Mark Zuckerberg: belief

Transcript context

…I think that's just one aspect of the flywheel. Part of what I generally disagree with on the fast-takeoff view is that it takes time to build out physical infrastructure. If you want to build a gigawatt cluster of compute, that just takes time. NVIDIA needs time to stabilize their new generation of systems. Then you need to figure out the networking around it. Then you need to build the building. You need to get permitting. You need to get the energy. Maybe that means gas turbines or green energy, either way, there’s a whole supply chain of that stuff. We talked about this a bunch the last time I was on the podcast with you. I think some of these are just physical-world, human-time things. As you start getting more intelligence in one part of the stack, you’re just going to run into a different set of bottlenecks. That’s how engineering always works: solve one bottleneck, you get another bottleneck. Another bottleneck in the system or ingredient that’s going to make this work well, is people getting used to learning and having a feedback loop with using the system. These systems don’t just show up fully formed with people magically knowing how to use them. There's a co-evolution that happens where people are learning how to best use these AI assistants. At the same time, the AI assistants are learning what people care about. Developers are making the AI assistants better. You're building up a base of context too. You wake up a year or two into it and the assistant can reference things you talked about two years ago and that’s pretty cool. You couldn’t do that even if you launched the perfect thing on day one. There’s no way it could reference what you talked about two years ago if it didn’t exist two years ago. So I guess my view is that there's this huge intelligence growth. There’s a very rapid curve on the uptake of people interacting with the AI assistants, and the learning feedback and data flywheel around that. And then there is also the buildout of the supply chains and infrastructure and regulatory frameworks to enable the scaling of a lot of the physical infrastructure. At some level, all of those are going to be necessary, not just the coding piece. One specific example of this that I think is interesting. Even if you go back a few years ago, we had a project, I think it was on our ads team, to automate ranking experiments. That's a pretty constrained environment. It's not 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, and what the results of those were. Then basically formulate new hypotheses for different tests that we should run that could improve the performance of the ad system. What we basically found was that we were bottlenecked on compute to run tests, based on the number of hypotheses. ifferent tests that we should run that could improve the performance of the ad system. What we basically found was that we were bottlenecked on compute to run tests, based on the number of hypotheses. It turns out, even with just the humans we have right now on the ads team, we already have more good ideas to test than we actually have either compute or, really, cohorts of people to test them with. Even if you have three and a half billion people using your products, you still want each test to be statistically significant. It needs to have hundreds of thousands or millions of people. There's only so much throughput you can get on testing through that. So we're already at the point, even with just the people we have, that we can't really test everything that we want. Now just being able to test more things is not necessarily going to be additive to that. We need to get to the point where the average quality of the hypotheses that the AI is generating is better than all the things above the line that we’re actually able to test that the best humans on the team have been able to do, before it will even be marginally useful for it. We'll get there I think pretty quickly. But it's not just, “Okay, cool, the thing can write code, and now all of a sudden everything is just improving massively.” There are real-world constraints that need to be overcome. Then you need to have the compute and the people to test. Then over time, as the quality creeps up, are we here in five or 10 years where no set of people can generate a hypothesis as good as the AI system? I don't know, maybe. In that world, obviously that's going to be how all the value is created. But that's not the first step. So if you buy this view, that this is where intelligence is headed, the reason to be bullish on Meta is obviously that you have all this distribution. You can also use that to learn more things that can be useful for training. You mentioned the Meta AI app now has a billion active users.…

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