{"version":1,"type":"story","url":"https://digestai.news/story/mit-study-finds-ai-solved-35-of-theory-problems-by-mid-2026","json":"https://digestai.news/story/mit-study-finds-ai-solved-35-of-theory-problems-by-mid-2026.json","markdown":"https://digestai.news/story/mit-study-finds-ai-solved-35-of-theory-problems-by-mid-2026.md","slug":"mit-study-finds-ai-solved-35-of-theory-problems-by-mid-2026","headline":"MIT study finds AI solved 35% of theory problems by mid-2026","summary":"A new study from MIT’s Neil Thompson and collaborators shows AI models now solve about 35% of theoretical problems in probability, statistics, economics, physics, and cosmology—up from 2% in mid-2024. The research tested OpenAI’s models (o1, o3, GPT-5 to GPT-5.5) on problems published after their training cutoffs, blocking internet access to ensure solutions were generated independently. The findings suggest AI’s progress in math will soon extend to other theory-heavy fields, though Thompson warns of mixed effects: faster discovery but also risks to verification and the role of human experts.\n\nThe study follows OpenAI’s controversial claim of solving the Navier-Stokes equations—a grand challenge in fluid dynamics—with an unreleased model. Critics, including a NYU mathematician, accused OpenAI of ‘gazumping’ collaborators at Anthropic. Since then, AI labs have announced hundreds of solved math problems, prompting calls for limits on AI use in research. Thompson’s team emphasizes AI won’t replace experts but may accelerate theoretical work, while noting models can still produce errors. Labs like Anthropic and OpenAI are now investing heavily in AI-driven scientific research, blending theoretical analysis with experimental capabilities.","keyPoints":["MIT study finds OpenAI models solved 35% of theory problems by mid-2026, up from 2% in mid-2024","Researchers tested models on problems published after their training cutoffs, with no internet access","Thompson warns AI may accelerate discovery but risks undermining human verification and expertise"],"whyItMatters":"If AI can solve 35% of theoretical problems, it could reshape scientific research—speeding breakthroughs but also raising questions about credibility and the role of human oversight.","category":{"slug":"research","name":"Research","url":"https://digestai.news/category/research"},"entities":{"companies":["OpenAI","Anthropic"],"models":["o1","o3","GPT-5","GPT-5.1","GPT-5.4","GPT-5.5"],"people":["Neil Thompson","Gabriel Manso","Claudio Mayrink Verdun"]},"firstPublishedAt":"2026-10-14T18:15:00Z","updatedAt":"2026-10-14T18:15:00Z","sourceCount":1,"hasPrimarySource":false,"sources":[{"outlet":"Wired AI","title":"AI Came for Math. Now It’s Chasing the Rest of Science","url":"https://wired.com/story/ai-is-coming-for-science-next-watch-out","publishedAt":"2026-10-14T18:15:00Z","type":"press","primary":false,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"MIT study finds AI solved 35% of theory problems by mid-2026\", 14 October 2026, https://digestai.news/story/mit-study-finds-ai-solved-35-of-theory-problems-by-mid-2026","publisher":"Digest AI","title":"MIT study finds AI solved 35% of theory problems by mid-2026","datePublished":"2026-10-14T18:15:00Z","url":"https://digestai.news/story/mit-study-finds-ai-solved-35-of-theory-problems-by-mid-2026"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}