Summary
Agentic AI could unlock social science abundance
Highlights
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To understand exactly why AI is already accelerating social scientists’ research so dramatically, let me walk you through one of my projects in detail. Earlier this year, I uploaded my published 2020 study on vote-by-mail policy in California, Utah, and Washington to Claude. The study examines whether switching to universal vote-by-mail—where every registered voter is automatically sent a ballot—affects turnout and partisan vote share. Counties in these three states adopted the policy at different times, creating a natural experiment. I then asked Claude to replicate the findings and extend the analysis with new election data. Claude Code wrote Python scripts to run difference-in-differences regressions to estimate the causal effect of the policy, just like we had in our original paper. It scraped county-level election results from the California Secretary of State, the Utah Lieutenant Governor’s office, and the Washington Secretary of State, and pulled Census voting-age population data from the American Community Survey. It identified the specific election in which each county first adopted universal vote-by-mail, merged the new data with the original 1996–2018 panel, ran the analyses, produced tables and figures, and wrote a first draft of the paper. All twelve coefficients from the original study’s main tables replicated exactly—indicating that Claude was able to automatically verify the original research. The extension added new election cycles and found that vote-by-mail increases turnout by about two percentage points but has no systematic effect on Democratic vote share. The entire project—data collection, coding, analysis, and write-up—took under an hour. In contrast, the original paper took us several months.
Esta es también mi experiencia. Pega que antes tomaba muchísimo tiempo y era muy técnica hoy por hoy se puede delegar a LLM’s, y es un Horizonte que se está expandiendo rápidamente.
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People are now building systems that automate entire stages of the research pipeline—generating, evaluating, and replicating research at scales no human team could match.
Y esto no sólo aplica para la investigación, sino que para cualquier campo, incluida la consultoría y gestión de proyectos.
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When I first started using AI to accelerate my research, I thought it might lead to smaller labs with fewer human researchers and more agents. But that’s not how it’s played out so far, for me at least. At first, I spent a long time working directly with Claude Code. I still do that. But the more I’ve done it, the more it’s become clear to me that having a human come up with ideas, apply judgment, and guide Claude is essential. To scale the work, I realized I therefore needed more humans, not fewer. And that’s how my lab has now grown to include more than 10 research fellows, all overseeing their own versions of Claude.
Javin’s paradox
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This spring, I’m teaching an undergraduate course at Stanford called “Free Systems: Preserving Liberty in an Algorithmic World.” The students will spend the quarter building working prototypes of AI-powered political tools—and the best ones will compete in a final contest judged by builders and investors. These students aren’t software engineers. They’re undergrads who happen to live in an era when the barrier between initial idea and working version has effectively collapsed. One person with a laptop and an API key can now prototype things that would have required a team of developers just months ago.
Desarrolladores descalzos
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If we’re serious about 100x knowledge production, we need to rethink not just how research is done but how it’s packaged and shared. The current format—a static PDF published in a gated journal, with replication files theoretically available upon request—is a relic. It made sense when producing a paper was expensive, and distribution was scarce, but neither is true anymore. Research should increasingly live as code repositories and open data. Of course, this was already possible before AI. But AI makes it so easy that there’s really no excuse anymore.
Los LLM pueden potenciar tremendamente el movimiento de open science, haciéndolo dinámico y actualizable en tiempo real.