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Consumers do not share one verdict on AI content. Research finds substantial concern about authenticity, trust and undisclosed brand use, but also widespread GenAI use and mixed reactions to AI-generated advertising. The phrase “soulless slop and zero talent” captures a criticism, not a representative finding about what everyone thinks.
Do consumers really think AI content is “soulless slop”?
There is no comparable population-wide statistic showing how many people dislike “AI slop.” The studies available ask different questions: whether people use or trust GenAI, how they react to AI-labeled advertising, whether they want disclosure, and how comfortable they are with AI-assisted or fully generated content. Those are related concerns, but they are not interchangeable.
General attitudes are mixed. In a 2025 U.S. study of more than 1,500 people, the Ad Council Research Institute found that 58% were very or somewhat familiar with GenAI, nearly two-thirds used it for personal or work tasks, and half trusted its outputs to some extent. One-third described it as extremely or very beneficial, while another one-third described themselves as extremely or very concerned. Most respondents acknowledged that GenAI makes mistakes. Ad Council Research Institute’s study therefore points to simultaneous use, perceived benefits and concern—not blanket rejection.
Why does AI use by brands draw skepticism?
People may judge familiar content differently once they believe a brand used AI to make it. In controlled experiments, the Nuremberg Institute for Market Decisions (NIM) found that identical advertising received more critical evaluations when participants were told it was AI-generated rather than human-made. In its second experiment, German participants saw six ads with either an AI-generated or human-generated label. The AI label lowered ratings of naturalness and usefulness and reduced willingness to research or purchase.
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NIM’s page also reports that 44% of participants knew AI could create marketing content, 28% understood how personal data was used for personalization, 25% thought they could recognize AI-generated content, 21% trusted AI companies and their promises, and 20% trusted AI itself. The page describes 1,000 respondents each in the U.S., U.K. and Germany; it does not clearly identify a publication date. Its experiment and survey measure different things: the former tests a label’s effect on responses to ads, while the latter asks about awareness and trust. NIM’s account of the findings does not establish that every AI-made ad will be rejected.
Gartner’s October 2025 survey of 1,539 U.S. consumers found that 50% preferred to do business with brands that do not use GenAI in consumer-facing messages, advertising and content. The same survey found 61% frequently questioned whether information used for everyday decisions was reliable, and 68% frequently wondered whether content and information they saw was real. These figures describe a preference about brand-facing content and broader doubts about reliability; they do not measure dislike of every AI application. Gartner published the results in March 2026. Analyst Emily Weiss summarized the marketing implication: “Marketers should treat GenAI as a trust decision as much as a technology decision.” Gartner’s release provides the survey details and statement.
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Does it matter whether AI assists a person or generates everything?
Yes. Comfort tends to be lower when people are asked about fully AI-generated content than when AI is framed as assisting a human creator. Baringa’s research, involving more than 5,000 respondents across the U.S., U.K., Europe and Australia in survey waves in March 2024 and January 2025, found that 53% were uncomfortable with content where AI assisted human creators, while two-thirds were uncomfortable with content generated completely by AI.
In the same study, three in four U.S. consumers wanted to know whether content was created by AI, down from 81% in 2024. Additionally, 61% wanted creative companies to be open about which AI they used, and 57% wanted visible labels showing how much AI was involved. These measures capture comfort and disclosure preferences, not a single judgment of quality. Baringa’s study reports the findings and survey timing.
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Does disclosure make people less likely to buy?
Not necessarily. A label can draw attention to AI use and lower evaluations, as NIM’s ad experiments demonstrate, but another survey found that disclosure did not automatically reduce younger consumers’ stated purchase likelihood.
The Interactive Advertising Bureau (IAB) surveyed 505 U.S. Gen Z and Millennial consumers who engaged with ads, along with 104 advertising executives; fieldwork ran from October 2025 to January 2026. Only 45% of surveyed consumers felt very or somewhat positive about AI-generated ads, compared with 82% of executives who believed those consumers felt positive. Very or somewhat negative sentiment was reported by 39% of Gen Z respondents and 20% of Millennial respondents. Yet 73% of these Gen Z and Millennial consumers said learning an ad was AI-created would either increase or make no difference to their likelihood of purchase.
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More than half of IAB’s surveyed consumers wanted disclosure for fully AI-generated ads and for AI-generated video or images. The findings apply to the study’s ad-engaged younger consumer sample, not all consumers; stated purchase likelihood is also not the same as observed buying behavior. IAB’s report describes the sample, fieldwork and results.
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Perceived authorship can affect opinions about who made something even when it does not change judgments of the work itself. In an experiment reported by Google Research, participants felt more negatively about the creator and were less satisfied when they believed AI had been used. The assigned creator did not affect their content judgments in that experiment. This distinction helps explain why a person might object to AI’s role or want it disclosed without necessarily finding every resulting piece worse. Google Research’s study summary describes the result.
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Why do survey results differ?
Studies that sound contradictory may be measuring different populations, content and outcomes. Check the scope before treating any one number as a verdict on public opinion:
- Geography: ACRI and Gartner report U.S. findings. NIM includes respondents from the U.S., U.K. and Germany, and its second experiment used German participants. Baringa surveyed across the U.S., U.K., Europe and Australia. IAB’s consumer sample was U.S.-based.
- Timing: Attitudes can shift, and a publication date is not always the fieldwork date. Gartner’s March 2026 release reports an October 2025 survey; IAB’s fieldwork ran from October 2025 through January 2026.
- What “AI content” means: General GenAI use, brand advertising, AI-assisted creative work and fully generated media are different cases.
- Method: A survey of stated beliefs is not equivalent to an experiment that holds an ad constant while changing its AI or human label.
- Audience and outcome: IAB focused on Gen Z and Millennials who engaged with ads. Trust, comfort, perceived authenticity, content judgments, willingness to research, and stated purchase likelihood are separate measures.
A YouGov report describes a multi-market study of almost 10,000 consumers in Australia, Canada, France, Germany, Singapore, the U.K. and the U.S., but its accessible page does not provide the detailed results needed to establish a direction or percentage here. YouGov’s report page gives its scope.
What should a brand take from the evidence?
Use AI only where there is a clear consumer benefit, and be straightforward about its role. The findings support taking concerns about trust and authenticity seriously; they do not show that disclosure alone creates trust, or that every consumer will reject AI-generated work. The right response depends on what the content is, whether a person meaningfully shaped it, and what the audience needs to know to evaluate it.
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