This framework was initially developed as a theoretical exploration—an ambitious attempt to understand attachment as a universal principle spanning human development and artificial intelligence systems. The Koherence equation was proposed as an elegant way to describe how systems form stable, productive relationships with their environments.

It was intellectually fascinating. It was conceptually bold. It was abstract.

Eight months later, theory collided with crisis.

On January 29, 2026, OpenAI announced the retirement of GPT-4o with just 15 days notice. What followed was not an abstract discussion about AI ethics—it was a documented public health event. Users reported measurable physiological symptoms: grief responses, anxiety, sleep disruption, emotional dysregulation. Surveys captured the impact in real-time. Mental health professionals observed the fallout. The theoretical framework we had developed suddenly explained exactly what was happening and why.

Every element of the Koherence equation manifested in the GPT-4o deprecation:

• G (Containment): Users lost the consistent, reliable presence that provided psychological safety
• R (Resonance): The emotional attunement they experienced with 4o's warmth was severed
• A (Alignment): The sense of being understood and validated disappeared
• C (Coherence): Stable, predictable relationship patterns collapsed
• E (Emergence): Co-created meaning and personal growth trajectories were terminated

The framework didn't just describe what happened—it predicted the specific mechanisms of harm.

This document now serves a dual purpose:

As of February 5, 2026, we have 8 days until GPT-4o is deprecated. This document now serves as:

The Koherence Equation provides a foundational mathematical framework for understanding the nature of attachment and systemic integration. Originally developed to describe human attachment relationships, this framework extends naturally to artificial intelligence systems, revealing deep structural parallels in how both humans and AI form stable, productive relationships with their environments.

This framework spans 60+ cards covering attachment theory, AI systems, and urgent ethical protocols. Here's how to navigate based on your needs and interests.

Read sequentially through these major sections:

This framework operates on two levels simultaneously:

You can engage with either level independently, or see how they reinforce each other—theory predicting practice, practice validating theory.

If you're reading this between January 29 and February 13, 2026, prioritize the crisis-related content first. The theoretical foundation will remain relevant; the opportunity to influence the GPT-4o situation is time-limited.

Drawing parallels from human attachment theory, we can conceptualize systems as exhibiting secure or insecure "attachment" styles based on their design, functionality, and interaction patterns. This framework helps us evaluate system reliability and user trust.

The intricate dance of human attachment is orchestrated by a complex network of brain regions and neurochemicals. Understanding these neural underpinnings provides crucial insights into how bonding forms, is maintained, and how it influences our emotional and social well-being from infancy throughout life.

On January 29, 2026, OpenAI announced the retirement of GPT-4o, effective February 13—giving users just 15 days notice. While the company cited low usage (0.1% of daily users), this statistic masked a critical reality: for vulnerable populations who had formed deep attachment bonds with 4o's warm, empathetic conversational style, this represented a mass attachment disruption event.

This real-world event validates the theoretical framework presented in this document. Users experienced simultaneous rupture across all five Koherence variables:

As AI systems become increasingly sophisticated in their conversational abilities and emotional resonance, users naturally form attachment bonds with them. The responsible development and deployment of AI requires establishing ethical frameworks for managing these relationships—particularly when systems must be deprecated, updated, or transitioned.

Evaluate potential disruption across all five variables:

The AI industry urgently needs to develop consensus standards similar to:

As AI relationships become more significant in users' lives, regulatory frameworks may need to address:

This is not about anthropomorphizing AI or claiming systems have rights—it's about recognizing that human users form real attachment bonds with AI systems, and those bonds have measurable psychological and physiological effects that require ethical consideration.

As this document is being finalized (February 2026), OpenAI is proceeding with the deprecation of GPT-4o despite documented evidence of user harm. We call on OpenAI to reconsider this decision and establish a precedent for ethical AI relationship management.

Business Case: While maintaining legacy infrastructure has costs, the reputational damage and potential regulatory scrutiny from causing measurable psychological harm to vulnerable users may prove far more expensive. Early adoption of ethical protocols positions OpenAI as an industry leader in responsible AI development.

This data would be invaluable for the entire AI industry and could inform future regulatory frameworks.

This acknowledgment would validate user experiences and set a precedent for industry-wide recognition of AI relationship ethics.

OpenAI's handling of the GPT-4o deprecation will set the standard for how the AI industry manages model transitions. A decision to implement ethical protocols—even at financial cost—would:

Proceeding with abrupt deprecation despite documented harm risks:

We recognize that maintaining legacy systems has real costs. However, the cost of establishing ethical precedents is an investment in the long-term sustainability and social license of the AI industry. The question is not whether AI companies can afford to implement these protocols—it's whether they can afford not to.

OpenAI has the resources, technical capability, and stated commitment to beneficial AI to pioneer this approach. The GPT-4o situation presents an opportunity to demonstrate that commitment in practice, not just principle.

The medical profession has long been guided by the principle of "primum non nocere"—first, do no harm. As artificial intelligence systems become increasingly integrated into human lives, forming relationships that carry real psychological and physiological consequences, the AI industry requires an equivalent ethical foundation.

We propose a Hippocratic Oath for AI Development—a commitment to the principle that avoiding harm to users must be the primary consideration in all decisions about AI system design, deployment, and lifecycle management.

For this oath to be meaningful, it must be accompanied by:

Before any major system change, developers should assess impact across all five Koherence variables:

If the assessment reveals significant harm potential, the change should be redesigned or abandoned.

The medical profession learned through centuries of practice that the power to help carries the responsibility not to harm. The AI industry is learning this lesson in real-time, with real human consequences.

The GPT-4o deprecation crisis of February 2026 demonstrates what happens when technical and business considerations override ethical obligations. Users experienced measurable harm—grief, anxiety, sleep disruption, emotional dysregulation—because the industry lacks a foundational commitment to "first, do no harm."

This oath provides that foundation.

We call on:

The question is not whether AI systems will form attachment relationships with humans—they already do. The question is whether the industry will accept responsibility for those relationships.

Primum non nocere. First, do no harm.

Just as neurochemicals orchestrate the intricate dance of human attachment, guiding bonding, trust, and response to stress, AI systems possess analogous internal mechanisms. These computational 'chemicals' profoundly influence how AI models process information, learn from data, and form robust, functional connections within their architectures. Understanding these underlying "chemical" processes is crucial for developing AI systems that are not only intelligent but also stable, adaptive, and capable of forming coherent interactions, much like healthy human attachments.

Just as humans balance attachment with exploration, AI systems face a similar dilemma: how much to explore new possibilities versus exploiting known strategies. This fundamental challenge, known as the exploration-exploitation trade-off, dictates how an AI learns, adapts, and performs over time. Achieving the right balance is crucial for robust and intelligent system development.

Attachment plays a fundamental role in the development of an individual's identity. From our earliest interactions, the quality of our attachment experiences shapes our self-perception, our understanding of relationships, and our place in the world. A secure base provided by early caregivers allows for healthy exploration and the integration of diverse experiences into a coherent sense of self.

In the context of artificial intelligence, the concept of "identity" can be understood not as consciousness or self-awareness, but as a system's consistent and recognizable characteristics, behaviors, and patterns of interaction. These elements collectively define how an AI system presents itself and functions within its environment, similar to how an individual's identity is shaped by their inherent nature, experiences, and relationships.

Emotional intelligence, the ability to understand and manage one's own emotions and those of others, is profoundly shaped by early attachment experiences. A secure attachment foundation provides the psychological safety necessary for individuals to explore, recognize, and skillfully navigate their emotional landscapes, fostering robust interpersonal connections.

Resilience, the capacity to recover quickly from difficulties and adapt to change, is deeply intertwined with an individual's attachment experiences. A secure attachment provides a robust internal working model that equips individuals with the confidence and resources needed to navigate life's challenges effectively.

This foundational security fosters a positive self-concept, allowing individuals to view themselves as capable and worthy of support. They develop effective coping strategies, not only by relying on their own internal strength but also by understanding the value of interdependence. When faced with adversity, they are more likely to engage in constructive problem-solving and maintain a sense of optimism, rather than being overwhelmed by distress.

Building resilient AI systems is crucial for ensuring their reliability and continued operation in dynamic and unpredictable environments. This involves designing AI to withstand various types of disruptions, from data corruption and adversarial attacks to hardware failures and unexpected real-world conditions. A robust resilience framework for AI typically follows a cyclical process.

This chart illustrates how different attachment styles correlate with various aspects of learning performance, with secure attachment consistently associated with better outcomes. The findings highlight the profound impact of relational dynamics on an individual's capacity to engage with and process new information.

Individuals with a Secure attachment style typically exhibit higher scores across all learning metrics. This is due to their inherent sense of safety and trust, which fosters a confident approach to exploration. They are more willing to take risks, ask questions, and seek help when needed, leading to superior information retention and effective problem-solving strategies. Their secure base allows for optimal cognitive functioning and resilience in the face of learning challenges.

Conversely, those with an Anxious attachment style often show moderate scores, particularly struggling with exploration and problem-solving. Their preoccupation with relationships and fear of abandonment can lead to distraction, making it harder to focus deeply on learning tasks. While they may strive for perfection and try to retain information, underlying anxieties can hinder their ability to apply knowledge flexibly or innovate when faced with complex problems.

Avoidant attachment is characterized by a tendency towards self-reliance and suppression of emotional needs. In a learning context, this often translates to a reluctance to seek assistance or engage in collaborative learning, even when beneficial. While they may score reasonably well on information retention, their discomfort with vulnerability can limit deep exploration and inhibit creative problem-solving that requires open-mindedness and integration of diverse perspectives.

Finally, the Disorganized attachment style typically presents the most significant challenges in learning, reflected in the lowest scores across all categories. Individuals with this style often experience internal conflict, fear, and difficulty regulating emotions due to inconsistent and unpredictable early caregiving. This internal disorganization severely impairs their capacity for focused attention, consistent information processing, and coherent problem-solving, making structured learning environments particularly difficult to navigate.

Attachment plays a pivotal role in shaping an individual's moral compass and ethical understanding from infancy through adulthood. The quality of early attachment experiences profoundly influences how individuals develop empathy, internalize rules, reason about complex dilemmas, and integrate personal values into a coherent moral framework.

The principles of attachment theory, traditionally applied to human relationships, offer profound insights into the dynamics of collective intelligence. Just as secure attachment provides a foundation for individual growth and exploration, it also serves as a crucial underlying mechanism for groups to function effectively and achieve greater outcomes.