Reading Time: 7 minutes

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“People don’t hate new technology. They hate feeling stupid or manipulated by it.”
— internet wisdom, quietly accurate

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Disinformation Security and the AI Slop Crisis

Why the internet didn’t break overnight, and why enterprises are now paying for it

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By 2028, nearly half of all enterprises are expected to adopt dedicated disinformation security products. That line usually gets framed as a forecast, the kind you skim past in a slide deck. It is more accurate to read it as a postmortem written slightly ahead of schedule.

Because something important already broke.

In late 2025, public trust in online content did not gently decline or slowly erode. It snapped. Not in a dramatic way. In a quiet, almost boring way. People didn’t storm platforms or announce their exit. They just stopped believing that what they were seeing was worth their time.

Feeds filled up. Search results started blending together. Brand voices, even from companies that once felt distinct, began to sound eerily similar. Not incorrect. Not offensive. Just empty. Competent, polished, and instantly forgettable. The internet didn’t feel wrong. It felt tired.

This phenomenon now has a name that fits its vibe perfectly: AI slop.

AI slop is not fake news. It is not propaganda. It does not rely on deception or intent. As outlets like The Atlantic and MIT Technology Review have pointed out, its damage comes from something more subtle.

AI slop is content that technically works and emotionally fails. It loads. It ranks. It converts, briefly. And then it evaporates from memory.

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Why This Feels Different Than Every Other “Trust Crisis”

The internet has survived worse lies. It has not survived this much sameness.

The internet has always had a truth problem. Clickbait headlines. Spam networks. SEO mills. Coordinated misinformation. We built defenses for those. Fact-checking ecosystems. Moderation policies. Media literacy campaigns. Each assumed the same thing: that falsehoods were the primary threat.

AI slop bypasses all of that because it is not lying. It is flooding.

When everything sounds plausible, well-structured, and politely confident, the brain stops evaluating. Cognitive effort becomes expensive. Instead of asking, “Is this true?” people ask a much faster question: “Is this worth my attention?” Increasingly, the answer is no.

As The New York Times noted in its coverage of AI content fatigue, users are not engaging in more skepticism. They are engaging in more avoidance. They scroll faster. They read less deeply. They remember almost nothing.

This is the psychological shift that matters.

People are not becoming harder to convince. They are becoming harder to interest. And indifference, not outrage, is what finally poisons trust.

The Coca-Cola Moment

When scale exposed the problem instead of solving it

If you want a clean, almost painfully clear case study of AI slop at enterprise scale, look at Coca-Cola and its recent AI-generated holiday advertising experiments.

This was not a lazy experiment or a novelty stunt. According to reporting from Business Insider and Futurism, the campaign involved tens of thousands of AI-generated variations, relentless prompt tuning, and serious investment. This was AI deployed with budget, governance, and executive buy-in.

In other words, this is what “doing it right” looked like. And still, the backlash landed hard.

What’s interesting is not that people criticized the ads, but how they did it. Viewers did not accuse Coca-Cola of spreading misinformation. They did not argue about facts. They struggled to articulate what felt wrong, only that something clearly was. The trucks shifted shape. Faces felt slightly misaligned. Emotional beats arrived late or not at all. The ad hit all the visual notes of nostalgia without ever triggering the feeling itself.

As one widely shared comment put it:

“It looks like Coca-Cola remembering itself incorrectly.”

That sentence matters because it points to the real failure. This was not a prompt problem. It was not a tooling problem. It was not even a creativity problem in the traditional sense. It was a provenance problem. At no point did audiences feel a human decision behind the work. There was no visible moment of authorship, no sense that someone had chosen this version because it meant something. The campaign felt assembled rather than authored. Generated rather than made.

And people can feel that difference instantly, even when they can’t explain it. For enterprise leaders, this is the uncomfortable lesson. Scale did not save the campaign. Optimization did not rescue it. More output did not produce more meaning. In fact, the very systems designed to guarantee consistency ended up flattening the thing Coca-Cola has spent decades protecting: emotional trust.

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It Wasn’t Just Coca-Cola

Advertising disasters are early warning signals

Other major brands learned similar lessons in 2025, and this is where the story stops being about taste and starts being about risk.

McDonald’s pulled an AI-generated holiday campaign in parts of Europe after it landed as tone-deaf and emotionally flat, as reported by The Guardian. Fashion brands rolled out AI visuals that slipped into the uncanny valley. AI-generated corporate spokespeople felt less relatable than saying nothing at all.

These examples matter not because they failed, but because of who failed. These are global giants with enormous budgets, experienced creative teams, legal review, brand governance, and access to the best tools available. If anyone should be able to deploy AI carefully, it is companies like Coca-Cola and McDonald’s.

And they still made what look like rookie mistakes. That tells us the problem is not talent, money, or effort. It is that AI systems scale faster than meaning. Even with oversight, something essential disappears when responsibility becomes diffuse.

People were not asking, “Was this made by AI?” They were asking, “Who thought this was a good idea?”

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The Old Trust Model Is Gone

Published no longer means authored

For years, publishing implied intent. If content came from a known organization, it was assumed a human had decided it was worth saying. That assumption no longer holds.

Content can now be assembled, localized, and deployed at scale with minimal human involvement. Audiences know this. As The New York Times has noted, people increasingly treat online content as provisional and disposable.

The default stance has flipped. Content is assumed synthetic until proven otherwise.

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Why AI Slop Is Worse Than Disinformation

Lies can be corrected. Noise just accumulates.

Disinformation has actors and goals. You can analyze it, counter it, and attribute it. AI slop has none of that. It emerges from systems that reward speed and volume over meaning. No one intends to flood the internet with forgettable content. It happens because incentives quietly push everything in that direction.

AI slop doesn’t persuade. It exhausts. From a systems perspective, this is entropy, and entropy only grows unless structure is added.

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Content Provenance: The Unsexy Fix

Trust needs receipts now

Every critical system relies on provenance. Software is signed. Transactions are logged. Supply chains are traced. Content largely wasn’t.

Frameworks like C2PA attach verifiable metadata to content showing how it was created, edited, and published, including human input and AI assistance. This metadata doesn’t tell people what to think. It tells systems what happened.

That’s the difference between persuasion and infrastructure.

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Stop Asking “Was AI Used?”

Ask who is accountable

The human-versus-AI debate misses the point.

Human-written content can feel empty. AI-assisted content can feel deliberate. What audiences respond to is accountability. Did someone stand behind this message, or did it simply emerge from a system optimized for output? Provenance makes that distinction visible.

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The Real Risk: Credibility Debt

The slow tax on future trust

Each low-integrity artifact makes the next high-integrity message harder to believe. Over time, this becomes credibility debt. Like technical debt, everything seems fine until the moment you need the system to perform. Then trust fails exactly when it matters most.

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Platforms Already See This

Algorithms are bored too

AI slop often spikes briefly and collapses just as fast. Platforms can measure this. Retention drops. Recall disappears. As provenance signals become available, platforms gain a new ranking lever: confidence. Content with clear origin holds attention longer. That isn’t ideology. It’s math.

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Why CTOs and CISOs Should Care

This damage does not trigger alerts

There was no breach. No attacker. No incident response. And yet reputational damage occurred.

AI slop introduces a new class of enterprise risk: trust degradation without a failure event. Security teams respond to things that break loudly. AI slop breaks quietly. By the time leadership notices, the cost is already baked in.

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Final Thought

The internet didn’t get worse. It got cheaper.

Experts are converging on the same conclusion: the real risk of generative AI is not deception, but saturation. When low-effort content becomes cheap and abundant, trust does not collapse loudly. It fades. People disengage. Attention thins out. Belief becomes conditional.

What worries experts most is not that machines can generate convincing media, but that endless, forgettable output trains audiences to stop caring at all. This kind of fatigue erodes trust faster than any single misinformation event. Trust can no longer be assumed. Content is increasingly treated as synthetic until proven otherwise. For enterprises, the implication is simple. Trust must be engineered before it is earned. Provenance and accountability are no longer optional. They are how meaning survives automation.

In a world flooded with content, credibility becomes the advantage.

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References

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