AI tools are quickly becoming a standard part of the design and development process. They're no longer just helpful additions; they are changing how we approach problems and create solutions. This shift is both exciting and challenging.
I use these tools. They help. They save time, reduce friction, and sometimes surface things I wouldn't have thought of on my own. That's powerful. However, I've started to notice a shift not only in the tools I use but also in how I think about the work itself.
It's easy to be tempted to go with the suggested approach because it shows up quickly, looks polished, and seems to make sense. But when you pause and take a closer look, gaps begin to appear. AI doesn’t see the bigger picture - the product goals, the messy edge cases, the team constraints, or the long-term impact. What seems like a shortcut can quietly set you on a path that leads to rework later.
That's where experience comes in. Sometimes what looks right in the moment is not right in the larger context. Knowing the difference requires perspective as much as technical skill.
I've been reflecting on what this means for people entering the field, whether designers, engineers, or those of us who do a bit of both. There was a time when you had to figure things out from scratch. You ran into walls, searched Stack Overflow, made mistakes, and got better because of it. The value wasn’t just in the answer; it was in the thinking it forced you to do. That friction helped us grow.
With AI, we get instant solutions that are faster, cleaner, and often technically correct. But are we still learning? Are we making the connections that build real understanding? Or are we skipping over the slow, valuable part of the process?
I think about fundamentals often - accessibility, hierarchy, naming conventions, or knowing when to question a pattern instead of simply repeating it. These aren’t just checkboxes; they are part of learning to see clearly. But if tools solve these things before a person understands why they matter, we risk losing something essential.
This is not about resisting change. It is about staying active in the process. Let AI support your work, but don’t let it shape your thinking without permission. These tools are trained on what’s been done, not what should be done. That responsibility remains with us.
Maybe that is the real shift - learning when to listen to the suggestion and when to pause, zoom out, and trust your own sense of direction.
That kind of instinct takes time to build. It doesn’t show up in a shortcut. Yet it helps us design better, code smarter, and build products that actually matter.
I think about where all of this is heading. What happens if more people never learn that kind of discernment? If companies are led by those who skipped the deeper thinking and trust output over insight, what kind of decisions will we make? What kind of things will we build?
It is not about resisting the tools. It is about staying present in the process - questioning, choosing, and thinking for yourself. The more we let go of that, the more we risk building things that are fast but fragile.
In the end, it’s not just the products that suffer when we stop thinking deeply; it’s the craft. And without the craft, what are we really doing?