There are more AI-powered developer tools launching every week than most people can track. Some of them are genuinely useful. Some are autocomplete with a GPT badge and a $20/month price tag.
This post is about how to tell the difference — and where AI actually earns its place in a development workflow.
The hype vs the reality
The marketing version: "AI writes your code for you."
The actual experience: AI writes a draft of code that you still need to read, evaluate, test, debug, and maintain. If you can't assess the quality of what it generates, you're not going faster — you're accumulating invisible technical debt.
This isn't an argument against AI tools. It's an argument for using them with accurate expectations.
Where AI is genuinely useful in dev workflows
After using these tools seriously for the past couple of years, the pattern that holds up is this: AI is good at translation and drafting. It's not good at understanding and deciding.
Concrete examples where AI earns its place:
- Boilerplate — repetitive, predictable code (CRUD endpoints, form validation, test stubs)
- Explanation — "what does this unfamiliar code do?" is a great AI prompt
- Translation — turning intent ("I want to paginate this query") into a working starting point
- Summarization — condensing logs, diffs, commit history into human-readable language
- First drafts of tests — the structure is usually right, the edge cases need your judgment
The common thread: you know what good looks like, and AI handles the mechanical part of getting there.
Where AI fails (and people don't say it enough)
- Deep codebase knowledge — AI has no idea how your specific system is wired together
- Architectural decisions — AI can propose patterns, but can't weigh long-term consequences for your context
- Security-sensitive code — generated code often looks correct and isn't. Don't skip review here.
- Tasks where "probably right" isn't enough — financial logic, data migrations, auth flows
The failure mode isn't dramatic. It's subtle: code that works in tests, passes review, and causes a production incident six months later because the AI pattern didn't account for your specific edge case.
The right mental model
The best AI dev tools reduce friction on tasks you already understand, freeing your attention for the parts that require actual judgment.
Not: "AI will do this for me."
But: "AI will handle the mechanical part so I can focus on the interesting part."
When a tool is designed around this — augmenting capability rather than replacing judgment — it's usually worth using. When it's designed to skip understanding entirely, it's usually not.
A concrete example: generating changelogs
I built WhatShipped to generate changelogs from git history. It's a small but illustrative case.
The AI doesn't decide what matters. You do. It reads the diff, groups commits by type, and translates technical messages into user-facing language. You get a structured draft in about a minute.
You still review. You still edit. But the blank-page problem — staring at 40 commits trying to figure out what actually changed for users — is gone.
That's the pattern: AI eliminates the mechanical friction, human judgment handles the rest.
The question to ask about any AI dev tool
Before paying for or building around an AI tool, ask:
"Does this make me faster at things I understand, or does it let me skip understanding things?"
If it's the former, it's probably worth it. If it's the latter, the speed is an illusion — and the debt will surface eventually.
The best AI dev tools don't make you a better developer by doing the work for you. They make you a more effective one by removing the parts of the work that don't require you.
That's a meaningful distinction. Worth keeping in mind as the space gets more crowded.
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