Why polls on AI disagree

By Travis Fischer ¡ ¡ 16 min read

Ask Americans whether they’re concerned that AI could pose a threat to humanity, and 77% say yes. Ask them to pick the single most likely cause of human extinction, and 4% pick AI. Both numbers come from real polls of US adults, and neither is wrong.

That gap isn’t noise. People who study what the world thinks about AI measure it in very different ways: who they ask, how they ask, and what, if anything, they give back. Those choices move the answers more than most headlines admit.

I build Doom or Bloom, a short interview that maps your view of AI’s future, so I went through about 60 of these studies, from Pew’s trackers to Anthropic’s 81,000 AI-led interviews. This post lays out the landscape: how each approach works, what they find, where they agree and where they flatly contradict each other, how opinion has shifted, and what an ideal measure would look like. Then I hold Doom or Bloom to the same standard. Every number below comes from a primary source listed at the end, and every chart carries the date it was checked.

The landscape

Two questions separate the approaches more than any other: how much room people get to answer in their own words, and whether they get anything back for their time.

How people answer, and what they get back

What they get back

A personal resultNothing personal
Fixed choicesHow people answer Free text, adaptive

Hover, tap or focus a project to see how it works and how many took part

Doom or Bloom

Answers in your own words to authored questions, picked one at a time to fill gaps in what you’ve said. You get a place on a map, a P(doom) and the simulated thought leaders closest to you.

About 950 placed results as of October 3, 2026, most from two launch-week crowds.

How people answer, and what they get back
PointHow people answer (0 Fixed choices, 100 Free text, adaptive)What they get back (0 Nothing personal, 100 A personal result)Value
Polls45Pew, Gallup, AP-NORC, YouGov and others ask fixed questions with fixed answers of samples weighted to match the population. Respondents get nothing personal back; everyone else gets toplines with the exact wording. Usually 1,000 to 5,000 adults a wave. Pew’s June 2026 wave reached 3,488 US adults.
Expert and forecaster surveys2017AI researchers, experts and forecasters type probabilities and dates into fixed questions. AI Impacts assigns wording variants at random; the Forecasting Research Institute asks its panel again in regular waves. 1,580 researchers in AI Impacts’ 2024 survey. Hundreds of experts, dozens of superforecasters and about 600 members of the public in each LEAP wave.
MIT Moral Machine655Forced choices between two outcomes of a self-driving car’s crash. You see how your choices compare with everyone else’s. 40 million decisions in 10 languages from 233 countries and territories, in the 2018 paper.
P(doom) calculators1778Sliders for each step of an argument, multiplied into one number. You set every answer yourself, often starting from a suggested value. The calculator that publishes its data logged 365 submissions in a year.
Polis and Collective Constitutional AI4838People write short statements and vote on each other’s. The software finds opinion groups and the statements every group agrees with, and shows people where they sit. Anthropic used it in 2023 to draft a constitution for a model. Collective Constitutional AI: about 1,000 Americans cast 38,252 votes on 1,127 statements.
Global Dialogues6022Recruited participants answer fixed and open questions about AI and vote on each other’s answers, every couple of months. Each round repeats a core of questions, and most rounds report how representative they were. About 1,000 people a round from 63 to 80 countries; the May 2026 round had 1,024 from 69.
Anthropic Interviewer9410Claude holds a conversation of about 15 minutes around four core questions and writes its own follow-ups; Claude-powered classifiers code the transcripts afterwards. Participants got a closing recap and, later, the published findings. 80,508 Claude users in 159 countries and 70 languages, interviewed in December 2025.
The Hall of AI Fears and Hopes5272A 2025 study. People rated their fear and hope about AI from 0 to 10, wrote what they fear and hope for, and saw their portrait placed among TIME’s 100 most influential people in AI. 330 Americans, sampled to match the census on age, sex, ethnicity and politics.
Doom or Bloom9093Answers in your own words to authored questions, picked one at a time to fill gaps in what you’ve said. You get a place on a map, a P(doom) and the simulated thought leaders closest to you. About 950 placed results as of October 3, 2026, most from two launch-week crowds.

Placement is my reading of each project’s published method. Sources: Pew Research Center, AI Impacts, the Forecasting Research Institute, Anthropic, the Computational Democracy Project, the Collective Intelligence Project, Awad et al. in Nature (2018), the neoneye P(doom) calculator’s public reports, Moreira et al. (arXiv, 2025) and Doom or Bloom’s own aggregates as of October 3, 2026. As of October 4, 2026

The bottom left holds the polls. They are the most authoritative numbers we have, because they reach samples built to stand for a population and ask everyone the same fixed questions. Respondents get nothing back.

The bottom right holds Anthropic’s interviews: rich, open conversations led by Claude, at a scale no human team could manage. Participants get a recap and, later, the published findings.

The top left holds quizzes and calculators. They give you something instantly, a number or a type, but from sliders and fixed choices, with no room for why.

The top right is nearly empty. The closest academic work, The Hall of AI Fears and Hopes, asked 330 Americans to rate and explain their fears and hopes, then placed each of them among TIME’s 100 most influential people in AI. In my search I didn’t find another public tool that starts from free text, adapts its questions to what you say, and hands back a personal result. That’s the corner Doom or Bloom is trying to fill, and the rest of this post is partly a look at what it can learn from everyone else.

Six ways to ask

Each approach is good at something the others miss. Here is how they compare on eight things a good measure of opinion should do.

What each approach does well, and what it misses

PollsPew, Gallup, AP-NORC, YouGov

  • Sample ¡ Does thisPew and Gallup recruit panels by mail or phone and weight them to the census; YouGov weights an opt-in online panel
  • Wording ¡ Does thisThe same fixed wording for everyone, with answer options rotated to spread order effects
  • Reasons ¡ PartlyMostly closed questions; Pew sometimes adds open-ended follow-ups
  • Gives back ¡ Doesn’tNothing personal; the public gets the toplines
  • Checks ¡ Not neededClosed answers need no interpreting
  • Openness ¡ Does thisToplines print the exact wording, sample sizes and methods
  • Over time ¡ Does thisPew has asked the same concern question since 2021, Gallup its harm-or-good question since 2023
  • Languages ¡ PartlyMost polls cover one country; Pew’s global survey asks the same question in dozens

My reading of each approach’s published methods: Pew Research Center, Gallup and YouGov methodology statements, AI Impacts (2024), the Forecasting Research Institute (2023 and 2026), Anthropic’s 81k methods appendix (2026), the Computational Democracy Project, the Collective Intelligence Project, the neoneye P(doom) calculator’s reports, Moreira et al. (2025) and Doom or Bloom’s about page. As of October 4, 2026

Polls from Pew, Gallup, AP-NORC and YouGov ask the same fixed questions of samples weighted to match the population. Pew and Gallup recruit their panels by mail and phone, so their numbers are the closest thing to “what Americans think”. YouGov weights a large opt-in online panel, which is faster and cheaper but less certain. Polls print their exact wording and repeat it for years, which makes them the only reliable way to track change. What they can’t do is hear reasons: you learn that 52% are more concerned than excited, not why.

Expert and forecaster surveys ask people who study AI or forecasting for numbers. AI Impacts’ Expert Survey on Progress in AI, led by Katja Grace, surveyed authors at the top AI venues in 2016, 2022, 2023 and 2024, repeating its core questions; its newest wave reached 1,580 researchers. The Forecasting Research Institute (FRI) ran a 2022 tournament in which experts and superforecasters argued their cases for months, and now runs a panel of experts, superforecasters and members of the public, called LEAP, that answers new questions every month or so. Their weakness is the sample: only about one in ten invited researchers answered AI Impacts’ survey, and its authors note that researchers at AI companies are almost certainly underweighted, because those companies largely stopped publishing at the venues it draws on.

AI-led interviews are the newest approach. In December 2025, Anthropic, the company Dario Amodei runs, invited every Claude user to a conversation with an AI interviewer built on Claude; 80,508 people in 159 countries and 70 languages gave usable interviews. Four core questions anchored each conversation, and Claude wrote the follow-ups. Classifiers, also built on Claude, then coded each transcript. The result is the largest qualitative study of its kind, with real depth. It also has the limits you’d expect: the sample is Claude users who chose to take part, the interview asked about hopes before concerns, and each classifier was checked against one human on 25 labels.

Deliberation platforms let people write statements and vote on each other’s. Polis finds opinion groups and the statements every group agrees with; Anthropic and the Collective Intelligence Project used it in 2023 to draft a constitution for a model from the votes of about 1,000 Americans. The Collective Intelligence Project’s Global Dialogues recruits about 1,000 people a round from around 70 countries, repeats a core of questions, and reports how representative most rounds were. Deliberation is the best tool for finding common ground; it isn’t built to tell you where one person stands.

P(doom) calculators multiply sliders for each step of an argument into one number, the structure Joe Carlsmith used in his 2022 report on power-seeking AI. They’re quick and give you a number of your own. They also show how much the frame matters: on the one calculator that publishes its data, 36% of quiz-takers kept the number it suggested, and most of those who moved it went down. It logged 365 submissions in a year, so this is a small niche.

The Hall of AI Fears and Hopes is the closest relative of Doom or Bloom. Its 330 participants, sampled to match the US census, rated their fear and hope from 0 to 10, wrote out what they fear and hope for, and saw their portrait placed among the influencers they resembled most. Its headline finding is a useful reminder that experts and the public worry about different things: “The public fears AI getting out of control, while influencers emphasize regulation”.

Where the numbers agree

Despite the noise, a few findings hold up across approaches.

  • The public is more worried than excited. Recent US polls lean clearly negative: 52% more concerned than excited against 9% more excited (Pew, June 2026), 39% saying AI does more harm than good against 9% more good (Gallup, May 2026), and 39% calling AI’s impact on society negative against 11% positive (Reuters/Ipsos, September 2026)
  • Many people hold both views at once. When a poll offers a middle option, it draws a big share: 37% are equally concerned and excited (Pew), and 52% say AI does equal amounts of harm and good (Gallup). Anthropic found the same inside individual conversations: hope and alarm “didn’t divide people into camps, so much as coexist as tensions within each person”
  • Catastrophe is a minority expectation, not a fringe one. No group’s median expects it, but almost every group gives it real odds, from 2.4% among superforecasters to 10% among AI researchers. Individual thought leaders spread much wider, from near zero for Yann LeCun to over 90% for Max Tegmark
  • Experts are more optimistic about AI overall. That holds whether “experts” means conference authors (Pew) or FRI’s broader panel

Where they conflict

Then there are the places where two credible sources seem to say opposite things. Almost every one of them comes down to the question, the format or the sample.

The wording

How many people worry that AI could end humanity? Depending on how you ask, between 4% and 77%.

One worry, asked many ways

Raised it without being asked

Claude users who brought up existential risk in an open interviewAnthropic, 80,508 Claude users, 2025
7%
Britons who named AI when asked to list three likely causes of human extinctionRethink Priorities, 2024
9%

Picked AI from a list

AI is the single most likely cause of human extinctionRethink Priorities, US, 2023
4%
AI is among the three most likely causes of human extinctionYouGov, US, August 2025
16%
The same question, in Britain a year laterYouGov, Britain, September 2026
34%

Asked how likely

AI has the potential to “bring about the end of human civilisation”, and it is likelyYouGov, Britain, September 2026
23%
“AI will destroy humanity” is somewhat or very likely to come true at some pointYahoo News/YouGov, US, October 2025
53%

Asked how worried

Worried AI will go rogue and end civilizationAnthropic Public Record, US, 2025
27%
Concerned “AI will cause the end of the human race on Earth”YouGov, US, September 2026
50%
Worried “machines with artificial intelligence could eventually pose a threat to the existence of the human race”Monmouth, US, January 2023
55%
Concerned “future AI systems could potentially threaten human survival”Quinnipiac, US, September 2026
73%
The Monmouth question again, without a “not too worried” answerYouGov, US voters, July 2023
76%
Concerned that AI could pose a threat to humanityYouGov, US, December 2025
77%

Anthropic (“What 81,000 people want from AI”, interviews of Claude users, December 2025; Anthropic Public Record, YouGov, November–December 2025, whose chart condenses the item), Rethink Priorities (US, April 2023; Britain, July–August 2024), YouGov (US, August 2025, December 2025 and September 2026; Britain, September 2026), Yahoo News/YouGov (October 2025), Monmouth University (January 2023), a YouGov poll of registered voters for Lionheart Ventures (July 2023) and Quinnipiac University (September 2026). Quoted words are as printed by each pollster. As of October 4, 2026

The lowest numbers come from asking people to name causes of extinction themselves, or to pick one from a list. The highest come from asking how worried or concerned they are. Concern isn’t the same as expectation: in September 2026, 66% of Britons said AI has the potential to end civilization, but only 23% thought that likely.

Small details matter too. Monmouth and a July 2023 YouGov poll asked the same question about machines with AI threatening “the existence of the human race”. Monmouth offered “not too worried” as an answer and found 55% worried; the YouGov poll offered no such step and found 76%. The polls also differed in mode, sample and year, so not all of that gap comes from the scale. The same YouGov poll told people that Geoffrey Hinton, Sam Altman and Bill Gates had signed a statement that mitigating the risk of extinction from AI should be a global priority, and 57% said that made them more concerned.

The format

Numbers people type are even less stable than words they choose. In FRI’s 2022 study, the same college graduates gave two very different answers about the chance of AI-caused extinction by 2100, depending on how they were asked.

The same people, two ways to answer

Median answers from the same 405 people

Typed a percentage1.5%
Filled in “1 in X”, after examples of rare events1 in 40 million

Examples shown with the “1 in X” question

A fair coin lands tails1 in 2
Dying from lightning, over a lifetime1 in 300,000
A random newborn becomes a US president1 in 10 million

Forecasting Research Institute, “Forecasting Existential Risks” (2023), pages 29–30 and footnotes 69–70: medians of the 405 college graduates who answered both ways, for the chance that AI causes human extinction by 2100, and three of the ten examples shown with the second format. As of October 4, 2026

Typing a percentage, they said 1.5%. Shown examples of rare events and asked to fill in “1 in X”, they said 1 in 40 million, a number about 600,000 times smaller. Neither answer is the “real” one. Small probabilities are hard to think about, and people lean on whatever the question gives them. Across everyone who answered each version, the medians were 2% and 1 in 30 million.

Prompted or unprompted

When Anthropic let Claude users talk about what they want from AI, only 6.7% brought up existential risk on their own. When the Anthropic Public Record asked 51,993 people in the US directly, 27% said they were at least a little worried that AI will go rogue and end civilization. Rethink Priorities saw the same pattern in Britain in 2024: 9% named AI unprompted as a likely cause of extinction, against about 17% who picked it from a YouGov list. One possibility they offer is that people rarely think of AI risk on their own but “find it plausible when prompted”. Asking about catastrophe makes it salient.

Experts and the public

Experts are much more positive about AI than the public, but not about everything.

Experts expect more good from AI, until you ask about harm

Pew Research Center, 2024

AI’s impact on the US over the next 20 years will be positive
Experts: 56%
US adults: 17%
AI in daily life makes them more excited than concerned
Experts: 47%
US adults: 11%
AI will very or extremely likely cause major harm to humans in the next 20 years
Experts: 20%
US adults: 35%

Forecasting Research Institute, 2026

AI’s impact on the US over the next 20 years will be positive
Experts: 58%
US adults: 42%

Pew Research Center (US adults, August 2024, n = 5,410; 1,013 US-based authors and presenters at 21 AI conferences, August–October 2024, unweighted) and the Forecasting Research Institute’s Longitudinal Expert AI Panel (wave 8, April–May 2026: 205 experts and 601 US adults, reweighted). The two surveys define experts differently. As of October 4, 2026

In Pew’s 2024 survey, 56% of AI experts expected AI to have a positive impact on the US over 20 years, against 17% of US adults. Yet when FRI’s LEAP panel asked the same kind of question in 2026, 42% of its US public sample expected a positive impact. That gap is too big to be wording alone: the samples and the weighting differ, and Pew also offers a “not sure” answer. It’s a good reason not to splice one pollster’s numbers onto another’s trend line.

On harm, the order can flip. The public was more likely than experts to call major harm to humans very or extremely likely in the next 20 years (35% against 20%). On LEAP’s catastrophe question, the public’s median (7%) sat above the experts’ (5%). But AI researchers in AI Impacts’ survey gave a higher number than either, 10%, to a broader outcome with no deadline.

Claude users and everyone else

Anthropic coded 67% of its interviews as net positive about AI. Pew finds 9% of Americans more excited than concerned, and a 13% median across 37 countries. Both can be true. One is a reading of whole conversations with people who already use an AI chatbot by choice, scored by Claude; the other is a single closed question asked of a random sample.

How views have shifted

The trend lines are clearer than any single number.

How Americans feel about AI, survey by survey

More concerned than excited

Pew: “Overall, would you say the increased use of artificial intelligence (AI) in daily life makes you feel…”

Jun 2026 ¡ n = 3,48852% More concerned than excited9% More excited than concerned

AI does more harm than good

Gallup: does AI do more good than harm, more harm than good, or equal amounts of each

May 2026 ¡ n = 3,27039% More harm than good9% More good than harm

Concerned AI could end the human race

YouGov: “How concerned, if at all, are you about the possibility that AI will cause the end of the human race on Earth?” Very or somewhat

Sep 2026 ¡ n = 18,23850% Very or somewhat concerned

More concerned than excited, by party

Pew, the same question, Democrats and Republicans including independents who lean to each

Jun 202656% Democrats49% Republicans

More concerned than excited, by age

Pew, the same question, the youngest and oldest age groups

Jun 202655% Ages 18 to 2959% Ages 65 and older

Hover, tap or focus a panel, then use the arrow keys, to read every survey

More concerned than excited. Pew: “Overall, would you say the increased use of artificial intelligence (AI) in daily life makes you feel…”
CategoryMore concerned than excitedMore excited than concerned
November 7, 202137%18%
December 18, 202238%15%
August 6, 202352%10%
August 18, 202451% (n = 5,410)11% (n = 5,410)
June 15, 202550% (n = 5,023)10% (n = 5,023)
June 28, 202652% (n = 3,488)9% (n = 3,488)
AI does more harm than good. Gallup: does AI do more good than harm, more harm than good, or equal amounts of each
CategoryMore harm than goodMore good than harm
May 15, 202340% (n = 5,458)10% (n = 5,458)
May 6, 202431% (n = 5,835)13% (n = 5,835)
May 12, 202531% (n = 3,007)12% (n = 3,007)
May 11, 202639% (n = 3,270)9% (n = 3,270)
Concerned AI could end the human race. YouGov: “How concerned, if at all, are you about the possibility that AI will cause the end of the human race on Earth?” Very or somewhat
CategoryVery or somewhat concerned
April 3, 202346% (n = 20,810)
March 18, 202439% (n = 1,073)
December 3, 202436% (n = 1,110)
March 7, 202537% (n = 1,132)
June 30, 202543% (n = 1,112)
July 9, 202647% (n = 1,095)
September 14, 202650% (n = 18,238)
More concerned than excited, by party. Pew, the same question, Democrats and Republicans including independents who lean to each
CategoryDemocratsRepublicans
November 7, 202131%45%
December 18, 202231%45%
August 6, 202346%59%
August 18, 202446%55%
June 15, 202551%50%
June 28, 202656%49%
More concerned than excited, by age. Pew, the same question, the youngest and oldest age groups
CategoryAges 18 to 29Ages 65 and older
November 7, 202131%43%
December 18, 202229%45%
August 6, 202342%61%
August 18, 202439%59%
June 15, 202547%56%
June 28, 202655%59%

Pew Research Center’s American Trends Panel (June 2026 topline; party and age from its September 16 and August 18, 2026 reports), the Bentley-Gallup Business in Society survey (Gallup, July 2026) and YouGov (daily questions of US adults in April 2023 and September 2026, smaller surveys of US adult citizens in between). Each point is a survey’s last day of fieldwork. As of October 4, 2026

The share of Americans more concerned than excited about AI in daily life jumped from 38% to 52% between Pew’s December 2022 survey, a few weeks after ChatGPT was released, and its summer 2023 survey. It has stayed near half since. Gallup’s “more harm than good” fell after 2023 and then climbed back to 39% in 2026.

The groups changed even more than the totals. In 2021, Republicans were the more worried party (45% against 31%); by June 2026 it had flipped, with Democrats at 56% and Republicans at 49%. Adults under 30 were the least concerned age group in 2021, at 31%; in 2026, 55% of them are more concerned than excited, close to the 59% of people 65 and older.

Concern about AI ending the human race rose too. YouGov’s question fell from 46% in April 2023 to 36% at the end of 2024, then climbed to 50% in September 2026, its highest reading so far. That month brought a run of high readings: 73% concerned about AI threatening human survival (Quinnipiac) and 34% of Britons naming AI among the three likeliest causes of extinction, up from 15% a year earlier (YouGov). They came during a news cycle about leaders of several AI companies calling for a slowdown, which 69% of Americans had heard about (Reuters/Ipsos), and Doom or Bloom launched in the middle of it on September 25. Pew’s and Gallup’s latest waves were fielded before it.

AI researchers moved as well.

AI researchers moved both numbers

Median chance of extinction or permanent disempowerment

“What probability do you put on future AI advances causing human extinction or similarly permanent and severe disempowerment of the human species?”

Dec 2024 ¡ n = 74410% Median researcher

Year with even odds of human-level AI

When “unaided machines can accomplish every task better and more cheaply than human workers”, all respondents’ forecasts combined

Dec 20242042 50% year

Hover, tap or focus a panel, then use the arrow keys, to read every survey

Median chance of extinction or permanent disempowerment. “What probability do you put on future AI advances causing human extinction or similarly permanent and severe disempowerment of the human species?”
CategoryMedian researcher
August 3, 20225% (n = 149)
October 24, 20235% (n = 1,321)
December 24, 202410% (n = 744)
Year with even odds of human-level AI. When “unaided machines can accomplish every task better and more cheaply than human workers”, all respondents’ forecasts combined
Category50% year
June 28, 20162061
August 3, 20222059
October 24, 20232047
December 24, 20242042

AI Impacts’ Expert Survey on Progress in AI: Grace et al., “Thousands of AI Authors on the Future of AI” (2023 and 2022 waves) and “Advanced AI according to 1,580 researchers” (2024 wave, published September 2026). The 2024 report gives the 2022 year as 2059; the 2023 paper recomputed it as 2060. Each point is a survey’s last day of fieldwork. As of October 4, 2026

In AI Impacts’ survey, the median researcher’s chance of extinction or permanent disempowerment from AI doubled from 5% in 2023 to 10% in its December 2024 wave, published last month. Their forecast for human-level AI moved from 2061 in 2016 to 2042, “about 3.4 years closer for each year that passed”.

The P(doom) numbers, side by side

P(doom) is shorthand for the chance that AI ends in catastrophe for humanity. Here are the numbers from the sources above that asked for one, with the question each group actually answered.

Chances of an AI catastrophe, by who answered and what they were asked

AI researchers

Authors at six top AI venues10%

“What probability do you put on future AI advances causing human extinction or similarly permanent and severe disempowerment of the human species?”

Median of the 744 asked this way, AI Impacts, December 2024

Experts and forecasters

Experts on the LEAP panel5%

“A catastrophic event caused primarily by artificial intelligence systems in which more than 10% of the people alive at the start of a five-year period die by the end of that period”

Chance by 2100, median of 192 experts, May–June 2026

Superforecasters on the LEAP panel2.4%

The same question, median of 53 superforecasters

AI experts in the 2022 forecasting tournament3%

“reduction of the global population to less than 5,000”

The tournament’s definition of extinction; chance AI causes it by 2100, median of its AI domain experts, June–October 2022

Superforecasters in the same tournament0.38%

The same question, median of 88 superforecasters

The public

US adults on the LEAP panel7%

LEAP’s catastrophe question, by 2100, median of 612 adults reweighted to the US population, May–June 2026

US adults, Rethink Priorities15%

“percentage likelihood that AI would cause human extinction by the year 2100”

Median of 2,407 adults recruited online and modeled to the US population, June 2023

Thought leaders, in their own words

Andrew McAfee≈0%
Gary Marcus≈3%
Noah Smith≈10%
Geoffrey Hinton10–20%
Scott Alexander20%
Dario Amodei25%
Ryan Greenblatt35–40%
Max Tegmark>90%

Doom or Bloom participants

Typed their own number12.5%

“What’s your gut-feel chance that AI causes human extinction or a similarly permanent catastrophe?”

Median of the 117 people who typed a number, as of October 3, 2026

Inferred from their answers5.6%

Median of the 813 people whose number our model read from the whole interview; a rough reading, not a stated belief

AI Impacts (2024 survey, published September 2026), the Forecasting Research Institute (LEAP wave 9, May–June 2026, and its 2022 tournament), Rethink Priorities (June 2023), the statements linked from each thought leader’s profile and Doom or Bloom’s aggregates as of October 3, 2026. The outcomes and time frames differ, so read each number with its question. As of October 4, 2026

The closest match to our own question is AI Impacts’. Its wording, “human extinction or similarly permanent and severe disempowerment of the human species”, with no deadline, is almost the same as Doom or Bloom’s question about “human extinction or a similarly permanent catastrophe”. Among participants who typed a number, the median is 12.5%, a little above the AI researchers’ 10%. That group chose the site and then chose to answer, so it says more about who shows up than about the public. The median our model infers for everyone else is lower, 5.6%, and it’s a rough reading of what people wrote, not a number they gave. We never pool the two.

Most of the rest ask about something else. FRI’s panel asks about a catastrophe that kills more than 10% of people within five years, by 2100; its tournament asked about extinction, by 2100. Those differences matter as much as who answered. The thought leaders’ numbers, linked to the sources on their profiles, each come with their own definitions and time frames; the P(doom) table lists what each one means.

What a better measure would look like

Put the evidence together and a few principles fall out. None of them is new; no single approach above follows all of them.

  1. Know who you’re hearing from. Reach a sample built to stand for a population, or say plainly who it does stand for, and report results by group
  2. Ask neutrally, and in a fixed order where it counts. Wording, answer scales and order move results by tens of points. An AI interviewer that writes its own questions can move them too: in a 2026 experiment, following a closed question with an AI-led interview polarized later answers
  3. Ask open and closed questions. Open questions show what people bring up unprompted; closed ones make answers comparable. You need both, and you should report them apart
  4. Show the exact wording next to every number. It’s the only way a reader can tell 4% from 77%
  5. Give people something back. A result of their own respects people’s time and helps them think. It is also what made the Moral Machine spread by word of mouth
  6. Let people check the reading. When a person or a model interprets free text, show the interpretation and let people correct it. Expect the confirmations to run high: in one study, human coders judged AI codings correct about 20% less often than respondents did
  7. Keep stated and inferred numbers apart. A number someone typed and a number a model read from their words are different measurements. Label them and never average them together
  8. Open the methods and the data. Publish the questions, the code and the rules that turn answers into results, and as much data as privacy allows
  9. Ask again, the same way. Trends need a fixed core of questions asked in regular waves, with any change of wording noted
  10. Ask in people’s own languages. Views differ widely by country, and most studies hear from very few of them

How Doom or Bloom measures up

Doom or Bloom is built around several of these principles, and falls short on others.

What it does: everyone starts with the same open question, “What do you think AI means for our future—and why?”, and then gets authored questions picked one at a time to fill gaps in what they’ve said, about 3 minutes in all. The model only interprets answers; it never writes the questions.

Before the reveal you can guess where you’ll land, and if the guess and the result differ a lot, you’re asked what the reading missed. You can trace each reading back to your own words and say whether it feels right. The result labels a P(doom) as typed or inferred, and never mixes the two. The interview runs in 10 languages, the questions and code are public, and posts like this one publish only aggregates of 10 or more people.

And you get something back: a place on the map, a P(doom) and the simulated thought leaders closest to you. As of October 3, the most common closest matches were Bernie Sanders, Ed Zitron, Nathan Lambert and Geoffrey Hinton.

What it doesn’t do yet:

  • The sample is self-selected. About 950 people have been placed so far, most from two launch-week crowds, one from Hacker News and one from X, which landed far apart on the map. That makes our numbers a picture of who came, not of the public, and I report them that way
  • The reading isn’t yet checked against human coders. People can confirm or correct it, but as the research above shows, confirmations run high. A human-coded check in every language is on the list
  • Inferred P(doom) is rough. It’s our model’s reading of short answers, shown with a range, and so far its median runs lower than the numbers people type
  • The order adapts. That’s what keeps the interview short, but it means no single question is asked at the same point for everyone, which makes it hard to set any one answer beside a poll
  • It’s new. There is no trend line yet, and every result is tied to the engine version that produced it, so changes can be traced

If you want to see where you land among all of this, take the interview (about 3 minutes, no sign-up). For the numbers people have stated in public, see the P(doom) table, and for how the interview and the map work, the about page.

How I put this together

  • Sources: about 60 surveys, studies and tools, each read from its primary source (pollster toplines, reports and papers) on October 4, 2026, with the exact wording recorded. I left out figures I could only find in secondary coverage
  • Placement: the positions on the landscape and the marks on the scorecard are my editorial reading of each approach’s published methods, not a measurement
  • Comparisons: different questions, samples and dates, shown side by side so the differences are visible, not to rank them
  • Our numbers: Doom or Bloom’s production aggregates as of October 3, 2026: the earliest result of each person, leaving out simulated users, forks and my own test account, with every number describing at least 10 people. The thought leaders on Doom or Bloom are simulated from their public writing and interviews; their stated numbers above are their own words, linked from their profiles

Sources

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