Azeem Azhar

And in that configuration file, if you're using an AI tool in the team, you should be checking that configuration file.

And if we've gone from GPT 5.2 to 5.4, it should seamlessly switch you over.

That

Kind of automatic evaluation is sort of churning through not that many tokens, but consistently churning through some usage.

But it means that we're never going to be more than a couple of hours late if there is a radical shift in model capabilities.

And I think that you can see that in some of the things that happened at Nvidia's GTC because Jensen talked a lot about other models and Nemotron and open source models they're working with.

So what happens is when you're using AI agents, usage increases.

And of course, it's in Jensen's interest to say that everybody should use agents, that every company needs an open call strategy because he is selling the thing that produces the tokens.

And these things are hungry, hungry hippos, and they love eating tokens.

So it's the same way that it's in the interest of an airline to tell you about beautiful destinations that they can fly you to.

But I think he's right.

I mean, I think that it is really important that you start to think about

what kind of open call strategy or agentic strategy or token application strategy you have in the firm.

So my inference loads have gone up hugely using these agentic systems.

And we're just starting to touch on the question of what happens when agents themselves trigger inference workloads.

Because right now what happens is that I am triggering them.

I'm either saying kick something off if these conditions happen or do this at this point in time or do this later.

now.

And right now, my sub-agents, those open-clawed agents that report into Rmini Arnold, they have access to a wide range of tools when I task them to do something, but they don't have access to the really token-expensive complex tools.

For example, the large-scale simulations, those 200 million token jobs.