Generative AI can boost coding productivity, but careless deployment creates technical debt that cripples scalability and destabilizes systems.
Edward Anderson, Geoffrey Parker, and Burcu Tan August 18, 2025 Reading Time: 8 min
Topics
Frontiers
An MIT SMR initiative exploring how technology is reshaping the practice of management.
Dan Page/theispot.com
Summary:
Generative AI tools can make developers up to 55% more productive, but rapid deployment …
Generative AI can boost coding productivity, but careless deployment creates technical debt that cripples scalability and destabilizes systems.
Edward Anderson, Geoffrey Parker, and Burcu Tan August 18, 2025 Reading Time: 8 min
Topics
Frontiers
An MIT SMR initiative exploring how technology is reshaping the practice of management.
Dan Page/theispot.com
Summary:
Generative AI tools can make developers up to 55% more productive, but rapid deployment creates dangerous technical debt. In brownfield environments with legacy systems, AI-generated code compounds existing problems when it’s deployed by inexperienced developers. To avoid costly system failures, organizations must establish clear guidelines, make technical debt management a priority, and train developers to use AI responsibly.
Listen to “The Hidden Costs of Coding With Generative AI” (12:04)
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.
Generative AI is growing explosively across knowledge work, particularly in software development. OpenAI’s GPT-4.1 focuses heavily on enhancing coding capabilities and is a step toward full automation. Organizations adopting these tools are anticipating major gains. And early research supports their optimism: GitHub has reported that programmers using Copilot are up to 55% more productive, and McKinsey has found that developers can complete tasks up to twice as fast with generative AI assistance.
But these positive indicators come with a major caveat. The studies were conducted in controlled environments where programmers completed isolated tasks — not in real-world settings, where software must be built atop complex existing systems. When the use of AI-generated code is scaled rapidly or applied to brownfield (legacy) environments, the risks are much greater and much harder to manage. As part of our ongoing research on the strategic management of AI-augmented software development, we conducted interviews with individuals involved in developing software — ranging from junior developers to lead software engineers and CIOs — across a diverse set of industries, including insurance, web hosting, social media, defense, management consulting, and fintech. Drawing on insights from these interviews, a review of the trade press, and our own economic modeling, we have identified several strategic trade-offs that companies should consider when adopting generative AI for software development.
Why Technical Debt Grows Faster With AI
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.
Topics
Frontiers
An MIT SMR initiative exploring how technology is reshaping the practice of management.
About the Authors
Edward Anderson is the Betty and Glenn Mortimer Centennial Professor for Business at the University of Texas McCombs School of Business and the University of Texas Supply Chain Management Center. Geoffrey Parker is the Charles E. Hutchinson ’68A Professor of Engineering Innovation at Dartmouth College and faculty director for the Arthur L. Irving Institute for Energy and Society. He is also a research fellow at the MIT Initiative on the Digital Economy. Burcu Tan is an associate professor at the University of New Mexico Anderson School of Management.