The Problem with Vibe Coding: You Still Need to Know What to Build – Solutioning Helps Fix It

Recently, I chatted with a few friends working in AI, and we all shared the same frustration: while AI is great at writing code, something still feels off when using it.

Let’s be honest—”Vibe Coding” has lowered the technical barrier. You can throw a vague instruction at AI, and it’ll spit out code. But here’s the catch: you still need to know what to build in the first place. It’s like being handed a razor-sharp knife but having no idea what to chop.

1. Code Is Easy, Problem-Finding Is Hard

The biggest pitfall of current AI coding tools? They solve “how to write code” but not “why write this code.”

I’ve seen too many teams dive headfirst into coding. AI generates code so fast it feels impressive—until they realize: Who actually needs this feature? Is there even a market for it? The end result is either unused or outdone by existing solutions.

This is classic “solution looking for a problem.” When all you have is a hammer, everything looks like a nail.

2. “Vibe Solutioning” Might Be the Cure

The concept of “Vibe Solutioning” mentioned in the article is intriguing. Instead of just writing code, it uses AI to help clarify what product to build, starting with market research.

For example:

Automatically analyzing competitors
Predicting user pain points
Generating product prototypes
Even evaluating business models

This is far more valuable than just writing code. After all, startups rarely fail because of bad code—they fail because of bad product direction.

3. Roles Are About to Change

If this trend takes off, the way product managers and developers work will shift dramatically.

Product managers might no longer manually write PRDs but instead learn to collaborate with AI on market insights. Developers won’t obsess over syntax details but spend more time on system design.

In short, AI automates execution, pushing humans to think higher-level. Those who can’t adapt to these tools risk being left behind.

4. Don’t Celebrate Too Soon

Of course, there are risks. The biggest question: Is AI’s market analysis reliable? Can its product proposals actually work?

Some early tools I’ve seen offer suggestions that are painfully obvious. For example, “A social app should prioritize user experience”—no kidding?

The key is maintaining critical thinking. AI is an advisor, not a commander.

5. Impact on Startups

What excites me most is how this could lower the barrier to real innovation.

Traditionally, startups had to hire teams for market research and product design. Now, a small team with AI tools can quickly validate ideas. Lower failure costs mean more experimentation.

But the flip side? Competition will intensify. When everyone has the same tools, what sets you apart is your unique understanding of the problem.

Final Thoughts

AI coding tools are cool, but don’t be fooled by sheer code volume. A million lines of garbage code are worthless compared to ten lines that solve a real problem.

Before starting your next project, try asking AI: “Is this problem worth solving?”—that might be more valuable than asking it to write code.

(The End)