Every week, another breakthrough. Another model that “changes everything.” Another breathless press release, another LinkedIn post with a robot emoji, another thread explaining why this one, this one, is different.

And somewhere in the back of your mind, a quiet voice: I don’t care anymore.

That’s AI fatigue. And almost nobody is talking about it.

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When Every Image Looks the Same

Cast your mind back to late 2022, early 2023. The first time you saw a Midjourney image, really saw one, it was genuinely startling. The detail. The lighting. The impossible scenes rendered with painterly precision. People were sharing them like they’d discovered fire.

Now look at your feed. You can spot an AI image in under a second. The tell-tale hyper-saturated skin. The fingers that are almost right. The dreamlike backgrounds that have no particular logic. The lighting that comes from everywhere and nowhere.

We didn’t just get used to AI imagery. We got bored of it. And then something worse happened. We started to distrust it. Every dramatic news photo now comes with a moment of suspicion. Every too-perfect product shot gets a second glance. Every viral image of a celebrity or politician triggers an internal audit before you let yourself react to it.

What began as wonder has curdled into a kind of low-grade visual anxiety. When everything could be fabricated, authenticity becomes the scarcest thing on the internet. Photographers are fighting to prove their work is real. Brands are scrambling to signal “this is an actual human.” And audiences, exhausted by the cognitive load of constant verification, have simply started caring less about images altogether.

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The Video Deluge Nobody Asked For

If image fatigue arrived fast, video fatigue is arriving faster, because the stakes are higher and the uncanny valley is deeper.

Sora. Runway. Kling. Pika. HeyGen. Synthesia. The tools multiplied before anyone had time to develop a cultural immune response. Suddenly, a talking head on LinkedIn could be a real person, a digital avatar, or a cloned voice layered over stock footage, and telling the difference requires effort most people won’t spend.

The early AI videos were easy to mock. The warping faces. The teeth that moved like they were made of liquid. People shared them as curiosities, laughed, moved on. But the models improved with terrifying speed, and now the failure modes are subtler. You don’t immediately clock what’s wrong. You just feel a vague wrongness, that uncanny register that sits just below conscious recognition.

The result? People are watching less, trusting less, and engaging less. Video, which was already the dominant format of the internet, is now contaminated by doubt. Creators who have spent years building authentic audiences on camera are now competing not just with other creators, but with synthetic versions of creators, cheaper, faster, never tired, never having a bad day.

For consumers, the rational response to this environment is disengagement. And that’s exactly what’s happening. Watch time metrics are fracturing. Completion rates are dropping. Not because the content is worse, but because the effort of caring feels less worth it when you can’t be sure what you’re looking at is real.

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Drowning in the Sea of Tools

Now layer on top of all this the sheer, overwhelming proliferation of AI tools, and you start to understand why so many people have quietly checked out.

There are, at last count, thousands of AI-powered tools competing for attention, adoption, and monthly subscription fees. There’s an AI for writing, an AI for editing your writing, an AI for turning your writing into a podcast, an AI for summarizing the podcast, an AI for turning the summary into a LinkedIn post, and an AI for scheduling that post at optimal engagement times.

Each one promises to give you back hours of your week. The collective irony is that evaluating, adopting, learning, abandoning, and replacing these tools has become its own full-time job.

This is tool fatigue, and it sits at the intersection of decision paralysis and productivity theater. The promise of AI productivity tools was that they’d reduce cognitive load. Instead, for many workers, they’ve added a new layer of it, the meta-work of managing your AI stack. Which tools do you trust with your data? Which ones are actually used by your team, and which ones are shelfware? Which subscription can you justify when renewal comes around?

Knowledge workers are particularly hit. The creative professional who was told AI would handle the tedious parts of their job now finds that the tedious part is AI, prompting, correcting, refining, fact-checking, and second-guessing outputs that are often almost right, which is somehow more frustrating than being clearly wrong.

Almost right means you can’t trust it but you also can’t dismiss it. You have to babysit it. And babysitting a machine that was supposed to liberate you is a particular kind of demoralizing.

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The Admission Nobody Will Make

Underneath all of this: AI fatigue is widespread, but admitting it feels professionally dangerous.
Not for me, obviously but, in tech circles, expressing boredom or exhaustion with AI is tantamount to admitting you don’t understand it, or worse, that you’re falling behind.

The discourse has been so thoroughly captured by evangelists and doomers that there’s almost no space for a third position: I believe this technology is significant, and I am also very, very tired of talking about it.

Corporate culture makes it worse. Organizations are pouring billions into AI transformation. Employees are expected to be enthusiastic adopters. In that environment, saying “honestly, I find this exhausting” isn’t candid, it’s career risk. So people perform enthusiasm they don’t feel. They sit through the AI demo, nod along, and go home feeling vaguely hollow.

And the companies selling these tools have every incentive to keep the hype machine running. Slowdowns in the narrative are bad for valuations. Nuance is bad for virality. So the breathless announcements keep coming, the launch events keep selling out, and the collective fatigue deepens in silence.

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What Comes After Exhaustion

Fatigue isn’t the end of a technology. It’s usually the beginning of its maturation. The hype cycle has a hangover phase for a reason: it burns off the noise and leaves behind what actually works.

The people who will get the most out of AI going forward aren’t the ones who chased every tool or shared every announcement. They’re the ones who stepped back, got selective, and asked the boring but essential question: does this actually make my specific work better?

That question cuts through fatigue. It’s not anti-AI. It’s just anti-hype. And right now, being anti-hype might be the most useful thing you can be.

The exhaustion is real. The tools are real. The transformation, some of it, in some places, is real too.

We just need to stop pretending we have to be excited about all of it.