AI is getting close to solving big problems, but the gulf is frustrating (www.mickmel.com)

AI is often impressively fast at producing polished outputs — from website layouts to clickable app mock-ups — but real-world use is exposing a persistent gulf between “looks right” and “works right.” In practice, generative models can assemble attractive UIs and simulate functionality in hours, yet they frequently misorder content, miss messaging goals, and hide the hard engineering work (backend logic, edge cases, integration, testing) behind a shiny façade. That creates a false sense of progress for clients and teams: mock-ups act as useful alignment tools but are often “smoke & mirrors,” not a durable reduction in cost or timeline. For the AI/ML community this is a crucial reality check. It highlights model strengths—pattern synthesis, multimodal layout, rapid prototyping—and their weaknesses: lack of systemic planning, stateful reasoning, specification adherence, and verifiable code quality. Technically this points to where progress matters most: better training corpora for end-to-end app behavior, advances in program synthesis and verification, human-in-the-loop workflows, and metrics that measure functional correctness, not just pixel fidelity. Expect UI/design generation to converge faster toward production readiness, while full-stack development will require more targeted advances before AI can substantially replace human engineering rather than just simulate it.