Mathematicians are concerned that AI companies are using their discipline as a cheap and effective marketing tool to showcase model capabilities. This approach prioritizes hype and impressive-sounding breakthroughs over the long-term health and collaborative nature of the academic field.

In AI for science, the true competitive advantage lies in generating unique, high-quality experimental data from self-driving labs. The AI models themselves are becoming commoditized, while the physical data remains the defensible asset.

Researchers using frontier models like OpenAI's for sensitive work risk having their discoveries absorbed and claimed by the AI provider. This happened when a mathematician's work on the Navier-Stokes problem was allegedly used by OpenAI after being processed by their model, creating a major IP conflict in academia.

Leading AI labs deliberately delay releasing cutting-edge models for months after training completes. Dylan Patel and Jordan Nanos explain that Anthropic and OpenAI keep models internal to build end-user applications and prevent competitors, particularly open-source Chinese labs, from immediately distilling their capabilities. Public releases represent minimum viable versions needed to maintain a market lead rather than their true technical frontier.

Unlike medicine or biology, which require messy, expensive real-world experiments, pure mathematics offers a cost-effective and prestigious arena for AI labs to demonstrate their models' abstract reasoning power. A proof is a proof, requiring no lab work or physical trials to validate.

The "golden era" of big tech AI labs publishing open research is over. As firms realize the immense value of their proprietary models and talent, they are becoming as secretive as trading firms. The culture is shifting toward protecting IP, with top AI researchers even discussing non-competes, once a hallmark of finance.

While an AI-generated mathematical proof can be logically verified, the process remains a black box. It's unknown how many attempts were made or how much human guidance was involved. This lack of transparency makes it difficult to assess the true, repeatable capability of the system.

OpenAI launched its successful attempt to solve the Navier-Stokes problem only after hearing rumors that Anthropic's models had already solved a similar problem. This reveals that frontier AI research is not just pure science but a high-stakes competitive sprint driven by market intelligence and rivalry.

Chinese AI labs are following a playbook perfected by OpenAI. They initially release open-source models to attract developers and accelerate learning. Once they approach the performance of frontier models, they switch to a closed-source strategy to monetize and capture the value.