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The available evidence does not show that technology companies and politicians coordinated to manufacture AI-doomsday fear for profit, or that catastrophic-risk warnings were deliberately used to hide present-day harms. It does show competing incentives, genuine disagreement about risk, and public concern spanning job losses, misinformation, data misuse and hypothetical catastrophe. Those are reasons to scrutinize who makes each claim and what policy it supports—not proof of a smoke screen.
What does the “smoke screen for profit” claim mean?
The claim combines two separate allegations: first, that warnings about catastrophic AI risks are being used to divert attention from nearer-term harms; second, that companies or politicians do so to gain money or another advantage. Evidence that an actor could benefit is not evidence that the actor deliberately created or amplified a warning for that purpose.
“Big Tech,” “politicians” and “AI risk” are not single, unified positions. A company may compete to build advanced systems while also warning about potential dangers; a policymaker may advocate oversight for reasons that differ from a vendor’s commercial interests. To substantiate a coordinated profit-seeking scheme, reporting would need to connect particular people or organizations to specific messages, show evidence of intent or coordination, and establish a material benefit. The evidence summarized here does not make those connections.
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Survey results measure responses to particular questions among particular populations. Concern about a concrete harm, support for regulation and belief that AI is the likeliest cause of human extinction are different measures; the figures below should not be read as one continuous trend.
#1 Best Overall
| Source and population | Finding | What the measure does—and does not—show |
|---|---|---|
| Pew Research Center, report published 2025; separate surveys of U.S. adults and AI experts fielded in 2024 | 56% of U.S. adults and 25% of surveyed AI experts were extremely or very concerned about AI eliminating jobs. 66% of U.S. adults were highly worried about people getting inaccurate information from AI. | Responses to specific questions about job loss and inaccurate information; not a measure of support for catastrophic-risk claims. |
| Rethink Priorities, 2023; U.S. online poll | 4% selected AI as the most likely cause of human extinction among the options offered, compared with 42% who selected nuclear war. | The report calls the estimates preliminary and cautions that topic novelty and question framing matter. This is not a measure of current opinion or of all people. |
| Scientific Reports, 2024; surveyed participants in Germany and Spain | 62.2% of German participants and 63.5% of Spanish participants supported or strongly supported “much stricter” regulatory oversight of commercial AI research. | Support for a particular oversight proposal among study participants; not a measure of U.S. opinion or necessarily of fear about extinction. |
| Anthropic, 2026; company-sponsored, YouGov-sourced online survey of 51,993 Americans fielded in November and December 2025 and weighted to U.S. Census benchmarks | 64% reported concern about AI-induced job loss, 56% about cognitive dependency and 52% about misinformation. | These are responses from a survey sponsored by an AI company. Its questions and method differ from Pew’s, so the results are not a direct comparison or evidence of a change over time. |
Together, these studies show that public concern is not confined to extinction scenarios. They do not establish whether one kind of AI coverage displaces another.
Is there evidence of a financial incentive to emphasize catastrophic risk?
There is an incentive argument worth examining, but it is not evidence of a deliberate fear-marketing plan. In a 2024 account of a Gladstone AI report commissioned by the U.S. State Department, TIME reported that the report argued competition for the economic reward of reaching artificial general intelligence (AGI) first could encourage companies to scale quickly. AGI here refers to a proposed class of broadly capable systems; it is not interchangeable with today’s AI products or with a hypothetical superintelligent system.
Rank #2
TIME reported that the federal contract for the work was $250,000, and that the report’s recommendations did not represent the views of the Department of State or the U.S. government. Those details show that risk analysis can enter government policy debates and that the report discussed a competitive economic incentive. They do not show that the report’s authors, AI companies or politicians fabricated warnings, coordinated their messages or acted primarily to increase profit.
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That possibility is a question about attention and coverage, not something these sources establish as fact. Pew’s U.S. findings include concerns about jobs and inaccurate information; its report also addresses impersonation and data misuse. The UK government’s 2024 public-attitudes tracker describes concerns including job displacement, data security and unequal distribution of AI’s benefits. It also reports support for some AI applications and the view that clear risk mitigation can reduce concern. The tracker says the prominence of existential-risk narratives before its fieldwork likely affected public recognition of them; that does not determine whether the visibility came from accurate warnings, sensational coverage or commercial strategy. The UK tracker does not measure whether catastrophic-risk discussion crowded out reporting on nearer-term issues.
Policy positions also differ. The Information Technology and Innovation Foundation (ITIF), a policy organization, told a 2023 Senate AI Insight Forum about proposed international assessment institutions and cautioned that AI safety research was then nascent. That is an organization’s policy statement, not a neutral scientific consensus. A proposal for oversight, a warning about a possible future outcome and evidence that a current harm has occurred each require different kinds of scrutiny.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can you judge a claim about AI risk and who benefits?
- Identify the risk horizon. Is the claim about an observed harm or misuse, such as inaccurate information or job displacement, or a forecast about future loss of control or catastrophe?
- Check the evidence type. Look for whether the statement rests on measured outcomes, a survey, a capability assessment, a scenario or expert judgment. A forecast is not an observed outcome, and uncertainty alone does not prove a forecast false.
- Read the proposed remedy. Ask who recommends disclosure, evaluations, oversight, restrictions or a pause on some activity, and what evidence they give for that specific intervention.
- Trace the interest without assuming motive. Note who funds, builds, regulates or advocates for the proposal and any disclosed financial or institutional stake. A documented incentive is a reason to examine a claim carefully, not proof that the claim was made in bad faith.
- Check the survey before citing public opinion. Confirm geography, field dates, sponsor, sample and question wording. Support for oversight does not automatically mean respondents believe AI will cause an existential catastrophe.
- Look for evidence connecting message to outcome. A claim of a coordinated smoke screen would need more than a vivid warning and a possible beneficiary: it would require evidence linking specific actors, communications, intent and material advantage.
Readers seeking a sustained critical perspective on AI claims may also consider AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference, by Arvind Narayanan and Sayash Kapoor. Princeton University Press’s publication record lists the book in 2025 and describes its discussion of AI’s uses and harms, misleading claims and existential-risk arguments. It is the authors’ perspective, not a substitute for evaluating evidence claim by claim. Princeton University publication record.
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