Coding Agents Social Sciences

Coding agents in the social sciences \ Anthropic Economic Research Coding agents in the social sciences May 27, 2026

Summary We present results from a survey of 1,260 social scientists about AI and coding agent use, fielded in February and March 2026. The vast majority of respondents (81%) have tried using AI chatbots in research, particularly for writing code and editing prose. But only 20% have adopted coding agents—tools like Claude Code that autonomously write and execute analysis code—into their work. There are sharp disparities in use of coding agents. Twice as many researchers with typically male names use coding agents as those with female names. Researchers at top universities are 40% more likely than others to use coding agents. Users of coding agents post more working papers and grant proposals than others in the same discipline and career stage, but this could reflect pre-existing differences among early adopters. Researchers are more optimistic about AI helping write publishable papers than about the effects of AI on the social sciences as a whole.

How are AI coding agents changing how we study the economy and society? 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. 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. A new survey on AI coding agent use among quantitative social scientists 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. Coding agents haven't reached most social scientists We measured overall AI use in two ways. First, we asked “Have you previously used genAI models to aid your research process?” 81% of respondents said yes. 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). Figure 1: Economists and political scientists report the most use of coding agents. Coding agent use is yes to “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)?” and a follow-up question verifying use of one of those tools, or Google Antigravity. Discipline categories described in appendix. Adoption is highly uneven Figure 1 shows there is large variation in the overall adoption rate, from 39% of economists and 25% of political scientists to single digits for public health (6%), education (4%) and communication (6%). This gradient roughly tracks differences across fields in overall AI use, but differences in coding agent adoption are steeper on average. Just over a quarter of doctoral students and postdocs use coding agents at least weekly; among tenured professors that rate falls by more than half. The researchers adopting coding agents are the juniors—more technologically fluent, more likely to be working directly with code and data, and facing stronger career pressures to produce research. Figure 2: Adoption is highest among early-career researchers. The blue series shows the percent reporting AI use to aid in research, by career stage. The orange series below shows the percent reporting regular coding agent use for research. Adoption differences extend beyond discipline and career…