Kylie Robison

This is what we're using the billions of dollars we raised. We're using it here. You know, OpenAI is valued at $150 billion, and that is insane to me. So I think, you know, this is them saying, like, look, this is what $150 billion evaluation looks like. We have the biggest data center project in history.

DeepSeq released this research paper about a model called V3, which was just designed for general purpose language tasks. And it was efficient and cost effective, which made a lot of AI researchers at the time go, wow, OK, I'm surprised that China could make this with way less resources. They were able to really optimize with what they had.

DeepSeq released this research paper about a model called V3, which was just designed for general purpose language tasks. And it was efficient and cost effective, which made a lot of AI researchers at the time go, wow, OK, I'm surprised that China could make this with way less resources. They were able to really optimize with what they had.

DeepSeq released this research paper about a model called V3, which was just designed for general purpose language tasks. And it was efficient and cost effective, which made a lot of AI researchers at the time go, wow, OK, I'm surprised that China could make this with way less resources. They were able to really optimize with what they had.

And then R1 came out, and it sort of blew everyone's minds. It's a reasoning model. And it's supposed to be on par with O1, OpenAI's reasoning model that only came out a few months ago. So people really freaked out about that because usually it takes a lot longer for China to match these capabilities, but they were able to do it in just a few months.

And then R1 came out, and it sort of blew everyone's minds. It's a reasoning model. And it's supposed to be on par with O1, OpenAI's reasoning model that only came out a few months ago. So people really freaked out about that because usually it takes a lot longer for China to match these capabilities, but they were able to do it in just a few months.

And then R1 came out, and it sort of blew everyone's minds. It's a reasoning model. And it's supposed to be on par with O1, OpenAI's reasoning model that only came out a few months ago. So people really freaked out about that because usually it takes a lot longer for China to match these capabilities, but they were able to do it in just a few months.

I really underestimated how much the finance people would freak out. I think that's my tech brain. But two things happened, in my opinion here. One, Davos happened. And Alexander Wang, the CEO of Scale AI, which helps provide data for some of these large language models, that CEO pointed to the fact that this model existed and that it was on par with O1 per their own benchmarks.

I really underestimated how much the finance people would freak out. I think that's my tech brain. But two things happened, in my opinion here. One, Davos happened. And Alexander Wang, the CEO of Scale AI, which helps provide data for some of these large language models, that CEO pointed to the fact that this model existed and that it was on par with O1 per their own benchmarks.

I really underestimated how much the finance people would freak out. I think that's my tech brain. But two things happened, in my opinion here. One, Davos happened. And Alexander Wang, the CEO of Scale AI, which helps provide data for some of these large language models, that CEO pointed to the fact that this model existed and that it was on par with O1 per their own benchmarks.

So people noticed that. And then I think the Wall Street Journal wrote a story about DeepSeek. And that's what sent the market freaking out that possibly half a trillion dollars worth of data centers wasn't the best moat. Perhaps we have been just burning money.

So people noticed that. And then I think the Wall Street Journal wrote a story about DeepSeek. And that's what sent the market freaking out that possibly half a trillion dollars worth of data centers wasn't the best moat. Perhaps we have been just burning money.

So people noticed that. And then I think the Wall Street Journal wrote a story about DeepSeek. And that's what sent the market freaking out that possibly half a trillion dollars worth of data centers wasn't the best moat. Perhaps we have been just burning money.

You might have seen Meta's stock bump as well. It seemed like everyone was going down and Meta went up. And my understanding is that perhaps the market is seeing open source as a real competitor, something that closed source companies should worry about.

You might have seen Meta's stock bump as well. It seemed like everyone was going down and Meta went up. And my understanding is that perhaps the market is seeing open source as a real competitor, something that closed source companies should worry about.

You might have seen Meta's stock bump as well. It seemed like everyone was going down and Meta went up. And my understanding is that perhaps the market is seeing open source as a real competitor, something that closed source companies should worry about.

Some of the sub-tweets from OpenAI VCs and Sam Altman himself at the time when B3 came out was like, well, they can copy our old work, but there's no way they're going to make the best frontier models. No one can really deny that it appears that they really innovated on a few key areas that really optimized these models. And that's what's really interesting.

Some of the sub-tweets from OpenAI VCs and Sam Altman himself at the time when B3 came out was like, well, they can copy our old work, but there's no way they're going to make the best frontier models. No one can really deny that it appears that they really innovated on a few key areas that really optimized these models. And that's what's really interesting.

Some of the sub-tweets from OpenAI VCs and Sam Altman himself at the time when B3 came out was like, well, they can copy our old work, but there's no way they're going to make the best frontier models. No one can really deny that it appears that they really innovated on a few key areas that really optimized these models. And that's what's really interesting.

Everyone likes to talk about Jevons paradox, which is scale will still matter because if we're able to do this so efficiently with lower costs, that means if we do it with way more chips, then it will create better models. So you're going to see a lot of people on the Internet telling people to buy the Nvidia dip because Nvidia will still matter.