Transcript — "AI Sells Labor, Not Software — Legendary Investor Elad Gil"

Auto-generated YouTube captions (en), cleaned via vtt-to-text. Speakers not labeled in source; the interviewer (Tim Ferriss) asks questions, Elad Gil answers. >> markers in the raw captions indicate speaker turns.

[00:00:00] I'm looking at a piece in front of me. This is from a while ago, but it's you discussing long-held dogma that ends up being unviable. So, for instance, the common-held belief after PayPal's sale to eBay that fraud will kill you in the payment space, right? Yeah. And I'm wondering how you orient yourself as an investor to stress test those types of dogma. It's really hard because you often end up. You start out with some set of beliefs. You think something's interesting. Or maybe you invest in it, maybe you start a company in it. And then it turns out the thing you think is really interesting turns out to be really hard and you get killed. And then 5 years later a company comes up that actually does it and wins. And the question is why? Why did the thing suddenly work when it didn't before? Or there's 10 attempts to do X and then suddenly — is it that technology got good enough? It could be a regulatory change. It could be a market shift. It could be whatever. An example: Harvey AI Legal,

[00:01:01] where selling to law firms traditionally has been awful. And Harvey's not much broader than that, right? They also have very strong enterprise adoption and lots of different people using them in different ways, but the dogma was always like building stuff for law firms is crappy as a business and you should never do it. But what AI did is it shifted things from selling tools to selling work product or selling units of labor. That's really the shift in generative AI. We're going from seats and we're going from software and SaaS and we're moving into a world where we're selling human labor equivalents. We're selling work hours or labor hours or whatever you want to call it — of cognition. And so Harvey is effectively helping really augment lawyers in different ways. And part of that's a knowledge corpus, but a lot of it is this tooling that really helps lawyers achieve the goals that they have in different ways in a collaborative manner in some cases. And so this is a fundamentally different type of product from what people were selling before. And so it opened up the market in a way that the market wasn't open before. There's actually a broader conversation around: is the world market-limited or

[00:02:00] founder-limited in terms of entrepreneurial success? The Y Combinator school of thought is that we just don't have enough founders, and if we had 10 times as many founders, we'd have 10 times as many big companies. And there's an alternate school of thought, which is how many markets are actually open in any given moment in time, and those are the ones where you can build big companies. Because if the market isn't open to innovation or change or whatever, or hasn't / is undergoing a shift, you can't really build anything anyhow, so why do it? And the striking thing about AI is it's opened up tons and tons of markets that were closed for a long time. And it's opened it up because of capabilities, but it's also opened it up because every CEO is asking themselves, "What's my AI story?" And there's way more openness to try things than I've ever seen in my life. And so we have this odd moment in time where things are massively available for founders to do new things. And if you're an AI company and you're not seeing explosive growth quickly, something's fundamentally broken. Because the markets are so open that you can suddenly grow at a rate that you've never grown before. There's always been cases of companies that just

[00:03:00] go like this. But again, you look at the ramps of OpenAI and Anthropic, and it's the fastest ramps to tens of billions ever. As percentages of GDP, it's crazy. If we come back to your comment of not necessarily market first and strength of team second all the time, but like you said, you 90% agree with that, right? And if you have an excellent team in a terrible market, that's going to be a difficult one to execute. How do you determine what is a good versus great market, or just what is a great market? What do you look for? And the example you gave — I might be overreading this — but you said that when Google shut down, I think it was Maven, right? That's an interesting kind of event-based approach as an input to investing, right? Because you're like, "Okay, if they're not going to build it," that suddenly creates a playing field for startups to play in that space. So could you speak to more of how you determine or look for great

[00:04:00] markets? I mean, there's a few different ways to think about it. One is, some people take the framework of "why now?" What's shifted now that makes it an interesting market — because people have been trying to do things for a long time in every market. And so that may be a regulatory shift, right? Samsara, the fleet-management company, benefited from the fact that there was regulation around needing in-cab monitoring of drivers. So you had some of the cameras watching people so they don't fall asleep while they're driving trucks on the road, right? And so that was their entry point to start building out a suite of software. But it was a regulatory shift. Sometimes there's technology shifts, like what's happening in AI. And the crazy thing about the AI shift is the foundation models instantly plugged into a massive set of markets, which is basically all enterprise data and information and email and just all white-collar work was suddenly available to AI. Because it was the perfect market for that. It also plugged into code, which is a type of white-collar work. So suddenly it just inserts into language, and language is used everywhere in enterprises as well as in consumer. And so there's just a massive market to tap into and transform, or set of markets. Robotics is a little

[00:05:00] bit different from that, because even if you had the world's best robotic model, the submarkets that already have robotic hardware are quite small on a relative basis. And so you don't have that instant runway that you would with language, unless you come up with something new there. That's kind of an aside — I think robotics is really interesting and will be important. It's more just that nuance of what's that instant thing you plug into commercially. And then there's regulatory shifts and technology shifts, there's incumbency or company shifts, competitive shifts. A company may blow itself up, it may get bought by a competitor. One company I'm excited about on the security side is called In-Q-Tel and they're basically competing in part with Hashi. Hashi got bought by IBM. Anytime you get bought by IBM, you slow down a lot usually. Suddenly it creates more opportunity for a startup. So I feel like there are these different things that can change at a given moment in time. [clears throat] It could be the market trying really fast, as Coinbase and crypto, right? You just have suddenly this adoption and proliferation of token types. There's lots and lots of different markets that are interesting. The commonality is usually, is it also

[00:06:00] big? Is there a big enough TAM? And there's two types of TAMs. There's fake TAM — just for people listening who might not have it, your total addressable market. Total addressable market. So what's the market you're in? And sometimes people come up with these fake markets. They're like, "Oh well, we are facilitating global e-commerce, and global e-commerce" — I'm making up the number — "is $30 trillion a year, and so we're in a $30 trillion-a-year market, and if we get just a tenth of a percent of that, that's $300 billion of revenue." And you're like, that's not your market. Your market is, you built this little optimization engine for SMB websites or whatever. That's not a $30 trillion market. And so really it's kind of defining the market. There's a really famous example of this where defining your market changes how you think about it. And so that was Coca-Cola, right? So Coke and Pepsi were roughly neck and neck in terms of market share for decades. And then one of the Coke CEOs said, "Hey, maybe we should be thinking about our share as share of liquid sold." Like drinks, not share of soda.

[00:07:02] And so they just went from 50% market share to 0.5%. And that's why they bought Dasani and that's why they entered all these other markets, right? Because they said, "Our definition of our market is wrong. We're not in the soda-pop business, we're in the drinks business." And so I think also conceptualizing what you're doing can really help change your scope of ambition or how you think about what you're doing. [Tim] If you're trying to spot — along the lines of "fraud will kill you in the payment space," right — any dogma in the AI world, the sphere of AI, anything that's hopped to mind where you think, maybe that's not true now, or maybe in like two years it'll be completely untrue, but people will have latched onto this belief as one of the "thou shalt not" or "thou shalt" commandments? I don't know. I mean, there's some things that have circulated in the past around what's the ROI on the capex spend, and whether it'll be paid back, and I think that stuff is probably off. But

[00:08:01] yeah, I think fundamentally there are moments in time where it's very smart to be contrarian. And there are moments in time where being consensus is the smartest possible thing you can do. And I think right now we're in a moment in time where being consensus is very right. And you can really overthink it: what's the contrarian thing? "We should go do a bunch of hardware stuff because blah blah blah." And maybe just buy more AI, you know what I mean? I think people make these things way too complicated.