Jonathan Ross

this has just made it absolutely nakedly clear that the models are commoditized, right? You've been asking the question, right? Like if there was any doubt before, that doubt's over. So what is the moat? And for me, I love Hamilton-Helmer's seven powers, right?

this has just made it absolutely nakedly clear that the models are commoditized, right? You've been asking the question, right? Like if there was any doubt before, that doubt's over. So what is the moat? And for me, I love Hamilton-Helmer's seven powers, right?

this has just made it absolutely nakedly clear that the models are commoditized, right? You've been asking the question, right? Like if there was any doubt before, that doubt's over. So what is the moat? And for me, I love Hamilton-Helmer's seven powers, right?

So marketing is the art of decommoditizing your product. And the seven powers are seven great ways to decommoditize your product. Scale economies, network effects, brand counter-positioning, cornered resource, switching cost, process power, right? The question is, who's going to do what? OpenAI, and you've got to give Sam Altman and that team credit.

So marketing is the art of decommoditizing your product. And the seven powers are seven great ways to decommoditize your product. Scale economies, network effects, brand counter-positioning, cornered resource, switching cost, process power, right? The question is, who's going to do what? OpenAI, and you've got to give Sam Altman and that team credit.

So marketing is the art of decommoditizing your product. And the seven powers are seven great ways to decommoditize your product. Scale economies, network effects, brand counter-positioning, cornered resource, switching cost, process power, right? The question is, who's going to do what? OpenAI, and you've got to give Sam Altman and that team credit.

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.