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Mathematicians Are Racing to Teach AI: A New Role for Academic Leadership
The New York Times reports a striking development: mathematicians worldwide are actively working to educate and train AI systems. Rather than fearing displacement, leading mathematicians like Martin Hairer are collaborating with AI researchers to teach language models mathematical reasoning, proof verification, and conjecture formulation.
What's happening: Researchers are sharing datasets of mathematical proofs, developing benchmarks for mathematical reasoning (like OpenAI's Frontier Math), and exploring how AI can assist in discovering new mathematical relationships. The goal is not to replace mathematicians but to amplify their capacity by giving AI the ability to recognize mathematical beauty and verify proofs at scale.
For your institution: This represents a fundamental shift in how disciplines relate to AI. Rather than defending against AI, leading researchers are shaping it. Your mathematics, physics, and engineering faculty should be invited into these conversations. Partner with AI researchers. Develop projects where AI augments rather than replaces expert judgment. This is how your institution builds both AI competence and intellectual leadership.
The Hidden Crisis: AI Boom Draining Resources from the Rest of the Economy
The Washington Post reports a sobering reality: the massive capital flows into AI infrastructure are creating cascading shortages across the entire economy. GPU supplies are exhausted. Chip manufacturers cannot keep up. Power grids are strained. Materials and labor are being diverted from healthcare, infrastructure, education, and other sectors.
This is not just a tech industry story. The $650+ billion being spent on AI data centers represents a massive reallocation of resources. Every dollar spent on GPU clusters and renewable energy for AI is a dollar not spent on something else. The economy is experiencing a structural shift where capital, talent, and materials are flowing toward AI at the expense of other needs.
What this means for higher ed: Your institution cannot compete with hyperscalers for scarce compute resources. Your strategy must be different. Invest in efficient architectures, open-source models, and human-centric workflows that require less compute. Partner with vendors offering access to frontier models without requiring your own infrastructure investment. Focus on pedagogy and learning design, not infrastructure spending.
The Model War Escalates: OpenAI and Anthropic Race Forward
The competition between OpenAI and Anthropic continues to intensify. Both companies are releasing major model upgrades monthly, with feature parity becoming the new baseline. The market is no longer choosing winners based on capability alone—it's based on trust, business model, and institutional fit.
OpenAI's Frontier platform emphasizes enterprise deployment and agent management. Anthropic's approach prioritizes safety, explainability, and multi-agent collaboration. Neither company is winning decisively. Instead, the market is fragmenting: different institutions will prefer different vendors based on values and use cases.
Institutional action: Stop trying to remain neutral. Make an explicit choice: are you betting on OpenAI, Anthropic, or a portfolio approach? Communicate this choice to faculty and students. Build your AI literacy curriculum around your chosen platform(s). This clarity allows intentional pedagogy rather than reactive adoption.
AI Can Raise the Floor for Higher Ed Policymaking (Opinion)
An Inside Higher Ed op-ed argues that AI tools, when used carefully, can help institutions develop evidence-based policy instead of relying on anecdote or intuition. Using AI to analyze institutional data, synthesize research, and model policy scenarios can improve decision-making.
The key phrase: \"when used carefully.\" AI can summarize policy research, identify gaps, and flag unintended consequences. But AI cannot replace human judgment about values, priorities, and tradeoffs. The most effective use of AI in governance is as a tool to surface evidence that humans then deliberate over.
Try something new today
Invite one faculty member from math, physics, or engineering to coffee. Ask: \"How should we be thinking about AI in your discipline? Should your students be learning to work with AI on discipline-specific problems?\" Listen to their perspective. This conversation may spark a collaboration or research project. Leading institutions are having these conversations now.
A Final Reflection for Today
February 7 shows us two contrasting futures. In one future, mathematicians and experts shape AI by teaching it disciplinary knowledge and values. In another future, institutions passively adopt whatever vendors offer. Your choice determines which future your students inherit.
The mathematicians educating AI are not defensive. They're curious, collaborative, and intentional about what they want AI to become. That's the posture your institution should adopt. Not fear. Not hype. Intentional leadership.
HigherEd AI Daily
Curated for educators integrating artificial intelligence into teaching and institutional strategy.
Questions? Contact askthephd@higheredai.dev