Azeem Azhar

That's happening across three vectors.

Vector number one is its generalizability, its ability to be applied in lots of different domains.

And the most sophisticated things I do with GPT-5, GPT-5 Pro are within my domain.

And then I see scientists doing it in their domain and I see other experts in their domain.

That generalizability is broadening.

The second is the complexity of a task that an AI system can do end to end is increasing.

Deep research is a first example of that, 10, 20, 50 minutes, hours worth of human work.

But we're seeing through the coding evaluations, we run our own as well.

This notion of doubling of task length that an AI system can reliably complete is extending.

The third area is the guardrails and the scaffolding

that is being built around these tools that may not be AI itself, but the agentic frameworks, agentic policy management, operations, exception handling, error recovery, these are all getting more and more mature.

And those three things are shifting independently of each other, often by different groups.

So the question of can an AI, whatever it is,

do this task is changing on certainly one aspect on its own is doubling every six or seven months.

So again, if you recognize that, I hope you do.

How has that instability shown up in the data that you have been able to analyze and get your hands on?

Yeah, this notion that orchestration is a fancy word for being the pointy-haired boss in the Dilbert cartoons, right?

That's what managers do.

We orchestrate resources within the company and across suppliers and subcontractors.

And yeah, the old word was just management.