OpenAI fought dirty on career-making math problem, says NYU mathematician

The controversy surrounding OpenAI’s rapid solution to the Navier-Stokes problem, following the preliminary findings of Buckmaster and Alpöge, points to a growing tension in AI-driven scientific discovery. OpenAI’s use of $22.5 million in compute to achieve a full proof, allegedly after learning of a rival’s specific approach, suggests that raw computational power can now be a decisive factor in solving complex mathematical problems. This dynamic could reshape how research is conducted and credited, particularly in areas with significant financial bounties like the $1 million Millennium Prize problems. For Asia, this incident underscores the increasing importance of AI model access and compute resources in advanced research. Companies and national AI initiatives across the region, from Singapore to South Korea, are investing heavily in AI infrastructure. The ability to deploy massive compute against specific research problems could become a new competitive frontier, influencing which entities lead in scientific breakthroughs. The ethical questions raised by Buckmaster about OpenAI’s conduct, including requests to remove Alpöge’s credit due to his Anthropic affiliation, highlight the need for clear guidelines in collaborative and competitive AI research. As Asian AI labs and startups increasingly engage with global research challenges, establishing transparent protocols for information sharing and attribution will be critical to maintaining trust and fostering genuine innovation.