Technical Debt Just Got A Bailout.

Everyone's Building New. Nobody's Fixing Old

I recently added a new feature to a hotel booking system that in 2024 would have taken me a week. In 2026, I did it in 3 hours, complete with proper documentation and comprehensive automated tests, all thanks to AI tooling. With efficiency improvements like this, you can understand why the AI hype cycle on X is white hot right now, but building new things is not all we need to be focused on.

Developers chase greenfield code because it's creatively satisfying, exciting, and comes with the opportunity of unearthing new value. In reality though, the software that makes real money is often boring. It's the legacy system that stuck around long enough to become irreplaceable. Nobody’s going to make TikToks about upgrading Rails 4 apps, but an overlooked impact of AI may be about to be felt in codebases that already exist, not ones yet to be imagined.

Too Critical to Replace, Too Fragile to Touch

Every organisation with software has legacy software, and it's usually sitting quietly at the centre of the business. This software represents the product-market fit that originally drove growth, and it often pays everyone's salaries.

But rapid growth creates technical debt. Features shipped fast with poor architecture. Developers added special-case to satisfy customers. Quick fixes became permanent. Over time, the systems involved became difficult to understand, hard to change safely, and risky to deploy.

After some time, those with the system knowledge move on from the business, and we get stuck with old codebases that are uneconomical to work on directly. As a workaround, we declare them "legacy" and build wrappers around them or new services alongside them. But this adds architectural complexity with each passing year, and never solves the core problem.

Sometimes, teams will decide to simplify everything with a full rebuild, but these are risky. They often fail completely, or run over budget and lose critical business logic that nobody documented. Many "v2" rewrites ship without ever fully replacing the legacy system, and so we just end up with two legacy systems rather than one.

AI Reverses this Entropy

Modern AI models (Claude Opus 4.5, GPT-5.2) and tools (Claude Code, OpenCode) turn all this on its head, making rescuing a legacy codebase viable where it wasn't before.

Code that would take a developer days to comprehend can be mapped and explained automatically in minutes. AI can trace dependencies, identify dead code, and surface implicit assumptions buried deep in the logic.

AI can speculate accurately about why strange decisions were made, recovering intent, not just structure. That bizarre conditional that checks for negative invoice totals? AI can explain the edge case that prompted it.

AI also excels at writing automated tests that verify existing behaviour, making it safer to change code you didn't write and don’t fully understand. Framework and dependency upgrades that once took days can be completed in hours with AI assistance. A developer can guide AI to restructure and simplify code, and add comments explaining complex functionality, essentially rebuilding the documentation that was never written.

The result of all this is that technical debt that was too expensive to pay down is suddenly cheap. Systems that have long since been declared "legacy" can now quite readily be brought back from the abyss, and made great places for developers to do work once again.

Rescue, Not Roulette

The trick to doing this well is to follow a structured approach — assess, stabilise, modernise.

• Assess Produces a clear picture of what exists, what's risky, and what's salvageable. Often this is the first honest documentation the system has ever had.
• Stabilise Stops the bleeding with critical fixes, improved deployment, and automated tests on key paths.
• Modernise Incrementally upgrades architecture, performance, and dependencies without betting the business on a rewrite.

This approach has a huge advantage of delivering standalone value at every stage. Work can be paused, adjusted, or handed off at any point and you get to keep all the benefits you’ve worked for so far. Keep at it, and the eventual outcome is that the system stops being "legacy" and becomes just another modern application the company can build on.

Contrast this with full rebuilds: high upfront cost, long time to value, risk of losing undocumented business logic, and a track record of failure that would make any CFO nervous.

AI Still Needs Human Judgment

Although AI accelerates dramatically, it can't replace the expertise that keeps modernisation on track. Like an extremely intelligent but inexperienced apprentice, it's brilliant at getting things done, but prone to missing context that a seasoned professional would catch immediately.

Your legacy system contains years of hard-won knowledge about how your business actually operates. Every codebase has quirks: timing dependencies, implicit integration contracts, behaviours that exist for reasons long forgotten. AI-generated code can look correct in review but fail in production because it missed these hidden constraints.

This is why rescue projects benefit from developers who know a technology stack inside out. They spot AI mistakes faster than generalists learning as they go. The AI provides leverage; the experienced developer provides judgment about where to apply it.

From Liability to Leverage

A modernised system isn't just stable — it's strategically flexible too.

Features that were "impossible" become straightforward when the foundation supports them. Integrations with new tools, APIs, and services become viable. Performance improvements unlock new markets or use cases. The business regains optionality - the ability to respond to opportunities without a six-month infrastructure project.

SMEs in particular benefit. They can't afford failed rebuilds, but they can afford disciplined, incremental rescue. The rescued application becomes a competitive asset again, not a source of anxiety.

The Invisible Advantage

The loudest AI stories are about new products built from nothing, but another important economic impact may be less visible. Smart companies are using AI to quietly rescue and modernise systems that competitors are still afraid to touch.

This creates a compounding advantage. While others are stuck with fragile legacy systems, rescued systems enable faster iteration. The rewrite reflex ("let's just rebuild it properly this time, I promise") will be replaced by disciplined rescue as the default.

The unsexy work of modernisation won't make headlines, but it's a fantastic use case for AI nonetheless. For SMEs, this shift levels the playing field. Enterprise-grade modernisation is now accessible at smaller budgets.

So, if you've got a system everyone's afraid to touch, the question isn't whether to fix it. It's whether you fix it before your competitor does.