Your AI Coding Agent Isn't a Tech Debt Machine.

Arun Rao
2 hours ago
6 min read

You're Just Using It Wrong.

The concerns about AI-generated code maintainability echo every major abstraction shift in computing history. Here's why that's a feature, not a bug — and how to close the loop with proper verification.

Somewhere in your organization right now, a senior developer is looking at a block of AI-generated code and saying some version of: "I don't fully understand what this does. We can't ship this."

It's a reasonable-sounding concern. It's also, historically speaking, wrong every single time it has been said — and it has been said at every major abstraction shift in computing.

This article is for the engineers who are trying to figure out where to draw the line, and for the product managers who need to make the business case for AI tooling despite a skeptical team. The short version: the skeptics aren't wrong about the risks. They're wrong about the solution.

We've Been Here Before

In the late 1980s and early 1990s, the industry was transitioning from assembly language to third-generation languages like C and eventually C++. The complaints were immediate and familiar: developers writing C don't understand what machine code the compiler generates. You lose control over the stack. How do you debug something you didn't write? The code might work but it won't be maintainable.

Then came the ORM era. When Hibernate and SQLAlchemy abstracted raw SQL, the same chorus: "developers won't know what queries are running under the hood. You'll get N+1 problems everywhere. You've lost the ability to reason about performance." Those concerns weren't wrong — they described real edge cases. But they were describing the learning curve of a new abstraction layer, not a fundamental flaw in the technology.

We are in year two of the AI-coding abstraction curve. The developers who internalize the new mental model now will be the force multipliers of 2027.

What the Data Actually Shows

The productivity gains are real, and they're well-documented. A 2022 controlled study by GitHub found that developers using Copilot completed a representative task 55% faster than those who didn't.[1] A more recent 2024 study by Cui et al., covering real-world deployments at Microsoft and Accenture, found a 26.08% increase in completed tasks for developers with Copilot access versus the control group, measured by pull requests, commits, and builds.[2]

Stack Overflow's 2024 Developer Survey found that 63% of professional developers currently use AI in their development process, with another 14% planning to start soon.[3] The number one benefit they're chasing? Increased productivity — not magic, just faster iteration.

But the maintainability critics aren't making things up either. GitClear's 2025 analysis of 211 million changed lines of code from 2020–2024 — including code from Google, Microsoft, and Meta repos — found that code duplication has spiked dramatically as AI adoption increased. The percentage of changed lines associated with refactoring dropped from 25% in 2021 to less than 10% in 2024. Copy-pasted code rose from 8.3% to 12.3% over the same period.[5] Code churn — lines reverted or updated within two weeks of being written — is on track to double compared to its pre-AI baseline.[5]

The maintainability critics are describing a real pattern. But they're diagnosing the wrong problem. The issue isn't AI-generated code — it's AI-generated code without a verification layer.

These trends are a direct consequence of teams using AI as a line-generator without pairing it with proper review and testing discipline. The tool isn't the problem. The workflow is.

The Comprehension Trap — and Its Actual Fix

There's a concern that surfaces consistently in engineering teams adopting AI coding tools: developers can produce large amounts of working code quickly, but may not have deep familiarity with every implementation detail the agent produced. This gets framed as a maintainability risk.

It's worth examining what's actually being claimed here. A team produced working, functional code faster than before — and the concern is that they haven't personally read every line? The more important question is: how did that team verify code quality before AI? In most cases, the answer was "a senior developer read it and felt good about it." That is not a more rigorous standard. It's a more familiar one.

The correct response to "we don't have full comprehension of this code" is not to slow down and read every line. It is to make the code prove itself.

Here's why this works: when you instruct your AI coding agent to generate comprehensive test cases alongside the implementation — unit tests, integration tests, edge cases, boundary conditions — you are creating an executable specification of what the code is supposed to do. You don't need to understand every implementation detail if you can verify behavior exhaustively. That is a more rigorous form of assurance than a developer nodding through a code review.

That 34-percentage-point confidence gap tells the story cleanly. AI-generated tests, when paired with AI-generated code, close the loop that critics are worried about.

The Verification Stack: Closing the Loop

The workflow that resolves the maintainability debate isn't complicated. It just requires treating AI as the generation layer while preserving human ownership of the verification layer. Here's what that looks like in practice:

Fig 4: AI Assisted Development Verification Stack

Google has reported that roughly 25% of new code written at the company is now AI-generated.[7] If Google's engineering culture, which is notoriously rigorous about quality and code review, can integrate AI generation at that scale, the argument that AI code is inherently unmaintainable starts to look like a workflow problem, not a technology problem.

Reframing the Concerns

Most of the legitimate concerns about AI coding tools dissolve when you map them to their correct solution. The following table captures the most common objections and what actually addresses them:

What This Means If You're a PM

If you're a product manager watching your engineering team debate whether to trust AI-generated code, here's your framing: the question is not whether the code is trustworthy. The question is whether your team has a verification layer that can answer that question systematically.

Teams that pair AI generation with automated test suites and static analysis are seeing quality improvements - Qodo's 2025 research found 70% of developers who saw "considerable" productivity gains also reported higher code quality — a 3.5x increase over teams that didn't see productivity improvements.[6] The productivity and quality gains are correlated. Teams that generate fast and verify rigorously outperform teams that either generate slowly or generate fast without testing.

The business case isn't "AI makes developers faster." It's "AI with a proper verification workflow produces better-tested, faster-shipped software than the previous approach."

The Bottom Line

The concern that AI coding agents create unmaintainable code is the same concern that was raised about every abstraction layer that came before - C compilers, ORMs, cloud infrastructure, auto-generated API clients. In every case, the industry adapted by building better verification tooling and raising the level at which engineers reason about systems. The net result was always the same: higher productivity, broader access to engineering capability, and a new baseline expectation for what a "senior" engineer understands.

We are in the early innings of that same transition. The engineers who will define the next decade are not the ones who insist on reading every line. They're the ones who have internalized a new mental model:

Own the architecture, specify the constraints, verify the behavior.

Let the AI handle the implementation.

The tools are not flying the plane. You are. The question is whether you trust your instruments.

References

GitHub / Microsoft (2022). "Research: Quantifying GitHub Copilot's Impact on Developer Productivity and Happiness." Developers using Copilot completed a representative coding task 55% faster. Via Visual Studio Magazine.
Cui et al. (2024). Real-workplace study at Microsoft and Accenture, showing a 26.08% increase in completed tasks (measured by pull requests, commits, and builds) for Copilot users versus control group. Via arXiv preprint, 2025.
Stack Overflow (2024). Developer Survey: 63% of professional developers currently use AI in their development process; 14% plan to start soon. survey.stackoverflow.co/2024.
Microsoft Research. Teams take approximately 11 weeks to fully realize the satisfaction and productivity gains of AI coding tools. Via GitHub Resources.
GitClear (2025). "AI Copilot Code Quality: 2025 Data." Analysis of 211 million changed lines of code (2020–2024) across repos including Google, Microsoft, and Meta. Refactoring share fell from 25% to <10%; copy/paste code rose from 8.3% to 12.3%; code churn projected to double vs. 2021 baseline. gitclear.com.
Qodo (2025). "State of AI Code Quality." Developer confidence in test suites: 27% without AI-generated tests vs. 61% with AI-generated tests. 59% say AI improved code quality overall; 81% among teams using AI for code review. 70% of high-productivity teams also saw quality gains (3.5x over low-productivity teams). qodo.ai.
Google, as cited by Qodo (2025): approximately 25% of new code at Google is AI-generated. qodo.ai/blog.