Early science acceleration experiments with GPT-5
Sébastien Bubeck, Christian Coester, Ronen Eldan, W. T. Gowers, Yin Tat Lee, Alexandru Lupsasca, Mehtaab S. Sawhney, Robert J. Scherrer, Mark Sellke, B. K. Spears, Derya Unutmaz, Kevin Weil, Yin, Steven, Nikita Zhivotovskiy
Papers open inside SciRah from the original publisher or repository; SciRah never hosts them.
Abstract
AI models like GPT-5 are an increasingly valuable tool for scientists, but many remain unaware of the capabilities of frontier AI. We present a collection of short case studies in which GPT-5 produced new, concrete steps in ongoing research across mathematics, physics, astronomy, computer science, biology, and materials science. In these examples, the authors highlight how AI accelerated their work, and where it fell short; where expert time was saved, and where human input was still key. We document the interactions of the human authors with GPT-5, as guiding examples of fruitful collaboration with AI. Of note, this paper includes four new results in mathematics (carefully verified by the human authors), underscoring how GPT-5 can help human mathematicians settle previously unsolved problems. These contributions are modest in scope but profound in implication, given the rate at which frontier AI is progressing.