Ajeya Cotra

If this is true, then all three anchors, seconds, hours, and years, were off by at least an order of magnitude.

But it turns out that none of this matters very much.

The highest and lowest anchors cancel out, so that the most plausible anchor, human brain with time horizon of hours to days, is around the average.

If you remove all of the other anchors and just keep that one, the model's estimates barely change.

But also, we're talking about crossing 12 orders of magnitude here.

The difference between the different time horizon anchors doesn't register much on that level, compared to things like algorithmic progress, which have exponential effects.

Maybe this is the model basically working as intended.

You try lots of different anchors, put more weight on the more plausible ones, take a weighted average of each of them, and hopefully get something close to the real value.

Bio anchors did.

Or maybe it was just good luck.

Still hard to tell.

Eliezer Yudkowsky argued that the whole methodology was fundamentally flawed, partly because of the argument above, he didn't trust the anchors, but also partly because he expected the calculations to be obviated by some sort of paradigm shift that couldn't be shoehorned into algorithmic progress, like how you couldn't build an aeroplane in 1900 but you could in 1920.

As of 2026, still before AGI has been invented and we get a good historical perspective, no such shift has occurred.

The scaling laws have mostly held.

Whatever artificial space you try to measure models in, the measurement has mostly worked in a predictable way.

There have really only been two kinks in the history of AI so far.

First, a kink in training run size around 2010.

Here's a graph showing different notable AI models in terms of the training compute used over time, running all the way from 1955 to 2025, and we see that the gradient changes sharply upwards on a line of best fit around all these points at around 2010.

Second, a kink in time horizons around 2024 and the invention of test time compute.

Here's a graph from Meta.