Eiso Kant

And we want to get to a world where developers can work with models that are as capable as them and potentially even one day more capable. Now, the reason I mentioned this is so we've got the human capability aspect, right? What's the gap that's there? How economically valuable is the domain?

Then there's a next area I think that you have to ask yourself is how easy is it going to be to close that gap? And that comes down to data. Where can we get extremely large scale web scale data to be able to close that gap in areas where the intelligence gap is really big? Because the bigger the gap in intelligence today, the more data that we need to close it.

Then there's a next area I think that you have to ask yourself is how easy is it going to be to close that gap? And that comes down to data. Where can we get extremely large scale web scale data to be able to close that gap in areas where the intelligence gap is really big? Because the bigger the gap in intelligence today, the more data that we need to close it.

Then there's a next area I think that you have to ask yourself is how easy is it going to be to close that gap? And that comes down to data. Where can we get extremely large scale web scale data to be able to close that gap in areas where the intelligence gap is really big? Because the bigger the gap in intelligence today, the more data that we need to close it.

If you kind of use this as a, where can we find the data size related to how large the gap is between human and machine intelligence, and how economically valuable it is in the real world already, I think the intersection between those is the places where companies like us get to exist.

If you kind of use this as a, where can we find the data size related to how large the gap is between human and machine intelligence, and how economically valuable it is in the real world already, I think the intersection between those is the places where companies like us get to exist.

If you kind of use this as a, where can we find the data size related to how large the gap is between human and machine intelligence, and how economically valuable it is in the real world already, I think the intersection between those is the places where companies like us get to exist.

GitHub 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. GitHub 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.

GitHub 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. GitHub 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.

GitHub 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. GitHub 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 is an inherent advantage from a capabilities race perspective. Another thing that we frame in our company over and over again is there's a capabilities race in the world. And to your point earlier, we say there's four things that matter. Agree with you on the three, but I'm going to add one. Compute, it's data, it's proprietary applied research, the algorithms, and talent.

And so there is an inherent advantage from a capabilities race perspective. Another thing that we frame in our company over and over again is there's a capabilities race in the world. And to your point earlier, we say there's four things that matter. Agree with you on the three, but I'm going to add one. Compute, it's data, it's proprietary applied research, the algorithms, and talent.

And so there is an inherent advantage from a capabilities race perspective. Another thing that we frame in our company over and over again is there's a capabilities race in the world. And to your point earlier, we say there's four things that matter. Agree with you on the three, but I'm going to add one. Compute, it's data, it's proprietary applied research, the algorithms, and talent.

Talent is absolutely key in this industry. 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.

Talent is absolutely key in this industry. 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.

Talent is absolutely key in this industry. 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.

No. $600 million that we've raised till date and the latest $500 million round is translates to us being able to be an entrant into the race.

No. $600 million that we've raised till date and the latest $500 million round is translates to us being able to be an entrant into the race.

No. $600 million that we've raised till date and the latest $500 million round is translates to us being able to be an entrant 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 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.