“P(doom) is a shorthand term used in the artificial intelligence safety community to represent the subjective probability that advanced AI will cause an existential catastrophe, such as human extinction or permanent disempowerment. The term is expressed as a percentage or fraction reflecting an individual’s personal estimation of risk.” – P(doom) – Artificial intelligence
Pressure around the term comes from the fact that it compresses a disputed forecast into a single number. That number is meant to capture a person’s subjective estimate of the chance that advanced AI leads to an existential catastrophe, usually framed as human extinction or permanent disempowerment, but the range of outcomes bundled into the label is broader than many casual uses suggest 1,5,8. Because the label is informal, it is useful only when the speaker also states the threat model, time horizon, and assumptions behind the number. Without those qualifiers, two people can both say they have a P(doom) of 10% while referring to very different futures.
The practical meaning is closer to a risk judgement than to a measurable property of the world. In the AI safety community, the term usually denotes subjective probability, not a frequency derived from repeated trials, and not a formally calibrated actuarial estimate 5,9. That distinction matters because there is no established statistical base rate for superhuman AI causing civilisation-scale failure. A person saying 20% is therefore reporting a credence, not a measurement, and the number reflects their views on capability progress, alignment difficulty, governance quality, and the likelihood of human intervention succeeding under stress 5,7.
Definition and scope
In substance, P(doom) asks how likely it is that advanced AI creates an outcome bad enough to qualify as existential catastrophe. Some uses focus narrowly on extinction, while others include permanent loss of human control, irreversible authoritarian lock-in, or lasting civilisational collapse 1,5,8. This range explains why estimates differ so dramatically. A narrower definition usually produces a lower figure, while a broader definition raises the number because it counts more failure modes and more ambiguous forms of harm.
The term also carries a strong asymmetry between cause and prevention. The usual AI safety usage counts AI as the cause of doom, not as a system that might avert other disasters along the way 5. That matters because a model could, in principle, reduce some risks while increasing existential risk elsewhere. A single percentage cannot express all of those trade-offs unless the forecaster makes explicit which pathway they are measuring.
How the number is formed
There is no standard formula for calculating P(doom), but a useful way to think about it is as a decomposition over several uncertain links in a causal chain. One stylised representation is P(\text{doom}) = P(\text{failure}) \times P(\text{catastrophic failure} \mid \text{failure}), where each term hides deeper uncertainty about model capability, deployment incentives, and governance response. In more detailed thinking, forecasters often mentally split the problem into alignment failure, misuse by humans, loss of control, and the ability of institutions to correct course before a catastrophic threshold is crossed 5,7.
That decomposition is not a formal consensus model, but it clarifies why the debate is so intractable. If a person thinks advanced systems will remain weak, or that current architectures cannot scale into dangerous agency, they will assign a low P(doom). If they think capability gains will outpace alignment research and that competitive pressure will delay safeguards, they will assign a much higher one 2,7. The uncertainty sits not only in the final outcome, but in every stage of the pathway leading there.
Why estimates diverge
Published and publicly discussed estimates cover an exceptionally wide range, from near zero to near certainty. A 2023 survey of AI researchers reported a mean estimate of 14,4% for the probability that future AI could lead to human extinction or similarly severe and permanent disempowerment within 100 years, with a median of 5% 1. Other compilations of public statements and commentary show similarly wide dispersion, with some safety-oriented researchers placing the risk in double digits or higher, while some mainstream machine learning researchers argue the risk is negligible 7,8,12.
The central reason for this spread is that P(doom) bundles empirical uncertainty with philosophical judgement. One school of thought treats advanced AI as an engineering problem that will remain controllable through better tools, robust deployment practices, and iterative oversight. Another argues that sufficiently capable systems may become strategically powerful in ways that current oversight cannot reliably contain 2,7. A third perspective emphasises governance, claiming that the decisive variable is not the internal logic of AI systems alone, but the incentives, institutions, and security practices surrounding their deployment. The label is the same, but the causal story differs.
Debates and tensions
One major tension concerns whether the term is too broad to be analytically clean. Critics say that lumping extinction, disempowerment, and civilisational collapse into a single probability encourages rhetorical overreach and hides different policy priorities 5,12. Supporters reply that the shared feature is not identical mechanism, but the same strategic endpoint: a future in which humanity loses its ability to shape its own destiny. That disagreement is not semantic trivia. It changes whether the right policy response is frontier-model restraint, governance reform, compute controls, or more focused technical alignment work.
A second tension is between subjective and operational meaning. Because P(doom) is not a directly observable quantity, it can be misused as a status signal rather than a disciplined forecast 5,9. Yet the concept still matters because decision-makers need some way to reason about low-probability, high-impact events. The economics literature on transformative AI has argued that even low probabilities of catastrophic outcomes can justify substantial investment in safety and alignment, because expected losses become large when the stakes include human extinction or permanent lock-in 2,3.
Mathematical and policy significance
From a decision-theoretic view, the expected value of preventive action can be sketched as \mathbb{E}[L] = p \times C, where p is the estimated probability of doom and C is the cost of the catastrophic outcome. If C is effectively infinite in moral or civilisational terms, even small increases in p become salient. That is why P(doom) appears so often in policy discussion. It offers a compact way to connect technical uncertainty with the scale of intervention that might be justified 2,3.
Still, the number should not be treated as a forecast with false precision. Good use of the term requires stating the horizon, the definition of doom, and the assumptions about model development and governance. The best readings of the literature treat P(doom) as a disciplined shorthand for uncertainty under deep time pressure, not as a settled statistic. It remains important precisely because the field has not resolved whether the relevant probability is close to zero, meaningfully non-trivial, or alarmingly high. Until that question is answered, the term will continue to structure the main argument in AI safety: whether humanity is managing a difficult engineering challenge or approaching a threshold that could permanently remove its agency 1,2,5,8.
References
1. P(doom) – 2024-06-19 – https://en.wikipedia.org/wiki/P(doom)
2. The Economics of p(doom): Scenarios of Existential Risk and … – arXiv – 2025-03-10 – https://arxiv.org/abs/2503.07341
3. The economics of p(doom): Scenarios of existential risk … – https://www.sciencedirect.com/science/article/pii/S0264999326002476
4. P(doom) Survey 2026: What Do AI Researchers Think? – 2026-06-04 – https://calcuja.com/research/ai-risk-survey-2026/
5. What is “p(doom)”? – 2026-07-24 – https://aisafety.info/questions/NM24/What-is-pdoom
6. The Perils of Artificial Intelligence: Safeguarding Humanity in an Age … – 2026-08-19 – https://www.aehsasfoundation.org/blog/2
7. What is P(doom)? AI Existential Risk Probability Explained – 2025-05-13 – https://calcuja.com/what-is-pdoom/
8. Appendix: Quantifying Existential Risks – Chapter 2 – https://ai-safety-atlas.com/chapters/v1/risks/appendix-quantifying-existential-risks/
9. What is “p(doom)”? – AISafety.info – 2026-03-03 – https://aisafety.info/questions/NM24/What-is-%22p(doom)%22
10. What Does P(DOOM) Mean? – https://www.cyberdefinitions.com/definitions/P(DOOM).html
11. Quantifying Expert Consensus on Existential Risk: A Biographical … – 2025-11-29 – https://blog.biocomm.ai/2025/11/29/quantifying-expert-consensus-on-existential-risk-a-biographical-and-statistical-analysis-of-the-top-50-scientists-and-leaders-in-artificial-intelligence-safety-and-alignment/
12. Why do Experts Disagree on Existential Risk and P(doom) … – 2025-01-25 – https://arxiv.org/html/2502.14870v1
13. What is P (doom)? – AI Glossary – Beginners in AI – 2026-05-16 – https://beginnersinai.org/glossary-what-is-p-doom/
14. What is “p(doom)”? – 2025-06-16 – https://aisafety.info/questions/NM24/What%20is%20%22p(doom)%22%3F
15. P (doom), Explained: What the AI Safety Debate Means for … – https://idrak.qa/insights/p-doom-explained-business-risk
