The Exchanges

Q what I mean by that, we have Deep Seat released something this week, we have OpenAI released something this week, we have Anthropic released something this week, we have Mistral released something, you know, 10 days ago, where, bloody, every single day there's a new release, that the world maybe gets apathetic. How do you think about that, and how does that inform how you think about product launches, messaging?

A Yeah, I, I mean, it is, uh, much more complex. And Instagram, you know, you, The things that you had to watch out for, the Big Rocks were very known in advance. It's like, don't launch anything WWDC week. That's gonna be a, you know, flurry of announcements of the September iOS event. You know, there might be some other Big Rock, like, holiday. This is so much easier from a product marketing perspective, where here, um, it reminds me a little bit of Crossy Road, where you're like, okay, the car's going by. Alright, there's a gap in the car, like, launch tomorrow, or like, now it's good, but oh, now we hear there's a rumor. It's so much harder, and I've heard from Uh, folks at other labs as well, that everybody's kind of trying to read the, the tea leaves and be like, all right, is anybody, is it quiet? All right. Is it okay to launch now? Or like, I think we're gonna, we can do it next Tuesday. So it's much harder. You know, it, it requires a completely different approach. And I give credit to our, literally our product marketing team, because they've had to orient from a point where, you know, we were cloud three, seven sonnet. We launched on Monday and we locked the blog post for that. Sunday night at nine PM, which is not best practice from a marketing perspective. You know, we were briefing press that day on Sunday. Thank you to folks that helped on the phone with us on Sun…

AI assessment note: “it does involve that sort of, uh, ability to react quickly and be nimble.”

Q I'm going to ask one question on this. It's not a trap to go down, but I'm, I've spoke to Alex Wang about it on the show and I saw poolside on the show, and they said, we deeply underestimate China's ability in AI. Do you agree that we underestimate it?

A Yeah, I think the deep seek piece that people seem surprised that there were sort of cutting edge research teams there. And if you were paying attention, that part should not have been the surprising piece. Um, you know, it's, um, and we saw Instagram was blocked in China fairly early. And then we saw the sort of emergence of a sort of like a parallel world of, uh, startups, When, if you take up Facebook and Instagram, what happens and what emerges and those products were often like very high quality. They like demonstrate a lot of creative thinking and, uh, and were built at scale too. Like they were solving problems. You know, people love talking about the, like the super app and, and we chat, and there was some technical challenges solved by those at scale that were of the same scale of challenges that Facebook was challenged was doing. So, uh, it was absolutely. Uh, be a mistake to have underestimated or continue to underestimate like China's ability to both, uh, train at the frontier, um, especially like if they get access to compute, um, and then continue to innovate there too. So I think it's a pretty Western centric view that I've definitely seen happen in more of traditional software around like, well, like maybe it's like caught in this, like, you know, nineties, early 2000 of you have like, oh, all they're doing is like replicating what's already been working elsewhe…

AI assessment note: “it was absolutely. Uh, be a mistake to have underestimated or continue to underestimate”

Q to be perfect, and actually the challenge is as they've got bigger, you have more and more weight and pressure placed on every release. Um, how do you think about that? Releasing it doesn't have to be perfect, let's get it in the hands of users, versus now Anthropic is a massive company with millions of users. It does. How do you think about that as, as the product leader?

A I think about this a lot, and especially because you have different surfaces and different audiences that have different, both, uh, expectations of stability or, uh, sort of desire to be on the cutting edge. And so, um, you know, in an API product, like what people value is predictability and, uh, stability and the opt-in of something that's more future facing. Right. And so it can be a very opt-in thing. So I remember we launched prompt caching, which is a Big cost savings for people. But initially we did that via like a beta header that you had to opt into. And a lot of what we do on the API is in that form. If you do that for our customer facing, like our more consumer stuff, that's really lame to have to like have people opt in or like really, you want to be able to sort of iteratively release and be experimental with folks. And you know, you don't want to totally break their experience, but you've got a little bit more of that permission. And then we have all these enterprise customers that are using cloud for work in an enterprise. Now I think AI adoption enterprise is still a, Early adopter product in the enterprise. So you can get away with more than, you know, if you're, you know, I don't know how many releases Salesforce does a year, but I know a lot of these companies do like two, right? Or three. And it's usually oriented around some big event that they can do. And …

AI assessment note: “different surfaces and different audiences that have different, both, uh, expectations of stability”

Q What happens when those are released? When Grok III releases theirs, like jokes aside, like, does everyone at Anthropic and OpenAI get by, oh, shit, they beat us again? Or like, oh, shit, we won. Yeah.

A I think it requires, like, one of the things I try to do, you know, to support the team there is remind, like, you know, it's the model releases are going to happen, and at any given point you are going to be, you know, it's the, you know, it's so over, we're so back, like that cycle. It's like, that is, you have to live that in AI, and you can't get too down about one release, because yeah, for sure, it is inevitable, and sometimes you're lucky, and there's like two or three months where The model that you launched or the product that you launched is still state of the art across all the things you really care about. Sometimes it lasts a week and you, you can't over rotate on either as you can't rest on your laurels.

AI assessment note: “it's so over, we're so back, like that cycle”

Q When you say about the legwork there, what I think to you, and you said about differentiated GTM and differentiated data pools or data sources, does this next generation wave of AI benefit existing vertical SaaS companies who have those already and can implement AI, or does it benefit bottoms up net newly created companies in those spaces? Which one more so?

A That's a great question. I think it can be both at the highest level. The very thing about AI and product design is Uh, you have to dance this very delicate dance of showing the future and dreaming up what the models are currently capable at their edges, you know, cause you want to design for where they'll be, gosh, three months from now, which is how quickly things are moving, but, um, not over promise and under deliver, because that's like a very trust breaking piece. And now if you're a startup, you can do a little bit more of the over promising because people are kicking your tires, the early adopters, they have a little bit more of that. Uh, sort of willingness to engage. It's much harder if you're, like, an existing verticalized SaaS company, and you say, we've added AI, and then people try to, it's like, it's not that good, or like, oh, I thought I was gonna do all these things, or, uh, you said it could do these 30 things, it does, like, two of them well. Um, I think that, like, each of those two groups have, like, a very different challenge. On the former, it's, you have established products, you have established behaviors, you want to skate to where the puck is going without alienating your existing customers. Um, I think we can dive in. I think there's some good patterns for doing that. And on the startup front, you probably don't get have the data. Um, and it's like…

AI assessment note: “I think it can be both at the highest level.”

Q to be perfect, and actually the challenge is as they've got bigger, you have more and more weight and pressure placed on every release. Um, how do you think about that? Releasing it doesn't have to be perfect, let's get it in the hands of users, versus now Anthropic is a massive company with millions of users. It does. How do you think about that as, as the product leader?

A I think about this a lot, and especially because you have different surfaces and different audiences that have different, both, uh, expectations of stability or, uh, sort of desire to be on the cutting edge. And so, um, you know, in an API product, like what people value is predictability and, uh, stability and the opt-in of something that's more future facing. Right. And so it can be a very opt-in thing. So I remember we launched prompt caching, which is a Big cost savings for people. But initially we did that via like a beta header that you had to opt into. And a lot of what we do on the API is in that form. If you do that for our customer facing, like our more consumer stuff, that's really lame to have to like have people opt in or like really, you want to be able to sort of iteratively release and be experimental with folks. And you know, you don't want to totally break their experience, but you've got a little bit more of that permission. And then we have all these enterprise customers that are using cloud for work in an enterprise. Now I think AI adoption enterprise is still a, Early adopter product in the enterprise. So you can get away with more than, you know, if you're, you know, I don't know how many releases Salesforce does a year, but I know a lot of these companies do like two, right? Or three. And it's usually oriented around some big event that they can do. And …

AI assessment note: “you have different surfaces and different audiences that have different, both, uh, expectations of stability”

Q I'm going to ask one question on this. It's not a trap to go down, but I'm, I've spoke to Alex Wang about it on the show and I saw poolside on the show, and they said, we deeply underestimate China's ability in AI. Do you agree that we underestimate it?

A Yeah, I think the deep seek piece that people seem surprised that there were sort of cutting edge research teams there. And if you were paying attention, that part should not have been the surprising piece. Um, you know, it's, um, and we saw Instagram was blocked in China fairly early. And then we saw the sort of emergence of a sort of like a parallel world of, uh, startups, When, if you take up Facebook and Instagram, what happens and what emerges and those products were often like very high quality. They like demonstrate a lot of creative thinking and, uh, and were built at scale too. Like they were solving problems. You know, people love talking about the, like the super app and, and we chat, and there was some technical challenges solved by those at scale that were of the same scale of challenges that Facebook was challenged was doing. So, uh, it was absolutely. Uh, be a mistake to have underestimated or continue to underestimate like China's ability to both, uh, train at the frontier, um, especially like if they get access to compute, um, and then continue to innovate there too. So I think it's a pretty Western centric view that I've definitely seen happen in more of traditional software around like, well, like maybe it's like caught in this, like, you know, nineties, early 2000 of you have like, oh, all they're doing is like replicating what's already been working elsewhe…

AI assessment note: “it was absolutely. Uh, be a mistake to have underestimated or continue to underestimate”

Q coding element in a minute. I do just have to ask, when we look at kind of, um, blockers or barriers to To progression. When you look today, what do you think the biggest blockers are? Cause this is one where I have completely disparate opinions from different people, whether it's Alex Wang, or whether it's, you know, Jonathan Ross at Grok. What is the blocker today? Compute data algorithms?

A It's getting the environments by which the models get trained in to better and better match real world challenges that aren't sort of single shot. Um, I know Alex has been thinking about this problem as well, because we talked about sort of evals for agentic behavior is like one sort of very specific version of the broader thing that I'm talking about, which is, um, even within the realm of software engineering, the work of a software engineer is not just to produce code. It's to understand what needs to get produced, to work out the timelines with their product management counterparts, um, to, um, deeply understand the requirements and deeply understand the user, um, Uh, use case that they're building for, and then also delivering whatever they built in a way that then can be tested and iterated on, and then as user feedback at the other end, if they're building some kind of public facing product, that's a hard, well, there's no evil for that, right? There's like, um, it's interesting that we call the, the sort of most, uh, sort of common software engineering thing, SWE bench, right? Like to actually be a SWE is a lot more than just, you know, I looked at a pull request. I produced this Pull request, you know, or pull it, pull this to stiff, and then you're going to accept it or not. So building environments and evaluations that better mirror that we think a lot about office p…

AI assessment note: “It's getting the environments by which the models get trained in to better and better match”

Q This is what Kevin was saying before you joined. It was a bit awkward. And that's the worst. Ok, final one. Like, would you have like insanely smart oh sorry, not insanely smart, like incredibly ambitious and hungry, or like super experienced IQ, experienced IQ or hungry hustler ambitious?

A I think the faith that the different phases of the company require different kind of people. You know, I think you always want somebody who's excited about what you're doing and, uh, and willing to jump in. I've, I've always been happy with people who have the sort of raw talent and desire to learn and don't see boundaries as a reason to stop working on something. Like that's a repeated theme I've, I've hit. Like the people I love working with are the ones that don't come up, you know, if they're IOS engineers, and they start having to do back-end work. They don't throw up their hands and go, ah, that's not my problem. Like, somebody else go, like, please, like, please help me here. It's the people that are like, you know what, like, I, um, like, one of our engineers was like, oh, you know, I haven't really done marketing stuff before, but somebody needs to put together our Google Play Store page. Like, I'm gonna go learn, like, what other companies are doing. Like, I'm gonna put together a proposal. Like, that sort of, like, desire to not see, uh, you know, your role as strictly defined by, like, the four bullet points that you think you do. That's, to me, the kind of people I repeatedly like working with. Now, once you scale, there's sometimes reasons why there are boundaries between teams. Like, there's more process. Like, you wouldn't want necessarily, like, your, you know,…

AI assessment note: “I think the faith that the different phases of the company require different kind of people”

Q Final one. Dario has said that this will be the generation that could live to a 150. I'm slightly, like, butchering and summarizing his quote. Obviously. Uh, but like, this could be the generation. I'm very optimistic. My mother has multiple cirrhosis that will find cures for diseases like MS with AI. Do you agree with his optimism? And how do you think about AI increasing longevity and human lifespan?

A I think the potential is huge. I think there's everything from at the, like today where AI is helping, which is in, um, Closing the loop on drug discovery and closing the loop on clinical trials, right? On Novo Nordisk, uh, used to take, I think it was something like 15 weeks to do their clinical trial reports, and now they use cloud and get it done in 20 minutes, and like, that's a step change. Now, there is years of research that proceeded at, so I'm not saying that we've cut years to weeks, you know, or years to minutes, but that's a point, you know, of the process that we can make faster, and that's like with the models today. Then you see, um, AHRQ, which is this, um, Science and Research Institute that, um, Patrick Callison and some others have started and funded, They're working on foundational models for cells, right? Where you have all of a sudden, ah, a real cell model that you can run experiments on, and that kind of thing should also accelerate drug discovery, um, and, and, and experimentation there tremendously, because all of a sudden you're, you're cutting the loop there. So, I'm very optimistic. There's a lot of places where AI is, I think, underutilized relative to its potential, and I think some of the smartest people in the field and the, like, smartest minds of my generation were working on, like, serving more targeted ads. Maybe that was true at one point.

AI assessment note: “I think the potential is huge.”

Q coding element in a minute. I do just have to ask, when we look at kind of, um, blockers or barriers to To progression. When you look today, what do you think the biggest blockers are? Cause this is one where I have completely disparate opinions from different people, whether it's Alex Wang, or whether it's, you know, Jonathan Ross at Grok. What is the blocker today? Compute data algorithms?

A It's getting the environments by which the models get trained in to better and better match real world challenges that aren't sort of single shot. Um, I know Alex has been thinking about this problem as well, because we talked about sort of evals for agentic behavior is like one sort of very specific version of the broader thing that I'm talking about, which is, um, even within the realm of software engineering, the work of a software engineer is not just to produce code. It's to understand what needs to get produced, to work out the timelines with their product management counterparts, um, to, um, deeply understand the requirements and deeply understand the user, um, Uh, use case that they're building for, and then also delivering whatever they built in a way that then can be tested and iterated on, and then as user feedback at the other end, if they're building some kind of public facing product, that's a hard, well, there's no evil for that, right? There's like, um, it's interesting that we call the, the sort of most, uh, sort of common software engineering thing, SWE bench, right? Like to actually be a SWE is a lot more than just, you know, I looked at a pull request. I produced this Pull request, you know, or pull it, pull this to stiff, and then you're going to accept it or not. So building environments and evaluations that better mirror that we think a lot about office p…

AI assessment note: “It's getting the environments by which the models get trained in”

Q Final one, I just have to ask before we do a quick fire, but we mentioned kind of some end products there and building them. When you think about building end products for consumers versus building the API division of the company, which is very significant. How do you think about the balance and the trade-offs there between building an API business and building an end user consumer business?

A There's, um, what we get out of, uh, each, um, and I, I think about that trade-off. I think we learn a lot more quickly with first party products. So, um, as a really, you know, specific example with cloud code within, you know, a week of it being deployed internally, we had found a way in which one of the sort of tools that it has access to, uh, the model wasn't using as well as it could have, and that made its way directly into three seven sonnet. Like that's a way in which internal dog fooding of the first party tool directly led to a model improvement in the next generation. There's like a few other places where we've hit that even building first party products. Much harder with a third party product, right? Like they'll tell you if something's wrong, but it's, it's a bit more arm's length. And even though we work really closely, including with some of those, uh, coding startups that you mentioned, it's still not the same. So there's a lot of value in what we learned there. Then there's the sort of stickiness and, and sort of, uh, we talked about brand and loyalty. I think it's easier from a consumer if you can build a brand around a product than just an API. You know, the fact that we power a lot of these coding products is visible to people. Like it's often the, Or default in the dropdown selector. And if you're in the know, you know, but not everybody does. And it's stil…

AI assessment note: “I think we learn a lot more quickly with first party products.”

Q kind of, startups building for where models will be. It's a very challenging time where startup products are so determined quality-wise by the quality of the models, and a change in model can seismically change a startup's output, be it a coding software or a legal platform, whatever that is. Should startups build for what we have today, or should we build for what we can project forward in time?

A A really good question. I've heard from multiple people that say, like, my startup was not a startup until Cloud three, five sonnet or the second cloud three, five sonnet. Uh, but I hear that from entrepreneurs that are this, this company was not a company until the small breakthrough where now, you know, the accuracy went up, I don't know, from 95 to 99. And now that's, you know, close enough for this industry or from sometimes it's like from 70 to 90, sometimes you get those kind of generational leaps as well. So, um, how to figure out where, where that is, like there's times where entrepreneurs have been knocking their heads against the wall within a particular space where whether it's Helping people code, whether it's helping with legal analysis, whether it's, um, you know, I mentioned healthcare or something in that space and the cobbled together probably undersells it. They're like lovingly assembled version of what they did, which probably involved multiple tools was either like price uncompetitive because it required, you know, an Opus class model and that was not going to be sort of supported by the underlying business is still worth doing because when the model arrives, you're not starting from square zero. And so Often the companies that do benefit from those model generation shifts are not the ones that suddenly start that day. Like, gosh, you know, it sounds like c…

AI assessment note: “is still worth doing because when the model arrives, you're not starting from square zero”

Q uh, McCall recently, who obviously raised that big new round. Um, but I asked him the question, and I, I'd love your thoughts, which is like, when we look at the future of data within models, will there be more synthetic data that compounds on top of each other, or will human data continue to be the predominant data source that drives model progression? How do you think about that?

A I think for the, um, for the models to improve, you do need a story around how do you perhaps seed it with an original human data, but then can generate all these synthetic environments by which it can sort of pathfind and Explore. Um, Claude's been having fun playing Pokemon this week, which is, you know, uh, has been a good, but kind of funny distraction for our own like research and engineering teams. I'm like, what is everybody doing? They're like, oh, we're watching the Claude plays Pokemon, uh, live stream. But I think games are an interesting example where, you know, you can imagine a lot of different runs through the same game within some constraint and rules that gets a lot harder when the problem space is less well-defined than, you know, did you make it out of the, Viridian forest. I never played Pokemon. I'm learning just watching this live stream. Um, but it's still important to be able to take sort of golden paths, but also synthesize, um, a variety of approaches through it so that you can still think about how the model can progress in the face of uncertainty. So I think it absolutely has to be a mix. And I think the best, uh, models will come from that combination of great, like for code it's, you know, being, having good foundational understanding of code and good examples, but then also being able to explore a really wide variety, uh, of paths, um, through tha…

AI assessment note: “So I think it absolutely has to be a mix.”

Q I would argue that brand is the most important thing. To your point, people aren't switching every day. They're kind of like, oh, I'm a Claude person or, oh, I'm a ChatGPT person. And they kind of identify already with their models. Do you agree with that statement or do you think that's too glib?

A I don't, I, I think that is right. I think, especially on the consumer front, um, you know, I was just reading, uh, Ben Thompson, you know, he has Nat Friedman and Daniel Gross on there pretty often, and they're talking about some people being Claude people and some people would chat GPT people. And I think that definitely happens where they, you like the personality, you like the interface design, you like the vibe again, you know, it actually reminds me a lot. Um, you know, we had this interesting back and forth with Snapchat over the years with Instagram. Um, and then even before that people would launch, A new product that's like Instagram, but just for super high-end photographers, or with this, like, additional twist, or just one photo a day, you know, it's be real. And, um, I had this, like, fake formula. I'm not the mathematician, clearly, at Anthropic, but it was, uh, you know, social networks are made of format, or formats that you have in your product, audience, and vibes. And format, you know, for Instagram, we had stories, we had feed, and then eventually we had video. Audience, you know, initially was sort of Hipstery photographers eventually grew to be anybody that's really interested in sort of visual storytelling or visual media. But the vibes of Instagram, even when we had more, uh, product similarities to a Snapchat, even to a Facebook, the vibes were very di…

AI assessment note: “I think that is right. I think, especially on the consumer front”

Q You know, when you look at what you're brilliant at today, you do so well, as we said on the code front, is there a roadmap here to put your own ID in? Code agent. And how do you think about that?

A You know, again, with the product focus lens, um, I think we have to pick our bets carefully. Um, and even building, we built cloud code, which we just released, um, as I sort of, uh, uh, command line agentic coding tool internally first, because we just wanted to accelerate our own team. And, uh, after seeing it play out for a couple of months, we're like, this is good. Like it's not, it's not a solution to all coding problems and doesn't obviate the IDE. But it's useful enough to us in enough cases that we want to see people use it out in the real world. And so then, you know, and shipping is never free, right? There's like, you got to name it something externally. We got to, you know, find the right, you know, packaging around it. There's a go to market fees. Um, so we, we do it carefully. I think my view of, of where the models are today is, um, you still need hands on keyboard and you still need that exchange of, Hey, I did this. Is this right? Um, Right. Well, let's pursue this direction down. Yes, this is great. Let's put up a request. No, we went down kind of like a false trail. Let's like unwind the stack metaphorically and, you know, maybe an actual, uh, usage and then, and then keep going. That's why I think that there is a, there is a role for this sort of in between, uh, IDE and the full on like cognition dev and like full on delegation of tasks within can be used …

AI assessment note: “doesn't obviate the IDE. But it's useful enough to us in enough cases”

Q What can and will you do to increase product speed on the first-person consumer side?

A I think there's two things. One is recognizing that we were running, I think, a larger company playbook for what is actually, like, we're still in, like, start, our products are, even if the company has good traction and, like, The API business is doing real well. People are using cloud AI and upgrading cloud AI pro. It's still early days and it's still like do or die or like make it or break it. So we need to operate in that way. And so, uh, that means getting the right people together sooner, faster, and ignoring organizational boundaries. We got too calcified, I think. And like, oh, well, this is on this team's plate versus this team's plate. And oh, you can't get this done this quarter because it's not on this team. I mean, I get why organizations evolve and some of that is natural, but We can't afford that right now. So it's been a lot more. Who are the right people? Let's get them together. Let's clear all the other distractions. And then like, like, let's clear out my calendar so that like I spend more of my time in product review and design review than I do in administration.

AI assessment note: “getting the right people together sooner, faster, and ignoring organizational boundaries”

Q Penultimate one that's open, but I'm interested. You guys have done the most incredible angel portfolio and investments. Any big lessons from doing that?

A Man, I looked back. I did a little, like, you know, reflection. It was like mid last year. I was like, there were like a few either, like, LP positions I took or individual investments. I was like, man, like, I did this because it felt like this was a thing that a lot of people were excited about, and I wasn't, and that, like, it's just such a, like, I have to keep relearning that lesson. My life is relearning lessons. I think everybody's is, right? And hopefully you get better at, like, on margin making fewer of those, the same mistakes over and over again. So that's a, that's a really big one. Um, and two, like, it's such a cliche, but, like, there's the founders where you're like, they will, like, they were gonna run through the wall of any problem that they hit, and then the ones that are like, oh, actually, they're really polished, and they came out of, you know, a Facebook or an Instagram, and they've got, like, a decent first demo, but, like, you ask yourself, like, the second something goes wrong here, are they the kind of person that are gonna be able to make the hard decisions and move quickly? Like, have they had that adversity in their careers? And, or can you, like, imagine themselves in that adversarial situation, and, You know, again, I look back and again, some, so much of this is like hindsight thinking. So you, you can learn the lessons you want to learn and t…

AI assessment note: “I did this because it felt like this was a thing that a lot of people were excited about”

Q kind of, startups building for where models will be. It's a very challenging time where startup products are so determined quality-wise by the quality of the models, and a change in model can seismically change a startup's output, be it a coding software or a legal platform, whatever that is. Should startups build for what we have today, or should we build for what we can project forward in time?

A A really good question. I've heard from multiple people that say, like, my startup was not a startup until Cloud three, five sonnet or the second cloud three, five sonnet. Uh, but I hear that from entrepreneurs that are this, this company was not a company until the small breakthrough where now, you know, the accuracy went up, I don't know, from 95 to 99. And now that's, you know, close enough for this industry or from sometimes it's like from 70 to 90, sometimes you get those kind of generational leaps as well. So, um, how to figure out where, where that is, like there's times where entrepreneurs have been knocking their heads against the wall within a particular space where whether it's Helping people code, whether it's helping with legal analysis, whether it's, um, you know, I mentioned healthcare or something in that space and the cobbled together probably undersells it. They're like lovingly assembled version of what they did, which probably involved multiple tools was either like price uncompetitive because it required, you know, an Opus class model and that was not going to be sort of supported by the underlying business is still worth doing because when the model arrives, you're not starting from square zero. And so Often the companies that do benefit from those model generation shifts are not the ones that suddenly start that day. Like, gosh, you know, it sounds like c…

AI assessment note: “still worth doing because when the model arrives, you're not starting from square zero.”

Q uh, McCall recently, who obviously raised that big new round. Um, but I asked him the question, and I, I'd love your thoughts, which is like, when we look at the future of data within models, will there be more synthetic data that compounds on top of each other, or will human data continue to be the predominant data source that drives model progression? How do you think about that?

A I think for the, um, for the models to improve, you do need a story around how do you perhaps seed it with an original human data, but then can generate all these synthetic environments by which it can sort of pathfind and Explore. Um, Claude's been having fun playing Pokemon this week, which is, you know, uh, has been a good, but kind of funny distraction for our own like research and engineering teams. I'm like, what is everybody doing? They're like, oh, we're watching the Claude plays Pokemon, uh, live stream. But I think games are an interesting example where, you know, you can imagine a lot of different runs through the same game within some constraint and rules that gets a lot harder when the problem space is less well-defined than, you know, did you make it out of the, Viridian forest. I never played Pokemon. I'm learning just watching this live stream. Um, but it's still important to be able to take sort of golden paths, but also synthesize, um, a variety of approaches through it so that you can still think about how the model can progress in the face of uncertainty. So I think it absolutely has to be a mix. And I think the best, uh, models will come from that combination of great, like for code it's, you know, being, having good foundational understanding of code and good examples, but then also being able to explore a really wide variety, uh, of paths, um, through tha…

AI assessment note: “So I think it absolutely has to be a mix.”

Q I would argue that brand is the most important thing. To your point, people aren't switching every day. They're kind of like, oh, I'm a Claude person or, oh, I'm a ChatGPT person. And they kind of identify already with their models. Do you agree with that statement or do you think that's too glib?

A I don't, I, I think that is right. I think, especially on the consumer front, um, you know, I was just reading, uh, Ben Thompson, you know, he has Nat Friedman and Daniel Gross on there pretty often, and they're talking about some people being Claude people and some people would chat GPT people. And I think that definitely happens where they, you like the personality, you like the interface design, you like the vibe again, you know, it actually reminds me a lot. Um, you know, we had this interesting back and forth with Snapchat over the years with Instagram. Um, and then even before that people would launch, A new product that's like Instagram, but just for super high-end photographers, or with this, like, additional twist, or just one photo a day, you know, it's be real. And, um, I had this, like, fake formula. I'm not the mathematician, clearly, at Anthropic, but it was, uh, you know, social networks are made of format, or formats that you have in your product, audience, and vibes. And format, you know, for Instagram, we had stories, we had feed, and then eventually we had video. Audience, you know, initially was sort of Hipstery photographers eventually grew to be anybody that's really interested in sort of visual storytelling or visual media. But the vibes of Instagram, even when we had more, uh, product similarities to a Snapchat, even to a Facebook, the vibes were very di…

AI assessment note: “I don't, I, I think that is right. I think, especially on the consumer front”

Q This is what Kevin was saying before you joined. It was a bit awkward. And that's the worst. Ok, final one. Like, would you have like insanely smart oh sorry, not insanely smart, like incredibly ambitious and hungry, or like super experienced IQ, experienced IQ or hungry hustler ambitious?

A I think the faith that the different phases of the company require different kind of people. You know, I think you always want somebody who's excited about what you're doing and, uh, and willing to jump in. I've, I've always been happy with people who have the sort of raw talent and desire to learn and don't see boundaries as a reason to stop working on something. Like that's a repeated theme I've, I've hit. Like the people I love working with are the ones that don't come up, you know, if they're IOS engineers, and they start having to do back-end work. They don't throw up their hands and go, ah, that's not my problem. Like, somebody else go, like, please, like, please help me here. It's the people that are like, you know what, like, I, um, like, one of our engineers was like, oh, you know, I haven't really done marketing stuff before, but somebody needs to put together our Google Play Store page. Like, I'm gonna go learn, like, what other companies are doing. Like, I'm gonna put together a proposal. Like, that sort of, like, desire to not see, uh, you know, your role as strictly defined by, like, the four bullet points that you think you do. That's, to me, the kind of people I repeatedly like working with. Now, once you scale, there's sometimes reasons why there are boundaries between teams. Like, there's more process. Like, you wouldn't want necessarily, like, your, you know,…

AI assessment note: “for the next few years, like, um, it's raw talent, ability to learn quickly”

Q Do you think it's been able to morph because you've actually gone in the same direction? You both are now married. You both now have kids. And it's kind of scaled in a similar trajectory, respectfully, to both of you.

A Yeah, I believe that. I think that there's a tremendous amount of empathy. I remember Kevin had kids first, and, you know, I look at myself, you know, as a, like, you know, mid to late twenties person, where, like, my friends were starting to have kids, and, like, realized how naive I was a lot about things. Like, the dumbest thing I ever said to somebody that was going out on leave was, do you have, like, a bunch of books signed up that you're gonna read during your, your, like, leave? And they're like, no, like, who's the second kid? They're like, I'm just Try to, you know, stay alive and, and see if I can read, you know, like, you know, like the news of the day at the end of the day, I feel good. I'm not going to read a book. So I learned, you know, you learn a ton as a first time parent and Kevin got to do that first. But I think for sure, I think the fact that, you know, we looked at each other post Instagram and we both had that drive to still build. And I think if that were different, then I don't think we would be working together. I think that would have made a much harder partnership if One person was like, great. I'm, you know, you know, no judgment, but like, I want to, you know, coast, or I don't want to do something, or I want to do something, you know, a little bit less intense, but both of us love building. So when we looked at each other, we were still on that …

AI assessment note: “Yeah, I believe that. I think that there's a tremendous amount of empathy.”

Q When you say about the legwork there, what I think to you, and you said about differentiated GTM and differentiated data pools or data sources, does this next generation wave of AI benefit existing vertical SaaS companies who have those already and can implement AI, or does it benefit bottoms up net newly created companies in those spaces? Which one more so?

A That's a great question. I think it can be both at the highest level. The very thing about AI and product design is Uh, you have to dance this very delicate dance of showing the future and dreaming up what the models are currently capable at their edges, you know, cause you want to design for where they'll be, gosh, three months from now, which is how quickly things are moving, but, um, not over promise and under deliver, because that's like a very trust breaking piece. And now if you're a startup, you can do a little bit more of the over promising because people are kicking your tires, the early adopters, they have a little bit more of that. Uh, sort of willingness to engage. It's much harder if you're, like, an existing verticalized SaaS company, and you say, we've added AI, and then people try to, it's like, it's not that good, or like, oh, I thought I was gonna do all these things, or, uh, you said it could do these 30 things, it does, like, two of them well. Um, I think that, like, each of those two groups have, like, a very different challenge. On the former, it's, you have established products, you have established behaviors, you want to skate to where the puck is going without alienating your existing customers. Um, I think we can dive in. I think there's some good patterns for doing that. And on the startup front, you probably don't get have the data. Um, and it's like…

AI assessment note: “I think it can be both at the highest level.”

Q And who can build those products? Which is, like, my, as an investor's big question often, which is when does a model provider move into an application provider? I'm just fascinated to hear your thoughts around what is attractive enough where you dedicate the resources to become an application provider, not just a model provider enabling.

A Two main criteria that I look at is, um, cause our team for all of Anthropic being big, you know, I think we crossed a thousand people. Our product team is, you know, maybe a 10th of that. Like it's, you know, by Instagram year two standards, very large, but by, you know, large SAS company, very small, we're somewhere in between all of those. And we're supporting like, you know, you have cloud code. Now we have the API, we have Claudia, we have cloud for work. So it is across a lot of different surfaces. So I think generalizability is really important. Even if we pick a persona or a vertical to go after, We are going to be building things that are, um, general purpose as a rule with maybe some specialization at the, like, user level, but not at the, I don't anticipate us building a lot of verticalized experiences that are, like, fairly bespoke to a given workflow or use case. So I think that's-

AI assessment note: “Two main criteria that I look at... generalizability is really important.”

Q You know, when you look at what you're brilliant at today, you do so well, as we said on the code front, is there a roadmap here to put your own ID in? Code agent. And how do you think about that?

A You know, again, with the product focus lens, um, I think we have to pick our bets carefully. Um, and even building, we built cloud code, which we just released, um, as I sort of, uh, uh, command line agentic coding tool internally first, because we just wanted to accelerate our own team. And, uh, after seeing it play out for a couple of months, we're like, this is good. Like it's not, it's not a solution to all coding problems and doesn't obviate the IDE. But it's useful enough to us in enough cases that we want to see people use it out in the real world. And so then, you know, and shipping is never free, right? There's like, you got to name it something externally. We got to, you know, find the right, you know, packaging around it. There's a go to market fees. Um, so we, we do it carefully. I think my view of, of where the models are today is, um, you still need hands on keyboard and you still need that exchange of, Hey, I did this. Is this right? Um, Right. Well, let's pursue this direction down. Yes, this is great. Let's put up a request. No, we went down kind of like a false trail. Let's like unwind the stack metaphorically and, you know, maybe an actual, uh, usage and then, and then keep going. That's why I think that there is a, there is a role for this sort of in between, uh, IDE and the full on like cognition dev and like full on delegation of tasks within can be used …

AI assessment note: “it's not a solution to all coding problems and doesn't obviate the IDE”

Q You said three years sounds ridiculous. A year would be much more realistic. I agree, and I get you when we look at the speed of scaling. Do we think that we hit a plateau or an asymptote in product releases, the speed of development? Because it feels so fast now to our point earlier. Do we hit that plateau or do we continue in this exponential progression movement?

A Is a question I think a lot about. I started the year by looking at our product development process and looking at where we are Clodified, like where are we using Claude and where we're not? And, uh, you look at him and say, okay, you know, Claude can be useful in sort of taking initial ID and creating a PRD out of it. And Claude can be useful, obviously in the coding side. Um, Claude can be useful in synthesizing a lot of conversations that people are having about a product and kind of like finding like the kind of thorny issues of disagreement, driving alignment and actually figuring out what to build is still the hardest part, right? Like that is actually like the only thing that is still best resolved by Just getting together in a room and talking through the pros and cons or going off and exploring it in Figma and coming back. And so like any dynamic system, if you optimize one piece, all of a sudden something else becomes the, um, uh, the, like the, the blocker or the, or the critical kind of path. And I think alignment, deciding what to build, solving real user problems and like figuring out a cohesive product strategy, still very hard. And probably like the models are more than a year away from solving that. That is the constraint. It's why I'm really bullish on at least startups being able to explore the space because, you know, I remember this from my, both Instagram …

AI assessment note: “if you optimize one piece, all of a sudden something else becomes the... blocker”

Q Do you worry about your five-year-old becoming more comfortable talking to models and agents than they are humans?

A I've had so many conversations with Alex Wang about this because he has this whole thing about how in the future most friends will be AI friends. And, um, you know, I, I don't think he's wrong. Um, and I think that there's, uh, there's ways in which Uh, that's already starting to be the case with, you know, people, uh, you know, having lots of online game experiences, and some of those are NPCs, and you might just have, like, more of, like, a comfortable sort of existence in there as well, even if you're not breaking through this. I do, I worry, she is so gregarious that, like, I'm not actually worried in her particular case, but, like, let's, uh, abstract to the broader sense. There is a lot you can learn, uh, from, you know, what it feels like, like, Here's the bull case. I was a fairly awkward, you know, you know, teenager, and I probably could have benefited from some practice mode, like AI interactions around some of these things to build it up. And at the same time, that's like not the real, it's doesn't feel like it's totally closing the loop around like the consequences of real interaction. Like it's the difference between reading about what it's like to have your first, like really hard argument with your high school girlfriend and then actually having it. And like, when you're in that moment, you know, it's, this is like now the, the classic, like, Is it the Chinese r…

AI assessment note: “I'm not actually worried in her particular case, but, like, let's, uh, abstract”

Q Does Europe become more or less relevant in an AI driven decade?

A I want them to do well because I, I love a lot of Europe and I have, um, you know, I lived in Portugal growing up as well. Um, I saw a funny, maybe somewhat defeatist argument where if real world experiences and human interaction become more valued, Europe becomes more valuable itself as like the perhaps world capital of sensory and, you know, uh, experiences. That feels weird as a, as if that's all you're resting on that, that feels a little, uh, limited in there as well. But I think will be really interesting from a Europe perspective or European perspective is what are the things like, I think I really respect about Europe is there's often been the case that there are, um, things about the lifestyle or the society that they hold very, very strongly that then they not always elegantly, but at least attempt to enshrine in either like best practices or even laws. And so even as we think about doing our product design and data privacy and, you know, selling to German users or German companies, there's a different set of questions that get asked that are often very helpful questions. And so maybe the, the bull case there is that those are actually questions that are relevant to everybody, and they will just be at the leading edge of asking some of those questions. I think from a lab's perspective, it's a lot harder question to answer. And I think there's maybe some combination of…

AI assessment note: “maybe the bull case there is that those are actually questions that are relevant”

Q Penultimate one that's open, but I'm interested. You guys have done the most incredible angel portfolio and investments. Any big lessons from doing that?

A Man, I looked back. I did a little, like, you know, reflection. It was like mid last year. I was like, there were like a few either, like, LP positions I took or individual investments. I was like, man, like, I did this because it felt like this was a thing that a lot of people were excited about, and I wasn't, and that, like, it's just such a, like, I have to keep relearning that lesson. My life is relearning lessons. I think everybody's is, right? And hopefully you get better at, like, on margin making fewer of those, the same mistakes over and over again. So that's a, that's a really big one. Um, and two, like, it's such a cliche, but, like, there's the founders where you're like, they will, like, they were gonna run through the wall of any problem that they hit, and then the ones that are like, oh, actually, they're really polished, and they came out of, you know, a Facebook or an Instagram, and they've got, like, a decent first demo, but, like, you ask yourself, like, the second something goes wrong here, are they the kind of person that are gonna be able to make the hard decisions and move quickly? Like, have they had that adversity in their careers? And, or can you, like, imagine themselves in that adversarial situation, and, You know, again, I look back and again, some, so much of this is like hindsight thinking. So you, you can learn the lessons you want to learn and t…

AI assessment note: “I did this because it felt like this was a thing that a lot of people were excited about”