The Exchanges

Q A lot of people break it down as compute data and then kind of models themselves. Well, I mean, yeah, compute data and then algorithms, really. So if we take those three, how do you think about what the biggest bottleneck is today in the progression of models? Is it the data that we mentioned, or is it one of the other two?

A We are making, in our space, Especially, I think, post the ChatGPT moment, like incredible advancements in the algorithms that are making learning more efficient. Internally, I, I have this thing that I say to the team, and they're probably tired of me hearing because I say it every single day. I say, all the work we do on foundation models, on one hand, is improving their compute efficiency for training or running them, or on the other hand, improving data. Now, the way to think about the algorithms and the improvement of compute efficiency is that's table stakes. All of us, OpenAI, Anthropic, Google, et cetera, are doing this, and we're just constantly improving here. And it's engineering and research combined. But the real differentiation between two models is the data. But compute matters tremendously for data. Because if you think about poolside, and we spoke about how do we get this data, and I mentioned the word synthetic, it means that we're generating it. It means that we're using models to generate data. To then actually use models to evaluate it, to then run it. And so, compute usually matters on this side of the generation of data. But once we have all of this data, where we started there, we spoke about, you know, neural nets essentially being compression of data that forces and generalizes learning. Now, when we have small models, We are taking huge amounts of dat…

AI assessment note: “the real differentiation between two models is the data”

Q interesting kind of stat, and it was in the two years subsequent the founding of Netscape, one percent of the value, enterprise value of internet companies was created. 99% 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?

A So I think, you know, there's the classic quote of, ah, you know, history doesn't repeat itself. It rhymes. It's failing for me from, from who it was. I think it was Mark Twain. And I think that might be the mistake that we're possibly making looking at the past. And, and the reason that is, is because we're on an exponential in terms of technological progress. 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. And it took some time to, to get there, and, and now I could be I could be wrong about this. Another thing that I could, 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. You know, if I, if I bring it all the way back and try to steel man the opposite side of the argument is maybe it's exactly that. And, and, and that the next couple of years are about these massive capabilities that we're moving the world closer towards AGI. And then when you look at the following five or 10 years, it's true. The huge economic value that's going to come from that will, of course, surpass the economic value that we have. I think the economic value is going to continue to surpass on the exponential that we're on. But what I don't agree with i…

AI assessment note: “the reason that is, is because we're on an exponential in terms of technological progress.”

Q Now, I want to just dive straight in. I think there's a lot of people looking at Poolside in the news and seeing the new round going, what is Poolside? Can you just provide some context? What is Poolside? What do you do? And let's start there.

A The poolside's in the race towards AGI. We think the future is going to play out, that the gap between machine intelligence and human level capabilities is going to continue to decrease. And, but are the path towards that, in our opinion, is by focusing on building the most capable AI for software development. And all of this comes back to a set of foundational beliefs that we have that I would say are different than some of the other companies in the space in terms of where both research is heading and where capabilities are heading. And so the term AGI is a loaded term. And the way that I like to kind of take the definition of things most commonly used is that at some point we are going to be in a world where across all sets of capabilities that we have as human beings, Machine intelligence is going to be as capable and if not more capable than us and surpass us. Now, our point of view is, is that that world is still quite a bit out and that we are actually going to end up in a place before that where we see human level capabilities in areas that are massively economically valuable and can drive abundance in the world for all of us that are not going to be equally distributed, not for every single thing. And what I mean by that is that If you think about foundation models today, and I have a kind of simple mental model about them, which is that we are taking large web skill d…

AI assessment note: “focusing on building the most capable AI for software development.”

Q interesting kind of stat, and it was in the two years subsequent the founding of Netscape, one percent of the value, enterprise value of internet companies was created. 99% 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?

A So I think, you know, there's the classic quote of, ah, you know, history doesn't repeat itself. It rhymes. It's failing for me from, from who it was. I think it was Mark Twain. And I think that might be the mistake that we're possibly making looking at the past. And, and the reason that is, is because we're on an exponential in terms of technological progress. 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. And it took some time to, to get there, and, and now I could be I could be wrong about this. Another thing that I could, 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. You know, if I, if I bring it all the way back and try to steel man the opposite side of the argument is maybe it's exactly that. And, and, and that the next couple of years are about these massive capabilities that we're moving the world closer towards AGI. And then when you look at the following five or 10 years, it's true. The huge economic value that's going to come from that will, of course, surpass the economic value that we have. I think the economic value is going to continue to surpass on the exponential that we're on. But what I don't agree with i…

AI assessment note: “reason that is, is because we're on an exponential in terms of technological progress”

Q Would you have done poolside if you had sold sourced?

A I think the question is, what, what could I 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 it 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. And frankly, I'm really grateful that that event did happen because that's how I met my co-founder. That's how I met Jason. He was the CTO at GitHub at the time, and it started this many-year conversation on what the progress in AI looks like and its applicability to software development.

AI assessment note: “I don't think I would have Become the person that allows me to go build poolside today.”

Q Paris is meant to be the AI hub of Europe, no?

A Where has talent historically been, even pre-Chatsuppie team moment, right, and 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 built an incredible talent base, and 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. But in terms of when you look at it from a numbers perspective and sheer size of people, I think, you know, Google separately and DeepMind 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.

AI assessment note: “Where has talent historically been, even pre-Chatsuppie team moment, right, and talent in AI?”

Q What would you do if you were Sam today? You just raised six billion.

A Look, I think Sam and OpenAI have Understood the importance of compute and have understood the importance of data. And what I imagine, you know, that, that 6.6 billion dollars is, is going towards is exactly those two things. 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 incredibly competitive market. And, and you find yourself with incredible pressures from all side. Uh, and you're building a platform and a consumer product at 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.

AI assessment note: “I think it is tricky to be Sam today because general purpose models”

Q What would you do if you were Sam today? You just raised six billion.

A Look, I think Sam and OpenAI have Understood the importance of compute and have understood the importance of data. And what I imagine, you know, that, that 6.6 billion dollars is, is going towards is exactly those two things. 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 incredibly competitive market. And, and you find yourself with incredible pressures from all side. Uh, and you're building a platform and a consumer product at 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.

AI assessment note: “I think it is tricky to be Sam today.”

Q Yeah, we mentioned Blackwell and what that will unlock. I think a lot of people have been waiting for GPT-V for quite a long time. When you think about what GPT-V needs to deliver, What does it need to deliver to be a step function change, and do you think it will?

A I think GPT-V, what it won't deliver, isn't a 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. We said, look, we're in this century. I think there's three mountains that humanity is 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, from the top of that mountain, we look back, and we're going to realize the ones before were exponentially smaller.

AI assessment note: “what it won't deliver, isn't a question we're going to look back on”

Q Yeah, we mentioned Blackwell and what that will unlock. I think a lot of people have been waiting for GPT-V for quite a long time. When you think about what GPT-V needs to deliver, What does it need to deliver to be a step function change, and do you think it will?

A I think GPT-V, what it won't deliver, isn't a 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. We said, look, we're in this century. I think there's three mountains that humanity is 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, from the top of that mountain, we look back, and we're going to realize the ones before were exponentially smaller.

AI assessment note: “what it won't deliver, isn't a question we're going to look back on”