ChatGPT Is the New “Doctor Google” – Why DIY AI Fails in Real Projects

 Intro

ChatGPT is the new “Doctor Google” — just for tools and code.

1. The Familiar Misunderstanding

Since ChatGPT became available to everyone, I keep seeing a familiar misunderstanding:

“If I can ask it, I can just build it myself.”

The assumption is simple: if an AI can generate code, scripts, or logic on demand, the step from idea to production suddenly feels trivial.

But access to information has never been the same as experience, responsibility, or accountability.

2. We Have Seen This Before: Doctor Google

This is not a new phenomenon.

Years ago, the same pattern appeared with “Doctor Google.”

Suddenly, medical information was available to everyone.

Symptoms, diagnoses, treatment options — all just a search away.

What did not suddenly appear was medical training, responsibility, or the consequences of getting things wrong.

Information became accessible.

Experience did not.

3. The Pattern Reappears in Companies

Today, the same pattern shows up inside organizations.

“We just need a small tool.”

“Just a script.”

“Just a macro.”

With AI generating code instantly, the perceived effort drops dramatically.

What used to require planning, coordination, and review now feels like a quick task someone can “just do.”

At this point, the solution often already exists — at least technically.

What does not yet exist is clarity around ownership, integration, and long-term responsibility.

4. Where AI Solutions Start to Break

In practice, AI-generated solutions rarely fail immediately.

They fail later — when they leave the demo environment and encounter reality.

Typical breaking points look like this:

- Demo vs. real data

- Performance in production

- Security & permissions

- Ownership & maintenance

- Fragmentation instead of standardization

5. Being Pro AI – But Realistic

This is not an argument against AI.

On the contrary — AI is an incredibly powerful accelerator.

The real leverage is not that everyone suddenly builds everything themselves.

The real leverage is that specialists become faster, more structured, and more effective with AI.

AI does not remove the need for experience, architectural thinking, or responsibility.

It amplifies whatever structure — or lack of structure — already exists.

6. The Hidden Bill

The problem with many DIY AI solutions is not that they are wrong.

It is that their true cost shows up later.

What looked fast and cheap in the beginning often becomes expensive in support effort, rework, and risk.

Performance issues appear under load.

Security gaps surface during audits.

Ownership questions arise when the original author moves on.

Just because something is technically possible does not mean it is economically sound.

7. A Pragmatic Position

Prototypes with AI? Absolutely.

Exploration, experimentation, and fast feedback are exactly where AI shines.

Productive solutions, however, require guardrails.

They need review, ownership, documentation, and a clear understanding of who is responsible once the demo phase is over.

Without that, speed simply turns into future cleanup work.

Conclusion

AI does not fail because it is too powerful.

It fails when responsibility, ownership, and context are removed from the equation.

Used well, AI accelerates good decisions.

Used carelessly, it accelerates future problems.

The difference is not the tool.

It is how seriously we treat what comes after the demo.