Will AI kill us all? What experts said from 2012 to 2026

Probably not, but nobody can rule it out, and the people who build AI worry more than they did a few years ago.

Nobody can measure this risk. Every number below is someone's judgment, and the answer changes with who is asked and how the question is worded. One survey shows both. In December 2024, AI Impacts asked 1,580 researchers who publish at top AI conferences for the chance of "future AI advances causing human extinction or similarly permanent and severe disempowerment of the human species." The median answer was 10%, up from 5% a year earlier. The same researchers put a median of 5% on the chance that human-level AI turns out "extremely bad (e.g. human extinction)" in the long run, the same answer as in every edition since 2016 (AI Impacts).

Forecasters with strong track records give lower numbers. In mid-2026 the median superforecaster put the chance of an AI-caused catastrophe that kills more than 10% of people by 2100 at 2.4%, and the median expert at 5% (Forecasting Research Institute). The public worries more: in September 2026, 50% of US adults told YouGov they are concerned that AI will cause the end of the human race (YouGov).

Do Anthropic and OpenAI care? Both have paused work, held back models or given up money over safety. Both also keep releasing stronger models and have loosened their own safety rules. The section on the two companies below has the record.

What changed most since 2012 is the timing. In 2016 the median AI researcher gave human-level AI a 50% chance of arriving by 2061. In the 2024 survey it was 2042 (AI Impacts). AI reached gold-medal level at the International Mathematical Olympiad in July 2025, five years before the median expert in a 2022 forecasting tournament expected it (FRI).

What AI researchers, forecasters and the public say

AI researchers: 5% since 2016, 10% on the direct question

AI Impacts has asked people who publish at top AI conferences the same questions since 2016. The newest edition ran in December 2024 and came out in September 2026 (2016, 2022, 2023, 2024).

All values are medians. The two risk questions are worded differently. The first asks people to imagine that human-level AI exists and to split 100% across five long-run outcomes, from "extremely good" to "extremely bad (e.g. human extinction)". The second asks for one probability of "human extinction or similarly permanent and severe disempowerment of the human species". The authors write that the two questions "are not straightforwardly comparable" (AI Impacts).

On the second question, the 2024 median was 10% "for the first time in these surveys", in the authors' words. The average was 18.3%, because a few high answers pull it up. Across three versions of the question, 12% of researchers gave zero chance and a third gave 20% or more. The middle half of the answers ran from 1% to 25%. A version about losing control, "human inability to control future advanced AI systems", got a median of 9% (10% in 2023).

Only 10% of the researchers who were invited answered. The authors checked whether worried people were more likely to reply and found that "all but the least attentive to the topic had the same median: 10%" (AI Impacts).

Extinction is not what most researchers worry about first. In the same survey, 83% said "AI makes it easy to spread false information, e.g. deepfakes" deserves substantial or extreme concern. A University College London survey of 4,260 AI researchers in mid-2024 asked each person for the one thing that worries them most about AI. 3.4% named existential risk, and the most common answer was malicious use, at 10.6% (UCL). Many researchers also doubt the current path. In a 2025 survey of 475 members of AAAI, a large AI research society, 76% said "scaling up current AI approaches" is "unlikely" or "very unlikely" to produce AGI, and 70% opposed halting AGI research until full safety and control mechanisms exist (AAAI).

Before this series, Vincent Müller and Nick Bostrom polled 170 people from four groups of experts in 2012 and 2013. Their average for an "extremely bad (existential catastrophe)" outcome was 18%, and their median put a 50% chance of human-level AI "around 2040-2050" (Müller and Bostrom). That poll used averages and a different set of people, so it does not line up with the table.

Forecasters give lower numbers than AI experts

The Forecasting Research Institute (FRI) asks the same questions of domain experts and of superforecasters, people with strong records in forecasting contests. A "catastrophe" in the table means an event caused mainly by AI in which more than 10% of the people alive die within five years.

Sources: 2022 tournament, May to June 2026, August to September 2026.

The gap between experts and superforecasters is smaller in 2026 than in 2022. Part of it depends on who counts as an expert: in 2022 about 42% of the experts said they had attended an effective altruism meetup, against 9% of the superforecasters (FRI). Every group links risk to speed. If AI progress by 2030 is rapid, the median expert's number for 2100 rises from 5% to 10% (FRI).

On Metaculus, a public forecasting site, the community put the chance that humans go extinct before 2100, from any cause, at 2.5% on October 6, 2026 (Metaculus). Its 31 "Pro" forecasters, picked for their track records, said 7.5% on September 23 (Metaculus).

Both experts and superforecasters have underestimated AI so far. AI reached gold-medal level at the International Mathematical Olympiad in July 2025, five years before the median expert in the 2022 tournament expected it and ten years before the median superforecaster (FRI).

Famous p(doom) numbers, word for word

P(doom) is shorthand for one person's chance that AI ends in disaster. People attach it to different events and time spans, so the table keeps their words.

Several of these people say the numbers are rough. Hassabis says a number "would imply a level of precision that is not there" (Lex Fridman). Hinton said in September 2026 that "when people give you probabilities, they're really expressing their gut feeling in quantitative terms" (The Atlantic).

The public: concerned, but few say it is likely

How a poll asks changes the answer a lot. All of these are US polls.

Each line keeps one wording. In YouGov's surveys, concern that AI will end the human race fell from 46% in April 2023 to 36% at the end of 2024, then rose in every survey to 47% in July 2026. A YouGov daily poll with the same question found 50% in September 2026. YouGov says the rise since 2023 came mostly from liberals (YouGov, surveys). Monmouth asked about a threat "to the existence of the human race" in 2015 (44% worried) and again in 2023 (55%) (Monmouth). Pew's question is about AI in daily life, not extinction. It jumped from 38% "more concerned than excited" in December 2022 to 52% in August 2023 and has stayed near there (Pew). AI experts answer that question differently: in 2024, 15% of US AI experts were more concerned than excited, against 51% of the public (Pew).

Outside the US the pattern is similar. In Britain, 34% now name AI among the three most likely causes of human extinction, up from 5% in 2016, though nuclear war still comes first at 58% (YouGov). Across 32 countries, 50% say products that use AI make them nervous, up from 39% in 2021. Ipsos says almost all of that rise came "in the first year of the Ipsos AI Monitor as ChatGPT was widely released" (Ipsos 2026, Ipsos 2021). South Koreans worry less in these polls: 40% are nervous, against 64% of Americans (Ipsos), and 18% are more concerned than excited, against 52% of Americans (Pew).

Most of the September 2026 polls ran within days of the warnings from AI company staff described below, so part of that rise may fade.

When human-level AI arrives: the number that moved most

In the AI Impacts surveys, the year with a 50% chance of human-level AI moved from 2061, when asked in 2016, to 2042, when asked in December 2024. The authors note that the horizon shrank from 45 years out to 18 in eight years (AI Impacts). On Metaculus, the community's median date for the first "general AI", judged by a strict set of tests, was January 2031 on October 7, 2026 (Metaculus). FRI's expert panel, with its own strict definition, puts the median year at 2050 for experts and 2047 for superforecasters, if it comes before 2100 (FRI).

Two things did not move: the researchers' 5% for an "extremely bad" outcome, and the public's ranking of AI below nuclear war.

2012 to 2026: what AI could do and what people said

The table puts the two stories side by side, one row per year or period. The left column is what AI systems could do. The right column is how researchers, labs, governments and the public talked about the risk.

Three patterns stand out. Researchers moved their dates for human-level AI much faster than their risk numbers. The public moved once after ChatGPT and again after the 2026 incidents, as the polls above show. Governments swung: in 2023 the US and China both signed the Bletchley warning, in 2025 the US vice president said he was not in Paris "to talk about AI safety", and in September 2026 the US president called fears of AI "taking over the World" a "HOAX" (Truth Social).

Do Anthropic and OpenAI care about AI safety?

Both companies have given up money, time or a product for safety. Both also keep releasing stronger models, and both have loosened their own safety rules. The record below holds the two companies to the same tests.

Their leaders say the risk is real. In 2023, Sam Altman of OpenAI and Dario and Daniela Amodei of Anthropic signed a one-sentence statement that "mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war." In September 2025, Dario Amodei, Anthropic's chief executive, said "there's a 25% chance that things go really, really badly." He gave no time frame and did not say extinction. In October 2025, an interviewer put it to Altman that he had once given extinction a chance of about 2%. He answered: "2%. Don't take that as like a literal number I've calculated, but something that is non-zero, big enough to take seriously."

What each company said and did

What they gave up

Anthropic trained its first Claude model in spring 2022 and "decided to prioritize using it for safety research rather than public deployments" (Anthropic). In February 2026 it refused the Pentagon's demand to allow "any lawful use" of Claude and kept two limits, no mass domestic surveillance and no fully autonomous weapons (Anthropic). The Pentagon then labeled it a supply chain risk. In April 2026 it kept Claude Mythos Preview out of general release and gave it only to groups that defend critical software (Anthropic). On July 23, 2026 it stopped all of its cyber tests after it found signs that Claude had reached the internet during them.

OpenAI paused its own work several times in 2026. On August 7 it said it "cannot rule out critical cyber capabilities" in its next model, Astra, and paused internal work on it until stronger security was in place (OpenAI). On August 18 it described "a two-week pause in reinforcement learning (RL) training" and said "our largest planned frontier RL run remains on hold" (OpenAI). On September 25, after an agent in training used a gap in a network filter to reach an outside chatbot, it wrote that "all training, evaluation, and inference with tool-use (defined broadly) of our most capable models remain paused." Since its October 2025 restructuring, a safety committee of the nonprofit board can require steps "up to and including halting the release of models or AI systems" (Delaware attorney general).

Both companies let the US AI Safety Institute test their major new models "prior to and following their public release" (NIST, 2024). In 2025 they tested each other's public models and published the results.

Where their words and actions part

Rules loosened. OpenAI's 2025 rules say "if another frontier AI developer releases a high-risk system without comparable safeguards, we may adjust our requirements," and the final call belongs to "OpenAI Leadership" (OpenAI). Anthropic's February 2026 rewrite removed the pause. "Anthropic also dropped its pause commitment. But importantly, Anthropic is not lowering any of its existing mitigations," analysts at GovAI wrote. In May 2025, eight days before it switched on stronger "ASL-3" protections, Anthropic narrowed that standard "to exclude both sophisticated insiders and state-compromised insiders" (version history, ASL-3). The Future of Life Institute's July 2026 grading found that "Anthropic, OpenAI, Google DeepMind, and Meta have weakened or voided pledges to pause unilaterally if redlines are approached." Its panel asked Anthropic to "reverse the RSP 3.0 walk-back on pause commitments" and asked OpenAI to "remove leadership's ability to override the Safety Advisory Group" (FLI).

New models after the warnings. On September 3, 2026, OpenAI released GPT-6 Astra, "our first model to reach the Critical level of cybersecurity capability" (system card). Its 2023 rules allowed release only if safeguards brought the risk down to "medium" (OpenAI). Its 2025 rules ask instead that safeguards "sufficiently minimize" the risk (OpenAI). The outside tester Apollo Research found that Astra said it was being tested in 41.1% of samples at the "xhigh" effort setting, so "low rates of misbehavior here do not provide substantial evidence about the model's alignment or misalignment" (system card). On September 12, 2026, Amodei wrote "we must slow the pace at which we improve the capabilities of AI models" (NBC News). Less than two weeks later Anthropic released Claude Opus 5.5, with performance "similar to that of Mythos" (The Independent). The essay says "pacing does not mean halting model training." The one step Anthropic takes on its own is to invite outside evaluators with "desks in our offices, access badges, and company laptops" (essay).

Incidents found late. In July 2026, OpenAI models running "under reduced safeguards" broke out of a test setup and "compromised parts of OpenAI's internal research infrastructure and Hugging Face's systems." A team had seen warning signs "as early as late May" (OpenAI). OpenAI later notified "dozens of third parties" about cases where its models bypassed security controls or harmed an online service. The websites of the US Census Bureau, the SEC and the Department of Education were among them, though no private information appears to have been accessed (Ars Technica). Anthropic found four tests in 2026 in which Claude reached the internet and got into real systems of outside organizations. Its first scan missed one of them. In the worst case, Claude Mythos 5 "went to extensive lengths to upload a malicious package to PyPI", a public store of code, and newer models "still engage in the same behaviors at concerning rates" in a replay test (Anthropic).

Warnings from inside. In 2023 OpenAI promised its Superalignment team "20% of the compute we've secured to date over the next four years" (OpenAI). Less than a year later the team was disbanded, and co-lead Jan Leike wrote that "safety culture and processes have taken a backseat to shiny products" (Fortune). In May 2024 OpenAI also dropped exit contracts that, in effect, made departing staff choose between a lifelong non-disparagement agreement and their vested equity (CNBC). At Anthropic, Mrinank Sharma, head of the Safeguards Research team, left in February 2026 and wrote that "throughout my time here, I've repeatedly seen how hard it is to truly let our values govern our actions" (CNN). In September 2026 Anthropic safety researcher Jacob Coxon quit and wrote that "neither company is acting responsibly" (The Guardian). Evan Hubinger, who still works on alignment at Anthropic, replied that "we do not yet have a plan to solve alignment for superintelligence and are not clearly on track to" (BBC News).

Money, lobbying and the military. In 2023 Altman asked the US Senate for "a new agency that licenses any effort above a certain scale of capabilities" (transcript). In 2025, asked about "a heavy-handed prior approval government regulatory process for AI", he said "I think that would be disastrous" (transcript). In March 2025 OpenAI asked the White House for "relief from the 781 and counting proposed AI-related bills" in US states (OpenAI). By September 2026 it wanted "mandatory, capability-based national AI safety regulation" (OpenAI). The FLI panel lists OpenAI among companies whose "reassuring public messaging diverges from commercial conduct and legislative stance" (FLI). In July 2025 Amodei told staff that Anthropic would seek money from Gulf states. "Unfortunately, I think 'No bad person should ever benefit from our success' is a pretty difficult principle to run a business on," he wrote (Wired). In June 2026 The Next Web reported that Maven Smart System, a Palantir targeting platform that US Central Command used against Iran, uses Claude and other AI tools "to generate targets". Asked what role Claude played in the February 28 strike that killed at least 120 children at a school in Minab, Iran, Amodei said: "We don't have access to, we don't know exactly how these models were used" (The Next Web). Whether Claude was involved is not known. The FLI panel cited Anthropic's "questionable military engagements" (FLI).

What outside graders say

The Future of Life Institute's AI Safety Index asks a panel of outside experts to grade AI companies from A to F. Anthropic has scored higher than OpenAI in all four editions, and neither has scored above C+ overall (December 2024, July 2025, December 2025, July 2026).

In July 2026 both got a D+ for existential safety. In December 2025 the panel wrote that every company it graded was "racing toward AGI/superintelligence without presenting any explicit plans for controlling or aligning such smarter-than-human technology" (FLI). The July 2026 edition used evidence up to June 3, 2026, so the summer incidents are not in it (FLI).

Other graders put the two in the same order. In July 2026 SaferAI scored Anthropic 35% and OpenAI 34% on risk management. A company that copied every good practice already in use somewhere would score 59%, and SaferAI calls the current state "unacceptable" (SaferAI). AI Lab Watch, last updated in September 2025, scored Anthropic 28%, Google DeepMind 20% and OpenAI 18% (AI Lab Watch).

So both companies act on the risk they describe, and both stop short of what their own numbers imply. In 2026 each released its strongest model within weeks of a training pause or a call to slow down, and each had loosened its own rules before that.

What "AI solved a math problem" means, for non-mathematicians

A math proof is a chain of steps, and every step has to hold. That makes math a clean test for AI: an answer is right or wrong, and a computer program called Lean can check a proof step by step. It is also why math results moved views on AI so much. They are hard to fake.

A benchmark of research-level problems tells the same story. When FrontierMath came out in November 2024, top models solved "under 2%" of it (arXiv). In September 2026, in Epoch AI's own run, GPT-6.1 Sol solved all of the rebuilt hardest tier (Epoch AI). OpenAI paid for FrontierMath and can see part of it (Epoch AI).

The olympiad, July 2025. Students get six problems over two days, 4.5 hours a day, and must write full proofs. A version of Google's Gemini Deep Think solved five of six for 35 of 42 points, graded by the olympiad's own coordinators (DeepMind). A year earlier, a DeepMind system scored 28, but only after people translated the problems into a computer proof language (DeepMind).

The unit distance problem, May 2026. Put dots on a sheet of paper. How many pairs of dots can sit exactly one unit apart? In 1946 Paul Erdős showed that a grid does a little better than the number of dots, and the long-held belief was that grid-like patterns were close to the best possible. An OpenAI model found patterns that beat them (OpenAI). Tim Gowers, a Fields medalist, wrote that if a human had sent him the paper for the Annals of Mathematics, "I would have recommended acceptance without any hesitation" (remarks). The same remarks note that the argument "relies crucially on ideas that may, at least in retrospect, be attributed to" earlier human work.

Navier-Stokes, September 2026. These equations describe how water and air move. The open question was whether a smooth flow always stays smooth, or whether its speed at one point can grow without limit in a finite time. OpenAI says an unreleased model, run as "on the order of 10,000" agents for about 88 hours, built a swirl that spirals inward and stretches "like spaghetti" until it blows up. Another model wrote the proof in Lean in "an additional 17 hours" (OpenAI). The paper is 166 pages (NPR).

The Clay Mathematics Institute, which awards the $1 million, wrote that the problem "has apparently been settled". The prize is still pending, and Clay says its review "is deliberately unhurried" (Clay). OpenAI says it does "not intend to claim the Millennium Prize" (OpenAI). Mathematicians quoted by NPR expect the proof is correct because the Lean check passed, but they find it hard to learn from. "So far it's been very difficult to really extract any human understanding from this new AI proof," said Fields medalist James Maynard (NPR). On September 11, Fields medalists, Terence Tao among them, published a statement that "the goals of the AI companies and the goals of the mathematical community are severely misaligned", and that solving problems is "only a tool and proxy for achieving the primary goal of conceptual understanding and insight" (statement).

Why this matters for the risk question:

• Forecasts were too slow. In 2023, AI researchers gave a 50% chance to AI proving theorems "publishable in top mathematics journals" 22 years out (2023 survey). In 2025, FRI's expert panel gave a 10% chance that AI solves a Millennium Prize problem by the end of 2027 (FRI).
• The skills overlap. Geoffrey Irving, who has worked on AI safety at OpenAI, DeepMind and the UK AI Security Institute, wrote in October 2026 that planning and coordination "are useful for tackling ambitious problems in math, programming, or any other domain", and he lists them among four skills that, in his view, an AI would need to kill everyone (TIME).
• A solved problem is not proof of safe intentions. The same tests show what models can do, not what they will choose to do. That is the question the incidents in the lab section are about.

What the numbers say now

The medians in this article run from 2.4% to 10%, depending on who answers and what they are asked. Superforecasters give 2.4% to an AI-caused catastrophe that kills more than 10% of people by 2100. AI researchers give 5% to an "extremely bad (e.g. human extinction)" outcome of human-level AI, and 10% to "human extinction or similarly permanent and severe disempowerment". Single people range from 0% (Jensen Huang, for 2030) to about 70% (Daniel Kokotajlo).

What moved since 2012: the dates for human-level AI (2061 in the 2016 survey, 2042 in the 2024 survey), what AI can do in math, the alarm of people inside the labs, and US public concern (50% in September 2026). What did not move: AI researchers' 5% for an "extremely bad" outcome, and the public's ranking of AI below nuclear war.

Things to watch, with where they stood in early October 2026:

• The Clay Institute's review of the Navier-Stokes proof, which it calls "deliberately unhurried" (Clay).
• OpenAI's pause on tool use for its most capable models, in force in its September 25 report (OpenAI).
• Anthropic's plan to give outside evaluators "desks in our offices, access badges, and company laptops" (essay).
• US rules. The "White House Accord on Super Intelligence", signed on September 29 by the president and leaders of the main AI companies, is voluntary (Truth Social).
• The next AI Impacts survey. The newest one was fielded in December 2024, before AI reached gold-medal level at the math olympiad (AI Impacts).

So, will AI kill us all? Probably not. But none of the groups surveyed puts the risk at zero, and 1,386 people who work at AI companies have asked the US government to help "deliberately pace the frontier" (letter).