Mathematicians Tristan Buckmaster and Andreas Thom Accuse OpenAI of Misappropriating Work for Navier-Stokes and Astra While Still Using Codex

Mathematicians Tristan Buckmaster and Andreas Thom accuse OpenAI of misusing their foundational research to solve complex problems like the Navier-Stokes existence and smoothness problem, which carried a $1 million bounty. Despite these allegations, both mathematicians continue to use OpenAI's AI tools, such as Codex and Astra, citing a lack of practical alternatives and highlighting a growing credit and attribution problem in AI development. For broader context, explore our AI News.

The Navier-Stokes Controversy and OpenAI's Response

The dispute gained significant attention when NYU mathematician Tristan Buckmaster accused OpenAI of leveraging his work to solve the complex Navier-Stokes existence and smoothness problem. This particular challenge is one of the Millennium Prize Problems, carrying a substantial $1 million bounty for its solution. Buckmaster's claims, made in September 2026, suggested that OpenAI's Codex model might have benefited from his prior interactions with the system.

In response, OpenAI conducted an internal investigation. The company subsequently amended its initial announcement, stating that their inquiry confirmed Buckmaster's Codex prompts over the preceding two months could not have influenced the system in any way, including its training data. Despite this denial, the incident brought the issue of intellectual credit in AI development to the forefront.

A Broader Pattern of Uncredited Work

The concerns raised by Buckmaster are not isolated. German mathematician Andreas Thom has also come forward with similar allegations against OpenAI. Thom stated that the company's Astra model, which achieved a significant breakthrough in August 2026, utilized techniques from geometric group theory that he had developed over two decades. This pattern suggests a systemic issue where AI labs may be incorporating advanced human research without clear acknowledgment or proper attribution.

Buckmaster further elaborated on the scale of OpenAI's efforts, noting that the company reportedly deployed tens of thousands of agents to arrive at the Navier-Stokes solution. He described the act of solving such profound problems without crediting the underlying human intellectual contributions as both "irresponsible" and "childish."

The Dilemma: Reliance Amidst Accusations

Despite their strong criticisms and belief that their work has been appropriated, mathematicians like Buckmaster continue to use OpenAI's tools. Buckmaster, for instance, still employs Codex for practical tasks such as tidying research papers and reconstructing logical steps in proofs. This paradoxical reliance stems from a perceived lack of practical alternatives in the current AI landscape.

Buckmaster characterized the situation as a monopoly, stating that there is "not much choice" for researchers seeking advanced AI assistance. This highlights a critical challenge: while AI tools offer unprecedented capabilities for accelerating research and problem-solving, the ethical frameworks for intellectual property and attribution have not kept pace with technological advancements. This dynamic forces academics to engage with systems they distrust, creating a complex ethical and practical dilemma.

Why This Matters for AI Development and Academia

This ongoing dispute between mathematicians and leading AI labs like OpenAI underscores a fundamental tension in the rapid evolution of artificial intelligence. The core issue revolves around credit and attribution, where AI systems achieve impressive results, but the human intellectual foundations upon which these achievements are built are not always clearly acknowledged. This situation could deter academic collaboration with AI developers and potentially stifle the open exchange of ideas crucial for scientific progress.

For the broader AI community, these accusations serve as a critical reminder of the need for transparent practices regarding data sourcing, model training, and the acknowledgment of human contributions. As AI tools become increasingly powerful and integrated into research, establishing clear ethical guidelines and robust attribution mechanisms will be essential to foster trust and ensure fair collaboration between human experts and advanced AI systems. This also opens a discussion on the need for more diverse and competitive AI tools to prevent monopolies and offer researchers genuine alternatives.

Conclusion

The accusations from mathematicians like Tristan Buckmaster and Andreas Thom against OpenAI highlight a significant ethical and practical challenge in the AI sector. While AI models like Codex and Astra demonstrate remarkable capabilities, the debate over intellectual property and proper attribution remains unresolved. The continued reliance of academics on these tools, despite their concerns, points to an urgent need for industry-wide standards for transparency and credit. Moving forward, fostering a more equitable and collaborative environment will be crucial for the sustainable advancement of AI and its integration into scientific research.

Sources

• Mathematicians Hate AI. They Can’t Quit It | WIRED
• OpenAI's feud with mathematicians is only escalating - TechCrunch
• OpenAI just wants to win
• OpenAI fought dirty on career-making math problem, says...