Thirty years ago, when I first saw the Internet, I was convinced it was going to become an incredibly powerful tool for businesses. But I also believed there was a catch: the technology had to become invisible.
That was harder than it sounds. In the early days, websites were often built by left-brain programmers who were understandably fascinated by what the technology could do. So websites became showcases for the technology. Animated GIFs. Splash screens. Flash intros. Confusing navigation. Things spinning, blinking and moving around simply because someone had figured out how to make them spin, blink and move around.
And when those websites sucked, critics didn’t just criticize those websites. They criticized the Web. They dismissed it as a fad, questioned why businesses needed it and painted the entire medium with a very broad brush.
The problem wasn’t the Internet. It was what people were doing with it.
Thirty years later, I’m beginning to wonder if we’re watching the same movie again.
Welcome to the Age of AI Slop
There is a growing backlash against obviously AI-generated content. You’ve probably seen it yourself. Artificial-looking social posts. Uncanny people in ads. Impossible food. AI-written articles that say a lot without actually saying anything. Videos where something just feels wrong even before you figure out exactly what it is.
We’ve even developed a wonderfully unflattering name for it: AI slop.
And this isn’t just a handful of AI-hating curmudgeons yelling on social media. Meltwater found that mentions of the term “AI slop” increased ninefold in 2025 compared with the same period in 2024, growing from about 461,000 mentions in all of 2024 to roughly 2.4 million by November 20, 2025.
The business implications are getting harder to dismiss. Gartner reported in March that 50% of U.S. consumers would prefer to give their business to brands that don’t use generative AI in consumer-facing content. In the same research, 68% said they frequently wonder whether the content and information they encounter is real.
That’s enough to get any marketer’s attention.
But I think we need to be careful about what conclusion we draw from it.
AI slop isn’t simply content created using AI. It’s what happens when the ability to produce something quickly and inexpensively becomes a substitute for judgment, creativity, accuracy, authenticity and effort.
And that’s a very different problem.
Do People Really Hate AI?
I don’t think they do. At least, not exactly.
People use AI constantly, sometimes knowingly and sometimes without giving it a second thought. AI can help detect fraudulent transactions. It can recommend something you might actually want. It can help analyze thousands of data points. It can answer a question in seconds instead of making someone dig through a website for ten minutes.
There isn’t much outrage when it works.
That sounds less like a population that hates AI and more like one that’s trying to figure out when it should be trusted.
And consumers are getting pretty good at recognizing it. Canva’s 2026 marketing research found 70% of consumers say they can usually spot AI-generated advertising because it feels like something is missing. Interestingly, among Gen Z and Millennials, 70% said they pay more attention to the “vibe” of an ad than to how it was made.
That distinction matters.
Maybe people don’t hate AI nearly as much as they hate being made aware that AI replaced effort.
Cheap. Fake. Generic. Lazy. Inaccurate. Soulless. Those are the things that hurt brands. AI has simply made it possible to produce them at unprecedented scale.
People may eventually stop caring whether something was made with AI. They’re unlikely to stop caring whether it’s good.
New Capability Doesn’t Automatically Create a New Need
This is where marketers can get themselves into trouble.
Every new technology creates an irresistible temptation to ask, “What can we do with this?” That’s a useful question during experimentation. It’s not necessarily a good marketing strategy.
The better question is: “Can this create something more valuable than what we already have?”
Suppose a business has a library of terrific professional photography. Real employees. Real customers. Real products. Real locations. Great user-generated video showing people actually enjoying the experience the brand promises.
AI can replace all of that with synthetic imagery.
But why would you?
Real photography has something a beautifully rendered AI image doesn’t: evidence. That’s actually your restaurant. That’s actually your product. Those are actually your people. Those customers actually had that experience.
In a world where almost anyone can generate almost any image, video or paragraph imaginable, that authenticity may become more valuable, not less.
AI is making content abundant. It may simultaneously be making authenticity scarce.
And scarcity has value.
The Technology Should Disappear
This brings me back to what we learned building websites in the 1990s.
Eventually, people stopped being impressed that a business had a website. They just expected the website to work. Nobody cared what software built it, what language it was programmed in or how clever the technology behind it was. They wanted to find something, learn something, buy something or accomplish something.
The technology became secondary to the experience.
We’ve applied that same philosophy at Trivera through wave after wave of emerging technology: search, social media, mobile, marketing automation, programmatic advertising, personalization and now AI.
The emerging technology changes. The rule doesn’t.
Technology isn’t the strategy. The customer experience and the business result are the strategy.
So here’s a simple test we’re increasingly applying to AI:
1. Does it make the experience better?
2. Does it make the marketing more effective?
3. Does it create something valuable that wasn’t previously possible or economically practical?
4. Does it strengthen rather than dilute what makes the brand authentic?
5. Can we measure a meaningful business benefit?
If the answer to those questions is no, being able to say “we’re using AI” isn’t much of an accomplishment.
There’s Another Audience Watching: AI Itself
Here’s where things get even more complicated for marketers.
AI isn’t just helping us create marketing. It’s increasingly deciding which marketing gets discovered.
Traditional search is being supplemented by Google AI Overviews, ChatGPT, Gemini, Perplexity and other AI-driven discovery tools. That means our content increasingly needs to be understandable not only to humans, but also to machines deciding which sources deserve to be retrieved, summarized and cited.
The emerging practices around AI search emphasize many of the same things good SEO has always rewarded: authority, clarity, relevance, structure and useful information. The iPullRank AI Search Manual goes further into concepts such as citation readiness, semantic relationships and content structured so retrieval systems can interpret it accurately.
All of that makes sense.
But it also creates a wonderfully absurd temptation:
Use AI to manufacture enormous quantities of content so other AI systems will discover your brand.
You can see where that ends.
AI absolutely can help knowledgeable marketers research topics, uncover gaps, structure information, analyze search behavior and produce clearer content. But if the objective becomes sheer production, we’ve simply invented a much faster machine for filling the Internet with stuff nobody particularly wants.
And there’s an even bigger danger.
You can win the AI citation and still lose the customer.
If your content gets mentioned in an AI answer but the human who eventually encounters your brand finds generic writing, questionable claims, synthetic imagery and nothing demonstrating genuine expertise, what exactly did you win?
SEO and GEO visibility that diminishes brand trust isn’t success.
We’re Using AI Too. A Lot.
We’re certainly not standing outside the AI revolution throwing rocks at everyone participating in it. Trivera uses AI extensively, and we’re constantly looking for new places where it can make us and our clients better.
But we’re also learning where the human involvement becomes more important rather than less.
Blogs and thought leadership. AI can help with research, challenge an argument, identify gaps, organize thoughts, polish writing and assist with imagery. But experience, opinion, judgment and the willingness to say, “No, that’s not what I mean,” still need to come from a human being with something worth saying.
Images and creative. Generative AI lets us create custom visuals that would once have required photography, illustration or budgets that simply weren’t practical. That’s a genuine new capability. But when real photography or human-created work tells the story better, using AI merely because we can makes no sense.
Our AI Deep Dive podcast. Chip and Nova are openly identified as AI-generated hosts. There’s no attempt to pretend otherwise. But considerable human involvement happens before anyone hears them: choosing the subject, developing the argument, supplying source material, reviewing what they say, correcting problems and producing the finished episode. The point isn’t that AI can make a podcast. The point is whether the resulting conversation is worth your time.
AI agents. These may be one of the most exciting applications we’re working with because a properly developed agent can answer customer questions instantly, work around the clock and make enormous amounts of company knowledge accessible conversationally. But that also means the AI is speaking directly for the brand. The knowledge, guardrails, testing and human oversight matter enormously.
Advertising, social, reporting and analytics. AI can analyze huge datasets, uncover patterns, improve targeting, assist with placement, summarize performance and identify opportunities humans might otherwise miss. Much of that AI can remain completely invisible to the customer.
There’s a common denominator in all of these.
We’re not trying to replace human judgment with AI. We’re trying to multiply what good human judgment can accomplish.
The Closer AI Gets to Your Customer, the More Careful You Should Be
I think marketers need to start thinking about AI risk on a spectrum.
1. Low brand exposure: Analysis and automation. Letting AI examine 50,000 rows of analytics data is very different from letting it talk to a customer. Research, reporting, workflow automation and pattern detection can deliver tremendous benefits with relatively little direct brand exposure.
2. Moderate exposure: Marketing assistance. SEO/GEO analysis, personalization, ad targeting, content research and optimization put AI closer to the marketing but still leave humans in control of what reaches the customer.
3. High exposure: Published creative. Writing, imagery, video, advertising and social content are the brand. If they feel generic, artificial or careless, customers aren’t going to blame the AI platform. They’re going to blame you.
4. Highest exposure: AI speaking for you. Agents and other customer-facing systems can answer questions, recommend products, qualify prospects and represent your organization in real time. Their potential is enormous. So is the damage they can do when they’re poorly conceived, inadequately trained or left without appropriate guardrails.
Here’s the counterintuitive part: as AI gets closer to your customer, human oversight should increase, not decrease.
Too many organizations are likely to do exactly the opposite because eliminating human involvement is where they expect the cost savings to come from.
That’s how efficiency turns into slop.
What This Means for You
1. Stop measuring AI adoption. Measure AI improvement. “How much AI are we using?” is the wrong question. Ask what became faster, better, more useful, more accurate or more profitable because you’re using it.
2. Audit what your customers actually see. Look at your AI-assisted content, imagery, advertising, video, social posts and customer interactions as a customer would. Does it feel unmistakably like your organization, or could your logo be swapped with a competitor’s without anyone noticing?
3. Don’t replace authenticity you already own. If you have real expertise, real employees, real customers, real photography, real experiences and a distinctive point of view, don’t automatically trade them for synthetic approximations simply because they’re easier to generate.
4. Increase human oversight as AI gets closer to the customer. Back-office analysis and customer-facing communication shouldn’t have identical guardrails. The more directly AI represents your brand, the more important knowledgeable humans become.
5. Don’t trade brand trust for SEO or GEO visibility. Make your content clear, authoritative and easy for AI systems to understand and cite. But make damn sure there’s something useful and credible waiting for the human being who eventually gets there.
6. Make the technology disappear whenever it can. Don’t use AI because you can. Use it when it creates an experience, insight or result that is genuinely better than what you could deliver without it.
Thirty years ago, we believed the Internet would become truly powerful for businesses when people stopped being impressed by the technology and simply expected it to work.
I think AI is heading toward the same place.
The brands that win won’t necessarily be the ones using the most AI. They’ll be the ones using it so well that eventually nobody cares that it’s AI at all.
