Quick Assessment
| Dimension | Assessment |
|---|---|
| Current Role | Independent researcher; previously at OpenAI Governance team (2022–2024) and Google DeepMind (2020–2022) |
| Education | MPhil in Philosophy of Physics, Cambridge; MSc in Machine Learning, UCL; undergraduate at Oxford |
| Key Contributions | "AGI safety from first principles" essay series (LessWrong, 2020); lead author on "The Alignment Problem from a Deep Learning Perspective" (ICLR 2024); essays on power-seeking and inner alignment that have shaped community discourse |
| Communication Role | Among the field's most cited essayists; widely regarded as one of the clearest written-articulation of the case for AI risk from a contemporary deep-learning perspective |
| Public Profile | Active on LessWrong, the Alignment Forum, and Twitter/X; engages with both safety-skeptical and safety-focused audiences |
Overview
Richard Ngo is an AI safety and governance researcher whose career has spanned Google DeepMind (2020–2022), OpenAI's Governance team (2022–2024), and independent research. He is widely cited as one of the clearest contemporary writers articulating the case for taking AI risk seriously from a deep-learning-grounded perspective — distinct from the earlier Yudkowsky-era arguments that pre-dated the modern transformer era.
His "AGI safety from first principles" essay sequence — published on LessWrong in 2020 — was an attempt to restate the safety argument in language accessible to mainstream ML researchers, and has been one of the most-linked introductory resources for the field. His subsequent paper "The Alignment Problem from a Deep Learning Perspective" (ICLR 2024, with Lawrence Chan and Sören Mindermann) was an effort to bring those arguments into the academic peer-reviewed literature.