Dwarkesh Patel

consultant is doing, you take that out of the bucket.

You take a little task that an accountant is doing, you take that out of the bucket.

And then you're just doing this across all knowledge work.

But instead, if we do believe we're on the path of AGI with the current paradigm, the progression is very much not like that.

At least...

It just does not seem like consultants and accounts and whatever are getting like huge productive improvement.

It's very much like programmers are like getting more and more chills of the way of their work.

If you look at the revenues of these companies, discounting just like normal chat revenue, which I think is like, I don't know, that's similar to like Google or something.

just looking at API revenues, it's like dominated by coding, right?

So this thing which is general, quote unquote, which should be able to do any knowledge work, is just overwhelmingly doing only coding.

And it's a surprising way that you would expect like the AGI to be deployed.

I actually, I'm not sure if that alone explains it because...

I personally have tried to get LLMs to be useful in domains which are just pure language in, language out.

Like rewriting transcripts, like coming up with clips based on transcripts, etc.

And you might say, well, it's very plausible that I didn't do every single possible thing I could do.

I put a bunch of good examples in context, but maybe I should have done some kind of fine-tuning, whatever.

So our mutual friend, Annie Matuszak, told me that

He actually tried 50 billion things to try to get models to be good at writing spaced repetition prompts.

Again, very much language in, language out tasks, the kind of thing that should be dead center in the repertoire of these LLMs.

And he tried in-context learning, obviously, with a few short examples.