Coding agents in the social sciences

The human sciences are shifting: for the first time, core research tasks can be handed off to machines. AI chatbots increasingly contribute to scientific research, including in the most prestigious publications and in the social sciences . This has spurred optimism that AI could boost research productivity—while also stoking fears about overloaded peer review and a deluge of academic AI slop.

But while turn-taking AI chatbots have primarily been used for writing assistance , coding agents could restructure social science research more radically. Agentic coding platforms like Claude Code and Codex can take a research idea and a dataset, write and run an analysis, interpret the output, and iterate autonomously. What had been irreducibly human steps in empirical research can, for the first time, be automated . At the extreme, researchers have built multi-agent pipelines to automate computer science research and autonomously execute social science research ideas .

These tools could accelerate science and make it more daring: fast research execution should mean cheap and plentiful discovery. They could also amplify disparities in research resources and exacerbate congestion in the scholarly record. More deeply, as AI handles a broadening swath of research tasks, its distinctive analytical choices could stamp our collective understanding of our economy, our society, and ourselves.

But what about those who have actually adopted increasingly capable coding agents into their workflow? Here, we asked “Do you regularly (more than once a week) use an AI coding assistant integrated into your command line (such as Codex, Cursor, or Claude Code)?” In a follow-up question, we verified that they used one of those tools (or Google Antigravity). 1

Only 20% of respondents use coding agents. Our survey came around two months after a flurry of discussion about Claude Code and Opus 4.6 that kicked off in late December of 2025. Yet even among interested respondents who self-selected into our survey, only ⅕ had adopted agents into their workflow. Claude Code is the most common coding agent tool reported, with 86% of users reporting Claude Code use (31% report using Codex, the next most common tool).

In this post, we offer a first look, drawing on a survey of 1,260 quantitative social scientists fielded in early 2026. The survey is the baseline wave of a larger ongoing study of how coding agents affect research productivity, including a randomized experiment providing researchers with access to Claude Code. We will publish results from this experiment in the future. For now, we report what the baseline survey reveals about who is using these tools and for what; how output differs between users and non-users; and what researchers expect about the implications of growing adoption.

We fielded the survey in late February and March 2026, targeting active quantitative social scientists. This was not a representative sample—respondents were recruited for a study that offered access to Claude Max accounts, so selection into the sample could tilt toward researchers curious about AI tools. However, the respondents were fairly similar to an earlier sample that received a more generic invitation (see Table A2 in the Appendix ).

Respondents were evenly split between economics, political science and sociology, each around a fifth of the sample, with management sciences and psychology close behind (see Table A1). We also received a smaller number of responses from public health, education and communications researchers. Roughly 40% were full or associate professors, 25% were assistant professors, and about 30% were doctoral students.