Blaise Agüera y Arcas

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213 appearances 2 recordings 1 series first heard Jul 2026 last heard 31 Jul

Blaise Agüera y Arcas’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 2 in all, peaking in Jul 2026 with 2.

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Freakonomics Radio · 682. Should A.I. Move to Space? · 24 Jul 2026 podcast

But we know from neuroscience and we know from psychology that your perspective is closer to what is really going on.

If you, for instance, do radical brain surgery on people and split their brains in half, which used to be done quite a lot as a last-ditch surgery for addressing epilepsy.

You can see that the two hemispheres of the brain are working independently of each other, and yet people's sort of impression of themselves remains unified.

So it's kind of like every part of the brain is working as a member of a team.

They all know that they're on Team You, and they're modeling themselves as a whole, and yet also in a kind of competition, as you're describing.

for who says the thing that is going to come out of the mouth.

And that's how neural nets work too.

Artificial neural nets have these soft max layers, as they're called, which are essentially an internal competition for which part of the network will get to emit the behavior.

This is in many ways an old idea.

One of the longstanding critiques of AI models is that they're just autocomplete on steroids, that they're just predicting the next token.

I always kind of thought in my heart, as it were, that that could not be the secret of intelligence.

Surely it's not just predicting the next token.

So I was as shocked as anybody when we made really large scale next token predictors and they started to get the answers right to hard mathematical word problems and write poetry and all this kind of stuff.

I found it very surprising as far as my intuition went, but if you really start to look at what it means to predict, the surprise dissipates a bit.

The reason we've got brains is because we live in a complex world and our actions can have effects on that world and our own futures that can either hurt us or help us.

And so in order to be able to distinguish between actions that will lead to good places or to bad places, that's exactly what you have to do.

You have to predict, not just predict the world in your absence, but do what statisticians call conditional prediction, meaning predict the world conditional on action A and action B, and then decide which prediction you like better.

In some sense, it's almost a tautology, like, of course, that's what brains are there for.

If you couldn't act to change your own future, then there would really be no point in having a brain.

And if you couldn't predict

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