There’s a lot of talk right now about what artificial intelligence is going to replace.
Developers.
Designers.
Writers.
Marketers.
Maybe entire companies.
I understand why.
What I’ve experienced with AI has been unlike anything I’ve seen in technology before—and I’ve been around technology for a long time.
But I’m beginning to believe we may be asking the wrong question.
The most interesting thing about AI isn’t what it replaces.
It’s what it multiplies.
And that distinction matters.
I’ve seen this movie before.
I started working with computers when DOS was still something you needed to understand.
I taught WordStar, Lotus 1-2-3, WordPerfect, Excel and eventually Windows.
Later, I worked in technology, became a product evangelist, and spent years thinking about how people interact with software.
Then I stepped away.
Not because I stopped loving technology.
Life simply took me somewhere else.
I built Jacquez Art.
For the past 15 years, we’ve created one-of-one custom framed sports jersey displays by hand. And instead of spending my days thinking about software, I spent them talking to customers, designing, selling, cutting mats, framing jerseys, embroidering patches, engraving nameplates, packing enormous boxes and solving thousands of tiny problems that only reveal themselves when you actually do the work.
For years, those two chapters of my life seemed almost unrelated.
Today they don’t.
Something changed.
During an earlier chapter of my technology career, I became fascinated with mobile learning.
I had ideas constantly.
Some were probably terrible.
Some were pretty good.
And occasionally I’d have one that I desperately wanted to see exist.
There was always a problem.
I couldn’t build it.
I could describe it.
Sketch it.
Prototype pieces of it.
Explain it to developers.
But somewhere between the idea in my head and the finished software was an enormous wall.
Money.
Developers.
Time.
Technical knowledge.
Priorities.
That wall has largely disappeared.
And I don’t think people fully appreciate what that means yet.
An idea can become something real before lunch.
Over the past several months, I’ve found myself doing something that would have seemed ridiculous to me just a few years ago.
I’ll have an idea in the morning.
I’ll start talking it through with AI.
We challenge the idea.
Refine it.
Think through the interface.
Consider the business model.
Find the holes.
Then I start building.
And sometimes, a few hours later, I’m clicking on the thing that existed only in my imagination that morning.
That still blows my mind.
Some of these experiments have grown directly out of problems I’ve encountered in my own life and businesses.
Jacquez Art is the custom sports memorabilia framing company I’ve spent the last 15 years building. We create one-of-one framed jersey displays by hand, and thousands of conversations with collectors have taught me as much about design, presentation, sales and customer behavior as I ever could have learned by studying the industry from the outside.
That experience recently led me to build the Jacquez Art Studio, an interactive design experience that lets someone explore colors, layouts, matting, patches and presentation ideas before we ever cut a piece of matboard. It takes knowledge I've accumulated through years of physically framing jerseys and turns some of that knowledge into software—while keeping the final craftsmanship in human hands.
In many ways, the Studio is the clearest example of what I'm talking about in this essay: AI didn't teach me how to frame jerseys. Fifteen years of framing jerseys taught me what the software needed to know.
PromptTOC.com, a Chrome extension, was born from a tiny frustration I kept having while using AI. My ChatGPT and Claude conversations were getting incredibly long, and I kept losing things I wanted to return to. PromptTOC creates a table of contents from your prompts so you can instantly jump back to an earlier part of a conversation.
SaveReel.co grew out of another frustration: I was saving valuable Instagram reels and carousels and then forgetting why I saved them. So I began building a tool that doesn’t merely save social content—it helps you extract the ideas, analyze them with AI, ask questions about them, and turn something you saved into something you can actually use.
Different products.
Different problems.
But they all began the same way.
Not because AI told me what to build.
Because I noticed something.
That’s an important distinction.
AI didn’t give me the problems.
Experience did.
This may be the part of the AI revolution I’m most fascinated by.
For years, we celebrated technical ability because technical ability determined what could be built.
If you couldn’t code, there were enormous limits on what you could create digitally.
AI is changing that equation.
But removing the technical barrier doesn’t remove the need for knowledge.
In some ways, it makes knowledge even more valuable.
Consider my world of custom jersey framing.
I could ask AI today to design software for framing sports jerseys.
It could generate something impressive.
Beautiful interface.
Color selectors.
Layout options.
Animations.
Maybe even AI-generated recommendations.
But would it know that a football jersey carries excess fabric that needs to disappear behind the presentation?
Would it understand why the player’s name needs breathing room?
Would it know when another photograph makes the composition worse rather than better?
Would it understand why a quarter inch of suede showing beneath another mat can completely change the visual hierarchy?
Would it know when the jersey has stopped being the hero?
Probably not.
Those aren’t software problems.
They’re domain problems.
I learned them by framing jerseys.
Hundreds of decisions.
Thousands of customer conversations.
Mistakes.
Experiments.
Things that looked good on a screen and terrible when built.
Things I thought customers would love that they ignored.
Things customers asked for that taught me something I hadn’t considered.
AI didn’t create that knowledge.
It can now multiply it.
That’s why “vibe coding” fascinates me.
The term makes some people uncomfortable.
I understand why.
If vibe coding means blindly asking AI to generate software you don’t understand, there are obvious limitations.
But I think something much bigger is happening underneath the funny name.
People who understand a problem deeply can suddenly participate in building its solution.
The craftsman can build software for craftsmen.
The teacher can build something for teachers.
The salesperson can build a better sales tool.
The photographer can create something for photographers.
The small-business owner who has heard the same customer frustration 2,000 times can finally say:
I know exactly how this should work.
And then actually build it.
That’s different.
Technology-first versus experience-first.
This has changed how I think about innovation.
Silicon Valley has traditionally worked something like this:
Build technology.
Find a market.
Learn the industry.
Iterate.
Sometimes that produces extraordinary companies.
But AI enables another path:
Learn an industry.
Live the problems.
Develop judgment.
Then build the technology.
That’s the direction that excites me.
Because now the person closest to the problem has a chance to become the person building the solution.
The advantage isn’t necessarily knowing more code.
It might be knowing more about the problem.
When everyone can make, taste becomes the advantage.
There’s another consequence of all this that I’ve been thinking about constantly.
If AI makes execution easier, what becomes more valuable?
Daniel Pink recently offered an answer I love:
“In an age of artificial intelligence, taste will be your killer app.”
Pink is talking about something much deeper than having good style.
He’s talking about discernment.
Knowing what’s good.
Knowing what’s mediocre.
Knowing what belongs.
Knowing what should be removed.
Knowing when something is technically impressive but fundamentally wrong.
And perhaps most importantly, knowing why.
AI can generate twenty logos.
Fifty headlines.
A hundred interface concepts.
Ten different ways to frame a jersey.
That’s extraordinary.
But abundance creates a new problem:
Which one?
When almost anyone can produce almost anything, execution is no longer necessarily the scarce resource.
Discernment is.
That idea resonates deeply with me because I’ve experienced it every day for years without necessarily calling it “taste.”
When I’m designing a Jacquez Art masterpiece, I might move something a quarter of an inch and immediately know it’s better.
I might remove a photograph that a customer originally wanted because the composition is becoming too crowded.
I might look at a framed jersey from ten feet away and know something is wrong before I can explain exactly what it is.
Sometimes the best design decision is adding something.
Very often, it’s having the courage to remove something.
Where did that judgment come from?
Not a prompt.
Not software.
Not a design textbook.
It came from making things.
Thousands of decisions.
Hundreds of finished pieces.
Mistakes.
Experiments.
Customer reactions.
Standing in front of something I created and asking myself:
Is this actually good?
Taste is developed by creating, not merely consuming.
And that may become even more important in the AI age.
Because AI can give all of us more options than we’ve ever had before.
But options aren’t the same thing as answers.
Someone still has to say:
That one.
AI gives us generation.
Experience gives us judgment.
And taste decides what deserves to survive.
Craftsmanship isn’t disappearing.
This is where my two worlds have collided in a way I never expected.
I run a business built around making physical things by hand.
And I’m simultaneously more excited about artificial intelligence than I’ve been about any technology in decades.
Those ideas don’t feel contradictory to me.
They reinforce each other.
At Jacquez Art, I’ve been experimenting with AI not to eliminate craftsmanship, but to bring customers closer to it.
Let someone explore an idea before we build it.
Let them see possibilities.
Let technology remove uncertainty.
Then walk into the studio and make the actual thing with our hands.
The computer doesn’t cut the suede.
It doesn’t sew the jersey.
It doesn’t decide that one more element would ruin the composition.
It doesn’t stand ten feet away from the finished piece and think:
Something isn’t right.
That’s still us.
AI amplifies the craft. It doesn’t have to replace it.
Maybe those years weren’t a detour after all.
This is the part I’ve been thinking about most.
There was a time when I wondered whether stepping away from technology meant I’d left something behind.
Now I wonder whether I was simply gathering the other half of the equation.
Technology taught me how to think about products.
Entrepreneurship taught me how to sell.
Customers taught me what people actually value.
Craftsmanship taught me patience and judgment.
Running a business taught me where the real problems hide.
And now AI has arrived and connected all of it.
Looking backward, the path suddenly makes more sense.
Steve Jobs famously talked about how you can’t connect the dots looking forward.
You can only connect them looking backward.
I’m beginning to understand exactly what he meant.
The opportunity isn’t AI.
It’s you × AI.
That’s the idea I keep coming back to.
AI by itself is extraordinary technology.
But perhaps its greatest impact won’t come from the technology alone.
It will come from what happens when it encounters someone who has spent 10, 20, 30 or 40 years learning something.
A teacher.
A mechanic.
A doctor.
An artist.
A carpenter.
An entrepreneur.
A designer.
A farmer.
Someone who knows things they don’t even realize they know.
Give that person the ability to build, analyze, experiment, create and iterate at a level previously available only to teams of specialists...
and something extraordinary happens.
Experience becomes executable.
Domain knowledge tells you what problem is worth solving.
AI gives you the ability to build the solution.
Taste tells you whether what you built is any good.
That, to me, is the opportunity.
And for the first time in a very long time, I don’t feel like I’m watching the next technology revolution happen.
I feel like I’m inside it.
And I can’t wait to see what we build.
— RJ