Technical Debt in the Age of AI-Assisted Coding

AI coding assistants have made us faster. That is undeniable. But faster at what? I have watched teams ship features in half the time while doubling their maintenance burden. The trade is not always worth it.

This is not an anti-AI polemic. I use coding assistants daily. But I have learned to use them strategically, and I have developed a rubric for when to resist the autocomplete.

The Speed-Understanding Tradeoff

When I write code manually, I understand every line. I know why each function exists, what edge cases it handles, what assumptions it makes. The code is slow to produce but cheap to maintain.

When I accept AI-generated code, I often understand the gist but not the details. The code appears faster but carries hidden costs:

• Debugging time. When AI-generated code breaks, I must first understand what it was trying to do. This reverse-engineering adds latency to incident response.
• Modification friction. Changing code I did not write requires understanding code I did not write. AI-generated code is often subtly different from how I would have written it, increasing cognitive load.
• Test coverage gaps. I tend to write fewer tests for code I accepted rather than wrote. The code "looks right," so I skip verification. This is a trap.
• Architectural drift. AI assistants optimize for local correctness. They do not see the broader system. Over time, accepted suggestions accumulate into an architecture nobody designed.

The Vibe Coding Phenomenon

I have observed a pattern in teams that adopt AI assistants aggressively. I call it "vibe coding."

Vibe coding works like this: the developer describes what they want in natural language, accepts the generated code, runs it, sees if it works, and iterates. The feedback loop is fast. The output often functions. But the developer never actually reads the code.

Vibe coding is fine for throwaway scripts and prototypes. It is dangerous for production systems. The developer has outsourced not just the typing but the thinking. When the system breaks—and it will break—they lack the mental model to diagnose it.

The tell: ask a vibe coder to explain how their code handles a specific edge case. They cannot, because they never thought about it. The AI might have handled it, or it might not have. Nobody knows until it fails in production.

My Rubric for AI Assistance

I use AI coding assistants selectively based on the type of work:

High AI leverage (use liberally):

• Boilerplate and scaffolding. Generate the skeleton; fill in the logic manually.
• Test generation. AI is excellent at generating test cases from function signatures.
• Documentation. Generating docstrings and README sections from code.
• Language translation. Converting working code from one language to another.
• Regex and one-liners. Tasks where the syntax is tricky but the logic is simple.

Low AI leverage (use cautiously):

• Business logic. The core of what your system does. You must understand this.
• Security-sensitive code. Authentication, authorization, cryptography. Review every line.
• Performance-critical paths. AI-generated code is rarely optimized. Profile before trusting.
• Error handling. AI assistants are optimistic. They generate the happy path well but often miss failure modes.
• Architectural decisions. AI sees files, not systems. Keep architecture in human hands.

The Maintenance Multiplier

Here is the math that changed how I think about AI-assisted coding:

Code is written once but read many times. If AI helps me write code 3x faster but makes it 2x harder to read, I have not saved time—I have borrowed it. The interest payments come due during every code review, every bug fix, every feature extension.

I now optimize for readability over writability. I often rewrite AI-generated code to match the style and patterns of the existing codebase, even when the generated code is technically correct. Consistency reduces cognitive load for the team.

What I Actually Do

My current workflow:

1. Plan first. Before touching the keyboard, I sketch the approach. What functions? What data flows? What error cases? AI cannot do this for me.
2. Generate scaffolding. I use AI to create file structures, boilerplate, and function signatures. This is pure time savings with low risk.
3. Write critical logic manually. The core algorithms, the business rules, the security checks—I write these myself. I need to understand them.
4. Generate tests with AI. AI is great at generating test cases. I review them to ensure coverage but rarely write tests from scratch anymore.
5. Review everything. Before committing, I read every line as if a junior developer wrote it. Because, in a sense, one did.

The Team Dynamics

AI-assisted coding changes team dynamics in ways we are still understanding:

• Senior engineers benefit most. They have the judgment to know when to accept and when to reject. They use AI as a force multiplier.
• Junior engineers face a different challenge. AI can stunt their growth if they accept suggestions without understanding. I now require juniors to explain any AI-generated code before merging.
• Code reviews take longer. The reviewer cannot assume the author understands the code. Questions shift from "why did you do it this way?" to "do you understand what this does?"

The Uncomfortable Truth

AI-assisted coding is not universally good or bad. It is a tool with tradeoffs. Teams that pretend there are no tradeoffs accumulate debt invisibly until it cripples their velocity.

The best teams I work with have explicit policies: when to use AI assistance, what requires human authorship, how to review generated code. They treat AI as a powerful but dangerous tool—like a chainsaw, not a magic wand.

Speed without understanding is not productivity. It is debt with a variable interest rate.