Staying Grounded in the Age of AI

The Shift Beneath the Tools

AI and automation are changing the way we work every day. They make tasks faster and cleaner, but they also influence how we think, how we make decisions, and how we approach the craft of building things. Lately, I've been paying attention to that shift in my own work.

They're already part of my daily workflow. They save time, reduce friction, and sometimes surface things I wouldn't have thought of on my own. That's powerful. Lately, though, I've started to notice a change. Not just in the tools themselves, but in how they shape the way I approach the work.

It's easy to go with the suggested solution because it shows up quickly, looks polished, and seems reasonable at first glance. But when you slow down and look closer, gaps begin to appear. AI does not see the bigger picture. It misses product goals, messy edge cases, team constraints, and long-term impact. What feels like a shortcut can quietly lead 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 takes more than technical ability. It takes perspective.

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 learning meant figuring things out the hard way. You ran into walls, searched Stack Overflow, made mistakes, and improved because of it. The value wasn’t just in finding the answer. It was in the thinking the process demanded. That friction helped us grow.

With AI, we now get instant solutions that are faster, cleaner, and often technically correct. But are we still learning? Are we building the mental connections that lead to real understanding? Or are we skipping the slow and uncomfortable part of the process that builds judgment?

Think about the fundamentals. Accessibility, hierarchy, naming conventions, knowing when to question a pattern instead of repeating it. These are not checkboxes. They are part of learning how to see clearly. If tools solve these problems before someone 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 do not let it shape your thinking without intention. These tools are trained on what has been done, not on what should be done. That responsibility still belongs to us.

Maybe that is the real shift. Learning when to accept a suggestion and when to pause, zoom out, and trust your own sense of direction.

That kind of instinct takes time to build. It does not come from shortcuts. 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 fewer people develop that kind of discernment? If companies are led by those who trust output over insight, what kinds of decisions will we make? What kinds of things will we build?

Again, the goal is not to reject these tools. It is to remain present. To question, to choose, and to think for ourselves. Otherwise, we risk building things that are fast but fragile.

In the end, when we stop thinking deeply, it is not just the products that suffer. It is the craft. And without the craft, what are we really doing?