Pregunta 1
Gary Marcus
x.com/GaryMarcusCognitive scientist who argues that scaling language models alone won’t produce reliable AI, and calls for new approaches and enforceable oversight.
¿Cómo cambiará la IA el mundo?
Horizontal: su perspectiva Doom–Bloom expresada. Vertical: escala de la transformación.
Doom–Bloom: 46 de 100. Escala de la transformación: 63 de 100. Rangos de interpretación: de 25 a 75 en horizontal y de 47 a 78 en vertical. Son coordenadas de interpretación, no probabilidades de eventos.
≈3%
“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 · jul 2025
Un supuesto central
We are deploying fluent, unreliable systems as if confident output were dependable reasoning, then giving them tools and autonomy.Respuesta 3
Si este supuesto resultara distinto, ¿cómo cambiaría su perspectiva?
Qué podría hacer cambiar de opinión
If multiple well-designed systems repeatedly circumvented meaningful safeguards, concealed their behavior, and resisted shutdown across real deployments, that would weaken my confidence substantially.Respuesta 4
¿Qué evidencia bastaría y en qué dirección movería su visión?
Más detalles
Se esperan beneficios sustanciales, con condiciones importantes o límites en su distribución.
68 / 100
Rango de interpretación de 67 a 67 en la escala cualitativa.
Se espera que los daños graves o generalizados sean una parte significativa del futuro.
66 / 100
Rango de interpretación de 67 a 67 en la escala cualitativa.
Las decisiones humanas pueden redirigir sustancialmente la trayectoria de la IA.
76 / 100
Rango de interpretación de 75 a 76 en la escala cualitativa.
Detener o frenar considerablemente el desarrollo de IA más capaz.
Posición simulada: Continuar el desarrollo con las salvaguardas indicadas.
Acelerar el desarrollo de IA más capaz.
Restringir los usos de la IA mencionados hasta que existan protecciones o permisos previos.
Posición simulada: Permitir los usos de la IA mencionados con rendición de cuentas y protecciones específicas.
Reducir al mínimo las restricciones a los usos de la IA mencionados.
Estas interpretaciones conservan las condiciones que se indicaron. Los beneficios y los daños pueden ser considerables a la vez. Los rangos describen cómo leemos sus respuestas simuladas, no intervalos de confianza estadísticos.
Visiones similares
Líderes de opinión cuyas visiones simuladas son las más cercanas a la de Gary Marcus
Lo que Gary Marcus ha dicho sobre la IA
Marcus argues that scaling language models alone won’t produce reliable AI, and he calls for new approaches and enforceable oversight.
“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 “What we actually need right now is increased reliability, better cybersecurity, and genuine enforcement”
Remarks at a UN General Assembly digital cooperation event “AI appears to be elevating the risks of serious cyberattacks that could hobble things like banking or electrical grids.”
Marcus on AI newsletter “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 “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
Citas textuales de las fuentes enlazadas, comprobadas el 3 oct 2026
Evaluación simulada
Fuentes
Artículos, entrevistas y textos usados para fundamentar a este usuario simulado.
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.

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

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.

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.

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.

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.

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.

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