The interesting question about AI in web development is no longer whether the tools work.
They do, well enough that most developers now use them daily without remarking on it.
The interesting question is which parts of the job they are actually good at, because the answer is narrower than the marketing suggests and wider than the sceptics allow.
Broadly: these tools are strong at producing a first draft of something well-understood, and weak at deciding what should exist. That distinction predicts most of what follows.
The Rise of AI in Web Development: Transforming the Future of Websites
Where it genuinely helps
- Boilerplate and scaffolding. Component structure, configuration files, test harnesses, repetitive markup. Well-defined work has a conventional shape and is tedious to type.
- Explaining unfamiliar code. Inheriting a codebase and asking what a function does is a legitimately good use, and considerably faster than reading it cold.
- First-pass review. Catching obvious errors, unhandled cases and inconsistencies before a human reviewer spends attention on them. Not a replacement for review, but a useful filter ahead of it.
- Accessibility checks. Flagging missing alternative text, poor heading structure and unlabelled form fields. The same limitation applies as with any automated accessibility tool: it catches a portion of issues and cannot tell you whether a task can be completed.
- Content drafting. Meta descriptions, alternative text suggestions, first drafts of documentation. All requiring review, all faster than starting from nothing.
Where it goes wrong
- Confident errors. Generated code that looks correct, uses a real-looking API and does not work, or works in a way that fails at the edges. This is the failure mode that costs the most time, because plausible wrong answers take longer to debug than obvious ones.
- Outdated patterns. Training data reflects what was common, which is not the same as what is current. Generated code frequently reaches for approaches the platform has since made unnecessary.
- Weight. Left unchecked, these tools reach for a library where a few lines would do, because that pattern is heavily represented in what they learned from. If you are working to a page weight budget, generated code needs checking against it specifically.
- Accessibility that looks right. A generated component with ARIA attributes applied incorrectly can be worse than one with none, because it misinforms assistive technology rather than simply leaving it to defaults.
- Security. Generated code is not reviewed code. Authentication, data handling and anything touching user input deserve human scrutiny regardless of how confident the output appears.
- The judgement it cannot supply
These tools do not know which feature is generating your support tickets, which step your customers abandon, or which wording confuses your particular audience.
That knowledge comes from your analytics, your support inbox and watching people use the thing. No amount of generation substitutes for it, and a fast first draft of the wrong feature is not progress.
Treat output as a draft to evaluate rather than a decision to accept.
Using it responsibly
Review everything that reaches production, particularly anything touching authentication, personal data or payment.
Do not paste client code, credentials or personal data into tools without checking where that data goes and what happens to it. Confirm your obligations with a qualified data-protection adviser rather than assuming.
Be honest internally about what was generated, because the next developer maintaining it deserves to know which parts were reviewed carefully and which were accepted quickly.
The energy question, carefully
Running these models consumes energy, and figures circulating about the cost per query vary enormously and are frequently unsourced. Precise-sounding numbers in this area deserve scepticism, including favourable ones.
What can be said is more modest. Where performance and environmental impact both apply, treat them as the same lever: reducing unnecessary data transfer and processing supports a faster experience while lowering estimated digital carbon, though those figures remain modelled rather than directly measured. If generated code ships heavier pages than hand-written code would, that is a real cost you can measure on your own site, whatever the model’s own footprint.
The W3C Web Sustainability Guidelines remain the reference for the delivery side.
Our sustainable web development work uses these tools where they save time and measures what actually ships.