Azeem Azhar

And it's important to understand that NVIDIA is more than just a chip company.

It's actually a systems company and it sells systems, not just chips, platforms, not just processors.

But the inference market is really different to the training market.

And that's really different to the graphics market that NVIDIA came from.

And I think it's really impressive to see the firm figure this out, not just figure it out, frankly, actually deliver on it at the scale at which they have to deliver.

We'd figured out, we'd thought, like NVIDIA, like lots of other people, that inference was going to be an important part of the market, that workload volumes were going to shift and ratios were going to shift.

And I've personally invested in a couple of startups that build chips to tackle this inference opportunity through much higher throughput or much lower energy usage.

It was clearly going to be important.

And I think that what we've seen with NVIDIA is almost going to be a case study of how a large firm can move really quickly with conviction, which is not something firms can normally do.

At this point, let's go back to what we're doing with all that inference.

What am I doing with it?

What are you doing with it?

What are your colleagues doing with it?

And this takes us back to Jensen's observation that every company needs an open core strategy.

So I have to go back a layer still.

What's driving all this growth?

Well, it's really happened in the last few months where agentic systems, I think Claude Code, the coding tool that Anthropic has built, was a good example of this, started to work well, which means that you could effectively leave them to their own devices.

And I think that that shift, and OpenClaw also symbolizes it, helps us understand the distinction between breakthrough research and development and the things you need to diffuse technologies into the market.

The debate that's gone on about the AI investment wave and AI in general

I think has confused this.