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Business Insider
It took OpenAI's agents 130 billion tokens to crack a 90-year-old math problem
OpenAI announced the successful resolution of the Navier-Stokes equations, an intricate set of formulas with a $1 million prize for its solution. This achievement involved around 10,000 AI agents sending approximately 2.7 million messages over a period of 88 hours, followed by a verification phase of another 17 hours. While the industry lauds this milestone, it has sparked criticism related to data usage concerns from researchers.
MonitorConfidence: HighShort term horizon
Published 9 Sept 2026, 16:20 UTC2 min read
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Brief
NewsBite reading:OpenAI agents solve complex math problem using extensive AI resources
TL;DR
- OpenAI solved the Navier-Stokes equations, a long-standing mathematical problem.
- The solution required 10,000 AI agents and 2.7 million messages.
- The total cost to OpenAI's token usage is estimated between $10 million to $40 million.
- The achievement has drawn scrutiny over the potential misuse of researchers' work.
- OpenAI celebrated the success but faced criticism from mathematicians regarding data usage.
Interesting facts
- The Navier-Stokes equations are crucial for understanding fluid dynamics, which has implications across physics and engineering.
- The problem has been unsolved for nearly a century, making this achievement notably significant in the mathematics community.
What changed
OpenAI's agents successfully solved a long-standing mathematical problem, marking a significant milestone in AI capabilities.
Unchanged: The fundamental nature of the Navier-Stokes equations and the controversy surrounding data usage in AI training.
What we still don't know
- Details on the ethical review processes for data usage.
- Further insights from the mathematical community regarding OpenAI's solution.
Decision signal
Decision brief
Actionable takeaways
- Monitor ongoing developments in AI and mathematics to assess their implications.
- Evaluate the ethical guidelines regarding AI training data usage.
- Engage with stakeholders in research to enhance collaboration frameworks.
- Consider implications of AI advancements on traditional fields like mathematics and physics.
Decision signal
Monitor
Technical significance
Meaningful
Competitive impact
Strengthens position
Source reliability
High
Impact certainty
High
Novelty
High
Execution risk
Medium
Strategic weight
High
Trend: Accelerating
- AI in scientific research
- ethical implications in AI training data usage
Personalized guidance
Audience lens
Board
- Consider establishing clear guidelines on AI data usage.
CTO
- Focus on maintaining transparent development practices with AI.
Startup founders
- Assess collaboration strategies with academic institutions.
Investors
- Evaluate potential risks related to AI training practices.
Policy
- Monitor regulatory developments related to AI and data governance.
Engineering
- Ensure robust infrastructure to support AI functions and ethics.
Developers
- Adopt best practices for ethical AI model training.
Sentiment signal
Sentiment
Overall: MixedMood: Cautious
The news carries a mixed sentiment, reflecting the excitement of an AI achievement marred by ethical concerns.
Drivers
Significant AI breakthrough in mathematicsCriticism over data usage ethics
By category
aiPositive
This achievement advances AI's credibility in tackling complex problems.
programmingNeutral
While significant, primarily involves AI capabilities rather than direct programming advancements.
Notable entities
OpenAIorganizationPositive
OpenAI's achievement showcases its AI's problem-solving potential.
Clay Mathematics InstituteorganizationNeutral
The institute offered the prize for the Navier-Stokes equations but remains passive in the context.
Tristan BuckmasterindividualNegative
Raised concerns about the ethical implications of data usage by OpenAI.
Levent AlpögeindividualNegative
Also contested OpenAI's data practices in relation to their research.
Market signal
Impact & reach
OpenAI's solution to the Navier-Stokes equations may reshape the application of AI in solving complex mathematical problems.
This breakthrough underscores the potential of AI in mathematical problem-solving, yet the data usage controversies reflect ongoing ethical debates in AI research practices.
researchersNegative
Concerns over their research data being used without permission could undermine trust in collaboration with AI developers.
Geographic focus
globalPositive
This achievement has potential implications for AI applications worldwide.
Likely winners
OpenAIAI research community
Likely losers
researchers concerned about data autonomy
Ripple effects
academic institutionsConfidence: Medium
Might reassess AI usage policies in research and collaboration.
Risk signal
Risk radar
Cybersecurity riskLow
No immediate cybersecurity threats reported.
Data governance riskMedium
Ongoing debates on data usage underscore potential future governance issues.
Reputational riskMedium
OpenAI faces reputational challenges due to data usage criticisms.
Execution riskLow
The execution of the AI function appears successful.
Infrastructure riskLow
Current infrastructure appears sufficient for AI applications.
Geopolitical riskLow
No significant geopolitical shifts indicated.
Regulatory riskMedium
Emerging concerns over AI data usage may attract regulatory attention.
Supply chain riskLow
Supply chain stability remains unaffected.
Talent displacement riskMedium
AI advancements might influence job dynamics in mathematical research.
AI liability riskMedium
Questions about model training data could create legal ramifications.