Azeem Azhar

They're prone to booms.

and they're prone to busts.

And being able to see an order book out like that is a real sign of health.

Well, this year, we learned at GTC that the scale of the committed orders

was now a trillion dollars for Blackwell's, for the new Vera Rubin products, just out through to 2027.

And the thing to understand is, yes, NVIDIA maintains a dominant market share, but it's not 100% of the market.

There's also competition and supply from Google's TPUs, from Amazon's Tranium, from AMD, and from others in the wings.

But there is a trillion dollars of orders, at least, and certainly more than that, every dollar going on chips to build manufactured intelligence.

And those chips are more power efficient and they're more powerful, delivering more for each dollar than the dollars spent last year and the dollars spent the year before that.

So hold that thought for a moment.

The reason that's happening is because the shape of AI is changing.

The way AI is being used is shifting.

When ChatGPT burst on the scene, the bulk of usage of compute for AI workloads was in what's known as training.

training, building these large language models that we would then go off and use in the inference phase.

And these large language models really did require large amounts of computing.

Astronomical is not an exaggeration.

10 to the 23, 10 to the 24 floating point operations or more to train.

And data centers were being consumed by the training rather than the inference stage of the process, which is where, of course, people like you and I actually end up using these models.

But that is changing.

Now, to understand that, let's talk about how manufactured intelligence makes its way out there.