A post on Dev.to this week got 132 reactions and 109 comments: “AI Is Creating a New Kind of Tech Debt, And Nobody Is Talking About It.” The author argued that AI-generated code looks clean, passes tests, and ships fast, but creates a hidden liability that traditional tech debt metrics do not capture. The post resonated because developers are seeing this in their own codebases and do not have a name for it yet.
I do: token dependency. Your codebase runs on tokens, not developers. And that changes the economics, risks, and maintenance model of software in ways the industry has not fully reckoned with.
What Token Dependency Looks Like
Traditional tech debt accumulates when developers take shortcuts: skipping tests, using quick hacks instead of proper abstractions, copy-pasting instead of refactoring. The developer who wrote the code usually understands what it does, even if they know it is not ideal. The debt is in the quality, not the understanding.
Token dependency is different. The code is often clean. It follows conventions. It passes linting. It might even have tests. But the developer who “wrote” it, meaning the developer who prompted the AI to generate it, does not fully understand how it works. They understand what it does at a high level. They verified the output. But they could not rewrite it from scratch, explain the edge cases it handles, or predict how it will behave when a dependency changes.
The understanding lives in the AI model that generated the code, and that model is accessed through tokens. When you need to debug, extend, or modify that code, you do not reach for your own understanding. You reach for the AI again. You paste the code back in, explain the problem, and ask for a fix. The AI becomes the maintainer, not just the author.
This works. Until it does not.
When Token Dependency Breaks
Token dependency creates fragility in scenarios that traditional development handles naturally:
Model changes break context. AI models get updated, deprecated, or replaced. The Claude that generated your authentication handler in January might reason differently than the Claude that debugs it in June. Subtle changes in how the model interprets your code can lead to debugging sessions where the AI contradicts its own earlier work. You lose the continuity that a human developer’s memory provides.
Context windows have limits. Even with million-token context windows, complex debugging sessions require loading the right context. If the developer does not understand which files are relevant to a bug, they cannot construct an effective prompt. The AI is only as good as the context it receives, and curating that context requires understanding the developer may not have.
Costs compound invisibly. Every debugging session, every modification, every code review that requires AI assistance is a token cost. For a single developer working on a small project, this is negligible. For an agency maintaining dozens of AI-generated plugins across multiple clients, the ongoing token cost of maintaining code that nobody on the team fully understands becomes a real line item. And unlike human developers, token costs scale linearly with the amount of code that needs attention.
Knowledge transfer fails. When a developer leaves a team, they normally transfer knowledge through documentation, code reviews, and conversations. When the “developer” was an AI, the knowledge transfer is a prompt history and a hope that the next person can reconstruct the context. Onboarding a new developer onto an AI-generated codebase is harder than onboarding them onto human-written code, because the code does not carry the author’s intent in the same way.
The WordPress Plugin Problem
This hits the WordPress ecosystem particularly hard because of how plugins are developed and maintained.
A typical WordPress plugin lifecycle involves an initial development sprint, followed by years of maintenance: compatibility updates for new WordPress versions, security patches, feature additions based on user requests, and bug fixes. The initial sprint is where AI excels. The maintenance phase is where token dependency becomes expensive.
When WordPress 7.0 ships with breaking changes to the block editor API, every plugin that uses block editor features needs updating. A developer who wrote their block registration code by hand can read the migration guide and update their code because they understand what their code does and why it does it that way. A developer who AI-generated their block registration code has to start from scratch: paste the old code into an AI, paste the migration guide, and hope the AI produces a correct update. If the AI gets it wrong, debugging requires understanding that the developer never built.
Multiply this across a commercial plugin with 50 files, several hundred hooks, and thousands of users expecting a timely update. The token cost of maintaining that plugin through a major WordPress version change is significant, and the risk of introducing bugs during AI-assisted updates is higher than with human-maintained code because the human in the loop does not serve as an effective safety net for code they do not understand.
The Understanding Gap Is the Real Cost
The token bill is not the expensive part. The understanding gap is. When nobody on your team genuinely understands how a critical piece of code works, you have outsourced your technical capability to an API. Your ability to make quick decisions about that code, to triage bugs under pressure, to evaluate whether a proposed change is safe, depends on the availability and quality of an external service.
This is a fundamentally different risk profile than traditional tech debt. Traditional tech debt slows you down. Token dependency makes you dependent. The code works until the day you need to understand it, and that day always comes.
For agencies, this creates a business risk that is hard to see in the metrics. Projects delivered faster with AI-generated code look like productivity gains. But the maintenance phase reveals the hidden cost: every modification requires re-engaging the AI, every debugging session is a context reconstruction exercise, and the team’s capability to respond to emergencies is limited by their ability to prompt effectively under pressure.
How to Build Without Creating Token Dependency
The solution is not to stop using AI. The productivity gains are too significant to abandon. The solution is to use AI in a way that builds understanding alongside the code.
Understand before you ship. When AI generates code, read it line by line before committing. Not just to verify it works, but to ensure you could explain every decision to another developer. If you cannot explain why the code uses a specific approach, you do not understand it well enough to maintain it.
Write the critical paths yourself. Let AI handle boilerplate, scaffolding, and repetitive code. But for the code that represents your core business logic, authentication, payment processing, data handling, write it yourself with AI as an assistant rather than an author. The difference is subtle but important: AI suggests, you decide and implement.
Document the intent, not just the implementation. Traditional code comments describe what the code does. For AI-assisted codebases, document why the code exists and what business problem it solves. When you need to modify the code later, the intent documentation tells you what to preserve and what can change, even if you need AI to help with the implementation details.
Test as your safety net. Comprehensive tests are the best insurance against understanding gaps. When you have tests that verify the code’s behavior across edge cases, you can modify the implementation with confidence even if you do not fully understand the current code. The tests tell you whether the modification broke something. Invest the time AI saved you in writing better tests.
Build team understanding through code review. Require that AI-generated code goes through the same review process as human-written code. The reviewer’s job is not just to verify correctness but to ensure at least two people on the team understand how the code works. If the reviewer cannot understand the code, it should not be merged regardless of whether it passes tests.
The New Definition of Technical Capability
Technical capability used to mean: can your team write this code? Now it means: does your team understand this code well enough to maintain it when the AI that wrote it is no longer available, has changed, or produces different results?
That is a harder bar to clear than it sounds. It requires discipline to slow down and understand code that AI could generate in seconds. It requires resisting the temptation to ship fast and figure it out later. It requires investing in documentation, tests, and code review that feel redundant when the code already works.
But the alternative, a codebase that runs on tokens instead of understanding, is not a productivity win. It is a liability that grows with every line of code your team does not genuinely own. The tech debt is invisible in the code. It lives in the gap between what your software does and what your team understands about how it does it.
The developers and teams that recognize this early and build practices to prevent token dependency will maintain a real competitive advantage. Not because they use less AI, but because they use AI without becoming dependent on it. They ship fast and understand deeply. That is the combination that wins.
Last modified: March 25, 2026
