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Cyberpunk 2077 2.5 Update Secretly Added The Witcher 3 Sword and Cosmetics
Cyberpunk 2077’s surprise 2.5 update added a new dual-wielding playstyle, fresh ways to customize protagonist V’s look, extra quests and plenty more, but players have discovered that developer CD Projekt Red secretly added a range of new The Witcher 3-themed cosmetics, too.
Canva releases its first image generation model
Canva is making its new image model available to all users, promising better, more customizable performance at lower costs.
Insta360 Mic review: A midrange mic kit that's anything but boring
In an ocean of boring microphones, Insta360’s Mic adds a (customizable) pop of color.
Introspection required if EU Inc fails, says start-up lobbyist
European start-ups have a new voice in Brussels. Startups for Europe, created from the merger of lobby groups Allied for Startups and the European Startup Network, is hoping to harness the momentum behind EU-Inc, a campaign that united start-ups and investors behind demands for a new pan-European company regime.At a launch event for the new association in Brussels on October 1, the mood was remarkably upbeat compared to a typical Eurobubble gathering, more Silicon Valley than Schuman roundabout.When asked whether this reflects a feeling that Europe is on the right track, chief executive Serena Borbotti-Frison said it’s a “scary” moment as the continent looks to boost its competitiveness amid a new technological arms race.“What is at stake is quite intense, and the way we can answer is by delivering, but also with hope,” she told Science|Business. “It’s what our industry does. It’s not about identifying a pain point and then saying it’s too hard.”For many in the start-up community, that sense of hope is dwindling. In March, the European Commission presented its proposal for EU Inc, naming its initiative after the grassroots campaign.EU Inc was meant to be a gamechanger: the ability to incorporate within 48 hours for less than €100 and scale up across Europe; a harmonised stock option regime enabling start-ups to compete for talent; and simplified insolvency procedures for innovative start-ups to reduce the cost of failure.However, haggling by EU governments has raised concerns that EU Inc will be stripped of the very features, including stock option harmonisation, that are meant to make it attractive for companies.“I fear we are proving to ourselves that the model doesn’t work,” Borbotti-Frison said of the policymaking process. “Everyone agrees we should have EU Inc. Everyone agrees it’s hard to scale and we need to scale to answer the competitiveness [challenge], and still we cannot deliver? [. . .] What does that say about ourselves?”The Commission has made EU Inc a cornerstone of its single market reform, and has agreed with the European Parliament and the Council of the EU to target an agreement by the end of 2026. However, several points are currently holding up an agreement, including national rules around employee participation in management decisions, and the provision that employee stock options always be taxed at the point of sale. Some EU governments have reportedly pushed back on the Commission’s choice of legal basis, contending that anything related to taxation requires unanimity in the Council.Trade unions have also protested against EU Inc, fearing it will be exploited by companies looking to undermine workers’ rights.“If this doesn’t work, it’s introspection time, on the way our institutions work, maybe the way our lobbies work, and maybe the way the cooperation between them works,” Borbotti-Frison said.“Is it in the national interest? Is it a gap between political will and technical ability to operate? Is it speed? I don't know, but something needs to be fixed.”Ahead of last week’s European Council meeting, EU-Inc campaigners tried a new strategy: appealing directly to EU presidents and prime ministers. In an open letter, they urged EU leaders to “step in and give your negotiating teams a clear mandate to change course and reverse their objections to the Commission’s original proposal.”Lessons from EU-IncLike its predecessors, Startups for Europe will represent national start-up associations, but it is also targeting investors. The founding sponsors are venture capital firm Atomico and EQT, the investor selected earlier this year to manage the €5 billion Scaleup Europe Fund.It also wants to harness the knowledge and cross-border experience of European success stories. “Take the list of the 100 biggest unicorns in Europe. We are speaking to all of them,” Borbotti-Frison said. The association plans to publish a full list of supporters following the Slush tech conference in Helsinki in November.Andreas Klinger and Simon Schaefer, two key figures in the EU-Inc campaign, are both on the board of the new association, and Startups for Europe wants to take the lessons from their campaign.What was unprecedented about EU-Inc was “how rooted it was, and how strong it was in bringing everyone on board behind one clear pain point and solution to fix it,” Borbotti-Frison said. “This is an inspiration for us, and then we need to turn it into something more systematic, more bridged in Brussels, more connected to the policymaking game, and to scale it.”If it’s possible to unite such a broad coalition, it’s because the European start-up ecosystem is now mature, she said. “Maybe it was not ten years ago. Maybe it was not five years ago. But now, it’s mature to speak with one voice.”Open and liberal EuropeThe association plans to campaign on files related to three “pain points,” Borbotti-Frison said: “start and scale in Europe, fundraise and invest in Europe, and making Europe the most innovative continent.”As well as EU Inc, she cited public procurement, the EU’s simplification push, the Savings and Investments Union, and files related to AI adoption and technological sovereignty as priorities for the coming months.What these files have in common is that they relate not to subsidies but to the framework conditions influencing the availability of mostly private, or in the case of procurement, public, funding.Related articlesEmployee participation disagreement delays MEPs’ vote on EU IncCommission urges EU governments not to water down EU IncEU’s ‘ultra-fast’ €5B scale-up fund is set up to win, says its fund managerThe budget dispute over the next Horizon Europe programme will also impact start-ups, which are supported notably through the European Innovation Council. But EU innovation funding is a drop in the ocean compared to venture capital and public procurement budgets.“If competitiveness and innovation are a priority, then the [EU budget] should reflect it,” Borbotti-Frison said. However, she added that the main question is not how much public money is invested, but “how do we create the right conditions for the market to solve the problem?”The Scaleup Europe Fund mixes both approaches, using €1 billion in EU funding to crowd in private investors and address Europe’s lack of large-scale, late-stage growth funds. “It’s a proof of concept. Ideally, then the market will do it itself,” Borbotti-Frison said.Startups for Europe is also focused on bringing an alternative political narrative. Amid competing visions for open, global markets on the one side and a protectionist Europe on the other, it wants to offer “a third voice, to say we can be proudly European and build a strong continent in a liberal economy, open to the world, and global.”It can be tempting, when thinking about how Europe can better support innovation, to look across the Atlantic. But while it’s important to compare statistics to see where Europe needs to catch up, that’s where the comparison should stop, Borbotti-Frison said. “On everything else, we need to do it our own way. We don't have the same history. We don't have the same political model. We don't have the same way of life.”Many of the key files that could benefit the start-up community, including EU Inc and the Savings and Investments Union, rely on closer integration within Europe, making it easier to operate across borders.While the Commission has made deepening the single market a political priority, support at national level is more precarious. The coming months will see pivotal elections in Spain and France, while the German government has also been coming under pressure domestically. In each country, far-right parties have been gaining ground.When asked if she was concerned the political situation could jeopardise this progress, Borbotti-Frison responded: “Of course. I would be lying if I said I wasn’t.” However, she continues to believe that cooperation is the only way forward. “Doing it alone is not the solution. It’s also not who we are, it’s not the model we are building, and it’s not the scale we need.”
Kansas Premium Meats shuts down, cites ‘lack of business’
A Pottawatomie County store specializing in offering locally-raised meats is shutting down permanently.
Scientists discover a surprising secret behind the human brain’s wrinkles
Scientists have discovered why common marmosets have unusually small, smooth brains despite starting development much like other primates. The secret lies in subtle changes to brain-building cells that slow neuron production, revealing new clues about how the large, folded human brain evolved.
China biotech RiboX reveals first data of circular RNA therapies in people
RiboX Therapeutics published first circular RNA clinical data in NEJM, showing RXRG001 improved saliva in xerostomia patients, ahead of Orna, Orbital, and Sail Biomedicines.
Forge Nano to Trade on NASDAQ as NANO After Completing Business Combination with Archimedes Tech SPAC Partners II Co.
Forge Nano common stock and warrants expected to begin trading on NASDAQ on October 22, 2026 under the symbols “NANO” and “NANOW,” respectivelyPublic listing positions Forge Nano to accelerate growth across semiconductor manufacturing equipment and advanced battery technologies DENVER, Oct. 21, 2026 (GLOBE NEWSWIRE) -- Forge Nano, Inc., a leading U.S.-based semiconductor equipment and advanced materials company pioneering Atomic Layer Deposition (“ALD”) technology for artificial intelligence (“A
Uranium from World War II reveals how close the Nazis were to an atomic bomb
A new look at the uranium and heavy water used by the Nazis reveals whether they were close to making a reactor for an atomic bomb.
Peek into hidden worlds revealed by the 2026 Nikon Small World contest
This year’s photomicrography contest captured the wonders of the very small, from flying ink droplets to the most adorable jumping spider you've ever seen.
Nikon’s Small World photomicrography competition winners
The 52nd annual Nikon Small World competition honours the world’s most remarkable images captured through a microscope
Research report: Digital Science and Sunmedia partner to bring research impact reporting to Japan’s medical affairs teams
Sunmedia will use Dimensions and Altmetric data in customized reports intended to assess attention, sentiment and real-world connections for Japanese medical affairs teams.
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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N. Syracuse Manufacturer Marks Milestone, Plans for the Future
Air Innovations has grown from a small local manufacturer into a global business, selling its products in 74 countries organizations. Some of its clients include NASA, Blue Origin and SpaceX. By Stefan Yablonski [caption id=attachment_11117 align=alignright width=450] Michael Wetzel, president and CEO, and his father, Larry, who currently is an adviser to the board of directors. Larry served as the chairman of the board until 2024, when the company was acquired by Altus Capital Partners, a private equity firm.[/caption] Air Innovations in North Syracuse hopes to add more employees and possibly even expand its facility in the near future. The company is a global leader in designing and building environmental process control systems for applications that can’t be addressed with standard HVAC equipment, according to Michael Wetzel, president and CEO. “We customize solutions for temperature, humidity, filtration and pressurization, with additional capabilities in direct-expansion, chilled-water and thermoelectric technologies. Our business now is entirely focused on specialty or niche markets.” Air Innovations celebrated its 40th anniversary on Sept. 18, marking four decades of doing business, manufacturing products and growing in Central New York. In the beginning [caption id=attachment_11138 align=alignright width=451] A worker, Ezzy Salehi, tests a Wine Guardian condenser unit at Air Innovations.[/caption] “A guy by the name of Jim Kelley started the business [known as Floratech] in 1986. It was focused on refrigerated display cases for supermarket fresh flowers,” Wetzel said. “My father [Larry] started Clean Room Technology in the early 1980s, then sold that to a French company called Clestra Cleanroom. “My father and I bought Floratech in 1996 — 30 years ago, 10 years after it was started. Then in 1999, Floratech bought a portion of Clestra and their building. At that point everything was still called Floratech. But, as that was not reflective of the diversity of the business, we rebranded as Air Innovations in 2001.” The company rebranded to pursue a strategy of diversification in specialty HVAC systems. Wetzel explained that since then, it has become a trusted global partner, earning the President’s “E” Award for Exports and securing contracts in the most demanding fields, including aerospace, defense and semiconductor manufacturing. Air Innovations has grown from a small local manufacturer into a global business, selling nearly 70,000 units in 74 countries and territories and developing custom environmental control systems for companies and organizations including NASA, Blue Origin and SpaceX. “We work with all the big names you ever heard of and some you never heard of,” Wetzel added. The Wetzels sold the business two years ago to Altus Capital Partners, a private equity firm focused on control investments in lower middle-market, niche manufacturing companies in North America. Founded in 2003, Altus Capital Partners’ headquarters is in Wilton, Connecticut, with an additional office in Lincolnshire, Illinois. “They are an equity firm. Both my father and I both maintain a small minority share. I am going to continue to be involved. I call myself a part-time CEO. I will continue that role for the foreseeable future; I don’t see any change there.” Building to the future “We are at 58 employees right now, which is a little bit down. We had a high of 70. That moves along with the activity we have,” Wetzel said. “We’ll start ramping up here in the fourth quarter into next year. More than 20% of our employees have been with the company for more than a decade. “I am fairly certain that sometime in the next several years we are going to have to enlarge the footprint of our facility and for sure we are going to have to add employees. We already are. We are probably going to have to add five or six people next year. We are always in the market for HVAC technicians; we always need those.” Air Innovations will remain in Central New York, with its current workforce staying in place, he added. “I believe the primary driver of our success over time is the diversity of our business which is a direct result of our willingness to tackle new challenges,” Wetzel said.
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The English Renaissance meets the Harlem Renaissance in “Play On!,” a sparkling show that marries a loose adaptation of Shakespeare's “Twelfth Night” with the music of Duke Ellington. Conceived by Sheldon Epps, who also directed the original 1996-97 production (first in San Diego, then on Broadway), the show is getting a marvelous revival under his direction to open Chicago Shakespeare Theater's 40th anniversary season.While the title “Play On!” refers to the famous quote about music being the food of love and thus to the dozens of Ellington songs—among them “It Don’t Mean a Thing,” “Don’t Get Around Much Anymore,” “I Got It Bad (And That Ain't Good),” “Solitude” and “I’m Beginning to See the Light”—incorporated into the plot, one of the highlights for me is the clever way Cheryl L. West's book reworks the story. Apparently, it's also been updated for this outing, among other things to give the women more agency. In the big opening number (there's a six-piece, horn-heavy onstage band), aspiring songwriter Vy (Hailee Kaleem Wright) is the one to “Take the 'A' Train” to “Drop Me Off in Harlem.” She wants to meet the famous Duke (Edred Utomi) — Ellington, not Orsino, that is — and she seeks the help of Jester (an engaging James Harkness) to find out how. When Jester tells her in no uncertain terms that the music industry is a man's world, she disguises herself as Vy-Man and persuades the Duke to listen to one of her songs. He's suffering from a creative block because he's pining for Lady Liv (Krystal Joy Brown), queen of the Cotton Club, and convinces Vy-Man to bring the Lady the song as if the Duke had written it, promising to reward her with employment. Vy reluctantly complies, and the predictable complications ensue. Lady Liv is attracted to Vy-Man, while Vy is attracted to the Duke and he to Vy, though it disturbs him because he thinks she's a man. The fallout when the deception comes to light bodes ill—at least for a while.Here's where West's book cannily diverges from Shakespeare. Vy's twin brother Sebastian really is dead, so West devises another love interest for Lady Liv basically by rehabilitating the Malvolio character, here named Rev (Alan H. Green), the Lady's uptight, self-important manager. His unrequited love for her is magically transformed after he gets his comeuppance (minus the torture) from Miss Mary (Alanna Lovely), her honey Sweets (James Earl Jones II) and Jester, the stand-in for Sir Toby, who has romantic problems of his own with a woman named CC (Kaylah Janssen). In other words, Rev gets the girl — and also a show-stopping yellow zoot suit from costume designer An-Lin Dauber. All the costumes are dandy, though those for the men have more pizzazz than the women's day dresses. The bilevel, slanted scenic design by Beowulf Boritt brings 1930s Harlem to life in a slightly expressionistic way with the help of Adam Honoré's lighting and Ray Nardelli's sound design. Edgar Godineaux's choreography adds to the period feel, though there could be even more of it.Most of all, the leads have splendid voices, and the performances are terrific. Green's Rev and Harkness's Jester get my vote for comedy chops, and Brown's Lady Liv probably will stay with me the longest for her complexity and big blues number, “I Ain't Got Nothin' But the Blues.”
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Studying Science in an Anti-Scientific Time
This Chronicle Review Deep Cut was first published in 2025. By Julianne Werlin Thanks for reading The Chronicle Review! Subscribe for free to receive new posts and support my work. Subscribe The last two decades have not been kind to science studies. Already bruised and battered by the “science wars” of the 1990s, by the 2000s sociologists of science — who had long argued that science had to be understood in its human context — were encountering the funhouse-mirror version of their own views in right-wing critiques of climate scientists. It was enough to give some prominent scholars pause. “Was I wrong to participate in the invention of this field known as science studies?” asked Bruno Latour, the most famous sociologist of science in the world, in 2003. “Should we apologize for having been wrong all along? Or should we rather bring the sword of criticism to criticism itself and do a bit of soul-searching here: What were we really after when we were so intent on showing the social construction of scientific facts?” In response, Latour proposed a change of method and attitude. Science studies, now chastened and contrite, would no longer seek to strip facts of their authority, but to enrich and enhance them. What this meant in practice was never fully clear. But his diagnosis of problems resonated, even if his solutions did not. Latour’s exercise in public hand-wringing signaled a shift in mood. By 2016, with the first election of Donald Trump, the idea that skepticism of science was a right-wing position, not a left-wing one, had become a commonplace, discussed eagerly in The New York Times. For liberals, vaccine denial during the Covid era and Trump’s second-term attacks on the funding of scientific research, covered extensively in these pages, added a new urgency to the defense of science on both practical and principled grounds. There was little appetite for a sociology or philosophy of science that emphasized the messiness and missteps of the scientific process rather than its integrity and triumphs. The distinguished historian of science Peter Dear’s new book, The World as We Know It: From Natural Philosophy to Modern Science (Princeton University Press, 2025), which seeks to tell the story of 18th- and 19th-century science for a general public, may seem comfortably remote from these bitter contests. In its 16 short chapters, Dear draws swift, sure sketches of major discoveries from Newton to Einstein, ably guiding the reader through debates on stellar nebulae, animal taxonomy, atomic chemistry, evolution, and other flashpoints in the history of science. But though a work of history, Dear’s book must still negotiate our polarized present. In a blurb on its back cover, the historian of biology Lynn K. Nyhart alludes to the “present moment, when scientific knowledge is being swamped by misinformation and its institutions are under siege.” Dear himself, in his conclusion, refers to the “yard signs” and “bumper stickers” that proclaim “Science Is Real.” One antidote to both anti-science propaganda and to reductive slogans in science’s defense is a robust history and philosophy of science: exactly what Dear’s scholarship has long offered. But his new book illustrates how difficult such work has become. Dear, an emeritus professor of history at Cornell University, is best known as a creative and philosophically minded historian of the Scientific Revolution. His edited volume The Literary Structure of Scientific Argument (1991) analyzed the rhetoric of Galileo’s thought experiments to brilliant effect. Discipline and Experience: The Mathematical Way in the Scientific Revolution (1995) was a major contribution to the analysis of scientific experiments in early modern Europe and beyond, while Revolutionizing the Sciences: European Knowledge in Transition, 1500-1700 (2001), now in its third edition, remains one of the best overviews of the profound epistemological upheavals of 16th- and 17th-century science. But Dear has also ventured into later periods. Nearly two decades ago, in The Intelligibility of Nature: How Science Makes Sense of the World (2006), he turned to the 18th and 19th centuries to sketch a history and theory of scientific knowledge. Aimed at a generalist audience, Dear’s simple prose belied serious philosophical ambitions. Science, Dear argued, has two aspects: “natural philosophy,” the quest to understand the true nature of the universe, and “instrumentality,” the applied skills and technologies that allow us to manipulate the world for human advantage. Both strands are ancient, but the innovation of the phenomenon we call “science” was to yoke them together. As Francis Bacon wrote, “Human knowledge and human power meet in one; for where the cause is not known, the effect cannot be produced,” an insight typically abridged to “knowledge is power.” Or, in Bertrand Russell’s characteristically blunt phrase, science has two functions, “1. to enable us to know things, and 2., to enable us to do things.” So far, so conventional. But Dear took issue with this familiar story in one respect. Bacon’s formula implied that theory and practice could be fused in a single method. In fact, Dear argued, the quest to understand nature and the desire to manipulate it were coupled only loosely. They could come together, but they could also pull apart. Focusing on natural philosophy, the “knowing” half of the scientific circle, he used a series of case studies from cosmology, taxonomy, chemistry, electromagnetism, and quantum theory to show how theories changed over time, in dialogue with practice, but never reducible to it. Take the case of matter, the basic substrate of the physical world. In the 18th and 19th centuries, scientists not only disagreed about what it was, but also about how well it could be known. In 1789, the chemist Antoine Lavoisier dismissed speculation about the atomic composition of chemicals as “discussions entirely of a metaphysical nature,” as he sought to orient chemistry away from philosophical abstraction and toward laboratory results. But in the 19th century, atoms returned with a vengeance in the atomic chemistry of John Dalton, who wanted to explain the underlying structure of nature. It was not just that Dalton and Lavoisier had a different set of experimental results, or even a different theory, Dear showed. What counted as an explanation had changed. In arguing that scientific theories depended on their intelligibility to human beings with complex arrays of beliefs and commitments, Dear was in sympathy with science studies. Key figures such as the sociologist Steven Shapin emphasized the importance of psychological mechanisms like trust in the formation and acceptance of scientific ideas. But in focusing on the scientific pursuit of knowledge, not its politics or economics, Dear resisted the more cynical perspective of some of his colleagues. The Intelligibility of Nature strikes a very different note than Shapin’s collection of a few years later, Never Pure: Historical Studies of Science as If It Was Produced by People With Bodies, Situated in Time, Space, Culture, and Society, and Struggling for Credibility and Authority (2010), to name just one example. Walking a very fine line, Dear depicted science as driven at once by complex social dynamics and by the intellectual power of real discoveries. It was a testament not only to his own acumen but also to the vitality of the field that he was able to do so. Nearly two decades later, The World as We Know It returns to the questions broached in The Intelligibility of Nature. Like Dear’s earlier study, it begins with the Newtonian cosmos and ends with debates between Einstein and Bohr on quantum physics. Beyond its terminal figures, many of the same characters appear in both studies, including the naturalists John Ray and the Comte de Buffon; the chemists Étienne François Geoffroy, Antoine Lavoisier, and John Dalton; as well as Charles Darwin and Michael Faraday, among others. Its subject is nearly identical: As in The Intelligibility of Nature, The World as We Know It considers natural philosophy as opposed to instrumentality, here defined as the “desire to create a picture of what the world is really like — the world as God knows it — rather than simply having instrumental or operational control over it.” The two books also overlap in insights — and sometimes even in language. The reader who has learned in The Intelligibility of Nature that Buffon’s method of classifying animals “reflected his belief in the importance of the senses and empiricism in learning about nature (a doctrine associated with Newton and the philosopher John Locke)” will experience a sense of déjà vu on reading, in The World as We Know It, that the classification “reflects Buffon’s commitment to the epistemological stance of Newton and his philosophical underlaborer John Locke, who stressed the role of the senses in creating natural knowledge.” Later in the passage, we read that, “because of its stress on understanding the ways of life of animals in their environments, Buffon’s approach could almost be labeled (anachronistically) as ‘ecological.’” And again, “an anachronistic way of putting it would be to say that Buffon recommends an ecological mode of understanding.” Likewise, in The Intelligibility of Nature we read of Dalton that he was initially drawn to meteorology, and that: He did a bit of experimental work on these things, especially having to do with the water-holding capacity of gases, but his real interest lay in understanding what, at an underlying natural-philosophical level, was really going on physically.” Whereas, in The World as We Know It: He did experimental work on these things, especially concerning the water-holding capacity of gases — although not at a very refined level quantitatively — but he was continually trying to develop a theoretical conception of what was happening physically. The final full chapter of each book closes on the same resigned note. Intelligibility: These days, most serious work on the natural philosophical underpinnings and implications of quantum mechanics is performed not by physicists but by philosophers of science. Among scientific practitioners themselves, Bohr’s attempt to use instrumentality as sufficient grounds for a respectable science has met with a high degree of success. And again, in The World as We Know It: Bohr’s and Einstein’s concerns are now questions that are mostly discussed by philosophers of science rather than by physicists themselves. The marginalization of these sorts of questions amounts to the demise of natural philosophy, at least in physics, in the 20th century. Quantum mechanics “works,” and that’s good enough for most scientists who encounter it. Examples could be multiplied. When handling the same subject matter, some echoes are inevitable. Scholars should not feel that because they have written “Isaac Newton died in 1727” in one book, in the next they must describe him as perishing, expiring, or meeting his maker. But the repetitions of language and arguments (not to mention six reproduced images) threaded throughout The World as We Know It are more extensive than that. They should have been caught by Princeton University Press or its reviewers, and revised. Because the two books have so much in common, it is easy to see where they diverge. The World as We Know It is a less theoretical work than its predecessor. The sociology of knowledge that organized The Intelligibility of Nature has been reduced in scale and folded into readings and examples. The taut interplay between history and theory, which gave the earlier study so much interest, is there, but it is subtle. In the earlier book, the case studies demonstrated theoretical claims about the character and development of science. In the later work, each narrative of discovery is an end in itself. Insights emerge from the history, but they do not control the organization of the study. With the theory muted, it can be hard to understand the logic uniting Dear’s sprawling history of more than two centuries of scientific knowledge. The 16 brisk chapters cover an enormous amount of material. But the closer the volume edges to comprehensiveness, the more obvious the missing elements become. A chapter on “Institutions and Pedagogy,” describing the birth of the research university, illustrates the problem. Inserted roughly halfway through the book, the unnumbered chapter is designed as an “entr’acte,” in acknowledgement that it does not fit the study’s organization around scientific discoveries. Yet as Dear is well aware, no general history of the development of modern science can bracket its 19th-century institutionalization and professionalization. It was the 19th-century university, after all, that gave us the “scientist,” a term coined by the Cambridge philosopher William Whewell. Dear’s history of scientific discoveries implies a wider social and institutional history, but it remains largely subterranean, only occasionally extruded under the pressure of the narrative. It must be said that the balance is not all on the side of the earlier study. There are some advantages to the more historical, less theoretical organization of The World as We Know It. Dear’s accounts of paradigm shifts are remarkably lucid, no small accomplishment given that they span fields and centuries. He makes excellent use of the wealth of new scholarship on Darwin and his influences, to which he has himself contributed. It is satisfying to see puzzle pieces from Buffon’s natural history, Georges Cuvier’s animal taxonomy, Thomas Robert Malthus’s demography, and Charles Lyell’s geology recombined seamlessly in the Darwinian jigsaw. Likewise, Dear’s discussion of stellar nebulae, divided between chapters two and 15, shows vividly how Newton, Kant, Herschel, and Hubble reimagined insights and observations as they sought to discover whether the diffuse light that filtered through early telescopes was a glowing, gaseous substance or discrete but distant stars. Yet despite such pleasures, the book remains a more modest work than The Intelligibility of Nature. Dear has clearly not changed his views about the history of science. But in the two decades that have elapsed, the discipline and the world around it have changed, making it harder to write a sociologically and philosophically ambitious history of science for the general reader. That is a shame, because in the age of Silicon Valley and biotechnology, both the reciprocity and the tensions Dear identified between the epistemological and instrumental ambitions of science are more evident than ever. In recent years, some of the most exciting new books on the history of science in early modernity have focused precisely on this pressure point. Important studies such as Pamela H. Smith’s From Lived Experience to the Written Word (2022), James Poskett’s Horizons: The Global Origins of Modern Science (2022), and Vera Keller’s The Interlopers: Early Stuart Projects and the Undisciplining of Knowledge (2023) all shed new light on how early modern science negotiated the relationship between its instrumental and epistemological ambitions, or in Russell’s phrase, knowing and doing. There is every opportunity for a philosophically informed approach to draw new insights from this wealth of material. Dear, unfortunately, has not done so in The World as We Know It. But for future scholars of the history of science, his body of work over nearly four decades may provide just the model. Julianne Werlin is an associate professor of English at Duke University. Thanks for reading The Chronicle Review! Subscribe for free to receive new posts and support my work. Subscribe
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Good science requires much more than evidence
Empiricists believe that scientific theories are true only if they make correct empirical predictions: if we can't verify these predictions, then there is no reason to expect that they tell us anything about reality. However, Professor of Philosophy at King's College David Kyle Johnson argues that this is mistaken. In response to Bas van Fraassen, who has written on this subject for the IAI, he argues that we have many reasons for believing a theory to be true, such as it being parsimonious and having large scope — qualities which have been used throughout the history of science as criteria for judging theories. In 2024, Princeton philosopher Bas van Fraassen wrote a short article for IAI News entitled “Science Does not Describe Reality: The Limits and Benefits of Explanation.” At the time, I was working on two projects (a science book and a Great Course) that, when completed, would (among other things) defend science’s ability to describe reality. I wanted to write a response to van Fraassen, but the projects demanded too much of my attention. But now that those projects are complete, I am in a position to explain the objections to van Fraassen’s thesis. Summarizing van Fraassen’s argumentIn a nutshell, his argument is this: What scientists do is make predictions based on theories and then invite others to test to see whether those predictions are borne out. “If theory T is right, result R should happen in experimental condition C.” Many think that running an experiment that shows that R happens in condition C is proof that theory T is true (and should be believed). But, van Fraassen argues, that R happened in condition C is only a reason to think that R happens in condition C. That’s the only observable, or empirical, result we can actually see, so that is all that we are really justified in believing. (The underlying theory that made that prediction still may or may not be true.) That is why he calls those who accept his position “empiricists.”Before I explain what is wrong with this position, let me explain what it gets right. What van Fraassen Gets RightFirst, a theory correctly predicting the results of an experiment is not, strictly speaking, “proof” that the theory is correct. The basic line of reasoning is this:If the theory T is true, result R should happen. Result R did happen. Thus, theory T is true.In other words:“If T then R. R. Thus, T.”This is a deductively invalid, fallacious mode of reasoning, called “affirming the consequent.” Those premises do not guarantee that the conclusion follows. “If I am in Pennsylvania, then I am in the USA. I am in the USA. Thus, I am in Pennsylvania.” Clearly that argument doesn’t guarantee its conclusion; neither does the above argument.However, science is not a deductive method of reasoning; it is an inductive one. And inductive arguments merely try to raise the probability of their conclusion, not “prove” them for certain. Thus, the fact that an experimental result doesn’t prove a theory is true is irrelevant; it’s not supposed to. That is not what scientific reasoning is about.___If the hypothesis that something exists clearly provides the best explanation for what we can observe, then the belief that it exists is what is rational.___Second, it’s also true that a single experimental result doesn’t provide enough justification to believe that a theory is true. Scientists do not come to a consensus about whether a theory is true by looking at a single experiment; they look at all the evidence to determine where the preponderance of evidence points. The mere fact that I am in the USA raises the probability that I am in Pennsylvania, but not nearly enough to conclude that I am in Pennsylvania. However, if I am also regularly encountering people wearing Eagles and Phillies hats and seeing advertisements for Founding Father tours, I probably am. In the same way, a theory successfully predicting the outcome of one experiment is not enough to conclude that it is true; but if a theory makes a wide variety of predictions that are all vindicated by multiple experiments, performed by many different groups and people, all actively trying to prove the theory false (which is what good scientific experiments are designed to do)—that is a good reason to conclude that the theory is true. In other words, if you repeatedly try to prove something false but can’t, that is a very good indication that it isn’t. The problem of underdeterminationIn reply, those whom van Fraassen calls “empiricists” often point to the “problem of underdetermination.” For any given theory that makes successful predictions about what happens in experimental conditions, there are countless other possible theories that make exactly the same predictions—and since they do, the experimental evidence in question is just as much reason to accept any of those theories as it is to accept our original one. Thus, the argument goes, we have no way of knowing whether our original theory is true, or one of the other “predictively equivalent” theories is true. SUGGESTED VIEWING How Occam's Razor changed the world With Johnjoe McFadden As an example of underdetermination, philosophers often offer this example. The evidence of fossils seems to provide reason to conclude that the universe is quite old; ancient fossils are what the “ancient universe” hypothesis predicts. But it’s also possible that God created an ancient-looking universe 200 years ago, complete with a “fake” fossil record for us to find, to make it look old. The existence of fossils is what that theory predicts too. So, the argument goes, the fossil record can’t be used as evidence that the universe is ancient; the fossil record supports the “200-year-old ancient-looking universe” theory just as much. The problem of underdetermination points out that, for any theory with supporting evidence, we could invent another completely different theory that predicts exactly the same evidence, and thus undermine that evidence’s ability to support the original theory.But this “argument from underdetermination” betrays a fundamental misunderstanding of what scientific reasoning is and how it works. To explain, let me use another rather strange (but very real and commonly discussed) philosophical thought experiment called “The Grue Problem.” The “Grue” ProblemSuppose I want to know what color emeralds are, so I observe a few, see that they all appear green, and form the theory that “all emeralds are green.” I thus predict that the next 100 emeralds I find will also appear to be green. And let’s say that happens, and so I thus conclude that my theory that all emeralds are green is true. Now, one might rightly point out that I don’t know for sure that all emeralds are green—I have not seen them all. Maybe some I haven’t found yet are blue. Fair enough. After all, we used to think that all swans were white, until we found some black ones. But let’s say that I go on a quest and collect every emerald in the universe—and, for the sake of the argument, let’s say I can do this, and can even know that I have every one of them. So, I collect them all, look at them all, and verify that they all appear green. Can’t I now rightly conclude that all emeralds are green?Inspired by the problem of underdetermination, a friend might argue that I cannot.Sure, they all appear green now—but there could be another kind of color called “grue.” Something is grue if it is green up to a certain point in time, but then spontaneously turns blue. And there could be an infinite number of “shades” of grue. Something that is one shade of grue will turn blue at noon today, but something that is another shade of grue will turn blue at noon tomorrow, etc. So yes, the emeralds appear green now; and since they haven’t turned blue yet, they are not some “past” shade of grue. But they might be some “future” shade of grue (e.g., they may turn blue at noon tomorrow). And since all future shades of grue are consistent with “being green up to this point in time,” the evidence you have is completely consistent with the notion that all emeralds are some future shade of grue, rather than green. Indeed, that they would appear green until now is what the “some future shade of grue” hypothesis predicts. So, we have just as much reason to conclude that all emeralds are some shade of grue as we do to conclude that they are green.You’re probably thinking “It’s not equally possible that the emeralds are grue; they are obviously green.” You are right! But explaining why your instinctive reaction to my friend’s argument is correct will help inform why van Fraassen’s “empiricist” is wrong and science actually does describe reality.The main mistake my friend makes is in thinking that the “empirical evidence” (of all the emeralds appearing green) is (or even should be) all I am using to draw the conclusion that all emeralds are green. It is not. I am also considering known facts about how colors typically work; things don’t usually change color, and when they do, they don’t do so suddenly, without cause, and all at once. So, even though the simple evidence of the emeralds all appearing green up to this point is consistent with them being some future shade of grue—that is even what the grue hypothesis “predicts”—it is still exceedingly unlikely that they are. The hypothesis that they are green is much more likely. Understanding science refutes the problem of underdetermination The misunderstanding about science that the “empiricist” pointing to “the problem of underdetermination” makes is similar to the one that my friend made. Science is not just about considering experimental evidence; in fact, it’s not even just about considering empirical evidence. As Ernan McMullin and many other philosophers of science have pointed out, the inference that makes science is “inference to the best explanation” (sometimes called “abduction” or “retroduction” by McMullin). While, depending on the circumstances, scientists use many different kinds of reasoning, science as a whole is, simply put, the process of considering multiple competing explanations and comparing them to certain criteria—explanatory criteria (the kinds of things explanations must be and do, by definition, to be a good explanation)—to see which explanation is the best.And these criteria do not deal only with empirical results. Yes, successful predictions raise the probability of a hypothesis, but it’s also the case that the more new assumptions a theory makes, the less likely it is to be true; every new assumption presents an opportunity for the theory to be false. (The fewer assumptions a theory makes, the simpler or “more parsimonious” it is said to be.) Likewise, the more existing evidence a theory contradicts, the less likely it is, because in order for a theory to be true, every piece of evidence it contradicts has to be somehow faulty. (The fewer established theories a new theory contradicts, the more “conservative” it is said to be.) And, obviously, the more something explains, the better explanation it is. (This is called having “scope.”) If a theory raises unanswerable questions, it’s not expanding our understanding, and thus is not wide-scoping. Scientists consider all these things when deciding which theory to accept, because the theory that adheres best to these explanatory criteria is most likely to be true.___the mere fact that a theory’s predictive success is why it “sticks around” or “becomes accepted” in the scientific community is not a reason to be skeptical about its truth.___The reason the grue hypothesis is no good is because it invents an entirely new kind of color—not just a new shade, but an entirely different category of color (a different way colors work)—and thus is not parsimonious. It raises unanswerable questions about how and why emeralds would just spontaneously change from green to blue (and thus does not have wide scope). And it contradicts what is already well established about how color works (and thus is unconservative).The theories that the problem of underdetermination points to do the same thing. Yes, the theory that God created an “ancient-looking universe” (complete with an ancient-looking fossil record) two hundred years ago is also consistent with the empirical evidence (of a fossil record) that presently exists; but that theory is monumentally unparsimonious (it requires many extra assumptions about infinite entities) and not wide-scoping (it raises unanswerable questions about why God would do such a thing). So, I can rationally conclude that it is false.Indeed, the history of science includes many examples of theories that were rejected because they lacked parsimony and/or scope, even before their falsity (and the truth of their competing theory) was confirmed by observation. As I point out in my book, geocentrism was rejected by the scientific community because it lacked parsimony, almost 200 years before the observation of parallax confirmed that the Earth moves around the sun. And the continuum theory of matter was completely rejected by 1860, because it couldn’t explain chemical reactions or organize matter into a periodic table, even though the experiments that eventually confirmed atomic theory weren’t even proposed until the 1870s. Solving the best of a bad lot problemIn response, the “empiricists” are likely to object, this time pointing to the “best of a bad lot” problem. “We have no reason to conclude that an explanation that has been shown to be better than others is true because we have no guarantee that the true theory was among those that we were considering.” If you have a “lot” (a collection) of poor explanations, even the best one is not true.The responses to this problem, however, are numerous. Peter Lipton argues that the set of hypotheses that scientists consider are not just randomly selected and thus unlikely to contain a theory that is true; they use existing knowledge to home in on what is most likely to be true. A doctor trying to explain abdominal pain is not going to bother wondering whether demons are causing it; they are going to stick with things like appendicitis and kidney stones, because they are most likely. Further, despite its name, inference to the best explanation is not just the process of comparing hypotheses and accepting the best one; we also want one that is a good explanation in its own right. If it’s not, we are going to keep looking until we find an explanation that is. And while that process does not guarantee that we will come across the exact truth, it’s very likely that the theory that emerges as the best will be at least approximately true, and thus we will be justified in believing it. And lastly, as I point out in my book, although many theories not based in scientific evidence (like geocentrism and continuum theory) have been completely overturned, science almost always progresses by the refinement of scientifically established theories—not their replacement. (Although our sun is not the center of the universe, heliocentrism was our first step in understanding the Earth’s true place in the universe.) So even if a theory we accept today may not be completely right, accepting it is a step on the journey toward the complete truth. The example of atomic theoryNow, it’s worth pointing out that most empiricists are not skeptical of all scientific theories; they are only skeptical of the ones that hypothesize the existence of “unobservables.” It’s not like they think that round Earth, heliocentrism, and germ theory are just useful (but untrue) “instruments” for making predictions about how to most efficiently navigate the seas, predict planetary motion, and treat diseases. They admit that the Earth actually is round, that the planets revolve around the sun, and that germs exist and cause disease. These facts and entities can be observed; the theories that suggest they exist are true. It’s only when theories start hypothesizing the existence of unobservables—like electrons, or quantum fields—that can’t be observed directly, that we should start doubting the theories’ truth. And to be fair to van Fraassen, in this published work, he doesn’t really say that we should doubt the truth of such theories. Only that scientific reasoning does not rationally force us to accept them as true. Skepticism, or agnosticism, he argues, is warranted. SUGGESTED READING Science is based in metaphor By Andrew Reynolds But the history of atomic theory throws a monkey wrench into that argument. Atoms used to be unobservable; again, scientists first accepted the theory that they exist because it had such grandiose explanatory power. But then atoms were seen with electron microscopes. They thus moved into the category of the observable. But this didn’t substantially change anything about what scientists were justified in believing; it certainly didn’t change any “atomic skeptics’” minds. It just confirmed what everyone already knew! If the hypothesis that something exists clearly provides the best explanation for what we can observe, then the belief that it exists is what is rational. The fact that the thing itself is not technically observable does not make doubting its existence rational.What’s more, the atoms were seen with an electron microscope. But electrons are themselves unobservable. How can an electron microscope reveal an atom if electrons are not real? This leads us to what is famously known as Hilary Putnam’s “miracle argument.” It’s (not) a miraclePhilosopher Hilary Putnam’s miracle argument is somewhat famous. Basically, he argued that a scientific theory being able to repeatedly make successful predictions—even about unobservables—without being at least approximately true would require a “miracle.” In other words, it would be such an improbable occurrence that it could not rationally be believed—the better explanation for why scientific theories about unobservables make the right predictions is because the unobservable entities and structures they hypothesize exist.Van Fraassen has addressed this argument. Indeed, in his original article, where he references Alison Gopnik’s point about orgasms not being “necessary for procreation” even though the desire for orgasm “drives the creation of progeny,” he is pointing to his own argument about how organisms are not naturally selected by evolution to get true beliefs; to the extent that belief-forming processes are selected for, the ones that are selected are the ones that are useful for survival—not the ones that produce true beliefs. In the same way, van Fraassen argues, scientific theories are accepted, not because they are true, but because they aren’t falsified; they make successful predictions; that’s what makes them “stick around.” So, the fact that they make successful predictions (and thus became accepted) is not a reason to think they are true.This argument is similar to C. S. Lewis’s “argument from reason,” which he used to argue against evolution: “The truth of evolution undercuts our ability to know that it is true.” Lewis’ argument famously failed; Elizabeth Anscombe, by Lewis’s own admission, demolished Lewis in a debate on the topic, and I have explained elsewhere why modified versions of the argument fail as well. In short, it’s because it fails to recognize that a belief-forming process can make an organism more likely to survive because it reliably produces true beliefs. It is therefore not the case that beliefs generated by naturally selected belief-forming processes are automatically false or unjustified.In the same way, the reason a theory “sticks around” could be because it’s not falsified, because it “makes correct predictions.” But the reason it makes correct predictions is most likely because it is true. (In pointing this out, I am echoing the arguments of Musgrave, Lipton, Psillos, and Kitcher, who refuted van Fraassen’s argument and analogy long before his 2024 article.) It would be a huge coincidence that it could do so without being true. And that is the point of Putnam’s miracle argument. Thus, contrary to van Fraassen’s suggestion, the mere fact that a theory’s predictive success is why it “sticks around” or “becomes accepted” in the scientific community is not a reason to be skeptical about its truth. ConclusionLet me close by saying something about the fact that empiricists don’t doubt the truth of all scientific theories, just the ones that hypothesize unobservables. In today’s society, this theory is dangerous to espouse publicly. The nuance between being skeptical about all of science, and just the highly technical theories about unobservables, is guaranteed to be lost to the average reader. And there is so much unjustified skepticism and doubt about the findings of science and experts that a headline like “Science Does Not Describe Reality” could easily fuel the anti-science movement, worsen climate change denial and cause the next pandemic.This is why I believe it was important to still respond to van Fraassen’s article, a full two years after he wrote it. Not only, as I have argued, does it represent a philosophically inaccurate view of how scientific reasoning works and what scientific reasoning can do, but it is a dangerous view to boot. It should thus be rejected.
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Intel Panther Lake Teardown, 18A, BSPD, GAAFET, SemiAnalysis STEEL
Panther Lake debuts the first commercial implementation of backside power delivery (BSPDN), introduces Intel’s first iteration of gate-all-around (GAA) transistors, and showcases their advanced packaging capabilities with its Foveros-S assembly. With Panther Lake, Intel’s manufacturing arc has shifted from nebulous roadmaps to shipped silicon, a significant milestone on their long road back to competitive semiconductor manufacturing. To evaluate the extent of Intel’s comeback, we tore down Panther Lake. The SemiAnalysis STEEL teardown lab breaks down advanced datacenter and AI hardware. To learn more about our pipeline or to commission a teardown, contact sales@semianalysis.com. WE’RE HIRING: Architecture, floorplan, packaging, manufacturing, and labs experts. Opportunities from system to transistor and everywhere in between. Check out our Careers page. Our teardown traces 18A from its four-sheet RibbonFETs (Intel’s marketing name for GAAFETs) and gate stacks through contacts, frontside and backside wiring, and the bonded carrier. We explain how these material and integration choices improve gate control and reduce resistance, while adding capacitance, thermal resistance, and process complexity. Our measurements put Panther Lake’s 18A compute logic and TSMC N3E GPU logic at similar logic density. However, 18A does not lead TSMC N3P, N2 or Samsung SF2 in peak density. Panther Lake’s CPU cores are incremental updates, and the high-end GPU still uses TSMC N3E. Panther Lake assembles one compute tile, one GPU tile, and one I/O tile atop a passive base tile using Intel’s Foveros-S advanced packaging. Both compute tile variants use Intel 18A. The Xe3 GPU options are a 4-core GT1 tile on Intel 3 and a larger 12-core GT2 tile on TSMC N3E. Both I/O tile variants use TSMC N6. [1], [2] Our analysis centers on the PTL-U compute tile, both the 4-core and 12-core GPU tiles, as well as the 12-lane I/O tile. In conventional chips, power and signal are routed through the same frontside metal stack towards the device frontend. Power rails consume scarce routing resources near the transistors, while tall via stacks carry VDD and VSS from the coarse upper wires to local rails. Backside power delivery (BSPD) moves the main power network behind the transistor layer, to the backside, separating it from frontside signal routing. We covered BSPD and its impacts in 2024. [3], [4], [5] Intel’s BSPD implementation, branded as “PowerVia”, routes power through dedicated backside metals to nano-TSVs, which connect those rails to local source/drain (S/D) contacts. Implementing that separation requires Intel to build the interconnect stacks from both sides of the wafer. The frontside comprises the M0-M14 signal stack, while the backside comprises the BM0-BM5 power stack. M0 and BM0 are closest to the transistors. The nano-TSVs connect the two sides, but Intel patterns and etches each via from the front after forming the contacts. A narrow via runs from the side of the contact deep into the silicon substrate. Intel then completes the frontside signal metal stack, bonds the wafer to a carrier, flips it and removes the original substrate until the buried via tips are exposed. The backside metal stack is then deposited directly on the revealed vias. The nano-TSV and backside-via profiles taper in opposite directions because Intel forms them from opposite sides of the wafer. The transistor structures form the FEOL. Local contacts and nano-TSVs connect them to the wiring. M0 begins the frontside interconnect stack. The silicon carrier remains attached above the frontside interconnects. It supports the device wafer during substrate removal and backside processing and remains part of the finished chip’s thermal path. PowerVia removes the main power distribution from the congested frontside metals, routing supply through shorter and wider backside wires. Its lateral landing still occupies area in the standard cell, so it recovers less cell area than a direct backside contact. [3] Nano-TSVs beside the logic devices carry VDD or VSS from the backside power network, while signal connections continue upward through the frontside metals. Backside Interconnects Samsung SF2 data is included for comparison to Panther Lake’s within this article. SF2 is the incumbent GAA foundry node but lacks BSPD, serving as a useful reference to evaluate 18A. A full teardown of Samsung’s S26 products, processed on SF2, will be shared soon.Nanosheet-cut EDS comparison. The PowerVia supply path runs from the backside Cu rails through Mo-lined W nano-TSVs to the local transistor contacts. In this cross section, the tapered connection spans roughly 150 nm from the contact level to BM0. The Ta liner confines Cu and promotes adhesion to the surrounding stack; the AlOₓ etch stop controls the next dielectric etch above the rail. Dielectric beneath the ribbons electrically separates the devices from the backside wiring and removes the conducting silicon body below the channel. [6] AlOₓ serves as an etchstop (ES), enabling endpointing and protecting the underlying layers. Low-volatility aluminum fluoride reaction products resist the fluorinated plasma, allowing a thin AlOₓ film to protect the metal while the surrounding low-k dielectric is removed. [6], [7]. While the BM0 and layers above the M1 lines show double AlOx layers, Our SMIC N+3 teardown showed single AlOₓ layers. SMIC uses a simpler local AlOₓ substack, while the remaining cap and etch sequence provide the required landing protection. So why double layers? The closely spaced AlOₓ doublets provide two protected endpoints in the etch sequence. Intel documents an AlOₓ/SiN/AlOₓ stack that explains the benefit. The main dielectric plasma etch stops on the first AlOₓ film; a selective wet clear opens that film; a second plasma etch removes the intermediate SiN and stops on the second AlOₓ film. The final wet clear exposes the metal landing surface. SiN is the intermediate dielectric in Intel’s published example. [8] The second stop protects the metal through a cap breakthrough. Wide openings can etch faster than narrow ones, and etch depth varies across the wafer. Metal under an early-clearing opening would otherwise be exposed while other openings still need more etching. Staged protection widens the process window and reduces metal erosion, corrosion and void formation. [8] TSMC documents AlN/AlOₓ/SiOC/AlOₓ above Cu, with AlN blocking Cu diffusion, and a simpler AlN/SiOC/AlOₓ variant that omits one AlOx film. [9] Levels with different opening sizes, aspect ratios, pattern densities and cap materials need different etch margins. A double AlOx stop is useful where another protected endpoint justifies the added processing. The extra film adds formation, selective opening and cleaning steps, plus another set of interfaces to control adhesion, moisture, and stress. These blanket films are opened through the existing via pattern, so each film does not require another lithography mask. AlOₓ adds parasitic capacitance when it replaces lower-k dielectric; two thin AlOₓ films can nevertheless contain less AlOₓ than one thick film. Total thickness, placement, and theintermediate dielectric determine the electrical cost. Deposition chemistry also changes AlOx permittivity and residual hydroxyl content, which can oxidize the underlying metal. [7], [8], [10], [11] The backside stack separates into relatively fine BM0-BM2 wiring near the devices and coarser BM3-BM5 power distribution. The largest pitch increase occurs between BM2 and BM3. BM0’s pitch closely matches the logic-row height, fitting local power delivery to the cell rows. Higher levels aggregate current through larger conductors: routing density becomes less important than low resistance and current capacity as the network approaches the package. This hierarchy provides wide power wiring for the power delivery network without consuming scarce frontside signal-routing resources. [3] The SemiAnalysis STEEL teardown lab breaks down advanced datacenter and AI hardware. To learn more about our pipeline or to commission a teardown, contact sales@semianalysis.com. WE’RE HIRING: Architecture, floorplan, packaging, manufacturing, and labs experts. Opportunities from system to transistor and everywhere in between. Check out our Careers page. Frontside Interconnects Intel 18A combines Mo-lined W contacts and nano-TSVs with a separate backside Cu power network. Samsung SF2 keeps power on the frontside, using Ti-based contact interfaces and Ta-based barriers and Co liners around Cu wiring. In 18A standard-cell rows, backside power rails supply the devices through nano-TSVs within the cells, freeing frontside routing resources. Samsung’s M0 accommodates both power and signal connections. From the device toward M0, the connection runs through a Ti-based S/D interface, W contact fill, a Mo-lined W via, and the Cu M0 wire. Mo supplies a conductive nucleation and adhesion layer for W, replacing the resistive TiN liner used in conventional W integration. This increases the effective conduction volume within the feature while retaining W fill and its established polishing, cleaning and etching processes. Intel’s Mo/W patent describes this integration tradeoff. The nano-TSV uses the same Mo-lined W construction in the backside supply path. [12] The move from TiN to Mo is an incremental change. While a full Co or Mo fill can also reduce the volume lost to liners in very small features, it requires new integration schemes that increase complexity and risk. Cu remains attractive for wider wires due to its low resistance. As wires and vias shrink, the diffusion barrier consumes an increasing fraction of their cross-section. [12], [12], [14] Intel uses Co/Ru liners at M0-M1, Co at M2-M4, and Nb at M5-M9. The lower-level liners help Cu adhere and reduce void formation during trench fills. Applied Materials’ Endura has new thermal control that facilitate wetting process, so the thin film continuity is good enough that good capillary pressure will drive Cu atoms to the via bottom without voiding. Intel’s choice to use Nb is particularly interesting. Intel’s Nb patent describes a conductive diffusion barrier intended to reduce the barrier’s contribution to resistance relative to conventional Ta-based barriers, particularly at via bottoms where all current crosses the barrier. The patent pairs Nb in coarser levels with the option of lower-cost PVD processing. [15], [16] The upper metal layers support thicker barriers formed through physical vapor deposition (PVD) despite its worse coverage and uniformity. Meanwhile, the lower metal layers require thinner barriers deposited through conformal atomic layer deposition (ALD). Co/Ru adds another material interface and requires controlled deposition and Cu fill. Changing liners and barriers by metal layer allows Intel to optimize interconnect resistance, process complexity, and reliability. [15, 16] RibbonFET, Intel’s name for its gate-all-around FETs (GAAFETs), replaces the FinFET’s vertical fins with four stacked horizontal silicon nanosheets, allowing the gate to surround the channel on every side. The path to GAAFET begins with the planar transistor. A planar MOSFET places the gate above the channel between its source and drain. Pairing an NMOS with a PMOS transistor creates a CMOS inverter, in which the NMOS pulls the output low for a high input, and the PMOS pulls it high for a low input. The gate must retain electrostatic control of the channel to ensure clean switching. As gate lengths shrank, the drain began to compete with the gate for that control, increasing off-state leakage. Electrostatic control was restored through an architectural evolution that raised the channel into a vertical fin and wrapping the gate around three sides. Called “FinFET”, this new architecture packed more effective channel width into a smaller footprint. Further scaling made it harder to maintain both drive current and leakage within smaller cells, and reintroduced the same problems planar MOSFETs faced. Nanosheet GAAFETs close the fourth side by replacing the vertical fin with a stack of horizontal nanosheets, each surrounded by the gate. The tighter electrostatic control suppresses leakage at shorter gate lengths while stacking adds effective channel width within the cell footprint. In a FinFET process, channel width changes in discrete steps as designers must add or remove whole fins. Nanosheet width can instead be adjusted continuously within the process’s design rules. Wider sheets increase drive current, while narrower sheets reduce capacitance at the cost of drive current. Intel 18A uses stacks of four nanosheets each and varies their widths across logic and SRAM. At the process level, adding more sheets to each stack increases effective channel width and drive current, but complicates fabrication. RibbonFET vs MBCFET Samsung began GAAFET production in 2022 with SF3E, following with SF3 and now SF2. Its ‘MBCFET’ provides a useful structural comparison with Intel’s first RibbonFET implementation. [17] STEEL is digging deeper into SF2, used in the Exynos 2600, and TSMC’s GAAFET N2, used in Apple’s A20 Pro, in upcoming newsletter articles. We’re throwing some teasers on X. Let’s compare Samsung SF2’s MBCFET with Intel 18A’s RibbonFET. Subscribe Even to the untrained eye, Intel’s extra nanosheet is obvious. Intel stacks four ribbons to Samsung’s three. Samsung’s sheets are much wider in these fields, so both sheet count and width matter to the available channel perimeter. Sheet width also changes which silicon surfaces carry current. On conventional (001) silicon, wide nanosheets emphasize the broad top and bottom surfaces, favoring electron transport; the larger sidewall contribution in a narrow sheet favors hole transport. Thinner sheets improve gate control but increase confinement and scattering. This makes width and thickness part of the NMOS/PMOS balance, alongside strain and threshold voltage. [18], [19] GAAFET designs like 18A use different work-function-metal (WFM) stacks for NMOS and PMOS. Around each ribbon, a thin SiOx interfacial layer separates the silicon channel from the HfOx high-k dielectric, with La providing dipole tuning and the WFM wrapping the dielectric. NMOS uses a TiAl-based stack, while PMOS uses TiN WFM. W fills the remaining gate trench, providing a lower-resistivity path where the work-function layers are no longer needed. In this field, the PMOS stacks leave room for W between ribbons, while the NMOS stacks occupy more of those gaps. A silicon-based dielectric marks the P/N boundary, allowing the PMOS and NMOS gates, sharing the same gate trench, to be processed sequentially. Fast logic paths, retention circuits, and SRAM need a family of threshold options. Changing threshold without substantially changing device dimensions, capacitance or fabrication complexity is valuable. FinFET processes typically use different work-function-metal stacks. In a four-ribbon GAA stack, the narrow sheet-to-sheet gap limits how much WFM can fit around each channel. La in the gate dielectric creates interfacial dipoles at the SiOx/HfOx boundary, shifting effective work function and tuning threshold voltage. This gives Intel another control alongside its NMOS and PMOS WFM stacks. Low-threshold devices improve critical-path drive; higher thresholds reduce leakage elsewhere. Dipole tuning is especially useful in GAA because it changes threshold without consuming the narrow intersheet gap with thicker WFM. Precise control of La incorporation, diffusion and interface quality has long been a challenge, limiting viability in high volume production but is now seen from every leading-edge foundry. Intel’s patent describes depositing a dipole-forming oxide above HfOx and annealing it toward the interfacial oxide before completing the work-function and fill metals. This separates threshold tuning from the space available for metal. Newer research addresses the thermal cost: imec’s 2026 dipole-middle research inserts the shifter between two HfOx depositions, shortening the diffusion path while protecting SiOx during patterning. [20], [21]Matched-cut EDS, Intel 18A (left) vs. Samsung SF2 (right). Intel retains raised source/drain epi beneath its contacts, while Samsung recesses W deep into the epi to form a V-shaped Ti-lined interface. The deeper contact increases metal-to-semiconductor area and shortens the current path from the lower sheets, reducing contact and spreading resistance. It also removes epi volume and brings the contact etch closer to the channel ends. Retaining more epi preserves the material available for strain transfer, especially from SiGe into PMOS. These geometries balance contact access against stress engineering and etch margin. [22], [23] Samsung stacks three sheets to Intel’s four ribbons, and both processes use sheet width to tune drive strength. In our Samsung cross-sections, widths range roughly from 19 to 30 nm in the NPU rows and 37 to 50 nm in the CU cell. The Samsung nanosheets taper, with the widest sheet at the bottom and the narrowest at the top. Both processes use HfOx gate dielectric and Ti-based work-function stacks, with Al in the NMOS stack. In the Samsung devices shown here, the dielectric and WFM occupy the intersheet gaps, leaving W above the top sheet. Intel’s PMOS stack leaves more room between ribbons, and W fills those gaps while the thicker NMOS stack leaves W mainly in the upper trench. Gate-stack EDS maps. The W between Intel’s PMOS ribbons provides a conductive path close to the lower gates. Where WFM fills the entire gap, the gate still surrounds the channel, but voltage reaches it through the more resistive work-function films. Thinner WFM and dipole tuning preserve room for low-resistivity fill; Mo and Ru are alternative fill metals being developed for further scaling. [24] A masked, sequential WFM flow explains the different gate heights and inter-nanosheet fill. The proposed sequence below shows how separate NMOS and PMOS work-function steps produce that geometry. Enabled by the BSPDN process, Intel replaces the dense-logic silicon subfin with dielectric, removing the parasitic conduction path below the ribbons and reducing substrate-related capacitance. A retained silicon body as in classical, non-SOI, planar and FinFET designs needs junction and punchthrough-stop engineering to suppress leakage. Dielectric isolation makes that leakage less sensitive to the subfin doping profile but adds removal and fill steps. It also weakens the direct thermal path through silicon, making the contacts, metal stacks and package more important for heat extraction. [24], [26] Fluorine is concentrated around selected Intel device structures in the maps. WF6 is a standard precursor for W nucleation and fill, while barrier films protect adjacent dielectrics from fluorine attack. Low-fluorine W processes reduce the residual-F burden. Chloride-based precursors avoid introducing F during W deposition, but require control of chlorine attack, nucleation and fill quality. The integration target is a continuous, low-resistance W path with a thin protective liner and minimal chemical damage to the surrounding stack. [20], [27], [28] The SemiAnalysis STEEL teardown lab breaks down advanced datacenter and AI hardware. To learn more about our pipeline or to commission a teardown, contact sales@semianalysis.com. WE’RE HIRING: Architecture, floorplan, packaging, manufacturing, and labs experts. Opportunities from system to transistor and everywhere in between. Check out our Careers page. We measured cell height, gate pitch, metal geometry, and ribbon dimensions at the XTEM sites shown below. The tables group these dimensions by site and device polarity. Our “sheet cuts” cross the silicon channel and show the ribbons end-on. “gate cuts” run along the channel through successive gates. The 18A logic cell dimensions point to a five-track logic library while the N3E and Intel 3 cell dimensions evidence a seven-track logic library. The DDR-PHY uses wider M0 wires and much larger spacing than core logic. That trades routing density for lower wire resistance and weaker coupling between neighboring nets. The geometry suits the current delivery and coupling requirements of analog, clock, and I/O circuitry. PowerVia lets 18A combine a compact cell height with wider M0 geometry by moving the main power rails off the signal-routing tracks. That relaxes local wire scaling while preserving a small cell footprint. Cell height and gate pitch set the geometric density; pin access and routability determine how much of it a real block can use. [29] The biggest takeaway from our gate-pitch measurements is that Intel 18A compute logic and TSMC N3E GPU logic have similar density in the Bohr representative-cell model. The 18A example is 18.6% denser than the Intel 3 GPU example. Gate pitches are nearly identical across the three sites, so cell height drives most of the difference. The Bohr model combines a four-transistor NAND2 spanning three gate pitches and a 32-transistor scan flip-flop (SFF) spanning nineteen pitches, weighting their densities 60:40. The sensitivity column shows how independently changing cell height and gate pitch by ±1 nm changes the result. This compares representative cell geometries; whole-die density also depends on cell mix and placement. The 18A P-core gives M0 substantially more metal cross section than the N3E vector engine. Treating each profile as a trapezoid gives 2.63 times the area per line and 1.84 times the area after normalization by routing pitch. The larger section reduces the geometric contribution to line resistance and lowers current density for a given current. Taller and wider wires also add capacitance, so circuit delay depends on the balance of resistance and capacitance. The DDR-PHY has less metal area per routing width than the 18A core fields, while remaining above N3E. [30] Area = height × (top CD + bottom CD) / 2, including liners. Area/pitch normalizes by routing width. Taper is the symmetric sidewall angle from vertical, with the largest angle belonging to the DDR-PHY. Compute tile The measurements show how ribbon dimensions and gate-stack geometry vary across the compute tile and between NMOS and PMOS to balance channel drive, gate load and the space needed for the dielectric/WFM stack across logic, SRAM and the DDR-PHY. Width mainly changes available channel perimeter; thickness also changes electrostatic control and carrier confinement. Gate-stack thickness then determines the space left for low-resistivity fill P-core and LP E-core logic Both the P-core and LP E-core use multiple nanosheet widths. Widths are measured on high-magnification XTEMs while wider-field images demonstrate additional width choices within the LP E-core. Multiple widths are expected even within an LP E-core. Timing-critical paths, buffers and cells with different fanout need different drive strengths. The lower-magnification fields show this width diversity beyond the sites quantified in the table. L2 and L3 SRAM GAA gives SRAM designers another way to balance the pull-up (PU), pass-gate (PG), and pull-down (PD) transistors. FinFET bitcells set device strength through fin count while GAA adds nanosheet width as a sizing knob. In a 6T SRAM cell, a strong pull-down relative to the pass-gate limits read disturbance, while a strong pass-gate relative to the pull-up improves writability. During a write, the pass-gate and write driver pull the node storing “1” below the inverter trip point. During a read, the pull-down holds the node storing “0” low. Bias, threshold voltage, mismatch and assist circuitry set the remaining margin. FinFET high-current cells commonly use a PU:PG:PD fin-count pattern of 1:2:2, a device-sizing ratio rather than a current ratio. Ribbon width lets Intel balance SRAM strengths without adding whole fins. The L2 cell uses its narrowest ribbons for PU and widest for PD, improving writability and read stability respectively. Intel’s disclosed HCC operates without assist; its denser HDC uses negative-bitline write assist. Pulling the selected bitline briefly below ground increases pass-gate overdrive so it can overpower the pull-up at lower supply voltage. That buys density and low voltage writability at the cost of boosting circuitry, switching energy, and additional voltage stress that must be controlled. [31], [32] Four rectangular ribbons give the perimeter = 8 × (width + thickness), before corner rounding. PG/PU is 1.49 and PD/PG is 1.16. The L3 structures closely resemble L2 in layout and cell height. Fewer L3 nanosheet widths are tabulated because fewer high-magnification images were available. DDR PHY The DDR-PHY trades density for controlled analog behavior and reliable off-chip signaling. It contains drivers, receivers, delay circuits, and calibration logic that set drive strength, sampling time, and voltage margin. Repeated four-sheet devices with similar widths fit the use of regular transistor units for matching and programmable drive. Its wider local wiring provides room for current delivery and separation of sensitive signals, while consuming more area than a dense core-logic grid. The layout serves the memory channel’s electrical requirements as well as digital logic density. [33] The SemiAnalysis STEEL teardown lab breaks down advanced datacenter and AI hardware. To learn more about our pipeline or to commission a teardown, contact sales@semianalysis.com. WE’RE HIRING: Architecture, floorplan, packaging, manufacturing, and labs experts. Opportunities from system to transistor and everywhere in between. Check out our Careers page. Intel 3 GPU devices Vector engine logic Intel 3’s XVE logic uses two-fin PMOS and NMOS devices with power rails in M0. Its cell height and M0 pitch give a seven-track geometry, two tracks more than the 18A logic. One-fin groups also appear among the two-fin devices. Intel 3 L2 SRAM The Intel 3 L2 SRAM uses the familiar HCC sizing pattern: one PU fin, two PG fins, and two PD fins. N3E GPU devices Vector engine logic The N3E XVE field contains repeated two-fin devices with seven-track cell geometry. N3E remains a FinFET process, giving Panther Lake a direct FinFET-to-RibbonFET comparison. N3E L2 SRAM The N3E L2 SRAM uses the same PU:PG:PD fin-count pattern of 1:2:2. Panther Lake-U follows Lunar Lake’s floorplan quite closely. Both pair 4 P-cores with 4 LP E-cores and NPU, media and display engines in similar locations. Lunar Lake also uses Xe2, the direct predecessor to Panther Lake’s Xe3 GPU. This makes Lunar Lake the most direct basis for our comparisons. Arrow Lake differs in core count and uses the older Xe-LPG GPU architecture, so we only use it where it offers a more direct component-level comparison. Compute tile Panther Lake compute-tile floorplans remain sparse even months after launch. Intel 18A’s backside metal and dielectric stack must be removed without damaging the underlying structures before a clean transistor-level floorplan can be imaged. Most published die shots hide or heavily process the background, but we are quite proud of the die shot we achieved and are excited to show the work we have done. We measured the areas of the key components on the compute tile and compared them with their Lunar Lake predecessors on TSMC N3B. These help us to capture changes in block area and compare the two chips across process nodes and designs. Our total tile areas exclude the scribe-line area. The compute-plus-GPU subtotal below uses the PTL-U compute tile and GT1 GPU; it excludes the I/O tile and passive base. Individual block areas use the boundaries marked on the floorplans The compute-plus-GPU row is recomputed from the displayed PTL-U and GT1 areas. Component rows use their stated per-region counts and are not an additive partition of the whole tile. The P-core area remains almost unchanged between Lunar Lake and Panther Lake, despite L2 capacity increasing from 2.5 MiB to 3 MiB. Arrow Lake uses the same Lion Cove core as Lunar Lake but also has a 3 MiB L2. Cougar Cove fits 20% more L2 into the same P-core area. The larger private cache keeps more of each core’s working set close to its execution units, reducing access to shared L3 and DRAM. Extra capacity adds storage leakage and lookup energy, so designers balance it against avoided lower-level accesses. The shared P-core L3 cache also shrank by 14.8%. [2] Cougar Cove combines a similar footprint with Intel’s reported power-efficiency improvements. RibbonFET’s tighter channel control reduces leakage, while PowerVia reduces supply droop and allows tighter voltage guardbands. [1] Darkmont’s four-core LP E-core cluster is 5.0% smaller than Skymont’s on Lunar Lake, with most of the reduction in its L2 regions. The 1 MiB region shrank by 8.4% and the 1.5 MiB region by 14.9%. The tag arrays also use one fewer visible row. Tags identify which memory addresses the data array holds, so rearranging them changes the cache’s layout and wiring without requiring less data capacity. [2] The LP E-cores share one L2. This pools capacity and avoids duplicating all the cache machinery, but the four cores contend for its banks and bandwidth. Their separate cluster also keeps light work away from the performance cluster and its L3, allowing that larger domain to sleep. [1], [2] Cache area includes more than the storage cells. Tags identify each line, decoders select rows, sense amplifiers read the small bitline signal, and wires connect to the banks. Splitting an array into smaller sections shortens wordlines and bitlines, improving access speed, but duplicates peripheral circuits. Panther Lake’s smaller cache regions therefore reflect the complete memory implementation, including how much of each region is devoted to storage. [34] Unlike Meteor Lake and Arrow Lake, Panther Lake has no separate SoC tile. The NPU, LP E-cores, memory controllers, PHYs, media and display engines now share the compute tile. This removes an active die and keeps CPU memory traffic on one die. The cost is moving PHY and I/O-related circuitry onto 18A: drivers, receivers and analog circuits must still meet external voltage, loading and signal-integrity requirements, so their area does not shrink like dense digital logic. [1], [2] The biggest shrink comes from the NPU, which occupies 36.9% less area. NPU 5 consolidates the same total INT8 MAC count into half as many neural compute engines. Each of the three NCEs has a larger MAC array to make the complete NCE envelope 22.6% larger than an NPU 4 engine. Consolidation also halves the number of scratchpads and SHAVE DSPs, from 12 to 6. The MAC array handles matrix multiplication and convolution, while SHAVE executes vector and custom operations that fit the array poorly. [1], [2], [35] The paired floorplans identify each NCE envelope and its scratchpad, MAC, and SHAVE regions. Each measured MAC polygon is counted once per NCE in the area accounting below. The scratchpads store weights, activations, and intermediate results near the MAC arrays, allowing repeated use without fetching them again from DRAM. Halving their number delivers the largest measured area saving but leaves less local storage for the same total MAC count. Layers that no longer fit locally require smaller working tiles or more transfers of intermediate data. The benefit depends on keeping the enlarged arrays busy while managing that tighter storage budget. [36] NPU 5 also adds native FP8. Using half the operand width of FP16 reduces storage and transfer demand, helping workloads fit the smaller local memory budget. Lower precision and format-dependent range make scaling and model validation part of deployment. Hardware activation functions further reduce work that would otherwise occupy the programmable DSPs. [1], [2] Microsoft requires an NPU to deliver at least 40 TOPS for Copilot+ PCs. Both Lunar Lake and Panther Lake meet this threshold, but Panther Lake uses significantly less silicon. GPU tiles Panther Lake is Intel’s first product with Xe3, its latest GPU architecture. It offers two different GPU tiles: a smaller GT1 tile with 4 Xe3 cores on Intel 3 and a larger GT2 tile with 12 Xe3 cores on TSMC N3E. Panther Lake allows us to compare the same GPU architecture across both Intel 3 and TSMC N3E. Wildcat Lake adds a third Xe3 implementation on Intel 18A. A future newsletter will detail Xe3 and its implementation differences across all three process nodes. GT2 scales Xe3 to a different physical layout, with render slices arranged vertically instead of GT1’s horizontal arrangement. Slice placement sets the distances to shared cache banks and the D2D interface. Those wires consume area and add delay, so scaling the number of Xe cores also requires a new balance of cache placement, routing and timing. [1] What’s immediately obvious is that the GT2 tile on TSMC N3E has much smaller Xe cores than GT1. These block areas include logic, caches, and routing. An Xe core on the GT1 tile is ~69% larger than one on Lunar Lake, and ~55% larger than one on GT2. Intel 3 therefore uses substantially more area per Xe core. The block-area gap exceeds the measured logic and SRAM density gaps, bringing routing, timing targets, cell mix, and floorplan allocation into the comparison. The measured vector/matrix engine region is almost unchanged between Lunar Lake and Panther Lake’s GT2 tile. Xe3 retains eight 512-bit vector engines and eight 2048-bit XMX engines per core. Its gains also come from feeding those engines more effectively: more resident threads hide stalls, and variable register allocation lets shaders trade registers per thread against the number of threads kept active. [1] The shared L1/SLM capacity increased by 33% from 192 KiB to 256 KiB, while its area increased only 5%, raising effective density by 27%. L1 retains reused cache lines, while software-managed SLM lets a thread group share data locally. Both reduce traffic to more distant memory. Allocating more SLM per group can also limit how many groups reside on a core at once. [1], [37] The GT1 tile carries 4 MiB of L2 against 16 MiB on the GT2 tile. GT1 divides its L2 cache into four 1 MiB banks, while GT2 uses eight 2 MiB banks. Each bank contains 128 macros, but each N3E macro stores 16 KiB, twice the Intel 3 macro’s 8 KiB capacity. The N3E macro is only 54% larger while holding twice as many bits, giving it 30% higher density: ~23.7 Mbit/mm² versus 18.3 Mbit/mm². Including bank-level circuitry, the gap widens to ~16.9 Mbit/mm² on GT2 versus ~10.4 Mbit/mm² on GT1. GT2 gains density with its macros storing more bits per unit area, and those macros occupy more of each cache bank. Larger macros spread decoder and sense-amplifier overhead across more storage, while a more compact bank layout reduces the share spent on control and routing. The compromise is longer wordlines and bitlines that carry more capacitance. [34] I/O tile Panther Lake uses two I/O tile variants, both fabricated on TSMC N6. The smaller one provides 4 PCIe 5.0 and 8 PCIe 4.0 lanes and serves lower-tier systems as well as those without a discrete GPU, while the larger one adds 8 PCIe 5.0 lanes, bringing the total to 20 lanes, for discrete-GPU connectivity. Panther Lake SKUs with the larger 10- or 12-Xe GPUs use the smaller I/O tile. [38] The smaller I/O tile adds a PCIe 4.0 block and a Thunderbolt block to Lunar Lake’s I/O layout, providing four additional PCIe 4.0 lanes and another Thunderbolt 4 port. Its repeated N6 blocks retain nearly identical areas and layouts. Reusing these proven PHYs and controllers avoids porting and requalifying external interfaces on 18A, where faster digital logic offers less benefit to circuits constrained by the off-chip link. [38] SemiAnalysis’s teardown lab (STEEL) dives deep into the world’s advanced datacenter and AI hardware. To learn more about our pipeline or to commission a teardown, contact sales@semianalysis.com. We’re hiring technical experts from system to transistor and everywhere in between. Check out our Careers page. Panther Lake offers scalability and modularity through its disaggregated packaging that partition compute, GPU, and I/O silicon into separate tiles allowing for a suite of tile configurations. This partitioning makes the package part of Intel’s node economics as it determines how much leading-edge wafer area each product consumes, which functions can remain on other processes, and how much configuration freedom Intel can offer from a shared set of tiles. Furthermore, fabricating the compute and GPU tiles separately confines the new 18A process to the compute tile and allows graphics and I/O to use other, more established, and more cost-effective processes. For Panther Lake, the GPU and I/O tiles are assembled alongside the compute tile on a passive silicon base using Foveros-S. Intel’s current technology brief lists a nominal 36 µm pitch for Foveros-S. Through-silicon vias (TSVs) in the base connect the fine wiring above to the larger package connections below. The functional tiles sit side by side on that passive base in a 2.5D configuration. [39] Our cross-section through the compute and GPU tiles shows the package’s wiring hierarchy. Microbumps connect each active tile to the passive silicon base; its fine redistribution layer (RDL) carries the short, dense tile-to-tile links. TSVs carry connections through the base to the package substrate, which fans them out to the much coarser motherboard solder joints. The base supplies interconnect, while computation remains in the active tiles above it. [39] At the compute-tile edge, the higher-magnification inset shows a local microbump spacing of approximately 25.24 µm and a feature width of 12.33 µm. These local spacings are finer than Intel’s nominal Foveros-S value. The X-ray fields further confirm tighter neighboring bumps, consistent across every die-to-die area found on each tile. Additional X-ray analysis is offered after the paywall. Putting the memory controller beside the CPU removes the D2D transfer that CPU memory requests required in Meteor Lake and Arrow Lake. This avoids the extra transmitter, receiver, and link traversal, saving interface energy and latency. Panther Lake’s separate GPU still crosses a D2D link to reach DRAM, so its larger local caches also help contain package traffic. [1], [40] Smaller dies are less likely to contain a random fatal defect, and screening them before assembly prevents one bad tile from consuming a complete package of good silicon. Reuse also spreads design and qualification work across more products. Against those gains, Intel pays for the passive base, D2D circuits, extra bonding and test steps, and losses during assembly. Cost per working product across the portfolio captures the combined effect of wafer yield, reuse, test, and assembly. [29] Wildcat Lake packaging Intel launched Core Series 3, formerly Wildcat Lake, on 16 April 2026 for value mobile and edge systems. Wildcat Lake keeps 18A but removes the passive base and combines more functions on one die to simplify the package. The two products therefore reveal two distinct ways to commercialize the same leading-edge process. [41] Wildcat Lake’s 18A die combines up to two Cougar Cove P-cores, four Darkmont LP E-cores, two Xe3 cores and a smaller NPU. A separate platform-controller die supplies I/O, connected through UCIe, Intel’s first processor implementation of the standard. Consolidating graphics remove a tile boundary and the passive base, reducing assembly complexity for a modest-bandwidth value product. It also ties CPU and graphics scaling to the same die, giving up Panther Lake’s ability to swap in a much larger GPU. [42], [43] In July 2021, Intel CEO Pat Gelsinger set out an ambitious process roadmap aimed at regaining performance leadership by 2025, later described as five nodes in four years. Five years and one CEO later, Intel’s comeback story is not as unambiguously positive as Pat may have hoped. [44], [45] Intel once set the pace for process technology, bringing high-k metal gate technology and FinFETs into volume production years ahead of the rest of the industry. Its 22 nm FinFET process reached consumers with Ivy Bridge in 2012. [46] Intel’s integrated device manufacturing (IDM) model allowed its architects and process engineers to co-optimize products and processes. Starting with Sandy Bridge, Intel dominated x86, while AMD struggled with Bulldozer. That lead faltered at 14 nm and broke at 10 nm. Intel targeted a massive 2.7× density increase, but the node arrived years late and required several revisions before it could support Intel’s full lineup. This delay forced Intel to stretch 14 nm across six generations, while TSMC moved ahead in process technology and AMD recovered in x86. By 2019, Intel was still shipping 14 nm across most of its product stack, with its 10 nm client ramp focused on Ice Lake mobile processors. Meanwhile, TSMC was shipping N7 and N7+, and AMD’s Zen 2 compute chiplets used N7 to raise core counts and improve efficiency. Intel’s process failures were central to its decline, but unsound business decisions furthered their downward slide. Product delays compounded product mistakes, pushing client, server, and FPGA roadmaps off schedule. Several attempts to enter AI (Nervana and Gaudi) and networking (Tofino) also failed to establish lasting businesses. Intel’s recovery has focused on consumer CPUs and advanced packaging. Tiger Lake, Alder Lake, Lunar Lake and now Panther Lake have restored Intel’s consumer roadmap. On the process side, Intel 4 shipped with Meteor Lake, Intel 3 with Granite Rapids and Sierra Forest, and Intel 18A with Panther Lake. Intel has also made advanced packaging part of its foundry offering. However, Intel is still playing catch-up in servers. Several Xeon generations arrived years late and trailed contemporary AMD and Arm server CPUs in performance, efficiency, and core count. The process roadmap is back, but Intel does not hold the same process-technology leadership position it held prior to 10 nm. The introduction of gate-all-around nanosheets and backside power delivery are two of the biggest changes to transistor integration in a decade. Intel took on both changes at once: 18A paired its first RibbonFET with PowerVia in Panther Lake. Panther Lake is a substantial manufacturing milestone. Our cross-sections show how RibbonFET and PowerVia reshape local contacts and wiring, while the floorplans show where architectural consolidation and process choices save area. A sustained competitive lead depends on product performance, cost, yield, and the next implementation. The SemiAnalysis STEEL teardown lab breaks down advanced datacenter and AI hardware. To learn more about our pipeline or to commission a teardown, contact sales@semianalysis.com. WE’RE HIRING: Architecture, floorplan, packaging, manufacturing, and labs experts. Opportunities from system to transistor and everywhere in between. Check out our Careers page.. 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Can the Left Make America Fun Again?
Hi friends, I’ve got a piece in The New York Times today about how collective joy — participatory, youthful, anti-corporate — can defeat Trumpism and transform our lives. The basic argument is that politics used to be embedded in communal life: dances, sports leagues, union halls. As that infrastructure disappeared — and more of everyday life was atomized and privatized — politics became something broadcast at people rather than something they did together. NYC-DSA and Zohran provide another model, where socials and face-to-face organizing are tied to a fight vs the corporations that fenced off so much of the good life. Rebuilding that kind of collective fun, I argue, could help bring young people back into politics and, by redistributing America’s wealth, make everyday life more joyful. I would super appreciate it if you’d read the piece, share it, or post comments on it. Thank you! p.s. FYI, Opinion writers don’t get any say over their headlines! More Take action to support the fight of licensed deck officers at Alaska Tanker Company and Overseas Shipholding Group to win a first union contract. This is one of the most important, and underreported, unionization campaigns in America: these workers have a huge amount of power over domestic energy distribution and maritime supply chains, and they’re still without a contract two years after having voted overwhelmingly to unionize. Please sign the public petition here, and if you’re an academic you can sign this support letter.
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Truth and Politics in the Age of Post-Truth
Get E-International Relations delivered to your inbox, free of charge. As you sign up, consider becoming a paid subscriber, or make a donation, to support our work. Subscribe This is an excerpt from Global Politics in a Post-Truth Era. You can download the book free of charge from E-International Relations. The term post-truth is by now so ubiquitous in describing the state of politics in the Western world and even beyond that the most pressing issue of the day appears to be about making democracy work in the era of post-truth rather than questioning the normalisation of the term itself in the first place. The critical turning point appeared to have occurred in 2016 when the term was chosen by the Oxford English Dictionary as ‘The Word of the Year.’ On its website, the Dictionary notes the transformation of post-truth from ‘being a peripheral term to being a mainstay in political commentary and connects ‘the spike in frequency’ directly with the Brexit referendum in the United Kingdom and the election of Donald J. Trump in the United States – both occurring in 2016. Paired with the noun ‘politics,’ post-truth is defined as an adjective ‘relating to or denoting circumstances in which objective facts are less influential in shaping public opinion than appeals to emotion and personal belief.’ The Oxford English Dictionary traces the first use of the term to a 1992 essay in The Nation by the playwright Steve Tesich as he reflected on the 1980s Iran-Contra scandal and the 1990–91 Gulf War: We are rapidly becoming prototypes of a people that totalitarian monsters could only drool about in their dreams. All the dictators up to now have had to work hard at suppressing the truth. We, by our actions, are saying that this is no longer necessary, that we have acquired a spiritual mechanism that can denude truth of any significance. In a very fundamental way we, as a free people, have freely decided that we want to live in some post-truth world (Tesich 1992, 13; emphasis added). He then continued, The Gulf War is over but the war at home goes on. The gulf between rich and destitute widens – between those of us who live in a modern post-industrial nation and those of us who live in the Third World countries of our inner cities. The present administration’s response to this internal crisis has shifted from benign to malignant neglect. The current levels of misery and decomposition of our cities and the economic gulags of our ghettos are acceptable. Since there is only so much hope to go around, there is a freeze on hope. The have-nots have now been reclassified as never-will-haves (Tesich 1992, 13). In the context of Tesich’s essay, truth includes both facts, as in what happens, and ‘moral absolutes,’ as in the ‘self-evident truths mentioned in our Constitution’ (Tesich 1992, 14). Together, these two notions of truth entail the obligation to both act and react. The two passages cited above show that even in the early post-Cold War years, Tesich was already deeply concerned that the American people are neither prepared to act on truth nor react to truth that is disconcerting, or what he calls ‘bad news’ (Tesich 1992, 12). Accordingly, Tesich’s insight as captured by the term post-truth is not that truth is irrelevant to what we know, but rather it is irrelevant to how we act. In contrast, ‘post-truth politics,’ as defined by Oxford as a term that pitches ‘objective facts’ against ‘emotion and personal belief’ such that the latter appears to have overtaken the former in shaping ‘public opinion’ and thereby, democratic politics. When Tesich’s post-truth politics and its current usage are juxtaposed, the two are in fact not identical in terms of what truth is and how it relates to politics. Against such consideration, this chapter provides a critical survey of selected historical events and theoretical considerations for the purpose of gaining a more nuanced understanding of the relation between truth and politics. Truth and Language One notable impact of post-truth politics is that language is being overtly deployed as a political weapon not only by politicians and those in power, but among the general population. It seems that once facts are irrelevant to the validity of what is being said, language becomes the platform for an all-out political contest in which what is said is no more than an act of will expressed as an assertion of power and/or an exercise in defiance. As early as in the writings of Thucydides (460–400 BCE), the vulnerability of logos – which is the human capacity for rational discourse in tumultuous times – was already noted. Thucydides belonged to a school of Greek thought which maintained that language is conventional and that the association between name and things is a result of ‘human use and habit’ rather than ‘nature or divine dispensation’ (Connor 1984, 99). The conventional nature of language means that it is incapable of providing its users with any independent criteria for judgement. In Thucydides’ study of the Peloponnesian War (431–405 BCE), the subversion of logos as seen in the manipulation of language was introduced through his account of the civil war in Corcyra (Thucydides 1972, 236-245). The war first broke out in 427 BCE between the pro-Athenian democratic faction and the pro-Spartan oligarchic faction before it spread to the rest of the Greek world. There was a general breakdown of law and order, resulting in extreme violence and death. Language underwent changes that were reflective of the events of the time. The English philosopher Thomas Hobbes (1588–1679) translated Thucydides’ description of the situation as follows: The received value of names imposed for signification of things, was changed into arbitrary. For inconsiderate boldness, was counted true-hearted manliness: provident deliberation, a handsome fear: modesty, the cloak of cowardice: to be wise in everything, to be lazy in everything... he that had been so provident as not to need to do the one or the other, was said to be a dissolver of society, and one that stood in fear of his adversary. In brief, he that could outstrip another in the doing of an evil act, or that could persuade another thereto that never meant it, was commended (Hobbes 1839–1845, 8: 348; Thucydides 1972, 242-243). What concerned Thucydides most was that the meanings of words are no more than linguistic conventions that require logos to support them. The Corcyrean civil war was a reminder that such capacity cannot be assumed. This is why history understood as an authentic account of what happens matters. Guided by logos, the historian’s task is ‘the search after truth’, which is established through ‘the absence of the fabulous,’ including ‘the rejection of myth and unverifiable stories’ (de Jonge 2017, 2). Thus, history plays an important role in facilitating the stability of language – without which distinctions and judgements cannot be rendered communicable. Just like Thucydides, Hobbes was a nominalist who subscribed to the view that there is no inherent meaning to words. Hobbes’ state of nature, where there is no common authority, is precisely one in which distinctions cannot be made. As Hobbes put it, there is ‘no Mine and Thine distinct’ nor is there distinction between ‘the notions of Right and Wrong, Justice and Injustice’ (1968, 188; emphasis in original). This is clearly an unsustainable condition for everyone. Behind Hobbes’ famous words that life in the state of nature is ‘solitary, poor, nasty, brutish, and short’ is his uncompromising argument for the ‘Leviathan’, which is described by the late American political theorist, Sheldon Wolin (1922–2015), as the ‘sovereign definer’ (Hobbes 1968, 186; Wolin 2004, 238–243). Accordingly, the social contract is importantly a ‘linguistic contract’ that binds all parties to ‘general names agreed upon’ (Peters 1979, 125; Hobbes 1968, 111). Through Thucydides and Hobbes, we can identify a line of thinking that sees politics as a contestation over the use of language, which is based on shared conventions and norms. Thus understood, politics is not a collective quest for the true nature of our being or an epistemological exercise in pursuit of true knowledge. Rather, politics is about humans living with one another by finding a shared language to communicate with one another on matters that affect them all. Truth is in that sense contingent on what is being said as opposed to setting the standard for it. In other words, truth is the embodiment of shareability among us. While truth as such ensures the viability of a political entity, truth by no means represents a normative standard to adjudicate among contesting actors in what they say, let alone in what they do. During Thucydides’ times, ancient China was also experiencing a tumultuous time as the ruling Zhou dynasty deteriorated into the period of the Warring States (481–221 BCE). This was when the rulers of Zhou no longer presided over the territories brought under its control through conquest. Instead of professing allegiance to the Zhou royalty, the lords of these territories became kings themselves to rule over their own land. The period was one marked by endless military campaigns as each of the seven major states aimed to conquer the rest (Lewis 1999). Yet, the period was also known for its intellectual vibrancy as different thinkers challenged the restoring of order among these warring states – including offering ideas and advice to rulers on good governance. Among these thinkers was Confucius (551–479 BCE). His thought, which came to be known as Confucianism, went on to become the state ideology of China from the Han dynasty (206 BCE–222 CE) until the end of the Qing dynasty in 1911 when China became a republic. The founding text of Confucianism was the Analects, which is a collection of dialogues between Confucius and his students as recorded by them. One key concept Confucius advanced is the ‘rectification of names’. Though the term only appears in one exchange in the Analects, it is considered a major aspect of Confucius’ thought. Generations of Confucian scholars from past and present, including Western academics, have debated over its interpretation (Makeham 2003). The passage in question reads as follows: Zi Lu said: ‘The monarch of the state of Wei wants you to govern the country, what is the first thing you plan on doing?’ Confucius said: ‘First it is necessary to rectify the names.’Zi Lu said: ‘Is that really what has to be done? You are being too pedantic, aren’t you now? How will you rectify these names?’Confucius said: ‘Zhong You, you are too unrefined. A gentleman, faced with a matter that he does not understand, takes a skeptical attitude. If names are not correct, one cannot speak smoothly and reasonably, and if one cannot speak smoothly and reasonably, affairs cannot be managed successfully. If affairs cannot be managed successfully, rites and music will not be conducted. If rites and music are not conducted, punishments will not be suitable. And if punishments are not suitable, the common people will not know what to do. So, when the gentleman uses names, it is necessary to be able to speak so that people understand. If one can say it, one can definitely do it. A gentleman should not be careless with words’ (Analects 13:3 in Cao 2016, 148). This translation is offered by the contemporary philosopher Cao Feng. As Cao notes, the passage is what started the whole debate over ‘exactly what kind of names did Confucius... wish to rectify’ (Cao 2016, 148). Of the three major groups of interpretation examined by Cao, beginning with classical Confucian scholarship, one is arguably the most influential and certainly most pertinent to the issue of language and politics. This is the interpretation that was first associated with the Neo-Confucian master, Zhu Xi (1130–1200), who argued that the ‘rectification of names is simply the rectification of politics’ (Cao 2016, 149). Within this interpretive framework, rectification has the dual functions of correction and prescription by ensuring that predetermined hierarchical roles are strictly adhered to through following ‘the system of rites and propriety’ (Cao 2016, 151). In addition, the rectification of names entails ‘using names to rectify actuality’ (Cao 2016, 151). Yet Cao rejects this whole body of interpretive works by arguing that these were ideas developed later. Instead, Cao suggests the following: The original meaning of Confucius’ rectification of names may, then, be quite simple. Confucius did not mean to establish a concrete, normative system of “names”; rather, Confucius was simply the first person in history to realize or remark upon the importance of language in politics. As a politician, he noticed and foresaw the impact that the indeterminacy, ambiguity, and arbitrariness of names would have on politics. Confucius recognized the political consequences of language’s not accurately expressing meaning or not being accurately received by people. He realized the great role that names, as a means of distinguishing right and wrong and establishing norms, could play in society and politics (Cao 2016, 168). Scholarly debates aside, Cao’s view has the advantage of identifying an interpretation of this controversial exchange in the Analects that enables its readers to extrapolate from it the observation that the meaning of words depends as much on their users as on their interpreters; neither of which can be predetermined. Accordingly, a good ruler is someone who can speak without ambiguity to his subjects and make good his words through actions. Stated differently, Cao’s view on what concerned Confucius then, who was living through times of protracted political upheaval, is that language is an indispensable political tool for those in power and can be deployed by both good and bad rulers alike. To sum up, this historical survey shows that politics as a war of words is by no means a distinctive marker of contemporary politics nor the Western world. Be it ancient or current, east or west, language is subject to manipulation to serve political purposes and when there is an outright war of words that defies existing norms, it is indicative of a political situation in which people no longer share a language and truth becomes irrelevant. Accordingly, political order entails stability in language use. Truth and Politics As previously noted, current use of the term post-truth as an adjective to the term ‘politics’ suggests that once there was a time when politics aligned with truth. Indeed, if we start with Plato’s Republic as the key founding text of Western political thought, we see that the ideal polis (city-state of ancient Greece) is where truth and politics converge, and justice is achieved. Written around 380 BCE when Athens was still coping with its defeat by the Spartans in the Peloponnesian War, the Republic is a defence of the rule of the ‘philosopher king’, defined as someone who is ‘guided by the truth’ and is ‘always and in every way’ in pursuit of the truth (Plato 1974, 490a). For Plato, truth converges with virtues and hence, letting the philosopher take charge of politics is to let the wisest and the most virtuous lead the less wise and the less virtuous even if it requires compulsion (Plato 1974, 499b). In Plato’s words, ‘…until the philosophers attain power in a city there will be no respite from evil for either city or citizens…’ (Plato 1974, 501e). The uncompromising rule of philosopher-kings is made palatable by the claim that we are all ‘earth- born brothers’ with random mixtures of gold, silver, bronze and iron, but only the gold can rule (Plato 1974, 414–415). Understandably, academic debates over the interpretation of Plato’s Republic revolve around the question of whether truth as attained in philosophy can be realised in politics as action and whether such a connection is even desirable. Without getting into the details of these scholarly exchanges, I suggest that they all share the concern that truth acquired through the rigorous process of philosophical reasoning is not without problem when applied to the real world of living humans. This is because not all humans are philosophers – and indeed, in Plato’s view, not everyone is capable of becoming a philosopher given the protracted and arduous training that one needs to endure to become one. Hannah Arendt (1906–1975), a highly influential political philosopher of the twentieth century, notes that ‘Plato was the first to introduce the division between those who know and do not act and those who act and do not know’ and that such a separation has remained ‘at the root of all theories of domination’ (Arendt 1958, 223, 225). What Arendt points out in this remark is not the gap between theory and practice, but rather that Plato provides the normative ground for those who know the truth to tell those who do not know what to do. Arendt herself took on the challenge of understanding the relationship between truth and politics through her analysis of Nazism and Stalinism. Arendt’s insight, which was controversial when The Origins of Totalitarianism was published in 1951, was to identify Nazism and Stalinism as representative of a new form of political rule – totalitarianism – making it distinct from ‘other forms of political oppression... such as despotism, tyranny and dictatorship’ (Arendt 1973, 460). Arendt defines totalitarianism as a self- contained system of ideas driven by their ‘logic’ and marked by a mode of reasoning called ‘ideological thinking.’ Ideological thinking as such is a distinctive form of political reasoning defined by three characteristics. First, ideological thinking claims to provide a ‘total explanation’ of all historical happenings, encompassing the past, the present, and the future. Second, while ideological thinking is an attempt to account for ‘factual reality’ in its totality, this mode of thinking is paradoxically divorced from reality and experience. This is because experience, being rooted in specificity, is clearly limited in its claim to totality. It follows that as its third characteristic, ideological thinking aims to emancipate thought from the limitation imposed by experience through a ‘specific method of demonstration.’ The method involves arranging facts into an ‘absolutely logical procedure’ that starts from an ‘axiomatically accepted premise’. It is, in short, a ‘process of deduction from a premise’ – which is the only possible movement in the realm of logic that leads to one definitive conclusion. This is how the ‘logicality of ideological thinking’ provides the basis for total explanation and the ‘principle of action’ for totalitarian movement (Arendt 1973, 468–474). Stated differently, totalitarianism is a form of uncompromising idealism that exists in one’s head irrespective of shared reality as experienced. It is the perverse form of what Arendt later refers to as ‘rational truth’, which is the product of the ‘speculative mind’ that belongs to philosophy, mathematics and the sciences. The ‘speculative mind’ is the mind working on its way to generate axioms and theories by relying on the cognitive capacity of the human brain (Arendt 1977, 231). The presence of others has no bearing whatsoever on rational truth. In contrast, ‘factual truth’ is what happens when humans are ‘living and acting together’ (Arendt 1977, 231). Factual truth is therefore particular and temporal by definition. But more importantly, factual truth is ‘political by nature’ because it can only be validated in the context of the human community (Arendt 1977, 238). In other words, factual truth is about humans in their plurality rather than in their singularity. For factual truth to be sustainable, we need what Arendt calls ‘common sense’ – understood as ‘community sense’ (Arendt 1982, 72). There is also another distinction about truth that Arendt makes which is pertinent to understanding how totalitarianism works. The issue is whether the so-called totalitarian ideology is simply an unnecessarily cumbersome way of saying that it is an all-round and polished lie. Here, Arendt calls totalitarian ideology the ‘modern lie’ – in contrast to the ‘traditional lie’. The latter is restricted by two conditions: that it is a lie about the ‘particulars’ and that it deceives only the ‘enemy’ – which means that the liar is not engaged in self- deception. The modern lie, however, involves a ‘complete rearrangement of the whole factual texture’. It also intends to deceive everyone alike, to the extent that those who initiate the lie eventually ‘fall victims to their own falsehood’ (Arendt 1977, 249–254). This distinction between the traditional and modern lie is important. It indicates that not only is totalitarianism a lie, but the lie is in fact too big to fit into the standard meaning of the word. To tell a lie from truth, we need a common standard. In the case of a traditional lie, the common standard is precisely ‘the fabric of factuality’. As such, a lie always appears as a ‘tear’ to that fabric. Yet, modern lies require making a surrogate reality with a completely different context for facts to fit ‘without seam, crack, or fissure, exactly as the facts fitted into their original context’. Arendt notes that as long as those who concoct the surrogate reality are prepared to engage in self- deception to ‘create a semblance of truthfulness’, there is nothing to prevent ‘these new stories, images, and non-facts from becoming an adequate substitute for reality and factuality’ (Arendt 1977, 253–254). Totalitarianism therefore requires a person to stand outside of the system itself in order to make sense of it. Arendt points out that it is futile for us to argue with (for example) a Nazi or a Stalinist on race or class. To confront totalitarianism requires more than just a confrontation with specific facts. It is a more fundamental confrontation between reality as experienced and its total rejection. This means that Nazism and Stalinism are more than radical ways of conducting racial conflict or class struggle. Rather: Totalitarian politics – far from being simply antisemitic or racist or imperialist or communist – use and abuse their own ideological and political elements until the basis of factual reality, from which the ideologies originally derived their propaganda value– the value of struggle, for instance, or the interest conflicts between Jews and their neighbours – have all but disappeared (Arendt 1973, xv). Accordingly: The ideal subject of totalitarian rule is not the convinced Nazi or the convinced Communist, but people for whom the distinction between fact and fiction (that is, the reality of experience) and the distinction between true and false (that is, the standards of thought) no longer exist (Arendt 1973, 474). Who then is the ‘ideal subject’ of totalitarianism? Although there is a revival of interest in Arendt’s observation on truth and politics since the onset of post- truth politics, not enough has been said about her reply to the question (Hyvönen 2018; Klinkler 2018; Lee 2019; Zerilli 2020). Yet if we take a look at Arendt’s portrait of this ideal subject, her view is even more pertinent to our current challenge. As a self-contained system of ideas that is detached from experience, totalitarian thinking offers consistency in ways that reality cannot. Arendt notes that this kind of consistency is especially appealing to those who have lost a sense of bearing in the world (Arendt 1973, 353). They constitute what she calls the ‘masses’. The ‘mass man’ is thus a highly isolated and atomised individual, who is ‘obsessed by a desire to escape from reality’ (Arendt 1973, 318, 352). In his ‘essential homelessness’ the mass man can ‘no longer bear its accidental, incomprehensible aspects’ (Arendt 1973, 352). This longing for escape from reality is a ‘verdict against the world’ in which one is ‘forced to live’ but ‘cannot exist’ (Arendt 1973, 352). Such a situation is ripe for the ‘revolt of the masses’ against ‘common sense,’ which is ‘the result of their atomisation, of their loss of social status’ and with it, the ‘whole sector of communal relationships in whose framework common sense makes sense’ (Arendt 1973, 352). In short, the masses are people who are considered to be ‘superfluous or can be spared … [who] cannot be integrated into any organisation based on common interest’ (Arendt 1973, 311). Whether one agrees with Arendt’s analysis of the social conditions in the interwar years that led to the formation of the ‘masses’, her portrait of the ‘mass man’ is a powerful account of what can happen to an individual who has become disposable to the society that they used to belong to. Hand in hand with this phenomenon is indeed the collapse of ‘common sense’ that leaves the dislodged individual scrambling for reconnection. By offering the masses a substitute reality validated by the ‘logic of an idea’ rather than by experience, totalitarianism makes it possible for them to live in a world where there is no condition of plurality. Between ‘facing the anarchic growth and total arbitrariness of decay’ and ‘bowing down before the most rigid, fantastically fictitious consistency of an ideology’, the masses will likely ‘choose the latter’ (Arendt 1973, 352). A world created by ideology, then, is more attractive to the masses not because ‘they are stupid and wicked, but because in the general disaster this escape grants them a minimum of self- respect’ by conjuring up ‘a lying world of consistency which is more adequate to the needs of the human mind than reality itself’ (Arendt 1973, 353). Moreover, it is precisely this ability to guarantee consistency that makes it possible for the leaders of totalitarian movements to demand ‘total, unrestricted, unconditional and unalterable loyalty of the individual member’ (Arendt 1973, 323). While this chapter is not a study of the social factors that led to specific contemporary developments such as the result of the Brexit referendum or the election of Donald Trump in 2016, Arendt’s ‘mass man’ seem to have found their counterparts among the supporters of Brexit and Trumpism. Studies of voters’ profiles in both events point to a disproportionate share of supporters from white male lower income backgrounds without university educations. These are individuals who have either already lost their jobs to globalisation or who are at high risk of losing their job with very little capacity to be retrained for the twenty-first century market economy of advanced industrialised states (BBC 2016; Pew Research Center 2018). In short, these displaced workers are part of the tapestry forming the modern equivalent of Arendt’s ‘mass man’ – ready to be the recruits of totalitarianism. Conclusion If we step back to take a more comprehensive approach to the relationship between truth and politics, both historically and theoretically, it is not so obvious that 2016 is a defining moment. From ancient China and ancient Greece to our so-called post-truth age, the indeterminacy of language appears to be a persistent political challenge. Language is always open to manipulation by its users to serve power. Nonetheless, the tendency these days is to single out the ubiquity of social media and the proliferation of Internet platforms as the incubator for the countless cyberwars of words that in turn fuel the real world of politics. In this context, language is no longer the conveyor of objective facts and accurate information, but rather the medium for expressing subjective opinions and feelings, as captured by the Oxford Dictionary’s definition of the term ‘post-truth politics’. In contrast, the historical approach used in this chapter shows that it is in fact too easy to point the figure at the Internet and digital communication for the political challenge that we face today. What is being suggested here is that with or without the Internet, language, be it used to convey facts or emotions, is simply a tool for communication that humans have invented. While language per se cannot adjudicate what is true or not true, what is right or wrong, it embodies such capacity when language is a shared norm. That shared norm cannot be sustained without some semblance of cohesion in any given community. Any war of words, irrespective of its platform, is indicative of the breakdown of such cohesiveness. Returning to the original context in which the term post-truth politics was first used by Steve Tesich helps to identify the important distinction between truth as the standard for knowledge and truth as the standard for action. The two do not necessarily coincide. Placed in the context of the history of political thought, this distinction is in fact at the heart of Plato’s Republic. Yet when the philosopher as the quintessential bearer of truth becomes king, the polis that he rules over is far from the ideal of a just state that it purports to be. Instead, it is one that is held together by lies and coercion. What Arendt reveals through her study of Nazism and Stalinism is that it is dangerous for the kind of truth that philosophy generates – ‘rational truth’ – to become the guide to politics. Politics guided by rational truth can only be uncompromising and follows a logic of its own that cannot afford the ambiguity and indeterminacy of reality as lived experiences of humans. A world of post-truth politics can be an opportunity for a new kind of politics if it is indeed about dismantling the idea that truth is the ideal that politics should strive for. But as it stands, the post-truth politics of today is more about invoking a past that never exists to judge the presence as a crisis in naming when the real crisis is about rebuilding a shared space that has room for all – be they of the left, right or centre; cosmopolitan or parochial; civic-minded or self-centred; liberal-minded or bigoted. Politics is and always will be about striving for that space, without which no human can thrive. References Arendt, Hannah. 1958. The Human Condition. Chicago: The University of Chicago Press. Arendt, Hannah. 1973. The Origins of Totalitarianism. Rev. ed. New York: Harcourt Brace Jovanovich. Arendt, Hannah. 1977. “Truth and Politics.” In Between Past and Future, 227–264. New York: Penguin Books. Arendt, Hannah. 1982. Lectures on Kant’s Political Philosophy, edited by Ronald Beiner. Chicago: University of Chicago Press. British Broadcasting Corporation. 2016. “Brexit vote: The breakdown.” BBC News, December 7, 2016. https://www.bbc.com/news/uk-politics-38227674 Cao, Feng. 2016. “A New Examination of Confucius’ Rectification of Names.” Journal of Chinese Humanities 2: 147–171. https://doi-org.subzero.lib. uoguelph.ca/ doi/ 10.1163/23521341-12340032. Connor, W. Robert. 1984. Thucydides. Princeton: Princeton University Press. de Jonge, Casper C. 2017. “Dionysius of Halicarnassus on Thucydides.” in The Oxford Handbook of Thucydides, edited by Sara Forsdyke, Edith Foster, and Ryan Balot. Oxford University Press. DOI: 10.1093/ oxfordhb/9780199340385.013.17 https://www-oxfordhandbooks-com.subzero. lib.uoguelph.ca/view/10.1093/oxfordhb/9780199340385.001.0001/oxfordhb-9780199340385 Hyvönen, Ari-Elmeri. 2018. “Careless Speech: Conceptualizing Post-Truth Politics.” New Perspectives 26 (3): 31–55. https://www.proquest.com/ scholarly-journals/careless-speech-conceptualizing-post-truth/ docview/2204515132/se-2?accountid=11233. Hobbes, Thomas. 1839–1845. The English Works of Thomas Hobbes, edited by Sir William Molesworth. London: John Bohn. Vol. 8. Klinkner, Melanie. 2018. “Reasonable Truth.” International Journal of Media & Cultural Politics 14 (3): 393–99. doi:10.1386/macp.14.3.393_7. Lee, Haiyan. 2019. “When Nothing Is True, Everything Is Possible: On Truth and Power by Way of Socialist Realism.” PMLA/Publications of the Modern Language Association of America 134 (5). Cambridge University Press: 1157–64. https://doi.org/10.1632/pmla.2019.134.5.1157 Lewis, Mark E. 1999. “Warring States Political History.” In The Cambridge History of Ancient China: From the Origins of Civilization to 221 BC, edited by Michael Loewe and Edward Shaughnessy, 587–650. Cambridge: Cambridge University Press. Makeham, John. 2003. “Zheng ming (Rectification of names).” In RoutledgeCurzon Encyclopedia of Confucianism, edited by Xinzhong Yao. Vol. 2: 813-814. New York: Routledge. Oxford Languages, n. d. “Word of the Year 2016.” Accessed 19 August 2017. https://languages.oup.com/word-of-the-year/2016/ Peters, Richard. 1979. Hobbes. Westport: Greenwood Press. Pew Research Center. 2018. “An examination of the 2016 electorate, based on validated voters.” Report, August 9, 2018. https://www.pewresearch.org/ politics/2018/08/09/an-examination-of-the-2016-electorate-based-on- validated-voters/ Plato. 1974. Republic. Translated by G. M. A. Grube. Indianapolis: Hackett. Tesich, Steve. 1992. “Watergate Syndrome: A Government of Lies.” The Nation: 12-14. January 6/13, 1992. Thucydides. 1972. The Peloponnesian War. Translated by Rex Warner. Harmondsworth, England: Penguin Books. Wolin, Sheldon. 2004. Politics and Vision: Continuity and Innovation in Westen Political Thought. Expanded Edition. Princeton: Princeton University Press. Zerilli, Linda M. G. 2020. ‘Fact-Checking and Truth-Telling in an Age of Alternative Facts.’ Le foucaldien 6, no. 1: 2, 1–22. Theresa Man Ling Lee is an Associate Professor of Political Science at the University of Guelph (Canada). Lee is a political theorist who specializes in contemporary political thought. Her research and teaching areas include continental philosophy, Marxism and critical theory, feminism, postmodernism, philosophy of social science, psychoanalysis, multiculturalism, human rights, disability studies, modern Chinese political thought and comparative political theory. Lee has published on a wide range of subjects in these areas, including intercultural pedagogy and the challenges of teaching as a person with multiple minority designations in a Canadian university classroom.
Caesars Entertainment director David Tomick sells $415,431 in shares
Caesars Entertainment director David Tomick sells $415,431 in shares
Insider Selling: Caesars Entertainment (NASDAQ:CZR) Director Sells 7,000 Shares
Caesars Entertainment, Inc. (NASDAQ:CZR - Get Free Report) Director David Tomick sold 7,000 shares of Caesars Entertainment stock in a transaction that occurred on Thursday, September 10th. The shares were sold at an average price of $29.67, for a total transaction of $207,690.00. Following the transaction, the director directly owned 38,911 shares in the company, valued at approximately $1,154,489.37. This trade represents a 15.25% decrease in their ownership of the stock. The sale was disclosed in a filing with the Securities & Exchange Commission, which is available through this hyperlink.
Please Stop Conflating Sports Betting Handle With Spending On Entertainment
Editor’s note: The Cashout for paid subscribers will publish on Tuesday. Every September there’s a huge uptick in how much people bet on sports with the start of football season. Which means it’s time to get ahead of a ridiculous narrative before it picks up steam on social media or in mainstream media. Subscribe Every few months I see some version of this: [ Charlie Bilello@charliebilello Americans bet over $165 billion on sports last year, which is more than they spent on movies, books, concerts and sports tickets - combined. 1:00 PM · Jun 28, 2026 · 4.1M Views 258 Replies · 674 Reposts · 4.23K Likes ](https://x.com/charliebilello/status/2071217050328895935?s=20) Fortune published this in July. The headline and start of the story tell the same narrative (emphasis added): Gambling becomes America’s favorite pastime as Americans spend more on sports bets than movies, arts, museums, and music combined: The North American box office totaled $8.87 billion in 2025, which is still 22% below pre-pandemic levels. Recorded music revenue hit a record $11.5 billion. Live music like concerts and festivals brought in $18.51 billion. Meanwhile, book publishers tracked by the Association of American Publishers reported $14.6 billion for the year. And the U.S. museum industry generated an estimated $16.4 billion. Add it up and the total comes to roughly $70 billion, which is less than half what Americans wagered on sports. The story, to its credit, goes on to tell us why sports betting is entirely different, although people who only read headlines will miss that nuance. (The tweet above definitely skips over the nuance.) I will mostly be preaching to the choir here, but it’s worth saying out loud: Wagering money on sports is not the same thing as spending on entertainment from a financial perspective. When you spend money on a movie or concert ticket, that money is gone. You exchanged money for an experience. It’s a good trade! I get to have fun for “x” dollars. (Unless the movie sucks, I guess.) When you bet money on sports, the money is not necessarily gone every time you wager it. It could be gone, but sometimes it’s not and you actually get more money back! That’s not “spending” money or something you should try to compare directly to entertainment spend. A far better comp would be how much people lose at sports betting. That number was more like $17 billion in the US last year, at least for state-regulated sportsbooks. Why are we trying to equate handle and entertainment spending? I dunno. It makes for splashy headlines, I guess. Now, people should be betting for entertainment, and not because they expect to win in the long term; no doubt about it. Anyway, here’s hoping I don’t see people saying Americans “spent” hundreds of millions of dollars on sports betting this fall. Mindway AI and DraftKings Expand Responsible Gaming Drive with Launch of Gamalyze American Football In time for the NFL kickoff, Mindway AI has expanded its partnership with DraftKings to launch Gamalyze American Football the second title in the neuroscience-based Gamalyze Sports collection. Following the success of Gamalyze Football (soccer) during the FIFA World Cup, this gridiron-themed update replaces static questionnaires with immersive, stadium-inspired gameplay. Backed by 10+ years of Aarhus University research, Gamalyze evaluates real-time decision-making to deliver instant, unbiased player risk assessments. Integrated into DraftKings’ platform and nationwide activations, it engages fans when excitement peaks. Ready to upgrade your player protection? Contact Mindway AI to try Gamalyze Sports today. Gambling news roundup 🚨The important stuff 1st NFL Sunday of Season ‘Champagne-Popping Day for Bettors’ (Covers): “The first NFL Sunday of the season was a champagne-popping day for bettors, while the book was left cleaning up the corks,” Caesars’ head of football Joey Feazel said prior to the Sunday night game. “Caesars Sportsbook reported that Detroit’s Jahmyr Gibbs, Baltimore’s Derrick Henry, Indianapolis’ Jonathan Taylor, and Atlanta’s Bijan Robinson were among the top five most-bet players to reach the end zone, and all of them cashed tickets.” [ Ben Fawkes@BFawkes22 A @CaesarsSports oddsmaker on how Week 1 has gone so far: “The first NFL Sunday of the season was a champagne-popping day for bettors, while the book was left cleaning up the corks.” ➡️8 of 10 most popular anytime TD scorers cashed 1:31 AM · Sep 14, 2026 · 276K Views 16 Replies · 16 Reposts · 615 Likes ](https://x.com/BFawkes22/status/2099310001927995849?s=20) 🔍 Are we already getting a “customer-friendly results” narrative in Week 1? NFL Commissioner Roger Goodell spoke with CNBC about the prediction markets industry: “We think that there needs to be stronger regulations into the predictive markets,” Goodell said. “We want to see that to protect the integrity of our game. We want to make sure we’re protecting the consumers that are on those platforms for the NFL. So, we’re continuing to have conversations with them and talk to them about the things that we think they need to do to strengthen that so that they could be potential partners at some point in the future.” More on the NFL + PMs: [ Straight to the Point Quid Pro Quo The NFL has asked Kalshi and other prediction markets to remove certain easy to manipulate markets. Does a potential partnership hang in the balance… Read more 6 hours ago · 19 likes · Steve Ruddock ](https://straighttothepoint.substack.com/p/quid-pro-quo?utm_source=substack&utm_campaign=post_embed&utm_medium=web&embedding_publication_id=1472492) Kalshi was on my podcast?: Kalshi was on my podcast [ The Event Horizon Episode 32: Talking Prediction Market Enforcement With Kalshi’s Robert DeNault Kalshi? On my podcast? Yep… Listen now 5 hours ago · 6 likes · 1 comment · Dustin Gouker ](https://nexteventhorizon.substack.com/p/episode-32-talking-prediction-market-enforcement-kalshi?utm_source=substack&utm_campaign=post_embed&utm_medium=web&embedding_publication_id=1472492) DraftKings Enhances Responsible Engagement Efforts With New Customer Initiatives, Tools and Education (press release): DraftKings Inc. today announced a series of new initiatives designed to promote responsible engagement and increase awareness and use of the tools and resources to help customers make informed decisions and engage responsibly. The efforts include a new digital advertising campaign featuring Kevin Hart and Nick Jonas that promotes responsible engagement, a PGA TOUR sweepstakes that promotes customer engagement with responsible engagement features, the launch of Gamalyze American Football in collaboration with Mindway AI, and new custom cool-off options that give customers enhanced flexibility when choosing a cool-off period. The initiatives build on DraftKings’ commitment to making responsible engagement an integral part of the customer experience and providing players with accessible tools and resources that help them make informed choices about their gaming experience. “At DraftKings, responsible engagement is embedded in how we operate and is essential to building trust with our customers and our long-term sustainability,” said Lori Kalani, Chief Responsible Gaming Officer at DraftKings. “We’ve continued to invest in tools and resources that give customers enhanced access to information about their play and more choices for managing how they engage. From My Budget Builder and My Stat Sheet to player-set limits and new custom cool-offs, we’re continuing to evolve our approach while promoting awareness and use of the tools and resources available to help customers play responsibly.” … DraftKings is launching a new digital advertising campaign featuring Kevin Hart and Nick Jonas that continues the pair’s road trip and promotes responsible engagement and awareness of the tools and resources available to help customers make informed choices about their gaming experience and engage responsibly. The campaign was initially launched on Sep. 10th and will reach customers through digital channels, bringing responsible engagement messaging to fans in an engaging way. bet365 Launches “The Matchup,” a New Weekly Video Podcast Hosted by Super Bowl Champions Chris Harris Jr. and Emmanuel Sanders (press release): "bet365, a global leader in online sports betting and gaming, today announced the launch of “The Matchup,” a new weekly video podcast hosted by former Super Bowl Champions Chris Harris Jr. and Emmanuel Sanders. Featuring current and former NFL players, the original series marks a significant, intentional investment in football as bet365 works to become a go-to destination for U.S. bettors throughout the season. “The Matchup” premieres September 10, with new episodes released weekly in both video and audio formats. Full episodes will be available on YouTube, Apple Podcasts, Spotify and wherever podcasts are available, with clips and additional content rolling out across YouTube, X, Instagram, Facebook, Threads, and TikTok. The show is being produced by Last Row Digital Media. Each week, Harris and Sanders will be joined by guests from across the game to break down the biggest NFL matchups, unpack the latest league news and share perspectives that go beyond the box score. By bringing together two former players with distinct perspectives and firsthand experience, “The Matchup” will give audiences timely context and a deeper understanding of the teams, players and storylines shaping the week ahead. “Every great football conversation starts with a matchup—who has the edge, what everyone is overlooking and what could change the game,” said Chris Harris Jr. “Emmanuel and I have experienced the game from different perspectives, and we want the audience to feel like they are part of the conversation and leave every episode knowing more about the week ahead.” Today’s iGaming companies face new litigation threats as expansive consumer protection statutes spark lawsuits with sky’s-the-limit aspirations, sometimes backed by private funders. Clients of Ifrah Law have a staunch defender in Robert Ward – an agile litigator and case tactician who responds to the changing state of play with creative legal defenses and efficient case-closing strategies. Robert draws on federal clerkship experience and appellate defense work to advise iGaming clients on litigation exposure, white-collar risk, and regulatory defense across multiple jurisdictions. Read More: Robert Ward on Helping iGaming Clients Outmaneuver Lawsuit-Obsessed Adversaries 🔮 Prediction markets Update on legal action in Connecticut: [ Daniel Wallach@WALLACHLEGAL Connecticut says gaming service license holders which received subpoenas over sports prediction markets “are not under investigation” by the @CTDCP but may possess information that could assist the state “in evaluating the conduct of prediction market platforms.” 2:31 PM · Sep 13, 2026 · 844 Views 5 Likes ](https://x.com/WALLACHLEGAL/status/2099143914271670576?s=20) American Gaming Association Opposes CFTC Bid to Block New York Prediction-Market Enforcement (DeFi Rate): “The brief points to the Ninth Circuit’s Aug. 28 decision in KalshiEX LLC v. Assad, which the AGA says supports New York’s authority to regulate sports contracts as gambling. It also cites Kalshi advertising, product comparisons and reporting on Kalshi’s sports volume to argue that the economic product resembles a sportsbook wager more than a conventional financial hedge.” [ Daniel Wallach@WALLACHLEGAL AGA intervenes in CFTC v. NY case to oppose CFTC request for preliminary injunction, says the CFTC’s arguments “obfuscate a simple truth: prediction markets offer sports betting.” Highlights CA9 decision, @DustinGouker (6x), and $1.32 billion in OSB taxes for NY (0 from PMs). Daniel Wallach @WALLACHLEGAL High stakes hearing on Monday in CFTC v. New York State. If SDNY judge Lorna Schofield denies the CFTC’s motion for preliminary injunction, the New York AG would have a clearer path for securing a state court geofencing order against Kalshi, Coinbase, and Gemini Titan. 2:08 AM · Sep 13, 2026 · 4.78K Views 1 Reply · 2 Reposts · 8 Likes ](https://x.com/WALLACHLEGAL/status/2098957043474280584?s=20) Gaming Commission remains member of gambling council after questions raised over ties to Kalshi (Mass Live): “The Massachusetts Gaming Commission will not follow the leads of gaming regulators in Ohio and Michigan and drop ties with the National Council on Problem Gambling over its new association with Kalshi.” Sponsor’s messageTrusted Voices: Conversations About Betting is designed to equip adults, including parents and coaches, with tools and resources to talk to young people about gambling, including information on warning signs, risks and proxy betting. The program is led by retired professional basketball player Randy Livingston and his wife, basketball agent Anita Smith, who share their personal stories related to problem gambling, with the hope of preventing others from experiencing similar harms. Learn more and join the conversation here. 📣 Industry news Gaming M&A: Latest Mergers, Acquisitions and Deals (SBC Americas): “But what else is happening in the world of gaming mergers, acquisitions, investments, and partnerships? Here, we round up some of the news you might have missed.” BetMGM Named Official Sports Betting and Online Casino Partner of the Minnesota Vikings in Canada (press release): BetMGM, a leading iGaming and sports betting operator, announced a multi-year partnership with the Minnesota Vikings, naming it the Official Sports Betting Partner and Online Casino Partner of the team in Canada. The agreement gives BetMGM rights to use Vikings marks across approved sports betting, online casino and marketing activities in Canada. In addition, Vikings Big Kick LuckyTap™, a casino game by Design Works Gaming, is available now exclusively at BetMGM Casino in Alberta and Ontario. “Vikings fans bring a level of passion and loyalty that extends well beyond game day,” said Matt Prevost, Chief Revenue Officer at BetMGM. “As the home of one of the largest collections of sports-themed casino content in North America, BetMGM is uniquely positioned to combine sports fandom with iGaming. We are eager to start football season off strong with another fantastic partnership especially as we expand our reach in Canada to Alberta.” Martin Nance, Minnesota Vikings Chief Marketing Officer, said, “As we continue to deepen our connection with Vikings fans across Canada, we’re proud to welcome BetMGM as the team’s official sports betting and online casino partner in Canada. Their commitment to innovation and fan engagement aligns well with our organization, and we’re excited to create memorable experiences that celebrate Vikings football and connect with Canadian fans in new and meaningful ways.” Caesars Entertainment Launches Ball Rush Jackpots™ in New Jersey,the Latest Proprietary Online Title from Empire Creative™ (press release): Caesars Entertainment, Inc. (NASDAQ: CZR) (“Caesars”) today announced the launch of Ball Rush Jackpots™, a new ball-and-peg style online casino game developed by Empire Creative™, the Company’s in-house game development studio. Now live in New Jersey across Caesars Palace Online Casino, Caesars Sportsbook & Casino and Horseshoe Online Casino, Ball Rush Jackpots is expected to become available in additional jurisdictions where Caesars offers online casino gaming following regulatory approval. The title expands Caesars’ growing portfolio of proprietary online casino titles, following the successful launch of Ca$hline™ earlier this year and further diversifying the Company’s lineup beyond traditional slots and table games. Ball Rush Jackpots follows the traditional ball-drop gameplay experience where players drop a ball down a board filled with pegs to land in a prize slot. As balls navigate independently through a field of pegs, every drop creates new opportunities to collect feature symbols, unlock bonuses and pursue jackpots, delivering a different experience with every round. “Ball Rush Jackpots delivers a different kind of casino experience for our players, combining easy-to-understand gameplay with meaningful customization, exciting bonus opportunities and jackpot potential,” said Matthew Sunderland, Senior Vice President and Chief iGaming Officer at Caesars Digital. “As we continue expanding our proprietary content portfolio, we’re focused on creating games that are simple to pick up, rewarding to revisit and designed around the features players enjoy most.” Bally’s Corporation Secures New Financing to Support the Development of the Bally’s Bronx Project (press release): Bally’s Corporation (NYSE: BALY) (“Bally’s” or the “Company”) announced today that it secured new financing led by WhiteHawk Capital Partners, LP to fund further development of the Bally’s Bronx project and general corporate purposes. The new financing comprises closing date term loan commitments in an aggregate principal amount of $400 million and delayed draw term loan commitments in an aggregate principal amount of $160 million (together, the “Facilities”). … Bally’s Chairman of the Board, Soo Kim, commented, “This important financing allows us to progress the pre-construction planning process so that we are ready to complete the remainder of the capital raise and remain on schedule. Furthermore, the additional liquidity provides us greater flexibility for other capital opportunities.” WORLD JAI ALAI LEAGUE LAUNCHES CENTENNIAL SEASON AND ENTERS NEXT CHAPTER IN MIAMI (press release): The World Jai Alai League will open its 2026 fall season on Tuesday, September 15 at Miami’s JAM Arena, beginning a milestone season that marks 100 years since professional Jai Alai first arrived in Miami. Season X, the tenth season of the Battle Court team-based competition launched in 2022, will also be the first since entrepreneur and sports asset investor Rob Gough acquired the World Jai Alai League in August 2026. The acquisition marks the beginning of a new chapter for the league. World Jai Alai League will maintain the existing Battle Court competition and operating format, with Chief Operating Officer Scott Savin overseeing Season X. Gough and his team will use the coming months to listen to players and fans, evaluate opportunities across the league, and execute plans for a broader evolution of Jai Alai’s presentation, live experience, media, marketing and commercial strategy beginning in 2027. “This is a really unique moment for Jai Alai,” said Rob Gough, Owner of the World Jai Alai League. “We have a sport with more than 100 years of history in Miami, extraordinary athletes and incredible speed, but I also believe there is enormous untapped potential.” bet365 reveals most popular online slots in 3 US states in August (SBC Americas): “In its monthly Gaming Insights and Performance report, growing online casino operator bet365 tracks player trends and game performance in online slots across its North American online casino markets.” The Closing Line is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe
OpenRGB 1.0 Launches for Open-Source RGB Customization on Windows, Linux, and macOS
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Iran Decries Austria’s Blocking of Nuclear Chief from IAEA Conference
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Business Monday: Hilo Pizza Co. serving up New York-style pies and slices that patrons can customize
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Scientists Detect a Mysterious Signal That Could Be Dark Matter
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Wilmington health tech founder launches new venture
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Peapack Private Expands Specialty Lending Capabilities with Launch of Yacht Financing Business
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Health-oriented Korean rice bowl restaurant opens on Whitney Avenue
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Chinese scientists find a hidden atomic structure that unlocks methane
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The British author who harvested mutant, jumbo peanuts from her atomic garden
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In 1942, scientists stacked roughly 45,000 graphite blocks beneath the stands of a Chicago football stadium and started the world’s first nuclear reactor — while one physicist waited nearby with an axe, ready to cut a rope and drop an emergency rod if the reaction ran out of control
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New Steelers HC Customized 2026 Offense For Aaron Rodgers
Aaron Rodgers and Mike McCarthy are back together once again, this time as members of the Pittsburgh Steelers. The quarterback is in his final NFL season, while McCarthy is back after a year away from the game.The two have a ton of rapport and also have a Super Bowl title together. The opponent in that […] The post New Steelers HC Customized 2026 Offense For Aaron Rodgers appeared first on HEAVY.
Two years after its rocky launch, Civilization 7's first paid expansion Earthrise is on its way - but first comes the atomic age
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Las Vegas families attend STEM Saturday at Atomic Museum
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A course correction Canada would concur with
I’ve always harbored a soft spot for Canada. My father was stationed there twice, once in the 1960s as the Atomic Energy Commission’s American liaison with the Canadian nuclear reactor program in Chalk River, Ontario and again in the 1970s as the State Department’s scientific attache at the U.S. Embassy in Ottawa. I had the […]
Scientists Are Using AI And Viruses To Help Defeat Superbugs
Deep learning models can help scientists build customized phages.
Scientists Are Using AI And Viruses To Help Defeat Superbugs
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