Consumers do not share one verdict on AI content. Many use generative AI and see benefits, yet skepticism rises when brands use it in customer-facing work—especially when people question its authenticity, reliability, or whether its use has been disclosed. The phrase “soulless slop and zero talent” captures a sharp criticism, not a finding that surveys establish as representative public opinion.
What the surveys actually say about AI content
General attitudes toward generative AI 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, and nearly two-thirds had used it for personal or work tasks. One-third described it as extremely or very beneficial, while one-third described themselves as extremely or very concerned. Half trusted GenAI outputs to some extent, although most respondents acknowledged that mistakes are common. The Ad Council Research Institute’s study measures broad familiarity and attitudes, not approval of every AI-made image, video, or advertisement.
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That distinction matters. Someone can use a chatbot for a task and still dislike an AI-generated ad, distrust a particular output, or object to a company using AI in its marketing. There is no single comparable statistic in the studies here that measures how many people dislike “AI slop.” The research asks different questions about GenAI overall, brand advertising, disclosure, comfort, and authorship.
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Why brand content can draw more skepticism
People may judge branded material through a trust lens as well as a quality lens. In a survey of 1,539 U.S. consumers conducted in October 2025, Gartner 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 that 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 consumers’ stated preferences and concerns; they do not mean that half reject all AI-made content or all companies that use AI. Gartner published the results in March 2026.
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An experiment from the Nuremberg Institute for Market Decisions offers a different kind of evidence: it tested what happens when the same advertising is labeled as AI-generated rather than human-made. In its second experiment, German participants saw six ads. The AI label led to less favorable judgments of naturalness and usefulness, and reduced willingness to research or purchase. Because the label was changed while the ad was held constant, this result points to the effect of perceived authorship in that experiment; it is not a measure of how every consumer would react to every AI ad. NIM’s account of the experiments also reports limited consumer confidence and awareness: across 1,000 respondents each in the U.S., U.K., and Germany, 44% 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 reviewed page does not clearly state a publication date.
Gartner analyst Emily Weiss, a senior principal analyst in its Marketing practice, summarized the business implication this way: “Marketers should treat GenAI as a trust decision as much as a technology decision.”
AI-assisted work and fully generated content are not judged alike
“AI content” can mean a human using AI for support, or a system generating the finished work. Consumer comfort differs across those cases. Baringa’s study, drawing on 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 consuming content where AI assisted human creators; two-thirds were uncomfortable with content generated completely by AI. Three in four U.S. consumers wanted to know if content was created by AI, down from 81% in 2024. The study also found that 61% wanted creative companies to be open about what AI they use, and 57% wanted visible labeling of how much AI was used. Baringa’s findings are not a universal measure of every country or kind of creative work, but they show why specifying the degree of AI involvement can matter.
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These distinctions help explain why a broad question such as “Do people like AI content?” can produce misleading answers. A person might be comfortable with a human editor using AI for a limited task but uneasy about a fully generated ad presented without explanation. A survey about comfort is also not interchangeable with one about purchasing, trust, or perceived quality.
Does disclosure make people reject AI-made ads?
Disclosure matters to many consumers, but the evidence does not support a simple rule that telling people about AI use always makes them reject an ad. The Interactive Advertising Bureau 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. More than half of the consumers wanted disclosure for fully AI-generated ads and AI-generated video or images. Yet 73% said learning that an ad was AI-created would either increase or make no difference to their likelihood of purchase.
The IAB also found a gap between consumer sentiment and executives’ assumptions: 45% of surveyed Gen Z and Millennial consumers felt very or somewhat positive about AI-generated ads, compared with 82% of advertising 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. These results apply to the study’s U.S. audience, not all generations or consumers everywhere. The IAB report distinguishes what consumers said from what advertising executives thought they would say.
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Disclosure can make AI use more noticeable, and an experiment suggests that an AI label can lower evaluations of a particular ad. But the IAB’s purchase-likelihood result shows why it is too broad to claim that disclosure automatically harms buying intent. The outcome depends on the audience, the content, and what is being measured.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsWhat people object to—and what the evidence cannot prove
- Questions about authenticity: In NIM’s experiment, the AI label lowered perceived naturalness and usefulness for the ads tested.
- Trust and reliability concerns: Gartner’s survey recorded widespread questions about whether information is reliable and whether content is real.
- Unclear or hidden AI involvement: Baringa found substantial interest in knowing whether AI was used and how much; IAB found demand for disclosure in specified ad formats.
- Discomfort with full generation: Baringa’s respondents were more uncomfortable with fully AI-generated content than with AI-assisted human-created content.
These findings help explain skepticism, but they do not establish that all AI content is low-quality, that consumers universally see it as talentless, or that every use of AI damages a brand. Nor do they provide a representative collection of consumer comments using the words “soulless slop.” Google Research’s experiment illustrates another distinction: participants felt more negatively about the creator and were less satisfied when they believed AI was used, while the assigned creator did not affect their judgments of the content itself. Google Research’s study suggests that perceived authorship and evaluation of the work can diverge.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to read apparently conflicting results
Studies on AI content are easier to interpret when you check what they measured, whom they surveyed, and when. Gartner’s preference for brands avoiding GenAI concerns consumer-facing brand content; ACRI asked about GenAI more broadly; Baringa separated assisted from fully generated work; and IAB focused on U.S. Gen Z and Millennial consumers who engaged with ads. NIM’s experiment changed the stated origin of an ad, while broad surveys ask respondents to report beliefs or preferences. Trust, comfort, naturalness, purchase likelihood, and content quality are related but different outcomes.
Dates also matter. Gartner released its findings in March 2026 based on October 2025 fieldwork, while IAB’s consumer research ran from October 2025 through January 2026. Baringa used survey waves in March 2024 and January 2025. These are snapshots, not timeless measures of public opinion.
YouGov describes a multi-market report based on almost 10,000 consumers across Australia, Canada, France, Germany, Singapore, the U.K., and the U.S., but the accessible report page does not expose detailed findings that would support a specific direction or percentage here. The report covers trust in the age of generative AI, but no numerical claim from it is used in this article.
What this means for brands and creators
The evidence points to conditional skepticism, not universal rejection. A practical approach is to make the purpose of AI use clear, keep a human accountable for the final work, and explain AI involvement when it is material to how an audience may understand the content. That is not a guarantee of trust: the studies do not show that disclosure alone makes content credible, useful, or appealing. They do show that consumers can care both about what they see and about how it was made.
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