Dwarkesh Patel

And this is very often fair.

It's easy to underestimate the progress that AI has made over the last decade.

But some amount of GoPro shifting is actually justified.

If you showed me Gemini 3 in 2020, I would have been certain that it could automate half of knowledge work.

And so we keep solving what we thought were the sufficient bottlenecks to AGI.

We have models that have general understanding, they have few-shot learning, they have reasoning, and yet we still don't have AGI.

So what is a rational response to observing this?

I think it's totally reasonable to look at this and say, oh, actually, there's much more to intelligence and labor than I previously realized.

And while we're really close and in many ways have surpassed what I would have previously defined as AGI in the past, the fact that model companies are not making the trillions of dollars in revenue that would be implied by AGI clearly reveals that my previous definition of AGI was too narrow.

And I expect this to keep happening into the future.

I expect that by 2030, the labs will have made significant progress on my hobby horse of continual learning, and the models will be earning hundreds of billions of dollars in revenue a year.

But they won't have automated all knowledge work.

And I'll be like, look, we made a lot of progress, but we haven't hit AGI yet.

We also need these other capabilities.

We need X, Y and Z capabilities in these models.

Models keep getting more impressive at the rate that the short timelines people predict, but more useful at the rate that the long timelines people predict.

It's worth asking, what are we scaling?

With pre-trading, we had this extremely clean and general trend in improvement in loss across multiples orders of magnitude in compute.

Albeit, this was on a power law, which is as weak as exponential growth is strong.

But people are trying to launder the prestige that three-training scaling has, which is almost as predictable as a physical law of the universe, to justify bullish predictions about reinforcement learning from verifiable reward, for which we have no well-fit publicly known trend.