What Happened With Bio Anchors?
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Welcome to the Astral Codex X podcast for the 12th of February, 2026. Title, What Happened With Bio-Anchors? This is an audio version of Astral Codex X, Scott Alexander's Substack. If you like it, you can subscribe at astralcodex10.substack.com. The original post was Biological Anchors, A Trick That Might or Might Not Work. 1.
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?
What are Biological Anchors and why are they significant?
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. So there was relatively constant growth in effective flops, amount of computation available adjusted by ability to use that computation efficiently. There was no obvious way to know how many flops AGI would take, but there were some intuitively compelling guesses.
For example, an AGI that was as smart as humans might need a similar level of computing capacity as the human brain. Kortra picked five intuitively compelling guesses, the namesake bio-anchors, and turned them into a weighted average. Then she calculated, given the rate at which available flops were increasing and the number of flops needed for AGI, how long until we closed the distance and got AGI? At the time, I found this deeply unintuitive, but it's held up. Improvement in AI since 2020 really has come from compute, the construction of giant data centers. Improvement in the underlying technology really has been measurable in effective flops, that is, the multiple it provides to compute, rather than some totally different, incommensurable paradigm.
And Kotra's anchors, the intuitively compelling guesses about where AGI might be, match nicely with how far AI has improved since 2020, and how far it subjectively feels like it still has to go. All of the weird hard parts went as well as possible. So, again, what went wrong? 3. In 2023, Tom Davidson published an updated version of BioAnchors that added a term representing the possibility of recursive self-improvement. the new calculations shifted the median date of AGI from 2053 to 2043. This doesn't explain why our own timeline seems to be going faster than BioAnchor's. Even 2043 now feels like on the late side. And anyway, recursive self-improvement has barely begun to have effects. But in 2025, John Crocs published a thorough report card on Davidson's model.
He took his numbers from Epic, who used real data from the 2020-2025 period that earlier forecasters didn't have access to, as well as the latest projections for what AR companies plan to do over the next few years, to come up with more formal projections.
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