Azeem Azhar

I'm thinking they could do this falling out of bed in the morning before their coffee, because that is the inherent way in which these companies have been architected.

I'm not sure if Dunder Mifflin, the paper manufacturer sales company, has the right cultural wherewithal and how many companies in America actually have that kind of cultural capacity and have the depth in the management and leadership talent to facilitate that.

So I'm going to leave that as a question, not for you or I to answer.

I'll leave it with listeners to think about because it's a big challenge.

The other thing we haven't talked about, though,

is the fundamental instability of the technology.

So you talked about a really important point, which again, I want people to hold on to this notion that maybe it's managers expectations of what AI might do in the next six months, 12 months, 24 months, that is slowing down the pace of hiring.

But there is another piece that's happening with AI, which is undeniable.

And that is, it is getting more and more capable.

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.