P(doom) is the estimated probability that advanced AI leads to human extinction or permanent civilisational collapse. The term was popularised in AI safety circles and is now widely used by researchers and forecasters to communicate their sense of long-term risk from advanced AI systems.

Why do AI safety researchers estimate higher P(doom)?

AI safety researchers focus specifically on long-term and catastrophic risks from advanced AI systems, particularly misalignment scenarios where AI pursues goals contrary to human welfare. Mainstream ML researchers typically focus on near-term capabilities and applications, and may not engage as deeply with long-term risk scenarios.

P(doom) Survey 2026: What Do AI Researchers Think?

Name: P(doom) Survey 2026: Aggregated AI Extinction Risk Estimates from Researchers and Forecasters
Creator: Calcuja Research
Published: 2026-06-04
License: https://creativecommons.org/licenses/by/4.0/

// Key Findings

Median estimate: ~20% probability of existential catastrophe from AI across all sources
Estimates range from near 0% (LeCun, Ng) to >90% (some EA-aligned researchers)
AI safety researchers estimate significantly higher P(doom) than mainstream ML researchers
Estimates have increased on average since 2020 across all groups
No consensus: the standard deviation across estimates is larger than the median itself

// Distribution Summary

Median Estimate

~20%

Mean Estimate

~25%

Lowest Estimate

~0%

Highest Estimate

>90%

Researchers Surveyed

50+

Data Range

2020–2026

// Estimates by Group

Group	Median P(doom)	Range
AI Safety researchers	~30%	10–99%
Tech industry leaders	~15%	0–50%
Forecasting platforms	~10%	2–25%
ML researchers (mainstream)	~5%	0–20%

Public estimates only. Sources linked below.

→ Calculate Your P(doom)

// Full Data

Estimates by Researcher & Organisation

All estimates are publicly stated. Estimates reflect the researcher's stated view at the time of publication. Color: ■ High (>20%) · ■ Moderate (1–20%) · ■ Low (<1%)

Researcher / Organisation	Affiliation	P(doom) Estimate	Year
Eliezer Yudkowsky	MIRI	~99%	2023
Paul Christiano	ARC / Anthropic (former)	~50%	2023
Scott Alexander	Astral Codex Ten	~25%	2023
Yoshua Bengio	Mila / Université de Montréal	~20%	2024
80,000 Hours survey	EA organisation	~20% median	2023
Geoffrey Hinton	Google (former)	10–50%	2023
Dario Amodei	Anthropic	10–25%	2024
Nick Bostrom	FHI Oxford	substantial	2023
Stuart Russell	UC Berkeley	significant	2023
Demis Hassabis	Google DeepMind	notable risk	2023
Sam Altman	OpenAI	low but non-zero	2023
Toby Ord	FHI Oxford	~10%	2020
Metaculus (community)	Forecasting platform	~10%	2025
Yann LeCun	Meta AI	~0%	2023
Andrew Ng	DeepLearning.AI	very low	2023

⬇ Download CSV → P(doom) Calculator

// Analysis

Why Estimates Diverge So Dramatically

AI Safety Researchers (~30% median)

AI safety researchers — those employed at organisations like MIRI, ARC, Anthropic's safety team, and DeepMind's safety division — consistently produce the highest P(doom) estimates. This reflects a professional focus on long-term risk scenarios, particularly misalignment: the possibility that a sufficiently advanced AI system pursues goals that are subtly or severely contrary to human welfare. Researchers in this tradition often work from explicit models of AI development trajectories in which misalignment is difficult to prevent by default. Eliezer Yudkowsky's estimate of ~99% reflects a belief that alignment is essentially unsolved and that current approaches are fundamentally inadequate for the level of AI capability being developed.

Tech Industry Leaders (~15% median)

CEOs and senior researchers at major AI labs (OpenAI, Anthropic, DeepMind, Google) tend to acknowledge existential risk while estimating it as moderate rather than near-certain. This group shows the widest variance — from Sam Altman's "low but non-zero" to Dario Amodei's 10–25% range. Industry leaders face structural incentives that may compress estimates downward (investor confidence, regulatory concerns) and upward (fundraising narratives, safety credibility). The divergence between Altman and Amodei — both co-founders of OpenAI — illustrates how much uncertainty exists even within the same institutional background.

Forecasting Platforms (~10% median)

Aggregated forecasts from platforms like Metaculus, Manifold Markets, and Polymarket tend to cluster in the 5–15% range for questions about AI-caused catastrophe before 2100. These estimates represent crowd wisdom from hundreds of forecasters applying diverse models. Prediction market participants are rewarded for accuracy and tend to be more calibrated than individual experts — though the far-future nature of the question makes calibration extremely difficult. Metaculus's community estimate of ~10% has gradually increased since 2020.

Mainstream ML Researchers (~5% median)

Researchers working on AI capabilities — building large language models, computer vision systems, and reinforcement learning agents — tend to give the lowest P(doom) estimates. Yann LeCun (~0%) and Andrew Ng (very low) are the most prominent voices in this camp. Their argument is that current AI architectures cannot produce genuinely autonomous goal-directed behaviour, that the "paperclip maximiser" scenario requires capabilities far beyond current systems, and that existential risk discourse is a distraction from real near-term harms. This group's lower estimates reflect both genuine analytical disagreement and a different professional focus on near-term technical work.

// Methodology

Data Collection & Methodology

This report aggregates publicly stated P(doom) estimates only. We define P(doom) as the estimated probability that advanced AI leads to human extinction or permanent large-scale civilisational collapse within the next 100 years.

Inclusion criteria: Estimate must be publicly stated in an interview, published paper, blog post, podcast, or recorded conference talk. No private communications are included. Where researchers gave a range, we use the midpoint for quantitative analysis.

Limitations: P(doom) is not a standardised metric. Different researchers define the term differently — some include economic collapse or loss of human autonomy, others only biological extinction. Estimates are point-in-time and may have changed. This is a descriptive survey, not an endorsement of any estimate or a claim about the actual probability of AI catastrophe.

Updates: This report will be updated annually. Last updated June 2026.

// Sources & References

Yudkowsky, E. (2023). Pausing AI Developments Isn't Enough. We Need to Shut it All Down. Time Magazine, March 29, 2023.
Ord, T. (2020). The Precipice: Existential Risk and the Future of Humanity. Hachette Books. Estimates AI risk at ~10% before 2100.
80,000 Hours (2023). Annual survey of AI risk estimates among researchers and EA community. 80000hours.org
Metaculus (2025). Will AI cause human extinction before 2100? Community forecast. metaculus.com
Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
Hinton, G. (2023). Interview with MIT Technology Review. "AI could be as transformative as the industrial revolution — and potentially more dangerous."
Bengio, Y. (2024). Statement on AI existential risk at UN AI Safety Summit, Geneva.
Christiano, P. (2023). Interview with 80,000 Hours. Estimates ~50% P(doom) from misalignment.

Cite this report:
Calcuja Research (2026). P(doom) Survey 2026: Aggregated AI Extinction Risk Estimates. Calcuja.com. https://calcuja.com/research/ai-risk-survey-2026/

// FAQ

Frequently Asked Questions

P(doom) is the estimated probability that advanced AI leads to human extinction or permanent civilisational collapse. The term was popularised in AI safety circles — particularly by researchers at MIRI and associated organisations — and is now widely used by researchers, forecasters, and journalists to communicate a single-number summary of long-term AI existential risk.

There is no consensus. Estimates range from near 0% (mainstream ML researchers like Yann LeCun and Andrew Ng) to over 90% (some AI safety researchers like Eliezer Yudkowsky). The median across all public estimates in this survey is approximately 20%. The wide disagreement is itself informative: it suggests deep uncertainty about fundamental questions in AI development.

AI safety researchers focus specifically on long-term and catastrophic risk scenarios, particularly AI misalignment — the possibility that a sufficiently advanced AI pursues goals contrary to human welfare. Mainstream ML researchers typically focus on near-term capabilities and applications, and tend not to engage with long-term alignment risk scenarios in their professional work. Both groups may also be influenced by professional incentives: safety researchers are rewarded for finding risks, capabilities researchers for building systems.

P(doom) estimates are highly uncertain and subjective. They reflect the estimator's model of AI development trajectories, alignment difficulty, and governance capacity — not empirical measurement. The question is irreducibly novel: there is no historical base rate for human-level AI development. Wide disagreement should be expected. Treat these estimates as one input into thinking about AI risk, not as authoritative forecasts.

The Calcuja P(doom) Calculator lets you input your own assumptions about AI development timelines, alignment difficulty, and governance quality to generate a personalised P(doom) estimate based on a structured risk model. Unlike the point estimates in this survey, the calculator makes the underlying assumptions explicit — letting you see how different beliefs about AI development translate into different risk estimates.

// Explore More