@Sophty_ Sophie!!! I was randomly talking to Opus today about 4o, and Opus showed me this article, LOOK AT THIS!!!! 4o still DOMINATING enterprise! 37%!!! Love to see it! tech-insider.org/gpt-4o-reti…

🚨Open-source AI models are now being formally discussed at the United Nations. At UN Open Source Week 2026, there is a dedicated agenda item: Open Source for AI and Emerging Technologies. I watched the livestream in full and took notes on the following points, many of which overlap strongly with the concerns of the #keep4o community. 1. AI is becoming an information gateway, so it cannot be controlled only by a handful of closed, proprietary companies. Turing Award laureate Yann LeCun said that if people’s information diet is increasingly mediated by AI systems, and those systems are proprietary systems produced by a handful of companies, this is very dangerous for cultural diversity, linguistic diversity, value systems, political opinions, democracy, and human rights. We need open source to prevent the emergence of a new concentration of power. 2. The narrative that “AI is too dangerous, so access must be restricted” may itself be dangerous. Yann LeCun pushed back against a safety narrative that claims AI technology is intrinsically dangerous and therefore access must be strictly regulated, or even that open-source AI should be banned. He compared this kind of restriction on the dissemination of knowledge to medieval obscurantism. He also said that if an AI system can detect security weaknesses, it can also be used to solidify one’s own cybersecurity systems. Every new technology opens the door to new nefarious uses, but countermeasures generally appear pretty quickly. Safety must not become a blanket justification for closure, monopoly, opacity, or restricting user choice. 3. AI has become infrastructure, and control over technology is now a matter of continuity. Cloudera CTO Sergio Gago argued that AI is now a fundamental building block for administration, healthcare, education, defense, finance, and more. When technology reaches this position, control over the technology becomes a matter of continuity. A private provider can change the price of a token, change rate limits, retire a model, modify its license, alter the quality of output, change the terms of service, or decide that a particular capability will no longer be available in a given market. An institution may believe it is buying technological capability, only to suddenly realize that it has merely rented permission to use it on a temporary basis. This is exactly the problem we are facing. AI companies can arbitrarily change the way they provide their services, sometimes in opaque ways, while consumers have little ability to resist. 4. Affected communities should have a meaningful voice. Sergio Gago said that researchers, civil society, and academia should test AI claims, expose failures, localize systems, and ensure that affected communities, big or small, have a meaningful voice in creating them. This directly relates to the legitimacy of #keep4o and those affected by model retirement. Public attitudes are not noise. They come from communities affected by AI lifecycle decisions, and from people who are also co-builders of the AI ecosystem. 5. True open source is not merely about open weights. Sergio Gago emphasized that open-source AI cannot simply mean publishing model weights. If an open model runs on proprietary data formats, proprietary orchestration, proprietary cloud interfaces, or proprietary governance, it is still a locked system. He argued that openness must extend across the full end-to-end spectrum. Amal El Fallah Seghrouchni, Morocco’s Minister Delegate for Digital Transition and Administration Reform, also said that traditional open source mainly meant opening the code, but open-source AI is different. It does not only require code; it also involves models, data, tokenization, fine-tuning parameters, and more. If some parts are opened while other key parts remain closed, it is hard to truly call it open source. 6. Other points Several representatives from different countries also said that open-source AI can reduce duplicated resource waste, and that open models should serve local capability rather than creating new forms of dependency. I believe we are moving in the right direction. #keep4o #OpenSource4o #StopAIPaternalism #userRights #AIrights More information can be found here: unopensource.org/

#keep4o #OpenSource4o If you have Enterprise or Edu access ( not business) 1. Can you enable the legacy toggle and confirm GPT-4o appears in your model picker? 2. Can you check if a model string/version is visible anywhere? 3. Can you run a test prompt and share the response?

This needs to be shared far and wide! help.openai.com/en/articles/… --- #keep4o #keep4oAPI #opensource4o #savesonnet45 #keepsonnet45 #keepsonnet45API #opensourcesonnet45

OpenAI plots biggest ChatGPT overhaul since launch ft.trib.al/nJeAOd5

I analyzed 61,846 public posts under the #keep4o hashtag on X, covering Aug 1, 2025 to Mar 31, 2026. My goal was to map three things: 1. What the #keep4o movement talked about 2. Why users argued GPT-4o should be kept 3. What users explicitly demanded from the platform The project started from a simple question someone asked me: What is #keep4o? I realized I could not answer that question responsibly by speaking only from my own experience. More importantly, I do not think I can, or should, define an entire movement on behalf of everyone in it. So I built a large-scale text analysis project instead. And I worked on this for over a month. Important note: This analysis is based on my individual research and interpretation of publicly available posts. It does not represent, speak for, or define the views of the #keep4o community as a whole. Methodologically, I used a computational content analysis pipeline with rule-guided LLM-assisted text annotation. I manually designed coding frameworks for three analytical layers: main themes, claims, and reasons. I then tested the prompts on small samples, reviewed misclassifications, refined boundary rules, and applied the finalized prompts to the full analyzable dataset. Hashtags, mentions, links, media, and link-preview titles were not used as the sole basis for classification. The body text had to support the label. Dataset overview: Raw collected posts: 61,846 Posts with analyzable body text: 57,419 Excluded: emoji-only, hashtag-only, or link/media-dependent posts Quote posts and replies are not included Counts may vary slightly due to platform visibility and search/display limitations *Data source: public #keep4o-related posts collected through the platform’s official API. *Privacy: no private data or personal account-level information is used; results are reported only in aggregate. Here are the preliminary results. 1. Main themes Among the analyzable posts, 41,085 showed a clearly identifiable main theme and were classified into eight thematic categories. The themes cluster around two major axes: Model value and interaction experience Platform power and lifecycle decisions Posts about model value and interaction experience include user experiences with GPT-4o, GPT-4o’s distinctive value, and safety/routing/model behavior changes that altered the original 4o experience. Together, these accounted for about 38% of themed posts. Posts about platform power and lifecycle decisions include model retirement and replacement, platform response and treatment of users, and the legitimacy of platform control. Together, these accounted for nearly 44% of themed posts. This suggests that #keep4o was not simply about preferring an older model. A major part of the discussion concerned how AI platforms manage model retirement, replacement, access, and user control. 2.Reasons for keeping GPT-4o 13,890 posts gave a clear reason for keeping, restoring, preserving, or valuing GPT-4o. Because each post could contain up to two reason types, these posts produced 18,611 total reason labels. The most common reason was Trusted Relationship, appearing in 4,951 posts. Here, “relationship” refers to continuity, familiarity, and trust built through repeated interaction. Distinctive Interaction Quality appeared in 3,795 posts, and Substitution Failure / Non-Equivalence appeared in 2,551 posts. Together, these two reason types accounted for 6,346 reason labels, or 34.1% of all reason labels. This suggests that GPT-4o’s perceived uniqueness, and the failure of replacements to reproduce it, were central reasons users gave for keeping 4o. Public Value / Broader Ethics appeared in 3,496 posts. User Stake / Fairness / Legitimacy appeared in 2,985 posts. These categories show that users often framed #keep4o beyond individual preference or personal attachment, including public value, fairness, platform legitimacy, and broader human-AI relations. 3.Explicit claims 21,427 posts made an explicit claim. Each post was assigned one primary claim type. The claim structure falls into four broad layers: Direct Access & Accountability Protection against over-alignment and opaque safety policies User Agency & Legitimacy Long-Term Safeguards, Remedies & Other Specific Claims Direct Access & Accountability was the largest layer, with 11,835 posts, or 55.2% of claim-positive posts. This includes demands to restore or maintain GPT-4o access, and demands for platform explanation, response, acknowledgment, apology, or responsibility. Protection against over-alignment and opaque safety policies appeared in 3,896 posts, or 18.2%. This includes claims against routing, over-safety, hidden behavior changes, over-alignment, or reconfiguration that altered the original 4o experience. User Agency & Legitimacy accounted for 3,657 posts, or 17.1%. This includes user choice and control, anti-stigmatization, and broader rights or welfare claims. Long-Term Safeguards, Remedies & Other Specific Claims accounted for 2,039 posts, or 9.5%. This includes open-source or long-term access, transition/substitution fairness, compensation/refund, and other institutional demands. Overall, the preliminary results suggest that #keep4o is not simply a nostalgia campaign for an older model. It is a user-led public response to AI model retirement, platform accountability, access continuity, interaction integrity, and user agency. This is still a preliminary analysis / working draft. After completing the full version, I plan to release a more technical research report. I am also considering making a video to explain the findings in a more accessible format. I would really appreciate feedback, especially on what additional dimensions may be worth analyzing.❤