The case for letting your UI tell you what to build next

Short summary

AI-augmented UIs can close the gap between user needs and product roadmaps by logging unresolvable requests and 'graduating' frequent patterns into hardcoded features. This reframes inference costs not as permanent overhead but as temporary signal collection, shifting validated patterns to zero-cost hardcoded views. The author prototypes this model against self-hosted media configuration, demonstrating how behavioral signals can drive product roadmaps that match user needs.

• •AI layer handles requests the static UI can't resolve, logging demand signals that reveal what users actually need
• •Frequent patterns 'graduate' to hardcoded features, shifting inference costs from cents-per-request to zero marginal cost
• •Reframes AI as temporary roadmapping infrastructure with a built-in exit ramp, not permanent runtime

Generated with AI, which can make mistakes.