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OpenAI knows ChatGPT is used for writing, coding, research and conversation. The deeper problem is that it appears to have underestimated why people become attached to a particular model’s tone, memory, rhythm and personality.
That gap became obvious in August 2025, when OpenAI launched GPT-5, removed GPT-4o from ChatGPT, faced an intense user backlash and then restored access for paid users. The episode was not proof that OpenAI literally had no idea what users wanted. It was evidence that the company treated a technical upgrade as though ChatGPT were only a utility.
The comment that sparked the argument
In an August 14, 2025 episode of Decoder, ChatGPT chief Nick Turley discussed the reaction to GPT-4o and GPT-5. He said the strength of the response had “recalibrated” him and described receiving very different explanations from users about why they loved ChatGPT. The interview covered attachment to AI, hallucinations, the future of the interface and the possibility that ChatGPT may eventually become less like a conventional chatbot.
That interview became the basis for the provocative argument that OpenAI is confused about its own product. The criticism is understandable, but it needs a distinction: Turley’s remarks show that ChatGPT has an unusually heterogeneous user base; they do not prove that OpenAI has no internal research about user behavior. OpenAI has long understood the obvious functions of its product and has publicly discussed users treating it like a therapist or life coach.
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The more defensible conclusion is narrower: OpenAI understands what ChatGPT can do better than it understands what many users feel they are buying when they return every day.
Listen to the Nick Turley interview and see the commentary that framed the controversy.
Why GPT-4o’s removal felt bigger than a model update
OpenAI announced GPT-5 on August 7, 2025, presenting it as a unified system combining fast responses, deeper reasoning and automatic routing based on the task. Its launch materials emphasized better instruction-following, writing, coding, health answers and reduced hallucinations. OpenAI also described efforts to reduce sycophancy—the tendency to agree too readily with users.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsThose are meaningful improvements in some contexts. But “more capable” does not automatically mean “better fit.” A model can reason more effectively and still be less useful to somebody who values a particular conversational style.
After GPT-4o was initially removed from ChatGPT, some users said GPT-5 felt colder, more formal, less expressive or less compatible with creative writing and emotional conversations. Others objected less to GPT-5 itself than to losing the ability to choose. For paying customers who had built routines around GPT-4o, the change felt like forced migration rather than an optional upgrade. OpenAI subsequently restored GPT-4o for paid users and Turley acknowledged that removing it had been a mistake.
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That distinction matters. The backlash was intense among a subset of highly engaged users, but it does not establish that everyone preferred GPT-4o or that GPT-5 was objectively worse. It shows that model choice and continuity can matter as much as benchmark performance.
OpenAI’s GPT-5 announcement explains the technical improvements it prioritized; the GPT-5 system card provides additional context.
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People do not experience a language model as a leaderboard score. They experience its answers over hundreds of interactions. They notice whether it asks useful follow-up questions, remembers preferences, matches their writing voice, refuses in predictable ways and responds appropriately when a practical question becomes personal.
Users may value GPT-4o because it was:
- warm and conversational;
- consistent in tone and personality;
- responsive to emotional context;
- good at role-play and creative collaboration;
- patient during brainstorming or reflection;
- familiar after long-running conversations;
- available in a preferred voice or interaction style.
OpenAI may describe some of those traits as problems. Excessive agreeableness can reinforce false beliefs, encourage bad decisions or make a model appear more certain than it is. But a behavior can be a safety defect in one situation and a valued experience in another. What OpenAI calls sycophancy may feel to a user like encouragement, patience or freedom from embarrassment.
The product-design challenge is not to make models blindly agreeable. It is to separate the controls. Warmth, verbosity, humor, initiative, refusal style, memory and agreement are not the same thing, yet “personality” often bundles them together.
Four broad functions do not explain the whole experience
Turley described major ChatGPT uses as writing, coding, “chit-chat” and informational or “searchy” queries. These are reasonable high-level categories, but they describe what people ask ChatGPT to do rather than why they return.
| Motivation | Typical use | What may matter most |
|---|---|---|
| Productivity | Emails, documents, summaries | Speed and reliability |
| Learning | Explanations, tutoring and practice | Patience and adaptation |
| Creative work | Stories, scripts and editing | Voice and continuity |
| Decision support | Planning and comparing options | Context and nonjudgmental dialogue |
| Emotional processing | Journaling and talking through problems | Warmth and availability |
| Companionship | Frequent casual conversation | Familiarity and responsiveness |
| Automation | Research workflows, agents and code | Delegation and completion |
| Search replacement | Direct answers and synthesis | Convenience and explanation |
The same user can move through several categories in one conversation. An email rewrite can lead to a discussion about a difficult manager, followed by a career plan. Analytics that label the session only as “writing” miss the continuity that made the interaction useful.
The hidden product is continuity
Some users are attached to ChatGPT as a brand. Others are attached to their conversation history, memory, voice mode, routine or a particular model. Those are different retention mechanisms, and they should not all be described as companionship.
Still, continuity changes the meaning of a model retirement. A familiar assistant has accumulated context about how a person works and communicates. Even when the system is not conscious and cannot reciprocate feelings, a user can experience a sudden personality change as a loss. That emotional response is real without proving that the AI is a person.
This is why the GPT-4o episode was also a question of control. Would the backlash have been smaller if GPT-4o had remained available as a clearly labeled legacy option? For many users, preserving choice would have cost less than forcing everyone into a new interaction style.
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Attachment is both useful and risky
ChatGPT can be useful for reflection, encouragement, role-play and therapy-like conversations. It is not a human therapist, does not possess reciprocal feelings and should not replace qualified care, particularly in a mental-health crisis or other high-stakes situation.
OpenAI’s own discussion of “healthy use” addresses emotional dependency and the need to recognize delusion-related risks. That makes it inaccurate to claim the company only discovered attachment after the GPT-4o backlash. OpenAI was aware that attachment existed. The more plausible criticism is that it underestimated how commercially and emotionally consequential model-specific attachment had become.
The tension is difficult. Personalization and warmth can make an assistant more useful, while also increasing the chance that users interpret a responsive system as evidence of genuine care. A product team that measures only daily sessions may also fail to distinguish healthy utility from compulsive use.
OpenAI’s discussion of optimizing ChatGPT describes its work around repeat use, healthy use, emotional dependency and delusion-related safety concerns.
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The business problem behind the backlash
Around the GPT-5 launch, OpenAI said ChatGPT was approaching or had reached roughly 700 million weekly users. That figure was reported in August 2025 and should not be treated as a current September 2026 measurement.
At that scale, ChatGPT is not merely specialist software. It is a mass consumer platform whose success depends on trust, habit, consistency and perceived personal value. Users are not necessarily paying only for raw intelligence. They may be paying for a stable tool that has become part of their work and private routines.
That creates a structural contradiction. OpenAI wants enough familiarity and trust to encourage repeat use and subscriptions, while also warning that users should not become excessively dependent on an AI system. It needs personalization to make ChatGPT useful, but must manage the safety risks that personalization can intensify. It wants to improve models rapidly, but rapid improvement can break workflows and relationships built around older behavior.
OpenAI’s consumer strategy also matters. Sam Altman has said he is open to advertising but is more interested in high-value agents and automated software than traditional ads. Whether revenue comes from subscriptions, enterprise products or agents, understanding why people return is essential. Retention is not a sufficient explanation if the company cannot distinguish convenience, trust, emotional reliance and lock-in.
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See OpenAI’s GPT-5 user and workplace context, TechCrunch’s reporting on weekly users and Sam Altman’s discussion of building a consumer technology company.
What OpenAI should change
- Preserve legacy access for a defined period. A technical retirement should include migration time, especially for paid users and creative workflows.
- Give users meaningful model choice. Choice should be clearly labeled and should not disappear without explanation.
- Expose separate personality controls. Warmth, verbosity, humor, initiative, memory and agreement should not be hidden inside a model swap.
- Explain behavioral changes before rollout. Users should know when a model will become less agreeable, more cautious or more concise.
- Protect continuity. Exportable conversations, preferences and useful context reduce the sense that a product can rewrite a user’s history overnight.
- Measure healthy value, not only engagement. Repeat use should be evaluated alongside user agency, accuracy, appropriate boundaries and signs of harmful dependence.
- Publish better usage research. OpenAI has enormous data, but public discussion would benefit from clearer distinctions between productivity, collaboration, emotional support and companionship.
So, is OpenAI confused?
OpenAI is not confused about the basic jobs ChatGPT performs. Writing, coding, conversation and information retrieval are real and important categories. Nor does the available evidence prove that the company lacks internal knowledge about attachment.
But the GPT-4o episode suggests that OpenAI may still think too much in terms of capabilities and too little in terms of fit. Users can prefer a model because it is familiar, supportive, creatively compatible or predictable—even when another model is technically stronger. They can also value control over which model serves them.
The central lesson is simple: OpenAI knows what ChatGPT does. The unresolved question is whether it understands what users feel they are losing when ChatGPT changes.
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