In many cases, AI isn’t removing friction—it’s shifting it. We’ve moved from “writing complex code” to “writing precise prompts and validating outputs.” The effort hasn’t disappeared; it’s just moved into: Prompt design Output validation Fixing edge cases, the AI didn’t anticipate So while developers may write less code, they often spend more time guiding and verifying the system. The real UX question is: who owns the complexity? From my experience with Candor Data Platform, the difference comes when systems go beyond generation and handle more of the workflow—like maintaining context, structuring pipelines, and supporting validation. That reduces the back-and-forth and the number of “almost correct” results. The shift that matters isn’t just AI that helps you work faster—it’s AI that takes on more responsibility for delivering reliable outcomes. Until then, many “AI UX improvements” are less about removing complexity and more about relocating it behind a cleaner interface.

Most classrooms are currently using AI as a chat-based tool, but effective learning goes beyond simple Q&A—it comes from building, testing, and iterating. In my experience with Candor Data Platform, the difference is in how AI is used. Instead of only asking questions and receiving answers, students can: Define a problem Generate structured code (Python/SQL/JavaScript) Test, debug, and iterate on solutions This shifts AI from being just a chatbot to a more guided development environment. The transition is straightforward: From prompting → to problem-solving If the goal is to help students develop real technical skills, then tools and workflows that encourage structured thinking and iteration become far more valuable than simple conversational interfaces.