing to do in the existing system. And then about half a decade or so ago, you have Seth Dallaire, who’s an ex-Amazon exec, moved to Walmart. I think he joined as chief growth officer. I don’t remember. And he was, before that, for a while at Instacart.

And now, over the last half a dozen years or so, what Walmart has done is they said, “What is the one thing we have that Amazon will never have despite having bought Whole Foods?” And that’s the physical presence. We know how our customer not just shops online on walmart.com or whatever.

We see them physically shop. That’s a proprietary strength that we have that Amazon can never have by design, structurally. Unless Amazon suddenly starts opening physical stores, which would completely destroy them. So that’s management logic antagonism, right? And the second thing here is path dependence.

A shopper that has shopped in Walmart over two or three generations, that’s a level of path dependence that you’ve built, a level of trust, a level of brand, a level of customer understanding that you’ve built about a customer and a community, a local community, that Amazon just cannot have by design.

So you have path dependence, right? And you have the self-improvement loop, which now needs to be built, right? So two of these were already there. And then what they did is, okay, we will now take what we understand of our customer online. We’ll take what we understand of our customers uniquely physically.

We’ll put this together, and now we try to delight our customer. And with that, they are now on such an amazing trajectory, and they went multi-paradigm, meaning you can buy online, you buy in store. We have a loyalty program. That’s the platform model, right? So you’re taking an industrial business with the physical stores.

You’re leveraging that to build your digital capabilities, and then you open it up through partnerships, et cetera, with a loyalty program. And now, again, you’ve built an insane compounding beast. So it does not matter where you start. You could be a pre-dot-com bubble tech startup.

You could be an established family-owned, a hundred or whatever, or half a century old business. It does not matter who you are and where you come from. What matters is, are you willing to think from first principles, from a systems thinking perspective? And are you willing to unlock leverage and do something that is different from the crowd, that is outside the scope of the paradigm in which you’re operating, in a smart way?

So it’s not just, oh, let’s just wake up in the morning and brainstorm ten ideas and then randomly do three. It’s, can we rigorously think from a systems perspective, understand the mechanisms, and really map it out logically as a causal model of what we’re trying to do? And if we go wrong, or if a specific thing doesn’t work out, then you update the causal model.

So you’re not just madly shooting—you’re not just throwing stuff at the wall and seeing what sticks. You have a causal model, and then you’re updating or modifying it based on empirical feedback, and so you’re closing the loop.

Matt: I’m so happy you did that, because it really, to me, threads why thinking in this corporate strategy way matters for investors.

It’s so interesting to be able to look at why is the company making the decision they are or not, and have this framework that you’re laying out in The System Gambit to do so. Give me the one lesson an investor could take away from this book that you would hope, if they read this, this is what’s in their head, this is how they’re thinking about the companies they own or invest in going forward.

Ritavan: It’s always hard to distill it into one lesson, but I think the real opportunity at any given level of assets, at any given level of size, is the question: what game are you choosing to play? And The System Gambit allows you to think that through from first principles in a way that allows you to unlock that leverage.

And it could also be on a time axis, right? So if everyone is trying to optimize the next quarter and play that game, the question is, do you want to play on a different time axis, right? And so concretely, you can map it on a bunch of things. It can be based on the assets you already own, physical or intangible.

It can be based on time. It can be based on a bunch of stuff, and that’s why it’s contextual to you. And The System Gambit, the book, is really just like a bunch of workouts and reps that, once you get in, you can choose to play the game you want, right? You’re just a healthier, better, smarter person, and then you play the game you are uniquely capable of playing.

Matt: Beautifully said. Ritavan, if people wanna find the book, say the name again, say where they can buy it, say where they can bug you on the internet.

Ritavan: So the book is The System Gambit, so that’s the system crossover from blue to black. The book is on Amazon, and you can go to thesystemgambit.substack.com.

That’s where I share stuff. Or go to the YouTube channel called The System Gambit for more. And otherwise, yeah, just follow on LinkedIn and engage on LinkedIn.

Matt: Make sure you do that. Chase Ritavan down. I’m telling you, I got an early copy of the book. So did Kai. We loved reading it and said, “This is gonna be such a fun conversation.”

So hope you learned something. Check out Ritavan online. Kai Wu, Sparkline Capital. Make sure you—his work on intangibles dovetails with this so nicely. We’re gonna write all this up on Excess Returns on the Substack. Wherever you’re watching and listening, thank you. Like, comment, subscribe, all the things below, and we are out.

Picking the peak of a real bubble, he says, is the holy grail of investing; the thing about the holy grail is that you cannot find it because it does not exist. That admission, from someone with every reason to claim foresight, is the constraint that shapes everything else he believes. If the top cannot be known in real time, the job changes. It stops being about prediction and becomes about something humbler: recognizing what kind of market you are in, and managing the one variable you actually control, which is yourself.

Lesson 1: You Can Spot the Regime, You Just Can’t Spot the Top

In December 1996, Alan Greenspan warned of “irrational exuberance” in asset prices. The stock market then rallied for more than three years before the internet bubble finally peaked. Andy returns to this moment because it makes his point for him. Here was the most powerful central banker on earth, naming the bubble out loud and then, with the market up forty percent from that very warning, cutting rates anyway. As Andy put it, “He had no idea and he’s the chairman of the Federal Reserve.”

Greenspan’s mistake wasn’t the warning, it was the implied confidence that anyone could act on it. The timing of a bubble’s collapse is unknowable even to the person with the best seat and the most information. The distinction Andy draws is one investors constantly blur. Saying you are in a bubble regime is a description; saying the bubble is about to pop is a call. He will happily make the first and refuses to pretend he can make the second.

This matters because everyone wants the conversation to be about whether you are a buyer or a seller, long or short. Andy keeps redirecting. A bubble regime just means the market is behaving differently than in a normal bull or bear cycle, and that difference is something you can observe and prepare for without knowing the date it ends.

Even retrospect is slippery. A bubble, in his telling, is only confirmed as one when it pops. Let a parabolic market grind sideways for years while earnings catch up, and no one will ever call it a bubble at all.

Lesson 2: Bubbles Bring Out the Worst in You

Imagine your neighbor is up a hundred percent on some semiconductor stock. Then his neighbor is up the same. Then everyone you know is suddenly, visibly rich, and you are not. Andy describes this precisely because he thinks it is the whole game. A neighbor beating you by a few points a year generates no emotion; nobody cares. But sudden, widespread wealth happening to everyone around you is incredibly compelling and resisting it takes a discipline most people do not have.

This is why his single biggest takeaway is also his least technical: bubbles bring out the worst in you, and you should know what you are capable of before you walk in. The market blows people up not when they misread a chart but when a regime engineered almost perfectly to exploit human nature catches them doing exactly what everyone around them is doing.

His practical advice is deliberately unglamorous. The best thing an investor can do in a parabolic market is stop looking at the markets. Find any habit that keeps you off the screens. At the barbecue where everyone is trading stock tips, change the subject. He knows it sounds like a non-answer, but he means it as a defense against the tickers, the alerts, the group chats, and the financial news that exist to generate emotion and are very good at it.

What makes the warning land is that Andy aims it at himself. He still describes knowing yourself as a lifelong project, one you make progress on only by failing and paying for it. Pain creates progress, Ray Dalio says. The point is not to avoid every mistake. It is to keep the mistakes you will make anyway from becoming the kind that change your life.

Lesson 3: When Your Portfolio Feels Safest, It Is Most Dangerous

There is a particular feeling that defines the late stage of a bubble. Your portfolio goes up almost every day. The drawdowns vanish. Volatility drops, and your account feels less risky than it has ever felt. This is the trap, and Andy thinks it is the literal mechanism by which bubbles inflate.

Faced with a portfolio that never seems to lose money, the natural human move is to reach for more. Instead of a basket of chip stocks, you buy the hottest one. Instead of the index, you buy the leveraged version, or the calls, or you buy on margin. Every one of those choices feels rational when your account has not had a red week in months. And that, he explains, is exactly how the bubble expands: people look at their placid, profitable accounts, conclude they are not taking enough risk, and lever up at the single worst moment to do it.

His answer is to distrust the feeling. Be skeptical of your own ability to measure how risky your portfolio really is, and cap your leverage in advance. His own rule is that he will never hold more than thirty percent more in assets than he has in cash, and in a regime he believes is a bubble, he tightens it further, down to ten percent. He also keeps a floor: even in the most violent markets, he stays at least partly long.

None of this is market timing. It is the recognition that low volatility describes the recent past, not the future, and that the moment your portfolio feels safest is when the next move is most likely to hurt.

Lesson 4: In a Fair Trade, Nobody Gets a Good Deal

When SpaceX ran the largest IPO in history, it sold roughly eighty-five billion dollars of stock against a two-trillion-dollar valuation: about four percent of the company. The shares priced at one thirty-five, opened around one-fifty, and ran into the one-seventies. By Andy’s read, that went very well, and why reveals how he thinks about IPOs in general.

His IPO writing carries a line he means half tongue-in-cheek: in the IPO machine, everybody lies. The issuers, the banks, the founders, the institutions, and the retail buyers all want the deal to happen, but each wants it on their terms, and the clearing price is just the point where those competing interests are grudgingly satisfied. In any trade, he insists, the ideal price is the one where neither side gets a good deal: both consider it fair, and nobody is taken advantage of. That holds for every transaction, not just the splashy ones.

An IPO is stranger than a normal trade, because it is only the first in a long sequence that will define a company’s life. The issuer is not trying to squeeze the last dollar out of one sale; it wants durable access to capital, stock its employees can sell, and currency to buy other companies. So giving away a little on the first four percent, while signaling that you are a hot, high-quality company, is a bargain SpaceX was glad to strike.

Lesson 5: Owning Only Stocks Is the Least Diversified Bet You Can Make

Most people who say they are diversified mean they own a lot of different stocks. Andy thinks this is backwards. Every asset on the planet has to be owned by somebody: your home, an apartment building, a bond, a stock, an ounce of gold. Together they make up what he calls the market portfolio, and the moment you decide to overweight stocks, you force someone else to be overweight something they would rather not hold. They will charge you for the privilege.

The real argument is about expectations. Stocks do not reward you when growth goes up; growth almost always goes up. They reward you only when the economy beats what everyone already expects, and to bet on that you have to believe you know something the entire world does not. Being optimistic is fine. Believing you can consistently outguess the consensus forecast is a bias, he argues, that you simply should not have.

So he owns the wider portfolio: more bonds, more inflation-protected securities, more gold and commodities than most investors, and fewer stocks. Bonds protect him if the economy disappoints. Commodities give him a second pro-growth exposure while gold hedges his confidence in central banks. The result is a lower-risk mix that, levered modestly, can target the same return as an all-equity portfolio while surviving the drawdowns that all-equity investors simply have to endure.

The Bottom Line: Manage the Player, Not the Market

An investor’s edge does not live where most people look for it. Andy does not believe you can outguess the top, outsmart consensus growth, or feel your way to the safe moment to add risk. The information you crave is either unknowable or already in the price. What is left is the part of the system you can actually govern, which is your own behavior inside a regime you can recognize but not control.

That is why his most repeated advice is psychological rather than analytical. Cap your leverage before the calm seduces you. Close the apps when your neighbors get rich. Hold assets beyond stocks so a bad growth bet cannot sink you. Not one of these requires a forecast, and every one is available to the ordinary investor. The market will always be better at exploiting your nature than you are at predicting its next move. Andy’s wager is that managing the first problem is the closest thing to a holy grail you will ever find.

Watch the full episodes with Andy below:

Main topics covered

• Ben Inker’s framework for easy bubbles versus hard bubbles

• Why the 2000 tech bubble was easier to navigate than the 2008 financial crisis

• How expected returns can help investors think about risk and reward

• Chris Mayer on why labels like AI, software or SpaceX can mislead investors

• Why investors need to understand what companies actually mean when they say AI

• The case that today’s market risk may be hiding in earnings rather than valuations

• How AI data center spending can boost current corporate profits before depreciation hits

• Why great 100-bagger stocks usually give investors many chances to buy

• How IPO supply from companies like SpaceX, OpenAI and Anthropic could affect market returns

• Chris Mayer’s approach to evaluating founders, compensation, incentives and culture

Timestamps

00:00 Intro to the Weekly Wrap and the new episode format

02:22 Ben Inker on easy bubbles, hard bubbles and 2000 versus 2008

08:12 Chris Mayer on SpaceX, AI and the danger of letting labels do the thinking

14:13 Ben Inker on earnings bubbles, AI spending and why valuations may look reasonable

19:38 Chris Mayer on 100-baggers and why investors do not need to buy immediately

22:53 Ben Inker on IPO supply, lockups and what new equity issuance can do to returns

28:03 Chris Mayer on evaluating founders, incentives, compensation and trust

34:38 Closing thoughts and the new Excess Returns Clips channel