Jonathan Ross
speaker
170 appearances
2 recordings
2 series
first heard Jan 2025
last heard 20 Oct
Jonathan Ross’s voice in public audio — every appearance, attributed to the second.
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Appearances
Because the other thing is, while it's sort of asymptotes, the question is, on this curve, where do you stop? It depends on how many people you have doing inference. You can either make the model bigger, which makes it more expensive, and then you train it on less. Or you make it smaller, and it's cheaper to run, but you have to train it more. So DeepSeq didn't have a lot of users until recently.
And so for them, it would have never made sense to train it a lot anyway. They would much rather have a bigger model. But now what you're going to see is all these other people either making smaller models or trying to make higher quality ones of the same size, but just training it more.
So they ran out of compute. And this is the other reason why chip startups are going to do just fine. because they ran out of inference compute. You train it once, but now... So you spend money to make the model, like designing a car, but then each car you build costs you money, right? Well, each query that you serve requires hardware. Training scales with the number of ML researchers you have.
Inference scales with the number of end users you have.
I think they marketed very well. Like you look at some of the publication and they make it sound like it's a philosophical thing. And, you know, they talk about they spent six million on the GPUs and everyone just zoomed in on that, neglecting the fact that Lama's first model was trained on like, I think, five million worth of GPU time. And it set the world on fire in a good way. And then
ignoring the fact that they spent a ton generating the data and all this. They're really good at marketing. I think they were probably surprised at how well it worked, but I think this is what they were going for.
What's up with the $500 billion Stargate effort?
I've gone back and forth on that. I actually did. So Gavin Baker tweeted some math. Before I saw that tweet, I came up with very similar math. However, talking to some people in the know, some of the comments are actually they've got it. But then you keep pressing and it's like, well, maybe is there some cutesiness to it?
What I think it is, is an acknowledgement that the models have been commoditized and infrastructure is what's important in terms of maintaining elite like scale. It's one of the seven powers. I think what you're seeing there is an attempt to move from having a cornered resource or something like that into a scale economy.
I don't think you get there in a short period of time with GPUs because most of the compute is inference. And so, you know, if you're talking about building out all the power, like it's going to take time. It's infrastructure. It's CapEx. The real win here is brand. That's what I would be doubling down on. I would be like hiring the best brand firms I could. I would do a complete makeover.
Much stronger. I think they're going to double down on that and they're going to focus on it. Who will lose? People who can't adapt to disruption. Anyone who just wants to keep going on a straight line and do what they were doing before is going to lose.
And the rate of disruption is probably going to increase because going back to the analogy of LLMs being the printing press, imagine if there were a couple of smartphones left over from an ancient civilization. All of a sudden, the printing press is invented and you're like, ooh, Uber's coming. I want a position for it. I know where this is going. We are the smartphones.
We know where generative age technology goes. And now everyone's like, well, we know how big this gets. Let's put money into it. I can't be the one who doesn't spend money on this because I know how big of an advantage it's going to be. It's like getting to add more workers to the workforce. And so I think...
the generative age, we're going to speed run it faster than whatever comes next because we know what it looks like.
I think with self-driving, the problem you had was the threshold. It had to be way superhuman. Because if you look at the number of miles driven by these self-driving vehicles, it's an enormous number. And the number of fatalities and incidents is lower per mile. But we have no tolerance whatsoever for them when it's a machine.
When you're writing poetry and code, it's very different versus doing a surgery or driving a car.
I would probably feel both better and worse. I'd feel better about my bet on building out more hardware. I would feel worse about trying to build out my own model. Why is Elon doing that? Just pick one up off the ground. Like, why are you making your own?
Long ago, I stopped having good days and bad days. It's yes, it's how many good things, it's how many bad things, right? When you run an organization, I'm both excited and nervous. And I'm excited and nervous about different things at the same time. The thing that I am most nervous about is that unlike nuclear war, you can use AI tools to attack each other.
Google just announced recently the first zero-day exploit found by an LLM that was previously unknown. Yeah, that's a scary one.
So how would you like me to have access to your phone? Not ideal. How would you like the CCP to have access to your phone? Even last night. That's a nation state and nation states have a lot of resources.
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