WhatA platform that pairs domain experts with fine-tuned LLMs to systematically attack open problems in mathematics and science by generating novel proof strategies and cross-domain technique suggestions.
SignalThe core story here is that an amateur used a general-purpose LLM to crack a 60-year-old open problem by prompting it to think creatively — the LLM applied a well-known formula from a related field that no specialist had thought to use, suggesting there is enormous untapped value in using AI for cross-pollination of techniques across research domains.
Why NowReasoning models like o1/o3 can now sustain 80+ minutes of chain-of-thought on a single problem, a capability that simply did not exist 12 months ago.
MarketResearch universities, national labs, pharma R&D, and math/science departments — roughly $50B+ global R&D spend on fundamental research; no direct competitor offers a structured workflow for LLM-assisted open problem solving beyond raw ChatGPT access.
MoatProprietary dataset of successful and failed proof attempts across problem domains creates a feedback loop that improves prompt strategies and model fine-tuning over time.
Amateur armed with ChatGPT solves an Erdős problemView discussion ↗ · Article ↗ · 760 pts · April 26, 2026
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