Problem Havers vs. Solution Creators: Why AI Changes Who Gets to Build

For most of history, there have been two kinds of people in business: the ones who see the problems and the ones who can build the solutions. The marketing director who knows exactly why the funnel is leaking but can't fix the website. The operations manager who could redesign the entire workflow on a whiteboard but needs a developer to make it real. The founder with a brilliant product idea and no way to prototype it.

I call them "problem havers" and "solution creators." And for decades, the gap between them has been expensive to cross. You either learned to code, hired someone who could, or watched your idea die on a sticky note.

AI is closing that gap. But not in the way most people think.

Table Stakes vs. The Real Unlock

Let's get something out of the way: if you're using AI to summarize documents, draft emails, and analyze spreadsheets, that's great. Genuinely. But that's table stakes now. Everyone is doing that. Your competitors are doing it. The intern is doing it.

The real unlock isn't using AI as a productivity tool. It's using AI as a building partner.

There's a massive difference between asking ChatGPT to "write me a marketing email" and using AI to build an automated email workflow that triggers based on customer behavior, pulls data from your CRM, and personalizes content at scale. The first one saves you twenty minutes. The second one changes your business.

The people who figure out this difference — who move from using AI as a fancy assistant to treating it as a co-builder — are the ones who will have an enormous advantage over the next five years.

You Don't Need to Learn to Code (But You Need to Think Like a Builder)

Here's where I might lose some people, so stick with me.

I'm not telling you to learn Python. I'm not telling you to take a bootcamp or get a computer science degree. What I am telling you is that to use AI as a building partner, you need to understand a few concepts that most "AI for business" content completely ignores.

Structured knowledge documents. AI works dramatically better when you give it well-organized context. A messy Google Doc full of bullet points and random notes? The AI will give you messy output. A clean Markdown document with clear sections, defined terms, and structured information? Now the AI can actually think with you. Learning to organize your knowledge in AI-optimized formats is one of the highest-leverage skills you can develop right now.

Datasets and information architecture. You don't need to become a data scientist, but you need to understand how information is organized. What's a CSV? How should your customer data be structured? What's the difference between a flat list and a relational dataset? When you understand how data flows, you can direct AI to build things that actually work with your information.

Integrations and APIs. Every tool you use — your CRM, your email platform, your project management software — has ways to connect to other tools. These connections are called APIs, and they're the plumbing of modern business. You don't need to write API calls by hand. But you need to understand that they exist, what they can do, and how to ask AI to wire them together.

Project structure. When you start building with AI, you'll quickly learn that organization matters. Where files live, how things are named, how a project is structured — these aren't just developer concerns. They're the difference between a project that works and one that collapses the moment you try to change something.

Embrace the Spaghetti

Here's the part nobody talks about: your first few builds will be terrible. And that's completely fine.

You'll use AI to create something, and it'll work — sort of. The code will be tangled. The logic will be fragile. You'll change one thing and three other things will break. Welcome to spaghetti code. Every developer in history has written it.

The difference is that with AI, scrapping it and starting over costs you an afternoon, not a month. You built version one, learned what you actually needed, and now you can direct AI to build version two with that knowledge. Each iteration gets cleaner, faster, and more robust.

This is the builder's mindset: ship something ugly, learn from it, rebuild it better. The cost of iteration has collapsed. Take advantage of that.

Start Small, Think Big

If this sounds overwhelming, here's your on-ramp: pick one small thing and build it.

• A portfolio site. Use AI to help you set up a simple website. Not a Squarespace template — an actual site you control, with files you can see and modify.
• An email automation. Connect your email tool to your CRM and have AI help you build a workflow that sends personalized follow-ups automatically.
• A data dashboard. Take that spreadsheet you've been staring at for months and have AI help you turn it into something interactive.
• A simple internal tool. That process your team does manually every week? Build a basic tool that handles it.

None of these require a CS degree. All of them require the willingness to sit with AI, think through the problem, and iterate until it works.

The Best Problem Framers Win

Here's the thing that makes this especially relevant for marketing people, business operators, and founders: the people who win with AI as a building partner aren't the best coders. They're the best problem framers.

If you can clearly articulate what needs to happen — the workflow, the logic, the user experience, the edge cases — AI can handle a huge portion of the how. The years you've spent understanding customer behavior, business processes, and market dynamics? That's not a consolation prize. That's the most valuable input in the entire equation.

A developer who doesn't understand your business will build the wrong thing efficiently. A business person who understands the problem deeply and can direct AI? They'll build the right thing, even if it takes a few tries.

The Line Is Dissolving

We're moving into a world where the ability to identify a problem and the ability to build a solution are no longer separated by a technical moat. The moat is shrinking every month as AI tools get more capable.

But — and this is important — the moat isn't gone. You can't just type "build me a SaaS product" into ChatGPT and expect magic. The people crossing the gap are the ones investing time in understanding how to work with AI as a building partner: organizing their knowledge, structuring their data, understanding integrations, and learning to think in systems.

The problem havers who develop builder thinking will have a massive edge. Not because they became developers, but because they stopped waiting for permission to build.

That's the shift. And it's already happening.