Nobody wants intelligence, period.

I want something else, right?

And that something else is always specific, or at least more specific.

There is this kind of observed phenomenon where if you over-engineer intelligence, you regret it because somebody figures out a more general and maybe potentially simpler method that scales better.

And a lot of the hard-coded decisions you made are things you end up regretting.

So I think it's fair to assume that like models will get better and algorithms will get better

and a lot of that stuff will improve.

Then the question we really ask is intelligence is great, but what problems are you actually trying to solve?

That idea that scaling model parameters and scaling just pre-training data is all you need,

exists nowhere anymore. Nobody thinks that. Actually, people deny they ever thought that at this point.

Now you see this massively human designed and very carefully constructed pipelines for post-training,

where we really encode a lot of the things we want to do. You see massive emphasis on retrieval

and web search and tool use and agent training.

There is clearly a sense in which the labs have already recognized that the old playbook doesn't work.

The question is, is that actually sufficient for making the best use and the most use of these language models?

It's not a problem of capabilities.

It's a problem of actually we don't necessarily

just need models. We want systems. The conventional wisdom says we're racing toward EGI

by making language models bigger and bigger. But what if the entire framing is wrong? On today's

episode, you'll hear from A16Z general partner, Martin Casado, and guest Omar Khatav,

assistant professor at MIT and creator of DS Pi.

Omar doesn't think we need artificial general intelligence.

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