Enabling independent research on how people use Claude

Earlier this year, we ran a pilot giving external researchers access to aggregate, real-world Claude usage data. Three research groups designed their own studies for Anthropic Insights, our privacy-preserving analysis tool; we ran the data collection on their behalf, and they conducted their own independent analysis. In this post, we share high-level results from those studies and what we learned running this pilot. We’re also providing an expression of interest form for researchers who may want to work with us in the future.

Ensuring the transition to transformative AI goes well requires understanding its impact on people and society. Right now, data on real-world interactions with AI is concentrated in a handful of labs. We think it would be good if more data was made widely available—to researchers, policymakers, and the general public.

Researchers outside the labs have two options. They can draw on analyses the labs publish, which reflect real usage but often answer the lab’s questions, rather than their own. Or they can use public datasets, which they can study however they like, but skew toward more casual use, and may not reflect how most people actually use AI. Neither is sufficient for independent research on how AI is actually being used.

METR is estimating real-world productivity gains from coding agents and how these increases in productivity change across model generations. Their analysis of Claude Code conversations is still underway, but early results suggest:

Understanding AI’s effects on society is too big a job for AI companies alone. Real oversight needs external researchers asking their own questions of real-world usage data and publishing what they find independently.

This pilot was an experiment: could external researchers conduct independent studies on our platform without compromising our users’ privacy? The effort was more challenging than we expected, and we learned many lessons, but so far the answer seems to be yes. Our partners pursued research we would not have thought to design ourselves, and each told us something new about AI’s real-world impacts. This is a promising first step, but there is far more to do.

The next step is for us to determine whether we can scale this program, both in what kinds of studies we can support given the constraints described above, and in how many we can run at once. We are starting slowly to ensure privacy, safety, and research quality. We want to gauge interest and understand what researchers would want to study. If you are a researcher and access to Anthropic Insights would let you pursue work you cannot do today, please fill out this form .

Kunal Handa led the project, collaborated with the partners on their research proposals, drafted research guidance and the contract, ran partners’ Anthropic Insights studies, helped build the technical infrastructure supporting partners’ research, contributed to the internal review of the Anthropic Insights outputs, and wrote the blog post. Miranda Zhang coordinated the partnerships and communications, and contributed to all parts of the work. Gabriel Nicholas coordinated the third-party privacy audit, internal review of Anthropic Insights’ outputs, and contributed to all parts of the work. Miles McCain wrote the guidance on interpreting Anthropic Insights outputs, developed technical infrastructure to support partners’ research, contributed to the internal review of the Anthropic Insights outputs, and provided feedback on the blog post and privacy threat model. Ryan Heller contributed technical infrastructure to support partners’ research. Saffron Huang contributed to the internal review of the Anthropic Insights outputs and provided key feedback and discussion. Thomas Millar and Suzanne Wang contributed technical infrastructure to support partners’ research and to the internal review of the Anthropic Insights outputs. Shan Carter, Mo Julapalli, Matt Kearney, Sarah Pollack, and Judy Shen contributed to the internal review of the Anthropic Insights’ outputs. Matthew Jagielski contributed to the privacy threat model. Shaoyi Zhang contributed technical infrastructure to support partners’ research. Heather Whitney, Ankur Rathi, Aisling Keenan, and David Saunders provided legal and privacy guidance throughout the project. Jake Eaton and Sylvie Carr contributed to the framing and writing of the blog post. Jack Clark and Michael Stern provided valuable guidance, support, and discussion throughout the process. Deep Ganguli provided detailed guidance, organizational support, and feedback throughout all stages of the project.

Additionally, we thank Miriam Chaum, Ishita Dasgupta, Esin Durmus, Adam Farina, Zoe Hitzig, Jerry Hong, Devin Kuokka, Hendson Lin, Maxim Massenkoff, Peter McCrory, Maryam Quasto, Nitarshan Rajkumar, Amie Rotherham, Divya Siddarth, Taylor Sorensen, Jerome Swannack, Alex Tamkin, Molly Villagra, Scott White, and Charles Yang for their helpful ideas, discussion, feedback, and support.

This spring, we piloted a program in which three external research institutions designed and ran their own studies on Claude usage data through Anthropic Insights (formerly named ‘Clio’), the privacy-preserving tool our own teams use to analyze usage patterns across millions of Claude conversations. We hope to scale this program in the future, so we also conducted an additional privacy audit of all data shared with third-party researchers to verify that our privacy protections held (see Appendix ).

We believe this is the first time external researchers have run public independent studies on an AI company's own usage data. Below, we discuss what the external teams found, what we learned running the pilot, and what we are weighing as we decide how to expand the program more widely. We are also publicly releasing the aggregate data from each project .

We partnered with three research groups: the Social and Language Technologies (SALT) Lab at Stanford University, the Human Information Processing Lab at the University of Oxford, and METR , a non-profit organization that evaluates frontier AI models. Each group developed its own research questions and used Anthropic Insights to conduct privacy-preserving analysis of roughly 250,000 Claude.ai or Claude Code conversations from April-May 2026.