Every week—no, every day—there’s a new AI tool. A new update. A new plugin. A new model that claims to be faster, smarter, more ethical, more open, or more private.
And while most of us in the tech and creative industries once looked at these releases with genuine curiosity, now many of us are quietly muttering: Make it stop.
Welcome to the age of AI fatigue. We are overwhelmed. Not just by the pace, but by the sheer multiplicity of tools that seem to emerge from nowhere and demand our attention with the urgency of a fire alarm.
You blink, and you’re obsolete. You blink again, and the tool you just learned has been absorbed into a larger model or deprecated entirely. This isn’t a trend—it’s an arms race dressed up as innovation.
The Tool-ification of Intelligence
Let’s talk about what’s really going on. We’re not merely seeing advancements in artificial intelligence; we’re witnessing the commodification of cognitive tasks.
What was once seen as miraculous—natural language processing, image generation, voice synthesis, autonomous reasoning—is now served to us in SaaS wrappers with $19.99/month pricing plans.
The philosophical question, “What does it mean to think?” has been replaced with “Can it export to PDF?”
We’re not building intelligence anymore. We’re building interfaces. And those interfaces are increasingly shallow. A GPT plugin that finds recipes. An AI that “brainstorms” with you but really just spits out glorified bullet points.
A hundred clones of ChatGPT wrapped in different UX masks and claiming to be “custom assistants.” It’s starting to feel like Web 2.0 all over again, but with machine learning duct-taped to the backend.
Innovation or Noise?
Here’s the uncomfortable truth: most of these tools are not innovations. They’re derivatives. Variants. Clones of a handful of foundational models with different branding and marginal tweaks. The illusion of progress is being sustained by volume, not value.
The ecosystem is becoming too dense to navigate. It’s like trying to find meaningful research papers in an academic field where every PhD student is required to publish weekly, regardless of whether they have anything new to say.
Signal is drowning in noise. And paradoxically, the very intelligence we’ve built to help us process information is now making it harder to tell what’s worth our time.
The New Gatekeepers of Productivity
For those of us in tech, writing, research, or design, the expectation is clear: you’d better be using AI. If you’re not leveraging tools to code faster, draft faster, edit faster, ideate faster—you’re inefficient. Obsolete. Dead weight.
But what if the productivity gains we’re being sold are illusions of efficiency rather than real improvements? Is speed always better? Are we actually doing better work, or just more of it, faster, with less reflection? When AI becomes not just a tool but an obligation, it stops being a benefit and starts becoming a leash.
And let’s not ignore the obvious: the pressure to keep up doesn’t fall equally. The AI-native elite—the engineers, VC-backed founders, power-users with custom prompts and API keys—are building a world tailored to their speed. The rest of us? We’re running to stand still.
Knowledge, Forgotten Overnight
One of the cruelest ironies of the AI boom is that expertise is now ephemeral. You might spend weeks mastering a new tool—learning its quirks, its limitations, building workflows around it—only to have it go out of date within a month.
This is not evolution. This is churn. And churn has a cost.
What do we lose when mastery itself becomes obsolete? When the reward for learning something deeply is replaced with a new login screen for a tool that does it “better” but differently?
We’re not just overwhelmed—we’re being conditioned to not bother with mastery. To skim, to tinker, to pivot. Permanently in beta, both the tools and ourselves.
The False Choice of Opting Out
You might say: just ignore the noise. Pick the tools that work for you and shut the rest out. Sounds reasonable—until you’re competing for a job against someone who used AI to do twice the work in half the time.
Until your clients expect generative prototypes in real time. Until the industry standard shifts and you realize that “opting out” just made you irrelevant.
This isn’t a treadmill you can step off. It’s a treadmill wired to your economic survival.
And let’s not even begin to discuss the ethical implications. We’re too busy building wrappers for ChatGPT to worry about data transparency, labor exploitation behind reinforcement learning, or the ecological impact of training trillion-parameter models.
The Need for a New Ethos
If the current trajectory is unsustainable—and I believe it is—then what replaces it?
We need a culture of slow AI. Of deliberate adoption. Of human-centered integration that prioritizes durability over novelty. We need frameworks that treat tools not as silver bullets, but as instruments requiring context, judgment, and reflection.
We need to reward depth, not just speed.
We need better curation, not more creation.
We need friction—not as a blocker, but as a signal that we’re thinking.
Because right now, the only thing faster than AI’s evolution is our willingness to abandon anything we can’t immediately monetize.
So here we are. Drowning in tools. Watching the tide rise, holding onto lifeboats built last week.
And wondering—not for the first time—whether intelligence without wisdom is just noise in a prettier font.
