• 2025: Early productivity study and a surprise
• February–May 2026: Reliance hardens, research stalls
• 2026 corporate reality check: Tokenmaxxing and costs
• Diverging perspectives: Speed vs. quality

Developers’ Reliance on AI Tools Raises Concerns About Code Quality Software development teams are racing to adopt AI coding assistants, but new evidence suggests the rush may be undermining both productivity and code quality rather than improving them.

§2025: Early productivity study and a surprise

In 2025, a landmark experiment measured how long open-source developers took to complete tasks by hand versus with AI tools. While participants felt AI made them more productive, the data showed the opposite: AI-generated code required extra time to find and fix errors, steer the models, and wait for outputs, ultimately slowing developers down.

§February–May 2026: Reliance hardens, research stalls

By February 2026, AI lab METR attempted to repeat the study to see if tools and skills had improved. It failed: most developers refused to participate because they would not work without AI, even for a few research tasks. METR instead ran a survey, published in May, in which technical workers self‑reported that AI had made them “twice as valuable” to their organizations, despite the earlier empirical finding that AI use had slowed them down.

§2026 corporate reality check: Tokenmaxxing and costs

Meanwhile, large companies confronted the cost of this dependency. Amazon shut down an internal token‑tracking leaderboard, Kirorank, after employees gamed it by overusing AI agents, driving up expenses without proving productivity gains. Uber burned through its entire 2026 AI budget in just four months, with executives acknowledging no clear increase in projects or measurable output.

This pattern, dubbed “tokenmaxxing” — treating high AI token consumption as a proxy for productivity — has been a defining but increasingly questioned corporate trend in 2026.

§Diverging perspectives: Speed vs. quality

Developers argue that AI helps them “produce code faster,” reinforcing the sense of personal efficiency. Researchers and industry observers counter that faster code is not necessarily better code and warn that AI‑assisted development may be generating more bugs, technical debt, and long‑term maintenance costs than it eliminates.

With empirical data, self‑perception, and corporate experience in tension, the debate is shifting from how much AI developers use to how rigorously its output is reviewed — and whether organizations can build processes that keep speed from eclipsing quality.

[1] Developers won’t work without AI anymore. The research says it might be making them worse. — “Developers won’t work without AI anymore. The research says it might be making them worse.”

[2] Coders are refusing to work without AI — and that could come back to bite them — “Coders are refusing to work without AI — and that could come back to bite them”

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