"It costs too much" is one of the most common things I hear about accessible development. And the same thought-process applies in AI-generated code. An LLM defaults to what's easiest to write, and correcting for accessibility feels like extra work. Or at least that's what it's been trained to think.
But what if the opposite were true?
I ran the same prompts through the same model twice (once without any accessibility guidance in context and once with a lightweight accessibility skill loaded) and what I found confirms what accessibility advocates have been saying all along: it actually costs more and takes longer to ship inaccessible code.
In this talk, I'll walk through the experiments, the numbers, and the failure modes it exposed. We'll look at what the AI actually built without accessibility context (spoiler: lots of custom code for features the browser already handles), why the accessibility-aware run converged faster, and what this means for how we ship AI-assisted code.
You'll leave with the exact prompts and context patterns needed to make your model ship faster, more accessible UI on the first try, plus a data-backed argument for making accessibility a default in every AI-assisted workflow - not just because accessibility means that everyone can use the products we build - but because it's cheaper too.
This talk has been presented at AI Coding Summit NYC, check out the latest edition of this Tech Conference.
