A growing majority of scientists are turning to AI to speed their work, with 62% in a recent Wiley survey saying they now use the tools, up from 45% a year earlier. Researchers report efficiency gains, more output, and quality improvements, and an arXiv analysis links AI use to faster career advancement and more citations. But the boom brings risks: high rates of concern about hallucinations, data security, opaque training data, and ethics have climbed to 87%. The pattern suggests AI may further advantage data-rich fields, potentially narrowing scientific diversity. Case studies, from astronomy’s rapid candidate triage to classroom-ready simulations powered by Claude, highlight the upside — and the dependence — that are reshaping how labs produce, vet, and communicate science.
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