AI Came for Math. Now It's Chasing the Rest of Science
AI may soon be the default way to solve theoretical problems in economics, high-energy physics, and other fields, MIT researchers tell WIRED.
By Will Knight | October 14, 2026
Artificial intelligence has already transformed mathematics, proving theorems and uncovering patterns that eluded human mathematicians for decades. Now, according to researchers at MIT, AI is poised to do the same for the rest of science.
In conversations with WIRED, MIT scientists suggest that AI could soon become the default tool for tackling theoretical problems in fields ranging from economics to high-energy physics. The shift promises to accelerate discovery and reshape how research is conducted.
From Math to All Sciences
The mathematical community has increasingly embraced AI as a collaborator. Systems like DeepMind's AlphaProof and other theorem-proving models have demonstrated the ability to solve complex problems and even suggest new avenues of inquiry. This success has not gone unnoticed in other disciplines.
MIT researchers believe the same techniques—large language models, reinforcement learning, and symbolic reasoning—can be adapted to domains where theoretical progress has traditionally been slow and labor-intensive.
Economics and Physics Next?
Economics, with its complex models and vast datasets, presents an obvious target. AI could help economists develop more accurate predictive models, test hypotheses at scale, and identify patterns in economic data that humans might miss.
High-energy physics, too, stands to benefit. The sheer volume of data generated by particle accelerators like the Large Hadron Collider requires sophisticated analysis. AI can sift through this data to detect rare events or subtle signals that could point to new physics beyond the Standard Model.
The Future of Theoretical Research
If AI becomes the default way to solve theoretical problems, the implications are profound. Researchers may spend less time on manual calculations and more time on creative problem-solving and interpreting AI-generated insights. The pace of discovery could accelerate dramatically.
However, challenges remain. AI models must be trained on domain-specific data, and their reasoning must be interpretable enough for scientists to trust their conclusions. MIT researchers acknowledge these hurdles but remain optimistic.
The article, originally published by WIRED, highlights a growing consensus: AI is not just a tool for mathematicians—it's on track to become an indispensable partner across the entire scientific enterprise.
This article was originally published by WIRED on October 14, 2026.
via Wired AI