Title: Coding agents in the social sciences

URL Source: https://www.anthropic.com/research/coding-agents-social-sciences

Markdown Content:
## 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](https://www.nature.com/articles/s41562-025-02273-8) and in the [social sciences](https://sociologicalscience.com/articles-v13-3-45/). 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](https://sociologicalscience.com/articles-v13-3-45/), 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](https://www.nber.org/papers/w34202). At the extreme, researchers have built multi-agent pipelines to automate [computer science research](https://www.nature.com/articles/s41586-026-10265-5) and autonomously execute social [science research ideas](https://osf.io/preprints/socarxiv/24xfq_v1).

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](https://cdn.sanity.io/files/4zrzovbb/website/403415e54964751190003985896630e56829e797.pdf)).

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).

![Image 1](https://www.anthropic.com/_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2F370ed50b68c61c0c3f9341d6327d8ccb552667cd-3840x2160.jpg&w=3840&q=75)

**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.

![Image 2](https://www.anthropic.com/_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2Fec30771367cfb20421e7c41b22dedd4762ae198b-3840x2160.jpg&w=3840&q=75)

**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 stage. We classify researcher names according to gender and find that those with typically male names have adopted coding agents at more than twice the rate of respondents with typically female names. High-status and private universities also see notably higher use. All of these differences are significant at the p<0.05 level. These differences are starker than the differences in overall AI use, and suggest higher inequality, at least in this early period of coding agent adoption.

The gender gap in coding agent use does not just reflect a gap in rates of trying AI. Among respondents who have tried using AI for research, there is even a slightly larger gender gap in regular coding agent use than in the overall sample. These differences also persist when comparing across genders in the same disciplines and career stages.

![Image 3](https://www.anthropic.com/_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2Fd695e1325aaa79f163b780a1b0942a1d468e05fa-3840x2160.jpg&w=3840&q=75)

**Figure 3: Adoption of coding agents shows sharp disparities.**_Differences in the percent of researchers using AI (left panel) and coding agents (right panel) by career stage, sex, and university attributes. Male and female categories are measured through name classification. Top 25 universities are defined based on Nature Index Leading Institutions. All differences in adoption between groups are statistically significant with p < 0.05._

## **Researchers mainly use AI to code and edit, not write**

Among researchers using AI, whether through coding agents or chatbots, what are they actually using it for? Debate about AI in academic research has focused heavily on writing: hallucinated literature reviews, “it’s not X, it’s Y” strewn across formulaic introductions, and the possibility of fully automated paper writing.

But Figure 4 shows that the most common use, for both coding agent users and others, is for coding up analysis of quantitative data: 97% of coding agent users and 77% of other AI users report using it to generate code. Next most common is editing prose, followed by asking for methods advice and background on prior research. Aggregating across coding agent users and others, only a third of all AI users have used it to draft prose at all. These patterns generally hold across disciplines, with only economists and management researchers commonly using AI to draft prose.

![Image 4](https://www.anthropic.com/_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2F76e73af10dd9410c9918d3f915b4a74fc5aae348-3840x2160.jpg&w=3840&q=75)

**Figure 4: The most common use cases are code and editing.**_The left panel shows the share of users who indicated each specific use case, split by whether they use coding agents or only genAI for research. Respondents can indicate multiple categories (the question is limited to research use cases). The right panel shows these same responses split by discipline._

## **Coding agent users are posting more working papers and sending out more grant proposals, but not submitting more to journals**

Are coding agents making researchers more productive? That’s the question motivating the broader study this survey kicks off. The experiment we are running on this question is still ongoing. But the baseline survey lets us compare coding agent users to others across a whole bunch of checkpoints in the research process. This comparison is purely descriptive: we compare researchers who select into coding agent use to those who do not, and expect that the two groups differ in a number of ways that we cannot adjust for. Differences should not be interpreted as causal, but as a first cut comparison between researchers using coding agents and those who are not.

![Image 5](https://www.anthropic.com/_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2F1d115c36e9b18bb674ddea85e662ccd71a9bf839-3840x2160.jpg&w=3840&q=75)

**Figure 5: Productivity differences in research output between regular AI coding agent users and other researchers.**_Outcomes are self-reported by respondents. Working papers posted, journal submissions and journal resubmissions are mutually exclusive categories. Model is described in appendix. Whiskers show 95% confidence intervals calculated using the robust standard errors scaled by the outcome mean. * p < 0.05, ** p < 0.01._

Figure 5 shows self-reported output over the six months before the survey at different stages of the research process, from projects started to papers submitted. The adjusted estimates compare coding agent users to others, controlling for career stage, discipline, and the week they completed the survey. Coding agent users are starting more projects, posting more working papers, submitting more grants, and possibly sending out more conference submissions.

So are coding agent users writing more papers? First, consider the differences in early pipeline output between coding agent users and others. Coding agent users are starting projects at a pace of around a quarter of a paper more and posting around a half of a working paper more than non agent users. In percentage terms, coding agent users look around 10% (empirical projects started) to 75% (working papers posted) more productive than others in their discipline and career stage.

However, this productivity difference only appears for these early pipeline measures. We find no evidence that coding agent users are submitting more new papers to journals or resubmitting papers more quickly. This could reflect the timeline of getting a paper to submission, as coding agent use is a recent phenomenon. But it could also reflect that coding agents are more useful at getting projects up and running than they are at the last mile of perfecting a paper for journal submission.

## **Researchers expect AI tools to raise productivity, but are less confident that they will improve social science overall**

We also asked researchers what they expected of AI tools. Does AI make social scientists more productive, in terms of writing publishable papers? And do they think AI will make the social sciences better or worse?

Researchers are optimistic about AI raising paper-writing productivity. On a 1 to 10 scale, 88% of respondents were above a 5, and half were at 8 or above. Figure 6 shows that these ratings vary strongly with AI use. The left side of the plot shows researchers that use AI for more types of tasks are more optimistic. The right side shows coding agent users are more optimistic than others.

![Image 6](https://www.anthropic.com/_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2F54cd57088b2e767d07f51a9f7ceea73083358018-3840x2160.jpg&w=3840&q=75)

**Figure 6: Researcher beliefs about how AI will impact productivity narrowly compared to beliefs about how AI will impact the overall field of research.**_Beliefs are measured on a 10-point slider scale. Productivity is: “In terms of writing publishable papers, do you think AI makes social scientists less or more productive” with the slider labeled “less productive to more productive.” Field impact is: “Do you think AI will make the social sciences worse or better," with the slider labeled “Worse to better.” Number of AI use cases adopted sums the categories listed in Figure 4._

The survey is drawing from people who are interested in trying these tools out, so it should not be surprising to see some optimism about productivity. But even among these optimists, there is a real gap between views about AI helping narrowly with publishable papers and broadly affecting the social sciences. 70% of respondents are more optimistic about paper productivity than about broader field impact. There are few researchers more optimistic about field impacts than about paper productivity, and many who are more pessimistic.

## Discussion

Social scientists who are using coding agents are posting more working papers and applying for more grants. Relative to others in their discipline and career stage, they are also starting more projects. But as of March 2026, they are not yet driving a surge in journal submissions. The increased number of project starts may be early evidence of productivity increasing. It could also indicate that early adopters were already more productive researchers.

Overall, early adoption of coding agents has tilted toward early career researchers, men, and those at higher status universities. Coding agent use is currently more unevenly distributed across these categories than LLM use more broadly. We also find suggestive evidence that researchers fear that the immediate benefits of rising paper productivity may come along with field-level costs. Perhaps more papers means congestion and competition for attention; perhaps respondents fear that some researchers will use AI tools in ways that exacerbate existing problems in social science, like selective reporting and risk-averse, incremental research.

There are several caveats to the findings in this report. The data presented here is based on an email survey of quantitative social scientists, recruited explicitly to participate in a study on workflows and AI use. We expect that the respondents are both heavier users and more optimistic about LLMs than non-responders. The early-stage productivity differences we see should be interpreted descriptively. The early adopters of coding agents may be more productive and otherwise different from non-adopters in many ways that we cannot measure directly in the survey. Finally, we only look here at the number of projects researchers report, and report nothing about their quality. In future updates on this study, we will show results comparing coding agent users to a clean comparison group, and assess whether the content, and not just quantity, of coding agent augmented work looks different.

Notwithstanding these limitations, we show that coding agents are diffusing into the social sciences. The way we study the economy and politics, for example, is increasingly through analysis decisions made in part by AI coding agents. We plan to bring more evidence in future reports on the potential and risks of this kind of automation.

Thomas Lyttelton, Maxim Massenkoff, Nathan Wilmers

### Acknowledgements

Tim Belonax, Keir Bradwell, Jake Eaton, Rebecca Hiscott, Peter McCrory, Kerry Persen, Sarah Pollack, Santi Ruiz, Heather Whitney, Jack Clark.

### Appendix

Available [here](https://cdn.sanity.io/files/4zrzovbb/website/403415e54964751190003985896630e56829e797.pdf).

### Citation

```
@online{lyttelton2026agents,
 author = {Thomas Lyttelton and Maxim Massenkoff and Nathan Wilmers},
 title = {Coding Agents in the Social Sciences},
 date = {2026-05-27},
 year = {2026},
 url = {https://www.anthropic.com/research/coding-agents-social-sciences},
}
```

### References

Alvero, A. J., Stoltz, D. S., Stuhler, O., & Taylor, M. A. (2026). Generative AI in Sociological Research: State of the Discipline. _Sociological Science_, _13_, 45-62.

Korinek, A. (2025). _AI agents for economic research_ (No. w34202). National Bureau of Economic Research. NBER Working Paper Series.

Liang, W., Zhang, Y., Wu, Z., Lepp, H., Ji, W., Zhao, X., ... & Zou, J. (2025). Quantifying large language model usage in scientific papers. _Nature Human Behaviour_, 1-11.

Lu, C., Lu, C., Lange, R. T., Yamada, Y., Hu, S., Foerster, J., ... & Clune, J. (2026). Towards end-to-end automation of AI research. _Nature_, _651_(8107), 914-919.

Wilmers, N., & Engzell, P. (2026). The Paper Factory. SocArXiv Preprints.

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