Ajeya Cotra
Technical Staff, METR
About
Ajeya Cotra works on threat modeling and risk assessment for loss-of-control risks from advanced AI at METR. She previously led the technical AI safety program at Open Philanthropy (now Coefficient Giving), where she developed the influential Biological Anchors framework for forecasting when transformative AI might arrive. She holds a B.S. in Electrical Engineering and Computer Science from UC Berkeley.
Key Contributions
- Developed the Biological Anchors framework, one of the most detailed attempts to forecast transformative AI from compute and brain-inspired reference classes
- Led Open Philanthropy's technical AI safety grantmaking, shaping which alignment and governance projects received early funding
- Analyzed compute scaling and training-cost trends before they became central to mainstream AI policy debates
- Now works at METR on threat models and evaluations for loss-of-control risks from advanced AI systems
- Her work is influential in effective-altruist AI safety circles, but its long-horizon assumptions remain contested by shorter-term and skeptical researchers
Videos & Interviews
Connections
Daniel Kokotajlo
DebatedExecutive Director, AI Futures Project
Cotra's biological anchors report gave AI forecasting its first serious model: compute, brain-derived reference classes, an explicit median. Kokotajlo's 'Fun with +12 OOMs of Compute' (2021) pressed on it from the short side, arguing that the model's own machinery implied far earlier dates than it reported; her 2022 update thanks him in the acknowledgements, answers his questions in the comments, and moves her median from 2050 to 2040. It is a rare public case of a forecast being argued down by an argument rather than a mood.
lesswrong.com · alignmentforum.org
Leopold Aschenbrenner
KindredFounder & CIO, Situational Awareness LP
An interpretive pairing of two people who count the same thing and feel differently about it. Cotra established the habit of forecasting AI in orders of magnitude of effective compute; Situational Awareness runs on exactly that arithmetic, stacking OOMs forward from GPT-4 to an automated researcher. What separates them is temperament rather than method — she publishes ranges and revises them, he publishes a trajectory and acts on it.
