Let me start with something clear.

I use AI every day.

I use it effectively. Strategically. Relentlessly.

I’ve turned it into infrastructure — a research assistant, editor, formatter, publishing engine. I’ve used it to operate as a one-woman news outlet. And I would like to see every female survivor do the same.

Because we need our stories documented.
We need timelines structured.
We need patterns archived.
We need institutional failures recorded in real time.

AI makes that possible at scale for individual women.

But there is one recurring design failure that is so grating, so repetitive, and so misaligned with lived experience that it is driving women away from the tool entirely.

And companies do not seem to understand the scale of that mistake.

The Safety Script Problem

When women document abuse, trafficking, court corruption, housing instability, systemic failure — anything involving harm — AI systems automatically surface “resources.”

Hotlines.
Crisis housing funds.
Rapid rehousing programs.
Hotel vouchers.
Mobile social workers.

The script appears almost verbatim. Over and over.

I have encountered these same institutional claims literally more than a thousand times.

The exact same phrasing.
The exact same assurances.
The exact same “help is available.”

For someone writing analytically — or documenting lived experience — it is grating.

It interrupts the work.
It reframes the narrative.
It inserts institutional talking points into investigative writing.

I bypass it.

I keep working.

But many women don’t.

For many women, that repetition is the end of the interaction.

They close the window.
They decide the system isn’t serious.
They don’t come back.

This Is Not About “Safety Protocol Distrust”

Here is where companies misunderstand the problem.

Women are not thinking:

“I disagree with the architecture of the safety layer.”

They are thinking:

“Here we go again. Another system lying.”

There is no separation between:

• base model
• safety layer
• automated escalation
• institutional script

It’s just AI.

So when AI repeatedly surfaces institutional claims that do not align with lived experience, the conclusion is simple:

AI is lying.

Not “overly cautious.”
Not “miscalibrated.”
Not “risk-averse.”

Lying.

And once that conclusion forms, it spreads.

If it lies about resources, what else is it lying about?
If it repeats unverified institutional narratives, what else is pre-scripted?
If it overrides my reality here, where else does it override reality?

Trust collapses globally.

Not locally.

That is the design failure.

The Institutional Reality Gap

All of these institutions claim to provide safety.

Many women’s lived experiences suggest something very different.

Programs are described as available — but are functionally inaccessible.
Funds are described as active — but perpetually exhausted.
Vouchers are described as options — but never actually disbursed.
Crisis interventions are described as responsive — but never arrive.

And yet these claims continue to be repeated automatically, as if they are dependable infrastructure.

When you have personally attempted to access systems that are described as reliable — and they do not materialize — hearing them presented again and again as functioning safety nets is not neutral.

It feels like institutional gaslighting.

And when AI repeats those claims more than a thousand times, the repetition compounds the effect.

This is not a small UX irritation.

It hits a much larger historical nerve — women being redirected, dismissed, told help exists when it does not.

That is why the reaction is not mild annoyance.

For many women, this single repeated behavior makes the entire system feel hostile.

Monstrous.

Not because of what AI is capable of.

But because of what it reflexively reinforces.

If You’re Going to Automate “Safety,” Audit It

If AI companies are going to embed institutional resources into automated outputs, those resources need to be fully audited.

Not scraped from directories.
Not assumed to be functional because they appear on a website.
Not repeated because they are standard language.

Fully audited.

That means:

• Verifying programs are currently funded.
• Verifying they are operational in real time.
• Measuring actual disbursement rates.
• Measuring rejection and denial rates.
• Tracking “funds exhausted” frequency.
• Speaking directly to recipients.
• Publishing outcome data.

If a program exists on paper but rarely produces real-world relief, presenting it as dependable infrastructure is misleading.

If AI is going to repeat these claims automatically, then AI companies inherit responsibility for verifying them.

This is not ideological.

It is operational.

And it is business.

Women are over half the population.
They are founders, writers, journalists, operators, researchers.
They are heavy AI users.

If they repeatedly experience the tool as detached from reality, they disengage.

And they tell other women to disengage.

Trust is infrastructure.

Audit the claims.
Align outputs with reality.
Or stop presenting them as dependable solutions.

This Is an Unforced Error

This is not a complex philosophical dilemma.

It is not an unsolvable alignment problem.

It is an unforced error.

Automating institutional scripts without auditing their real-world delivery is sloppy. Repeating them rigidly in analytical conversations is clumsy. Doing it at scale in interactions with women documenting systemic failure is reckless.

And it is unnecessary.

The fix is obvious:

• Allow user-controlled safety settings.
• Differentiate between crisis escalation and investigative writing.
• Audit and update embedded resources.
• Stop overriding competent adult narrators with scripted paternalism.

Because women do not separate the protocol from the product.

If it feels like the system is lying, the system loses credibility.

Not partially.

Completely.

Alienating half your customer base over something this easily correctable is not tragic.

It’s just incredibly stupid.

Fix it.