The Dangerous Gap Between AI POC and AI Product (And Why We Keep Ignoring It)

A quick one on my AI battles. Before I dive in, let me be clear: I’m a huge believer that AI will help us. I use AI every day. My team uses AI every day. I have 20+ years in software engineering and 10+ years in product management.

So when I share this concern, it comes from experience.

The Story That Triggered This

Today I received a request that went something like this:

“Hey Alex, this guy coded something in 45 hours that our team couldn’t deliver in 3-6 months!”

I wasn’t shocked by what they built. I was shocked because we’re still approaching AI this way.

In my opinion, that’s not the right way to tackle AI. We’re putting immense pressure on engineers and product teams, claiming something that isn’t even a POC is production-ready.

Let’s Define the Three Stages

If you’re not familiar with software development stages, here they are:

1. POC (Proof of Concept)

Reaching a POC is pretty easy—a couple of weeks, maybe. You prove the solution works in isolation.

2. MVP (Minimum Viable Product)

You have enough functionality. You’ve added quality checks. It’s starting to look real.

3. Product

Something stable that’s easy to scale. It’s evolving with incremental delivery. It’s production-ready.

The journey from AI POC to AI Product is long. Platforms can help, but it’s still a different game.

The Dopamine Problem

Here’s the second thing: human behavior. If you’re a doer, you prefer to do something yourself rather than wait for someone else.

That’s where vibe-coding is amazing—progressive learning. You don’t need to know everything upfront. You create, you learn, you deliver, you see results.

This releases dopamine. You stay motivated. (This is what excited me as a developer years ago.)

The Core Problem

We’re seeing AI POCs and believing they’re product-ready.

From both a product and engineering perspective, this is frustrating. If we view a product through the POC lens, everything is underestimated or not even considered.

I wrote a detailed document sharing these concerns. But here’s the problem: If you read it under the lens that “everything should be faster and easier with AI,” you won’t even read the document.

Because reading, digesting information, processing it, connecting neurons—that takes time.

But you have a product someone built in 15 minutes. And that seems fine. Until it’s a very big problem.

The Integration Problem

Another concern: Most AI POCs are built independently (which is great for speed—removing dependencies is a product/engineering rule).

But what happens when you try to integrate it into your real environment?

• You vibe-coded in Java, but your codebase is Python—now what?
• You developed outside your architecture constraints
• You have microservices, monitoring systems, rules, security protocols

When you AI POC or vibe-code, you bypass all the rules. And that’s fine for prototyping!

But at some point, you need to merge your code into production or build an entirely new architecture outside your existing one.

The Time Illusion

Because you’re viewing AI work through the “AI is super fast” lens, you won’t estimate properly.

The result? One of two outcomes:

1. Everything fails
2. “Don’t worry, just vibe-code another 15 minutes, and another 15 minutes, and another…”

Those minutes become hours. Hours become days. Days become weeks. Weeks become quarters.

And you know what? That’s exactly the same time your amazing engineering team would have needed—with their years of experience and deep knowledge.

My Opinion: AI is a Tool

As humans, we need to do a better job using this tool.

For Leaders: Educate Your Company

Explain what AI can do (it’s magic in many ways) but also explain the “false friend” aspects.

Yes, it’s fast. But there’s a law in software: Go slow in the beginning to go faster in the end.

With AI, we’ve reversed the order: Go fast in the beginning, then… who knows where we’re going at the end?

For Vibe-Coders: Keep Going

I’m not saying stop. The opposite. Keep doing your thing. Keep contributing to improving the models. We need all of you.

But keep in mind: People will start questioning what you’re doing because AI gives power to individuals who couldn’t code before.

Imagine: You couldn’t talk, then with AI you can suddenly speak in 15 minutes. That’s amazing, right?

But then you discover you’re only speaking a limited language. When someone asks for more, you need to keep learning.

That’s how we evolved as humans. With AI, it will be faster—yes. Magic—no. Free—no.

For Developers: Do a Better Job

First, embrace AI. That’s real.

But also: Explain what you’re doing. Don’t just justify it—communicate it.

For years, developers were seen as introverted people. “Don’t worry, they’re just a developer.” That shouldn’t be the case anymore.

You should be seen as someone who can communicate beyond the code you’re writing.

For Experienced Developers: See with Fresh Eyes

Experience is both a pro and a con. You’re biased by everything you know. You see all the edge cases.

If you want to cover all cases, it’ll require additional work (with or without AI).

Use AI to see with fresh eyes. Think in sprints: What’s the simplest piece of functionality I can deliver that’s available to customers?

Deliver that. Remove the fat. Start adopting.

With AI, without AI, vibe-coding, traditional coding, singing a song—it’s really up to you.