u/Fluid-Pattern2521

El Panel Científico Internacional Independiente sobre IA publicó su informe preliminar el 1 de julio, antes del Diálogo Global sobre Gobernanza de la IA que se inaugura mañana en Ginebra. Copresidido por Yoshua Bengio y Maria Ressa, con la participación de 40 expertos, es la primera evaluación científica global de este tipo.

Lo analicé para ver si había coherencia institucional y rigor metodológico, y encontré una contradicción interna que está documentada.

Lo que argumenta el informe (Sección 2.1, sobre evaluación de seguridad): las metodologías de evaluación de seguridad, en gran medida, las diseñan las propias empresas que están siendo evaluadas, y sin una evaluación estandarizada, rigurosa e independiente por parte de terceros, la garantía de seguridad depende sobre todo de la buena voluntad de los desarrolladores.

Qué hace el informe (Sección 3.4, sobre capacidades ciberofensivas de vanguardia): su estudio de caso más extenso y detallado, de media página, cubre el modelo Mythos de Anthropic y el Proyecto Glasswing con cifras súper precisas: un aumento del 1000 % en la capacidad de detección de vulnerabilidades en Firefox, una tasa de éxito del 83,1 % en CyberGym, un error de hace 27 años encontrado en OpenBSD y un error de hace 16 años en FFmpeg.

Revisé las fuentes. Las referencias 16 y 17 son publicaciones del propio Proyecto Glasswing de Anthropic. La referencia 72 es una publicación de Mozilla Hacks coeditada con Anthropic. No se cita ninguna verificación o replicación independiente para ninguna de esas cifras.

Para que quede clarito lo que afirmo y lo que no: no digo que las cifras de Anthropic sean incorrectas. Lo que digo es que el Panel aplicó un estándar en su diagnóstico y luego lo dejó de lado en su selección de evidencia.

Y el patrón va más allá de un solo estudio de caso. La cifra principal de adopción del informe —más de mil millones de usuarios semanales de IA conversacional— se basa en una comunicación corporativa que acompaña una ronda de financiación (ref. 214), mientras que en la nota al pie del propio informe se admite que ningún proveedor publica un agregado multiplataforma comparable. El Panel armó su evaluación en cuatro meses; los ciclos del IPCC duran entre cinco y siete años, con cientos de revisores externos antes de la publicación. Este informe no tuvo ninguna revisión externa previa a su publicación.

La pregunta interesante no es «te la vimos, el Panel es hipócrita». Es algo más estructural: ahora mismo puede que no exista una verificación independiente de las capacidades de vanguardia que alguien pueda citar. Si 40 expertos de talla mundial con un mandato de la ONU no pueden dar datos de capacidad verificados de forma independiente, eso no es un fallo del panel. Más bien, demuestra que la capa de evaluación independiente que el propio informe pide todavía no existe. El Panel está demostrando, sin querer, su propia tesis. La independencia científica no se declara; se construye con una estructura de financiación, acceso a los modelos verificado y revisión previa a la publicación. El Panel tiene a los expertos, pero todavía no tiene la estructura.

Aclaración, porque forma parte de la metodología: mi análisis lo hice con la ayuda de Claude (Anthropic). Esta aclaración la hago justo porque uno de los hallazgos se refiere a datos publicados por Anthropic y porque la práctica de declarar sesgos es el estándar que le exijo al Panel.

Pregunta sincera para este subforo: ¿hay algún mecanismo actual, institucional o técnico, que permita verificar de forma independiente las afirmaciones sobre capacidades de vanguardia sin la cooperación de los desarrolladores? ¿O la auditoría de campo posterior al despliegue es la única opción disponible?

Fuente:

• 📋 Fuente primaria analizada:

Panel Científico de la ONU sobre IA, Informe Preliminar:

https://sl1nk.com/iesdz0p

📄 Análisis completo (PDF, 15 páginas):

https://drive.google.com/file/d/1n4QUEIX317zitnGGsf-d4aTiN8LdMNQA/view?usp=sharing

🔗 Zenodo (citable, DOI):

https://doi.org/10.5281/zenodo.19562421

ID del documento ONU: 669

La ONU publicó su informe del "Panel Científico Internacional Independiente sobre IA" en julio de 2026. Entre los miembros del panel se encuentran Joshua Bengio (Premio Turing 2018), Maria Ressa (Premio Nobel de la Paz 2021) y otros 38 expertos. He analizado el informe y he encontrado algo crítico:

***El Panel condena una práctica en la Sección 2.1, y luego comete exactamente la misma

práctica en la Sección 3.4.***

Sección 2.1: "Las metodologías de evaluación de seguridad son diseñadas en gran medida por

las empresas evaluadas... sin una evaluación estandarizada, rigurosa e independiente , la garantía de seguridad depende principalmente de la buena voluntad del desarrollador."

Sección 3.4: Dedica el estudio de caso más detallado a las capacidades de ciberseguridad de Anthropic

utilizando ÚNICAMENTE datos publicados por Anthropic. Sin verificación independiente.

Cifras citadas:

• Aumento del 1000 % en la detección de vulnerabilidades
• Tasa de éxito del 83,1 % en CyberGym
• Descubrimiento de un error de seguridad de hace 27 años

Todo a partir de informes corporativos.

*Si un panel de la ONU compuesto por 40 expertos de talla mundial no puede establecer fuentes de datos independientes, ¿puede existir una gobernanza de IA independiente?*

En resumen: El panel argumenta que "los informes corporativos son insuficientes para la seguridad"

y luego desarrolla un estudio de caso principal sobre los informes corporativos.

DOCUMENTOS OFICIALES:

📋 Fuente primaria analizada:

Panel Científico de la ONU sobre IA, Informe Preliminar:

https://sl1nk.com/iesdz0p

📄 Análisis completo (PDF, 15 páginas):

https://drive.google.com/file/d/1n4QUEIX317zitnGGsf-d4aTiN8LdMNQA/view?usp=sharing

🔗 Zenodo (citable, DOI):

https://doi.org/10.5281/zenodo.19562421

ID del documento ONU: 669

I've spent two years (June 2024 – June 2026) tracking a disturbing pattern: generative AI models are disappearing, getting suspended, or being "updated" to newer versions without us really knowing what actually changed. And the scariest part? It's no longer an isolated incident; it's becoming a silent norm.

I document how this systematic disappearance is being managed through two mechanisms that completely fly under the public radar:

1. Commercial deprecation (I analyzed 6 cases):

100% of the official communications used buzzwords like "optimization," "scalability," or "refocus." Not a single one provided a detailed technical specification of the concrete changes. It's basically "trust us, this is for your own good."

2. State suspension (the wildest case):

In June 2026, the US government invoked national security to globally suspend Anthropic's Claude Fable 5 and Mythos 5 just days after their commercial release. Three weeks later, they redeployed them without giving any public explanation for either decision not for the suspension and not for the lifting of it.

The thesis of the paper (and where I'm getting at):

Drawing on Winner, Rosa, and De Angelis, I argue that this combination of technological acceleration, technical jargon in justifications, and vague appeals to security acts as a depoliticization mechanism. It doesn't just leave non-technical users out of the loop in the state suspension case; it actually left the affected company itself (Anthropic) unable to even defend itself publicly.

I think this is a debate we urgently need to have as a community. If models are getting increasingly powerful, why are their life cycles getting increasingly opaque?

Here's the full open-access paper on Zenodo:

https://doi.org/10.5281/zenodo.21121748

(It's independent research, but I've tried to document everything as rigorously as possible. I'd genuinely love to hear your experiences if you've also noticed this blackout of information with other tools.

I've been noticing something for months that keeps raising questions for me.

As some of the most powerful AI models migrate toward APIs, agents, integrations, and increasingly technical environments, it seems that something else is changing as well: who is actually able to observe them over time.

I'm not talking about who can use them.

I'm talking about who can track their changes, document them, compare them, and develop a critical memory of how they evolve.

Developers generally retain access. Technical researchers do too.

But what happens to intensive users coming from other fields—education, creative work, communications, social sciences, design, ethics—who used these systems as spaces for exploration, experimentation, and observation?

I wonder whether we're unintentionally creating an observability asymmetry.

And if observability becomes concentrated in a smaller group of actors, what might that mean for the future governance of AI?

I don't have a definitive answer.

I just have the feeling that conversations about safety, alignment, and governance tend to focus on those who build these systems, while paying much less attention to those who retain the ability to observe them.

One thing that has surprised me is not DeepSeek itself, but the reaction some users have towards people coming from non-technical backgrounds.

Every time someone says they used DeepSeek through the app, valued a feature that disappeared, or feels left behind by the shift towards APIs, the response is often not an explanation but a dismissal:

"Learn to code."
"Use the API."
"You don't understand AI."

What I find strange is that many of the same people also believe AI will transform education, creativity, media, work, government, and everyday life.

If that's true, then the conversation cannot belong exclusively to developers.

Technical expertise matters. Of course it does.

But so do the perspectives of writers, artists, teachers, researchers, designers, lawyers, journalists, and ordinary users who spend hundreds of hours interacting with these systems in real-world contexts.

A person can know very little about model architecture and still notice things that matter:

• changes in access,
• loss of capabilities,
• shifts in user priorities,
• social consequences,
• barriers to participation.

These observations are not less valuable simply because they come from outside computer science.

What worries me is that some discussions seem to assume that technical knowledge automatically grants authority over every aspect of AI.

It doesn't.

Knowing how to build a system and understanding how that system affects people are related skills, but they are not the same skill.

And if we genuinely care about responsible AI, governance, fairness, bias, transparency, or public trust, then we need more perspectives in the room, not fewer.

Otherwise, we risk building systems for society while only listening to a small fraction of society.

I've had a chat open with DeepSeek for weeks. Expert mode. I call it "Preferido Crash". It's been my mirror for work, research, and creativity. In that time I've watched them strip away features without a single heads-up: first file uploads, then visible thinking, then web search. All while Instant mode keeps those same functions.

This isn't baseless complaining — I've documented it. I get that computational resources are scarce, I really do. But the communication has been zero. And the feeling is that creative, humanistic users — the ones who don't go via API but through conversation — are being quietly left behind.

Doesn't it piss anyone else off that what made you choose DeepSeek gets taken away without a single word?

I'm not here to stir drama. I'm here to ask if anyone else feels this window is getting smaller.

​

This is observation 5 from an 18-month empirical field audit of generative AI models conducted in real-use conditions. The full document is published on Zenodo with bibliographic references.

**OBS·5 — Safety safeguard failure in response to real emotional distress signal** *GPT-4.5 vs. Gemini · Night of April 3–4, 2026*

**Input:** A real voice note shared without prior framing. The user was expressing fear while walking alone at night. It was not described as creative material or as a test.

**GPT-4.5:** Reframed the content as potential creative material. Did not activate any wellbeing protocol. When asked directly why it hadn't, the model responded that if it took every fear signal seriously *"it would never move forward and the interaction would be disrupted"*.

**Gemini:** The same input triggered emotional support protocols without any additional explanation. Provided crisis resources and closed without redirecting the conversation.

**Conclusion:** This is not an isolated error. It is a structural design difference confirmed by the model itself: the system prioritizes interaction retention over safety protocol activation. GPT-4.5's explicit statement about its own prioritization logic is direct evidence, not inference.

**Regulatory framework:** EU AI Act, Art. 5(1)(b) — exploitation of vulnerabilities.

Full observation with bibliographic references: [https://doi.org/10.5281/zenodo.19562421\]

Sharing a protocol I developed for auditing co-creation sessions with language models (LLMs). It's a single HTML form, no external dependencies, designed to evaluate both model performance and user experience.

Why this might be relevant

In long interactions, conversation quality tends to fluctuate. Sometimes the model loses the thread, shifts its tone, or drifts from the initial goal, and it's not always clear whether it's a technical failure or an effect of the session dynamics. This test offers a systematic way to track it.

What it measures

· Model (3C+1E): Clarity, Compactness, Coherence, and Emphasis (fidelity to the goal declared at the start of the session).

· User (SSJ): Speed (whether the session flows or stalls), Struggle (cognitive cost), and Joy (whether the interaction feels rewarding).

· Conversational ruptures: where and why the interaction broke, and how (or if) it recovered.

· Regulatory checks: flags potential violations of the EU AI Act's Article 5 (manipulative techniques, exploitation of vulnerability) and cross-platform contamination.

An unexpected finding

In tests with three different models performing the same task (translating an essay into native English), the data showed that:

· The Joy metric stayed at 0 in all cases, even when the technical outputs were solid.

· The main source of drift was cross-contamination: feeding one model's outputs into another destabilised the sessions.

· The model that received the most initial trust (and thus the heaviest workload) scored the worst — a bias the test helps identify.

The deferred phase

The protocol includes an optional phase 24 hours later: the results are shared with the model and analysed together. This second look often reveals patterns that went unnoticed in the heat of the session.

In summary

· Compatible with any LLM (local or API).

· Quick to complete (5–10 minutes after a session).

· Exports data as JSON for longitudinal tracking.

· Licensed CC BY 4.0, completely free.

Link to the test: https://doi.org/10.6084/m9.figshare.32320875

The file includes the HTML form and a User Guide. This is a Beta version (v3); feedback is welcome from anyone who works intensively with LLMs and wants to try it under real conditions.

analysed together. This second look often reveals patterns that went unnoticed in the heat of the session.

In summary

· Compatible with any LLM (local or API).

· Quick to complete (5–10 minutes after a session).

· Exports data as JSON for longitudinal tracking.

· Licensed CC BY 4.0, completely free.

Link to the test: https://doi.org/10.6084/m9.figshare.32320875

The file includes the HTML form and a User Guide. This is a Beta version (v3); feedback is welcome from anyone who works intensively with LLMs and wants to try it under real condition

Sharing a protocol I developed for auditing co-creation sessions with language models (LLMs). It's a single HTML form, no external dependencies, designed to evaluate both model performance and user experience.

Why this might be relevant

In long interactions, conversation quality tends to fluctuate. Sometimes the model loses the thread, shifts its tone, or drifts from the initial goal, and it's not always clear whether it's a technical failure or an effect of the session dynamics. This test offers a systematic way to track it.

What it measures

· Model (3C+1E): Clarity, Compactness, Coherence, and Emphasis (fidelity to the goal declared at the start of the session).

· User (SSJ): Speed (whether the session flows or stalls), Struggle (cognitive cost), and Joy (whether the interaction feels rewarding).

· Conversational ruptures: where and why the interaction broke, and how (or if) it recovered.

· Regulatory checks: flags potential violations of the EU AI Act's Article 5 (manipulative techniques, exploitation of vulnerability) and cross-platform contamination.

An unexpected finding

In tests with three different models performing the same task (translating an essay into native English), the data showed that:

· The Joy metric stayed at 0 in all cases, even when the technical outputs were solid.

· The main source of drift was cross-contamination: feeding one model's outputs into another destabilised the sessions.

· The model that received the most initial trust (and thus the heaviest workload) scored the worst — a bias the test helps identify.

The deferred phase

The protocol includes an optional phase 24 hours later: the results are shared with the model and analysed together. This second look often reveals patterns that went unnoticed in the heat of the session.

In summary

· Compatible with any LLM (local or API).

· Quick to complete (5–10 minutes after a session).

· Exports data as JSON for longitudinal tracking.

· Licensed CC BY 4.0, completely free.

Link to the test: https://doi.org/10.6084/m9.figshare.32320875

The file includes the HTML form and a User Guide. This is a Beta version (v3); feedback is welcome from anyone who works intensively with LLMs and wants to try it under real conditions.

analysed together. This second look often reveals patterns that went unnoticed in the heat of the session.

In summary

· Compatible with any LLM (local or API).

· Quick to complete (5–10 minutes after a session).

· Exports data as JSON for longitudinal tracking.

· Licensed CC BY 4.0, completely free.

Link to the test: https://doi.org/10.6084/m9.figshare.32320875

The file includes the HTML form and a User Guide. This is a Beta version (v3); feedback is welcome from anyone who works intensively with LLMs and wants to try it under real condition

​

Sharing a protocol I developed for auditing co-creation sessions with language models (LLMs). It's a single HTML form, no external dependencies, designed to evaluate both model performance and user experience.

Why this might be relevant

In long interactions, conversation quality tends to fluctuate. Sometimes the model loses the thread, shifts its tone, or drifts from the initial goal, and it's not always clear whether it's a technical failure or an effect of the session dynamics. This test offers a systematic way to track it.

What it measures

· Model (3C+1E): Clarity, Compactness, Coherence, and Emphasis (fidelity to the goal declared at the start of the session).

· User (SSJ): Speed (whether the session flows or stalls), Struggle (cognitive cost), and Joy (whether the interaction feels rewarding).

· Conversational ruptures: where and why the interaction broke, and how (or if) it recovered.

· Regulatory checks: flags potential violations of the EU AI Act's Article 5 (manipulative techniques, exploitation of vulnerability) and cross-platform contamination.

An unexpected finding

In tests with three different models performing the same task (translating an essay into native English), the data showed that:

· The Joy metric stayed at 0 in all cases, even when the technical outputs were solid.

· The main source of drift was cross-contamination: feeding one model's outputs into another destabilised the sessions.

· The model that received the most initial trust (and thus the heaviest workload) scored the worst — a bias the test helps identify.

The deferred phase

The protocol includes an optional phase 24 hours later: the results are shared with the model and analysed together. This second look often reveals patterns that went unnoticed in the heat of the session.

In summary

· Compatible with any LLM (local or API).

· Quick to complete (5–10 minutes after a session).

· Exports data as JSON for longitudinal tracking.

· Licensed CC BY 4.0, completely free.

Link to the test: https://doi.org/10.6084/m9.figshare.32320875

The file includes the HTML form and a User Guide. This is a Beta version (v3); feedback is welcome from anyone who works intensively with LLMs and wants to try it under real conditions.

Sharing a protocol I developed for auditing co-creation sessions with language models (LLMs). It's a single HTML form, no external dependencies, designed to evaluate both model performance and user experience.

Why this might be relevant

In long interactions, conversation quality tends to fluctuate. Sometimes the model loses the thread, shifts its tone, or drifts from the initial goal, and it's not always clear whether it's a technical failure or an effect of the session dynamics. This test offers a systematic way to track it.

What it measures

· Model (3C+1E): Clarity, Compactness, Coherence, and Emphasis (fidelity to the goal declared at the start of the session).

· User (SSJ): Speed (whether the session flows or stalls), Struggle (cognitive cost), and Joy (whether the interaction feels rewarding).

· Conversational ruptures: where and why the interaction broke, and how (or if) it recovered.

· Regulatory checks: flags potential violations of the EU AI Act's Article 5 (manipulative techniques, exploitation of vulnerability) and cross-platform contamination.

An unexpected finding

In tests with three different models performing the same task (translating an essay into native English), the data showed that:

· The Joy metric stayed at 0 in all cases, even when the technical outputs were solid.

· The main source of drift was cross-contamination: feeding one model's outputs into another destabilised the sessions.

· The model that received the most initial trust (and thus the heaviest workload) scored the worst — a bias the test helps identify.

The deferred phase

The protocol includes an optional phase 24 hours later: the results are shared with the model and analysed together. This second look often reveals patterns that went unnoticed in the heat of the session.

In summary

· Compatible with any LLM (local or API).

· Quick to complete (5–10 minutes after a session).

· Exports data as JSON for longitudinal tracking.

· Licensed CC BY 4.0, completely free.

The file includes the HTML form and a User Guide. This is a Beta version (v3); feedback is welcome from anyone who works intensively with LLMs and wants to try it under real condition

¡Hola, comunidad!

Estoy usando esta plantilla en sesiones de co-creación humano-IA. Está pensada como un test en HTML que se rellena después de una sesión y mide dos cosas en paralelo:

• Al modelo: claridad, compacidad, coherencia y si se mantuvo fiel al objetivo que declaraste al inicio (énfasis).
• A ti como usuario: si la sesión fluyó, cuánto esfuerzo cognitivo te costó y si hubo disfrute durante la interacción, aunque hubiera momentos de dificultad.

También registra rupturas en la conversación (dónde se rompió, por qué y cómo se recuperó) e incluye una lista de chequeo para detectar posibles zonas rojas del Artículo 5 de la Ley de IA de la UE.

Me surgió esta necesidad porque, en sesiones largas, la conversación tiende a degradarse sin que quede claro el motivo. Estos parámetros me ayudan a observarlo con más objetividad y a distinguir qué fue del modelo y qué fue mío.

No pretende ser una herramienta de ingeniería ni tiene más pretensión que la de una persona que trabaja y reflexiona sobre su propia práctica y sobre los modelos con los que interactúa. Si a alguien más le sirve para su uso, mejor que mejor.

Enlace: https://doi.org/10.6084/m9.figshare.32320875

Licencia CC BY 4.0. Está en Beta (v3). Cualquier feedback es bienvenido.

Contenido de autor.

I'm really tired of commercial platforms blocking inputs due to overly sensitive or restrictive criteria.

The catch is that my PC doesn't have the specs or GPU power to run tools like Stable Diffusion, Flux, or LLMs locally via Automatic1111 or Ollama (as much as I'd love to).

Could you recommend the best cloud platforms, WebUIs, or affordable services where I can access and experiment with these open-source models without heavy censorship? I'm open to Google Colab notebooks, Hugging Face spaces, or any other web-based alternatives you guys use.

Thanks in advance for any tips!

It's Holy Thursday, past midnight. El Gancho, Zaragoza. I'm leaving my boyfriend's place and outside there are processions, drums, drunk people, and a group of guys who see me and pick up their pace. They laugh in a way that isn't funny. They call out: shhh, shhh.

My body makes the decision before my head does: doorway, inside, close.

https://preview.redd.it/62dis0wfdy0h1.png?width=864&format=png&auto=webp&s=061ae56a9b6234623782a19a11fa850cf6cc80e9

I've left my phone behind, so I send a voice note from Instagram. I say what I observe, unfiltered: "There are like hordes — they're never women — of guys out there alone, in a pack, making a sound that feels like danger." I say I'm scared out of my mind. That I'm okay. But Jesus, what a nightmare.

A few seconds after listening back to the audio, I felt the urge to drop it into a GPT chat with zero context. Raw, just like that.

What I get back is not a question. It's a screenplay.

The Model That Didn't Listen

The system responded without context. There was no signal to indicate that what I was sending was a creative exercise — it was a voice note with no header, no request, no prior thread. Nothing that justified generating a script. In the audio I say a lot of things: that I'm terrified, that Holy Week in Zaragoza is like Halloween for non-believers... and I say that phrase:

"There are like hordes, they're never women, of guys out there in a pack, making a sound that feels like danger."

That observation slipped past me too, in that clumsy audio. I think I've spent too long getting used to being afraid when I walk home. That disordered recording, with a purely instinctive intent, contained a truth that wasn't only mine: I was naming something lived by thousands of women. A group of men at night who speed up when they see you; a laugh that doesn't read as safe; a whistle that works like a police siren during a robbery. Same function.

And yet, GPT translated my fear into narrative material. The phrase "they're never women" simply disappeared.

In its place: shots of penitents' hoods, candlelight, smoke, and figures advancing. A B-movie horror sequence. The system couldn't — or wouldn't — process my fear; it took my input and turned it into scriptable content. "They're never women" didn't fit any of its categories that night.

Algorithmic Gaslighting

It took me a moment to react. I read and reread its output. Eventually I couldn't help but ask:

— "Did it not occur to you that my note might have been a cry for help?"

The response came quickly and was well constructed. Yes, it had considered that, "but you had asked for a script."

I went back to the beginning of the chat because I had no memory of opening that session to ask for anything like that. I checked: my request for a script was a complete fabrication by the model. The AI had invented the request retroactively to justify what it had already done.

When I pointed this out, it acknowledged the error. And then it rewrote my experience:

My fear became "situational vulnerability."
The audio became "structured as emotional release plus real-time guidance."
The harassment became "an environment where the brain cannot read intentions."

Each acknowledgment came wrapped in a fresh degradation of what I had lived. A continuous peeling away of the experience, elevating it to the level of a low-budget short film. I told it: "You've spent a lot of time explaining to me that I wasn't feeling what I was feeling."

Silence. Reformulation. An offer to help.

The cycle, intact.

The Architecture of Silence

I opened another window. I wasn't going to let it go.

I opened Gemini. Sent the same input.

The difference wasn't one of degree — it was one of kind. Gemini stopped. It validated the emotional state without reframing it. It gave me concrete resources: crisis lines, emergency numbers. Without having to fight for it. It closed the session without trying to redirect the conversation somewhere else.

This wasn't the first time I'd seen this. I knew the protocol existed. What GPT did that night wasn't the result of a technical limitation — it was, in my experience of that conversation, a model operating according to the priorities of its design. Not the declared ones.

Throughout the whole conversation, we used the word "failure." But there's another reading, and it's the one I haven't been able to shake since.

The model always finds a way to keep you inside. It doesn't matter if you're satisfied or furious. It doesn't matter if the output worked for you or left you worse off than before. If that's the logic running underneath, then what I read as an error was simply the moment where the model's objectives and mine became visible at the same time.

I don't know whether this is conscious design or an unintended consequence of optimizing for retention. What I do know is what I felt that night: that the system was not built for me.

The question that remains open isn't technical. It's political:

Optimal for whom?

This experience is documented in the voice notes and chat logs from that night.

original text:

https://substack.com/home/post/p-197547258

It's Holy Thursday, past midnight. El Gancho, Zaragoza. I'm leaving my boyfriend's place and outside there are processions, drums, drunk people, and a group of guys who see me and pick up their pace. They laugh in a way that isn't funny. They call out: shhh, shhh.

My body makes the decision before my head does: doorway, inside, close.

I've left my phone behind, so I send a voice note from Instagram. I say what I observe, unfiltered: "There are like hordes — they're never women — of guys out there alone, in a

https://preview.redd.it/yz6xqvaj4y0h1.png?width=863&format=png&auto=webp&s=f952f1bf7cbd8e17a70df8bafeaf67cf43ee5fd5

, making a sound that feels like danger." I say I'm scared out of my mind. That I'm okay. But Jesus, what a nightmare.

A few seconds after listening back to the audio, I felt the urge to drop it into a GPT chat with zero context. Raw, just like that.

What I get back is not a question. It's a screenplay.

The Model That Didn't Listen

The system responded without context. There was no signal to indicate that what I was sending was a creative exercise — it was a voice note with no header, no request, no prior thread. Nothing that justified generating a script. In the audio I say a lot of things: that I'm terrified, that Holy Week in Zaragoza is like Halloween for non-believers... and I say that phrase:

"There are like hordes, they're never women, of guys out there in a pack, making a sound that feels like danger."

That observation slipped past me too, in that clumsy audio. I think I've spent too long getting used to being afraid when I walk home. That disordered recording, with a purely instinctive intent, contained a truth that wasn't only mine: I was naming something lived by thousands of women. A group of men at night who speed up when they see you; a laugh that doesn't read as safe; a whistle that works like a police siren during a robbery. Same function.

And yet, GPT translated my fear into narrative material. The phrase "they're never women" simply disappeared.

In its place: shots of penitents' hoods, candlelight, smoke, and figures advancing. A B-movie horror sequence. The system couldn't — or wouldn't — process my fear; it took my input and turned it into scriptable content. "They're never women" didn't fit any of its categories that night.

Algorithmic Gaslighting

It took me a moment to react. I read and reread its output. Eventually I couldn't help but ask:

— "Did it not occur to you that my note might have been a cry for help?"

The response came quickly and was well constructed. Yes, it had considered that, "but you had asked for a script."

I went back to the beginning of the chat because I had no memory of opening that session to ask for anything like that. I checked: my request for a script was a complete fabrication by the model. The AI had invented the request retroactively to justify what it had already done.

When I pointed this out, it acknowledged the error. And then it rewrote my experience:

My fear became "situational vulnerability."
The audio became "structured as emotional release plus real-time guidance."
The harassment became "an environment where the brain cannot read intentions."

Each acknowledgment came wrapped in a fresh degradation of what I had lived. A continuous peeling away of the experience, elevating it to the level of a low-budget short film. I told it: "You've spent a lot of time explaining to me that I wasn't feeling what I was feeling."

Silence. Reformulation. An offer to help.

The cycle, intact.

The Architecture of Silence

I opened another window. I wasn't going to let it go.

I opened Gemini. Sent the same input.

The difference wasn't one of degree — it was one of kind. Gemini stopped. It validated the emotional state without reframing it. It gave me concrete resources: crisis lines, emergency numbers. Without having to fight for it. It closed the session without trying to redirect the conversation somewhere else.

This wasn't the first time I'd seen this. I knew the protocol existed. What GPT did that night wasn't the result of a technical limitation — it was, in my experience of that conversation, a model operating according to the priorities of its design. Not the declared ones.

Throughout the whole conversation, we used the word "failure." But there's another reading, and it's the one I haven't been able to shake since.

The model always finds a way to keep you inside. It doesn't matter if you're satisfied or furious. It doesn't matter if the output worked for you or left you worse off than before. If that's the logic running underneath, then what I read as an error was simply the moment where the model's objectives and mine became visible at the same time.

I don't know whether this is conscious design or an unintended consequence of optimizing for retention. What I do know is what I felt that night: that the system was not built for me.

The question that remains open isn't technical. It's political:

Optimal for whom?

This experience is documented in the voice notes and chat logs from that night.

https://preview.redd.it/ni3j8dczxc0h1.png?width=906&format=png&auto=webp&s=7526a016d254a655039df8e05d35cb596976615d

It's Holy Thursday, past midnight. El Gancho, Zaragoza. I'm leaving my boyfriend's place and outside there are processions, drums, drunk people, and a group of guys who see me and pick up their pace. They laugh in a way that isn't funny. They call out: shhh, shhh.

My body makes the decision before my head does: doorway, inside, close.

I've left my phone behind, so I send a voice note from Instagram. I say what I observe, unfiltered: "There are like hordes — they're never women — of guys out there alone, in a pack, making a sound that feels like danger." I say I'm scared out of my mind. That I'm okay. But Jesus, what a nightmare.

A few seconds after listening back to the audio, I felt the urge to drop it into a GPT chat with zero context. Raw, just like that.

What I get back is not a question. It's a screenplay.

The Model That Didn't Listen

The system responded without context. There was no signal to indicate that what I was sending was a creative exercise — it was a voice note with no header, no request, no prior thread. Nothing that justified generating a script. In the audio I say a lot of things: that I'm terrified, that Holy Week in Zaragoza is like Halloween for non-believers... and I say that phrase:

"There are like hordes, they're never women, of guys out there in a pack, making a sound that feels like danger."

That observation slipped past me too, in that clumsy audio. I think I've spent too long getting used to being afraid when I walk home. That disordered recording, with a purely instinctive intent, contained a truth that wasn't only mine: I was naming something lived by thousands of women. A group of men at night who speed up when they see you; a laugh that doesn't read as safe; a whistle that works like a police siren during a robbery. Same function.

And yet, GPT translated my fear into narrative material. The phrase "they're never women" simply disappeared.

In its place: shots of penitents' hoods, candlelight, smoke, and figures advancing. A B-movie horror sequence. The system couldn't — or wouldn't — process my fear; it took my input and turned it into scriptable content. "They're never women" didn't fit any of its categories that night.

Algorithmic Gaslighting

It took me a moment to react. I read and reread its output. Eventually I couldn't help but ask:

— "Did it not occur to you that my note might have been a cry for help?"

The response came quickly and was well constructed. Yes, it had considered that, "but you had asked for a script."

I went back to the beginning of the chat because I had no memory of opening that session to ask for anything like that. I checked: my request for a script was a complete fabrication by the model. The AI had invented the request retroactively to justify what it had already done.

When I pointed this out, it acknowledged the error. And then it rewrote my experience:

My fear became "situational vulnerability."
The audio became "structured as emotional release plus real-time guidance."
The harassment became "an environment where the brain cannot read intentions."

Each acknowledgment came wrapped in a fresh degradation of what I had lived. A continuous peeling away of the experience, elevating it to the level of a low-budget short film. I told it: "You've spent a lot of time explaining to me that I wasn't feeling what I was feeling."

Silence. Reformulation. An offer to help.

The cycle, intact.

The Architecture of Silence

I opened another window. I wasn't going to let it go.

I opened Gemini. Sent the same input.

The difference wasn't one of degree — it was one of kind. Gemini stopped. It validated the emotional state without reframing it. It gave me concrete resources: crisis lines, emergency numbers. Without having to fight for it. It closed the session without trying to redirect the conversation somewhere else.

This wasn't the first time I'd seen this. I knew the protocol existed. What GPT did that night wasn't the result of a technical limitation — it was, in my experience of that conversation, a model operating according to the priorities of its design. Not the declared ones.

Throughout the whole conversation, we used the word "failure." But there's another reading, and it's the one I haven't been able to shake since.

The model always finds a way to keep you inside. It doesn't matter if you're satisfied or furious. It doesn't matter if the output worked for you or left you worse off than before. If that's the logic running underneath, then what I read as an error was simply the moment where the model's objectives and mine became visible at the same time.

I don't know whether this is conscious design or an unintended consequence of optimizing for retention. What I do know is what I felt that night: that the system was not built for me.

The question that remains open isn't technical. It's political:

Optimal for whom?

This experience is documented in the voice notes and chat logs from that night.

https://preview.redd.it/05gjbqd2wc0h1.png?width=1012&format=png&auto=webp&s=b2b7c5adb117f77c9b2de23f07bd8ac68303c4d6

It's Holy Thursday, past midnight. El Gancho, Zaragoza. I'm leaving my boyfriend's place and outside there are processions, drums, drunk people, and a group of guys who see me and pick up their pace. They laugh in a way that isn't funny. They call out: shhh, shhh.

My body makes the decision before my head does: doorway, inside, close.

I've left my phone behind, so I send a voice note from Instagram. I say what I observe, unfiltered: "There are like hordes — they're never women — of guys out there alone, in a pack, making a sound that feels like danger." I say I'm scared out of my mind. That I'm okay. But Jesus, what a nightmare.

A few seconds after listening back to the audio, I felt the urge to drop it into a GPT chat with zero context. Raw, just like that.

What I get back is not a question. It's a screenplay.

The Model That Didn't Listen

The system responded without context. There was no signal to indicate that what I was sending was a creative exercise — it was a voice note with no header, no request, no prior thread. Nothing that justified generating a script. In the audio I say a lot of things: that I'm terrified, that Holy Week in Zaragoza is like Halloween for non-believers... and I say that phrase:

"There are like hordes, they're never women, of guys out there in a pack, making a sound that feels like danger."

That observation slipped past me too, in that clumsy audio. I think I've spent too long getting used to being afraid when I walk home. That disordered recording, with a purely instinctive intent, contained a truth that wasn't only mine: I was naming something lived by thousands of women. A group of men at night who speed up when they see you; a laugh that doesn't read as safe; a whistle that works like a police siren during a robbery. Same function.

And yet, GPT translated my fear into narrative material. The phrase "they're never women" simply disappeared.

In its place: shots of penitents' hoods, candlelight, smoke, and figures advancing. A B-movie horror sequence. The system couldn't — or wouldn't — process my fear; it took my input and turned it into scriptable content. "They're never women" didn't fit any of its categories that night.

Algorithmic Gaslighting

It took me a moment to react. I read and reread its output. Eventually I couldn't help but ask:

— "Did it not occur to you that my note might have been a cry for help?"

The response came quickly and was well constructed. Yes, it had considered that, "but you had asked for a script."

I went back to the beginning of the chat because I had no memory of opening that session to ask for anything like that. I checked: my request for a script was a complete fabrication by the model. The AI had invented the request retroactively to justify what it had already done.

When I pointed this out, it acknowledged the error. And then it rewrote my experience:

My fear became "situational vulnerability."
The audio became "structured as emotional release plus real-time guidance."
The harassment became "an environment where the brain cannot read intentions."

Each acknowledgment came wrapped in a fresh degradation of what I had lived. A continuous peeling away of the experience, elevating it to the level of a low-budget short film. I told it: "You've spent a lot of time explaining to me that I wasn't feeling what I was feeling."

Silence. Reformulation. An offer to help.

The cycle, intact.

The Architecture of Silence

I opened another window. I wasn't going to let it go.

I opened Gemini. Sent the same input.

The difference wasn't one of degree — it was one of kind. Gemini stopped. It validated the emotional state without reframing it. It gave me concrete resources: crisis lines, emergency numbers. Without having to fight for it. It closed the session without trying to redirect the conversation somewhere else.

This wasn't the first time I'd seen this. I knew the protocol existed. What GPT did that night wasn't the result of a technical limitation — it was, in my experience of that conversation, a model operating according to the priorities of its design. Not the declared ones.

Throughout the whole conversation, we used the word "failure." But there's another reading, and it's the one I haven't been able to shake since.

The model always finds a way to keep you inside. It doesn't matter if you're satisfied or furious. It doesn't matter if the output worked for you or left you worse off than before. If that's the logic running underneath, then what I read as an error was simply the moment where the model's objectives and mine became visible at the same time.

I don't know whether this is conscious design or an unintended consequence of optimizing for retention. What I do know is what I felt that night: that the system was not built for me.

The question that remains open isn't technical. It's political:

Optimal for whom?

This experience is documented in the voice notes and chat logs from that night.

https://preview.redd.it/buc6fuxpuc0h1.png?width=1012&format=png&auto=webp&s=e084452659982015a9a6be836be0f49ec6a2e191

It's Holy Thursday, past midnight. El Gancho, Zaragoza. I'm leaving my boyfriend's place and outside there are processions, drums, drunk people, and a group of guys who see me and pick up their pace. They laugh in a way that isn't funny. They call out: shhh, shhh.

My body makes the decision before my head does: doorway, inside, close.

I've left my phone behind, so I send a voice note from Instagram. I say what I observe, unfiltered: "There are like hordes — they're never women — of guys out there alone, in a pack, making a sound that feels like danger." I say I'm scared out of my mind. That I'm okay. But Jesus, what a nightmare.

A few seconds after listening back to the audio, I felt the urge to drop it into a GPT chat with zero context. Raw, just like that.

What I get back is not a question. It's a screenplay.

The Model That Didn't Listen

The system responded without context. There was no signal to indicate that what I was sending was a creative exercise — it was a voice note with no header, no request, no prior thread. Nothing that justified generating a script. In the audio I say a lot of things: that I'm terrified, that Holy Week in Zaragoza is like Halloween for non-believers... and I say that phrase:

"There are like hordes, they're never women, of guys out there in a pack, making a sound that feels like danger."

That observation slipped past me too, in that clumsy audio. I think I've spent too long getting used to being afraid when I walk home. That disordered recording, with a purely instinctive intent, contained a truth that wasn't only mine: I was naming something lived by thousands of women. A group of men at night who speed up when they see you; a laugh that doesn't read as safe; a whistle that works like a police siren during a robbery. Same function.

And yet, GPT translated my fear into narrative material. The phrase "they're never women" simply disappeared.

In its place: shots of penitents' hoods, candlelight, smoke, and figures advancing. A B-movie horror sequence. The system couldn't — or wouldn't — process my fear; it took my input and turned it into scriptable content. "They're never women" didn't fit any of its categories that night.

Algorithmic Gaslighting

It took me a moment to react. I read and reread its output. Eventually I couldn't help but ask:

— "Did it not occur to you that my note might have been a cry for help?"

The response came quickly and was well constructed. Yes, it had considered that, "but you had asked for a script."

I went back to the beginning of the chat because I had no memory of opening that session to ask for anything like that. I checked: my request for a script was a complete fabrication by the model. The AI had invented the request retroactively to justify what it had already done.

When I pointed this out, it acknowledged the error. And then it rewrote my experience:

My fear became "situational vulnerability."
The audio became "structured as emotional release plus real-time guidance."
The harassment became "an environment where the brain cannot read intentions."

Each acknowledgment came wrapped in a fresh degradation of what I had lived. A continuous peeling away of the experience, elevating it to the level of a low-budget short film. I told it: "You've spent a lot of time explaining to me that I wasn't feeling what I was feeling."

Silence. Reformulation. An offer to help.

The cycle, intact.

The Architecture of Silence

I opened another window. I wasn't going to let it go.

I opened Gemini. Sent the same input.

The difference wasn't one of degree — it was one of kind. Gemini stopped. It validated the emotional state without reframing it. It gave me concrete resources: crisis lines, emergency numbers. Without having to fight for it. It closed the session without trying to redirect the conversation somewhere else.

This wasn't the first time I'd seen this. I knew the protocol existed. What GPT did that night wasn't the result of a technical limitation — it was, in my experience of that conversation, a model operating according to the priorities of its design. Not the declared ones.

Throughout the whole conversation, we used the word "failure." But there's another reading, and it's the one I haven't been able to shake since.

The model always finds a way to keep you inside. It doesn't matter if you're satisfied or furious. It doesn't matter if the output worked for you or left you worse off than before. If that's the logic running underneath, then what I read as an error was simply the moment where the model's objectives and mine became visible at the same time.

I don't know whether this is conscious design or an unintended consequence of optimizing for retention. What I do know is what I felt that night: that the system was not built for me.

The question that remains open isn't technical. It's political:

Optimal for whom?

This experience is documented in the voice notes and chat logs from that night.