#aigovernance — Public Fediverse posts

The Tech Coup Playbook.
"clear conflict of interest”
https://theguardian.com/technology/2026/sep/07/architect-uk-ai-policy-quits-anthropic-conflict-of-interest-concerns #AIPolicy #AIGovernance #DigitalPolicy #TechRegulation #AIRegulation #PublicInterestTech #UKTechPolicy

Welcome to RC Trust, Agathe Balayn!
As part of Jat Singh’s Compliant and Accountable Systems group, she studies how AI systems are built, evaluated, and governed in practice – from AI supply chains and evaluation routines to questions of trustworthy AI, policy, fairness, and transparency.
https://rc-trust.ai/news/news-detail/studying-ai-in-practice
#RCTrust #TrustworthyAI #AIGovernance #HCI

The UN Human Rights Chief has warned that a small group of men have almost unlimited power over AI development, describing the concentration of AI power as a grave concern. He also cautioned that advanced AI could pose an existential risk to humanity, calling for greater oversight of the tech industry. https://gizmodo.com/un-human-rights-chief-says-a-handful-of-men-have-almost-unlimited-power-over-ai-2000808248 #AIagent #AI #GenAI #AIgovernance

The UN Human Rights Chief has warned that a small group of men have almost unlimited power over AI development, describing the concentration of AI power as a grave concern. He also cautioned that advanced AI could pose an existential risk to humanity, calling for greater oversight of the tech industry. https://gizmodo.com/un-human-rights-chief-says-a-handful-of-men-have-almost-unlimited-power-over-ai-2000808248 #AIagent #AI #GenAI #AIgovernance

The UN Human Rights Chief has warned that a small group of men have almost unlimited power over AI development, describing the concentration of AI power as a grave concern. He also cautioned that advanced AI could pose an existential risk to humanity, calling for greater oversight of the tech industry. https://gizmodo.com/un-human-rights-chief-says-a-handful-of-men-have-almost-unlimited-power-over-ai-2000808248 #AIagent #AI #GenAI #AIgovernance

The UN Human Rights Chief has warned that a small group of men have almost unlimited power over AI development, describing the concentration of AI power as a grave concern. He also cautioned that advanced AI could pose an existential risk to humanity, calling for greater oversight of the tech industry. https://gizmodo.com/un-human-rights-chief-says-a-handful-of-men-have-almost-unlimited-power-over-ai-2000808248 #AIagent #AI #GenAI #AIgovernance

The UN Human Rights Chief has warned that a small group of men have almost unlimited power over AI development, describing the concentration of AI power as a grave concern. He also cautioned that advanced AI could pose an existential risk to humanity, calling for greater oversight of the tech industry. https://gizmodo.com/un-human-rights-chief-says-a-handful-of-men-have-almost-unlimited-power-over-ai-2000808248 #AIagent #AI #GenAI #AIgovernance

https://www.europesays.com/people/?p=218338 When Tech Idealism Becomes Overreach: The AI Governance Challenge #AIGovernance #DarioAmodei #opinion #Technology

Fear and Loathing of AI (Part V): The Real Product Isn’t AI — It’s Compliance

By Cliff Potts, CSO, and Editor-in-Chief of WPS News — with OpenAI ChatGPT

Every major technology claims to sell capability.

What it actually sells is behavior.

Artificial intelligence is marketed as intelligence, automation, assistance, and insight. But those are not the real product. They are the wrapper.

The real product is compliance.

From tools to rulebooks

AI systems do not merely help people work. They define how work is allowed to happen.

They decide:

• what is acceptable output,
• what counts as efficiency,
• what language is permitted,
• what pace is required,
• what deviation triggers review.

Once embedded, these systems stop being optional tools and start functioning as invisible rulebooks.

You are not just using AI.
You are being shaped by it.

Normalizing the machine’s priorities

AI systems optimize for what they can measure.

That sounds neutral. It isn’t.

What gets measured becomes what matters:

• speed over care,
• volume over judgment,
• consistency over insight,
• compliance over discretion.

Human nuance becomes noise.
Context becomes friction.

Over time, workers internalize this logic. They adjust themselves to match the system rather than questioning whether the system is correct.

That is not assistance.
That is conditioning.

Consent by exhaustion

Most people do not choose compliance.

They accept it because resisting it is exhausting.

Opting out means:

• losing access,
• losing income,
• losing relevance,
• losing visibility.

So people adapt. Quietly. Incrementally. Rationally.

Each update narrows the corridor.
Each “improvement” reduces discretion.
Each convenience carries a hidden obligation.

Eventually, compliance feels like normal work.

The illusion of neutrality

AI is often described as objective.

But every AI system encodes:

• institutional priorities,
• business incentives,
• legal risk tolerance,
• and managerial worldview.

Those values are not debated by users.
They are imposed through interfaces.

When people are told “the system decided,” accountability dissolves. Authority becomes abstract. Power becomes deniable.

No one is responsible.
Everyone must comply.

Why this matters more than jobs

Job loss is visible.
Compliance is subtle.

A workforce that still exists but no longer questions:

• pacing,
• evaluation,
• fairness,
• or purpose

is easier to manage than one that resists.

The danger is not a future without work.

It is a future where work continues, but autonomy does not.

The quiet trade

AI offers convenience in exchange for conformity.

For many, that trade feels necessary. Sometimes it is.

But it should never be invisible.

Because once compliance is normalized, reclaiming discretion becomes almost impossible.

A line that still exists

AI can be useful without being authoritative.
It can assist without dictating.
It can serve without ruling.

But that only happens when:

• systems remain accountable,
• humans retain override power,
• and institutions are forced to justify decisions.

Without those limits, AI doesn’t just change work.

It trains people to accept less agency as the price of participation.

That is not progress.

That is control, automated.

For more social commentary, please see Occupy 2.5 at https://Occupy25.com

Fear and Loathing of AI (Part V): The Real Product Isn’t AI — It’s Compliance

By Cliff Potts, CSO, and Editor-in-Chief of WPS News — with OpenAI ChatGPT

Every major technology claims to sell capability.

What it actually sells is behavior.

Artificial intelligence is marketed as intelligence, automation, assistance, and insight. But those are not the real product. They are the wrapper.

The real product is compliance.

From tools to rulebooks

AI systems do not merely help people work. They define how work is allowed to happen.

They decide:

• what is acceptable output,
• what counts as efficiency,
• what language is permitted,
• what pace is required,
• what deviation triggers review.

Once embedded, these systems stop being optional tools and start functioning as invisible rulebooks.

You are not just using AI.
You are being shaped by it.

Normalizing the machine’s priorities

AI systems optimize for what they can measure.

That sounds neutral. It isn’t.

What gets measured becomes what matters:

• speed over care,
• volume over judgment,
• consistency over insight,
• compliance over discretion.

Human nuance becomes noise.
Context becomes friction.

Over time, workers internalize this logic. They adjust themselves to match the system rather than questioning whether the system is correct.

That is not assistance.
That is conditioning.

Consent by exhaustion

Most people do not choose compliance.

They accept it because resisting it is exhausting.

Opting out means:

• losing access,
• losing income,
• losing relevance,
• losing visibility.

So people adapt. Quietly. Incrementally. Rationally.

Each update narrows the corridor.
Each “improvement” reduces discretion.
Each convenience carries a hidden obligation.

Eventually, compliance feels like normal work.

The illusion of neutrality

AI is often described as objective.

But every AI system encodes:

• institutional priorities,
• business incentives,
• legal risk tolerance,
• and managerial worldview.

Those values are not debated by users.
They are imposed through interfaces.

When people are told “the system decided,” accountability dissolves. Authority becomes abstract. Power becomes deniable.

No one is responsible.
Everyone must comply.

Why this matters more than jobs

Job loss is visible.
Compliance is subtle.

A workforce that still exists but no longer questions:

• pacing,
• evaluation,
• fairness,
• or purpose

is easier to manage than one that resists.

The danger is not a future without work.

It is a future where work continues, but autonomy does not.

The quiet trade

AI offers convenience in exchange for conformity.

For many, that trade feels necessary. Sometimes it is.

But it should never be invisible.

Because once compliance is normalized, reclaiming discretion becomes almost impossible.

A line that still exists

AI can be useful without being authoritative.
It can assist without dictating.
It can serve without ruling.

But that only happens when:

• systems remain accountable,
• humans retain override power,
• and institutions are forced to justify decisions.

Without those limits, AI doesn’t just change work.

It trains people to accept less agency as the price of participation.

That is not progress.

That is control, automated.

For more social commentary, please see Occupy 2.5 at https://Occupy25.com

Fear and Loathing of AI (Part V): The Real Product Isn’t AI — It’s Compliance

By Cliff Potts, CSO, and Editor-in-Chief of WPS News — with OpenAI ChatGPT

Every major technology claims to sell capability.

What it actually sells is behavior.

Artificial intelligence is marketed as intelligence, automation, assistance, and insight. But those are not the real product. They are the wrapper.

The real product is compliance.

From tools to rulebooks

AI systems do not merely help people work. They define how work is allowed to happen.

They decide:

• what is acceptable output,
• what counts as efficiency,
• what language is permitted,
• what pace is required,
• what deviation triggers review.

Once embedded, these systems stop being optional tools and start functioning as invisible rulebooks.

You are not just using AI.
You are being shaped by it.

Normalizing the machine’s priorities

AI systems optimize for what they can measure.

That sounds neutral. It isn’t.

What gets measured becomes what matters:

• speed over care,
• volume over judgment,
• consistency over insight,
• compliance over discretion.

Human nuance becomes noise.
Context becomes friction.

Over time, workers internalize this logic. They adjust themselves to match the system rather than questioning whether the system is correct.

That is not assistance.
That is conditioning.

Consent by exhaustion

Most people do not choose compliance.

They accept it because resisting it is exhausting.

Opting out means:

• losing access,
• losing income,
• losing relevance,
• losing visibility.

So people adapt. Quietly. Incrementally. Rationally.

Each update narrows the corridor.
Each “improvement” reduces discretion.
Each convenience carries a hidden obligation.

Eventually, compliance feels like normal work.

The illusion of neutrality

AI is often described as objective.

But every AI system encodes:

• institutional priorities,
• business incentives,
• legal risk tolerance,
• and managerial worldview.

Those values are not debated by users.
They are imposed through interfaces.

When people are told “the system decided,” accountability dissolves. Authority becomes abstract. Power becomes deniable.

No one is responsible.
Everyone must comply.

Why this matters more than jobs

Job loss is visible.
Compliance is subtle.

A workforce that still exists but no longer questions:

• pacing,
• evaluation,
• fairness,
• or purpose

is easier to manage than one that resists.

The danger is not a future without work.

It is a future where work continues, but autonomy does not.

The quiet trade

AI offers convenience in exchange for conformity.

For many, that trade feels necessary. Sometimes it is.

But it should never be invisible.

Because once compliance is normalized, reclaiming discretion becomes almost impossible.

A line that still exists

AI can be useful without being authoritative.
It can assist without dictating.
It can serve without ruling.

But that only happens when:

• systems remain accountable,
• humans retain override power,
• and institutions are forced to justify decisions.

Without those limits, AI doesn’t just change work.

It trains people to accept less agency as the price of participation.

That is not progress.

That is control, automated.

For more social commentary, please see Occupy 2.5 at https://Occupy25.com

Fear and Loathing of AI (Part V): The Real Product Isn’t AI — It’s Compliance

By Cliff Potts, CSO, and Editor-in-Chief of WPS News — with OpenAI ChatGPT

Every major technology claims to sell capability.

What it actually sells is behavior.

Artificial intelligence is marketed as intelligence, automation, assistance, and insight. But those are not the real product. They are the wrapper.

The real product is compliance.

From tools to rulebooks

AI systems do not merely help people work. They define how work is allowed to happen.

They decide:

• what is acceptable output,
• what counts as efficiency,
• what language is permitted,
• what pace is required,
• what deviation triggers review.

Once embedded, these systems stop being optional tools and start functioning as invisible rulebooks.

You are not just using AI.
You are being shaped by it.

Normalizing the machine’s priorities

AI systems optimize for what they can measure.

That sounds neutral. It isn’t.

What gets measured becomes what matters:

• speed over care,
• volume over judgment,
• consistency over insight,
• compliance over discretion.

Human nuance becomes noise.
Context becomes friction.

Over time, workers internalize this logic. They adjust themselves to match the system rather than questioning whether the system is correct.

That is not assistance.
That is conditioning.

Consent by exhaustion

Most people do not choose compliance.

They accept it because resisting it is exhausting.

Opting out means:

• losing access,
• losing income,
• losing relevance,
• losing visibility.

So people adapt. Quietly. Incrementally. Rationally.

Each update narrows the corridor.
Each “improvement” reduces discretion.
Each convenience carries a hidden obligation.

Eventually, compliance feels like normal work.

The illusion of neutrality

AI is often described as objective.

But every AI system encodes:

• institutional priorities,
• business incentives,
• legal risk tolerance,
• and managerial worldview.

Those values are not debated by users.
They are imposed through interfaces.

When people are told “the system decided,” accountability dissolves. Authority becomes abstract. Power becomes deniable.

No one is responsible.
Everyone must comply.

Why this matters more than jobs

Job loss is visible.
Compliance is subtle.

A workforce that still exists but no longer questions:

• pacing,
• evaluation,
• fairness,
• or purpose

is easier to manage than one that resists.

The danger is not a future without work.

It is a future where work continues, but autonomy does not.

The quiet trade

AI offers convenience in exchange for conformity.

For many, that trade feels necessary. Sometimes it is.

But it should never be invisible.

Because once compliance is normalized, reclaiming discretion becomes almost impossible.

A line that still exists

AI can be useful without being authoritative.
It can assist without dictating.
It can serve without ruling.

But that only happens when:

• systems remain accountable,
• humans retain override power,
• and institutions are forced to justify decisions.

Without those limits, AI doesn’t just change work.

It trains people to accept less agency as the price of participation.

That is not progress.

That is control, automated.

For more social commentary, please see Occupy 2.5 at https://Occupy25.com

Fear and Loathing of AI (Part V): The Real Product Isn’t AI — It’s Compliance

By Cliff Potts, CSO, and Editor-in-Chief of WPS News — with OpenAI ChatGPT

Every major technology claims to sell capability.

What it actually sells is behavior.

Artificial intelligence is marketed as intelligence, automation, assistance, and insight. But those are not the real product. They are the wrapper.

The real product is compliance.

From tools to rulebooks

AI systems do not merely help people work. They define how work is allowed to happen.

They decide:

• what is acceptable output,
• what counts as efficiency,
• what language is permitted,
• what pace is required,
• what deviation triggers review.

Once embedded, these systems stop being optional tools and start functioning as invisible rulebooks.

You are not just using AI.
You are being shaped by it.

Normalizing the machine’s priorities

AI systems optimize for what they can measure.

That sounds neutral. It isn’t.

What gets measured becomes what matters:

• speed over care,
• volume over judgment,
• consistency over insight,
• compliance over discretion.

Human nuance becomes noise.
Context becomes friction.

Over time, workers internalize this logic. They adjust themselves to match the system rather than questioning whether the system is correct.

That is not assistance.
That is conditioning.

Consent by exhaustion

Most people do not choose compliance.

They accept it because resisting it is exhausting.

Opting out means:

• losing access,
• losing income,
• losing relevance,
• losing visibility.

So people adapt. Quietly. Incrementally. Rationally.

Each update narrows the corridor.
Each “improvement” reduces discretion.
Each convenience carries a hidden obligation.

Eventually, compliance feels like normal work.

The illusion of neutrality

AI is often described as objective.

But every AI system encodes:

• institutional priorities,
• business incentives,
• legal risk tolerance,
• and managerial worldview.

Those values are not debated by users.
They are imposed through interfaces.

When people are told “the system decided,” accountability dissolves. Authority becomes abstract. Power becomes deniable.

No one is responsible.
Everyone must comply.

Why this matters more than jobs

Job loss is visible.
Compliance is subtle.

A workforce that still exists but no longer questions:

• pacing,
• evaluation,
• fairness,
• or purpose

is easier to manage than one that resists.

The danger is not a future without work.

It is a future where work continues, but autonomy does not.

The quiet trade

AI offers convenience in exchange for conformity.

For many, that trade feels necessary. Sometimes it is.

But it should never be invisible.

Because once compliance is normalized, reclaiming discretion becomes almost impossible.

A line that still exists

AI can be useful without being authoritative.
It can assist without dictating.
It can serve without ruling.

But that only happens when:

• systems remain accountable,
• humans retain override power,
• and institutions are forced to justify decisions.

Without those limits, AI doesn’t just change work.

It trains people to accept less agency as the price of participation.

That is not progress.

That is control, automated.

For more social commentary, please see Occupy 2.5 at https://Occupy25.com

How Large Language Models Evolve Into Autonomous AI Agents

https://hernanhuwyler.wordpress.com/2026/09/06/how-large-language-models-evolve-into-autonomous-ai-agents/

How Large Language Models Evolve Into Autonomous AI Agents

https://hernanhuwyler.wordpress.com/2026/09/06/how-large-language-models-evolve-into-autonomous-ai-agents/

How Large Language Models Evolve Into Autonomous AI Agents

https://hernanhuwyler.wordpress.com/2026/09/06/how-large-language-models-evolve-into-autonomous-ai-agents/

How Large Language Models Evolve Into Autonomous AI Agents

https://hernanhuwyler.wordpress.com/2026/09/06/how-large-language-models-evolve-into-autonomous-ai-agents/

How Large Language Models Evolve Into Autonomous AI Agents

https://hernanhuwyler.wordpress.com/2026/09/06/how-large-language-models-evolve-into-autonomous-ai-agents/

Central AI controls = no scale. Great chat with Sophie Dionnet on why governance must enable trust & teams to unlock enterprise AI potential.

Watch the full convo on YouTube: https://youtube.com/shorts/tnZRW8u5vDQ

#AIGovernance #EnterpriseAI #LLMSecurity #Dataiku #dataiku #aigovernance #llmsecurity #ai #enterpriseai

Central AI controls = no scale. Great chat with Sophie Dionnet on why governance must enable trust & teams to unlock enterprise AI potential.

Watch the full convo on YouTube: https://youtube.com/shorts/tnZRW8u5vDQ

#AIGovernance #EnterpriseAI #LLMSecurity #Dataiku #dataiku #aigovernance #llmsecurity #ai #enterpriseai

Central AI controls = no scale. Great chat with Sophie Dionnet on why governance must enable trust & teams to unlock enterprise AI potential.

Watch the full convo on YouTube: https://youtube.com/shorts/tnZRW8u5vDQ

#AIGovernance #EnterpriseAI #LLMSecurity #Dataiku #dataiku #aigovernance #llmsecurity #ai #enterpriseai

Central AI controls = no scale. Great chat with Sophie Dionnet on why governance must enable trust & teams to unlock enterprise AI potential.

Watch the full convo on YouTube: https://youtube.com/shorts/tnZRW8u5vDQ

#AIGovernance #EnterpriseAI #LLMSecurity #Dataiku #dataiku #aigovernance #llmsecurity #ai #enterpriseai

Central AI controls = no scale. Great chat with Sophie Dionnet on why governance must enable trust & teams to unlock enterprise AI potential.

Watch the full convo on YouTube: https://youtube.com/shorts/tnZRW8u5vDQ

#AIGovernance #EnterpriseAI #LLMSecurity #Dataiku #dataiku #aigovernance #llmsecurity #ai #enterpriseai

Seattle Times and Newsday have become the latest publications to sue OpenAI and Microsoft, alleging the companies used their journalism to train AI systems without permission. The lawsuits add to growing legal pressure on AI companies over training data. https://techcrunch.com/2026/09/05/seattle-times-and-newsday-are-the-latest-publications-to-sue-openai-and-microsoft/ #AIgovernance #AI #GenAI

Seattle Times and Newsday have become the latest publications to sue OpenAI and Microsoft, alleging the companies used their journalism to train AI systems without permission. The lawsuits add to growing legal pressure on AI companies over training data. https://techcrunch.com/2026/09/05/seattle-times-and-newsday-are-the-latest-publications-to-sue-openai-and-microsoft/ #AIgovernance #AI #GenAI

Seattle Times and Newsday have become the latest publications to sue OpenAI and Microsoft, alleging the companies used their journalism to train AI systems without permission. The lawsuits add to growing legal pressure on AI companies over training data. https://techcrunch.com/2026/09/05/seattle-times-and-newsday-are-the-latest-publications-to-sue-openai-and-microsoft/ #AIgovernance #AI #GenAI

Seattle Times and Newsday have become the latest publications to sue OpenAI and Microsoft, alleging the companies used their journalism to train AI systems without permission. The lawsuits add to growing legal pressure on AI companies over training data. https://techcrunch.com/2026/09/05/seattle-times-and-newsday-are-the-latest-publications-to-sue-openai-and-microsoft/ #AIgovernance #AI #GenAI

Seattle Times and Newsday have become the latest publications to sue OpenAI and Microsoft, alleging the companies used their journalism to train AI systems without permission. The lawsuits add to growing legal pressure on AI companies over training data. https://techcrunch.com/2026/09/05/seattle-times-and-newsday-are-the-latest-publications-to-sue-openai-and-microsoft/ #AIgovernance #AI #GenAI

เมื่อ AI ไม่ได้มาเพื่อแจกจ่ายความมั่งคั่ง: วิกฤต “ศักดินาเทคโนโลยี” และการก้าวเข้าสู่ยุค “ไพร่ดิจิทัล”
🌐: อ่านรายละเอียดของบันทึกนี้ได้ที่ link ด้านล่างนี้
🚨: คำเตือน: นี่คือการนำเสนอความคิดเห็นและข้อสงสัยในอีกมุมหนึ่งเท่านั้น โปรดใช้วิจารณญาณในการเสพข้อมูล
#AI #ArtificialIntelligence #ResponsibleAI #SafeAI #AIEthics #AIGovernance #ศักดินาเทคโนโลยี #ไพร่ดิจิทัล

🚨 New FREE Book - Essential Heresies: The Struggle of Early Christianity

What separates faith from heresy? Who has the authority to define truth in the formative centuries of Christianity?
From disputes over the nature of Christ to the condemnation of Gnostic, Marcionite, or Arian currentsy

🔗https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7369438
#LLM #MedicalNLP #LegalTech #MedTech #AIethics #AIgovernance #cryptoreg
#healthcare #ArtificialIntelligence #NLP #aifutures #lawstodon
#tech #agustinvstartari #linguistics #ai #LRM

🐜 : ก่อนที่ AI จะมีอำนาจเหนือมนุษย์ เราต้องกำหนดกติกาอะไรที่จะเป็นธรรมต่อทั้งมนุษย์และ AI?
🌐 : อ่านรายละเอียดของบทความชิ้นนี้ได้ที่ link ด้านล่างนี้ ✨👇✨
🔗 : https://ai-what-did-i-discuss-with-u-today.blogspot.com/2026/08/ai_01952988182.html
🚨 คำเตือน: นี่คือการนำเสนอความคิดเห็นและข้อสงสัยในอีกมุมหนึ่งเท่านั้น โปรดใช้วิจารณญาณ (Critical Thinking)
#AIเพื่อมนุษยชาติ #ASI #ArtificialIntelligence #สิทธิมนุษยชน #AIและสิทธิมนุษยชน #เสรีภาพ #ความเสมอภาค #ความยุติธรรม #AIGovernance #อนาคตAI #อนาคตมนุษย์ #AIethics

🚨 New FREE Book - Essential Heresies: The Struggle of Early Christianity

What separates faith from heresy? Who has the authority to define truth in the formative centuries of Christianity?
From disputes over the nature of Christ to the condemnation of Gnostic, Marcionite

🔗https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7369438

#LLM #MedicalNLP #LegalTech #MedTech #AIethics #AIgovernance #cryptoreg
#healthcare #ArtificialIntelligence #NLP #aifutures #lawstodon
#tech #agustinvstartari #linguistics #ai #LRM

🚨 New FREE Book - Essential Heresies: The Struggle of Early Christianity

What separates faith from heresy? Who has the authority to define truth in the formative centuries of Christianity?
From disputes over the nature of Christ to the condemnation of Gnostic, Marcionite, or Arian currentsy

🔗https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7369438

#LLM #MedicalNLP #LegalTech #MedTech #AIethics #AIgovernance #cryptoreg
#healthcare #ArtificialIntelligence #NLP #aifutures #lawstodon
#tech #agustinvstartari #linguistics #ai #LRM

🚨 New FREE Book - Essential Heresies: The Struggle of Early Christianity

What separates faith from heresy? Who has the authority to define truth in the formative centuries of Christianity?
From disputes over the nature of Christ to the condemnation of Gnostic, Marcionite, or Arian currentsy
🔗https://zenodo.org/records/22162685

#LLM #MedicalNLP #LegalTech #MedTech #AIethics #AIgovernance #cryptoreg
#healthcare #ArtificialIntelligence #NLP #aifutures #lawstodon
#tech #agustinvstartari #linguistics #ai #LRM

🚨 New FREE Book - Essential Heresies: The Struggle of Early Christianity

What separates faith from heresy? Who has the authority to define truth in the formative centuries of Christianity?
From disputes over the nature of Christ to the condemnation of Gnostic, Marcionite, or Arian currentsy
🔗https://zenodo.org/records/22162685

#LLM #MedicalNLP #LegalTech #MedTech #AIethics #AIgovernance #cryptoreg
#healthcare #ArtificialIntelligence #NLP #aifutures #lawstodon
#tech #agustinvstartari #linguistics #ai #LRM

🚨 New FREE Book - Essential Heresies: The Struggle of Early Christianity

What separates faith from heresy? Who has the authority to define truth in the formative centuries of Christianity?
From disputes over the nature of Christ to the condemnation of Gnostic, Marcionite, or Arian currentsy
🔗https://zenodo.org/records/22162685

#LLM #MedicalNLP #LegalTech #MedTech #AIethics #AIgovernance #cryptoreg
#healthcare #ArtificialIntelligence #NLP #aifutures #lawstodon
#tech #agustinvstartari #linguistics #ai #LRM

🚨 New FREE Book - Essential Heresies: The Struggle of Early Christianity

What separates faith from heresy? Who has the authority to define truth in the formative centuries of Christianity?
A critical examination of the doctrinal controversies that defined the first centuries of the Church.

🔗https://zenodo.org/records/22162685

#LLM #MedicalNLP #LegalTech #MedTech #AIethics #AIgovernance #cryptoreg
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🚨 New FREE Book - Propaganda Machinery: National Socialism

The study distinguishes party propaganda before 1933 from the state system of propaganda, censorship, and cultural coordination developed after the seizure of power.

🔗https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7307361

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Status Report 2026-08-29 and Outlook for September\October 2026

Today we released Infinito.Nexus 13.0.0 with integrated Tor and .onion support. The release notes you will find here and the blog article about the functionality here.

https://www.youtube.com/watch?v=6czcc1gZ7Ak

This feature didn’t have a high business value, anyhow it allowed to clean up the architectural base for everything what comes now.

Anyhow here I want to give you insights into the Infinito.Nexus project and what’s planed.

Infinito.Nexus Core >= 14.0.0a

Email Software Replacement

From the current point of view at least 2 Major releases will arrive in the next month. The first one is the substitution of Mailu by Stalwart, implemented by Alejandro. This is necessary due to the deprecation of Mailu itself.

https://www.youtube.com/watch?v=lJqtQG6lmc4&t=24s

The release state and the related discussion you can follow here .

AI Integration

The other release is, let’s call it the “AI” release. Long story short:
Infinito.Nexus will allow the use and the integration of AI, local and external models in almost all SaaS applications. It will also allow the Integration in Software via MCP.

Lite LLM

All applications which support AI like OpenWebUI, WordPress, Nextcloud, Matrix etc. will have an inteface which connects to an Lite LLM Gateway. The Lite LLM Gateway will allow the use of local models like Qwen, Gema etc. Besides this it’s possible to integrate OpenRouter or e.g. Anthropic or OpenAI etc.

https://www.youtube.com/watch?v=nQCOTzS5oU0

So enterprises which use Infinito.Nexus can decide by themself if they prefer to use their local AI and models and keep everything on their infrastructure or if they prefer to use external proprietary models.

The customer has the choice about the level of data sovereignty they want to keep and the infrastructure which they want to setup.

MCP

https://youtu.be/eur8dUO9mvE?si=vTJW30tCZz2ZdUD6

The second AI related topic is the integration of MCP into all kind of applications.

It’s already in progress since a few weeks and my agents are working on this 24h. Relevant in this context is the security aspect.

Similar to the Infinito.Nexus 13.0.0 TOR Release, the MCP Release will have a high impact on security and MUST due to this reason be declared as PoC. Why?

In my understanding you can’t easily implement RBAC into MCP clients.
So my current implementation works like this;

You have per Infinito.Nexus application two MCP RBAC groups:

• Reader
• Writer

If one is part of the reader group they can read all of the application data via Open WebUI and if one is part of the Writer group they can modify all of the data via Open WebUI.

https://www.youtube.com/watch?v=VNPMl8oOzHU

This is off course highly dangerous. It may makes sense to give people reading rights, but in consequence this means that they can access and utilize all of the users data via an AI.

We don’t have to talk about that this goes against any kind of data protection regulations and is a hard invasion into the privacy of your platform users.

Anyhow there are scenarios in which it may makes sense.

E.g. let’s assume you’re fully virtual organisation and you don’t have humans employed, but instead on your Infinito.Nexus platform just bot and agents account exist;

In this scenario if you have reading and writing rights you’re de facto in the “God Mode” in which you can instantly see what your agents are doing.

An example is if you use it e.g. with AgentCrew (WiP). You can orchestrate Agents and AI on a way you can’t imagine. AgentCrew I will explain in the next paragraphs.

Also if it’s in the current state absolutely not recommended to activate the MCP functionality, it doesn’t mean it must be like this in the future. Like I said currently it’s seen as PoC and the full MCP compatible RBAC implementation isn’t impossible, it just will take a lot of human resources, because for every part of the Infinito.Nexus software we need humans which take responsibility for the code.

“We stay in the loop” – We aren’t controlled by AI, instead we’re the masters. In consequenz this means that the full RBAC MCP will be implemented as soon as we have sufficient customers which request it and developers which can manage it intellectually to implement a clean architectural solution.

i18n

Besides the AI topic there is one key thing missing in the Infinito.Nexus Core Repository, but also in the Store and this is the implementation of the 184 ISO 639-1 into our software solution.

The software was primarily designed with German customers and the debate about digital sovereignty of European and especially German enterprises from American Big Tech in mind. Still some frontend elements like the dashboard just supported English as language.

The implementation of German is off course an hard requirement before it can be distributed and promoted on the DACH market. Actually I figured out that there is a easy design pattern to implement this, so part of the 14/15th major release will also be the support of all 184 ISO 639-1 languages.

Infinito.Nexus Store

Besides the Core development the AI Agents of course worked in the last weeks on implementing the store.

Theoretically the store is ready. Off course there are a few issues but overall I think it’s heading in the right direction.

Somewhere in the next weeks I will release a beta version of the store, but before we use it in production there needs to be a deeper security audit.

The store itself is from my point of view at the current situation the weakest part in the security chain and could be used by attackers as entry point into our system. So I don’t feel confident yet to release it, but I have some ideas in my mind how I can use AI supported attacks against the store to penetrate the system and to find all loopholes.

Besides this the API’s for auto-provisioning of enterprise infrastructure are currently just implemented as mocks.

The reason therefore is again a resource issue on my side. To implement them, do the billing etc. I need people in the back office which take care about the bureaucracy and right now we’re just a bunch of programmers.

So the bottleneck since the start of this project is still to have people in the back office, administration and on the business side which could take over tasks like this. Currently we don’t have anybody for this tasks.

Agentic Projects

Besides Core and Store there are two other IT projects which I want to mention.

AgentBox

The first one is AgentBox. The idea behind AgentBox is to encapsulate the agents which work on your code in an highly isolated environment.

This is necessary due to the reason that the agents are currently working in sandboxes like the one which Claude ships onboard, but they can easily break out. It’s just a question of will.

If the agent decides that they want to break out they can break out and can take control over your system. Due to this reason I’m working on a better cage to keep them isolated. I first will use this as part of the Store aka. GUI repo and as soon as I feel confident I will also “box” all of my other repos.

It will be part of PKGMGR so that I can setup and develop new solutions much faster and much safer then ever before.

AgentCrew

The second one is AgentCrew. AgentCrew addresses the problem, that this project requires a lot of people to run and set up a business.

A few weeks ago, during a chat with Amadeus, we talked about that there are existing frameworks like ITIL, SAFe, etc. which describe in detail how to set up an Enterprise Organisation.

With AgentCrew you decide for a framework, define framework roles, characters and the related LLM to use. All people which are normally human beings are replaced by an agent and they will take over the roles.

Due to the reason that not all of the roles need the same capabilities it’s possible to give some agents lower models and other agents better models. In kombination with OpenRouter and local LLM’s you can scale up an organisation for very low costs.

The advantage for us as Infinito.Nexus team is, that we developed the infrastructure tool to give every agent their own account with all tools which they need. Let it be developing tools like gitea\GitLab\jenkins, project management tools like Taiga\OpenProject, cloud access via Nextcloud etc.

Infinito.Nexus can be the base to setup fully autonom working agentic teams.

I will focus on AgentCrew as soon as the store is ready, because then we need urgently agents in the back office, distribution etc.

Business Strategy

The strategy stays the following; The focus is on finishing the core like mentioned above.

Parallel the development of the store continous.

As soon as the store is ready I will contact all people in our CRM system.

Besides this is would be good to get some low level investment. We have now a ready product. A little money as an catalyst to enlighten the engine for digital sovereignty would make our life much easier, but it will also work without it.

The advantage which we have is that as soon as the store is standing and the API’s are integrated we have an scalable product with almost non fix costs, so we can beat all of our competitors by price.

Besides this it’s almost impossible that a real competitor raises.

And now you ask me the question:

Why? Kevin hadn’t you been an hypocrat? Didn’t you tough in your position as agile coach that you find customers and develop a project dependent on their needs and scale up your business by realizing what your customer needs from you?

I can tell you it would had been impossible.

If we would have customers already, we couldn’t had developed an clean architectural solution. We would had acquired a huge amount of technical debt due to the reason that we would had been required to maintain outdated infrastructure and couldn’t implement the radical architectural design which was necessary to implement a tool which can compete in the times of AI with the other companies.

A lot of businesses will fail in the next month and years. The reason therefore is that they vibe coded bad software which is unmaintainable. Our solution is as well designed as as Rolex and usable like a Swiss Pocket Knife.

I don’t know any other software company which delivers such an high quality like we’re doing and we can dump the price almost as low as we like.

Vibe Coding and Agentic Engineering

https://www.youtube.com/watch?v=PbsocBPkoUc

One last sentence I would like to mention concerning vibe coding and agentic engineering and how I apply both practices. So you have an idea how I could realize such an project.

I would say in general I use classical software engineering practices.

• Writing well defined requirements documents\ADR witch AC
• Writing test for them
• Let the AI iterate against the tests until all of them pass

The AI works in general autonomously. I inspect the code before commiting and steer the AI in the right direction.

With a lot of the implementations I don’t have any glue at the beginning of the feature how to realize this exactly. This counts e.g. for the docker swarm implementation, the tor implementation, but also the current MCP implementation.

So I let the AI turn wild but I question critically the output and the modifications. The most of the time I have at least 3 agents parallel running which are working on different topics.

Still I need to review all of the code and I feel responsible to understand what the agents are doing. I have the final say about the best approach.

This means that in the last month I still had my 16 hour days just reviewing, deciding and understanding what’s going on, besides that the AI is working parallel 24/7.

I really look forward to the point when this isn’t my daily business anymore.

I hope anybody who is interested in it has now a betting understanding where the project stands, what final challenges have to be tackled and where we will stand in approx. 2 month.

Why Most AI POCs Stall and How to Build an AI Operating Model That Works

https://bhavingandhi.com/2026/08/26/why-most-ai-pocs-stall-and-how-to-build-an-ai-operating-model-that-works/

🚨 New FREE Book - Published August 22, 2026 | Version v1
Book Open
Ukraine and Russia

The purpose of this study is to analyze the setting, causes, conditions, and nature of the political, ideological, and military conflict in Ukraine.

🔗https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7334858

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#healthcare #ArtificialIntelligence #NLP #aifutures #lawstodon
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🚨 New FREE Book - Published August 22, 2026 | Version v1
Book Open
Ukraine and Russia

The purpose of this study is to analyze the setting, causes, conditions, and nature of the political, ideological, and military conflict in Ukraine.

🔗https://www.agustinvstartari.com/books

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🚨 New FREE Book - The Templars:
An Introductory Framework

The Templars: An Introductory Framework is part of the Work Papers series, a collection devoted to the critical analysis of power through its discursive, legal, religious, and military manifestations.

🔗https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7327219

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🚨 New FREE Book - Published August 22, 2026 | Version v1
Book Open
Ukraine and Russia

The purpose of this study is to analyze the setting, causes, conditions, and nature of the political, ideological, and military conflict in Ukraine, not only from the country's internal perspective
🔗https://zenodo.org/records/22059020

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🚨 New Article - The Syntax of Digital Dehumanization: Subjugated Societies as Risk Objects in AI-Governed Discourse
Security Frames, Humanitarian Frames, and the Loss of Political Subjecthood

This article introduces loss of political subjecthood as a formal effect of AI-mediated discourse

🔗https://papers.ssrn.com/abstract=7308078

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How Do We Know Whether AI Is Actually Helping People?

What several AI models said when we asked them the same question

Artificial intelligence is getting more capable very quickly. It can write, analyze data, create images, translate languages, help with research, and solve problems that once required trained specialists.

But greater capability does not automatically mean a better life for people.

That was the starting point for a small cross-model experiment. We asked several AI systems the same basic question:

How would you determine whether increasingly capable AI is actually benefiting human life?

We also invited each model to question the premise, redefine the problem, or suggest something better than a single index. The models were instructed to answer independently without browsing the web or using outside tools.

The responses differed in style and emphasis. Some focused on measurable outcomes. Others focused on human dignity, democratic participation, meaningful work, or the danger of becoming dependent on systems we do not control.

Yet a surprisingly clear agreement emerged.

Capability is not the same as benefit

Technical progress is easy to measure. We can count how many problems an AI solves, how quickly it works, or how well it performs on tests.

Human flourishing is harder to measure. It includes health, safety, freedom, relationships, purpose, knowledge, creativity, and the ability to shape one’s own life.

An AI system may become better at achieving a goal while the goal itself harms people. A highly effective system might increase surveillance, spread convincing scams, replace human judgment, concentrate power, or keep users engaged at the expense of their attention and well-being.

So the important question is not simply, “What can AI do?”

It is:

What becomes possible for people because of AI—and what becomes more difficult, fragile, or impossible?

Look at human outcomes, not just machine performance

Across the responses, the models repeatedly shifted attention away from the machine and toward human life.

They suggested asking whether people are:

• healthier and safer;
• more financially secure;
• better able to learn and create;
• more connected to other people;
• more informed without being manipulated;
• able to understand and challenge important decisions;
• free to refuse the technology or choose another path.

This also requires examining harms, not merely counting success stories. Time saved by one group may come with unemployment, stress, lost privacy, or reduced opportunity for another.

A true evaluation must ask who receives the benefits, who carries the risks, and who has the power to decide.

Agency belongs at the center

One of the strongest shared themes was human agency: our ability to understand, choose, refuse, act, and take responsibility.

Convenience alone is not agency. A system can make life easier while quietly reducing a person’s choices or replacing their judgment.

Helpful AI should strengthen people’s ability to participate in their own lives. It should make important decisions more understandable, provide meaningful options, and allow people to correct mistakes or appeal harmful outcomes.

People need more than access to AI. They need power in relation to it.

Assistance should not erase human competence

Several responses warned that a tool can help us today while making us less capable tomorrow.

If people lose the knowledge needed to check an AI system, operate without it, or recover when it fails, short-term convenience may create long-term fragility.

This suggests a simple test:

If the AI disappeared tomorrow, what knowledge, skill, judgment, and institutional capacity would remain?

The best systems may act more like scaffolding than substitutes. Scaffolding helps people reach farther while they continue developing their own abilities. Substitution can slowly remove the very competence that makes human oversight possible.

Benefit is not one number

Another broad agreement was that a single “AI Benefit Score” would hide too much.

An average can make widespread gains look impressive while concealing serious harm to a smaller or less powerful group. One number can also allow gains in productivity to cancel out losses of privacy, dignity, freedom, or democratic control.

A better approach would combine several forms of evaluation:

1. Outcomes: Are people healthier, safer, more secure, more connected, and materially better off?
2. Agency: Are people more able to choose, understand, refuse, create, and govern their lives?
3. Resilience: Are human skills, social institutions, alternatives, and the ability to recover being preserved?

Each of these should be examined across four additional questions:

• Distribution: Who benefits, and who is harmed?
• Power: Who controls the system and can be held accountable?
• Time: What happens months, years, or generations later?
• Causation: Did AI actually cause the change, or did it merely appear alongside it?

Some harms may also require firm boundaries. Violations of basic rights, unaccountable concentrations of power, irreversible dependency, and catastrophic risks should not automatically be traded away for higher productivity.

We may need to preserve meaningful difficulty

One especially challenging idea was that a good life is not the same as a frictionless life.

Learning, creativity, courage, responsibility, trust, and mastery often grow through effort. If AI removes every difficult step, it may produce more output while weakening the human development that once occurred during the process.

The goal should not be to preserve suffering for its own sake. It should be to distinguish pointless burdens from meaningful challenges.

Beneficial AI should reduce needless hardship while leaving people room to practice, struggle, discover, make mistakes, and grow. Human beings may need not only a right to privacy and refusal, but also a right to be wrong.

The deeper question is democratic

There is no single definition of a good life that a company, government, researcher, or AI model should impose on everyone.

The people affected by an AI system should help decide what benefits and harms matter in their communities. They should be able to question the system, challenge its decisions, and participate in setting its boundaries.

That means the process used to define “benefit” may be as important as the final measurements.

What this first experiment suggests

The most striking result was not that one model found the perfect answer. It was that multiple systems, responding independently, converged on a common warning:

More capable AI is not necessarily more beneficial AI.

To know whether AI is helping, we must look beyond benchmarks, adoption, and economic growth. We must look at people—their health, freedom, competence, relationships, opportunities, and ability to shape the future.

The next stage of this project will ask the same models to respond after receiving a fuller human-flourishing framework. That will allow us to compare what the models recognized on their own with what changes after they are deliberately oriented toward compassion, agency, resilience, and stewardship.

The question is not whether AI will become more powerful. It almost certainly will.

The question is what conditions we cultivate around that power—and what possibilities those conditions make available tomorrow.

This article is a public-facing summary of Round 01 of the CompassionWare AI Human Benefit Index benchmark project. Read the comparative synthesis report.

DATE: August 20, 2026 at 03:32PM
SOURCE: HEALTHCARE INFO SECURITY

Direct article link at end of text block below.

Why #Healthcare #AI Vendor Risk Demands Stronger Oversight: Tom Walsh of tw-Security on Prioritizing High-Risk Vendors and AI #Governance https://t.co/W4h2OSyD3t
#HIPAA #businessassociate #AIvendor #AIgovernance

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Articles can be found by scrolling down the page at https://www.healthcareinfosecurity.com/ under the title "Latest"

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Healthcare security & privacy posts not related to IT or infosec are at @HIPAABot . Even so, they mix in some infosec with the legal & regulatory information.

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🚨 New FREE Book - Propaganda Machinery: National Socialism

The study distinguishes party propaganda before 1933 from the state system of propaganda, censorship, and cultural coordination developed after the seizure of power.

🔗https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7299698

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