I've watched it happen too many times. Talented founders with brilliant technical backgrounds pour their hearts, souls, and savings into sophisticated AI solutions that ultimately go nowhere. Months of coding, training models, and optimizing algorithms—all wasted because nobody actually needed what they built.

This isn't just frustrating to observe; it's a completely avoidable tragedy. The pattern is painfully predictable: founders become enchanted with their clever technical solution before confirming that real people have the problem they're trying to solve.

The Seductive Trap of AI Solutionism

There's something uniquely intoxicating about building AI solutions. The technology is cutting-edge. The possibilities seem limitless. The technical challenges are intellectually stimulating. It's easy to get lost in the elegance of your architecture or the impressiveness of your model accuracy.

But here's the cold, hard truth: nobody cares how sophisticated your natural language processing model is. Nobody is impressed by your recommendation engine's novel approach. Nobody will pay for your solution just because it uses the latest large language model.

What they care about—the only thing they care about—is whether you can solve real problems first.

Why Founders Fall Into This Trap

Several factors make founder-solution love affairs particularly common in AI:

• Technical fascination: Many AI founders come from technical backgrounds and genuinely enjoy solving complex problems—sometimes for their own sake.
• Hype cycle pressure: There's enormous pressure to build "AI solutions" while the market is hot, leading to forced applications.
• Solution-first thinking: "I have a great AI technique, now what can I apply it to?" rather than "Here's a painful problem, what's the best way to solve it?"
• Overestimation of value: Assuming that because something is technically difficult, people will automatically value it.

Problem-First Thinking: The Only Path That Works

The successful founders I've observed take a radically different approach. They become obsessed with a specific problem before they ever write a line of code or train a single model.

This problem-first mindset changes everything:

1. They talk to potential customers extensively before building
2. They validate that the problem is painful enough that people would pay to solve it
3. They understand the nuances and context of the problem deeply
4. They evaluate multiple potential solutions, AI being just one option
5. They build minimal implementations to test assumptions quickly

Remember: people don't wake up in the morning hoping to use more artificial intelligence. They wake up with specific frustrations, challenges, and jobs to be done.

The Reality Check: Nobody Cares About Your AI

This might sound harsh, but it's a reality check every AI founder needs: nobody cares about your AI. Full stop.

What they care about is:

• Saving time
• Reducing costs
• Increasing revenue
• Eliminating frustration
• Looking good to their boss/peers
• Achieving their goals more easily

AI is just a means to these ends—nothing more. The sooner you internalize this, the sooner you can build something people actually want.

How to Avoid Building AI Nobody Wants

If you're currently building an AI solution or considering it, here's my straightforward advice:

1. Start with customer interviews, not code

Talk to at least 20 potential customers before you build anything. Ask open-ended questions about their problems, not about your solution. Listen for emotional signals that indicate real pain points. If they don't express genuine frustration or excitement when discussing the problem, you're probably chasing a lukewarm opportunity.

2. Quantify the pain

How much time or money is this problem costing people? How frequently do they experience it? What workarounds are they currently using? If the answers don't reveal significant pain, reconsider your focus.

3. Validate willingness to pay

Don't just ask "would you pay for this?" (everyone says yes). Instead, try to get commitments: "If I build this solution that does X for $Y per month, would you be willing to prepay for 6 months to get early access?" Real commitments separate polite encouragement from genuine market demand.

4. Build minimum viable solutions

Your first version might not even use AI. Could you deliver value with a manual process first? Could you build a simple rules-based system before implementing machine learning? Focus on solving the core problem in the simplest way possible.

The Path to AI Products People Actually Want

The most successful AI products I've seen share a common trait: they solve a specific, painful problem so well that the AI technology becomes invisible. Users don't think "I'm using an AI solution"—they think "Finally, someone solved this frustrating problem!"

That's the paradox of building great AI products: the better your AI actually is, the less people will notice or care about the AI itself. They'll just be delighted that their problem is being solved elegantly.

This is why the mantra "focus on the problem, not the solution" isn't just good advice—it's the fundamental difference between success and failure in AI entrepreneurship.

So before you spend another day fine-tuning that model or optimizing that algorithm, ask yourself: am I in love with my solution, or am I obsessed with solving a real problem? Your honest answer might be the difference between building strength through failure or joining the graveyard of clever AI solutions nobody wanted.

Remember this: People don't care about your AI. They care about their problems. Focus there first, and everything else will follow.