Generative AI can be a powerful productivity booster in coding — but only when deployed thoughtfully. Used carelessly, it can cripple scalability, destabilize systems, and leave companies worse off.

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When an organization rapidly introduces new software into existing systems, it can inadvertently create a tangle of dependencies that compounds its technical debt — that is, the cost of additional technological work that will be needed in the future to address shortcuts taken and quick fixes made during development. Technical debt is the hidden underbelly of digital technology. It is the 60-year-old COBOL code in banking systems that was never properly documented or updated. It is the shortcut of representing the current year with two digits instead of four, leading to the Y2K crisis, which cost hundreds of billions of dollars to fix globally. The buildup of technical debt causes slower development cycles, increased complexity, and security vulnerabilities, potentially leading to system failures.

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You can think of technical debt as operating much like financial debt. The “principal” is the work needed to modernize and refactor code; the “interest” is the ongoing complexity tax that slows maintenance, complicates scaling, and raises the risk of failure. While some debt is unavoidable, implementing AI-generated code is often akin to borrowing at a much higher interest rate. As one of the developers we interviewed said, “The problem with AI is that it can’t see the big picture.” Developers we interviewed also told us about code duplications, integration problems, dependency conflicts, a lack of context awareness, and myriad other problems that come with coding with AI. Indeed, when GitClear analyzed millions of lines of code from 2020 to 2024, it uncovered an eightfold increase in duplicated code blocks and a twofold increase in code churn — both measures of declining code quality. The “2024 Accelerate State of DevOps” report from Google’s DevOps Research and Assessment team found that a 25% increase in AI usage improves code review and documentation but results in a 7.2% decrease in delivery stability. So, what looks like rapid progress today could turn into costly setbacks tomorrow.

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Letting technical debt compound is dangerous. Southwest Airlines’ 2022 meltdown — which stranded over 16,900 flights and cost the airline over $750 million — was rooted in technical debt in its crew-scheduling systems. Technical debt drove the massive 2024 CrowdStrike outage that led to worldwide failures in health care delivery. In May 2025, Newark Liberty International Airport in New Jersey was plagued by massive delays and hundreds of flight cancellations that were caused by a combination of antiquated air traffic control technology and staffing shortages. Failures like these show how invisible risks can suddenly cripple even major organizations. Without deliberate efforts to “pay down the principal,” organizations risk becoming overwhelmed — first slowly, then all at once.

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