at Anthropic focuses on how AI can increase the pace of scientific progress. Through internal research and collaborations with partners, we work to improve our models’ core scientific capabilities, investigate model-generated hypotheses in our molecular biology lab, build products, and provide support that allows researchers to safely speed up AI-assisted discovery across disciplines.
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Research
The scientific and discovery process consists of feedback loops: designing and conducting an experiment, interpreting the results, revising the question, and beginning again. We’re working to tighten these loops by developing tools that make researchers more efficient or help them develop entirely new approaches, and by building training environments and evaluations that help us improve Claude’s performance on scientific tasks.
AI for Science
We work with universities, research institutes, biotech companies, and academic medical centers—through direct API access, co-developed custom agents, applied AI technical support, and more—to get Claude into the hands of scientists. This allows us to incorporate feedback based on real-world evaluation and applied research across domains, demonstrating the tangible impacts AI is having on science.
The missing map of the sky
Brice Ménard, an astrophysicist at Johns Hopkins University and a researcher at Anthropic, explains how he worked with Claude Science to produce the first complete map of the sky in UV light.
Read moreClaude-shaped science
Guest author Prof. Matthew Schwartz describes what happened when he stopped fighting Claude and allowed Claude to find “Claude-shaped” problems: ones best suited to the capabilities of the current generation of LLM tools. This led him to build BootLoops, a toolkit for exact calculations in quantitative science, which he has been applying across scientific fields alongside experts.
Read moreFormalizing Fermat's Last Theorem
We are sharing the first complete computer-checked proof of Fermat’s Last Theorem. Claude worked largely autonomously over 11 days to write the proof in the Lean programming language.
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ARTICLES
Explore work from other teams
Alignment
47 posts
Works to understand the risks of AI models and develop ways to ensure future ones remain helpful, honest, and harmless.
Economics
25 posts
Studies and monitors the impact of AI systems on individuals, firms, and economies.
Engineering
27 posts
Builds the infrastructure and systems that make Claude reliable and trustworthy.
Frontier Red Team
33 posts
Stress-tests AI systems to understand the full extent of their current capabilities and anticipate what comes next.
Interpretability
35 posts
Studies how large language models work internally as a foundation for AI safety.
Societal Impacts
24 posts
Studies how AI affects people, both individually and collectively.
Science
18 posts
Explores how AI can increase the pace of scientific progress through both research and collaborations.
Alignment
47 posts
Works to understand the risks of AI models and develop ways to ensure future ones remain helpful, honest, and harmless.
Economics
25 posts
Studies and monitors the impact of AI systems on individuals, firms, and economies.
Engineering
27 posts
Builds the infrastructure and systems that make Claude reliable and trustworthy.
Frontier Red Team
33 posts
Stress-tests AI systems to understand the full extent of their current capabilities and anticipate what comes next.
Interpretability
35 posts
Studies how large language models work internally as a foundation for AI safety.
Societal Impacts
24 posts
Studies how AI affects people, both individually and collectively.
Science
18 posts
Explores how AI can increase the pace of scientific progress through both research and collaborations.