Quick Assessment
| Dimension | Assessment |
|---|---|
| Primary Role | Executive Director, Center for AI Safety (CAIS); AI safety researcher |
| Key Contributions | Developed MMLU and ETHICS benchmarks for evaluating language models; proposed the GELU activation function (adopted in BERT and GPT-4 series); foundational work on out-of-distribution detection; co-authored papers on robustness and ML safety; coordinated the May 2023 statement on AI extinction risk |
| Key Publications | A Baseline for Detecting Misclassified and Out-of-Distribution Examples (ICLR 2017); Gaussian Error Linear Units (GELUs) (arXiv 2016); Measuring Massive Multitask Language Understanding (ICLR 2021); Aligning AI With Shared Human Values (ICLR 2021); Natural Adversarial Examples (CVPR 2021); Unsolved Problems in ML Safety (arXiv 2021); Introduction to AI Safety, Ethics, and Society (CRC Press, 2024); Superintelligence Strategy (arXiv 2025) |
| Institutional Affiliation | Center for AI Safety (CAIS), San Francisco; advisor to xAI and Scale AI |
| Education | B.S. with Honors, Computer Science, University of Chicago (2018); Ph.D., Computer Science, UC Berkeley (2022) |
| Influence on AI Safety | CAIS produces safety research, educational resources, and policy advocacy; Hendrycks co-authored NIST AI Risk Management Framework input (2022) and co-authored Superintelligence Strategy (2025) with Eric Schmidt and Alexandr Wang |
Overview
Dan Hendrycks (born 1994 or 1995) is a computer scientist and AI safety researcher who serves as executive director of the Center for AI Safety (CAIS), a San Francisco-based nonprofit he co-founded in 2022 with Oliver Zhang. During his doctoral research at UC Berkeley — advised by Jacob Steinhardt and Dawn Song — he developed several benchmarks that became widely used reference points for evaluating large language models, including MMLU and the ETHICS dataset, both published at ICLR 2021. His dissertation, titled Machine Learning Safety, was completed in 2022.
Prior to his benchmark work, Hendrycks co-authored two papers that became foundational in the deep learning literature: a 2016 arXiv preprint proposing the GELU activation function (later adopted in BERT, GPT-2, and subsequent transformer architectures), and a 2017 ICLR paper establishing a simple baseline for out-of-distribution detection using maximum softmax probabilities, which accumulated over 3,800 citations on Semantic Scholar and is regarded as a foundational reference in the OOD detection literature.
Through CAIS, Hendrycks has combined continued technical research with field-building and policy engagement. In May 2023 he coordinated a public statement asserting that AI extinction risk should be treated as a global priority, which drew over 350 initial signatories — a count that grew to more than 500 as the page remained open — including Turing Award winners and executives from major AI laboratories. In 2024 he published an open-access textbook, Introduction to AI Safety, Ethics, and Society, through CRC Press (Taylor & Francis). In March 2025 he co-authored Superintelligence Strategy with former Google CEO Eric Schmidt and Scale AI CEO Alexandr Wang.