GPT-5 for AI-assisted discovery
Many hope that AI will be smart enough" to make breakthrough scientific discoveries in the near future, such as find a cure for cancer. Some research efforts have sought to create an AI Scientist" that can make discoveries in an automated or semi-automated way; for a recent example, see [1]. Others [2] have called out the methodological pitfalls of some of these attempts. Still others question whether a truly original discovery is even possible at all for an AI.
OpenAI released GPT-5 on August 7. Some thought it lackluster and falling behind compared to expectations. Others however found performance in some areas to be much advanced compared to its predecessor.
Two recent reports show the new model's utility. Scott Aaronson published a paper last month [3], [4] in which a key technical step in the proof of the main result came from AI." Also, Terence Tao reported earlier this month [5] his use of ChatGPT to find a first counterexample to an unsolved mathematics problem.
I'm sure this resonates with the experience of other researchers using the tool. Recently, in the course of a discussion I had with ChatGPT, it came up with a new algorithmic recipe for something I was working on, based on ideas combined from several papers but in itself apparently original. That was a very simple case-but on a grander scale, connecting two ideas together in a novel way can lead to a real breakthrough. For example, Faltings' proof of the Mordell Conjecture in 1983 was based on recognizing a subtle internal connection among some already existing theorems.
There is always the specter of concern that an idea maybe was already in the training data." It can be difficult to prove otherwise. But deep domain experts like Scott Aaronson and Terence Tao are likely to know with high likelihood whether the idea is truly an original never-before-published result or not.
If past is prologue, we can hope for more powerful models in the future that can solve increasingly hard problems.
Notes[1] Yixuan Weng, Minjun Zhu, Qiujie Xie, Qiyao Sun, Zhen Lin, Sifan Liu, Yue Zhang, DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressively." https://arxiv.org/abs/2509.26603.
[2] Ziming Luo, Atoosa Kasirzadeh, Nihar B. Shah, The More You Automate, the Less You See: Hidden Pitfalls of AI Scientist Systems." https://arxiv.org/abs/2509.08713.
[3] The QMA Singularity." https://scottaaronson.blog/?p=9183
[4] Scott Aaronson, Freek Witteveen, Limits to black-box amplification in QMA." https://arxiv.org/abs/2509.21131.
[5] https://mathstodon.xyz/@tao/115306424727150237
The post GPT-5 for AI-assisted discovery first appeared on John D. Cook.