Full Transcript: D.A. Wallach on Finding Alpha in Biotech

A Deep Dive into Biotech Investing

Matt: You are watching Excess Returns, the channel that makes complex investing ideas simple enough to actually use, where better questions lead to better decisions. Biotech investing — it’s like a lottery ticket. Nobody knows what’s going on, nobody knows anything, right? So who’s with me here today? I have D.A. Wallach, musician, venture capitalist, co-founder of Time Bio Ventures. He believes that actual persistent alpha still exists in this space. So I’m excited for this conversation. D.A., welcome to Excess Returns.

D.A.: Thank you.

Matt: Let’s dive right into the deep end. Biotech — one of the few places where active alpha is real and persistent. Every quant kid in the community and everywhere else is yelling at you right now saying, “How dare you.” Why do you think this is to be true?

D.A.: Sure. Well, basically making sense of biotech companies and valuing these companies requires processing a lot of very domain-specific information. And that is still a type of work that only a small number of market participants are really expert at doing. And so the argument would be that the biotech hedge funds that basically drive most of the activity around these stocks are being paid by the market for the service that they’re providing. And that would be consistent with efficient markets, but still explain why there is money to be made and why money has been made by specialist firms. And we’re talking here about the public market. The private market’s a different story. There, there’s probably even greater asymmetry to specialist knowledge.

Matt: Okay, so just around the idea of a biotech company — full stop. Introduce this bag of options framework, because I think this is really cool.

D.A.: Sure. So the typical biotech company in its early stages is not selling any product commercially. It’s a company that exists to try and get new drugs through a highly regulated development process, and then ultimately launched commercially.

And so we can distinguish between biotech companies that are in that phase — where they’re not selling a product — from biotech companies that are selling a product. And that latter group is going to be valued in a more traditional way based on future cash flows. The development-stage biotech companies are really distinguished by the fact that there is a high degree of uncertainty that any of their products will get to the market.

So let’s just imagine a hypothetical biotech company. They’ve got three drugs that they want to bring to market, and each of those projects started a year after the one before it. So they’ve got these three staggered projects, and each of those projects is going to move through the development life cycle.

It’s going to probably start in what we would call the preclinical phase, which basically means that scientists are researching that drug in the Petri dish, so to speak. And then they’re going to do animal experiments — unfortunately, but animal experiments are still a major part of how we try to figure out whether drugs are going to be safe and effective in humans.

So that’s all the preclinical phase of drug development. Any project in that stage of its life has a very, very low probability of making it to the finish line. And the finish line here would be an FDA approval and then a commercial launch. So just to give you a general orientation, a drug at that stage might have somewhere between a five and 10% chance of making it to the finish line.

And then what will happen is the drug, if it has generated evidence that suggests it’s worth spending money to take it further, will submit an application to the FDA or a different regulator in another country, and it will apply for permission to start doing human clinical trials. The first human clinical trials — phase one trials — are typically going to be focused on determining whether the drug is safe, and then the phase two and phase three trials are going to be focused on trying to figure out if the drug is actually effective. And a drug, to get approved at the end of the day, has to satisfy both of those criteria. It has to be proven safe and effective in the eyes of regulators.

And if it satisfies those criteria, it will get a regulatory approval and then the company will have permission to start selling it on the market. As a drug progresses through each of these phases, the probability goes up that it’s going to cross the finish line. And so you can value any one of these companies typically by basically doing a sum of the parts of the net present value of each of its programs.

So this company that we’re talking about — that has three drugs — you’re going to calculate the net present value of each of those projects. And in each case, you’re both going to be discounting the potential future market for that program, and you’re going to adjust it by the probability that those revenues will ever occur in the future. You’re also going to be adjusting for the likelihood that they occur, and the costs associated with developing that drug. So the valuation is going to be a sum of each program’s net present value. That net present value is going to include the uncertain future revenues and the uncertain future costs that go along with that program.

Matt: Okay. Inside of this, how do you think about — for the MBAs and nerds — base rates? What do you tack this back to?

D.A.: Yeah. Well, we have a pretty long history of people trying to develop drugs. And so you can look at what the historical success rates have been. And when I talk about success rates, you can either think of that as the aggregate success rates. Say we’ve got a drug starting at a preclinical phase, and we’re trying to figure out what’s the chance that it gets across the finish line — that’s going to require you to multiply the probability of it transitioning from each phase to the next through all of those phases I just described. So that’s how you get, for a brand new concept, something like a five to 10% total probability of success. That would be like your base rate.

Now, for the transition between each of these intermediary phases, you can figure out what base rates are based on drugs in the past that have faced that precipice and either made it across or not. So you would use base rates for as granular a part of the evolution of a project as you can. And your goal is to try and anchor your estimate of the likelihood that things succeed to what has been historical experience across different drugs.

And of course, for any project, you’re going to do a better job the more precise your reference class is. So, for example, drugs that are antibodies are going to have different base rates than drugs that are small molecules. And a lot of the art of biotech investing that specialists like us are engaged in comes down to figuring out what are the right base rates to use for a given situation, and then how might you adjust those base rate expectations depending on the specific facts of the drug in question.

Matt: How much of that is — like, the total addressable market for each of these drugs?

D.A.: So the total addressable market is basically how big the pot of gold is at the end of the rainbow. The probability of success estimate is what you are multiplying that market size by. So you’re trying to fundamentally estimate two different things. One, how likely is this thing to get to the finish line? And two, if it gets to the finish line, how much money is it going to make?

And the “how much money is it going to make” part is also quite subjective, because you’re going to need to figure out: will this drug be the drug that doctors always choose to give patients, or will it only capture a certain market share for patients with that disease? And you also need to figure out what Medicare or other insurance companies are likely to pay for this drug. And that’s going to be the outcome of a complex set of strategic and negotiating circumstances for each drug.

So there are a ton of these variables that all have error bars around them. And the professional biotech investor’s job is to try and have a view about each of these variables. And as a result of those views, figure out what you think the company should be worth today based on all the uncertainty that surrounds it.

Matt: Okay. So between the bag of options, between all the different layers, calculating how we get towards the TAM — if we even get to that payoff — the last four years you’ve said have not been so kind to this space. Could you sort of walk through what happened, why has everybody been down on this? Why has this not gotten a lot of attention or focus, and why does the market environment kind of create opportunities and cycles here?

D.A.: Sure. Well, I’m going to talk about the public market here, and we can get into the private market if you want to. But as a rough rule of thumb, the private market sort of follows the public market with a lag in terms of what’s going on, what its climate is.

In the past few years, we had this kind of coincidence of negative developments for biotech. So interest rates went up. That’s a major headwind to these companies because — this isn’t totally true, but I’m going to give you kind of rough heuristics — the story people tell that has some truth to it is that the valuations of these companies, calculated in the manner that we talked about, are heavily dependent upon cash flows that are in many cases eight to 10 years away in the future. And so when the discount rate changes, these companies see their valuations fluctuate in a very sensitive way to those discount rates, because they’re not making any money today. Therefore, all of their value is really contingent upon this big bolus of cash way out in the future. So that’s kind of one fact that worked against biotech starting in ‘21, ‘22.

The other is that within the public markets, biotech is competing with all the other risky sectors for investor interest. And so I think what we saw was that for a couple of years there, most of the hedge funds and a lot of bigger generalist allocators of capital decided that biotech was just not an area they wanted to be in. They wanted to do other stuff during that period, and in particular the big tech and the AI narrative created a competitor to biotech for that risk capital. So money flowed out of the biotech sector into other parts of the market, and that just created a massive headwind for all of these companies.

So during that period you had going on what typically sort of drives this sector — companies doing clinical trials, sometimes getting acquired by large pharma companies. These are the things that drive returns. But those returns were occurring against a backdrop of money being sucked out of the sector. And so it was like all the prices were going down together. And only if you were extremely careful and specific with your bets would you find these little instances where you could make money.

And of course, what we hope for the rest of the time in more normal environments is that there should be hopefully kind of like a steady-state risk premium of some kind to investing in biotech. In other words, you would hope that it’s sort of a good place to always have some money. And then if you’re good, you would generate returns far in excess of that by doing the kind of specialist investing that we talked about. Yeah. On the other hand, this is, as we were describing it, a market that goes through cycles. And so it was just definitively a bad place to be for the past few years. And we came out of that — starting about halfway through 2025, there was this massive resurgence of risky biotech investing, capital flowing back into the sector. And you saw returns even in the sort of passive biotech indexes of 80, 90, 100 percent.

Matt: I want to talk about the last time that cycle turned. So I want to go back before four years. Can we just unpack what happened in and around the pandemic to the space, because I think that’s an important context and catalyst to set up where we are now.

D.A.: Sure. So what I just walked you through was sort of the comedown from the sugar high of the pandemic.

When the pandemic occurred, of course, we all directly experienced how central biotech can be to everybody’s life. Most people only discover this when something really bad happens to them — like they get cancer, or they get neurodegenerative disease or heart disease — and then their eyes are opened to just how profound this sector is and its contributions to everyone’s lives are. But for most of us, we’re going through life day to day, hopefully healthy. And you just sort of don’t think about what’s happening in biotech world.

Well, obviously COVID was a rude awakening, because at the outset people were just desperate for a solution. The biotech industry ended up providing a pretty decent solution, but then that of course became itself highly contested with the mRNA vaccines. What we did see was that at the beginning, enthusiasm was out of control because everyone realized, whoa, there’s going to be a lot of money made around this industry helping us get out of the pandemic. And so valuations went way up, enthusiasm was on high.

And then as I described it, you sort of came off this sugar high. Interest rates went up. The world kind of reverted to normalcy. The air was let out of the balloon of the companies that had provided the vaccines, which — at a certain point in time — the assumption was that the mRNA companies are going to be producing a blockbuster product that everyone’s getting every six months or every year. Of course, sentiment turned against those vaccines in a big way. And so people’s expectations there were changed. And so now we’ve just come out of this big rut that that drove us into.

Matt: Is there any inkling of — and by all means, it’s nowhere near a pandemic-type awareness on the space — is some of the stuff that’s going on with AI helping draw money back? Or as you paired it, biotech and tech sometimes get lumped into the same growth investor attraction. Is there anything that we could see around AI that could be the same size or scale of what we saw in the pandemic?

D.A.: Well, I would say by and large they’re competing with each other for capital — meaning both early-stage tech and biotech are viewed by public market investors as highly risky places to deploy. When we’re not talking about investing in the big, big companies — investing in Alphabet’s a totally different story from investing in a $3 billion market cap AI company, right? And that’s what more of these clinical-stage biotechs look like. One to $10 billion valuations, there are hundreds of these companies. And that as a category is viewed as one place where you could place some risky bets if you wanted to.

So I think the AI narrative has continued to outcompete biotech in terms of the argument that it’s made to risk-seeking public investors. There certainly is a narrative within biotech around what AI may do to the industry. And I would say just as with every sub-sector where there’s an AI story, there are people chasing that. It’s not a thesis that we have leaned into particularly heavily, but I can definitely articulate to you the arguments that people would make for how transformative AI’s going to be within biotech.

Matt: I bring this up because this is one of the places that I’m hearing it, and it’s your work that helps make me think of this regularly. The generalist level appreciation for biotech is probably — you tell me if it’s more a key determining factor in flows. And when I hear generalist growth investors talking about what AI can bring to industries and directly referencing biotech over and over and over again — “here’s how it’s going to help with these things” — I go, this isn’t just a story, this is a flow story. What’s happening there? And then maybe you could lay out how they’re painting this.

D.A.: Yeah. Well, I’m not sure whether that story has actually driven flows into biotech companies. It has certainly been part of the sales pitch that AI companies make to attract capital to themselves. So just as one example, in Jensen from NVIDIA’s presentations — if you watch them — there’s almost always a major callout to biotech and how transformative the GPUs are already proving in various parts of the biotech sector.

The large pharmaceutical companies, just like all the large companies in different sectors, are also making this case to investors that they’re investing heavily in AI and they’re going to deploy their capital to kind of leveling up technologically. And so that also may be attracting some flows to the large pharma companies. But I think it’s also particularly clear that it’s such a competitive industry that however much the large pharmas have to retool, it’s probably unlikely that any of them is going to have a durable competitive advantage as a result of AI — meaning multiple of them are all going to run at this in a similar way. And that’s probably going to do the same thing to their economics across the board, which hopefully for the sake of patients is going to increase the number of new drugs that we get and decrease the prices of drugs. That I hope is what happens, but that’s going to play out over a 20 or 30 year time horizon. It’s not something that’s going to change overnight.

Matt: Let’s talk about the role that specialists play inside of the business, both as allocators and inside of these companies, because I feel like this is something that — if this is your thing — this is kind of where you have to stay and where they have to figure out ways to keep you. How do you think about specialists — investors and allocators in particular first?

D.A.: Sure. Well, the specialists in the public biotech markets are basically a number of hedge funds who almost exclusively do biotech. And there are, call it, between 30 and 80 of those firms that matter — meaning they’re managing a billion dollars or more, something like that. None of them is that massive. You don’t see $150 billion biotech hedge funds. Some of the best players in this space have remained, even for a 20 or 30 year career, quite small. And I think part of why they’ve done that is because they are mainly trafficking in these small-cap, low-float stocks where it is very difficult to scale a strategy beyond a certain point.

So it is a market that’s very dominated by specialists. The specialists who do this for a living, by and large, know what they’re doing and are quite skilled. And they’re paid by the market for that skill. And historically, I would say the large allocators — be it the pension funds or the endowments and so forth — in most cases will have some allocation to the biotech public markets, and in many cases, allocations to the private biotech markets.

This whole ecosystem is a kind of self-reinforcing machine where you need the private market to create biotech companies that will go public. Going public, in most cases, is going to be where the returns are produced for the private investors. And then big pharma is the ultimate engine because the big public large-cap pharma companies are ultimately who buys the companies that have gone public.

Every once in a while a commercial-stage public biotech company will decide not to sell. And so occasionally you will have an independent public biotech company that chooses to remain independent, and a few of those have grown to be quite large in their own right. Amgen would probably be the greatest example of this — but that’s a decades-old story. Amgen, Vertex, Regeneron — those would be companies that are not large pharmas, but that have become quite large independent commercial-stage companies because they didn’t sell.

But the day-to-day bread and butter of returns in this industry is that large pharma companies buy biotech companies, often out of the public market. And that’s where the returns come from for the public hedge funds, the specialists, and they recycle that capital back into the public market. And that’s what enables new companies to IPO.

Matt: So let’s talk on the private side just for a second there.

D.A.: Yeah.

Matt: On the private side, does that mean the level of specialization is that much tighter — like the only people trafficking in this space are extreme specialists?

D.A.: You know, there are a few examples of firms that do tech and biotech, but they’re few and far between. Most of the venture firms in biotech mainly just do biotech. There are some firms like ours that take a bit of a broader view than only investing in drugs. So as an example, we do drugs — which in the industry people call therapeutics — we also do diagnostics, we do research tools, we do medical devices, and we’ll also look at opportunities in healthcare innovation more broadly. Companies that are doing something that might enhance the experience of going to the hospital or going to the pharmacy.

But I would say the big concentrated players in our landscape mainly focus on drugs, and that’s what they do. And they often — maybe even more than in tech — have a very hands-on approach to almost building companies. So they’re not quite venture foundries, but it’s more like that than in the tech world, where usually what a VC is doing is finding a company that some entrepreneur has already started — that they’ve maybe already financed for a year or two — and then they’re just sort of piling onto that.

In biotech, it’s much more common that the venture capital firms will be quite involved with these entrepreneurs from the very inception of a company.

Matt: Inside of that — if they’re that much more involved — that also is a big part of the differentiation, I feel like, between being a venture or angel tech investor and being in biotech specifically. Fair?

D.A.: Yeah, I think so. Venture capital broadly is the closest thing you find in the investment world to entrepreneurship — meaning venture capitalists are close to the entrepreneur, they’re often right there in the trenches from the early days. And obviously there’s a high range in terms of how involved they are with building early-stage companies. I think biotech investors, to my earlier points, are on average more involved in early company building and financing than tech investors often are.

But it also depends on the entrepreneurs. Some entrepreneurs are really self-contained and they know what they’re doing — they’re going to build something that’s likely to be successful irrespective of who’s on their cap table. You get some of that in biotech, but it is a very capital-intensive type of company building. And therefore the investors, and the ability to create a financing story and the right investor syndicate from the early days, can be extremely important — maybe more important than it is in tech.

Matt: It kind of feels that way, because you can’t have the same type of generalist founder type come in and try to build one of these portfolios. Like, they would at least need to surround themselves with a different level of specialist to even want to enter this space and not just assume they’re going to burn a lot of money.

D.A.: I think that’s true. That’s certainly been my experience. I came into this from this sort of tech generalist venture mindset. And what I pretty quickly figured out was there is a glide path for companies, if they’re built in the right way. The science obviously is what determines whether companies succeed and progress. But in a perfect world, you’d like to set these businesses up with the right investors, with the right kind of management team, from the outset, such that if the science plays out the way that you hope it does, all the other risks have been mitigated.

And I think part of why that’s important is these companies, as we discussed, don’t make money for a long time. So what you do get as time goes on is more and more data that reduces how risky investors would feel the bet is. Nevertheless, there is still a huge amount of narrative at play, and the investors ultimately have to believe in the scientific story that the company is chasing. Whereas with, say, an enterprise software company, within the first year or two you’ve got all kinds of evidence as to whether or not it’s working. You can talk to customers, you see revenues, you see what the retention rates are and what the lifetime value is. And so you can anchor an investment analysis to all these facts. In biotech, there’s still so much narrative that it becomes especially important that you have contextualized a project with all of the things that signal to the market that it’s on the path to success.

Matt: It’s so interesting to think too — because as a private investor — that also cements: you kind of have to be there for a long time when nothing is happening.

D.A.: Well, it looks like nothing’s happening — and everyone can be forgiven for feeling like, wow, nothing’s happening. Because what is happening is a bunch of experiments typically that generate a lot of data that to the untrained eye is very hard to make sense of. And that’s where we get back to this point about why specialists get paid in this field — our job is to look at a body of evidence from mouse experiments and make an assessment as to whether those are convincing results. And if we’re right about that, we’ll double down on an investment. If we’re wrong about that, we can make errors. And if you don’t know how to look at that information, it’s just a total coin toss.

Matt: I say that partly in jest, in the sense of — just because the price isn’t moving doesn’t mean — it’s the ultimate value investor epithet. It’s the ultimate Buffett look-through earnings. It’s the ultimate, any of these cases where you go, there’s things happening below the surface that price isn’t communicating to you.

D.A.: That’s right. And the way that you package up the entire story around — and the belief in what’s going on — that has to get built over time. That can’t be flighty in the generalist sense.

Matt: How did you come to understand that role — moving from the tech space into the biotech space — where you saw how much longer it had to take for that narrative to sort of gel? That you go, “oh, this is the commitment, this is the way we look at progress,” because it is a very different ecosystem.

D.A.: Sure. It took me several years of looking at these companies, and I’m of course still learning every day. It’s a very deep craft, I would say. And people who have been investing in biotech for decades have, through that experience, learned a lot of lessons about the nuances of both the narrative side of this and the substantive side of it.

And so all I can tell you is that both dimensions are really important. And I think that’s something we can talk about more broadly in investing — this interplay between reality, facts, and narrative. That combination, and getting it right, is absolutely integral to building these companies, maybe more so than any other types of businesses I’ve ever looked at.

Matt: This is sort of AI adjacent on this question too. I want to talk about — number one, I wonder, with some of the generalists looking at this stuff — it’s like, “well, AI can teach me how to do anything, so now I can read some of these clinical trials or I could read some of this stuff, and maybe that helps draw them back in.” But also, I look at an industry like this and I wonder: do people feel like we’re already drowning in ideas? There’s more trials that we can come up with than we can ever imagine running. Is any of this stuff actually going to help get more of those ideas into test mode faster and farther? Is there any there there for AI, as you see it, for the portfolio companies?

D.A.: Yeah, absolutely. I think we’re all across the economy right now going through this experience of learning how to use these tools and finding ways that they can augment our abilities and our existing processes. And biotech’s no different.

So there is a superficial story that you can tell — again, in any sector — about how AI’s going to change everything, and that may over the long run be true. But what matters is: what are the ways in which that transformation is investible? And when we look at biotech and we kind of explode out all of the things that need to happen to get a drug from idea to the market, we’re no longer thinking of this as “what does AI do to drug discovery writ large?” We’re thinking about a thousand component parts of the drug discovery lifecycle, and we’re asking: is there anything AI can do for each of these that makes it better, faster, cheaper, more predictive? And in some of those areas the answer’s going to be yes. In some of those areas the answer’s going to be no.

What’s exciting right now is everyone has a lot of incentive to do that analysis. And so you’re starting to see companies that have a different character — they have more computer science-y people in the mix — and they are asking these questions in a very thoughtful way. And by their own report, some of them are harvesting major efficiency gains as a result. So you hear that when you listen to the pharmaceutical companies talk about what they’re doing internally. You hear it when you talk to smaller startups, and obviously they can be very creative and agile. But I think it’s an incremental thing, and I think it only plays out over a longer time horizon.

Matt: Any changes that you could see coming with success rates? Or ways it could actually help the clinical trial process, or even — from the drug companies looking downstream — at companies they might want to acquire sooner?

D.A.: Sure, yeah. You would hope that ultimately we will see success rates go up. And part of what circumscribes just the size of this sector — it’s a sector I obviously believe is very important to civilization, and I wish that there were more money in it, meaning I wish more capital were flowing into it. But investors are simple — money goes where the returns are.

And so what has sort of capped returns in this market — or at least what has capped the scale of returns — has been these low probabilities of success. And there are a lot of different theories about why those success rates are so low. I’ll just give you one and I’m not endorsing it, but people have argued, for example, that basically we picked all the low-hanging fruit over the past hundred years. Like all of the diseases that were easy to cure with a drug, we figured out how to address with a drug. What has that left us with? Well, all of the hardest, most stubborn diseases.

Obviously the invention of penicillin was like a game changer for human history. But we figured out how to kill bacteria with a drug — and that was kind of a solved problem by and large. Well, now curing pancreatic cancer is a much more complicated problem. So it’s like — on the one hand, yeah, our tech keeps getting better and that should bring down our failure rate. But at the same time, everything we solve leaves us with harder things remaining to be solved. And that may work in the opposite direction.

So at the end of it all, my hope is that better technology leads to higher rates of success, therefore to higher returns in this sector, and therefore to more capital going into this sector. And that’s the optimistic vision of the future.

Matt: I’m curious in your own process how something comes together. How an idea finds you, how you find an idea — private market — I want like actual day-in-the-life type stuff. How does something even get on your radar? How does a conversation even start? How do you even start the process of “we might want to write a check and get involved with this idea”?

D.A.: Yeah. Well, the first thing is — for all the reasons we just talked about — this is a capital-constrained industry. In other words, there are more good ideas looking for money than there is money looking for good ideas. And that favors folks in the business we’re in, because it means that without a lot of proactive effort, we hear from a ton of people who are trying to raise capital.

So I often think about investing as sort of like offense and defense. I love playing offense, meaning I love reading a book that gives me a divergent idea, and then I go and I try to find companies in that space. And I feel like when I do that, I’m going to end up doing things that other investors won’t do, and you can have an ability to differentiate. But also, we are just inundated with people trying to raise money. And there are a lot of these little biotech companies out there running around trying to scrounge up the cash that is required to take their thing to the next phase.

And going back to the beginning of our conversation, these companies have room for a lot of creativity in the weeds, but they’re relatively templatized. I mean, if you’ve got a little concept for a new drug in the preclinical phase, it’s very clear what your goal is. Your goal is: do a bunch of preclinical work. If it’s good, raise enough money to get into your first clinical study. If that data’s good, raise enough money to do your second clinical study. And so forth.

And so everyone kind of knows what they’re trying to accomplish. And what we are attempting to assess are all of the variables that we’ve talked about earlier in the conversation — what do we think the probability of success is? How strong an idea do we believe this is? What is the scientific rationale for this mechanism of a drug potentially solving a big problem that patients have? And ultimately, we want to understand how physicians out there in the real world think about treating a particular set of patients, and can this company give them something innovative for their toolkit that’s going to be game-changing for those doctors and for those patients. And at the end of it all, we want to back companies that we think have a high likelihood of succeeding in that way.

Matt: How do you think about the portfolio construction element? You write a check, you write a check, you write a check. How does that come together?

D.A.: Well, I think it’s essential because — again — these are low-probability stories. Meaning every individual company, we hope they’re all going to work. And just zoom out to venture capital broadly — everyone knows that you’re going to have a high failure rate. Fred Wilson from Union Square Ventures — as a rule of thumb — would say a third of your companies are going to be zeros, a third of them are going to return capital, a third of them are going to make money. And all your returns are of course going to come from that last group.

In biotech, the distribution of success and failure is probably a little bit different. I would say my impression is that there are fewer thousand-baggers, but people’s hit rate is maybe a little bit higher in biotech — meaning specialist investors, because they are underwriting these companies in a pretty rigorous and probabilistic way from the beginning, are largely building a portfolio to overcome the intrinsic uncertainty that each project carries, and that everyone appreciates each project carries.

So the key is to size your bets appropriately and have enough of them that you overcome those low individual odds for each individual company. In our case, as private venture investors, we have sized our portfolio around 20. So roughly 20 bets — we have viewed as roughly the right balance between diversification and having enough upside on any individual company.

Matt: 20 bets per fund, or is it evergreen?

D.A.: Like 20 bets per fund.

Matt: Okay. Inside of that 20 bets per fund — we all know the portfolio math — but how do you guys think about volatility variance? Define it first inside of a fund, and then tell me how you think about it in portfolio construction.

D.A.: Yeah, look, I’ll be the first to admit — private strategies in general are of course guilty of this so-called volatility laundering, to use Cliff Asness’s term. But you also have to distinguish what of that is, let’s call it, disingenuous versus inevitable.

Matt: I want to give you the room to actually explain the practitioner version of it, not accuse you of volatility laundering.

D.A.: So the Cliff Asness argument is: you’ve got these private credit funds or these private equity firms, and they own a bunch of companies that are doing hundreds of millions in revenue. And not only that — these investors who work at Blackstone or Apollo or whatever — Asness has pointed out — these are some of the most sophisticated people at private market valuation that have ever existed. I mean, if these guys don’t know how to put a price today on one of these companies, nobody does. So if they are carrying portfolio companies at marks — valuation marks — that are not tethered to the day-to-day business realities on the ground, they are effectively masking the volatility that their investors should assume is being realized by these portfolios.

So it’s like — if you had a mutual fund that owned 20 public SaaS companies, of course every day you would see the prices go up and down for all of those underlying companies. But in some private equity fund that owns a very comparable 20 software companies, the reporting to limited partners is going to suggest that basically there’s no volatility. They’re all worth this month what they were worth last month, or maybe a little bit more. So I think the suggestion in that critique is that these investment firms should be more honest about what their underlying holdings are worth, and they should mark those much more dynamically so that their customers have a more honest picture of the true volatility of what they own.

Now, in our world — again, these are really hard businesses to price. A preclinical biotech company is in fact only priced when it raises money. I mean, that is when you find out what the market thinks it’s worth. And we as an investor in a company can do our best to guesstimate that. But it’s nothing like the precision that you would expect from Blackstone valuing some company that sells textbooks or whatever. And so the convention in our space with a private venture fund is that we basically mark companies when they raise capital — we view those as the pricing events — and the price at which a company is able to sell new issuance is what we would mark our holding to. And we try to do it in as objective and transparent a way as we possibly can. And we try to capture the volatility that certainly does exist in our portfolio. But again, the resolution of that volatility is just inevitably masked by the fact that these companies don’t price daily.

Matt: How do you guys think about diversification when you’re putting together a fund?

D.A.: We largely view these risks as independent of each other. Certainly there are some shared risks — like in the past few years, what we talked about — if all the money rushes out of the biotech sector, obviously that is a shared risk that our companies face. But we basically just try to have diversification across as many dimensions as we can.

In a portfolio of 20 companies, we’re not going to have 20 Alzheimer’s companies. We’re going to have companies working in different disease areas. We’re going to have companies working with different drug technologies — different modalities, as people in our market would say. So small molecules, biologics, cell therapies, peptides — different fundamental technologies. We have some regional diversification — we have companies in the US, we have companies in Europe, we have a company in Australia.

And it’s no different from how I think about my personal portfolio, which is: on the pie chart, I just want a lot of colors. I want a lot of different colors and I don’t want any one color to be too big, where if it blew up, it would destroy me. And it’s the same thing here.

Matt: Let’s talk a little bit about China — thinking about this on a global scale. You’ve had some really interesting stuff in this direction. We keep hearing China’s going to be the big story — between the manufacturing, between the stuff, between what we learned about COVID and putting some of the vaccines together. Give us an update. How are you thinking about China? Are you investing there? Are you avoiding it? What’s the lay of the land?

D.A.: Yeah, we’re invested in one US-based company that so far is going to do its clinical studies in China — its first human clinical studies — and there will probably be more of that across our portfolio. That is basically what has happened in the past year or two that everyone’s talking about, which is that people have come to believe that China is a very good place to do clinical studies, and in particular to do your first human clinical studies.

There are a few reasons for this. One, excellent scientists available in China to do this work. Two, a very large population and in many ways lower regulatory hurdles to enrolling patients in trials. In biotech, time is money — when you’re running a clinical trial, you really want it to happen as quickly as possible. The rate limiter there is often enrolling patients who qualify for the trial enrollment criteria. And China’s proven to be capable of recruiting and enrolling patients much more rapidly than here. So what this means for small biotech companies is: if they’re considering whether they do their first study in China or in Europe or the US, it is now in many cases going to be more attractive to do it in China, because it’s going to be cheaper, faster, and the quality is going to be very high.

Matt: The historic issues we hear on all the other stuff — in every other industry and everything else, you’re not supposed to trust stuff in China, all these things — it doesn’t feel like, when you’re talking about it, that you think any of that applies. It’s like a good US company with the right partners — it’s just the population size, the access, all the things you just laid out — this is a clear and obvious thing to pursue.

D.A.: Yeah. Well, I don’t think it’s monolithic. There are a lot of different vendors in China, there are a lot of different clinical trial sites in China. They’re not all high quality — and they’re not all high quality here either, by the way, or in Europe. So the question is: if you’re working with the cream of the crop in China, are you going to be getting something that’s inferior to what you’d get in the US? And the answer to that has become no.

Now I’m talking about this in a completely apolitical way. There are of course totally reasonable arguments to be had about the geopolitics of this, and whether it’s strategically risky for the United States to be yielding some of these activities to China. And regulators in the US are having a pretty robust debate right now about whether or not we need to change some of the ways we do things to remain competitive for these parts of drug development.

The other thing that the US has really going for it is ethnic diversity. For the FDA to approve drugs for large populations of patients, they want to see that the drug has been tested across a quite heterogeneous patient population. That’s of course much harder to come by in China, and it’s one of the great benefits of doing work in the US.

Matt: Do you think — specifically for US biotech investors, public or private — how aware should they be of thinking about this globally versus thinking of this just inside of the US?

D.A.: I think it’s imperative to think about it globally. And it’s not unlike being a manufacturer of something in the eighties in the US, right? There’s this massive outsourcing that is taking place to China. Companies that engage in it are probably going to have a significant cost advantage. So it’s to any entrepreneur’s peril to not be aware of what’s going on and to consider the options. And the same goes for investors.

So again, it’s a moving target. Things may change, public policy may change. We could get into a war with China that would obviously change things. But the other point I would just make is that the large pharmaceutical companies are multinationals. And so in many cases they’re based in Switzerland or Japan — or in the US. They’re going to go where the best drugs are originating. And they don’t care whether a drug was invented in China or invented in Missouri — they want the best drugs. They want to buy them at the cheapest price they can get them at, and they want the best clinical data that they can generate. And so it is a global industry, and China is becoming increasingly important to that global marketplace.

Matt: Alright, let’s zoom out. I’m going to take you very, very high level — some of our favorite closing questions. If there was one thing you could teach the average investor — and you decide which level you want to answer this on: if you want to think about it from the biotech place, go there; if you want to just think about your own experience and now getting involved with the highly risky and speculative aspect of direct private investments and raising these funds — what’s one lesson you’d teach the average investor?

D.A.: I kind of pride myself on, over time, trying to become an ambidextrous investor. And what I mean by that is that I often find that investment wisdom, commentary, and conversation typically bifurcates people along — to put it crudely — a value versus growth mindset. And my experience as an investor, largely in private markets but increasingly in public markets, has been that different things work at different times, and it is to one’s detriment to join a particular ideology and define yourself by that ideology.

And there’s an interesting thing that happens. I’m going to characterize it in a very vague way here, but if you just imagine this oscillation in the markets between value and growth — and I know those aren’t perfect descriptions, but: value, think Warren — “I want to buy high-quality stuff cheap” — and growth, “I want to chase the latest exciting thing and buy things that are going up.” You never know when it’s going to switch in terms of what behavior or what strategy is dominating positive returns at a given moment. But it does switch back and forth.

And the irony is that it can last in one regime for long enough that partisans on either side of this divide only ever become convinced that they’re right. In other words, if you’re the grumpy, crotchety value investor who hates how expensive Nvidia is — for the past several years, it’s like at some point things will revert, and a bunch of people who own that are going to lose a bunch of money. And you’re going to take from that the lesson that you were right all along. “Look at all those pigs getting slaughtered. I’m so smart. What idiots.” But you’ve missed out on the returns for the decade — or whatever, the two decades — that they were making money.

And so the duration of these cycles is such that very few investors actually learn that the right answer is to not be partisan. The right answer is to have an open mind and to try to understand what environment you’re presently living in, and to try to have the mental flexibility to apply the right toolkit to that particular environment. You can’t choose what the market is wanting to do at any given moment. You can’t choose where the world’s going. You don’t get to choose whether AI is over-hyped or under-hyped, or whether it’s going to deliver or not. And in fact, we don’t know — nobody knows what’s going to happen.

So the best that you can ever do is try to unemotionally assess what the current conditions are and make a good judgment about how you should take risk in that environment. And that sort of flexibility and agnosticism is the thing I would recommend to myself and to all other investors.

Matt: Follow up to that — what’s one thing that one of your peers in this space would disagree with you on?

D.A.: Hmm. Well, I am naturally inclined to systematizing things. And so I’m quite biased towards systematic and quantitative investing broadly. And I think particularly in biotech, most of my peers are biased against that, because — as we discussed — making sense of these companies and putting prices on them is a very delicate handcraft. And so anyone who lives and breathes that craft is very familiar with all the nuance that it entails and therefore is naturally predisposed to think that it will never be done by computers. And the same goes for venture capital.

But when I just zoom out and look at what I believe is the long history of markets and professional investment strategies, what I see is that we are inevitably just going to keep getting more and more systematic over time. And if you look at active investing 10 years from now, it’s going to have a higher share of quant than it had today. And obviously if you look today, it has a higher share of quant than it had 10 years ago. I think that is the big, 50-to-100-year story of our industry. And so I always want to be on the side of more technology, more systemization, more science applied to investing.

Of course, the risk of that is to overestimate our ability to be scientific in a domain that is non-stationary — where markets change over time and where the way that we adapt to changing markets further changes those markets. So there are a lot of reasons to be humble about systematic approaches. But if I had a bias, it would be very much in favor of more and more systemization.

Matt: Okay. I have one more bonus question for you before I let you go — and this is because I won’t forgive myself if I don’t ask you at least one music-adjacent question. So if venture biotech was a style of music — a scene, if you will — take it wherever you want to go. Is it K-pop? Is it CBGBs in the seventies? Is it hyperpop?

D.A.: That’s a great question. Well, I don’t want to pat ourselves on the back too much, but as we’ve talked about, I think it’s highly skilled.

Matt: Okay, go on.

D.A.: So now we’re in the territory of jazz, math rock, classical. It’s definitely not —

Matt: Don Cab? Fox?

D.A.: It’s not CBGBs — shots fired — punk. We’re not —

Matt: Yeah.

D.A.: So it’s like — I think it’s free jazz. Because there’s a lot of — it’s people who like risk. I mean, it’s really crazy — people who have a huge appetite for risk.

And it’s people who are not mainly in it for the money. That’s one nice thing about being a biotech investor — most of the people in biotech are not in it for the money. Because if that’s what they cared about, they wouldn’t be doing this — they’d be doing something else in the investment world, because it’s not the best place to make money.

And third — as we’re talking about — it’s highly specialized, it’s very craft-oriented. And so I think it is filled with people who love the practice. They love the science. And that’s another nice thing about this biotech environment — most of the people I’m interacting with on a daily basis, they didn’t start as investors, they started as PhDs or physicians. Their North Star in life is to try and help people who are suffering from unfortunate medical issues. And then what makes it so interesting is that the science itself is so fascinating and so endless.

Matt: I’m taking that answer. I’m asking for comments on it. I want feedback from some people who are going to care about this — you know who you are. We’re coming to debate this to a town near you at a podcast discussion panel. D.A., if people want to find out more about you, read about your firm, bug you on the internet, where should we send them?

D.A.: Sure. I’m on X. My handle is DA Wallach — D-A-W-A-L-L-A-C-H. I’ve got a website, which is just dawallach.com. I occasionally write on Substack, which is whatever their URL is slash DA Wallach. And I’m da@dawallach.com.

Matt: This was enormously educational. Really fun to talk through on all these levels, because there’s just not a lot of you out there in the world doing this kind of thing. So thank you so much for the time. Alright, man — it’s probably a good thing you’re watching Excess Returns. That’s D.A. Wallach. I’m Matt Ziegler. Like, comment, subscribe, all the things below. We are out.