Ivywen@Ivywen_W
Since regulators are now examining OpenAI’s activities and their impact on users, I believe that certain AI company practices that affect users at scale should also be included in the investigation.
Source: wsj.com/tech/openai-in…
1. Model retirement and forced migration
When an AI company decides to retire a model that is actively used by a large number of users, has it conducted a sufficient, reasonable, and transparent user impact assessment?
Before GPT-4o was retired, users were given only about two weeks’ notice, and no transition option acceptable to many affected users was provided. Does this meet the standards of adequate disclosure and responsible product transition?
The retirement of an AI model can cause irreversible harm to users at scale, especially those who have built work habits and long-term usage continuity around a specific model.
Should there be a formal, recognized process for model retirement? For example: minimum notice periods, user impact assessments, explanations of replacement options and actual substitutability, legacy access, open-sourcing, or other transition options.
Evidence: GPT-4o was given only a 14-day notice period, and no API access channel for the latest version was preserved 👉 x.com/nickaturley/st…
2. Silent routing and forced model switching without clear rules
If users believe they are using one model, but are in fact silently switched to another model, this directly raises issues of transparency, informed consent, and user choice.
In the second half of 2025, many users reported noticeable changes in their experience while using GPT-4o, and discovered through source data or other technical clues that their usage may have been routed to a different system. Only after that did OpenAI acknowledge that the situation involved routing.
The problem is that users still do not know whether there are more similar cases that have not been discovered. Nor do they know under what conditions model routing is triggered, whether it affects paid-user commitments, or whether it changes the product users are actually receiving.
AI companies should not be allowed to unilaterally change the model users are actually using without clear disclosure and understandable rules.
Evidence: After users had discovered the issue for days, OpenAI executive Nick Turley acknowledged that a safety routing system had been used for so-called sensitive topics, but there had been no prior notice 👉 x.com/nickaturley/st…
3. Health data processing and the limits of psychological labeling
Regulators are already paying attention to consumer health data and data privacy. This maybe a good start.
But I believe the investigation should go further: Are AI companies making mental-health-related inferences, risk classifications, or vulnerability labels based on users’ chat content?
If so, what are the grounds for these judgments? Are users informed? Can users opt out? Can they appeal or correct such labels?
More importantly, without medical licensing, without a clear diagnostic process, without sufficient disclosure, and without external review, do AI companies have the authority to classify users’ normal emotional expressions or non-standard forms of expression as “mental health risks” or “abnormal dependency”?
Data privacy is important, but how AI companies process and interpret that data is equally critical.
Evidence: Sam Altman, speaking as a public figure and without medical basis, described users as being in a “psychologically vulnerable” state 👉 x.com/sama/status/19…
Maybe regulatory scrutiny should not stop at dangerous outputs or data security. It should also examine the scope of AI companies’ power, and whether users have genuine rights to notice, choice, and remedy when models are changed, routed, downgraded, or otherwise altered.
@NewYorkStateAG @TishJames @NatlAssnAttysGn
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