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Friday

  1. Ethan Mollick

    Ethan Mollick on DeepMind's intelligence economics research: agent coordination capability is the key to economic transformation

    Ethan Mollick commented on Google DeepMind's "The Economics of Intelligence" research, pointing out that an organization is not merely the sum of its best individuals, so a single agent becoming stronger will not change the economy on its own; only agents that can work in coordination may bring about transformation. The research, released by Alex Imas, Andrew Koh, and Shane Legg, proposes two thresholds of economic capability—capability and coordination—and argues that economic transformation depends on whether AI becomes a super-strong individual, super-strong collaboration, or both.

    Ethan Mollick commented on Google DeepMind's "The Economics of Intelligence" research, pointing out that an organization is not merely the sum of its best individuals, so a single agent becoming stronger will not change the economy on its own; only agents that can work in coordination may bring about transformation. The research, released by Alex Imas, Andrew Koh, and Shane Legg, proposes two thresholds of economic capability—capability and coordination—and argues that economic transformation de

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    #hot-signal#English text is being prepared.#Agents#Papers/Research
  2. ScienceNet

    Li Jianshu and Wang Jian on AI and Mathematics: From an Excess of Answers to Two-Way Empowerment

    ScienceNet reporters interviewed Li Jianshu, an academician of the Chinese Academy of Sciences, and Wang Jian, an academician of the Chinese Academy of Engineering, to discuss the controversies and directions surrounding AI's entry into mathematical research. The article mentions that in May of this year, Google DeepMind's AI agent solved 9 previously unsolved Erdős problems and tackled 44 open mathematical conjectures; OpenAI recently published 722 mathematical manuscripts generated by its internal AI models on GitHub; and 25 Fields Medalists, including Terence Tao and Deng Yu, jointly issued an open letter warning of a serious misalignment between AI and mathematics. Li Jianshu believes that AI may change the paradigm of mathematical research and points out that mathematics has done far from enough for AI; Wang Jian believes that AI's reasoning ability currently comes mainly from code, and that learning deeper reasoning from mathematical data will bring greater gains. The Institute for Advanced Study in Mathematics at Zhejiang University, where the two work, and Zhijiang Laboratory jointly launched the Future Mathematics Program, which focuses on four aspects: mathematical foundation models, formal verification, AI mathematical mechanisms, and interdisciplinary empowerment.

    hinese Academy of Engineering, to discuss the controversies and directions surrounding AI's entry into mathematical research. The article mentions that in May of this year, Google DeepMind's AI agent solved 9 previously unsolved Erdős problems and tackled 44 open mathematical conjectures; OpenAI recently published 722 mathematical manuscripts generated by its internal AI models on GitHub; and 25 Fields Medalists, including Terence Tao and Deng Yu, jointly issued an open letter warning of a serio

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    #science#Industry news#Papers/Research#DeepMind
  3. Story1 reports · 1 facts

    Google DeepMind paper proposes that AI's economic impact depends on agents' coordination capabilities

    On October 30, 2026, Google DeepMind published the paper "The Economics of Intelligence," proposing that the economic impact of AGI be characterized by two thresholds—competence (single-task capability) and coordination (agents' ability to collaborate)—and arguing that the economic impact depends on agents' coordination capabilities rather than on individual models becoming stronger. The paper states that AI has already shown changes in lean startups, flattened organizational structures, and a slowdown in hiring for some entry-level positions, but that large-scale unemployment has not yet occurred; most benchmarks test only a single model, whereas firms compete as teams. The paper cites a 100-person test in which groups with complementary pairings performed 32% better than randomly paired groups. On the same day, Ethan Mollick commented on the research, pointing out that an organization is not merely the sum of its best individuals, that a single agent becoming stronger will not change the economy on its own, and that only agents capable of working in coordination may bring about transformation.

    On October 30, 2026, Google DeepMind published the paper "The Economics of Intelligence," proposing that the economic impact of AGI be characterized by two thresholds—competence (single-task capability) and coordination (agents' ability to collaborate)—and arguing that the economic impact depends on agents' coordination capabilities rather than on individual models becoming stronger. The paper states that AI has already shown changes in lean startups, flattened organizational structures, and a s

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  4. Story1 reports · 1 facts

    Google releases the Gemini 4 Carbon model

    On October 28, 2026, Google DeepMind released Gemini 4 Carbon, calling it its strongest model, ranking first with 59 points on the Artificial Analysis Intelligence Index v5.0, and scoring 88.2% on SEC-bench Pro, 54.8% on ExploitGym, and 71.0% on CWE-bench. The model targets long-horizon engineering tasks and was first opened to some cybersecurity defenders, then expanded through the Fairwind Program, and afterward made available to developers, enterprises, and consumers. On October 29, Arena.ai said Gemini 4 Carbon ranked second in Agent Arena with a net improvement of 16.2% and pushed the Pareto frontier forward; the same day, Yuchen Jin cited Artificial Analysis data to raise reservations, noting that the evaluation used the Antigravity CLI. On October 30, the Artificial Analysis Intelligence Index showed it in first place with 59 points, 1 point higher than Claude Opus 5.5. On October 31, Perplexity CEO Aravind Srinivas shared an early evaluation, saying it set a new SoTA on the WANDR benchmark.

    On October 28, 2026, Google DeepMind released Gemini 4 Carbon, calling it its strongest model, ranking first with 59 points on the Artificial Analysis Intelligence Index v5.0, and scoring 88.2% on SEC-bench Pro, 54.8% on ExploitGym, and 71.0% on CWE-bench. The model targets long-horizon engineering tasks and was first opened to some cybersecurity defenders, then expanded through the Fairwind Program, and afterward made available to developers, enterprises, and consumers. On October 29, Arena.ai

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  1. ScienceNet

    Should AI Hit the Brakes: Divergent Positions and the Battle Over Commercial Motives Among Tech Giants

    The debate over whether the AI industry should slow its research and development pace continues to heat up. On September 12, Anthropic CEO Dario Amodei published an article calling on the industry to proactively slow the pace of capability improvements in frontier models, warning that recursive self-improvement could spiral out of control. OpenAI CEO Altman, xAI founder Musk, DeepMind founder Hassabis and others publicly expressed support. On September 16, Meta CEO Zuckerberg said that devoting the vast majority of compute to serving people rather than racing to pursue recursive self-improvement is one of the best ways to develop this technology safely, and said labs that do not focus on alignment will fall behind. Opposing voices are equally strong: Trump called AI safety concerns a hoax, and Nvidia CEO Jensen Huang believes innovation and safety can go hand in hand and that no new legislation is needed; while big short Michael Burry pointed out that executives talk about slowing down out of self-interest, and said large language models are not AI and will not become AGI.

    proactively slow the pace of capability improvements in frontier models, warning that recursive self-improvement could spiral out of control. OpenAI CEO Altman, xAI founder Musk, DeepMind founder Hassabis and others publicly expressed support. On September 16, Meta CEO Zuckerberg said that devoting the vast majority of compute to serving people rather than racing to pursue recursive self-improvement is one of the best ways to develop this technology safely, and said labs that do not focus on al

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    #science#Industry news#Safety / alignment#OpenAI
  2. ScienceNet

    Multiple teams train LLMs on historical data to test whether AI can reproduce general relativity

    According to a recent report in Nature, since the beginning of this year multiple teams have attempted to build retro LLMs whose training data is cut off before a certain historical point in time, to test whether the models can rediscover later scientific ideas. The results show that AI is still a considerable distance from Einstein-style scientific breakthroughs. Independent researcher Michael Hera trained Machina Mirabilis on pre-1900 data; when faced with the photoelectric effect, the model once proposed that light would break down into a large number of mutually independent pulses, but in most cases it failed and was merely imitating historical texts. Google DeepMind researcher Tom Zahavi points out in the paper "LLMs Don't Leap" that current LLMs cannot yet complete a reasoning leap like Einstein's, and that the abductive reasoning on which scientific breakthroughs depend often occurs on anomalous phenomena with very little data or in conflict with existing theories.

    t, the model once proposed that light would break down into a large number of mutually independent pulses, but in most cases it failed and was merely imitating historical texts. Google DeepMind researcher Tom Zahavi points out in the paper "LLMs Don't Leap" that current LLMs cannot yet complete a reasoning leap like Einstein's, and that the abductive reasoning on which scientific breakthroughs depend often occurs on anomalous phenomena with very little data or in conflict with existing theories.

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    #science#Papers/Research#Reasoning#English text is being prepared.

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Thursday

  1. One Useful Thing

    Ethan Mollick: In the Age of AI Agents, We Can Already See the Outline of "Thing"

    Ethan Mollick argues that since late 2025, AI agents such as Claude Code, OpenAI Codex, and OpenClaw have shifted humans from collaborating with AI to managing AI, entering a new stage. He cites benchmarks including the METR long-task curve, GPQA (the strongest AI scores 94%), and GDPval (the latest AI meets or exceeds top humans in 82% of scenarios), showing that AI capabilities are still improving exponentially. He takes the Software Factory built by a three-person team at StrongDM as an example: this model stipulates that code must not be written or reviewed by humans, and each engineer spends at least $1,000 per day on AI tokens. He also mentions that on February 22, a fictional report by Citrini Research triggered market volatility; on February 26, Block announced 40% layoffs; and on February 27, the Pentagon and Anthropic clashed over rules for Claude usage. He believes this week foreshadowed a future in which AI capabilities, markets, employment, and policy are intertwined. Dario Amodei of Anthropic, and Demis Hassabis of OpenAI and Google DeepMind, have all mentioned that recursive self-improvement (RSI) is advancing.

    e also mentions that on February 22, a fictional report by Citrini Research triggered market volatility; on February 26, Block announced 40% layoffs; and on February 27, the Pentagon and Anthropic clashed over rules for Claude usage. He believes this week foreshadowed a future in which AI capabilities, markets, employment, and policy are intertwined. Dario Amodei of Anthropic, and Demis Hassabis of OpenAI and Google DeepMind, have all mentioned that recursive self-improvement (RSI) is advancing.

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    #newsletter#English text is being prepared.#Agents#English text is being prepared.

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