There’s a lot of noise around AI right now - and understandably so. It’s powerful, fast, and often feels like magic. But I’ve been thinking about what it really means for designers who’ve built their craft through years of iteration, feedback, and learning by doing.

AI tools can research, summarize, generate ideas, and even produce visual directions in seconds. And yes, they can save an enormous amount of time. But if used the wrong way, they can also short-circuit the very learning process that makes a designer valuable - understanding context, making trade-offs, reasoning through constraints, and building empathy for users.

AI should be treated as a supporting layer, not a replacement. Use it for research, domain knowledge, early ideation, and even for exploring alternate perspectives. But once the foundation is set, it’s the human insight - the ability to reason through ambiguity - that makes a product truly meaningful and scalable.

It’s easy to get caught up in the demos - mockups, prototypes, and AI-generated UI screens that look impressive at first glance. But when you go deeper, most of them don’t scale well. They miss the invisible layers: accessibility, security, performance, error states, user flows, edge cases - the real work that makes a design production-ready. Many of the people bragging about AI design tools can put together something that looks cool, but not something that holds up in a live product.

The right path is balance. Let AI handle the repetitive and research-heavy parts, while you focus on reasoning, scaling, and designing for real users and real systems. The more you use AI as a partner rather than a shortcut, the more it reinforces your own learning curve - instead of flattening it.

Because in the end, tools evolve. What stays valuable is the designer’s ability to think - to connect dots, to craft experiences that make sense, and to make technology feel human. AI can accelerate that journey, but only if it doesn’t replace the path you need to walk to learn.