{
  "id": 9542201,
  "title": "AI has taken on a Millennium Problem. Is that scary?",
  "url": "https://urgent.news/2026/09/24/ai-has-taken-on-a-millennium-problem-is-that-scary",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-24T10:30:09.000Z",
  "source": {
    "name": "The Indian Express",
    "slug": "the-indian-express",
    "url": "https://indianexpress.com/article/opinion/columns/ai-taken-on-millennium-math-problem-is-that-scary-10892239/"
  },
  "original_language": "en",
  "account": "OpenAI recently announced their internal AI system generated a proof for the Navier-Stokes existence and smoothness problem, one of mathematics' seven Millennium Prize Problems. While the claim requires careful examination by mathematicians, the more significant aspect is that AI systems can now engage in research previously the pinnacle of human intellectual efforts. Universities must respond by elevating the level of human inquiry. Just as calculators did not halt mathematics education, AI should not either; instead, it necessitates a substantial shift in teaching methods.\n\nTechnical proficiency will remain crucial, but alone it will no longer suffice. A doctoral student must learn to recognize significant questions, formulate them rigorously, integrate knowledge across disciplines, employ AI systems wisely, scrutinize their outputs, and link computational results to real-world phenomena. Consequently, doctoral training should become more ambitious, and undergraduate education must undergo a significant overhaul. Lectures followed by problem sets where students replicate established methods under exam conditions still dominate science and engineering education. However, AI can now solve many such problems convincingly, demanding a shift from \"solve this equation\" to \"decide what should be modeled, what assumptions are defensible, what data are missing, how the answer could fail, and how you would test it in the real world.\"\n\nThis shift also impacts faculty. Institutions like India's leading IITs, IISc, IISERs, and major universities should consider if research agendas designed pre-AI are ambitious enough for the capabilities now becoming available. Code and experiments can be created and planned far quicker than before. However, this does not negate the necessity of scientists and engineers; it merely alters where their limited human judgment should be directed: towards choosing the right problem, establishing constraints, identifying fallacies, designing decisive experiments, and comprehending consequences.\n\nUniversities must respond deliberately. AI literacy should become a core part of every science and engineering student's training. With intellectual execution becoming considerably cheaper and quicker, ambition, problem selection, and the ability to translate ideas into reality become even more critical sources of advantage. The Navier-Stokes announcement should therefore not be seen as a story of a machine competing with mathematicians but as a warning to universities that the intellectual benchmark is moving. The appropriate response is not panic or resistance but demanding more from students, faculty, and institutions.",
  "summary": null,
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}