The Thrill of Surprise

In recent research published in Nature, James Evans, Qianyue Hao, Fengli Xu, and Li Yong ask how AI use has shaped scientific careers and science as a whole. 

They analyzed more than 40 million research papers spanning four decades of natural science to understand how AI is reshaping science. The findings reveal a paradox.

For individual scientists, AI is a career accelerator. Researchers who adopt AI publish three times more papers with fewer authors, receive nearly five times more citations, and become research leaders nearly 1.5 years earlier than peers who don't. AI papers appear nearly 20% more frequently in top-quartile journals. Annual citations run approximately 100% higher than non-AI papers across three decades of follow-up.

For science as a whole, AI is a narrowing force. AI-augmented research covers nearly 5% less topical ground and generates more than 20% less engagement among follow-on researchers. The contraction appears in the vast majority of the 200+ subfields that were examined. Citation patterns show a starker concentration: in AI research, less than a quarter of papers capture 80% of all citations.

The mechanism is straightforward: AI use has shifted to where data is abundant, at an accelerating pace as models have grown larger. AI gravitates toward well-lit problems and away from foundational and emergent questions where data is necessarily sparse. The result is collective hill-climbing—everyone scaling the same popular peaks rather than searching for higher mountains.

This creates "lonely crowds" in the scientific literature: clusters of researchers converging on identical problems without building on each other's work.

The pattern holds across biology, chemistry, physics, medicine, materials science, and geology. It persists and increases through each wave of AI—from conventional machine learning through deep learning to today's generative models and LLMs.

This isn't inevitable. Models that are powerful at prediction can be inverted to identify what is surprising (to those predictions), and enable us to consider and theorize entailments to surprising new data and findings. But without deliberate intervention, local incentives have and will likely continue to push scientists to optimize what's already known rather than discover what isn't.

The history of major discoveries is linked to new ways of seeing nature. If we want AI to accelerate breakthroughs rather than automate the familiar, we need AI systems tuned to surprise that expand sensory and experimental capacity—not just cognition.

Paper: https://rdcu.be/eY5f7
Science commentary: https://lnkd.in/gcsAfkz9
Nature commentary: https://lnkd.in/gtpZ5AU8
Nature podcast: https://lnkd.in/gSiVmrDD

Project Team

James Evans
Qianyue Hao, Tsinghua University
Fengli Xu, Tsinghua University
Li Yong, Tsinghua University and Zhongguancun Academy