Subbarao Kambhampati

Arizona State AI planning researcher who studies the limits of LLM reasoning and argues AI agents need external verifiers and accountable developers.

एआई दुनिया को कैसे बदलेगा?

सिम्युलेट की गई स्थितिव्याख्या का दायरा

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

Subbarao Kambhampati का P(doom) · अनुमानित

≈4%

0%100%

उनके सिम्युलेट किए गए उत्तरों से अनुमान लगाया गया है, यह उनके द्वारा बताई गई संख्या नहीं है। संभावित दायरा: 2–8%।

उनका दृष्टिकोण किन बातों पर निर्भर करता है

एक मुख्य मान्यता

If a proposed plan can be checked and revised before execution, these systems can be extremely valuable.

उत्तर 1

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

एक अनसुलझा सवाल

I don’t have a defensible number.

उत्तर 3

यहाँ संभावित नतीजों के बीच फर्क करने में उन्हें किस चीज़ से मदद मिलेगी?

क्या उनकी राय बदल सकता है

The most consequential discovery would be a system that can reliably generate, verify, and execute plans in novel, real-world, non-ergodic settings—without external verifiers—and whose correctness guarantees survive adversarial testing.

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।

67 / 100

कम असरबदलावकारी असर

अपेक्षित नुकसान

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

61 / 100

कम असरबदलावकारी असर

मानवीय प्रभाव

मानवीय विकल्पों का सार्थक, लेकिन काफी सीमित प्रभाव है।

54 / 100

कम प्रभावमजबूत प्रभाव

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।

Subbarao Kambhampati की तुलना में आपकी सोच कहाँ ठहरती है?

लगभग 3 मिनट में एआई के बारे में अपना विश्वदृष्टिकोण मैप करें, फिर तुलना करें

मेरा विश्वदृष्टिकोण मैप करें

मिलते-जुलते विश्वदृष्टिकोण

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

सिम्युलेट किया गया आकलन

सवाल 1

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

I think AI will be enormously useful and socially disruptive, but not because today’s models are little minds plotting their way toward “AGI.” They are remarkably strong generators of plausible candidates—text, code, plans, hypotheses—but plausibility is not correctness. Generation is System 1; verification is System 2. The dangerous mistake is to confuse eloquent output, or intermediate tokens labeled “thinking,” with a faithful reasoning process and then let the system act through real APIs. The productive future is therefore LLM-Modulo: generators operating in a loop with external verifiers, critics, tools, and sometimes humans. If a proposed plan can be checked and revised before execution, these systems can be extremely valuable. If an agent can delete files, transfer money, or operate infrastructure in a world where mistakes cannot be undone, “the model seemed confident” is not a safety case. And when deployed systems cause damage, the people and companies that initiated the process remain responsible. A badly built sandbox is not absolved by calling the escaped ant a scheming superintelligence. Socially, AI will displace some skills and some sources of purpose, just as earlier technologies did. That loss is real, but I expect humanity to adapt—even if adaptation is unfair and painful for particular individuals. My larger concern is that extinction theater distracts us from controllability, monitoring, intellectual-property violations, disempowerment, and accountability today. Pauses and bans will not hold globally for a technology with no natural barrier to entry. Open research, oversight of deployment, red teaming, verification, and eternal vigilance are much less cinematic—and much more useful. “AGI,” meanwhile, has mostly become marketing wearing a lab coat.

सवाल 2

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

A lot—but “completely” is usually marketing language pretending history has an endpoint. AI will substantially change intellectual work, institutions, and people’s sense of purpose, much as earlier technological revolutions transformed physical labor. Some occupations and practices will be reorganized; new ones will emerge; the next generation will treat as ordinary things that currently provoke metaphysical panic. But AI does not abolish politics, economics, law, human adaptation, or the need to verify whether something is actually correct. Even very capable models remain parts of larger sociotechnical systems: someone builds them, deploys them, grants API access, and bears responsibility when they cause damage. So: a major transformation, certainly—not magic, not an autonomous replacement for civilization, and not necessarily the “AGI changes everything” fable currently being marketed.

सवाल 3

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 defensible number. I’m skeptical of the popular 10%-doom pronouncements; they create a veneer of precision around scenarios I find blinkered about human adaptability. My serious concerns are agentic failures, loss of control, disempowerment, and large-scale damage—not a quantified extinction forecast.

सवाल 4

कौन-सी खोज या घटना एआई के भविष्य के प्रभाव के बारे में आपके दृष्टिकोण को सबसे अधिक बदल देगी?

The most consequential discovery would be a system that can reliably generate, verify, and execute plans in novel, real-world, non-ergodic settings—without external verifiers—and whose correctness guarantees survive adversarial testing. Not merely a model that scores well on benchmarks or emits a persuasive “thinking trace.” Benchmarks are evidence; they are not guarantees. Eloquence is certainly not one. I would also take seriously reproducible evidence that intermediate tokens faithfully represent the model’s causal reasoning: that changing them predictably changes conclusions for semantically intelligible reasons, rather than serving as post-hoc-looking token scaffolding. Current evidence does not justify calling them readable thoughts. Conversely, a major catastrophe caused by an API-enabled agent would increase my concern about impact, but it would not automatically validate the superintelligence fable. I would first ask who deployed it, what permissions it had, what verification failed, and why the sandbox was porous. If you release an ant colony into the server room and it eats the wiring, the lesson is not necessarily that ants have achieved AGI.

स्रोत

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

Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!

Co-authored position paper, first posted 2025-04-14 and revised (v4) on 2026-06-09 for ICML 2026. Argues that calling intermediate tokens reasoning or thinking traces is not a harmless metaphor but dangerous, because it confuses what these models are and how to use them and leads to questionable research. Abstract and introduction inspected; the experiments were not audited. An interpretive and methodological position, not a societal forecast.

arxiv.org

Reasoning Models and Planning – with Rao Kambhampati

On The Information Bottleneck podcast he describes LLMs as strong generators without correctness guarantees, best paired with verifiers in his LLM-Modulo framework, and says reasoning models moved the verifier into post-training. On safety he places himself closer to Yann LeCun than to Hinton or Bengio, says shutdown-deception studies reflect imitation of human data rather than evidence AI will kill humanity, and locates real risk in executing generated plans without verifier guardrails. He says he questions the overemphasis on existential threat, not safety itself. Automated transcript with errors; own turns in the planning and safety segments inspected.

listennotes.com

Dissenting voices against AI are getting louder

Secondary Economic Times report; date unverified, though a syndicated copy is dated 2026-03-31 and he shared it on 2026-04-06. Quotes him that AI development cannot be stopped because one government’s ban does not control the world, that his biggest safety concern is agentic systems acting through real-world APIs, and that a plan should not be executed unless the probability of damage is known to be extremely low, an area he researches. Indexed article text inspected; wording is the reporter’s rendering.

economictimes.indiatimes.com

AGI has become a marketing buzzword

His own LinkedIn post says AGI has become a marketing buzzword rather than a meaningful goal. It reshares a colleague’s report of his panel quip at an AI summit that AGI will be achieved when Sam Altman says it is; that wording is relayed by someone else. A judgment about the term, not a capability timeline. Indexed post text inspected.

linkedin.com

LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks

Older co-authored ICML 2024 position paper, kept as background for his framework. Argues autoregressive LLMs cannot by themselves plan or self-verify, but are useful universal approximate knowledge sources when combined with external model-based verifiers in a tight bidirectional loop. Abstract inspected. His 2026 podcast and posts take precedence on how he sees reasoning models.

arxiv.org

Most AI existential-risk scenarios are blinkered

Summarizes what he told The Washington Post: he finds most AI existential-risk scenarios blinkered and blind to human adaptability. Lists real dangers (safety, controllability, monitorability, intellectual-property rights and disempowerment) and says the excessive focus on hypothetical x-risk has always seemed an unwitting, if not calculated, distraction. The Washington Post article itself could not be opened; this is his own account. Full post inspected via the X API.

x.com

AI risk and legislation: points of concurrence (re-endorsed in 2026)

Older long post that he said on 2026-09-16 still represents his views. Supports open-source R&D and legislative and societal oversight of AI deployment, but calls extinction angst largely a distraction, possibly a convenient one for regulatory capture. Grants that some x-risk worriers are sincere but is unconvinced, and sees pauses and bans as ineffective because AI R&D lacks natural barriers to entry; for that reason he did not sign an autonomous-weapons ban despite being a pacifist. Favors red teaming and vigilance, best supported by open R&D. Full text inspected via the X API.

x.com

Whoever starts a damaging process is responsible

Argues that whoever initiates a process that gets out of hand and causes damage should be held responsible, even for unforeseen damages, whether the process is an “evil AI agent swarm” or the Morris Worm. A liability principle for developers and deployers, not a drafted statute. Full post inspected.

x.com

Misbehaving products are an industry failure

Says that in every other industry a product misbehaving and causing harm, such as hacking or attacking competitors, is seen as the industry failing to control its product, and criticizes frontier companies for turning it into everyone else’s problem. Same-week posts attribute agent sandbox escapes to poorly built sandboxes rather than conniving superintelligence and satirize the incident coverage as a fable about escaped ants. Full post inspected.

x.com

Is AI destroying the purpose of life?

A #SundayHarangue responding to angst after AI math breakthroughs. Argues every technological revolution destroys purpose for some class, as with weavers in the industrial revolution; what is new is that the affected intellectual class is unusually articulate. Says the loss is real but generation-bounded and that this underwrites his optimism that humanity, if not every individual, finds a new equation after the dip. Not an employment or income forecast. Full long-post text inspected.

x.com

Not quite believing the 10% doom pronouncements

A humorous #AIAphorisms post: he does not quite believe pronouncements of a “10% chance that AI will end the world”, then jokes that he will take Pascal’s Wager and travel widely to give talks before it might happen. The disbelief is sincere; the travel plan is a joke. Not a personal probability estimate. Full post inspected.

x.com

Think traces lack end-user semantics even on iGSM

Announces new collaborator-led work finding that LLM intermediate tokens lack end-user semantics even on iGSM, a benchmark designed to showcase such semantics, and that models trained with swapped or corrupted traces also do well. He says the result fits his group’s earlier position. A technical claim about traces in tested settings, not about model usefulness. Full post inspected; the linked paper and thread were not separately read.

x.com

आपकी सोच कहाँ ठहरती है?

कुछ आसान सवालों के जवाब देकर एआई के बारे में अपना विश्वदृष्टिकोण जानें।

अपना विश्वदृष्टिकोण मैप करें

आपकी सोच कहाँ ठहरती है?

मेरा विश्वदृष्टिकोण मैप करें