Scott Alexander

In our like guesstimates of AI,

how much faster algorithmic progress would be going, the progress multiplier for the middle level.

We basically do assume that you get massive diminishing returns to having more minds running in parallel.

And so we totally buy all of that.

and also the research taste thing.

So, like, here are some important inputs to AI R&D progress today.

Research tastes.

So, like, the quality of your best researchers, the people who are managing the whole process, their ability to learn from data and, like,

make more efficient use of the compute by running the right experiments instead of flailing around running a bunch of useless experiments.

That's research taste.

Then there's like the quantity of your researchers, which we just talked about.

Then there's the serial speed of your researchers, which currently is all the same because they're all humans.

And so they all run at basically the same serial speed.

And then finally, there's how much compute you have for experiments.

So what we're imagining is that basically serial speed starts to matter a bunch because you switch to AI researchers that have like orders of magnitude more serial speed than humans.

But it tops out.

Like, we think that, like, over the course of our scenario, if you look at our, like, sliding stats chart, it goes from, like, 20x to, like, 90x or something over the course of the scenario, which is important but, like, not huge.

And also we think that, like,

Once you start getting like 90x serial speed, you're just like bottlenecked on the other stuff.

And so like additional improvements in serial speed basically don't help that much.