ai slop and trust

AI slop, mass-produced AI content of low quality, eats away at the trust customers place in what you publish. At the same time, research from 2025 and 2026 shows that a simple “made with AI” label can lower the credibility of accurate content, while consumers do ask for honesty. You resolve that transparency paradox by tying disclosure to the situation: in customer contact you always say a machine is talking, for marketing content you personally stand behind every word, and internal AI use is nobody’s business. The differentiator that remains is being human: work that feels like machine work loses either way.

why is everything suddenly called ai slop?

Because in 2025 the saturation became official language. Merriam-Webster chose “slop” as its word of the year 2025 and defines it as low-quality digital content, usually produced in large quantities with artificial intelligence (Merriam-Webster, 2025). A dictionary only picks a word once everyone is already using it, and everyone is using it because everyone sees the problem. Spotify removed over 75 million AI-generated spam tracks in 2025 (CNBC, 2025). That is not a linguistic footnote. That is a market visibly silting up.

For you as a business owner this is a market signal. Your newsletter, your website and your socials now compete in an environment customers have come to associate with slop. So the question is simple: do you ride along in that stream, or do you set yourself apart from it? That most AI projects strand earlier still, between pilot and production, is a related but different story, which I work out in the production gap.

do customers still trust ai content?

Less and less, and the Dutch are more skeptical than average. The global study by the University of Melbourne and KPMG among 48,000 respondents in 47 countries shows that 33 percent of Dutch people are willing to trust AI, against 46 percent worldwide (KPMG, 2025). The research agency Norstat found that 44 percent of Dutch people disapprove of AI use in advertising, websites and brochures, rising to 52 percent if AI in advertising keeps growing; among people over sixty, disapproval sits at 53 percent (Norstat via Emerce, 2025).

The uncomfortable part: that same consumer barely recognizes AI content. Earlier Norstat research shows that over half of young people struggle to identify AI-generated content (Norstat via Emerce, 2025). “They will never notice” is therefore the wrong reassurance. Disapproval runs on suspicion and on discovery after the fact, and the suspicion is growing faster than the ability to detect. Gartner measured in 2025 that 53 percent of consumers have little trust in AI-powered search results and summaries (Gartner, 2025). And internationally, trust in AI-generated content fell from 73 to 55 percent between 2023 and 2025 according to the consultancy Capgemini, in every age group, Gen Z included (Capgemini via MarTech, 2026).

why doesn’t “just be transparent” simply work?

Because an AI label can lower credibility even when the content is accurate. Two lines of research show this independently of each other, and they measure different things. The first is about trust in the sender: across thirteen experiments with over 5,000 participants, Schilke and Reimann saw that people consistently place less trust in whoever openly discloses AI use, even when those people work with AI a lot themselves (Organizational Behavior and Human Decision Processes, 2025). The second is about the credibility of the content itself: an experiment with 433 participants around science communication on social media found that an AI label made accurate information less credible and misinformation more credible, the so-called truth-falsity crossover effect (JCOM, 2026). Research from the University of Twente points the same way: AI labels on news articles depress perceived credibility, regardless of what the text actually says (University of Twente, 2025). The caveat belongs right next to it: those last two studies were done on news and science communication, and whether the effect carries over one to one to advertising is not proven.

This chafes, for me too. I build with AI for clients every day, and I also believe you should be honest about how you work. The research says that honesty costs you credibility per piece of content. Anyone selling “just be transparent” as the simple answer is keeping that price quiet. Except the opposite advice, concealment, is a time bomb: according to YouGov, 32 percent of consumers would trust a brand less once its AI use becomes known, against 15 percent who would then trust the brand more (YouGov via MarTech, 2026). What you gain in credibility per piece of content, you can lose in brand trust on the day it comes out.

human-made as a selling point

The countermovement, meanwhile, is commercially proven. Polaroid won an Ad Age Creativity Award 2026 with a global campaign that explicitly rejects AI and data centers and positions the analog, tangible photo as the answer, including a billboard against data centers at Coney Island (Ad Age, 2026). Scale is no longer a differentiator; anyone can generate a thousand texts a day. Realness is.

Still, “anti-AI” is too blunt an answer for most companies, and the research shows why. According to research by the software company Bynder, 82 percent of consumers have no problem with brands using AI for copy, as long as the result feels human (Bynder via MarTech, 2026). So the acceptance is there, but it is conditional. The condition is called quality and feel. Customers tune in to the substance, far more than to the label.

my line: three kinds of ai use, three kinds of honesty

For myself and for clients I use a classification of three lanes, each with its own disclosure rule.

Customer contact. If a customer is talking to a machine, they deserve to know. Always, from the first line. A chatbot posing as a human borrows trust it can never repay; the discovery hits your whole brand and echoes through every contact that follows. Openness pays here too: the software company Qualtrics’s own trend research finds that customers are more willing to share data with organizations that are transparent about how it is used (Qualtrics, 2026). That this openness is also becoming a legal requirement in Europe is something I cover separately in the ai act without panic. And it can be done without losing trust: the Dutch National Voice Monitor 2025 sees trust in AI-supported customer contact rising, especially among younger people, while 78 percent of Dutch consumers have now dealt with a chatbot at some point, up from 70 percent a year earlier (Frankwatching, 2025). Research by Y.digital and Markteffect saw negative attitudes toward AI chatbots drop from 52 to 32 percent (Consultancy.nl, 2026). Trust follows proven performance.

Marketing content. Here AI is a tool to me, the way the spell checker and photo editing have been for years. I feel no duty to note under every blog post which tools I used; the photographer does not credit his lights either. Two hard limits do apply. One: every number, every claim and every experience in the text must be real and checked by a human, with a name underneath that stands for it. Letting AI write up an experience you never had is deception, label or no label. Two: synthetic media that suggests something real, an AI photo of your “team”, a generated product shot that is not your product, a fabricated review, is always deception. No disclosure repairs that; you simply leave it alone.

Internal. Analyses, drafts, summaries, code: here you owe nobody a disclosure. The client buys the result and your responsibility for it. What your kitchen looks like is your own business, as long as what leaves the kitchen meets your standard.

The dividing line beneath these three lanes is always the same: deception begins where the receiver concludes something about the sender that is untrue. That they are talking to a human. That you tested the product yourself. That the review came from a customer. If those conclusions hold, AI assistance is simply honest work.

checklist: how to stay out of the slop

1. Sort all your AI use into the three lanes: customer dialogue, public content, internal. Edge cases follow the strictest lane.
2. Have every bot introduce itself as a digital assistant in its first line, and always offer a route to a human.
3. Set one publishing rule: nothing goes live without human final editing and a name that signs for it.
4. Check every number and every claim against the original source, never only against the AI summary of it.
5. Ban outright: fabricated reviews, fabricated experiences and generated images that suggest something real.
6. Do the read-aloud test: read the text out loud and cut everything you would never say to a customer that way.
7. Reread your existing content every quarter with the same eyes; slop sneaks in through the back door of time pressure.

What you do not need to do: run AI detectors on your own texts or stick a “100 percent human-made” badge on every page. Detectors are unreliable, and such a badge answers a question your customer never asked. Your customer asks whether your work is right and whether it feels like you.

My position: I use AI every day and make no secret of it, and at the same time nothing that leaves my workshop may feel like machine work. Those two convictions get along fine as long as you keep the lanes apart: machines identify themselves in dialogue, humans sign for content, and internally you do whatever works. The label is the side issue; being answerable for the substance is the main thing.