OpenAI claimed that its recent GPT-5 update improved user safety in a model evaluation involving more than 1,000 self-harm and suicide conversations. OpenAI claimed that its recent GPT-5 update improved user safety in a model evaluation involving more than 1,000 self-harm and suicide conversations. More than a million ChatGPT users each week send messages that include "explicit indicators of potential suicidal planning or intent", according to a blogpost published by OpenAI on Monday. The finding, part of an update on how the chatbot handles sensitive conversations, is one of the most direct statements from the artificial intelligence giant on the scale of how AI can exacerbate mental health issues. In addition to its estimates on suicidal ideations and related interactions, OpenAI also said that about 0.07% of users active in a given week - about 560,000 of its touted 800m weekly users - show "possible signs of mental health emergencies related to psychosis or mania".

Open AI CEO Sam Altman speaks during a summit on June 2, 2025 in San Francisco, California. Open AI CEO Sam Altman speaks during a summit on June 2, 2025 in San Francisco, California. Founded in 2015 as a nonprofit, rather than a for-profit company, it promised to develop AI "in the way that is most likely to benefit humanity." With billions of dollars in investments from Microsoft, Japanese bank SoftBank, and chipmaker Nvidia, however, OpenAI has proposed changing its corporate structure to give investors more control over its technology. Critics of the change include cofounder-turned-competitor, Elon Musk, and nonprofits concerned about OpenAI's adherence to its mission.

OpenAI released initial estimates about the share of users who may be experiencing symptoms like delusional thinking, mania, or suicidal ideation, and says it has tweaked GPT-5 to respond more effectively. For the first time ever, OpenAI has released a rough estimate of how many ChatGPT users globally may show signs of having a severe mental health crisis in a typical week. The company said Monday that it worked with experts around the world to make updates to the chatbot so it can more reliably recognize indicators of mental distress and guide users toward real-world support. In recent months, a growing number of people have ended up hospitalized, divorced, or dead after having long, intense conversations with ChatGPT. Some of their loved ones allege the chatbot fueled their delusions and paranoia.

The Download: what to make of OpenAI's Atlas browser, and how to make climate progress I tried OpenAI's new Atlas browser but I still don't know what it's for OpenAI rolled out a new web browser last week called Atlas. It comes with ChatGPT built in, along with an agent, so that you can browse, get answers, and have automated tasks performed on your behalf all at the same time. I've spent the past several days tinkering with Atlas. I've used it to do all my normal web browsing, and also tried to take advantage of the ChatGPT functions--plus I threw some weird agentic tasks its way to see how it did with those. My impression is that Atlas is fine? But my big takeaway is that it's pretty pointless for anyone not employed by OpenAI.

A data center, which can use as much electricity as Philadelphia, is the new American factory, creating the future and propping up the economy. "I do guess that a lot of the world gets covered in data centers," Sam Altman, the C.E.O. of OpenAI, has said. Drive in almost any direction from almost any American city, and soon enough you'll arrive at a data center--a giant white box rising from graded earth, flanked by generators and fenced like a prison yard. Data centers for artificial intelligence are the new American factory. Packed with computing equipment, they absorb information and emit A.I. Since the launch of ChatGPT, in 2022, they have begun to multiply at an astonishing rate. "I do guess that a lot of the world gets covered in data centers over time," Sam Altman, the C.E.O. of OpenAI, recently said. The leading independent operator of A.I. data centers in the United States is CoreWeave, which was founded eight years ago, as a casual experiment. In 2017, traders at a middling New York hedge fund decided to begin mining cryptocurrency, which they used as the entry fee for their fantasy-football league. To mine the crypto, they bought a graphics-processing unit, a powerful microchip made by the company Nvidia. The G.P.U. was marketed to video gamers, but Nvidia offered software that turned it into a low-budget supercomputer. "It was so successful, from a return-of-capital perspective, that we started scaling it," Brian Venturo, one of CoreWeave's co-founders, told me. "If you make your money back in, like, five days, you want to do that a lot." Within a year, the traders had quit the hedge-fund business and bought several thousand G.P.U.s, which they ran from Venturo's grandfather's garage, in New Jersey.

Ed Zitron Gets Paid to Love AI. He's one of the loudest voices of the AI haters--even as he does PR for AI companies. Either way, Ed Zitron has your attention. In his day job, Ed Zitron runs a boutique public relations firm called EZPR. This might surprise anyone who has come to know Zitron through his podcast or his social media or the newsletter in which he writes two-fisted stuff like "Sam Altman is full of shit and "Mark Zuckerberg is a putrid ghoul." Flacks, as a rule, tend not to talk like this. Flacks send prim, throat-clearing emails to media people who do, on rare occasions, talk like this. Flacks want to touch base, hop on the phone, clear up a few things about the allegation that their CEO is a "chunderfuck." And that really is one of the things with guys like Sam Altman and Dario Amodei from Anthropic," Zitron was saying over burgers on a fine Manhattan afternoon in September. "I work with founders all the time. I'm a founder myself, I guess--I don't like the title. But when you are a person that has to make more money than you lose, otherwise you lose your business, and you see these chunderfucks burning 5, 10 billion dollars in a year--and everyone's celebrating them? We were talking about whether any of Zitron's ranting about the AI industry had cost him business on the PR side of the ledger. There was the one client who felt Zitron was being a little mean toward Altman, the CEO of OpenAI and the biggest chunderfuck of all, as far as Zitron is concerned. Founding a company is hard, the client said. "I said, 'I appreciate the comment, but, like, this isn't about you,'" Zitron told me. "His company is burning billions of dollars.

I tried OpenAI's new Atlas browser but I still don't know what it's for My impression is that it is little more than cynicism masquerading as software. OpenAI ChatGPT Atlas introducing is being displayed on a mobile phone with the company's branding seen in the background, in this photo illustration. Taken in Brussels, Belgium, on 23 October 2025. OpenAI rolled out a new web browser last week called Atlas. It comes with ChatGPT built in, along with an agent, so that you can browse, get direct answers, and have automated tasks performed on your behalf all at the same time. I've spent the past several days tinkering with Atlas.

Abstract--Generative artificial intelligence (AI) models in smart grids have advanced significantly in recent years due to their ability to generate large amounts of synthetic data, which would otherwise be difficult to obtain in the real world due to confidentiality constraints. A key challenge in utilizing such synthetic data is how to assess the data quality produced from such generative models. Traditional metrics such as sample-wise Euclidean distance and distributional distances applied directly to raw generated data inadequately reflect higher-order temporal dependencies and cross-temporal relationships between real and synthetic series, and thus struggle to discriminate generative quality. In this work, we propose a novel metric based on the Fr echet Distance (FD) estimated between two datasets in a learned feature space. The proposed method assesses synthetic data quality via distributional comparisons in a feature space derived from a model tailored to the smart grid domain. Empirical results demonstrate the superiority of the proposed metric across downstream tasks and generative models, enhancing the reliability of data-driven decision-making in smart grid operations. ENERA TIVE models in the electric energy sector have been an active field of research in the past few years, thanks to their potential to create realistic and diverse scenarios for system planning, reliability assessment, and renewable energy integration--ultimately enhancing grid resilience and operational efficiency. These models, such as Generative Adversarial Networks (GANs), allow researchers to access much larger sets of synthetic data across multiple time scales that would otherwise be unavailable due to confidentiality constraints [1]. In contrast to traditional methods that involve creating synthetic power networks and subsequently using commercial-grade simulation software to generate electrical measurement variables [2], these generative approaches leverage a data-driven methodology.

Generative AI tools are increasingly used to create portrayals of people in occupations, raising concerns about how race and gender are represented. We conducted a large-scale audit of over 1.5 million occupational personas across 41 U.S. occupations, generated by four large language models with different AI safety commitments and countries of origin (U.S., China, France). Compared with Bureau of Labor Statistics data, we find two recurring patterns: systematic shifts, where some groups are consistently under- or overrepresented, and stereotype exaggeration, where existing demographic skews are amplified. On average, White (--31pp) and Black (--9pp) workers are underrepresented, while Hispanic (+17pp) and Asian (+12pp) workers are overrepresented. These distortions can be extreme: for example, across all four models, Housekeepers are portrayed as nearly 100\% Hispanic, while Black workers are erased from many occupations. For HCI, these findings show provider choice materially changes who is visible, motivating model-specific audits and accountable design practices.

W.J.T. Mitchell's influential essay 'What do pictures want?' shifts the theoretical focus away from the interpretative act of understanding pictures and from the motivations of the humans who create them to the possibility that the picture itself is an entity with agency and wants. In this article, I reframe Mitchell's question in light of contemporary AI image generation tools to ask: what do AI-generated images want? Drawing from art historical discourse on the nature of abstraction, I argue that AI-generated images want specificity and concreteness because they are fundamentally abstract. Multimodal text-to-image models, which are the primary subject of this article, are based on the premise that text and image are interchangeable or exchangeable tokens and that there is a commensurability between them, at least as represented mathematically in data. The user pipeline that sees textual input become visual output, however, obscures this representational regress and makes it seem like one form transforms into the other -- as if by magic.