Dwarkesh Patel

And then two, the end is not here yet.

I'm curious to bounce two other ways in which the analogy might be different.

And the reason I'm especially curious about this is because I think the question of how fast AI is deployed, how valuable it is when it's early on is like potentially the most important question in the world right now, right?

Like if you're trying to model what the year 2030 looks like, this is the question you want to have some understanding of.

So another thing you might think is, one, you have this latency requirement with self-driving where you have – I have no idea what the actual models are, but I assume like tens of millions of parameters or something, which is not the necessary constraint for knowledge work with LLMs.

Or maybe it might be with computer use and stuff.

But anyways, the other big one is – maybe more importantly, on this CapEx question –

Yes, there is additional cost to serving up an additional copy of a model, but the sort of OPEX of a session is quite low and you can amortize the cost of AI into the training run itself, depending on how inference scaling goes and stuff.

But it's certainly not as much as like building a whole new car to serve another instance of a model.

So it just, the economics of deploying more widely are much more favorable.

The latency requirements and the implications for model size.

Yeah.

Do you have any opinions on whether this implies that the current AI build-out, which would like 10x the amount of available compute in the world in a year or two, and maybe like 100, more than 100x by the end of the decade, if the use of AI will be lower than some people naively predict, does that mean that we're overbuilding compute?

Or is that a separate question?

Yeah, that's right.

Let's talk about education in Eureka and stuff.

One thing you could do is start another AI lab and try to solve those problems.

Yeah, curious what you're up to now.

And then, yeah, why not AI research itself?

And so what are you working on there?