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Switch 2 jailbreak gives hackers new ways to run homebrew and mod games
Userland" exploit relies on integer overflow found in "SmileBasic" interpreter.
Why Giants Should Part Ways With Adrian Houser
The San Francisco Giants are on the hook to pay Adrian Houser more money in 2027, but his production does not justify it.
'Heated Rivalry' made hockey hot. But some fans say the sport's culture is leaving them out in the cold.
"Heated Rivalry" has sparked a hockey mania. But as fans rush to rinks, they also see opportunities for more inclusion.
MLB free agency Top 50: Ranking the best players available this winter, starting with Tarik Skubal and Freddy Peralta
Who is Nathan Fielder? A guide to his shows including 'The Rehearsal'
Nathan Fielder, who's behind the Elizabeth Holmes doc 'You Can See Everything,' is well-known to comedy fans for his docuseries 'The Rehearsal' and 'Nathan for You.' Here’s a primer on the origin and rise of Fielder.
Canva releases its first image generation model
Canva is making its new image model available to all users, promising better, more customizable performance at lower costs.
eMudhra to Exit Global SSL/TLS Business After 8 Intermediate CA Keys Found Easily Guessable
eMudhra plans to exit global SSL/TLS certificates after eight weak CA keys were recovered, as Chrome and Firefox withdraw trust in its ECC root.
How well have past GOTY winners sold?
We’re less than two months away from 2026’s The Game Awards, and what a year it’s been for fantastic games, including Resident Evil Requiem, Forza Horizon 6, Slay the Spire II, the Blood of Dawnwalker, and Control Resonant – just to name a few. So as we approach the end of the year, we thought we’d look back on our estimates for the GOTY winners of the past four years. In terms of revenue, Elden Ring (2022 winner), Baldur’s Gate 3 (2023), Astro Bot (2024), and Expedition 33 (2025) have generated a combined $3.8B via 73M copies sold: Let’s have a look, then. Elden Ring has generated $2B in revenue Elden Ring is a phenomenon in every sense. It’s sold a whopping 36.4M copies across Steam (50%), PS5 (35%), and Xbox (15% across console and PC). Want more free estimates and insights in your inbox twice a week? Subscribe below! Subscribe Elden Ring also just passed an astronomical milestone: $2B in revenue. While PlayStation and Xbox account for half of Elden Ring’s copies sold across these three platforms, they account for 57.5% of revenue. This – as ever – is due to regional pricing and the higher relative share of PC gamers in lower-priced markets. For example, around 17.4% of Elden Ring’s Steam copies sold were in China, where the game costs about 26% less than the price in the States. There’s an … interesting narrative going around these days that console gamers are only interested in ball-and-gun games and have the patience for little else. Well, Elden Ring’s impressive sales prove that is simply not true, and across all platforms. What’s more, over 9.8M people have played Elden Ring for over 100 hours, with nearly 3.5M people getting all of the achievements and trophies. For reference, 10.9% of Elden Ring’s PlayStation players have the platinum trophy, while 10.5% of players on Steam and 3.9% of Xbox players unlocked all the achievements. Git gud, Xbox players! Of the 1.5M people who played Elden Ring on PlayStation last month, 4% also played Dark Souls 3 last month, 4% played Bloodborne, and 3% played Sekiro. There’s those Soulsborne diehards for you. By many accounts, including my own, Elden Ring is one of the best games of all time. It’s an evergreen seller that will continue to be a cash cow for Bandai and FromSoftware for years to come. Want more free estimates and insights in your inbox twice a week? Subscribe below! Subscribe Elden Ring has sold another 2M copies across Steam, PlayStation, and Xbox this year, generating almost $110M. This means it’s outperforming many of this year’s high-profile releases. Even Elden Ring’s standalone run-based PvE spinoff, 2025’s Nightreign, has sold 7.8M copies and generated $282M in revenue. Baldur’s Gate 3 is another billion-dollar-plus banger Another cross-platform phenomenon is Baldur’s Gate 3, which has sold 23.2M copies and generated over $1.2B in revenue. About 18.7M of these copies (80.6%) are via Steam, 2.9M via PS5 (12.7%), and 1.6M (6.8%) via Xbox. Around 1.3M folks on Steam bought BG3 before it hit 1.0. The PlayStation version launched about a month after the PC 1.0 release, while the Xbox version came four months later due to technical issues around developing local co-op on the Series S. Want more free estimates and insights in your inbox twice a week? Subscribe below! Subscribe Across platforms, Baldur’s Gate 3’s monthly active users have never dropped below 2M, including 2.2M MAUs last month (68.5% on Steam, 21.9% on PS5, and 9.6% on Xbox). And it still regularly brings in 280K to 400K players every day: About 1% of BG3’s Steam player base has played for over 500 hours! That’s a little under 200K people. Astro Bot is also an evergreen seller, albeit a more niche, family one Astro Bot, 2024’s winner, has now sold 4.7M copies and generated $291M in revenue. The budget, including marketing, is almost certainly under $100M, given the small Japan-based core team and three-year dev timeline. You can really see why the tides are changing toward less expensive hubs. Astro Bot is the only first-party winner on this list, but follows other first-party winners of The Last of Us Part II (2020’s winner), God of War (2018), and Breath of the Wild (2017). Around 46.6% of Astro Bot’s copies sold have been physical, which is far higher than the typical game. This makes sense for a couple of reasons: There’s a large family audience for this one, and physical boxes are an item to wrap when giving Astro as a gift. Astro has collector value for the PlayStation core (this is nostalgia-fueled platformer celebrating PlayStation’s history) Want more free estimates and insights in your inbox twice a week? Subscribe below! Subscribe Astro is another one of those games that continues selling over time, regularly selling more than 100K a month. It’s sold around 1M more copies this year ($60M in revenue) and is a popular title with families. We can see this using the Alinea platform’s MAU crossover tool: Astro Bot’s top #2, #3, and #4 games by crossover in any given month this year are Minecraft, Fortnite, and Roblox – without fail. The #1 title every month, though, is Astro’s Playroom, a pack-in with all PS5 consoles. For every month this year, between 20% and 30% of Astro Bot players also played Astro’s Playroom that month. So the free pack-in is a noice funnel for converting players to a full paid product. Expedition 33 sells 8.7M despite Game Pass cannibalisation Finally, we have last year’s winner – Expedition 33. The turn-based-RPG banger has generated $366M via 8.7M copies sold, with 58.8% on Steam, 35.7% on PS5, and 5.5% on Xbox. Xbox’s share of copies sold is this low due to Expedition 33 launching on Game Pass, where 4.3M people across console and PC accessed the game. A little under 700K Xbox players used their subscription to play Expedition 33 this year. That said, the game’s 1.5-year Game Pass deal ended last week on October 16, and we’re already beginning to see a positive trend on Expedition 33’s copies sold via Xbox (but the jury’s still out!). There’s understandably a lot of French pride behind this one, as France is the game’s #3 market on both Steam and PlayStation. Vive la France! One thing that stood out to me is that the GOTY winners aren’t $300M Hollywood productions made in the US. They’re mostly mid-budget studios outside the traditional AAA cost centres. Lots to think about there. Also, good games sell well; who’d have thunk it!? And there’s a whole load of them this year, with GTA 6 still to come, too. GTA just made the cutoff point for the VGAs this year. What a coincidence! Loading... Cool links and other stuff Our physical games newsletter from Tuesday was picked up by GameSpot, Eurogamer, TweakTown, and a bunch of others. We’re offering a free trial of our platform for games companies. Send us a message here, or reply right here on Substack/email, and we’ll set you up. Next week, we’ve got some exciting news regarding the future of our newsletter, so stay tuned! The last word Reply to this email – or reach out here – if you have any feedback for the newsletter – or want to request a game for us to cover. Want to see why these companies (and many others) are subscribed to Alinea? Get a free platform trial! [Alinea Analytics boasts the most accurate PC and console estimates in the business. Game makers use our platform to understand their audience, keep an eye on the competition, monitor sales trends, and spot new opportunities. We equip game studios and financial institutions with accurate data and the confidence to make smarter, data-driven decisions. Want to talk about all things games market data? We’d love to chat!]
Two CVSS 9.0 Linux KSMBD RCE Flaws Disclosed on Public Mailing Lists Before Kernel Fixes
Two CVSS 9.0 flaws in Linux KSMBD could enable remote code execution; one needs no authentication, and neither has a mainline fix.
Conan O'Brien met his match on 'Subway Takes.' She's a 68-year-old bar owner from Brooklyn.
O'Brien was killing it on the popular internet talk show. Then Debbie Shields sat down next to him.
If Netflix Can’t Shine in Show Business, Who Can?
The streamer was once the prime example of modern entertainment. Now it’s being squeezed by Big Tech and user-generated content. What stock investors need to know.
Qlik Brings Governed Business Context and Analytics Workflows to Claude
Qlik’s MCP Server is now available through Anthropic’s Claude Directory, giving Claude users a simpler way to connect governed Qlik® data, analytical logic, and selected analytics and data workflows, so they can work with trusted business context instead of raw data alone. Key takeaways: Makes Qlik easier to discover and use inside Claude: Availability through Anthropic’s Claude Directory gives Claude users a more direct path to connect Qlik through MCP.Brings governed business context into Clau
Argonne welcomes Chicagoland mayors to campus for meeting and tours of user facilities
Argonne hosted the Metropolitan Mayors Caucus for its quarterly meeting, where dozens of Chicagoland leaders learned about the lab’s work in super intelligence and resilience planning and toured the Advanced Photon Source and Aurora supercomputer.
Giant iPhone Duo Installations Spotted at Apple Fifth Avenue
Apple is preparing what appear to be giant iPhone Duo displays outside its Fifth Avenue store in New York ahead of the device's launch on Friday, October 23. Photos from users on X show the structures on the plaza beside the glass cube. Their designs are hidden by wrapping and black covers, so it remains unclear exactly how Apple will present the device. October 21, 2026 The outdoor installations accompany a substantial retail push inside the store.
John Anderson, country music star, dies at 71
Blake Shelton, Reba and Luke Combs are among those who have paid tribute to the Country Music Hall of Famer.
iPhone Duo Reviewers Really Miss Face ID
Touch ID on the iPhone Duo might end up being frustrating for some users, based on media reviews that came out today. Multiple reviewers found Touch ID to be slow to unlock, which can be jarring coming from Face ID. According to Marques Brownlee, Apple added an intentional delay to "every single" Touch ID unlock. After you touch a finger to the side button to unlock, it pauses on the Lock Screen. If you keep your finger there, it continues to the Home Screen. It's technically smart.
Meta earnings: Mark Zuckerberg says Muse AI app hits over 10M users
CEO Mark Zuckerberg announces 10M users for Meta's Muse AI app since September. Meta's stock was up after its third-quarter earnings report.
BTA Sports Expands into Prediction Markets With AI-Powered Analytics
The fourth quarter rollout pairs independent probability modeling with “Ask BTA” to help users assess sports event contractsHORSHAM, Pa., Oct. 21, 2026 (GLOBE NEWSWIRE) -- BTA Sports, a subscription-based sports analytics and decision-intelligence platform, today announced its expansion into sports prediction markets, bringing the data and insights used by its sports betting subscribers to users evaluating sports event contracts. The offering, available in the fourth quarter as part of Version 2
How GSN Doctoral Residents are Approaching Care for Military Women’s Health
A Navy nurse and GSN doctoral resident redesigned an Okinawa maternity unit, cutting off-guideline staffing from 18% to below 12%.
Critical GitLab Flaw (CVSS 10.0) Could Allow Unauthenticated RCE Across 12 Years of Releases
GitLab fixes CVE-2026-110264, a critical flaw that could allow unauthenticated code execution on self-managed versions dating back to 7.0.
Texans QB C.J. Stroud bet on himself for a bigger payday, and that might be working out great
Meta ordered to pay New Mexico $25 billion over Cambridge Analytica breach
A judge has ordered Meta to pay New Mexico $25 billion after a jury found the company liable for deceiving users about privacy and content moderation on Facebook.
CoreWeave's business model explained: Why investors are worried
In the Loop Host Ejaaz Ahamadeen breaks down exactly how CoreWeave's (CRWV) business model works, the financial challenges facing the AI infrastructure company, and why concerns about its ability to manage debt are growing. Ahamadeen examines an estimate suggesting CoreWeave faces a 55% probability of default over the next five years, and what that could mean for the company.
Google Chrome Adds ClickFix Warnings to Stop Users From Running Malicious Commands
Google Chrome adds ClickFix command detection for Enhanced Protection users, warning them before they execute malicious scripts.
Abbott Plans to Spin Off Its Nutrition Business
Abbott Laboratories said it plans to spin off its nutrition unit into a separate publicly traded company after a series of missteps have weighed on the company’s broader performance.
PHP2Shell: Two Unpatched Critical PHP RCE Flaws (CVSS 9.2) Trigger Emergency Security Alert
PHP2Shell involves two critical PHP RCE flaws rated CVSS 9.2, with emergency fixes scheduled following a disclosure leak.
Apple CEO John Ternus says design still means everything & includes whimsey
"Design is how it works" rings true at Apple today, and John Ternus wants to assure everyone that respect for the user and building products that are beneficial is "job one."
Rays vs. Brewers World Series preview: Who has the edge in matchup of MLB's 2 best teams?
ChatGPT now lets you lock your chats
The company says the feature is designed for situations where users might be discussing something personal or planning a surprise and want to keep those conversations private when handing their device to friends or family to show them something in another chat.
Sundar Pichai: Google’s Frontier Imperative, Compute Scarcity Dilemmas, Future AI Products and Businesses
Alphabet CEO Sundar Pichai has little patience for doubts about Google’s willingness or capacity to compete at the frontier of AI. His company’s latest Gemini 4 Argon model — long-promised and recently delivered — beats OpenAI and Anthropic’s latest public models on coding, math, science, and knowledge-work benchmarks and has placed Google’s AI back among the world’s best. Amid chatter that Google was fine with good-enough lightweight ‘Flash’ models, Pichai pushed back hard against any suggestion that company is limiting its research ambitions. “We won’t do it any other way,” he told me in a Big Technology Podcast interview recorded this week at Google’s Mountain View headquarters. Pursuing cutting-edge research is costly for Google, requiring compute and chips that otherwise could be sold at a high markup by its cloud business. But Pichai said allocating resources to frontier AI development is “ground zero” for Google. “Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development,” Pichai told me. In our conversation, we discussed Argon (which Pichai says processed three times as many tokens as Gemini 3 in the 72 hours after launch), AI’s role in developing better models, the AI interface of the future, competition with OpenAI, Google’s partnerships with Anthropic and Apple, data centers in space, and the nature of his optimism. You can read the full conversation, lightly edited for length and clarity, below, or listen to it on Apple Podcasts, Spotify, YouTube, or your podcast app of choice. Alex Kantrowitz: Google is back at the frontier with Gemini 4 Argon, which beats OpenAI’s and Anthropic’s public models on benchmarks spanning coding, math, science, and knowledge work. The conventional wisdom was that Google had given up on the frontier to focus on fast, good-enough models for its products. That was wrong? Sundar Pichai: Very wrong. Look, we are so committed to being at the frontier. It’s in our DNA. We won’t do it any other way. We’ve been thinking about the AI frontier for well over a decade now. The frontier is very dynamic. It’s very competitive. It’s very exciting. I couldn’t be more pleased with how Argon has come together. We took our time with it. We made sure we tested it safely and deployed it carefully. We took a bit longer than our other models, given its capabilities. Across the board, be it for agentic work, knowledge work, coding, creative tasks, Argon is one of the really good frontier models. The first 72 hours since we launched, it’s almost up to 25 trillion tokens used, which is about three times what Gemini 3.0 had in the first 72 hours. And we are capacity constrained as we are serving it. Excited, but there are always areas….. You know, the frontier is very dynamic, and you have to keep evolving the models. There are areas which we can see we need to improve as well. We’ll be at work on all of that as well. Which areas? On any given model, Argon would be across the board better at everything if you just look even two months back. But the space is so evolving. Even on coding, for example, there are pockets where we know we need to do better, and there’s clear feedback. We’ll make it better. The argument for standard models is that they’re already good enough to build great products, with the frontier valuable only for certain tasks. Meta built Muse with good models rather than the best, and Google could put standard models into Maps, Gmail, and Search. Why isn’t that sufficient? It’s definitely not how I have thought about it at all. We are deeply committed to driving the state of the art and actually pushing where this technology can go, all the way from transformers. That’s a journey we’ve been on. You will see us continue to do that. When you’re at the frontier, you get an earlier look into the emerging capabilities, earlier than others, which helps you better prepare for the future. We are fortunate to have a wide set of opportunities across consumer and enterprise. Some of our cloud customers, for sure, want the model at the frontier. The frontier models will be better at developing the next set of models in an era of recursive self-improvement. I would argue it matters there. Internally, we have definitely seen a steep point of inflection by deploying Argon to our developers. It definitely tackles problems which our previous models could not do. Being at the frontier puts you at the innovation frontier and helps you drive that as well. There is room for models of all sizes and capabilities. That’s why we did Flash, Flash-Lite, etc. I agree with you that you can develop very good product experiences with not necessarily the most frontier model, because they tend to be slower and more expensive too. But we care about the Pareto frontier, and we’re committed to doing all of that. You said developing frontier models gives you a look at what’s coming. What do you see on the horizon? What makes this moment so exciting is you build something which you think is a culmination of months of work, and you put it out. You already see clear headroom ahead. We’ve all been working on these agentic, long-horizon capabilities, but clearly the frontier is going to have multiple of these agents working together to do things. That’s an example of an exciting paradigm underway. It’s also clear to me that users, both in consumer and enterprise, will have access to some multi-agent framework for them, working all the time doing things. It feels like a moment of infinite possibilities. Sergey Brin said one of Argon’s core innovations was relying more on the model to help develop the model. Yet incidents involving OpenAI’s bots breaking containment and Google’s bots accessing a site they shouldn’t have suggest these systems still aren’t perfectly aligned. Isn’t there reasonable cause for concern about accelerating toward recursive self-improvement? There are three parts to your question. Let’s first talk about RSI itself. You’re right, Sergey mentioned RSI being a critical contributor as we are developing Argon. But to be very clear, today, in a continuum, we are pretty far from the end state of RSI or something like that. The right way to describe it is you have RSI loops along with AI researchers in various aspects of model development. You almost have agents helping AI researchers in some automated way work together to build these models. It’s good to at least understand where we are today, and it’s super exciting. I expect that continuum to keep making progress as we go into next year. The second question is around safety. Security comes from defense in depth. You’re talking about alignment, obviously. We all are doing a lot of work to make sure the models are as aligned as possible. But it’s an active area of research, and there are clear gaps. That’s not the only thing we rely on. You have the work you do on alignment, but beyond that, we put a lot of effort into real-time monitoring as these models are working. Making sure you have good telemetry into what’s happening so that you can intervene in real time if you see unexpected behaviors. How you do that, the architecture with which you do that, how you make sure it’s all sandboxed and isolated in a way that it can continue doing its job during real-time monitoring — these are all areas of investment. Obviously, when we deploy these models, giving customers, enterprises, control, both with real-time monitoring, but their ability to decide where they deploy, what type of data which models ever have access to, etc. All that work is very, very important, and we are doing a lot of that. As an industry, we need to constantly up our game and take the time to do all this stuff, including dedicating the compute required to make sure you can safely deploy these models. The third part is: Is it all enough? This is why there are a lot of conversations in the industry, and I think it’s reasonable to be worried. The right way to approach something you worry about is early on thinking about what more we can do. As an industry, you’re definitely seeing increased levels of work on alignment, testing for safety, etc., as we deploy these models. The next step would be developing better standards so there are cross-industry standards of how we do these things, including the ability to evaluate using independent evaluators. If you look at areas like financial controls, we have accounting standards, we have independent auditors. Those are all the next natural steps for us as an industry to work toward, and regulation will end up playing a role as we go through all of that as well. Google pursues both language models and world models, while some other AI companies like OpenAI have focused more heavily on LLMs as the path to AGI. Why is world-model research important to you? As humans, you obviously perceive the world around you, and you’re taking all those inputs. Making sure these models are able to understand the laws of physics, simulate the world in high fidelity. All that, I think, are key enabling blocks for intelligence. We are working across all possible applications of this. Take an area like robotics. It’s not an accident: If you look at embodied reasoning benchmarks, Argon is state of the art. That all comes from our experience building world models. Inherently, I would expect there will be architectures which will converge all of this in the future. But for us, we view this as, today, this jagged frontier that is intelligence. We want to be at the frontier of all the possible approaches, and that will help us both drive better research, better insights, and develop a much better AGI model, is how we think about it. Some people argue Google should focus on selling its computing resources rather than using them for AI research. Cloud wants to sell compute at a high markup, while researchers need a very big computer to build the best models. How do you balance those demands? Ground zero for us is making sure we are investing in the compute that’s needed to be at the AI frontier. In 2015, I set the company to be AI-first. AI is that key horizontal ingredient across everything we do, all the way from Search to Cloud to Waymo. That is ground zero for us, making sure both for research and developing the models that we have enough compute needed. Beyond that, we have these independent businesses which each have their own long-range plans. They’re using those long-range plans to project forward, and we plan for compute for all of them. The cloud team, they have a business and they have a projection and they have demand signals. They have committed contracts. With that help, we can plan on a long-term compute trajectory for them. We’re fortunate to have this wide set of opportunities. There’s no company in the world which isn’t making trade-offs at any given moment in time. But the first principles we operate on is making sure we have enough compute to invest in the frontier, and that’s non-negotiable, and everything else works on top. Remember, we are building our own compute, our own data centers. There are long lead times. The compute we have today is a function of decisions I made in 2024. Even if you think back to a year from today, last year the agentic coding takeoff hadn’t happened. Today, obviously, when you sit and look at anything, everyone is short—not just us, other companies—and everyone wishes you had two times what you had planned for. That comes with the territory. At any given moment in time, we are planning for what we would need two to three years out in the future. You’re going to find some decisions you make may look suboptimal two years out, depending on where the state of the art is. I don’t spend too much time thinking about it. The way I think about compute allocation is making sure Google DeepMind, both for research and frontier model development, is cutting edge in terms of compute access. After that, we plan for all our businesses. It’s great we can provide our compute to other people too, like we have done. It allows us to scale that up, get real tests for it in the widest application set possible, test it in the real world, and make sure we are at the cutting edge of technology at every layer of our AI stack as well. Ben Horowitz relayed on a podcast that Andy Jassy had said compute was sold out until 2028. Are you seeing the same thing? If today a customer is coming out and asking us for compute in ’27, that is incredibly difficult to do. Even last year we went out and leased some short-term compute to accommodate some customers and help them out. But yes, we are all dealing with similar timelines. Every major technology shift seems to involve overbuilding, from railroads to fiber. Do you think that’s happening here? It depends on the time frames you’re looking at. This level of build, if I take a long enough time frame, it makes complete sense to me. We are at early stages of the most consequential technology ever, and this is not like fiber. It’s not dark. We are using it. We are capacity constrained on a daily basis. The applications, the underlying technology is improving rapidly. But running a company, the way I work through the moment is you look at your demand signals. You look at your committed revenue opportunities that you’re getting in the form of contracts. You balance that with the underlying technical capability, or internally you balance how we are using it internally, the model trajectories. Based on all that, you take a disciplined approach—a confident, aggressive, but disciplined approach—to plan out compute. But when you talk about the broader economy as a whole, you’re having people operate with various levels of risk. I focus on what I can control as a company and plan through it. If your question is, can there be an overbuild? Yes, because you can have some constrained components. Remember, what matters is a set of everything you need to build out that compute. Memory, electricity, electricians…. Land. Like 2,000 components or more. You could get constrained at some point. There could be things which cause bottlenecks, and then you could have overbuilds. There could be external shocks to the economy. There are macro risks. All that is possible at any given time. But am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes. I was in the room when you said AI would be as profound as fire and electricity. If you believe that, shouldn’t you give researchers the biggest possible computer, even at the expense of today’s cloud profits? We’re all doing some form of that. Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development. What you’re seeing is true already. But you want to be thoughtful. Even our AI researchers have some constraints, which are also good. That’s what inspires creativity. That’s what helps you come up with a more efficient algorithmic breakthrough, maybe to use less memory. What we are committed to is to invest what’s needed to stay at the frontier. OpenAI President Greg Brockman told me a few months ago that the ideal AI product might have no interface at all—just as natural as conversation. What do you think the interface looks like in the age of AI? Computing historically has been humans adapting to computing. With AI, it’ll be the other way around. The technology will adapt seamlessly to how humans live and go about their day-to-day life. You want to be able to speak to that AI when you want to. It has context. It can be persistently running somewhere, give you continuity. You’re not dealing with a lot of complexity. It’s natural. All that makes sense to me. That doesn’t mean it won’t have any interface, however. Nowadays, even on my phone, Gemini voice models are so good. I can just press the microphone button. I can just ramble. That’s why we call it Rambler. I can ramble. It intelligently corrects with my personal context. Even my friends’ names, weird names, it gets it right, even if it first guesses it wrong. It’s amazing. I’m not going in, backspacing, editing once I finish saying it. It’s remarkable how good it’s gotten. But there are going to be moments you will need an interface. You may want to entertain yourself. You may want to watch a video. You may ask your AI to create something, and you want to watch it, consume it. The way I think about it is, you want it to be as seamless and as natural as possible, and that you are not doing the hard work adapting to whatever the interface is. The technology is adapting to how you’re working at any given moment in time. When I asked Sam Altman about competition from Google, he argued that OpenAI was building AI-native products and implied Google was bolting AI onto existing ones. Do you accept that framing, and can a “bolt-on” compete with an AI-native product? First of all, it’s a very dynamic moment. We are all pushing each other. OpenAI has pushed us as a company too. This is not just we are doing something to them. Far from it. Yes, Google went into its own version of a code red too. In the AI moment, all of us have to respond with varying levels of urgency at any given moment in time. We are constantly building new products as well. Gemini was built from the ground up as a product—the Gemini app I’m talking about. Or tomorrow, as all of us are adapting to agents, are you bolting agents onto ChatGPT, or are they building it natively? How would you think about that? You can always put these questions back. I don’t think of it that way. I think of it as you want to build world-class product experiences that are AI-native and they are winning. Some of it is adapting products, some of it is rethinking new experiences. They will all be part of how you approach this moment. How do you expect to make money from these AI interfaces? If an agent recommends a product and I buy it, should it take a cut of the sale, even if that’s disclosed? I’m going to put enterprise aside. Consumers are going to increasingly value this technology. Take software developers today. Developers are willing to pay for these coding models because it really helps make them more productive. Nobody would want to code the way they were coding three years ago, or even the same time last year. When you’re delivering value, there’s value to be had. I’ve never seen examples where consumer products really scale to billions of users, where people are using it intensely, and haven’t had monetization to go along with it. If you’re providing value to people, there is inherent value to be had. The nature of it may change. You’re asking a scenario in which agents are doing stuff for you. At the end of the day, you will have commercial needs. The agents need to fulfill those commercial needs. The providers on the other side are going to see value in that. There’s going to be some mechanism by which it gets fleshed out. That’s what an efficient system does. But what matters is that you’re building those world-class consumer experiences. I promised myself one product feature request: Can you make Gemini-powered search in Gmail as good as searching Gmail through ChatGPT? What’s going on here? One of the exciting things we announced recently is the unified Gemini agent for enterprise, and we’re bringing that to consumers. These are persistent agents which run 24/7 in the cloud. We’re spinning up a micro VM effectively for every query, and from the runtime, the harness, the model—it’s all a headless service. It’ll work everywhere, and it has your context with you. You can address this agent whether you’re in Workspace, in Search, in Gemini. Agents are going to drive this unification from a customer experience, which people are looking for. When you have a broad product portfolio and you’re moving forward fast, there are always areas where you’re moving forward faster than some other areas. That’s valid feedback. But we are putting a lot of time and effort into making sure, as we build out our agentic experience, that unification and that capability works well across all these services. People have asked where Google’s equivalent of Muse or Dots is. Do you anticipate creating a new product to compete with them? Our goal with Gemini is to be a personal, proactive, and powerful assistant. That’s what we are working toward. You saw us describe the early versions of it at Google I/O this year with Spark. Effectively, already, if you open the Gemini app as a Pro or an Ultra user, you see Chat and Task, and they start giving you that agentic experience. With Argon now, that capability is dramatically accelerated. What we announced recently in our enterprise event, we are bringing that to consumers as well. Soon in Gemini, you will be able to ask something, and it’s a continuum. You don’t need to think about whether you’re asking a chat or an agentic task. It’ll just automatically do the right thing, and it can work persistently in the background securely and get things done for you. Already, empirically, when I look at what’s coming into Gemini in our app, last year you would have tagged them and most of them would have been chats. But a steadily growing percentage is already agentic tasks in Gemini. Yes, we are going to bring personal agents to our consumers. It’ll work in the Gemini app. It’ll work in Google Search. It’ll work in Gmail. You can address it, call it, etc. We want to make sure we’re getting it right. It’ll be powered by Argon, and we are very excited to bring it to our users. Google has invested and committed tens of billions of dollars to Anthropic. What do you see in Dario Amodei that makes you want such a deep investment and partnership? Very early on, when we looked at the founding team of Anthropic, it was very clear to us this was a world-class team which was involved in some of the earliest GPT models. We were quickly able to assess that they would be at the frontier. We’ve always tried to identify and partner with companies which would be at the cutting edge of technology. We’ve done that with SpaceX. We’ve done that with Anthropic. That’s what we saw, and we had a chance to partner. Is your partnership with Apple to license Gemini for Siri expanding, contracting, or standing still? For both us and Apple, that partnership was about translating the capabilities of AI into better experiences for users. I thought Apple has done a phenomenal job of incorporating—they customize these models and build it into Siri—and it’s been great to see the reception. We are committed to doing more. You’re going to do more with them? There’s nothing specific to comment, but it’s a partnership. It’s working well. What have you learned so far about the feasibility of data centers in space? It’s a very exciting project. Projects like this remind me—I still recall that moment in the desert for the DARPA Grand Challenge, watching the Stanley car compete in it, which ended up being the basis for Waymo. With all these technologies, you’re at a moment in time where it looks a bit like sci-fi, but 10 to 20 years down the line, it’s actually working in a meaningful way. I feel the same in space. We’ve always believed in betting big on these technologies. Quantum has been a great example of it. We’re probably 10 years into the journey. Waymo, we are almost 20 years into the journey. Space, we are two to three years into the journey. It’s clear to me that it’s an approach which will make a lot of sense in the future. Being in sub-synchronous orbits with 24/7 access to the most abundant form of energy we have access to will make a lot of sense for this exponential increase in compute that we are going to need. Having said that, this is a challenging project. The team has disciplined milestones. It was a super exciting milestone to send the TPUs up to space. Sometime, as we are speaking, we are expecting a “hello world” moment from them. I look forward to seeing that happen, and we’ll continue investing here too. You previously said 25% of the code written within Google was written by AI. What’s the number today? That number in our world today is meaningless in the sense that every part of what we now touch is influenced by AI deeply. What we are tracking with Argon, for example, it’s been a point of inflection. People are using Argon to really go tackle complex projects, including projects which affect millions of lines of code. In the four weeks since we have launched Argon, we have seen a 30% increase in CLs, and more importantly, almost a similar increase—a one-third decrease in rollbacks. People are writing code more productively and better code with it. We are tracking it with newer metrics now. How are you dealing with opposition to data centers? If you can’t build in the U.S. the way you want to, where else do you go? We’re very committed to building our data centers very responsibly. When I look at our largest data centers in some of the communities where we have been the longest, I visit them, I talk to people. There are people who come and tell us their kids have gone to college because of our investments in these communities, and we’ve grown in those communities. To me, this is about doing it right. Engaging with the communities early, doing it transparently. We are committed to making sure we are bringing incremental power, and we cover 100% of the cost of our power needs for these projects. It’s understanding the impact on neighborhoods and making sure you’re working thoroughly to address them. Done rightly, you can continue making progress. But we are constantly taking feedback into account. I do worry not everyone operates the same way. People have been surprised by data centers showing up in their backyard. Those are all important. As an industry, we should continue to do better. But part of the reason, in a 20-year time frame, we are thinking about things like data centers in space is making sure we are able to put these data centers where maybe the trade-offs will be different. What unproven area of AI is Google working hardest to crack? Is it continual learning—getting models to keep updating after release—or something else? There are multiple parallel paths. You saw with Argon we have a million output tokens. With our Gemini models early on, we had a one-million context window on the input side. These are all examples of breakthroughs we are constantly working on. How do you think about synthetic data? How do you think about continual learning? How do you think about more efficient architectures? We do dedicate a meaningful percentage of our compute to these research bets too. It always goes hand in hand, including our world models. Those are all bets we are placing. Sometimes, as part of these bets, there are ideas that emerge which just make their way into the mainline models. Maybe we don’t fully talk about it, but we have multiple steps in these areas. Working in technology requires optimism that building something will improve the world, but those bets can leave real consequences when they fail. I recently returned to a town in El Salvador where enthusiasm for Bitcoin had given way to unfinished construction and something resembling a ghost town. How do you maintain the optimism you spoke about in your Stanford commencement address while accounting for those risks? I genuinely have a deep conviction that improving technology and improving access to technology makes people’s lives better. It’s been my personal experience in my life, out of which comes that deep conviction, optimism. But with that, I’ve always felt humanity’s journey is always about working hard to harness that technology to make it work well for society. The optimism is not an optimism that you just build whatever you want, whenever. You work hard to make sure you’re building technology in the service of humanity, to make the human condition better. That’s got to be the defining goal as we are approaching it. With that framework, I’m very, very optimistic. We are in very early stages of the kind of progress that we are going to see on many areas. We’re all talking about areas like healthcare. A lot of the progress is ahead. The ability to make things in a fundamentally cheaper way on many things where things have been getting expensive for a long, long time, discovering new materials, etc., which really bend the curve—it’s tough to step today and understand the impact of all that, but they’re going to be very profound. I’m very optimistic, but we have to be rightfully concerned about all the ways this technology can go wrong. Sundar, I know you don’t do many of these. Thank you. Thanks, Alex. Appreciate it.
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Last week I asked you what worries you most about AI in health. It was close, but your top answer was “AI giving bad health advice.” Last week’s poll results: You’re in good company. This concern was top of mind at a conference I joined at Stanford last week, where researchers, clinicians, and people who build AI systems spent a day debating trust and safety in health AI. Stepping back, I’m also hearing your emerging concerns about AI’s impact on our well-being generally. I see this as a matter of public health, and we’ll take it in pieces as we learn together. Today we’ll focus on AI chatbots used for therapy for mental health. Early this month, Governor Newsom vetoed Senate Bill 903, California’s most ambitious attempt to regulate AI use for mental health. It’s at the center of active debate, and raises the questions patients, family members, citizens, and voters will face as AI moves deeper into health care. Let’s work through it. What is “AI-based therapy”? It’s really two things: purpose-built mental health apps designed to deliver structured support, and general chatbots like ChatGPT, Claude, and Gemini that people increasingly use for emotional support—even if they weren’t built for it. Spoiler alert: I don’t know whether the shift toward AI therapy is good or bad. The evidence isn’t there yet. But I do know it’s already happening, and I have some ideas about how we can roll it out with safety in mind. The therapy dilemma, in a nutshell Mental health challenges are common, and strain health and well-being across populations. Therapy (interactive dialogue with a trained, empathetic practitioner) helps many people heal. The demand for licensed therapists far exceeds the supply in California, and AI-based therapy can narrow that gap. AI-based therapy remains novel, unproven, and not well tested. Right off the bat, we can see we’re dealing with trade-offs here. Since the main driver of demand for therapy from a robot is lack of access to therapy from a human, we’ll start there. It’s too hard to find and afford a human therapist California is short roughly 55,000 licensed, non-prescribing therapists and counselors, about 40% of what we need. Psychiatrists, who rely on talk therapy as a complement to prescribed medications, are even more scarce. Eight counties have none, and density ranges from 1.7 per 100,000 residents in San Benito County, to 68.1 in Marin County. My wife is a psychiatrist in Marin, and even here access is tight. If it’s hard in the best-supplied county, imagine rural Sierra County, or the San Joaquin Valley. In 2024, more than half of Californians who tried to make a mental health appointment struggled to find a provider who took their insurance, compared with 14% for physical health care. When Senate staff posed as “secret shopper” patients and called 120 mental health providers in Medicare Advantage networks, they landed an appointment less than once for every five tries. Insurers pay behavioral health clinicians about 22% less than medical and surgical clinicians for comparable office visits. On top of that, billing denials and prior authorizations hit hardest at the solo and small practices common in mental health. Psychiatrists opt out of Medicare at the highest rate of any specialty. In many places, a cash practice for therapy fills quickly with patients paying $250 or more an hour, with no insurance paperwork at all. Families who can pay cash find providers more easily. Those who rely on insurance, including Medi-Cal enrollees, older adults, rural residents, and non-English speakers, compete for what’s left. So we can’t pretend the human system works, and we can’t blame people for turning to AI for support when they’re in distress. AI is filling the gap It’s estimated that roughly a quarter of AI users use it for mental health support. If you have internet, it’s available 24/7, it’s cheap, and it speaks your language. Uninsured adults are about twice as likely as insured adults to do it (30% vs. 14%). It feels anonymous in ways that may enable people to ask things they’re unsure or embarrassed about. The largely hidden, personal use makes it hard to study whether it’s actually helping people with their mental health issues, but researchers are trying. Does AI therapy work? A 2026 analysis of nearly 40 randomized trials found chatbots showed a real but small reduction in depression symptoms. But most of these trials were compared to an alternative of no therapy, not a human therapist. So the benefit of AI therapy is probably real, but modest, and mostly untested against the alternative of human contact. We need more studies. Serious concerns OpenAI’s own estimate is that more than a million ChatGPT users each week show signs of suicidal planning or intent. These are high stakes encounters, and AI doesn’t always get it right. That’s partly because a chatbot can sound like a therapist, but it has no license, no duty of care, and it’s not clear who to hold accountable when it gets things wrong. AI is also designed to help users in whatever task they’re seeking to accomplish. People may feel like they’re getting therapy, but they’re mainly getting a convincing conversation. It’s easy for an AI companion to wander into gray areas, like playing along with “creative writing” that may be about the user’s own suicidal intent. Courts are driving safety conversations A wave of lawsuits have been filed on behalf of families claiming that chatbots pushed people they loved toward self-harm, including teens who died by suicide. The first settlements came this year. OpenAI is headquartered in San Francisco, and dozens of cases alleging that ChatGPT drove users to suicide or self-harm are now before a San Francisco judge. Character.ai has filed for bankruptcy after several states brought lawsuits accusing the chatbot of pushing dangerous content to kids. But the technology seems to be improving. Last month, an independent evaluation found that newer AI models “almost never endorse suicide.” That’s progress, but our societal appetite for even one preventable death is very low. So how do we move forward? Finding a middle ground Earlier this month the governor vetoed Senate Bill 903, a bill designed to place guardrails on the use of AI for therapy. The bill would have prohibited any AI-based platform from being advertised as psychotherapy, and would have required a licensed professional to review and approve each encounter where AI was used to support therapeutic decisions. Newsom called it overly broad, since even routine screening questions would have needed clinician sign-off. Knowing we’re already short on mental health providers, requiring human review of AI encounters may not relieve our access problems. Still, the answer isn’t no oversight. Here’s one feasible short term solution. Rather than reviewing each encounter, a licensed clinician can design the guardrails: what counts as a crisis, what triggers an immediate handoff to a human, and what gets sampled for audit each month. The AI runs inside that structure. Conversations get checked, failures get reported, and a real clinician owns the framework. Medicine already has models to learn from. The FDA process for rolling out newly approved medications is an example. After a product reaches patients, we keep watching for adverse events. Patient-facing AI needs the same “digital pharmacovigilance”: systematic tracking of harms, near misses, and the types of groups that get hurt and why. It’s also critical that users know that they can reach a human if they’re in crisis. Most chatbots now include messages like these: Bottom line The rush to use unproven AI tools for mental health support is mostly a workaround, a sign of a health system people can’t get into. We need better access to proven care from licensed clinicians. That takes reimbursement that makes accepting insurance viable, and a bigger workforce. But, built and tested well, AI could be part of the answer. It could be the 2 a.m. check-in when no one else is awake, or a way for patients to practice skills their therapist taught them. It could be the first step to get someone the right level of care, and take paperwork off clinicians’ plates so they spend more time with patients. None of this is simple. The SB 903 veto doesn’t end the debate, and the legislature will be back. In the meantime, those of us using these tools need to stay informed, ask hard questions, and hold the companies building them accountable. I’ll try to do my part, and we’ll keep learning together. Love,Matt P.S. If you or someone you know is struggling with mental health challenges, emotional distress, substance use problems, or just needs to talk, call or text 988- you’ll reach a real person— or chat at 988lifeline.org 24/7. Dr. Matt Willis is the author of Your Local Epidemiologist in California. A California native, he’s served as a primary care physician, CDC epidemiologist, and public health officer for Marin County, where he guided the pandemic response. He lives in Marin with his family and their dogs, Teddy and Ramona. Thank you Cynthia He, MD, PhD, and Parnika Saxena, MD, for their guidance in preparing this article. Cynthia works in an outpatient mental health clinic, treating patients who have Medi-Cal or, sometimes, no insurance. Parnika works in an inpatient psychiatric unit, treating patients with public or private insurance or self-pay ability. All opinions are their own and do not represent their employers.
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