The Role of AI in Modern Web Development

The interesting question about AI in web development is no longer whether it writes usable code. It does. The question is which parts of the job it actually shortens, and which parts it quietly makes longer.

Where It Genuinely Helps

The biggest wins are in work that is well specified and tedious: migrations, test scaffolding, type definitions, the fourth CRUD endpoint that looks like the previous three. Anything with a clear shape and a verifiable result.

It is also unreasonably good at the first ten minutes of an unfamiliar codebase — answering where something lives and how the pieces connect, faster than reading the files yourself.

Where It Costs You Time

Confident wrong answers are expensive. A subtly incorrect implementation takes longer to debug than an empty file, because it looks finished and reads plausibly. Review effort scales with how convincing the output is, not how correct it is.

Architecture is the other weak spot. Models optimise for the code in front of them, not for the decision you will regret in eight months.

Working With It Well

Keep the Feedback Loop Tight

Types, tests, and a linter turn a plausible answer into a checked one. The teams getting the most out of these tools are the ones that invested in verification first.

Stay the Author

Generated code you have not read is code nobody on the team understands. Treat every suggestion the way you would a pull request from a fast, well-read contributor who has never seen your product.