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Two-Year Update on My Personal AI Timelines (Ajeya Cotra)
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Written by Ajeya Cotra (Open Philanthropy), this 2022 post updates her influential biological anchors report and is widely cited in AI safety circles as a serious attempt to forecast transformative AI timelines using empirical and methodological reasoning.
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Summary
Ajeya Cotra updates her 2020 biological anchors-based AI timeline forecasts, weighing factors that compress timelines (continued scaling, faster-than-expected progress, compute efficiency gains) against those that extend them. The post reflects on how LLM advances have shifted her probabilistic estimates for transformative AI. It serves as an important public record of expert timeline reasoning evolving in response to empirical evidence.
Key Points
- •Updates the 2020 biological anchors framework after two years of rapid AI progress, particularly in large language models.
- •Identifies timeline-shortening factors: sustained deep learning scaling, compute efficiency improvements, and faster-than-expected capability gains.
- •Also considers factors pushing toward longer timelines, maintaining calibrated uncertainty rather than a single confident estimate.
- •Demonstrates a Bayesian approach to forecasting transformative AI, openly revising priors in light of new evidence.
- •Provides a key data point in the public discourse on AI timelines from a prominent researcher at Open Philanthropy.
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Two-year update on my personal AI timelines — AI Alignment Forum
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Two-year update on my personal AI timelines
by Ajeya Cotra
2nd Aug 2022
19 min read
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I worked on my draft report on biological anchors for forecasting AI timelines mainly between ~May 2019 (three months after the release of GPT-2) and ~Jul 2020 (a month after the release of GPT-3), and posted it on LessWrong in Sep 2020 after an internal review process. At the time, my bottom line estimates from the bio anchors modeling exercise were:[1]
Roughly ~15% probability of transformative AI by 2036[2] (16 years from posting the report; 14 years from now).
A median of ~2050 for transformative AI (30 years from posting, 28 years from now).
These were roughly close to my all-things-considered probabilities at the time, as other salient analytical frames on timelines didn’t do much to push back on this view. (Though my subjective probabilities bounced around quite a lot around these values and if you’d asked me on different days and with different framings I’d have given meaningfully different numbers.)
It’s been about two years since the bulk of the work on that report was completed, during which I’ve mainly been thinking about AI. In that time it feels like very short timelines have become a lot more common and salient on LessWrong and in at least some parts of the ML community.
My personal timelines have also gotten considerably shorter over this period. I now expect something roughly like this:
~15% probability by 2030 (a decrease of ~6 years from 2036).
~35% probability by 2036 (a ~3x likelihood ratio[3] vs 15%).
This implies that each year in the 6 year period from 2030 to 2036 has an average of over 3% probability of TAI occurring in that particular year (smaller earlier and larger later).
A median of ~2040 (a decrease of ~10 years from 2050).
This implies that each year in the 4 year period from 2036 to 2040 has an average of almost 4% probability of TAI.
~60% probability by 2050 (a ~1.5x likelihood ratio vs 50%).
As a result, my timelines have also concentrated more around a somewhat narrower band of years. Previously, my probability increased from 10% to 60%[4] over the course of the ~32 years from ~2032 and ~2064; now this happens ove
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