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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.

Oct 20 • 1:47 PM EDT • Technology • bigtechnology.com

Coming to XBOX Game Pass: Final Fantasy VII Remake Intergrade, The Séance of Blake Manor, and More

Ghosts to question and a planet to save. Sounds like a perfectly reasonable few weeks to me! We have another batch of games coming soon to Game Pass, so clear a little room in that “Play Next List” and let’s get to it!

Oct 20 • 12:00 PM EDT • Technology • news.xbox.com

Ubisoft plans cuts at German studios, adding to wave of workforce reductions

Ubisoft is planning to reduce its workforce in Germany, where it operates a trio of studios, by about 100 workers, Game File has learned. The company wants to offer employees buyouts to, should enough people accept, avoid having to lay anyone off. “In Germany, we initiated discussions with the Works Council last week regarding a proposed voluntary departure plan across Ubisoft Dusseldorf, Ubisoft Berlin & Ubisoft Mainz,” a company rep told Game File. A “works council” is a union-like group representing employees. “The voluntary plan is the primary measure being proposed to support the intended organizational changes,” the Ubisoft rep added. The company declined to confirm the scale of the potential headcount cut. Of the impacted studios, Mainz might be the best known to the public. It leads development of the Anno series and shipped the well-reviewed strategy game Anno 117: Pax Romana in November 2025. Berlin and Dusseldorf operate as support studios within Ubisoft’s creative network. Berlin recently supported the development of Rainbow Six Siege and the Rayman Legends Retold remake. Dusseldorf also contributed to those projects, as well as The Settlers and Ubisoft-branded escape rooms. Dusseldorf is the biggest of the three studios, with some 400+ workers, compared to just over 100 at the other two, a source familiar with the reductions and the teams told Game File. More than half of these new reductions of about 100 people are expected to happen at Dusseldorf. The planned cuts were announced at a town hall for the German studios on Tuesday of last week. In an email follow-up, the studio group’s managing director, Benedikt Grindel, said the cuts were intended to “best position our German studios for the future... with a clearer focus on their role, capabilities and organizational setup needed for the work ahead.” The email was an echo of the past. In January 2025, Grindel had emailed the Ubisoft developers in his region (or studio “zone”) to explain that a round of 185 cuts at the time, including about 65 at Dusseldorf, were “essential to improve our operational efficiency and ensure the long-term sustainability of our studios, the zone and the company.” A half-decade of continued job cuts Ubisoft has been aggressively cutting its headcount for years. At the end of September 2022, it had 20,729 workers. By the end of March 2026, that total was down to 16,590. Studio management is trying to trim costs as it grapples with a changing industry and a drought of releases, let alone hits. The company announced earlier this year that it had reduced its costs since 2022 by “nearly €325 million” ($362m) and planned to lower them through 2028 by another €180 million ($200m). In June the company said it was closing down its Winnipeg and Belgrade studios and cutting roles in its Barcelona team. Even commercial and creative success hasn’t spared studios from this ongoing company-wide contraction. As Game File was nailing down details around the proposed cuts in Germany, Ubisoft management was informing workers of cuts in its Singapore studio, which shipped a hit remake of Assassin’s Creed IV: Black Flag this past July. Some 70 positions in Singapore are being eliminated “to align the studio with upcoming production needs,” per a company rep’s statement to Game File. In early 2024, a large team in the Singapore studio launched the multiplayer pirate game Skull & Bones. It performed poorly but has been supported with regular content updates since. Many of Ubisoft’s studios around the world are heavily subsidized via regional grants and tax breaks. Singapore had been among them for years, but received no such local assistance in the 12 months ending this past March. News of the Singapore cuts broke today alongside word that Ubisoft is also proposing the closure of RedLynx, pending negotiations with officials. The Finnish studio behind the motorbike series Trials was founded in 2000 and sold to Ubisoft in 2011. As reported by VGC (and corroborated by Game File), Ubisoft management told employees it could not come up with a new, sustainable assignment for RedLynx. The 2025 workforce at Ubisoft Dusseldorf were straight-up cuts. This time, Ubisoft is hoping the reductions at its trio of German studios will largely come from workers taking a payout to leave. The company offered such voluntary dismissals at its Massive Entertainment studio (The Division) a year ago. For the German studios’ buyouts this time around, Ubisoft and the Works Council had discussed a formula that’d pay a worker’s monthly salary multiplied by 1.5 and by the number of years they’ve worked at the company, according to Game File sources. The formula is still subject to negotiations with the Works Council and has not yet been part of an official offer to workers. It could be adjusted up or down, and it could be supplemented with additional bonuses. Asked about the floated payout formula, a Ubisoft rep told Game File, “The terms of a potential voluntary departure program remain subject to negotiation with employee representatives. No final agreement has been reached, and we won’t comment on specific proposals while discussions are ongoing.” Game File is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe Item 2: Epic’s AI adventures Epic Games (Fortnite, the Unreal engine) is all-in on using AI to try to transform the way it makes video games and game-making tools. It’s leaders appear to be undeterred by any resistance among gamers and some game developers around generative AI—and instead emboldened by advances, particularly in AI-assisted programming. A recap of the last three days: On Monday, Bloomberg reported that Epic management told workers last week that development of an upcoming game combining Star Wars and Marvel characters would be “AI first.” Employees would be expected to use AI models such as Claude to code the game. A manager told Epic’s developers there would be “[n]o more hand-written code” and that art for the game would be “generated with full AI workflows,” per the outlet. Just ahead of that story running, Epic founder Tim Sweeney tweeted: “We have a huge pivot underway here at Epic, moving developers around to retool for maximum AI acceleration. The aim isn’t to do the same stuff with fewer people, but to enable everyone here to build MORE and BETTER stuff, and to bring the benefits to all teams using Unreal.” In a follow-up, Sweeney predicted that most game development processes would be at least three times faster a year from now than they were in January 2026, thanks to AI. To a person on X who told him he was failing to read the room, Sweeney said: “Much of the room wants it to be 2016 again, but it’s not. Our job, as the provider of tools and technology to much of the industry, is to design for the present that is and the future that will soon be.” On Wednesday, Epic announced it was releasing a faster launcher for its Epic Games Store marketplace. It did so by stating in a blog post as clear as could be that the launcher is an AI-enabled, vibe-coded project: Programming is hard. So, instead of doing that, we vibe coded a new Epic Games Launcher that’s way faster and more responsive than the current one, and has a new look. Programming is hard? That was unexpected phrasing from a company founded by a legendarily great programmer. Back in 2011, Sweeney told me that he’d picked up programming quickly as a kid and rejoiced at the power it gave him. “I could look at any program that anybody else had written and, with enough effort, build something cooler.” Item 3: The Xbox Labs experiment (and one of the people behind it) Microsoft’s Xbox division, which is flooding the zone with announcements lately, yesterday revealed a new internal tech-tinkering team called Xbox Labs. In the words of Xbox boss Asha Sharma Labs is “a small group of game makers, engineers and scientists whose job is to turn emerging ideas into things our players and developers can experience.” Conventional wisdom coming out of the announcement is that Labs is a team that’ll focus on ideas driven by generative AI. In her announcement, Sharma cited some of the dizzying achievements in tech and gaming over the past month as AI agents and other AI tools have been deployed to swiftly figure out how popular games work, then rebuild them and mix them up. She mentioned seeing ”games rapidly decompiled and rebuilt to run on hardware they were never originally designed to support” and said “[p]erhaps more surprisingly, we’ve seen many of the biggest games in the world have been remixed into the source code of another.” (Minecraft plus Elden Ring, for example, or Fortnite + Super Monkey Ball + Overwatch + Diablo + others.) Sharma also cited the work of Windows PC games being ported to run in browsers, a seeming reference to a recent AI-assisted fan port of Halo. Or perhaps Call of Duty or GTA. Sharma didn’t use the specific term “AI” in her memo, but she came close, citing AI “agents” and their potential for game-making tools that Labs could help craft: I think XBOX has an important role to play here. Coding became the first major workload for agents because they can write, run, and test software. Games share many of those same properties, and people spend more than 72 billion hours a year playing XBOX games, roughly five times the estimated time spent writing code worldwide. We’ve spent 25 years learning how to make great games, and I want our game makers to help shape what these new technologies make possible. As we learn, we’ll share the tools we build with developers across the industry, just as we always have. When I asked an Xbox rep if Labs was an AI-centric team, they said genAI was not the focus, describing it as having a broader mandate for using new tech in new ways in gaming. As an example, they pointed to Xbox’s recently launched disc-to-digital gaming usage rights program. Microsoft didn’t specifically name anyone involved full-time in Xbox Labs yesterday, but on social media and LinkedIn, I spotted Adrian Mato IDing himself as leading the creatio of tools at Labs and talking up the launch. Mato first caught my attention in late September, when he said on X that he was “starting to build a new team at Xbox focused on products and tools for how games get built and distributed as agentic tools become more capable.” At the time, he framed AI-based tools as a supplement to traditional game dev tools. “I don’t think we need to replace them,” he said. Since then, he’s publicly shown interest in AI and in games. He’s recently spent some of his X feed marveling at the power of AI agents and praising the lighting in the latest Gears of War. Following the Labs announcement yesterday, he enthused about the Xbox Labs’ stylish landing page. He wrote: “We built some fun tools to art-direct shaders in real time, experimented with interactive ASCII art, and sprinkled in lots of tiny details. “Move your mouse to leave a colorful trail that reveals the original footage, or interact with the video to trigger bursts of particles. “A lot of love went into making it all feel just right. “More to come”

Oct 14 • 4:31 PM EDT • Technology • gamefile.news

Ryan Seacrest’s Six-Pack Did Not Come Easily: ‘500 Crunches a Day’ and a ‘Perfectly Calculated’ Diet

Oct 12 • 8:18 AM EDT • Entertainment • yahoo.com

Vibe-Coded Browser Ports Of Games Work Perfectly, Unfortunately

Call of Duty: Black Ops, Halo: CE, Grand Theft Auto: Vice City, Skate 3 and many more titles have been decompiled and ported to web browsers overnight

Oct 9 • 5:45 PM EDT • Technology • kotaku.com

What If You Perfectly Remembered Every Day of Your Life? Turns Out, It’s Not So Nice.

One teenager’s mind replays the past 5 to 7 hours a day. Scientists just discovered what that costs him.

Oct 7 • 9:00 AM EDT • Science • popularmechanics.com

Did the Tigers Fail With Tarik Skubal?

Wasted postseason dominance and a painful blockbuster trade define the Tarik Skubal era. Explore whether Detroit squandered a generational ace or played their hand perfectly for the future.

Oct 5 • 11:17 PM EDT • Sports • sports.yahoo.com

Physicists unveil the most accurate atomic clock ever ‪—‬ and it could literally redefine the second

Physicists have used radioactive lutetium to create what they say is the most accurate atomic clock ever, keeping time perfectly up to 19 decimal places.

Oct 1 • 1:11 PM EDT • Science • livescience.com

Wesley to return to Gasperini’s disposal next week

Roma has started the season almost perfectly: four wins and a draw against Inter in the first five matchdays. Where can the team improve? Some individuals haven’t yet shown their best form, includin...

Oct 1 • 3:35 AM EDT • Sports • sports.yahoo.com

The Family Business Advantage: Third Quarterly Power Breakfast of 2026 explores how family businesses balance legacy and innovation

Photo by GFBJAnother successful Power Breakfast is in the books. The Greater Fayetteville Business Journal hosted its third quarterly Power Breakfast of 2026 on Tuesday, Sept. 29. Local leaders and decision-makers gathered to network and learn from the panel about how established principles drive long-term stability while allowing for operational evolution. The panel featured Managing Partner of Luigi’s Italian Chophouse & Bar, Nate Ernst; Inside Sales at Hubbard Supplyhouse, Mary Grace Hurley; Chief Operations Officer for Copiers Plus, Drew Smith; and CEO of Holmes Security Systems, Stephen Wheeler. The panel was moderated by Lauren Willets, director of the Family Enterprise Center at UNC’s Kenan-Flagler Business School. Throughout the discussion, the panelists returned to a common idea: family businesses thrive when they preserve the relationships and values at their core while remaining open to change.Each panelist shared personal stories that carried practical lessons. Stephen Wheeler emphasized the role of relationships with customers and employees, saying, “What makes a family business successful today is relationships.” Mary Grace Hurley echoed Wheeler’s sentiment, describing relationships as a lasting inheritance, including a customer connection that began when she was young and continued into her working life. “If something’s given to you, that’s going to cost you too,” added Nate Ernst. He explained this philosophy by sharing that, in his view, earning responsibility builds credibility and confidence.Another hot topic was the idea of tradition versus change. For these businesses that have been around for a century or even more, it can be difficult to escape the “we’ve always done it this way” caveat, but these speakers emphasized how change has been an integral part of their growth and longevity. A family business can honor its history without being bound by it.“Innovation and tradition–they don’t have to be competing with one another,” shared Hurley. She pointed to changes in how businesses operate, including the adoption of computers and new management practices. Ernst stressed that adaptation is essential, while Wheeler described how his family’s electrical contracting business had evolved into a security-focused company. The details of the work may change, but identifying and upholding core values is key to embracing change while maintaining the character of a business. “I think it’s just important to remember why you started in the first place. What was your mission? What are your core values? And to reevaluate those,” shared Drew Smith.In addition to the first-hand accounts and wisdom shared by panelists, Moderator Lauren Willets recommended two novels during the panel for those seeking additional educational resources. Those two novels are “Hug of War: How to Lead a Family Business with both Love and Logic”by Cathy Carroll and “In the Company of Family: How to Thrive When Business Is Personal” by Melissa Mitchell-Blitch. Preparing the next generation requires more than handing over a title or company. Wheeler argued that future leaders need genuine passion and a vested interest in the business. The panelists were in agreement that succession is a process, and there is not one clear answer for how to perfectly prepare for it. Leaders must cultivate readiness, keep communication open and make room for the next generation to earn responsibility while being sure not to neglect the business in the here and now. “Today’s Power Breakfast was one of our best events yet! Between the passion and in-depth knowledge of the panelists, the great audience questions and the guidance and expertise of Mrs. Willets, the conversation was both engaging and informative,” shared GFBJ Publisher Faith Hatton. “A lot of small businesses in Fayetteville are family-owned and grown, and it’s great to be able to spotlight some of the businesses that have stood the test of time by strengthening the next generation of leadership and preparing them for ownership. I hope everyone received some inspiration and ideas that they can bring back to their own businesses and organizations!”We thank everyone who attended this Power Breakfast and hope you’ll all join us for the next Power Breakfast on Dec. 8. Details about the event will be shared on our social media and in future email updates. To learn about becoming a Power Breakfast sponsor, contact the Greater Fayetteville Business Journal at 910.240.9697 or marketing@bizfayetteville.com.

Sep 29 • 3:42 PM EDT • Business • bizfayetteville.com

Stanley Parable Co-Designer Perfectly Explains Why People Play 1,600 Hours Of Dota 2 And Still Hate It

This is exactly how I've felt

Sep 28 • 9:42 AM EDT • Technology • kotaku.com

It's Your Business | Couple brings gym to Fisher with Jacked Rabbit Fitness

Travis Enghausen and Ashley Warlow hope to have a grand opening on the weekend of Nov. 14 for their business, which they’ve decided to call Jacked Rabbit Fitness — perfectly

Sep 27 • 5:15 AM EDT • Business • news-gazette.com

🤯 Bonkers idea: club in Brazil loans out manager

Players who are loaned to another club — for example to gain match practice — are perfectly normal in the football business. Loans of this kind are part of everyday life.In Brazil, however, a high...

Sep 26 • 8:01 AM EDT • Sports • sports.yahoo.com

"Inter means family". The exclusive interview with Lothar Matthäus

"I am proud to be able to tell my kids and my friends that I played for this Club, that I was an Inter player." Those words perfectly capture the deep love and special bond Lothar Matthäus developed ...

Sep 24 • 8:00 AM EDT • Sports • sports.yahoo.com

Opinion: Kobach's politics of tragedy underline our nation's immigrant crime double standard

When a native-born American commits a murder, we generally understand the concept of individuality perfectly well. We ask about motive, rage, domestic conflict, ideology, drugs, mental illness, opportunity or the peculiar facts of that person's life.

Sep 24 • 4:33 AM EDT • Politics • kansasreflector.com

Calvin Klein Introduces the New Perfectly Fit Bra Starring Teyana Taylor

NEW YORK--(BUSINESS WIRE)--Sep 22, 2026--

Sep 22 • 9:34 AM EDT • Business • rutlandherald.com

Serie A: Roma and Inter Milan in a crazy draw!

A crazy top-of-the-table clash!On Saturday night, the showdown between AS Roma and Inter Milan gave us a completely wild 2-2 draw.Perfectly set on their way by a goal from Manu Koné in the sixth minu...

Sep 19 • 1:57 PM EDT • Sports • sports.yahoo.com

Analysis: Wake-up call for below-par Gunners

Arsenal's season had been going perfectly until this heavy loss - against a side who beat them by the same margin at Emirates Stadium in 2023. The Gunners had conceded just one goal in the league this term during their impressive winning streak, but the performance against the Seagulls was far from the standards Mikel Arteta has demanded from his side. Bruno Guimaraes was given his first league start, but the Brazilian failed to click with Declan Rice and Martin Odegaard in the middle of the pitch.

Sep 19 • 12:59 PM EDT • Sports • sports.yahoo.com

The Big Holes in Wearable Heart Rate Variability And Readiness Scores

On September 9th, Apple announced it was revamping its Apple Watch Health Sensing System, rolling out a Readiness score (0 to 10), and increasing the frequency of heart rate variability (HRV) outputs 24-fold. This can be viewed as upping its competition with various other consumer wearable sensors. These “readiness scores” as a composite of multiple metrics, with heart rate variability (HRV) being front and center for most. The majority of Americans are now using wearable sensors, which equates to well over 100 million adults. HRV and Readiness scores are increasingly being marketed as a measurement of autonomic nervous system health, a digital marker for future disease, a clock for biological age, and a holistic metric to promote healthspan and even longevity. (Apple also introduced a new longevity tab and “Health Age”.) None of this has been proven. In this edition of Ground Truths I am going to review what we know about heart rate variability and readiness scores. Heart Rate Variability HRV is the variation in normal heart cycle timing. The variability of the heart rate, the barely perceptible millisecond changes in time between consecutive heart beats (see R-R intervals in the Figure below, left panel), is due to interplay between the sympathetic and parasympathetic (vagal nerve) inputs. Distinct from heart rate, individuals with the same heart rate can have very different HRVs. It is a rough reflection of the autonomic nervous system (ANS) activity, inadequate to say whether a person’s ANS function is abnormal. For more than three decades, heart rate variability (HRV) has been measured and several studies have found an association of low HRV and clinical outcomes, particularly a link with higher all-cause and cardiovascular mortality. There have also been less well established links of low HRV to risk of early cognitive impairment, dementia, mental illness, Type 2 diabetes and substance abuse. An important reminder is that HRV is a surrogate marker without any established cause-and-effect relationship. If you increase your HRV, that doesn’t mean it will improve health outcomes. In fact, there is no hard evidence for that. All that work linking to health outcomes was done with electrocardiogram (ECG) derived HRV. Now, in the era of consumer wearables, this is getting assessed differently, by optical pulse (yes, the lights you see) plethysmography (PPG) or what is called pulse rate variability (PRV). They are not the same, as shown below (right panel) and only concordant when the delay between the ECG and pulse is kept constant, which basically means at rest. I should mention there’s also what I will call MPV, a mattress mechanical movement sensor, a derived heart rate variability, that companies like Eight Sleep use, even further away from directly measuring HRV. HRV has not one uniform measurement but many different types of quantification, such as RMSSD, the magnitude of difference between successive R-R intervals of normal sinus beats (N-N) or SDNN, the standard deviation of NN intervals, both in milliseconds. SDNN is one of the so-called frequency domain HRVs (others are LF, HF, LF/HF). Different wearable sensors use different metics; Apple has relied on SDNN and nearly all of the others use RMSSD, which is generally considered the more accurate metric. There’s also the different length of time measured, such as for a matter of minutes, all day, or an overnight’s sleep. Short measurements are especially problematic since they don’t capture enough of respiratory modulation and other factors that influence HRV. Share Ground Truths How well does HRV correlate with PRV? There are very limited studies, especially independently done. One that is commonly cited was conducted by Air Force researchers in only 13 healthy adults assessing Oura ring 3 and 4, Whoop 4.0, and Garmin Fenix 6 and showed a correlation coefficient of 0.88 to 0.97 and a mean absolute percentage error from 6 to 10%. The correlation is not a perfect 1.0, but there’s at least a fairly high level of correlation. HRV is supposed to increase during the night due to takeover of the parasympathetic nervous system, and higher during deep sleep. A recent example of my 1 week, all day “HRV,” and one during sleep is shown below. As you can see, the N of 1 data are inconsistent for the same days from different sensors (Oura, AppleWatch, Fitbit Air, and Eight Sleep) by patterns, absolute numbers, and comparison with prior days and weeks. The largest study in over 8 million Fitbit users (the old version, not Google Fitbit Air, introduced in May 2026) gives you a sense of the effect of age, sex, and the 2 different main HRV (here PRV) metrics, with RMSSD on the left and SDRR (=SDNN) on the right below. That study, from data collected in 2018, is a major outlier, since all the more recent ones are tiny with respect to sample size. Many of the companies have not had independent evaluation of their HRV, such as Eight Sleep, but have published a low standard error on their website. There are some other published studies on the correlation between HRV and PRV, but they are all small and only in healthy adults. A scoping review emphasized the lack of study in underrepresented individuals, including the aged, people of color (which affects the PPG signal), and individuals who are underweight or obese. Add the typical adult age 60 plus with one or more chronic diseases. For example, one study in over 900 adults found poor correlation of HRV and PRV, non-uniformly underestimated across many chronic diseases (cardiovascular, endocrine, neurological, respiratory, and others), concluding PRV is “an invalid surrogate for HRV.” A recent systematic review of 43 studies comparing HRV and PRV found reasonable pooled absolute standardized error (HRV as gold standard) but only 10 of the studies provided quantitative synthesis in ideal conditions. Their main conclusion was similarly cautious: “PPG-derived HRV [PRV] should not be regarded as universally interchangeable with ECG-derived HRV across all devices, populations, and recording contexts.” Factors Affecting HRV and PRV That gets me to the long list of factors that affect HRV (and PRV) besides the device, the type of measurement (RMSSD, SDNN or others), the person’s signal, the sensor site, the duration of data capture, if weighting by sleep stage is used, how artifact is processed and corrected. And this list is not complete!: Oura puts out data from their community of users (who input data) on what affects their overnight HRV. The factors currently provided are: no alcohol (increase 8%), melatonin (increase 2%, float tank (increase 2%), wine (decrease 4%), and party (decrease 14%) in overnight HRV. Must be some big parties! Subscribe What is a PRV measurement good for? It has been falsely characterized as an index of “autonomic balance” and a specific indicator of stress. A 2018 review of the studies available for HRV and its relationship to stress, not using any of the current wearables, found that stress can lower HRV. But so can many other factors. The non-specificity of the signal, indexed to the table above, is striking. Evidence from a UK Biobank study of over 46,000 participants with actual HRV looked at genetically predicted HRV, a genetic risk score, that failed to show the expected HRV-mortality link, indicating that _HRV is likely not causa_l, but rather a reflection of person’s physiologic state. A review of consumer wearable HRV data from 5 longitudinal studies showed that nighttime PRV was not associated with perceived stress, and surprisingly higher HRV, in the largest cohort (N=717 participants), was correlated with higher stress. An Oura ring cohort of 525 first-year college students found a link between overnight PRV and perceived stress, but that was also seen with resting heart rate, sleep, and respiratory rate. Several very small studies have examined the relationship of HRV and athletic injuries or guiding training with mixed, and predominantly negative results. HRV biofeedback training with paced breathing had no significant effect on reducing stress or raising HRV, as demonstrated with sham controlled trials. When HRV for multiple days showed a decline in conjunction with body temperature, the Oura ring published data for prediction of Covid. The WHOOP company sponsored an observational study, published in 2026, of 30,000 users for 72 weeks, without a control group, that reported reduced alcohol intake (5.8 % points) by self-report. That doesn’t tell us much, and particularly about the merits of HRV for behavioral change. If you use the same device and conditions as longitudinal trends for multiple (at least 2-3) weeks that may be the one way to get something useful from the measurements. Data for overnight sleep with minimal motion and using RMSSD is the best proxy for real HRV. The reason to look at trends rather than any given night is that it more likely represents something, even though you won’t know with certainty what the “it” is. Keep in mind there are no data, no peer-reviewed evidence, to show that HRV fluctuation in-person has any correlation with health outcomes. Share Readiness Scores These are proprietary scores that integrate different metrics for each of the wearables: no algorithms have been disclosed. They are unvalidated against health outcomes. In a review of 14 composite health scores of readiness and recovery, HRV contributed 86% to the scores, followed by reading heart rate (79%), physical activity and sleep duration (both at 71%). That review noted the substantial variability in measurement protools and lack of standardization. Sleep staging is notoriously inconsistent and inaccurate by these sensors, which adds further to the HRV uncertainties for what the scores, which use sleep stage data, mean. Only resting heart rate has been shown consistently across devices to be extremely accurate. I’ve made a Table to summarize what we know about which metrics are included, the scores, any peer-reviewed studies that compared the readiness score with health outcomes, and the corresponding (if any, NA-not available) citation. You will note that some companies do not use the term “readiness," such as WHOOP for recovery, and Garmin, which has 2 different scores, one of which is Body Battery. Eight Sleep uses the term “Fitness Score.” They all include HRV; Apple includes a new metric they call “Recovery HRV” which among other components uses 7-days of sleep, but it is unclear what this means or how it is differentiated from other scores (there are clearly no data for outcomes). We have no knowledge of how the different components are weighted or whether any of these scores are better than resting heart rate, HRV alone, physical activity, or any other single metric. Since none of these are standardized, they are not interchangeable, so if you get a 90 for Oura that has no relationship to a 90 on a Google Fitbit Air. Notably, the company can update its algorithm for readiness score at any point without notification to device users. Without any useful evidence of actionability for these scores or established relationship with health outcomes, it is hard to make a case for their value. At the Apple recent announcement they showed their Readiness score (0-10) on the watch (Figure below) but there are no published data on this score, not even on their website. It’s available only on their new Watch Series 12 or Ultra 4 [of course, ;-)]. That exemplifies the problems with these scores, lack of data and evidence for being meaningful to promote health. Perhaps the best study (which isn’t saying much) is the WHOOP Recovery for golfer performance, because it did correlate with an objective outcome, even though there was no control group and the authors were all from the company. Among the 389 pro golfers, an absolute 10-per cent point increase in Recovery score was associated with about 0.5 fewer strokes per round. But that’s hardly a health outcome! WHOOP is also conducting a study in over 2,700 runners to see if their recovery score will be linked to less injuries and improved performance, but that is not yet published and has no control group or randomization. Putting This in Context For two decades I’ve been enthusiastic about the potential for digital health and particularly wearable biosensors. Over the years, we’ve seen some great progress for their ability to promote physical activity and accurately detect atrial fibrillation (the first FDA cleared deep learning AI for consumers). That work was the subject of rigorous research. But there are holes in the data and evidence for other metrics. One notable one is the “VO2 max” story that I wrote about earlier this year. At that time many subscribers asked me to cover heart rate variability, which I finally got to here. When I dived into the research and publication for HRV and readiness scores, I expected to find at least some that were of high quality and demonstrated their utility by linkage to health outcomes. To my surprise, I found none. The wearable sensor measurements for HRV (PRV) are, for the most part, accurate, but that validation work has only been done in small studies of healthy adults and does not take into account the long list of factors, from the device, software side, and the user side, that affect HRV measurements. Moreover, this metric chiefly relies on optical sensing and, as we have learned for heart rate PPG sensing, may be less accurate in people of color. Keep in mind that all of the health outcome association evidence comes from ECG-derived HRV; none are from wearable sensor data. I will repeat the key point: there's no peer-reviewed evidence to show that in-person HRV fluctuation—or efforts to raise your HRV— has any correlation with health outcomes. For those of you who look at your HRV on awakening, or even 2+ week trends of it being low, I hope this context helps to relieve any anxiety. Yes, low HRV (not PRV) has been shown to increase risk of some diseases as summarized above. But efforts to raise your HRV—a surrogate metric— has not been established for improving any health outcomes. HRV does not have any evidence of causality (the genetic evidence actually goes against this possibility). In this summary, I have not included data for other wearable sensors such as Polar, Samsung, Withings, Suunto, Amazfit, Coros, Ultrahuman, or additional mattress sensors. These are beyond my first-hand experience, and as far as I know from my in-depth review none have any peer-reviewed published data that differ from the 6 sensors I’ve reviewed here. From the points I’ve gone over above, we’re not ready for readiness scores. Besides being proprietary, they are predominantly based on metrics that have their own issues. It’s compounding the problem, like building a house without a solid foundation that has never undergone a rigorous inspection, and then selling it. Like I mentioned for PRV, you can look at trends over weeks rather than any single day, to get a handle, but even that may not be helpful. There’s simply no evidence that these scores meaningfully relate to health outcomes. I’d emphasize they might, but that requires doing prospective or randomized studies to prove it. None exist. There’s great promise for HRV/PRV utility_. For example**,**_ Prof Maiken Nedergaard, who discovered the brain glymphatics that are essential in eliminating metabolic waste products from the brain during sleep, has posited that HRV could be a non-invasive marker for neuromodulator oscillations, brain-body regulatory circuits, and brain clearance. That would be extremely useful, but like everything else on HRV and readiness scores it requires solid research and validation. The lay media isn’t helping much to get the story straight. Earlier this year The Economist published a piece entitled “The most useful indicator of your overall health” which ordained HRV as an “accumulated stress score.” That’s akin to the false assertion about VO2max: “V02 max is the singular most powerful marker for longevity.” As I’ve summarized here, that is not established. The fact is that so many things can lower HRV, including physical exercise (especially an intense workout), reduced sleep quality, stress, the list above, no less the device, signal, and software. Whatever fluctuations observed have not been correlated with any health outcome. Sadly, “datamaxxers” are widely using HRV and readiness scores that have never been validated to mean anything. We already know that for some people using the sensors for sleep metrics, it can induce “orthosomnia,” an obsession to get high sleep quality, with associated high levels of anxiety. In an experiment done by a company to promote sleep quality for its employees, “For those employees who did use the trackers, many reported feeling perfectly rested until their tracker told them they had had a terrible night. Others were told that they had slept like a baby when they had actually been lying awake worrying about the quality of their sleep. “ The same problem can result from preoccupation with HRV or readiness scores, with anxiety that would lead to further reduction in both. It you are using a wearable like >100 million American adults, it’s OK to look at these data, but contextualized with the major caveats reviewed here. If you are one to require evidence that HRV or readiness scores are linked to health outcomes, you may not even want to look. The companies make it hard to turn them off! Let me end with the companies that make and sell wearables. Apple’s doubling down on HRV (24-fold more reporting and heart rate very 5 seconds) and introduction of a Readiness score tells us that consumers have bought into these metrics and they are joining the club. However, all of this is occurring with a backdrop of tens millions of users, claims about the data that are not backed up by adequate evidence, marketing way out in front of whatever limited data exists, and not being transparent about their readiness score algorithms. The companies can well afford to do the research that is needed to connect these metrics with health outcomes show, once and for all, that increasing HRV or using readiness scores promotes our health. If they believed and invested in the products they are selling, we’d not be in this position of not knowing. That’s essentially where we are with HRV and readiness scores. Perhaps someday this will change and we’ll have good reason to embrace them. NB: I wrote this post. No AI. I have no conflicts of interest with any of its content. Loading... Ground Truths has 215,000 subscribers from every US state and 214 countries. There are over 300,000 followers of Ground Truths so more than 90,000 folks who can easily convert to be free subscribers. Your subscription to these free essays and podcasts makes my work in putting them together worthwhile. If you’re not a subscriber, please join! If you found this interesting PLEASE share it! Share Ground Truths The proceeds from all voluntary paid subscriptions go to support our summer internship program. It enabled us to accept and support a record number of 62 summer interns that joined us in 2026! These are high school, college and medical students selected from thousands of applicants. We couldn’t do this expanded program without the funds coming in through Ground Truths. Thank you!

Sep 19 • 10:05 AM EDT • Technology • erictopol.substack.com

'A Talent for Murder' review: Alden Ehrenreich gives sexy menace in this Patricia Highsmith-inspired thriller

All hail Helen Mirren, who is perfectly cast as an iconically vicious dame.

Sep 19 • 5:00 AM EDT • Entertainment • mashable.com

Scientists find that “perfect” systems may be surprisingly fragile

Complex systems may work better when their parts are not perfectly alike. Northwestern physicists found that carefully balanced variation, or “disorder,” can make networks such as power grids, ecosystems, neurons, and materials more stable. Even random differences sometimes improved stability compared with completely uniform systems. The discovery could help researchers design more resilient technologies and explain why natural systems are so rarely perfectly uniform.

Sep 18 • 8:00 PM EDT • Science • sciencedaily.com

Huge Steelers TE has perfectly acceptable excuse for practice absence

The Pittsburgh Steelers saw one of their biggest players miss practice ahead of Week 2, but he may have a perfectly good excuse.

Sep 18 • 12:01 PM EDT • Sports • sports.yahoo.com

Countdown to Stuttgart: TV coverage, kits, record

Head-to-head: Strong runThe Bundesliga record between BVB and VfB Stuttgart is almost perfectly balanced: in 112 meetings, VfB Stuttgart have won 44 times, Borussia Dortmund 42 times, and 26 matches e...

Sep 18 • 4:40 AM EDT • Sports • sports.yahoo.com

This new Android skin copies iOS 27 so perfectly, it might even fool Apple

HONOR has revealed Magic OS 11 based on Android 17, and while it brings new AI features, it also copies Liquid Glass down to a T.

Sep 17 • 3:47 AM EDT • Technology • androidauthority.com

Europa League opener perfectly timed for Celtic after Ibrox shambles

A European clash before a vital Glasgow Derby is usually seen as a hindrance, but this is one that could benefit Celtic greatly…Martin O’Neill, Manager of Celtic, arrives at the stadium before the...

Sep 16 • 2:20 AM EDT • Sports • sports.yahoo.com

Andy Roddick pinpoints what Alexander Zverev did ‘perfectly’ against Ben Shelton in the US Open final

Like London buses, two Grand Slam titles have come along at once for Alexander Zverev after spending over a decade in pursuit of his first....

Sep 14 • 6:00 AM EDT • Sports • sports.yahoo.com

The System Is Not the Patient

Across OECD countries, healthcare spending has long grown faster than GDP, while health-adjusted life expectancy has stagnated or declined and up to 20% of spending remains directed to low-value care. Outcome-based care measures success by patient outcomes relative to resources used, yet no health system has implemented it end to end. The World Economic Forum convened expert workshops to identify practical solutions. This paper argues that full system redesign, perfectly aligned incentives and flawless measurement systems are not prerequisites for progress, highlighting instead six interconnected priorities. The challenge is now to connect existing building blocks into a coordinated system focused on improving patient outcomes.

Sep 13 • 8:00 PM EDT • Health • weforum.org

Postpartum politics: What it means to run for government as a new mum

Victorian Greens candidates Nina Crawley and Lini Fernando reflect on their experiences running a political campaign just months after giving birth. From the challenges and compromises to the support networks that wrapped around them, and why showing up vulnerably in public space matters. Subscribe In 2024, when my second child was four months old, I decided to run for local government. Fast forward two years, I am again four months postpartum, with my third child, and this time running in the state election. But this time has hit differently. Going through pregnancy, birth and postpartum while being a political candidate has had a more profound and personal impact than I ever expected. I reached out to my fellow Greens candidate for the state election, Lini Fernando, who gave birth 20 days before me, and her experience closely mirrored mine. Postpartum has its own unique challenges that we’ve not seen reflected in stories about women running for office. When people discuss the “juggle”, they’re usually talking about the practical mechanics of looking after small children - childcare arrangements, pumping schedules and logistics. As mums of multiple children, we were both aware of the logistical changes and difficulties that accompany giving birth and the months after. We assumed that combining this with running for election would be hard, but that we could organise our way out of any problems. What neither of us anticipated was the intense vulnerability that comes with putting yourself into the spotlight of a state election campaign at a time when you are physically exhausted and navigating things like breastfeeding and feeling like a stranger in your own body. There have been challenges that neither of us anticipated. Firstly, needing to buy new clothes because the supportive leggings that are a staple of the postpartum wardrobe weren’t going to cut it for community events. Lini held her first fundraiser - a trivia night - just three weeks postpartum. A business-casual wardrobe, several sizes bigger than pre-baby, was needed, and fast. As women, how we look is closely scrutinised, and the internalised fatphobia is loud. We are conditioned from our earliest days to believe that our appearance is tied to our worth, and taught how we are allowed to show up in public. Women who dare to put themselves into the spotlight also receive more abuse than their male counterparts, including criticism of their appearance. To stand in front of members of your community and try to convince them that you’re the best person to represent them in parliament is a challenge in and of itself. To do it with leaking breasts, a pelvic floor that could betray you at the first sign of a sneeze, a body that still feels unfamiliar, and with half your hair falling out (hello postpartum hair loss!) is another thing altogether. It requires courage, allowing yourself the grace to be vulnerable in public, and the belief that showing up, however imperfectly, matters. And the vulnerability isn’t just physical; it also includes factors like having to ask a campaign team to accommodate the realities of having a baby. Asking for help is hard, and it’s a skill that both new parents and hopeful political candidates need to become comfortable with. Parents understand the disappointment that comes when a call for help or connection is rebuffed, like a friend cancelling a much-needed coffee. That rejection is amplified for political candidates calling dozens of people each week. Many people don’t realise running for Parliament, when you aren’t the incumbent, is a volunteer role supported largely by other volunteers who have to squeeze volunteering in around their paid work. Calls need to be made and meetings held after work hours - usually during “witching hour” (5-8 pm), that special time of night when babies cry for no reason. I found that I was struggling to make these commitments, or having to tap out and ask for them to be rearranged. When I spoke to Lini, we realised that she had had a similar experience, this push-pull of being the candidate and wanting to show our faces at meetings and help recruit new volunteers at ‘calling parties’, but also being the only people who could settle our newborns. Sometimes we could merge the two worlds; Lini and I realised that we had both attended 7:30pm meetings online, sitting in a dark room, struggling though breastfeeding and settling our babies. Although we’re both committed to breaking the stigma around breastfeeding, it can be hard to keep your composure while trying to feed a young baby as they try to latch, spew and fuss. Ultimately, Lini and I agreed that showing up vulnerably in public space matters. Leaders should possess empathy and understanding for those who feel raw and in transition; from the dad who just got evicted to the teen who acquired a disability - anyone who feels they aren’t the version of themselves they expected right now. The patriarchy tries to tell us otherwise, but women shouldn’t have to wait until they’re ‘recovered’, no longer vulnerable, before they feel entitled to participate in public life. In fact, we could all benefit from our leaders being a little more vulnerable. *This piece was cowritten, but has been presented in the first person for the sake of style. About the authors Nina Crawley is former teacher and community organiser who now works in human rights and inequality advocacy for a non-profit. She’s a mother of three keen to get back to running as soon as her pelvic floor allows! She’s contesting the seat of Ivanhoe for the Greens in this year’s state election. Lini Fernando is a lawyer working in diverse areas of law including anti corruption and public law. She’s a mother of two and a gardening and good policy enthusiast. She is currently contesting the seat of Croydon for the Greens in this year’s state election. Thanks for reading Cheek Media Co. ! This post is public so feel free to share it. Share

Sep 10 • 10:31 PM EDT • Politics • cheekmedia.substack.com

Complete Your Evolution With “Perfect Eclosion – Perfectly Ultimate Great Moth”! [YA01]

The Perfectly Ultimate Great Moth takes flight!

Sep 9 • 8:18 AM EDT • Technology • ygorganization.com

College Football Has Completely Lost Its Mind

College football has been back for one full week . That’s it. And somehow we already have a tweet perfectly summarizing just how ridiculous this season has become. LSU is dealing with a legal fight that has reached the point where SEC leaders could vote on whether to expel the school from the conference. Western…

Sep 8 • 4:05 PM EDT • Sports • sports.yahoo.com

The core anti-Pangram constituency is phony writers

Welcome to The Closing Argument, our verdict on the news, plus everything The Argument published and appeared in this week. The Verdict, by Kelsey Piper A lot of people hate AI for drowning the internet in slop and displacing human creatives. You might think that these people would be thrilled by a program that can identify and flag AI-generated work. But many people who hate AI also hate Pangram, an impressively accurate AI detector that produces very few false positives, according to high-quality independent research. This loathing found its most recent expression in a bizarre Wired article that amplifies a wide range of false arguments from Pangram skeptics: that AI detectors are racist (they’re not) or ableist (they’re not)1 or don’t work (this one does)2. Rather than starting from the premise that it would be really valuable to have a working AI detector and scrutinizing Pangram to make sure it lives up to that promise, the article starts from the premise that Pangram obviously has bad vibes and sets out to discover why. (One answer: The CEO microwaved lunch during an interview!) Why aren’t writers who resent Pangram more eager to distinguish themselves from posters who are having ChatGPT ghostwrite all their work? There are a few different things at play, but I think one key dynamic is this: These are often the same people. A core constituency for arguments like Wired’s is “writers” who are using AI to write their posts but resent this fact being made public. They stand to lose from AI detectors because AI detectors call them out. Share When Substack announced a partnership with Pangram, a sizable fraction of the platform’s authors rose up in outrage. Some people had experience with other AI detectors that didn’t work, and some felt that AI writing ought to be encouraged — both of which are fair enough. But the fury was fueled by authors passionately insisting that Pangram kept wrongly accusing them on almost every post they published. Pangram can be wrong, especially on short passages. But in the overwhelming majority of cases where Pangram looked at a whole blog post rather than a single paragraph, the program correctly detected work as AI. Even if we can’t be 100% sure in any individual case, we can notice the trend in the aggregate. Wired quotes Sam Illingworth, a professor of critical AI literacy at Edinburgh Napier University, who said, “I don’t think [AI detectors] work. … I think that detectors are prejudiced against certain people.” This spring, he said that he used AI for research and editing; this summer he acknowledged that in addition to research and editing, he also uses it “now and then for a turn of phrase I keep.” Pangram informs us that most of his posts are 100% AI-written (some are mixed AI-and-human) and that 100% AI-written posts started appearing last year. The favorable interpretation is that, as someone whose recent work Pangram consistently mistakes for AI, Illingworth sees Pangram’s flaws firsthand. The unfavorable interpretation is that the tool’s credibility trades off against his own, because its account of who writes his posts is not consistent with his. Illingworth is not alone in insisting the detector does not work. “It’s a Monday night in late July, and I’m pasting my own Substack draft into an AI detector for the first time. Not because I’m worried. Because I’m curious. I wrote every word of it myself, the way I write everything, out loud in my head first, in a rhythm that still carries the accent of a language I learned to think in before I learned to write in this one,” begins one Substack post by an author indignant that Pangram flags her writing as AI. “That’s the part I had to sit with before I could write this.” If you, like me, have worked with AIs a lot, you won’t even need Pangram there. The author of that piece is AI. It is obviously AI! I checked 10 other posts on the blog, and they were also obviously AI! “I have spent 22 years as an award-winning author and essayist. And now this bullshit tool that learned from MY writing is going to accuse me of plagiarism,” popular Substack writer Carlyn Beccia wrote in an anti-Pangram post. “For the last and final time, AI DETECTORS DO NOT WORK.” Here’s what her writing sounded like in 2020: “Most success stories revolve around one person who believed enough in you to say — give it a try. We call these people muses, but they are so much more than that. They are the strong man giants whose shoulders we stand on so we can see the horizon. Throughout the editing process, I am sure my editor felt less like a giant of greatness and more like Atlas with the world on her shoulders.” Pangram says it was 100% human-written. (So were other articles of hers in 2020.) And here’s what it sounds like now: “And then there is the reshuffling, which sounds like paperwork until you remember that empires have always loved paperwork. The Trump regime recently moved special education oversight from the Education Department to HHS. That is not a minor bureaucratic transfer. It changes the frame.”3 Pangram says this is mostly AI. I am not convinced that Pangram is wrong. In my own experience, Pangram is very effective on long texts — if it marks a long essay as AI, the essay was either written by AI or deliberately written to sound like AI. On tweet-length or paragraph-length texts, it’s not perfectly reliable. It seems to work just as well for non-native English speakers as for native speakers. While I have seen dozens of claims of pre-2022 text returning a positive result on Pangram, I have tried repeatedly to corroborate those and have never once succeeded. To be clear, I’m absolutely not saying that complaining about Pangram suggests a writer is a cheat. There are plenty of other concerns. Most are mistakenly premised, such as the false claim that Pangram trains on work you put into it, but others are reasonable: It’s unreliable on sufficiently short text segments; it creates an internet witch-hunt mentality when there are defensible uses of AI; people who are open about their AI use are sometimes blamed, when the only people really doing something wrong are the ones who hide it; and it isn’t perfect, so a single positive result shouldn’t be taken as decisive proof of misconduct. I’m happy to concede that Pangram is imperfect and possible to over-rely on. If an author’s posts are mostly non-AI and one scans as partially AI, this could be a mistake. It’s also possible to defeat Pangram with intentional effort. If you think we need something more robust and more reliable, fine! But if you don’t like AI and don’t like Pangram, your attitude really ought to be that it’s a shame that our existing tools for AI detection are imperfect, and it’s important to invent better ones. If your stance is instead that AI is evil and so is looking for AI, then ultimately you’re contributing to the sloppification of the internet. You’re making it harder for people who write their own text to distinguish themselves, and providing cover for authors who are definitely not writing their own text. Subscribe Top stories this week, by Kobe Yank-Jacobs Commentators often worry that the decline of reading could mean the decline of liberal democracy — but deliberative governance actually predated widespread literacy. This week, Maibritt Henkel pointed out that many liberal cultures thrive on a culture of raucous, interactive debate, which can happen in-person as easily, if not more so, than in text. Read on, dear reader, to learn more. It may actually cheer you up: [ ](https://www.theargumentmag.com/p/reading-wont-save-liberalism) [ Reading won’t save liberalism ](https://www.theargumentmag.com/p/reading-wont-save-liberalism) Maibritt Henkel · Sep 1 [ Read full story ](https://www.theargumentmag.com/p/reading-wont-save-liberalism) It used to be bad to downplay climate change (if we mean denying its reality). Today, it’s actually good to downplay climate change (if we mean keeping the issue out of political fights so we can make real progress). This counterintuitive view is called “climate hushing,” and contributor Alex Trembath argued that it’s the key to enacting good policy going forward. Get up to speed on the latest in climate politics: [ ](https://www.theargumentmag.com/p/we-wont-stop-climate-change-by-talking) [ We won’t stop climate change by talking about it ](https://www.theargumentmag.com/p/we-wont-stop-climate-change-by-talking) Alex Trembath · Sep 2 [ Read full story ](https://www.theargumentmag.com/p/we-wont-stop-climate-change-by-talking) Canada’s Prime Minister Mark Carney is standing up for the forces of liberalism against Donald Trump’s assault on our closest ally. So, why is one prominent left-wing commenter attacking Carney? Columnist Matthew Yglesias explains: [ ](https://www.theargumentmag.com/p/when-economic-liberty-means-central) [ When "economic liberty" means central planning ](https://www.theargumentmag.com/p/when-economic-liberty-means-central) Matthew Yglesias · Aug 31 [ Read full story ](https://www.theargumentmag.com/p/when-economic-liberty-means-central) 🌟Abundance Wins of the Week🌟 Shout out to my home state of California, whose legislature passed two housing bills this week. One requires a housing density minimum in transit-oriented areas of the state’s most transit-rich cities (Los Angeles, Sacramento, San Francisco, Oakland, San Jose, San Diego, and Long Beach). Another bill closes two common loopholes used by third parties to delay housing projects, including an opportunity to challenge maps of a given lot created by the developer after a project has been approved. Shout out to our big-state rival, Texas, where pro-housing SB 785 took effect this week. The new state law requires municipalities to have at least one district permitted for manufactured housing without a special permit. This will promote zoning changes in 44% of municipalities. The company Fervo Energy signed a deal with Google for what is set to be the world’s largest enhanced geothermal facility, proving a technology can work at a scale that could be crucial to a decarbonized, energy-abundant economy. Share Worth watching… This week, forecasting legend Nate Silver joined Jerusalem, Matt, and Jeremiah on a crowded episode of the pod. It was a party. And speaking of parties, our round table debated the factional lines within the Democratic Party: Is it possible to be center-left and also anti-establishment? Watch or listen to find out: The Rio Grande Valley in Texas is one of the areas that swung hardest toward Trump in 2024, but musician Bobby Pulido is running to flip it back. Right now, The Argument/Split Ticket’s model has him running 1.1 points behind his Republican opponent in a district Trump won by 18 points. Tune in to find out how he differs from national Democrats and what he thinks he can to do win this seat. On Thursday, Bernie Sanders was among the first politicians to call for a ban on superintelligence. My guest this week, Andrea Miotti, has been lobbying to ban superintelligence for several years, most recently in the U.K., U.S., and Canada. I asked him how he gets legislators to take speculative risks seriously, and we dug into what plans might look like to actually pass a ban. Listen to learn more: What’s News with The Argument The Argument recommends, by Kobe Yank-Jacobs If we ever lose track of Justin Zuckerman, I’ll have the team split up and check all the movie theaters in the city. I’m shocked (and a little jealous) at how often he gets out to see good cinema. This week, he brings us It Ends, a “horror-ish movie,” which he described as a “Gen Z Waiting for Godot.” I was intrigued before he added that it was “funny, dark and existential” — at which point I was fully locked in. I guess I also had existential themes on the mind this week. One morning, a single line from a poem I read long ago just came to me: “Is death a stretch of time in which a life is just a flash?” The poem is called “From 20,000 Feet” by Heather McHugh. I suggest the version in The Vintage Book of Contemporary American Poetry, which, by tradition, my best friend and I always bring backpacking, extra weight be damned. We’ll stay thematically dark for just one more rec from senior editor John Robert Thomason, whose upcoming trip to Baltimore reminded him to suggest Michael Clune’s memoir White Out (about being addicted to heroin in Baltimore in the 1990s). Ben Lerner’s blurb called it _“_Disturbing, brilliant, hilarious—it’s as if Proust had written Jesus’ Son.” John told me this “seems accurate, with the caveat that I’ve never read Proust.” I, personally, have also shelved Proust until retirement. At this point, I was a little concerned for the team since all of us tossed out such psychologically grim material — maybe we’re all down about the end of summer — but thankfully, at least a few of us were into more upbeat selections. Milan Singh recommended Made In’s 5-quart stainless clad saucier, suggesting he had some fun in the kitchen. Jeremiah Johnson asked if he could recommend yet more Carly Rae Jepsen. Once denied, he offered up Game Changer instead_,_ calling it “the most wildly creative game show, maybe in the entire history of the genre.” Eli Richman mentioned _Lucky, “_a B-tier show with an A-tier cast and an S-tier theme song.” I suppose he was recommending the song, Horns of a Bull. Still, despite these cheerier recs, I couldn’t help but be concerned that we were subconsciously worried about racing toward summer’s end — the end of swim trunks and splashing around. Then a message landed reminding me of the three-day weekend to come. A whole extra day for sunshine. One more swim in the lake. I was grateful this called to mind yet another line from a poem, one by Mary Oliver, the patron saint of every gorgeous summer day (and about whom a documentary is now out). Speaking to God in the poem “Thirst,” she wrote: grant me, in your mercy,a little more time. Love for the earthand love for you are having such a longconversation in my heart. We have merch! We have quarter-zips, keychains, hats, and stickers. Each one is a great conversation starter in its own way. Buy them here. Subscribe More to read: [ The Opening Argument ](https://www.theargumentmag.com/p/seeing-like-an-elite)[ Seeing like an elite ](https://www.theargumentmag.com/p/seeing-like-an-elite) Kobe Yank-Jacobs · Sep 4 What do Americans hear when they watch Sen. Bernie Sanders or Vice President JD Vance rail against the elite? Are they thinking of right-wing tech billionaires? Imperious college professors? Liberal magazine writers? [ Read full story ](https://www.theargumentmag.com/p/seeing-like-an-elite) [ The Weekly Scroll ](https://www.theargumentmag.com/p/god-is-back-on-the-timeline)[ God is back on the timeline ](https://www.theargumentmag.com/p/god-is-back-on-the-timeline) Jeremiah Johnson · Sep 5 This week, we’re talking about Substack’s new religious debates, the continuing MAGA influencer scandal, how to retire from YouTube, and aura battles. [ Read full story ](https://www.theargumentmag.com/p/god-is-back-on-the-timeline) 1 This was a legitimate thing to worry about, but doesn’t seem to be a problem given the actual technology: Marijke Van Vlasselaer, Filip Van Droogenbroeck, and Bram Spruyt’s 2026 paper looks at whether Pangram is less accurate when evaluating the work of non-native speakers of English. It isn’t. That’s because Pangram isn’t really looking at how “unusual” a piece of writing is but whether it has the characteristic patterns of LLMs. The Wired article makes an odd gesture at a different Pangram-is-racist argument. There were several controversies this summer during which upcoming books were canceled after being discovered to be AI-written: “The three novels mentioned earlier — Shy Girl, Daggermouth, and Call Me, easily publishing’s biggest AI-detection scandals — were all written by writers of color.” But come on! As Zvi Mowshowitz put it: “They were not primarily written by people of color. They were primarily written by AIs.” That’s the whole problem! And as my colleague Jeremiah Johnson recently observed with respect to Cambridge’s Arday scandal, a community being unwilling to call out fraud if it’s perpetuated by people of color is not an anti-racist tendency; it is ultimately quite racist. 2 Many non-Pangram detectors don’t work, but many possible airplane designs also don’t work, and no one considers this a general argument against aviation; the thing that matters is whether any detection works. 3 The phrase “empires have always loved” mysteriously became all the rage in the last few years. See here, here, here, here and here. Before 2025, the only use of that phrase I have been able to find is a single Tweet. The logic of the sentence, too, is incoherent in an AI-characteristic way. “...sounds like paperwork, until you remember that empires have always loved paperwork”? Does remembering that make it sound less like paperwork? It’s a craft-ful sentence, but what is it actually saying??

Sep 6 • 6:05 PM EDT • Technology • theargumentmag.com

32 reactions | AfD leader Tino Chrupalla called Germany's extreme...

AfD leader Tino Chrupalla called Germany's extreme summer "perfectly normal" and dismissed concerns as scaremongering. But temperature records — and climate science — tell a different story....

Sep 6 • 5:16 AM EDT • Science • facebook.com

DeKriek completes trilogy with ‘My Mother at Last’

SIKESTON — For LaDona DeKriek, telling her life story has never been about presenting a perfectly polished journey.

Sep 5 • 1:55 AM EDT • Entertainment • semissourian.com

Matt Campbell is Perfectly Buying Into Penn State's History

Penn State football history is perhaps the biggest part of the program. Penn State, from top to bottom, has done a lot of good throughout its existence. The program has been among the top in college football year in and year out, and that doesn't look likely to change anytime soon, given how much money the school is allocating to the program.

Sep 3 • 11:15 PM EDT • Sports • sports.yahoo.com

The datacenter fight could permanently transform US politics. Which party will seize advantage?

The movement against datacenters are uniting Americans because they perfectly illustrate how a few individuals hold all the power to control decisions that impact our lives

Aug 30 • 9:00 AM EDT • Politics • theguardian.com

Omega Just Released a Mini Moonwatch

Say hello to a revamped collection of perfectly proportioned 38-mm Speedmasters.

Aug 25 • 2:00 AM EDT • Technology • wired.com

St. Pete resident gives back through junk removal business

“After completing several jobs, I noticed how many perfectly usable items were ending up in the landfill.”…

Aug 20 • 10:19 AM EDT • Business • stpetecatalyst.com

The new Instagram logo is the perfect embodiment of AI slop

Opinion: Some remarks about the new wordmark accidentally being perfectly awful.

Aug 13 • 1:32 PM EDT • Technology • arstechnica.com

The U.S. Healthcare System is Already Mostly Socialized

Now that Abdul El-Sayed is the Democratic nominee for Michigan’s open Senate seat, the outcome of this crucial race will depend mainly on two things: Whether Republicans succeed in demonizing El-Sayed because he’s brown and a Muslim, and whether they succeed in demonizing him as a left-wing radical. I will defer for the now the question of whether bigotry will be effective this year, although I will address it another day. What I want to address instead is El-Sayed’s signature issue — healthcare. Yes, El-Sayed advocates Medicare for All, a position the usual suspects denounce as “socialism.” I will get to the politics of his stance soon. Today, however, I want to talk about the substance. We should be clear that El-Sayed doesn’t even call himself a socialist. On the contrary, he explicitly disavows the label, saying “I’m a capitalist who just knows about capitalism”. In fact, as I explained in a previous post, the policies that El-Sayed advocates qualify him as a social democrat, not a socialist. Still, El-Sayed advocates a system under which the government would pay almost all medical expenses. Isn’t that socialism? Well, if that is the criterion then much of U.S. healthcare is already socialist. In fact, the government’s role is so large that U.S. healthcare is better described as partially privatized socialism than as anything resembling a free market. Let’s start with the raw numbers on health insurance, which pays the bulk of Americans’ medical expenses. Americans receive health insurance either from private companies or from public programs, overwhelmingly Medicare and Medicaid. Here’s what the breakdown of coverage and spending between private and public looked like in 2024: While a majority of Americans with health insurance get it from private companies, well over 100 million of us are covered by government programs instead. And these government programs pay more total dollars for medical bills than the private insurers do. Why do government programs that cover a minority of the population account for a majority of health insurers’ outlays? Mainly because Medicare covers seniors, and we’re expensive. Also, while Medicaid covers many younger Americans, disabled and elderly beneficiaries account for most of its spending. Even by the raw numbers, then, U.S. health insurance looks as much public as private. But that understates the government’s role: Private health insurance in the U.S. is both highly regulated and heavily subsidized. This is obviously true for the insurance coverage purchased by tens of millions of Americans through the exchanges established by the Affordable Care Act. But employment-based health insurance, which covers 161 million Americans, is far more of a government creation than most people realize. For employment-based insurance exists largely because of huge implicit government subsidies that come with major strings attached. Here’s how that works. If your employer pays you $10,000, that’s taxable income. But if your employer pays $10,000 toward a company health insurance plan that meets certain criteria, it isn’t taxable. That’s a huge effective subsidy, running at more than $300 billion a year. Take that subsidy into account, along with the more than $100 billion per year spent subsidizing policies purchased under the ACA, as well as the outlays on Medicare and Medicaid, and net effect is that the government pays for more than two-thirds of Americans’ health insurance. Granted, there are strings attached. Roughly speaking, employment-based health plans can’t discriminate based on medical history — that is, they can’t deny coverage to employees with pre-existing conditions — and they also can’t offer the plans only to highly compensated employees — a health plan that is only for the C-suite is considered taxable income. So given the restrictions of the federal tax code, employer-based health insurance is a lot like Obamacare, which also prohibits discrimination based on medical history and provides subsidies to make insurance affordable to lower-paid workers. The fact is that virtually all of private healthcare insurance is already well under some form of government control through regulation and tax subsidies. How should we think about this system? First of all, is Medicare socialist? Don’t tell anyone, but the way Medicare operates — it covers every senior’s medical expenses, whatever they turn out to be, while being funded by taxes that depend on one’s income — could be summarized by the old Marxist slogan “From each according to his ability, to each according to his need.” And back in 1961, when Ronald Reagan was the face of a last-ditch effort by the American Medical Association to block the creation of Medicare, he denounced Medicare as “socialized medicine” and warned that it would destroy our freedom. In reality, American freedom survived until Jan. 20, 2025, while Medicare is immensely popular, with an 82 percent favorable rating in the most recent KFF survey. Medicaid, government health insurance for lower-income Americans, comes in at 73 percent. This is in sharp contrast to public views about private insurers, who are widely disliked if not hated and accused of denying essential care. Furthermore, if Medicare is socialism, then the whole U.S. health insurance system, with the government paying most of the bills and placing conditions on private insurers, is almost equally socialist. If you insist on calling any program the government pays for and regulates socialist, then U.S. healthcare would best be described as a partially privatized form of socialism. And one must ask, what is the point of the part that is privatized? Do private insurers add anything positive? Or are they simply parasites, skimming off money that could have been paying for care? The case for viewing insurers as parasites is strong. Many Medicare benefits are now delivered via Medicare Advantage, that is, plans paid for by the government but run through insurance companies. MedPAC, an independent advisory commission, estimates that last year Medicare Advantage plans were overpaid by $84 billion, thanks to practices such as “upcoding,” in which patients’ health problems are overstated to get larger sums from Medicare. Assessing the waste from employer-based insurance is harder, but most evidence suggests that private insurers have substantially higher administrative costs than Medicare does. Furthermore, the complexity of private insurance, with multiple payers all trying to deny care when they can, is a huge burden both on providers — doctors, nurses, and hospitals — and on patients. What do private insurers offer to compensate for these costs? One must go through intellectual contortions to avoid the conclusion that they are, in fact, parasites. Which brings me back to El-Sayed. In supporting Medicare for All, he’s basically calling for an end to the partial, parasitic privatization of a healthcare system that is already hugely dependent on public funding. When one looks at it that way, it’s not at all a radical proposal. Admittedly, the political lift requires overcoming the opposition to higher taxes to pay for Medicare for All. Political realism rather than economics was the reason I and many other progressives supported Obamacare, with all its complexities, rather than demanding an immediate move to Medicare for All. But the political calculus may very well have shifted since the 2000s, as Americans are fed up with a system of unaffordable premiums, rampant claim denials, and byzantine claim administration. Moreover, funding healthcare insurance through taxation would allow for a much more progressive distribution of the cost. That is, compared to the present system, middle-income households would pay less as high-income households would pay more. And the U.S. would stop wasting large sums on pointless administrative costs. So let’s be clear: El-Sayed’s healthcare platform is perfectly reasonable on the merits of cost, fairness and health efficacy. And with private insurers never more hated, while millions of Americans are losing their health insurance, 2026 may be its time. MUSICAL CODA

Aug 11 • 6:33 AM EDT • Health • paulkrugman.substack.com

Mathematicians prove perfectly fair elections are impossible

Mathematicians have shown that no electoral system can perfectly balance local representation, proportional national results, and a fixed-size parliament once enough parties compete. A newly proposed voting method could soften these unavoidable trade-offs and produce outcomes that are much closer to fair.

Aug 2 • 8:00 AM EDT • Science • sciencedaily.com

The Pauls return for Days of '85 fair entertainment

The Pauls will return as free fair entertainment on Friday, Aug. 7, at 7:30 p.m. during the Days of '85 Fair & Rodeo at the fairgrounds in Ekalaka. The family band promises to deliver an uplifting musical experience that fits perfectly with the community spirit of Carter County's annual celebration. Originally from Florida and now settled on a ranc...

Jul 31 • 5:11 AM EDT • Entertainment • ekalakaeagle.com

Backrooms perfectly captures the lost art of '90s boredom

The hit thriller is making younger viewers unexpectedly nostalgic for an era they never actually experienced.

Jul 30 • 3:28 PM EDT • Entertainment • yahoo.com

After Hobbes vs Rousseau: Politics must move beyond human nature

Since the great debate between Hobbes and Rousseau, our political theory has been organised around a single question: is human nature essentially good or essentially corrupt? Graham Harman, the philosopher behind object-oriented ontology, argues that this is simply the wrong question to be asking. The real forces governing our collective fate - climate change, artificial intelligence, geography, and pandemics - are non-human; therefore, questions about human nature are not the central ones. Instead, Harman proposes a politics reorganised around our relationship to non-human objects and environments, in which quasi-government institutions incentivise and deter actions with the aim of extending the survival of the human race. “It is easier to imagine the end of the world than the end of capitalism.” This has become one of the most popular phrases among twenty-first-century intellectuals. Often misattributed to Fredric Jameson, it has been warmly embraced by such influential authors as Slavoj Žižek and the tragically departed Mark Fisher, and has fueled the notion that the major problem with the political Left is that it simply needs to become more speculative and creative. Naturally, this is never a bad idea in any human pursuit. But what if the political distinction between Left, Right, and Center itself results from a failure of imagination? SUGGESTED READING Hobbes vs Rousseau: are we inherently evil? By Robin Douglass This particular division has been with us only since the summer of 1789. During the simmering prelude to the French Revolution, it referred originally to the physical position in the National Assembly of radical figures on one side and loyal monarchists on the other. These positions are linked with two entirely different camps of modern political theory. On the Left we find a series of authors ranging from the moderate (Locke) to the supercharged (Rousseau) to the truly radical (Marx), all of them contending that exploitation of the many by the few justifies revolutionary overthrow of the existing order. On the Right they are countered by the likes of Machiavelli, Hobbes, and Schmitt, a group of thinkers convinced that corrupt human nature can be tamed only by force. In the words of the Nazi supporter Schmitt: “One could test all theories of nature and state and political ideas according to their anthropology and classify these as to whether they consciously or unconsciously presuppose [humans] to be by nature evil or by nature good.” This is the division we continue to face today, even in the daily retail politics of Western democracies.___Our species has regularly proven itself capable of both the noblest gestures and the most scathing cruelties, making it hard to define us as essentially either emancipatory or brutal in our aims.___The most refreshing feature of David Graeber and David Wengrow’s 2021 book The Dawn of Everything: A New History of Humanity was their awareness that the forced choice between Rousseau and Hobbes (as they put it) is no longer sufficient. Our species has regularly proven itself capable of both the noblest gestures and the most scathing cruelties, making it hard to define us as essentially either emancipatory or brutal in our aims. Far from being stationed “beyond good and evil,” we seem to be fully entangled with both. We are not naturally good or evil, Graeber and Wengrow contend, but naturally experimental and imaginative. They rest their case on the archaeological record, which seems to bear witness to numerous episodes of seasonally shifting governments and festivals, which vacillate in the same society (even in the same year) between the egalitarian and the dictatorial. Such praise of political experiment makes a good fit with the generally anarchist position defended by these authors. Yet it is also what I find most disappointing in their position. Although they are right that the forced choice between Hobbes and Rousseau falls short of what our new situation requires, the authors simply replace it with praise for the human being as a political tinkerer and performer. In other words, they give us just another theory of human nature, one that would not have been out of place in the sixteenth or seventeenth century. What other options might we have?___The time is ripe for a retheorization of politics on the basis of something other than human nature… We are not just alienated and limited by objects, but also empowered and unleashed by them___One fresh approach would be to treat politics within a broader framework than human nature, given that the polis does not exist in a vacuum filled exclusively with people. When Bruno Latour went to observe the baboons of Kenya with the San Diego anthropologist Shirley Strum, they jointly concluded that these primates were more socially interdependent than our own species. Each baboon is so reliant on the dynamics of the group that they find themselves locked into constant surveillance of their potentially dangerous peers. The group structure turns out to be inherently volatile. Humans, by contrast, are able to stabilize their societies to a large degree through the mediation of inanimate objects. Although one’s position in society may seem to fluctuate over time, considerable stability is produced through such items as apartment keys, wedding rings, and identification cards, all of them serving to secure us a place distinct from that of our fellows. The Italian philosopher Maurizio Ferraris has written at length about the crucial role of documents in establishing social relations, making him a hero among notaries in his country. Proper names and license plates cement our position amidst other humans, as do symbolic achievements such as graduation from a top university or distinguished wartime service with its various medals and ribbons. None of this is found among baboons, despite their closeness to us on the evolutionary tree: after all, we did share a common ancestor just 25 to 30 million years ago. SUGGESTED VIEWING The goodness paradox With Richard Wrangham The time is ripe for a retheorization of politics on the basis of something other than human nature, whether understood in the manner of the Left or of the Right. We are not just alienated and limited by objects, but also empowered and unleashed by them. But initially, it is true, we face non-human things primarily in the form of constraints. It would be difficult, perhaps impossible, to form a human civilization underwater, deep underground, atop Mount Everest, or in outer space. Human dependence on the non-social background takes on subtler form in discussions of political geography, especially unloved on the more idealistic Left. Consider the luckless position of Poland during World War II, sandwiched between Hitler on one side and Stalin on the other. By contrast, note the good fortune of the United States in not having a military peer positioned on any land border. Anglo-American geopolitics, since the writings of the American naval commander Alfred Thayer Mahan and the British geographer Sir Halford Mackinder, has been wedded to the notion that one or more liberal offshore naval powers should prevent any authoritarian “heartland” regime from gaining effective control of Eurasia. That remains the case whether the opponent is the impatient Napoleon, the swashbuckling German Kaiser, the villainous Hitler, the murderous Stalin, or the industrial juggernaut Chinese. In their current Foreign Affairs article “Heartland vs. Rimland,” Michael Beckley and Hal Brands analyze the current and coming state of the world on the basis of these same rather durable principles. In recent years a similar outlook has been embraced, in inverted form, by the Putin-loving Russian philosopher Aleksandr Dugin, an icon of the Rightist intellectual underworld. For Dugin, the Anglo-Saxons are an evil force bent on destroying the spiritually superior people of the land: much like Athens in its failed war with Sparta, or Carthage in its eventual collapse against Rome. But the shared notion on both sides is that any strategy that fails to take geography into account is likely to end up as a defeated form of excessive optimism.___When dealing with environmental degradation, then, we are doing business with forces that promise dire effects on our species as a whole.___More striking for us today than geography is the non-human object called climate, which threatens rimland and heartland equally. Even if we contend that humans are one hundred percent responsible for triggering the climate crisis, it remains non-human in the sense of being an independent force beyond our effective control. In a major work published just before his death in 2002, The Structure of Evolutionary Theory, the American paleontologist Stephen Jay Gould noted the tendency of evolutionary theorists to focus too closely on the fate of individuals in view of their successful or failed adaptation to circumstances. With climate change, however, we confront a force that goes well beyond the scope of any individual life. Whereas Richard Dawkins extended evolutionary questions downward from individuals to the sub-individual gene, Gould pushed it upward, speaking of entire species or clades as impacted by environmental change. The asteroid that struck the Yucatán 66 million years ago is only the most famous example of this sort. In the wake of that catastrophe, large animals needing abundant supplies of food were at a fatal disadvantage by contrast with the smaller birds and mammals of the planet. When dealing with environmental degradation, then, we are doing business with forces that promise dire effects on our species as a whole. The effect is to shift our attention away from individual beings towards the survival of the group.In the United States in particular, the COVID-19 pandemic sparked considerable backlash among those who asserted a nearly absolute degree of individual freedom. In California I encountered those who demanded that their businesses be allowed to reopen, that it was enough to cover their mouths but not their noses, or that they should have the right to enter any public area despite refusing vaccination. Yet public health is probably the clearest argument against libertarianism, which often merely asserts the autonomy of individuals against any claims of the commons. In normal times the individual is worth defending; under conditions that threaten the survival of part or all of our species, compulsion may be our only resort, as with perfectly reasonable blackouts and rations in wartime. To rebuild human society under the constraints of climate change will be one of the great challenges of the century to come, as will our improvised grappling with artificial intelligence. Such phenomena pose existential risks for humanity, and leave far less room for tolerance of individual brattiness. Cases are mounting of devastating wildfires caused by arson. Recently it was reported that there is even a website where one can bet on the location of coming wildfires, raising the specter of deliberate blazes set to collect on wagers. The death penalty has steadily lost support in a world where many stress the infinite value of human life. But if we focus instead on entire ecosystems, those who purposely burn the forests may begin to look worse than serial murderers.___The politics of the future may be splintered by disagreement over such topics as how to manage our environing background and how to permit or restrict the entry of new objects into the political sphere.___So far I have spoken of force, authority, and punishment: measures sure to have greater appeal to those who identify with what we know as the Right. But other implications of the post-anthropocentric world are likely to have different backers. If non-human objects are seen as structuring political space, it stands to reason that they can no longer be introduced solely by the pace of technical breakthrough. Fields ranging from cybernetics to energy will demand increased politicization once they are reconceived not just as products of individual human investment, but as renovations of collective political geography. This need not involve world government, with its inherent risk of tyranny, but may involve quasi-governmental enforcers of prestige along the lines of a less corrupt FIFA or International Olympic Committee, punishing deviant states not with violence but with less tangible incentives.Politics will never lose its historic links with conflict and faction. But rather than factions aligned by differing theories of human nature, the politics of the future may be splintered by disagreement over such topics as how to manage our environing background and how to permit or restrict the entry of new objects into the political sphere. This will prove just as incommensurable with the modern Left/Right spectrum as a globe is with a two-dimensional map.

Jul 30 • 8:00 AM EDT • Politics • iai.tv

Graham Platner’s Downfall Shows How Democratic and Republican Political Operatives Have Lost the Plot

A former chief of staff to Andrew Cuomo and Chris Christie writes for Vanity Fair about how both parties have lost the plot, as the Maine Senate situation so perfectly illustrates.

Jul 23 • 11:49 AM EDT • Politics • vanityfair.com

<i>The Odyssey</i> Is Perfectly Tolerable Entertainment

The movie is enchanting despite its frustrating qualities.

Jul 18 • 5:03 PM EDT • Entertainment • nationalreview.com

"Red monsters" in space: why these ancient galaxies shatter scientists’ beliefs - Futura-Sciences

The Puzzle of Early Galactic Giants In the standard picture of galaxy formation, large halos of dark matter form first. Their gravity pulls in ordinary matter, mostly gas, which can then cool, collapse, and form stars. But this process is not supposed to be perfectly efficient. Star formation is usually...

Jul 9 • 7:30 AM EDT • Science • futura-sciences.com

25 Movies That Perfectly Capture What An American Summer Actually Feels Like

Bask in the essence of summer with these essential seasonal picks.

Jul 4 • 9:00 AM EDT • Entertainment • huffpost.com

Scientists find thousands of earthquakes in a perfectly straight line in Alaska, revealing a hidden 'microplate'

Tiny earthquakes that emerge in a strikingly linear pattern revealed the Yakutat microplate, which may be focusing volcano and earthquake activity.

Jun 25 • 6:00 AM EDT • Science • livescience.com

MASTER CLASS: Tap in to the power within to make healthy choices

Intention is a very powerful psychological attribute contributing toward any behavior change. Without an intent to change, there's almost zero chance it will occur – especially with respect to health behaviors. This week, I'll present some tips for maximizing intention in an effort to make good behaviors "stick." Plus, I'll introduce an exercise that fits perfectly into physical activity program.

Jun 21 • 9:00 AM EDT • Health • arkansasonline.com

Central City Flower Girls: A treasured tradition marks its 94th year

CENTRAL CITY OPERA Central City News: The 94th presentation of the Central City Flower Girls got off to its usual booming start, thanks to a perfectly timed dynamite blast that reverberated from an undisclosed location within earshot of the Teller House Garden. The window-rattling boom may startle those unfamiliar with the time-honored tradition, but it generates knowing smiles and a smattering of applause from everyone else. Once the sound subsides, master of ceremonies Roopesh Aggarwal, vice president/investor relations […]

Jun 16 • 11:24 AM EDT • Politics • coloradopolitics.com

Is SMIC N+3’s Metal Pitch Smaller than Intel 18A’s?

Almost four years ago, we published that SMIC had started shipping 7 nm (N+1) chips. Now, SMIC is shipping its third-generation 7 nm (N+3) process in Huawei’s Kirin 9030, with a minimum metal pitch of 32.5 nm, about 10% tighter than the 36 nm minimum metal pitch shipping in Intel’s latest Panther Lake CPUs on 18A. The headline is true, but incomplete cherry picked metric. N+3 reaches the density of TSMC N6 through aggressive DUV multi-patterning and design-technology co-optimization (DTCO), but it pays for that in complexity, efficiency and process control. We found this and more in our reverse engineering and teardown where we cover SMIC’s N3 process technology, Huawei’s packaging, memory, architecture, and more. SemiAnalysis has been building a state-of-the-art teardown lab in Oregon capable of analyzing the world’s most advanced and important chips over the last year and half. We have already generated revenue on advanced datacenter chip teardowns including our recent reverse engineering of a major TSMC customer’s COUPE CPO optical engine + EIC 3D stack. This is the first public report from the SemiAnalysis Teardown Engineering & Evaluation Lab, or STEEL for short. The lab is aggressively scaling up and out and we’re excited to announce it publicly. This is a bit of inconvenient timing for TechInsights as they are private equity owned and currently being sold while having enjoyed virtually no credible competition for decades. This has led to TechInsights underinvesting in CAPEX. SemiAnalysis exceeds TechInsights in revenue despite no venture or private equity ownership and being founded only 6 years ago. Because we have no external investors and are founder led, we move faster, build faster, and we can release client chip teardowns for free regularly, while focusing on datacenter for our major clients. Here’s the first public image from our lab, the HiSilicon Kirin 9030 Pro SoC: This report will detail our teardown of the Kirin 9030 and our findings on SMIC’s N+3 process, the most advanced in China. For comparison, we’ll show our teardown of the MediaTek Helio G99, made on TSMC N6. Through this comparison, we can look at the effect of export controls – SMIC N+3 and TSMC N6 are comparable nodes, but one is heavily export-controlled, the other free to use the West’s most advanced equipment. Here we see both China’s progress and constraints. SMIC N+3 reaches TSMC N6-class logic density, but it requires far more aggressive DUV multi-patterning, so it does not match N6 on process maturity or cost. The Kirin 9030 Pro performs similarly to three-year-old Android flagships, and trails far behind the current flagship SoCs from Apple, Qualcomm, MediaTek, and Samsung. The efficiency gap is even wider. Export controls have not stopped Huawei and SMIC from shipping advanced silicon, but they have forced a different path. Without EUV, SMIC is leaning harder on DUV multi-patterning, DTCO, and increasingly complex integration. The roadmap continues forward through tighter design rules and backside power, but each step adds cost and process risk. Huawei’s τ scaling and LogicFolding show another path: stacking active logic and recovering density through advanced packaging and system-technology co-optimization (STCO). To understand the Kirin 9030, we must first understand Huawei’s SoC history. HiSilicon is Huawei’s chip design arm, responsible for the Kirin smartphone SoCs, Kunpeng server CPUs, Ascend AI accelerators, and switch/router networking silicon. Before export controls, Huawei was TSMC’s largest customer – the only customer on TSMC’s first EUV node, N7+, and among the first on N5, alongside Apple. That ended in late 2020. Huawei switched to Qualcomm SoCs in its flagship smartphones, though export controls limited them to 4G-only variants. In late 2023, Huawei returned to in-house silicon with the Kirin 9000s, a successor to the Kirin 9000, fabricated on SMIC N+2 instead of TSMC N5. In the following years, they released the Kirin 9010 and 9020 on the same N+2 process. These chips used Huawei’s in-house TaiShan CPU cores and Maleoon GPU. We have not torn down a Kirin 9020 ourselves, so the predecessor die shot is from Kurnal. The die shots show how Huawei spent its silicon budget: which functional blocks are where, and how their areas compare to the predecessor. First, a quick guide to the major blocks on the die. The total die area is nearly identical, but the 9030 uses that area more aggressively. A denser process lets Huawei fit an extra middle CPU core, more GPU and NPU cores, and larger caches into the same footprint. In contrast, the Helio G99 is a much smaller, low-cost SoC, built for budget smartphones rather than a flagship device. While the Kirin 9030 is ~140 mm², the G99 is only ~29 mm², roughly one-fifth the area. The underlying TSMC process technology, however, is directly comparable as a baseline for analyzing SMIC’s. The Kirin 9030 is an evolutionary refresh, not a clean-sheet design. Its CPU, GPU and NPU cores carry over the 9020’s families, and the gains come from three levers: the SMIC N+2-to-N+3 process step, DTCO and floorplan work, and incremental microarchitecture. Area is where the first two show up, and the 9030 scales well here. Performance and efficiency are the harder test. Huawei’s design holds up better than its node would suggest, but the chip still trails, both because N+3 sits behind the leading-edge nodes and because its cores, while competent, remain a few generations behind the newest designs. The new prime core is an incremental update. The main changes are a 10% frequency increase from 2.5 GHz to 2.75 GHz and a doubling in the L2 cache from 1 MiB to 2 MiB. Despite the increased cache, the core size decreased by 7.6%. Excluding the private L2 cache, the core size decreased by 21%. This is a large reduction for an incremental node. Compared with the TaiShan New V120 core in the Kirin 9020, the Kirin 9030’s middle core is almost unchanged architecturally, yet each core shrinks by ~22%. Most of that comes from the move from N+2 to N+3, with layout likely accounting for the rest. Visually, the most noticeable change is the increase from 3 middle cores to 4. There is also a 20% increase in the shared L3 cache of the big cluster. This helps to improve multi-core performance without sacrificing much in terms of area. Even with each core shrinking, the big CPU cluster’s total area is essentially unchanged. The per-core savings went back into an additional middle core and larger caches. The tiny cores shrank less than the prime core (excluding its L2 cache) and less than the middle cores. This is likely because fixed overhead is a larger share on a small core. We cannot resolve any architectural changes from the die shot alone, but the per-clock and efficiency gains shown below point to more than pure process and layout scaling. The area reduction was offset by a doubling of the shared L2 cache from 2 MiB to 4 MiB, leaving the total tiny CPU cluster area slightly larger. Area is the easiest improvement to see from a die shot, but it is only one part of PPA (power, performance, area). For modern logic, power and performance matter just as much, and often more. Since Dennard scaling broke down in the mid-2000s, voltage and frequency have not scaled in step with transistor dimensions, so each node has had to fight harder for gains in performance and efficiency. The starkest comparison is not Kirin 9020 versus Kirin 9030 Pro. Apple’s efficiency cores run circles around Huawei’s prime core. Apple’s low-power core delivers 20% higher integer performance while drawing only 1 W, compared with 4.5 W for Huawei’s prime core. N+3 matches TSMC N6, but N6 is several generations old. Apple and Qualcomm build on N4 and N3P, which are denser and sit on a better voltage-frequency curve, giving them a larger transistor budget and more performance per watt. The 9030’s own cores did improve. The middle and tiny cores gained 17% and 14% in per-clock integer performance over the 9020, with floating-point flat on the middle core and up 11% on the tiny. The tiny core improves cleanly, with performance rising while power falls and efficiency increases by 45% in integer and 24% in floating point. The middle core is mixed: integer performance rises but power rises faster, cutting integer efficiency by 7%, while lower power lifts floating-point efficiency 16%. Per-clock gains at the same or lower frequency are microarchitectural, so the cores are tuned, not just shrunk. Both also failed to hold their rated maximum frequencies, pointing to thermal, power, or stability limits. Per clock, the middle core sits around Arm Cortex-A720 and the tiny core near the Cortex-A520; absolute performance trails because Huawei clocks them much lower. The prime core is roughly Cortex-X2 class per clock, a 2021 design. Apple’s 2020 M1 Firestorm core is still 35% higher per clock and 57% faster in absolute integer performance at a similar 4.5 W. The current leading edge is further ahead again: the Apple M5 P-core is 60% higher per clock and 2.7× faster, the Arm C1 Ultra 45% higher and 2× faster. Matching older high-end cores per clock is a genuine design achievement. What Huawei cannot match is the voltage-frequency curve and transistor budget of leading-edge nodes, which let Apple, Qualcomm and others spend more transistors in the same area on wider cores, larger caches and deeper buffers while running at lower voltage. Huawei’s LogicFolding roadmap is one answer, stacking active logic to recover density and shorten signal paths. We return to it later. The GPU compute units (CUs) changed more visibly than the CPU cores, moving to a more rectangular layout for both the arithmetic logic unit (ALU) clusters and the CU overall. Even with ray-tracing support added, a CU shrank ~28%. However, that shrink is offset by the increase from 4 to 6 CUs and the area outside the CUs grew 33%. Overall, the GPU cluster is larger by ~10%. The GPU is where Huawei makes its biggest gains. The Maleoon 935 is not competitive with current flagships, but it is a large step up from the 920 and reaches older-flagship territory. In 3DMark it is 70% faster in Wild Life Extreme (WLE) and 79% faster in Steel Nomad Light (SNL) than the 920; with 11% higher clocks and 50% more CUs, the ~67% theoretical uplift roughly matches WLE and is beaten by SNL. It edges ahead of the Snapdragon 8+ Gen 1 in WLE and SNL, and the Dimensity 9200 and Apple A16 in WLE, but stays far behind newer parts: the Snapdragon 8 Elite Gen 5 and Dimensity 9500 are ~2.4–2.6× faster in WLE and ~3.2× faster in SNL. The Maleoon 935 is Huawei’s first GPU with hardware-accelerated ray tracing; there it lands slightly ahead of the Exynos 2200, and on par with the Apple A16, with current flagships up to 3.7× faster. The Neural Processing Unit (NPU) saw the largest structural changes of any block, moving from a Lite and a Tiny core in the Kirin 9020 to a Lite and two Tiny cores in the Kirin 9030. Both core types also show significant layout changes. This is a reversal in Huawei’s NPU design. The Kirin 9000 5G, its last flagship chip on TSMC N5, used two Lite and one Tiny core. The series of SoCs on SMIC N+2 moved to one Lite and one Tiny core, likely for area savings. With the Kirin 9030, Huawei has shifted back toward a larger multi-core NPU cluster, but with the additional area going to a Tiny core rather than a Lite core. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. Before diving into the process stack, the package and memory are worth separating from the SoC itself. The Pro variant of the Kirin 9030 carries 12 GB of Samsung DRAM, with two stacks of four dies each. The dies were identified as the K4L2E165YD, a 12 Gb LPDDR5X-9600 device fabricated on Samsung’s 1a node, the fourth generation of its 10 nm-class DRAM after 1x, 1y and 1z. 1a has shipped in volume since 2022, so this is current memory rather than older-node inventory. The 16 GB Pro Max variants we obtained were found with both CXMT and Samsung packages. The CXMT package is marked CXDD7JEDM, with two stacks of four dies, packaged in week 45 of 2025. The inferred die dimensions from X-ray computed tomography (CT) are consistent with a known density of ~0.3 Gib/mm² for the CXMT G4 process, roughly equivalent to other manufacturers’ 1z processes. The Kirin 9030 uses a typical integrated package-on-package (iPoP) stack: multiple DRAM dies in a memory package sit above an organic redistribution layer (RDL) interposer, which sits above the SoC and package substrate. The full package is then mounted to the printed circuit board (PCB) through ball-grid array (BGA) solder bumps. The memory package substrate is a thin bismaleimide-triazine (BT) laminate carrying the LPDDR5X stack. The organic RDL interposer over the SoC routes the PoP signals around the die and carries possible dummy thermal copper pillars. The package substrate, a thicker Ajinomoto Build-up Film (ABF) build-up over a BT core, fans the flip-chip bumps out to BGA pitch and embeds the power planes. The whole stack is organic. The only silicon is the SoC and the LPDDR5X dies; there is no silicon interposer. Keeping it all-organic brings the package’s coefficient of thermal expansion (CTE) close to the PCB’s, reducing board-level warpage, and avoids the cost of a silicon interposer the SoC’s bandwidth does not need. In an iPoP stack, the memory package connects to the organic RDL interposer through an array of solder bumps. Underfill fills the gap around those bumps, adding stiffness and protecting the joints from mechanical stress. The Pro and Pro Max variants differ here, which we cover behind the paywall. The die shot and architecture tell us how Huawei allocated its silicon budget. The process tells us what SMIC can manufacture. We use the Helio G99 as the process reference for TSMC N6. Both SMIC N+3 and TSMC N6 are evolutions of previous 7 nm-class nodes. We used targeted TEM cross-sections through logic and memory regions, imaged in both fin-cut and gate-cut directions. Each cross-section caption gives its horizontal field width (HFW), the real width of the imaged area. We start at the transistor fins, then move up through standard cells, local interconnect, and SRAM. SMIC has not overtaken Intel or TSMC. It uses aggressive DUV scaling and DTCO to reach N6-class density, but that density doesn’t translate into comparable performance and efficiency, for two reasons: the node gap to leading-edge nodes, and Huawei’s core designs. Fin Profile One of the most important knobs in a FinFET process is the fin profile: the shape of an individual fin and the channel where current passes from source to drain. The ideal fin is tall, narrow, and nearly vertical. A taller fin increases effective channel width, while a narrower fin improves electrostatic control by thinning the body the gate must control. Push either too far, and the process pays for it: weaker drive current, fragile fins, taper, footing and line-edge variation that hit yield and device variability. The Intel 22 nm, 14 nm, and 10 nm fin cross-sections show how FinFET nodes have improved over time. 22 nm fins were a first-generation structure, relatively short, wide and strongly tapered. The shape limits current density and reduces gate control uniformity across the height of the fin. At 14 nm and 10 nm, Intel pushed the fins taller and narrower while also making the sidewalls more vertical. Rather than shrinking the device, these changes increase the effective channel width per fin and improve electrostatic control. The trade-off is that taller fins at tighter pitches are much more difficult to manufacture. Now, let’s compare the Helio G99 on TSMC N6 with the Kirin 9030 on SMIC N+3. Both processes are in the same class, with fin pitch of 30-32 nm on N+3 and 34 nm in our N6 cross-section. The pitch for N6 is especially interesting as N7’s HD library is generally listed with a 33 nm fin pitch, and N6 did not shrink pitches directly. Its density gains came from DTCO instead of tighter pitches. The 34 nm pitch was stable across our sampled region and serves more as a comparison against the SMIC N+3 we have not investigated further. Pinning down N+3’s fin patterning scheme takes more than one core unit. The CPU cores show a dense ~32 nm pitch, with the pitch between N-P fin pairs alternating between 78 and 88 nm. Logic alone may be consistent with dual-pitch mandrels of 120 and 110 nm, but this is a complex and unusual approach. Combining the pitch from the the 8T SRAM, which has more complex repeat unit, with the CPU core sequence allows us to reverse engineer the patterning steps with more confidence. As both the logic and SRAM should share the same base grid, a single CD mandrel lithography pattern with 128 nm pitch undergoing SAQP produces a die-wide ~32 nm grid (128 nm/4), which supports the pitch sequencing seen in both logic and SRAM cells. In the sampled cross-sections, N+3 shows a taller, narrower, higher-aspect-ratio fin than N6. The measured fin aspect ratio is ~9.5:1 on N+3 versus 7.8:1 on N6. N+3 also shows less top rounding, with an estimated radius of ~2 nm, compared with 2.8 nm on N6. Even though the fin widths differ, the ratio of top rounding to fin width tells the same story, with N+3 at 0.37 and N6 at 0.44. In a geometric sense, lower is better; a perfectly rectangular fin would have no top-rounding penalty. These are single-digit-nanometer features measured from a handful of cuts, so treat the absolute numbers as approximate. The important result is the relative gap: N+3’s fins are consistently taller, narrower and less rounded than N6’s. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. Standard Cell A standard cell is the basic building block of chip layout: a fixed-height row pairing one NMOS and one PMOS transistor that share a gate, tiled in a grid to build logic blocks. The key dimensions are contacted gate pitch (CGP), cell height (CH), fin count, and the lower-metal routing grid. To measure density, we use the Bohr metric: a weighted average of NAND2 gate area (60%) and scan flip-flop area (40%). This represents a realistic mix of combinational and sequential logic. This metric has its limitations, especially for complex cell layouts like TSMC’s FinFLEX, which alternates cells with different fin counts. Even so, it is the best metric for a pure process-level comparison. Another important measurement is the fin pitch; it refers to the distance between two fins of the same transistor. In a FinFET process, multiple fins are used in each transistor to increase the drive current and thus performance. TSMC N6 ships both a high-density (HD) library with 2 PMOS and 2 NMOS fins per cell, and a high-performance (HP) library with 3 of each. More fins under the shared gate mean more effective channel width. HP cells switch harder at the cost of area. Designers mix the two on a die, primarily spending HP cells on timing-critical paths, and matching their PPA targets. In the Cortex-A55 core of the Helio G99, we found a cell height of 240 nm for the HD cell. MediaTek has used HD cells in the G99 to minimize die size and thus cost. As an SoC for budget smartphones at ~$100, this is essential. By contrast, we found only one library in the Kirin 9030, with 2 NMOS and 2 PMOS fins. This suggests a narrower library strategy than TSMC N6, where both HD and HP libraries are widely used. This likely reflects the smaller customer base and the more constrained domestic design and electronic design automation (EDA) ecosystem. In all three CPU cores of the Kirin 9030, we found cell heights of 228 nm, 5% smaller than on N6. This is also a reduction of 9.5% over SMIC N+2’s cell height of 252 nm. SMIC N+3 and TSMC N6’s HD library both feature a CGP of 57 nm. For SMIC, this is a 9.5% shrink over N+2. In the past, CGP and cell height alone may have been enough to compare transistor density. Now, however, we must consider scaling boosters and DTCO as well. SMIC’s density gain does not come from EUV. It comes from using every available DTCO booster aggressively. First is fin depopulation: reducing the number of NMOS and PMOS fins in each cell. The first FinFET nodes started with 3 or 4 fins for each transistor. SMIC N+3 and TSMC N6 HD both use only 2 fins per transistor, trading drive strength for density. Next is contact over active gate (COAG). By landing the gate contact directly over the active gate, instead of out over the isolation region, the cell height drops. N+3 integrates COAG while N6 does not. Our N+3 gate-cut cross-sections indicate COAG, with the gate contact sitting over the active region, while N6 shows an off-gate contact. Last is single diffusion break (SDB). Diffusion breaks are inserted between cells in the same row to provide electrical isolation, but they also introduce local layout effects (LLE), layout-dependent shifts in electrical characteristics. In the past, a double diffusion break was used, consuming the space of two CGPs. SMIC N+3 and TSMC N6 instead use SDB, saving area but increasing LLE sensitivity. This must be controlled at the process level and accurately modeled in the process design kit (PDK) so that EDA tools can account for it. Overall, SMIC N+3 has a transistor density of 113.4 MTr/mm², slightly above TSMC N6 at 107.7 MTr/mm². Even without EUV, SMIC has achieved density beyond TSMC’s mature N6 node which utilizes EUV. Metal Stack The smallest critical dimension in the teardown is M0; SMIC N+3 uses a 32.5 nm local metal pitch. That is smaller than the 36 nm M0 pitch on Intel 18A in Panther Lake. However, this does not mean that SMIC has a better process than Intel 18A or TSMC N3P. M0 is a local intra-cell routing layer. Its usefulness depends on the full interconnect stack: M1 and M2 pitch, track count, via and line resistance, design rules, mask count, overlay control, and routing flexibility. The 32.5 nm M0 is consistent with self-aligned quadruple patterning (SAQP), whose four-population line-width loading we read coarsely as alternating widths of 21.5 to 24 nm; M1 and M2, at 38 and 40 nm, are consistent with self-aligned double patterning (SADP), a single A/B split. On TSMC N6, M0, M2, and M3 sit at a relaxed ~40 nm and are consistent with SADP-class double patterning, with no need for quadruple patterning. That said, we measure M2 for example at ~43 nm, likely inflated by sparse routing. We do not assign any specific layer to EUV from our cross-sections; the distinction we can draw is double versus quadruple patterning, not lithography wavelength. Transistor-level density in the front-end-of-line (FEOL) sets an upper bound, but the design is ultimately limited by what the interconnect stack can route. The lowest metals are the most important for standard-cell density, but the semi-global and global layers determine how usable that density is at the block and chip level. Two axes are commonly used for chip cross-sections: the fin-cut and the gate-cut. The micrograph above is a fin-cut and shows metals 0 through 3. This axis lets us see the even-numbered metals, with M0 right above the fins. There are two kinds of M0 lines. The first are the power rails; these are wide wires for the VDD and VSS running horizontally at the top and bottom edges of each standard cell. The wide wires measure 55 nm across, more than double the other M0 lines. Their width minimizes resistance and reduces IR drop. The second kind are intra-cell wires, short segments within the cell that connect terminals to M1. These have alternating widths between 21.5 and 24 nm. The M0 pitch is 32.5 nm, a 19% reduction versus N+2 and N6. At this pitch, DUV patterning requires more aggressive multi-patterning, increasing mask count, overlay sensitivity, process complexity and cost. M0 is below what a single DUV-defined spacer (SADP) can resolve, so SMIC cascades a second spacer step (SAQP). The cross-section reflects the cost: the M0 trenches are visibly more re-entrant (narrower at the bottom than the top) than M1 or M2 on the same chip and carry a bright barrier-rich foot where the trench meets the etch-stop layer. That shape is partly the intended damascene profile, as a slightly narrow bottom helps void-free copper fill, but its magnitude at M0 is driven by the tight pitch and the higher trench aspect ratio. Intel 18A supports an M0 pitch of 32 nm, although Panther Lake has only shipped with a looser 36 nm pitch. This is due to Intel’s heavy usage of HP libraries. Among leading-edge nodes, 18A has the loosest M0 pitches due to PowerVia. With power routing moving to the backside, congestion is reduced, and the entire front-side metal stack can be used for signal routing. M2 is the first true inter-cell routing layer. It runs horizontally like M0 but spans across multiple cells to carry block-level signals. The M2 pitch sets the cell’s track height – the number of M2 tracks that fit between the VDD and VSS rails, defining what the library calls a 6-track or 7.5-track cell. This layer is the most important, limiting the routing of entire blocks. SMIC N+3 features a 5.7-track cell. The M2 pitch is 40 nm, a 5% decrease over N+2 and the same as N6. This shrink keeps the pitch at the edge of what is possible with double patterning. Future nodes will need to increase the number of masks for M2 as reducing the number of tracks is much harder due to the limitations in routing. The micrograph above is in the perpendicular direction, the gate-cut, and shows metals 0 through 4. This allows us to see and measure the odd-numbered vertical metal layers. The M1 pitch is 38 nm, 9.5% less than N+2 and 33% less than N6. The M1-to-gate ratio matters because it sets local routing flexibility. N+2 and N+3 use a 3:2 ratio, while N6 uses a 1:1 ratio, explaining the huge differences in M1 pitch. The more M1 lines there are compared with the gates, the more flexibility there is for power and signal crossing within the cell. Routing flexibility enables more complex and better cells. Clean fractional ratios are also preferred as a grid is periodic and improves layouts. The 3:2 ratio gives SMIC more local routing flexibility than a strict 1:1 grid, but it also complicates layout and patterning. This is a DTCO choice, with SMIC increasing process complexity to recover density and routability without EUV. This 3:2 ratio is not very popular in the leading-edge nodes. TSMC has only used it on N7+, the N5 family, and the short-lived N3(B). They have switched back to a 1:1 ratio for N3E. Intel has only used it on the 10 nm/Intel 7 family, with Intel 4, 3 and 18A all using a 1:1 ratio. Samsung is the only one still using a 3:2 ratio at the leading edge, using it in the SF4 and SF3 families. It remains to be seen if SMIC will remain at a 3:2 ratio or move to a 1:1 ratio with its future nodes. The industry is still actively exploring these local-routing ratios. At VLSI 2026, imec will be presenting work on even higher ratios, including a 2:1 scheme that can reduce area by up to 14%. We will be covering the conference in a future newsletter article. Subscribe We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. The final local interconnect layer for N+3 is M3, with a pitch of 44 nm. The M3 pitch is the same as on N+2 and 10% larger than on N6. The semi-global layers carry the majority of block-level signal routing. They have a coarser pitch than the lower local layers. On leading-edge nodes, they are designed to sit at the limit of DUV single patterning. M4 through M11 pitches were found divided between 80–82 nm (M4–M6), 128 nm (M7–M10), and 148 nm (M11). Given limited sampling, it is possible these are divided further in dense routing areas. At the top are two giant metal layers, M12 and M13. These have kept the same pitches as N+2 at 1920 nm and 4600 nm respectively. While the lower layers’ pitches are generally fixed by the process and library, the upper layers vary much more in pitch and count, depending on the design. Even two smartphone SoCs on the same process can have wildly different metal stacks. The Helio G99 carries fewer routing layers, reaching coarse metal pitches of 850 nm by M9, while the larger and higher-performance Kirin 9030 keeps fine pitches until M11. SRAM At the leading edge, SRAM is much more difficult to scale than logic. TSMC’s latest nodes have seen little to no bitcell scaling, while logic still has more DTCO levers to pull. While looking for other logic libraries in the GPU compute units, we stumbled upon the SRAM. The most common type of SRAM has 6 transistors (6T), but this cell had 8 transistors (8T) instead. 8T SRAM adds two transistors to form a dedicated read port. Unlike a 6T cell, where reading disturbs the storage, the decoupled read port removes read-disturb, improving read stability and letting the cell be pushed harder for performance. At first glance, the cut looked like an unusual logic library, with each cell row having 3 fins of one polarity and 5 fins of another. The rows also alternated in orientation. Energy-dispersive X-ray spectroscopy (EDS) resolved our confusion. The cut had not landed on the GPU logic, but on the SRAM macro beside it. The unusual fin pattern was due to the SRAM library. We return to EDS in the process flow analysis behind the paywall. SRAM libraries are not like traditional logic libraries. Due to the unequal number of PMOS and NMOS transistors, they require specialized rules and layout libraries. They do not need the flexibility of logic libraries, so they are hyper-optimized for one purpose: dense, reliable memory. The SRAM cell we found is a 1:2:2-2:2 cell. This means there is 1 fin per pull-up (PU) PMOS transistor, and 2 fins per pull-down (PD) and pass-gate (PG) NMOS transistor. These 2 PU, 2 PD and 2 PG transistors would usually form a single 6T high-current cell (HCC). An 8T HCC adds a read-pull-down (RPD) and a read-pass-gate (RPG) NMOS transistor, each with two fins. We measured a cell height of 406 nm, which brings the bitcell size to 0.0463 µm². That is a theoretical peak density of 21.6 Mib/mm². We estimate that a 6T HCC would have a cell height of 292 nm and a size of 0.0337 µm². This is ~12% larger than a 6T HCC on Intel 3 and 4. We also estimate the 6T high-density cell (HDC) to have a cell height of 228 nm and a size of 0.0260 µm². This is coincidentally the same as the logic standard-cell height measured earlier. The estimate puts the cell near Samsung 7LPP/5LPP and slightly below TSMC N7/N6. That is a theoretical peak density of 38.5 Mib/mm². 6T HDC is arguably the most important cell as it is used for the largest caches in a chip, the L3 caches and system-level cache (SLC). Both the Kirin 9020 and 9030 have split the SLC into 4 banks to raise total SLC bandwidth. In the Kirin 9030, the SLC increased from 2 MiB to 3 MiB per bank. Correspondingly, the number of arrays within the bank also increased by 50%, from 16 to 24. Each array can store 128 KiB and forms an orderly pattern on the die shot. From the Kirin 9020 to the Kirin 9030, the area of a 128 KiB SLC array decreased from 0.0477 mm² to 0.0392 mm², an 18% shrink. The achieved density is 25.5 Mib/mm², 66% of the theoretical maximum. While the SLC was quite similar across both chips, the L3 has seen some major changes, particularly in terms of its layout. The total capacity also went up from 10 MiB to 12 MiB. Much like the SLC, the L3 is also split into 4 banks. In the Kirin 9020, an L3 bank consisted of 16× 128 KiB arrays and 16× 32 KiB arrays. However, an L3 bank in the Kirin 9030 instead consists of 48× 64 KiB arrays. In the Kirin 9020 L3, a 128 KiB array was 0.0513 mm² and a 32 KiB array was 0.0154 mm². The size of the 128 KiB array is different on the L3 and SLC as the assist circuitry for the two arrays differs depending on their purpose. In the Kirin 9030 L3, a 64 KiB array is 0.0210 mm². Although not a like-for-like comparison, normalized for capacity, it is 18% smaller than the 9020’s 128 KiB L3 array and 31% smaller than its 32 KiB L3 array. The achieved density is slightly lower than the SLC, at 23.8 Mib/mm², 62% of the theoretical maximum. Unlike the L3 and SLC, the prime cores’ private L2 cache uses a 2-bank design. As the prime cores’ L2 is latency-critical, it likely uses 6T HCC instead of 6T HDC. The 9020 has 16 arrays in each bank while the 9030 has 32. Each array has a capacity of 32 KiB. A 32 KiB array in the L2 shrank from 0.0171 mm² to 0.0142 mm², ~17% smaller. The density is 17.6 Mib/mm², ~59% of the theoretical maximum for 6T HCC. SRAM scaled well from N+2 to N+3, shrinking by ~19%, close to the theoretical logic shrink. The caveat is that N+2’s bitcells were unusually large, bigger than comparable 7 nm-class nodes, so part of the gain is catch-up rather than true scaling. With the insights from STEEL’s teardowns, we will be doing a deep dive into SRAM in a future newsletter article. Subscribe Everything above came out of a single STEEL teardown: die annotation, block-level area analysis and TEM cross-sections through logic and SRAM. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Future Roadmap The same cross-sections that pin down N+3 also show where SMIC can go next. Although N+3 is already close to the practical limits of DUV multi-patterning in several layers, SMIC still has a few scaling levers left. A theoretical N+4 would likely start with cell height. N+3 uses 5 M0 tracks between its power rails. Moving to 4 M0 tracks, as on SMIC N+2 and TSMC N6, could reduce cell height by roughly 15%. The routing grid is only one side of the shrink; the front end also must fit into the smaller cell. One possible FEOL lever is reducing the p-to-n isolation spacing from two diffusion grid units to one. Intel used this scaling booster on Intel 4, and TSMC did so on its N3 family. This path trades layout flexibility for density. Fewer M0 tracks reduce local routing resources, while tighter p-to-n spacing raises integration and design-rule difficulty. M2 is also constrained by the cell height shrink. For SMIC to maintain a ~5.7-track cell, M2 would need to move toward ~35 nm. That would move another layer into SAQP territory. SMIC could also reduce the CGP from 57 nm to 54 nm. Intel reached a similar CGP on Intel 10 nm/Intel 7 without EUV. The local interconnect is also tougher. If SMIC keeps the 3:2 M1-to-gate ratio, M1 would need to shrink to 36 nm and would likely require SAQP as well. If SMIC moves to a 1:1 ratio, M1 could relax to 54 nm, but it would give up routing flexibility. Under this theoretical path, we estimate that SMIC N+4 could reach a cell height of 198 nm and a CGP of 54 nm, implying a Bohr density of 137.8 MTr/mm², on par with TSMC N5 or Samsung SF4. However, the difficulty is cumulative. Each step is individually plausible, but together they make N+4 harder than the transition from N+2 to N+3. It will likely take longer, cost more, and carry less process margin. A theoretical N+5 would require a larger integration shift. One possible path is backside contacts (BSCon), moving power routing and source/drain contacts to the backside, which would reduce front-side routing pressure and enable another reduction in cell height. The front-side metal pitches could relax to reduce the process complexity. M0 would likely relax slightly to ~34 nm, and M2 and M4 pitches could relax further. CGP is unlikely to shrink much further. Even with EUV, 48 nm has been the practical limit for yield and process control. This approach would allow N+5’s cell height to fall to 170 nm and its CGP to 53 nm. This implies a Bohr density of 163.6 MTr/mm², on par with Intel 18A’s HP library. However, this would not make N+5 cost-competitive with the leading edge. It would reach a similar density through a much more expensive route. The integration difficulty rises sharply, with new process flows for backside alignment, wafer thinning, contact reveal, and backside metallization. Past this point, standard density and interconnect scaling become increasingly unattractive. That is where Huawei’s roadmap stops looking like a normal foundry roadmap and starts looking like a packaging roadmap. At ISCAS 2026, Huawei unveiled its tau (τ) scaling law, reframing process scaling in the time domain. τ is the time cost of data movement and processing: switching delays in transistors, RC signal propagation delays in circuits, compute, memory, and networking latency. Outside Huawei’s terminology, this is called system-technology co-optimization. This is Huawei’s answer to its lack of EUV lithography. Without EUV, planar density cannot keep pace with TSMC, Intel, or Samsung. If transistor density cannot shrink further, Huawei’s alternative is to shorten wires, reduce buffering, and stack logic vertically. “LogicFolding”, Huawei’s implementation of this new scaling idea, is, in practice, an aggressive 3D stacking approach. AMD V-Cache places SRAM above or below a CPU die. AMD’s MI350X places active interposer dies (AIDs) underneath accelerator and compute dies (XCDs), with the AIDs handling cache, IO interfaces, the network-on-chip (NoC) and embedded metal-insulator-metal (MIM) capacitors. With LogicFolding, parts of the same logic block are split across multiple active dies bonded face-to-face at ultra-fine pitch. This allows Huawei to shorten some critical paths and reduce buffer overhead, not merely add cache capacity or offload the IO and interconnect. Shortening wires is where the higher clocks come from. A large share of a modern core’s delay and energy budget goes into driving long interconnects and the repeater buffers along them. LogicFolding distributes a block’s critical-path gates across multiple stacked tiers bonded at a very fine pitch, so the bond interface behaves like an additional metal layer and the longest paths get shorter. That is how Huawei expects to recover frequency and efficiency it cannot get from the process alone. Huawei’s roadmap shows its intent. Prime core frequency is targeted to rise from 2.75 GHz in the Kirin 9030 to roughly 5 GHz by 2031, far beyond what planar scaling alone could deliver. Prime cores with 3.1 and 3.39 GHz clocks are being tested in its labs, although their power consumption is unknown. Beyond that, chips are in the design, simulation or pathfinding phase, meaning the frequencies are targets. However, the direction matters more: LogicFolding also helps with performance, not just density. The catch is that Huawei’s density claim is not directly comparable to foundry densities. A stacked design can report more transistors per package footprint by adding active layers, even if each patterned die remains well behind TSMC or Intel in front-end density. This is how Huawei can claim to reach foundry 14A-equivalent density by 2031. This is not a like-for-like foundry comparison, with Huawei using stacked logic and measuring density per package footprint. On a normalized Bohr-density basis, SMIC N+3 is ~114 MTr/mm², 38% less than Intel 18A’s HD library. Huawei’s 3D roadmap closes the gap by stacking active logic, reaching 215 MTr/mm² by 2030. In 2031, the roadmap density jumps to 295 MTr/mm², implying either a third active layer, partial EUV insertion or aggressive planar DUV scaling. Huawei’s methodology makes other foundries look much denser as well. Applying it to AMD’s MI450X with an N2 top die on an N3P base die yields a theoretical density of 460.2 MTr/mm² in 2026, compared with Huawei’s 295 MTr/mm² in 2031. This Kirin 9030 does not use LogicFolding, remaining in a conventional mobile SoC package. Instead, it forms the baseline for how far Huawei and SMIC can push planar scaling. Future teardowns of Kirin and Ascend chips will show both planar logic density and Huawei’s hybrid bonding solutions. Export controls changed China’s optimization problem rather than ending it. EUV restrictions raised the cost and complexity of leading-edge manufacturing without freezing it. SMIC reaches N6-class logic density through DUV immersion, SAQP and DTCO, while Huawei shifts more of the burden onto architecture, packaging and system-level integration. Future nodes will be tougher. N+3 still had room to tighten local metals and reduce cell height and CGP. Further scaling without EUV leaves fewer levers. More aggressive multi-patterning adds masks and overlay error. SMIC can keep pushing DUV, but each step will get more expensive and less forgiving. The design side is just as critical. Huawei had domestic EDA tools and flows before the Kirin 9030, with the Kirin 9000s, 9010 and 9020 making that clear. Huawei was able to ship multiple consumer SoCs on SMIC N+2 and N+3 while cut off from the Western EDA stack. US export controls restricted EDA tools for advanced chips in 2022 but did not target tools for more mature chips. In 2025, the US government briefly placed much broader restrictions on EDA software from Synopsys, Cadence, and others, before lifting them less than two months later as part of a trade deal tied to rare earths. Huawei has been unable to access those tools because it remains on a US trade blacklist. That forced Huawei, SMIC and Chinese academic institutions to build their own tools and flows. Researchers at Peking University recently announced a prototype EDA tool for Huawei’s LogicFolding architecture, which requires a new flow to handle the multilayer layout and floorplan. This is not the same as replacing the full Synopsys or Cadence stack, but it shows where domestic EDA is headed: toward tighter co-optimization between architecture, process and packaging. These advances are also diffusing into the Chinese ecosystem. SMIC is licensing its N+2 and N+3 processes to HLMC/Hua Hong at the government’s direction rather than by choice. If the same process learning feeds into Ascend accelerators for AI training and inference, the choke point shifts from one named fab to an ecosystem. Alibaba’s T-Head silicon arm and Cambricon, a Chinese AI chip designer that is expected to supply ByteDance, could also be major beneficiaries. Sanctions aimed at SMIC alone become less effective once the manufacturing knowledge has spread to other fabs and design houses. China is not closing the gap with Intel, Samsung and TSMC. The teardown shows the opposite in several places: no EUV, no backside power, higher process complexity, and visible trade-offs. But China is still advancing. If domestic chips become good enough for phones, inference, networking and security-sensitive workloads, they can matter strategically without matching TSMC at the leading edge. Behind the paywall, we show what else STEEL can do, with material and process flow analysis of SMIC N+3, and analysis of the Kirin 9030 package. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline, access the full Kirin 9030 and SMIC N+3 analysis or to commission a custom teardown, contact sales@semianalysis.com.

Jun 14 • 3:14 PM EDT • Technology • newsletter.semianalysis.com

In Retrospect: Poland, Russia, Europe

In April I spent a few days with Zryw, a Polish non-profit organization that recruits young people to enter politics and public life. Their aim is to build a new generation of civic leaders in Poland. I’ve followed their evolution from the beginning; my son is one of the founders. (“Zryw” means “impulse” or “burst of speed” in Polish). Julie Chmielewska, one of Zryw’s leaders, interviewed me at the event. Here is the conversation, lightly edited for grammar and context: Julia Chmielewska: Assuming that culture shapes institutions, could we conclude that some cultures are more prone to authoritarianism? Anne Applebaum: I don’t think so. Every culture, every nation, and every country has the potential for change. When we look at history, we can see that nations do change. Poland has changed, for example, from the 1980s to today. There’s a completely different mentality here than the one I encountered when I first visited, in 1988. The current generation sees the world through a different frame. So no, I don’t believe that certain nations are doomed to authoritarianism, or to anything. Of course, there are political and cultural traditions, but they can always be challenged. JC: So do you think there’s something intrinsic in human nature that pushes people to democracy, freedom? AA: I think there is something in human nature, a deep instinct, that pushes people towards freedom, yes. People who live in authoritarian countries – and I’ve heard this from them over and over again – sooner or later find themselves angered by a feeling of helplessness. They can see the unfairness of crooked courts, irrational political decisions. They naturally dislike the fact that somebody has all the power while they have none. In those circumstances, people who have no political background at all sometimes are politicized just by that frustration. I’m thinking of Svetlana Tikhanovskaya, for example, who was a Belarusian housewife, until the circumstances of her husband’s arrest forced her to become a politician. I met a woman who is a Uyghur from China, and who had come to live in Turkey. After several of her family members were arrested, she too was propelled into a political role. These kinds of experiences make people interested in democracy or political reform, even if they thought they didn’t care. I also think that the opposite is true: some people instinctively prefer homogeneity, order, hierarchy. They don’t like the confrontation of opinions or the noise of debate, and they lean towards a single leader or a unified regime. I think that most of modern history is shaped by the clash between those two different sides of human nature. JC: Which force do you find more powerful today? AA: I think they both have a lot of power and there’s no way of predicting who wins. Even in countries that seem very shut off or stuck in an authoritarian system can change. We’ve seen that happen. And vice versa, I think democracies can also begin to fail and slide into authoritarianism. So it’s very important not to be complacent in either direction. JC: I want to go into your history and your experiences. You’ve lived in Russia during a time of incredible change in the late 80s, and then you came back to do research in post-Soviet Russia. AA: I was a student in Russia just before the collapse of the Soviet Union. I then traveled and worked there extensively in the 1990s, and I met people who really were full of hope. They hoped that Russia could become a democracy, or at least a place that guaranteed freedom of speech, freedom of the press. These weren’t ideas that came from Washington. Although now the narrative in the Kremlin is different, I met many people who were working towards exactly that kind of transformation. A friend of mine ran a series of seminars – once a year, and then later several times a year, in cities all over Russia – designed to get people to discuss politics in new ways. She invited young politicians and journalists, as well as foreigners. I went to many of them, in Moscow as well as Volgograd and Novosibirsk. Over two decades, hundreds of people came to these seminars, and all of them wanted Russia to be different. I remain convinced that many Russians still want Russia to be different. It’s just that the old system, in the form of Putin, a representative of that system, returned. The KGB reimposed their power. And this wasn’t because of something to do with culture, this was to do with money and politics, and who was in charge. JC: You’ve partially answered my question... I wanted to ask whether that excitement of the 1990s was an exception from the Russian historical norm which has been rather authoritarian? AA: Of course, if you look at the history of Russia since the 18th century, there have always been liberal groups, reformers and revolutionaries. There were the Decembrists, in the early 19th century, and the rural populists later on, who tried to organize the peasantry. A significant group of liberal Russians appeared at the turn of the 20th century, people like the father of the writer Vladimir Nabokov, who was a Russian politician before the revolution. These groups were small in number, but sometimes had a broader significance. The Russian dissident movement in the 1950s and 1960s actually invented the modern human rights movement. It was their idea to hold the leaders of the Soviet Union accountable for the language they used, to remind them of the treaties they had signed and the promised they had made. They created the Helsinki group, for example, that held Soviet leadership to the language of the Helsinki Treaty, which formally ended the Second World War and which contained guarantees of human rights, at least on paper. That idea spread to Eastern Europe, and similar tactics were used here in Poland. (Benjamin Nathans recently wrote a great book about the Soviet dissidents, To the Success of Our Hopeless Cause) JC: But seemingly, even if they do ever get power, they could never hold on to it. AA: We don’t know, because they have never really had power. But in any case, we also don’t know what will happen in the future. There is no reason to assume that Russia will forever be condemned to totalitarianism. Even during the first fifteen years of Putin’s regime there was some form of democratic politics. Only when the full-scale war began did he bring everything to an end: he shut down all remaining independent organizations and killed his most important opponent, Alexi Navalny. But does that mean he is popular, that he enjoys legitimacy? Even now, we don’t really know what Russians think about him, or about the war. There is no way of doing real polling in Russia because nobody will answer an independent pollster over the phone. JC: Do you think that the flock of young people, especially young men, who fled Russia after the start of the war represent a continuation of liberal movements that refuse to partake in the authoritarian state, or do you think they are moving for the sake of self-preservation – “I don’t want to go to the army, so I leave”? AA: I’m sure it’s both. I actually know many people who left Russia for political reasons either in the very last years before the war, or else just after the war began. JC: And do you see a change in the young generation versus the old one that have fled? AA: It’s hard for me to say because the people who I know were mostly already politicized. I do know some younger people who left, but most of them were already either active in the opposition, or working as journalists. Many of them very self-consciously think of themselves as maintaining Russian culture abroad, just as in the 20th century the Soviet dissidents or the anti-communist White Russians once thought of themselves as people who maintained the true Russian culture, outside of Bolshevik Russia. JC: Let’s move on to Poland. One of your first experiences with Poland was reporting on Margaret Thatcher’s first state visit, in the autumn of 1988. You followed her around the farmers’ market. AA: She was making a symbolic gesture. She wanted to welcome free markets, but the only one she could really visit was the farmers’ market at Hala Mirowska. JC: And I want to talk about this area because today you can look out of any window in that district, Mirów, and see rows and rows of skyscrapers. How does it feel to see a country built from the ground in such a short time, both politically and economically? AA: You know, when you’re living in a period of dramatic change, it’s always hard to know whether the change is positive or negative. The transformation in Poland created winners and losers, as well as new classes of people, new political movements of different kinds. At the time, it sometimes just felt like turbulence. Now, we can look back at the 1990s and early 2000s in Poland and see in retrospect that the country was headed in towards prosperity and European integration. In around 2009 or 2010, I remember speaking to a woman who worked cleaning government offices, and who asked me if her daughter should study in Amsterdam or in London. This seemed to me to be a real transformation, and it happened very fast: within a few years, Poles stopped feeling like second-class Europeans, and a generation of younger Poles appeared who had no qualms about crossing borders. JC: We again live in turbulent times. Do you think we’re heading towards positive outcomes? AA: I can’t make predictions, because everything that happens tomorrow depends on what people do today. Also, history doesn’t have a trajectory. It doesn’t move in a particular direction. You only think it does afterwards. As I said, you can look afterwards at Poland in the 1990s and you could say this was a period of great progress. But not everybody at the time saw that. Sometimes people are very wrong about a given country’s trajectory. I remember people writing about Ukraine in the 1990s, about how dangerous it was that Ukraine was independent, that this was going to be an anti-semitic state, that Ukrainian nationalism was going to come back... and although you can say many bad things about Ukraine, the country made many bad choices, actually it became a pretty tolerant state which now has a Jewish president and until recently a Muslim defense minister. JC: In the 1990s Poland’s and Ukraine’s GDP were roughly similar. Ukraine was even a bit richer. So the starting point was similar for both of these countries, but they’ve turned out very, very differently. Even if we stop in 2014. Why do you think Ukraine “chose East”? Was it external influence? AA: No, I think Ukraine was in a different position. First of all, central and eastern Ukraine had been part of the Soviet Union since the 1920s, and had therefore experienced a longer period of occupation, repression, Sovietization and even Russification. Ukraine did not have, as Poland did, an alternative elite: a large cadre of economists and politicians, who already had some experience in opposition politics and in independent thinking. In Poland, the transformative finance minister, Leszek Balcerowicz, had already thought about how an economy might be decentralized before he took office. We can argue about how successful he was, but at least he was prepared to try something new. In Ukraine, people weren’t prepared. For one, there hadn’t been much space even for an illegal opposition in the previous couple of decades. Ukraine was also in many ways still emerging from colonial occupation. For the previous couple of centuries, many of the most capable Ukrainians had moved to Russia, and the first generation of leaders in independent Ukraine tended to be former Soviet apparatchiks. They didn’t immediately seek to become closer to western Europe. Instead, they looked for some kind of economic and political “third way” and an accomodation with Russia. Ukraine became stuck in this position, half-in, half-out and became pretty profoundly corrupt. JC: The Polish alternative elite had a vision of where they wanted the country to go. I think the general perception now is that this vision ends with the next election, right? Is this a natural process of gaining power? AA: Their vision, and it was shared by the time by politicians who now call themselves both left and right, was that Poland should join the EU and NATO, as fast as possible. My husband (Radek Sikorski) was in the very first government that said it wanted to join NATO. At the time this was taken very badly in Washington: the demand for NATO membership absolutely came from Poland, not from the US. But even then, in the early 1990s, many Poles already understood that the Russian sphere of influence would grow once again, that it was important to become part of the transatlantic alliance and an integrated Europe as fast as possible: NATO, the EU, the Council of Europe, all of the trading and security alliances. That consensus lasted through several different governments and over many years, even governments containing former communists. For twenty years, right, left and center disagreed about many things, but they all agreed on the direction that the country should go. In retrospect, that consensus created the basis for prosperity in Poland. JC: And regarding the Polish-Ukrainian relations. We know that antagonisms, e.g. the myth of Wołyń [a massacre of Poles that took place in what is now Western Ukraine, in 1943] are increasingly used in Polish political discourse today. In Ukraine, nationalist historians make heroes out of UPA, the Ukrainian revolutionary army, which is held responsible for this tragedy. Where do you see a start for burying the hatchet? Is the reconciliation of our nations possible? AA: So here’s a strange thing: For more than a decade after they both gained independence, Poland and Ukraine participated in a number of projects that were designed to solve exactly these kinds of Polish-Ukrainian differences. Books were published that were jointly written by Polish and Ukrainian historians, monuments were built. These projects were successful: If you’d asked me this question in 2010, I would have said that there are no real historical issues between Poland and Ukraine, that the two countries had mostly reconciled. For a number of reasons, this began to shift. In Poland, children and grandchildren of victims of the Wołyń massacre felt their tragedy hadn’t been fully recognized. In Ukraine, nationalist groups wanted the UPA partisans who had fought against the Soviet Union recognized for their heroism, despite the fact that some in this broad and not well-defined group were also linked to Wołyń. In both eastern Poland and western Ukraine, the memory of the ethnic cleansing that took place there in the late 1940s - Poles were moved West, Ukrainians East - to accomodate new borders has left feelings of displacement and bitterness. The wave of Ukrainian immigration, which started before the war, may have provoked some of this. But don’t underestimate the role played by Russia, which has always wanted to create discord between Poland and Ukraine, and pumps out propaganda to that effect in both countries. Vandalism on both sides of the border, damage done to cemeteries and monuments, is sometimes carried out by provacateurs working for Russia, just to create bad feelings. The Polish-Ukrainian conflict also hides a different reality: although there are real grievances and bad memories, the truth is that the biggest tragedies both nations experienced in the twentieth century were inflicted by the Soviet Union and Nazi Germany. That should unite these two countries, not divide them. JC: This week (19.04.2026 – Ed.) in Hungary we saw that using historical narratives to create grievance has no longer worked. Are you optimistic about the direction after Orban’s loss? AA: The Hungarian election was really important because it shows that people can actually resist powerful, dominant propaganda. The ruling party Fidesz controlled all of the TV, 90% of the newspapers, the constitutional court, the bureaucracy... and yet people had the nerve to vote against it and resist it. It’s also an important election because Hungary created a huge network of well-funded think tanks and foundations that were funding other far right groups and projects. So that loss is going to have a knock-on effect in other places too, maybe even in Poland. JC: What you’ve said about resisting propaganda reminds me of one of the interviews you did about talking to Russians in the post-Soviet era who claimed that people knew that the system was lying. Why do people choose to believe in these regimes anyway? AA: Because it’s useful. Stephen Kotkin, an American historian, once described how people would learn how to “speak Bolshevik”: It was just a language that you needed to use in order to get by in society. If you didn’t talk the way the regime wanted you to talk, then you might have problems, or lose your job, or your kids would not get into university. It was a way of getting along. Communist Poland was similar, at least in the early years. When I was writing a book about the Sovietization of Eastern Europe after World War II, I met a lot of people who wanted to talk a lot about the war, when many of them had done really heroic things. But almost nobody wanted to talk about the decade afterwards, between 1945 and 1956. This was when you had the short period of terror, violence and Polish Stalinism. To stay safe, people felt the need to just go along with what others were saying, or stay out of politics, or use communist jargon without necessarily believing it. I don’t judge them for it, and I wouldn’t describe all of those people as collaborators. It’s easy to feel moral certainty in retrospect, because we know that communism ended. But if you were living in 1949, it might not have been clear to you that communism was going to end. In very repressive societies, maybe about 10% of people who are really evil – collaborators, torturers. Maybe 10% of people are really brave and who find ways to resist. And then around 80% of people have kids, they have parents, they have to have jobs and they’ve had to get through it somehow. Strange though it may sound, I’ve found that the work I did on that book is now very useful in explaining some of what’s going on in the United States today. There are plenty of members of the Republican Party in Congress who know perfectly well that Donald Trump says many things that aren’t true. But they avoid criticizing him because it’s inconvenient, because they would get in trouble, because someone would attack them on the internet. So actually you don’t even need mass terror to get people to conform. JC: Is Gen Z reinventing agency? AA: We’ll see. In any generation you can find groups of people who want agency. If you live in a society that’s unfair, for example where a tiny number of people have captured most of the wealth, then at a certain point people rebel. This is always easier for younger people, who have you have less to lose. The question now is how the internet will affect the relationships between citizens and the state, and how it will change organizations. The “Twitter revolutions” people spoke about as early as 2008-9, mass protests organized online, have often proved to be very superficial. Although you can easily join something just by clicking, the work of creating a real movement requires physical participation. Successful mass movements of the past, for example the Civil Rights movement in the United States, were built by people who had spent many years together. They had participated in non-violent workshops together. They practiced and prepared. Just using the you can get everybody to show up at a certain moment, but then when the protest is broken up, nothing happens. It’s also true that the repressive regimes have more recently learned how to use the internet to undermine leaders and movements. Most of all, they now understand how to make people cynical. The form of propaganda that most modern authoritarians use - and I think the Russians really pioneered this - deploys sneering cynicism. Instead of praising the rulers, the regime propagandists mock the opposition movement leadership for being supposedly corrupt or sexually degenerate, with the goal of undermining any kind of idealism. JC: MAGA grew on the internet? AA: MAGA is a real grassroots movement. They had symbols, red hats, they showed up at rallies. But you’re right that the cynicism, the sarcasm and the distrust that the movement embodies came originally from the internet, and this is different. Fifty years ago, most European political parties had a social base in real organizations, in trade unions for Social Democrats, in church groups for Christian Democrats. As people stopped going to church and as the trade unions became smaller, the social base of these parties disappeared. Now people who wouldn’t have known one another in the past meet on the internet and create new movements, many of which don’t last. JC: Is history ending? Going in stale circles? AA: History never ends. In truth, human beings want contradictory things at the same time: order and freedom, stability and change. These things are never resolved, and when a society swings too far in one direction, people always push it the other way. But each time that happens, the result is different. Certainly it is true that we are now in a period when old philosophers and ideas from the 1930s are undergoing a revival. Carl Schmitt, who was a philosopher who was very influential on the Nazis, is back as an influence on both the far left and the far right, once again inspiring people to think that all politics has to be existential, that all politics is about defining and defeating enemies. In the US, some are also bringing back dominionism, the idea that we need a Christian and not a secular state. It’s also true that we are living through a more fundamental shift. The old left-right divide, which was mostly about the size of the state, is much less important now. Instead, we are arguing about more fundamental things: what are our rights, what protects our freedom. Or how much power do we want to give small elites, versus how much power to give ordinary people. In America a small group of very wealthy people now control information, because they control the social media platforms and AI, which means they determine what people will read and learn. Finding a way to preserve preserve zones of autonomy and independent thinking, this is going to be the main task for the next generation. JC: What is the one book that a person interested in Central Europe should read? AA: You could read my book “Iron Curtain: the Crushing of Eastern Europe” which tells the story of how the Soviet Union imposed its culture on the region. Or read Tim Snyder’s history of the region, “The Reconstruction of Nations: Poland, Ukraine, Lithuania, Belarus 1569-1999.” And everyone should read “The Captive Mind” by Czesław Miłosz, which describes exactly the phenomenon we were talking about: why do people collaborate? I joined Scott Galloway and the brilliant Fiona Hill in a discussion about the wars in Ukraine and Iran, the future of global power, and what these conflicts reveal about America's role in the world. Scott told us that this was the 400th edition of his podcast, which is quite a number: My friend Francesco Chiamulera runs a brilliant series of literary events, Una Colline di Libri, A Hill of Books, in the Prosecco Hills, a region of vineyards and elegant hill towns in the Veneto, north of Venice. We did an event in Vittorio Veneto and also drove and biked around the countryside. Buy Autocracy Inc Buy Iron Curtain Open Letters, from Anne Applebaum is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe

Jun 13 • 4:15 AM EDT • Politics • anneapplebaum.substack.com

A Scientific Achievement That’s Totally Random

Encryption systems rely on “random” numbers, but conventional computers can’t generate them perfectly. New research shows that quantum physics can.

Jun 9 • 1:10 PM EDT • Science • nytimes.com

Sophie Cunningham's 'business mode' look aged perfectly after Fever win

The Fever guard kept things simple before Monday night's matchup in Washington, then helped Indiana leave town with one of its most dramatic victories of the season.

Jun 9 • 7:52 AM EDT • Business • sports.yahoo.com

The Braves Should Avoid the Tarik Skubal Deadline Trap

Tarik Skubal is the kind of pitcher who makes a perfectly reasonable front office start spouting bad ideas. Which, honestly, makes sense. Skubal is one of Major League Baseball’s best left-handed aces, a two-time reigning American League Cy Young Award winner, and one of the few starters in baseball who could make a great team …

Jun 4 • 11:17 PM EDT • Sports • sports.yahoo.com

The cult of objectivity has broken consciousness science

With 22 competing theories and no consensus in sight, the science of consciousness is lost. In this exclusive IAI interview, Berkeley psychedelics researcher and bestselling author of a new book on consciousness Michael Pollan insists that even though consciousness lies beyond traditional forms of scientific investigation, we should resist becoming dualists who see it as something radically different from ordinary stuff in the world. Instead, he makes the case for reimagining science itself: it must stop reaching for objective descriptions of physical reality, and instead embrace subjectivity as the indispensable core of all inquiry. Alasdair Craig: How did your experiences with psychedelics get you interested in consciousness?Michael Pollan: One of the things that happens on psychedelics is that they foreground consciousness. What formerly seemed perfectly transparent and ever present—and yet, non-existent, as water is to fish—suddenly you see it. The metaphor I use in the book is that psychedelics sponge the windscreen. Suddenly you realize: wow, there’s a windscreen, there is this phenomenon that’s mediating my relationship to reality. This is not an unusual realization on psychedelics. I think a lot of people are called to think about consciousness after psychedelic experiences. So that was the proximate inspiration for A World Appears: A Journey Into Consciousness (2026).And then there was a more specific way in which psychedelics influence the book. That was a particular experience I had on psilocybin. I had the sensation that the plants in my garden were conscious and returning my gaze, with their own point of view and agency, and I got curious as to how to evaluate an insight like that. I mean, what’s the truth quotient of insights you have on psychedelics? And I was reading William James—who, of course, wrote beautifully about mystical experience—and he said, we can’t judge the metaphysics here, but we should pay attention to two criteria: (1) How useful is it to think this is true? He was a pragmatist, of course. And (2) can we subject it to other ways of knowing to validate it? Because maybe we should treat these insights as hypotheses. And that’s kind of how I approach psychedelics, as a great generator of hypotheses, but they need to be tested against other ways of knowing. And so that led me down this long path exploring the science of plant intelligence and plant sentience.AC: I love your phrase, “smudging the windowpane of conscious experience.” So do you now start to think, actually, even when I’m not on psychedelics, I’m just seeing a screen all the time, which is not reality, but rather is my conscious experience, behind which reality is hidden?MP: Well, you know, not when I’m brushing my teeth and making breakfast! But yes, in meditation, certainly. I mean, one of the things you realize is that my experience happens to be this way now, but it could be another way. And many things can affect it. My morning coffee changes the screen in various ways, sharpens certain things.___The only tool we have to understand consciousness is consciousness.___There’s this wonderful metaphor in Aldous Huxley’s book, The Doors of Perception, where he thinks we channel consciousness, and that what the brain does is edit it, and there’s a reducing valve. And psychedelics open the reducing valve wider. I don’t know that I’m ready to buy into the idea that consciousness exists outside brains, but I do love that metaphor because there are times when you are more conscious than others. I know I’ve switched from window pane to valve, but you know, a camera aperture can open wider or narrower. And I do have a sense of that. I have a sense of when I’m being less conscious, and when I’m being more conscious, by which I mean, you know, more present, taking in more sensory information. So when you start thinking about consciousness as a matter of degree, that changes things also.I think when we scroll on social media, obviously, we’re conscious. But minimally so, because we don’t have to generate our own thoughts. We’re basically taking these little hits from other people and thinking their thoughts.The process of writing this book made me kind of relativize everyday consciousness as just one type, which is exactly what William James said. Normal consciousness is one type, separated by the flimsiest of screens from the other types.AC: If we stick with the metaphor of the smudge on the window that you get when you take psychedelics, it seems like we’re saying that consciousness has become like an object that you’re looking at, right? You’re looking at the smudge and that just is consciousness. But sometimes we’ll talk about consciousness as a state that you’re in, rather than an object you can perceive. You’re either conscious or you’re not conscious. So it’s difficult to really know what sort of thing we’re talking about when we talk about consciousness, right?MP: Yeah. When you say that consciousness is an object, what’s the subject? Consciousness also? The only tool we have to understand consciousness is consciousness. So we’re in a closed loop and when we get a perspective on consciousness, we’re not achieving any distance from it. That’s one of the problems with studying consciousness. Science is predicated on an objective, God’s eye view of things, and you never get that. There’s no such thing. SUGGESTED VIEWING The brain filters consciousness With Àlex Gómez-Marín But, yeah, there is this sense, I think, that consciousness is something you can approach. Certainly, when you’re writing and trying to recreate a conscious experience, it’s a fiction, but you’re standing back from it, and looking at it. But there’s no real subject-object distinction.AC: That’s so interesting, because when philosophers start talking about consciousness, they often talk about what they call “qualia”, which is their word for the felt experience of, say, the color red. And I think that way of speaking often makes us think of the qualia as an object, which you can see out there: the redness of red. But, as you say, we also want to collapse the subject-object distinction when we talk about consciousness. So there’s something strange going on here…MP: Oh, yeah, there are many strange things going on! *AC: Let’s talk about the varieties of consciousness. Apart from perceptions, there are, pains, emotions, thoughts, and so on. What is it that holds all this diversity together so that they all count as instances of consciousness? What is it that they all have in common?MP: Yeah, that’s a really good question. I mean, there is sensory information, right? Which we perceive coming from the world outside ourselves. And then there’s internal information, which is what’s called interoception. And this is very important too. This is where feelings come from—what are sometimes called homeostatic feelings—that are intended to get the brain to keep us in these narrow ranges of temperature and glucose and so on. That’s a very important part of consciousness. And according to some researchers, who I think are probably right, this is where consciousness begins, with these very primal feelings, like hunger and thirst and warmth and itch, and things like that. And then there’s this third thing, which is our thinking about thinking.And what holds all this together is, I think, the sense of self. Consciousness generates a self: it is probably an organizing principle. All this stuff is happening to the self. It’s a construct, I think, but it is very useful. It can connect all these different streams of consciousness to a single being or a place, metaphorically speaking.* AC: The first half of your book is about the science of consciousness. Is consciousness just another thing, like black holes or the origin of life, which we don’t yet understand, but which we should have every hope that science will one day get to the bottom of, or is there something special about consciousness that makes this unlikely?MP: It depends how you define science. As we define science now, it’s going be very difficult. Science since Galileo has had a set of rules or assumptions, which is that the proper remit of science is to deal with the objective, quantifiable, third-person world. And the physical sciences have done amazing work under that set of limitations. Galileo—for tactical reasons, not for philosophical reasons—said science should not deal with the soul, which at the time was what consciousness was considered to be. So what is subjective, what is qualitative, what is first person, we’re going to leave to the church, he said. And this was a good agreement, and it probably kept many scientists from getting burned at the stake. But it leaves science in an awkward spot, now that it wants to venture into that territory of qualities, of first-person experience. And I don’t think it has the proper tools. I think it will have to add to its toolkit in some way, and find a way to bring in phenomenology, or lived experience and introspection, which science is usually dismissive of.The challenge—we alluded to it earlier—is that the only tool you have to study consciousness with is consciousness. And what I mean by that is, the whole scientific enterprise is a manifestation of consciousness. As much as scientists like to flatter themselves with this idea that they attain a godlike perspective on what they study, when it comes to consciousness, there’s no such thing. And it’s fine for all the things you can step outside of and study from that perspective. But two areas it won’t work are consciousness and cosmology.___It could be that we live in this anomaly of not believing nature is conscious. And maybe psychedelics are teaching us something we’ve forgotten.___With cosmology, too, you have to study the universe from inside the universe. It’s not that cosmologists haven’t made progress. We know a lot about the age of the universe and expansion and things like that. But we can’t penetrate what happened before the Big Bang. We just don’t have the tools because we can’t stand outside.The fact that there are 22 full-scale theories of consciousness tells you all you need to know: we’re kind of lost. And I was surprised to find that we are more lost than I thought we were when I started this. I thought that there’d be leading theories, or that we had experiments that we could conduct that would nail some of this down. And the fact is, we don’t.So, I don’t know if science can solve it. But, of course, we can change the rules of science. A really influential book for me was a book called The Blind Spot, by two physicists, Marcello Gleiser and Adam Frank, and a philosopher, Evan Thompson. It’s a reminder that science is a social practice. It’s organized in a certain way and it can be organized in a different way. But it has this huge blind spot at the center of it, which is its failure to recognize that it is a form of consciousness itself, and it doesn’t have a good way of taking account of lived experience.AC: On the one hand, I think you want to resist traditional dualism, which would say that consciousness is something magical, something supernatural, right? But on the other hand, you’re accepting that there is this division in reality between the stuff that you can investigate objectively and measure, and so on—the stuff that science has been good at finding out about—and consciousness, which, for some reason, you can’t objectively measure. Some would take this to suggest that the dualism is actually right. Is there a way of reconciling these two strands of your thinking?MP: To call it magical is full of value judgement. I mean, you could just say immaterial. The interesting thing is, I think, that intuitively we’re all dualists. And I don’t know whether that’s because we’re children of Descartes and other cultures are less dualist than we are. Or it may be because that’s how consciousness feels. It feels different than the body. It feels separate from the body. But I don’t think we should trust that intuition.AC: So let’s think about ways that science might be changed to get at consciousness. I suppose the starting point would be the way that you begin the second half of your book. You have this nice line from William James, where he says, “We now begin our study of the mind from within.” Could you give us some sense of the ways of studying the mind from within? What does this involve?MP: I don’t know what that science looks like. But one of the points I’m making is that it depends on the humanities. I found that I learned as much, if not more, about consciousness, from reading novels and poetry. There is a sense of the fullness and complexity of consciousness that you don’t get in any scientific account. I interviewed this stream-of-consciousness writer and I approached her as an expert on consciousness, and she is. So I think that dialogue is really important.I’m reading Ben Lerner’s new novel, Transcription. Just read the first page of this novel: it’s a man falling asleep on a train, going to conduct an interview, and there is the experience of being in the train, facing backwards, there’s the view outside, but there’s a reflection of himself through which he’s seeing outside. There’s the memory of dropping his kids off at school. All this is going on in one moment of consciousness. Scientists aren’t going anywhere near that. But I think it’s useful for them to think about. That’s where we’re trying to go: like, how does such a layered experience happen? You know, it also makes you wonder, could a computer ever do this? And it gives you some doubt.*AC: Before we get to computers, perhaps we could talk briefly about plants. How did you come to feel that they are sentient?MP: Well, it was a psychedelic experience, and it’s not unusual on psychedelics. In fact, there’ve been studies about how people become animists during psychedelic experience. Their belief in animism arises after a single psychedelic experience.You know, most scientists would say this is all a myth. But we have only 400 years of the age of enlightenment and science. Before that, we were animists, and if you go around the world, most indigenous cultures are still animists, and children are animists, by and large, until they go to school. So it could be that we live in this anomaly of not believing nature is conscious. And maybe psychedelics are teaching us something we’ve forgotten.As I said, I wanted to test this idea, and see whether there was conventional scientific evidence for it. And there’s fascinating work going on in a field that calls itself—in a rather cheeky way—plant neurobiology. These are people who fully realize there are no neurons in plants, but they’re trolling conventional botanists! And they’re doing experiments that are demonstrating that plants can learn and remember, which is kind of amazing: how do you store a memory without neurons? They’re finding plants that can solve problems, that can hear and see. In fact, they have 20 senses to our five or six. For example, a bean plant looking for something to twine, and given a pole within eight inches or so of it, knows where that pole is and will aim its spiraling reach at that pole. What is that about? Is it bouncing signals off of it?One of the spookiest things is the fact that the same anesthetics that put us out put plants out. You might think that plants are already out, but they’re not. They’re just slow. If you take a plant with a fast behavior, like a Venus flytrap, and give it an anesthetic, it will stop reacting. So you have to ask yourself, what is lost when the plant is anaesthetized? Some state of alertness, of awareness, of its environment, of reactivity. I’m not prepared to call that consciousness, but maybe it’s sentience—which is a simpler form of consciousness, and involves the ability to sense what’s going on in your environment and assess what’s good and bad for you and act appropriately. That may be a universal quality of life.AC: Let’s turn from plants to AI. You point out that LLMs are trained on the internet rather than direct interactions with reality. You put this really nicely when you say the internet is not the world so much as a shadow cast by the world. So LLMs are living in this kind of shadow world, or in Plato’s cave, as you put it. What’s the significance of this for AI consciousness and moral consideration? And, to recall the beginning of our conversation, if we’re stuck behind a veil or screen of consciousness, are we also stuck in a kind of shadow world?MP: I don’t think they’re the same shadow worlds because our world includes nature and other people. And even if it’s mediated in some ways... There is a reality out there, and we are in relation to it, through consciousness. There’s friction with it. What’s missing from the LLMs is that friction with the real world. There’s more to the world than human representation, which is what they’re dealing with. And that means that so far, AI is a cultural technology for combining thoughts we’ve already had. SUGGESTED VIEWING Cracking the code for thought With Roger Penrose, Sabrina Gonzalez Pasterski, Max Tegmark, Güneş Taylor Genuine creativity is more than collage. A lot of what AI does now is tantamount to collage, putting this with that and you know, that’s part of art, certainly. But there are also genuinely new ideas that come into the world that don’t come in through manipulating the representations we already have. So it remains to be seen whether AIs can be truly creative in that more radical sense.But my arguments against AI becoming conscious have to do with the fact that the idea of AI consciousness is based on this faulty metaphor that the brain is a computer. There are dimensions in which the brain is like a computer—it certainly does its share of computation. But computers have this sharp distinction between hardware and software, and brains don’t. In a brain, hardware and software are identical. Every memory is a physical change to the brain. Brains are not interchangeable in the way computers are. So this idea that you could take the consciousness out and somehow run it on another substrate, a silicon substrate, seems foolhardy.Anyway, it’s a more extended argument, and I may be wrong. And when we move to computers that aren’t based on silicon—neuromorphic computers and things like that—all bets may be off.AC: Regarding your point about LLMs being stuck in the world of our representations—the internet or whatever—and not being able to interact with reality, are these representations, the world of the internet, themselves part of reality? We’ve created the internet, but it’s still a real thing that exists. And so while the LLMs may be trapped within that part of reality, they’re nevertheless within reality and interacting with it and so on. So it’s not obvious that there’s a barrier there to them becoming conscious and being creative.MP: Yeah, I mean, if our consciousness is built out of human culture, which is what we’re talking about with all those representations. But I tend to think consciousness comes a bit more out of nature and friction with the natural world, but...AC: So there’s a fundamental divide between the natural world and human artefacts?MP: And we lose track of that, and especially people in this computer world. You know, the people who are creating these AI systems, my guess is they’ve been into video games since the age of three, and they didn’t go outside a lot. And their own contact with nature is somewhat limited. I could be wrong, it’d be a very interesting thing to ask, all the wizards of AI, whether they like to go fishing and hiking and do things like that. Or were they on computers from a very early age? I interviewed Blake Lemoine, the Google engineer who came to believe that LaMDA, the Google LLM out at the time, was conscious and deserved personhood. It was very clear that he didn’t see a distinction between this bubble in which he lived and the real world. And I think we’ve all been distanced from the natural world, and this is taking us even further away. But it’s still there, you know, and we still have bodies, and we’re still mortal, and we don’t live in a simulation.AC: I think that’s a great place to finish. Michael Pollan, thank you very much!

Jun 3 • 8:00 AM EDT • Science • iai.tv

One SEC coach perfectly explains flaw in College Football Playoff debate

Mike Elko will tell you the truth about why coaches' opinions on the College Football Playoff shouldn't matter.

May 27 • 5:13 AM EDT • Sports • usatoday.com

Is President Trump healthy? What we know about his recent medical exam

President Trump said, "Everything checked out PERFECTLY" after a three-hour visit to Walter Reed. Here are the details.

May 26 • 5:45 PM EDT • Health • koat.com

Is President Trump healthy? What we know about his recent medical exam

President Trump said, "Everything checked out PERFECTLY" after a three-hour visit to Walter Reed. Here are the details.

May 26 • 5:45 PM EDT • Health • kcra.com

Betting On Herself: After 30 Years Of Service, Local Leader Launches Consulting Business To Help Teams Thrive

SOUTHERN MARYLAND — After 30 years in federal service, Mandy Miedzinski didn’t walk away with a perfectly mapped-out plan — she walked away with a feeling she couldn’t ignore: she still had more to give. What began as a simple conversation with a mentor quickly gained momentum. One introduction led to another. Ideas turned into […]

May 26 • 10:00 AM EDT • Business • thebaynet.com

New MatterChat Model Helps AI to ‘See’ the Language of Science

From writing emails to generating computer code, much of the artificial intelligence prevalent in our daily lives has succeeded by mastering one domain: text. However, this leaves a major blind spot in the physical sciences, where models depend on the high-resolution, three-dimensional data of the physical world, like the intricate lattice of atoms in a crystal. Delivering on the promise of using AI for science requires teaching these data-driven text models to seamlessly “talk to” physics-based models. Now, a new AI framework from Lawrence Berkeley National Laboratory (Berkeley Lab), called MatterChat, solves this problem by creating a specialized “bridge.” It connects the conversational power of a Large Language Model (LLM) with a physics-based AI that models “interatomic potentials”: the complex physical forces between atoms. The resulting system already significantly outperforms general-purpose AI tools like GPT-4 at predicting material properties, and the team hopes it can accelerate scientific discovery by serving as a robust research partner that provides grounded insights and generates step-by-step instructions for synthesizing novel materials. A paper describing this work was recently published in Nature Machine Intelligence. “Traditional simulations can provide the physical rigor required for materials science, yet their computational cost remains prohibitive for high-throughput screening. Conversely, while LLMs excel at rapid knowledge synthesis, they inherently lack the ‘structural vision’ to interpret materials directly from their underlying atomic coordinates,” said Yingheng Tang, a postdoctoral researcher in Berkeley Lab’s Applied Math and Computational Research Division (AMCR) and lead author on the paper. “MatterChat was built to solve this dilemma, empowering LLMs with a structural ‘vision’ that allows researchers to leverage their full potential for solving complex, real-world materials challenges.” Empowering language models to solve complex challenges in materials science To build MatterChat, the Berkeley Lab team drew inspiration from technologies like Vision Question Answering (VQA) and Text-to-Image (T2I) generation. In these tasks, AI must translate high-level text concepts into visual images, or vice versa. Doing so requires developers to build tools that “bridge” two fundamentally different forms of data. The researchers adapted this concept to the physical sciences. With MatterChat, they created a “bridge model” that successfully connects an LLM’s general knowledge with the deep understanding of the atomic-scale world encoded in scientific interatomic potentials. Until now, researchers using LLMs to solve materials problems usually had to feed them raw data files as if they were just strings of text. It’s like asking an AI to understand a complex 3D engine based only on a parts list: the LLM can read the names, but it can’t “see” how the atoms fit together in space. MatterChat solves this by training a specialized AI bridge model, pre-trained on millions of crystal structures and an LLM, to align the LLM’s representation of the world with the interatomic potential’s representation of the world. The bridge model can translate physical insights into a format the LLM can actually understand. By giving the LLM these “scientific eyes”— a scientific “inductive bias” in the terminology of AI — the Berkeley Lab team has transformed it into a robust research tool capable of providing grounded scientific insights into complex materials challenges, such as predicting thermal stability or analyzing electronic band gaps. “We think of atoms as living in a physical space, but from a machine learning perspective, they are just vectors living in some very non-trivially structured manifold in a high-dimensional Euclidean space; and, of course, the same is true for the sentences and paragraphs that we use to express our ideas about those atoms,” said co-author Michael Mahoney, Berkeley Lab’s Scientific Data Division (SDD) AI Initiative Research Lead. “The bridge model basically gets those two structures to ‘talk with’ each other.” As a proof-of-concept of this general approach, the team trained their bridge model on a dataset curated by pairing nearly 143,000 stable atomic structures from the Materials Project with their corresponding physical properties. This training data was automatically assembled using the Materials Project’s API and deliberately enriched with properties fundamental to microelectronics design — like formation energy and bandgap — allowing MatterChat to learn the complex patterns connecting a material’s atomic blueprint to its functional performance. To validate their model, the researchers benchmarked MatterChat against a suite of other AI systems, from general-purpose LLMs to other specialized scientific AI methods. The results show that MatterChat consistently outperformed its competitors across a range of tasks. The model was more accurate in classifying material types and demonstrated superior precision in predicting numerical properties. For example, it excelled at predicting a material’s bandgap, a property critical for designing new electronics from high-capacity energy storage to next-generation computer chips. “Our design is significantly more efficient because we don’t have to build a massive AI model from the ground up,” said co-author Zhi (Jackie) Yao, a research scientist in Berkeley Lab’s AMCR. “Instead, we take two powerful, pre-trained models — a structural encoder for materials physics and an open-source LLM — and use them off-the-shelf. The only component we actually train is the lightweight ‘bridge model’ that translates between them. It’s the difference between building an entire car factory and simply designing a smart adapter that connects a world-class engine to a world-class navigation system. This approach is not only computationally efficient, but also makes the system modular, so we can easily upgrade components or adapt the bridge for other scientific domains in the future.” Crucially, this modular design highlights exactly how institutions like Berkeley Lab and the Department of Energy are carving out a highly valuable niche in the booming AI landscape. Rather than competing with Silicon Valley tech giants to build ever-larger language models from scratch, the lab is focusing on the specialized connective tissue that makes commercial AI useful for hardcore science. Because the bridge model approach underlying MatterChat is forward-compatible, it is perfectly positioned to leverage these parallel tracks of innovation. As Mahoney pointed out, “We expect that industry will continue to develop improved LLMs, and we expect domain scientists and facilities will continue to generate new data. An important part of scientific machine learning is not simply to solve problems on today’s data, but instead to develop general methods that will be forward-compatible with orders of magnitude more data, whether from scientific domains or from LLMs.” According to Yao, the MatterChat project, which was initially developed and enhanced with funding from a Berkeley Lab Laboratory Directed Research & Development (LDRD) Program, will now expand its capabilities. In a collaboration with Fermilab, MatterChat is already contributing to a U.S. Department of Energy Genesis Mission project — called Accelerating eXtreme Environment Specs-to-Silicon (AXESS) — that aims to speed up the development of next-generation, high-speed, radiation-hardened detectors for challenging particle physics experiments by using advanced 3D integrated circuits (chiplets) and AI-driven data analysis. In addition to the LDRD support, the team also credits supercomputing resources at the National Energy Research Scientific Computing Center (NERSC), located at Berkeley Lab, with MatterChat’s success. “We are incredibly grateful to NERSC; this research simply would not have happened without access to the Perlmutter supercomputer through their AI for Science program,” said Tang. Wenbin Xu, a NERSC postdoctoral fellow at the time, was also a major co-author of the work, as was Benjamin Erichson, a research scientist in Berkeley Lab’s SDD, highlighting the benefits of AMCR-SDD collaboration on AI for science. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.

May 18 • 11:00 AM EDT • Science • newscenter.lbl.gov

'Remarkable Bright Creatures' doesn't aim high, but lands perfectly

Last week we had talking sheep, this week it’s an octopus. Based on the best-selling book of the same name, “Remarkably Bright Creatures” is another example of live action family

May 13 • 11:00 PM EDT • Entertainment • galvnews.com

Physicists find evidence that the universe isn't perfectly uniform — potentially unraveling a 100-year-old model of cosmology

The universe may not be perfectly uniform after all, a new series of papers hints. If confirmed, this could upend a nearly 100-year-old model of cosmology.

May 12 • 1:17 PM EDT • Science • livescience.com

240-million-year-old giant “sand creeper” found hidden in retaining wall

A forgotten fossil hidden inside a garden wall has turned out to be one of Australia’s most remarkable prehistoric discoveries. Scientists have now identified the 240-million-year-old amphibian, Arenaerpeton supinatus, revealing an almost perfectly preserved skeleton—complete with rare traces of skin. This ancient river predator, about 1.2 meters long, looked somewhat like a giant salamander but was bulkier and armed with fearsome fang-like teeth.

May 5 • 8:00 PM EDT • Science • sciencedaily.com

'Aberdeen not safe yet'

Aberdeen fans, we asked for your views on Friday night's 2-2 draw against Livingston. Chris: To be perfectly honest after last night my worry is Stephen Robinson and his team selection. As time passes my real fear is who Robinson will show the door and who will come in.

May 2 • 8:10 AM EDT • Sports • sports.yahoo.com

What if humans could regrow tissue? Texas A&M study moves science closer

Researchers have successfully regenerated skeletal and connective tissue — even if not perfectly formed — demonstrating the next, critical step in limb regeneration.

Apr 23 • 10:11 AM EDT • Science • stories.tamu.edu

‘The Neighbourhood’: Creative Team Behind ITV’s Latest Big Entertainment Bet On Host Graham Norton’s Perfectly “Mischievous & Witty” Tone: “We Feel Lucky To Have Got Him Inbetween Taylor Swift Video Shoots”

Graham Norton clearly doesn’t do things by halves and he spent a full month living around the corner from contestants on The Neighbourhood, ITV’s constructed reality series that launches Saturday nigh...

Apr 23 • 4:00 AM EDT • Entertainment • yahoo.com

Why the Party Photography Business Is Booming

Not only are brands investing in more stylistic content, but celebrities, influencers and wealthy shoppers are hiring their own photographers to ensure a steady stream of perfectly staged pictures.

Apr 20 • 12:28 AM EDT • Business • businessoffashion.com

Archaeologists discover perfectly circular ancient Egyptian temple that may have been used for sacred water rituals

The temple included a network of water infrastructure, revealing the importance of the ancient city and the Nile.

Apr 17 • 4:59 PM EDT • Science • livescience.com

Does anyone really want to deal with Kanye West?

A UK ban coming days after selling out two nights in California perfectly encapsulates Ye’s decades long career.

Apr 13 • 12:51 PM EDT • Entertainment • cnn.com

“AI polls” are fake polls

A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.

Apr 11 • 11:38 AM EDT • Technology • natesilver.net

I've been a Kindle user for 10+ years — and I may never buy another one

Amazon is cutting off older Kindles that still work perfectly fine, and it’s enough to make me rethink buying another model ever again.

Apr 11 • 5:30 AM EDT • Technology • androidauthority.com

Amazon Pulls Support for Perfectly Fine Older Kindles

In an email to customers, Amazon announced that it would be ending service for Kindle devices older than the 2012 edition. Those devices will lose access to the Kindle Store.

Apr 8 • 12:54 PM EDT • Technology • wired.com

Artemis II’s Awe Reminds Us of Science’s Fragility and Importance

Three words uttered yesterday from Mission Control to Reid Wiseman, commander of Artemis II, perfectly capture the groundswell of emotion many of us have experienced over the last week as a team of astronauts flew beyond the previous reaches of human space travel: “Amaze. Amaze. Amaze.”

Apr 7 • 10:10 AM EDT • Science • bloomberg.com

MXene breakthrough boosts conductivity 160x with perfect atomic order

A new breakthrough is transforming MXenes—ultra-thin, high-tech materials—into something far more powerful and precise. Researchers have developed a cleaner, more controlled way to build these materials using molten salts and iodine, eliminating the messy chemical processes that once left their surfaces disordered. The result is a perfectly arranged atomic structure that lets electrons flow with remarkable ease, boosting conductivity by up to 160 times.

Apr 3 • 8:00 PM EDT • Science • sciencedaily.com

Unique nonprofit serving single moms selects new leader

SPARKHOPE AUTOMOTIVE Denver News: Jason Spears has been named chief executive officer of SparkHope Automotive. While its name might lead one to believe it is a car dealership, SparkHope Automotive, formerly Hands of the Carpenter, is a faith-based nonprofit serving employed single mothers by addressing a critical and often overlooked barrier to stability: reliable transportation. “Jason’s leadership and heart for community perfectly align […]

Mar 31 • 1:39 PM EDT • Politics • coloradopolitics.com

Movie Review: 'Two Prosecutors' is a perfect nightmare of state corruption

Sergei Loznitsa’s “Two Prosecutors” is a nightmare of government corruption so perfectly composed that, by the time it reaches its chilling conclusion, you feel nearly as entrapped as its young

Mar 26 • 12:42 PM EDT • Entertainment • yakimaherald.com

Patience and Hard Work Paying Off for Karl - Kansas State University Athletics

Cadyn Karl remembers the at-bat so perfectly, as you'd expect from a guy who had never reached such heights before in his three seasons as centerfielder on t...

Mar 25 • 8:00 AM EDT • Sports • kstatesports.com

Causal Inference Is Eating Machine Learning

Your ML model predicts perfectly but recommends wrong actions. Learn the 5-question diagnostic, method comparison matrix, and Python workflow to fix it with causal inference.

Mar 23 • 11:00 AM EDT • Science • towardsdatascience.com

A DNA test ruled him out. He was convicted of murder anyway.

Forensics has its share of high-profile problems. There’s phony “junk science,” like bogus bite-mark and blood spatter analyses that have sent innocent people to jail. And there’s the steady drumbeat of lab scandals, where analysts make up results. But there’s a subtler and more pervasive problem at work in many cases: lab analysts and lawyers who take perfectly legitimate science and twist it. Globe Opinion is exploring these distortions with a series we’re calling “Twisted Science.” And there may be no better place to start than with the case of Terrel Barros.

Mar 22 • 9:57 PM EDT • Science • bostonglobe.com

The '80s Car Feature That Would Fit Perfectly In The AI Hellscape Of Today

The '80s car feature that would fit perfectly in the AI hellscape of today is Chrysler's Electronic Voice Alert that gave drivers warnings by speaking to them.

Mar 22 • 11:25 AM EDT • Technology • jalopnik.com

35 photos that show what life looked like in 1973

A senior portrait so perfectly 1973 it could have been Michael Kelso's yearbook photo — the side-swept hair, the wide lapels, the expression of a guy who knows exactly how good-looking he is. The menu behind him says coffee is 15 cents.

Mar 20 • 1:04 PM EDT • Technology • aol.com

49ers Might be Playing the Mac Jones Trade Market Perfectly

Mar 7 • 10:00 PM EST • Sports • sports.yahoo.com

A perfectly balanced atom just broke one of nuclear physics’ biggest rules

Physicists have discovered a surprising new “Island of Inversion” in a place no one expected: among nuclei where the number of protons equals the number of neutrons. For decades, these strange regions—where atomic nuclei abandon their usual orderly structure and become strongly deformed—were thought to exist only in highly neutron-rich isotopes far from stability. But experiments on molybdenum isotopes revealed that molybdenum-84 behaves dramatically differently from its close neighbor molybdenum-86, even though they differ by just two neutrons.

Mar 7 • 7:00 PM EST • Science • sciencedaily.com

For the first time, light mimics a Nobel Prize quantum effect

Scientists have pulled off a feat long considered out of reach: getting light to mimic the famous quantum Hall effect. In their experiment, photons drift sideways in perfectly defined, quantized steps—just like electrons do in powerful magnetic fields. Because these steps depend only on nature’s fundamental constants, they could become a new gold standard for ultra-precise measurements. The discovery also hints at tougher, more reliable quantum photonic technologies.

Mar 1 • 7:00 AM EST • Science • sciencedaily.com

5 Tips To Pick a Perfectly Fresh, Ripe Lemon Every Time

To choose a ripe, juicy lemon, look for one with a bright yellow peel that feels firm and heavy. The fruit is an excellent source of vitamin C.

Feb 21 • 7:00 AM EST • Health • health.com

Opinion | He Studied Cognitive Science at Stanford. Then He Wrote a Startling Play About A.I. Authoritarianism.

A brisk theatrical thriller, “Data” perfectly captures the slick, grandiose language with which tech titans justify their potentially totalitarian projects.

Feb 16 • 5:03 AM EST • Science • nytimes.com

Scientists use AI to crack the code of nature’s most complex patterns 1,000x faster

Order doesn’t always form perfectly—and those imperfections can be surprisingly powerful. In materials like liquid crystals, tiny “defects” emerge when symmetry breaks, shaping everything from cosmic structures to everyday technologies. Now, researchers have developed an AI-powered method that can predict how these defects will form and evolve in milliseconds instead of hours. By learning directly from data, the system accurately maps molecular alignments and complex defect behavior, even in situations where defects merge or split.

Jan 29 • 7:00 AM EST • Science • sciencedaily.com

The Institute of Science and Technology Austria (ISTA) receives €5 million donation for AI research

The Institute of Science and Technology Austria (ISTA) has received a donation of 5 million euros from Canadian philanthropist and entrepreneur Garrett Camp to help advance AI as a trustworthy, human-centered technology that benefits society. ISTA’s commitment to responsible AI, coupled with its focus on frontier research and interdisciplinary culture, make it perfectly placed to pursue this goal.

Jan 24 • 7:00 AM EST • Science • eurekalert.org

In 1994 We Got To See How Long Impact "Craters" Last On Gas Giants, Courtesy Of Comet Shoemaker-Levy 9

The comet was discovered in its final stage before impact. As luck would have it, NASA had a probe on its way to Jupiter, perfectly placed to capture its demise.

Jan 17 • 7:00 AM EST • Science • iflscience.com

Patrick Mahomes makes feelings about Matt Nagy perfectly clear

Patrick Mahomes and the Chiefs failed to make the postseason this year, and now they may be losing their offensive coordinator.

Jan 16 • 12:27 AM EST • Sports • sportingnews.com

Chiefs' final power rankings spot perfectly encapsulates horrific 2025 season

The 2025 season is now firmly in the rearview mirror.

Jan 6 • 4:38 PM EST • Sports • sportingnews.com

Trump regrets handing Dems health 'ammunition' with October scan — clarifies it was a CT, not an MRI

President Donald Trump underwent a CT scan in October amid health scrutiny, with White House physician reporting perfectly normal results.

Jan 2 • 9:07 AM EST • Health • foxnews.com

Democracy is under attack, right here in Colorado

Remember during COVID, when the people screaming the loudest for government-mandated jabs were the very same people chanting “my body, my choice” when it came to abortion — I mean, “women’s health care”? They’re also the folks who insist a 12-year-old is far too young to get a tattoo, but perfectly mature enough to make […]

Dec 28, 2025 • 3:00 AM EST • Politics • coloradopolitics.com

No. 23 Virginia takes care of business at home versus Maryland

“You’re not going to play perfectly,” Odom said. “This is not a game of perfect.”

Dec 21, 2025 • 1:12 AM EST • Business • cavalierdaily.com

Commentary: What the Dodgers are doing isn't normal in pro sports. Be sure to appreciate it

The Dodgers are perfectly fine with spending more money than anyone else to win championships, and fans should appreciate it considering what's happening in Anaheim.

Dec 13, 2025 • 7:30 AM EST • Sports • latimes.com