“I am shocked by the scale of cognitive effort that you can concentrate in such a short period of time. If you think about what 130 billion tokens are — if it was a single human thinking as a full-time job, stretched back to back, that would be a human thinking for like 4,000 years, eight hours a day, working a normal work week. So starting from ancient Sumeria up till today, a single sequential human thinking that long, concentrated in 88 hours.”

…scale up inference compute, you can see what the base capabilities of the models will be a few years into the future. And I feel like you're in a similar position now to help us understand what future capabilities will look like, given the enormous scaling of agent sizes that we can do right now.

…And so multi-agent is a way of scaling test time compute in parallel instead of purely serial. And it is like less efficient because it doesn't have, it's not like a single agent has all the context to itself, but it is like a very effective way of scaling test time compute if it's done well.

let's talk about the parallelization penalty and then we can talk about the qualitative stuff because the truth is that we don't have very good science on multi-agent scaling up to this kind of scale. So when we released 5.6, I think that was the first time that we had a proper multi-agent system…

Is it a linear serial time speedup or a sublinear speed up as you increase the number of parallel agents?

I would say it's slightly sublinear, though it does depend a lot on the problem. So math, for example, is quite parallelizable. It's not the most parallelizable thing, but it is very parallelizable. I think web search, things like doing a deep research report where you have to look through a bunch…