Stop Trying to Use AI—Start Building With It

Everyone's using AI wrong.

Not wrong as in "incorrect syntax" or "bad prompts." Wrong as in focused on the wrong thing entirely.

The conversation about AI is dominated by productivity. Write emails faster. Summarize documents quicker. Research in minutes instead of hours. Do more of what you're already doing, just with less friction.

That's the appetizer. That's what they show you to get you interested.

The real opportunity isn't productivity. It's capability.

The Productivity Trap

Productivity improvements are nice. If you can draft an email in one minute instead of five, you saved four minutes. If you can research a topic in ten minutes instead of an hour, you saved fifty minutes.

Add it up and you get—what? A few extra hours per week? Maybe a day per month?

That's valuable. I'm not dismissing it. But it doesn't change what you can do. It just changes how fast you do it.

The ceiling is the same. You're just hitting it more efficiently.

Here's the problem with productivity gains: your competitors get them too. When everyone can write emails faster and research quicker, the advantage disappears. You're all running faster on the same treadmill.

The Capability Shift

Building is different.

When you use AI to create something that didn't exist before—a custom workflow, a tool tailored to your exact problem, an automated system that does work without your involvement—you're not just moving faster.

You're doing things you couldn't do before.

That's a different kind of advantage. It doesn't normalize when your competitors adopt AI for productivity. It persists because you built something specific to your business, your problems, your needs.

Custom solutions can't be copy-pasted. They have to be imagined, specified, iterated, and refined. The business owner who builds them has an advantage that isn't erased when everyone gets the same chatbot subscription.

What "Building" Actually Looks Like

I'm not talking about becoming a software engineer. I'm talking about directing AI to create solutions.

The difference is important.

A software engineer understands systems at a fundamental level. They know languages, frameworks, architectures, best practices developed over decades. That expertise takes years to develop and can't be shortcut.

But here's what's changed: you don't need that expertise to build. You need it to build at the highest levels, to create the most complex systems, to solve the hardest problems. But for business applications—the tools and workflows and automations that small businesses need—you can get there without the deep expertise.

You need to be able to describe what you want clearly. You need to be able to evaluate whether what you got matches what you asked for. You need to be able to iterate when it doesn't work the first time.

Those are different skills than traditional coding. They're more accessible. And they're enough to create real value.

The Gap Between Having and Using

Most businesses have access to AI tools right now. Subscriptions are cheap. The interfaces are approachable. Getting started is trivially easy.

But having access isn't the same as using the access. And using the access for productivity isn't the same as using it to build.

The gap is in imagination and execution.

Imagination: Can you see the problems in your business that custom solutions could solve? Can you envision workflows that don't exist yet but should?

Execution: Can you translate that vision into a specification clear enough for AI to act on? Can you iterate through the inevitable problems until you get something that works?

Most people stop at the productivity layer because that's what's easy. Ask the chatbot a question, get an answer. Write an email, get a draft. Low friction, low effort, low ceiling.

Building requires more investment. You have to think harder about what you actually want. You have to push through the frustration when it doesn't work the first time. You have to learn a new kind of skill.

But the returns are proportionally higher.

What You Should Be Asking

The default AI question is: "How can AI make my work faster?"

Better question: "What could I build that I couldn't build before?"

Think about your business. Where are the gaps? What workflows are broken because no tool exists for them? What data do you have that you can't act on? What would you create if creation was free?

Those are the opportunities. Not in doing the same things faster, but in doing new things entirely.

The email you write 30% faster is a marginal improvement. The system you build that alerts you to problems before they become emergencies is a structural advantage.

The Meta-Skill

Here's what I've learned building with AI: the skill isn't prompting. It's thinking.

You have to know what you want. You have to be able to describe it clearly. You have to recognize when what you got isn't right and be specific about how it's wrong.

That's really a business skill, not a technical skill. It's the same thing you do when you hire a contractor: specify what you want, evaluate what you get, iterate until it's right.

The difference is that AI is infinitely patient, available 24/7, and costs almost nothing per iteration.

If you can think clearly about problems and communicate clearly about solutions, you can build. If you can't, no amount of prompting tips will save you.

The Invitation

Stop using AI.

Start building with it.

The productivity gains are fine. Take them. But don't stop there.

Look at your business and ask: What should exist that doesn't? What would I create if I could?

Then create it.

The tools are here. The window is open. The only thing between you and capability is the decision to build.