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Matan Grinberg

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2026-06-13
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2026-06-13
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  1. 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. And 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

    2026-06-13 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  2. They're just like, basically, they saw what happened with the cloud. They saw scars of being 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. Brave new world.

    2026-06-13 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  3. Honestly, EY, the accounting firms, is one of our largest customers. I know. You said shocking. They are so agent native. It's crazy. They're one of our largest customers.

    2026-06-13 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  4. I mean, it's just a shame because 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.

    2026-06-13 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  5. Yes, it actually 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 disingenuous and wrong, but it's like really hurt the psychology of a lot of developers, like just people.

    2026-06-13 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  6. Past is an indicator of the future, and like there's just been more random chaotic, turbulent events at OpenAI. But like from a business perspective, to me, that's like both great choices.

    2026-06-13 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  7. In my mind, the answer here is I think they're approximately equivalent. Like to me, it doesn't really matter. The biggest reason that affects the EV is like volatility of the company. That's the only thing. Because I think from the business perspective, they're both very well suited and like well positioned there.

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  8. 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 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 you're in season, you're fucking locked in. You'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.

    2026-06-13 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  9. 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 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. You 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.

    2026-06-13 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  10. 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. And so we can do that. And, you know, 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.

    2026-06-13 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  11. 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 Grindslop comes from intermediate metrics. Like, 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 analogy 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, you know, get things done. But 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,

    2026-06-13 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  12. Yes, 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 if 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 infosis or cognizant or whatever. We are not a services company. If we need FDEs to make the product work, we have a shit product. Like 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.

    2026-06-13 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  13. 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.

    2026-06-13 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  14. Ambitions, what are ambitions? I don't think it makes sense for them to do that. 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.

    2026-06-13 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  15. Me, and this is speaking strongly biased as an application person. 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.

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  16. Yes, but I think that's at least in the United States the beauty of having states 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 versus the states that do allow for data centers to be created people will prosper they'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 the United States and I think in Europe I mean it's tough. I think there were some good positioning that Europe had you know a few years ago, a few decades ago with nuclear that I think hasn't been you know 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 on the energy side

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  17. Probably on the Frontier Model Lab side, there's so much to do on the 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. Other 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.

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  18. 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. But 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.

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  19. 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 a different nation, which is one concern. And I think that the concern is there 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 it in the US, is there concern with the model itself? And 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 adversarily. 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 trying to intentionally kind of break whatever it is that you're doing. Suppose 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 adversarial.

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  20. 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 they're probably going to be in the next couple years some pretty big incidents that occur because of AI generation. They're honestly probably have been. 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

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

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  22. 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 factor. Also, like I'm not going to be, 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. My sense is, and we're still in the early innings, 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.

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  23. 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 open eye versus anthropic. And, you know, for some enterprises, if there are things that are like very sensitive or very secure, you might want to do that. But for a lot of, like 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

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  24. 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. So 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, you know, 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 cloud code and codecs 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? Do we want to use an open model? Do we want to use Frontier? You know, which one of those?

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  25. 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. And 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. And the world

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  26. 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.

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  27. Yes, she is, first of all, she is 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. She'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.

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  28. We were having a conversation. I was like, look, Francesca, like, if you want more ownership of factory, you could just join us. And he was like, kind of as a joke, I was like, oh, you could just join us if you want more. Like, this is the highest 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 returns for them and we're the biggest fans of theirs and kind of we still have a very deep relationship. And she was very close with their firm affinity from her investing days. Then, you know, we were introduced. We got along really well. And so then that was how the connection was made there.

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  29. That was one of the best hires that I've ever made at Factory 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, that was another kind of weird coincidence, but she's like just in the process of 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. She was like kind of hounding.

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  30. I don't want to name names, but there's this one investor in particular who's still in the game, but more of the old guard, I'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, 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 at 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's hot everyone's going to be excited it matters when it's not and there are going to be tough times how how do they behave then

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  31. So 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.

    2026-06-13 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  32. 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, but 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. Retrospectfully, it's like, oh, 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 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.

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  33. 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. Oh, yeah, we'd solve that easily. We'd 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, we want to give you a check.

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  34. I didn't even know who the hell they were. It was just like, oh, these random people, whatever. Yeah, I'll go talk to them. I wish I was recorded because I'm sure I came across as so arrogant.

    2026-06-13 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  35. 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. I had some shitty demo that I built. Eno is thousandx 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. And keep in mind, like, my parents immigrated from the Soviet Union to the United States with basically nothing. The fact that I was...

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  36. Yeah, because it was so like it was incredible. We had the exact same reasons for getting interested. I totally didn't want to dirty it with that kind of thing.

    2026-06-13 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  37. 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 he responded that day and invited me down to Sandhill. 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. At the end of it, he was basically like, it was great to meet you, Maton. 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, like for your resume, if you like, voluntarily go there, or you should start a company. And I was like, okay, thank you so much. I appreciate you taking the time. Like, I'm going to, you know, 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 like a pitch.

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  38. 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.

    2026-06-13 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  39. The way to actually solve this problem was not in academia, but in the industry. And to properly solve it in the industry, you would have to start a company. 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 Teals is 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 know, 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, why 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. So it was a theoretical physicist. Like I remember this guy's name.

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  40. 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. And 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 three or like n equals four, four 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. So 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.

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  41. 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 on 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 the kind of this existential crisis. Like, what do I do? And so, you know, kind of realized it was probably going to be either quantance, 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 a 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. You could always go and do quantifence. Stay at Berkeley, explore, learn some stuff, whatever. So I was like, okay, fine. You know, I'll do that, you know. Ended up taking my first CS classes at Berkeley. I learned like to code for physics for like simulation.

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  42. 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 work with a great advisor there. And then only at Berkeley I realized it kind of all comes crashing like holy shit, I've just been doing this because it's hardened because someone said I couldn't do it. What the hell do I do with the rest of my life? Like this is crazy. Like everything came crashing.

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  43. 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, are you kidding me? She thinks I need to retake geometry. Like I'll show her. And so my first order on Amazon ever was textbooks for algebra 2, trigonometry, pre-calc, calculus one, two, and three, differential equations, and maybe a linear algebra textbook. So bought those textbooks. And then the summer between middle school and high school, I studied all of those, like did all the problems and 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. 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.

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  44. 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 to 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. Like if you go into a conversation trying to sell something, especially to engineers, don't waste your time. If you go in trying to have...

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  45. 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.

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  46. 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 your 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. And 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.

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  47. I think the biggest bottleneck by far, working with all these organizations, is the human side of it. It's just like behavior change.

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  48. Maybe there's like some short term blips, but 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.

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  49. 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 would need to be very, very careful about the cases where you do want the government to say, hey, we want to step in here.

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  50. 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. I think there are 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, you know, 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. But 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, you know, short term, it might be slightly worse, but it...

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