The Exchanges

Q What do you think is the strongest opinion that you have that most people disagree with?

A I would say the opinion that we have that I think in the space that we are in is the most controversial, um, is, uh, the way that we treat What product is at factory? Um, 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, 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. Um, 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, 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 who are, like, wholly at the office, and you're not allowe…

AI assessment note: “the opinion that we have that I think in the space that we are in is the most controversial”

Q I sort of doubt it, please. First time it's ever been said on the show. Um, tell me, can you sell to enterprises today without an FDE model?

A Yes. Have a good product. The, 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. It basically, if there's a customer where if we just give them our product, they'll scale to like a million in six months, I'll throw in FDEs if they're going to scale them to a million dollars worth in three months. Great. They accelerated that. If I'm sending in FDEs as services, Like, I'm not Accenture here. Like, I'm not trying to be like, or 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 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.

AI assessment note: “Yes. Have a good product.”

Q Rory and Jason, and Rory was basically saying, you know, the fundamental question is, will we see an increase in GDP? Um, Coming from AI and the coding developments that we're seeing, and will it lead to GDP increasing above the two percent average for the last 200 years? Do you think we will see meaningful productivity gains from the AI tooling that we're seeing, or is Uber's concerns validated?

A So I think, yes, absolutely, we will, we will see tremendous growth from these tools. I think it takes time to permeate through, um, because you can tell, like, on an individual basis, like, almost like on a problem by problem basis, we can solve problems faster with these tools. Now, companies generally organize around solving problems, um, 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, solve the same number of problems with fewer people, 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, I think, a question that a lot of businesses will be, will be grappling with.

AI assessment note: “yes, absolutely, we will, we will see tremendous growth from these tools.”

Q 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.

A So a couple things. Uh, 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. Um, 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 three phases that we're seeing happen in these enterprises. Um, so phase one, this was a, 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. Uh, CTO, hey, what's our AI strategy? Let's make sure we adopt AI. And so then phase two was kind of 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 kind of like the, the debauchery, the long night, you know, 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, like, is this helping our business? Um, that…

AI assessment note: “one, it's easy when there's only one... two is, it's also really expensive.”

Q Brave new world. Uh, final one. What have you changed your mind on most in the last 12 months?

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

AI assessment note: “I thought it might be just one or two companies that run away”

Q When we think about kind of 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?

A Yes. This is a, this is a great point. Um, this resource allocation problem of token, it's not just tokens, it's like dollars, it's tokens, it's people. 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? Um, 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, um, but that might not be your core competency. 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, like how many features do they ship in a quarter? Instead, it's like, 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, uh, more dramatically move the needle on that business outcome. And I think this is, 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 fo…

AI assessment note: “what is the core competency for our business? What actually matters for the business”

Q committing half a billion dollars. Yeah. That would suggest the opposite. Yes. Um, I had Brendan on from McCall 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. 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?

A I would disagree. I'd pretty strongly disagree, um, for a couple 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 gonna 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. 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, it kind of, an example of this is like, 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? Probably not. 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. Um, and I think it's somewhat similar here, which is like, just because you can build a lot of these things does not mean you should. And in fact, oftentimes you want…

AI assessment note: “I would disagree. I'd pretty strongly disagree, um, for a couple things.”

Q What do you think is the strongest opinion that you have that most people disagree with?

A I would say the opinion that we have that I think in the space that we are in is the most controversial, um, is, uh, the way that we treat What product is at factory? Um, 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, 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. Um, 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, 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 who are, like, wholly at the office, and you're not allowe…

AI assessment note: “the product at factory is the entire journey from the very first time”

Q 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?

A 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, uh, you know, uh, a better user long-term. It makes them more agent native. They get more out of our product. We can then follow them through that journey, enable the salespeople so they know how to talk about it or they know how to demo it. This, like, is this like a full stack engineer that goes way beyond just engineering, but into sales, into marketing, into enablement and all that. Um, 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? Are you, do you memorize all the different nuances of the…

AI assessment note: “Yes, it very seriously changes, and this is actually why we're selecting”

Q resources, there are times when it is suboptimal from a human morality, societal standpoint in a lot of cases, To see, uh, engineers at Anthropic working on optimizing core 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?

A Um, I think it's a, it's, 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, like weapons, like you're definitely going to need, you know, some involvement there. Um, I think there's some incentivization that can be helpful just because, uh, there's, there might be some problems for 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. Um, you know, talking about that one, um, 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 CO two 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…

AI assessment note: “I think generally I'm pretty reluctant. I think you need to have a very good case”

Q I sort of doubt it, please. First time it's ever been said on the show. Um, tell me, can you sell to enterprises today without an FDE model?

A Yes. Have a good product. The, 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. It basically, if there's a customer where if we just give them our product, they'll scale to like a million in six months, I'll throw in FDEs if they're going to scale them to a million dollars worth in three months. Great. They accelerated that. If I'm sending in FDEs as services, Like, I'm not Accenture here. Like, I'm not trying to be like, or 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 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.

AI assessment note: “Yes. Have a good product.”

Q 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 wasn't me. Um, what do you think of the grind slop?

A 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, Uh, imagine trying to measure who won a basketball game by who sweat the most. Like, you could sweat a ton, but look at the scoreboard. Like, are you doing what actually needs to be done or not? And I think for us, we want to focus on, like, getting the best players. I don't care if you sweat a ton or if you sweat very little. If you're scoring a lot, great. We want you on our team. Um, now generally, for most people, you have to sweat if you want to, you know, get things done. Um, 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 a, 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.

AI assessment note: “And grind slop comes from intermediate metrics.”

Q When we think about kind of 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?

A Yes. This is a, this is a great point. Um, this resource allocation problem of token, it's not just tokens, it's like dollars, it's tokens, it's people. 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? Um, 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, um, but that might not be your core competency. 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, like how many features do they ship in a quarter? Instead, it's like, 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, uh, more dramatically move the needle on that business outcome. And I think this is, 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 fo…

AI assessment note: “what is the core competency for our business? What actually matters for the business”

Q Per individual. Yeah. How do you respond or think about that?

A We, 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 like 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 kind of realizing. And so what's happened with Uber publicly has happened privately with a lot of customers of ours. And yeah, there's a little bit of shock where it's like, okay, wait, let's put in these user limits. But then you come into …

AI assessment note: “we're just getting towards this world where you have very nuanced resource allocation”

Q What is the belief that would invalidate yours?

A The bare case against factory is if one model provider gets significantly better than all of the others. Um, 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, well, 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 kind of fluctuates every week. Like even already people have a, 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 it's like, okay, we want to put, you know, companies might want to just completely go in with them. Um, but then that's a monopoly for the entire economy to be worried about.

AI assessment note: “The bare case against factory is if one model provider gets significantly better”

Q 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.

A So a couple things. Uh, 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. Um, 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 three phases that we're seeing happen in these enterprises. Um, so phase one, this was a, 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. Uh, CTO, hey, what's our AI strategy? Let's make sure we adopt AI. And so then phase two was kind of 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 kind of like the, the debauchery, the long night, you know, 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, like, is this helping our business? Um, that…

AI assessment note: “one, it's easy when there's only one of them... two is, it's also really expensive.”

Q 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 to date that you think will be incredibly common in the next few years?

A 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. Um, so it's way beyond what a typical engineer does. And it kind of feels like, again, owning more of a business outcome, more entrepreneurial, higher agency, just, like, spreading their reach beyond just-

AI assessment note: “I think it's kind of this like GM or general manager Like role”

Q 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 stack in every function.

A The age of the polymath is back. Like, I, 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, like art and engineering and, um, sculpture. Um, and so you could get to the frontier of these disciplines with it in multiple disciplines within your lifetime. And then growing up in, you know, the early 2002 1010, pre AI fields were so deep. In my case, theoretical physics and strength, it was so deep that you could spend literally 50 years catching up on all of the literature and academia that's existed before you contribute anything new. 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 and being okay with that, like no, knowing there are unknowns an…

AI assessment note: “The age of the polymath is back.”

Q What's the best way someone's tried to wee?

A 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. Um, 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'm, 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 after, when it wore off, I was like, oh my god, he got me, like, he did it, like, he did the thing, he made me feel special, and like, A lot of investors, when a company is hot, are going to do that, and they're really good at it. That's why they're great investors. Um, 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 going to be excited. Um, it matters when it's not, and there are going to be tough times. How, how do they behave then?

AI assessment note: “he's really good at making you feel good about yourself... he made me feel special”

Q 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 stack in every function.

A The age of the polymath is back. Like, I, 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, like art and engineering and, um, sculpture. Um, and so you could get to the frontier of these disciplines with it in multiple disciplines within your lifetime. And then growing up in, you know, the early 2002 1010, pre AI fields were so deep. In my case, theoretical physics and strength, it was so deep that you could spend literally 50 years catching up on all of the literature and academia that's existed before you contribute anything new. 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 and being okay with that, like no, knowing there are unknowns an…

AI assessment note: “The age of the polymath is back.”

Q When you say it's gonna be crazy, what does that actually mean?

A Like, the amount of code that's being generated, the, uh, I think code generated is growing exponentially. The security efforts aren't growing in kind, and so I think there's kind of a lag there. Um, I think there are probably going to be in the next couple years some pretty big incidents that occur because of, There honestly probably have been. I just, whatever incidents that have occurred, no one's going to admit, or typically they'll be reluctant to admit, um, if it was like AI involved or not. Um, 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. And so I think security, like the higher the stakes, it's going to grow in importance. And so I think the security part of the market is really important.

AI assessment note: “code generated is growing exponentially. The security efforts aren't growing in kind”

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

A I think generally, um, 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, 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.

AI assessment note: “I think spending on that type of stuff, but obviously in the more, like, intellectual domain”

Q committing half a billion dollars. Yeah. That would suggest the opposite. Yes. Um, I had Brendan on from McCall 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. 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?

A I would disagree. I'd pretty strongly disagree, um, for a couple 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 gonna 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. 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, it kind of, an example of this is like, 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? Probably not. 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. Um, and I think it's somewhat similar here, which is like, just because you can build a lot of these things does not mean you should. And in fact, oftentimes you want…

AI assessment note: “I would disagree. I'd pretty strongly disagree, um, for a couple things.”

Q resources, there are times when it is suboptimal from a human morality, societal standpoint in a lot of cases, To see, uh, engineers at Anthropic working on optimizing core 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?

A Um, I think it's a, it's, 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, like weapons, like you're definitely going to need, you know, some involvement there. Um, I think there's some incentivization that can be helpful just because, uh, there's, there might be some problems for 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. Um, you know, talking about that one, um, 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 CO two 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…

AI assessment note: “I think it's a, it's, it's certainly useful in some cases.”

Q Ok, so it's not good for the consumer if the model is tied to the application. Ok, cool. But bluntly, we are seeing codex and claw code eat a huge part of the market. What does the market look like in three years in terms of market maturation?

A 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 gonna need to be agnostic. And so, you know, you could be agnostic by saying, hey, every engineer, we're gonna give you Cloud Code, and Codex, and Gemini CLI, and all these other tools. Um, 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, Um, 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?

AI assessment note: “every CIO I speak to is really keenly aware of, we cannot, you know, throw”

Q 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?

A I think eventually we'll stop seeing them as model releases, and they'll feel more continuous. Like, just how before it was you know, GPT-II, then GPT-III, then GPT-III, then then GPT GPT-III then-III, then GPT then GPT then GPT GPT then GPT GPT GPT GPT GPT GPT-IV then,, then GPT then GPT then GPT-IV, then GPT, then GPT, then- Whether it's us or like 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, um, 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.

AI assessment note: “we'll stop seeing them as model releases, and they'll feel more continuous.”

Q In terms of, like, being there in person and the sales process, You got Sequoia very, very early on. Sequoia obviously one of the best and most prominent investors. Can you just tell me the story of how you got Sequoia having never had a job and only being paid to do physics?

A Yeah. So I was obsessed with physics basically since I was 12 because, um, I was a bad student, uh, 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 II, Trigonometry, Precalc, Calculus I, II, and III, differential equations, um, and maybe a linear algebra textbook. So I bought those textbooks, and then the summer between middle school and high school, I studied all of those, like, did all the problems in all of them, um, and then in high school took exams to place out of all of those classes. Um, 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 gonna 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. Um, ended up going to, uh, to Princeton because they had a great physics professor I wanted to work with. Um, he's, uh, this famous professor named Juan Maldicena, and I was like the first undergrad to work with him and write a paper with him. Then I ended up coming to Berkeley to do my PhD and, you know, work…

AI assessment note: “So I was obsessed with physics basically since I was 12”

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

A 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, um, but again, the thing that's very difficult, uh, 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. Um, but the benefit though is, you know, Theoretically, we get this balancing act where we don't go too crazy in any which direction. Um, you know, other parts of the world where it's more authoritarian is like, this is the thing we're doing. We are doing it. We're acting now. You get to move quickly. Now there's less kind of correction because what if you're going on the wrong course, but in cases like AI, where it's pretty clear, like for build out, you need to build data centers. You need, uh, energy, um, 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.

AI assessment note: “Probably on the, like, frontier model lab side.”

Q Does Ivanka Trump provide value? People will look at it and be like, oh, branding, just a name, whatever, and I didn't mean that disparagingly at all. I think people often think that with kind of famous celebrity names. Does she actually provide value?

A Yes. She is, first of all, she is 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, um, on the cap table.

AI assessment note: “there is, like, kind of dirty work investor help that she helps out with”

Q When you say it's gonna be crazy, what does that actually mean?

A Like, the amount of code that's being generated, the, uh, I think code generated is growing exponentially. The security efforts aren't growing in kind, and so I think there's kind of a lag there. Um, I think there are probably going to be in the next couple years some pretty big incidents that occur because of, There honestly probably have been. I just, whatever incidents that have occurred, no one's going to admit, or typically they'll be reluctant to admit, um, if it was like AI involved or not. Um, 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. And so I think security, like the higher the stakes, it's going to grow in importance. And so I think the security part of the market is really important.

AI assessment note: “the amount of code that's being generated, the, uh, I think code generated is growing exponentially”