Developers won’t code without AI anymore. That’s not a boast; it’s a trap.

Last year, a study found AI slowed coding despite faster code generation. Developers spent extra time fixing bugs, steering the AI, and waiting for results. When researchers tried to repeat the experiment in early 2026, participants refused to work without AI—even briefly. The tool has become a crutch, not just an accelerator.

Developers believe AI doubles their value. Surveys confirm this perception. Reality paints a different picture. Corporations pumping millions into AI tools see no clear jump in shipped projects or productivity. Amazon killed its internal leaderboard after staff gamed AI usage to inflate scores and run up costs. Uber burned through its 2026 AI budget in just four months, with executives admitting no gains in output.

This “tokenmaxxing” craze—measuring AI use by token volume—turned toxic fast. More tokens don’t mean better code. They mean more AI queries, more errors, and a higher bug fix workload.

That workload is no small detail. AI-generated code creates more problems than human-written code. One code-review firm found AI code brought 1.7 times more issues. Another startup says 44% of AI tokens go to fixing AI’s own bugs. Independent researchers warn these bugs pile up, creating long-term maintenance debt that slows down development over time.

AI Coding’s Hidden Cost: Maintenance Debt

Programmer James Shore put it bluntly: if you double your coding speed without halving maintenance costs, you’re just signing up for permanent overtime. AI may spit out code faster, but the cleanup is brutal. Bugs hide in AI-generated code, often harder to detect and fix than human errors. This slows teams down in the long run.

Advanced AI coding agents like Devin, made to fix AI bugs on the fly, don’t solve the problem outright. Their creators admit the tools perform like junior or mid-level programmers, not seasoned engineers. These agents require human oversight and can’t be left unsupervised.

Experts recommend treating AI output like junior developer work: review everything, maintain strong quality checks, and keep critical tasks like architecture and security in human hands. Developers must understand where AI fails as well as they know their own languages.

The result is an uneasy paradox. Developers love AI for the speed and help it offers. Companies spend heavily on AI tools expecting productivity boosts. Neither side is getting what they want. AI dependency has outpaced the infrastructure needed to manage its risks.

For now, the AI coding market rides on hype and hope. The question is whether the industry can build the quality assurance systems, routing layers, and review processes to turn AI from a liability into a genuine productivity tool. Without that, faster code means faster technical debt—and that ship has sailed.