AI is eating Software (?)

4% of commits on GitHub generated by Claude Code (and growing fast)

Developers & AI

Web Standards and Front-End Development

• Technologies that enable front-end development (CSS, JS APIs, packaging)
• Guidance on how to deploy front-end code:
  • Accessibility
  • Internationalization
  • Sustainability
  • Security & Privacy
• Adoption support: validators, polyfills, documentation

AI-Generated Code & Front-End DX

[It] can take years of Interop work to get a feature to the point it becomes “Baseline newly-available”. […] If it’s not in the LLM training data, it doesn’t exist.

Developer experience features (syntactic sugar, convenience APIs) are competing against established […] patterns in LLM training data

Dead framework theory

(see also Docs CG discussion on impact on documentation)

AI-Generated Code & Accessibility

The [...] results suggest that objective error count is too high to rely on LLM technology at all in digital accessibility work, even under explicit expert guidance.

AI-Generated Code & Accessibility

LLMs excel at addressing basic accessibility requirements but struggle with complex accessibility requirements, particularly ARIA-related attributes, performing worse than human developers.

Advanced prompting techniques consistently generate code with lower accessibility issues than human-written code [but with] inherent limitations in addressing accessibility through prompting alone.

AI-Generated Code & Sustainability

AI-Generated Code for Standards Adoption

• Accelerate generation of polyfills, validators?
• Impact of LLMs on documentation: production & consumption

Questions

• Impact on prioritizing features?
• Impact on writing guidelines?
• Impact on adoption support?
• Incorporating AI-generation in adoption strategy?
  • Can we influence more directly the relevant AI tools?
  • Will this move the Web vs Native needle again?