It is like a band that tours every single year for a decade. At first, the tickets sell out in seconds because the energy is high and the novelty is fresh. But eventually, the fans stop caring. The music is still technically proficient—the guitar solos are clean, the vocals are on pitch—but the experience has become a commodity. The magic is gone, replaced by a predictable routine.
The AI industry has reached that same plateau.
Why is boredom setting in?
The novelty of a talking computer has evaporated. We have moved from the shock of seeing a model write poetry to the annoyance of seeing “AI-powered” slapped onto every single SaaS landing page like a cheap sticker. This is the “AI malaise” discussed in a recent piece by MIT Tech Review, and it stems from a massive gap between ubiquity and actual utility.
The tech is everywhere, but it isn’t actually doing much that changes the day-to-day workflow of a developer or a manager. Most of the “features” released in the last year are just wrappers around a chat interface. Who actually enjoys spending twenty minutes prompting a bot to write an email that should have taken two? (I certainly don’t). The industry has optimized for the “wow” factor of a demo, but the actual user experience is often just a slower, more temperamental version of the old way.
The honeymoon period is officially over.
Can agentic systems fix this?
The only way out of this malaise is to stop treating the LLM as a destination and start treating it as a component. The current obsession with the chat box is a failure of product design. A text box is not a product; it is a command line for people who don’t know how to code. The real shift happens when the model stops asking for a prompt and starts executing a sequence of tasks autonomously.
If a system can actually navigate a file system, hit an API, and verify its own output without a human holding its hand every three seconds, the boredom vanishes. But we aren’t there yet. Most “agents” currently in production are just loops that hallucinate in a circle until they hit a token limit or a timeout. There is a massive difference between a model that can describe how to fix a bug and a system that can actually open the PR and pass the CI pipeline.
By Q4, the market will pivot away from praising parameter counts and start rewarding agentic reliability.
Is the ROI actually there?
This is where the friction becomes real. Running a 70B model is expensive, and the latency on the high-end APIs is still enough to make a developer twitch. When you factor in the cost of GPUs and the energy bills, the math for a lot of these “AI-enhanced” enterprise tools doesn’t add up. Companies are paying a premium for marginal productivity gains that are nearly impossible to quantify, all while adding a layer of unpredictability to the output.
It is like buying a professional-grade industrial oven to toast a single slice of bread. The capability is there, but the use case is trivial. The malaise exists because the cost of deployment is still higher than the perceived value of the output. Or maybe the value is there, but it’s buried under a mountain of prompt engineering that no one actually wants to do.
Until the cost per token drops another order of magnitude or the autonomy increases, the industry will continue to feel like it is spinning its wheels in the mud. We are waiting for the moment the technology stops being a curiosity and starts being a utility. Until then, it’s just more noise in the feed.
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