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.

एआई दुनिया को कैसे बदलेगा?

सभ्यता-स्तरीय बदलावक्रमिक बदलावDoomBloom
सिम्युलेट की गई स्थितिव्याख्या का दायरा

आर-पार: उनका व्यक्त किया गया Doom–Bloom दृष्टिकोण। ऊपर: बदलाव का स्तर।

Doom–Bloom: 100 में से 46। बदलाव का स्तर: 100 में से 63। व्याख्या के दायरे: क्षैतिज रूप से 25 से 75, लंबवत रूप से 47 से 78। ये व्याख्या के निर्देशांक हैं, घटनाओं की संभावनाएँ नहीं।

Gary Marcus द्वारा बताया गया 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 · जुल॰ 2025

उनका दृष्टिकोण किन बातों पर निर्भर करता है

एक मुख्य मान्यता

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

अगर यह मान्यता अलग साबित होती, तो उनका दृष्टिकोण कैसे बदलता?

क्या उनकी राय बदल सकता है

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

कौन-सा प्रमाण पर्याप्त होगा, और उससे उनका दृष्टिकोण किस दिशा में बदलेगा?

अधिक जानकारी

अपेक्षित लाभ

काफ़ी लाभ की उम्मीद है, लेकिन उनके साथ महत्वपूर्ण शर्तें या वितरण संबंधी सीमाएँ होंगी।

68 / 100

कम असरबदलावकारी असर

गुणात्मक पैमाने पर व्याख्या का दायरा 67 से 67 तक है।

अपेक्षित नुकसान

गंभीर या व्यापक नुकसान के भविष्य का एक ठोस और अपेक्षित हिस्सा होने की उम्मीद है।

66 / 100

कम असरबदलावकारी असर

गुणात्मक पैमाने पर व्याख्या का दायरा 67 से 67 तक है।

मानवीय प्रभाव

मानवीय विकल्प एआई की दिशा को काफी हद तक बदल सकते हैं।

76 / 100

कम प्रभावमजबूत प्रभाव

गुणात्मक पैमाने पर व्याख्या का दायरा 75 से 76 तक है।

विकास की गति

अधिक सक्षम एआई का विकास रोकें या उसकी गति काफी धीमी करें।

सिम्युलेट की गई स्थिति: बताए गए सुरक्षा उपायों के तहत विकास जारी रखें।

अधिक सक्षम एआई के विकास की गति बढ़ाएँ।

एआई के उपयोग के नियम

जिन सुरक्षा उपायों या अनुमति का पहले से होना ज़रूरी है, उनके लागू होने तक चर्चा किए गए एआई उपयोगों को प्रतिबंधित रखें।

सिम्युलेट की गई स्थिति: लक्षित जवाबदेही और सुरक्षा उपायों के साथ चर्चा किए गए एआई उपयोगों की अनुमति दें।

चर्चा किए गए एआई उपयोगों पर प्रतिबंध कम से कम रखें।

इन व्याख्याओं में उनकी बताई गई शर्तें बरकरार रखी गई हैं। लाभ और नुकसान, दोनों पर्याप्त हो सकते हैं। ये दायरे बताते हैं कि हम उनके सिम्युलेट किए गए उत्तरों को कैसे समझते हैं, ये सांख्यिकीय विश्वास-अंतराल नहीं हैं।

Gary Marcus की तुलना में आपकी सोच कहाँ ठहरती है?
लगभग 3 मिनट में एआई के बारे में अपना विश्वदृष्टिकोण मैप करें, फिर तुलना करें

मिलते-जुलते विश्वदृष्टिकोण

ऐसे जाने-माने विचारक जिनके सिम्युलेट किए गए विश्वदृष्टिकोण Gary Marcus के विश्वदृष्टिकोण से सबसे अधिक मिलते हैं

Gary Marcus ने एआई के बारे में क्या कहा है

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

लिंक किए गए स्रोतों से शब्दशः उद्धृत, 3 अक्टू॰ 2026 को जाँचा गया

सिम्युलेट किया गया आकलन

सवाल 1

आपके विचार में एआई हमारे भविष्य के लिए क्या मायने रखती है—और क्यों?

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.

सवाल 2

लोग एआई के भविष्य के प्रभाव को किस हद तक आकार दे सकते हैं?

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.

सवाल 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.

सवाल 4

कौन-सा सबूत इस बारे में आपका दृष्टिकोण बदल देगा कि लोग शक्तिशाली एआई को नियंत्रित कर सकते हैं या नहीं?

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.

स्रोत

इस सिम्युलेट किए गए उपयोगकर्ता को तथ्य-आधारित बनाने के लिए इस्तेमाल किए गए लेख, इंटरव्यू और रचनाएँ।

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
आपकी सोच कहाँ ठहरती है?
कुछ आसान सवालों के जवाब देकर एआई के बारे में अपना विश्वदृष्टिकोण जानें।
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