For years, we’ve all lived with technical debt.
We knew it was there. We knew it slowed us down. But when it came to priorities, there was always something else more urgent — another feature to ship, another business request to handle.

At QIMA, one of our core values is to embrace change — and with the rise of AI in software development, the game has changed.

Today, technical debt doesn’t just slow down your developers. It actively limits the efficiency and relevance of your AI tools. That shift changes how we should think about code quality, prioritization, and engineering velocity.

When Code Confuses AI, Everyone Slows Down

Modern AI tools — whether it’s GitHub Copilot, Cursor, or internal copilots — don’t read code like humans. They rely on patterns, structure, and consistency.

Technical debt breaks all of that.

Example:
Your codebase still uses the term vendor, but the business switched to supplier years ago. The docs say “supplier,” the PRD says “supplier” — but the code still expects vendor. Developers work around it. AI does not. It blindly generates code around the wrong abstraction.

This disconnect causes:

  • Confusing code suggestions
  • Broken automation
  • AI hallucinations based on outdated patterns

In short: technical debt introduces ambiguity that both humans and machines now have to fight against.

Clean Code Now Has Compounding Value

We used to treat clean code as a developer experience benefit. Nice to have, but not urgent.

That’s no longer true.

Today, clean code enables smarter AI-assisted development:

  • Better context for code generation
  • Faster test automation
  • More reliable refactoring
  • More accurate debugging

In other words, the value of reducing technical debt is now multiplied by the value of unlocking AI productivity.

AI Makes It Cheaper to Tackle Debt — Changing the ROI Equation

Historically, tech debt was ignored because the cost of fixing it outweighed the perceived benefit. That equation has flipped.

Thanks to AI, the effort to reduce debt is now dramatically lower:

  • AI helps refactor, rename, and restructure in minutes
  • It can suggest patterns, apply changes, and propagate terminology
  • It supports bulk updates with less manual effort

This means we can finally “make it count.”
Effort is down. Impact is up.
It’s no longer a hard trade-off. It’s a clear opportunity.

Are We More Willing to Fix Code for AI Than for Ourselves?

It’s a bit ironic.
Developers have lived with tech debt for years — voicing the pain, flagging the inconsistencies — and nothing changed. But now that AI also struggles with it, we’re suddenly motivated to clean it up.

Are we valuing the machine’s efficiency more than the developer’s experience?

Maybe.
But maybe this isn’t about valuing machines over people.
Maybe it’s about finally aligning tech health with business velocity — and using AI to justify the investments we always knew were necessary.

This Is What Embracing Change Looks Like

At QIMA, we don’t just adapt — we embrace change to stay ahead.
The rise of AI in software development is not a distant future — it’s already changing how we write, review, and maintain code.

If we want to fully unlock its value, we need to clear the path.

That means:

  • Prioritizing strategic debt reduction
  • Aligning code with current domain models
  • Making our systems easier to navigate — for humans and machines

Technical debt is no longer just a developer headache. It’s a barrier to AI-driven velocity.

By cleaning it up, we simplify the experience, we improve trust, and we accelerate everyone.
And that’s how we make it count.