AI for Developers: What to Understand Before You Sign Off on the Work

A client asks your freelance developer for a quote on a new feature, and they mention they'll lean on AI for parts of it. A candidate lists "AI-assisted development" on a resume. Your ops manager mentions the contractor "used Claude for most of that script." None of this requires you to write a line of code yourself, but all of it puts you in a position where you're expected to have a view on AI for developers, without ever having used one of these tools. That gap between what you're asked to approve and what you actually understand is the real problem, and it's worth closing before the next invoice or hiring decision, not during it.

What actually changed for developers

The tools people mean when they say "AI for developers" are things like GitHub Copilot, Cursor, and Claude Code. In practice, they suggest the next few lines of code as a developer types, or write a whole function or small script from a plain description, or read through an existing codebase and explain what a section does. They don't replace a developer's judgment about what the software should do or how it should behave when something goes wrong. What they change is pace, and the kind of mistake that shows up. A developer using these tools well ships faster on routine work. A developer using them carelessly ships code that runs on the first try and fails in a way nobody predicted, because nobody, human or AI, actually checked it against the rest of the system.

That distinction, careful use versus careless use, is invisible from the outside. You can't tell which one you're getting by looking at a demo or a quote. You can only tell by what you ask.

The question that matters more than "did AI write it"

Most owners who worry about AI for developers ask the wrong first question: did they use AI. The better question is what got checked afterward. A developer who writes a feature by hand and never tests it against real data is a bigger risk than one who used AI for the first draft and then verified it carefully. The tool isn't the variable that predicts quality. The review step is.

So instead of asking "are you using AI," ask: "what did you test this against before calling it done, and who looked at it besides the person who wrote it." A developer or agency with a real process will have a specific answer, not a vague reassurance. A small e-commerce owner learned this the expensive way after a contractor used an AI tool to add a discount-code feature, tested it once with a made-up code, and shipped it. It worked in the demo and quietly let two real codes stack together for a week before a customer noticed the total was wrong. The AI wasn't the failure. Nobody had tested the interaction between two features that already existed.

Judging speed claims without knowing the codebase yourself

You will hear that AI makes a developer two or three times faster, and there's truth to that for a lot of routine work: boilerplate, repetitive edits, first drafts of a form or a report. But speed claims are easy to make and hard to verify from where you sit, especially for AI for developers specifically, where the productivity gains vary enormously by the kind of task.

A more useful approach is to ask for checkpoints instead of a final promise. Break a project into pieces small enough that you see something working every few days rather than waiting three weeks for one big reveal. This isn't about you evaluating code quality, which you likely can't do. It's about seeing whether the pace holds up across the whole project or only in the parts that were easy to demo early. If a developer resists breaking work into visible checkpoints, that resistance is worth more information than any speed claim they've made.

Where the real risk sits

The risk that gets the least attention is code nobody, including the person who "wrote" it, fully understands six months later. AI-generated code that works today can be harder to maintain than hand-written code, because it was assembled quickly rather than reasoned through, and the person who approved it may not remember why a particular piece exists. If your business depends on that script or that internal tool continuing to work, ask who could fix it if the original developer left tomorrow. If the honest answer is "nobody, really," that's a liability you're carrying without knowing it.

A second, quieter risk: developers sometimes paste real customer data, an email list, order records, internal financials, into an AI tool while asking it to help debug something. Most of these tools have settings that keep that data private, but the default isn't always private, and a developer moving fast doesn't always check. If your business touches customer information at all, it's worth asking directly whether real data ever goes into these tools, and under what settings.

What this doesn't require from you

None of this requires learning to code or becoming the kind of person who reviews pull requests. It requires a short list of questions you ask before approving a quote or signing off on finished work: what got tested, who reviewed it besides the person who built it, whether real customer data touched any AI tool along the way, and who could maintain this if the original developer disappeared tomorrow. Those four questions catch most of the actual risk in AI for developers without requiring any technical background at all.

Getting this built into how you already work with developers

Asking good questions on one project is a start. Making it a habit, so every contractor, freelancer, or in-house hire gets the same short checklist before their work ships, is what actually protects a business over time rather than just once. If you'd rather have this set up properly, with the right checkpoints and questions built into your process from the start instead of pieced together after something goes wrong, that's exactly the kind of system we build.