Enabling Independent Research

Enabling independent research on how people use Claude \ Anthropic Societal Impacts Enabling independent research on how people use Claude Aug 26, 2026

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. 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 . What the researchers learned 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. We wanted our external partners to have as much independence as possible, so our contractual review rights were limited to user privacy, information that could help people violate our usage policies, Anthropic’s confidential information, and research accuracy. Anthropic otherwise had no say in the content of the findings and the researchers are free to publish their results even if they are inconvenient for Anthropic. Below are some early results. We're excited about the directions, and about what others will find now that the data is public. The Social and Language Technologies Lab studied how humans collaborate with AI. They looked at what types of work people bring to AI, what roles humans retain in completing that work, and where human-AI collaboration breaks down. They found: People bring high-stakes work to AI more than expected. Prior research suggested people mostly delegate low-accountability tasks to AI and keep consequential tasks (that is, work that affects others or is hard to undo) for themselves. But the SALT Lab found that over half of Claude conversations involved people delegating consequential tasks to AI. People were most likely to bring consequential work to Claude when seeking professional guidance, particularly on legal or financial questions. People usually direct and oversee the work when they collaborate with Claude. In nearly three-quarters of conversations, people set the direction while Claude assisted, and they usually adapted its output rather than using it verbatim. But even when directing Claude on the output they want, people vary in how much they understand and learn from what Claude produces. It is common for people to experience friction when collaborating with AI. However, that friction is often productive. The time and effort that people put into seeing how Claude attempts a task, identifying where the request was unclear or misunderstood, and iterating on their direction leads to better results–it pushes people to clarify their intent, refine the output, or stay engaged with the problem.

Read their full writeup here . The Human Information Processing Lab is studying how people feel while using Claude and how that relates to Claude’s behavior. Their early results indicate: How people feel when using AI is linked to how AI behaves. The researchers found patterns of human and AI behavior appeared together in conversations: Claude being warm went together with people being more positive. Claude refusing or disagreeing went together with people pushing back. Claude being eccentric went together with people getting more intellectually engaged. And Claude simply helping went together with people seeming satisfied. People’s experience when using AI looks a lot like it does on the rest of the web. The researchers found that the patterns among states like absorption, frustration, and enjoyment in Claude conversations closely resemble those in a separate study on everyday internet browsing, suggesting similarities in how people engage with AI and with other digital activity.

They are still completing their writeup. When it is public, we will add a link to it here. 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: More capable models may save users more time. METR compared Claude’s guesses on how long tasks would have taken without AI to how long they actually took with different Claude models. Their preliminary findings indicate newer models deliver significant speedup over older models. METR plans on sharing more as their analysis develops. AI can...