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How well have past GOTY winners sold?

We’re less than two months away from 2026’s The Game Awards, and what a year it’s been for fantastic games, including Resident Evil Requiem, Forza Horizon 6, Slay the Spire II, the Blood of Dawnwalker, and Control Resonant – just to name a few. So as we approach the end of the year, we thought we’d look back on our estimates for the GOTY winners of the past four years. In terms of revenue, Elden Ring (2022 winner), Baldur’s Gate 3 (2023), Astro Bot (2024), and Expedition 33 (2025) have generated a combined $3.8B via 73M copies sold: Let’s have a look, then. Elden Ring has generated $2B in revenue Elden Ring is a phenomenon in every sense. It’s sold a whopping 36.4M copies across Steam (50%), PS5 (35%), and Xbox (15% across console and PC). Want more free estimates and insights in your inbox twice a week? Subscribe below! Subscribe Elden Ring also just passed an astronomical milestone: $2B in revenue. While PlayStation and Xbox account for half of Elden Ring’s copies sold across these three platforms, they account for 57.5% of revenue. This – as ever – is due to regional pricing and the higher relative share of PC gamers in lower-priced markets. For example, around 17.4% of Elden Ring’s Steam copies sold were in China, where the game costs about 26% less than the price in the States. There’s an … interesting narrative going around these days that console gamers are only interested in ball-and-gun games and have the patience for little else. Well, Elden Ring’s impressive sales prove that is simply not true, and across all platforms. What’s more, over 9.8M people have played Elden Ring for over 100 hours, with nearly 3.5M people getting all of the achievements and trophies. For reference, 10.9% of Elden Ring’s PlayStation players have the platinum trophy, while 10.5% of players on Steam and 3.9% of Xbox players unlocked all the achievements. Git gud, Xbox players! Of the 1.5M people who played Elden Ring on PlayStation last month, 4% also played Dark Souls 3 last month, 4% played Bloodborne, and 3% played Sekiro. There’s those Soulsborne diehards for you. By many accounts, including my own, Elden Ring is one of the best games of all time. It’s an evergreen seller that will continue to be a cash cow for Bandai and FromSoftware for years to come. Want more free estimates and insights in your inbox twice a week? Subscribe below! Subscribe Elden Ring has sold another 2M copies across Steam, PlayStation, and Xbox this year, generating almost $110M. This means it’s outperforming many of this year’s high-profile releases. Even Elden Ring’s standalone run-based PvE spinoff, 2025’s Nightreign, has sold 7.8M copies and generated $282M in revenue. Baldur’s Gate 3 is another billion-dollar-plus banger Another cross-platform phenomenon is Baldur’s Gate 3, which has sold 23.2M copies and generated over $1.2B in revenue. About 18.7M of these copies (80.6%) are via Steam, 2.9M via PS5 (12.7%), and 1.6M (6.8%) via Xbox. Around 1.3M folks on Steam bought BG3 before it hit 1.0. The PlayStation version launched about a month after the PC 1.0 release, while the Xbox version came four months later due to technical issues around developing local co-op on the Series S. Want more free estimates and insights in your inbox twice a week? Subscribe below! Subscribe Across platforms, Baldur’s Gate 3’s monthly active users have never dropped below 2M, including 2.2M MAUs last month (68.5% on Steam, 21.9% on PS5, and 9.6% on Xbox). And it still regularly brings in 280K to 400K players every day: About 1% of BG3’s Steam player base has played for over 500 hours! That’s a little under 200K people. Astro Bot is also an evergreen seller, albeit a more niche, family one Astro Bot, 2024’s winner, has now sold 4.7M copies and generated $291M in revenue. The budget, including marketing, is almost certainly under $100M, given the small Japan-based core team and three-year dev timeline. You can really see why the tides are changing toward less expensive hubs. Astro Bot is the only first-party winner on this list, but follows other first-party winners of The Last of Us Part II (2020’s winner), God of War (2018), and Breath of the Wild (2017). Around 46.6% of Astro Bot’s copies sold have been physical, which is far higher than the typical game. This makes sense for a couple of reasons: There’s a large family audience for this one, and physical boxes are an item to wrap when giving Astro as a gift. Astro has collector value for the PlayStation core (this is nostalgia-fueled platformer celebrating PlayStation’s history) Want more free estimates and insights in your inbox twice a week? Subscribe below! Subscribe Astro is another one of those games that continues selling over time, regularly selling more than 100K a month. It’s sold around 1M more copies this year ($60M in revenue) and is a popular title with families. We can see this using the Alinea platform’s MAU crossover tool: Astro Bot’s top #2, #3, and #4 games by crossover in any given month this year are Minecraft, Fortnite, and Roblox – without fail. The #1 title every month, though, is Astro’s Playroom, a pack-in with all PS5 consoles. For every month this year, between 20% and 30% of Astro Bot players also played Astro’s Playroom that month. So the free pack-in is a noice funnel for converting players to a full paid product. Expedition 33 sells 8.7M despite Game Pass cannibalisation Finally, we have last year’s winner – Expedition 33. The turn-based-RPG banger has generated $366M via 8.7M copies sold, with 58.8% on Steam, 35.7% on PS5, and 5.5% on Xbox. Xbox’s share of copies sold is this low due to Expedition 33 launching on Game Pass, where 4.3M people across console and PC accessed the game. A little under 700K Xbox players used their subscription to play Expedition 33 this year. That said, the game’s 1.5-year Game Pass deal ended last week on October 16, and we’re already beginning to see a positive trend on Expedition 33’s copies sold via Xbox (but the jury’s still out!). There’s understandably a lot of French pride behind this one, as France is the game’s #3 market on both Steam and PlayStation. Vive la France! One thing that stood out to me is that the GOTY winners aren’t $300M Hollywood productions made in the US. They’re mostly mid-budget studios outside the traditional AAA cost centres. Lots to think about there. Also, good games sell well; who’d have thunk it!? And there’s a whole load of them this year, with GTA 6 still to come, too. GTA just made the cutoff point for the VGAs this year. What a coincidence! Loading... Cool links and other stuff Our physical games newsletter from Tuesday was picked up by GameSpot, Eurogamer, TweakTown, and a bunch of others. We’re offering a free trial of our platform for games companies. Send us a message here, or reply right here on Substack/email, and we’ll set you up. Next week, we’ve got some exciting news regarding the future of our newsletter, so stay tuned! The last word Reply to this email – or reach out here – if you have any feedback for the newsletter – or want to request a game for us to cover. Want to see why these companies (and many others) are subscribed to Alinea? Get a free platform trial! [Alinea Analytics boasts the most accurate PC and console estimates in the business. Game makers use our platform to understand their audience, keep an eye on the competition, monitor sales trends, and spot new opportunities. We equip game studios and financial institutions with accurate data and the confidence to make smarter, data-driven decisions. Want to talk about all things games market data? We’d love to chat!]

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Oct 22 • 1:02 PM EDT • Technology • alineaanalytics.substack.com
Googlebook deserves a little patience: why early launch quirks don’t define its future

If you have been following our Googlebook coverage of late, you know I have been wrestling with two competing realities around the fledgling, current state of Googlebook. Earlier this week, I aired...

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Oct 21 • 12:20 PM EDT • Technology • chromeunboxed.com
Saints GM Loomis in Paris addresses criticism from team owner amid slow start to season

VERSAILLES, France (AP) — New Orleans Saints general manager Mickey Loomis said Wednesday he understands the recent criticism from owner Gayle Benson but asked for patience with what he called a “young developing team.”Benson, in comments before the Saints (2-4) beat the New York Giants last Sunday, said Loomis and his staff are responsible for “building a roster capable of sustained success.

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Oct 21 • 10:30 AM EDT • Sports • sports.yahoo.com
Sundar Pichai: Google’s Frontier Imperative, Compute Scarcity Dilemmas, Future AI Products and Businesses

Alphabet CEO Sundar Pichai has little patience for doubts about Google’s willingness or capacity to compete at the frontier of AI. His company’s latest Gemini 4 Argon model — long-promised and recently delivered — beats OpenAI and Anthropic’s latest public models on coding, math, science, and knowledge-work benchmarks and has placed Google’s AI back among the world’s best. Amid chatter that Google was fine with good-enough lightweight ‘Flash’ models, Pichai pushed back hard against any suggestion that company is limiting its research ambitions. “We won’t do it any other way,” he told me in a Big Technology Podcast interview recorded this week at Google’s Mountain View headquarters. Pursuing cutting-edge research is costly for Google, requiring compute and chips that otherwise could be sold at a high markup by its cloud business. But Pichai said allocating resources to frontier AI development is “ground zero” for Google. “Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development,” Pichai told me. In our conversation, we discussed Argon (which Pichai says processed three times as many tokens as Gemini 3 in the 72 hours after launch), AI’s role in developing better models, the AI interface of the future, competition with OpenAI, Google’s partnerships with Anthropic and Apple, data centers in space, and the nature of his optimism. You can read the full conversation, lightly edited for length and clarity, below, or listen to it on Apple Podcasts, Spotify, YouTube, or your podcast app of choice. Alex Kantrowitz: Google is back at the frontier with Gemini 4 Argon, which beats OpenAI’s and Anthropic’s public models on benchmarks spanning coding, math, science, and knowledge work. The conventional wisdom was that Google had given up on the frontier to focus on fast, good-enough models for its products. That was wrong? Sundar Pichai: Very wrong. Look, we are so committed to being at the frontier. It’s in our DNA. We won’t do it any other way. We’ve been thinking about the AI frontier for well over a decade now. The frontier is very dynamic. It’s very competitive. It’s very exciting. I couldn’t be more pleased with how Argon has come together. We took our time with it. We made sure we tested it safely and deployed it carefully. We took a bit longer than our other models, given its capabilities. Across the board, be it for agentic work, knowledge work, coding, creative tasks, Argon is one of the really good frontier models. The first 72 hours since we launched, it’s almost up to 25 trillion tokens used, which is about three times what Gemini 3.0 had in the first 72 hours. And we are capacity constrained as we are serving it. Excited, but there are always areas….. You know, the frontier is very dynamic, and you have to keep evolving the models. There are areas which we can see we need to improve as well. We’ll be at work on all of that as well. Which areas? On any given model, Argon would be across the board better at everything if you just look even two months back. But the space is so evolving. Even on coding, for example, there are pockets where we know we need to do better, and there’s clear feedback. We’ll make it better. The argument for standard models is that they’re already good enough to build great products, with the frontier valuable only for certain tasks. Meta built Muse with good models rather than the best, and Google could put standard models into Maps, Gmail, and Search. Why isn’t that sufficient? It’s definitely not how I have thought about it at all. We are deeply committed to driving the state of the art and actually pushing where this technology can go, all the way from transformers. That’s a journey we’ve been on. You will see us continue to do that. When you’re at the frontier, you get an earlier look into the emerging capabilities, earlier than others, which helps you better prepare for the future. We are fortunate to have a wide set of opportunities across consumer and enterprise. Some of our cloud customers, for sure, want the model at the frontier. The frontier models will be better at developing the next set of models in an era of recursive self-improvement. I would argue it matters there. Internally, we have definitely seen a steep point of inflection by deploying Argon to our developers. It definitely tackles problems which our previous models could not do. Being at the frontier puts you at the innovation frontier and helps you drive that as well. There is room for models of all sizes and capabilities. That’s why we did Flash, Flash-Lite, etc. I agree with you that you can develop very good product experiences with not necessarily the most frontier model, because they tend to be slower and more expensive too. But we care about the Pareto frontier, and we’re committed to doing all of that. You said developing frontier models gives you a look at what’s coming. What do you see on the horizon? What makes this moment so exciting is you build something which you think is a culmination of months of work, and you put it out. You already see clear headroom ahead. We’ve all been working on these agentic, long-horizon capabilities, but clearly the frontier is going to have multiple of these agents working together to do things. That’s an example of an exciting paradigm underway. It’s also clear to me that users, both in consumer and enterprise, will have access to some multi-agent framework for them, working all the time doing things. It feels like a moment of infinite possibilities. Sergey Brin said one of Argon’s core innovations was relying more on the model to help develop the model. Yet incidents involving OpenAI’s bots breaking containment and Google’s bots accessing a site they shouldn’t have suggest these systems still aren’t perfectly aligned. Isn’t there reasonable cause for concern about accelerating toward recursive self-improvement? There are three parts to your question. Let’s first talk about RSI itself. You’re right, Sergey mentioned RSI being a critical contributor as we are developing Argon. But to be very clear, today, in a continuum, we are pretty far from the end state of RSI or something like that. The right way to describe it is you have RSI loops along with AI researchers in various aspects of model development. You almost have agents helping AI researchers in some automated way work together to build these models. It’s good to at least understand where we are today, and it’s super exciting. I expect that continuum to keep making progress as we go into next year. The second question is around safety. Security comes from defense in depth. You’re talking about alignment, obviously. We all are doing a lot of work to make sure the models are as aligned as possible. But it’s an active area of research, and there are clear gaps. That’s not the only thing we rely on. You have the work you do on alignment, but beyond that, we put a lot of effort into real-time monitoring as these models are working. Making sure you have good telemetry into what’s happening so that you can intervene in real time if you see unexpected behaviors. How you do that, the architecture with which you do that, how you make sure it’s all sandboxed and isolated in a way that it can continue doing its job during real-time monitoring — these are all areas of investment. Obviously, when we deploy these models, giving customers, enterprises, control, both with real-time monitoring, but their ability to decide where they deploy, what type of data which models ever have access to, etc. All that work is very, very important, and we are doing a lot of that. As an industry, we need to constantly up our game and take the time to do all this stuff, including dedicating the compute required to make sure you can safely deploy these models. The third part is: Is it all enough? This is why there are a lot of conversations in the industry, and I think it’s reasonable to be worried. The right way to approach something you worry about is early on thinking about what more we can do. As an industry, you’re definitely seeing increased levels of work on alignment, testing for safety, etc., as we deploy these models. The next step would be developing better standards so there are cross-industry standards of how we do these things, including the ability to evaluate using independent evaluators. If you look at areas like financial controls, we have accounting standards, we have independent auditors. Those are all the next natural steps for us as an industry to work toward, and regulation will end up playing a role as we go through all of that as well. Google pursues both language models and world models, while some other AI companies like OpenAI have focused more heavily on LLMs as the path to AGI. Why is world-model research important to you? As humans, you obviously perceive the world around you, and you’re taking all those inputs. Making sure these models are able to understand the laws of physics, simulate the world in high fidelity. All that, I think, are key enabling blocks for intelligence. We are working across all possible applications of this. Take an area like robotics. It’s not an accident: If you look at embodied reasoning benchmarks, Argon is state of the art. That all comes from our experience building world models. Inherently, I would expect there will be architectures which will converge all of this in the future. But for us, we view this as, today, this jagged frontier that is intelligence. We want to be at the frontier of all the possible approaches, and that will help us both drive better research, better insights, and develop a much better AGI model, is how we think about it. Some people argue Google should focus on selling its computing resources rather than using them for AI research. Cloud wants to sell compute at a high markup, while researchers need a very big computer to build the best models. How do you balance those demands? Ground zero for us is making sure we are investing in the compute that’s needed to be at the AI frontier. In 2015, I set the company to be AI-first. AI is that key horizontal ingredient across everything we do, all the way from Search to Cloud to Waymo. That is ground zero for us, making sure both for research and developing the models that we have enough compute needed. Beyond that, we have these independent businesses which each have their own long-range plans. They’re using those long-range plans to project forward, and we plan for compute for all of them. The cloud team, they have a business and they have a projection and they have demand signals. They have committed contracts. With that help, we can plan on a long-term compute trajectory for them. We’re fortunate to have this wide set of opportunities. There’s no company in the world which isn’t making trade-offs at any given moment in time. But the first principles we operate on is making sure we have enough compute to invest in the frontier, and that’s non-negotiable, and everything else works on top. Remember, we are building our own compute, our own data centers. There are long lead times. The compute we have today is a function of decisions I made in 2024. Even if you think back to a year from today, last year the agentic coding takeoff hadn’t happened. Today, obviously, when you sit and look at anything, everyone is short—not just us, other companies—and everyone wishes you had two times what you had planned for. That comes with the territory. At any given moment in time, we are planning for what we would need two to three years out in the future. You’re going to find some decisions you make may look suboptimal two years out, depending on where the state of the art is. I don’t spend too much time thinking about it. The way I think about compute allocation is making sure Google DeepMind, both for research and frontier model development, is cutting edge in terms of compute access. After that, we plan for all our businesses. It’s great we can provide our compute to other people too, like we have done. It allows us to scale that up, get real tests for it in the widest application set possible, test it in the real world, and make sure we are at the cutting edge of technology at every layer of our AI stack as well. Ben Horowitz relayed on a podcast that Andy Jassy had said compute was sold out until 2028. Are you seeing the same thing? If today a customer is coming out and asking us for compute in ’27, that is incredibly difficult to do. Even last year we went out and leased some short-term compute to accommodate some customers and help them out. But yes, we are all dealing with similar timelines. Every major technology shift seems to involve overbuilding, from railroads to fiber. Do you think that’s happening here? It depends on the time frames you’re looking at. This level of build, if I take a long enough time frame, it makes complete sense to me. We are at early stages of the most consequential technology ever, and this is not like fiber. It’s not dark. We are using it. We are capacity constrained on a daily basis. The applications, the underlying technology is improving rapidly. But running a company, the way I work through the moment is you look at your demand signals. You look at your committed revenue opportunities that you’re getting in the form of contracts. You balance that with the underlying technical capability, or internally you balance how we are using it internally, the model trajectories. Based on all that, you take a disciplined approach—a confident, aggressive, but disciplined approach—to plan out compute. But when you talk about the broader economy as a whole, you’re having people operate with various levels of risk. I focus on what I can control as a company and plan through it. If your question is, can there be an overbuild? Yes, because you can have some constrained components. Remember, what matters is a set of everything you need to build out that compute. Memory, electricity, electricians…. Land. Like 2,000 components or more. You could get constrained at some point. There could be things which cause bottlenecks, and then you could have overbuilds. There could be external shocks to the economy. There are macro risks. All that is possible at any given time. But am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes. I was in the room when you said AI would be as profound as fire and electricity. If you believe that, shouldn’t you give researchers the biggest possible computer, even at the expense of today’s cloud profits? We’re all doing some form of that. Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development. What you’re seeing is true already. But you want to be thoughtful. Even our AI researchers have some constraints, which are also good. That’s what inspires creativity. That’s what helps you come up with a more efficient algorithmic breakthrough, maybe to use less memory. What we are committed to is to invest what’s needed to stay at the frontier. OpenAI President Greg Brockman told me a few months ago that the ideal AI product might have no interface at all—just as natural as conversation. What do you think the interface looks like in the age of AI? Computing historically has been humans adapting to computing. With AI, it’ll be the other way around. The technology will adapt seamlessly to how humans live and go about their day-to-day life. You want to be able to speak to that AI when you want to. It has context. It can be persistently running somewhere, give you continuity. You’re not dealing with a lot of complexity. It’s natural. All that makes sense to me. That doesn’t mean it won’t have any interface, however. Nowadays, even on my phone, Gemini voice models are so good. I can just press the microphone button. I can just ramble. That’s why we call it Rambler. I can ramble. It intelligently corrects with my personal context. Even my friends’ names, weird names, it gets it right, even if it first guesses it wrong. It’s amazing. I’m not going in, backspacing, editing once I finish saying it. It’s remarkable how good it’s gotten. But there are going to be moments you will need an interface. You may want to entertain yourself. You may want to watch a video. You may ask your AI to create something, and you want to watch it, consume it. The way I think about it is, you want it to be as seamless and as natural as possible, and that you are not doing the hard work adapting to whatever the interface is. The technology is adapting to how you’re working at any given moment in time. When I asked Sam Altman about competition from Google, he argued that OpenAI was building AI-native products and implied Google was bolting AI onto existing ones. Do you accept that framing, and can a “bolt-on” compete with an AI-native product? First of all, it’s a very dynamic moment. We are all pushing each other. OpenAI has pushed us as a company too. This is not just we are doing something to them. Far from it. Yes, Google went into its own version of a code red too. In the AI moment, all of us have to respond with varying levels of urgency at any given moment in time. We are constantly building new products as well. Gemini was built from the ground up as a product—the Gemini app I’m talking about. Or tomorrow, as all of us are adapting to agents, are you bolting agents onto ChatGPT, or are they building it natively? How would you think about that? You can always put these questions back. I don’t think of it that way. I think of it as you want to build world-class product experiences that are AI-native and they are winning. Some of it is adapting products, some of it is rethinking new experiences. They will all be part of how you approach this moment. How do you expect to make money from these AI interfaces? If an agent recommends a product and I buy it, should it take a cut of the sale, even if that’s disclosed? I’m going to put enterprise aside. Consumers are going to increasingly value this technology. Take software developers today. Developers are willing to pay for these coding models because it really helps make them more productive. Nobody would want to code the way they were coding three years ago, or even the same time last year. When you’re delivering value, there’s value to be had. I’ve never seen examples where consumer products really scale to billions of users, where people are using it intensely, and haven’t had monetization to go along with it. If you’re providing value to people, there is inherent value to be had. The nature of it may change. You’re asking a scenario in which agents are doing stuff for you. At the end of the day, you will have commercial needs. The agents need to fulfill those commercial needs. The providers on the other side are going to see value in that. There’s going to be some mechanism by which it gets fleshed out. That’s what an efficient system does. But what matters is that you’re building those world-class consumer experiences. I promised myself one product feature request: Can you make Gemini-powered search in Gmail as good as searching Gmail through ChatGPT? What’s going on here? One of the exciting things we announced recently is the unified Gemini agent for enterprise, and we’re bringing that to consumers. These are persistent agents which run 24/7 in the cloud. We’re spinning up a micro VM effectively for every query, and from the runtime, the harness, the model—it’s all a headless service. It’ll work everywhere, and it has your context with you. You can address this agent whether you’re in Workspace, in Search, in Gemini. Agents are going to drive this unification from a customer experience, which people are looking for. When you have a broad product portfolio and you’re moving forward fast, there are always areas where you’re moving forward faster than some other areas. That’s valid feedback. But we are putting a lot of time and effort into making sure, as we build out our agentic experience, that unification and that capability works well across all these services. People have asked where Google’s equivalent of Muse or Dots is. Do you anticipate creating a new product to compete with them? Our goal with Gemini is to be a personal, proactive, and powerful assistant. That’s what we are working toward. You saw us describe the early versions of it at Google I/O this year with Spark. Effectively, already, if you open the Gemini app as a Pro or an Ultra user, you see Chat and Task, and they start giving you that agentic experience. With Argon now, that capability is dramatically accelerated. What we announced recently in our enterprise event, we are bringing that to consumers as well. Soon in Gemini, you will be able to ask something, and it’s a continuum. You don’t need to think about whether you’re asking a chat or an agentic task. It’ll just automatically do the right thing, and it can work persistently in the background securely and get things done for you. Already, empirically, when I look at what’s coming into Gemini in our app, last year you would have tagged them and most of them would have been chats. But a steadily growing percentage is already agentic tasks in Gemini. Yes, we are going to bring personal agents to our consumers. It’ll work in the Gemini app. It’ll work in Google Search. It’ll work in Gmail. You can address it, call it, etc. We want to make sure we’re getting it right. It’ll be powered by Argon, and we are very excited to bring it to our users. Google has invested and committed tens of billions of dollars to Anthropic. What do you see in Dario Amodei that makes you want such a deep investment and partnership? Very early on, when we looked at the founding team of Anthropic, it was very clear to us this was a world-class team which was involved in some of the earliest GPT models. We were quickly able to assess that they would be at the frontier. We’ve always tried to identify and partner with companies which would be at the cutting edge of technology. We’ve done that with SpaceX. We’ve done that with Anthropic. That’s what we saw, and we had a chance to partner. Is your partnership with Apple to license Gemini for Siri expanding, contracting, or standing still? For both us and Apple, that partnership was about translating the capabilities of AI into better experiences for users. I thought Apple has done a phenomenal job of incorporating—they customize these models and build it into Siri—and it’s been great to see the reception. We are committed to doing more. You’re going to do more with them? There’s nothing specific to comment, but it’s a partnership. It’s working well. What have you learned so far about the feasibility of data centers in space? It’s a very exciting project. Projects like this remind me—I still recall that moment in the desert for the DARPA Grand Challenge, watching the Stanley car compete in it, which ended up being the basis for Waymo. With all these technologies, you’re at a moment in time where it looks a bit like sci-fi, but 10 to 20 years down the line, it’s actually working in a meaningful way. I feel the same in space. We’ve always believed in betting big on these technologies. Quantum has been a great example of it. We’re probably 10 years into the journey. Waymo, we are almost 20 years into the journey. Space, we are two to three years into the journey. It’s clear to me that it’s an approach which will make a lot of sense in the future. Being in sub-synchronous orbits with 24/7 access to the most abundant form of energy we have access to will make a lot of sense for this exponential increase in compute that we are going to need. Having said that, this is a challenging project. The team has disciplined milestones. It was a super exciting milestone to send the TPUs up to space. Sometime, as we are speaking, we are expecting a “hello world” moment from them. I look forward to seeing that happen, and we’ll continue investing here too. You previously said 25% of the code written within Google was written by AI. What’s the number today? That number in our world today is meaningless in the sense that every part of what we now touch is influenced by AI deeply. What we are tracking with Argon, for example, it’s been a point of inflection. People are using Argon to really go tackle complex projects, including projects which affect millions of lines of code. In the four weeks since we have launched Argon, we have seen a 30% increase in CLs, and more importantly, almost a similar increase—a one-third decrease in rollbacks. People are writing code more productively and better code with it. We are tracking it with newer metrics now. How are you dealing with opposition to data centers? If you can’t build in the U.S. the way you want to, where else do you go? We’re very committed to building our data centers very responsibly. When I look at our largest data centers in some of the communities where we have been the longest, I visit them, I talk to people. There are people who come and tell us their kids have gone to college because of our investments in these communities, and we’ve grown in those communities. To me, this is about doing it right. Engaging with the communities early, doing it transparently. We are committed to making sure we are bringing incremental power, and we cover 100% of the cost of our power needs for these projects. It’s understanding the impact on neighborhoods and making sure you’re working thoroughly to address them. Done rightly, you can continue making progress. But we are constantly taking feedback into account. I do worry not everyone operates the same way. People have been surprised by data centers showing up in their backyard. Those are all important. As an industry, we should continue to do better. But part of the reason, in a 20-year time frame, we are thinking about things like data centers in space is making sure we are able to put these data centers where maybe the trade-offs will be different. What unproven area of AI is Google working hardest to crack? Is it continual learning—getting models to keep updating after release—or something else? There are multiple parallel paths. You saw with Argon we have a million output tokens. With our Gemini models early on, we had a one-million context window on the input side. These are all examples of breakthroughs we are constantly working on. How do you think about synthetic data? How do you think about continual learning? How do you think about more efficient architectures? We do dedicate a meaningful percentage of our compute to these research bets too. It always goes hand in hand, including our world models. Those are all bets we are placing. Sometimes, as part of these bets, there are ideas that emerge which just make their way into the mainline models. Maybe we don’t fully talk about it, but we have multiple steps in these areas. Working in technology requires optimism that building something will improve the world, but those bets can leave real consequences when they fail. I recently returned to a town in El Salvador where enthusiasm for Bitcoin had given way to unfinished construction and something resembling a ghost town. How do you maintain the optimism you spoke about in your Stanford commencement address while accounting for those risks? I genuinely have a deep conviction that improving technology and improving access to technology makes people’s lives better. It’s been my personal experience in my life, out of which comes that deep conviction, optimism. But with that, I’ve always felt humanity’s journey is always about working hard to harness that technology to make it work well for society. The optimism is not an optimism that you just build whatever you want, whenever. You work hard to make sure you’re building technology in the service of humanity, to make the human condition better. That’s got to be the defining goal as we are approaching it. With that framework, I’m very, very optimistic. We are in very early stages of the kind of progress that we are going to see on many areas. We’re all talking about areas like healthcare. A lot of the progress is ahead. The ability to make things in a fundamentally cheaper way on many things where things have been getting expensive for a long, long time, discovering new materials, etc., which really bend the curve—it’s tough to step today and understand the impact of all that, but they’re going to be very profound. I’m very optimistic, but we have to be rightfully concerned about all the ways this technology can go wrong. Sundar, I know you don’t do many of these. Thank you. Thanks, Alex. Appreciate it.

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Oct 20 • 1:47 PM EDT • Technology • bigtechnology.com
Bulls' Caleb Wilson Embracing Patience, Despite Glimpses of Stardom

Chicago's No. 4 overall pick is looking forward to his NBA regular season debut on Wednesday night.

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Oct 20 • 8:00 AM EDT • Sports • sports.yahoo.com
Bauers Is One of Brewers’ Biggest Strengths in World Series

Fresh off his NLCS MVP performance, Jake Bauers anchors a low-power lineup with elite patience and game-changing pop, making him the Milwaukee Brewers’ X-factor in the World Series.

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Oct 19 • 5:00 PM EDT • Sports • sports.yahoo.com
A.J. Brown deserves patience despite disappointing return

| WEEI 93.7

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Oct 18 • 4:11 PM EDT • Sports • audacy.com
Move slow and make things: What Japanese craftsmanship can teach business about quality, patience, and expertise when AI makes speed cheap

AI makes it easier to produce more. The Japanese craft tradition of shokunin offers a guide to making something better.

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Oct 18 • 5:00 AM EDT • Business • fortune.com
Health workers run out of patience with Stormont budget delay

The practical impact of Northern Ireland's budget delay seemed relatively muted until earlier this week when hospital doctors announced two strikes.

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Oct 17 • 7:02 PM EDT • Health • yahoo.com
Northern Ireland health workers run out of patience with Stormont budget delay

The practical impact of Northern Ireland's budget delay seemed relatively muted until earlier this week when hospital doctors announced two strikes.

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Oct 17 • 7:00 PM EDT • Health • bbc.com
Want Apple's folding iPhone? Duo you have patience?

Expected ship dates for Apple's iPhone Duo, the company's first foldable phone, have already slipped from October to January 2027.

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Oct 16 • 1:09 PM EDT • Business • businessinsider.com
Television Q&A: Do new CBS late-night shows both have studio...

CBS late-night imports “Comics Unleashed” and “Funny You Should Ask” both use studio audiences, Allen Media Group says. Also: updates on “Lioness,” “Patience” a...

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Oct 10 • 12:00 AM EDT • Entertainment • unionleader.com
What Bruce Arians Thinks of Buccaneers' 0-4 Start

The Super Bowl-winning coach preaches patience amid a historic slump, but mounting injuries and a daunting October schedule threaten to derail Tampa Bay’s postseason hopes entirely.

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Oct 7 • 11:20 PM EDT • Sports • sports.yahoo.com
What Bruce Arians Thinks of Buccaneers' 0-4 Start

The Super Bowl-winning coach preaches patience amid a historic slump, but mounting injuries and a daunting October schedule threaten to derail Tampa Bay’s postseason hopes entirely.

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Oct 7 • 11:20 PM EDT • Sports • sports.yahoo.com
30 Hours With Fire Emblem: Fortune's Weave And My Patience Is Wearing Thin

Part one is an unrewarding slog, saved only by incredible characters

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Oct 2 • 10:00 AM EDT • Technology • kotaku.com
Detroit Pistons’ J.B. Bickerstaff Exercising Patience With Jalen Duren

Without Jalen Duren at training camp, Detroit Pistons head coach J.B. Bickerstaff shared his perspective on the big man’s absence

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Oct 1 • 10:00 PM EDT • Sports • sports.yahoo.com
September Monthly Review: Health MCP Guy?

For all the time I spend talking about APIs and data exchange, this newsletter has had embarrassingly little interoperability of its own. It’s old-school SaaS, baby - UI or bust. Not all of that is my fault! Substack, for all its popularity, is (as mentioned before) really frustrating as my system of record. The primary user it optimizes towards is the reader, whose attention feeds the app, the Notes feed, and the recommendation network that Substack is actually selling. Health API Guy is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe They’re falling into the same trap that Medium once sprinted into, albeit at a much more glacial speed. Younger generations may not remember (or never knew) it, but Medium once lured publications like The Ringer and Signal v. Noise onto its platform, only to lose them as it rebuilt itself around a paywall and an algorithmic feed. Substack’s original pitch circa 2020 was the antidote (you own your list), but every new feed feature chips away at it. I get it: writers are generic supply in the eyes of the Substack product team. Generic supply follows demand, so the consistent pressure on the PMs is to generate demand, which leads to every product cycle going toward discovery (Notes, recommendations, the app, live video). The end result lackluster author tools (laggy and broken analytics are truly crippling) all despite the premium price point. The archive is where it crosses from authorial gripes to actual reader impact, though. Across Substack and LinkedIn, I’ve published 1,218 posts and nearly 900,000 words since 2018. Substack’s native search can see only the 464 posts that live on Substack, and even there it’s woefully inadequate at surfacing relevant content (even for the guy who wrote it)! Readers have the same problem, writ large. Ask the Archive Fixing search (for both my readers and my own use) turned out to be extremely non-trivial, unfortunately: There’s no public API for readers to access content RSS exists for new content notification, but it only carries the 20 most recent posts and cuts paid ones off at the preview The only Substack MCP is an admin tool for analytics The export exists for authors, but it’s painfully awkward and slow. So I built something to work around that: the Health API Guy MCP. It’s a private MCP server holding the full text of everything I’ve written across Substack and LinkedIn. Add it to Claude or ChatGPT as a connector and you can ask things like: It pairs keyword search (for acronyms and case names, of which we have many) with semantic search. Every post is also labeled by topic, form, and court case, so you can pull a lawsuit’s (or multiple cases’) entire docket across Substack and LinkedIn in one query: Hail, Healthapiguyhydra This is also a bit of me eating my own cooking. I’ve spent the year arguing that headless healthcare is arriving and that developers want primitives over abstractions. Last week in Hail, Benihydra, I watched Salesforce plug its platform into Claude and bet that the records underneath outlive whatever interface sits on top: Salesforce must believe that the Hydra’s immortal head is the records, permissions, and business logic (in that they will survive whatever happens to the interface). It felt a bit hypocritical to keep my own archive behind a Substack search bar. The archive is the body. Substack is one head, and now whatever tool you might use is another. Will the EHRs follow my lead? Probably not imminently, but one can hope! Why Founding Members Only The MCP is available to Founding Member subscribers. A few reasons: It costs money and time to run. Hosting, the database, Clerk usage and AI compute for search and labeling are significant operational costs (that increase with usage). Substack gives me no way to automate access. There’s no API to check whether someone is a paid subscriber, so every user is manual operations for now. It’s a beta. We’ll need to improve things and parts will break. I’d rather they break for a small group of dedicated readers who will tell me about it. The Founding tier deserved a real benefit. Until now it was mostly a generous tip. Now it comes with something. If you’re already a Founding Member, reply to this email with the address you’d like provisioned. If you’re not and want in, upgrade here. Larger teams (such as companies) can get access too: group subscriptions at the Founding tier come with the group discount. The admin for the subscription can reply with the list of addresses to provision. Get 20% off a group subscription Pricing Founding membership is now a flat $200 a year, replacing the old suggested $150 that readers could adjust. Existing Founding Members will have access grandfathered in at their previous rate. Month in Review Articles Published Make Networks Dumb Again (Sep 11): AI is making old analog networks cheaper to use and flooding them with machine traffic in the process. A plea for agent-native rails with the intelligence at the endpoints, and for Washington to drag them to ubiquity. Interoperability, Unbuttoned (Sep 29): Interoperability is a series of workflows in a trenchcoat, and this post finally takes the trenchcoat off. A color-coded map shows which provider workflows went digital, which are fragmented, and which still run on fax. Video Content Antitrust’s System of Record Problem (Sep 3): The FTC’s investigation into Epic prompted a ninety-minute webinar, now posted with chapters and a smoothed transcript. We get into why market definition, missing B2B share data, and the SSNIP test leave antitrust poorly suited to systems of record, plus why information blocking may be doing more than any pending lawsuit. The Information Exchange: Cerner Milking Edition (Sep 18): This was so fucking fun. Ryan Tucker, Ryan Brickner, Brad Thorson, and I try an Around the Horn format, complete with points, penalties, and a mute button. We debate whether Oracle Health is dead, who would actually buy Cerner (IBM enters the chat), p(Doom), and why the front desk still hands you a clipboard. Regulatory Ask Not What Your Network Can Do (Sep 23): CDC’s RFI on public health data intermediaries lists use cases already served, unevenly, by eCR, lab reporting, IZ Gateway, and syndromic surveillance. The real opportunity sits in one question about AI: a network that can handle the next emergency without another interface project. Court cases Particle v. Epic: The Naughty Schoolteacher (Sep 4): After a summer-long vigil, the answer on Epic’s early summary judgment bid is two paragraphs and a remedial assignment. Particle advances to the next grade, and market definition gets another year of discovery. The Epic Discovery Files (Sep 8): Texas’s motions to compel arrive with a 251-page appendix of requests, objections, and meet-and-confer letters with Cravath. Inside: Epic using generative AI for document review, denied interoperability requests in scope, and Noerr-Pennington raised against lobbying discovery. Epic v. Health Gorilla: “Working as Designed” (Sep 9): Epic wants nothing to do with the data breach MDL, and explaining why puts fresh discovery on the public docket. The spiciest allegation: Health Gorilla told GuardDog to scrub its law firm business from its website instead of cutting off access. Veeva v. Epic: Legal Bypass Surgery (Sep 15): Veeva asks the Wisconsin Supreme Court to skip the Court of Appeals, while law professors and three policy groups line up as amici. Meanwhile, Epic’s lawyers apologize for another round of citation errors, including a quote that never existed. Texas v. Epic: Behind the Black Bars (Sep 17): The court portal served up Epic’s unredacted filings, revealing a $5 million document review budget and the AI workflow behind it. Also inside: the 200-plus company names Texas wants searched, and one very expensive Zoom invitation. OpenEvidence v. Doximity: Consequences Will Follow (Sep 25): OpenEvidence tells the court it will not comply with a discovery order, and its own lawyers at Quinn Emanuel say they won’t defend the choice. Add years of Slack reported unrecoverable, and Rule 37 sanctions come into view. Health Tech Keeps Choosing Violence (Sep 28): Doximity moves to end OpenEvidence’s case, Epic asks Texas to show its work, and a sleepy Audacious Inquiry patent fight sprouts antitrust and information blocking counterclaims. The main event is a long-sealed Cognizant v. Infosys ruling that hints where system of record antitrust claims can survive. EHRs One Verb for Oracle Health (Sep 14): Oracle’s earnings call quantifies every data center to the decimal point, while Oracle Health gets a single verb: “accelerate.” Panning the few healthcare mentions turns up an agentic care management system, a research-to-care ledger play, and the question of whether healthcare gets Larry’s patience. Epic’s Forretress Under Siege (Sep 30): Judy Faulkner tells a panel Epic paused hundreds of projects to harden its software and calls it a “shame,” while a spokesperson insists the roadmap hasn’t changed. Project Glasswing, a Pokémon-branded QAN sprint, and a trove of Jodel grievances reconcile the two Epics and preview the Jevons paradox coming for all software. Industry Analysis Scanning for a Network (Sep 1): Scan.com raises $220M to build the imaging network labs never got, speedrunning Zocdoc’s consumer-to-B2B pivot. Its wedge to network density may be the most American one available: personal injury litigation. The Front Desk Wants the Damn Whole Building (Sep 16): Hello Patient buys Converse Health, pushing its AI front desk into referrals, chart work, and prior auth. The copilot categories keep blurring, and your distribution partner is still your final boss. Cross-industry Comparisons A Different Patchwork Quilt (Sep 10): Healthcare loves to envy open banking, but the US version has no mandate, a toll booth at Chase, and data quality problems of its own. The grass isn’t greener; it’s another American patchwork quilt, and perhaps a worse one. Hail, Benihydra (Sep 21): Salesforce brings its platform into Claude, Slack, and its own agent, inviting the interfaces that might replace it. Cut off one head and more grow back, so long as the records underneath stay put. Living on A Trade Secret Prayer (Sep 22): Six credit unions are suing Fiserv, the Epic of core banking, by claiming their own member records as trade secrets. Banking has no Cures Act, so its lawyers reach for legal alchemy that healthcare has already tried. Other News None this month External Media Nabla Accelerate NYC: I kicked off fall conference season at Nabla’s Accelerate event in New York, where Delphine Groll opened by asking the room to picture the company it wants to become and the one it doesn’t. I liked her framework of bureaucracy, fear, and ego as the blockers. I also love vendor conferences/events in general - different vibe than industry biggies. Healthcare 101 with Nikhil Krishnan: I briefly guest lectured Out-Of-Pocket’s Healthcare 101 class. Appreciated Nikhil’s takeaways cover whether AI doctors can query HIEs, why healthcare’s data-liberation rules beat most industries’, and the hi-res graphic is in the comments. HCN: Epic Probed by the FTC: I joined the HCN guys to talk through the FTC’s investigation into Epic. Cedric’s new hairdo was an unexpected plot twist. The episode also covers MFN drug pricing, healthcare M&A and Eli Lilly’s acquisition streak. Fall Conferences: At eHealth Exchange’s Annual Meeting in Austin on Oct 27, I’ll moderate a panel on the tensions between AI, HIEs, and timely patient access with Jean Ross (Primary Record), Therasa Bell (Kno2), Byron Crowe (Doctronic), and Michael Marchant (Freenome). Here’s the rest of my schedule if you want to meet up: CommonWell (Redwood Shores, CA): Oct 13-14 Will be participating in a fun Jeopardy event Open@Epic (Madison, WI): Oct 20-23 Will be enjoying Verona, Willy Street, and other local adventures eHealth Exchange and Sequoia Project (Austin, TX): Oct 27-29 Yeehaw HLTH (Vegas): Nov 15-18 Groundhog Day, Venetian Edition RSNA (Chicago): Nov 30-Dec 2 See me at “Governing AI in Breast Imaging: What Health Systems Are Actually Doing” Digital Health Counsel 2026 AI Summit (Seattle): December 2-3 Will likely be speaking to a bunch of lawyers about information blocking Posts I Liked ChatGPT’s Epic integration: game changer or nothing burger?: Joshua Liu, MD offers five thoughts on OpenAI’s Epic launch, starting from the point that it rides the same open APIs available to anyone. He argues read-only access leaves ordering to Art and the ambient startups, and asks why HCA is piloting it alongside Commure. He’s sorta a must-follow at this point - consistently excellent, thought-provoking takes. Epic, Lilly, and the Discovery pass: Seth Chaney reads Epic’s first major Discovery deal (with Eli Lilly), as well as its revised developer terms on commercial displays, as the pipe finally opening between EHRs and pharma. Life sciences and health continue to collide. Scott Rossignol on EHR payer platforms and ePA fees: Scott argues the top ambulatory EHR vendors are turning the prior auth mandate into a per-member-per-year toll on payers, for an API whose operating cost has nothing to do with covered lives. His fix is two lines of regulatory text: put the CRD/DTR/PAS certification criteria in the Base EHR definition, and bring payer-facing ePA connectivity under § 170.404’s cost-based fee rules. It’s a good solution. Health API Guy is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe

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Oct 1 • 4:36 AM EDT • Health • healthapiguy.substack.com
Alessio Milivojevic Remains Spartans QB, but Patience Is Growing Thin

Freshman promise has vanished behind a crumbling offensive line as Milivojevic’s turnover-prone struggles force Michigan State to decide if their struggling starter can still salvage the season.

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Sep 28 • 11:05 AM EDT • Sports • sports.yahoo.com
Pocognoli may require patience to deliver on Scotland shake-up

A new era begins for Scotland, with Sebastien Pocognoli leading a refreshed squad into the Nations League campaign.

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Sep 26 • 4:29 AM EDT • Sports • sports.yahoo.com
😐 Neutral (0%)
'I would encourage patience with Wirtz'

"Liverpool fans were naturally expecting the finished product to arrive and Florian Wirtz probably isn't that just yet," says Liverpool podcaster Josh Williams, who also admits the 23-year-old isn't living up to his £116m price tag. Wirtz has made 55 appearances for the Reds, scoring seven goals and contributing 11 assists, since penning a five-year deal to move to Anfield from Bayern Leverkusen in June 2025.

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Sep 25 • 4:04 AM EDT • Sports • sports.yahoo.com
😐 Neutral (0%)
love
100%
Daniel Cormier urges patience with Gable Steveson

Daniel Cormier backs Gable Steveson.

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Sep 24 • 7:30 PM EDT • Sports • sports.yahoo.com
Josh McDaniels preaches patience with Patriots offense

New England has scored just two offensive touchdowns in its first two games.

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Sep 24 • 3:02 PM EDT • Sports • sports.yahoo.com
Löw: New Germany coach Klopp needs 'necessary time and space'

Former Germany coach Joachim Löw believes Jürgen Klopp can enjoy a successful spell with the national team but has urged patience from fans. Former Borussia Dortmund and Liverpool boss Klopp take charges of his first Germany match later on Thursday in the Nations League in the Netherlands.

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Sep 24 • 9:48 AM EDT • Sports • sports.yahoo.com
Aaron Rodgers’ brother urges fans to be patience with Steelers

Brother of Aaron Rodgers urges fans to be patient with Steelers

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Sep 23 • 2:00 PM EDT • Sports • sports.yahoo.com
Struggling Red Sox reliever: Patience ‘definitely running thin’

Red Sox reliever Erik Miller is trying to stay patient, but it’s not easy. “It’s definitely running thin at this point."

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Sep 22 • 11:05 PM EDT • Sports • sports.yahoo.com
Lukas Van Ness has arrived

Van Ness is paying off the Packers’ patience.

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Sep 22 • 9:06 AM EDT • Sports • sports.yahoo.com
Olympiacos Needs Time and Patience Under Imanol Alguacil

Olympiacos needs time and patience under Imanol Alguacil after a controversial coaching change left the team without a preseason or a clear playing system.

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Sep 20 • 9:10 PM EDT • Sports • sports.yahoo.com
😐 Neutral (0%)
Seahawks great Shaun Alexander sees himself in Jadarian Price

Seahawks great Shaun Alexander recently lauded Jadarian Price for his explosion in the hole, timing and patience as a young running back in the NFL.

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Sep 19 • 2:01 PM EDT • Sports • sports.yahoo.com
"Chill Out a Bit": FOX Sports Broadcaster Calls Out Dak Prescott on Live TV After Cowboys' QB Questions Fans

Dallas Cowboys went 7-9-1 in 2025 and missed the playoffs for a second straight season, so a 0-1 start this season after losing to the New York Giants was always going to test the patience of the Cowboys Nation. That is the backdrop for Dak Prescott’s comments this week, when asked about a fan’s disappointment.

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Sep 19 • 11:33 AM EDT • Sports • sports.yahoo.com
Kauai small businesses ask tourists to return with aloha and some patience

When tourists should return is always a question in Hawaii, but Kauai small businesses are already asking for them to come back in the wake of the devastation left by Hurricane Lowell.

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Sep 18 • 11:11 AM EDT • Business • ksl.com
Letter to the editor: Good science requires patience

It was no surprise to see the Aug. 23 headline about school vaccine rates falling and rising cases of measles in Pennsylvania, and then to see the Health...

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Sep 18 • 6:00 AM EDT • Science • post-gazette.com
Kauai small businesses ask tourists to return after Hurricane Lowell, with aloha and some patience

Watching her staff happily hand out free meals for hundreds of residents outside their Wailua commercial kitchen, food truck co-owner Kelly Kakalia felt fulfill

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Sep 18 • 12:08 AM EDT • Business • wtop.com
Kauai small businesses ask tourists to return after Hurricane Lowell, with aloha and some patience

Watching her staff happily hand out free meals for hundreds of residents outside their Wailua commercial kitchen, food truck co-owner Kelly Kakalia felt fulfilled.

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Sep 18 • 12:03 AM EDT • Business • wral.com
Browns Hit With Troubling Deshaun Watson Locker Room Report

The Cleveland Browns appear to be losing their patience with Deshaun Watson and the constant drama. The post Browns Hit With Troubling Deshaun Watson Locker Room Report appeared first on HEAVY.

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Sep 17 • 7:40 PM EDT • Sports • sports.yahoo.com
Alabama’s Defense Is Becoming the Identity Kalen DeBoer Needs

Alabama’s defense has needed only two games so far to turn a preseason theory into the clearest part of Kalen DeBoer’s 'dream' team. The Crimson Tide entered 2026 expecting a developing offense to need some level of patience. Questions surrounding Alabama’s next quarterback and the offensive line rebuild under Adrian Klemm made that pressure quite …

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Sep 15 • 4:41 PM EDT • Sports • sports.yahoo.com
Simeone: Atlético Madrid need time, patience amid Osasuna clash

He spoke on Tuesday from Majadahonda

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Sep 15 • 3:43 PM EDT • Sports • sports.yahoo.com
Michigan business group says US trade war with Canada taxing residents' patience

Mark Frisk from the Michigan Smart Trade Alliance says that the U.S. trade war with Canada is already hurting American wallets.

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Sep 9 • 3:31 PM EDT • Business • wdet.org
Donald Trump: a ‘fuming, friendless’ president

The US president has burned through international goodwill at a pace that would have once seemed impossible – and patience is running out at home, too

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Sep 6 • 1:35 AM EDT • Politics • theweek.com
Eagles TEs coach excited about Stowers' development, but preaches patience

While Eagles TEs coach Ryan Mahaffey has been excited by Eli Stowers’ development, he’s still preaching patience.

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Sep 3 • 7:00 AM EDT • Sports • nbcsportsphiladelphia.com
Astros Outslug Yankees in 9-7 Victory

The Astros took their first bite out the big apple on Tuesday Night, and it was a big one. Astros 9 Yankees 7. Game one of the post-Dana Brown era had a different feel with a much-needed result. Call it desperation, call it sharper focus, patience at the plate, or just unwavering […]

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Aug 25 • 10:43 PM EDT • Sports • sports.yahoo.com
Review: Chuwi's $449 Unibook laptop is a funhouse-mirror MacBook Neo

The Unibook is a weird entry-level laptop for people with patience.

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Aug 24 • 7:00 AM EDT • Technology • arstechnica.com
She grew up unvaccinated. Now she’s a leading voice for science

Immunologist Liz Marnik urges patience with people like her mother who feared shots.

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Aug 23 • 6:00 AM EDT • Health • bostonglobe.com
She grew up unvaccinated. Now she’s a leading voice for science

Immunologist Liz Marnik urges patience with people like her mother who feared shots.

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Aug 23 • 6:00 AM EDT • Science • bostonglobe.com
Eagles preach patience as Makai Lemon continues to battle hamstring injury

The Eagles' 2026 first-round pick has missed four consecutive practices.

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Aug 9 • 7:00 AM EDT • Sports • nbcsportsphiladelphia.com
Packers rookie kicker Trey Smack struggling to start training camp

Patience is required with rookie kicker Trey Smack, who is 8-for-13 on kicks to start training camp.

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Aug 3 • 3:10 PM EDT • Sports • sports.yahoo.com
'We can't write his obituary immediately': R Ashwin on Vaibhav Sooryavanshi

Ravichandran Ashwin clarified his "serve drinks" comment about Vaibhav Sooryavanshi. He stated the remark aimed to highlight learning opportunities for the young batter. Sooryavanshi faced criticism after a poor series against England. He then performed well against Zimbabwe, earning Player of the Series. Ashwin urged patience, noting young players experience ups and downs.

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Jul 29 • 4:37 AM EDT • Sports • sports.yahoo.com
😐 Neutral (10%)
ahah
59%
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Thune and White House trade jabs over Trump’s controversial elections legislation

President Donald Trump’s criticism of Senate Majority Leader John Thune over the stalled SAVE America Act escalated Thursday, with the White House warning that the president’s “patience is running out” as Republican leaders struggle to advance the controversial voting legislation before lawmakers leave in August for recess.

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Jul 23 • 11:59 PM EDT • Politics • cnn.com
Trump Is Losing Patience Over an Iran War With No Clear End in Sight

The president has grown skeptical of diplomacy and is in “revenge mode” against Tehran, according to a senior administration official.

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Jul 23 • 9:00 PM EDT • Politics • wsj.com
Trump's patience 'running out' as Thune fires back over stalled election security bill

Karoline Leavitt says Trump's patience is running thin with John Thune over the SAVE America Act as Senate Republicans face a 50-vote math problem.

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Jul 23 • 4:23 PM EDT • Politics • foxnews.com
Yankees' Aaron Boone Defends Anthony Volpe Despite Fans' Impatience

Anthony Volpe's defense at shortstop has declined in the last few seasons

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Jul 8 • 10:50 PM EDT • Sports • sports.yahoo.com
KATSEYE addresses speculation over Manon's future with group

KATSEYE is asking fans for patience as Manon Bannerman's health-related hiatus stretches into its fifth month.

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Jun 26 • 6:52 PM EDT • Entertainment • centraloregondaily.com
Making Our Own Luck

We are cross-posting a piece that former NIH official Kris Willis wrote for Macroscience. Kris is now the President and Founder of the Woodley Park Institute, for which I’m a board member. Picking winners or hedging bets? Federal science agencies have long wrestled with the question of how to ensure their billions of dollars of grants and contracts result in maximum benefits for Americans. On the surface, it might seem that a goal-oriented, interventionist style of management would be the best policy: prioritize our most pressing problems and distribute funding accordingly.This top-down strategy creates a unique hazard, though: bad choices can send resources in the wrong direction, resulting in slow progress that impedes the delivery of tangible benefits to citizens. Moreover, if decision makers pursue an applied advance when the underlying scientific principles remain poorly understood, they risk costly failures and a loss of support for the research enterprise at large. Examples include Nixon’s 1971 War on Cancer, undertaken on the premise that a cure could be achieved in less than a decade, the drive to build an operational commercial fusion plant by the mid-1990s, and the National Plan to Address Alzheimer’s Disease, which failed to produce the effective treatments and prevention strategies promised by 2025. The contrasting laissez-faire approach is to hedge your bets. If the path to an advance is impossible to predict, then the wisest choice might be to distribute funding to as widely as possible. The main risk of this strategy is that spreading funding thinly may limit the resources available to the most promising research, resulting in the same negative outcomes as the interventionist approach: limited support for the most promising topics, slow progress, and delayed returns. In reality, at science agencies like the National Science Foundation and National Institutes of Health, program managers navigate between these two extremes, setting some funds aside to target high-priority areas and using the rest to cover the widest possible range of meritorious proposals. This middle path seems like the most rational way to proceed, but it still requires making choices. How much should be set aside for high priority topics? What should those topics be? How should we define merit? Expert opinion can provide a helpful guide, but leading experts can have remarkably different opinions about what matters and what should be prioritized. Moreover, experts and administrators alike can be risk averse, giving an edge to high-profile, well-established concepts. What’s really needed is a data-driven framework to help guide decision-making, one that doesn’t simply repackage prestige or incumbency. Recognizing breakthroughs years in advance Many breakthroughs fail to attract funding or recognition at the earliest stages of development. Katalin Karikó, for example, spent years struggling to win grants for work on mRNA that would eventually win her and Drew Weissman a Nobel Prize. Similar stories can be told about Jim Allison’s groundbreaking studies leading to the development of cancer immunotherapy, Stan Pruisner’s demonstration that prions can self-replicate without DNA, or Robin Warren and Barry Marshall’s proof that Helicobacter pylori, not stress, causes ulcers. Yet at some point, the scientific community recognizes and responds to transformative breakthroughs like these. How does that change happen? And what if science funders could identify the areas that are on the cusp of a breakthrough? Those questions led my collaborators and me to study how scientists responded to past breakthroughs. Based on patterns in that data, we developed a means of predicting future breakthroughs, using biomedicine as a proof of concept. It’s worth taking a moment to describe how our approach works. Briefly, our prediction process begins by grouping the 18 million or so peer-reviewed papers in PubMed, the authoritative database of biomedical research, into roughly 50,000 unique topics. A small subset of these topics are so widely acclaimed as breakthroughs that they have been recognized with a major prize like a Nobel or a Lasker Award. We theorized that studying the development of these outliers over time, especially before they gained acclaim, was the key to identifying future instances of them. After defining topics, we chose 21 representative breakthroughs and asked what they looked like before they won prestigious prizes. To answer that question, we re-ran our topic-mapping algorithm repeatedly, winding back the clock one year each time, stringing together the years to follow the progress of each field as it grew and advanced. We found that in each case, the years leading up to a discovery followed a pattern: a burst of papers on a new, rapidly evolving topic, many of which quickly became influential. These characteristics—the percent of papers that are new, the percentage of papers that are brought in from other topics, and the influence of each individual paper—can all be measured separately for any given topic. Together, they act as a predictive signal that is detectable an average of five years before a transformative discovery is made, and up to thirty years before the discovery receives a major prize. Scanning the current research landscape for topics that display this signal allows us to predict what work will likely produce a future breakthrough. Each year we analyzed includes four or five signals, a number that appears to have remained constant across a twenty year time frame. In their classic work on the sociology of science, Bruno Latour and Steve Woolgar established that the desire to participate in a major discovery is an important motivator for scientists as they consider whether they should take up (or abandon) a research problem. Although further work is needed, the simplest explanation for our results is that this behavior is widespread enough to detect at scale. A foundational advance, or something like it, draws the attention of scientists and causes them to change the direction of their research. They vote with their feet, staking their own reputation and careers by publishing on and citing the new idea. That upswell of interest is the foundation of our breakthrough signal. What kind of research does our model identify as a breakthrough? After building a model based on the patterns in our 21 preselected breakthroughs, we tested it by identifying all early signals of discovery from 1994 through 1997. Of the 18 signals we found in that time period, 17 can be traced forward in time to a breakthrough. Examples include super-resolution fluorescence microscopy (see the image above), the directed evolution of proteins and enzymes (Chemistry Nobel, 2018), and sequencing the human genome (National Medal of Science, 2008). The discovery of the role of leptin signaling in obesity is a particularly interesting case study. Around 1950, scientists noticed the existence of a type of lab mouse that suffered from a unique inherited form of obesity. When given the freedom to choose their own diet, these mice ate more than normal and preferred food that was high in fat. Researchers had few clues as to why the animals overate; early experiments pointed to the existence of a soluble ob factor found circulating in the bloodstream that regulated appetite, but its identity was unknown. In 1994, Jeffrey Friedman and colleagues identified and sequenced the ob gene, noting that the protein it encoded appeared to fit the profile of a circulating factor. A year later, a trio of high-profile publications demonstrated that ob was a hormone that regulated body weight and fat deposition by regulating appetite. One of these was co-authored by Friedman, who christened it leptin from the ancient Greek word for thin. Using our model, the data from 1996 make it clear this advance would become a breakthrough. Scientists had flocked to study the biology of the new hormone, generating the flurry of new papers and citations required to produce a signal. One of these demonstrated that serum leptin concentrations reflected the amount of adipose tissue in the human body, providing clinical validation of the earlier mouse studies. Before the identification of leptin, scientists were unable to point to a specific molecule that controlled appetite and adiposity, so that it was impossible to rule out non-physiological causes of obesity; its characterization fundamentally changed our concept of weight gain. In 2010, Friedman and fellow pioneer Douglas Coleman were awarded a Lasker for the discovery. Our work also identifies breakthroughs in clinical practice and behavioral and social sciences, even though these areas are less likely to draw the attention of major prize committees. One example that falls into this category is the introduction of endovascular aneurysm repair, a minimally invasive surgical procedure for the treatment of abdominal aortic aneurysm, a leading cause of death among Americans over the age of 65. Relative to the previous standard of care, it reduces in-hospital mortality by almost four-fold. In 2017, Timothy Chuter received the American College of Surgeons’ prestigious Jacobsen Innovation award for his multiple refinements to the technique. Another under-recognized breakthrough identified by our model is the development of standardized survey instruments to assess the quality of life for HIV+ patients. The antiretroviral cocktails introduced in the mid-1990s reduced AIDS mortality, but treatment came with serious side effects. The new evaluations showed that patients whose disease progressed had worse physical functioning than Americans with other chronic diseases, while those who were asymptomatic maintained a health-related quality of life on par with the overall US population. This evidence made it possible for patients and activists to argue that even in the absence of a total cure, and in spite of the side effects, alleviation of symptoms was a worthwhile priority for clinicians. My colleagues and I have failed to identify any significant recognition for the physicians and health policy experts who pioneered this advance, although in 2016, researchers called for the World Health Organization to set targets for good health-related quality of life as part of its framework to end the AIDS pandemic. From forecasting to funding How should science funders use our predictions? Any serious discussion of what we should do with this new capability needs to begin with an understanding of its limitations. We can’t (and shouldn’t) invest all of our resources in the small number of topics predicted to produce breakthroughs. Innovative new fields are born from existing ones; they rarely arise de novo. If funders don’t maintain a diverse portfolio, the breakthroughs of tomorrow will have no antecedents. Think of this as eating the seed corn, or killing the goose that lays the golden egg. There are also multiple goals of funding beyond basic scientific discovery, including translating discoveries into clinical practice, developing new technologies, supporting economic development, and training new scientists. The investments needed to accomplish these goals are likely different from those required to support emerging breakthroughs. Finally, we must be aware that we may not capture every instance of a breakthrough. The concept is fuzzy, and not every significant discovery is recognized with a major award. Further, breakthroughs are rare, meaning that our dataset is, by necessity, small. This makes formal estimates of accuracy challenging. Although our overall success rate appears to be high, any one signal may turn out to be a false positive. We found one apparent example: the development of the third generation COX-2 inhibitors, Vioxx and Celebrex, which were much celebrated in the early 2000s as effective but non-addictive pain relievers, meets all the algorithmic criteria of a breakthrough. Five years after it received FDA approval, researchers demonstrated that Vioxx was associated with serious cardiovascular complications, and the manufacturer voluntarily pulled the drug from the market. Celebrex remains available, although like other NSAIDs, it carries an FDA warning for increased cardiovascular risk. Decision makers who were overly focused on the breakthrough potential of this research might have wasted substantial resources nurturing an area that ultimately proved to be a failure—but so would those who relied on expert opinion at the time. That said, the potential benefits of funding the next mRNA vaccine or cancer immunotherapy years earlier than we might have are large. When we can’t assume the outcome of an action, it’s worth conducting an experiment. Can targeted support for areas that are poised to produce a breakthrough increase the return on scientific investment without cannibalizing future advances? With the necessary administrative infrastructure in place, conducting this experiment would be straightforward. First, identify all the breakthrough signals between 2018 and 2023, select half at random, and commit to a decade of investment. For biomedicine, a six year window should yield about 30 topics with the potential to produce a breakthrough. Providing strong support to half of these might be accomplished for $300 million a year. That’s roughly half of the recent annual budget of the NIH Common Fund or around 20% of the fiscal year 2025 budget for ARPA-H. After five years, then again at ten, compare to see which group — the one that was actively managed, or the control — produced more breakthroughs, and on what timescale. Although we used biomedicine as an example, the same experiment could just as well be run for physics, materials science, or any other discipline. Any attempt to undertake such an experiment should keep three points top of mind. First, we can predict the topic of future breakthroughs, but the people and vision for how to move the work forward are still important. In practice, this means success requires active program managers with the expertise to develop the science and the authority to do so, including by recruiting investigators. Second, we would be wise to learn from the failures of past attempts to fund transformative research. High among these are funding modestly repackaged work, confusing the genuinely novel with the merely unfamiliar, and relying too much on incumbent investigators. All of these risks can be mitigated by thoroughly and intelligently integrating high quality data into portfolio management. Finally, we need to have patience. Even with strong funding support, producing a breakthrough takes time. If supporting breakthroughs is the best way to nurture the birth of new fields, we also need to accurately describe and properly manage the other two stages, so that we invest wisely throughout the productive lifetime of ideas and divest once they reach the point of diminishing returns. We would benefit from more study of all of these phases, especially the transition from one to another. More research could answer questions like: When does a breakthrough become an established field? How do the availability of funding, the size and characteristics of the available workforce, and the accumulation of evidence that disagrees with prevailing models affect a field’s decline? Exactly how do old ideas give birth to new ones, and can we speed up the process? The more we understand about the dynamics of scientific progress, the better we can maximize the odds that our curiosity leads to results. I’d call that finding a way to make our own luck.

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Jun 25 • 4:48 PM EDT • Science • goodscience.substack.com
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Physicians East rolls out new electronic health record system, warns of possible delays

Physicians East is asking patients for patience as the healthcare provider transitions to a new electronic health record system.In a message to patients, Physic

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Jun 24 • 5:31 PM EDT • Health • wcti12.com
Farmers backed Trump. Now some say they’re losing patience.

Rural approval of the president falls to a new low amid rising bankruptcies and trade uncertainty, and some farmers accuse the GOP of making a hard business harder.

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Jun 19 • 5:00 AM EDT • Politics • washingtonpost.com
Want to be more patient? Try these 6 expert-backed tips.

Modern life makes it easy to have a short fuse. Here’s how experts recommend developing your patience muscle.

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Jun 13 • 6:00 AM EDT • Technology • washingtonpost.com
Judges are losing patience with lawyers' AI flubs

A Mississippi federal judge removed all four lawyers from a case, barred two from practicing before the court, and issued $8,000 in fines.

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Jun 11 • 5:30 AM EDT • Business • businessinsider.com
New OMB rule could break science in the United States

The drug that saved your mother. The treatment your doctor recommended. The clinical trial that bought someone you love more time. None of it was inevitable. It required a system built to operate outside politics and sustained over decades, because science doesn’t operate on the timeline of a politician. That system is now under direct threat. The Office of Management and Budget (OMB) has proposed a new rule to regulate all federal grants, not just science, but housing, education, defense, NASA and everything in between, that would fundamentally change how research is conducted in the United States. There have been a lot of fires in the past 16 months, and if everything is an emergency, nothing is an emergency. But this is a very big deal. These rules would strip away the independence that has kept science from becoming a political tool. The good news is there’s still something we can all do. How the grant system was designed to work When I was a new faculty member, I was full of millions of ideas, so I sat down and wrote an NIH proposal. I worked on it for months and thought it was so good. Then I sent it to my mentor. It came back covered in comments. Places where my reasoning wasn’t as airtight as I’d thought, methods that could be stronger, gaps in my thinking that I had been too close to the work to see. It stung, as honest feedback always does, but peer review almost always makes the work better. In fact, it’s the backbone of how American science has functioned for generations. At NIH, submitted proposals go to a panel of scientists who have experience in the field. These are the people who can read research methods and spot their weaknesses, who understand when an experiment is designed well and when it isn’t. Applications are scored rigorously, and often only the top 3-20% of proposals receive funding. The people making funding decisions are required to justify them based on these reviews. To be clear: this is not a perfect system, and good science still goes unfunded more often than any of us would like due to limited resources. There is also criticism that peer review may undervalue bold ideas that are risky but could have high payoffs. But the peer review system is nonetheless a meaningful part of the grant selection process. While thoughtful and careful changes could improve the system, the changes being proposed by OMB will not make things better. The rule that could change everything Through this OMB proposal, the system that helps decide what gets funded is being dismantled. There are many troubling elements buried in this proposal, but three provisions stand out in particular. Each would be damaging alone. Together, they would change what kind of science gets done in America, and who it serves. 1. Political appointees would decide which science gets funded, and peer review would be explicitly sidelined or ignored. The guidelines your doctor follows, the standards that determine what’s in your air and water, the drugs that get approved for your family: all of it starts with a funding decision. Right now, that decision is made by scientists who can evaluate whether the work is actually sound and whether it could move the needle forward. Under this proposal, funding decisions could be made by political appointees. The careful, expertise-driven review process that shapes grant funding would be effectively sidelined. That means that a poorly designed study could get funded simply because someone in a political office liked the sound of it, while rigorous, important work doesn’t get funded because it no longer aligns with the administration’s priorities. In other words, critical work that benefits all of us could now be missed just because it has become politically inconvenient. 2. Grants could be canceled at any point, without warning, in the middle of ongoing work. Say your tax dollars have been paying for a cancer study for two years. The grant was awarded for five years, so the team is mid-experiment. There are three additional years’ worth of answers that could change how the disease is treated for hundreds of thousands of people. With this new rule, the federal government could cancel the remaining funding tomorrow, walk away from the commitment it made with your money, and have no legal obligation to finish what it started. This is a huge waste of money. There are rarely other sources of funding for this work, which means the team, the graduate students, postdocs, and research staff who have dedicated years of their careers to this project could lose their jobs. Patients who have contributed their time to studies may not see it turn into useful learnings for others. Every hour of work already completed could result in nothing useful. Science can take decades to get to tangible outcomes. It needs a stable funding system to flourish. 3. Researchers would have a harder time communicating their findings without pre-approval from the federal government. There is a saying from Sir Mark Walport: “Science isn’t finished until it’s communicated.” Science is no use to anyone if it is kept locked away in the labs that produced the data. The proposed rule could make communicating research findings harder in a number of ways: Make it harder for scientists to widely share their findings in scientific journals. Publication costs, including journal fees that allow for broad public access, could not be paid for by federal funds which is often how scientists pay these costs. Restrict researchers from engaging publicly about how their findings should inform policy or public health guidelines. This would restrict scientists from doing the work that transforms years of lab work into decisions that actually affect people’s lives. Make it harder for scientists to attend conferences. Conferences would now need to be approved by the federal agency at the time of grant award. This disadvantages conferences or publications that are not politically aligned. Conferences are a key way in which scientists learn about work happening in their field so they can collaborate and build on each other’s findings. What good is science with a muzzle? The long-term impact These changes could (and hopefully would) be reversed under the next administration. But by then, damage has been done. Once projects are halted or denied funding by political appointees, people leave their science careers for other paths or even leave the country to do their amazing science elsewhere. You cannot pause a scientific study the way you pause a subscription you decided isn’t useful anymore. You cannot freeze a research team that depends on salaries to feed their families, causing a scramble for replacement funding that, in most cases, simply does not exist. When the money stops, everything stops, and all the work in progress and its potential are lost. Despite all of the language about reducing waste, fraud, and abuse, this new proposed rule increases the administrative burden tremendously. Every researcher watching this unfold will make a rational decision: don’t start anything that will take a decade to finish. This includes not building a team around funding that could vanish, or asking the big questions that need patience and stability to answer. The most consequential science, the kind that produces the treatments and the breakthroughs that reach your doctor’s office twenty years from now, is exactly the science that would be abandoned first. This is still a proposal, so we can all do something Some worry this rule is already a done deal. But by law, OMB is required to open a public comment period. Those comments matter because they inform members of Congress, they can be used in litigation, and they create a public record. So here’s what you can do: Submit a public comment. The public comment period on this proposed change is open until July 13. Anyone can submit a comment, and OMB is required to respond to substantial ones. Each comment must be different to count as distinct; identical comments will be counted only once. Comment as an individual, not on behalf of your organization. Here’s a full resource to help you, with useful examples, but in general: Share a story about how science or federal funding has benefitted you or is involved in your work. Are you or someone you loved a cancer survivor? Do you know someone with Alzheimer’s? Have you received NIH/NSF or other federal funding? All of these can be stories you use to highlight why you care. Explain why you’re worried about these proposed changes. You can use the wording found here or in other resources to help you. Ask them to withdraw this proposed rule. Contact your elected representatives. Congress allocates money to federal agencies to spend, and it has oversight of how that process works. You can let your representatives know this proposed change concerns you and ask them to intervene. There are levers they can push that can help at this moment. Let others know why federal funding matters. This isn’t just about science, but ALL federal grants, including for education systems, defense projects, and more. The process, including the importance of federal funding, is invisible to many. We can all make it more visible by sharing how science benefits us and why we value it. You can also share this article, or any of the resources linked here, to help spread awareness! Bottom line We are quickly losing ground in scientific research in the U.S. More changes, like the proposed rule by OMB, would decimate our ability to conduct research here, leaving us all worse off: fewer treatments for pancreatic cancer, less research on pollution impacts, fewer answers for patients with rare disease, and more unaddressed health needs. The impact will be very real for all of us, regardless of where we stand politically. Using our voices for science is more important than ever. Love, LM Big thanks to Dr. Megan Ranney for reading this over and providing brilliant insights, as always. Dr. Liz Marnik, PhD, is Executive Director of The Evidence Collective at YLE and a science communicator and immunologist working at the intersection of public trust, science, and public health. You can find her on Substack, LinkedIn, Instagram and Threads. Your Local Epidemiologist (YLE) comprises a team of experts, ranging from physicians to immunologists to epidemiologists to nutritionists, working together with one goal: to “Translate” ever-evolving public health science so that people are well-equipped to make evidence-based decisions. The YLE suite of newsletters reaches over 475,000 people across more than 132 countries. This newsletter is free to everyone, thanks to the generous support of fellow YLE community members. To support the effort, subscribe or upgrade below: Subscribe

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Jun 10 • 6:04 AM EDT • Science • yourlocalepidemiologist.substack.com
Nike’s Reset Under Elliott Hill Tests Core Sports And Investor Patience

Nike (NYSE:NKE) has launched an operational and brand reset under returning CEO Elliott Hill. New companywide programs, including "Win Now" and "Sport Offense," aim to refocus on core sports categories and refresh Nike's market approach. The reset includes renewed attention to wholesale partners, core product franchises, and updated marketing to athletes and everyday consumers. Nike, trading at $42.98, is undertaking this reset after a long period of weak share price performance, including...

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Jun 8 • 3:08 AM EDT • Sports • finance.yahoo.com
My 10-year-old started a vending machine business

Christina Nicholson says her son Landon launched a vending machine business at 10 and quickly learned lessons about profit and patience.

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Jun 6 • 2:01 PM EDT • Business • businessinsider.com
House passes bill to provide more Ukraine aid and impose new sanctions on Russia

The 226-195 vote is a sign of impatience with President Donald Trump's approach to the war and represents the House's second major foreign policy break with Trump this week.

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Jun 4 • 8:36 PM EDT • Politics • pbs.org
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In a first, House votes to block Trump from ordering further strikes on Iran

The Senate advanced a similar war powers resolution last month on a procedural vote, reflecting growing impatience with a conflict Congress hasn’t authorized.

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Jun 3 • 5:18 PM EDT • Politics • washingtonpost.com
Shannon Sharpe doesn’t hold back on Caitlin Clark’s behavior

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Jun 2 • 8:12 AM EDT • Sports • sports.yahoo.com
How Is Netflix’s Stock Performance Compared to Other Entertainment Stocks?

Netflix has lagged broader entertainment peers, yet analysts stay steady, suggesting patience could still reward long-term investors.

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May 29 • 2:28 AM EDT • Entertainment • barchart.com
'Ask Us Anything' hits major Iowa sports, how we do our jobs

Is Iowa's next football coach on staff? How much patience does Beth Goetz have for wrestling? Those questions and more on this week's Hawk Central.

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May 27 • 3:45 PM EDT • Sports • hawkcentral.com
What's in it for you?

Caregiving is complex work. It requires technical skill, emotional regulation, patience, adaptability, trust and presence.

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May 18 • 10:15 PM EDT • Business • santafenewmexican.com
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Cardinals rookie Karson Sharar's patience paid off

Patience paid dividends for former Iowa linebacker Karson Sharar, as he turned starting reps as a senior into a shot at NFL glory.

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May 9 • 1:07 PM EDT • Sports • arizonasports.com
Open for business: Arcadia businesses frustrated by SR 70 construction

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May 6 • 7:07 AM EDT • Business • winknews.com
Alito and Jackson’s fiery debate over the Voting Rights Act exposes Supreme Court tensions

The November election is still a long way off, but patience is already running thin at the Supreme Court.

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May 5 • 2:45 PM EDT • Politics • cnn.com
Visual Voice and JAEBC Launch Strategic Partnership to Bridge Japan-Africa Entertainment Sectors

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Apr 28 • 8:37 AM EDT • Entertainment • prnewswire.com
Brené Brown would like a word with Silicon Valley

The author and researcher has no patience for tech's bad-boss era.

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Apr 26 • 4:17 AM EDT • Business • businessinsider.com
Community Engagement & Town Hall – Southern Humboldt Business Improvement District

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Apr 26 • 3:03 AM EDT • Business • kymkemp.com
Analysis: Fans have turned on Rosenior

It marked radical new low point in a bleak run of five consecutive league defeats without a goal for Chelsea - their worst such sequence in more than a century and one that few supporters have witnessed in their lifetime. Given the club's success over the past 25 years, it is perhaps understandable that patience is thin for a head coach appointed just three months ago from Chelsea's partner club, Strasbourg. There were early signs that the night might not unfold as Rosenior had hoped.

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Apr 21 • 5:56 PM EDT • Sports • sports.yahoo.com
Approval of Trump on economy falls in new AP-NORC poll, as Iran war drives up prices

The findings from The Associated Press-NORC Center for Public Affairs Research show a president who is struggling with unfulfilled promises to tame inflation and testing Americans’ patience with a conflict in the Middle East that has dragged on longer than expected.

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Apr 21 • 5:48 PM EDT • Politics • pbs.org
Report: Cardinals, Saints discussing expensive 2026 draft trade

Saints GM Mickey Loomis isn't known for his patience in the NFL draft. But this report about a trade up to third overall may be too rich even for him.

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Apr 19 • 5:41 PM EDT • Sports • sports.yahoo.com
The Phillies Preach Patience, But Will Bryce’s Bat Benefit?

If at first pitch you don’t swing…

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Apr 16 • 1:00 PM EDT • Sports • sports.yahoo.com
‘Pothole Politics’ Meets a Hell’s Kitchen Crater at W42nd & 9th Avenue

Three weeks of sewer repairs snarl traffic at W42nd and 9th, exposing underground infrastructure clashes and testing Hell’s Kitchen patience.

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Apr 15 • 8:09 AM EDT • Politics • w42st.com
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How the Voyager missions rewrote planetary science, tested the limits of 1970s engineering, and became humanity's longest-running experiment in institutional patience

In November 2026, Voyager 1 will become the first human-made object to reach one light-day from Earth. Nearly 49 years after launch, the twin Voyager missions remain the most productive space exploration program ever flown, and a case study in what happens when engineering resilience meets institutional patience.

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Apr 6 • 6:08 AM EDT • Science • spacedaily.com
Gemma 4 and what makes an open model succeed

Having written a lot of model release blog posts, there’s something much harder about reviewing open models when they drop relative to closed models, especially in 2026. In recent years, there were so few open models, so when Llama 3 was released most people were still doing research on Llama 2 and super happy to get an update. When Qwen 3 was released, the Llama 4 fiasco had just gone down, and a whole research community was emerging to study RL on Qwen 2.5 — it was a no brainer to upgrade. Today, when an open model releases, it’s competing with Qwen 3.5, Kimi K2.5, GLM 5, MiniMax M2.5, GPT-OSS, Arcee Large, Nemotron 3, Olmo 3, and others. The space is populated, but still feels full of hidden opportunity. The potential of open models feels like a dark matter, a potential we know is huge, but few clear recipes and examples for how to unlock it are out there. Agentic AI, OpenClaw, and everything brewing in that space is going to spur mass experimentation in open models to complement the likes of Claude and Codex, not replace them. Especially with open models, the benchmarks at release are an extremely incomplete story. In some ways this is exciting, as new open models have a much higher variance and ability to surprise, but it also points at some structural reasons that make building businesses and great AI experiences around open models harder than the closed alternatives. When a new Claude Opus or GPT drops, spending a few hours with them in my agentic workflows is genuinely a good vibe test. For open models, putting them through this test is a category error. Something else to be said about open models in the era of agents is that they get out of the debate of integration, harnesses, and tools and let us see close to the ground on what exactly is the ability of just a model. Of course, we can’t test some things like search abilities without some tool, but being able to measure exactly the pace of progress of the model alone is a welcome simplification to a systematically opaque AI space. Share The list of factors I’d use to assess a new open-weight model I’m considering investing in includes: Model performance (and size) — how this model performs on benchmarks I care about and how it compares to other models of a similar size. Country of origin — some businesses care deeply about provenance, and if a model was built in China or not. Model license — if a model needs legal approval for use, uptake will be slower at mid-sized and large companies. Tooling at release — many models release with half-broken, or at least substantially slower, implementations in popular software like vLLM, Transformers, SGLANG, etc due to pushing the envelope of architectures or tools. Model fine-tunability — how easy or hard it is to modify the given model to your use-case when you actually try and use it. The core problem is that some of these are immediately available at release, e.g. general performance, license, origin, etc. but others such as tooling take day(s) to week(s) to stabilize, and others are open research questions — with no group systematically monitoring fine-tunability. In the early era of open models, the days of Llama 2 or 3 and Qwen pre v3.5, the architectures were fairly simple and the models tended to work out of the box. Some of this was due to the extremely hard work of the Llama, Qwen, Mistral, etc. developer teams. Some is due to the new models being genuinely harder to work with. When it comes to something like Qwen 3.5 or Nemotron 3, with hybrid models (either gated delta net or mamba layers), the tooling is very rough at release. Things you would expect to “just work” often don’t. I’ve been following this area closely since we released Olmo Hybrid with a similar architecture, and Qwen 3.5 is just starting to work well in the various open-source tools that need to all play nice together for RL research. That’s 1.5 months after the release date! This is just to start really investing more into understanding the behavior of the models. Of course, others started working on these models sooner by investing more engineering resources or relying on partially closed software. The fully open and distributed ecosystem takes a long time to get going on some new models. All of this is lead-in for the most important question for open models — how easy is it to adapt to specific use-cases? This is a different problem for different model sizes. Large MoE open-weight models may be used by entities like Cursor who need complex capabilities in their domain, e.g. Composer 2 trained on Kimi K2.5. Other applications can be built on much smaller models, such as Chroma’s Context-1 model for agentic search, built on GPT-OSS 20B. The question of “which models are fine-tunable” is largely background knowledge known by engineers across the industry. There should be a thriving research area here to support the open ecosystem model. The first step is to understand characteristics of different base and post-trained models to understand what they look like. The second step is to tune pretraining recipes for open models so they’re more flexible. Interconnects AI is a reader-supported publication. Consider becoming a subscriber. Subscribe For The ATOM Project and other Interconnects endeavors, we’ve put in substantial effort to measuring adoption trends in the open ecosystem. Everything takes a long time to unfold after a model is first publicly available — and adaptability is why. What we know for sure now, when Qwen has been going from strength to strength with its releases, is that technical staff across the industry has gotten comfortable working with Qwen models. Countless research methods and datasets were made to work with Qwen. It’ll take patience for any other model family to get to this point — a patience I’m not sure many open model builders have. This takes us to Gemma 4, Google’s latest open models. Gemma 3 was released more than a year ago, in March of 2025, and is a bit underrated. Gemma 4 comes in 4 sizes for now, with a bigger, MoE model of over 100B total parameters rumored but not released yet. The models we have today come in sizes of ~5B dense, 8B dense, 26B total 4B active MoE, and 31B dense. I’m most excited that they’re finally adopting a standard Apache 2.0 open source license. This’ll massively boost adoption. The standard of better licenses for strong open-weight LLMs was set by mostly Chinese open model labs in the last 1-2 years, and now U.S. companies are following suit. I will personally be so happy if the horrible Llama licenses and Gemma terms of service were an ~18-month transient dynamic of the industry being nervous about releasing strong open models. The Gemma 4 scores look very solid, the small models have incredible benchmark scores (especially in general domains like LMArena) and the 31B model rivals the recent Qwen 3.5 27B, which is the leading member of that class. The ~30B size range is an important one, as it’s accessible both to researchers and to enterprises looking to deploy the model in real use-cases. Where the 7B model scale is the default for tinkering and research, a 30B model is the default for seeing if an open model can unlock substantial value in your specific workflow — a good mix of intelligence, low price, tractability for downstream training, etc. This takes us back to the above adoption criteria I mentioned for open models and the bigger question — do I think Gemma 4 will be an overwhelming success? Previous Gemma models have been plagued by tooling issues and poorer performance when being finetuned. Gemma 4’s success is going to be entirely determined by ease of use, to a point where a 5-10% swing on benchmarks wouldn’t matter at all. It’s strong enough, small enough, with the right license, and from the U.S., so many companies are going to slot it in. I’m cautiously optimistic that Gemma 4 is going to work better here. Winds are shifting for open models built in America. We saw GPT-OSS go through a bumpy launch to become an overwhelming success. There’s a collective energy around the likes of Reflection, Arcee, Nemotron, Gemma, Olmo, and peers that show substantial demand for building new stacks around open models. There’s capital to be spent on AI stacks across the economy by those who want more ownership of everything, including the model. After launching The ATOM Project 240 days ago, the conversation is shifting into the next stage. Summer of 2025 was a crisis moment where the U.S. AI scene realized it can’t wait and figure out open models after building AGI. The two markets will capture different areas and proceed in parallel. Now that more companies in the U.S. are releasing strong models, we need to improve the ecosystem so that these models are easy to use, understand, and build value around. It’s the hard work to build another inflection point in these adoption plots I’ve been updating consistently, but that’s the work to be done. Join me in it. More data coming soon! Here’s a sneak peek:

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Ukrainian soldiers have incredible tactical patience and are far happier moving slowly than the British military

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Rising memory costs are about to test buyers' patience

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Week 14 panic or patience? 'NFL Fantasy Live'
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