Azeem Azhar

It was going to be about the Arab Spring and people speaking with one voice and it ended up being anything but that.

So...

One of the things that I've got some experience and some scar tissue from is trying to understand exactly the path that a technology takes from here to there.

And as I said, China seems to have a different approach to AI, which seems to focus on deployment rather than really, really breaking the frontier.

I mean, is that a fair way of characterizing it?

I'm not sure.

Well, I mean, there is that necessity because ultimately they don't have the chips and Huawei is starting to produce chips that can be suitable for training high-end LLMs, but they are way, way behind where NVIDIA sits right now.

But there is also this question about what actually drives

the economic success of a technology like this.

So I'm sure you know Jeff Ding well.

Jeff had his book recently and he made the case that it's less about breakthrough innovation and more about broad diffusion and deployment.

One of the things that we see are

state pronouncements, you know, the state council in China saying we want 70% deployment for everything from industrial applications to philosophy by 2027, and that to be 90% by 2030.

So a focus on deployment.

How do you, how do you interpret that melange of messages?

That's really important because it feels to me that there are different versions of techno-accelerationism.

There is a techno-accelerationism that comes out of Silicon Valley, which is really about building AGI, then ASI, then ASI that builds itself.

But there's also policy accelerationism.

And as a slightly distant observer of China,

What I see is a type of techno-acceleration on the deployment side.