Lisa Su

How could you possibly... compete against Intel because they had 10x the people that you do. And I was like, you know what? It's not about how many people you have. It's like, do you have good ideas? And in some sense, we were constrained because we had less people. We had to out-innovate and come up with new ideas and a new way of doing things.

I think that's what we saw a bit with the DeepSeek moment. they didn't have the compute. They didn't have the gobs and gobs of compute that we have at USAI Labs. They didn't have the chips. They didn't have the compute. And so what they had to do was think of a different way to innovate.

I think that's what we saw a bit with the DeepSeek moment. they didn't have the compute. They didn't have the gobs and gobs of compute that we have at USAI Labs. They didn't have the chips. They didn't have the compute. And so what they had to do was think of a different way to innovate.

I think that's what we saw a bit with the DeepSeek moment. they didn't have the compute. They didn't have the gobs and gobs of compute that we have at USAI Labs. They didn't have the chips. They didn't have the compute. And so what they had to do was think of a different way to innovate.

And I mean, the truth is, when you're trying to do something, let's call it a little bit after the leaders, it takes less resources. And what they did was they aggregated some very good ideas that came from other top AI labs. What I think we learned from it is you can slow down with restrictions, but you're not going to stop progress.

And I mean, the truth is, when you're trying to do something, let's call it a little bit after the leaders, it takes less resources. And what they did was they aggregated some very good ideas that came from other top AI labs. What I think we learned from it is you can slow down with restrictions, but you're not going to stop progress.

And I mean, the truth is, when you're trying to do something, let's call it a little bit after the leaders, it takes less resources. And what they did was they aggregated some very good ideas that came from other top AI labs. What I think we learned from it is you can slow down with restrictions, but you're not going to stop progress.

I mean, you have to imagine that there are really smart people out there across the world. And if you give them a set of constraints, they're going to innovate around it.

I mean, you have to imagine that there are really smart people out there across the world. And if you give them a set of constraints, they're going to innovate around it.

I mean, you have to imagine that there are really smart people out there across the world. And if you give them a set of constraints, they're going to innovate around it.

It forwarded something that I really believed, which is, Two things. One is that there are lots of ways to solve problems. And, you know, you learn different ways. The second thing, which actually was really positive, is that we saw the power of open source. Like, I'm a big, big believer in open source because it allows people to collaborate on a problem.

It forwarded something that I really believed, which is, Two things. One is that there are lots of ways to solve problems. And, you know, you learn different ways. The second thing, which actually was really positive, is that we saw the power of open source. Like, I'm a big, big believer in open source because it allows people to collaborate on a problem.

It forwarded something that I really believed, which is, Two things. One is that there are lots of ways to solve problems. And, you know, you learn different ways. The second thing, which actually was really positive, is that we saw the power of open source. Like, I'm a big, big believer in open source because it allows people to collaborate on a problem.

And what you saw with DeepSeek is, you know, put aside DeepSeek being a Chinese lab. You know, DeepSeek was an open source model that And now you see, like, every research group learning from some of the techniques of DeepSeq to make their models better. And I actually thought that was really cool.

And what you saw with DeepSeek is, you know, put aside DeepSeek being a Chinese lab. You know, DeepSeek was an open source model that And now you see, like, every research group learning from some of the techniques of DeepSeq to make their models better. And I actually thought that was really cool.

And what you saw with DeepSeek is, you know, put aside DeepSeek being a Chinese lab. You know, DeepSeek was an open source model that And now you see, like, every research group learning from some of the techniques of DeepSeq to make their models better. And I actually thought that was really cool.

Like, we saw in the realm of a short number of weeks people, you know, kind of changing the way they thought about deepseq. you know, adding some innovation from what they learned.

Like, we saw in the realm of a short number of weeks people, you know, kind of changing the way they thought about deepseq. you know, adding some innovation from what they learned.

Like, we saw in the realm of a short number of weeks people, you know, kind of changing the way they thought about deepseq. you know, adding some innovation from what they learned.

You know, they have a lot of smart engineers. I think we have better technology. Our goal in life is to continue to stay ahead. And that's why I said, you know, at the end of the day, we have to spend like 90% of our time thinking about how do we go faster? How do we innovate faster? How do we use all of the great capabilities that we have?