GoKawiilAI is turning scientists into publishing machines—and quietly funneling them into the same crowded corners of research.
That’s the conclusion of an analysis of more than 40 million academic papers, which found that scientists who use AI tools in their research publish more papers, accumulate more citations, and reach leadership roles sooner than peers who don’t.
But there’s a catch. As individual scholars soar through the academic ranks, science as a whole shrinks its curiosity. AI-heavy research covers less topical ground, clusters around the same data-rich problems, and sparks less follow-on engagement between studies.
The findings highlight a tension between personal career advancement and collective scientific progress, as tools such as ChatGPT and AlphaFold seem to reward speed and scale—but not surprise.
“You have this conflict between individual incentives and science as a whole,” says James Evans, a sociologist at the University of Chicago who led the study.
And as more researchers pile onto the same scientific bandwagons, some experts worry about a feedback loop of conformity and declining originality. “This is very problematic,” says Luís Nunes Amaral, a physicist who studies complex systems at Northwestern University. “We are digging the same hole deeper and deeper.”
Evans and his colleagues published the findings 14 January in the journal Nature.
A long-standing interest in how science evolves
For Evans, the tension between efficiency and exploration is familiar terrain. He has spent more than a decade using massive publication and citation datasets to quantify how ideas spread, stall, and sometimes converge.
In 2008, he showed that the shift to online publishing and search made scientists more likely to read and cite the same highly visible papers, accelerating the dissemination of new ideas but narrowing the range of ideas in circulation. Later work detailed how career incentives quietly steer scientists toward safer, more crowded questions rather than riskier, original ones.
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