They can generate flows, create interfaces, iterate on layouts, write microcopy, and produce work that would have been considered productive output from a mid-level designer not long ago.

Once that really sinks in, a lot starts to click.

For years, technology rewarded people who could produce. The more screens you could design, the more concepts you could explore, and the faster you could execute, the more leverage you had.

AI is rapidly reducing the cost of production.

Production is becoming abundant and judgment is becoming scarce. That’s why I believe designers who came up through traditional design disciplines are particularly well-positioned for what’s coming next.

Many of us spent years studying typography, hierarchy, color theory, composition, grid systems, rhythm, visual storytelling, and all the subtle details that make something feel clear, intuitive, and memorable.

The advantage goes beyond simply knowing design fundamentals, It’s learning how to think and develop judgement.

Traditional design education teaches you to make decisions. What deserves attention? What can be removed? What should someone feel? What’s the clearest way to communicate an idea? How do individual parts work together as a system?

Those skills become incredibly valuable when AI can generate almost anything.

AI can produce endless variations. It can create hundreds of screens in minutes. What it still needs is direction. It still needs someone to recognize what’s working, what isn’t, what feels right, and what should exist in the first place.

You can already see this playing out. AI-generated work is everywhere, yet strong design judgment remains relatively rare. Taste remains rare. At the same time, I think we’re underestimating how much AI is changing the way products get made.

For decades, software creation was divided between the people who imagined products and the people who built them. Designers designed. Engineers engineered. Ideas passed through layers of translation before they became reality. That separation shaped entire organizations, workflows, and careers.

AI is beginning to collapse that distance.

The person with the idea is increasingly becoming the person who can realize it. This is why I don’t view AI as another productivity tool. The shift feels much larger than that.

We’re moving from a world where creation required coordination between specialized roles to one where a single person can direct teams of agents toward an outcome. Instead of spending weeks documenting every decision, we’re increasingly defining intent, constraints, systems, and outcomes. Execution becomes more conversational. More iterative. More immediate.

I keep thinking about all the ideas I had over the years that never made it beyond a Figma file. Projects built for fun. Experiments with no PRD. Concepts that pushed far beyond what a company would prioritize.

Most of them stopped for the same reason: I couldn’t build them myself. They required engineers, funding, time, or all three. That constraint is disappearing.

For the first time, many designers can move from idea to working product without waiting for permission, resources, or a team to form around them. I don’t think we’re simply getting better tools. I think we’re witnessing a fundamental change in how products come into existence.

For years, designers were responsible for defining experiences while someone else was responsible for building them.

That distinction is starting to fade.

And for designers who understand systems, communication, visual thinking, and human behavior, that may be one of the most exciting opportunities we’ve ever seen.

Some people love it. Some people hate it. Many seem to hold both feelings at the same time.

Part of that reaction comes from how quickly AI has entered everyday life. For some, it’s a productivity tool. For others, it’s a direct threat to their livelihood. Entire industries are beginning to rethink how work gets done, and many workers have already felt the impact firsthand.

Recently, a court in China ruled that companies cannot replace employees solely with AI. Decisions like this reflect a growing concern shared around the world: how do we embrace technological progress without leaving people behind?

So why do we fear AI?

Much of it comes down to security and purpose. Work has always been more than a paycheck. It provides structure, identity, community, and a sense of contribution. When people worry about AI replacing their role, they are often worrying about something deeper: whether they will still be needed.

At the same time, AI is becoming the default source for answers. Increasingly, our first instinct is to ask a machine rather than another person. Every question routed through an AI assistant is one less conversation with a coworker, friend, mentor, or stranger. It’s worth asking what happens when that behavior scales across society.

Do we become more informed? More productive? More isolated?

Yet there are also compelling reasons to embrace AI. It can provide instant access to knowledge, help us learn new skills, automate repetitive work, and accelerate creativity. Designers can generate concepts in seconds. Developers can build faster. Researchers can synthesize information at a scale that would have been impossible only a few years ago.

The technology is undeniably useful.

What interests me most is not whether AI is good or bad. It’s why so many of us feel both excitement and anxiety at the same time.

Perhaps the tension comes from the fact that AI amplifies two competing forces. It increases our capabilities while simultaneously raising questions about our relevance. It makes us more efficient while reducing the need for certain forms of human interaction.

The long-term impact may have less to do with the technology itself and more to do with how we choose to integrate it into our lives.

If we delegate too much, we risk weakening the skills and relationships that make us human. If we reject it entirely, we may miss one of the most powerful tools ever created.

The real question isn’t whether AI will change society.
It’s how society changes when intelligence becomes abundant.

The concern goes beyond people losing money on sports or elections. Prediction markets add financial weight to ordinary beliefs. A guess, opinion, or gut feeling becomes something people can win from, lose from, and emotionally defend.

When someone loses, the loss has to land somewhere. Some people will accept it as bad judgment. Others will blame the outcome, the platform, the audience, the refs, the voters, the producers, the judge, or the system. The bet creates a financial wound, and the mind looks for a story to explain it.

Over time, people may become more resentful toward the things they lose money on. A person who loses on a team may start to hate the team. A person who loses on an election may feel the process was rigged. A person who loses on a reality show may become angry at the audience or the type of people who made that outcome happen.

This is where prediction markets could change culture. More outcomes become charged with personal financial meaning. A game, a trial, a breakup, an election, a company announcement, a weather event, or a TV finale becomes something people feel they won from or were robbed by.

That creates a society with more people experiencing the future like a running balance sheet. Every event becomes something gained, lost, owed, or taken. People start to feel like reality should pay them for being right. When the reward never comes, the reaction can turn into anger, suspicion, shame, or the urge to win it back.

Over the next 10 years, the social effect could be a more reactive population. People become more attached to being right, more embarrassed by being wrong, more willing to chase losses, and more suspicious of outcomes that cost them money.

A gambling society may become a society where people lose the ability to be wrong gracefully.

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