AI coding assistants and coding agents have changed the speed of software development. A developer can now generate features, write tests, refactor code, and investigate bugs in a fraction of the time.
But there is an uncomfortable question:
Are we becoming faster at creating problems we will have to maintain later?
Recent research on AI-generated code has found that a significant portion of AI-introduced issues can survive in real-world repositories. One large study analyzed more than 300,000 AI-authored commits and found that 24.2% of tracked AI-introduced issues remained in the latest revision.
The problem isn't AI itself.
The problem is speed without verification.
The New Technical Debt
Traditional technical debt usually comes from shortcuts:
- "We'll clean this up later."
- "This architecture is good enough for now."
- "Let's skip the tests."
- "We need to ship this feature today."
AI can make those shortcuts much easier to take.
Instead of writing 100 lines of questionable code manually, developers can generate 1,000 lines in minutes.
That changes the economics of technical debt.
AI doesn't remove technical debt. It can accelerate the creation of it.
The "Looks Good" Problem
AI-generated code often looks convincing.
It may have:
- Clean naming
- Helpful comments
- Proper formatting
- Reasonable abstractions
- Unit tests
- Familiar design patterns
But code that looks professional isn't automatically correct.
An AI agent can misunderstand a business rule, introduce an unnecessary dependency, create a subtle security issue, or solve the wrong problem while producing perfectly readable code.
That's why code review becomes more important, not less important.
Developers Are Becoming Reviewers
The role of the developer is changing.
Instead of spending most of the day typing code, developers increasingly need to:
- Define the problem clearly.
- Give AI enough context.
- Review the generated solution.
- Verify assumptions.
- Run meaningful tests.
- Check security and performance.
- Understand the architecture.
- Decide whether the code should actually ship.
This is especially important as AI moves from simple autocomplete toward agents that can plan tasks, modify repositories, run tools, and execute multi-step workflows.
How Do We Avoid AI-Driven Technical Debt?
A few simple rules can make a huge difference.
1. Don't merge code you don't understand
If an AI-generated change is too complicated to explain, it isn't ready for production.
2. Keep tests independent from the AI
Don't ask AI to write the implementation and then blindly trust the tests it generated.
The tests should verify the requirements, not simply confirm that the AI's implementation behaves the way it expected.
3. Review architecture, not just syntax
A pull request can pass linting, tests, and security scans while still making the architecture worse.
Ask:
"Should this code exist here at all?"
4. Measure what AI is changing
Track things like:
- Production incidents
- Defect rates
- Code complexity
- Security findings
- Review time
- Rework
- Dependency growth
- Test coverage
AI productivity should be measured by business and engineering outcomes, not simply lines of code generated.
5. Give agents boundaries
AI agents should have limited permissions.
Don't give an agent unrestricted access to production systems simply because it makes automation easier.
The more autonomous the system becomes, the more important permissions, observability, testing, and rollback mechanisms become.
The Real Advantage of AI
The best engineering teams won't be the ones that generate the most code.
They'll be the ones that can generate, verify, and maintain high-quality software faster.
AI gives developers incredible leverage.
But leverage works both ways.
If your engineering process is strong, AI can accelerate good engineering.
If your engineering process is weak, AI can accelerate the problems too.
The future isn't about humans versus AI.
It's about humans becoming better at directing, reviewing, and governing increasingly capable machines.
The fastest developer isn't necessarily the one who writes the most code.
It's the one who knows what code should be written and what code shouldn't.
