You sit down to build a feature. You open Claude Code, write a detailed prompt, and watch as it generates hundreds of lines of seemingly perfect code in seconds. You should feel productive, right? Instead, you feel… drained.

You’re staring at a screen full of code you didn’t write, trying to understand someone else’s logic, hunting for the subtle bugs you know are probably hiding in there. Twenty minutes later, you’re still debugging an issue that would have taken you ten minutes to avoid if you’d just written it yourself. You close your laptop feeling more like a code reviewer at a pull request marathon than a developer who built something.

If this resonates with you, you’re not alone. What you’re experiencing has a name: AI Fatigue, or what some developers call the Reviewer’s Burden. It’s becoming one of the most common complaints about AI-assisted development, and it’s worth understanding why this productivity tool sometimes feels like it’s killing the very thing that made you love coding in the first place.

From Maker to Manager: The Identity Shift

Programming is a creative act. When you write code from scratch, you’re a Maker. You’re in the flow, building logic structures in your head, watching them come to life line by line. There’s a puzzle-solving dopamine loop that happens when you figure out an elegant solution to a complex problem. This is why most of us became developers.

When you rely heavily on AI coding tools (especially agentic ones like Claude Code or Cursor), something shifts. You become a Manager. Your day transforms into writing instructions, clarifying requirements, and then reviewing someone else’s work. You’re still involved in the creation process, but you’re no longer the one creating.

Here’s the thing: most developers consider code review to be one of the more tedious parts of their job. It’s necessary, it’s valuable, but it’s rarely the part you look forward to. AI-assisted coding forces you into code review mode constantly. Every function, every component, every module becomes a pull request from a colleague you can’t quite trust. The creative joy gets replaced with suspicious vigilance.

The Cognitive Asymmetry Problem

There’s a famous saying in software engineering: “Code is harder to read than it is to write.” This isn’t just developer laziness talking. It’s a real truth about how we process information.

When you write code yourself, you build the mental context as you go. You know why variable x exists, why you chose that particular data structure, what edge case you’re handling in that weird-looking conditional. The code isn’t just syntax on a screen. It’s a crystallization of your thought process.

When Claude generates that code, all that context is missing. You have to reverse-engineer its logic to verify correctness. You’re doing code archaeology on fresh code, which is as absurd as it sounds. What pattern is it using here? Why did it structure the function this way? Is this handling the edge case I mentioned, or did it miss it?

This reverse-engineering is pure cognitive overhead. It’s mental work that generates no value beyond verification. And because AI is prone to hallucinations and subtle logic errors, you can’t afford to skim. You have to maintain a state of hypervigilance, carefully checking every line. This constant suspicion is exhausting in a way that trusting your own logic never is.

The Uncanny Valley of Code

AI-generated code often falls into what we might call the “uncanny valley of software development.” It looks almost perfect. The indentation is flawless, the naming conventions are reasonable, the structure seems sound. And that’s exactly what makes it dangerous.

Finding a missing semicolon is easy. Your IDE screams at you, the error message is clear, the fix is obvious. But finding a logic error where the AI misunderstood your business rule but wrote syntactically perfect code? That’s incredibly draining. The code compiles. The tests might even pass. But somewhere in there, a subtle assumption is wrong, and you won’t discover it until you trace through the entire execution path.

I’ve watched developers spend thirty minutes debugging an AI’s interpretation of “update the user’s preferences” because the AI created a new preferences object instead of updating the existing one. The code was beautiful. It was wrong. And finding that wrongness required the developer to fully understand code they didn’t write, which meant they might as well have written it themselves in the first place.

The Interruption Tax

Flow state is that magical condition where time disappears and you’re completely immersed in the problem. It’s where the best code gets written. It’s also where programming stops feeling like work and starts feeling like play.

AI-assisted coding is flow state’s natural enemy. You’re constantly context-switching between your editor, your terminal, and the chat interface. You’re interrupting your own thought process to explain to Claude what you want. And sometimes, trying to describe a complex abstract concept in English to an AI feels harder than just expressing it directly in code.

When you’re in flow, you think in code. You see the solution in Python or JavaScript, and your fingers translate that vision directly to the keyboard. When you use AI, you have to translate that vision into a prompt, wait for the AI to translate it back into code, then verify that the round-trip translation preserved your intent. Each translation is a tax on your mental energy and an interruption to your flow.

Reclaiming the Craft

None of this means AI coding tools are bad or that you should delete them. It means you need to be intentional about how you use them. The goal isn’t to maximize AI usage. It’s to maximize your own effectiveness and satisfaction.

Code the core, generate the chore. Keep the interesting stuff for yourself. Write the complex business logic, the clever algorithms, the creative architecture. That’s the fun part, the part that keeps you sharp. Use Claude for the tedious stuff you’d rather not think about: writing boilerplate, generating unit tests, creating TypeScript interfaces, formatting regex patterns, scaffolding CSS layouts. Let the AI handle what one developer called “the digital equivalent of washing dishes.”

Treat it like a junior developer. You wouldn’t ask a junior intern to architect your core feature on their first day. You’d have them handle well-defined, bounded tasks where mistakes are cheap. Apply the same principle to AI. Use it to explain error messages, suggest libraries, generate documentation, or write first-draft code you plan to heavily refactor. Keep the architectural keyboard for yourself.

Build AI-free zones. Designate specific times or tasks where you turn the AI off completely. Maybe it’s Friday afternoons, or maybe it’s whenever you start a new feature. Give your brain a chance to remember what it feels like to build something from scratch, to solve a problem without a copilot. You might be surprised how much you missed it.

The key is recognizing that writing code and prompting an AI to write code are different activities. One is synthesis, the other is review. One is creation, the other is verification. Both have value, but they produce very different feelings. If you find yourself energized by the former and exhausted by the latter, you’re not broken. You’re a developer who loves development, and that’s exactly as it should be.

A Note for People Learning to Code

There’s something else worth mentioning here. If experienced developers find AI-assisted coding draining because it robs them of the creative joy and forces them into management mode, what does that mean for beginners?

When you’re learning, you’re not just building software. You’re building your ability to think in code. You’re developing the mental models that let you see solutions, the pattern recognition that helps you spot bugs, the intuition that guides your architectural decisions. These skills don’t come from reading code. They come from writing it, from struggling with it, from making mistakes and fixing them.

If you lean too heavily on AI during the learning phase, you risk becoming fluent in prompt engineering before you’re fluent in programming. You might be able to produce working code, but when that code breaks in a way the AI doesn’t expect, you won’t have the foundation to fix it yourself. You’ll be a manager who can’t do the work they’re managing.

Wrapping Up

You’re not crazy for finding AI-assisted coding exhausting. Writing code is a craft, a creative act that engages your problem-solving mind in a deeply satisfying way. Prompting is a management task that requires a different kind of thinking. It’s natural to find one energizing and the other draining.

The developers who seem happiest with AI tools are the ones who’ve found their own balance. They use AI for leverage, not as a replacement. They keep the interesting problems for themselves and delegate the tedious ones. They remember that the goal isn’t to write less code. It’s to build better software while maintaining the joy that made them developers in the first place.

Because at the end of the day, if the tools meant to make us more productive are making us miserable, something has gone wrong. The craft matters. The joy matters. And finding a way to work that preserves both is worth more than any productivity metric.