In 2026, researchers have discovered that developers cannot work without AI coding tools. While AI undoubtedly helps programmers generate code faster, some researchers warn that it may not improve code quality, which could lead to future problems. Specifically, in February 2026, the respected AI research lab METR published a surprising revelation: Most developers are unwilling to work, even on limited tasks, without AI. METR aimed to update groundbreaking research from 2025 on AI coding productivity. In that study, researchers measured how long open source developers took to complete tasks manually versus with AI. While developers reported that AI made them more productive, they were shocked to learn it actually slowed them down. Although AI generated code faster, developers spent additional time finding and fixing errors, guiding the AI, and waiting for it to complete tasks. When METR attempted to repeat the experiment to measure advances in AI and coder proficiency, they were unable to do so because developers were unwilling to participate “because they do not wish to work without AI.” Instead, METR published a survey in May allowing technical employees to self-report their AI productivity gains. Unsurprisingly, they perceived AI as doubling their value to their organizations. However, recent headlines about the wild expenses of so-called tokenmaxxing, coupled with recent research, cast doubt on such self-perceptions. Tokenmaxxing, or using the number of tokens a person uses as a proxy for productivity with AI, has been the trend of 2026 so far, and it may already be over. Amazon shut down its internal token-tracking leaderboard, Kirorank, after employees were gaming it by excessively using AI agents, leading to skyrocketing costs. Employees demonstrated that AI use does not automatically translate to increased productivity. Uber exhausted its 2026 AI budget within the first four months of the year, and COO Andrew Macdonald recently stated on a podcast that such spending hadn’t led to a measurable increase in projects or productivity. AI-generated code also does not necessarily reduce ongoing code maintenance needs and may even increase them, as programmer and author James Shore elegantly argued in a viral blog post on Hacker News. “You write code twice as quick now? Better hope you’ve halved your maintenance costs,” he wrote. “Otherwise, you’re screwed. You’re trading a temporary speed boost for permanent indenture.” There’s other evidence that AI can increase code maintenance woes. A viral tweet from Aiswarya Sankar, founder and CEO of reliability engineering agent startup Entelligence AI, proclaims that companies are spending 44% of their tokens on bug fixes that their AI generated. Meanwhile, code-reviewing tool company CodeRabbit analyzed open source pull requests and found that AI produced 1.7x more problems than human code. While these are admittedly self-serving stats from those trying to sell AI code reviewing tools, independent researchers have also found such issues. Researchers from the respected Singapore Management University published a report in April warning that “AI-generated code can introduce long-term maintenance costs into real software projects.” Given that programmers love their AI assistants, what’s the solution? Those who want to sell you AI coding agents suggest that developers can use AI coding agents to handle the bone-wearying tasks of fixing code as fast as AI spits it out. However, even Cognition founder and CEO Scott Wu admits that, while Devin can work independently, he’d currently rate its skill between a junior and mid-level programmer, depending on the task. This is not a hand-it-off and forget it solution. The SMU researchers suggest a more human approach. Programmers should know what tasks AI does and doesn’t do well as deeply as they know their favorite coding languages. They need strong quality assurance systems designed for AI and they are stuck with carefully reviewing the AI’s work as if it were a junior dev. Meanwhile, the researchers say (and Wu agrees), humans should still be doing the big-picture work like software architecture and security design.
Blogger's Review: The current application of AI in coding provides convenience for programmers, but reliance on AI-generated code may lead to higher maintenance costs. Developers should remain vigilant when using AI to ensure that code quality is not sacrificed. Future development work requires human-AI collaboration rather than a simple replacement relationship.