Terence Tao

In a very similar way to how the internet drove the cost of communication down to almost zero.

Yeah.

which is an amazing thing, but it doesn't create abundance by itself.

Yeah, so now the bottleneck is different.

So we're now in a situation where suddenly people can generate thousands of theories for a given scientific problem.

And now we have to verify them, evaluate them.

And this is something which

we have to change our structures of science to actually sort this out.

So in fact, traditionally, we build walls.

So in the past, before we had AI slop, we had sort of amateur scientists have their own theories of the universe, many of which were basically of very little value.

And so we put these like peer review publication systems and things to kind of filter out and try to isolate the high signal ideas to test.

But now that we can generate these possible explanations at massive scale, and some of them are good and a lot are terrible,

I mean, human reviewers, they're already being overwhelmed, actually.

Many, many journals are reporting AI journal submissions are just flooding their submissions.

So it's great that we can generate all kinds of things now with AI, but it means that the rest of the aspects of science have to catch up.

Yeah.

verification, validation, and assessing what ideas actually move the subject forward and which ones are dead ends or red herrings.

And that's not something we know how to do at scale.

For each individual paper, we can discuss it, have a debate among scientists and get to a consensus in a few years.

But when we're generating a thousand of these every day, this doesn't work.