AI-Generated Designs: Structure, But Perhaps Lacking Intent

If there is something I have learned about using AI for design, it is that AI-induced design entropy is real.

For me, learning to design was a process of iteration, critique, and deep problem-solving. I spent years refining my ability to translate business goals into UI that actually serves user needs. I wasn’t just pushing pixels—I was thinking about hierarchy, flows, interactions, and the underlying strategy that makes a product successful.

That meant I had to break down problems at their core—understanding how people navigate interfaces, what cognitive loads exist in certain layouts, and how different design patterns influence behavior.

I would spend hours researching best practices, dissecting design systems, and reworking wireframes based on real user feedback. I had to be intentional. Every decision had to map back to the business intent and user goals.

Enter AI, and everything is different. The mechanics of designing have become more immediate, but the strategy is often lost in translation.

With tools like Bolt.new, V0.dev, and Cursor, you can generate polished wireframes and even near-production-ready UI in seconds. And it looks good. The typography is balanced, the spacing is consistent, and the buttons have all the right states. It’s impressive—until it isn’t.

At a certain point, you realize that the AI-generated UI lacks depth. It’s the same issue I used to run into when working with early Material Design or Bootstrap products—the scaffolding is there, but it doesn’t inherently answer the business needs.

I’ve used AI-generated designs that, on the surface, seem viable. But then I start breaking them down. The layout isn’t aligned with the user’s mental model. The affordances aren’t clear. The flow doesn’t actually facilitate the core task. It’s all almost right, but that last 10%—the part that makes a product intuitive and frictionless—is missing.

It’s like designing in reverse. Instead of starting with a problem and designing toward a solution, you get a visually complete interface that you then have to retrofit into a meaningful experience.

And that’s where the entropy creeps in. You iterate, patch, tweak, and suddenly, you have a UI that looks cohesive but feels disjointed. Because the intent wasn’t there from the start (unless you’ve properly set up the model context to provide an accurate output *more on that later).

I now spend more time prompting AI to explain its choices to me than just generating raw designs. I ask it to refactor components based on principles I actually understand and can defend. AI is great at executing patterns, but it doesn’t understand why those patterns exist in the first place.

Yes, vibe-driven design is real. But it’s also a means to an end —perhaps a dead end.

In the coming years, an overwhelming amount of AI-generated UI will flood the industry, and companies will waste significant resources trying to fix what should have been designed with purpose from the start.

Even if AI gets exponentially better, refactoring these interfaces into truly functional products will be expensive—both in time and cost. Because the real work of design isn’t just making something look good. It’s ensuring that it works.

The lesson here? You can leverage AI to move faster, but you can’t outsource design thinking. If you do, you’ll always reach a point where you have to scrap it all and start again, except now, you’ll be exhausted and further from your goal than when you began.

Good design isn’t just about output—it’s about intent.

From AI-generated to business-aligned. Need a designer who gets both?
Reach out: joshua@jshmllr.com | LinkedIn | X