Pod Insights a16z Podcast Finding a Single Source of AI Truth With Marty Chavez From Sixth Street About this episode a16z General Partner David Haber talks with Marty Chavez, vice chairman and partner at Sixth Street Partners, about the foundational role he’s had in merging technology and finance throughout his career, and the magical promises and regulatory pitfalls of AI.
This episode is taken from “In the Vault”, a new audio podcast series by the a16z Fintech team. Each episode features the most influential figures in financial services to explore key trends impacting the industry and the pressing innovations that will shape our future.
Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Speaker 00 I cannot believe he said this to me in 1981, but he said, "The future of the life sciences is computational." The through arc is my entire career. I've been building digital twins of some financial or scientific or industrial reality. We looked at that and thought, wow, we'd better do something about this very large unhedged position. That was the history of Dodd-Frank. Like, we don't really know what went wrong in the financial crisis. So let's just go regulate everything. And I think 99% of it was red tape that did not make the world a better place. This was one of the many early nuclear winters of AI.
0:45 Speaker 00 I walked right into it.
0:47 Steph Smith? Hello, everyone. Welcome back to the A16Z Podcast. This is your host, Steph Smith. Now, today we have a very special episode from a new series called "In the Vault." This series features some of the most influential voices across the finance ecosystem, including, of course, our guest today, Marty Chappas. Marty is now the partner and vice chairman of Sixth Street Partners. However, he's long had a knack for spotting how a healthy serving of technology can disrupt other industries. From his PhD of applied artificial intelligence to medicine, to being one of the founding engineers of the team that created SECDB. That's the software that perhaps couldn't predict the global financial crisis, but famously helped Goldman survive it.
1:29 Steph Smith? So today, Marty sits down with A16Z general partner David Haber, and they talk about a lot more, including where the puck is moving in this new wave of technology and the role of regulators and lawmakers within that. And of course, if you liked this episode, don't forget to check out our new series, "In the Vault." You can find that on our A16Z live feed, which we'll also include in the show notes. There you can find other episodes with global payments CEO Jeff Sloan and Marco Argenti, the CIO of Goldman Sachs. All right, David, take it away.
2:08 David Haber? Hello and welcome to In the Vault, A16Z's fintech podcast series where we sit down with the most influential leaders in financial services. In these conversations, we offer a behind the scenes view of how these leaders guide and manage some of the country's most consequential companies. We also dive into the key trends impacting the industry and of course, discuss how AI will shape the future. Today, we're excited to have Marty Chavez on the show. Marty is currently a partner and vice chairman of Sixth Street Partners, a global investment firm with more than $75 billion in assets under management. Prior to Sixth Street, Marty spent over two decades at Goldman Sachs, where he held a variety of senior roles, including chief information officer, chief financial officer, head of global markets, and served as a senior partner on the firm's
2:52 David Haber? management committee. He was also one of the founding engineers behind the legendary software system, SECDB, which many believe helped Goldman avoid the worst of the global financial crisis. In our conversation, Marty talks through the evolution of technology in financial services and the potential impact of artificial intelligence. Let's get started.
3:11 Steph Smith? As a reminder, the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. For more details, please see a16z.com/disclosures.
3:30 David Haber? Awesome. Marty, thank you so much for being here. We really appreciate it.
3:33 Speaker 00 David, it's a pleasure. I've been looking forward to this.
3:36 David Haber? Marty, you've had a fascinating career. Obviously, you played a really pivotal role in turning the Wall Street trading business into a software business, especially during your time at Goldman Sachs and also now at Sixth Street. But you also serve on the boards of the Broad Institute, on Stanford Medicine, and a bunch of amazing companies. Maybe walk us through your career arc and what is sort of the through line in those experiences?
3:59 Speaker 00 Well, let me talk about a few of the things I did and then the arc will become apparent. So I grew up in Albuquerque, New Mexico. I had a moment... Really liked the movie The Graduate when I was about 10 and my father put his arm around my shoulder and said, Martin, computers are the future and you will be really good at computers. And this is 1974 and it was maybe not obvious to everybody. It was obvious to my father. He was a technical illustrator at one of the national laboratories and there was this huge computer that they had just bought that his team used to draw these beautiful
4:42 Speaker 00 blueprints for the weapons in the nuclear arsenal. And they really had the latest and greatest equipment when it was very clunky and very expensive. And my dad knew where it was going. So in New Mexico, you don't have a ton of choices, especially at that time. It's basically tourism and the military industrial complex. And so I went for the military industrial complex and my very first summer job when I was 16 was at the Air Force Weapons Lab in Albuquerque. The government had decided that blowing up bombs in the Nevada desert was really problematic in a lot of ways. And some scientists had this idea, crazy
5:26 Speaker 00 at the time, that we could simulate the explosion of bombs rather than actually detonating them. And they had one of the early Crayon supercomputers. And so for a little computer geek kid, this was an amazing opportunity. And my very first job was working on these big FORTRAN programs that would use Monte Carlo simulation. So I got an early baptism in that technique. And you would simulate individual competent electrons being scattered out of a neutron bomb explosion and then calculate the electromagnetic pulse that arose from all that scattering. And my job was to convert this program from MKS units
6:08 Speaker 00 to electron rest mass units. And so that certainly seemed more interesting to me than jobs in the tourism business. And so I did that. And then the next big moment was I went to Harvard as a kid. And I took sophomore standing. And did you by any chance, did you do sophomore standing?
6:27 David Haber? I didn't do sophomore standing. Also went to Harvard. I think you, I think we also studied about, you studied about chemistry.
6:31 Speaker 00 Yeah.
6:32 David Haber? Yeah, we said it. So, yeah.
6:34 Speaker 00 So you have to declare a major, a concentration right away if you take sophomore standing. And I didn't know that. And I didn't know what major I was going to declare. It was going to be some kind of science, for sure. And I went to the Science Center and the science professors were recruiting for their departments. And I remember Steve Harrison sitting opposite a table saying, what are you? And I was a little bit like a Hogwarts question, I suppose. And I said, I'm a computer scientist. And I cannot believe he said this to me in 1981, but he said, the future of the life sciences is computational. And that was amazing, right? And so profound and so prescient. And I thought, wow,
7:18 Speaker 00 this must be true. And he said, we'll construct a biochem major just for you. And we'll emphasize simulation, we'll emphasize building digital twins of living systems. And so I walked right into his lab, which was doing some of the early work on X-ray crystallography of protein capsids and working to set up the protein databank. And who knew that, well, even back then, he wanted to solve the protein folding problem. And I remember he said, it might take 50 years, it might take 100 years, and we might never figure it out. And that's obviously really important because that protein databank was the raw data for, for AlphaFold, which later came in and solved the
8:00 Speaker 00 problem. And so the through arc is my entire career, I've been building digital twins of some financial or scientific or industrial reality. And the amazing thing about a digital twin is you can do all kinds of experiments and you can ask all kinds of questions that would be dangerous or impossible to ask or perform in reality. And then you can change your actions based on the answers to those questions. And so for Wall Street, if you've got a high fidelity model of your trading business, which was something that I, with many other people, worked on as part of a huge team that made SEC DB happen.
8:41 Speaker 00 Then you could take that model and you could ask all kinds of counterfactual or what-if questions. And as the CEO of Goldman Sachs, Lloyd Blankfein, who really commissioned and sponsored this work for decades, would say, "We are not predicting the future. We are excellent predictors of the present." And I've been doing some variation of that ever since.
9:04 David Haber? Fascinating. I want to spend more time digging into TechDB because that was also a prescient decision, obviously, during the financial crisis. But maybe just going back, I know you ended up doing some graduate work in healthcare and in AI. How did you go from that into Wall Street? Maybe walk us through that transition because it's not probably obvious maybe for most. And then I would love to dig into your time at Goldman and as a founder, et cetera.
9:28 Speaker 00 I got so excited about these problems of building digital twins of biology that it seemed obvious to me that continuing that in grad school was the right thing to do. I actually wanted to go out and start making money. And I really owe it to my mom, who convinced me that if I didn't get a PhD, then I wasn't going to do it. I'm sure she was right about that. And so I applied to Stanford. That was my dream school. And so what happened is I was working on this program in artificial intelligence in medicine that had originated at Stanford under Ted Shortliff, who was extremely well-known even back then
10:11 Speaker 00 for building one of the first expert systems to diagnose blood bacterial infections. And so I joined his program and we, and a bunch of my colleagues in the program, took his work and thought, can we put this work, this expert system inference, in a formal Bayesian probabilistic framework? And the answer is you can, but the downside is it's computationally intractable. So my PhD was finding fast randomized approximations to get provably nearly correct answers in a shorter period of time. So this was amazing as a project to work on, but we Realized pretty early on that the computers were way too slow to get anywhere
10:55 Speaker 00 close to the kinds of problems we wanted to solve. The actual problem of diagnosis in general internal medicine is you've got about a thousand disease categories and about 10,000 various clinical findings, laboratory findings, or manifestations, or symptoms. And the joint probability distribution that you have to calculate is therefore on the order of thousand to the 10,000. And this is a big problem. And we made some inroads, but it was clear the computers were just not fast enough. And we were all despondent. And this was one of the many early nuclear winters of AI. I walked right into it. I remember I stopped saying artificial intelligence. I was embarrassed, right? Like, this is not anything like artificial intelligence.
11:40 Speaker 00 And a bunch of us were casting around, looking for for other things to do. And I didn't feel too special as I got a letter in my box at the department. And the letter was from a headhunter that Goldman Sachs had engaged. And I remember the letter, I probably have it somewhere. It said, I've been asked to make a list of entrepreneurs in Silicon Valley with PhDs in computer science from Stanford, and you are on my list. And in 1993, before LinkedIn, you had to go do some digging to construct that list. And I thought, I'm broke and AI isn't going anywhere anytime soon. And I have no idea what to do.
12:23 Speaker 00 And I have a bunch of college friends in New York and I'll scam this bank for a free trip. And that's how I ended up at Goldman Sachs. And it didn't seem auspicious. I just liked the idea. They were doing a project that seemed insane. The project was, we're going to build a distributed, transactionally protected.
12:44 Speaker 00 Object oriented database that's going to contain our foreign exchange trading business, which is inherently a global business. So we can't trade out of Excel spreadsheets and we need somebody to write a database from scratch in C. And fortunately, I had not taken the database classes at Harvard because if I had, I would have said, that's crazy. Why would you write a database from scratch? And I don't know anything about databases. And so I just had the fortune to join as the fourth engineer and the three person core SEC DB design team. And then in a very lucky move, one day the boss comes into my office and said, the desk strategist for the commodities business
13:28 Speaker 00 has resigned. Congratulations. You are the new commodity strategist and go out onto the trading desk and introduce yourself. He was never going to introduce me to them. And we were kind of scared of them, to be honest. And so there I was in the middle of the oil trading desk, kind of an odd place for a gay Hispanic computer geek to be in 1994 Wall Street.
13:54 David Haber? It's such an amazing story. And you know, one of my favorite lines, which I believe and I repeat often, is that opportunities live between fields of expertise. And I personally love exploring those intersections. I feel like your career has sort of been at these intersections. Maybe fast forward kind of into the financial crisis, you know, famously, you know, my understanding is that SEC DB really helped the firm navigate that period and really saved Goldman Sachs. So what was it about SEC DB that was different than other Wall Street firms who, you know, lost billions and millions of dollars in that moment? And how did you guys sort of navigate that?
14:24 Speaker 00 Yes. Well, you know, this is where we're going to start to get into the top culture, right? It's because of course you have to mention the big short when you start talking about these things, right? And so SEC DB showed the legendary CFO of Goldman Sachs during the financial crisis, David Vinear, that we and everybody else had a very large position in collateralized debt obligations, CDOs that were rated AAA.
14:52 Speaker 00 So in SEC DB, it's another thing and it has a price. And that price can go up and down. And there's simulations where it gets shocked according to probability distribution. And then there's non-parametric or scenario-based shocks. And we looked at that and thought, wow, we better do something about this very large unhedged position, namely sell it down or hedge it. We didn't know that the financial crisis was coming, of course. And we got in the press and elsewhere accused of all kinds of crazy things like they were the only ones who hedged, so they must have known it was coming. We were just predictors of the present and thought, better hedge this position, hence the big short.
15:36 Speaker 00 And the question was, if Lehman fails, what happens then? And we talk about Lehman as if it is a single thing. We had risk on the books to 47 distinct Lehman entities with complex subsidiary guarantee, non-guarantee, collateralized, non-collateralized relationships. And so it was super complicated, but in SEC DB, it was all in there and you could just flip it around. You could just as easily run the report from the counterpart side. Now, I make it sound like it was perfect. It was, it was a little less than perfect. We had to write a lot of software that weekend, but the point is, we had everything in one
16:20 Speaker 00 virtual place and it was a matter of bringing it together. So this is also part of the legend, but it's also factual. We had our courier show up at Lehman's headquarters within an hour of its filing bankruptcy protection for the 47 entities. And we had 47 sheets of paper with our closeout claim against each of those entities rolled up firm-wide across all the businesses. And it took many of the major institutions on Wall Street months to do this.
16:58 Speaker 00 And so that was the power of SEC DB. And of course, it was wildly imperfect, but it was something that nobody else had.
17:06 David Haber? Just to like piggyback on that last point, what impact has regulation had historically on technology's impact on financial services? And I think about, you know, the different asset classes, for example, in global markets that shifted to be traded electronically, right? Like, was it often historically driven by regulatory change, emergent technologies? Both, you know, I'm curious about that and also how it informs the future.
17:32 Speaker 00 Yes, well, so regulation is a powerful driver of change and so is technological change. And some things are just inevitable. I'm a strong believer in capitalism with constraints and rules. And we can and will have a vigorous debate about the nature of the rules and the depth of the rules and who writes the rules and how how they're implemented and all that matters hugely. But to say, oh, we don't need any rules or trust us, we'll look after ourselves. I just haven't seen that work very well. And so in some cases, the regulators will say something. For instance, in the Dodd-Frank legislation, there's a very short paragraph that says
18:14 Speaker 00 that the Federal Reserve shall supervise a simulation. It was called the DFAST simulation, the Dodd-Frank, and I don't even remember what the rest stands for, right? And that will be part of the job of the Federal Reserve, a simulation of how banks will perform in a severely adverse scenario. And that was a powerful concept, right? You have to simulate the cash flow, the balance sheet, the income statement several quarters forward in the future. None of this was specified in detail in the statute, but then the regulators came in and really ran with it and said, you will simulate nine quarters in the future, nine quarters in the future, right? The whole bank, all of it, end to end.
18:59 Speaker 00 And then, in a very important move, the acting supervisor for regulation, at the time, Dan Terulo. Reserve Governor said, we're going to link that simulation to capital actions, whether you get to pay a dividend or whether you get to buy your shares back or whether you get to pay your people, right? Because he knew that that would get everybody's attention. If it's just a simulation, that's one thing. But if you need to do it right before you can pay anybody, including your shareholders and your people, then you're going to put an awful lot of effort into it. So that caused a massive change and made the system massively safer and sounder. We saw that in the pandemic.
19:43 Speaker 00 There's actually a powerful lesson for us in the early days of electronic trading, for the early days of artificial intelligence, right? There was a huge effort by the regulators to say, we've got to understand what these algos are thinking. Because they could manipulate the market, they could spoof the market, they could crash the market. And we would always argue, you're never going to be able to figure out or understand what they are thinking. That's a version of the halting problem. But at the boundary between a computer doing some thinking and the real world, there's some API, there's some boundary. And at the boundary, just like in the old days of railroad control,
20:29 Speaker 00 at those junctions, you better make sure that two trains can't get on a collision track, right? And it's the junction where it's going to happen. But then when the trains are just running on the track, just leave them running on the track. Just make sure they're on the right track. That's going to be an important principle for L L M's and A I's generally as they start agenting and causing change in the world. We have to care a lot about those boundaries.
20:55 David Haber? And Man, it's a good transition to present day. You were a huge force in the digitization of Goldman Sachs and Wall Street in general and kind of the rise of the developer as decision maker. Maybe talk a little bit about generative AI specifically today. How is this technology different from, you know, the AI of your PhD in 1991? And what are the impacts that you see, not just in financial services, but perhaps in other industries as well?
21:17 Speaker 00 Well, for full disclosure, I remember late eighties, early nineties and this program at Stanford, we were, we were the Bayesians, right? And then we would look at these connectionist neural network people. And I hate to say it, but it's true. We felt sorry for them. We thought. Like, I don't work, simulate neurons? You gotta be kidding. Well, so they just kept simulating those neurons. And look what happened. Now, in some ways, there's nothing new under the sun. I had a fantastic talk not so long ago with Yoshua Bengio, who's really one of the four or five luminaries in this in this in this renaissance of AI that's that's delivering these incredible results.
22:01 Speaker 00 And he was talking about how his work is based on taking those old Bayesian decision networks and coupling them with with neural networks, where where the neural networks designed the Bayesian networks and vice versa. And so some of these ideas are coming back. But it is safe to say that the thread of research, or the river of research that took this connectionist neural network approach is the one that's bearing all the fruit right now. And David, the way I would describe all of those algorithms, because they are just software, right? Everything is turning equivalent, right? But they're very interesting software. They they started off with images, images of cats on the internet.
22:46 Speaker 00 People love putting up pictures of cats. Well, now you've got billions of images that people have labeled as saying, this image contains a cat. And you can assume all the other images don't contain a cat. And you can train a network to see whether there's a cat or not. And then all the versions of that. How old is this cat? Is this cat ill? What illness does it have? All of these things, over the last, maybe starting 10 years ago, you started to see amazing results. And then after the transformer paper, now we've got another version of it, which is fill in the blank, or predict what comes next, or predict what came before. And these are the transformers and all the chat bots that we have right now. It's amazing. I wish we all understood in more detail
23:32 Speaker 00 how they do the things that they do. And we're starting to understand it. It all depends on the training set. And it also depends crucially on on a stationary distribution, right? So the reason all this works on, is it a cat or not a cat? Is that cats change very slowly in evolutionary time? They don't change from day to day. But things that change from day to day, such as markets, it's a lot less clear how these techniques are going to be powerful. But here they are. They're doing amazing things. We're using this in my firm and we're using it in production and we're deeply aware of all the risks and we have a lot of policies around it.
24:12 Speaker 00 It reminds me a lot of the early Wild West days of electronic trading where we're authorizing a few of us to do some R and D, but very careful about what we put into production. And we're starting with the easy things.
24:31 David Haber? It feels like a unique moment, or maybe there's unique to me, a lot of momentum happening, both bottoms up and top down. Bottoms up because, you know, I don't know, something like 40% of Fortune 100 is using maybe GitHub Copilot in some new organization or a Microsoft AI product. And then conversely, every CEO or every board member, right, can plug a prompt into one of these models and kind of understand intuitively the magic and imagine the impact that it could have on their business. And so it seems like the employees of many of these companies want the productivity gains that you're describing. Boards are like, you know, how is this going to impact the human capital efficiency of our company? Like, where can we deploy this technology?
25:13 David Haber? I guess when other CEOs of large companies, you know, come to you for your advice, like, how are you advising them on how to deploy AI in their organizations? Where within those companies? Like, what's the opportunity you see maybe in the near term and, you know, in the middle or long term?
25:27 Speaker 00 Really, first order of business. And this is something that we worked on at Goldman for a long time and I'm happy that we left Goldman in a place where it's going to be able to capitalize on Gen AI really, really quickly, which is having a single source of truth for all the data across the enterprise, a time traveling source of truth. So what is true today and what did we know to be true at this, at the close of business on some day three years ago, right? And we have all of that and it's cleaned and it's curated and it's named and we know that we can rely on it because all of this training of AIs is still garbage in, garbage out.
26:11 Speaker 00 And so if you don't have ground truth, then all you're going to do is fret about hallucinations and you're just going to be caught in hallucinations and imaginings that are incorrect and not actionable. And so. Getting your single source of truth right, that data engineering problem, I think a lot of companies have done a terrible job of it. I'm really excited about the new Gemini 1.5 context window, a million tokens. Like that one, I just want to shout that from the mountaintops. Like if you've been in this game and you've been using RAG, Retrieval Augmented Generation, which is powerful, but you run into this problem of I've got to take a doc,
26:54 Speaker 00 a complicated doc that references pieces of itself and chunk it. Well, you're going to lose all of that unless you have a really big context window. Breaking that quadratic time complexity of the length of the context window is just monumental. And I think over the next few months, you're going to see a lot of those changes. Problems that were really hard are going to become really easy. I don't know. What do you think?
27:18 David Haber? Look, I think every company needs to kind of, using Goldman maybe as the analogy, so much of the organization, but you know, in particular, even many parts of the federation, I think can and should be leveraging software. And a lot of those workflows can be augmented with AI, right? From legal to compliance to vendor onboarding to, you know, risk management as we're talking about. But I think it's going to have a profound impact on the enterprise. Obviously, we're quite biased. I guess one topic that, you know, people debate quite often is the impact of regulation on the adoption of this technology. I'm just curious, your view on, you know, the government's role in this, you know, in general AI and what advice you have in kind of accelerating this versus, you know, what responsibility they have?
27:59 Speaker 00 Well, one of the things that I learned during the financial crisis was a huge amount of respect for the regulators and the lawmakers. They have a really tough job and, and really important to, to collaborate with them and to become a trusted source of knowledge about how a business works. And I just lament the number of people who just go into a regulator and they're just talking their own book and, and hoping that the regulator lawmaker won't understand it. I think that is a terrible way to approach it and has the very likely risk of just making them angry, right? Which is definitely not the right outcome. And so I've been spending a lot of time.
28:38 Speaker 00 With regulators and legislators in a bunch of different jurisdictions. And you already heard a bit of what I have to say, which is let's please not take the approach that we first took with electronic trading. That approach was write a big document about how your electronic trading algo works. And then step two was hand that document over to a control group who will then read the document and assert the correctness of the algo. Right? This is the halting problem squared. It's not just a bad idea. It's an impossible idea. And instead, let's put a lot of emphasis, a lot of standards and attestations at all
29:23 Speaker 00 the places where there's a real world interface, especially where there's a real world interface to another computer. Right? So the analogy is in electronic trading, there was not a lot you could do to prevent a trader from shouting into a phone an order that would take your bank down. Right? How are you going to prevent that from happening? And but what you really worried about was computers that were putting in millions of those trades, right? Even if they were very small, they could do it very fast and you could you could cause terrible things to happen.
30:03 Speaker 00 And so another thing I'm always telling the regulators is, you know, please, please, the concept of liability, right? They start with this idea, let's make the LLM creators liable for every bad thing that happens with an LLM. To me, that is the exact equivalent of saying, let's make Microsoft liable for every bad thing that someone does on a Windows computer. Right? They're fully general. And so these LLMs are a lot like operating systems. And so I think the regulation has to happen at these boundaries, at these intersections, at these control points first, and then see where we go. And I would like to see some of these regulations in place sooner.
30:48 Speaker 00 Other than later, unfortunately, the pattern of human history is we usually wait for something really bad to happen. And then go put in the cleanup regulations after the fact and generally overdo it. That was the history of Dodd-Frank, like, we don't really know what went wrong in the financial crisis. So let's just go regulate everything. And I think 99% of it was red tape that did not make the world a better place. And some of it, such as the CCAR regulations, was profound and did make the system safer and sounder. And I would want us to do those things first and not just the red tape.
31:26 David Haber? Well, I know you're also very passionate about life sciences. You started your graduate career there and I believe you now sit on the board of Recursion Pharmaceuticals.
31:35 Speaker 00 Yes.
31:35 David Haber? Yeah, maybe talk through kind of the implications that you're seeing for generative AI in life sciences and biotech in particular.
31:42 Speaker 00 Well, it's epic, isn't it? I had an amazing moment just a couple of months ago. I had the opportunity of being the fireside chat host for Jensen of Nvidia at the JP Morgan Healthcare Event. And there was a night that Recursion was sponsoring. And we really talked about everything he learned from chip design. So Jensen, incredibly modest, will say, well, he was just the first in that generation of chip designers who were the first to use software to design chips from scratch. And it was really the only way he knew how to design it. And he likes to say that Nvidia is a software company, which it is, right? But that seems counterintuitive because it's supposed to be a hardware company.
32:27 Speaker 00 And he talks about the layers and layers of simulations that go into his business. Those layers do not go all the way to Schrödinger's equation. And we can't even do a good job on small molecules, right? Solving Schrödinger's equation for small molecules, but it does go very low. And it goes very high to what algorithm is this chip running? And that's all software simulation. And he said in that chat that at some point, he then has to press a button that says, take this chip and fabricate it. And the pressing of that button costs $500 million. And so you really want to have a lot of confidence in your simulations.
33:07 Speaker 00 Well, drugs have that flavor, very much so, except they cost a lot more than $500 million by the time they get through phase three. And so. It seems obvious to all of us that you ought to be able to do these kinds of simulations and find the drugs. Now, the first step is going to be just slightly improve the probability of success of a phase two or phase three trial. That's going to be incredibly valuable because right now so many of them fail and they're multi-billion dollar failures. But eventually, will we be able to just find the drug? The needle in the haystack nature of this problem is mind blowing.
33:49 Speaker 00 There are, depending on the size of the carbon chain, but let's just pick a size, there's about 10,000 trillion possible organic compounds and there are 4,000 approved drugs globally. So that's a lot of zeros. And if AIs can help us navigate that space, that's going to be huge. But I'm going to bet that we will map biology in this way. It's just biology is so many orders of magnitude more complicated than the most complicated chip. And we don't even know how many orders of magnitude and how many layers of abstraction are in there. But the question is, do we have enough data so that we can train the LLMs to infer the rest of biology?
34:32 Speaker 00 Or do we need an awful lot more data? And I think everybody's clear, we need more data. I think what we're less clear on is do we need 10 orders of magnitude more data or 100 more orders of magnitude? We just don't know.
34:47 David Haber? Amazing time to be alive.
34:49 Speaker 00 Best time ever. We say this at the Alphabet board and it's what an incredible group of people. And when I hear Sergey and Larry say it's the best time ever to be a computer scientist, you know, of course I agree with that. It's magical.
35:04 David Haber? Totally. Awesome. Well, Marty, thank you so much for your time. Always a pleasure. You've had such a fascinating career and we really appreciate you spending time with us.
35:12 Speaker 00 David, great talking with you. Be well.
35:14 David Haber? Thanks. I'd like to thank our guests for joining In the Vault. You can hear all of our episodes by going to a16z.com/podcasts. To learn more about the latest in fintech news, be sure to visit a16z.com/fintech and subscribe to our monthly fintech newsletter. Thanks for tuning in.
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