Keith Rabois

Careful.

It is the best one yet.

I mean, every year we have this.

Before we start, Nick, can you please show Elon's tweet about how they did on the AGI benchmark?

It's absolutely incredible.

Two things.

One is how quickly, starting in March of 2023, so we're talking about less than two and a half years, what this team has accomplished.

and how far ahead they are of everybody else as demonstrated by this.

But the second is a fundamental architectural decision that Elon made, which I think we didn't fully appreciate until now.

And it maps to an architectural decision he made at Tesla as well.

And for all we know, we'll figure out that he made an equivalent decision at SpaceX.

And that decision is really well encapsulated by this essay, The Bitter Lesson, by Rich Sutton.

And Nick, you can just throw this up here.

But just to summarize what this says, it basically says in a nutshell that you're always better off when you're trying to solve an AI problem, taking a general learning approach that can scale with computation, because it ultimately proves to be the most effective.

And the alternative would be something that's much more human labored and human involved that requires human knowledge.

And so the first method, what it essentially allows you to do is view any problem as an endless scalable search or learning task.

And as it's turned out, whether it's chess or Go or speech recognition or computer vision, whenever there was two competing approaches, one that used general computation and one that used human knowledge, the general computation problem always won.

And so it creates this bitter lesson for humans that want to think that we are at the center of all of this critical learning and all of these leaps.

In more AI-specific language, what it means is that a lot of these systems create these embeddings that are just not understandable by humans at all, but it yields incredible results.

So why is this crazy?