Finding a Single Source of AI Truth With Marty Chavez From Sixth Street

0:00I 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 better do something about this very large unhatched 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.

0:45I walked right into it. Hello everyone, welcome back to the A16C podcast. This is your host, Staffsman. Now today we have a very special episode from a new series called In the Volt. This series features some of the most influential voices across the finance ecosystem, including of course our guest today, Marty Chapitz. 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 SectorDB.

1:22That's the software that perhaps couldn't predict the global financial crisis, but famously help Goldman survive it. 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 like this episode, don't forget to check out our new series in the vault. You can find that on our A6 Seam's E Live feed, which will also include the show notes. There you can find other episodes with global payment CEO Jeff Sloan and Marco Argenti, the CIO of Goldman Sachs.

1:57All right, David, take it away.

2:08Hello and welcome to In the Vault, A6 Seam's E's Fintech podcast series where we sit down with the most influential leaders in financial services. In these conversations, we offer 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.

2:41Prior to Sixth Street, Marty spent over two decades of 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 firms management committee. He was also one of the founding engineers behind the legendary software system, SektDB, 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. As a reminder, the content here is for informational purposes only.

3:14should 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 slash disclosures. Awesome, Marty. Thank you so much for being here. We really appreciate it. David, it's a pleasure of been looking forward to this. 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.

3:47But you also serve on the board to 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? 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 like 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 was 1974, and it was maybe not obvious to everybody.

4:29It 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 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, is 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 at the time that we could simulate the explosion of bombs rather than actually detonating them.

5:34And they had one of the early Kray -1 supercomputers and so for a little computer geek kid, this was an amazing opportunity. And my very first job was working on these big 4 -trans programs that would use mycaro simulation. It's like an early baptism in that technique. and you would simulate individual comps and 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 to electron rest mass units. And so that certainly seemed more interesting to me than jobs in the tourism business.

6:15And so I did that. And then the next big moment was I went to Harvard, I was a kid, and I took sophomore standing. And did you, by any chance, did you do sophomore standing? I didn't do sophomore standing. I also went to Harvard. I think we also studied about chemistry. Yeah, we said it out. So yeah. So you have to declare a major concentration right away if you take sophomore standing. And I didn't know that. And I didn't know what major is going to declare. I 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.

6:50And I remember Steve Harrison sitting opposite a table saying, what are you? And I was a little bit like 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 prashant. And I thought, wow, 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 data bank.

7:42Who 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 data bank was the raw data for alpha -fold, which later came in and solved the 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.

8:22And 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 SektiB happen, 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 Blankfine, who really commissioned and sponsored of 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.

9:05It's fascinating. I don't want to spend more time kind of digging into CECDB because that was also a pressure 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. Kind of, how did you go from that into Wall Street? Maybe walk a few kind of that transition because it's not probably obvious maybe for most and then you know Would love to kind of dig into your time at Goldman and the founder etc. 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.

9:50I'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, an artificial intelligence in medicine that had originated at Stanford, under Ted Shortliffe, who was extremely well known even back then, for building one of the first expert systems to diagnose blood bacterial infections. And so I joined this 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 answers you can, but the downside is it's computationally intractable.

10:37So 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 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 in 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 1 ,000 to the 10 ,000.

11:18And this is a big problem. And we made some inroads, but it was clear that 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 as embarrassed. This is not anything like artificial intelligence. A bunch of us were casting around looking for other things to do. I didn't feel too special as I got a letter in my box at the department. The letter was from a headhunter. The Goldman Sachs had engaged. I remember the letter. I probably have it somewhere.

11:59where it said, I've been asked to make a list of entrepreneurs in Silicon Valley with PhDs and 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. And I have a bunch of college friends in New York, and I'll scam this bank for 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 object -oriented database that's going to contain our foreign exchange trading business, which is inherently a global business.

12:51So 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 Sector B 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 has resigned. Congratulations. You are the new commodity strategist and go out onto the trading desk and introduce yourself.

13:36He was never going to introduce me to them and we were kind of scared of them to be honest. And so there 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. It's such an amazing story. And 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. It may be fast forward kind of into the financial crisis. Famously, you know, my understanding is that SECDB really helped the firm navigate that period and really saved the growth.

14:14So what was it about Sekti B that was different than other Wall Street firms who you know lost billions of millions of dollars in that moment and and how did you guys sort of navigate that? 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 Sekti B showed the legendary CFO of Goldman Sachs during the financial crisis David Vineer that we and everybody else had a very large position in collateralized debt obligation CDOs that were rated AAA. So in Sector B, 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 a 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 unhaged position, namely, sell it down or hedge it.

15:16We didn't know that the financial crisis was coming. Of course, you know, 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. 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, non -guaranteed, collateralized, non -collateralized relationships. And so I was super complicated, but in Sektibi, it was all in there.

16:07And you could just slip 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 a little less than perfect. We had to write a lot of software that weekend. But the point is we had everything in one virtual place and it was a matter of bringing it together. So, it's also part of the legend, but it's also factual. We had our career 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 firmwide across all the businesses.

16:49And it took many of the major institutions on Wall Street months to do this. And so that was the power of sector B. And of course it was wildly imperfect, but it was something that nobody else had. Just to piggyback on that last point, what impact has regulation had historically on technologies impact on financial services? And I think about the different asset classes, for example, in global markets that shifted to be traded electronically. Was it often historically driven by regulatory change, emergent technologies, both? I'm curious about that and also how it informs the future. Yes, well, so regulations, a powerful driver of change, and so is technological change, and some things are just inevitable, a strong believer in capitalism with constraints and rules and way 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 they're implemented and all that matters hugely.

17:57But 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 that the Federal Reserve shall supervise a simulation. It was called the D -Fast 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 and a severely adverse scenario. And that was a powerful concept, right?

18:37You 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. And then in a very important move, the acting supervisor for regulation at the time Dan Tarulo, the 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?

19:20Because he knew that that would get everybody's attention if it's just a simulation. and 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 in Sounder. We saw that in the pandemic. There's actually a powerful lesson for us in the early days of electronic training 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.

20:03They 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, 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.

20:44That's going to be an important principle for LLMs and AI's generally as they start agenting and causing change in the world, we have to care a lot about those boundaries. 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 is decision -maker. Maybe talk a little bit about generative AI specifically today. How is this technology different from 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?

21:18Well, for full disclosure, I remember late 80s, early 90s, and this program at Stanford, we were the basins, right? And then we would look at these connectionists to 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 got to be kidding. Well, so they just kept stimulating 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 Benjiro, who's really one of the four or five luminaries in this renaissance of AI that's delivering these incredible results.

22:01And 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 threat of research or the river of research that took this connection as neural network approach is the one that's bearing all the fruit right now. And 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.

22:41They started off with images, images of cats on the internet. People are 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 it'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.

23:22And 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 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 a stationary distribution. So the reason all this works on, is it a cat or not a cat, is the 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 are 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 It reminds me a lot of the early Wild West days of of electronic trading where we're authorizing a few of us to do some R &D, but very careful about what we put into production.

24:29And we're starting with the easy things. It feels like a unique moment or maybe there's a 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, and some in an organization or a Microsoft AI product. And then conversely, every CEO or every board member 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.

25:06Boards are like, how is this gonna impact the human capital efficiency of our company? Like where can we deploy this technology? I guess when other CEOs of large companies come to you for your advice, how are you advising them on how to deploy AI in their organizations? Where within those companies? What's the opportunity you see in the near term and in the middle or long term? Really first order of business. 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, time -traveling source of truth.

25:51So 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 AI's is still garbage garbage in, garbage out. And so if you don't have ground truth, then all you're going to do is is is fret about hallucinations and you're just going to be caught in hallucinations and 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.

26:35I'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. If you've been in this game and you've been using RAC, Retrieval Augmented Generation, which is powerful, but you run to this problem of I've got to take a doc, 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 context window is just monumental. 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.

27:17I don't know. What do you think? I think every company needs to kind of using Goldman, maybe as the analogy, so much of the organization, but 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 on -boarding to 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 people debate quite often is the impact of regulation on the adoption of this technology.

27:49I'm just curious, your view on the government's role in this in generally I and what advice you have in kind of accelerating this versus, you know, what responsibility they have? 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 really important 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 hoping that the regulator lawmaker won't understand it.