Eiso Kant, CTO @Poolside: Raising $600M To Compete in the Race for AGI | E1211

(instrumental music plays) Who has earned the right to be in the race to AGI? And 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. You do not want to look back on that moment and not have given it everything you've got, because it's a race. And the latest $500 million round translates to us being able to be an entrant into the race. We don't get the luxury of stumbling on the capabilities race, or the go-to-market race.

Ready to go? (instrumental music plays) I so... Dude, I am so excited for this. This is also the first time that we've actually met in person. You are far more incredibly good-looking in person, so thank you so much for joining me today.

Well, thank you, Harry. It's a pleasure to be here. And it's- glad that we finally met in person. It's been a minute since we've known each other.

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.

So Poolside's in the race towards AGI. We think the future is gonna play out that the gap between machine intelligence and human-level capabilities is gonna continue to desc- decrease. And... But our- 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 s- a set of foundational beliefs that we have, that I would say are different than some of the other companies in this 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 that is 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 gonna 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-scale data, and we're compressing it into a neural net, and we're forcing generalization and learning. And this has led to things like incredible language understanding in these models. But it's also led to things where we look at it and we say, "These models are kind of dumb. Why aren't they able to do X, Y, or Z?" And our point of view is that the, the reason why they're not able to do X, Y, or Z has to do with how they learn, and the most important part, I think, of what I said is the scale of data. When we have web-scale data, we can get language understanding. But when we have areas where we have very little data, models really struggle to learn truly more capable areas. And I mean improvements in reasoning, improvements in planning capabilities, improvement in deep understanding of things. And so, while as humans we don't require so much data, the way to think about models is that they require magnitudes order more data to learn the same thing. Our focus is on software development and coding, and it's for a very specific reason. The world has already generated an incredibly large dataset of code. Uh, to put a little bit into context, like usable code for training, so what we refer to as about three trillion tokens, and if you look at kind of usable language in English on the internet for training, we're talking about anywhere between 10 and 15 trillion tokens. There's a massive amount of code that the world has developed. Over 400 million code bases are publicly on the internet. So why don't we have this incredible AI that's able to already, you know, do everything in coding? It's because coding is not just about the output of the work. Right? The code that we have online represents the final product, but it doesn't represent all of the thinking and actions that we took to get there. And that's the missing dataset. The missing dataset in the world to go from where models are today to being as capable as humans at building software is the dataset that represents being given the task, all of your intermediate reasoning and thinking, the steps that you do, the code that you write and try to run, and then it fails, and you learn from that- those interactions, and all the way to kind of getting that final product. And that intermediate dataset, that's what Poolside exists on creating.