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  "text": "@RonyVernet That’s not how AI works.\n\nLLMs sample outputs from a frozen probability landscape via interpolation and next-token probabilities. Every single generation requires external verification: a tool or a subject-matter expert.\n\nThat’s precisely the point Tao is making.\n\nMathematicians still have to interpret, validate and understand the results. They must judge whether something is correct, partial or hallucinated. Only then can it be integrated and taught. AI does not understand mathematics.\n
It does not solve mathematics. It also cannot create new knowledge or understanding in people.\n
AI is software. It cannot learn or understand things for you. Only a human can learn or understand. AI generates outputs. Only an external verifier can ground them.\n\nThis is a gap in AI literacy. Hype continues to oversell what the technology can actually do.",
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    "text": "🚨 Terence Tao warning about AI in mathematics.\n\nThe greatest living mathematician new essay “Mathematics in the Age of AI” is one of the most significant individual responses from a leading researcher examining frontier AI’s impact on the discipline.\n\nThe core message: AI will make generating correct proofs far cheaper and faster, but that creates a deeper problem. \n\nMathematicians cannot possibly read, understand, verify, and teach thousands of new results. The scarce resource shifts from finding proofs to making sense of them.\n\n[ Why this matters beyond mathematics ]\n\nThe same shift is already visible in software. Vibe coding is flooding codebases with exponentially more lines of code. Generation is cheap. Understanding, maintaining, and owning the system is the new bottleneck.\n\n[ Tao’s analysis ] \n\nFive pressure points that apply to any field deploying AI:\n\n1 Proof abundance, not scarcity.\n
AI produces correct outputs faster than humans can digest them. Human verification and explanation become the costly, critical step.\n\n2 Attribution and ownership collapse.\n
When models generate large volumes of work, credit, responsibility, and intellectual ownership grow unclear.\n\n3 Distorted incentives.\n
Prestige and funding start rewarding volume and speed of generation over depth of understanding and lasting insight.\n\n4 Press-release science.\n
Results appear on commercial timelines before the community can properly evaluate significance or correctness.\n\n5 Loss of autonomy.\n
Research priorities quietly shift toward what is easy to automate rather than what is actually important.\n\nThe leap: generation → abundance → sense-making\n\nWe have moved from tools that assist to systems that can flood a field with output. \n\nTao’s point is not about toy problems. It is about frontier capability arriving in domains where human understanding still defines value.\n\n[ What this means for your industry ]\n\nThe same risks apply wherever AI is used in high-stakes work: software, law, medicine, finance, engineering. \n\nThe core insight is simple: AI generates output, not understanding.\n
Verification and sense-making cannot be automated away. Human accountability remains non-negotiable.\n\nTools generate. Humans still have to understand.",
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