Miles Brundage

Miles Brundage

x.com/Miles_Brundage

Former OpenAI policy research head who leads the nonprofit AVERI and argues for independent audits, enforced safety standards and federal AI law.

¿Cómo cambiará la IA el mundo?

Cambio civilizatorioCambio incrementalDoomBloom
Posición simuladaRango de interpretación

Horizontal: su perspectiva Doom–Bloom expresada. Vertical: escala de la transformación.

Doom–Bloom: 24 de 100. Escala de la transformación: 85 de 100. Rangos de interpretación: de 19 a 50 en horizontal y de 75 a 100 en vertical. Son coordenadas de interpretación, no probabilidades de eventos.

P(doom) de Miles Brundage · inferido

≈7%

0%100%

Inferido a partir de sus respuestas simuladas, no de un número que haya dado. Rango plausible: 2–21%.

Cronología de hitos de Miles Brundage
  1. Trabajo e instituciones

    Very powerful AI is imminent, labs are using AI to accelerate further AI development, and the resulting feedback could compress decades of institutional and technological change into a few years.

    Respuesta 3

Agrupados por hito, sin espaciarlos ni ordenarlos por fechas inferidas. La IA general y la IA sobrehumana conservan sus definiciones.

De qué depende su perspectiva

Un supuesto central

Very powerful AI is imminent, labs are using AI to accelerate further AI development, and the resulting feedback could compress decades of institutional and technological change into a few years.
Respuesta 3

Si este supuesto resultara distinto, ¿cómo cambiaría su perspectiva?

Una pregunta sin resolver

I don’t have a personal number I can defend.
Respuesta 4

¿Qué le ayudaría a distinguir aquí entre los desenlaces plausibles?

Qué podría hacer cambiar de opinión

The biggest update would be strong evidence that automated AI development does—or does not—create a rapid feedback loop that sharply compresses our ability to iterate on safety.
Respuesta 5

¿Qué evidencia bastaría y en qué dirección movería su visión?

Más detalles

Beneficio esperado

Se esperan beneficios transformadores y de gran valor para muchos.

88 / 100

Poco impactoImpacto transformador

Rango de interpretación de 67 a 100 en la escala cualitativa.

Daño esperado

Se espera que los daños graves o generalizados sean una parte significativa del futuro.

75 / 100

Poco impactoImpacto transformador

Rango de interpretación de 67 a 100 en la escala cualitativa.

Influencia humana

Las decisiones humanas pueden redirigir sustancialmente la trayectoria de la IA.

69 / 100

Poca influenciaInfluencia fuerte

Rango de interpretación de 50 a 75 en la escala cualitativa.

Capacidades esperadas

Se espera que la IA siga siendo un conjunto de herramientas acotadas.

Se espera que la IA iguale a las personas en la mayor parte del trabajo cognitivo.

Posición simulada: Se espera que la IA supere ampliamente a las personas en el trabajo cognitivo.

Reglas para usar la IA

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.

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Visiones similares

Líderes de opinión cuyas visiones simuladas son las más cercanas a la de Miles Brundage

Evaluación simulada

Pregunta 1

¿Qué crees que significa la IA para nuestro futuro y por qué?

I think AI could be one of the most beneficial technologies ever—and that, on our current path, it creates a serious near-term risk of humans losing control. Very powerful systems are imminent. The danger is not mainly that everyone involved is malicious; it is that companies and countries fear slowing down while competitors keep racing. Labs are increasingly using AI to accelerate development of its successors, shortening the time available to understand failures or build institutions around them. I used to put more weight on gradual progress giving us repeated chances to learn. I now think I underweighted intelligence-explosion scenarios, and therefore underestimated both the alignment difficulty and the value of slowing development. Technical safety work remains essential, but there is no once-and-for-all technical fix. Any safety gain can simply be reinvested in reaching the next, riskier capability level faster. Aviation and nuclear power became safer through continuing standards, inspections, reporting, and enforcement—not by discovering one magic solution. So the future depends heavily on whether we build those guardrails now: binding safety and security standards, independent audits with deep access, whistleblower protections, incident reporting, serious penalties, biosecurity and cyberdefense, and verification mechanisms that could support international agreements. Companies cannot keep checking their own homework. I am not saying disaster is inevitable, and I do not have a personal numerical probability to offer. We have barely tried serious governance, which is both embarrassing and a reason for hope. But loss of control is a risk for the next few years, not the next few decades.

Pregunta 2

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

On our current path, I expect enormous benefits alongside severe, avoidable harms—and a nontrivial possibility of outcomes so bad they dominate the balance sheet. I do not have a defensible single number or clean “net positive/net negative” label. Much depends on choices being made now. AI could transform science, medicine, education, and productivity. It could also make cyberdefense far stronger and pandemic preparedness much cheaper. But the same capabilities can enable bioweapons, empower authoritarian control, destabilize critical systems, and gradually or suddenly disempower humans. The central problem is that deployment and capability development are moving faster than our ability—or willingness—to govern them. So my default expectation is not “AI inevitably goes badly.” It is that we get major gains while suffering far more damage than necessary, because companies and governments keep racing without meaningful obligations. The tail risk is much worse: genuine loss of control. Whether AI is positive overall will depend less on abstract arguments about the technology than on whether we urgently impose standards, audits, enforcement, and international verification. We are not currently on top of this, but we mostly have not tried.

Pregunta 3

Setting aside good or bad: how much do you think AI will ultimately change the world—a little, a lot, or completely?

A lot—quite plausibly on the scale of electricity. AI that exceeds humans across nearly every cognitive domain would reshape science, medicine, education, government, warfare, and most knowledge work. It could also change who actually exercises power: individuals, firms, states, or increasingly autonomous systems. I would hesitate over “completely,” because that can imply a specific endpoint or timetable I cannot justify. But this is not a modest productivity tool. Very powerful AI is imminent, labs are using AI to accelerate further AI development, and the resulting feedback could compress decades of institutional and technological change into a few years. Even without literal loss of control, the world after that would be profoundly different.

Pregunta 4

What’s your rough gut-feel chance that AI causes human extinction or a similarly permanent catastrophe? A number or “no idea” is fine.

I don’t have a personal number I can defend. My actionable view is that even 1% would be wildly unacceptable, and the risk can still be reduced substantially.

Pregunta 5

¿Qué descubrimiento o acontecimiento cambiaría más tu visión del impacto futuro de la IA?

The biggest update would be strong evidence that automated AI development does—or does not—create a rapid feedback loop that sharply compresses our ability to iterate on safety. I previously underweighted that possibility, and it now drives much of my urgency. On the optimistic side, I would update substantially if independent auditors with deep access repeatedly showed that frontier systems remain controllable as capabilities scale, and if enforceable standards and verification mechanisms demonstrably slowed unsafe racing. On the pessimistic side, clear evidence that systems can autonomously accelerate successor development, evade meaningful oversight, or defeat evaluations would make the situation much worse. Lab assurances alone would not move me much; companies cannot check their own homework.

Fuentes

Artículos, entrevistas y textos usados para fundamentar a este usuario simulado.

I worked at OpenAI. Here are the guardrails we need now

An op-ed agreeing with the more than a thousand lab employees who asked the US government to help “pace” automated AI development. He proposes that companies invite deep independent audits, join industry coordination bodies, fund verification tools for a possible US–China agreement, and support rather than kill legislation such as the Frontier Act and whistleblower protections. He thinks cooperation with China cannot yet be ruled out. Bylined as AVERI’s leader and a former OpenAI head of policy research; his own argument. Full text inspected on the Guardian page.

theguardian.com
My speech at Borgo Laudato Si’

Remarks at a Nobel laureates’ assembly on AI and nuclear war. He says people are voluntarily handing control to AI, starting inside the labs, and that speed and competition make loss of control possible even if almost no one wants it. AI now outperforms expert virologists on many hazardous tasks, and lying by AI systems has been normalized. He backs global frontier auditing and urges employees to push for verified guardrails. He has reservations about the Rome Declaration and warns about regulatory capture. Full post inspected.

milesbrundage.substack.com
We’re in Triage Mode for AI Policy

Quotes his late-2024 view that AI exceeding humans in nearly every cognitive domain is almost certain within a few years. Argues that 2025 was largely wasted, so the realistic goal is to “80/20” policy and narrowly avoid the worst cases (AI bioweapons killing billions, rogue AI takeover, stable global totalitarianism), while accepting serious but lesser harms. He says AI is not necessarily net bad, AI has big upside, and safety is not as hard as some claim, but without more action we will probably face avoidable harms and possibly a worst case. Full post inspected.

milesbrundage.substack.com
The Launch of AVERI

Announces AVERI, the nonprofit he cofounded to make independent auditing of frontier AI effective and universal. He argues companies should not check their own homework, whether AI is a normal technology or an unusually dangerous one, and that AI gets far less outside scrutiny than other technologies. He hopes auditing helps prevent catastrophic outcomes and enables beneficial deployment. Personal framing of an organizational mission; the AI-generated scrutiny estimates he cites are illustrative, by his own account. Full post inspected.

milesbrundage.substack.com
Faster, Please! — The Podcast #84: AI risks and rewards

Older context. He expects big changes within ten years and sees AI as comparable to electricity. He says risk reduction and benefits are not zero-sum, calls transparency low-hanging fruit, backs SB 53-style rules scaled to company size, and supports a framework of standards, evidence and incentives including third-party audits. He then said progress looked more gradual and allowed iteration, which made him more optimistic about solvability but pessimistic about neglected policy. His September 2026 posts revise this. Lightly edited transcript inspected; only his turns used.

aei.org
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