Why defining matters more than solving in the age of AI and innovation

Problem solving is celebrated often. It’s become a badge of honor to fix, optimize, and deliver. But what if the real skill isn’t in solving what’s visible, but in defining what’s worth solving? A problem isn’t always a bug or a pain point. Sometimes it’s a hidden opportunity, a gap in perception, or a simple human desire waiting to be noticed. From historical best innovators to modern AI founders aren’t just efficient solvers but brilliant definers, too.

1. Defining vs. Solving

In computer science, students are taught to solve problems algorithmically: find the shortest path, optimize the runtime, minimize the cost. It’s logical, structured, measurable but it starts with one assumption: that the problem is already defined. In the real world, most breakthroughs come not from better solutions, but from better definitions. They start with questions like:

• Why do people behave or think this way?

• What’s missing that no one noticed?

• Is the “problem” even the real issue?

Defining requires traits that don’t fit neatly into a textbook. You could call it instinct, but more truthfully, it’s the art of seeing differently.

2. When a “Problem” is just desire not every problem is something broken.

Sometimes, it’s just something people wish existed. The iPhone didn’t fix phones. Spotify didn’t fix music. Airbnb didn’t fix hotels. They all redefined satisfaction. They saw that what people truly wanted wasn’t repair it was reimagination. When you satisfy desire, you solve a “problem” that no one knew how to describe. That’s the quiet genius of defining well.

3. What this teaches PMs and TPMs enthusiasts?

For people yransitionaing into Product Managers and Technical Program Managers roles, this mindset changes everything. A PM’s job isn’t to run through backlogs it’s to uncover why the backlog exists in the first place. To connect what customers feel with what teams build. The PM defines the “what” and the “why.” The TPM engineers the “how.” When transitioning into these roles especially from engineering this shift is everything. You move from execution to direction. From “What do I build next?” to “What’s truly worth building?” That’s how good managers become strategic leaders.They stop solving faster and start defining smarter.

4. What this teaches CS students and startup owners?

 Computer Science education often rewards clean logic but the AI era rewards context. As AI tools become powerful enough to generate code, the question is no longer “Can I solve this?” It’s “Is this even the right thing to solve?” For CS students, that means thinking beyond syntax and efficiency. The standout developers of the AI age will be those who connect technology to real human context who can say, “This matters,” not just, “This runs.” For AI startups, this is the difference between noise and relevance. Many teams today race to automate fewer stop to ask why automation matters there at all. True disruption doesn’t come from solving faster. It comes from reframing the question. Uber didn’t fix taxis. It redefined convenience. ChatGPT didn’t fix search. It redefined access to thought. The next wave of AI innovation will come from founders who don’t just solve problems, but see differently. The world doesn’t lack problem solvers. It lacks people who define problems with insight and imagination. Whether you’re managing a product, leading a team, writing code, or building an AI model, the principle is the same: Don’t just ask “How can I fix this?” Ask “What truly needs fixing and why?Because sometimes, a problem isn’t a problem at all. It’s an opportunity waiting to be recognized.

Disclaimer: To improve the flow of his writing, writer utilized AI for grammar and structure refinement. All opinions and insights expressed here are his own.

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