Hidden Costs of Coding with Generated AI

Generating AI can become a powerful productivity booster In coding – but only if you unfold thoughtfully. Careless use can make scalability unstable, system unstable, and make businesses even worse.

Generator AI is growing explosively across the work of knowledge, especially in software development. Openai's GPT-4.1 focuses on enhancing coding capabilities, making it a step towards full automation. Organizations adopting these tools expect great profits. And early research supports optimism. Github has discovered that programmers using Copilot are up to 55% more productive, while McKinsey can help developers complete up to twice as many tasks as they generate AI assistance.

However, these positive indicators come with great warnings. This research requires programmers to carry out in a controlled environment where they complete isolated tasks rather than in real-world settings, and software must be built on complex existing systems. When the use of AI-generated code is scaled quickly or applied to brownfield (legacy) environments, the risks are much greater and much more difficult to manage. As part of our ongoing research into the strategic management of AI-Augmented Software development, we conducted interviews with individuals involved in software development, from junior developers to software engineers and CIO leads in a variety of industries, including insurance, web hosting, social media, defense consulting, and Fintech. Using these interviews, reviews of trade presses, and insights from our own economic modeling, we identified some strategic tradeoffs that companies should consider when employing generated AI for software development.

Why AI will grow faster technical debt?

If an organization quickly deploys new software on an existing system, it can incorrectly create tangles of its dependencies. Technical debt – That is, the cost of additional technical work that will be required in the future to address shortcuts and quick fixes made during development. Technical debt is the hidden abdomen of digital technology. The 60-year-old COBOL code for the banking system was not properly documented or updated. This is a shortcut that expresses the current year in two digits rather than four digits, leading to the Y2K crisis. Accumulation of technical debt can slow down development cycles, increase complexity, create security vulnerabilities, and lead to system failures.