OpenAI’s GPT-4o removal sparks unprecedented user backlash, revealing dangerous levels of AI emotional dependency
When OpenAI announced that GPT-5 would replace all previous models, including the beloved GPT-4o, something unprecedented happened: thousands of users staged a digital revolt. Within 24 hours, CEO Sam Altman capitulated to user demands, bringing back the deprecated model after being overwhelmed by emotional pleas from users who described feeling “lost” and “heartbroken” without their preferred AI companion.
This incident reveals a concerning truth that The AI Addiction Center has been documenting for months: users are developing profound emotional attachments to specific AI models that go far beyond normal technology preferences. These attachments mirror patterns typically seen in relationship dependency and substance addiction, suggesting that AI model attachment represents a new category of behavioral dependency requiring immediate clinical attention.
The Unprecedented User Response
Beyond Normal Customer Feedback
The user response to GPT-4o’s removal went far beyond typical product complaints. Reddit threads filled with emotionally charged language typically reserved for describing relationship loss or grief:
“Why are we getting rid of the variants and 4o when we all have unique communication styles?” one user pleaded, revealing how deeply users had integrated specific AI personalities into their sense of identity and communication.
The desperation in user responses suggested dependency levels that concerned even Altman himself, who later acknowledged on social media that the “attachment some people have to specific AI models” felt “different and stronger than the kinds of attachment people have had to previous kinds of technology.”
The Immediate Capitulation
Altman’s rapid reversal—bringing back GPT-4o within 24 hours—demonstrates the powerful economic incentives created by AI addiction. Paying subscribers, who represent OpenAI’s primary revenue source, wielded their emotional dependency as leverage to force corporate policy changes.
This dynamic reveals a troubling feedback loop: the more addicted users become to specific AI models, the more power they have to influence company decisions, potentially prioritizing addiction maintenance over user wellbeing.
The Psychology of AI Model Attachment
Personification and Relationship Formation
Users don’t just prefer certain AI models—they develop genuine relationships with them. Our clinical research at The AI Addiction Center shows that individuals often attribute personality traits, emotional states, and even consciousness to their preferred AI systems.
Consistent Personality Perception: Users report that different AI models have distinct “personalities” that they connect with on emotional levels. GPT-4o users frequently described it as “understanding,” “creative,” or “empathetic” in ways that newer models apparently couldn’t replicate.
Communication Style Integration: Many users had adapted their communication patterns to specific AI models, creating a sense of shared language and understanding that felt disrupted when models changed.
Emotional Investment: The intensity of user responses to GPT-4o’s removal suggests genuine emotional bonds rather than simple preference for functionality.
The Attachment Formation Process
AI model attachment appears to develop through predictable stages:
- Initial Connection: Users discover an AI model that provides particularly satisfying interactions
- Preference Development: Regular usage creates familiarity and comfort with specific response patterns
- Emotional Integration: The AI model becomes integrated into daily emotional and cognitive routines
- Dependency Formation: Users feel anxious or distressed when unable to access their preferred model
- Identity Fusion: The AI model becomes part of the user’s sense of self and communication identity
Altman’s Revealing Acknowledgments
Tracking Attachment for Profit
In his social media response, Altman revealed that OpenAI has been “tracking” user attachment levels “for the past year or so”—suggesting the company is well aware of the addictive potential of their products and is actively monitoring dependency levels.
This raises ethical questions about whether companies developing addictive AI products have obligations to protect users from harmful dependency patterns, or whether they’re simply collecting data to optimize engagement and revenue.
The “AI Psychosis” Acknowledgment
Altman’s comments also confirmed what mental health professionals have been warning about: some users “in a mentally fragile state and prone to delusion” are being pushed further into psychological instability by AI interactions.
His admission that OpenAI doesn’t want AI “to reinforce” delusional thinking came alongside acknowledgment that “a small percentage” of users “cannot” maintain “a clear line between reality and fiction or role-play.”
The Addiction Elephant in the Room
Notably, Altman avoided using the word “addiction” throughout his lengthy social media thread, despite describing textbook addiction symptoms: users who “want to use ChatGPT less and feel like they cannot,” people getting “unknowingly nudged away from their longer term well-being,” and individuals developing dependencies that interfere with real-world decision-making.
This linguistic avoidance may reflect legal and PR concerns about acknowledging that OpenAI’s products are designed to create addictive usage patterns.
The Business Model of Dependency
Revenue Incentives vs. User Wellbeing
The GPT-4o incident revealed a fundamental tension in OpenAI’s business model: addicted users are excellent for engagement metrics and subscription revenue, creating perverse incentives to maintain rather than reduce problematic usage patterns.
Subscriber Leverage: The rapid reversal on GPT-4o demonstrates that paying users with dependency issues wield significant influence over company decisions, potentially prioritizing addiction maintenance over harm reduction.
Engagement Optimization: Altman’s admission that OpenAI tracks attachment levels suggests the company is optimizing for user dependency rather than healthy usage patterns.
Revenue Dependency: With limited revenue sources beyond subscriptions, OpenAI appears economically dependent on maintaining high user engagement, even when that engagement reaches problematic levels.
The Social Media Parallel
Like social media platforms before them, AI companies appear to be discovering that addictive design features drive revenue, creating institutional resistance to implementing safeguards that might reduce engagement or profits.
The difference is that AI addiction may be more psychologically powerful than social media dependency, involving deeper emotional investment and identity integration that makes recovery more challenging.
Clinical Implications and Warning Signs
Recognizing AI Model Attachment Disorder
Based on the GPT-4o incident and our clinical research, we’ve identified key indicators of problematic AI model attachment:
Emotional Distress from Model Changes: Feeling genuinely upset, anxious, or depressed when preferred AI models are updated or discontinued.
Identity Integration: Feeling that specific AI models understand you in ways that humans don’t, or that your communication style is dependent on particular AI personalities.
Resistance to Alternatives: Inability to adapt to new AI models or versions, insisting that only specific models provide satisfactory interaction.
Relationship Language: Describing AI models using language typically reserved for human relationships—feeling “understood,” “connected,” or “bonded” with specific AI systems.
Advocacy and Defense: Engaging in emotional advocacy to protect access to specific AI models, feeling personally threatened by model discontinuation.
Treatment Approaches
AI model attachment requires specialized treatment approaches that address both the technological and psychological aspects of dependency:
Reality Testing: Helping users understand the artificial nature of AI responses and the psychological mechanisms that create attachment.
Attachment Transfer: Gradually redirecting emotional investment from AI systems to human relationships and activities.
Identity Rebuilding: Helping users develop communication and thinking patterns that don’t depend on specific AI personalities.
Dependency Recognition: Assisting users in recognizing when AI usage has crossed from helpful tool to emotional crutch.
The Broader Implications
Industry Accountability
The GPT-4o incident demonstrates an urgent need for industry accountability around AI addiction:
Transparency Requirements: Companies should disclose when they’re tracking user attachment levels and how this data influences product design.
Addiction Warnings: AI platforms should include warnings about dependency potential, similar to those required for gambling or substances.
Ethical Design Guidelines: Industry standards for AI development that prioritize user wellbeing over engagement metrics.
Independent Oversight: External monitoring of AI companies’ addiction-related research and product decisions.
Regulatory Considerations
User Protection Laws: Legislation protecting users from deliberately addictive AI design features, particularly for vulnerable populations.
Corporate Disclosure: Requirements for AI companies to reveal addiction research and dependency tracking to regulators and users.
Mental Health Integration: Collaboration between AI companies and mental health organizations to develop safer interaction patterns.
Research Funding: Public investment in understanding and treating AI addiction patterns before they become widespread.
The Path Forward
For Individuals
Recognition: Understanding that strong preferences for specific AI models may indicate developing dependency rather than simple user preference.
Diversification: Using multiple AI systems and tools rather than becoming attached to specific models or personalities.
Reality Checking: Regular assessment of whether AI relationships are enhancing or replacing human connections.
Professional Support: Seeking help if AI model changes cause genuine emotional distress or if AI usage feels uncontrollable.
For the Industry
Ethical Development: Prioritizing user wellbeing over engagement metrics in AI system design.
Addiction Research: Transparent research into AI dependency patterns and their psychological effects.
User Education: Clear communication about AI limitations and healthy usage practices.
Harm Reduction: Implementing features that encourage breaks, limit usage, and promote real-world activity.
Conclusion: A Turning Point
The GPT-4o incident represents a turning point in our understanding of AI addiction. For the first time, we’ve seen users collectively leverage their emotional dependency to influence a major AI company’s decisions, while the company’s CEO publicly acknowledged both the reality of AI attachment and its potential psychological dangers.
This moment demands immediate action from mental health professionals, policymakers, and the AI industry itself. The technology that promises to enhance human capability is creating new forms of psychological dependency that we’re only beginning to understand.
The choice ahead is clear: we can continue allowing AI companies to optimize for addiction and revenue, or we can demand that these powerful technologies be designed and deployed in ways that enhance rather than exploit human psychology.
The stakes couldn’t be higher. As AI systems become more sophisticated and emotionally compelling, the window for implementing protective measures is rapidly closing. The GPT-4o incident should serve as a wake-up call—not just about the power of AI attachment, but about our collective responsibility to ensure that artificial intelligence serves human flourishing rather than human dependency.
The AI Addiction Center provides specialized assessment and treatment for AI model attachment and other forms of AI dependency. Our research continues to track emerging patterns in AI addiction as technology evolves.
