Column Uses AI Math Breakthroughs to Question Corporate AI Value and Decision Quality

In a commentary published by TechOrange, columnist Spencer Liu examines the growing corporate use of AI for reports and meeting notes, questioning whether increased output has improved managerial decisions. Liu contrasts mathematician Grigori Perelman's decade-long proof of the Poincaré conjecture—which established reusable methodological tools—with reported 2026 achievements where OpenAI deployed ten thousand AI agents over 88 hours to solve the Navier-Stokes equations, primarily yielding an answer. Citing mathematician Terence Tao's 2026 lecture warning that optimizing solely for speed misaligns progress, Liu argues that businesses face a similar pitfall: rapid automated drafting easily outpaces verification capacity. The column concludes that enterprise AI success should be measured by whether teams understand the underlying insights to improve decisions, rather than simply tracking the volume of generated tasks.

Key takeaways

• The column contrasts human proofs that leave reusable methods with automated AI agents that deliver immediate answers.
• The Clay Mathematics Institute's deliberately slow verification reflects how verification struggles to keep pace with rapid AI output.
• Cites Terence Tao's view that blindly maximizing output speed misaligns core benchmarks and harms long-term health.
• Urges organizations to focus on team comprehension and decision validity rather than solely tallying automated reports.