The designer & AI : What happens when everyone can build ?

I’ve spent the past week down a bit of a design rabbit hole. It started with a few conversations about how AI is changing the way designers work. Then came podcasts, YouTube videos, LinkedIn posts, and research that sent me from one perspective to another. What caught my attention wasn’t really the idea that AI can make design faster. We’ve heard that before. It’s was ‘what happens after everything becomes faster’.

When you can generate a prototype in minutes, move between code and canvas, explore ten directions instead of two, and turn an idea into something tangible without following the traditional design process, the question becomes:

If everyone can build with AI, what makes a designer valuable?

I don’t think the answer is simply knowing Figma better, producing polished interfaces faster, or even knowing how to use the latest AI tools.

I’m beginning to see that it’s something deeper.

• It’s having the taste to recognize what is good.
• The judgment to know what is worth pursuing.
• The product thinking to understand the problem behind the interface.
• And the ability to communicate why one direction is better than another.

AI is changing how we make things. Maybe what’s more interesting is how it changes what we need to be GOOD at as designers.

The Process Is Changing

One of the first things I noticed while going down this rabbit hole was how much the actual design process is changing. For years, the process felt relatively familiar:

• Understand the problem
• Research
• Explore,
• Open Figma
• Iterate
• Get feedback
• Refine,
• and eventually hand things over to engineering.

That workflow is still very much alive, but it no longer feels like the only way to design. Today, a designer can move from an idea to a working prototype through AI and code in a fraction of the time. You can explore multiple directions, test interactions earlier, and make changes without waiting for every stage of the traditional process to happen first.

The State of AI Design report found that 91% of designers now use AI in their work at least weekly, 76% have used AI coding tools, and 50% say they have shipped code to production.

Another finding from Figma’s 2026 AI report really made me pause.

The number of developers doing design work has risen from 44% to 60%, while designers participating in development has nearly doubled, from 21% to 41%. And despite all this change, 90% of respondents say design is at least as important as it was before AI, with 57% saying it is more important. That tells me something important. The story isn’t that AI is making design less relevant.

It’s making the ability to design part of more people’s jobs.

Developers are moving closer to the canvas. Designers are moving closer to code. Product teams are becoming more fluid. And I think that’s a good thing.

What’s interesting isn’t that designers are leaving Figma or becoming engineers. It’s that we’re gaining more ways to move an idea from our heads into something we can actually experience. That means we can explore more, learn faster, and get closer to the real problem earlier.

But it also means something else:

When execution gets cheaper, judgment becomes more valuable.

And that’s where I think the conversation gets really interesting.

Taste Becomes the Differentiator

Recently I came across a LinkedIn post by Alison on how to build product taste in the era of AI, where she shared five ways to develop it.

One of them really stayed with me:

“Study the decisions behind great products frequently.”

I can’t emphazise how profound I think this is because there is a difference between using a great product and actually studying it.

When you consistently look at products this way, you start noticing things you would have otherwise missed.

• Why did they choose this interaction?
• Why is this information here and not somewhere else?
• What did they deliberately leave out?
• What alternative solutions might they have considered?

While I’m at the verge of making this a weekly practice, I believe when we do this consistently, over time, we start building something that isn’t always visible in the portfolio: a mental library of decisions.

We begin to see more problems. More patterns. More approaches. More trade-offs. More importantly, we begin to recognize when two products are solving a similar problem in completely different ways. You start asking why one feels more intuitive, more considered, or simply better. And I think that, is how we begin to develop taste as a designer.

I believe this doesn’t just make us better at recognizing beautiful interfaces. It gives more references to draw from when faced with a new problem because the more great products we study, the more informed our decisions become.

Build Your Toolkit

I had an interaction with a Design Lead at one of the world’s leading fintech companies, and our conversation reinforced this for me:

Even with all the new ways we have to build and prototype, some things haven’t changed.

• Deeply understanding the user before building,
• Being able to articulate the reasoning behind product decisions
• Knowing what to prioritize.

In fact, I think they matter even more than building an impressive UI, because when the cost of exploring an idea drops, it’s tempting to explore everything. But more options don’t automatically lead to better decisions. That’s where having a strong mental toolkit becomes useful.

You see a problem and think, I’ve seen something like this before. Maybe it was solved through progressive disclosure. Maybe another product approached it through personalization. Maybe there was a completely different solution that hadn’t been considered. That exposure changes the way you think. You start generating better alternatives. You ask better questions. You become more deliberate about your trade-offs and more confident explaining why one direction might work better than another.

Every product you thoughtfully analyze adds another reference to your toolkit, the more references you have, the more possibilities you can see when you’re solving something new.

AI can give us more ways to explore a problem. But the quality of what we explore still depends heavily on what we know to look for.

Building Isn’t the Whole Story

I was watching a YouTube video by Heigi Jeong where she made a point that I think is easy to overlook in all the excitement around becoming better at AI tools, according to her:

Getting better at AI doesn’t solve every problem.

AI is incredibly good at things like automating tasks, analyzing information, generating variations and recombining what already exists. But there are still things that require a deeper understanding of people: originality, empathy, trust and context. For her, these all lead to one important skill: storytelling. And I don’t mean storytelling in the traditional sense of simply presenting a polished case study.

She describes it through the lens of the hero’s journey: Conflict → Failed Attempts → Breakthrough

The conflict is the user’s problem. The failed attempts are everything that came before, the ideas that didn’t work, the assumptions that proved wrong, the approaches the team tried and abandoned. And the breakthrough is the solution you eventually arrived at. I found this particularly interesting because the failed attempts are often the most valuable part of the story. They explain why the final solution looks the way it does.

This reminded me of something Tony Fadell said on Lenny’s Podcast about storytelling. Reflecting on his time working with Steve Jobs, he described storytelling as a skill Steve continually sharpened, particularly the ability to explain why something matters, not just what it is. And I think that distinction is especially relevant to design. We can show people what we built, but the story helps them understand why it matters.

AI can help you generate a solution. It can even help you articulate it, but it doesn’t have your team’s experience of trying something, learning from it, changing direction, and eventually arriving somewhere better. To Heigi,

“Context is what turns a solution into a story”

As more people become capable of building things quickly, I dare say that being able to communicate the thinking, decisions and journey behind what you built will matter just as much as the thing itself.

The more I’ve thought about all of this, the less interested I’ve become in whether AI will replace designers. I’m more interested in what it is asking us to become.

Maybe the designer of the future isn’t the person who can produce the most screens, move the fastest in Figma, or write the best prompts.

Maybe it’s the person who understands the user deeply enough to know what problem is worth solving, has enough taste to recognize a great solution, enough judgment to make the trade-offs, and enough storytelling ability to bring others along.

AI is making building easier. That doesn’t make design less important. It makes the thinking behind the design harder to ignore.

The designer & AI : What happens when everyone can build ? was originally published in Bootcamp on Medium, where people are continuing the conversation by highlighting and responding to this story.