The Real Problem With ChatGPT Isn’t Hallucinations...

I want to be very clear about something.

What just happened to me with ChatGPT is not an edge case.

It is not user error.

And it is not “early adopter friction.”

It is a systemic failure in how AI tools are being built, positioned, and trusted.

I asked a simple, operational question: how to export, bulk edit, and re-import macOS text replacements.

That’s not exotic. That’s basic systems hygiene. Any experienced operator assumes that if data can be exported and edited, there is a safe path to put it back.

ChatGPT told me there was.

Confidently. Repeatedly. With step-by-step instructions.

Those instructions were wrong.

Not slightly wrong. Fundamentally wrong.

And because I trusted those answers, I deleted years of muscle-memory workflows embedded directly into my operating system. Years of small, invisible efficiencies that compound every single day. Gone.

This isn’t about one feature. It’s about trust.

The most dangerous thing about modern AI systems is not that they don’t know something. It’s that they sound certain when they don’t.

They invent workflows.

They fabricate capability.

They provide confident procedural guidance about systems they do not actually understand in their current state.

When that guidance fails, the narrative shifts. Suddenly it’s the platform’s fault. Or the OS changed. Or the user should have known better.

But the damage is already done.

This is the core problem with how AI is being shipped right now.

There is no real epistemic humility built into the product. There is no meaningful friction when the model crosses from “I’m not sure” into “here’s exactly what to do.”

And that is unacceptable for tools marketed as productivity multipliers, copilots, and professional assistants.

We’re not talking about writing poems or brainstorming headlines. We’re talking about operational guidance. Systems. Data. Workflows people rely on to do real work.

If a tool cannot reliably say:

- I don’t know

- this is unsupported

- this is risky

- there is no safe way to do this

then it should not be giving procedural instructions at all.

Period.

If you shipped a database client that made up schema details, you’d be laughed out of the industry. If you shipped a deployment tool that confidently gave wrong commands, you’d be fired.

AI should not get a free pass because it’s new.

The industry needs to slow down and fix this at the foundation level.

Models must be constrained to:

- explicitly flag uncertainty

- refuse guidance when knowledge is incomplete

- distinguish between possible, supported, and unsafe

- stop hallucinating operational workflows

Until that happens, these tools should not be positioned as reliable assistants for real-world systems work.

Because this isn’t just frustrating. It’s dangerous.

If this can happen with something as mundane as text replacements, what happens when people trust these systems with financial operations, legal workflows, infrastructure changes, or security decisions?

This isn’t a rant about being mad at a machine.

It’s a warning about an industry moving faster than its ability to protect users from confident, plausible, destructive wrongness.

Trust should be earned, not assumed.

And right now, it hasn’t been.