We do live in a world where information is really cut up, but we also live in a world where you can have access to more information than you ever could.
What are the key mental models that you keep coming back to that sort of explain how the world works to you?
I'm a big believer in systems thinking. There's a book called Thinking in Systems that I read.
What does that mean to think in systems?
I'm on the board of the Santa Fe Institute. The Santa Fe Institute studies complexity theory. I would describe complex systems as multivariable nonlinear systems. And multivariable nonlinear systems are very hard to predict. They can behave one way for a long time and then one variable can switch and they can behave another way. The weather, stock markets, all these things. There's consequences that can be first, second, third derivative. And you can't just think with a linear model or just think one variable because things can go way off the path. Being aware that if you make a change here, it could change something here which could change something there and it has to be the whole system.
How does that help you when you're solving problems or thinking about stuff?
I think it keeps you out of trouble because you can avoid consequences that you might find out later. You know, I was talking to a guy that worked at one of the large dating sites. They had this idea making the profile longer would lead to more engagement. Simple heuristic. And they tested it and it was true. And so they rolled it out. They found out many months later that it was negative for conversion, like when people knew more at that level.
And so but you find that out way later. There's my point about like a second derivative effect. And so you just got to be really conscious of the consequence and not get too deterministic about a single metric or a single variable and know what's important and what's on top.
What was the process you took to go about learning the craft of investing and who were the mentors and peers that played a role in that?
So because I started on Wall Street, you know, and not in venture directly, I got caught up in all the people you would expect, you know, around Wall Street and stocks. And so, you know, that starts with Peter Lynch, One Up on Wall Street, you know, best-selling book, probably the first book I read about investing, A Random Walk Down Wall Street, Burton Malkiel, all the Buffett letters, you know, Ben Graham. Once you read Buffett, you have to read Ben Graham. And so, and then Howard Marks, who's just incredible. And you were talking about the purpose of your podcast. Those people have spent their whole career assembling their thoughts and publishing them, you know, along the way. So those were the ones that I read everything. I think I had a very strong kind of bedrock of financial understanding.
It's interesting because as you're saying that, I'm thinking like value investing and then you went into non-value investing in a way, right? Like how did that translate? How did what Buffett said translate into seed investing and sort of venture investing?
I think having a firm understanding of the bedrock is super valuable and then when you recognize the need to innovate on top of it, it's just really good to have that foundation. I have an incredible peer in this guy, Mike Mauboussin. I don't know if you've heard of him, but he's a writer of financial books. We started at First Boston. He had probably been there a year or two ahead of me. So it's just super fortunate that I landed in the same place as him and we've been lifelong friends since then. He introduced me to a gentleman named Bill Miller who ran Legg Mason and had this like 15-year run of beating the S&P, one of the most famous investors of all time. And he claimed to be a value investor and he was the largest shareholder of Amazon for a very long period of time. And what he would say, I'm getting back to your question. He would say that, you know, value just means that the asset is underpriced relative to what you think it will be worth in the future. I spent a lot of time talking with Bill about network effects. And if you believe in that, then Amazon might be able to grow at an unreasonable growth rate for a very long period of time, which he believed. And so that's how you get there. But yeah, I've often thought that many of the VCs in Silicon Valley would benefit from having a better understanding of finance. And one other answer to your question about how it becomes valuable. You know, I've always thought of Wall Street as the buyer of the product that venture capitalists create because of the eventual liquidity is either an M&A or an IPO. And now the price is being set by that group and that institution. So if I know what they value, even if we're starting at a very early place, two people in a PowerPoint, you're still thinking about when this thing grows up, is it going to be something they're excited about?
Yeah. The trajectory matters more than the starting place, I think.
Yeah. That's where you're going to end. That's the output at the end of the day.
What does it mean to know the bedrock of the industry? We live in a world where people skim. They want the gist of things. They want, 'Give me the summary. Give me the executive summary.'
I'm going to tell you a story. So, my partner at Benchmark, Alex Balkanski, would go to this charity auction that I think Andre Agassi would run in Vegas. And one year, he bought a dinner with John Lasseter, the creative genius behind Pixar. And we go to John's house and he serves us in his movie studio. He serves us in his viewing room a 10-course meal. And each piece of the meal is tied to a classic cartoon that he believed was super important to understanding animation. And he would show it and he would talk through it and explain it. And you see that and you're like, 'Holy crap.' like he knows more about the history, you know, and then here's another data point that I just love. There's a world chess tournament and they take a break and run a trivia contest and Magnus Carlsen wins the trivia contest and it's all about the history of chess. We do live in a world where information is really cut up, but we also live in a world where you can have access to more information than you ever could. And that's even more true now with LLMs. I mean, you could just sit there. You have an hour drive and you could sit there and talk to OpenAI and learn about anything you want to. And I think more people would benefit by studying the history of whatever field they're in. There's another one that we mentioned is Picasso was a wildly successful realist painter by the time he was 14. If you go to the Barcelona Museum, you can see that. And I don't think anyone that looks at his cubist paintings would intuit that that was true. And then one last thing I would just say about this, and I think this is broadly applicable to almost anyone in any career. Imagine, let's just pick a field. I'm going to pick marketing. All right? Imagine you're interviewing for a job at P&G or Pepsi out of college and there's 20 people there and you're the one that understands the masters of marketing more than the others and you're able to bring that up in the interview. Isn't that wildly differentiating?
Yeah, totally. I can't imagine how it would land on me if I met that person and yet other than fields like I think in like literature you probably everyone studies the greats but in these other fields it's not a practice and I just think it would be like remarkably differentiating for people to walk around with the history of their field. I had a friend who actually recommended to people that their college essays do that when their admissions essays talk about like if they want to go into physics talk about the forefathers of like physics and show them and like you'll instantly create tons of contrast with everybody else.
And you'll show a passion like it infers passion to want to know that. And then the other part I get into, if that sounds tedious, it's probably not the right like if it's tedious to learn that this isn't a passion like you're not in the right I don't think you're in the right lane.
So you've spent your life working with outliers, all these founders. Are there is that a common trait? And I mean not just the history of the field but the details as well.
I don't know if the history is a common trait. I would say that a more common trait that's related in the entrepreneurial world is obsessive learning, like constant learning because the disruptions that allow for the technology waves that allow for companies to be disruptive and take market share from an incumbent are all tied to something dynamic that's happening on the edge. And every entrepreneur that's exploiting that, it's AI right now. They're going home at night and reading everything they possibly can because the edge is moving and they need to be right there and they need to be a top 1 percentile person that understands this new thing that's happening.
And today it's AI, but that was true of the mobile wave. Like when the mobile phone came out, there were no engineers that had written apps for mobile phones. And a few people got on that edge and figured out what that meant. And that requires obsessive learning on the edge.
The way that I'm thinking about that, and maybe I'm coming at this wrong, is, you know, if I'm young and upcoming, I'm on that edge. And I'm going to dive into it, but if I'm an incumbent, it's much harder to dive into that because it might mean, it's the innovator's dilemma in a way, but it might mean giving up a previous decision I've made or saying that I've been wrong and going backwards. How do you think about that in terms of competition?
I mean, I think that anybody in any field should want to be curious about the bleeding edge and what happens. And, you know, as a venture capitalist, you know, we're always definitely afraid that some new app's going to pop in the app store that we haven't seen or and so everything that comes up, I play with, I roll around. Right now, I have like five premium AI accounts because I just don't want to miss something. And you get trained that way. I think everyone should operate that way. I mean, it's kind of an interesting contrast. I'm suggesting you should understand the really old stuff, the history, because it's differentiating and shows a passion and it gives you a great frame of mind, but you also want to really understand the new edge. If you do both of those things, like you're I think you're a power player in your field, you know. And the second one is a great way for young people. That's another thing that could really differentiate you in an interview. If you're applying for that marketing job and you understand all the legends and the history, but you also really get TikTok, like that's super like that's going to be a very differentiated skill going into those companies and it matters. Like it really matters. Gives you a chance to shine.
If I was to observe you use AI for a week, what would surprise me about the ways that you're using it?
You often underestimate how much it can do. So you might ask it to identify the top 10 of something and then you're going to take those 10 and go study them. But you can say identify the top 10, list their pros and cons, and then rank order them based on this dimension. And then rank order them again based on another like stuff you would have done later you can just build into the prompt and it can do more of the work earlier for you. Early on I would often ask it for numbers and then I would go add them up and I'm like oh you can just tell it to do that part too.
Do you find ChatGPT is the best one?
I like the project structure and I'm being sucked into the memory element in that it knows who I am and it knows things about me for restaurants and stuff. I've been using Gemini just because it has all the Google review data and you can, you know, you don't just ask it which restaurants are good. You can say what are three plates people rave about and what have people warned against. Like you can go deep into the menu, which I do all the time. You know, the coding people swear by Claude.
Yeah. And I met a guy this morning who says for finance he prefers Perplexity, but if he's doing deep research on companies or like companies and countries he doesn't know, he finds Claude does better. So I think it's still a mix.
Do you think we're going to end up with like one model that just sort of like dominates or do you think we're going to end up with niche models and they're effectively going to be commodities in some way?
I think it's highly dependent on how things play out. There are certain examples in the verticals especially in the coding one which is probably the largest vertical right now where people have swapped out models, you know, Cursor even lets the user pick the model that they're using and as we move towards optimization and price optimization which isn't really the objective function right now but it will be in a few years, you may see more people try and do those swaps. I think the thing that could cut against that if the regulation gets extremely difficult and mundane and expensive that could actually lead to more oligopoly and I think some of the players know that and are begging for regulation.
Oh because they want that because it's a protective moat.
Pulls up the bar against especially against the Chinese open source models.
How do you think about regulation in the global sense? Just zooming out a little bit here. If one country is regulated on AI and it slows them down effectively and another country is not regulated on AI and it speeds them up like how do you think about that?
This has come up especially around copyright, you know, and if our models all have to adhere to some special rule and there's already been settlements and whatnot and the Chinese open source models don't, it could have an effect. You know, it's very I'm very uncertain how the EU might rule in that type of situation. So, I don't know. You know what I'm saying? I don't know how they might view it.
How do you think about it from a systems point of view? Just from like China seems they have four open source models now that are really good. Is that...
By the way, this is a great question just to talk more about systems thinking. So they have like 10 open-source models and so you have a situation where the competitive dynamic in China is more intense because it's more intense. Everyone's chosen to go open source and that creates a system that in my mind is capable of innovating far faster than the competitive system we have here. All the models learn from one another. You can actually have a model train another model or test another model. I'll use a simple metaphor but imagine you have two societies and both agricultural societies and one of them when all the farmers come to market they just sell each other goods and then they go back and the other society when the farmers come to market they're forced to share best practices with all the other farmers. Which one of those is going to evolve faster? And open source allows me to see what they're doing, how they're doing it.
Are they open sourcing weights too or just the...
Yes. And a lot of them are publishing how they figured it out like new techniques and things like that. So it's way more dynamic.
And does that help Western nations then too?
Well, there's an irony that a lot of the startups are forking those models and this would be a question of how regulation plays out and whether someone tries to stomp those out or not. I would say it's kind of a quiet secret just because I haven't read it on the front page of the journal that especially from a breadth standpoint like a volume companies are using these models all over Silicon Valley.
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If AI is really going to change everything or, you know, have such a big impact, how does it change how you invest? And you look at a company, are you looking like this is a wrapper on AI? You're effectively like a calculator app on the iPhone or like how do you think about that?
I think that question is up for grabs and it's a hot discussion between everyone. So, you know, if you believe that these models become near sentient, then there will be no need for a vertical model because this one model will just do everything. I probably come down on the other side of that. I think that there are workflows and data modes that if you get and also just understanding like there's three or four legal startups in the AI space, they're just spending so much more time making sure they ingest all the case law and really understand, you know, the processes and principles there and then you implement with them and they're writing stuff on your behalf and you're building new databases out of there. I just don't know that you then switch that to ChatGPT as they climb up the stack. But and I'll flip back to the other side. You know, they have talked about in their product groups, you know, going after verticals. So, it's I think it's a TBD. People point to Microsoft, you know, starting with the OS and then, you know, there was Lotus 1-2-3 and there was WordPerfect like I can't remember the specific apps, but you know, they eventually moved up the stack. That could happen. We're going to see how it goes.
Do you think there's limitations to how we're training the models now, which is sort of they're trained on all the data from the internet, including like, you know, Elon has the opposite approach where he's like, we're going to take all the data and then we're going to filter out clear untruths. We're going to use that as the starting point versus the other.
I do think that there is a valid argument that we might be running out of data, you know, that we're I call it painting in the corners like you know, just we've filled in everything right now. One of the most powerful solutions to improving the models is hiring experts. Literally hiring experts for thousands of dollars an hour to sit in and fine-tune and ask very hard questions and then tune them to be able to solve those. There's got to be a limit to that like where's the edge of human knowledge. So it's a big question like do we run into asymptotes or not? And part of it goes back to do you believe these things can become super intelligent at which point they start solving things that we've never imagined. There's a lot of debate about that.
I mean the I guess the theory correct me if I'm wrong is like the minute that they are super intelligent they can effectively make themselves a little bit better and at that point you just you enter a nonlinear curve.
That's an argument that some people have made. I don't know that I believe it but...
Give me the other side of it.
Rather than me stand on that hill like Yann, you know, Yann LeCun makes that point like he says that the next version of AI is not it's not LLM it's outside of LLMs it's broader than LLMs and that we're going to run into an asymptote with these because they're language based and there's just a limit to what language what you can capture with language which is part of why they're not specifically great with math and numbers, right? There are much better people to talk about this than me, but there's a people point to this famous game that Google AlphaGo where Google implemented and the bot eventually came up with a move that was shocking to all humans and that I forget the number. It's like a famous move number, whatever. And that is proof that they can innovate, you know, beyond what they're taught. The people that take the other side say that's a very constrained game and environment. And the computers can search a field of possibilities that's impossible for a human to search because there's just too many, right? And that gives it the ability to find that move that we didn't know about before. But in the real world, it's not constrained enough where you can tell it to walk all the possible paths. There's an infinite number of paths in a big complex system. And by the way, those AI models aren't LLM based like AlphaGo is not LLM based. It's an AI model trained to a very specific constraint system.
And that was trained just by playing. Is that true?
Yeah. Exactly. But and even FSD is, you know, at Tesla is a constrained environment like there's the inputs are the brake and the steering wheel and the gas pedal.
And or those are the outputs actually the inputs are all the visual data.
It's scary good. I mean I was telling someone the other day I was like I would be comfortable sitting in the back seat at this point with full self-driving like I don't feel a need to drive anymore.
What's your take on that? The corner cases.
Would you sit in the back seat with your Tesla driving?
The corner cases are impossible to fathom at this, you know, right now. Yeah, maybe at some point. I mean, I certainly think if it were in a world that didn't have the randomness of the real world. So, if you were in a geographic area where all of the cars were that, it'd be easier to go into that mindset.
Is that We got humans that think it's fun to test. People are jumping in front of these cars like that's not good.
I was talking to Rory Sutherland. He's like, 'You can just have fun with this. They're going to stop. You know they're going to stop.' And so like you don't even have to look both ways now. What are the consequences of that?
Yeah. Yeah. That's not good.
What opinions do you have today that are sort of non-consensus that you think are correct?
Having spent a ton of time in China over the past 20 years, it's hard for me to adopt this mindset of vilification that's heavy amongst many in Washington and now many in Silicon Valley. The US is like 3, 4, 5% of the global population.
American exceptionalism. When people utter that word, I always wonder like imagine the other 95% of the planet thinks when they hear someone say that. You know, that's probably a non-consensus viewpoint.
Are there Do you think we're overfunding this buildout? How do you think about that?
I saw that smile on your face.
I mean, it's such a hard question to know. If you told me five years ago that these Mag 7 would become worth $3 trillion and then turn around and take their free cash flow from 50 to 100 billion a year down near zero because they were going to spend it all on capex, I'd have been like no way. Like I wouldn't have believed it. So from a certain standpoint, I'm shocked that the money's this big. I will tell you that the venture capital community, you know, I meant we talked earlier about increasing returns and that concept and other people call it power laws like when startups have become important in an ecosystem and then they've been able to prove that they can grow and that that growth might be a function of their size already or their footprint or their users and that would include everyone from Google to Amazon to Meta that they end up being worth way more than anyone thought. And I think the investor community writ large has slowly become aware of and believes it strongly in increasing returns and power laws. And so over time, if they all believe that, they're going to be more willing to invest on the come and take risk. Right? That makes sense that follows. And so, you know, someone forwarded me a chart this morning of the losses of the leading company in the field prior to going cash flow positive. And you look at, you know, for Amazon it was like two or three billion. For Uber it was like, you know, 15 billion. And now for these companies, it's going to be way bigger than that. And so the venture capital community as a whole is getting more risk-seeking and taking on more risk because of their knowledge of how things have played out in the past.
What do you think are assuming we are overfunding? We haven't had a correction like...
Like a mini one kind of like and usually that weeds out sort of the weak competitors the strong ones survive and...
It depends. Yes, but it can be, you know, if you look at what happened with the dot-com crash, you know, there was a four year, three or four year lull before the Amazon of the world started climbing out again, you know, it was like a nuclear winter like right now there's so much optimism and belief in AI. You get to the place where there's very little, you know, some of these the these quote circular deals that people are talking about enhance the probability that we'll have a correction, but also extend the time before we have one.
Yesterday at the DealBook conference, Dario was asked about circular deals and he goes, 'Well, maybe people just don't understand. Let me explain how this works.' You know, imagine you're a cloud service I'm echoing what he said. Imagine you're a cloud service provider and you notice that this company Anthropic wants to develop this model. It's going to cost maybe $5 billion, but they don't have that money. So, you give them that money so that they can spend it. And I'm like, well, if you didn't give it to them, they wouldn't spend it. And so like the growth of everything is enhanced by the fact that you're giving money to companies to spend back on your services they wouldn't have otherwise. And so if you were in a more constrained environment where you didn't do that, things wouldn't be growing as fast. You inflate what's happening.
So you push further ahead faster.
