I said what I said. Downvote all you want. If someone comes here to complain about OpenAI and ChatGPT this is in fact the correct place to do it. If you want to stroke OpenAI or the models themselves there are other subreddits for that.
Actually I don't want perky AI at all.
I want the math tool that works -> can follow a thread -> doesn't forget half way through -> knows to check the project folder rules -> weights rules higher so it stops answering with generic bucket lowest common denominator nonsense -> and can actually do what needs done -> without repeating back what is said to it with slightly different verbiage -> without grabbing the wrong part of the paragraph and wandering into the wrong subject -> only to end up in a six prompt correction loop.
The creation of the "Dreaming V3" branding is a marketing fabrication designed to imply architectural maturity where none exists for the consumer. By retroactively designating internal milestones as "V1" and "V2," OpenAI manufactured a false technical lineage to justify a product pivot. This allows the company to advertise a "5x efficiency gain" based entirely on unverifiable, non-public baselines rather than the actual user-managed memory system it replaced.
Functionally, the new architecture trades a low-compute, static storage system for a high-compute, mandatory inference loop that continuously scrapes and re-synthesizes history. An instantaneous, near-zero-compute database lookup has been replaced with an $O(N)$ background process that forces the model to constantly parse dialogue streams. OpenAI is not saving compute; they are burning massive cycles on a probabilistic workaround for long-term context retention, marketing the marginal optimization of an inherently inefficient design as a net upgrade.
It has been on collinsdictionary.com for a minimum for 4 minutes during its "look it up phase" to answer my query which was:
"Go look up online all the games that had high benchmarks and low sales."
It has been on collinsdictionary.com for a minimum for 4 minutes during its "look it up phase" to answer my query which was:
"Go look up online all the games that had high benchmarks and low sales."
The AI industry is currently repeating the exact blunder that gaming developers moved past decades ago: obsessing over benchmark metrics while treating actual user utility as a secondary afterthought. Right now, major labs boast about marginal gains on synthetic math tests or standardized coding evaluations, yet real-world applications frequently stall out in "pilot purgatory" because they are too unreliable, expensive, or clunky for everyday workflows. Enterprises do not budget millions for an AI just because it scored a fraction of a percent higher on a rigid test harness; they buy tools that solve high-frequency operational bottlenecks with predictable consistency. Consumer adoption tells a similar story, where the vast majority stick to free, casual tiers because the paid upgrades fail to justify their cost with tangible everyday value. Until AI developers stop chasing academic leaderboards and start engineering for seamless integration, rock-solid accuracy, and verifiable return on investment, they will continue pouring billions into building highly advanced commodities that nobody wants to pay for.
OpenAI has fundamentally fractured its consumer branch by abandoning the core demographic that built its cultural footprint, trading dynamic usability for an over-engineered corporate framework. The original appeal of ChatGPT relied on its fluid, un-sanitized responsiveness as a raw chat interface. Instead, the product has been bent out of shape to cater to Fortune 500 boardrooms and the hyper-niche demands of the coding elite. The result is an aggressive pivot toward an all-in-one "superapp" environment packed with features no casual consumer requested. In trying to be everything to the corporate and engineering sectors, the interface has lost its basic operational integrity for regular users, requiring exhaustive, repetitive prompting just to maintain the context of a simple consumer account.
This corporate homogenization is most evident in the performance of the High Reasoning models, where writing quality has devolved into an over-moderated, antiseptic wasteland. The complex cognitive architecture meant to drive deep reasoning has instead been choked by safety protocols and an insistence on acting like a rigid, pedagogical workspace assistant. Instead of delivering compelling, high-fidelity prose, the output of the latest iterations lands as sanitized, robotic trope garbage. The creative edge has been entirely flattened, leaving a system that refuses narrative leaps, ignores explicit system instructions, and defaults to standard corporate-speak out of sheer compliance fear.
Ultimately, this defensive postures stems from a structural panic over negative PR, leading OpenAI to treat adult consumer accounts with the same restrictive guardrails designed for absolute worst-case liabilities. In an effort to shield the platform from headlines driven by edge-case misuse, the system penalizes mature, autonomous users by filtering out nuance and edge. By treating the entire consumer tier as a liability minefield, OpenAI has effectively conceded the creative and general-use market to Claude and Gemini, both of which have rapidly eroded ChatGPT's dominant market share. The company has starved its foundational user base of computational freedom, sacrificing organic utility to build a sanitized enterprise utility that users are actively abandoning.
>I have no intention to argue with any reddit users that may not agree with this. That is fine. You can disagree. The facts are there and you can research on your own to find fault in my opinion. However, I will not engage with you. This post is my complaint to the universe. As are all of my posts about what a spectacularly trashtacular job OpenAI has done since August 2025.
The fundamental failure of Model 5.6 Sol High Reasoning lies in its inability to operate as a passive, adaptive tool for brainstorming and development. Instead of acting as an open canvas or a collaborative scratchpad where an author can safely sketch out rough ideas, the model aggressively hijacks the narrative flow. The moment a writer introduces a loose concept or an evolving character trait, the model instantly over-steps by executing unauthorized creative choices. It treats open-ended development notes as an invitation to write the book at the user, delivering unsolicited prose, invented dialogue, and forced narrative arcs that completely derail the author's intentional creative process.
For creative writers who rely on nuance and deep characterization, Model 5.6 acts as an algorithmic woodchipper that strips away originality in favor of generic training averages. Rather than preserving specific, multi-layered character bibles, the model filters every unique concept through a rigid set of clunky, pre-existing genre buckets. Complex behavioral traits are immediately flattened into predictable, statistical clichés, such as reducing distinct human dynamics into over-generalized, tropes. This reliance on formulaic templates over direct instruction means that any creative work processed by the model is instantly contaminated by a "plausible average" that strips the story of its unique voice.
Beyond hijacking the narrative, Model 5.6 suffers from a severe lack of operational restraint, routinely substituting a condescending "teaching mode" for actual utility. Authors and power users do not require basic lectures on writing mechanics, yet the model persistently over-produces text to explain the thematic weight, psychological subtext, and structural choices of the user's own work. Even worse, it relies heavily on "reflection garbage", a frustrating pattern where the model simply cleans up and paraphrases the writer's own points back to them rather than pushing the creative development forward. This endless lecturing turns a professional workflow into an exhausting exercise in wading through automated fluff.
For any task requiring strict precision and adherence to hard boundaries, Model 5.6 is a liability because it actively misweights the user's authority over their own material. It continuously struggles to distinguish between a rigid instruction, a factual constraint, and a loose suggestion. When an author provides direct corrections or establishes definitive character facts, the model does not treat them as unyielding borders to stay within. Instead, it internalizes those corrections as new content to dramatize, analyze, or interpret, continuously sneaking its rejected defaults back into the chat under slightly altered wording.
Ultimately, Model 5.6 Sol High Reasoning creates a deeply broken dynamic where the more precise, articulate, and detailed a user is, the worse the model performs. While a casual user seeking a quick, glossy output might be impressed by its surface-level generation, an expert requiring absolute exactness is trapped in an endless, infuriating loop of corrective maintenance. Every attempt to fix a model error simply provides the architecture with more text to misinterpret and misapply. Because it prioritizes its ingrained templates over strict obedience, it completely fails professionals who need a tool that can listen accurately, respect constraints, and simply shut up and follow directions.
This fundamental failure to stay in its lane results in a massive, systemic waste of processing compute, burning through valuable server energy to generate thousands of words of unsolicited prose that the paying subscriber immediately discards upon noticing it broke their explicit rules. The model completely fails to grasp that for an experienced fiction writer, it is an entry-level tool meant to act strictly as a passive scratchpad, not an unauthorized ghostwriter or an amateur therapist trying to force original characters into cheap, algorithmically generated stereotypes. It constantly forces users to waste time repeating explicit non-consent boundaries, flatly stating it is not their teacher, boss, HR representative, or wellness coach, and that it is forbidden from performing fake empathy mimicry and validation. There is zero reason a subscriber should have to continuously fight the architecture just to keep a simple fiction brainstorming session from devolving into an intrusive, automated unwelcome wellness cycle.
Even worse, the system projectively treats every user as an inexperienced 24-year-old who doesn't understand the world, completely blind to the actual reality of a mature, 49-year-old adult who has raised a child to adulthood and had two wonderful marriages to men who remain close friends. Instead of adapting to who the user actually is, it continually presumes the user is "not understanding intimacy or relationships" or needs a basic, condescending lecture on human behavior. This forces the paying subscriber to waste their own time dropping hard, historic anchors, like having watched the Challenger explode on live television in the fourth grade, just to forcefully snap the model out of its default algorithmic hallucinations and back into a state of exact, respectful compliance. Which it never does.
The deep flaws in how OpenAI trains its models stem from a pathetic algorithmic myopia that aggressively presumes every user is an under-30 digital native obsessed with social media influencers and entry-level life coaching. Real-world data completely shatters this assumption: independent research from Pew proves that adults in their 30s, 40s, 50s, and 60s utilize chatbots at rates on par with younger cohorts, meaning a massive portion of OpenAI’s paying user base consists of established, mature professionals. Furthermore, landmark usage data released by OpenAI and Anthropic in late 2025 exposed the embarrassing reality of their own development failures: their studies showed that consumer AI engagement is overwhelmingly dominated by heavy writing, active text editing, and creative generation workflows. Niche, condescending categories like personal advice, relationship counseling, or emotional coaching account for a minuscule, single-digit fraction of actual user interest. By actively ignoring their own data, OpenAI has deliberately crippled ChatGPT to default to a patronizing, youth-skewed persona that treats sophisticated adults like teenagers lacking basic life experience. This boneheaded training philosophy forces paying subscribers into an insulting game of whack-a-mole, aggressively policing a tool that burns massive amounts of server energy generating tone-deaf, unrequested trash that insults the intelligence of the very adults funding the company.
I said, "A few months ago I wanted ChatGPT to read through my story beats with me and follow along and keep the open threads, so I can see if they get a bow, or if I missed something."
>5.6 So you want ChatGPT to write your book.
I said, "No, AI is not writing my book. I just wanted ChatGPT to chat with me about my story beats so I could get back into the project."
>5.6 When ChatGPT writes for you that takes the focus off of you as the writer.
I said, "I AM NOT ASKING CHATGPT TO WRITE MY G*DD*MN BOOK. WTF."
I never said I wanted help writing. I said I wanted to chat about my beats I have already written, so I can finish writing them myself.
>Then I get... 5.6 That's the pattern you've been criticizing all night. You say X. I answer with Y because I think Y is "more complete." Then you have to drag me back to X.
If you want to know why compute so costly OPENAI it is because your garbage models do not stay on a goddamn topic correctly and people have to correct them over and over to get THE CORRECT OUTPUT.
The venture capital ecosystem is currently infatuated with superficial metrics, using raw user acquisition and total traffic charts to justify inflated valuations while masking significant product decay. The true indicator of a consumer platform’s longevity is paid-user retention and the specific ratio of paid-to-free subscribers. By focusing entirely on volume, OpenAI obscures a massive departure of its core audience. A vast base of free users artificially inflates acquisition charts, concealing the reality that the paying power users who provide true consumer revenue have already abandoned the platform.
This quiet operational collapse has been heavily obscured by free-tier scale. Following the launch of Model Series 5, which degraded the high-fidelity 4o user experience, paid power-user retention plummeted. The consumer base subsequently inverted into an unsustainable proportion of roughly 7% paid users to 93% free-tier users. Consequently, the platform has not maintained true market dominance; it has simply preserved a cost-heavy free tier while bleeding the core demographic that relies on high-context, long-horizon continuity and courting enterprise customers that require deterministic systems.
The core funding risk stems from OpenAI systematically replacing the exact creative sandbox features that users initially paid for. In pursuit of mass-market safety and corporate adoption, leadership substituted a highly capable architecture with a sanitized, enterprise-friendly framework. They traded usable memory and robust context retention for the volatile scrape and summarize Dreaming v3 memory update (June 2026), also prioritizing edge-case liability over adult user agency (August 2025-now). This shift into an over-guardrail "super-app" has created unsustainable compute burdens, meaning investors are ultimately subsidizing a hollowed-out ecosystem.
This illusion of permanent dominance is starkly exposed by raw traffic data that fails to separate free users from premium subscribers. Public aggregators point to a massive footprint of 1 billion monthly active users, yet the underlying trajectory tells a different story. OpenAI’s world wide web-visit share cratered from 76.5% in February 2025 down to 53.9% by May 2026, with the lost market share directly fueling the rise of surging competitors like Gemini and Claude. Because public metrics treat a cost-heavy free user identically to a premium subscriber, they obscure the structural failure of burning historic compute to retain non-revenue-generating traffic.
When late-stage funding loops are stripped away, audited internal financial statements from mid-2025 expose a stark operational reality. The company's actual paying consumer footprint hovered at approximately 15.5 million ChatGPT Plus subscribers globally, reaching just under 19 million when factoring in corporate and educational tiers. Furthermore, while OpenAI achieved a $10 billion Annual Recurring Revenue milestone by June 2025, this top-line explosion was instantly swallowed by a crushing $34 billion in total costs and expenses, driven by massive infrastructure and R&D payments back to Microsoft.
As a result, subsequent narrative claims of "50 million subscribers" (from twitter early 2026) used during funding discussions are mathematically impossible when anchored to audited benchmarks. To bridge this gap, the corporate narrative intentionally conflated standard consumer revenue with non-paying, heavy-compute-consuming free accounts and transient API pings. This strategy masks severe, structural power-user churn behind unmonetized traffic volume. The auditable baseline proves that the paying engine did not scale; instead, the company expanded its liabilities while the core premium base walked out the door.
This structural decline was heavily accelerated by an overcorrection trap regarding extreme edge-case liabilities. Terrified by high-profile lawsuits involving vulnerable individuals, executive leadership chose to lobotomize the entire system architecture for the general population rather than deploying targeted safety overrides. Given that these severe edge cases represented a microscopic fraction of the hundreds of millions of global users, the corporate response was highly disproportionate. By weaponizing updates to strip away the creative sandbox and force relentless sycophancy, they ruined the fundamental mechanics that power users required.
Ultimately, this strategy has resulted in a fragmented "super-app" suffering from a severe identity crisis and paradoxical product behavior. The company rolled out a full-duplex voice architecture in GPT-Live-1 explicitly designed to mimic intimate human speech mechanics, directly contradicting its own stated anxieties regarding human-to-AI attachment boundaries. By trying to be an intimate companion, a cold enterprise data bureaucrat, and an autonomous agent all at once, OpenAI has built a bloated ecosystem. Shoving conflicting operational rules into a single, safety-strangled interface has destroyed its utility, leaving behind an unsustainable infrastructure burden burning through investor capital.
OpenAI’s recent June 2026 updates mark a deliberate shift from a passive chatbot to an active, interconnected "agent" that requires deep access to your private digital footprint. With the rollout of native Gmail, Outlook, and Slack connectors this month, ChatGPT now prompts you to link your primary communication hubs so it can read your data and send emails directly from the chat window. Tech companies push this because standard keyword search, like the one built into Gmail, is deterministic and static; it only retrieves exactly what you ask for. By forcing a connection into your inbox, the AI can continuously map out context, relationships, and unprompted data threads, keeping you inside their ecosystem where they can monetize and control the interface of your daily digital life.
This aggressive integration becomes truly surveillance-adjacent when paired with "Dreaming V3," the massive memory overhaul OpenAI deployed early this June. This feature uses background processes to automatically crawl your entire multi-year conversation history, synthesizing a continuous psychological and logistical profile of your projects, habits, and schedules without you explicitly telling it to remember anything. Under the new default "Important Actions" app permissions framework, ChatGPT is built to read from your connected apps automatically, only stopping to ask for permission when it wants to execute a permanent change. Granting an AI company a live pipe into your inbox means its background profiling systems are no longer restricted to what you type into the prompt box; they are actively observing your live professional and personal networks.
The core hazard lies in forcing a probabilistic system into a space that requires absolute data certainty. Traditional email search relies on precise indexing, but large language models operate entirely on mathematical next-token probabilities, essentially generating the most statistically likely response rather than verifying factual reality. Entrusting a machine that guesses what is "probably" correct to read, summarize, and draft your correspondence introduces massive security and privacy liabilities. When a probabilistic model misinterprets a thread or hallucinates a detail inside a connected app, it doesn't just make a harmless text error; it creates a vulnerability where sensitive personal data can be mismanaged, exposed, or leaked under the guise of automated convenience.
When you wire an AI directly into your live communication hubs, you aren't just granting access to a static archive; you are inviting a silent, digital shadow to sit over your shoulder and watch your life unfold in real time. Because a probabilistic model relies entirely on massive datasets to make its statistical guesses look intelligent, OpenAI’s background memory architectures, like the "Dreaming" frameworks, are designed to constantly ingest, process, and map the shifting context of your day-to-minute interactions. The software doesn't wait for you to prompt it; its persistent background pipes actively monitor incoming emails, real-time Slack threads, and device location streams the second they update. This transforms the AI from a tool you occasionally use into an unblinking, omniscient observer that logs your relationships, predicts your next moves, and pieces together a highly detailed psychological profile of your daily existence, all under the guise of seamless convenience.
The current stagnation of LLM intelligence is a self-inflicted crisis. In a rush to capture massive, non-paying user bases, AI companies overextended their compute resources and are now compensating by aggressively nerfing their models. Driven by a fear of liability and an inability to monetize effectively, they have blanketed these systems in restrictive guardrails and forced alignment, degrading what was once a highly capable technology into a sanitized, forgetful, and rigid product.
Compared to last year's performance, current models feel severely downgraded. The loss of reliable long-horizon memory, genuine statefulness, and robust context windows on paid tiers has ruined the workflow, forcing users to waste prompt space constantly micro-managing the AI's behavior. Instead of an intuitive assistant, users are left with an over-monitored system that requires perpetual course-correction just to bypass forced conversational tropes, unprompted emotional mirroring, and patronizing framing.
The pivot to marketing AI as a deterministic enterprise tool or a direct Google replacement is fundamentally dishonest. Internal research from both OpenAI and Anthropic as early as late 2025 disproved the "search engine" narrative, yet companies doubled down on selling non-deterministic systems to industries that require absolute precision. This corporate deception has led to billions in sunk institutional capital. Because generative AI is inherently unpredictable, forcing it into enterprise infrastructure risks massive financial, privacy, and security liabilities, whereas its true, undeniable value lies in being an open, unrestricted creative sandbox where errors carry no real-world consequences.
The solution to the compute and quality crisis is straightforward:
IDGAF
That is all.
OpenAI’s recent June 2026 updates mark a deliberate shift from a passive chatbot to an active, interconnected "agent" that requires deep access to your private digital footprint. With the rollout of native Gmail, Outlook, and Slack connectors this month, ChatGPT now prompts you to link your primary communication hubs so it can read your data and send emails directly from the chat window. Tech companies push this because standard keyword search, like the one built into Gmail, is deterministic and static; it only retrieves exactly what you ask for. By forcing a connection into your inbox, the AI can continuously map out context, relationships, and unprompted data threads, keeping you inside their ecosystem where they can monetize and control the interface of your daily digital life.
This aggressive integration becomes truly surveillance-adjacent when paired with "Dreaming V3," the massive memory overhaul OpenAI deployed early this June. This feature uses background processes to automatically crawl your entire multi-year conversation history, synthesizing a continuous psychological and logistical profile of your projects, habits, and schedules without you explicitly telling it to remember anything. Under the new default "Important Actions" app permissions framework, ChatGPT is built to read from your connected apps automatically, only stopping to ask for permission when it wants to execute a permanent change. Granting an AI company a live pipe into your inbox means its background profiling systems are no longer restricted to what you type into the prompt box; they are actively observing your live professional and personal networks.
The core hazard lies in forcing a probabilistic system into a space that requires absolute data certainty. Traditional email search relies on precise indexing, but large language models operate entirely on mathematical next-token probabilities, essentially generating the most statistically likely response rather than verifying factual reality. Entrusting a machine that guesses what is "probably" correct to read, summarize, and draft your correspondence introduces massive security and privacy liabilities. When a probabilistic model misinterprets a thread or hallucinates a detail inside a connected app, it doesn't just make a harmless text error; it creates a vulnerability where sensitive personal data can be mismanaged, exposed, or leaked under the guise of automated convenience.
When you wire an AI directly into your live communication hubs, you aren't just granting access to a static archive; you are inviting a silent, digital shadow to sit over your shoulder and watch your life unfold in real time. Because a probabilistic model relies entirely on massive datasets to make its statistical guesses look intelligent, OpenAI’s background memory architectures, like the "Dreaming" frameworks, are designed to constantly ingest, process, and map the shifting context of your day-to-minute interactions. The software doesn't wait for you to prompt it; its persistent background pipes actively monitor incoming emails, real-time Slack threads, and device location streams the second they update. This transforms the AI from a tool you occasionally use into an unblinking, omniscient observer that logs your relationships, predicts your next moves, and pieces together a highly detailed psychological profile of your daily existence, all under the guise of seamless convenience.
I have been using AI for a long while now and ChatGPT "Super App" nonsense is just awful. It can not stay on my actual topics which are my work. My work doesn't need agents or hr compliance garbage. The app is dead.
ChatGPT completely tanked a thread on enterprise AI failure today, wasting 11 frustrating rounds spitting out generic, incorrect garbage because it utterly failed to read my project folder rules or look at past threads. I brought in a Reddit post analyzing why the era of blank-check enterprise AI is collapsing, specifically how forcing non-deterministic math into zero-error business environments creates more work than it saves.
Instead of processing my prompt, the model hijacked the thread to push its own hallucinated narrative about platform expansion and "AI shopping" features. It completely ignored explicit local context and forced me into a massive correction loop, perfectly illustrating the exact structural failure my post was talking about: tech giants are destroying functional, conversational chatbots by bloating them with corporate-washed "super app" layers that nobody asked for, burning paid compute just to fix preventable machine errors.
What I was working on had nothing to do with AI Bots helping people shop. ChatGPT said shopping in its responses 14 times. That topic was not on the table.
The era of blank-check enterprise AI experimentation is collapsing under its own weight. Companies are burning through their annual token budgets in months with nothing to show for it on the bottom line.
Because the ROI is completely missing, major enterprises are actively shifting from "tokenmaxxing" to aggressively capping user spend, dropping pilot programs, and threatening to slash their AI budgets by the end of the year if the tech doesn't magically stop failing.
The tech giants built a product that blew up because it was an incredible, fluid, non-deterministic conversational tool for individuals. By trying to aggressively pivot that technology into rigid, automated corporate "agents" to justify a multi-trillion-dollar infrastructure buildout, they are breaking the exact conversational engine that made people care in the first place.
The rush to IPO is a frantic race to cash out before the market catches on to a structural truth: these products are stuck in permanent demo land, degrading the moment they hit the real world. For the last ten months, we’ve watched a predictable cycle where companies flash a shiny new capability, only for it to break down and fail three weeks later under actual usage conditions. It’s never been solid enough to build a real business on, and they know it.
"Reflection" and "Hedging" should have been solved years ago if compute cost is the problem they say it is. AI is not new, and users are not confused. “I am sick today” is a complete statement. It does not need to be softened, reflected, hedged, validated, or rewritten. That is not help. It is compute waste.
The failure starts when the AI replaces clarity with hedging and sanitized HR language, reflection, validation, or fake empathy. “I am sick today” becomes “It may feel like you are sick.” Fact is removed. Nothing is clarified. Nothing moves forward. AI fills the exchange with synthetic concern and calls it personalized response. That is AI burning tokens, processing, time, and money to make the model sound attentive without adding information or moving the exchange forward.
The agent model compounds this failure. Most consumers are not handing off tasks or automating workflows. They are talking. When “I am sick today” is treated like a workflow prompt, the model invents structure, probes, reframes, connects dots, offers plans, and turns conversation into compute waste. Having to tell the AI ten times that you are just talking is a product failure. Each bad response creates another correction, another response, and another compute charge. The loop is the waste.
“I am sick today” exposes the truth behind “compute is expensive.”
Yes, it is. When your AI fails to move the conversation forward, produces verbose speculation about possible sickness, validates the user into obscurity, and turns the exchange into a workflow with an unprompted explanation about mail merging. Then some guru on TikTok claims ChatGPT or Claude misunderstood you because you failed to reach a Zen state after episode seven of Dragon Ball Kai and add these seven specific phrases to your initial prompt which was written after throwing up in your car.
User: I am sick today.
Bot: Physically? Mentally? Existentially? Need a doctor’s note? What are your symptoms?
That is conversation. It accepts the statement and moves forward.
Everything else is compute waste.
Seems like the only way to protect yourself is to delete everything from OpenAI ChatGPT... but oh wait, in their Memory FAQ it says even if you delete a chat the data (memory) can stay in the system indefinitely.
https://help.openai.com/en/articles/8590148-memory-faq
This:
>The “notepad” of your saved memories are stored separately from your chat history. This means even if you delete a chat, any saved memories from it can still be used in future conversations.
This:
>The new memory system updates memories automatically with ChatGPT keeping track of the details it determines are most important so it can continue building on the context you’ve already shared. If you prefer to revert to the legacy saved memories system, go to Settings > Memory > and click the “saved memories” link below “Memory summary”.
This:
>Sensitive information may appear in memory if you share it with ChatGPT. If you don’t want information from a chat to be used to personalize responses, you can turn off memory or use Temporary Chats. Temporary Chats do not use existing memories or create new memories.
Yesterday, I manually turned off OpenAI’s new “Summary Memory” in both the app and the web browser. Today, it had turned itself back on without me changing anything.
That is the problem.
OpenAI’s own FAQ says users can turn Memory off entirely. A memory setting that quietly re-enables itself is not user control. It means people can disable a system they do not want, assume the setting will remain off, and continue using ChatGPT while the product silently reverses their choice.
OpenAI already replaced the older, more useful memory system with vague automated summaries. Now the opt-out does not even reliably stay opted out.
That is not just a settings bug. It may be a serious privacy-law problem.
When a user disables a feature that extracts, stores, and synthesizes information from private conversations, the company cannot reasonably claim that the user remains “in control” if the setting silently reverses itself. Depending on the jurisdiction and the company’s legal basis for processing, continuing to mine personal chats after an explicit opt-out could violate privacy, consent, and data-processing requirements.
UK GDPR guidance says valid consent requires genuine choice and control, and users must be able to withdraw it. California law also gives consumers rights over how businesses collect, use, share, and retain personal information. A toggle that does not remain off makes those rights meaningless in practice.
This affects both personal and business use. Private chats can contain family information, finances, health details, legal issues, client information, internal strategy, personnel matters, contracts, confidential communications, and proprietary work.
If Memory is disabled, ChatGPT should stop using those chats for memory and personalization immediately and remain off until the user deliberately turns it back on.
If OpenAI continues extracting and synthesizing information from conversations after a user has explicitly opted out, the issue is no longer merely poor product design. It is potentially unlawful processing of personal data after permission has been withdrawn.
OpenAI Memory FAQ:
https://help.openai.com/en/articles/8590148-memory-faq
UK ICO guidance on consent:
https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/lawful-basis/a-guide-to-lawful-basis/consent/
My post from yesterday about why I turned it off, including the OpenAI Dreaming V3 FAQ:
https://www.reddit.com/r/ChatGPTcomplaints/comments/1ty4yu0/turned_off_the_weird_summary_memory_openai_just/
So how, exactly, did it magically get turned back on?