Generative AI excels in codebases that are relatively clean, well-structured, and adhere to best practices. In these environments, LLMs can easily follow patterns, understand context, and generate useful suggestions or boilerplate code. Companies with younger, high-quality codebases will likely see the biggest productivity gains, as AI tools can navigate their code with precision and speed.
The team at Gauge.sh see the same thing:
There is an emerging belief that AI will make tech debt less relevant. Since it’s getting easier to write code, and easier to clean up code, wouldn’t it make sense that the typical company can handle a little more debt?
The opposite is true - AI has significantly increased the real cost of carrying tech debt. The key impact to notice is that generative AI dramatically widens the gap in velocity between ‘low-debt’ coding and ‘high-debt’ coding.
Companies with relatively young, high-quality codebases benefit the most from generative AI tools, while companies with gnarly, legacy codebases will struggle to adopt them. In other words, the penalty for having a ‘high-debt’ codebase is now larger than ever.
High-debt codebases are often a patchwork of custom solutions, undocumented hacks, and interdependent modules. This complexity is kryptonite for AI models, which struggle with code that deviates from standard patterns. LLMs are trained on large datasets filled with best practices and typical coding paradigms, not on the idiosyncrasies of a company’s specific, legacy system. As a result, AI-generated suggestions are more likely to make faulty assumptions, or even worsen existing issues in high-debt environments. In practical terms, this means the productivity boost that AI offers in low-debt environments simply isn’t likely to translate to high-debt codebases.
This widening gap has turned tech debt into an arguably more urgent and strategic problem. Back in 2003, Nicholas Carr's provocative Harvard Business Review article “IT Doesn’t Matter” argued that as IT became ubiquitous, its strategic value diminished. Carr's point was that once a technology is available to everyone, it ceases to be a source of competitive advantage.
While Carr was correct about IT’s ubiquity, he couldn’t have predicted how this ubiquity would lead to layers of accumulated complexity in codebases. Today, many companies, especially in industries like finance, are shackled by these legacy systems. For decades, banks and investment firms poured billions into proprietary trading systems, risk management platforms, and customer-facing applications. These were the “crown jewels,” meant to give them a competitive edge.
But ironically, the very systems that once differentiated them have now become corporate concrete shoes. They are mired in layers of custom code that can’t easily be modernized or replaced. Rather than enabling innovation, these systems prevent it. Companies find themselves allocating massive resources just to maintain the status quo, with little bandwidth left for new projects or innovation.
The problem isn’t just that tech debt exists; it’s that the cost of carrying it has escalated. Generative AI tools are a force multiplier, but only for those who are already well-positioned to take advantage of them. For companies with modern, well-maintained codebases, AI is a powerful accelerator. For those with tangled, legacy systems, AI is not a shortcut but a spotlight, highlighting the inefficiencies and fragility of their code.
In this new reality, tech debt is no longer just a drag on velocity—it’s a strategic risk. Companies that fail to address their tech debt may find themselves falling further behind, not just in their ability to deliver software but in their capacity to leverage the next wave of AI-driven innovation.