Refactoring the Bill: Will AI Make Tech Debt a Thing of the Past?

AI has really lowered the cost of writing code.  And not just core tech stacks like C# or Java.  It applies across the entire delivery pipeline, right down to CI/CD scripts and infrastructure.  

So this got me thinking about tech debt differently.   

In the pre-AI era, the biggest hurdle to addressing tech debt was usually cost. Fixing it required dedicated developer time, and every hour spent refactoring was an hour not spent on the next release. Teams constantly faced trade-offs: implement a feature the “quick and dirty” way and create debt, or do it properly and risk missing deadlines. Opportunity cost was the silent killer.

AI removes this barrier.  Instead of being forced to compromise solutions just to hit a deadline, we can increasingly have agents implement the right approach up front.  This is a fundamentally different way of thinking about software development.

And, does this mean existing tech debt magically evaporates?  Not automatically—but there is a real opportunity for alleviating this pain.

Dedicated Prioritization: We need deliberate systems to task agents with tech debt in isolation. I’ve always believed in keeping refactoring decoupled from feature work—it makes tracing cause and effect vastly easier when diagnosing changes in system behaviors.

Guardrails and Quality Control: Agents need strict constraints so they don't compound the problem. Left unchecked, an agent can just as easily introduce subtle debt of its own or deeply entrench existing anti-patterns (something I've already seen happen in practice).

Sure challenges abound, but the opportunity is real. If we implement the right discipline and tooling around these agents, we might finally make crushing technical debt a relic of the past.