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Every AI Commit Is Someone's Future Legacy Code

Every AI Commit Is Someone's Future Legacy Code
Photo by Muhil Mohan / Unsplash

Someone asked me last week if legacy code will disappear now that AI writes so much of our software. My first answer was a quick "no." Then I looked into it, and the real answer turned out to be more interesting than a simple no.

The story we tell ourselves

The pitch for AI coding tools always sounds the same. Feed the model your old codebase, and it will read the messy parts, explain the parts nobody remembers, and modernize what needs to change. In theory, legacy code stops being scary. It becomes just another input for a model with a big context window.

There is some truth here. In March 2026, Anthropic launched the Claude Partner Network with a $100M commitment, and legacy code modernization was one of the main use cases they pointed to. Cleveroad, a company that works on these migrations, explains that AI-assisted modernization reads existing code and translates it step by step, instead of throwing it away and starting over. A full rewrite is risky. You lose business rules that got buried in the code over the years, rules nobody wrote down anywhere else. AI tools are good at pulling that hidden logic out before anything gets touched.

So on the enterprise side, there is a real market forming around this. IBM i shops, old RPG systems, COBOL wrappers, all of it. Companies like ARCAD, Fresche, and Profound Logic are building tools and services around exactly this problem. If legacy code was disappearing, nobody would be selling shovels for it.

But here is the part I didn't expect

While AI tools get better at explaining old code, they also seem to be making developers less willing to touch it.

GitClear published research that LeadDev covered recently, and the numbers are hard to ignore. Legacy refactoring, meaning changes to code that is more than 12 months old, has dropped 74% since 2023. Developers are not going back to clean up what exists. They are building new instead, because that is what AI tools are good at and fast at. The research also found a rise in "error masking," where the model writes code that never throws an error no matter what input it gets, instead of handling the input properly. That looks fine today. It becomes tech debt the moment someone has to figure out later why the error handling is there at all.

This connects to something Google's DORA report found too: every 25% increase in AI usage adds about 7.2% more instability to a system. More AI does not automatically mean more stability. Sometimes it means the opposite, just with more code shipped around it.

So which is it?

Both things are true at the same time, and I think that is the honest answer.

At the big, expensive, well-funded end of the industry, AI is genuinely helping teams face 25-year-old systems that no human fully understands anymore. That work was basically impossible to staff before. Now a modern developer can be productive on a legacy system they have never seen, because the AI can explain what the code does, not just what it says.

But at the daily, ordinary end, where most of us actually work, AI is quietly creating a new kind of legacy code. Code that was generated fast, that passes the tests, that satisfies the prompt, and that nobody wants to open again in a year. Faros points out that developers increasingly care about net productivity, not just how fast the first draft appears, and that tools requiring constant correction lose favor quickly. That is a sign teams are noticing the same thing I noticed while reading all this: speed at the start does not mean less maintenance later. It can just move the maintenance further down the road.

Legacy code is not going anywhere. If anything, AI is teaching us how legacy code gets born in the first place, and that part was always true, long before any of these tools existed.

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