Ajeya Cotra

Ajaya Khotra's Biological Anchors report was the landmark AI timelines forecast of the early 2020s.

In many ways, it was incredibly prescient.

It nailed the scaling hypothesis, predicted the current AI boom, and introduced concepts like time horizons that have entered common parlance.

In most cases where its contemporaries challenged it, its assumptions have been borne out and its challenges proven wrong.

But its headline prediction, an AGI timeline centered around the 2050s, no longer seems plausible.

The current state of the discussion ranges from late 2020s to 2040s, with the more remote dates relegated to those who expect the current paradigm to prove ultimately fruitless, the opposite of Ejea's assumptions.

Kotra later shortened her own timelines to 2040, as of 2022, and they are probably even shorter now.

So, if its premises were impressively correct, but its conclusion 20 years too late, what went wrong in the middle?

2.

First, a refresher.

What was BioAnchors?

How did it work?

In 2020, the most advanced AI, GPT-3, had required about 10 to the power of 23 flops to train.

Flops are a measure of computation.

Big, powerful computers and data centers can deploy more flops than smaller ones.

Kotra asked, how quickly is the AI industry getting access to more compute or more flops?

And how many flops would AGI take?

If we can figure out both those things, determining the date of AGI arrival becomes a matter of simple division.

She found that flops had been increasing at a constant rate for many years, and if you looked at planned data center construction, it looked on track to continue increasing at about that rate.

New technological advances, algorithmic progress, made each flop more valuable in training AIs, but that process also seemed constant and predictable.