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What would “a smoke screen for profit” actually mean?
The title’s claim combines several ideas that need to be tested separately: that warnings of catastrophic AI risk are exaggerated or insincere; that companies or politicians use them to distract from nearer-term harms; and that doing so produces a financial or political benefit. Evidence of an incentive is not evidence that someone acted on it, and evidence that a warning benefits an organization does not establish that the warning is false.
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“AI” also covers different things. Present-day systems can produce inaccurate information or be used for impersonation and other misuse. Artificial general intelligence (AGI) refers to a hypothetical system with broad capabilities; claims about it involve forecasts, not observed outcomes. Scenarios involving loss of control or human extinction are more speculative still. Treating these as one risk makes it easier to confuse a real concern with a proven prediction.
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What do surveys say people are worried about?
Public concern is not synonymous with fear of extinction. The surveys below ask different questions, cover different populations, and were conducted in different places and years; their percentages should not be read as a single trend or compared as though they measure the same thing.
| Source and population | Finding | What it measures |
|---|---|---|
| Pew Research Center, 2025 report; U.S. adults and AI experts surveyed separately in 2024 | 56% of adults versus 25% of experts were extremely or very concerned about AI eliminating jobs. Separately, 66% of adults were highly worried about people getting inaccurate information from AI. | Responses to particular questions about jobs and inaccurate information—not views about extinction or a universal measure of AI risk. |
| Rethink Priorities, 2023 U.S. online poll | 4% selected AI as the most likely cause of human extinction among the offered options; 42% selected nuclear war. | A preliminary estimate from a question with a defined set of options. The report warns that topic novelty and wording matter; it is not a measure of current opinion or all people’s views. |
| Scientific Reports paper, 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 specified regulatory measure in those study populations—not a finding about U.S. opinion or a direct measure of catastrophic-risk anxiety. |
| Anthropic report, 2026; YouGov-sourced survey of 51,993 Americans, fielded in November and December 2025 and weighted to U.S. Census benchmarks | 64% were concerned about AI-induced job loss, 56% about cognitive dependency, and 52% about misinformation. | Reported responses in a survey sponsored by Anthropic, an AI company. Its questions and method differ from Pew’s, so these figures do not establish a trend or provide a like-for-like comparison. |
The pattern is more complex than a public swept up in one doomsday story. Pew’s findings foreground jobs and inaccurate information. Rethink Priorities’ extinction question found AI was selected far less often than nuclear war, while the report also describes support for some risk-reduction measures. A person can favor oversight without believing extinction is likely; those are different questions.
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The UK Government’s 2024 public attitudes tracker likewise discusses concerns about job displacement, data security, and the unequal distribution of AI’s benefits, alongside support for some applications and the view that clear risk mitigation can reduce concern. It says awareness of existential-risk narratives likely reflected their visibility before the survey. That observation does not establish whether the visibility came from accurate warnings, sensational media coverage, or a commercial strategy. The tracker’s then-minister, Viscount Camrose, said its findings “will continue to underpin the government’s approach to AI and data”; this describes the government’s stated use of the survey, not proof that its risk narrative was impartial or self-serving.
Is there evidence of a financial incentive to warn about catastrophe?
One relevant example is a 2024 account by TIME of a Gladstone AI report commissioned by the U.S. State Department. TIME says the report argued that the potential economic reward for being first to achieve AGI could incentivize companies to scale quickly. That is an attributed argument about a competitive incentive—not proof that every AI company faces the same motive, that any particular warning was made for profit, or that the report’s risk claims were fabricated.
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TIME reported that the federal contract was worth $250,000. It also noted that the report’s recommendations did not reflect the views of the State Department or the U.S. government. The contract and the report’s policy proposals make it reasonable to examine who funds risk analysis and how it enters government debate. They do not establish that the contractor or officials coordinated a fear campaign or that the report’s recommendations enriched anyone.
There can be incentives on more than one side of this debate. A company racing to build a system may have a commercial reason to scale; an organization advocating safeguards may have an institutional interest in its policy agenda. Identifying either interest is a starting point for scrutiny, not a shortcut to judging a claim’s truth. The available material does not document a specific politician’s financial gain from doomsday messaging or show that politicians as a group use such warnings to divert attention.
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Are extinction warnings a distraction from harms happening now?
They can compete for attention in public debate, but the sources here do not measure whether catastrophic-risk coverage crowds out reporting on current harms. Present-day concerns—including job effects, inaccurate AI-generated information, impersonation, and data misuse—deserve attention in their own right. So do uncertain future scenarios when they are clearly described as forecasts or possibilities rather than established outcomes.
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It is possible to discuss both without declaring one a decoy. A useful test of any argument is to ask what kind of evidence it offers:
- Observed or reported present-day problems: examples of misuse, errors, or other harms, and surveys asking people about their concerns.
- Projected future risks: capability forecasts, scenario analysis, or expert judgment about what advanced systems might do.
- Policy proposals: recommendations such as disclosure, evaluation, oversight, or limits on specified high-risk activity. A recommendation signals a proposed response; it does not show that the feared outcome is certain.
Keeping those categories distinct makes it harder to use an uncertain future scenario to dismiss a documented current harm—or to treat a real current harm as proof that every catastrophic forecast is false.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What policies are being proposed, and by whom?
Policy arguments do not divide neatly into “doomers” and “boosters.” They differ over which risks matter most, how strong the evidence is, and what response is proportionate. In a 2023 statement to a Senate AI Insight Forum, the Information Technology and Innovation Foundation (ITIF), a policy organization, discussed proposed international assessment institutions and cautioned that AI safety research was then nascent. That is an organizational policy position, not a neutral scientific consensus. Read ITIF’s statement.
The Gladstone AI report described by TIME proposed federal interventions in response to the risks it discussed; the account also makes clear those recommendations were not the U.S. government’s position. Neither that example nor ITIF’s statement alone establishes that a particular policy is necessary or effective. Readers should examine the proposed measure, the risk it targets, the evidence offered, and the costs or trade-offs—not infer certainty from the fact that a government forum or report discussed it.
How to judge the next “AI doomsday” claim
When a company executive, policymaker, researcher, or advocacy group makes a dramatic claim, check the substance and the messenger separately:
- What system and time horizon? Does the claim concern a deployed tool, a future AGI system, or a hypothetical loss-of-control scenario?
- What is the evidence? Is it an observed incident, survey response, model evaluation, forecast, or expert judgment? Those forms of evidence answer different questions.
- What action is being requested? Is the proposal transparency, evaluation, oversight, restriction, or another intervention? Does the argument explain why that response follows from the evidence?
- Who is speaking, funding, or sponsoring the claim? Disclose relevant organizational roles and interests, but do not treat an interest as proof of deception.
- What does a cited poll actually represent? Check geography, field dates, population, sponsor, wording, and whether it asks about a specific harm, regulatory support, or extinction likelihood.
For readers seeking a critical perspective on AI claims, Princeton University Press lists AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference by Arvind Narayanan and Sayash Kapoor in its 2025 publication record. The authors address AI’s capabilities and limits, misleading claims, harms, and existential-risk arguments; it is a perspective to consider, not a substitute for examining evidence claim by claim.
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