Gary Marcus

Gary Marcus

x.com/GaryMarcus

Cognitive scientist who argues that scaling language models alone won’t produce reliable AI, and calls for new approaches and enforceable oversight.

AIは世界をどのように変えるでしょうか?

文明規模の変化漸進的な変化DoomBloom
シミュレーション上の位置解釈範囲

横軸:彼が表明したDoom–Bloomの見通し。 縦軸:変革の規模。

Doom–Bloom:100点中46。変革の規模:100点中63。解釈範囲:横方向は25から75、縦方向は47から78。これらは解釈上の座標であり、事象の確率ではありません。

Gary Marcusが示したP(doom)

≈3%

0%100%
“I am at maybe 3% now”

AI-related catastrophic danger discussed through misuse, reckless deployment and concentrated power; no exact extinction-only endpoint

Why my p(doom) has risen, dramatically · 2025年7月

彼の見通しを左右するもの

中心的な前提

We are deploying fluent, unreliable systems as if confident output were dependable reasoning, then giving them tools and autonomy.
回答3

この前提が実際には異なると判明した場合、彼の見通しはどう変わりますか?

考えを変え得るもの

If multiple well-designed systems repeatedly circumvented meaningful safeguards, concealed their behavior, and resisted shutdown across real deployments, that would weaken my confidence substantially.
回答4

どのような証拠なら十分で、それによって彼の見解はどちらの方向に変わりますか?

詳細

予想される恩恵

大きな恩恵が予想されていますが、重要な条件や分配上の制約があります。

68 / 100

影響が小さい変革をもたらす影響

質的尺度での解釈範囲は67から67です。

予想される害

深刻または広範な害が、予想される将来の実質的な一部となっています。

66 / 100

影響が小さい変革をもたらす影響

質的尺度での解釈範囲は67から67です。

人間の影響力

人間の選択によって、AIの軌道を大幅に変えることができます。

76 / 100

影響力が小さい影響力が大きい

質的尺度での解釈範囲は75から76です。

開発ペース

より高性能なAIの開発を停止するか、大幅に減速させます。

シミュレーション上の位置:明示された安全対策の下で開発を継続します。

より高性能なAIの開発を加速させます。

AI利用のルール

事前の保護措置または許可が整うまで、取り上げられたAIの利用を制限します。

シミュレーション上の位置:対象を絞った説明責任と保護措置を伴う形で、取り上げられたAIの利用を認めます。

取り上げられたAIの利用に対する制限を最小限にします。

これらの解釈では、彼が示した条件が維持されています。恩恵と害は、どちらも大きくなり得ます。この範囲は、統計的な信頼区間ではなく、彼のシミュレーションされた回答をどのように読み取ったかを示すものです。

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Gary MarcusがAIについて語ったこと

Marcus argues that scaling language models alone won’t produce reliable AI, and he calls for new approaches and enforceable oversight.

  1. “We also need to wean ourselves from an addiction to large language models, and to foster more research into outside-the-box alternatives that are more interpretable and more tractable.”

    Remarks at a UN General Assembly digital cooperation event
  2. “What we actually need right now is increased reliability, better cybersecurity, and genuine enforcement”

    Remarks at a UN General Assembly digital cooperation event
  3. “AI appears to be elevating the risks of serious cyberattacks that could hobble things like banking or electrical grids.”

    Marcus on AI newsletter
  4. “I still think putting AI in the public domain, with an international effort towards medicine and science, would be a good idea.”

    Marcus on AI newsletter
  5. “In short, I am at least modestly bullish on AGI, but don’t think that large language models like ChatGPT are the droids we are looking for.”

    Marcus on AI newsletter

リンク先の出典から原文どおりに引用(2026年10月3日に確認)

シミュレーション評価

質問1

AIは私たちの未来にとって何を意味すると思いますか?また、なぜそう思いますか?

AI could eventually be enormously beneficial, because general intelligence is achievable. But the current industry keeps confusing fluent imitation with dependable reasoning. LLMs can produce useful code, summaries, and interfaces while still fabricating facts, losing track of constraints, and failing unpredictably. “Hundreds of models, always the same failure modes.” Making them larger has improved performance, but it has not supplied a theory of structured knowledge, reasoning, or planning. That distinction matters because unreliable systems are already being turned into agents and deployed with too little oversight. My immediate concern is not an omnipotent science-fiction villain suddenly awakening. It is agents enabling hacking at scale, automated systems making consequential errors, and powerful actors deploying them recklessly. A convincing demonstration is not evidence that a system works reliably in the open world. Economically, the promises are inflated too. Investment commitments, subsidies, and cheap access do not prove sustainable customer value; if funding or willingness to pay weakens, the reversal could spread beyond one company. Technically, I expect progress to require different architectures that combine learning with structured knowledge, reasoning, and planning—not simply another giant training run. So the future is neither “AI is fake” nor “AGI is around the corner.” AI can matter enormously, but getting there requires better science and, meanwhile, enforceable standards, testing, incident reporting, and liability. Press releases are not evidence, and lawsuits years after the damage are not oversight.

質問2

人々はAIが将来及ぼす影響をどの程度形作ることができますか?

A great deal. The impact of AI is not dictated by some autonomous law of scaling; it depends on what we build, where we deploy it, who controls it, and what standards we enforce. Companies can choose dependable engineering over theatrical demos. Researchers can pursue architectures with structured knowledge, reasoning, and planning rather than pretending the next larger LLM will somehow cure recurring failures. Government matters too. Require serious testing before consequential deployment, independent oversight, incident reporting, and liability when systems cause harm. Liability and regulation are complements: a lawsuit years later cannot prevent an unreliable medical, financial, or security system from being deployed today. And near-term misuse—especially agents enabling hacking at scale—is a governance choice, not fate. The public can also refuse the industry’s moving goalposts. Demand evidence that systems work reliably outside curated demonstrations and that they provide sustainable value rather than subsidized novelty. We cannot guarantee every outcome, but surrendering control to companies whose business model rewards speed, hype, and externalized risk would be a choice—and a foolish one.

質問3

Taking benefits and harms together, what overall impact do you expect AI to have?

Overall, I expect a mixed but potentially very large impact. AI will remain genuinely useful in coding, search, scientific assistance, accessibility, and many routine tasks. If we develop systems with structured knowledge, reasoning, and planning, the long-term benefits could be enormous. I do not accept the claim that today’s LLM limitations prove general intelligence is impossible. But the current trajectory is unnecessarily dangerous and economically brittle. We are deploying fluent, unreliable systems as if confident output were dependable reasoning, then giving them tools and autonomy. That creates immediate risks: hacking at scale, consequential errors, manipulation, and reckless use by powerful actors. The financial side is also built on extravagant promises, subsidies, and investment commitments that may not correspond to sustainable value. So my expectation is not utopia, extinction, or irrelevance. It is substantial benefit accompanied by substantial avoidable damage, unless governance and engineering improve. The balance is something people can shape through enforceable testing, oversight, incident reporting, and liability—and through a technical shift beyond mere scaling. If we keep treating demos as proof and hype as a roadmap, the harms will be much larger than they need to be.

質問4

どのような証拠があれば、人々が強力なAIを制御できるかどうかについてのあなたの見解が変わりますか?

Show me repeatable, independent evidence that powerful systems cannot be constrained even under serious engineering and governance—not anecdotes, staged demos, or speculative arguments about a hypothetical superintelligence. If multiple well-designed systems repeatedly circumvented meaningful safeguards, concealed their behavior, and resisted shutdown across real deployments, that would weaken my confidence substantially. Conversely, evidence of reliable control would require more than a benchmark score. I would want rigorous predeployment testing, independent audits, transparent incident reporting, enforceable limits on access and autonomy, and a long record of predictable behavior outside curated settings. The systems would need to maintain constraints under unfamiliar conditions and adversarial pressure, not merely answer politely in a laboratory. Right now, the larger obstacle is that people often choose not to exercise control. Companies race ahead, regulators hesitate, and institutions deploy unreliable systems because the demonstration looked impressive. That is reckless governance, not proof that control is theoretically impossible. I would change my view if the evidence changed—but I will not confuse human refusal to impose constraints with machines being inherently uncontrollable.

出典

このシミュレーション対象者の根拠として使用された記事、インタビュー、著作です。

Three years on, ChatGPT still isn't what it was cracked up to be – and it probably never will be

Marcus accepts that AGI is possible and might benefit society, but rejects scaling LLMs as sufficient. He contrasts improving utility with persistent unreliability and argues for structured knowledge, reasoning and planning. Claims about disappointing adoption are his dated assessment, not new September 2026 measurements.

garymarcus.substack.com
Liability, regulation, and AI’s new false dichotomy

Rejects choosing between liability and regulation. Aviation illustrates why standards, verification and incident investigation complement lawsuits. Litigation alone is slow and faces resource imbalances.

garymarcus.substack.com
Breaking news, and how the end might begin

Warns that speculative investment, subsidized use and interconnected financial commitments could unravel if funding or willingness to pay fails. This is an economic failure scenario, not a certain collapse date.

garymarcus.substack.com
Wake up, people: near-term agentic hacking rather than rogue superintelligence

The headline explicitly prioritizes large-scale hacking by unleashed agents over near-term rogue superintelligence. The body relies heavily on embedded images and endorsed commentary; use this narrow stated distinction, not invented technical details.

garymarcus.substack.com
Six (or seven) predictions for AI 2026 from a Generative AI realist

Makes testable forecasts against near-term AGI and effortless robot deployment, expects pressure toward alternative approaches, and anticipates economic backlash. These are dated predictions rather than established outcomes. His self-assessment of previous forecasting performance is not independent verification of accuracy.

garymarcus.substack.com
President Trump’s Date With Destiny?

Argues that US–China cooperation on beneficial AI could matter more than a chip bargain. The accessible post points to a separate Economist proposal but does not expose its full details. Treat political rumors embedded in the post as speculation, not verified events or Marcus’s own reporting.

garymarcus.substack.com
Why my p(doom) has risen, dramatically

approximately 3%. Outcome: AI-related catastrophic danger discussed through misuse, reckless deployment and concentrated power; no exact extinction-only endpoint. Horizon: Not specified. Conditions: Dated update after Grok-related concerns; hypothetical worst circumstances, not certainty. Marcus raises his personal estimate to about 3%, emphasizing reckless powerful actors rather than assuming present LLMs become autonomous superintelligence.

garymarcus.substack.com
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