Can AI Fix Your Technical Debt? An Honest Assessment
TL;DR: AI can meaningfully accelerate technical debt resolution, but only for specific categories of work, and only with human oversight. The right framing is not “can AI fix your technical debt” but “can AI do the repetitive parts so engineers can focus on the hard parts.”
Why engineers are asking this question right now
Technical debt is expensive. AI can write code. The logical conclusion, that AI should be able to eliminate technical debt, is intuitive but wrong in ways worth understanding.
The question matters because a lot of money is flowing toward AI-based code tools, and engineering leaders are being asked to evaluate them. The claims range from credible to reckless. “AI can handle your migrations” is a category that deserves scrutiny.
Here’s an honest answer.
What AI is genuinely good at in this context
Repetitive, pattern-based transformations
The best-defined category of technical debt work is structural transformation: update this import statement, replace this deprecated API call, reformat this block to match the new convention. This work is mechanical, high-volume, and well-specified.
AI, particularly when paired with static analysis tools like AST parsers, handles this category well. The transformation rule is clear, the change is deterministic, and the output can be validated by a compiler and a test suite.
Java version migrations are a strong example. Moving from java.security.acl to the modern replacement, updating javax to jakarta namespaces for Spring Boot 3, removing deprecated method calls, these are exactly the patterns that automated transformation handles reliably.
Identifying debt you didn’t know existed
AI models trained on large codebases can surface patterns that manual review misses: dead code, duplicated logic, inconsistent error handling across similar functions, dependencies that are imported but unused. This audit function, cataloguing the debt rather than resolving it, is something AI does well today.
Generating boilerplate for replacements
When a deprecated pattern needs to be replaced with a modern one, an AI can often generate the replacement and submit it for human review. This is faster than writing the replacement from scratch, even accounting for review time.
What AI is not good at in this context
Making judgment calls about your system
AI doesn’t understand your system. It understands code patterns in aggregate. When a migration involves deciding whether to retain a particular abstraction, consolidate two legacy services, or accept a behavior change, that’s a judgment call that requires knowing why the original code was written the way it was. AI can provide input but shouldn’t decide.
High-confidence transformations without validation
An AI that writes code and auto-merges without human review is a production incident waiting to happen. This isn’t a hypothetical concern; it’s a well-documented failure mode of early AI code automation tools. The transformation can be syntactically correct and semantically wrong in ways that only surface in production.
The answer is not to remove human review. The answer is to reduce how long human review takes. Well-structured automated PRs (one change per PR, clear description of what changed and why, automated test results attached) make human review fast. Opaque, multi-file AI-generated changes make it dangerous.
Debt that requires architectural decisions
Some technical debt isn’t structural; it’s architectural. A codebase that evolved from a monolith to a set of poorly-bounded microservices has debt that can’t be fixed by transforming code. It requires decisions about service boundaries, data ownership, and API contracts. AI can inform those decisions but can’t make them.
The hybrid model that actually works
The most effective approach to AI-assisted debt reduction combines two things:
AST-based transformation for structural changes. Abstract Syntax Tree analysis provides deterministic, compiler-verified transformations for well-defined migration patterns. This is more reliable than pure LLM generation for mechanical changes because it operates on the parse tree rather than generating code from statistical patterns.
LLM assistance for context-dependent changes. Where the transformation isn’t mechanical, where it requires understanding what a code block is trying to do, an LLM can propose a replacement that a human then reviews and accepts, rejects, or modifies.
The key constraint is this: every change should be a PR, and every PR should be human-reviewed before it merges. This isn’t a limitation on automation; it’s what makes automation safe enough to run at scale.
What to ask when evaluating AI code tools
If you’re evaluating tools that claim to address technical debt with AI, the questions that separate credible from reckless:
Does it auto-merge? If yes, reconsider. No automated tool should merge to production without human sign-off. This is non-negotiable for any codebase that handles real user data.
How does it validate changes? “The AI checked it” is not validation. Look for tools that run your existing CI pipeline against the proposed changes, surface test failures, and only generate PRs when the changes pass automated checks.
What categories of debt does it actually address? Tools that claim to fix “any technical debt” are overstating. Tools that specify they handle Java version migrations, dependency upgrades, security patches, and code standardization (and can show you examples of the transformations they run) are making claims you can verify.
How does it handle failures? Automated transformation will sometimes produce incorrect output. What does the tool do when that happens? Does it surface the failure clearly, or does it quietly skip the problematic file?
The honest summary
AI can fix a meaningful portion of your technical debt, specifically the high-volume, pattern-based, structural portion. That’s not nothing; it’s often the majority of the mechanical migration work that’s been sitting on the backlog for years.
AI cannot fix the debt that requires architectural judgment, system knowledge, or decisions about your business. That debt still requires engineers.
The engineering leaders who get the most value from AI in this space are the ones who treat it as a force multiplier on their engineers’ time, not a replacement for engineering judgment. Run the automation, generate the PRs, put them in the review queue. Let engineers focus on the changes that actually require them.
Frequently asked questions
Can GitHub Copilot fix technical debt?
Copilot is a code completion tool, useful for writing new code faster, not designed for running systematic transformations across an existing codebase. It will suggest completions as you manually edit files; it won’t autonomously migrate your Java 8 repositories to Java 21.
What about using ChatGPT or Claude to rewrite legacy code?
You can paste a file into a chat interface and ask for a rewrite, but this doesn’t scale. It requires manually processing every file, reviewing outputs without the benefit of your CI pipeline, and integrating changes without automated testing. For a handful of files, fine. For an enterprise codebase, not a real solution.
Is “agentic” AI code automation safe?
Agentic AI that operates autonomously across a codebase without human checkpoints carries significant risk. The safety profile depends entirely on whether there are meaningful review gates before changes hit production. An agentic system that proposes changes for human review is different from one that makes changes autonomously.
Kaydence uses a hybrid AST + LLM approach to automate technical debt resolution at scale. Every transformation runs against your existing CI pipeline. Every change ships as a PR. No auto-merge, ever. See how it works >
