Mark Zuckerberg

There's a very rapid curve on the uptake of people interacting with the AI assistance and like the learning feedback and kind of data flywheel around that.

And then there is also the build out of

supply chains and infrastructure and regulatory frameworks to enable the scaling of a lot of the physical infrastructure.

But I think at some level, all of those are going to be necessary and not just the coding piece.

I guess one specific example of this that I think is interesting, actually, even if you go back a few years ago, we had a project on, I think it was on our ads team, to automate

ranking experiments, right?

That's like a pretty constrained environment.

It's not like write open-ended code.

It's basically look at the whole history of the company, every experiment that any engineer has ever done in the ad system, and look at what worked, what didn't, what the results of those were, and basically formulate new hypotheses for different tests that we should run that could improve the performance of the ad system.

And what we basically found was

We were bottlenecked on compute to run tests based on the number of hypotheses.

It turns out even with just the humans that we have right now on the ads team, we already have more good ideas to test than you actually have either kind of compute or...

really cohorts of people to test them with.

Right.

Cause I mean, even if you have like three and a half billion people using your products, you still want each, you know, each test needs to be statistically significant.

So it needs to have, you know, some number of whatever it is, hundreds of thousands or millions of people.

And, um,

And there's kind of only so much throughput that you can get on testing through that.

So we're already at the point, even with just like the people we have, that...

that we already can't really test everything that we want.