20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory

19:52Harry Stebbings:Will we see a contraction then, given the hangover period being realized? We might see a short-term contraction of usage of the very frontier models. But I think it's healthy. I think it's healthier to do that than to be blind to it and then have a real sudden change there. Uber announced last night, I think it was, or yesterday, that they were having a$1 ,500 budget per individual. How do you respond or think about that? I've literally seen this with dozens of our customers. And this is a lesson on our post-sales team, where initially we came in, we were like, oh, by the way, we have these user limits, but here, these are the models go crazy.

20:28This was before we had routing. It happened a couple of times with customers where the usage would go crazy. They hadn't spent the time to actually determine what parts of the code base do we want to dedicate these tokens to versus not. And then they were like, oh my God, we're spending so much. This is crazy. We need to put in token limits. And at first, the first time this happened, we were like, oh my God, their usage went down. What's going on? But spending time with them, we realized, wait, we need to make sure with every customer, we are having a very clear conversation with them of, you know, it looks like you guys are spending a lot of tokens on some of these things.

21:00Have you thought about consciously? Yes, we want to do this. Sometimes we'll proactively set in those user limits. It's better to be aware as you're going up as opposed to just going crazy and then kind of realizing. And so what's happened with Uber publicly has happened privately with a lot of customers of ours. And yeah, there's a little bit of shock where it's like, okay, wait, let's put in these user limits. But then you come into a question of, well, wait, this team is really important. They should have a different user limit than that team. And we're just getting towards this world where you have very nuanced resource allocation throughout your org.

21:29Harry Stebbings:To me, the biggest question that I ask myself, and I think we need to ask ourselves as an ecosystem today, is if Mark Benioff says that he spends$300 million on Anthropic for his devs, That is 3.8 % of salaries. Okay, great. What will that number be in three years' time? Because if it's still 3.8, fuck. If it's 20, fuck again, but fuck positive. And if it's Brendan at McCaw who says that he's spending more on tokens than he is on headcount, fuck again, but even more positive. What do you think that percent of dev salary is in three years? I think it's actually a more nuanced question than we might think.

22:10I actually think it can be as low as 0 % for some individuals, and it can be as high as thousands, tens of thousands of percent for some individuals.

22:19Harry Stebbings:And what's the dependence there? It depends on what the unique skills of those individuals are. And I'm saying individuals and not devs in particular, because I think the way we even organize roles is going to be very different, where I'm not sure dev as a word makes sense. Traditionally, it's like custodians of code, right? The people who do anything relating to code are engineers or developers. everyone is going to be loosely interacting with code in your org, whether they're sales or marketing. But I think the difference is there are going to be certain people where, again, we're coming back to resource allocation, they get more leverage by using more tokens.

22:49And then they're going to be certain people where actually they don't really need tokens at all. And that's not how they deliver value to the business. Like for example, maybe our best salesperson, the way we use them best is not by having them use tokens, but by going and meeting people face to face. That's an obvious example because they don't write code in the first place. But I think similarly, maybe there are some engineers who they actually do their best work by spending time with users, spending time with their customers, maybe doing some data analysis that's not very token expensive. But then there are going to be others who are delegating to dozens of droids in parallel, working on a ton of different crazy features and refactors and migrations.

23:23But I don't think it's going to be a consistent number across the board. In fact, I would argue that if your org has a standard number where it's like, we want every engineer to be at this percent of their salary and token use, you're probably painting with way too wide a brush.

23:36Harry Stebbings:If I were to say, give me an average number, what will that average be? What will the median be? I would say order of magnitude will probably be comparable to salary. Comparable to salary? Like on the same order of magnitude. Within the three-year timeline? Yeah. What percent of tasks today using frontier models could be done with open source models? Probably 80 to 90%. It's typically the planning that really needs the frontier models. But is that not like the most, I'm sorry, I'm really dim. Harry, that's nonsense. But if it's 80 to 90%, does that not just present the biggest bear case ever against Codex or Claw Code?

24:17Harry Stebbings:Because you're just taking away 80 to 90 % of that time. Well, it depends because that 10 to 20 % could be the most important tokens. 10 to 20 % of the tokens, but those are really, really important because it's kind of decision-making tokens, perhaps. Sure. But it's very similar to how we structure human orgs. Oftentimes, leadership makes very key decisions that determine the fate of the company, and they don't spend the most hours. If you look at the human hours of a company, most human hours are not spent on making the decisions. They're on gathering data or implementing things. But then there's a select few hours where it's like, here is where we're going to make this irreversible decision on the strategy.

24:53And those people that make those decisions are also typically paid a lot.

24:56Harry Stebbings:Yeah, but the assumption there would be then that you'd have to increase that spend for that 10 % even higher. And that's what's happening already. It's like the frontier models are, sometimes they're getting more expensive or you're using the ultra high reasoning or, you know, this type of thing. And so it's like, okay, this planning thing is the very key thing. We'll spend on it. But it doesn't necessarily mean that most of your tokens are going there. It's just for certain key steps, maybe you want to spend a lot and it's worth that allocating the budget there. But then once it comes to, okay, we have the plan now let's implement, the open models are typically really good.

25:27Harry Stebbings:When we think about what you're willing to spend on, I asked Brandon how much it costs to hire great AI researchers, and he was like tens of millions of dollars. Have you found the same? And is it impossible to hire great AI researchers in competition with Anthropic and OpenAI? We are a very opinionated organization. And so the people who like the opinionated stances that we take are willing to not necessarily go and try to maximize the dollars that they can get out of in the market. That said, it is still pretty competitive. What do you think is the strongest opinion that you have that most people disagree with?

26:02I would say the opinion that we have that I think in the space that we are in is the most controversial, the way that we treat what product is at factory. I think there's some very commonly held beliefs at the labs or at some of our competitors who are also doing kind of software development. And this is honestly growing up in the Bay Area. There's a very common Silicon Valley fallacy, which is there's like research is like the pinnacle. And then there's engineers who implement the research. You know, they're not quite there, but you know, they're still great. And then there's sales and marketing and all that dirty stuff.

26:32Oh, if only we could build a better product and it would sell itself and we wouldn't need to deal with, you know, sales and marketing. And it's just completely delusional. The product at factory is the entire journey from the very first time they hear our name till their 10th renewal after a decade of being a happy customer. The software is a big part of that journey, but so too is the marketing that we do and the people that we have running that. Same with the sales process, the people that present themselves in discovery calls or in demos or in solution engineering. That entire thing is the product.

27:02Everyone is first class. It's not like we have engineers who are wholly at the office and you're not allowed to speak to them unless you're an engineer. It's like, no, no, no. We have engineers and salespeople sitting next to each other. There are no engineer corner, sales corner, any of that stuff. Everyone is completely intermixed. When salespeople close a deal, engineers say we closed a deal. When engineers ship a feature, salespeople say we shipped a feature. It is entirely one team, entirely cohesive. There's no first class or second class. And this is shockingly controversial in the Bay Area and in particular in coding or in AI.

27:33It's messed up. It's so messed up. And I think that the reality is it will come to haunt some of these companies one day. Because I think right now where there's a gold rush and everyone's desperate to sign and get more tokens from these people, it's easy. In my mind, it's kind of like they're astronauts in space where there's no gravity your muscles will atrophy gravity will come back and if you don't have a good sales and marketing team because you don't give it respect the second gravity returns all of your muscles will be atrophied and you won't be able to compete and i would say this name a legendary company that has a shit sales or marketing team you can't

28:06Harry Stebbings:no but i can name companies that have shit products but great sales and marketing teams that's that's the ironic thing exactly and in fact it seems like many more like most legendary I'm not going to name them because I'll get in trouble. I'm sure we're thinking of some of the same ones. If Chad Peets were here, he would say them. Yeah, that's right. Does what it takes to be a great engineer change when you essentially become prompter and manager of agents versus creator and doer of tasks? Yes, it very seriously changes. And this is actually why we're selecting for, very intentionally, like this culture that we just mentioned is really important because the best engineers are going to be the ones that don't see sales and marketing as dirty work.

28:44But as again, an important part of the product, because as an engineer, you're no longer, you know, just your job is ship feature. It's no, you are owning full end to end outcomes of here's the way the customer is behaving. Here's how maybe we can change that behavior that makes them a better user long term. It makes them more agent native. They get more out of our product. We can then follow them through that journey, enable the salespeople so they know how to talk about it or they know how to demo it. This like, this is like a full stack engineer that goes way beyond just engineering, but into sales, into marketing, into enablement and all that.

29:15And those are the parts of engineering that really, really matter. Those are the parts that have made engineers typically good founders is when they have that. And the parts of engineering that become less important are funny enough, the things that the Silicon Valley has really bragged about a lot, which is like competition winning or like Olympiad type. Are you like as fast as possible at coding? Do you memorize all the different nuances of these different languages? Those are the parts that don't matter. You memorized some coding language or some, you know, syntax of a coding language that someone else didn't, it doesn't matter.

29:43Harry Stebbings:People like not the credentialism, but I think they misunderstand VC mindset right now. And as a VC, I'm happy to share how we feel, which is just like fundamentally there is intense uncertainty around what Anthropic and OpenAI will do and who they will kill at the application layer. And so in a world where we desperately seek certainty, we look for validators. And the validators of someone being a math Olympiad or you name it, whatever that is, that validation, in the wake of not having other certainty, that serves as a good crutch. Yes, but it's a crutch. It's a crutch. It's helpful. It's a good indicator.

30:18Like generally you can't win competitions if you're dumb, right? Like it's pretty rare. However, for these types of engineers that we're looking for, that's cool, but that's kind of irrelevant. Like what have you built? How have you taken ownership and agency of things end to end? This is not like we have people on our team that have won Olympiads and I think they're great and it's fantastic and a lot of my friends have. But there's also a certain like, especially there's some high schools that like really focus on like you must do the math olympic like you must do this the amy to then go to the imo and like this is the path to success where actually that's kind of anti-signal because there it's like you're not owning your fate or choosing your agency you're kind of going through the funnel but then there are people on our team who are like from the middle of nowhere where no one else in their high school ever did this stuff and they kind of took the agency of like i think this is really fun i'm really competitive i want to compete at this and they kind of go and do those competitions on their own those are when the signal is still positive for this kind of engineer of the future i don't think i've told you this

31:12Harry Stebbings:but you know who you always remind me of who matt damon and goodwill hunter oh that's so i mean i'll take that to the bank have you not been told that before let me say that one more time you look identical that's very kind we're gonna put up like an image here and you're gonna do a side by side yeah all right we have a clip this is the clip this is the this is the starter to the show they're gonna be like uh-huh i totally get it that makes absolute sense can i ask you when we go back to actually what makes devs great and how we think about structuring the team, what role does not exist today that you think will be incredibly common in the next few years?

31:45So it's starting to exist more and more, but I think it's kind of this like GM or general manager like role for someone who used to be an engineer, where basically you own end to end an outcome that is not just a shipped feature, but like a business outcome. So even at factory, we have this now where there are people who will own the marketing copy if they're going to be releasing something. They'll own the outcomes in the product metrics. They'll own enabling the salespeople. So it's way beyond what a typical engineer does. And it kind of feels like, again, owning more of a business outcome, more entrepreneurial, higher agency, just like spreading their reach.

32:21Harry Stebbings:You know, I think that's just like every function. It's like, I believe it or not, I sometimes post on different social media platforms. and I just said like my biggest advice to any students today would just be just be full stack in whatever you do. If you're doing marketing, create the copy, make sure that it's ready to post, post it at the right time, amplify it. You have to be in every element from start to finish. Yes. Not just, oh, I just do the copy and then I hand it over to designers to create the visuals and then they hand it over to a social team. Is that not just the same for every function?

32:50Harry Stebbings:We're expecting everyone to be full stack in every function. The age of the polymath is back. Like, growing up, I was so, like, I was obsessed with math and physics, and I was so jealous that hundreds of years ago, people like Da Vinci or Euler or Newton could be polymaths, and it was because their fields were relatively shallow. Chemistry wasn't that built out. Mathematics wasn't that built out. Physics wasn't that built out, in Da Vinci's case, like art and engineering and sculpture. And so you could get to the frontier of these disciplines in multiple disciplines within your lifetime. And then growing up in the early 2000s and 2010s, pre-AI, fields were so deep, in my case, theoretical physics and strength, it was so deep that you could spend literally 50 years catching up on all of the literature and academia that's existed before you contribute anything new.

33:35And so it was like, this was infuriating to me because it was so frustrating. With AI, we're now completely the opposite. These tools can get you up to speed to the frontier. Obviously, with a lot of uncertainty about certain details, you won't have the depth of other people, but it'll get you to the frontier way faster than ever before. And so now if you're someone that's good at thinking around constraints, thinking about systems, holding uncertainty in your head and being okay with that, like knowing there are unknowns and knowing that you can still push the frontier forward despite that, you can be a polymath.

34:05You can push forward and create innovations on how to do developer marketing. Well, at the same time, pushing forward the frontier of token caching for software development agents at the same time as like, you know being an incredible solution engineer like these are things that you can now do all at once and so this is something that's very top of mind for me and in our hiring process we want to find the people that can be those polymaths the era is totally back polymaths are

34:29Harry Stebbings:back i've had a lot of people say on the show that agent operations will be like with the leading function that doesn't exist today that will be very common in three to five years do you agree with that what is the definition of agent operations agent operations is the creation of agents and the maintenance of them. So to be able to go into different functions and say, ah, social media, I'm going to create agents that allow you to create, distribute, share posts. Ah, marketing and design, I'm going to create agents that allow you to create visuals, share them amongst each other, edit them, collaborate on them.

35:01I think to some degree, everyone should be able to do that on their own. But I could imagine a world where there's kind of someone whose job it is, is to like find places that aren't as efficient and similar to operations now, like in organizations, but now it's just agentified. So they're using agents to make the organization more efficient wherever possible. But I think in general, if you have people that in certain functions that aren't proactively doing that, probably a bad sign. What do we do today that we'll look back on and go, oh my God, I can't believe we did that. For an engineering team, like writing release notes.

35:30That's crazy that people used to spend hours of time writing release notes or like writing documentation. So not everyone knows what release notes is.

35:37Harry Stebbings:What is release notes? So it's like, you know, basically cataloging the changes that you've made in the last whatever month or so, and sending it out to either internal or external to your users. And generally, this and documentation. Stripe has a really great reputation. They had incredible documentation. So many APIs had horrid documentation. Stripe was like the pinnacle. They were so good at this. Spent a lot of time doing it. Five years from now, it's going to be like, oh my god, I cannot imagine, cannot believe that these people that get paid so much money spent hours of their time doing this.

36:06I think that's something that we definitely won't do. Does that reduce the impact of Stripe's great documentation if everyone is equalized? Yes, but I think Stripe has plenty of places that they can differentiate. And I think it's a better world where everyone has documentation as good as Stripe's. How does the product review and especially that code review process change in the next few years? What's cool about this agent-native software development is review has been a big problem. Because basically, first phase of rolling out AI coding tools was, oh my god, look how much code we can generate.

36:36It's incredible. I'm generating a ton of stuff. Phase two was some poor staff engineer who has to review hundreds of these slop PRs that are like, don't adhere to your standards, are completely misformatted and all this stuff. But what's great about having this kind of like full end-to-end software factory, as it were, is it's now very clear the ROI of investing in things that make your agents more kind of ready for production. So examples of this are making sure your agents have access to up-to-date documentation, making sure agents can spin up a remote machine so that they're not just generating the code, but they can actually run it and see what the outputs are and iterate based on that to make sure that it's actually good.

37:15Setting up things like CICD or good linters or good pre-commit hooks. These are all things that the best organizations at like developer experience would invest a lot of resources in, but they would do it because it makes it easier for engineers to work, easier for them to onboard. But the impact of doing that well is just like one-to-one kind of correlated to how many engineers you have. With agents, though, the impact of that is now like 10x or 100x, depending on how many agents you're using. Because the better your devx, the better your agent ends up adhering to your standards, which means there's less time that that poor staff engineer has to go through reviewing your PR, which means you're kind of faster throughput in your software development.

37:54Harry Stebbings:When agents are the buyers and you're selling to agents, how does the world change and does the value of great API increase? That value is increasing, especially because the thing that makes it easier for agents tends to be the same as the things that make it easier for humans. At some point in theory, that could change, where if you're actually training models or training agents to be as efficient as possible, communicating to each other. But then the downside there is it's not as human readable. But if you think about agent to agent, agent to agent doesn't give a shit about UI or design, but it does fundamentally care about data structures, potential integrations, documentation.

38:27Do you know what I mean? Yeah, yeah, yeah. Yeah. So I think one thing that if you don't have careful standards in place, it can get bloated pretty quickly. But I think the best organizations who are the most agent native actually put in a lot of guidance on like, here's like the UI side of things and how things need to be. Being very aggressive about like pruning anything that's unnecessary, making sure there's not like bloated, like, I don't know, comments in all of your code that's like kind of gratuitous or there are ways around it. But that's kind of where the human's job changes a little bit, where their job goes from part of our name.

38:56Our name is Factory. Part of why it's called Factory is because the future of software development is where these organizations, instead of having engineers that build the software, they're going to have engineers that build the factories that build their software. Visually, whenever I say this, I always think of Tesla's factories. I don't know if you've ever seen videos of the inside of Tesla's factories. It's all these robotic arms going and you have the assembly line going through. And there might not be as many humans in that assembly line, but you know damn well that humans designed this process to optimize the throughput, to produce more Teslas in this case.

39:25And so in this new world of software development, And human engineers are not going to be involved as much in writing the actual code, but they're the ones that are going to be involved in how do we make sure it's not just creating all this bloat or it's technically getting the job done and passing tests, but doing it in a way that is really dramatically increasing debt. So they're kind of like building the scaffolding around this factory that produces their software.

39:45Harry Stebbings:Do you worry about labor displacement when we move from working in the factory to working on the factory? Short term, yes. Long term, no. Short term, yes, because it's just a shock to the system where there are all these big layoffs that are happening that are pretty aggressive and thousands, tens of thousands of people that had a job that no longer do. And so I think that does worry me. Long term though, I am very not worried because the reality is there is a huge number of problems in the world, ridiculous number of problems in the world, and a large percent of them can be solved or can be helped with software.

40:16Very few of those problems that can be solved with software are we currently solving with software. And so if we're going to be flooding the job market with tons of engineers, that means that we can now allocate them on the broader economy to solve more of these problems in the world. And if we have more engineers who are going and solving more problems in the world, that is a net good.

40:34Harry Stebbings:What problem is not currently being solved with software that will be enabled by this new technology? Because everyone's like climate change. And I'm like, great. You know how many people I've found doing climate change technology? Well, none. Yeah. Well, and maybe part of that is because all like the Googles have been hiring all these engineers. So distributing great engineering talent to more problems, I think is going to be a good thing. The economy has to match though and properly incentivize them. And that's something that I think will take a little bit of time, which is like the intermediate period, but like so many health problems, like so much of pharmaceutical research can be advanced with better engineering.

41:07The thing that really upsets me with some of the people who are talking about, you know, pausing AI development, or it's a bad thing and it's going to, you know, harm society. Dementia is kind of a go-to example where everyone understands how big of a deal that is. that is something that can be solved with better AI and better software. It's a matter of time. We will solve it and we can solve it. And by saying you want to slow down AI, that's saying these people who have relationships with loved ones who have dementia, you're like, no, no, no, sorry, you guys, you got to maintain that relationship for a little bit longer.

41:35We're scared. We don't know about AI. I think it's pretty harmful and it's pretty selfish to say that it's something that, to me, it doesn't make sense.

41:41Harry Stebbings:Do you agree with government intervention? In what capacity? In free markets, when you think about the allocation of resources, There are times when it is suboptimal from a human morality societal standpoint in a lot of cases to see engineers at Anthropic working on optimizing claw code when they could be working on optimizing healthcare systems or optimizing more critical or mission critical things immediately. Governments can intervene, offer subsidies, offer economic incentives. Do you agree with that or do you believe in Adam Smith's invisible hand? It's certainly useful in some cases. Like, I don't think anyone would argue that the government should never intervene ever in the economy because there are some things, especially as it relates to like military uses or safety or things like weapons, like you're definitely going to need some involvement there.

42:27I think there's some incentivization that can be helpful just because there might be some problems for a society that maybe capitalism doesn't see the immediate feedback loop of. And so you might want to juice the incentives a little bit to get an outcome that you're looking for. Generally, I'm pretty reluctant. I think you need to have a very good case for why you need to do that. Even like the example of climate change, talking about that one, it's obviously a very sensitive subject or a very important subject for a lot of people. You could make the case that the faster we develop AI, the sooner we solve climate change, because AI can help us solve a ton of these problems.

42:58But to develop AI faster, you might need to consume fossil fuels and emit them and emit CO2 into the atmosphere. And so the question is like, short term, it might be slightly worse, but it ends up getting us to solve the problem way sooner instead of dragging it out over 50 years or 100 years. There's some of these cases where the natural kind of free market will incentivize it the right way. And there's some cases where it won't. But I think you need to be very, very careful about the cases where you do want the government to say, hey, we want to step in here.

43:23Harry Stebbings:Do you think we are in an AI infrastructure bubble? Maybe there's like some short term blips, but like long term, absolutely not. Like not even close. There might be similar corrections to like this thing at Uber where, oh, we were going a little haywire. We weren't allocating it appropriately. And there's like, okay, let's lower consumption a little bit, but like on the net, absolutely not. What bottleneck do we have today that will be completely solved within a few years? I think the biggest bottleneck by far, working with all these organizations is the human side of it. It's just like behavior change.

43:51Harry Stebbings:And what you're saying there is like, it's selling into large enterprises and how they do change management? Yeah. Or even on an individual level. Like if you're an engineer who's been an engineer for 30 years, it's hard to change those patterns. Like you're stuck in ways to a certain degree, but there's also a funny thing where some of these engineers who've been engineers for a very long time or who have been engineering managers, they might be more reluctant to use these tools, but sometimes they're better because they know how to delegate. They know how to deal with some of the junior engineers where if you tell them the wrong thing, they're off in the cave doing the wrong thing for seven days, they come back with something completely useless.

44:20And then on the other end of the spectrum, there are people earlier in career who don't have as much of a standardized workflow that they're used to. So they're more eager to adopt these new workflows, but they don't know how to manage people. They don't know how to delegate as well. So there's kind of an interesting balance there.

44:33Harry Stebbings:When you look at now, you sell to some large enterprises in the world in some cases. What do you know now about selling to large, large enterprise that you wish you could tell young Matan two years ago? So this is the first job I've ever had, which I think is always a funny thing to say, because prior to this, I was a theoretical physicist. Literally never, never like coffee shop, any of that, literally never have had a job, like never have been paid to do anything aside from physics until this, which is a whole separate thing. All right, Matt Damon. But I will say the thing that has been the craziest learning, and this is obvious to anyone who's in sales or like Chad and Chris, to them, it's obvious.

45:08To me, the thing that was the most visceral altering thing was meeting people face to face makes such a big difference if you're trying to sell them something, but also you should never try to sell something. You should always try to understand their problems and see if the solution that you might have can actually help them solve that problem. If you go in a conversation trying to sell something, especially to engineers, don't waste your time. If you go in trying to have genuine curiosity about, it's really easy because these organizations do their engineering so differently. And I find it fascinating how like all of these different banks, you know, consulting firms, pharmaceutical companies, they have the most different ways of building software.

45:43And it's really interesting to go talk to them and to understand it. The best way of talking about it with them is face to face. People love talking about their problems and they love talking about all of the bureaucratic nightmares that they have to deal with. And then by understanding all of that, you can actually get a sense, you know, is our software a good fit for them? Will it help solve their problems? It's also just so fun to then like meet up with them a year later and be like, I remember when you had to deal with that bullshit and now you don't have to. And that's just such a rewarding feeling of making their lives better in that way.

46:09Harry Stebbings:In terms of like being there in person and the sales process, you got Sequoia very, very early on. Sequoia, obviously one of the best and most prominent investors. Can you just tell me the story of how you got was a quarter having never had a job and only being paid to do physics? Yeah. So I was obsessed with physics basically since I was 12 because I was a bad student and my geometry teacher told me that I had to retake geometry in high school. And like, I never tried in school, but I always prided myself on being good at math. And she told me that I was like, you kidding me? She thinks I need to retake geometry?

46:38Like I'll show her. And so my first order on Amazon ever was textbooks for algebra two, trigonometry, pre-calc, calculus one, two, and three, differential equations, and maybe a linear algebra textbook. So I bought those textbooks. And then the summer between middle school and high school, I studied all of those, like did all the problems in all of them. And then in high school, took exams to place out of all of those classes. And then I asked my dad what the hardest math was. He said string theory, which is technically physics, not math, but I was like, okay, I'm going to be a string theorist.

47:07And that was literally all I cared about for basically the next 12 years of my life. All I cared about was math and physics. Ended up going to Princeton because they had a great physics professor I wanted to work with. He's this famous professor named Juan Maldesena. And I was like the first undergrad to work with him and write a paper with him. Then I ended up coming to Berkeley to do my PhD and, you know, work with a great advisor there. And then only at Berkeley, I realized like it kind of all comes crashing, like, holy shit, I've just been doing this because it's hard. And because someone said I couldn't do it.

47:33What the hell do I do with the rest of my life? Like, this is crazy. Like everything came

47:36Harry Stebbings:What caused that crashing down moment? And why did it take so fucking long? 12 years? 12 years. You're slow. I have tunnel vision. When I get obsessed with a problem, it is all I think. I was at law school for two weeks. It was a quick, quick realization. See, some people are faster. You know, I wasn't quite as quick. Honestly, part of it was being, as part of a grad student at Berkeley, you have to teach classes. And I was teaching a class to like whatever, 18-year-olds who didn't give a shit about physics. And I was like, oh my God, this would literally be the rest of my life. It's like sitting and doing lectures and doing these classes.

48:07On Rate My Professor, I think I had a one out of five. I was like, horrible. Yeah, it wasn't a good fit. But it was kind of this existential crisis, like, what do I do? And so, you know, kind of realized it was probably going to be either quant finance, which is what a lot of math and physics people do, big tech or startups. I ended up doing the quant finance interviews, like every good physicist does, and almost took it, almost went to New York to do it. And then last second, I had an advisor that I spoke to who was like, you know what? Stay at Berkeley for a bit. Don't do it. You're always going to be good at math.

48:34You could always go and do Quant Finance. Stay at Berkeley, explore, learn some stuff, whatever. So I was like, okay, fine, I'll do that. Ended up taking my first CS classes at Berkeley. I learned to code for physics, for simulations and all this stuff, but never in a formal class. And I'm very competitive. And I found that in these classes, I was doing better than some of the CS students, which was very competitively satisfying. I was like, oh, okay, I'm going to take more of these. And then it wasn't until I took a seminar in what was called program synthesis at the time. Now we call it code generation.

49:02And it just completely nerd sniped me. Because the idea here is not machine learning for video or audio or images, but it's code with the explicit purpose of creating itself. And there's something just so fundamental about that. And a decade of physics, like physicists and mathematicians, they're never interested in the case of like n equals 3 or like n equals 4, 4 dimension. It's always like, what is the n-dimensional solution? What is the arbitrary, the fundamental solution to things? And there was something so fundamental about this idea of like code generating itself. And it just got me obsessed.

49:32So I stayed at Berkeley. And for the next year, that was kind of what I spent my time on. My advisor was very chill and just allowed me to just, you know, take AI courses. And eventually I realized that the way to actually solve this problem was not in academia, but in the industry. And to properly solve it in the industry, you'd have to start a company.

49:48Harry Stebbings:But I knew nothing about starting companies. Because again, all I cared about was math and physics, didn't know anything about this. So what does someone who wants to learn about starting companies do? Well, they order on Amazon, Peter Thiel's zero to one, and they look up on YouTube how to start a company. and so you know read zero to one incredible book i know it's so cliche but like to someone who didn't growing up in the bay area shockingly i just like did not care about any of that and reading this it was like so concise beautifully written all that you know loved that and then you know was watching these videos a lot of them like y combinator you know videos and all this stuff and then i stumbled upon this i think it was like a stanford vc club podcast with this guy whose name i recognized because at princeton when i wrote that paper with juan maldesena I had cited one of his papers.

50:33So it was a theoretical physicist. Like I remember this guy's name, but he was on this podcast talking about how he sold a company for a billion dollars and was an investor at this place called Sequoia. And he also, in this video, seemed like pretty sociable and normal, which I don't know if you've interacted with - I'm not sure, dude. Theoretical physics. You know, compared to theoretical physicists, like he can maintain eye contact. You know, he was somewhat normal. Very rare.

50:55Harry Stebbings:That's such a low bar. He can hold himself in a social setting. Yeah. And so I was like, okay, who is this guy? You know, I got to talk to him. So I ended up writing him an email being like, hey, I'm Matan. I also used to be a physicist. I wrote a paper with Juan. Didn't say the last name because it's like, if you know, you know. I would love to get your advice. And, you know, he responded that day and invited me down to Sand Hill. And it was supposed to be a 30-minute meeting. But we end up going on this walk and ends up being a three-hour walk. And on this walk, it turns out we had very similar reasons for getting interested in physics, very similar reasons for leaving physics.

51:26At the end of it, he was basically like, it was great to meet you, Matan. you absolutely need to drop out of your PhD and you should either join Twitter right now because Elon just took over and it's hardcore for your resume if you voluntarily go there or you should start a company. And I was like, okay, thank you so much. I appreciate you taking the time. I'm going to go think about it. But in the meantime, I had already known about Factory. I didn't want to ruin the meeting with a pitch. You didn't want to transactionalize this.

51:50Harry Stebbings:Yeah, because it was so... We had the exact same reasons for getting interested. I totally agree. Didn't want to dirty it with any of that. No, it's kind of like an LP where at the end you're like, I don't want to watch for a check. Exactly. And then the crazy thing, the next day I go to a hackathon in San Francisco and see across the room, this guy who also went to Princeton, who I like recognized, but I didn't like know super well, end up talking to him. He's also interested in this problem. We like, we joke that it was like intellectual love at first sight. This is my co-founder, Eno. And basically that day forward, we spend like every day talking nonstop.

52:24I had some shitty demo that I built. Eno is thousand X better of an engineer than I ever will be. And so he and I, for the next like 72 hours, like put together this better demo. And then I call up this investor and I'm like, hey, I have something cool I want to show you. So we hop on a call and I show him this demo. And I'm like, what do you think? He's like, eh, it's okay. I'm like, are you fucking kidding me? This is going to change the world. What are you talking about? He's like, okay, well, would you work on it full time? And I was like, yeah, absolutely. He was like, okay, drop out of your PhD and send me a screenshot.

52:49And keep in mind, like my parents immigrated from the Soviet Union to the United States with basically nothing. The fact that I was doing a PhD to them was like their pride and joy. like it was the thing that they were the most proud of. There was so much momentum, so much momentum. You know, he answered my email. We got along well. I met the co-founder the next day. And I was like, you know what? Fuck it. Dropped out, sent him a screenshot. And he was like, all right, you have a meeting with the Sequoia Partnership tomorrow morning. Be ready to present. You've never presented to a venture partnership before.

53:18No. So what happens? I made a shitty deck. We go to the Sequoia HQ, put some slides together. Keep in mind, I didn't even know who the hell they were. Like, it was just like, oh, these random people. like, okay, whatever. Yeah, I'll go talk to them. I wish it was recorded because I'm sure I came across as so arrogant. How did it go? I thought it went fine. They asked some questions. I think I was pretty, again, I didn't know anything about VC land or startup land or any of that stuff. Retrospectively, I know like Alfred and Pat and Roloff, they were all like in there. They're all asking questions and I was probably dismissing some of them.

53:46Oh yeah, we'd solve that easily. We do this, we do that. Keep in mind, this was in April of 2023. So this was like way before anyone was thinking about agents, way before people were even using Copilot. We were talking about fully autonomous software development agents. And it was kind of a blur. You know, the next day, Sean calls me and he's like, hey, you want to give you a check? How big was the check? A million dollars. A million dollars. And you know what he gives me shit for? You know what the terms? Five post? Five post.

54:13Harry Stebbings:I mean, I'm not being rude. Why did they bother doing a partnership meeting in the nicest way? That's like a coffee. I know it's a dick comment, but when you managed it, seven, eight billion. It was a different time. It was a different time. Early 2023 was a different time. I think he bought 20 % post just for listeners. I mean, on last funding round, that'd be like a$300 million position, not including dilution. And it was one of those things. A lot of people I spoke to were like, you should go shop that around. You can get better terms because it's Sequoia. And it's just like, when you have a connection like that, there's a certain thing to me where obviously you want to maximize the position for the business.

54:47But no one else would have believed in me except him. No one else would have understood. I literally had never had a job before. No other partner I would have met. Retrospectively, it's like, oh, yeah. Yeah, no one else would have done it. And it's one of those things where like trust and loyalty and like belief, to me that matters so much more than like the price tag you get or whatever. I want to make sure that the people that I have in my corner, because we're building a legendary company, it's not just going to be 10 years. This is like a lifetime. Would you tell founders to take a discount for Sequoia?

55:12So generally, yes. I mean, they're the best firm. In particular, if there's like a special connection with you and the partner, or there's a special reason why them in particular. But I think what really matters is you want to have people that are there for you when the days are tough and when it's not obvious. because when you're a hot company raising a hot round, everyone's your best friend. It is their job to make you feel special and they are really good at it. What's the best way someone's tried to wee? I don't want to name names, but there's this one investor in particular who's like still in the game, but more of the old guard.

55:39I'll say that much. And I remember beforehand people were like, people told me like, by the way, he's really good at making you feel good about yourself. And I was like, yeah, whatever. I can deal with that. That's fine. And then I remember leaving the meeting being like,

55:49Harry Stebbings:I'm the fucking man. This is my destiny. I'm going to build a legendary company. Like I got this. And then like 30 minutes after when it wore off, I was like, oh my God, he got me. Like he did it. Like he did the thing. He made me feel special. And like a lot of investors, when a company is hot, are going to do that. And they're really good at it. That's why they're great investors. I think for me, what's really important as we've built out our board in particular is people who have like deep conviction when it's not obvious. Like that's what really, really matters. Because when a company is hot, everyone's going to be excited.

56:17It matters when it's not. And there are going to be tough times. How do they behave then? How did you get Ivanka Trump as an investor? That was one of the best hires that I've ever made at factory was this woman, Francesca. And so the way that Francesca and I met was at a random conference. I was seated next to her and Alex Paul, who's one half of the chain smokers. And obviously, people know them as the chain smokers. They're also incredibly good investors, incredibly good investors, which sometimes people are surprised by. And we got along quite well. Weirdly enough, Francesca and I also grew up in the same hometown, which is a whole and had a ton of.

56:50that was another kind of weird coincidence, but she's like, just in the process of like them wanting to put a check in and the way she did diligence and just the way that she kind of carried herself so clear, like she was a killer and they wanted some allocation. I was like, no, no, no, sorry. Like, you know, it's going to be this. And she was fucking relentless, like came to our office, like was like, Hey, like we need to get to this much. How can we do it? I'm going to make these, if I do this and this and this, the business value that we provide to you is going to make it worth this more so than giving that allocation to someone.

57:17She was like kind of hounding. You know, we were having a conversation. I was like, look, Francesca, like, if you want more ownership of a factory, you could just join us. And it was like, kind of as a joke. I was like, oh, you could just join us if you want more. Like, this is the heist we can do. But then we kind of both were like, oh, interesting. And, you know, we talked about it a bit more and then realized, wait, this is an incredibly strong fit. And so we ended up bringing Francesca on board. Alex was, you know, it was kind of tough because she was incredible and they were very close. He's since been happy because she's helped us deliver a lot of, you know, returns for them and we're the biggest fans of theirs and we still have a very deep relationship.

57:50And she was very close with their firm affinity from her investing days. Then we were introduced, we got along really well, and so then that was how the connection was made there. Does Ivanka Trump provide value?

58:01Harry Stebbings:People will look at it and be like, oh, branding, just a name, whatever. And I don't mean that disparagingly at all. I think people often think that with kind of famous celebrity names. Does she actually provide value? Yes. She is, first of all, she's one of the kindest and smartest people that I've met. There are people that you meet that, you know, are famous that are kind of like a letdown or like, oh, they're different than I expect. She is genuinely so kind, so intelligent. And like people throughout tech, throughout the world really love her. And for good reason. And she has an incredible network.

58:31She's so generous with her time. There is kind of dirty work investor help that she helps out with that some other investors who are more known as investors do not do. she and the firm more broadly really earned that right on the cap table that's really good to hear

58:44Harry Stebbings:i i hate the statement i i'm not sure if anchor was quite your hero but like people say never meet your hero so always disappoint and i think it's just total bullshit yeah i'm meeting doug leone who was one of my heroes did not fucking disappoint like i left being more like god he should have been even more of like a poster boy for me because he was so great oh yeah so i i totally agree with you that that's very funny i would love just your thoughts on some market composition that I'm struggling with, which is like when you look at cognition, you look at Claw Code, you look at Kodaks, you look at Cursor now with Grok, how does this market evolve and mature?

59:19Harry Stebbings:Is this an AWS Azure GCP? Is this an Uber Lyft? What is the mature state of this market? Yeah. So I think what is necessary for the best outcome for the consumers is going to be models that are separate from the applications. You as a consumer do not want to use applications that are provided for you by the same people that are giving you the model because the incentives are misaligned. The incentives are misaligned. Why? Because if let's say the example of coding, like if I'm a model provider and I'm working with a large enterprise and I'm giving you a coding tool, I want you to use as many tokens as possible because I'm an API business and I get more money the more tokens you use.

59:53And I don't have a huge incentive to be more token efficient other than like, yeah, I want to give a good product experience, but not strong incentive versus if you have model providers and you have an application layer that allows that enterprise to decide between the different providers. If you're a model provider, you better damn well be the best or the cheapest or the fastest, or else you'll never get tokens through to you. So it puts the best incentives on the model providers. There's that independent agent in our case there. And then that gives the best prices to the enterprise. It also gives them the best in terms of like, if one model is really good at this language or that language, it allows them to kind of adjust between them.

1:00:29And the world where you're like vendor locked in, then you can slowly get like laziness and slower shipping. And as a consumer, you end up getting a worse experience.

1:00:38Harry Stebbings:Okay, so it's not good for the consumer if the model is tied to the application. But bluntly, we are seeing Codex and Clawcode eat a huge part of the market. What does the market look like in three years in terms of market maturation? This is going to be different from cloud. I think cloud, a lot of people suffered because the cloud providers came and said, hey, look, sign this three-year deal. We're going to give you a big discount. We'll get everything good for you. It'll be all right. Come on in. And then they would do that. and then they would jack up the prices. And once you're standardized on one, it's going to take you two years to switch to something else.

1:01:07So good luck, you're stuck with us and we're going to charge you more. Everyone has scars from that. So now every CIO I speak to is really keenly aware of, we cannot throw our lot in with just one model provider. We're going to need to be agnostic. And so you could be agnostic by saying, hey, every engineer, we're going to give you CloudCode and Codex and Gemini CLI and all these other tools. But then the problem is now you're asking your engineers to use 10 different tools, or you can use someone like Factory, where you can use one tool and you can kind of decide, kind of like in an auction on a task-by-task basis, which model provider do we want to use?

1:01:38Do we want to use an open model? Do we want to use Frontier? You know, which one of those?

1:01:41Harry Stebbings:Can you help me understand the paradox of, hey, we need to be more cost-efficient with Ratplit. We're going to run the same prompt on three models at the same time. And I don't mean that. There's no diminishment to Ratplit. That's like them providing a great product. Well, so I haven't seen them or that use case as much in the enterprise. I could see for maybe consumer use cases where you're not as cost-sensitive because you're not doing things at crazy scale, where it's kind of fun to see, oh, I wonder what Gemini does versus OpenAI versus Anthropic. And for some enterprises, if there are things that are very sensitive or very secure, you might want to do that.

1:02:10But if you're a non-technical person building an internal dashboard, you probably don't need 10 different models to generate different iterations of it.

1:02:17Harry Stebbings:In terms of the market maturation, what happens to the lovable and replic market? We saw OpenAI release a competitive product last night. I just don't know what happens there. Can you help me understand that? It's not obvious to me. and part of it is because not too many people that are close to me use those tools frequently. Like most of the people that I know either don't use like AI tools or they're like technical and using factory. Also, like I'm not going to be, no, none of my friends don't use factory. Like, what do we, come on, we wouldn't be friends. So I need to understand a little bit more about that user.

1:02:46My sense is they're probably, and we're still in the early inning, so I'm sure they're quite agile to figure out what is the exact niche that they want to occupy, but it's not super obvious to me what the kind of focus is. Because my understanding is some of them have been pivoting towards the enterprise a little bit?

1:02:59Harry Stebbings:I think they've been pivoting towards the enterprise in non-developer centric functions. So like, hey, if I'm lovable of the world, I'm going to sell to sales teams, marketing teams, customer support teams to allow you to create amazing materials with no experience developing. I mean, in that case, I think that niche does make sense a little bit more. I think it would be ill-advised if they were to try and go to the niche of non-technical people writing code for code's sake. Because I think that is going to be run by, like, if you're going to need enterprise controls over who has access to what databases and what code and all that stuff, that's going to be run by the engineers.

1:03:30That's going to be where factory goes. If it's things like if a salesperson wants to build a customized demo app or a customized website for something, I could see in some cases that having some value there.

1:03:40Harry Stebbings:Are we entering a danger zone for security? A huge amount of net new code created that may not be as secure as previous and we're seeing just the worst hack security leaks and this is just the start? Yes. Yeah, it's going to be crazy. When you say it's going to be crazy, what does that actually mean? Like code generated is growing exponentially. The security efforts aren't growing in kind. And so I think there's kind of a lag there. I think there are probably going to be in the next couple of years some pretty big incidents that occur because of AI generated. There honestly probably have been.

1:04:09I just, whatever incidents that have occurred, no one's going to admit, or typically they'll be reluctant to admit if it was like AI involved or not. But also I think we haven't even seen the most adversarial behavior yet. Like I think people can use these tools to be quite adversarial. I think security, like the higher the stakes, it's going to grow in importance. And so I think the security part of the market is really important.

1:04:28Harry Stebbings:Do you think US startups should be allowed to operate so extensively on Chinese open source models? Yes. Using an open model is fine. Like there are kind of two concerns. There's one concern is if you're sending your data externally to like a different nation, which is one concern. And I think that the concerns there are about like we don't want to send our data to China generally. I mean, you should probably want to keep your data to yourself regardless or like within country regardless. But I think the separate concern is like, oh, the model itself. Like even if we host in the US, is there concern with the model itself?

1:04:57And to explain some of the concern there, I think the idea that some people have is like, I don't know if you've seen in like those spy movies where there's like a code word where suddenly someone starts acting like you say the right word and then they're like in robot mode where they're going to go act adversarially. I think the concern is that some of these models might secretly have that ingrained within where you say a trigger word and then suddenly, even if it's hosted in the US, it's going to like send data somewhere else or it's going to start, you know, trying to intentionally kind of break whatever it is that you're doing.

1:05:23Suppose any nation were to try and do that. Suppose they wanted to make a model that had one of these trigger words that's going to go and act adversarially. Theoretically, you would want to do that as late as possible. Because if you do that in an early model and someone discovers it, they're literally never going to use your models ever again. So I don't see that as a big concern. And also, if you're deploying correctly, like not as a consumer, but in the enterprise, if you're deploying correctly, data exfiltration or like kind of some of this adversarial stuff, generally you can fight against.

1:05:49But I do think just from a, I'm quite patriotic. I think it's pretty embarrassing that we don't have frontier open models in the United States. So I do hope to see us, you know, reclaim superiority there.

1:05:59Harry Stebbings:Europe is significantly behind, especially on the model development side. Do you think Europe is too far behind to catch up? Probably on the frontier model lab side. There's so much to do on the like infra build out and energy side of things. But again, the thing that's very difficult in the different parts of the world is you have democratic countries where things generally are slower. Suppose you say, we need to do this thing. You need to get a lot of support. You need to convince certain people to do things. You need to pass legislation. It takes a long time. But the benefit, though, is theoretically, we get this balancing act where we don't go too crazy in any which direction.

1:06:30Other parts of the world where it's more authoritarian is like, this is the thing we're doing. We are doing it. We're acting now. You get to move quickly. Now, there's less kind of correction because what if you're going on the wrong course? But in cases like AI, where it's pretty clear, like for build out, you need to build data centers. You need energy, and energy requires a lot of build out as well that has a huge amount of lead time. You can act faster. In the West, things are slower. So that's one thing that kind of goes against us. It's a little bit slower to get this stuff done, especially when there's all the politics that you have to deal with it.

1:06:59Harry Stebbings:Do you worry about the public backlash to data center development that we've seen? I think it's like 40 out of 100 data centers post-approval don't actually get built out in the end. Do you think data centers will be seen as a symbol of wealth concentration and technology superiority? Yes, but I think that's, at least in the United States, the beauty of having states, is we get some selection where we can have different experiments of like, what's it like for a state that says no to all data centers? Well, okay, there won't be as many jobs that get created there. Whereas the states that do allow for data centers to be created, people will prosper.

1:07:28They're going to have great jobs. They'll see the downstream benefits of it. But it's nice. It's like we have little Petri dishes to test out and see how things work. That is the beauty of the United States. And I think in Europe, I mean, it's tough. I think there was some good positioning that Europe had a few years ago, a few decades ago with nuclear that I think hasn't been delivered on as much as of late. But that would have been a world in which Europe would have a way to bounce back a lot in AI on the energy side.

1:07:53Harry Stebbings:100%. I blame the Germans. And that's our German audience gone. Dude, I want to do a quick fire round with you. So I say a short statement, you give me your immediate thoughts. Nebius versus CoreWeave, who has a larger market cap in five years time and why? To me, and this is speaking from strongly biased as an application person, like I'll take the grab bag. It doesn't matter. I actually hope for a world in which our users don't even know which one is under the hood. For you, I would want CoreWeave to be bigger. Why? Because Nebius, I think, have more ambitious plans to be full stack, which will eat into some of your plans in a way that CoreWeave don't.

1:08:30Harry Stebbings:Ambitions. What are ambitious? I don't think it makes sense for them to do that.

1:08:35Okay. Businesses need to think about their core competencies. If people are trying to expand beyond their core competencies, Kirkland and Ellis, great. Look, have fun. It's not your core competency. I don't think it makes sense.

1:08:45Harry Stebbings:Yeah, I get you. I think you could argue that it's a lot more adjacent there. Do we have a series of businesses like a Nebius, like a McCaw, where customer concentration is like 90 % of revenues? Will we see more of that? Yeah. Yeah, probably. Is that a bad thing or a good thing? It's bad if you're an investor in one of those companies because it's a little riskier. But I think you can find a steady state. It's just scary. You just know there's kind of a sort of Damocles above your head of like, it's just risky. It's risky. A sort of Damocles. First time it's ever been said on the show. Tell me, can you sell to enterprises today without an FDE model?

1:09:18Yes, have a good product. The thing about the FDE thing blows my mind is like for us, when we do FDE, the way I think about it is their goal should be acceleration. Basically, if there's a customer where we just give them our product, they'll scale to like a million in six months, I'll throw in FDEs if they're going to scale them to a million dollars worth in three months. Great. They accelerated that. If I'm sending in FDEs as services, like I'm not Accenture here. Like I'm not trying to be like, or Infosys or Cognizant or whatever. We are not a services company. If we need FDEs to make the product work, we have a shit product.

1:09:49Like the point of FDEs should be accelerate and get them consuming faster. If you're putting in FDEs because that's the only way you'll get a deal done, I'm sorry, my friend, you have a shit product.

1:09:58Harry Stebbings:What do you think of the whole grind slop element? We talked a little bit before about the show with Nico at Corgi, which generated a little bit of discussion online. Yeah, just a little bit. Just a little bit of discussion online. Harry, you're ruffling feathers as always. Dude, I said nothing. This is honestly, it's like someone comes to your party and does something well and is like, that's me. What do you think of the grind slop? I feel like a lot of the things we've talked about actually is like something everyone needs to be wary of is intermediate metrics. And grind slop comes from intermediate metrics.

1:10:29Like, oh, generally to do things, you need to spend time on it. So let's focus on how much time do we spend instead of like, are we doing the thing? The analogies I use is imagine trying to measure who won a basketball game by who sweat the most. Like you could sweat a ton, but look at the scoreboard. Like, are you doing what actually needs to be done or not? And I think for us, we want to focus on like getting the best players. I don't care if you sweat a ton or if you sweat very little. If you're scoring a lot, great. We want you on our team. Now, generally for most people, you have to sweat if you want to get things done.

1:10:58but I think you are doing a bad job on hiring if you need to like mandate certain crazy hours or you need a bed in the office. It's like, dude, get a good night's sleep. Like you don't need a bed in the office. Like just go get an apartment nearby that's nice and cozy. Get eight hours of sleep. If you as an important member of your team at your company can get your job done on two hours of sleep, you're not doing very high leverage work. Do you know what I did think?

1:11:22Harry Stebbings:It was an amazing opportunity for Eight Sleep to do like an amazing social campaign. I would have delivered it. I would have got the founders outside being like, we got you covered. It's funny. Like that's literally like when we were 30 people, we had like a, what we call a surge, like a pretty aggressive, like two week sprint. And as part of it, I got everyone on the team eight sleeps, like fully free, whatever,$3 ,000 per person, like the decadence of startups. Right. But I think the idea there is like, we are optimizing for output and the people that we are bringing onto the team, it's like SEAL Team 6, like the NBA All-Stars, like it is worth every dollar to make them more productive, to deliver on these ambitious goals that we have.

1:11:57And so we can do that. And you know, this, at least for me, I think eight sleep helps with my sleep engineer, like, great, let's do it. They're going to be better. They're going to have more of their wits about them. They'll be sharper. And the type of engineering work that we do is not just like grunt work. How can we spend as many hours to do it? We have droids for that. The work that we do is like might require like really deep thought, really kind of like every ounce of brain power that you have. In which case, if you didn't sleep well, like you're not going to make as good of a decision.

1:12:24Harry Stebbings:If I gave you unlimited money, what would you spend on today that you're not spending on? I think generally we will see the best companies treat teams more and more like whatever SEAL Team 6 or NBA, like professional athletes. Not in the way that Google did it with like, oh, you get like a bounce castle and all this like weird shit. But like we're like athletes. It is kind of like it seems like they're getting pampered, but it's kind of a burden. Your diet is monitored. You get like you have to do your like hour long massage after a game to make sure your muscles are recovered for the next game.

1:12:53You have to do like an ice bath and all this stuff. Like it seems glamorous, but sometimes it's not. I think spending on that type of stuff, but obviously in the more like intellectual domain, I think that's what more and more companies will do. If I could spend an incremental dollar to make every person sleep that much better, recover that much better, be that much better at making decisions, it's probably worth it.

1:13:12Harry Stebbings:You're such an American. Do you know what I like? I like Lee Munchello. Do you know what I like? I like smoking. Do you know what I want to do? I want to sit in the sun under the intense vitamin D rays and I want to take in life with my friends. And you guys are like optimized to the extreme. Did you see the Stephen Bartlett video the other day? You might not know. This guy, Stephen Bartlett, he said, I drank two lots of wine and it ruined three days of my life because I didn't sleep. And then the next day I ate more. I podcasted worse. I didn't go to the gym and then I slept badly again and three days ruined okay honestly i get that to be fair the first year at factory i would drink a whiskey every night and my argument was and you'd probably agree with this was like for robustness like if you want to be a robust human you can't have like one drink ruins the next five days of your life like the wind blows and then you're like you're ruined right to some degree i get it like you want to have some of this stuff but i also think maybe you know again looking to athletes what they do is they have in season and out of season maybe it's like when When you're in season, you're fucking locked in.

1:14:14You're not drinking. You're like optimizing all this stuff with your eight sleep. And then take a week off, go on the beach, drink some, you know, mojitos or whatever the hell people drink on the beach. Well, an out of season, you're Charlie Sheen.

1:14:25Harry Stebbings:Yeah, man. If you can recover after, you know, to each their own. Oh, God, that would be the funniest thing ever. Work hard, play hard. Yeah. Okay, you can invest in one company on IPO day. Sorry, dude. Anthropic or OpenAI? In my mind, the answer here is I think they're approximately equivalent. To me, it doesn't really matter. The biggest reason that affects the EV is volatility of the company. That's the only thing. Because I think from a business perspective, they're both very well suited and well positioned there. So you're saying anthropic? Probably. Past is an indicator of the future. And there's just been more random, chaotic, turbulent events at OpenAI.

1:15:04But from a business perspective, to me, it's both great choices.

1:15:08Harry Stebbings:Has Dario done a misservice or disservice to the ecosystem by saying, we're going to take your jobs, we're going to take your jobs, we're going to take your jobs? Yes. It actually, this like really upsets me. So on one hand, I maybe just implied Anthropic there. But on the other hand, I think that has been not only like disingenuine and wrong, but it's like really hurt the psychology of a lot of developers, like just people in the world. Does AI a disservice? Does the world a disservice? because this is, again, talking about the use cases that are going to, the problems that will be solved for society.

1:15:38This feeds fuel of like, we should slow down AI, we should stop doing it. And honestly, it's for selfish reasons that they did that. Because if you're trying to raise unprecedented amounts of money, you know, hundreds of billions of dollars, whatever, the best way to convince people to do that is to say, all of capitalism is gone. The only company that's left will be me. So you better give us your dollars. And then suddenly when it comes to IPO, when now suddenly all the humans and the people that you might be replacing now have money, that you want them to put in your IPO, then suddenly it's, whoa, whoa, whoa.

1:16:06Oh, no, humans are pretty important. There are going to be jobs again. You know, we like you guys. That pisses me off.

1:16:11Harry Stebbings:I totally agree. And what's ironic is the ones who've never said it are the ones who've never needed the money. When you look at a Zark or a Damas, they've always had a very different stance to Sam and Dario when it comes to labor displacement and jobs. It's really interesting. The ones who need it and the ones who don't. It's a shame because like for all the philosophizing about AI and intelligence and all this stuff, It's like incentive is driving the outcome and the incentive is I want to raise a lot of money. Which legacy company do you think has most embraced AI well? Honestly, EY, the accounting firms, is one of our largest customers.

1:16:43I know. You said shocking. They are so agent native, it's crazy. They're one of our largest customers.

1:16:49Harry Stebbings:How? They just push it down the organization? They're just like, basically, they saw what happened with the cloud. They saw scars of being like late and kind of not jumping onto it aggressively. they have some great engineering leaders there who are like, look, this is going to be scary. Some people are going to get upset. It's not going to be the easiest thing, but we are going to make our agent native if it's the last thing we do. And they were honestly pretty early to it as well. To me, I think that's one of the most interesting things seeing is like, they're more agent native than some like startups, which is wild.

1:17:19Harry Stebbings:Brave new world. Brave new world. Final one. What have you changed your mind on most in the last 12 months? What I've changed my mind on the most in the last 12 months is there was a brief period of time where I thought it might be just one or two companies that run away with being kind of the frontier and the best. What seems pretty clear to me is it's probably going to be at least four that are going to probably be approximately as good. And that is a win. That is the win for humanity. The bad case for humanity is when there's one that's really, really good. I think there's probably going to be at least four, if not many others.

1:17:49And that's something that it seems there's like kind of growing evidence of, which kind of my sense is it's a hot take because I think right now people are a little bit enamored with maybe one or two, but.

1:17:57Harry Stebbings:Listen, Matt Damon, and it's been so wonderful to have you on the show. I'm going to let you go back to Robin Williams.

1:18:05Harry Stebbings:And the show was brought to you by Eight Sleep. I'm kidding. Dude, it's been so much fun. Thank you so much. Thank you for having me. But before we leave you today, here's a question for any founder listening. What would your marketing team do with an extra 30 hours a week? That's roughly what teams get back when they stop manually building every campaign, every workflow, every email. Right now, your best marketing people are spending their days cloning templates, configuring audience segments, and chasing data across systems. That's not what you hired them for. Conversion is the first marketing automation platform where AI agents handle the execution.

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