Astral Codex Ten

Bio Anchors

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Bio Anchors

Article

Bio Anchors is a recurring organization in the Astral Codex Ten archive, appearing 2 times across 2 issues between June 20, 2023 and February 12, 2026. The archive places it in contexts such as “The commentary around Bio Anchors made me suspect that every AI timelines prediction is based on vibes”; “Ajeya Cotra of Bio Anchors updated her estimate”; “This is how I treat Bio Anchors’ successors”. It most often appears alongside AGI, Ajeya Cotra, Bio Anchors.

Metadata

  • Category: Organizations
  • Mention count: 2
  • Issue count: 2
  • First seen: June 20, 2023
  • Last seen: February 12, 2026

Appears In

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

Davidson On Takeoff Speeds
June 20, 2023 · Original source
The basic Bio Anchors model Compute-Centric Framework (from here on CCF) update Bio Anchors to include feedback loops: what happens when AIs start helping with AI research? In some sense, AIs already help with this. Probably some people at OpenAI use Codex or other programmer-assisting-AIs to help write their software. That means they finish their software a little faster, which makes the OpenAI product cycle a little faster. Let’s say Codex “does 1% of the work” in creating a new AI. Maybe some more advanced AI could do 2%, 5%, or 50%. And by definition, an AGI - one that can do anything humans do - could do 100%. AI works a lot faster than humans. And you can spin up millions of instances much cheaper than you can train millions of employees. What happens when this feedback loop starts kicking in? You get what futurists call a “takeoff”. The first graph shows a world with no takeoff. Past AI progress doesn’t speed up future AI progress. The field moves forward at some constant rate. The second graph shows a world with a gradual “slow” takeoff. Early AIs (eg Codex) speed up AI progress a little. Intermediate AIs (eg an AI that can help predict optimal parameter values) might speed up AI research more. Later AIs (eg autonomous near-human level AIs) could do the vast majority of AI research work, speeding it up many times. We would expect the early stages of this process to take slightly less time than we would naively expect, and the latter stages to take much less time, since AIs are doing most of the work. The third graph shows a world with a sudden “fast” takeoff. Maybe there’s some single key insight that takes AIs from “mere brute-force pattern matchers” to “true intelligence”. Whenever you get this insight, AIs go from far-below-human-level to human-level or beyond, no gradual progress necessary. Before, I mentioned one reason Davidson doesn’t like these terms - “slow takeoff” can be fast. It’s actually worse than this; in some sense, a “slow takeoff” will necessarily be faster than a “fast takeoff” - if you superimpose the red and blue graphs above, the red line will be higher at every point1. CCF departs from this terminology in favor of trying to predict a particular length of takeoff in real time units. Specifically, it asks: how long will it take to go from the kind of early-to-intermediate AI that can automate 20% of jobs, to the truly-human-level AI that can automate 100% of jobs? (“Can automate” here means “is theoretically smart enough to automate” - actual automation will depend on companies fine-tuning it for specific tasks and providing it with the necessary machinery; for example, even a very smart AI can’t do plumbing until someone connects it to a robot body to do the dirty work. CCF will talk more about these kinds of considerations later.) In order to figure this out, it needs to figure out the interplay of a lot of different factors. I’m going to focus on the three I find most interesting: How much more compute does it take to train the AI that can automate 100% of the economy, compared to the one that can automate 20%?
Inline links: https://substackcdn.com/image/fetch/$s_!31WF!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F434b15a2-51c0-4b26-a887-41da480ee3ca_942x350.png, “takeoff”, Codex, 1
Like Bio Anchors, CCF ranks all current and future AIs on a one-dimensional scale: how much effective compute does takes to train them? It assumes that more effective compute = more intelligence. See the discussion of Bio Anchors for a justification of this assumption.
Inline links: the discussion of Bio Anchors
Training a current AI like GPT-4 takes about 10^24 FLOPs of compute2. Bio Anchors has already investigated how much compute it would take to train a human-level AI; their median estimate is 10^35 FLOPs3.
Inline links: 2, 3
What Happened With Bio Anchors?
February 12, 2026 · Original source
First, a refresher. What was Bio Anchors? How did it work?
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. Cotra picked five intuitively compelling guesses (the namesake Bio Anchors) and turned them into a weighted average.
In 2023, Tom Davidson published an updated version of Bio Anchors that added a term representing the possibility of recursive self-improvement. The new calculations shifted the median date of AGI from 2053 → 2043. This doesn’t explain why our own timeline seems to be going faster than Bio Anchors: even 2043 now feels on the late side, and anyway recursive self-improvement has barely begun to have effects.
Inline links: an updated version of Bio Anchors