Sam McCandlishCo-founder and Chief Architect · AnthropicResearcher↗
Researcher · Founder
Tom Brown
AnthropicCo-founder
Brown led the GPT-3 paper and engineering effort, showing that a large language model could perform many new tasks from examples in a prompt without being retrained. The result established in-context learning as a defining capability of modern language models; he later co-founded Anthropic.
Career & education
Current roleAnthropic2021-presentCo-founder
Anthropic2021-presentPreviously
OpenAI2018-2020Research Engineering Lead
Google DeepMind2017-2018Member of Technical StaffOpenAI2016-2017Member of Technical Staff
OpenAI2018-2020Google DeepMind2017-2018OpenAI2016-2017Work and education history is primarily focused on AI-relevant roles and may not be comprehensive.
Citation Trend
Total citations to scholarly work, including research papers, technical reports, books and theses, as recorded by Google Scholar. Each point is a dated snapshot.
citations154,526As of Oct 1, '26
citations+4,117Since last snapshot, Sep 1, '26
Selected contributions
Constitutional AI: Harmlessness from AI Feedback
With Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen and others ·Introduces a way to train AI assistants using a written set of principles. Models critique and revise their own answers, then learn from AI-generated feedback to reduce harmful responses while relying on fewer human judgments.
Language Models are Few-Shot Learners
With Ben Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam and others ·Introduced GPT-3, showing that a large language model could perform varied tasks from written prompts and a few examples, without separate training for each task.
Scaling Laws for Neural Language Models
With Jared Kaplan, Sam McCandlish, Tom Henighan, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford and others ·Measured how language model performance changes with model size, training data and computation, finding predictable relationships that helped guide how to allocate resources for training.
Based on public sources. If accuracy is important, verify details using the links or references.
For corrections, updates, or requests to remove information, contact us.
Reviewed August 8, 2026 · Checked for accuracy and new information.
