李德毅院士：当机器成为论文的第一读者——图灵方程将不再沉寂
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TL;DR — Chinese Academy of Engineering academician Li Deyi argues that in the AI era, machines—not humans—are becoming the first and primary readers of basic-research papers, so scholarly influence will shift from human citation counts to whether work is retrievable, callable, and reusable by AI systems ("the silicon-based circle"). He uses Turing's 20-year-dormant reaction-diffusion equations as the cautionary historical case that he believes will no longer repeat.
- The Turing equation case study: Turing's 1952 The Chemical Basis of Morphogenesis lay ignored for ~20 years due to five compounding factors — conflict with the then-dominant gene-determinism paradigm (Watson/Crick DNA, Wolpert's positional-information gradients), cross-disciplinary math/biology barriers plus a journal audience mismatch, no experimental validation (no compute to simulate patterns, no assays to track morphogen diffusion), Turing's death two years post-publication leaving no advocate or successors, and the counterintuitive claim that diffusion creates order. It resurfaced only via Gierer–Meinhardt's 1972 computational simulations and Prigogine's dissipative-structures theory.
- New impact metric: the traditional loop (humans read → cite → publish) is being displaced by machine retrieval and invocation. Explicit AI usage — in RAG pipelines and tool calls — is logged automatically, bypassing the gap between being depended on and being cited (Matplotlib: ~20k citations vs. ~300k dependent packages).
- Non-paper artifacts count: GitHub code, a lemma in a formal math library, or an agent-callable tool may exert more influence inside AI systems than a top-journal paper, since dissemination through the silicon circle is faster and broader than through the human "carbon-based" circle.
- Human role reframed: as AI drives the cost of routine problem-solving down, scarcity moves from "producing results" to "being worth using" — the researcher's value shifts to choosing fundamental problems, designing workflows, and verifying outputs, with imagination, consciousness, and pre-evidential insight held as irreplaceably human.
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李德毅院士：当机器成为论文的第一读者——图灵方程将不再沉寂
TL;DR — Chinese Academy of Engineering academician Li Deyi argues that in the AI era, machines—not humans—are becoming the first and primary readers of basic-research papers, so scholarly influence will shift from human citation counts to whether work is retrievable, callable, and reusable by AI systems ("the silicon-based circle"). He uses Turing's 20-year-dormant reaction-diffusion equations as the cautionary historical case that he believes will no longer repeat.
- The Turing equation case study: Turing's 1952 The Chemical Basis of Morphogenesis lay ignored for ~20 years due to five compounding factors — conflict with the then-dominant gene-determinism paradigm (Watson/Crick DNA, Wolpert's positional-information gradients), cross-disciplinary math/biology barriers plus a journal audience mismatch, no experimental validation (no compute to simulate patterns, no assays to track morphogen diffusion), Turing's death two years post-publication leaving no advocate or successors, and the counterintuitive claim that diffusion creates order. It resurfaced only via Gierer–Meinhardt's 1972 computational simulations and Prigogine's dissipative-structures theory.
- New impact metric: the traditional loop (humans read → cite → publish) is being displaced by machine retrieval and invocation. Explicit AI usage — in RAG pipelines and tool calls — is logged automatically, bypassing the gap between being depended on and being cited (Matplotlib: ~20k citations vs. ~300k dependent packages).
- Non-paper artifacts count: GitHub code, a lemma in a formal math library, or an agent-callable tool may exert more influence inside AI systems than a top-journal paper, since dissemination through the silicon circle is faster and broader than through the human "carbon-based" circle.
- Human role reframed: as AI drives the cost of routine problem-solving down, scarcity moves from "producing results" to "being worth using" — the researcher's value shifts to choosing fundamental problems, designing workflows, and verifying outputs, with imagination, consciousness, and pre-evidential insight held as irreplaceably human.