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'The Body' review: Netflix's lovechild of 'Yellowjackets' and 'The Crucible' is sinfully fun
A Catholic school dance team tangoes with the Devil in Netflix's "The Body."
Moana, The Invite and “What Customers Want”
(Welcome to my weekly streaming ratings report, the single best guide to what’s popular in streaming TV and what isn’t. I’m the Entertainment Strategy Guy, a former streaming executive who now analyzes business strategy in the entertainment industry. If you were forwarded this email, please subscribe to get these insights each week.) Before we get going, I wanted to acknowledge that two of the most important figures in sports media (at least to me and my team) are stepping back from two of the best sports shows of all time. First off, Ernie Johnson will no longer host possibly the greatest studio show in the history of all sports, Inside the NBA. (Charles Barkley has said in the past that he’s retiring when Ernie does, which would be even worse!) EJ is one of the greatest to ever do it, and the NBA won’t be the same without him. Next, Michael Wilbon is taking a leave of absence from PTI, my favorite sports news show. I’ve been listening to the podcast for this show since the 2010s, if not earlier, treating it as my daily sports page. Wilbon, along with his co-host Tony Kornheiser, is also one of the greatest to ever do it. The sports media landscape will be worse without these two in it! Wishing all the best to Wilbon and EJ. Okay, on to this week’s issue. We’ve got two big shows—the second season of Prime Video’s The Terminal List and the third season of Netflix’s _Nobody Wants This—_but both were bested by an acquired title landing on Netflix. All that, plus an Original film from Mexico makes the charts but a formerly huge foreign TV show does not, a notable creator’s latest standup special flops, Ted Lasso finishes as a top season four series of all time, big numbers for the NBA’s new season (finally), the top animated shows on linear and streaming, movie budgets, and all the flops, bombs and misses, and a whole lot more. But first, on the film side, I’m taking a look at Moana and A24’s buzzy film from last summer. Let’s dive right in! (Reminder: The streaming ratings report focuses on the U.S. market and compiles data from Nielsen’s weekly top ten viewership ranks, Luminate’s Top Ten Data, JustWatch and Reelgood interest data, Samba TV household viewership, company datecdotes, Netflix hours viewed data, Google Trends, and IMDb to determine the most popular content. While most data points are current, Nielsen’s data covers the weeks of October 19th to October 25th, 2026. You can find a link to my terminology here.) Subscribe Film - What Do Customers Want? If I could remove one phrase from entertainment journalism, it would be “what customers want”. It’s not that it’s not wrong per se, but I read a lot of articles that use that phrase, but then don’t provide evidence of what customers actually want.1 For example, look at the bar chart below. It shows customer interest as measured by how much customers actually spent at the American box office, comparing four films to four others. Which films would you say represent “interest” the most? The one on the left certainly seems bigger, right? So it seems like “customers” wanted to see those films more. So here’s that chart with names attached: That’s right, four very buzzy films—The Invite, You Can See Everything, Teenage Sex and Death at Camp Miasma and Primetime—went up against four notable “flops” or underperformers—Moana, Disclosure Day, Minions and Monsters, and Supergirl—and the latter clearly had more interest. That doesn’t mean they’re more profitable2 or well-liked by critics, but if you’re asking what customers want to spend their money on, it’s those movies. I’d especially argue that You Can See Everything had about as much media coverage as a film can get—I saw/read about it constantly; whenever random NBA podcasts are talking about it, you know it’s everywhere—and yet Moana will likely best it. I bring this up, because this week features a showdown between a notable box office flop, Moana on Disney+, and a buzzy dramedy, The Invite on HBO Max. Moana was widely considered to be a flop, and it was a disappointment at the box office, only earning $126 million domestically (and $323 million globally). For the budget, that’s a big miss, down from Moana 2’s heights of $1 billion globally. But as I’ve written a few times this summer, we often confuse “this film is profitable” with “more customers want to watch this film”, which is not the same thing. But that’s the box office. Let’s look at the streaming numbers, starting with the Moana live-action reboot. Where, yes, Moana (2026) also disappointed. It made Nielsen, but under 10 million hours, which is low for kid-skewing Disney+ films: It did make Samba TV, but at tenth place for only one week: Those numbers stand in stark contrast to some of Moana’s previous superlatives on streaming. Moana (the original animated film) has made the Nielsen charts 130 times since 2021. Moana 2 opened to 33.3 million hours on Nielsen. Moana was the biggest film on streaming in 2023 and 2024, second in 2020 and 2021, and top six in 2022 and 2025. Moana 2 was second overall in 2025. To be clear, the Moana live-action reboot didn’t work, given how much it cost to make. It’s absolutely a disappointment. But more families paid to see it than people paid to see most of A24’s buzzy films this year, with one notable (horror film) exception. Okay, on to The Invite, A24’s buzzy summer release, which just came to HBO Max. Mainly, I’m curious: Who made money from this film? Or who lost money: the stars of The Invite or A24? No, seriously, read this Deadline article for the details. The Invite’s budget was allegedly “under $15 million” (so...$14 million?) then A24 bought it for “a sum north of $10 million”, which I saw elsewhere as over $12 million (so...$13 million?), but then it just made $26 million in the US. So basically, The Invite’s investors only made money after they sold foreign territories—it made $33 million overseas—but A24 might just make its money back in the US in the theatrical window, assuming it had $0 in marketing. I read one article analyzing this film’s marketing campaign which assumed no marketing spend, even though that article went on to detail how much marketing and PR and ads this film had. For what it’s worth, I saw a lot of marketing for it. A24 will likely just break even after factoring in other windows, but no matter what, this isn’t a hugely profitable juggernaut from Hollywood’s most-heralded film studio. (And, I’d note, most coverage of IP or superhero failures doesn’t account for profitability in future windows.) Really, I’m guessing that the stars—Olivia Wilde, Seth Rogen, Ed Norton and Penelope Cruz—had or kept their backend, hence why they did so much press for this film, which brings me to my actual takeaway, which I just mentioned two weeks ago about Backrooms: I don’t trust A24’s reported film budgets anymore. Either A24 is strategically releasing low numbers to get good press or they’ve really cracked the code on how to make movies at prices no one else can approach. But I’m skeptical about that last point. You don’t keep elite talent happy by keeping budgets in check—see: Dan Lin getting fired—and by all accounts, A24 keeps talent very happy. There’s another option: not paying big name actors their actual quotes upfront by giving away back end/profit participation. Offering backend is one great way to keep the reported budget lower than it actually is, but it makes the film less profitable for A24. (By the way, I’m still running down the numbers on Backrooms’ budget. If you know its actual budget—not the reported $10 million number—reach out to me on my contact page. I’ve heard rumblings I’m on to something, but would love something more concrete.) Okay, enough asking questions about A24’s business model. To the streaming ratings! The Invite came to HBO Max on 23-Oct, so we could look at the streaming numbers now… …and I don’t have any for you since it didn’t make any of the charts I track. And that’s pretty much the moral of the streaming story. Even though it disappointed on streaming, Moana wins this round, too. Quick Notes on Film The only other notable new straight-to-streaming film was S_urviving Hurricane Otis_, a Mexican Original on Netflix. It made Nielsen with 3.4 million hours and Luminate with 4.8 million hours. Overall, I’d call that very solid for a foreign-language title, but still far from a hit. Overall, the Luminate and Nielsen charts felt very light this week. Gladiator II also came to Netflix non-exclusively (it’s still streaming on Paramount+) and had 5.0 million hours according to Nielsen. That’s up from the 3.7 million hours it had when it premiered on Paramount+ a year and a half ago. Netflix’s Animals fell to 4.9 million hours according to Nielsen and fell off the Samba TV charts. Peacock/Universal’s Disclosure Day actually made it for a third week, but only at 2.8 million hours. Apple TV’s Sacrifice was down to 2.7 million from 3.2 million. Still, for an Apple TV film to make two weeks is solid (for them). Toy Story 5 made a fifth week on the charts. We’re just getting started with this issue, but the rest is for paid subscribers of the Entertainment Strategy Guy, so if you’d like to find out… ...how The Terminal List did after a four-year break... ...what acquired surprising title topped the charts… …where Ted Lasso finished in the all-time charts… ...big NBA opening week ratings... …a controversial new reality show that missed the charts... ...what very notable foreign TV show flopped... ...all the flops, bombs and misses... ...and a whole lot more... ...please subscribe! We can only keep doing this great work with your support. If you want an idea of just how much content you’d get in a full issue, check out this older issue.
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Last Saturday, 31 October, I went to the University of Hong Kong’s Beijing Centre for its Talent Forum. It was the fourth stop in the series this year, following events in Shanghai’s Zhangjiang, Shenzhen’s Qianhai, and on HKU’s main campus. The programme covered a remarkable distance in one afternoon. HKU’s Xiao-Jing Wang discussed computational neuroscience, intelligence, and the prefrontal cortex—before even delivering his inaugural public lecture to students back in Hong Kong. His colleague Bing Zhang took us from the Big Bang to artificial intelligence. Tsinghua’s Xue Lan asked how AI might reshape the institutional infrastructure underpinning higher education. There were lively panels featuring industry leaders from JD.com, Xiaomi, Haier—and Kaiser Y Kuo on the closing panel discussing innovation and international connections. I cannot pretend to have followed every detail of the brain models or the astrophysics. The talk that stayed with me was closer to something we have covered before: Zhou Zhonghe 周忠和 on papers, evaluation, and scientific trust. Zhou is a palaeontologist, an Academician of the Chinese Academy of Sciences, and an international member of the US National Academy of Sciences. He is also editor-in-chief of The Intellectual (知识分子), China’s leading science commentary platform. His subject was uncomfortable enough for a room full of successful academics: what happens when the system for recognising good science starts rewarding something else? Part of my interest came from our sister newsletter, The East is Read, which covered the controversy around “Geng Tongxue” in May. A PhD dropout from Beihang University, Geng has publicly challenged papers by prominent researchers through detailed online allegations of manipulated images and fabricated data. At Tongji University, an investigation into a challenged Nature paper found academic misconduct, and the dean of its life sciences school was removed. The affair brought doubts about academic integrity well beyond laboratories and university committees. How could someone outside the established hierarchy spot problems in papers that had passed through prestigious journals and institutions? And what, exactly, had all that prestige guaranteed? Zhou approached these questions through two essays published in July by The Intellectual: one by Wang Xiaodong, director of the National Institute of Biological Sciences, Beijing, and a response by Wang Liming, of Shanghai’s Shangsi Institute of Natural Sciences. Both began with the controversy over Geng. Their arguments deserve a wider audience, so I want to put parts of them alongside what Zhou said on Saturday. Wang Xiaodong’s starting point is evolution. A research system selects for the behaviour it rewards. Grants, jobs, journals, talent programmes, and prizes collectively determine which scientists prosper and which practices get passed down from advisors to students. “Whatever the system rewards is what it will evolve to produce,” he writes, citing the ancient Chinese saying: The King of Chu loved slender waists, and many in the palace starved to death. That is a more demanding diagnosis than saying there are dishonest people in science. Punishing a fraudster does not change the selective environment that made a particular kind of research profitable. His account also gives scientific success an awkward role in the story. As Zhou put it on Saturday, science became a victim of its own success. Once research became central to industry, national power, and economic growth, universities and laboratories expanded, and public money followed. That created an administrative need for rapid decisions: who should get the next grant, the next laboratory, the next appointment? The difficulty is that the true value of a discovery may take years or decades of testing and building to become clear. Resource allocation cannot wait that long. Publication counts, journal prestige, impact factors, and citations therefore become convenient ways to make an instant judgement. “The real problem is not that these indicators have no value,” Wang Xiaodong writes, “but that they are trying to answer a question that only time can answer.” Telling administrators simply to stop counting papers will never be enough. They still have budgets to distribute and appointments to make. Something has to replace the shortcut. Zhou described the consequences inside publishing. Papers acquire ever more requirements: a complete story, polished presentation, extensive datasets, and accessibility to readers beyond the immediate field. Individually, those demands sound reasonable. Together, he warned, they produce formulaic “eight-legged” papers 八股文 and rigid reviewing, while genuinely original or non-consensus work struggles to get past journal editors—some of whom wield immense gatekeeping power despite having limited laboratory experience themselves. Meanwhile, the demands for sprawling, “complete” narratives have fuelled a wider replication crisis. Wang Xiaodong uses a mahjong metaphor to describe the result: put the complex papers published by the exact same lab over several years side by side, and they often look like “Thirteen Orphans” (十三不靠)—every tile completely unrelated to the next, and none of them reproducible by outsiders. Zhou also spoke frankly as an editor who has trouble finding reviewers. Everyone wants someone else to review their work; qualified senior researchers have little time to return the favour. The labour is anonymous, unpaid, and brings almost no institutional recognition, so it increasingly gets passed down to postdocs and graduate students. And while anonymity protects reviewers from rank and personal favours, hyper-specialisation means an author’s most qualified reviewer is often a direct competitor, shielded from accountability. The protection and the problem coexist. Wang Liming takes the argument a step further. The decisive change, in his essay, is the instant conversion of academic honour into material resources. Once a paper can be cashed in for funding, a job, students, commercial investment, or even a company valuation, scientists are alienated from their work: they must keep minting this “research currency” to keep their teams alive. He is careful not to romanticise the past. The old world of gentleman scientists—financially independent figures like Boyle and Darwin who needed nothing from external funders—was also a closed club that excluded people without inherited wealth, women, and much of humanity outside the West. Professional science allowed ordinary people, himself included, to become researchers. Nobody should want to undo that. The problem, in his account, is an exchange system that becomes too uniform and too difficult to correct. His most pointed Chinese example is the “hat” (帽子): the prestigious talent designation that brings continuing access to money, laboratory space, students, and institutional influence. “If papers are the circulating currency of the research system, then a ‘hat’ is a bond that pays interest over the long term,” he writes. A paper at least requires another round of peer review. A title in the Chinese context can lock in a judgement made years ago and turn it into permanent status rent, regardless of whether its holder is still doing good science. Here, official institutions hire, organise, and evaluate scientists. Unlike honours awarded by autonomous academic communities in the West, a Chinese “hat” blends non-scientist administrative power with state authority. Once granted, it turns a past judgement into permanent status rent that is nearly impossible to revoke—because taking a hat off would imply that official authority erred in putting it on. Both Wangs see China as experiencing, at compressed speed, pressures present across global science. Research expansion, rising investment, publication growth, and elaborate evaluation metrics arrived in China within two or three decades rather than two or three centuries. Zhou agreed with that international framing—noting that recent US White House science policy documents and the new president of the US National Academy of Sciences, a fellow palaeontologist who recently visited Zhou’s institute in Beijing, have raised strikingly similar warnings about consensus-bound peer review, short-term project grants, and the replication crisis. Still, Zhou emphasised that China’s traditional hierarchy, personal networks (guanxi), and the ethos that “a good scholar becomes an official” (学而优则仕) amplify those pressures at home. So what would change things? Here Zhou’s tone differed from the call for a revolution in scientific trust in Wang Xiaodong’s essay. As an evolutionary biologist, Zhou favoured patchwork repair (修修补补). Living systems survive through accumulated adjustments; dramatic mutations are usually fatal. Science cannot return to a small, slow-moving pastoral era, especially after the successive accelerations of professionalisation after the Second World War, the internet, and now artificial intelligence. But it can alter the local environment in which researchers work. He pointed to domestic experiments designed to support unconventional research and diversify funding, including Tencent’s New Cornerstone Investigator Program, the Xplorer Prize, and the Future Science Prize. He also mentioned participating in reviews for “major non-consensus projects” under the National Natural Science Foundation of China. One guiding principle he recalled was wonderfully direct: “Failure is allowed; corruption is not” (允许失败，不允许腐败). That distinction would mean quite a lot if taken seriously. An honest attempt that fails should not have to disguise itself as a success to survive an assessment. Wang Liming’s essay adds a sharp caveat to the optimism about new domestic funders. Multiple organisations do not automatically produce multiple ways of judging science. If the same pool of senior domestic scientists makes the decisions through the same two-round review procedures, it is still “fake diversity.” He argues for three distinct kinds of money: broad baseline funding governed by fair procedures; patient funding for questions that take ten years; and non-consensus funding based on individual taste, deliberately empowering a few genuine experts to make biased, contrarian bets on eccentric ideas that would inevitably be averaged out by a committee. Replacing one monopoly with another achieves little; a healthy ecosystem needs variance. Zhou’s proposals for publishing were similarly concrete: strip papers of their resource-allocation burden and return them to recording discoveries and establishing priority; separate dissemination from evaluation, as eLife and Peer Community In do; adopt more modular “micro-publications” and real-name endorsements; recognize the labour of editors and reviewers; and give replication work proper credit rather than treating it as an unpublishable dead end. AI makes every part of this debate more urgent—and Zhou wove it throughout his talk. In his own preparation, he used AI to survey the declining influence of academic monographs, noting how tools are already changing how scholars gather information. More fundamentally, he placed AI as the latest great acceleration in scientific history: just as the internet sped up the pace of research and tightened external resource pressures, AI is compressing the cycle again. In the essays Zhou cited, AI cuts both ways. Wang Xiaodong expects large language models to erase much of the English-writing disadvantage long faced by Chinese scientists, further swelling the flood of papers coming out of Chinese labs. Wang Liming sees AI accelerating both ends of the trust crisis: it lowers the cost of paper mills and fabricated images just as quickly as it lowers the cost of automated image forensics, data auditing, and code reproduction. “Whoever builds the verification layer wins,” Wang Liming writes. His proposed alternative to the static, packaged “story paper” is a continuously versioned “unit of discovery” containing a falsifiable claim, raw data, analysis code, experimental parameters, and an ongoing log of independent checks—where negative results and failed replications are recorded rather than buried. Zhou ended his keynote by bringing AI back to the question of scientific honour and credit. Citing a recent Intellectual discussion on “What AI Means for Mathematics” featuring Fields Medalist Deng Yu 邓煜—at a moment when AI is already batch-solving mathematical conjectures, as HKU Vice-President Lin Chen noted in his opening remarks—Zhou agreed that human researchers must remain the principal agents: AI is a tool that frees scientists from tedious calculation to think about deeper, messier real-world problems. Yet that partnership creates a thorny new evaluation problem. When a breakthrough is made with AI, what is the human contribution and what is the machine’s? And since AI models distil centuries of accumulated human scholarship, is it right that the credit and value flow so heavily to the private AI companies building the tools? The old struggle over how to assign scientific honour is acquiring a whole new layer. What I took away was a serious discussion of how science can remain trustworthy as its scale, incentives, and tools change. These were scientists with real standing inside China’s system questioning how that standing is produced, and proposing changes that would affect their own institutions and peers. That made the afternoon at HKU’s Beijing Centre worthwhile. At the close, Wang Feng, Director of Communications and Public Affairs at HKU, said the value of the forum series—which heads to Hangzhou in December and cities like Chengdu early next year—would lie in the real cooperation it helped bring about. The university has plenty to celebrate: this week it topped the QS Asia rankings for a second consecutive year, alongside its 11th-place position in the QS World University Rankings 2027. For me, bringing this debate to an audience beyond the people who review papers and allocate grants was a useful contribution. Share [ The East is Read PhD dropout’s exposé forces an academic fraud reckoning in China A former doctoral student turned blogger has shaken China’s academic establishment by publicly accusing prominent university scholars of fabricating data and manipulating papers, triggering investigations at several leading institutions and drawing rare attention from state media… Read more 5 months ago · 43 likes · 8 comments · Yuxuan JIA ](https://www.eastisread.com/p/phd-dropouts-expose-forces-an-academic?utm_source=substack&utm_campaign=post_embed&utm_medium=web&embedding_publication_id=47580)
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OpenAI: ChatGPT Search Is About 20% of Google’s, 250 Million People Speak With ChatGPT, and Chat Is Merging With Work
OpenAI says ChatGPT growth is accelerating. The company added 200 million users over just the past two months, jumping from 1 billion to 1.2 billion weekly active users in 60 days. Its search volume, it says, is approximately 20% of Google’s. Its users create 1.5 billion images with it per week. And its voice mode, between dictation and speaking with ChatGPT, now has 250 million combined weekly active users. ChatGPT consumer product lead Adam Fry told me this during a brief fireside chat at an OpenAI-hosted dinner in New York City on Monday night. “The growth is just going big,” he said. During our conversation, Fry also revealed plans to merge ChatGPT’s Chat and Work modes (so that confusing toggle up top will soon go away), agreed with feedback that ChatGPT-created restaurant signage looks too uniform, and addressed the cost to serve its personal assistant Dots. Here’s a look at our full conversation, lightly edited for clarity and length. We also cover image generation, interactive interfaces, advertising, education, and public unease about AI: Alex Kantrowitz: OpenAI has analyzed ChatGPT’s search volume and compared it with Google’s. What did you find? Adam Fry: ChatGPT has been growing quite quickly. ChatGPT’s volume of search-like queries is now roughly one-fifth the volume of queries of Google. It’s a pretty amazing statistic when you think about it, mainly because we started just about a year and a half ago. It’s hard to remember this, but ChatGPT at a time didn’t have any up-to-date information. You’d ask a question, it would say something like, “It’s past my training date,” and nobody had any idea what that meant. You’re saying ChatGPT’s search volume is one-fifth of Google’s. Bing’s share of the search market is around 5%. Does that make you multiple times larger than Bing? To say that more directly, ChatGPT has gone from one billion to 1.2 in just the last two months. That’s roughly 100 million users in 30 days, and another 100 million in another 30 days. The growth is just going big, and we’re staying very focused on how fast ChatGPT is growing. What counts as a search? I might have 20 prompts back and forth in a ChatGPT conversation. Does each one count as a query? Story continues below… A MESSAGE FROM OUR SPONSOR Move upmarket without a rebuild Moving upmarket means saying yes to what enterprise buyers ask for first: SSO, directory sync, and audit logs. WorkOS gives you all three, so your team keeps building the product instead of the checklist. Get enterprise-ready. Continued from above…. Adam Fry: What people came to ChatGPT for to start, if you go back about two years, is really writing help and coding help. Those wouldn’t be things that you really search for. What we’ve seen over the last year is people have grown into all these other topics. Things like you’re looking up a person or people or a place or a thing, and it requires up-to-date information. That’s a key element. Another big part is finance questions or health questions, where you really want to make sure it’s up-to-date information that’s quite factual, and we’ll do a web search for those questions. It’s basically anything that a user would have traditionally gone to a traditional search engine for, but is now deciding to go to ChatGPT for. Does each conversation with a search embedded count as one search, or does every question that requires a web search count? Every time you ask a question and go search the web, that’s a search. You used to work at Google. How would you say the nature of search is changing? One of the things that people really love about ChatGPT is this personalization and this understanding of the broader journey. If you look in the past, people would go for a Q&A box, and you’d ask a one-off question, like how tall is the Eiffel Tower. But there’s so much context in our lives around, “Maybe I’m taking a trip to Paris.” It’s actually helpful for ChatGPT to respond, “Here’s how tall the Eiffel Tower is, but I know you’re going on a trip to Paris.” It’s that ability for people to actually maintain context over time that has led them to start using ChatGPT in this way. You’ve also announced 250 million weekly active users of voice input, including dictation and conversations with voice mode. We are at 250 million weekly active users. What’s amazing about this is, if you look at those 250 million, what we’re seeing is it’s actually also incremental to the product. It’s no longer just a novelty. People aren’t just trying out voice. We’re seeing them use voice input day to day in their daily life, and it’s grown so quickly. Those 250 million users are using voice input, and that could just be a quick question or a long conversation. We’re seeing both behaviors. We’re seeing people just not want to type. You just hold it and start talking to it, or it turns into a longer conversation where people just sit with it. The big reason for this is the frontier capabilities of our models that we’ve shipped. We shipped what we call GPT Live 1, and then we shipped GPT Live 1.1. The big change with this is that it’s much more natural to speak with. It’s something we call backchanneling and interruptibility. Mhhhm… What you just did there is what the model does, because it’s really good at having a bidirectional conversation. That push to the frontier—we were the first to really release this at scale—has made it so much more natural for people to chat. That’s still only about 20% of the user base. From the outside, that looks small. Why is it exciting from the inside? It’s very exciting for a few reasons. We see from the data that when people start using voice, they stay around using voice. One of the things, transparently, we can do a better job of is bringing more people into discovering it. We see that the number of people who discover it could be a lot higher. But once they do, it’s a really new way for people to interact with AI. We’ve made a lot of improvements to it too, where we’re just going to see the number go up. You have background mode, so now by default, if you close the app, it’ll show, “Hey, the conversation’s still going.” If you want to walk around the house or you’re putting away the groceries, you can still have a chat. That kind of continuity has also changed how much people use it. Dots launched on September 29 and is about a month old. How’s it doing? It’s doing great. We talk to people about their Dot, and the first thing that everyone says is, “I love my Dot.” One of the reasons we’re seeing this is the proactivity, its ability to reach out to you and figure out things. The biggest use case I see all the time is, “Did I forget something today that I was supposed to do?” You can just say to your Dot, “Remind me of things at the end of the day that I may have forgotten. Somebody emailed me or messaged me,” and it’ll do that. This Dot changes the paradigm of the relationship with AI, where it’s actually helping you and giving you time back. You’re more conservative with permissions than some competitors. I tried to give my Dot my credit card, and it wouldn’t take it. With the creator of OpenClaw in-house, why are Dots’ permissions more limited? This is a space where we want to be very thoughtful and conservative because it’s a new space where you’re entrusting your Dot to take care of things on behalf of you that matter and are meaningful. We’ve just talked about how you go to this 1.2 billion user growth and asking questions. Now we’re asking people, “Can you ask ChatGPT or your Dot to help get things done?” But when you ask it to help get things done, it’s a whole new level of trust. We’ve built up a tremendous amount of trust in giving the right answers to your questions, and we want to make sure that when we build this new product, it has a high degree of trust. It’s a long game. We think this is going to be a new market for a very long time, and we want to make sure that we’re being this trusted player in the market to start. I initially thought Dots would be available on my $20 plan, then realized I had to spend $100 a month to get it. I upgraded, but I’d like to downgrade at some point…. We hope to keep earning your business and make the value good enough. It’s a really good point. ChatGPT’s goal more broadly, which I think is unique for us in the market, is we are focused on bringing intelligence to everyone safely across the entire world. For Dots in particular, there’s no difference. We don’t have anything to share tonight about the date of when Dots will go to a broader set of users. But you can rest assured we’re working very hard, and there are a couple things we have to get right. Over time we’re going to work on more efficient, agentic models that are able and capable of doing things for you. The first one is, right now we wanted to make sure that your Dot is actually really helpful. We started with some of our best models, but over time we’re going to work on more efficient, agentic models that are able and capable of doing things for you. The second thing is around proactivity. We’re still dialing in: How much do I message you to help you get things done? We can always play around with that to understand how much usage should a user use to have a successful Dot. We’re going to work on both, and our goal and ambition is to bring it out to all users. How much background computing does a Dot require? I’ve read estimates that serving a $100 subscriber might cost OpenAI $3,000 in compute, and mine seems to read every email that arrives. How often is it checking connected data sources? Right now, Dots are on the compute-heavier side, and this is one of the things we want to make sure that we can dial back over time. But the goal is to give you the best product. This is our premium, always-on personal assistant. Once we nail that, we’ll figure out how to dial back the compute needs. The thing we’re seeing also from our models, as we train frontier models, is just how—I can’t even keep up with how quickly we have a new model, and each new model is more efficient. If you looked at 6.1 Sol when it came out relative to Astra, much more efficient. I’m very excited about the next set of models we have coming too that’ll be even more efficient that we can use. Elon Musk said the cost of serving Grok Bot was unsustainably high relative to revenue. Is efficiency the key to winning against competitors, or is there more to it? There are a couple of factors that go into it. One is the cost side, but the other is the revenue side. On ChatGPT and on our consumer business, we’ve been extremely proud of our ability to diversify different revenue streams in a way that’s growing really fast. To give you another stat, we announced several months back that we had passed 50 million subscriptions, and the number has grown considerably since then. On ads, within 200 days, we reached more than a billion in ARR, and that business is growing extremely quickly. We also have credits. Rather than paying for subscriptions, users can buy more credits. Between credits, subscriptions, and ads, I’m confident that the value of a Dot is going to be able to pay for itself. Elon has subscriptions too. This is clearly an expensive product to serve. One thing we continue to bet on at OpenAI and ChatGPT more broadly is that the models will continue to get much better and much cheaper, way faster than any of us think. If you look back at the historicals on it, it continues to be true. From what I’ve seen, I see no slowing down of that. What is Meta doing right with Muse? It has spent a lot of time atop the App Store charts. There are a couple of things they’ve done well. The first is people have been craving simplicity in their products. One of the things we have stated publicly as well is, at times AI products can be too complex. We’ve announced we’re merging ChatGPT’s Chat and Work. We’re making it much more simple in the products. You don’t have as many toggles or sliders between efforts and different model pickers. One of the things they’ve done well is this simplicity. You just go in and you can ask a question. You don’t have to think too much about what you’re doing. That’s one thing that seems to have resonated with users, which we also believe in. Second, there’s something really nice about being able to access your agent across many different channels. We just announced and launched Dots on iMessage. Whether you talk to your Dot on email or iMessage or within the ChatGPT app, the ubiquity in getting your assistant out there is going to be key. Those are two things that, if I had to be nice to our competitors, I would say were two big ones. So Alexandr Wang’s meme-tweeting strategy isn’t something you’re going to emulate? I have a different tweeting style. ChatGPT still asks me to choose between Chat and Work, even though some people call this technology AGI. Will that distinction go away? Couldn’t agree more. We have to solve this. Asking users to choose — we’ve heard repeatedly from all of our users it’s too much to understand. When should you go to Chat? When should you go to Work? It’s solvable. There’s real research to solve to make sure the models can understand what your intent is. Are you asking for a fast, quick reply? Are you asking me to go do a long agentic task? We’re going to solve it, and the plan is to bring Chat and Work together. So one interface will work out whether I’m asking for a conversation or a task. When? Coming soon. You told me the majority of ChatGPT’s 1.2 billion users are using image functionality. What are they doing? Today, 1.5 billion images are created per week. It’s hard to even wrap my head around that number. They’re doing a wide range of things. One of the most interesting things they’ve started to do is really use it for productivity use cases. The first thing it started with—I don’t know if you saw the ’80s trend, but it was quite fun. You could take a photo of yourself and say, “Put me and my family in ’80s outfits.” I did this for my family, and it was a really fun poster. But what we’re also seeing is businesses for their menus, or posters for birthdays. People are using it almost in a productivity-like fashion to create visual diagrams of what they would like to see. We’ve actually brought the latency down on images as well, so it’s gotten a lot faster. It went from a very high number of seconds to getting much faster. As it comes down even further, the number of images people are creating and the number of use cases is going to grow a lot. My feature request: When a restaurant makes a menu or sign, prompt it for more specificity so it doesn’t look like every other AI-generated restaurant sign. Or offer a shuffle button for a different style. I agree with the feedback, and we’ll take it to the team. I’m seeing ChatGPT generate little interactive interfaces within its answers. What’s the direction for Intelligent UI, and how are people using it? I am extremely excited about Intelligent UI. If you go back a couple years, these chat apps were all text, and now that’s changing. They’re going to be visuals. Intelligent UI and our first attempt at this with GPT-6 Intelligent UI is really just the beginning. As you go to the extreme, what you can imagine is, let’s say you’re using voice mode, and you just say, “Show me how offside works.” Immediately it should show you a visual diagram, and you say, “No, I don’t quite understand it.” It should just repopulate. That’s where the future of this technology goes. It’s almost like you’re talking to a canvas that will update and change. People are finding it helpful for a lot of things. My favorite one is—it’s hard to admit that I had no idea how hearts work. The last biology course I took was in high school. I just asked it, “How does the heart work?” and it instantly gave me a beautiful interactive diagram of where blood flows and how it goes through. I’m really, really bullish on this for learning in particular. You’ve described useful applications in finance, health, and learning, and ChatGPT has 1.2 billion users. If people are getting so much value, why is public polling on AI so negative? The reality with AI is it is going to change the nature of how work gets done. For a lot of people, that is something that can feel scary at times, and we’ve all felt that. But I remain confident that when people actually use the product and they use AI, it very quickly changes how they perceive it. We’re at 1.2 billion, but there are still a lot of people who haven’t used it. The more people use it, get familiar with it, how it can actually help you in your life, it’s going to feel a lot less scary. Big Technology is a reader-supported publication. To receive new posts and support our work, please consider becoming a paid subscriber. Thanks!! Subscribe
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Why 2026 Is a Health Care Election
_Ypsilanti, Michigan_THE MIDTERM ELECTION IS ABOUT a lot of things: gas prices and groceries, the spread of corruption, and the future of democracy. And yet, as much as any single issue, it’s about health care. You can tell by listening to all the Democrats talking about high deductibles, drug costs, and insurance companies as they campaign around the country. Or you can tell by reading all the public-opinion surveys in which voters consistently rank health care as one of their top concerns. But if you really want to know why this is a health care election, your best source isn’t the politicians or the pollsters. It’s the people on the front lines. One of them is Raymond Rion, a family medicine physician and executive director of Packard Health, a small network of clinics spanning Ann Arbor and Ypsilanti on the western outskirts of the Detroit metro area. Packard is what’s known as a Federally Qualified Health Center, where anybody can get care regardless of income or insurance status. And like similar safety-net providers all over America, for the past decade it has benefited directly from the Affordable Care Act. “We almost went bankrupt in 2011,” Rion told me in a conversation last week, recalling how in the old days staff at Packard would work the phones furiously to get supplies and find specialists who would take referrals. Now many more patients have either Medicaid or private coverage they’ve purchased through HealthCare.gov, the online marketplace that the law created, so that the proportion of uninsured patients has fallen from around one in three to one in ten. It’s a lot easier to find referrals for these insured patients, though thanks to the infusion of paying customers Packard can do much more on its own. In particular, Rion said, the organization has opened two new branches, started a pharmacy, begun offering prenatal services, and vastly expanded behavioral health care by bringing on a half-dozen new doctors, nurses, and case workers. Now a retrenchment is underway. Coverage options on the HealthCare.gov marketplace got more expensive because Donald Trump and the Republicans refused to renew temporary subsidies that had been reducing the price of insurance. On top of that, the first cuts to Medicaid as part of last year’s “One Big Beautiful Bill” are starting to take effect. Rion told me Packard has already laid off a provider in anticipation of less revenue next year.1 Patient volume is down, he went on to explain, in part because uninsured patients sometimes avoid care altogether out of fear a single visit will lead to more treatments with more bills. And postponing care in that way can have serious medical consequences, as his colleague Ron Romero told me. “So you have somebody with diabetes, their chronic condition isn’t getting cared for,” said Romero, who like Rion is a family physician. “Their sugars rise, and they end up having other medical problems. Their kidneys start to get damaged. They start to have eye damage. They increase risk for heart attacks and strokes. . . . They end up in the emergency room.” And things are likely to get worse. Partly that’s because next year most of the remaining Medicaid cuts will take full effect, throwing more people off coverage. And partly it’s because forces that have nothing to do with Trump or Republican actions are making medical care more expensive for everyone, including those with employer insurance and Medicare. That will lead to still more people delaying or skipping care, ending up in the emergency rooms, and putting yet more cost pressure on providers. Rion wasn’t taking a position on the election or political parties, he made clear. He was just describing the reality he sees around him: “The whole system is being pressured. Nobody is immune to this stuff.” He’s right. And here’s the thing: This has all happened before. We know happens next.
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