I think AI is doing something good for design: it makes the field more accessible than ever. Someone who has never opened Figma can still build an interface. A PM with no design background can make a prototype to get an idea across. A developer can get fairly polished screens from a few lines of prompt.
I don’t see anything wrong with that. Technology is helping more people turn ideas into something they can see, click and test. But it also left me with a question: when making an interface becomes this easy, are we starting to take the thinking behind it too lightly? This post is what I think about that question, with two studies for extra perspective.
When the answer arrives before the question
Before AI, getting a design request meant understanding the problem, looking at similar products, analyzing information and trying several options. Some tasks that looked simple took a long time. Should people log in with email or phone first? Should we ask them to create a password right after the OTP step? If we add one more step to the sign-up flow, is it still a reasonable length? One icon family for everything, or split by role?
Sometimes I tried one direction, dropped it and started over. That process wasn’t always fun, but looking back, I learned a lot from the times I had to find the answer myself.
Now AI can propose a solution almost instantly. Describe a sign-up screen and it returns a full layout with a form, buttons and the familiar error states. It can look good, make visual sense, and sometimes beat the first option I would have come up with. What bothers me isn’t the visual quality. It’s that when an answer arrives this fast, it is very easy to skip asking my own questions.
Instead of asking what users need, I start asking AI how to design it. Instead of thinking about how to solve the problem, I tweak a solution that already exists.
A design that looks reasonable doesn’t mean I understood the problem.
The worry is not being able to explain my own decisions
I have received very short requests from PMs, sometimes just a reference image and one line: “See which one is usable.” That is a perfectly normal situation in product design. The PM may already picture what they want to see, but not necessarily how to organize the information or the interaction. A designer’s job is not to recreate the reference, but to understand which parts of it actually fit the product.
Another example is choosing icons for the first version of an app. The team had several icon families: 3D Clay, 3D Glossy, 2D and line. AI can build all four on the same screen in minutes, and all four look fine. For the first version, the team temporarily picked two: 3D Glossy for things you can tap, minimal line icons for information.
The icon families and the rule for the first version of the app. The mockup, colors and content have been changed to protect the project’s confidentiality.
The rule sounds simple, but behind it are questions I have to be able to answer: why split by role instead of by screen, do people looking at a 3D icon understand it is something to tap, what happens when an info item later becomes tappable, and why this is only a temporary choice for the first version.
AI can make a better icon set than I can. But if I can’t explain those things, I am only picking an option that looks fine, not making a design decision. I think the ability to produce an interface will make less and less difference. The ability to explain and defend a design decision is what is worth investing in.
Everyone can make UI. Is that a good thing?
I think yes, but not entirely. AI is removing part of the technical barrier in the field, and I support that. Not everyone with a good idea needs to master Figma. Not every PM needs to become a designer to present an idea. If AI helps someone visualize their thinking quickly, collaboration across teams can get more effective. The more accessible the tools, the wider the room for creativity.
The problem starts when we confuse knowing how to make an interface with knowing how to design a product. Anyone can make a very professional-looking login screen without ever thinking about validation, error states, accessibility, or how to organize information across a whole app. On simple screens that is not a visible problem. As the product gets more complex, the gaps in knowledge show up:
- If users drop off midway and come back, where do they continue from?
- If a phone number is already linked to another account, what do they see and what do they do next?
- Do a new device and a familiar device follow the same flow?
- If the product has many states, how does the interface respond?
Take the sign-up flow of a super app I am working on: the main steps are just phone number, OTP, password, basic info, face and PIN. The hard part is the remaining branches: what a user sees when blocked because the phone number is already linked to another account, which step a user returns to after dropping off, and why a device only becomes trusted after the whole registration is complete. Those screens rarely appear in a nice screenshot, but without them the product can’t be used.
A registration flow with blocked, leave-midway and resume branches. Illustrative mockup, content changed to protect the project’s confidentiality.
I don’t think only designers are allowed to answer those questions, but anyone making design decisions should care about them. Two products looking alike doesn’t mean the two people who made them understand the same things. I think this will be a big challenge when evaluating designers in the future.
Beginners improve faster, but may miss important lessons
If I were starting to learn design today, I would probably use AI a lot: asking about layout, hierarchy and typography, generating many options to compare. That is an advantage earlier generations didn’t have. But beginners can fall into a trap: getting a good result before understanding how it was made.
Before AI, getting a layout wrong forced me to find out why. I read, looked for examples, tried and adjusted. Those slow tasks built my design intuition. With AI, some of those hard stages can be skipped. That is not necessarily bad, because nobody needs to do everything the hard way just to prove they have skills. But if we keep skipping the parts that need thinking, I wonder where our knowledge will grow from.
Lee et al. (2025) surveyed 319 knowledge workers who use generative AI at least weekly. People who trusted AI more reported using less critical thinking, while people more confident in their own ability reported more critical thinking [1]. This is self-reported data and does not show that AI reduces intelligence, but it is worth reflecting on how we spend our thinking effort.
New designers need AI just as much as experienced ones. But more than a correct answer, they need the ability to recognize why the answer is correct.
The first option is hard to let go of
Working with AI, I noticed that the first option tends to have a lot of influence. When AI gives a fairly complete layout, the easiest thing is to keep editing it: change a color, swap a component, adjust spacing, add or remove a few elements. In the end there is a new interface, but the structure of thinking is almost the same as the first option. From the outside the designer is still working, but inside, the range of exploration may have narrowed very early.
Wadinambiarachchi et al. (2024) ran an experiment with 60 people: the group using an AI image generator for ideation fixated more on the initial example, known as design fixation [2]. The result doesn’t represent every UI design situation, but it points to a real risk: tools that suggest ideas can also limit the directions we think of next.
How I want to use AI
That is why I am changing how I use AI. Instead of always asking AI to propose a design, I ask more about my own assumptions: what is the weakness of this option, is there a completely different approach, if I removed its main element would the goal still be met, in which case would the current solution fail? I want AI to play the critic, not only the one who hands out answers.
I also don’t think the future of UI/UX is a race to see who makes the most screens in the shortest time, because if speed is the standard, AI will keep gaining the advantage. I hope technology helps designers cut repetitive work so they can spend time on things worth thinking about: understanding users, talking with PMs, understanding the business context, weighing trade-offs, checking usability, working with developers to get the solution into the real world. These things rarely show up in a pretty screenshot, but they directly affect product quality.
When PMs or developers can build prototypes themselves, designers get more material to discuss. Instead of saying “that’s not your job”, I would rather ask: how do we turn that first prototype into a solution with real value? But AI helping more people take part in making products doesn’t mean every option created should become a product. That makes evaluating design even more important.
Conclusion
Any technology that makes work easier can make us dependent on it, and AI is no exception. I don’t see that as a reason to limit AI, but as a reminder about how I work. If AI saves me two hours of building an interface, do I spend those two hours understanding the problem better? If AI gives me five options, do I really evaluate them or just pick the prettiest? If a screen is generated almost complete, do I still have the patience to ask whether it needs to exist at all?
When making an interface is no longer rare, designers need to find their value in deeper skills. Not to prove we are better than AI, but because a good product always needs more than a good-looking interface.
If you are thinking about this too and want to talk, message me through the contact section!
References
Both studies below have limited scope and do not replace my own point of view.
- Lee, H.-P., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., Wilson, N. (2025). The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers. CHI 2025.
- Wadinambiarachchi, S., Kelly, R. M., Pareek, S., Zhou, Q., Velloso, E. (2024). The Effects of Generative AI on Design Fixation and Divergent Thinking. CHI 2024.
Originally published at yurickart.com.
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