How AI Is Changing the Way Developers Write Code

A few years ago, AI coding assistants were a novelty. Today, they're a standard part of a professional developer's toolkit — not because they write perfect code, but because of where they genuinely save time.

Where AI tools actually help

• Boilerplate and repetitive code — CRUD endpoints, form validation, test scaffolding
• Explaining unfamiliar code — a fast way to understand a legacy codebase or an unfamiliar library
• Rubber-duck debugging — describing a bug out loud (to an AI) often surfaces the fix before you even get an answer
• First drafts — a starting point for a function or component that you then refine by hand

None of these replace understanding what the code does. They remove friction from the parts of development that were never really about problem-solving in the first place.

Where it still falls short

AI-generated code frequently looks correct while containing subtle bugs — an off-by-one error, a missed edge case, an insecure default. It also has no real understanding of your specific business logic or architecture decisions. Trusting it blindly, especially in production code, is how technical debt quietly piles up.

The developers getting the most value from AI tools treat them the way you'd treat a fast, occasionally overconfident junior developer: useful for a first pass, but everything gets reviewed before it ships.

What this means for the job

The bar for "can write code that runs" has dropped. The bar for "can design a system, make good architectural trade-offs, and know when generated code is wrong" hasn't moved — if anything, it matters more now, because it's the part AI can't reliably do for you.

In practice, AI tools have made me faster on the repetitive 20% of the job, which leaves more time for the 80% that actually requires experience: architecture, performance, security, and understanding what a client actually needs versus what they asked for.

A practical way to use AI responsibly

• Use it to generate a first draft, never a final answer
• Always read and understand every line before it ships, no exceptions
• Be extra skeptical around security-sensitive code — auth, permissions, input handling
• Use it to explain unfamiliar code, then verify that explanation against the actual source

Teams that adopt these habits tend to see real productivity gains. Teams that skip the review step tend to discover the cost later, usually in the form of a security incident or a very confusing bug report.

Where this is heading

AI tools will keep improving, and the boilerplate they handle well will keep expanding. That's a good thing — it frees experienced developers to spend more time on the parts of the job that were always the actual value: understanding a business problem deeply enough to build the right thing, not just a working thing.