Jonathan Ross

How do you think about that? What you see is a bunch of people who are concerned about training and the need for it. And everyone's still thinking that most of compute is training. And that there's going to be less of it because someone trained a model on 2000 GPUs and the nerfed A800 version with slower memory or whatever it is. And they're like, oh, people aren't going to need as many chips.

But again, Jevin's paradox, right? The more you bring the cost down, the more people consume. So for the last five to six decades, like clockwork, once a decade, the cost of compute has gone down 1,000x. People buy 100,000x as much compute, spending 100 times as much. So every decade, they spend 100 times as much. So you make it cheaper, they want more.

But again, Jevin's paradox, right? The more you bring the cost down, the more people consume. So for the last five to six decades, like clockwork, once a decade, the cost of compute has gone down 1,000x. People buy 100,000x as much compute, spending 100 times as much. So every decade, they spend 100 times as much. So you make it cheaper, they want more.

But again, Jevin's paradox, right? The more you bring the cost down, the more people consume. So for the last five to six decades, like clockwork, once a decade, the cost of compute has gone down 1,000x. People buy 100,000x as much compute, spending 100 times as much. So every decade, they spend 100 times as much. So you make it cheaper, they want more.

What's really happening is every time one of these models gets cheaper, we see our developer count just skyrocket. And then it comes back down a little bit, but the slope is higher than when it started. Better models create more demand for inference. More demand for inference then has people going, I should train a better model. And the cycle continues.

What's really happening is every time one of these models gets cheaper, we see our developer count just skyrocket. And then it comes back down a little bit, but the slope is higher than when it started. Better models create more demand for inference. More demand for inference then has people going, I should train a better model. And the cycle continues.

What's really happening is every time one of these models gets cheaper, we see our developer count just skyrocket. And then it comes back down a little bit, but the slope is higher than when it started. Better models create more demand for inference. More demand for inference then has people going, I should train a better model. And the cycle continues.

So I think over the long term, the only thing I say is Warren Buffett and Charlie Munger in the short term, the market is a popularity contest. In the long term, it's a weighing machine. I can't tell you about the popularity contest, but in terms of the weighing machine part, this is a misunderstanding. It's actually more valuable thanks to deep seek, not less valuable.

So I think over the long term, the only thing I say is Warren Buffett and Charlie Munger in the short term, the market is a popularity contest. In the long term, it's a weighing machine. I can't tell you about the popularity contest, but in terms of the weighing machine part, this is a misunderstanding. It's actually more valuable thanks to deep seek, not less valuable.

So I think over the long term, the only thing I say is Warren Buffett and Charlie Munger in the short term, the market is a popularity contest. In the long term, it's a weighing machine. I can't tell you about the popularity contest, but in terms of the weighing machine part, this is a misunderstanding. It's actually more valuable thanks to deep seek, not less valuable.

Okay, so Jevin's paradox was actually discovered by Jevin as recently made famous in Satya's tweet. However, I did beat him to that by quite a bit. And just as Satya likes to say that he made Google dance, I'm going to say I made Satya dance. He might take exception to that. But less than a month before he posted that, I did a cute little tweet on it.

Okay, so Jevin's paradox was actually discovered by Jevin as recently made famous in Satya's tweet. However, I did beat him to that by quite a bit. And just as Satya likes to say that he made Google dance, I'm going to say I made Satya dance. He might take exception to that. But less than a month before he posted that, I did a cute little tweet on it.

Okay, so Jevin's paradox was actually discovered by Jevin as recently made famous in Satya's tweet. However, I did beat him to that by quite a bit. And just as Satya likes to say that he made Google dance, I'm going to say I made Satya dance. He might take exception to that. But less than a month before he posted that, I did a cute little tweet on it.

So what's really happening here was in the 1860s, this guy Jevin, he actually wrote a treatise on steam engines, which I guess is what you did for fun back then in England. He realized every time steam engines became more efficient, people would buy more coal, which is the paradox.

So what's really happening here was in the 1860s, this guy Jevin, he actually wrote a treatise on steam engines, which I guess is what you did for fun back then in England. He realized every time steam engines became more efficient, people would buy more coal, which is the paradox.

So what's really happening here was in the 1860s, this guy Jevin, he actually wrote a treatise on steam engines, which I guess is what you did for fun back then in England. He realized every time steam engines became more efficient, people would buy more coal, which is the paradox.

But if you think about it from a business point of view, when the OPEX comes down, more activities come into the money. So people do more things. And so what's happened is every time we've seen the cost of tokens for a particular level of quality of models come down, We've actually seen the demand grow significantly. Price elasticity, baby.

But if you think about it from a business point of view, when the OPEX comes down, more activities come into the money. So people do more things. And so what's happened is every time we've seen the cost of tokens for a particular level of quality of models come down, We've actually seen the demand grow significantly. Price elasticity, baby.

But if you think about it from a business point of view, when the OPEX comes down, more activities come into the money. So people do more things. And so what's happened is every time we've seen the cost of tokens for a particular level of quality of models come down, We've actually seen the demand grow significantly. Price elasticity, baby.

Today, there's this wonderful business selling mainframes with a pretty juicy margin because no one seems to want to enter that business. Training is a niche market with very high margins. And when I say niche, it's still going to be worth hundreds of billions a year. But inference is the larger market. And...