20VC: Raising $500M To Compete in the Race for AGI | Will Scaling Laws Continue: Is Access to Compute Everything | Will Nvidia Continue To Dominate | The Biggest Bottlenecks in the Race for AGI with Eiso Kant, CTO @ Poolside

24:04I think the intersection between those is the places where companies like us get to exist. My immediate thought jumps to GitHub. I'll GitHub not best place to do that. Good Hub today has this incredible data set almost all of the code in the world. GitLab is a player, but only in the private side, right? What sits behind the accounts of developers. GitLab is massive in public code and it's massive in private code, but private code no one's allowed to train on. Not us, not open AI. So all of us have access to the same public data, and it's the output data. And so there isn't an inherent advantage from a capabilities race perspective.

24:39Another thing that we frame in our company over and over again, is there's a capabilities race in the world. And to your point, early we said, there's four things that matter. Greer, you on the three, but I'm going to add one. and compute its data, its proprietary plied research, the algorithms, and talent. Talent is absolutely key in this industry. Now, in the go to market race, it's talent first and foremost, but it's also product and distribution. And distribution, Microsoft definitely has an incredible positioning in the world. Can I say in terms of the compute element that we didn't discuss, when we think about compute underpins all of this and all of the data challenges that we mentioned, it's $600 million enough.

25:12No, $600 million that we've raised till date and the latest $500 million round translates to us being able to be an entrance into the race. And what that means is that the 10 ,000 GPUs that we've now brought online this summer that came from this capital allow us to make incredible advancements in model capabilities because of the our ability to take reinforcement learning from code execution feedback and generate extremely large amounts of data and then train very large models with it. It is enough for this moment in time, but over time, it won't be enough. How much do you think you'll need? It's a very good question.

25:48There are real physical, real world constraints behind this. We've seen crazy numbers thrown out in our industry of compute cluster sizes and things like that, but the world actually still needs time to catch up with the real ability to do so. Today, interconnecting more than 32 ,000 GPUs is extremely challenging. We're starting to be able to possibly interconnect 100 ,000. But right now, a million GPU cluster, a 10 million GPU cluster for training of models has both true algorithmic things that we have to overcome to be able to do this and also has actual physical limitations still in the world.

26:21So we're not living in a world right now where unlimited money can buy you unlimited advantages. It's why we get to exist with 10 ,000 GPUs. It does cash correlate to compute. And what I mean by that is if you have cash, can you go to your store and say, I want this amount of compute, or is it more? I think again, it depends on how much cash and how much compute. We about a year and a half ago, when we started as a company, there was a true imbalance between supply and demand in the world that even as a, you know, frontier eye company starting, everyone wants you to win and videos and incentivize to hyperscreeners.

26:55Everyone is incentivized actually to make early stage companies succeed with compute. It's a lot easier when you're an early stage eye company to get compute than it is when you're an enterprise because they understand this is where the future is heading towards. But even then there was a real mismatch between demand and supply and we had to do an incredible amount of work of understanding the market, building relationships and having planned A to Z to get there. In the last six months, the world has still a huge supply shortage and we can see this. If you're an early stage startup, there's lots of paths for you.

27:26If you're a Frontier AI company, you need to make decisions about who do you partner with, What do you work with? How much do you do yourself? I'm making decisions today that will impact us on compute in 12 or 18 months from now. It's very rare to be at early stage companies where you have to make decisions right now that impact you, you know, on physical infrastructure a year and a year and a half later. Have we seen that demand supply imbalance change? The world has still far more demand for GPU and GPU -like compute than that supply that's available. Larry Alessons said on a stage recently, it will require $100 billion to enter the race.

28:03That is the entry price. Do you agree with that as an entry price? If you want to become a hyperscaler that is able to put data centers all over the world, we're GPUs in it that are going to allow you to serve these models to everyone and infrastructure player, that's probably it. And that's probably just a starting point. If we look at the massive capex investments that all of the cloud companies are doing, they're far above $100 billion when you look at them over the course of a couple of years. Now in the race towards more and more capable AI closing that gap between human intelligence and machine intelligence, I think we are all pushing the frontier more and more possible.

28:42We're seeing how that gap closes as we're scaling up our models and scaling up our data. I don't think anyone has a definite answer of how many dollars is it going to take from here to there? If we knew that we knew the outcomes, we're all on the frontier of what's possible right now. You very, why is he called it a drunken ball fight? One of my friends who runs, one of the hyperskators, the other day said it's like the Manhattan Project. Where everyone's gonna actually try to get out, but no one actually can. It's fortified inside chips around the table and we've got to keep going. How far in all we do you think?

29:12Is this just the tip of the iceberg and there's a huge amount left to be spent by the incumbents? How do you see that? I think we need to separate spend from getting the world's most possible capable AI by closing this gap, getting to AGI closing the gap between human intelligence and the train. They're not the same thing because if you look at these models as investments that we're making to get intelligence out on the other end that needs to be economically valuable to end users. With lots of layers and applications and things in between, the model, the creation of models is Capix. The operating of them, the inference to running of them is Opix.

29:53But the Opix to run them requires extremely large skill physical footprint in the world. Very simple, if we would spend $100 making a model and it will ever return $2 or $3 in terms of value to the world, it makes no sense that what will punish it at one exist. The huge scale out that has to happen in the world, for AI to become something that can tackle all of our world's problems and can feed an all of from our software to our daily lives, requires huge footprint to run these models. It requires massive inference, and that means it requires data centers all over the world, close to end users, latency matters, and that requires a massive buildout.

30:30And I think this is one of the largest buildouts that we've seen in physical infrastructure since the last couple of decades in the cloud. In terms of that kind of build out of physical infrastructure, it was David Khan and trying to get exactly what he said. But he said, essentially, you will never train a frontier model on the same data center twice. The evolution of models is now outpacing the development of data centers. Do you agree with him when you hear that? We're in a world today where the amount of data centers that can hold and power and have enough energy to power increasingly magnitude order or larger of clusters is a very small number.

31:08I think he's absolutely right in this sense. Now, the data centers from two years ago versus the data centers in terms of size and power requirement that we're going to see in the next two years look radically different. Not just because the scale of number of servers and nodes that we're interconnecting. This is the difference between inference, right? For inference, we don't need all of the machines to be connected to each other in the same place. For training, we need them all to be connected to each other in the same room, in the same place. And so that massively changes what a data center looks like.

31:36I think the show has done so well because I ask questions. Why do you need that for training and not for inference? I think it's a good question. When we're scaling up the size of these models and we're training them on more and more data and we're using more and more compute for it at every single step that we're taking in the learning, every set of samples of data that we show the model, we need them to communicate with each other and share what they've learned across the optimization landscape. And so this is means that if I would, you know, have two data centers that sit far away from each other, the amount of information that they have to share with each other all of the different servers, and we're talking here thousands and tens of thousands soon of servers, you know, would make it so slow that it wouldn't be economically viable to train these models.

32:19Once I'm running a model, I'm using a lot less servers to run it. So think of it as having lots and lots of copies of the model during training over lots and lots of machines that at every time they see data they learn they need to communicate with each other to continue to improve in their learning. Can I ask you, when we look at the build out and the chips required and the compute required, to what extent is it continuing in video monopoly and to what extent it is the more even playing field? The dynamic that exists in the world today, we all had a depth of gratitude in video. When I started in this space in 2016, we were stacking 1080 Ti chips in racks of servers at our office.

32:56And Nvidia already back then understood that AI was going to be world changing. And no other company, the exception of maybe Google, had that deep of a realization, but Nvidia had massive conviction on this and continued to double down here and has made more and more incredible hardware. The company had fast followed and that was Google. It's why we're on the fifth generation of TPUs and the company had a fast follow -up on that was Amazon. The reason I mentioned DOS3 specifically is that they are all building extremely large volume of chips and constantly iterating on faster and better and better generations of chips or training and for inference.

33:32They're for me to three primary players in the race just from sheer volume of what they're producing in the fabs versus what they're bringing online to end users. Now we have some other companies in this space AMD, right, competitor Nvidia doesn't have their own cloud, right? Is reliant on being in this competitive nature from a price perspective, within video. And so their ramp up is entirely determined by the demand of the world wanting to use their chips. The demand of the world for AI for a Google and an Amazon with their own silicon is not the demand for chips, demand for AI. The way I look at this is that we're going to be in a world where those three and possibly new entrants are possibly AMD may be catching up, but I think really those three and Microsoft with their own silicon one day are going to be the Driving force in this industry to what extent has innovation in the space been held up by everyone Ozing and video is knee black will have to say that was quite happy black will was delayed Why because I'm training on H200s and so the compute that I brought online and at the end of August these 10 ,000 H200s means that the longer the next generation chips is delayed, it helps me in a competitive nature in the world.

34:43But also, there's a lot of marketing around the next generation of chips. And again, we have to separate training and inference. Pretty much what we've seen consistently with every two -year generation of training from Nvidia is about a 2X performance increase. But training is about 2X every two years. On inference, though, I think there's a lot of hope on Blackwell. Because it looks like for inference Blackwell might potentially unlock a much, much larger game. When Blackwell is released, are you forced, given the competitive nature of the landscape, to get Blackhold 2 and to spend hundreds of millions of dollars on Blackhold chips upgrading from H -200s?

35:20The way we think about this, and I think the way to think about it, is that these chips, when we become two times more efficient. The operations we're doing on them is still the same. It's matrix multiplications and additions and such. It's it's it's it's math that we're doing on these chips The black world generation for us from a training perspective doesn't unlock anything new It just means that we have to we can do more with a certain set of chips my H200s become less valuable in the world But it does not necessarily mean I have to go upgrade to the next generation We mentioned black well and what that will unlock I think a lot of people have been waiting for GPT -5 for quite a long time.

35:55When you think about what GPT -5 needs to deliver, what does it need to deliver to be a step -function change, and do you think it will? GPT -5, what are it won't deliver, isn't the question we're going to look back on in a decade from now. In a decade from now, we're going to look back to this moment, and it's similar, I think, how we look back to the early days of the computer, the early days of the internet, the early days of Google and others, and realize that we didn't fully internalize yet how much the world is going to unlock in value and abundance. We wrote this blog post when the fundraising announcement came out.

36:26We said, look, probably in this century, there's three mountains that humanity's going to climb. AGI is one of the mountains, the other is energy, and the other is space. And so I think as we're going to keep progressing, we're going to keep looking at the next mountain and we've from the top of that mountain, we look back and we're going to realize the ones before we're exponentially smaller. We mentioned like it's 600 million enough for you. I'm being slightly unfair here, but I don't understand how 6 billion is enough for OpenAI. You mentioned them in the hyperscalers, but when you look at what Zach has said he'll spend, what Google has said they'll spend on Larry Page saying that he's willing to go bust in the race to win, and then Larry Allison, I didn't understand how 6 billion is anywhere near enough.

37:07If we come back to the ingredients of the capabilities race, compute, tailon, data, proprietary planetary applied research. What we are going to find is that for compute, dollars have a direct one -on -one effect. But when we look at data, when we look at proprietary applied research and we look at talent, it is not as straightforward as dollars in magic, success out on the other end of it. I think we've had lots of examples in technology history already where we have seen these giants that seemed unbeatable, IBM in the early days of the personal computer. And so if we live in a world where we could perfectly translate dollars to successful outcomes, whoever can put more there is going to win in the race towards AGI dollars are critical for compute.

37:53And remember there's still time constraints. There's real world physical constraints of how large we can make these compute clusters for training. And it's that time and physical constraint, right? The constraint of what the chip is able to do, what the networking is able to pass through, that allow companies like us to have time and to do things and build massive advantages on the data, on the talent, and on the proprietary applied research. Is there such thing as proprietary knowledge in this market? Given the incestuous nature of jumping between companies and the knowledge that moves with those people, is there such thing as proprietary knowledge?

38:27I think you're fair to say that a lot of knowledge moves around. What do you make of large corporates funding these companies? Do you have corporates in Pulsat? If you look at our capital raise or last $500 million around, you'll see that there's none of the big hyperscaters, Google, Microsoft, Amazon, we're part of the round. Was that deliberate? That was deliberate from us. Because I think right now there's a future we see ahead of us for the world. And right now we see a path towards that future that we can do as a standalone company. And we have to acknowledge the fact that, you know, we're all in the same race.

39:03And so to me, you can make strategic decisions along the way where you decide to say, hey, we're partnering up together in one way or another, like in an equity, like you refer in equity relationships. It wasn't something that we had to do at this point. And it's something that, frankly, we very consciously decided not to do right now. There is one corporate that became part of our round and that was very deliberate and it was in video. And it's because we collaborate really closely with them. I think that the nature of large technology companies choosing to invest, you know, in Frontier AI companies is frankly the game theory optimal thing for them to do.

39:39Do you think we will continue to see the consolidation of small players like inflation, like adapt, like character, continue to get acquired by the large incumbents? I think there's very few left to be acquired to be very honest. Who is left? Go here. One. Rika. Rika, R -E -K -A. Small but very capable team from what I can see from the outside. Where are they? I believe they're in Europe, Mistral. And I'm hard pressed to think of more companies. I mean, X -A -I is very unlikely to be acquired, but they're a very capable player in this space. And look, and I focus, of course, on work in large language models and work towards AGI.

40:16So I think there is a very few companies that today are sufficiently far along and that are still left. You can buy OpenAI 156. You can buy an... and actually, the kind of of your investors said, this was a great question, which I agree with. So you can buy OpenAI 156, Anthropic at 40, which is their suggested new round, or X .AI at 24, which one do you buy and why? It is an unfair question, but it's a good question. I would love to spend a day with the current leadership team of every single one of these and then make a decision. They each have inherent advantages to them. XAI has understood that compute mattered and put an incredible team there and did what no one really had done at that speed.

41:02They built a 100 ,000 GPU, 332K interconnected clusters in Tennessee in the span of months. Once, XAI showed up with Elon strength, the ability to build physical infrastructure incredibly fast in the world. Open AI had the incredible chat GPT moment and has built this incredible business around both chat GPT and the usage of their APIs and is clearly ahead and revenue as others like has publicly stated. And then I think Anthropic has incredible, thoughtful researchers and a very rigorous approach in what they do, a very rigorous scientific approach in terms of moving things forward. and so, well, I can see strengths in all three of those.

41:41It would really be a day with the leadership team of each to determine where I will put my own money. That's awesome. Luckily, it's not your own money. Your adventure investor for this. And so which one would you go for? I'm not a yellow venture investor. What would you do if you were Sam today? You just raised six billion. I think Sam and OpenAI have understood the importance of compute and have understood the importance of data. What I imagine that $6 .6 billion is going towards is exactly those two things. Where from the outside, I think it is tricky to be Sam today. I think it's tricky to be Sam today because general purpose models that aim to be everything for everyone is a incredibly competitive market and you find yourself with incredible pressures from all sides.

42:26You're building a platform and a consumer product in exactly the same time. and more so than that, you're building a consumer product that from the outside is seeming to be for everyone. And I think that's a really hard thing to do. Funny, there's a brilliant Elon Musk interview. I think it was with Rogen, and he says that a lot of people think that life to be me. It's not that fun. That one's that with me. And you actually really hear the sadness in his voice. It's when I think about a lot to be very honest, and you're getting me even with it. It's probably one of the only things you could have said that would have got me a bit emotional because I think about it a lot.

42:57I saw it many years ago and I think I understand what he means very well. I was earlier today with it with a founder. I really respect. And we're funny of talking a little bit about this is that building what we're building. It's not a choice. It's an obsession. And you bring everything you've got to it. And we were talking about, hey, how do you deal with waking up at three in the morning and your mind doesn't stop racing going back and forth and then sharing each of our techniques and probably going back home tonight and trying them. I think Elon is one one of the most impressive examples of someone who has done this or such a prolonged amount of time at moments in time when the entire world, you know, refused to align around his view.

43:38I think there's companies in the world that get built because they were at the right moment at the right time and there's companies in the world that get built that shouldn't have the right to exist. Everything is against them. And I think Elon has done that not once. He's done it multiple times. and it brings me to another quote that I heard on an interview from him or about him as Peter Teal. And Peter Teal said, when we all worked with Elon, we thought he was crazy, we'd take so much risk. And then he went and started, you know, Tesla and SpaceX. And we thought he was even crazier. And if one of those two companies would have worked out, we would have said he got lucky.

44:10But both of those companies worked out and beyond and to extremes. But if something Elon understands about risk that the rest of us don't. And for me, that's the second quote that's been on my mind most of this year. Another fantastic teal quote is when he compared actually crypto and AI. He said that crypto specifically really embodied decentralization. And if that were the case, then AI would embody centralization. When I was in high school in 2008, my very first startup was a virtual digital currency. My views on crypto have changed a lot over the years. The notion of decentralization and what it can mean for the world is incredible, are incredible ideals.

44:47The problem I think that we've seen in crypto is a quote that I learned, it might have been my high school economics professor, and he said, bad money drives out good money. If you think about this in environments, when bad actors come in, it drives out the good actors, because we want to be in environments with other good actors. And I think the promise of crypto started from great actors and found itself due to the incentives of the ability to make money very quickly in lots of distorted ways to bring a lot of bad actors to it. The bad actors have driven out a lot of the good actors over time.

45:19There's still a true amazing idealist in that space. Now, the thing in AI that we don't have that, we have a set of people around the world who fundamentally might disagree on how to get there, but all see that in 10, 15 years we're going to look back and realize we have this incredible shift in the world by being able to close the gap between machine and human intelligence. And so while that might drive today some centralization because of the sheer amount of resources required that are scarce, right? Capital is the least scarce part of this, right? The talent is scarce. The proprietary, you know, applied points of view and research, those are scarce.

45:55I think that does lead itself to a small number of companies. I think we've seen that over and over in history. We look at when the massive boom of automobiles, right? The hundreds of automobile companies that started and how many actually survived. We've had this over and over again and so I don't think this is something new. What I would like to see is that it's not just Google Amazon and Microsoft. It's going to be an open -eyed and throughopic of pull -side and a set of companies who are able to get that massive escape velocity needed to sit alongside those companies and build the next generational businesses.

46:31You mentioned bad actors there and it made me think of tourists. And when I thought of tourists, some reason I thought of like bluntly and this sounds awful, but like people who are not in it for the long term or who are in it for a story. And a lot of public company CEOs and large company CEOs, and this is not tourists or bad acts of the tool, but they have to tell an AI story and they have to show that they are spending money on AI and innovating in some way. My question to you is when you, you know, you mentioned obviously the GTM team build out. When you think about the revenues that we're seeing today, are we well -passed the experimental until budget phase, are we into true deployment, true commitment?

47:08How do you see that from enterprise? So I think it depends on the use case. There's lots of experimental use cases still. Inters use cases that are far past the experimental side, AI for software developers. I don't think anyone in the world any more questions that software development moving forward is gonna be a, but foreseeable future developer led AI assisted world and increasingly AI assisted world. which use case do you see that you least understand or think has long term potential? I think there are use cases that are commoditizing very quickly. Speech recognition, I think, is one of them image generation is one already where we're seeing more and more commoditization over time.

47:48You've mentioned talent before being such a crucial part that we haven't really unpacked because we have discussed the models, the data, the compute. The talent perspective is one that you also have taken quite a different approach on. You know, you're a PN based company. The big question that a lot of you are in the past is said that we have discusses. Why did you decide to keep this as a European based company? I want to set the records straight. We're an American company and we've got incredible people from all the way from San Francisco to Israel. But a decision that we made early on, Jason and I, my co -founder, and I, we were planning on building this company in the Bay Area.

48:22And we did the work in the first days of the company and the work was, let's make a list of everyone we think from both that we knew and also externally on research papers and GitHub repos that we think potentially could be great for us. The list ended up with about 3 ,300 people. A lot of work done. 3 ,300 people that we saw ranged from having experience on distributed training to GPU optimizations to work on data, to reinforcement learning, experience with large language model. The whole breadth of what it takes to build what we're building from a model perspective. In that list was a location column.

48:55And as you can expect, the number one representative, geo in that was the Bay Area, not even the United States. It's purely the Bay Area. But what really was striking for us is that there was a huge part of that list that was not in the Bay Area. It was spread out across Europe and Israel, from the UK to Switzerland to Tel Aviv to Amsterdam, you know, Harris, like all of these different places. Well, we couldn't see a clear, deep talent concentration in one place. Probably UK actually being the one with the largest talent concentration. We didn't realize that it was probably worth spending some time talking to people there.

49:30And so I went and had conversations and we realized one thing. We said, there's incredibly capable people here who want to stay here geographically. They don't want to move to the Bay Area to join some of the other companies in the space, but they're not finding massively ambitious young companies that have huge visions to join. And so we saw it as an advantage, right? To the thing, the four things in that, you know, capabilities race, we need to build unfair advantages for every single one of those. And so we said great, let's build up talent, you know, here on this continent in Europe, as as we will in the United States.

50:03And frankly, I'm very grateful that we did. How many people do you have in London? So London for us is about 15 people. How many in Paris? Uh, two. Sorry, maybe I'm not allowed to get that. Paris is meant to be the AI hub of Europe now. The way to think about it is where has talent historically been even pre -chatchy -be -team moment in talent in AI and who helped build that talent in this space? The number one company we have to give credit to is DeepMind. DeepMind build an incredible talent base. They built it out of London. Meta did some work in building a very incredible talent base and it did it between London and Paris.

50:39But in terms of when you look at it from a numbers perspective and sheer size of people, Google separately and deep -mind as part of Google had made much larger investments. And then there's another talent pool that we do often talk about publicly that is just absolutely extraordinary, which is Yandex. Yandex built an incredible company in Russia with some of the world's most capable researchers and engineers, many of which have since left Russia and have kind of become a diaspora all over Europe. When we look at that talent, when we think about work ethic, it's one thing which Europe is often chastised for.

51:10So in terms of what life balance, how do you approach that and feel about implementing standards of what with teams? There's a tweet, and if I recall, it's from Aaron, Levi from Box, early on in post -chat GPT moment, and he wrote something along the lines of, if you feel like you're working extremely hard on reasonable hours as AI is now booming, you're probably right to do so. Because it's in these first years, and I'm probably going beyond what the tweets are, but in the first year is it's where the table gets set. Who has earned the right to be in the race to HGI? The way that I've always looked at this is my personal perspective and so is my co -founder and so is Margaret Rita, has put us in a place where we're gonna look back on this moment 10 years from now, just like we would look back to the moment of mobile, internet, and realize that that was the moment where the table got set.

52:02And you do not wanna look back on that moment and not have given it everything you've got But because it's a race and look, most startups are not races. Most startups are against yourself. But AGI is a race and so our view always has been the team that we build is a team that is deeply passionate to be in that race. And frankly, when you decide to join a race and you're upfront about it, you decide to be trying to become the gold medalist and swimming. That means that there are sacrifices that come with that. You don't get to have it all. And so that's something that we've been super open about with people from day zero.

52:35It's on our first intro call we talk about like do you want to join a race? And frankly, I have found no shortage of people in Europe that want to do that. I think there's a there's a stereotype about Europe, but the fact of the matter is the people who want to join races and do truly their life's work, they're built differently and you can find them all over the world in every single country. You just got to do the work to find them. Chase Cohen had an interesting kind of stat. In the two years subsequent, the founding of Netscape, 1 % of the enterprise value of internet companies was created.

53:0899 % was in the chasm between that subsequent two years and now, meaning actually it is such a long process and so much is to come. Does that not go against the idea of it being a race? And is now different. You know, history doesn't repeat itself at rhymes. I think it was Mark Twain. I think that might be the mistake that we're possibly making looking at the past. And the reason that is is because we're on the next potential in terms of technology promise. I think in 1996 with Netscape, if I'm getting the year right, there wasn't this amount of people and capital that understood what the future might look like in the next 10 years.

53:45And it took some time to get there. Now I could be wrong about this. Another thing that I could see as a possible avenue of why I tend to disagree is there's a big difference between what was required to be built in 1996 versus what's required to be built today. Bring it all the way back and try to steal man the opposite side of the argument is maybe it's exactly that and that the next couple of years are about these massive capabilities that were moving the world closer towards AGI. And then when you look at the following five or ten years, it's true. The huge economic value that's going to come from that will use, of course, surpass the economic value that we have.

54:23I think DeGeneres is going to continue to surpass on the exponential that we're on, but what I don't agree with the companies that are being built today will not have a set of companies amongst them that will become the giants of the future that have helped enabled us. I think the concern that I have is you will use a huge amount of dollars to get to a level of advancement and technology that will then be leveraged by other people to building incredibly valuable companies. If we look at battery in particular, whether it's going to be an unbelievable breakthrough throughs in battery technology by companies that you will never have heard of that got acquired went out of business and then were bought for their IP and it's a case of actually it takes a huge amount of money to uncover new breakthroughs and then those breakthroughs are taken by someone else.

55:06So if we take the battery analogy, I would actually think a little bit about BYD started as a battery company. There is the largest volume of electric car sold in the world and I do think there is a lot to be said about deep vertical integration. Look at poolside. We're building foundation models with a mission towards AGI right now in the world focused on bringing more and more capabilities of AI to software development, building a truly undentend business. Because I agree with you that the value is not going to only accumulate at the model layer. It's going to accumulate all the way to the end user.

55:37And so in our point of view, the way that we kind of, I think, get to avoid, you know, what the future plays out in your hypothetical scenario is just by truly doing it end to end. But I still actually look at this thinking that there will be more value built on top of us in the future than what we can possibly unlock only ourselves last one And then we'll do a quick fire you mentioned B .Y .D. unbelievable journey Our China really to is behind the you know they're not there's a couple of interesting things that might not be as obvious unless you're in our industry is the Research that still gets published openly that doesn't get help back that is most interesting is all coming out of China in vast spades and majority, something that wouldn't necessarily be obvious.

56:15But if you think about the game theory optimal thing to do, because they're not on the forefront of the world, seen of AI, actually opening up some of that research is the game theory optional. You know, optimal thing to do to be able to continue to act track talent, because that's really what opening up your research does, right? That attracts talent to you. I think China is at an incredible level of capabilities, and in no way could be discarded or thought of as yours behind on AI or AGI progress. We're working on technologies that we can see have massive societal impact. I think it's really important to be good stewards of that technology and that progress.

56:50And I think part of that is acknowledging that what we know about is the technology. What we know about is our users, our customers. But we should be careful in terms of trying to know what's best for the world and how to think about massive geopolitical conflicts and things like that. And so what I have said in the past is that the best thing that we can do is the West is to keep making it as attractive as possible, or talent from China we consider as a competitor on a very large skill to come to our countries. The easier we make it for one of those four major ingredients in the capability racing, frankly, one of the most important ones to help us accelerate, I think is probably the most practical advice I can give.

57:32I'm gonna do a quick fire on because I can talk to you all day. So I say a short statement, you give me your immediate thoughts. So that's how I can. Let's do it. Let's try it. So what have you changed your mind on most in the last 12 months? I think in the last 12 months it's a continued realization of the importance of scale, of data and not only compute. Do you regret not selling source to GitHub? It was probably the dumbest financial decision of my life, considering it was an all -stock offer and get up sold to Microsoft, I think less than a year later. And it would have three acts, the price.

58:03Far higher than that. But I'm really grateful I didn't. Why? I'm sitting here. Would you have done poolside if you had sold sourced with complete honesty? I think the question is what could have been able to do continuing on sourced mission? Because sourced mission was the mission we're talking about today with poolside. And back in 2016, there were very few people who believed there was ever possible for AI to write code. But no, I don't think there's really no regrets there. I wouldn't be sitting where I am today and I don't think I would have become the person that allows me to go build poolside today.

58:34And frankly, I'm really grateful that that event did happen because it's how I met my co -founder. Right? It's how I met Jason. He was the CTO get up at the time and it started this many year conversation on what the progress in AI looks like and it's applicability to software development. What do you think is the biggest misconception of AI in the next 10 years? That it's going to the progress is going to halt. What would cause progress to halt? Global conflict that disrupts the supply chain of chips. That is fucking me see for a quick far I'm sorry, I can't recall. I'm fat, but in 60 seconds, if you could have any board member in the world, who would it be?

59:09Mark Zuckerberg. Why? I think we all should give a lot of credit to Mark Zuckerberg in terms of having built an incredible company with a lot of conviction on what the future would look like when most people didn't agree with him. If you look at what he's done on AR and VR in the last decade, right, from buying Oculus to where it is today, when most of the world just wanted him to stop, that required an an incredible ability to have conviction for a future that's going to massively change the world, to get to AGI requires an incredible conviction for a technology to change the world. He's one of the people who has done that and someone very different than the people I have currently on our board.

59:47What's the worst thing that could happen for AI with regards to regulation? Regulation that fundamentally halts progress for small companies. The reality of regulation in many cases is that it becomes an expensive bureaucratic overhead, and that harms the most young startups. It doesn't harm companies that have raised massive amounts of capital. What specific regulations should be taken away? The world is finding a balance, and the balance that I'd like to see that the world is, and I think it's moving towards it, is to regulate the end -user application of AI. Just like we've regulated the end -user applications of any type of technology before, It's not the database that does harm, you know, how it's used.

1:00:28And so for me, I would love us for us to continue to hold companies massively accountable for the end use of their technology to users, to consumers, less so trying to put limitations on how much compute you're allowed to train on. We're building tools that are closing this gap between human capabilities and machine intelligence. We are not building the Terminator. So what do you think of DST's Yuri Melna? I really like Yuri. I got to know him through several occasions over the last year, but I never got to understand him till I read his book. Most people don't realize that Yuri has a book or a manifesto to find it online for free.

1:01:05And earlier you hurt me referenced looking from one top of the mountain to the next, and that's actually it's a metaphor that I took from Yuri. And because I think what Yuri has stated and said is that the sheer importance of scientific progress. What he often refers to is humanity's story. We have this incredibly special thing on this tiny little blue marble in the universe that is so immensely large. I think he really embodied the notion of, we cannot let that flame die out. To him and to Elon's credit, sharing the fact that the world is one of the ways of doing that is to become a space -faring civilization.

1:01:44His whole view is that progress that's happening towards AGI helps us to be able to take this special thing that we have, you know, humanity and spread it across a universe. And there is people who say this, and there's people who believe it, and who understand how special this is and what it can mean. And Yuri's one of those people, but at the same time, he's been an incredible capitalist. He's been an investor that, from what I can see from the outside, that understood that huge technology waves were happening all over the world in different places. And it was a truly global investor. What he saw was happening in the US, he would invest in India, he would invest in Indonesia and across Asia and all of these different places.

1:02:24And I think very few people from the investment landscape have taken strong conviction on what technology is going to bring in the next 10 years and then found a way to place bets all over the globe. Have people tried to buy poolside? No comment. Boat living. Biggest pro, biggest con. So I think we have to put some context here. Several years ago, me and my better half and our golden retriever, we were living in a fancy apartment. We decided to get an old sailing boat to fix up together. We put this old sailing boat in the harbor, ceiling was falling apart. I mean, we did together and we were passionate about it, and we loved being on the water, loved being on open ocean in particular.

1:03:00At some point, we were spending more evenings and nights on the boat where the ceiling was falling apart than the fancy apartment. And we looked at each other and we said, why do we still have the fancy apartment? Now, we got a slightly nicer boat after that and home became a sailboat. And I've gotten this question from friends never, I don't even know how you know this by the way, I've gotten the question from friends For me, I think the same thing about living on a boat is exactly the same thing of wanting to move the world You know forward and and I think a part of it is the drive of a freedom Right the freedom of options the freedom of like what can what can adventure in the future hold and then the simplicity of not I've just being with you know the person you love and your dog in a small space and you've got your good internet connection you do your work, all the other stuff doesn't matter.

1:03:43The watches, the cars, the houses, none of this stuff is ever going to be the things you think back on. It's the journey. And for me, a boat kind of allows for all of that. It keeps life simple. And it gets me, allows me to fully immerse myself in poolside. And I have to admit, since I started this company, I've been home less than a month. And so it's been a lot travel. But it's the journey that matters. It's not the material stuff. What specifically did you think mattered that no longer does matter? Stuff. Did you go through a phase of getting stuff? I did. I was lucky. I went through it in my early 20s and a lot of stuff.

1:04:18And then I got rid of all of it. For me to share realization is that it's the journey with the people. The outcomes that we want to see in the world, they're part of the obsession. They're that if you can see a future that looks like one that ends up being this incredible future, you want to build it. But that moment once you reached out, which will always be never moving, goal is not the interesting moment. The interesting moment is every single day with the people. And I think for me, I just learned more and more over the years that I love people. And I love the people I work with, I'm my team.

1:04:47I think in every single person, you're something incredible. And you cuddle the stuff, you cuddle the money, and you pick the hardest, biggest thing you could possibly, you know, focus your life on. And then do it with amazing people. You get to have this incredible experience. Penultimate one is investors, we are at investment. I'm always, there's always a section called pre -mortem, which is projecting ahead of time a reason why a company won't work. Do the right pre -mortem on poolside? What is the number one reason why it wouldn't work? We're in a race. If we stumble, we put our foot off the gas pedal, we lose.

1:05:22And I think in any race, there's 101 places you can stumble. We don't get the luxury of stumbling on the capabilities race or to go to market race. We have to be excellent in both. If we make a misstep on either of those, we can fall behind. And if we fall too much behind, we're no longer in the race and we don't matter. What question, final one, what question are you not asked often or ever? You should be asked. I'm surprised how I usually don't have what motivates you. And people ask about the business, they ask about the outcomes, they ask about the future you see. Very, very few people ask you about your why.

1:05:55And I think that's probably the most important question. When we talked about earlier about looking at those three companies, my first question to all of them, you know, and I have asked just some of them is the why when you why do you want the eventual outcome that you are pursuing so the ciphercy why do you do what you do I think that says a lot about a person and I think and at the end of the day in any race in any ambitious endeavor it's the people that make it happen it's not the dollars in the account those are the resources and inputs that you need and it's the people and so for me the why is a really important question to ask people.

1:06:29And then I think it goes back to the twirth, is five wise. Why do you do what you do? I say, I realize that I'm not calm or at peace when I'm not working on the hardest possible problem that lends to what I care about in the world. Why? Because my brain's never going to turn off. I'm always going to wake up at 4 in the morning. It's always going to be this constant going over and over of like what you care about. And when I've done that in the past, or things that weren't the hardest possible. The most ambitious possible thing in terms of what mattered in the world, the thing that mattered the most.

1:07:04I just didn't feel at ease. And now probably never worked. I've always worked hard, but I've never worked as hard as I have at Bullseye. Well, it's intense and it's stressful. I feel peace. Listen, I cannot thank you enough for doing this. I so appreciate the speed of doing it after the round. And this has been so much fun to do. Thank you, Harry. I really appreciate it. My word, I love doing that show. If you want to watch the full interview, you can find it on YouTube by searching for 20 VEC where you can see ISO in the studio. It was a fantastic one. But before we leave you today, this episode is presented by Brex.

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1:10:20As always, I so appreciate all your support and stay tuned for an incredible episode on Wednesday with Jeff Wang at Sequoia who runs the $10 billion hedge fund. An incredible show to come there.