Everyone Can Build. Not Everyone Can Design

There’s a question that keeps coming up in conversations around me lately: Did AI replace designers?

My answer is yes. But not all of them.

I think we’re experiencing a fundamental shift in how we define the value of designers and engineers. For decades, we’ve measured people’s abilities largely through what they can produce. The quality of their designs, the code they write, the prototypes they build, and the technical skills they’ve accumulated over the years. These things have shaped how we evaluate talent, build careers, and even define ourselves professionally.

But AI is changing that relationship. When almost anyone can generate a working application, design an interface, or build a game without necessarily understanding the tools behind it, we need to reconsider what actually makes someone good at their job.

I think the answer has less to do with making things and much more to do with knowing what should be made.

The Declining Value of Digital Craftsmanship

I use the term digital craftsmanship to describe the manual work involved in creating digital products. Writing code, designing interfaces, building prototypes, conducting research, and transforming ideas into something tangible.

For years, digital craftsmanship was one of the most important aspects of our professions. Designers spent years mastering tools like Figma, developing their understanding of typography and visual hierarchy, and learning how to create polished interfaces. Engineers invested similar effort in programming languages, frameworks, architecture, and the countless technical details required to build software. Even research involved its own craftsmanship, from conducting interviews to organizing findings and communicating insights.

We considered these skills valuable because they were difficult to acquire. Someone who could create beautiful interfaces or write exceptional code had an advantage over someone who couldn’t. We even used these abilities as evidence of our professional value, proudly listing Figma, HTML, CSS, JavaScript, prototyping, and dozens of other tools on our CVs.

Today, AI can perform an increasing amount of that work for us. You can describe an application and watch AI generate it. You can ask it to build a game, create a website, write components, or produce a working prototype. The results aren’t always perfect, and sometimes they’re genuinely terrible, but the underlying capability is improving incredibly quickly.

What’s interesting to me isn’t whether AI can produce something as well as an experienced professional today. It’s that the ability to produce digital things is becoming accessible to almost everyone. Someone who has never written code can build an application, and someone who has never opened Figma can generate a surprisingly polished interface.

The technical barrier between imagining something and making it real is becoming smaller every day. As that barrier disappears, I believe digital craftsmanship will gradually lose its position as one of the primary ways we differentiate good designers and engineers from everyone else.

This doesn’t mean craftsmanship suddenly becomes worthless. Understanding how something is made still matters, but simply being able to make it is no longer the competitive advantage it once was.

Everyone Can Build. Not Everyone Can Design.

Imagine two people using exactly the same AI model, with access to the same tools and capabilities. Both ask AI to design and build an application. Both receive something functional, visually polished, and technically impressive.

Yet one ends up creating an extraordinary product while the other produces something completely forgettable.

The difference isn’t necessarily who knows Figma better, who can write cleaner CSS, or even who has mastered prompt engineering. It’s that one person knows what good looks like and has the ability to guide the work toward it.

An experienced designer might immediately recognize that the information hierarchy is wrong, an interaction feels unnatural, or an otherwise beautiful interface doesn’t actually solve the user’s problem. They might notice that the application is unnecessarily complicated or that a technically impressive feature adds very little value. Another person might look at exactly the same result and think it’s finished simply because it looks professional and everything seems to work.

But recognizing a problem is only part of the skill. A good designer also needs to understand why something isn’t working and what would make it better. This requires more than aesthetic preferences. It requires an understanding of human behavior, interaction patterns, accessibility, information architecture, technical constraints, and the actual problems people are trying to solve.

That understanding becomes increasingly important when execution is no longer the primary limitation. AI can generate dozens of alternatives in minutes, but someone still needs to evaluate them, challenge assumptions, and decide which direction is worth pursuing.

The value of design is shifting from producing solutions toward making decisions about them.

From Craftsmanship to Decision-Making

Making decisions has always been part of design. What’s changing is the relationship between deciding and executing.

Traditionally, even a relatively simple design direction could require hours or days of manual work to explore properly. Building an interactive prototype, implementing a new interface, or testing multiple approaches involved significant effort. The cost of execution naturally limited how many ideas we could investigate.

AI changes that equation by reducing the effort required to turn an idea into something tangible. Instead of manually constructing every variation, we can generate working alternatives and experience them much earlier in the process.

This creates an interesting shift in responsibility. When generating solutions becomes easier, the challenge is no longer having enough options. It’s understanding which options deserve attention.

A designer might receive ten different implementations that all appear reasonable. Some might be visually impressive but unnecessarily complex. Others might be technically elegant but difficult to use. A few might solve the immediate problem while creating new problems elsewhere.

The ability to evaluate these trade-offs, recognize the consequences of different approaches, and make a decision becomes increasingly important.

And sometimes the best decision isn’t choosing between the available options. It’s recognizing that none of them solves the right problem.

This is where I believe designers will create the most value in the AI era. Not by producing more screens or prototypes than everyone else, but by ensuring that what gets produced is actually worth building.

But How Do We Develop Good Judgment?

There’s a contradiction in this argument that I find particularly interesting.

For years, craftsmanship wasn’t simply a way to produce things. It was also how we learned to become better designers.

We developed judgment through experimentation, repetition, and failure. By manually creating interfaces, building prototypes, and exploring different solutions, we gradually learned why certain decisions worked and others didn’t. The process of making something helped us understand what made it good.

If AI increasingly handles that process, what happens to the next generation of designers?

Someone entering the profession today might be able to generate sophisticated interfaces without ever learning the fundamentals of visual design or interaction. They might produce results that look professional without understanding the decisions behind them.

That raises an important distinction between being able to recognize something attractive and understanding why it’s effective.

Good judgment doesn’t come from simply having an opinion. It requires knowledge, experience, critical thinking, and the ability to connect decisions to their consequences. A beautiful interface can still be difficult to use, and a technically impressive product can still fail to solve a meaningful problem.

I don’t think AI eliminates the need to learn these fundamentals. If anything, it makes them more important. But it might change how we learn them.

Instead of spending most of our time manually producing work, perhaps we’ll spend more time experimenting, evaluating alternatives, questioning results, and understanding why particular solutions succeed or fail.

The challenge will be making sure that easier execution doesn’t lead to shallower understanding.

I don’t have a definitive answer to how this will change design education or professional development. But I think it’s one of the most important questions we should be asking as AI becomes part of everyday creative work.

The Freedom to Create Almost Anything

There’s another side to this transformation that I find incredibly exciting.

Historically, the gap between imagining something and building it was enormous. Ideas were often constrained by technical knowledge, available resources, time, or the cost of implementation. Even experienced professionals had to make decisions based on what they could realistically execute.

AI is reducing many of those limitations.

A designer can explore functional applications without manually writing every line of code. An engineer can experiment with visual directions without mastering every design tool. Someone with an idea for a game, application, or digital experience can create something tangible without assembling an entire team.

This doesn’t mean every idea will suddenly become a great product. But it does mean far more ideas can be explored.

I think this is one of the most significant creative opportunities AI offers. When execution becomes less expensive, experimentation becomes more accessible. We can investigate directions that previously seemed too ambitious, test unconventional ideas, and learn from working experiences rather than imagining how they might behave.

The boundaries between traditional professional roles may also become less rigid. Designers can participate more directly in implementation, engineers can contribute more deeply to the experience, and people without formal training can begin creating things that were previously beyond their reach.

The opportunity isn’t simply to produce the same work faster. It’s to explore possibilities that the cost of craftsmanship previously prevented us from pursuing.

But that freedom comes with a new responsibility. When almost anything becomes possible to build, deciding what deserves to exist becomes even more important.

So, Did AI Replace Designers?

I believe AI is already replacing parts of what we traditionally considered design work, and it will continue to replace more of the manual craftsmanship involved in creating digital products.

If someone’s primary professional value comes from knowing how to operate a particular tool or manually execute a predefined solution, that value is becoming increasingly vulnerable. The same applies to engineering work where the primary contribution is translating clear instructions into code.

But having the ability to create something doesn’t automatically mean having the judgment required to create something exceptional.

I don’t think AI will make everyone a good designer, just as access to a camera doesn’t automatically make everyone a good photographer. The tools may become accessible to everyone, but the ability to see possibilities, understand problems, and make meaningful decisions remains something that needs to be developed.

What excites me is that AI allows us to focus more of our energy on those decisions while giving us an extraordinary ability to execute them. What concerns me is whether we’ll continue developing the knowledge and experience required to make those decisions well.

Perhaps the biggest transformation isn’t that AI is replacing designers, but that it’s forcing us to reconsider what being a designer actually means.

For decades, digital craftsmanship helped define how good we were at making things. In the AI era, our judgment will increasingly define how good the things we make actually are.

I don’t think the future belongs to the people who can produce the most. I think it belongs to those who know what deserves to be produced.

And perhaps that’s what being a good designer was supposed to be about all along.