Inside the economics of OpenAI (exclusive research)

this is a kind of complicated exercise.

And one of the things that comes out from this is this question of that short model life.

And the family that we looked at was only really the preeminent family for a few months.

Now, we know that Enterprises don't change the API they're using the day a new one comes out.

There's always a bit of a lag.

But consumers do, right? Because that's what you get access to on ChatGPT.

And you may remember that when GPT-4 was set aside from ChatGPT, it was an emotional support tool for many users.

And they were very upset with how methodical and mechanical GPT-5 now felt.

And I think one of the uncertainties is, To what extent do you actually learn and prepare for your next model based on the short life of the existing model, right? There are a couple of elements to it, right? One, I think, is a little bit more nebulous, which is that by having a really good model, even if it lasts for a short period of time, you maintain your forward momentum in the market in terms of customers liking you and your enterprise sales and so on.

And that feels less tangible than the second bit that I think is perhaps a bit harder to unpick, which is what do you learn? about running better and better models from actually having run a better model, even if it only lasts for four months.

And that learning might be sort of down in the weeds in sort of R&D and particular choices you make in training data and reinforcement learning.

It might also be in operations, right, in just operating a model of that scale.

And I think it's quite hard for us to know.

I suspect it's hard for OpenAI or any of the other foundation models to know the contribution of that second part to the model itself, right? So in a sense, who in this kingdom is actually able to see with two eyes? I'm not sure, you know, many can at this point.

It's interesting.

It was making me think of GPUs and how do you, I was talking to some finance folks about, okay, well, what is the value of these H100 chips going to be in a few years? And everyone's kind of shrugging their shoulders like, you know, and sort of putting it out there.

And it's kind of seemingly like a parallel to these models.

Like, what is the value of, you know, GTP4 like three years ago was, you know.

So how do you think about that? One question I have is like, you know, you talked a bit about compute and costs there.

And, you know, this may be a little down in the weeds, but the cost of compute in sort of buildings models is going down.

And how do you sort of see that kind of going forward?

So the cost of compute of building these models, I don't think it's quite going down.

I do see it as going up time and again.

The pre-training seems to be going up.

And despite rumors to the contrary, pre-training is not dead at all.