Jonathan Ross

They've got amazing brand power, like no one else in this space. And that's going to serve them for a really long time. But what you see Sam trying to do is scale, right? He's trying to go scale. That's why we hear about Stargate and $500 billion, right? That's the power he would like to have, but the power he has right now is brand. And he's trying to bridge that. But what about the others?

They've got amazing brand power, like no one else in this space. And that's going to serve them for a really long time. But what you see Sam trying to do is scale, right? He's trying to go scale. That's why we hear about Stargate and $500 billion, right? That's the power he would like to have, but the power he has right now is brand. And he's trying to bridge that. But what about the others?

They've got amazing brand power, like no one else in this space. And that's going to serve them for a really long time. But what you see Sam trying to do is scale, right? He's trying to go scale. That's why we hear about Stargate and $500 billion, right? That's the power he would like to have, but the power he has right now is brand. And he's trying to bridge that. But what about the others?

Actually, I don't think it's enough spending. And the reason is, so we saw this happen at Google over and over again. We do the TPU. So why did we do the TPU? The speech team trained a model. It outperformed human beings at speech recognition. This was like back in 2011, 2012. And so Jeff Dean, most famous engineer at Google, gives a presentation to the leadership team. It's two slides.

Actually, I don't think it's enough spending. And the reason is, so we saw this happen at Google over and over again. We do the TPU. So why did we do the TPU? The speech team trained a model. It outperformed human beings at speech recognition. This was like back in 2011, 2012. And so Jeff Dean, most famous engineer at Google, gives a presentation to the leadership team. It's two slides.

Actually, I don't think it's enough spending. And the reason is, so we saw this happen at Google over and over again. We do the TPU. So why did we do the TPU? The speech team trained a model. It outperformed human beings at speech recognition. This was like back in 2011, 2012. And so Jeff Dean, most famous engineer at Google, gives a presentation to the leadership team. It's two slides.

Slide number one, good news, machine learning finally works. Slide number two, Bad news, we can't afford it. And we're Google. We're going to need to double or triple our global data center footprint at probably a cost of $20 to $40 billion. And that'll get a speech recognition. Do you also want to do search and ads?

Slide number one, good news, machine learning finally works. Slide number two, Bad news, we can't afford it. And we're Google. We're going to need to double or triple our global data center footprint at probably a cost of $20 to $40 billion. And that'll get a speech recognition. Do you also want to do search and ads?

Slide number one, good news, machine learning finally works. Slide number two, Bad news, we can't afford it. And we're Google. We're going to need to double or triple our global data center footprint at probably a cost of $20 to $40 billion. And that'll get a speech recognition. Do you also want to do search and ads?

There's always this giant mission accomplished banner every time someone trains a model. And then they start putting it into production. And then they realize, oh, this is going to be expensive. This is why we've always focused on inference. And so now think about it this way. At Google, we always ended up spending 10 to 20 times as much on the inference as the training back when I was there.

There's always this giant mission accomplished banner every time someone trains a model. And then they start putting it into production. And then they realize, oh, this is going to be expensive. This is why we've always focused on inference. And so now think about it this way. At Google, we always ended up spending 10 to 20 times as much on the inference as the training back when I was there.

There's always this giant mission accomplished banner every time someone trains a model. And then they start putting it into production. And then they realize, oh, this is going to be expensive. This is why we've always focused on inference. And so now think about it this way. At Google, we always ended up spending 10 to 20 times as much on the inference as the training back when I was there.

Now the models are being given away for free. How much are we going to spend on inference? And now with the test time compute, I've asked questions of DeepSeq where it took 18,000 intermediate tokens before it gave me the answer.

Now the models are being given away for free. How much are we going to spend on inference? And now with the test time compute, I've asked questions of DeepSeq where it took 18,000 intermediate tokens before it gave me the answer.

Now the models are being given away for free. How much are we going to spend on inference? And now with the test time compute, I've asked questions of DeepSeq where it took 18,000 intermediate tokens before it gave me the answer.

I mean, it just makes sense, right? You don't train to become, you know, a cardiovascular surgeon and then that's what you do for 95% of your life and then you perform 5%. It's the opposite. You train for a little and then you do it for the rest of your life.

I mean, it just makes sense, right? You don't train to become, you know, a cardiovascular surgeon and then that's what you do for 95% of your life and then you perform 5%. It's the opposite. You train for a little and then you do it for the rest of your life.

I mean, it just makes sense, right? You don't train to become, you know, a cardiovascular surgeon and then that's what you do for 95% of your life and then you perform 5%. It's the opposite. You train for a little and then you do it for the rest of your life.

I don't know what the solution is. There's carrot and there's stick, right? So you can either use a stick, block it. That might be effective. I don't know that the U.S. has really done that before. There's also the carrot, which is it's kind of interesting how it's being offered for free in China and not just in China, but to anyone else. And then others are doing that, too.

I don't know what the solution is. There's carrot and there's stick, right? So you can either use a stick, block it. That might be effective. I don't know that the U.S. has really done that before. There's also the carrot, which is it's kind of interesting how it's being offered for free in China and not just in China, but to anyone else. And then others are doing that, too.