What does a web developer’s workday look like now that AI can write code?

The most effective developers I’ve seen use AI as a force multiplier rather than an autopilot. They decide what should be built, break the work into small tasks, provide the relevant context, and review the result carefully. For a frontend feature, I might design the component structure and interaction myself, then use AI for boilerplate, test cases, documentation, or a review of accessibility and edge cases.

As you’re learning, don’t skip the fundamentals. Build parts manually first so you can recognize bad generated code. Then use AI in limited ways: ask for explanations, compare approaches, generate tests, or handle repetitive code. If you cannot explain what the generated code does, it is not ready to ship.

AI is useful for scaffolding, small components, repetitive CRUD code, and especially tests, but it usually needs strong direction. Frontend work involves accessibility, semantic HTML, responsive behavior, visual consistency, maintainability, and complicated interactions. AI can produce something that looks fine while duplicating code, choosing poor abstractions, or creating accessibility problems.

A common workflow is generate, inspect, test, refactor, and generate again. You can make the results much better by giving the tool project rules, existing component patterns, linting requirements, and accessibility feedback. But you still need to understand the code well enough to decide whether the proposed solution belongs in the application.

A normal workday is much broader than typing code. Developers spend time reading requirements, discussing designs, planning architecture, reviewing pull requests, debugging, testing, documenting decisions, handling releases, and communicating with clients or teammates. Depending on the team, actual new code might only take around a third of the day.

AI speeds up implementation, but it does not know the business context. It will not reliably understand which edge cases matter, whether a feature conflicts with an existing requirement, or why a seemingly harmless database change could break another service. Those decisions are still the developer’s responsibility.

Cursor making a homepage quickly does not mean it replaced five days of professional work. It can produce a convincing first draft, but production code has to survive real users and real failures: mobile browser issues, incorrect API responses, authorization mistakes, huge inputs, slow networks, migrations, monitoring, and future maintenance.

AI can also confidently invent APIs, change unrelated files, put secrets in unsafe locations, mishandle error states, or solve a bug by introducing another one. Asking it to explain or repair its own code can help, but it is not a guarantee—the model may simply rewrite the problem without identifying the underlying cause. The person responsible for the application still needs to diagnose and verify everything.