OpenAI vs Anthropic vs Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning

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The world going forward, there is going to be nothing that no one can build. Everyone is trying to commoditize the other. Value accrual is a time-dependent phenomenon.

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Meet Matan Grinberg, CEO and co-founder of Factory. Before Factory, he was a physicist. He spent 12 years trying to be one of the best string theorists in the world. Now he's changing the world of software development. He works with some of the biggest enterprises in the world. He does look like Matt Damon from Goodwill Hunting, but he is one of the best founders I've met.

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So many of the tasks that we're doing, we don't need the very frontier to do it. We might see a short-term contraction of usage of the very frontier models. I think it's pretty embarrassing that we don't have frontier open models in the United States. Name a legendary company that has a shit sales or marketing team. You can't. The age of the polymath is back. We will see the best companies treat teams more and more like SEAL Team 6 or like professional athletes.

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Ready to go?

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Matan, it is so good to have you in the studio. You've just insulted my continent with the suggestion that we've only come up with bottle caps while you came up with Transformers. Not wildly untrue. But this is going to be a fun show. So thank you so much for joining me. [SPEAKER_02] Thank you for having me, Harry. [SPEAKER_02] It's a pleasure to be here. Now, I was just doing a show yesterday with Rory and Jason, and Rory was saying, the fundamental question is, will we see an increase in GDP coming from AI and the coding developments that we're seeing? And will it lead to GDP increasing above the 2% average for the last 200 years?

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Do you think we will see meaningful productivity gains from the AI tooling that we're seeing? Or is Uber's concerns validated?

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So I think, yes, absolutely, we will. We will see tremendous growth from these tools. I think it takes time to permeate through. Because you can tell, on an individual basis, almost on a problem-by-problem basis, we can solve problems faster with these tools. Now, companies generally organize around solving problems. And if you're organized around solving problems, and you have some set of personnel, you might say, this is the number of problems we can solve at a given time based on how many people that we have. Everyone is now going to be able to solve more problems with the same number of people. We can solve the same number of problems with fewer people.

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But it takes time for the resource allocation to adjust. A lot of businesses will have to ask, do we want to solve more problems now because of the increased leverage that we get? Or do we want to solve the same problem, but now we can do it in a more efficient manner? That's a question that a lot of businesses will be grappling with.

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Do you think we will have fundamentally smaller teams, which ultimately suggests that number two is the option that most people take? Or do you think we will have actually the same size teams and we'll just go after a more expansive area?

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It's really not obvious because there are dynamics that it's hard for me to predict. But what I will say is, again, bringing it back to problems, all of these companies are now going to have to think, okay, we have all this new leverage. Do we want to solve the same problem? Do we want to increase our ambition and solve a bigger problem? Or do we want to solve more problems that our users maybe have?

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I was watching Andre Karpathy, and he was talking recently about the 10x engineer actually is wildly misunderstood. And you won't see the 10x engineer, you'll actually see a smaller number of 100x engineers and the rest, and this bifurcation of engineering talent. Do you think that is the right way to look at the future of engineering talent?

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Directionally, yes. Because what is a 10x or 100x engineer? I don't necessarily agree with the language around it, but— [SPEAKER_03] Why not? I think it just implies as if 10x of what? Is it pure output? When you say 10x, it means how much code they're writing. Yeah, now I can write a billion lines of code with these tools. It might be shit lines of code, though. So maybe the way that I think about it is load-bearing individuals in an org. It's if you remove this person, things fall. In some orgs, there might be people where if you remove them, nothing happens, and they're not load-bearing in that case. And so basically these people who have very high leverage

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are now being handed a tool that gives them even more leverage. And so using the language of 10x or 100x, yes, they're levered up. They can have even more impact.

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But with that leverage language, those who know how to use leverage will be able to have even more impact. And those who don't will on a comparative basis be that much less valuable to a business.

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When we think about the two different parts, you said that, hey, you have the option of you can do more with the same size teams or you can reduce teams and do what you already did. If I am thinking as a leader today, what would be your biggest advice to me on how I should think about resource allocation for tokens internally?

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Yes, this is a great point. This resource allocation problem of tokens, it's not just tokens. It's dollars, it's tokens, it's people. This is going to be the thing that over the next 24 months, every C-suite is going to be thinking about. And I think the right way to go about it is, what is the core competency for our business? What actually matters for the business that we are doing? And then how do we allocate resources accordingly? In other words, if you're a logistics company, your core competency is probably not software development. Now, you might have had a lot of software engineers as a means to an end to deliver on your logistics goals, let's say.

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But that might not be your core competency. This is, I think, going to be the thing that over the next 24 months, every C-suite is going to be thinking about. And I think the right way to go about it is, what is the core competency for our business? What actually matters for the business that we are doing? And then how do we allocate resources accordingly? In other words, if you're a logistics company, your core competency is probably not software development. Now, you might have had a lot of software engineers as a means to an end to deliver on your logistics goals, let's say. But that might not be your core competency.

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And so what you should be thinking about is not, how do we get more engineers to make more features because that's what engineers have in the past been judged by, how many features do they ship in a quarter? Instead, it's, what are the actual output metrics that matter for our business? And how do we now allocate resources, whether it's dollars, whether it's tokens, whether it's headcount, to more dramatically move the needle on that business outcome? And I think this is great for the world because I think part of the reason why so many organizations got so bloated is because we were in a period of time where everyone was focusing on intermediate metrics.

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If you're an engineering team, we wanted to ship three features this quarter. Did you ship three features? We shipped four. What a great quarter. That doesn't necessarily matter for the business at all. And so now it's coming back to what matters in the first place. What are the business metrics that we want to move the needle on? Is it customer satisfaction? Is it revenue? Is it market share? And you can tie back every individual's work to that, whether it's marketing, sales, engineering, all of it. [SPEAKER_03] Kirkland announced a $500 million spend.

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[SPEAKER_03] You're friends with Winston from Harvey, fantastic guy, who obviously I'm sure has, I don't know if you guys have spoken about this actually, but it's a big spend, $500 million across five years to internally build their own Harvey or Lagora. [SPEAKER_03] How did you think about that? I mean, it's fun talking about core competencies. Kirkland spending half a billion dollars to build their own AI tools. My understanding is that building AI technology is not a core competency of that firm.

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[SPEAKER_02] I think I was surprised to see it. [SPEAKER_02] Now, I actually think this is good for Harvey because it's nothing like trying to do something yourself to make you realize, this is actually really difficult. [SPEAKER_02] This doesn't actually matter for us to have the in-house ability to build this ourselves. [SPEAKER_02] Let's go and have someone who is an expert in this to build this for us. [SPEAKER_02] That is my sense. [SPEAKER_02] My favorite is also the amount of people that say, we told you how easy it was. And you're, it's so easy they're committing half a billion dollars. That would suggest the opposite.

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I had Brendan on from a call the other day and he was fundamentally saying that the next 12 months would be the most value accruing 12 months for AI infrastructure companies.

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[SPEAKER_03] We would see that the models of the products and the AI application layer companies would be most at risk denigrated. Would you agree with that? I would disagree. I'd pretty strongly disagree. For a couple of things. One, actually sticking with the Kirkland thing, I think as an example, we're so used to a world where moat in software was, I know how to do this and you don't. And so you're going to pay me because I have the engineers who know how to build this and you simply cannot. Now, the world going forward, there is going to be nothing that no one can build. Every single piece of software, anyone will in theory be able to build.

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Now, back to the resource allocation though, is it worth your time and your energy to go and build it? Or should you go to someone else who has already built it or can do it faster? To me, an example of this is, suppose we had a very busy day at work. I could probably go and pick up lunch for everyone on the team. I know how to do it. I know how to walk out the door, place an order, hold the bags, bring them in. Now, just because I know how to do it, is that an efficient use of my time?

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[SPEAKER_02] Probably not. [SPEAKER_02] I'm probably going to say, you know what, for my resource allocation, I'm going to pay someone to go and do that for us because at factory, our core competency is not that the CEO goes and gets lunch for everyone. [SPEAKER_02] And I think it's somewhat similar here, which is, just because you can build a lot of these things does not mean you should. [SPEAKER_02] And in fact, oftentimes, you want to be really ruthless about what are the few things that you and your team own and do end to end. [SPEAKER_02] And then if it's not relevant to your core business and your core competencies, outsource it.

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What would you do, but it's not core competency for you? And so you don't do it because of focus. Oh man, I enjoy making breakfast. [SPEAKER_02] I haven't done it in three years. [SPEAKER_02] There's nothing, it's just not time efficient. [SPEAKER_02] It just doesn't make sense to spend my time doing that. [SPEAKER_02] All right, okay.

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But it is something that I do enjoy. But to your point on model applications, infrastructure, [SPEAKER_03] What would you like to do, but it's not core competency for you? [SPEAKER_03] And so you don't do it because of focus. [SPEAKER_03] Oh man, I enjoy making breakfast. I haven't done it in three years. There's nothing like it's just not time efficient. It just doesn't make sense to spend my time doing that. All right, okay. But I, it is something that I do enjoy. But to your point on model applications, infrastructure, I'm not sure if you've seen there's a meme of the Microsoft org chart and it shows different segments and they all have guns pointed at each other.

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Just to show in Microsoft there's a lot of bureaucracy and everyone's fighting for who gets to do what. I think that image is pretty accurate to what's happening right now with the models, the application companies and the infrastructure companies where everyone is trying to commoditize the other. Everyone is trying to say, oh no, this one is irrelevant. All the value is going to be here. All the value is going to be there. The reality is value accrual is a time dependent phenomenon. It's not that there is one person who steady state gets all of the value. That's not how it works. It's maybe for this next year, this person is who has the pricing power, who gets the value.

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This next period of time, these people get it. We are all, whether overtly or not, and maybe I'm saying the quiet part out loud, everyone is trying to commoditize the people that are not them. So for example, we're model agnostic. We want to give our customers the best pricing, the best performance, the best speed for whatever task they want to do in their software development. And we want to make sure that OpenAI, Anthropic, Google, Microsoft are all under pressure to make sure they give the best models for as cheap, as quick as they can and don't feel they can just charge whatever they want.

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Now, similarly, the model companies want to make it such that the applications are all trivially easy to build and really the product is the model. And then the infra companies have their own spin on this. But the reality is everyone's trying to commoditize the one that's not them. So from Merkur's perspective, it's very much in their interest that models that have access to proprietary data get differentiated value and capture a huge amount of value because that validates their business model. It's a big push and pull of who can get the leverage. What is the belief that would invalidate yours?

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The bear case against factory is if one model provider gets significantly better than all of the others. So basically, I think a key thing for us is that all the models are going to be roughly as good as each other. They'll be good at one is a little bit better at review, one is a little bit better at testing, one's better at Python, this and that. It all fluctuates every week. Even already, people have a hard time keeping track. What model is number one? What's the latest thing that came out? If one model ends up going way above all the others, that's a case where companies might want to just completely go in with them.

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But then that's a monopoly for the entire economy to be worried about. [SPEAKER_03] Is the rate of model development sustainable? [SPEAKER_03] And what I mean by that is I was with the founder of Nebius the other day and he was talking about every few weeks we see new models. [SPEAKER_03] And I said, you're wrong, every few days. [SPEAKER_03] Yes. [SPEAKER_03] Especially when we look at Chinese open source, it's three or four a week. [SPEAKER_03] Is that rate of model development a feature of the time that we're in or is it an ongoing characteristic or trait of this environment? I think eventually we'll stop seeing them as model releases and they'll feel more continuous.

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Like before it was GPT-2, then GPT-3, then GPT-3.5, then 4, 4.1.

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[SPEAKER_02] But then you get more and more and 4.523. [SPEAKER_02] Eventually they're just going to not announce it and it's just, hey, look, here's our model that's continuously getting better because already people [SPEAKER_02] then GPT-3, [SPEAKER_02] then GPT-3.5,

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then 4, 4.1.

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And then you get more and more, 4.523. Eventually they're just going to not announce it and it's just like, hey, look, here's our model that's continuously getting better because already people have fatigue. Engineers at the enterprises that we work with can't keep up with every single model that comes out, nor should they. And I think that's the whole case for the application layer, whether it's us or a Harvey or whoever else is, we're going to figure out what model is best for what use case, where the trade-off is between cost, quality, speed, and we'll just deliver that to you based on the task that you have because it's hard to focus on what matters for your business and then also keep track of all these models that keep coming up.

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[SPEAKER_03] A big question that people have is around the rise of open source and whether everyone is concerned by the amount they're spending on tokens just being so much larger than they thought, hey, we spent our annual budget and it's May. Maybe we should move to open source. Yeah. And we're seeing more and more great companies use frontier models, see where they can get to and then move to open source to get as close to that as possible. Yeah. [SPEAKER_03] How do you feel about that being a considerable threat to maiming the market for frontier models?

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I think it's a really important counterbalance because it basically allows you to make the trade-offs of what tasks do you want to put what level of intelligence on. And I think it's a really important counterbalance because a lot of enterprises will realize so many of the tasks that we're doing, we don't need the very frontier to do it. And we can do it much faster, much cheaper with these open models. And again, it's part of the resource allocation and to do good resource allocation, you want to be able to be anywhere in that cost, quality, speed trade-off.

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[SPEAKER_03] I love the GIFs on Twitter or memes on Twitter when it's like, you know, me naming a file and it's like the massive cigar with the blowtorch.

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Oh, yeah. I love that. No, because it's such overkill. But also, there's a funny dynamic that emerges, which is there's an ego thing where, oh, no, no, the work that I'm doing, only a frontier model could handle. Oh, this mere open model can't deal with the work that I'm dealing with. And this is even admittedly, when I first started switching over, I'd be like, ah, I don't think an open model can handle this. And it's like, no, it probably can. And it's a funny thing to mentally deal with of deciding, you know, manually or then having the router do it for you.

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[SPEAKER_03] My question is, enterprises like security, they like reliability, they like ease. Yes. And when you have frontier models which are packaged perfectly, priced clearly, and it's secure, do they not just go for that, the easy option over trying to be smart and intelligent routing to different open models?

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So, a couple things. One, it's easy when there's only one of them. But again, as we said, a new model comes out every week. And if you have to go through the full enterprise process to get every new model in, it's not very easy. Two is, it's also really expensive. And if you're seeing your costs go up like crazy and not having an ROI case, it doesn't make as much sense. And I think something that's interesting is there's kind of like

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As we said, a new model comes out every week. And if you have to go through the full enterprise process to get every new model in, it's not very easy. Two is, it's also really expensive. And if you're seeing your costs go up like crazy and not having an ROI case, it doesn't make as much sense. And I think something that's interesting is there are three phases that we're seeing happen in these enterprises. Phase one, this was a couple months ago, was board yells at CEO, hey, Mr. CEO, what's your AI strategy? CEO's like, shit, I don't know. CTO, hey, what's our AI strategy? Let's make sure we adopt AI. And so then phase two was AI at all costs, token maxing, part of your performance reviews, we're going to measure how much you guys use AI, everyone, you have to adopt. That was phase two, right? Get as many people to adopt as possible. Phase two happened a lot faster than people might have expected. And so now we're ending phase three. Like phase two was the debauchery, the long night, taking shots, having a great time, using all the AI. Phase three is the hangover where you go and look at the bill and it's like, oh my God, we are spending so much. I have no idea what the ROI is. Does this—is this helping our business? That's where a lot of these companies are at now. And I think this is why routing is so important because they're realizing, and this is a true story. One of the CIOs I was speaking with realized we've been spending hundreds of thousands of dollars per month on people asking Opus 4.8 questions like, hey, how's it going? What are my macros from the food I ate today? What's the weather like? And it's like, guys, we don't need the frontier of human intelligence to be doing this stuff for us. Let alone, it's not even work related in some cases.

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[SPEAKER_03] Will we see a contraction then given the hangover period being realized? I think there are, 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. [SPEAKER_03] 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?

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I've literally seen this with dozens of our customers where initially, 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. This was before we had routing and it happened a couple 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, when we weren't—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. Have you thought about consciously—yes, we want to do this. Sometimes we'll proactively set in those user limits just so it's better to be aware as you're going up as opposed to just going crazy and then 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.

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as you're going up as opposed to just going crazy and then 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. [SPEAKER_03] To me, [SPEAKER_03] the biggest question [SPEAKER_03] that I ask myself [SPEAKER_03] and I think we need to ask ourselves

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[SPEAKER_03] as an ecosystem today [SPEAKER_03] is if Mark Benioff says [SPEAKER_03] that he spends 300 million [SPEAKER_03] on Anthropic, [SPEAKER_03] okay, [SPEAKER_03] for his devs, [SPEAKER_03] that is 3.8% of salaries. [SPEAKER_03] Okay, [SPEAKER_03] great. [SPEAKER_03] What will that number be [SPEAKER_03] in three years' time? [SPEAKER_03] Because if it's still 3.8%, [SPEAKER_03] fuck. [SPEAKER_03] If it's 20%, [SPEAKER_03] fuck again, [SPEAKER_03] but fuck positive. [SPEAKER_03] Yeah. [SPEAKER_03] And if it's [SPEAKER_03] Brandon at McCaw [SPEAKER_03] who says that he's spending [SPEAKER_03] more on tokens [SPEAKER_03] than he is on headcount,

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[SPEAKER_03] fuck again, [SPEAKER_03] but even more positive. [SPEAKER_03] Yeah. [SPEAKER_03] What do you think [SPEAKER_03] that percent of dev salary is in three years? I think it's actually a more nuanced question than we might think. I actually think it can be as low as 0% for some individuals and it can be as high as tens of thousands of percent for some individuals. [SPEAKER_03] 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,

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it's custodians of code, right? The people who do anything relating to code are engineers or developers. I think 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. And then there are going to be certain people where actually they don't really need tokens at all.

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[SPEAKER_02] And that's not [SPEAKER_02] how they deliver value [SPEAKER_02] to the business. [SPEAKER_02] Like for example, [SPEAKER_02] maybe our best salesperson, [SPEAKER_02] the way we use them best [SPEAKER_02] is not by having them [SPEAKER_02] use tokens, [SPEAKER_02] but by going and meeting [SPEAKER_02] people face to face. [SPEAKER_02] That's an obvious example [SPEAKER_02] because they don't write [SPEAKER_02] code in the first place. [SPEAKER_02] But I think similarly, [SPEAKER_02] maybe there are some engineers [SPEAKER_02] who they actually do [SPEAKER_02] their best work [SPEAKER_02] by spending time [SPEAKER_02] with users, [SPEAKER_02] spending time

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[SPEAKER_02] with their customers, [SPEAKER_02] maybe doing some data analysis. [SPEAKER_02] That's not very token expensive,

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but then there are going to be others who are delegating to dozens of droids in parallel, working on tons of different crazy features and refactors and migrations. But 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, 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. [SPEAKER_03] If I were to say, [SPEAKER_03] give me an average number, [SPEAKER_03] what will that average be? [SPEAKER_03] What will the median be? I would say order of magnitude, it'll probably be comparable to salary.

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Comparable to salary? Like on the same order of magnitude. [SPEAKER_03] Within the three-year timeline? [SPEAKER_03] Yeah. [SPEAKER_03] What percent of tasks today [SPEAKER_03] using frontier models [SPEAKER_03] could be done [SPEAKER_03] with open source models? Probably 80 to 90 percent. It's typically the planning that really needs the frontier models. [SPEAKER_03] But is that not the most, [SPEAKER_03] I'm sorry, [SPEAKER_03] I'm really dim. [SPEAKER_03] Harry, [SPEAKER_03] that's nonsense. [SPEAKER_03] But if it's 80 to 90 percent, [SPEAKER_03] does that not just present [SPEAKER_03] the biggest bear case ever [SPEAKER_03] against Codex or Claw Code?

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[SPEAKER_03] Because you're just taking away [SPEAKER_03] 80 to 90 percent of that time. Well, it depends because those, that 10 to 20 percent could be the most important tokens. It's maybe right,

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I'm really dim. Harry, that's nonsense. But if it's 80 to 90 percent, does that not just present the biggest bear case ever against Codex or Claw Code? Because you're just taking away 80 to 90 percent of that time. [SPEAKER_02] Well, it depends because those, that 20, 10 to 20 percent could be the most important tokens. [SPEAKER_02] It's maybe like, right, 10 to 20 percent of the tokens, but those are really, really important because it's decision-making tokens, perhaps. [SPEAKER_02] Sure.

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[SPEAKER_02] But it's very similar to how we structure human orgs, which is oftentimes leadership makes very key decisions that determine the fate of the company, and they don't spend the most hours. [SPEAKER_02] If you look at the human hours of a company, most human hours are not spent on making the decisions. [SPEAKER_02] 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. [SPEAKER_02] And those people that make those decisions are also typically paid a lot in a company.

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But the assumption there would be then that you'd have to increase that spend for that 10% even higher. [SPEAKER_02] And that's what's happening already. [SPEAKER_02] It's like the frontier models are sometimes they're getting more expensive or you're using the ultra high reasoning or this type of thing. [SPEAKER_02] And so it's okay, if this planning thing is the very key thing, we'll spend on it. [SPEAKER_02] But it doesn't necessarily mean that most of your tokens are going there. [SPEAKER_02] It's just for certain key steps, maybe you want to spend a lot and it's worth allocating the budget there.

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[SPEAKER_02] But then once it comes to okay, we have the plan now let's implement, the open models are typically really good. [SPEAKER_02] 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. Yeah. Have you found the same? And is it impossible to hire great AI researchers in competition with Anthropic and OpenAI? [SPEAKER_02] We are a very opinionated organization. [SPEAKER_02] And so it's very self-sacrifice.

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[SPEAKER_02] 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.

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That said, it is still pretty competitive.

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What do you think is the strongest opinion that you have that most people disagree with? [SPEAKER_02] I would say the opinion that we have that I think in the space that we are in is the most controversial is the way that we treat what product is at factory. [SPEAKER_02] I think there's some very commonly held beliefs at the labs or at some of our competitors who are also doing software development. [SPEAKER_02] And this is honestly, growing up in the Bay Area, there's a very common Silicon Valley fallacy, which is there's research is like the pinnacle. [SPEAKER_02] And then there's engineers who implement the research.

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They're not quite there, but they're still great. And then there's sales and marketing and all that dirty stuff. If only we could build a better product and it would sell itself and we wouldn't need to deal with sales and marketing. And it's 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. Now, 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.

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That entire thing is the product. Everyone is first class.

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till their 10th renewal after a decade of being a happy customer. Now, 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. Everyone is first class. It's not we have engineers who are wholly at the office and you're not allowed to speak to them unless you're an engineer. 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. Everyone, there's no first class or second class and this is shockingly controversial in particular in the Bay Area and in particular in coding or in AI. It's messed up. It's so messed up and I think 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, 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.

SPEAKER_02

[SPEAKER_03] No, but I can name companies that have shit products but great sales and marketing teams. That's the ironic thing on the flip side. And in fact, it seems many more, many more, many more. [SPEAKER_03] 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. I mean, yes, 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?

SPEAKER_02

Yes. It very seriously changes and this is actually why we're selecting for, very intentionally, 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 but as, again, an important part of the product because as an engineer, you're no longer 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 is a full-stack engineer that goes way beyond just engineering but into sales, into marketing, into enablement and all that and those are the parts of engineering that 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 Silicon Valley has really bragged about a lot which is competition winning or Olympiad type, are you as fast as possible at coding or do you memorize all the different nuances

SPEAKER_02

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 Silicon Valley has really bragged about a lot, which is competition winning or Olympiad type, are you as fast as possible at coding or do you memorize all the different nuances of these different languages? Those are the parts that don't matter. If you memorized some coding language or some syntax of a coding language that someone else didn't, it doesn't matter.

SPEAKER_02

[SPEAKER_03] The credentialism but I think they misunderstand VC mindset right now and as a VC, I'm happy to share how you feel, how we feel which is 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. [SPEAKER_03] Yeah.

SPEAKER_02

But it's a crutch. It's helpful. It's a good indicator. Generally, you can't win competitions if you're dumb, right? However, for these types of engineers that we're looking for, that's cool but that's irrelevant. What have you built? How have you taken ownership and agency of things end to end? This is not, 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 a certain, especially there are some high schools that really focus on you must do the math Olympiad, you must do this, the AMC, to then go to the IMO and this is the path to success where actually that's anti-signal because there it's like you're not owning your fate or choosing your agency. You're going through the funnel but then there are people on our team who are from the middle of nowhere where no one else in their high school ever did this stuff and they took the agency of I think this is really fun, I'm really competitive, I want to compete at this and they do those competitions on their own. Those are when the signal is still positive for this kind of engineer of the future.

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[SPEAKER_03] I don't think I've told you this but do you know who you always remind me of? Who? [SPEAKER_03] Matt Damon in Good Will Hunting. That's, I'll take that to the bank. [SPEAKER_03] Have you not been told that before? Let me say that one more time. [SPEAKER_03] You look identical. That's very kind of. I'll take that. [SPEAKER_03] We're going to put up an image here and you're going to do a side-by-side. Yeah, all right, we have a clip. This is the starter to the show and they're going to be like, uh-huh, I totally get it. That makes absolute sense.

SPEAKER_02

[SPEAKER_03] Can I ask you, when we go back to 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?

SPEAKER_02

I think so it's starting to exist more and more, but I think it's this GM or general manager 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 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 feels like, again, owning more of a business outcome.

SPEAKER_02

that is not just a shipped feature, but 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 feels like, again, owning more of a business outcome, more entrepreneurial, higher agency, spreading their reach beyond just...

SPEAKER_02

[SPEAKER_03] that's every function? It's like, believe it or not, I sometimes post on different social media platforms, and I just said, my biggest advice to any students today would just be, just be full-stacking 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? We're expecting everyone to be full-stacking every function.

SPEAKER_02

The age of the polymath is back. Growing up, 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. So chemistry wasn't that built out. Mathematics wasn't that built out. Physics wasn't that built out. In Da Vinci's case, 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 theory were 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. And so, it 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, knowing there are unknowns and knowing that you can still push the frontier forward despite that, you can be a polymath. You can push forward and create innovations on how to do developer marketing while at the same time pushing forward the frontier of token caching for software development agents at the same time as being an incredible solution engineer. 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 back.

SPEAKER_02

[SPEAKER_03] I've had a lot of people say on the show that agent operations will be, 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?

SPEAKER_03

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. [SPEAKER_01] Ah, marketing and design. I'm going to create agents that allow you to create visuals, share them amongst each other, 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. [SPEAKER_01] Ah,

SPEAKER_03

marketing and design. I'm going to create agents that allow you to create visuals, share them amongst each other, edit them, collaborate on.

SPEAKER_03

[SPEAKER_02] I think to some degree everyone should be able to do that on their own. But, I could imagine a world where there's someone whose job it is to 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 in certain functions that aren't proactively doing that, it's 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, writing release notes, that's crazy that people used to spend hours of time writing release notes or writing documentation. So, not everyone knows what release notes is. What is release notes? So, it's 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, like Stripe has a really great reputation. They had incredible documentation. So many APIs had horrid documentation. Stripe was the pinnacle. They were so good at this. Spent a lot of time doing it. Five years from now, it's going to be, oh my God, I cannot imagine, cannot believe that these people that get paid so much money spent hours of their time doing this. I 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.

SPEAKER_03

[SPEAKER_02] Totally agree with that.

SPEAKER_02

[SPEAKER_03] How does the product review and especially code review process change in the next few years? Yeah, I think what's cool about this agent native software development is review has been a big problem because basically, the first phase of rolling out AI coding tools was, oh my God, look how much code we can generate. It's incredible. I'm generating a ton of stuff. And then phase— Phase shit.

SPEAKER_02

Yeah. Phase two was some poor staff engineer who has to review hundreds of these slop PRs that don't adhere to your standards, are completely misformatted and all this stuff. But what's great about having this kind of 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 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. Setting up things like CICD or good linters or good pre-commit hooks, these are all things that the best organizations at 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 correlated to how many engineers you have. With agents though,

SPEAKER_02

CICD or good linters or good pre-commit hooks, these are all things that the best organizations at 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 one-to-one correlated to how many engineers you have. With agents though, the impact of that is now 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 faster throughput in your software development.

SPEAKER_02

[SPEAKER_03] When agents are the buyers and you're selling to agents, how does the world change and does the value of great API increase? I think 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,

SPEAKER_02

[SPEAKER_03] agent-to-agent doesn't give a shit about UI or design, but it does fundamentally care about data structures, potential integrations, documentation. Do you know what I mean?

SPEAKER_02

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 the UI side of things and how things need to be, being very aggressive about pruning anything that's unnecessary, making sure there's not bloated comments in all of your code that's gratuitous. There are ways around it, but that's where the human's job changes a little bit, where their job goes from—I mean, part of our name, our 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. And so in this new world of software development, humans, 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 building the scaffolding around this factory that produces their software.

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[SPEAKER_03] Do you worry about labor displacement when we move from working in the factory to working on the factory?

SPEAKER_02

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 these are 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. And very few of those problems that can be solved with software

SPEAKER_02

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. And very 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.

SPEAKER_02

[SPEAKER_03] What problem is not currently being solved with software that will be enabled by this new technology? Because everyone's climate change. And I'm great. You know how many people have found doing climate change technology?

SPEAKER_02

Well, none. Yeah. Well, and maybe part of that is because all 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 the intermediate period. But so many health problems, so much of pharmaceutical research can be advanced with better engineering. And the thing that really upsets me with some of the people who are talking about pausing AI development or any of this, it's a bad thing and it's going to harm society. Dementia is 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, no, no, no, sorry. You guys, you got to maintain that relationship for a little bit longer. We'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. It doesn't make sense.

SPEAKER_02

[SPEAKER_03] 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?

SPEAKER_02

I think it's certainly useful in some cases. 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 military uses or safety or things like weapons, you're definitely going to need some involvement there. I 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 but I think 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 and you could make the case that the faster we develop AI, the sooner we solve climate change

SPEAKER_02

I'm pretty reluctant. I think you need to have a very good case for why you need to do that.

SPEAKER_02

Even 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 and 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, 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 and so there's some of these cases where the natural 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 careful about the cases where you do want the government to say, hey, we want to step in here.

SPEAKER_02

[SPEAKER_03] Do you think we are in an AI infrastructure bubble? Maybe there's some short term blips but long term, absolutely not. Not even close. I think there might be similar corrections to this thing at Uber where, oh, we were going a little haywire, we weren't allocating it appropriately and there's okay, let's lower consumption a little bit but on the net, absolutely not. [SPEAKER_03] 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 behavior change.

SPEAKER_02

[SPEAKER_03] And what you're saying there is selling to large enterprises and how they do change management?

SPEAKER_02

Yeah, or even on an individual level. If you're an engineer who's been an engineer for 30 years, it's hard to change those patterns. 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've 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 an interesting balance there.

SPEAKER_02

[SPEAKER_03] When you look at now, you sell to some of the largest enterprises in the world in some cases. What do you know now about selling to the large enterprise that you wish you could tell young Matan two years ago?

SPEAKER_02

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 coffee shop, any of that, literally never have had a job. Never have been paid to do anything aside from physics until this which is a whole separate thing. But I will say the thing that has been the craziest learning and this is obvious to anyone who's in sales or Chad and Chris, you know, 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. That's another thing. If you go in a conversation trying to sell something especially to engineers

SPEAKER_02

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. That's another thing. 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, and it's really easy because these organizations do their engineering so differently and I find it fascinating how all of these different banks, consulting firms, pharmaceutical companies, they have the most different ways of building software and it's really interesting to go talk to them and to understand it and the best way of talking about it with them is face to face and 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, is our software a good fit for them? Will it help solve their problems? It's also just so fun to then 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.

SPEAKER_02

[SPEAKER_03] 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 Sequoia having never had a job and only being paid to do physics?

SPEAKER_02

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 I never tried in school but I always prided myself on being good at math and when she told me that I was like, are you kidding me? She thinks I need to retake geometry? I'll show her and so my first order on Amazon ever was textbooks for Algebra 2, Trigonometry, PreCalc, Calculus 1, 2, and 3, 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, 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. And 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 Maldacena and I was 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 all comes crashing down. Holy shit, I've just been doing this because it's hard and because someone said I couldn't do it. What the hell do I do with the rest of my life? This is crazy, everything came crashing down.

SPEAKER_02

[SPEAKER_03] What caused that crashing down moment and why did it take so long? 12 years? You're slow. [SPEAKER_03] I have tunnel vision. When I get obsessed with a problem it is all I think. [SPEAKER_03] I was at law school for two weeks. It was a quick realization. See some people are faster. I wasn't quite as quick. Honestly part of it was being a grad student at Berkeley you have to teach classes and I was teaching a class to like 18 year olds who didn't give a shit about physics and I was like It is all I think.

SPEAKER_03

I was at law school for two weeks.

SPEAKER_03

[SPEAKER_02] It was a quick realization. See some people are faster. I wasn't as quick. Honestly, part of it was being a grad student at Berkeley. You have to teach classes, and I was teaching a class to 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 sitting and doing lectures and doing these classes. On Rate My Professor, I think I had a one out of five. I was horrible. It wasn't a good fit. But it was an existential crisis—like, what do I do? And so I 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. 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. You could always go and do quant finance. Stay at Berkeley, explore, learn some stuff." So I was like, "Okay, fine." So I 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—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. A decade of physics—physicists and mathematicians—they're never interested in the case of n equals 3 or n equals 4, four dimensions. It's always like, what is the n-dimensional solution? What is the arbitrary fundamental solution to things? And there was something so fundamental about this idea of code generating itself, and it just got me obsessed. So I stayed at Berkeley, and for the next year that was what I spent my time on. My advisor was very chill and just allowed me to take AI courses. And eventually I realized that the way to actually solve this problem was not in academia but in industry. And to properly solve it in industry, you'd have to start a company. But I knew nothing about starting companies. Because again, all I cared about was math and physics. I 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 I read Zero to One. Incredible book. I know it's cliche, but to someone who didn't—growing up in the Bay Area, shockingly, I just did not care about any of that. And reading this, it was so concise, beautifully written. Loved that. And then I was watching these videos—a lot of them like Y Combinator videos and all this stuff. And then I stumbled upon this. I think it was a Stanford VC Club podcast with this guy whose name I recognized because at Princeton, when I wrote that paper with Juan Maldicena, I had cited one of his papers.

SPEAKER_03

[SPEAKER_02] loved that. And then I was watching these videos, a lot of them like Y Combinator videos and all this stuff, and then I stumbled upon this I think it was a Stanford VC Club podcast with this guy whose name I recognized because at Princeton when I wrote that paper with Juan Maldicena I had cited one of his papers, so it was a theoretical physicist. 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 pretty sociable and normal, which I don't know if you've interacted with. I'm not sure, theoretical physicist compared to theoretical physicist though. Look, he can maintain eye contact. He was somewhat normal.

SPEAKER_03

Very rare. Such a low bar. [SPEAKER_02] He can hold himself in a social setting.

SPEAKER_03

[SPEAKER_02] Yeah. And so I was like, okay, who is this guy? I gotta 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. Would love to get your advice. And 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 it 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. And at 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, like 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 gonna go think about it. But in the meantime, I had already known about Factory, it was just I didn't want to ruin the meeting with a pitch."

SPEAKER_03

You didn't want to transactionalize this. [SPEAKER_02] Yeah, because it was so – Like it was incredible. Like we had the exact same reasons for getting interested. [SPEAKER_02] I totally get it. Yeah. Didn't want to dirty it with – no, it's like an LP where at the end you're like, I don't want to ask for a check. Yeah, exactly.

SPEAKER_03

[SPEAKER_02] 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 recognized but I didn't know super well. End up talking to him. He's also interested in this problem. We joke that it was intellectual love at first sight. This is my co-founder. And basically that day forward we spend every day talking non-stop. I had some shitty demo that I built, and he's a thousandx better engineer than I ever will be. And so he and I for the next 72 hours 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, 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 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 their pride and joy. It was the thing that they were the most proud of. There was so much momentum. So much momentum. 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, "Alright."

SPEAKER_03

[SPEAKER_02] The fact that I was doing a PhD to them was like their pride and joy. It was the thing that they were the most proud of. There was so much momentum, so much momentum. 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, alright, you have a meeting with the Sequoia partnership tomorrow morning. Be ready to present. You've never presented to a venture partnership before. So what happens? I made a shitty deck. [SPEAKER_02] Put some slides together.

SPEAKER_02

[SPEAKER_03] We go to the Sequoia HQ, put some slides together. Keep in mind, I didn't even know who the hell they were. It was just like, oh these random people, 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?

SPEAKER_02

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. So retrospectively, I know that Alfred and Pat and Roloff, they were all in there, they were all asking questions, and I was probably dismissing some of it. Oh yeah we'd solve that easily, we'd do this, we'd do that. Keep in mind, this was in April of 2023, so this was 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 a blur. And the next day, Sean calls me and he's like, hey, you want to—I want to give you a check.

SPEAKER_02

How big was the check? A million dollars. A million dollars. And what he gives me shit for—you know what the terms were? Five post. [SPEAKER_03] I mean, what—I'm not being rude—why did they bother doing a partnership meeting, like in the nicest way, that's like a coffee. I know it's a dick comment. But when you managed like seven, eight billion, it was a different time. Early 2023 was a different time. Things got crazy. [SPEAKER_03] 20% post? [SPEAKER_03] Yeah, just for—you know, listeners, on last funding round, that'd be like a 300 million dollar position, not including dilution.

SPEAKER_02

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. Like no other partner I would have met—retrospectively it's like, oh yeah, no one else would have done it. And it's one of those things where trust and loyalty and belief to me matter 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.

SPEAKER_02

Would you tell founders to take a discount for Sequoia? So generally yes. I mean they're the best firm. In particular, if there's 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 woo you?

SPEAKER_02

I mean there are just some people I don't even know. I don't want to name names. There's this one investor in particular who's still in the game.

SPEAKER_02

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 mean there are just some people I don't even know. I don't, I don't want to name names. 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, 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. Like I am, this is my destiny. I'm gonna build a legendary company. Like I got this. And then 30 minutes after when it wore off, I was like, oh my god, he got me. Like he did it. He made me feel special. And a lot of investors when a company is hot are gonna 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 deep conviction when it's not obvious. That's what really matters. Because when a company's hot, everyone's gonna be excited. It matters when it's not. And there are gonna be tough times. How do they behave then?

SPEAKER_02

[SPEAKER_03] How did you get Ivanka Trump as an investor through?

SPEAKER_02

So one of the best hires that I've ever made at Factory was this woman Francesca. 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 Chainsmokers. And obviously people know them as The Chainsmokers. They're also incredibly good investors, incredibly good investors, which sometimes people are surprised by. And we got along quite well. And weirdly enough, Francesca and I also grew up in the same hometown, which is a whole thing. And had a ton of, that was another weird coincidence. But just in the process of them wanting to put a check in and the way she did diligence and just the way that she carried herself, so clear. She was a killer. And they wanted some allocation. I was like, no, no, no, sorry. You know, it's gonna be this. And she was relentless. She came to our office like, hey, we need to get to this much. How can we do it? I'm gonna make these. If I do this and this and this, the business value that we provide to you is gonna make it worth this more so than giving that allocation to someone. So she was hounding. We were having a conversation. I was like, look Francesca, if you want more ownership of Factory, you could just join us. And it was as a joke. I was like, oh you could just join us if you want more. This is the highest we can do. But then we both were like, oh interesting. 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, it was 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. We still have a very deep relationship. And she was very close with their firm Affinity from her investing days. Then we were introduced. We got along really well. And that was how the connection was made there.

SPEAKER_02

[SPEAKER_03] Does Ivanka Trump provide value? People will look at it. returns for them and we're the biggest fans of theirs we still have a very deep relationship and 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 [SPEAKER_03] does Ivanka Trump [SPEAKER_03] provide value [SPEAKER_03] people will look at it [SPEAKER_03] and be like [SPEAKER_03] oh branding [SPEAKER_03] just a name [SPEAKER_03] whatever [SPEAKER_03] and I don't mean [SPEAKER_03] that disparagingly at all [SPEAKER_03] I think people often [SPEAKER_03] think that with

SPEAKER_02

[SPEAKER_03] famous celebrity names [SPEAKER_03] does she actually [SPEAKER_03] 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 are famous that are a letdown or they're different than I expect she is genuinely so kind so intelligent 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 dirty work investor help that she helps out with that some other investors who are more known as investors do not do and so she and the firm more broadly really earned that right on the cap table

SPEAKER_02

[SPEAKER_03] that's really good to hear [SPEAKER_03] I hate the statement [SPEAKER_03] I'm not sure [SPEAKER_03] if Anchor was quite [SPEAKER_03] your hero [SPEAKER_03] but people say [SPEAKER_03] never meet your hero [SPEAKER_03] so always disappoint [SPEAKER_03] and I think [SPEAKER_03] that's just total bullshit [SPEAKER_03] yeah [SPEAKER_03] I remember meeting [SPEAKER_03] Doug Leonie

SPEAKER_03

who was one of my heroes did not disappoint I left being more

SPEAKER_02

[SPEAKER_03] god he should have been [SPEAKER_03] even more of a [SPEAKER_03] poster boy for me [SPEAKER_03] because he was so great [SPEAKER_03] oh yeah [SPEAKER_03] so I totally agree [SPEAKER_03] with you that [SPEAKER_03] that's very funny [SPEAKER_03] I would love just [SPEAKER_03] your thoughts on some [SPEAKER_03] market composition [SPEAKER_03] that I'm struggling with

SPEAKER_03

which is when you look at cognition you look at claw code you look at Kodaks you look at

SPEAKER_02

[SPEAKER_03] cursor now with [SPEAKER_03] Grok [SPEAKER_03] how does this market [SPEAKER_03] evolve and mature [SPEAKER_03] is this an AWS [SPEAKER_03] Azure [SPEAKER_03] GCP [SPEAKER_03] is this an Uber [SPEAKER_03] Lyft [SPEAKER_03] 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 we get the incentives are misaligned why because if let's say the example of coding

SPEAKER_02

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 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

SPEAKER_02

on the model providers there's that independent agent in our case there

SPEAKER_03

[SPEAKER_02] and then that gives [SPEAKER_02] the best prices [SPEAKER_02] to the enterprise [SPEAKER_02] it also gives them [SPEAKER_02] the best [SPEAKER_02] in terms of [SPEAKER_02] if one model is really good [SPEAKER_02] at this language [SPEAKER_02] or that language [SPEAKER_02] it allows them to [SPEAKER_02] adjust between them [SPEAKER_02] and the world where [SPEAKER_02] you're vendor locked in [SPEAKER_02] then you can slowly [SPEAKER_02] get laziness [SPEAKER_02] and slower shipping [SPEAKER_02] and as a [SPEAKER_02] consumer [SPEAKER_02] you end up getting [SPEAKER_02] a worse experience okay so it's not good for the consumer if the model is tied

SPEAKER_03

to the application [SPEAKER_02] the best in terms of if one model is really good at this language or that language it allows them to adjust between them and the world where you're vendor locked in then you can slowly get laziness and slower shipping and as a consumer you end up getting a worse experience okay so it's not good for the consumer if the model is tied to the application okay cool but bluntly we are seeing Kodaks and Klockode eat a huge part of the market what does the market look like in three years in terms of market maturation

SPEAKER_03

[SPEAKER_02] 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 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 codex and Gemini CLI and all these other tools but then the problem is now you're asking your engineers to use ten different tools or you can use someone like factory where you can use one tool and you can decide 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 which one of those

SPEAKER_03

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 yeah and I didn't mean that there's no diminishment to Ratplit that's providing a great product but 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 fun to see oh I wonder what Gemini does versus OpenEye versus Anthropic and you know for some enterprises if there are things that are 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 ten different models to generate different iterations of it totally get that

SPEAKER_03

in terms of the market maturation what happens to the lovable and Ratplit market we saw OpenEye release a competitive product last night I just don't know what happens there

SPEAKER_03

[SPEAKER_02] 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 AI tools or they're 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 my 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 focus is because my understanding is some of them have been pivoting towards the enterprise a little bit but I think from the enterprise perspective

SPEAKER_03

[SPEAKER_02] they're probably 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 focus is because my understanding is some of them have been pivoting towards the enterprise a little bit, but I think from the enterprise perspective, the

SPEAKER_02

[SPEAKER_03] I think they've been pivoting towards the enterprise in non-developer-centric functions. So if I'm lovable of the world, I'm gonna sell to sales teams, marketing teams, customer support teams to allow you to create amazing materials with no experience developing.

SPEAKER_02

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 gonna be run by engineers. If you're gonna need enterprise controls over who has access to what databases and what code and all that stuff, that's gonna be run by the engineers, that's 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.

SPEAKER_02

[SPEAKER_03] 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. [SPEAKER_03] Yeah, it's gonna be crazy.

SPEAKER_02

When you say it's gonna be crazy, what does that actually mean? The amount of code that's being generated, I think code generated is growing exponentially. The security efforts aren't growing in kind, and I think there's a lag there. I think there's probably going to be in the next couple years some pretty big incidents that occur because of AI. There honestly probably have been. I mean, whatever incidents that have occurred, no one's gonna admit, or typically they'll be reluctant to admit if it was AI involved or not. But also, I think we haven't even seen the most adversarial behavior yet. I think people can use these tools to be quite adversarial, and so I think security, the higher the stakes, it's gonna grow in importance. And so I think the security part of the market is really important.

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Do you think US startups should be allowed to operate so extensively on Chinese open source models? [SPEAKER_03] Yes. Using an open model is fine. I think there are two concerns. One concern is if you're sending your data externally to a different nation, which is one concern, and I think the concerns they're about is we don't want to send our data to China generally. I mean, you should probably want to keep your data to yourself regardless. But I think the separate concern is the model itself—like even if we host in the US, is there

SPEAKER_02

The concerns they're about—we don't want to send our data to China generally. You should probably want to keep your data to yourself regardless. But I think the separate concern is the model itself. Even if we host in the US, is there concern with the model itself? To explain some of the concern there, I think the idea that some people have is, I don't know if you've seen in those spy movies where there's a code word where suddenly someone starts acting like you say the right word and then they're in robot mode where they're going to 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 send data somewhere else or it's going to start trying to intentionally break whatever it is that you're doing.

SPEAKER_02

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 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. I don't see that as a big concern. And also, if you're deploying correctly—not as a consumer, but in the enterprise—if you're deploying correctly, data exfiltration or some of this adversarial stuff, generally you can fight against.

SPEAKER_03

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 reclaim superiority there.

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[SPEAKER_03] Europe is significantly behind, especially on the model development side. Do you think Europe is too far behind to catch up?

SPEAKER_02

Probably on the frontier model lab side. There's so much to do on the infrastructure 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 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

SPEAKER_02

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, it's 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 correction because what if you're going on the wrong course. But in cases like AI, where it's pretty clear that 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, but things are slower. So that's one thing that 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.

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[SPEAKER_03] Do you worry about the public backlash to data center development that we've seen? I think it's 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?

SPEAKER_02

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 it's 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. 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 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.

SPEAKER_02

[SPEAKER_03] 100%. I blame the Germans. And that's our German audience gone. Dude, I want to do a quick fire round with you. So when 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, I'll take the grab bag. It doesn't matter. I actually hope for a world in which I don't even our users don't even know which one is under the hood.

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[SPEAKER_03] 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. Ambitions. What are ambitions? I don't think it makes sense for them to do that. Okay, businesses need to think about their core competencies. And 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. [SPEAKER_03] Yeah, I get you. I think you could argue that it's a lot more adjacent there, but I get you.

SPEAKER_02

Businesses need to think about their core competencies. If people are trying to expand beyond their core competencies, Kirkland and Ellis great, have fun, it's not your core competency. I don't think it makes sense. [SPEAKER_03] Yeah, I get you. I think you could argue that it's a lot more adjacent there, but I get you. Do we have a series of businesses like a Nebius, like a McCall, where customer concentration is 90% of revenues? Will we see more of that? [SPEAKER_03] Yeah, probably. Is that a bad thing or a good thing?

SPEAKER_02

It's bad if you're an investor in one of those companies because it's riskier. But I think you can find a steady state. It's just scary. You just know there's a sword of Damocles above your head of like, if they ever, you know, it's just risky. It's risky. [SPEAKER_03] A sword of Damocles. First time it's ever been said on the show. Tell me, can you sell to enterprises today without an FDE model?

SPEAKER_02

Yes. Have a good product. The thing about the FDE thing blows my mind. For us, when we do FDE, the way I think about it is their goal should be acceleration. If there's a customer where we just give them our product, they'll scale to a million in six months, I'll throw in FDEs. If they're going to scale them to a million dollars in three months, great, they accelerated that. If I'm sending in FDEs as services, I'm not Accenture here. I'm not trying to be like 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. 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.

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[SPEAKER_03] What do you think of the whole grind slop element? We talked a little before about the show with Nico at Corgi, which generated a little discussion online. Just a little bit. [SPEAKER_03] Of discussion online. Harry, you're ruffling feathers as always. [SPEAKER_03] Dude, I said nothing. This is honestly like someone comes to your party and does something well and it's like that's me. What do you think of the grind slop?

SPEAKER_02

I feel like a lot of the things we've talked about actually is something everyone needs to be wary of: intermediate metrics. Grind slop comes from intermediate metrics. You know, generally to do things you need to spend time on it, so let's focus on how much time do we spend instead of are we doing the thing right. The analogy I use is imagine trying to measure who won a basketball game by who sweat the most. You could sweat a ton but look at the scoreboard. Are you doing what actually needs to be done or not? For us, we want to focus on 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, but I think

SPEAKER_02

and I think for us we want to focus on 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, but I think you are doing a bad job on hiring if you need to mandate certain crazy hours or you need a bed in the office. It's like dude, get a good night's sleep. You don't need a bed in the office. 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. But do you know I did think it was an amazing opportunity for Eight Sleep to do an amazing social campaign.

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[SPEAKER_03] opportunity for Eight Sleep to do an amazing social campaign.

SPEAKER_02

I would have delivered it. I would have got the founders outside being like we got you covered. That's literally like we. Wouldn't that be funny? When we were 30 we had like 30 people. We had what we call a surge, a pretty aggressive two week sprint. And as part of it I got everyone on the team Eight Sleeps, fully free, whatever, three thousand dollars per person. The decadence of startups, right. But I think the idea there is we are optimizing for output. And the people that we are bringing onto the team, it's seal team six, the NBA all-stars. It is worth every dollar to make them more productive and to deliver on these ambitious goals that we have. So we can do that. And 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 grunt work, how can we spend as many hours to do it. We have droids for that. The work that we do might require really deep thought, really every ounce of brain power that you have. In which case if you didn't sleep well, you're not going to make as good of a decision.

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[SPEAKER_03] If I gave you unlimited money, what would you spend on today that you're not spending on?

SPEAKER_02

I think generally we will see the best companies treat teams more and more like seal team six or NBA professional athletes, not in the way that Google did it with a bounce castle and all this weird stuff. But where athletes it seems like they're getting pampered, but it's a burden. Your diet is monitored. You have to do your hour-long massage after a game to make sure your muscles are recovered for the next game. You have to do an ice bath and all this stuff. It seems glamorous but sometimes it's not. I think spending on that type of stuff, but obviously in the more 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.

SPEAKER_02

Next game you have to do an ice bath and all this stuff. It seems glamorous, but sometimes it's not. I think spending on that type of stuff, but obviously in the more 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.

SPEAKER_02

[SPEAKER_03] You're such an American. Do you know what I like? I like Limoncello. 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 optimized to the extreme. Did you see the Stephen Bartlett video the other day? Do you mind? [SPEAKER_03] I don't know this guy Stephen Bartlett. He said I drank two glasses 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. Three days ruined.

SPEAKER_02

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 for robustness. Like if you want to be a robust human, you can't have one drink ruin the next five days of your life. The wind blows and then you're ruined, right? So to some degree I get it. You want to have some of this stuff, but I also think maybe, again looking to athletes, what they do is they have in season and out of season. Maybe it's when you're in season you're locked in, you're not drinking, you're optimizing all this stuff with your Eight Sleep, and then take a week off, go on the beach, drink mojitos or whatever the hell people drink on the beach. Or out of season you're Charlie Sheen.

SPEAKER_02

[SPEAKER_03] Yeah. If you can recover after, you know, to each their own. Oh god, that would be the funniest thing ever. Work hard, play hard. [SPEAKER_03] Okay, you can invest in one company on IPO day. Sorry dude, Anthropic or OpenAI? 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 reason, because I think from the business perspective they're both very well suited and well positioned there.

SPEAKER_02

[SPEAKER_03] So you're saying Anthropic probably just past is an indicator of the future, and there's just been more random chaotic turbulent events at OpenAI. But from a business perspective, to me that's like they're both great choices. [SPEAKER_03] But Anthropic, okay we got that good. Has Dario done a 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?

SPEAKER_02

Yes. This 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 disingenuous and wrong, but it's really hurt the psychology of a lot of people, developers, 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 solve the problems for society. This feeds fuel to 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.

SPEAKER_02

a disservice because this is again talking about the use cases that are going to the problems that will be solved for society. This feeds fuel of 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, 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, oh no humans are pretty important. They're going to be jobs again. We like you guys. That pisses me off.

SPEAKER_02

[SPEAKER_03] 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 Damus, they've always had a very different stance to Sam and Dario when it comes to labor displacement and jobs. Yes, it's really interesting. The ones who need it and the ones— It's a shame because for all the philosophizing about AI and intelligence and all this stuff, it's incentive is driving the outcome. And the incentive is I want to raise a lot of money. [SPEAKER_03] Which legacy company do you think has most embraced AI?

SPEAKER_02

Honestly, EY, the accounting firms. It's one of our largest customers. I know you said shocking. [SPEAKER_03] It's shocking. They are so agent native. It's crazy. [SPEAKER_03] They're one of our largest customers. How? They just push it down the organization. [SPEAKER_03] They just saw what happened with the cloud. They saw scars of being late and not jumping onto it aggressively. And 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.

SPEAKER_02

To me I think that's one of the most interesting things seeing is they're more agent native than some startups, which is wild. [SPEAKER_03] Brave new world. Brave new world. [SPEAKER_03] Final one. What have you changed your mind on most in the last 12 months?

SPEAKER_02

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 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.

SPEAKER_02

Probably going to be at least four that are going to 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 growing evidence of, which my sense is it's a hot take because I think right now people are a little bit enamored with maybe one or two.

SPEAKER_02

[SPEAKER_03] Listen, Matt Damon, it's been so wonderful to have you on the show. I'm going to let you go back to Robin Williams and the show was brought to you by 8sleep. I'm kidding, dude. It's been so much fun. Thank you so much. [SPEAKER_00] Thank you for having me. 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. But 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

SPEAKER_02

to be at this percent of their salary and token use, you're probably painting with way too wide a brush.

SPEAKER_03

If I were to say, give me an average number, what will that average be? What will the median be?

SPEAKER_02

I would say order of magnitude, it'll probably be comparable to salary. Comparable to salary? like on the same order of magnitude.

SPEAKER_03

Within the three-year timeline? Yeah. What percent of tasks today using frontier models could be done with open source models?

SPEAKER_02

Probably like 80 to 90 percent. It's typically the planning that really needs the frontier models.

SPEAKER_03

But is that not like the most, I'm sorry, I'm really, you know, dim.

SPEAKER_03

Harry, that's nonsense. But if it's 80 to 90 percent, does that not just present the biggest bear case ever against Codex or Claw Code? Because you're just taking away 80 to 90 percent of that time.

SPEAKER_02

Well, it depends because those, that 20, 10 to 20 percent could be the most important tokens. It's maybe like, right, like 10 to 20 percent 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, which is like, oftentimes, oftentimes leadership makes very key decisions that determine the fate of the company. and they don't spend the most hours. Like, 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,

SPEAKER_02

here is where we're going to, you know, make this irreversible decision on the strategy. And those people that make those decisions are also typically paid a lot

SPEAKER_03

in a company. But the assumption there would be then that you'd have to increase that spend for that 10% even higher.

SPEAKER_02

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, if 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. When we think about

SPEAKER_03

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. Yeah. Have you found the same? And is it impossible to hire great AI researchers in competition with Anthropic and OpenAI?

SPEAKER_02

We are a very opinionated organization. And so it's very self-sacrifice. Like the people who like the opinionated stances that we take are willing to not necessarily go and try to, you know, maximize the dollars that they can get out of, in the market. That said, it is still, you know, pretty competitive.

SPEAKER_03

What do you think is the strongest opinion that you have

SPEAKER_02

that most people disagree with? I would say the opinion that we have that I think in the space that we are in is the most controversial is 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. Oh,

SPEAKER_02

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. Like 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. Now, 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. Like that entire thing is the product. Everyone is first class. It's not like we have engineers

SPEAKER_02

who are like 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 like engineer corner, sales corner, any of that stuff. It's like 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. Everyone, there's no like first class or second class and this is shockingly controversial in particular in the Bay Area and in particular in coding or in AI. It's messed up. It's so messed up and I think

SPEAKER_02

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 like desperate to sign and get more tokens from these people, it's easy. They're kind of like, 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.

SPEAKER_03

No, but I can name companies that have shit products but great sales and marketing teams. That's the ironic thing on the flip side. And in fact, it seems like

SPEAKER_02

many more, many more, many more.

SPEAKER_03

Like, 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. I mean, yes, 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?

SPEAKER_02

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 but 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, you know, a better user long-term. It makes them more agent native. They get more out of our product. We can then follow

SPEAKER_02

them through that journey, enable the salespeople so they know how to talk about it or they know how to demo it. This, like, is this like a full-stack engineer that goes way beyond just engineering but into sales, into marketing, into enablement and all that and 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

SPEAKER_02

or do you memorize all the different nuances of these different languages? Those are the parts that don't matter. Like, if you memorized some coding language or some, you know, syntax of a coding language that someone else didn't, it doesn't matter. People like not

SPEAKER_03

the credentialism but I think they misunderstand VC mindset right now and as a VC, I'm happy to share how you feel, how we feel which is just, like, fundamentally there is intense uncertainty around what Anthropik 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. Yeah.

SPEAKER_02

But it's a crutch. It's a crutch. It's helpful. It's a good indicator. Like, 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, it's like, 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? And 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 Olympi- like, you must do this,

SPEAKER_02

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.

SPEAKER_03

I don't think I've told you this but do 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.

SPEAKER_02

Have you not been told that before?

SPEAKER_03

Let me say that one more time

SPEAKER_02

You look identical.

SPEAKER_03

That's very kind of, I'll take that. We're going to put up, like, an image here and you're going to do a side-by-side. Yeah, all right, we have a clip. This is the clip, this is the starter to the show. And they're going to be like, uh-huh, I totally get it. That makes absolute sense. Can I ask you, when we go back to actually, like, 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?

SPEAKER_02

I think, so 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,

SPEAKER_02

spreading their reach beyond just...

SPEAKER_03

that's just, like, every function? It's like, you know, 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-stacking 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? We're expecting everyone to be full-stacking every function.

SPEAKER_02

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 in, like, you know, hundreds of years ago, people like Da Vinci or Euler or Newton could be polymaths, and it was because their fields were relatively shallow. So, like, chemistry wasn't that built out. Mathematics wasn't that built out. Physics wasn't that built out. In Da Vinci's case, 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, you know, the early 2000s and 2010s, pre-AI, fields were so deep, in my case,

SPEAKER_02

theoretical physics and strength theory were 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. And 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

SPEAKER_02

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. You can push forward and create innovations on how to do developer marketing while at the same time pushing forward the frontier of, like, 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. Um, the era is totally back. This, polymaths are back.

SPEAKER_03

I've had a lot of people say on the show that agent operations will be like, with the leading, uh, 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.

SPEAKER_01

Ah,

SPEAKER_03

marketing and design. I'm going to create agents that allow you to create visuals, share them amongst each other, edit them, collaborate on.

SPEAKER_02

I 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 in certain functions that aren't proactively doing that, it's 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? I mean, for an engineering team, like writing release notes, that's crazy that people

SPEAKER_02

used to spend hours of time writing release notes or like writing documentation. So, not everyone knows what release notes is. 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, you know, and sending it out to either internal or external to your users. And generally, like this and like documentation, like Stripe has a really great reputation. They had incredible documentation. Like 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

SPEAKER_02

that these people that get paid so much money spent hours of their time doing this. I think that's something that, you know, 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. Totally agree with that.

SPEAKER_03

How does the product review and especially like code review process change in the next few years?

SPEAKER_02

Yeah, I think what's cool about this agent native software development is review has been a big problem because basically, you know, first phase of rolling out AI coding tools was, oh my God, look how much code we can generate. You know, it's incredible. I'm generating a ton of stuff. And then phase, phase shit. Yeah. 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 like 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

SPEAKER_02

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. Setting 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

SPEAKER_02

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.

SPEAKER_03

When agents are the buyers and you're selling to agents, how does the world change and does the value of great API increase?

SPEAKER_02

I think 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,

SPEAKER_03

agent-to-agent doesn't give a shit about UI or design, but it does fundamentally care about data structures, potential integrations, documentation.

SPEAKER_02

Do you know what I mean? 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, you know, 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, I mean, part of our name,

SPEAKER_02

our 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 like 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, you know, produce more Teslas

SPEAKER_02

in this case. And so in this new world of software development, humans, human engineers are not going to be involved as much in like 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 there's, they're kind of like building the scaffolding around this factory that produces their software.

SPEAKER_03

Do you worry about labor displacement when we move from working in the factory to working on the factory?

SPEAKER_02

Short term, yes. Long term, no. Short term, yes, because it's just a shock to the system where, you know, there are all these big layoffs that are happening that are pretty aggressive and, you know, these are 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. And very few of those problems that can be solved with software are we currently solving with software. And so if we're going

SPEAKER_02

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.

SPEAKER_03

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 have found doing climate change technology?

SPEAKER_02

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. And like the thing that really upsets me with some of the people who are talking about, you know, pausing AI development or any of this, like, oh, it's, you know, it's a bad thing

SPEAKER_02

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. Like it's a matter of time. Like we will solve it and we can solve it. And by saying you want to slow down AI, that's saying like 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. We're scared. We don't know about AI. I think it's like, it's pretty harmful and it's pretty selfish to say that it's something that to me, it doesn't make sense.

SPEAKER_02

It doesn't make sense.

SPEAKER_03

Do you agree with government intervention? In what capacity? In free markets. When you think about like 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?

SPEAKER_02

I think 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, you know, some involvement there. I 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 but I think generally I'm pretty reluctant. I think you need

SPEAKER_02

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 and, you know, you could make the case that the faster we develop AI, the sooner we solve climate change because AI, you know, 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, you know, that emit CO2 into the atmosphere and so the question is like, you know, short term, it might be slightly worse but it ends up getting us to solve the problem way sooner instead of dragging it out

SPEAKER_02

over 50 years or 100 years and so 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 careful about the cases where you do want the government to say, hey, we want to step in here.

SPEAKER_03

Do you think we are

SPEAKER_02

in an AI infrastructure bubble? Maybe there's like some short term blips but like long term, absolutely not. Like not even close. I think 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.

SPEAKER_03

What bottleneck do we have today that will be completely solved within a few years?

SPEAKER_02

I think the biggest bottleneck by far working with all these organizations is the human side of it. It's just like behavior change. Like especially if you're-

SPEAKER_03

And what you're saying there is like selling to large enterprises and how they do change management?

SPEAKER_02

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 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've 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

SPEAKER_02

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.

SPEAKER_03

When you look at now, you sell to some of the largest enterprises in the world in some cases. What do you know now about selling to the large, large enterprise that you wish you could tell young Matan two years ago?

SPEAKER_02

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. But I'm not 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, you know, to them it's obvious. To me, the thing that was the most kind of visceral altering thing was meeting people face to face makes such a big difference if you're trying to sell them something

SPEAKER_02

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. That's another thing. Like if you go in a conversation trying to sell something especially to engineers like don't waste your time. If you go in trying to have genuine curiosity about and 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 and it's really interesting to go talk to them

SPEAKER_02

and to understand it and the best way of talking about it with them is face to face and 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, you know, making their lives better in that way. In terms of like

SPEAKER_03

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 Sequoia having never had a job and only being paid to do physics?

SPEAKER_02

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 when 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, PreCalc, Calculus 1, 2, and 3, 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

SPEAKER_02

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. And 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, 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

SPEAKER_02

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. Like what the hell do I do with the rest of my life? Like this is crazy like everything came crashing.

SPEAKER_03

What caused that crashing down moment and why did it take so fucking long? 12 years? 12 years.

SPEAKER_02

You're slow.

SPEAKER_03

I have tunnel vision. When I get obsessed

SPEAKER_02

with a problem it is all I think.

SPEAKER_03

I was at law school for two weeks.

SPEAKER_02

It was a quick realization. See some people are faster. You know I wasn't as 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. Like I didn't yeah it wasn't 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

SPEAKER_02

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 you know 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 quant finance stay at Berkeley explore learn some stuff whatever. So I was like okay fine you know I'll do that you know so ended up taking my first CS classes at Berkeley. I learned like to code for physics for like simulations and all this stuff

SPEAKER_02

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

SPEAKER_02

like n equals 3 or like n equals 4 four dimensions it's always like what is the n dimensional solution what is the arbitrary the fundamental you know 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 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 but I knew nothing about starting companies so

SPEAKER_02

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 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

SPEAKER_02

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 Maldicena I had cited one of his papers so 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 physicist you know compared to theoretical physicist though look he can maintain eye contact you know he was somewhat normal

SPEAKER_03

very rare such a low bar

SPEAKER_02

he can hold himself in a social setting yeah and so I was like okay who is this guy you know I gotta 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 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 and at the end of it

SPEAKER_02

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 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 gonna you know I'm gonna go think about it but in the meantime I like had already known about Factory it was just I didn't want to ruin the meeting with like a pitch you didn't want to transactionalize this yeah because it was so

SPEAKER_03

like it was incredible like we had the exact same reasons for getting interested

SPEAKER_02

I totally get yeah yeah like didn't want to didn't want to dirty it with no it's kind of like an LP where at the end you're like I don't want to ask for a check yeah exactly 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 you know and basically that day forward we spend like every day talking non-stop I had some shitty demo that I built you know is thousandx better of an engineer

SPEAKER_02

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 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 the fact that I was doing a PhD

SPEAKER_02

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 I'm 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 alright you have a meeting with the Sequoia partnership tomorrow morning be ready to present you've never presented to a venture partnership before

SPEAKER_03

so what happens I made a shitty deck

SPEAKER_02

put some slides together

SPEAKER_03

we go to the Sequoia HQ put some slides together

SPEAKER_02

keep in mind I didn't even know who the hell they were like I didn't it was just like oh these random people like okay whatever yeah I'll go talk to them I wish it was recorded 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 so like retrospectively I know like Alfred and Pat and Roloff they were all like in there they were all asking questions and I was probably dismissing some of oh yeah we'd solve that easily we'd do this we'd do that keep in mind this was in April of 2023

SPEAKER_02

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 and 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

SPEAKER_03

I mean what I'm not being rude why did they bother doing a partnership meeting like in the nicest way that's like a coffee like I know it's a dick comment

SPEAKER_02

but when you managed like seven eight billion it was a different time early 2023 was a different time things got crazy

SPEAKER_03

20% post yeah just for for you know listeners I mean on last funding round that'd be like a 300 million dollar position not including dilation

SPEAKER_02

and it was one of those things a lot of my 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 like obviously you want to maximize you know the position for the business but like no one else would have believed in me except him no one else would have understood like I literally had never had a job before no like no other partner I would have met retrospectively 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

SPEAKER_02

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 gonna be 10 years this is like a lifetime would you tell founders to take a discount for Sequoia 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

SPEAKER_02

to make you feel special and they are really good at it what's the best way someone's tried to wee? I mean there are just some people I don't even know I don't I don't want to name names there's this one investor in particular who's like 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 like I am like this is my destiny I'm gonna build a legendary company like I got this and then like 30 minutes

SPEAKER_02

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 gonna 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's hot everyone's gonna be excited it matters when it's not and there are gonna be tough times how do they behave then how did you get Ivanka Trump as an investor through so one of the best hires that I've ever made at factory

SPEAKER_02

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 you know we got along quite well and 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 just 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

SPEAKER_02

like she was a killer and they wanted some allocation I was like no no no sorry like you know it's gonna 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 gonna make these if I do this and this and this the business value that we provide to you is gonna make it worth this more so than giving that allocation to someone so she 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

SPEAKER_02

like this is the highest we can do but then we kind of both were like oh interesting 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 you know he's since been happy because she's helped us deliver a lot of you know returns for them and you know we're the biggest fans of theirs and you know 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

SPEAKER_02

the connection was made there

SPEAKER_03

does Ivanka Trump provide value 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

SPEAKER_02

yes she is first of all she's one of the kindest and smartest people that I've met and like 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 just in 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 like there is like kind of dirty work investor help that she helps out with that some other investors who are more known as investors do not do and so she and the firm more broadly really earned that right on the cap table

SPEAKER_03

that's really good to hear I hate the statement I'm not sure if Anchor was quite your hero but like people say never meet your hero so always disappoint and I think that's just total bullshit yeah I remember meeting Doug Leonie 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 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

SPEAKER_03

evolve and mature is this an AWS Azure GCP is this an Uber Lyft what is the mature

SPEAKER_02

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 we get often 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 and I don't have a huge incentive

SPEAKER_02

to be more token efficient other than like you know 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

SPEAKER_02

it allows them to kind of adjust between them and the world where you're like vendor locked in then you can slowly get like laziness and slower shipping and as a you know consumer you end up getting a worse experience

SPEAKER_03

okay so it's not good for the consumer if the model is tied to the application okay cool but bluntly we are seeing Kodaks and Klockode eat a huge part of the market what does the market look like in three years in terms of market maturation

SPEAKER_02

this is going to be different from cloud I think cloud a lot of people suffered because you know 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

SPEAKER_02

to be agnostic and so you know you could be agnostic by saying hey every engineer we're going to give you cloud code 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 do we want to use an open model do we want to use Frontier you know which one of those

SPEAKER_03

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 yeah and I didn't mean that there's no diminishment to Ratplit that's like

SPEAKER_02

then providing a great product but 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 OpenEye 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 totally get that

SPEAKER_03

in terms of the market maturation what happens to the lovable and Ratplit market we saw OpenEye kind of release a competitive product last night I just don't know what happens there

SPEAKER_02

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 gonna 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 my 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

SPEAKER_02

is some of them have been pivoting towards the enterprise a little bit but I think from the enterprise perspective the like

SPEAKER_03

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 gonna sell to sales teams marketing teams customer support teams to allow you to create amazing materials with no experience developing sure

SPEAKER_02

I mean in that case I think that that niche does make sense a little bit more I think the if the 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 gonna be run by like if you're gonna need enterprise controls over who has access to what databases and what code and all that stuff that's gonna be run by the engineers that's gonna be where factory goes if it's things like you know 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

SPEAKER_03

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 a start yes yeah it's gonna be crazy

SPEAKER_02

when you say it's gonna be crazy what does that actually mean like the amount of code that's being generated the I think code generated is growing exponentially the security efforts aren't growing in kind and I so I think there's kind of a lag there I think there's probably going to be in the next couple years some pretty big incidents that occur because of AI there honestly probably have been I just whatever incidents that have occurred no one's gonna 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

SPEAKER_02

and so I think security like the higher the stakes it's gonna grow in importance and so I think the security part of the market is really important do you think US startups

SPEAKER_03

should be allowed to operate so extensively on Chinese open source models

SPEAKER_02

yes using an open model is fine I think the 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 they're 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 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 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

SPEAKER_02

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 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 adversarially theoretically you would want to do that as late as possible because if

SPEAKER_02

you do that in an early model and someone discovers it they're literally never going to use your models ever again 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 I do think just from a you know 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

SPEAKER_03

there Europe is significantly behind especially on the model development side do you think Europe is too far behind to catch up

SPEAKER_02

probably on the like frontier model lab side on the like 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 like 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 you know theoretically we get this balancing act where we don't go too crazy in any which direction you know other parts of the world where it's more authoritarian is like this is the thing we're

SPEAKER_02

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

SPEAKER_03

it 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

SPEAKER_02

yes but I think that's at least in the United States the beauty of having states is we get some like 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 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 of the United States and I think in Europe I mean it's tough I think there was some good positioning that Europe had you know a few

SPEAKER_02

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

SPEAKER_03

100% I blame the Germans and that's our German audience gone dude I want to do a quick fire round with you so when 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

SPEAKER_02

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 I don't even our users don't even know which one is under the hood

SPEAKER_03

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 ambitions what are ambitions I don't think it makes sense for them to do that

SPEAKER_02

okay businesses need to think about their core competencies and like 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

SPEAKER_03

yeah I get you I think you could argue that it's a lot more adjacent there but I get you do we have a series of businesses like a Nebius like a McCall where customer concentration is like 90% of revenues will we see more of that yeah yeah probably

SPEAKER_02

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 if they ever you know it's just risky it's risky

SPEAKER_03

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 yes

SPEAKER_02

have a good product this the thing about the FDE thing blows my mind is like like for us when we do FDE like 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 like 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

SPEAKER_02

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

SPEAKER_03

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

SPEAKER_02

just a little bit just a little bit

SPEAKER_03

of discussion online

SPEAKER_02

Harry you're ruffling feathers as always

SPEAKER_03

dude I said nothing this is honestly it's like it's like someone comes to your party and does something well and it's like that's me what do you think

SPEAKER_02

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 like oh you know 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 right like 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

SPEAKER_02

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 can get your job done on two hours of sleep you're not doing very high leverage work but do you know I did think it was an amazing

SPEAKER_03

opportunity for eight sleep to do like an amazing social campaign

SPEAKER_02

I would have delivered it I would have got the founders outside being like we got you covered like that's literally like we wouldn't that be funny when we were 30 we had like 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 three thousand dollars per person like you know 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 six like the NBA all-stars like it is worth every dollar to make them more productive and you know to deliver on

SPEAKER_02

these ambitious goals that we have and 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 let's 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

SPEAKER_03

if I gave you unlimited money what would you spend on today that you're not spending on

SPEAKER_02

I think generally we will see the best companies treat teams more and more like whatever seal team six 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 where like athletes it is kind of like it seems like they're getting pampered but it's kind of a burden like 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

SPEAKER_02

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

SPEAKER_03

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 race 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

SPEAKER_02

do you mind

SPEAKER_03

I don't know this guy Stephen Bartlett he said I drank two glass 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

SPEAKER_02

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 you know ruins the next five days of your life like the wind blows and then you're like you're ruined right so 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

SPEAKER_02

with your eight sleep and then you know take a week off go on the beach drink some you know mojitos or whatever the hell people drink on the beach or an out of season you're Charlie Sheen yeah

SPEAKER_03

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 Anthropik or OpenAI in my mind

SPEAKER_02

the answer here is I think they're approximately equivalent like to me it doesn't really matter the biggest reason that affects like the EV is like volatility of the company that's the only because I think from the business perspective they're both very well suited and like kind of well positioned there so you're saying Anthropik probably just past is an indicator of the future and like there's just been more like random chaotic turbulent events at OpenAI but like from a business perspective to me that's like they're both great choices

SPEAKER_03

but Anthropik okay we got that good 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

SPEAKER_02

yes it actually this like really upsets me so on one hand I maybe just implied Anthropik 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 people 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 this 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

SPEAKER_02

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 oh no humans are pretty important they're going to be jobs again you know we like you guys that pisses me off

SPEAKER_03

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 Damus they've always had a very different stance to Sam and Dario when it comes to labor displacement and jobs yes it's really interesting the ones who need it and the ones

SPEAKER_02

it's just 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

SPEAKER_03

which legacy company do you think has most embraced AI well

SPEAKER_02

honestly EY the accounting firms it's one of our largest customers I know you said shocking ah it's fucking shocking they are so

SPEAKER_03

agent native

SPEAKER_02

it's crazy

SPEAKER_03

they're one of our largest customers how they just push it down the organization

SPEAKER_02

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 and they have some great engineering leaders there who are like look this is going to be scary some people are going to get upset you know 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

SPEAKER_03

brave new world final one what have you changed your mind on most in the last 12 months

SPEAKER_02

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 like that is the win for humanity like the bad case for humanity is when there's one that's really really good like 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

SPEAKER_02

right now people are a little bit enamored with maybe one or two but

SPEAKER_03

listen Matt Damon it's been so wonderful to have you on the show I'm going to let you go back to Robin Williams and the show was brought to you by 8sleep I'm kidding dude it's been so much fun thank you so much

SPEAKER_00

thank you for having me