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Health Data Utilities as Essential Public Infrastructure: A Cooperative Federalism Architecture
Federal interoperability initiatives like Trusted Exchange Framework and Common Agreement transport clinical records across boundaries but fail to aggregate and enrich state-governed datasets, including Medicaid claims, prescription monitoring records, and social service registries.
Ukrainian science part of European security architecture
Ukrainian scientists are already contributing to the development of a new security architecture, including by helping build a drone ecosystem. — Ukrinform.
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.
PAHO Director calls for country ownership and regional solidarity in reshaping global health architecture
Berlin, 15 October 2026 (PAHO) – Pan American Health Organization (PAHO) Director and WHO Regional Director for the Americas Dr. Jarbas Barbosa called for countries and regional institutions to have a stronger role in shaping ongoing reforms of the global health system during the World Health Summit 2026 in Berlin this week, arguing that clearer responsibilities across national, regional and global levels can reduce duplication and help limited health resources deliver greater impact.
A Major Labor Boom in CNY? Not Here Yet
With so many major projects going on — Micron, I-81 viaduct and others — job growth is predicted to increase. But so far the number of jobs in the region has declined By Aaron Gifford [caption id=attachment_11065 align=alignright width=350] Amy Stage, deputy director at the Manufacturers Association of Central New York (MACNY): “For us, the conversation isn’t just about filling jobs. It’s about creating sustainable pathways that help people see a future for themselves in manufacturing.”[/caption] The I-81 viaduct project in Syracuse and Micron facility construction in Clay are expected to create a major labor boom for Central New York. But for the time being, the number of jobs here has actually decreased in the past year. The New York State Department of Labor reported that, for the annual comparison of June 2025 to this past June, private sector jobs decreased by 2,200 or less than 1% to 260,000. Job gains were concentrated in leisure and hospitality (+700), private education and health services (+400) and other services (+300). Job losses for that annual period were reported in the areas of trade, transportation and utilities (-1,500); professional and business services (-1,000); financial activities (-500); mining, logging and construction (-300); manufacturing (-200); and information (-100). The public sector job count rose over 12 months, with most growth concentrated in education, the state Department of Labor reported. Looking ahead, the Central New York economy is expected to benefit from both the Micron and I-81 viaduct projects over a period of several years. Initial job growth is foreseen in construction and computer and electronic manufacturing. Spin-off jobs will be created in the areas of professional and business services, health care and education, the state Department of Labor said in an email response to Oswego County Business magazine. “Population growth tied to the buildout of Micron Technology semiconductor complex in Clay is also expected to bring more workers and residents to the region, increasing demand for transportation services,” the agency said. New York Department of Labor highlighted current or projected job growth trends in various sectors. An increase in defense-related contracts at local high-tech employers has continued to support employment growth in the computer and electronic product manufacturing sector and Micron is expected to generate thousands of direct and indirect jobs as the project progresses. This sector paid an annual average wage of $117,500 in 2025 and employment in the sector is projected to grow by over 60% through 2032, making it the fastest-growing industry in the region. Typical occupations in this industry include electrical, electronic and electromechanical assemblers, software developers and industrial engineers, the state agency reported. Specialty trade contractors include several different types of tradespeople — electricians, plumbers, pipefitters and steamfitters and HVAC (heating, ventilation and air conditioning) mechanics and installers. Many of these positions are trained through apprenticeships, the state Department of Labor reported. The Interstate 81 construction project is generating local jobs, including construction laborers, projected to grow at a 6.4% rate, and operating engineers and other construction equipment operators, projected to grow at a 7.3% rate over the next several years. Construction of the Micron Technology facility is also “creating thousands of job opportunities that will continue over the next two decades,” the state agency reported. The specialty trade contractors sector paid an annual average wage of $86,200 in 2025. For Central New York, the challenge isn’t creating jobs, it’s filling them. Amy Stage, a deputy director at the Manufacturers Association of Central New York (MACNY), said meeting this unprecedented growth is a much larger task than just replacing retirees. “We’re seeing workforce gaps at every experience level,” she said. “Companies need entry-level employees, experienced technicians, supervisors, engineers and skilled trades professionals. The opportunity is tremendous, but we need more people entering these career pathways.” Employers are looking for people who can troubleshoot, read technical information, use computers and learn new processes, not just perform repetitive production work, Stage said. To address this demand, the MACNY created the Manufacturers Talent Institute, which aims to connect students and job seekers with meaningful career pathways while helping employers build their workforce. Twenty-one companies have so far signed up to provide opportunities for more than 800 students. “For us, the conversation isn’t just about filling jobs,” Stage said. “It’s about creating sustainable pathways that help people see a future for themselves in manufacturing.” Several manufacturing facilities in the region are in the midst of expansion: • TTM Technologies, a leading U.S. manufacturer of advanced electronics and interconnect solutions for aerospace, defense and high-technology markets, which has a plant in DeWitt, completed its buildout of an additional facility, adding 400 jobs and expanding production of high-tech circuit boards for defense and critical electronics applications. • In Cortlandville, the Byrne Hollow Crossing Plant, a dairy manufacturing and processing facility, is increasing its operations, creating 100 new jobs. • The Chobani yogurt company is building a 1.4-million square foot dairy processing facility in Oneida County. This will create more than 1,000 good-paying jobs and double Chobani’s New York state workforce, Stage said. Stage noted several ways that local, state and federal governments can help spur manufacturing job growth in this region. Elected leaders and public agencies could work with industry representatives to create pre-apprenticeship and registered apprenticeship programs seeking to upskill workers and improve employee retention. New York state has a variety of grants, tax incentives and financing programs to support site preparation, infrastructure, workforce development and capital investment. The U.S. Department of Labor has a pay-for-performance incentive program that provides employers $3,500 per new apprentice hired and retained for 90 days. MACNY was previously awarded an $87,000 federal grant that will cover 25 apprentices at nine companies. The manufacturing skills gap in the United States, at its current rate, could result in 2.1 million unfilled jobs by 2030, according to a study by Deloitte and The Manufacturing Institute. In New York state, that gap is on pace to exceed 100,000 unfilled positions within six years. Manufacturing companies will be counting on more women to help fill that gap. While women make up half of the overall U.S. workforce, they only account for 29% of the manufacturing workforce in this country, Stage said. “Pre-apprenticeship programs like Real Life Rosies help to bring awareness to the manufacturing industry to fill some of the gaps, but we have a long way to go and it starts with educating people at younger ages about careers in manufacturing and earn-and-learn opportunities,” she said. As for Micron, that $100 billion-plus project, considered the largest private investment in New York state history, is expected to eventually create up to 50,000 jobs throughout the region, factoring in the spin-off opportunities for construction and supply chains. The plant’s foundation was poured on July 9. “As America celebrates its 250th anniversary, data and memory are foundational to the modern economy — and Micron is increasing our U.S. investments to more than $250 billion through 2035 to meet that moment,” the company’s president and CEO, Sanjay Mehrotra, said in a news release. Bechtel, Micon’s construction partner in the project, reported that 4,500 people will be employed to help build the facility, including skilled craft professionals, local union members representing various trades and apprentices. The Syracuse-based CenterState CEO organization tallies the regional labor force at 397,582 workers (Onondaga, Cayuga, Cortland, Madison and Oswego counties). Job growth isn’t limited to the Central New York region. For the larger area covering portions of the Mohawk Valley, the North Country, the Southern tier, and the western part of the state, there are 155,601 employees in the advanced manufacturing sector, and 155,275 construction workers. The agency’s 2026 Economic Forecast report noted that 41% of community and business leaders surveyed said a shortage of skilled workers in this area remains a problem, though 77% shared a positive economic outlook for the year ahead. Beyond the semiconductor and advanced manufacturing areas, local business leaders also see growth in small manufacturing companies, storage businesses, housing, unmanned flight technology and general construction. “Productive reuse of property,” the report said, “will hopefully continue to preserve unique architecture while driving business.” The report identified several challenges for economic growth in the region, including an aging workforce, outdated equipment, the rising cost of raw materials and “the disconnect between the speed of business needs and the pace of talent readiness.”
Daldrop Opens for Business in Southern Oswego County
$10 Million Facility in the L. Michael Treadwell Oswego County Industrial Park marks the largest expansion outside of Germany in the company’s 73-year history By Stefan Yablonski [caption id=attachment_11076 align=alignright width=450] Daldrop SBB CEO Jens Daldrop addresses attendees at the 2025 groundbreaking for the project. Daldrop also chairs the advisory board of Germany-based Daldrop Group.[/caption] A new era of cleanroom technology is underway in Central New York. The Daldrop Group’s advanced manufacturing facility and U.S. headquarters is physically built within the L. Michael Treadwell Oswego County Industrial Park. This industrial park is located in the town of Schroeppel. It marks the largest expansion outside of Germany in the company’s 73-year history. Daldrop SBB is based in Neckartailfingen, Germany. It is a global supplier of cleanroom technology, primarily to the pharmaceutical industry. It designs and builds advanced cleanroom environments for the pharmaceutical, biotechnology and medical technology industries. It nvested $10 million in the new project. “We broke ground in 2025; we were under construction all winter. We just opened this spring, early spring,” said Benjamin Morrow, operations manager and control. The company consolidated sites in Salina and DeWitt into the industrial park in Schroeppel, Morrow added. “The next move here is to maximize every square inch of this facility and then we are already looking at the expansion. So, it is more capital investment on equipment — definitely higher-end automation in additional equipment so we can produce more components and then to support the ever-growing base in North America,” Morrow continued. “We are the headquarters for service, maintenance and spare parts. So as Daldrop has more and more installations, they need more and more service, maintenance and spare parts. So that will come out of this facility as well — it already is.” “As a global leader in GMP (global manufacturing support) pharmaceutical cleanrooms for decades, I am very proud to bring manufacturing jobs to Upstate New York,” said Mike Sullivan, president of Daldrop US operations. Key to choosing Southern Oswego County [caption id=attachment_11077 align=alignright width=450] Daldrop SBB at the L. Michael Treadwell Oswego County Industrial Park when the company announced the start of construction.[/caption] Sullivan said that the selection of the Schroeppel industrial park was mainly due to the help of Operation Oswego County and two people in the organization. “Prior to being purchased by Daldrop International from the Stuttgart region of Germany, Vince Bongio was a founding partner in SBB Inc. as well as our head of engineering. We based out of Syracuse due to Vince’s skills in HVAC and the manufacturing capabilities found in Syracuse,” said Sullivan. Bongio moved to Syracuse from California in the 1970s to work in Carrier’s Industrial Division of Large-Scale Petrochemical Refrigeration. Next, he advanced to General Electric’s TV Tube Plant where he got into cleanrooms ,leading to his role of head of engineering for Clestra Cleanrooms where he met Sullivan, their New England distributor. “Vince Bongio is a phenomenal engineer. Formerly of Carrier, he grew up in California and went to Cal Poly,” Sullivan explained. “Carrier hired him right out of college and he moved to Syracuse. He is the reason we are in Syracuse today. He is retiring this year. We are very proud and his legacy is having brought Daldrop to Syracuse.” In 2000, Sullivan, Bazinet, Bongio Inc (SBB) was formed with engineering and manufacturing, based in Syracuse, and sales and construction based in Massachusetts as it is today. “SBB Inc. started in Syracuse. I had a design–build contracting firm out of Massachusetts that was primarily doing all the new technologies coming out of MIT,” Sullivan said. “I started a manufacturing side. A big thing for cleanrooms is the HVAC. Most cleanroom suppliers are architecturally focused people. The strength in Syracuse is HVAC. So, SBB developed with me running sales and construction out of Massachusetts and manufacturing out of Syracuse.” “Daldrop wanted to have a relationship in the States and of those who approached us, they were the only ones who really understood HVAC. So, we were the right fit for them. We did a joint venture focused out of Massachusetts from 2012 to 2018 leveraging their technologies,” Sullivan explained. “The commitment was if we brought their product to the States, we would do that if they built it here. “The pieces that had to be in place to make the investment, they wanted to acquire us, we were great with that. If you looked at business recommendations the general guidance was to build in a low labor area — something like North Carolina — but Syracuse had a strong background in HVAC and we chose Syracuse and Oswego County to be our headquarters in the States. So, it is built off of history, technology and the existing supply chain — bringing jobs back to the area.” During the initial discussions with Daldrop about coming to the U.S., SBB agreed to sell under the conditions Daldrop would manufacture in the U.S. There are many areas that offer incentives and economic packages, but the strength of HVAC expertise and skilled manufacturing was a big part of the discussions for site selection. Coming home [caption id=attachment_11078 align=alignright width=451] Construction moves forward in March at the L. Michael Treadwell Oswego County Industrial Park.[/caption] The second key person to building in Oswego County is Ben Morrow, Sullivan added. “Ben has been working with Daldrop SBB for more than 12 years and has advanced to COO of manufacturing and engineering. Ben grew up in Phoenix, so building a factory for the Germany company in Phoenix is a strong success for both his community and the appreciation of Ben’s skills as a team leader,” Sullivan said. “I went to high school in Phoenix. So, this is like full circle for me. It’s a small world,” Morrow said. “I hadn’t been back to Phoenix since graduation. This is one of the properties that John Trimble of C&S, he is the former president of C&S, he just retired, suggested. He and I were very good personal friends. John was from Mexico and I was from Phoenix. We knew each other from high school. And the architect of this building, Ed Smart of smartDESIGN Architecture in Batavia — I went to high school with him. So, this was a project that’s full circle a coming home kind of thing. The three of us knew each other — kind of like it was meant to be. Very serendipitous.” Schroeppel was one option for Daldrop to consider; the original intent was just to get a warehouse to store some product, he added. The facility is Daldrop’s largest outside of their native country. Total payroll at the facility will be approximately $2.4 million once fully staffed. “Here is 32,000 square feet and we plan to expand and put in an additional 10,000 square feet,” Morrow said. “We have 17.5 acres. We can put at least three more facilities here, just on this property. The master plan submitted for the current building is the first of possible three buildings on the campus.” “If we can find the right people and advance the production according to the plan, we should be able to grow for the next decade. One of the first jobs we filled was our new HR manager, with a primary focus posting and filling new positions,” Sullivan said. Stop the exodus, start hiring [caption id=attachment_11080 align=alignright width=452] Principals at the local Daldrop SBB headquarters, from left: Ben Morrow, Mike Sullivan and Jens Daldrop.[/caption] “I have been coming to Syracuse for 30 years. One of the things I had a hard time watching was the exodus of factories. One of the toughest things is to watch it with my partners’ kids who had to move away for jobs. It is important for us to give opportunity where people’s kids don’t have to leave the area to get work,” Sullivan said. “My responsibility is to find people who have skill sets that want to work and then I have to build opportunity for that work to happen. “After years of watching companies and Vince’s three sons all move out of the areas to get jobs, it is important to us to provide opportunities for the Syracuse region and help grow our company. We have been hiring for our new factory and will continue for the next few years.” “We are hiring and will continue to hire. Currently we have about 19 on staff right now,” Morrow said. “We expect to grow that by about 10 to 15 in the next 12 to 18 months or so. We are at 19 currently — one in the wings. It’s been great being able to hire locally. All these folks that we’ve been hiring, they all live within a few miles from here.” “We hired someone this past week (end of August). She grew up in the area, raised a family and is working down in Florida. She just took a job with us so she can move back and be with her family. We will be moving her back and she should be starting in October. We are very happy to see this happen,” Sullivan said. “I feel strongly about having a spot for the kids to not have to move out of the area. It is better to have people moving into the area.” Currently, Daldrop SBB has openings for finance manager, COO of construction and delivery, as well as mechanical, electrical designers and Revit modelers, construction managers, commissioning agents and many other skills that are needed. Along with Daldrop’s reputation in life science cleanrooms, there is a strong history in semiconductor, electronics, glass, photonics, aerospace and other markets. This may be helpful with any new technologies building in the market such as the Micron expansion and the support network, Sullivan said. “We acquired this property and closed on it two months before any announcement from Micron. It is serendipitous that they’re right down the street from us,” Morrow added. “Everybody started thinking ‘you guys are supporting Micron.’ We were here first. That is not a goal of ours. We may or may not … they are talking to us; it is an opportunity.” The long run [caption id=attachment_11079 align=alignright width=450] Benjamin Morrow, operations manager and control at Daldlrop, speaks during a recent ribbon-cutting event in Schroeppel.[/caption] “Long term, we will expand the Phoenix facility,” Sullivan said. “We built our facility with a campus approach. Sustainability is big. We designed our rooms to be more sustainable. Our building has no gas lines for heat. We put geothermal wells in the ground. We put heat pumps in for heating and cooling out of those wells and we put a large layer of solar panels up on the roof. We worked with National Grid for metering. Our building is a high efficiency model. We upgraded all the instillation and the glass filtering. So, it is a very green building. We will have a very minimal carbon footprint. It also adheres to LEED (Leadership in Energy and Environmental Design) construction standards.” “We are a bunch of HVAC engineers. We did a large scale geothermal and spent the money up front because we knew it made sense in the business sense in the environmental sense and sustainability,” Morrow said. “We have insulated metal panels on walls and roof; we are an envelope from the exterior.” Daldrop, as a global player, has a strong culture of sustainability that is found in all its products including the new facility. The building has been designed with (sixteen) 400-foot deep geothermal wells, high efficiency construction methods, approximately 70 kilowatt solar photovoltaic panels, electric vehicle chargers and 26% recycled content. “Daldrop thinks of long-term investing,” Sullivan said. “On the roof, photovoltaic panels supplement our power. Over half the energy that I use is coming from the sun. We have chargers up front for employee electric vehicles. This place is built to last. This slab that we are walking on, normal buildings are four inches, some go to six inches, we are nine inches all the way through — except where the heavy equipment is — that is 10 to 12 inches thick, solid concrete. It allows us to reconfigure for whatever type of equipment we want to bring in,” Morrow said. “We are authorized by Underwriters Laboratory to self-certify so we don’t need to have an inspector come out and recertify. We get audited often, but we certify our own electronics. The control system in this building is rather high-end. Our thermostat in every room is zoned, independent. We analyze the data; incorporate it with our solar panels.” “There is a lot of competition for people to work. It’s not just about how much salary they are going to make — it’s about the work environment, the ability for stability. We want to make sure our employees have the best environment to work in,” Morrow added. Similarities “One of the reasons I believe why Daldrop chose this area, Phoenix in particular, is because it is a village, a river-based village. Neckartailfingen in Germany where Daldrop is based is also a river-based village, manufacturing community. So, there is a lot of similarities — that smalltown feel,” Morrow said. “We have a lot of connections through multiple employees that lead back to Carrier, also. Everything came together, the timing everything.” “We are thankful for all the economic development support. Everyone has been helping make this happen. So, keep it up, it is working, that is all I got to say, Sullivan said. “If you look at historical industrial manufacturing, this region has skills that have been un-utilized. The northern industrial region has a lot of skills that if we can tap before they go away, we can leverage them. That is what we are trying to do. You’d be surprised at the supply chain left behind from Carrier — that supply chain is still there.” “Operation Oswego County has been fantastic,” Morrow added. “I can’t say enough good things about that group.” What Is Cleanroom Technology Cleanroom technology is a system of specialized engineering, filtration and operational controls used to maintain an extremely low concentration of airborne particles, microbes and chemical vapors. How It Works • Air Filtration: Uses HEPA or ULPA filters to remove 99.97% of tiny particles down to 0.3 microns. • Airflow Control: Directs air in a steady, unidirectional (laminar) or filtered turbulent pattern to push dust away from work areas. • Environmental Regulation: Maintains precise control over temperature, humidity and positive air pressure to block outside air leaks. • Specialized Attire: Requires personnel to wear full-body garments (“bunny suits”), gloves and masks to stop human shedding
Autodesk names Diana Colella to lead architecture, engineering and construction business
Proven product leader to advance Forma industry cloud and AI strategy and drive the next phase of AEC growth SAN FRANCISCO, Oct. 6, 2026 /PRNewswire/ -- Autodesk, Inc. (NASDAQ: ADSK) today...
Autodesk names Diana Colella to lead architecture, engineering and construction business
/PRNewswire/ -- Autodesk, Inc. (NASDAQ: ADSK) today announced that Diana Colella has been named executive vice president of its architecture, engineering and...
Autodesk names Diana Colella to lead architecture, engineering and construction business
Proven product leader to advance Forma industry cloud and AI strategy and drive the next phase of AEC growth
URI lecture series explores regenerative landscapes through science, planning, and design
KINGSTON, R.I. – Oct. 6, 2026 – The University of Rhode Island’s 34th annual Landscape Architecture Lecture Series will bring three nationally recognized practitioners and scholars to campus this fall to explore how science, planning, and design can work together to create more resilient and regenerative landscapes. The free public lectures carry added significance this […]
Gallery of Unearthing the Ground: Architecture and the Politics of Waste - 2
Image 2 of 15 from gallery of Unearthing the Ground: Architecture and the Politics of Waste. Fresh Kills Park. Image © USEPA Environmental-Protection-Agency - December 6, 2012 via Wikipedia under Public Domain
Gallery of Unearthing the Ground: Architecture and the Politics of Waste - 3
Image 3 of 15 from gallery of Unearthing the Ground: Architecture and the Politics of Waste. Alsergrund, Viena. Image © C.Stadler/Bwag, via Wikipedia under CC BY-SA 4.0
Gallery of Unearthing the Ground: Architecture and the Politics of Waste - 4
Image 4 of 15 from gallery of Unearthing the Ground: Architecture and the Politics of Waste. Agbogbloshie. Image © Muntaka Chasant, via Wikipedia under CC BY-SA 4.0
Gallery of Unearthing the Ground: Architecture and the Politics of Waste - 1
Image 1 of 15 from gallery of Unearthing the Ground: Architecture and the Politics of Waste. Incineration Line in Roskilde / Erick van Egeraat. Image © Tim Van de Velde
Gallery of Unearthing the Ground: Architecture and the Politics of Waste - 5
Image 5 of 15 from gallery of Unearthing the Ground: Architecture and the Politics of Waste. Fresh Kills Park. Image © erial View Courtesy of NYC Department of Parks and Recreation
New quantum chip taps into weird quasiparticles to get qubits to communicate over long distances
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Argonne: Building Scientific Computing Ecosystems for AI-Enabled Discovery
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18 of the most unique new buildings in the world: photos
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Global Fund Leadership Shortlist Unveiled Amid Donor Friction And Push For Reform
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Huawei unveils Mate 90 phones, leans on homegrown chip design to offset US curbs
Huawei Technologies [RIC:RIC:HWT.UL] unveiled its Mate 90 smartphone series on Thursday, betting on its in-house operating system and redesigned chip architecture to boost its performance in the face of US curbs on shipments of advanced semiconductors.
Fresh off World Cup wins, Kansas City leaders say sports could be city's promising new brand
Those involved in the World Cup's KC2026 effort and economic development leaders say Kansas City can build a brand around its expertise in sports architecture and increasing its profile in sports media, tech, health and youth sports.
UCLA receives $75 million in NSF grants for quantum technology
Two UCLA quantum computing institutes received grants from the National Science Foundation worth a combined $75 million Aug. 25. The institutes – Fault-Tolerant Quantum Systems, Architectures and Applications and Challenge Institute for Quantum Computation – aim to advance quantum technologies, which function faster than classical computers or standard digital devices.
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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Oracle Health rolls out AI solutions for RCM, oncology as part of broader healthcare, life sciences strategy
ORLANDO, Fla.—Healthcare is entering an AI era, and the industry needs a fundamentally different architecture designed for AI from the start, Oracle Health executives said Wednesday. | Healthcare is entering an AI era, and the industry needs a fundamentally different architecture designed for AI from the start, Oracle Health executives said Wednesday.
The Beats 360 are essentially true wireless earbuds disguised as headphones.
As confirmed by Beats product manager Jeff Bruksch, the new Beats 360 headphones use a true wireless architecture with each earcup containing its own battery, antenna, and processing components. It allows Beats to “significantly reduce the cabling between the two sides,” according to Bruksch. > Because we don’t have the traditional system including a charging case, we have a cable for power supply and power management between the left and right sides so that you don’t have to charge both sides and the left and right sides power on/off together. Having a cable for power management only explains the lack of audio support through the USB-C.
Nvidia establishes Israeli research group focused on AI for medicine and science
The Applied AI Architecture group has begun recruiting researchers in Israel and will work on AI applications ranging from genomics and drug discovery to new materials.
Why NuoData Believes Enterprises Need a Different Way to Connect Data, AI, and Automation — Not Another Place to Store It
NuoData’s leadership team sat down to answer six questions about the platform’s origins, its position alongside Snowflake, Databricks, Microsoft Fabric and Google Cloud, and where they believe enterprise data and AI architecture is headed next. The answers have been lightly edited for length. NuoData describes itself as an end-to-end enterprise Data, AI and Agentic orchestration […]
Michael Baker International Names Brian Pursch, SHRM-SCP, VP, Human Resources Business Partner Lead
/PRNewswire/ -- Michael Baker International, a global leader in engineering, architecture, consulting, and digital solutions, today announced that Brian...
Washington's Grant Veto Sets a Warning for British Science
A political oversight board at the NIH is a domestic American story, but its logic travels, and the NHS's own research architecture cannot assume immunity
World of Warcraft: Forever drops GeForce GTX 780 support, but 2012 Radeon HD 7850 is still supported
World of Warcraft: Forever raises its official GPU minimum to GTX 10 series and RDNA 1. A separate architecture cutoff still allows some much older GPUs.
Engrams Embedding Entendre: Codesign for Efficient DRAM/SSD Offloading
Engram extends standard token embeddings with learned multi-token lookups. Recurring local patterns retrieve vectors directly, reducing the need to reconstruct them through attention and feed-forward layers. With Engram model architecture optimization, it allows for lower HBM capacity to be needed for models at the same quality. This does not mean there won’t be an insane demand for HBM but it just means that model architecture will continue to innovate around constraints. This model architecture design is naturally codesigned for parameter offloading: each token accesses a few embedding rows whose addresses depend on token IDs, not hidden states. The runtime can prefetch those rows from host DRAM while earlier layers compute, keeping the table outside HBM without transferring entire weight matrices. Our Memory model contains our latest estimates of quarter by quarter HBM, DRAM, & NAND supply and demand. Offloading frees HBM for model weights and KV cache, potentially supporting larger batches or more concurrent sessions. When DRAM becomes the next constraint, NVMe offers another tier. Recommendation systems already cache frequently or recently accessed embedding rows in faster memory while backing colder rows with SSDs. After NVIDIA roadmap had to change due to massively despec’ing Rubin Ultra from 1024GB to now ~200GB of HBM per chip, model architecture optimizations like emgram maybe helpful. Our DeepSeek-V4.1-Flash configuration uses roughly 189 GiB of memory for Engram. We replace it with a memory-mapped (mmap) file and measure serving performance with offload to SSD. Later on in our report, we will show our Engram offloading experiments along with the official InferenceX agentic inference serving results on Engram models like DeepSeekv4.1 Flash across all 6 NVIDIA GPU SKUs along with MI355X. Unsurprisingly, the CUDA Moat is still mogging MI355X on the ultra popular DeepSeekV4.1 Flash model. We also show how even on high capacity HBM SKUs, offloading emgrams to DRAM could result in even better performance for most of the pareto than keeping the emgram in HBM. Our benchmark has been widely reproduced, validated and/or supported by almost every major buyer of compute from Google Cloud to Microsoft Azure to Oracle, to Meta and many more. Furthermore, it has the support of the ML community including from vLLM, LMCache, SGLang, PyTorch, Huggingface and the support of major labs like OpenAI, MiniMax, ZAI, Qwen, Moonshot Kimi, etc. Star the InferenceX GitHub repository if you find the open-source benchmark and data useful!. InferenceX is the only inference benchmark in the world to have TPUv7, Jalapeño, Nvidia Rubin NVL72, AMD, and soon, SambaNova and Trainium. Due to how realistic AgentX scenario is to real world agentic inference workloads, AMD has committed to collaborating on MI455X UALoE72 too. DeepSeek did not release the original paper’s two trained Engram models. We replicated its setup on fineweb-edu using the released code and training hyperparameters, at an estimated 6E18 FLOPs per run. We observed the same U-shape scaling: Engram improved performance over pure MoE baselines. We also reproduced DeepSeek’s results, where earlier-layer representations with Engram resembled those of later layers. Like the original Engram paper, it is possible to probe Engram’s gate scores to see what n-grams DeepSeek-V4.1-Flash makes the most use of. Our gate scan finds names, code fragments, relational phrasing, and boilerplate. These examples prioritize interesting-ness over gate strength. One unexpected result was Wright : Ace Attorney. These examples suggest learned memory optimizes the training objective, not a judgment of which facts deserve storage. Licenses, bibliography fragments, API scaffolding, and website furniture can provide prediction shortcuts, so the value of additional Engram capacity may depend on what survives data preparation. This does not show that table capacity is “wasted”: the evaluation-corpus scan establishes neither training exposure nor the capacity occupied by each category. For offloading, strong gates do not identify cache-hot rows. Low gates do not automatically save reads either: computing the gate requires the retrieved key and undoes the performance gain of a fused kernel. Skipping reads would require a separate usefulness predictor before retrieval. In the original paper’s inference-time ablation, factual-knowledge benchmarks retained just 29–44% of their original performance, while reading comprehension retained 81–93%. This is due to the training–inference mismatch. The resulting degradation therefore measures this trained model’s dependence on Engram, not the performance difference between models trained with and without it. In our ablations, suppressing Engram worsens token likelihood across all evaluated domains, especially encyclopedia text and several code corpora. Surprisingly, GSM8K accuracy stays within measured run-to-run variation and removing Engram has no effect. Engram is not a detachable dictionary beside an unchanged MoE. Removing it changes downstream features and expert selection. We tested whether rerouting hurts or compensates by holding tokens fixed in a teacher-forced experiment on CRUXEval, a code-reasoning benchmark of small Python functions where the model predicts a function’s output from its code and an input, and scoring the reference answer. Removing Engram raised answer loss from 0.2848 to 0.3093 bits/token. Forcing the ablated model to use the original Engram-on expert choices made it worse still, at 0.3375 bits/token. Rerouting partially compensates for the missing memory. Memory features and expert selection work together, rather than following a clean “memory stores facts; experts reason” division. On the same CRUXeval, removing Engram during either phase reduced accuracy and increased generated tokens; removing it throughout produced the largest changes. Keeping Engram for prefill leads to more correct answers than keeping for decode likely due to semantically richer KV cache transferred to decode workers, allowing it to mitigate some of the performance loss. Engram’s table is large, but each lookup is small. DeepSeek-V4.1-Flash requests 24 rows at each of two Engram layers, about 12.4 KiB per processed token position across the model, or 3.1 KiB per GPU when split across four GPUs. Currently as of Day 7 since Model Release, MI355X is still 2-4x worse performance per dollar compared to B200 even when normalized by Mi355X’s lower TCO. Our full total cost of ownership breakdown comes from our AI Cloud TCO Model along with monthly market surveys of over 100+ gpu clouds & gpu cloud customers. On the Day 0 release of DeepSeekv4.1 Flash, NVIDIA vLLM works out of the box with zero issues across all 6 SKUs: H100, H200, B200, B300, GB200, GB300! This was thanks to the amazing work by the NVIDIA & Interact teams! In comparison, AMD vLLM did not work on day 0 for DeepSeekv4.1 Flash.Source: SemiAnalysis InferenceX AMD’s vLLM documentation points to using vllm/vllm-openai-rocm:deepseekv41-flash-0909, but from hour 0 of the model release to hour 23, AMD has not publicly released the image. AMD claims, “SPEED IS THE MOAT,” yet it has still not released it by the 23rd hour. We wish that, going forward, the AMD team has a better process for hour 0 model releases. Eventually when they did publicly release for “day 0” image support, performance-wise, it is currently up to 14.8x worse perf per dollar than H200 and up to 42x worse perf per dollar than B200/B300. The power of the CUDA MOAT is NVIDIA’s collaboration with its massive 6 million-developer community ecosystem including most of the vLLM & SGLang & Tokenspeed maintainers which means that CUDA is optimized on day 0. Overall, AMD did make significant improvements but the performance per dollar is still currently 2-4x worse than B200. AgentX Engram DRAM Offloading Improving Performance The HBM and DRAM offload use the same GPU kernel to select and dequantize rows. With HBM, it reads GPU memory; with Unified Virtual Addressing (UVA), it reads pinned host memory directly. Both support full decode graphs. Moving the table into HBM accelerates only the sparse lookup, leaving decoder computation and communication unchanged, resulting in little overall benefit while consuming memory otherwise available to KV cache. Another benefit of Engram offloaded to DRAM which means you can reduce the communication overhead by using less HBM GPUs per replica. For example, when enabling Engram offloading on B300, we are able to switch from TP4 to now TP2 which improves the pareto curve by up to 1.6x. When iso-model quality, less HBM is required as the engrams could be offloaded to host DRAM. Thus HBM bandwidth matters way more than HBM capacity. For inference workloads where memory bandwidth matters the most, 4-hi HBM provides the best $/bandwidth and therefore lowest cost per token. If China continues to make more and more revolutionary model architecture innovations, soon it could potentially 0Hi HBM stacks. [ ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) [ Long Live the Short King: Why 4-hi HBM Wins ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) Myron Xie, Bryan Shan, and 3 others · Sep 13 [ Read full story ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) Moreover, on B300 and week-0 stack, moving the Engram table back to HBM did not improve results and stay within run-to-run variance. This is a result of the work optimizing DRAM offload, such as async, and overlap. SSD Offloading On B200, we created a unoptimized vLLM fork and stored the Engram tables in memory-mapped files on local SSDs. File backing lets the OS reclaim table pages when other applications need RAM. Pages already cached in memory can be served without reading the SSD again. Furthermore, note that we were unable to turn on GDS The unoptimized SSD implementation changes how rows reach the GPU. It copies row IDs to the CPU, deduplicates them, gathers the requested rows into pinned buffers, copies those rows back to the GPU and dequantizes them. This work runs between segments of the GPU execution graph. Native UVA performs row selection and dequantization directly on the GPU, avoiding the CPU round trip. A warm filesystem cache removes physical SSD reads, but leaves the coordination, row gathering and transfers. This is why a file already cached in RAM can still perform worse than a pinned DRAM table. The comparison measures the whole serving path; it does not separate the time spent on each of these operations. B200 DRAM dominates both measured SSD serving curves in total tokens per dollar and P90 interactivity. Near 125 tokens/s/user, DRAM delivers 121 million total tokens per dollar versus 52 million for SSD. For production serving, SSD offloading is likely not worth the tradeoff. On the B200 configurations we measured, SSD offloading loses on both measures: every observed SSD point has a DRAM alternative that delivers higher P90 interactivity and more total tokens per dollar. The only points where Cheaper storage does not automatically produce a cheaper inference service. Moving Engram to SSD leaves the same four expensive GPUs and the rest of the server in place. Reclaiming RAM only creates an economic benefit if it enables a cheaper server configuration or additional useful capacity. The current unoptimized path provides neither benefit, and the filesystem cache still consumes RAM when table pages are resident. Next we will look at the mechanisms and specific implementation of ngrams from DeepSeekv4.1 Flash, LongCat, Qwen3.8 Flash Next.
Rooted Architecture Studio PLLC
Founded in 2021, Rooted Architecture Studio PLLC is a client-centered and wellness-centered architecture firm proudly built and led by a woman.
Has Your Business Banking Experience Fallen Behind? Here’s How to Catch Up
Design what users want, rather than focusing on your existing architecture and how it can be made fixed.
Opinion: Bridgeport residents have every right to demand answers
Chicago Architecture Center CEO Eleanor Gorski apologizes for remarks that upset Bridgeport residents and clarifies her views on the proposed Amtrak facility.
Apple explains how the iPhone 18 Pro’s new Reference Image camera mode works
A new post on Apple’s Security Research blog details the fascinating tech, architecture, and thinking behind the iPhone 18 Pro’s new Reference Image camera mode.
Why Fear of Unchecked AI Belongs in Product Architecture
Years ago, sitting on-site with data science teams at large institutions, I kept watching the same scene play out. A model, sometimes a sophisticated one, sometimes a logistic regression somebody trained in an afternoon, would get promoted into a decision that mattered. Whether a small business got a loan. Whether a case got flagged. Decisions […]
Apple's Siri AI Can Be Swapped Out for Claude, ChatGPT, Code Shows
Code sleuth "pdfu" has uncovered iOS 27 and macOS Golden Gate private frameworks that show Apple has designed its new Siri architecture to work with third-party AI models at what appears to be a surprisingly deep level. One mechanism called Model Delegation allows Claude to appear as a Siri extension in the same way as the existing built-in ChatGPT extension.
Bone Health Without the Hype
I am at the Australasian Menopause Society meeting and we had a fantastic lecture on Bone Health on Day 2, called, “Everything you need to know about bones - except the boring bits!” Spoiler alert, there were no boring bits. The lecture was given by Dr. Amanda Beech, who is a senior staff specialist in Obstetric Medicine and Endocrinology at the Royal Hospital for Women, where she is director of the Bone Density Unit. I want to summarize my top 10 take-aways for you. Share Osteoporosis is a disease of low bone mass, disruption of bone microarchitecture, and fragility of the skeleton. It is a silent disease…until someone develops a fracture. The diagnosis of osteoporosis is based on either fragility/minimal trauma fractures or a T-score of less than -2.5 on a DXA (Dual-Energy X-ray Absorptiometry) or bone scan. It is important to recognize that about 50% of minimal trauma fractures occur when women have low bone mass (often referred to as osteopenia), but not osteoporosis. This tells us that bone strength is more complicated than a number on a DXA report. I am going to add a little here that wasn’t in the talk (one can only include so much in 25 minutes), as many women might read the above paragraph and think, “Shouldn’t we be treating everyone with low bone mass if this is when 50% of the fractures occur?” Many fractures happen for women with low bone mass because there are so many women with low bone mass –it is a statement about the population on a whole, not about the fracture risk of low bone mass. For you as an individual, bone density is only one component of fracture risk and many women with lower bone mass will not have a significantly elevated fracture risk. This is why individualized risk assessment matters. Subscribe We build bone in our teens and twenties, and by the time we are about thirty we have reached peak bone mass. This is why, when we discuss risk factors for osteoporosis, it is important to consider factors that might have affected us in our teens and twenties when we were actively building bone, such as nutritional restriction/energy imbalance. It is important to ask women about a history of eating disorders, and for those with such a history, getting a bone scan in perimenopause or at age 50 is likely warranted. (This image below isn’t from the talk, but is similar to one that was). Bone mass is relatively stable until about two years before the final menstrual period, and perimenopause marks a time of rapid bone loss for women. Using urinary markers of bone loss we know that the time of significantly accelerated bone loss starts about 1 year before the final period and ends about 2 years after the final period. The Study of Women’s Health Across the Nation (SWAN) shows that over the menopause transition women lose an average of 10.6% of the bone at their lumbar spine and 9.1% at the femoral neck While this wasn’t covered in the lecture, I want to add that the rapid bone loss during perimenopause is clearly related to more than estradiol, because during this time estradiol levels can be erratic and not uniformly low. Bone loss during perimenopause is likely a multi-hormone event. For example, rising FSH (follicle stimulating hormone) tracks with bone loss better than estradiol, and there is evidence that FSH may act directly on bone. While decreased estrogen matters, there is clearly more going on. FSH and bone is an active area of research. Subscribe Screening for osteoporosis was recommended fairly broadly before age 65, and was taken from the “Practitioner’s Toolkit for Managing Menopause,” from Monash University. Here is a screenshot from the toolkit that was used as a slide: I am going to add a little bit more about body weight here for clarification. Low body mass (typically 152.1 lb): 0 points (risk neutral) 60–69 kg (132.3–152.1 lb): 3 points (adds some risk)
'Ex-Utero' project uses art, science to explore regenerative medicine
On a sunny spring day at Penn State, Cristin Millett, professor of art in the College of Arts and Architecture's School of Visual Arts, inserted a catheter needle into the umbilical vein of a placenta.
Pease Tradeport continues as NH economic engine
New report cites Tradeport’s ‘uniquely important role in New Hampshire’s economic architecture’ as executive director looks toward ‘retooling’ for life science users
Scientists visualize proteins' hidden water architecture that may help define biological function
For decades, scientists have understood proteins primarily through two defining features: their amino acid sequence and their three-dimensional structure. This framework has driven major advances in biology, ...
AirPods 5 Announced With Improved Noise Cancellation, Audio Quality, and More
Apple today introduced AirPods 5, bringing Active Noise Cancellation to its entry-level wireless earbuds at a starting price of $129. Apple says AirPods 5 remove up to 50% more external noise than AirPods 4 with Active Noise Cancellation. A redesigned acoustic architecture and updated Adaptive EQ also promise richer, more detailed sound, while an improved Transparency mode makes nearby voices and sounds more natural.
Apple Unveils A20 Pro as First 2nm Smartphone Chip
Apple today revealed the A20 Pro chip in the iPhone 18 Pro and iPhone 18 Pro Max, built on a brand new architecture using 2nm process technology. Apple described the A20 Pro as "an exceptional chip that sets a new bar for mobile computing". The chip has a 6-core CPU design with two new super cores that Apple says are up to 20% faster, calling them "desktop class" and "the fastest in any smartphone," alongside four new efficiency cores.
A new strategy enables precise fabrication of nanoporous carbon structures
Nanoporous carbon materials have attracted significant interest for applications in lightweight structural materials, microdevices, and bio-integrated systems due to their low density, chemical stability, electrical conductivity, and tunable mechanical properties. However, existing fabrication approaches often struggle to simultaneously achieve precise patterning, controllable porosity, and desirable mechanical performance. A research team led by Professor Min Qiu at Westlake University has developed a new fabrication strategy, termed Mix-ice lithography (Mix-IL), that enables direct construction of nanoporous carbon structures with tunable pore architectures and complex geometries. The study has been published in Science Bulletin.
Epic v. Health Gorilla: “Working as Designed”
We’ve been on a bit of a legal bender since just before Labor Day, so let’s keep a good thing going! As predicted, Epic does not want its case against Health Gorilla consolidated with the data breach cases, so it filed a motion to vacate the conditional transfer. And because they had to explain to the Panel why this case is different, they put a good chunk of previously unseen Epic v. Health Gorilla discovery record on the public docket! Roll the tape! The Case Against Consolidation Before we get to the fun stuff, let’s do the housekeeping. Epic's motion boils down to three arguments (sorted in ascending order of how well they land): A judge has already decided this. Judge Olguin declined to relate the fraud action to another ELU case (Patterson v. UVA Health), holding that the two do not call for determination of substantially similar questions of law or fact. Epic asks the Panel to treat that (a Rule 42 ruling) as law of the case. The overlap is thin. The three common questions Health Gorilla and the Panel identified are neither numerous nor complex (the qualities the Panel looks for before centralizing). Moreover, they are outnumbered by the questions unique to each side: The breach cases turn on security architecture, notification timing, and patient-level access. The fraud action turns on false treatment attestations and breaches of the framework agreements. Consolidation costs more than it saves. 17 of 21 parties in the fraud action do not appear in a breach case. The cases are also at very different stages: Olguin has a scheduling order running through a January 2028 trial while the breach actions are much more nascent. I like the last two more than the first because they attack the basic economics of an MDL: there just may not be enough shared discovery here to justify dragging all the defendants and a relatively mature fraud case all the way to Florida. On the other hand (while I know exceedingly little about MDL and Rule 42), I have a hard time seeing why a district judge's decision about his own docket would control what the Panel does with the separate § 1407 question. I might be reading between the lines too much, but I sorta think Epic's own brief half-concedes this, framing the two provisions as "two sides of the same coin" but then adding "respectfully submit" (which is often lawyer speak for “we know this is a reach”). The Ladder Gets More Rungs Anyway, the arguments are not why you're here (aside from the lawyers, maybe). Back in June, we discussed how Epic was using the smaller defendants to build an evidentiary ladder toward Health Gorilla and Unit 387. Turns out discovery has been adding a few rungs of its own.
Mapping the Global Health Landscape: Analysis of Fourteen International Organizations
The global health architecture includes a number of diverse international organizations with different mandates, governance models, financing structures, and operational approaches. This KFF resource looks across a range of variables to provide a descriptive mapping of 14 key global health and related international institutions. It is intended to inform discussions about synergies and coordination, duplication, comparative advantage, shared challenges, and architectural reform.
Everything to know about Lucas Museum of Narrative Art
The highly anticipated $1-billion building opens Sept. 22. Learn more about its architecture, must-see experiences, rocky start and the questions surrounding "narrative art."
Humpback whales can sing for up to 16 hours without stopping, and scientists have found their songs are synchronized with every dive — different themes line up with the descent, the deep-water phase and the climb back to the surface
The whales aren't just singing—they're composing underwater symphonies that match the architecture of their own bodies in motion, revealing a hidden layer of sophistication in one of nature's most mesmerizing performances.
CCHS plans Colorscape weekend history tours
Chenango County Historical Society will offer two Norwich tours during Colorscape Chenango, focusing on local architecture, buildings and history.
Lavish private bath used by Roman emperor and 'high-status residents' discovered at imperial palace in Serbia
The ancient bath complex was tucked in a tower in the corner of the royal palace and reveals unique architecture.
Museums for fall 2026: Science at the MSI, women all-stars at Art Institute and the annual architecture Open House
Whether you like architecture, history, science or maybe bugs, museums around the Chicago area have your thing this fall.
Keurig Dr Pepper sells stake in Chobani ahead of split; Corgan enters new market with M&A
Keurig Dr Pepper is selling its stake in Chobani and using the proceeds to reduce debt ahead of a planned split. Private equity firm Align Capital Partners completed an exit, while architecture firm Corgan revealed its latest acquisition to enter another fast-growing market. Read on to learn about these deals and more as September gets off to a quick start.
Holly & Smith Architects opens a Baton Rouge office
Holly & Smith Architects has opened a Baton Rouge office, the firm announced Tuesday. The move gives the Hammond-based architecture firm a physical presence in a market where it has worked for yea… Already an INSIDER? Sign in. Already an INSIDER? Sign in
HFG Architecture opens Rogers office
HFG Architecture, a Wichita, Kan.-based healthcare architecture and design firm, recently relocated its Bentonville office into a 1,000-square-foot space in Rogers and named Northwest Arkansas native Josh Danish as managing...
The IDA Health Window Is Gaining Traction: Four Responses to the Proposal
I recently proposed an IDA Health Window as the future of multilateral global health financing. The proposal is beginning to gain traction. It has received interest (mostly positive, but always with good, constructive challenges) from colleagues associated with the Accra Reset; Africa CDC and the African High-Level Ministerial Committee on Global Health Architecture Reform; low- and middle-income country health and finance officials; key high-income donors including the UK, France, Germany, and Norway; the World Bank and the Asian Development Bank; the World Health Organization, Wellcome Trust, and civil society.
Leaked DLSS 5 is Being Tested on RTX 20-series "Turing" GPUs, Now Runs on Emulators and Old DirectX Titles
The leaked DLSS 5 DLL keeps finding new homes, and support has now spread to RTX 20-series "Turing" GPUs. That architecture is three whole generations behind the NVIDIA-recommended "Blackwell," and modders are showing no signs of stopping either. Developer ShortFuse has released new DLSS-NR...
Apple Seemingly Made a Mistake When Announcing the New Mac Studio
Apple's press release for the new Mac Studio with M5 Max and M5 Ultra chips last week initially stated that the computer had "next-generation SSD architecture built on PCIe Gen 6". However, as spotted by the French blog MacGeneration, Apple removed the PCIe 6.0 mention from the announcement shortly after it was published.
Seoul National University of Science and Technology develops AI framework to test and improve vision-system robustness
A new artificial intelligence (AI)-based system shows how natural damage to traffic signs can mislead deep neural network (DNN)-based vision systems. The framework, AdvWT, uses a generative model to learn realistic damage patterns and then optimize them to trigger misclassification. Tests across multiple AI architectures showed strong attack success, transferability and physical-world robustness. The approach can identify vulnerabilities and improve AI generalization to real-world damage.
GE HealthCare announces 3 new ultrasound imaging systems
Each system is powered by GE HealthCare's cSound imaging architecture and includes advanced AI capabilities.
North Carolinians Continue to Trust Elections Close to Home More Than Elections Across the Nation, Catawba College-YouGov Survey Finds
Catawba College is ranked #4 among the best colleges in the South. Located in historic Salisbury, NC, it is a private, coeducational liberal arts college established in 1851. Our mission is to enrich the educational experiences of students and to prepare them for productive, meaningful lives of purpose. Our campus consists of over 30 buildings on 276 acres, a beautiful blend of neogothic architecture set on a shaded hillside adjacent to its own ecological preserve. We are just two hours from the Appalachian Mountains and four hours away from some of the best beaches on the Atlantic Coast.
Business People: Meteorologist Tucker Antico Joins KARE-11 from Boston station
MEDIA Twin Cities NBC-TV affiliate KARE-11, Golden Valley, announced that Tucker Antico joined as its Sunrise meteorologist. He most recently was a meteorologist at Fox affiliate Boston 25 (WFXT-TV). ARCHITECTURE/ENGINEERING WSB,…
Stellantis bets on long-idled Belvidere to launch this key technology
Stellantis will use its Belvidere plant to launch the Jeep Cherokee on the STLA One platform, a new architecture it hopes to use on 30-plus models.
The Apophis encounter in 2029: A rare opportunity for planetary science and defense
The near-Earth asteroid Apophis will make an extremely close flyby of Earth on April 13, 2029, at a distance of approximately 38,000 km—an event that occurs on average only once every 7,500 years, offering an unprecedented opportunity for planetary science and planetary defense. However, significant uncertainties remain regarding the physical parameters of Apophis, such as its shape, spin state, internal structure, and material composition, and predictions of tidal effects—including potential orbital changes, spin acceleration, surface material displacement, and dust ejection during the flyby—are highly dependent on model assumptions. Although multiple nations have planned exploration missions, how to coordinate global observational resources within the limited time window, optimize rapid-response mission architectures, and translate scientific understanding into effective planetary defense strategies remain critical challenges to be addressed.
HNTB looks to 'set a new bar for the NHL' with Stars' new arena
The Stars selected the Kansas City-based architecture firm to build their new arena in Plano at The Shops at Willow Bend.
The global health aid architecture is collapsing. The faith economy isn't
“Washington has rediscovered faith as a delivery mechanism. The bigger opportunity is faith as a source of capital,” writes Dr. Alaa Murabit.
Intuidy’s AI bet isn’t on smarter models; it’s that your business’ next breakthrough is already in your data
The Lenexa-based CEO of Intuidy isn’t betting his company’s future on building a smarter artificial intelligence model. OpenAI, Anthropic, Google and others are already fighting that battle. Grayson Smith instead wants Intuidy to build the architecture around those models.
Sporting KC co-founder Cliff Illig to headline KC Sports Impact Panel at KU exhibition closing event
The University of Kansas School of Architecture & Design’s “Shaping the Future of Football” exhibition concludes Aug. 21 at Keystone CoLAB in Kansas City with the KC Sports Impact Panel. Featured speaker Cliff Illig, principal owner and co-founder of Sporting Kansas City, will discuss Kansas City’s 2026 FIFA World Cup opportunity, the fan experience, and the future of sports, entertainment and economic growth in the region.
Is the World Bank Ready for a Bigger Role in Global Health Financing?
The global health architecture is facing a financing crisis. Various proposals have been put forward, including an IDA Health Window—a ring-fenced fund at the World Bank for countries to seek support for financing their health systems. But could the World Bank absorb and effectively channel a greater share of health financing in the future? As the largest and most influential multilateral development bank, it is an attractive option: it works directly with country governments, provides financing through national budgets, offers both loans and grants, and supports investments across sectors based on country demand.
Shopify Inc. (SHOP) Soars as Jim Cramer Applauds AI-Driven Business Formation
Software platforms faced selling pressure due to concerns that generative AI tools would replace traditional software architectures. However, Shopify Inc. (NASDAQ:SHOP) broke away from sector headwinds after second-quarter financial results showed AI tools driving business formation rather than displacing its e-commerce infrastructure. Because Shopify serves small and medium-sized businesses requiring turnkey checkout, payment processing, and […]
Studio Maks tops science pavilion in Austria with undulating mirrored roof
Thousands of mirrors line the gently undulating ceilings of the VISTA Science Experience Centre in Austria, completed by Studio Maks.
Enterprise connectivity boosts AI performance and security
AI and cloud demands reshape enterprise connectivity. Optimum Business provides scalable solutions for secure and efficient network architecture.
How Cohere Health digitizes clinical policies using Amazon Bedrock AgentCore
In this post, you learn how Cohere Health built a multi-tenant agentic architecture on AgentCore using AgentCore Runtime’s secure MicroVM isolation, unified tool access through AgentCore Gateway, AgentCore Memory, and the Agent Skills open standard to rapidly scale policy digitization capabilities, while preserving transparency, version control, and human oversight.
Butterfly lilies wing their way to Science
Many animals and plants show left-right differences in how their bodies or organs are arranged, but we still know little about how such asymmetries evolve or how genes control them in natural populations. Mirror-image flowers, in which female and male sex organs are deflected to opposite sides in left- and right-handed forms, provide a notable example of such asymmetry. Here, we studied South African butterfly lilies, where individual plants produce only left- or right-handed flowers. Their mirror-image flowers promote outcrossing by depositing pollen from each floral form onto a specific region of a pollinator’s body that matches the stigma position of the opposite form. This suggests that selection for efficient pollen transfer between forms maintains this natural polymorphism. Our work uncovers a hemizygous supergene that coordinates the left-right orientation of female and male sex organs in mirror-image flowers by linking two independently acting genes for style and stamen handedness in a single, nonrecombining genomic segment. This architecture ensures the coinheritance of a matched pair of organ orientations that is essential for efficient cross-pollination between floral forms. At the developmental level, we show that floral handedness emerges from the interaction between an intrinsic tissue chirality and a gravitropic response that orients the style and median stamen relative to gravity. Together, these findings reveal how a supergene, hormone and microRNA signaling, tissue chirality, and an external physical cue combine to produce and maintain a stable left-right polymorphism in natural plant populations.
Viewpoint: Europe needs a new scientific contract
The White House's new science strategy should be required reading in the Berlaymont and every European capital.Not because Europe should copy it. Parts of it should concern Europeans deeply. But because it asks the question Europe has so far avoided while steadily losing ground in the global race for technological leadership.Europe already possesses many of the world's finest universities, scientists and research-intensive companies. It produces world-leading research in fields ranging from quantum technologies and biotechnology to climate science. Yet time and again Europe generates the scientific breakthroughs while others build the companies, industries and strategic advantages around them, particularly in digital technologies. Increasingly, we find ourselves regulating technologies whose underlying capabilities were developed elsewhere.This is not primarily a scientific failure. It is an institutional one.The settlement that shaped modern science policy emerged after the Second World War. In Science, the Endless Frontier, Vannevar Bush argued that governments should invest in fundamental research while protecting scientists from political direction. Public investment was justified precisely because no government, university or company could predict where the next transformative discovery would emerge.That scientific contract served liberal democracies extraordinarily well. It underpinned decades of advances in medicine, computing, energy and communications, but the world for which it was designed no longer exists.Artificial intelligence is transforming not only technology but the production of knowledge itself. Advanced chips, computing power, data and scientific instruments have become strategic assets. Scientific knowledge now diffuses globally almost instantly. Competitive advantage increasingly lies not in making discoveries first, but in possessing the infrastructure, capital, engineering capability and institutions required to translate those ideas into products, industries and strategic advantage.The old boundaries between science, industrial policy, national security and geopolitics have largely disappeared. Dual-use technologies now underpin both economic power and military deterrence.The US has recognised this shift. In July, the White House published Science: A New Golden Age, a comprehensive attempt to rethink the relationship between science and national power. The report builds on Bush's legacy, but departs from his framework in important ways. Science is no longer treated simply as the source of future discoveries. It is presented as the foundation of economic resilience, technological leadership and national security. AI, advanced infrastructure and the capacity to build become as important as discovery itself.Yet the report also reveals a striking paradox. The same administration calling for “a new golden age” of US science has simultaneously proposed deep cuts to the federal research enterprise while expanding political influence over the allocation of public research funding. Europe should therefore distinguish between the report's diagnosis and its prescription. The diagnosis, that science has become a strategic asset, is compelling. But the prescription is self-defeating. Long-term technological leadership depends on strong universities, academic freedom and the institutional autonomy that makes scientific discovery possible. This distinction matters. Bush's achievement was never simply to make science serve national strength. It was to establish a division of labour in which governments invested in research without attempting to direct discovery itself. Scientific breakthroughs emerge precisely because researchers retain the freedom to pursue questions whose value cannot yet be demonstrated and whose conclusions cannot be anticipated.Neither China, nor the USAs science becomes more strategically important, the temptation to politicise it inevitably grows. But scientific freedom is not an obstacle to technological leadership, it is an essential precondition.China represents the opposite model. For decades it has integrated science, manufacturing, infrastructure and industrial policy into a single national strategy. Its ability to mobilise resources around critical technologies is formidable. But it does so within an authoritarian system where scientific priorities are increasingly inseparable from political power.Europe should imitate neither model. It should learn from the strategic ambition of the US without importing its growing politicisation of science. And it should learn from China's capacity to build without adopting its concentration of power.Instead, Europe needs a scientific contract of its own. Bush's scientific contract protected the freedom to discover because the defining challenge of the 20th century was producing new knowledge. Europe's next scientific contract must preserve that freedom while ensuring that scientific discoveries can be converted into technological, industrial and strategic capability. Because in a transactional world, Europe's freedom to shape its own future depends on its capacity to build it.Related articlesThe US ‘golden age’ science strategy: what Europeans need to knowEuropean researchers sound the alarm that Trump rule changes will hit cooperationNIH turmoil risks reciprocal EU-US access to health research fundingThis does not mean subordinating free science to competitiveness or industrial policy. On the contrary. Scientific autonomy remains the first principle of Europe's next scientific contract. Governments should not direct scientific discovery. Their responsibility is to ensure that Europe's scientific, industrial, financial and regulatory institutions work together rather than in isolation.Meeting that responsibility requires far more than research funding. European researchers need access to world-class computing infrastructure, advanced laboratories, shared datasets and scientific facilities. Innovative companies need patient capital, testing environments and a genuine single market in which they can scale without facing 27 different regulatory regimes. Public procurement must evolve from purchasing existing technologies to creating markets for new ones. Regulators must become partners in experimentation rather than guardians of yesterday's rules.A connecting architectureEurope does not need to invent the Defense Advanced Research Projects Agency (Darpa) model from scratch. Britain's Aria, Germany's Sprind and Wellcome Leap already show that Europe can build institutions capable of taking high-risk, mission-oriented bets while preserving democratic accountability. The challenge now is not to create more isolated initiatives, but to connect them to Europe's broader scientific, industrial and financial system so that breakthrough discoveries can scale.Above all, Europe must stop treating research, innovation, industrial policy, finance and security as separate policy domains. The challenge is no longer to create excellent institutions in each of these fields, but to make them work as one European innovation system. Europe's strength will increasingly depend not on excellence within institutions, but on excellence across them.Europe's ambition should be neither to imitate the US nor to emulate China. It should demonstrate that scientific freedom, democratic legitimacy and technological capability are not competing objectives, but mutually reinforcing strengths. In an age of geopolitical rivalry, Europe's greatest advantage may ultimately be its ability to attract the world's best talent by combining scientific freedom, democratic openness and technological ambition.Bush's scientific contract helped define the 20th century because it protected the freedom to discover. Europe's challenge is not to abandon that achievement but to extend it. The defining task of Europe's next scientific contract is to preserve the freedom to discover while building Europe's capacity to translate scientific discovery into technological power.Kaare Nielsen is founder of Pomus, a Danish think tank on science and innovation.
Sapphire Intros Radeon RX 9050 8GB Pulse Graphics Card
Sapphire today introduced its Radeon RX 9050 Pulse 8 GB graphics card. Based on a compacted variant of its RX 9060 XT Pulse board design, the card forms AMD's new entry-level in the RX 9000 series generation of GPUs powered by RDNA 4 graphics architecture. AMD carved the RX 9050 out of the 4 nm...
Repairs to Madame John's Legacy, one of French Quarter's oldest buildings, causes concern
One of the oldest buildings in the New Orleans French Quarter and a rare surviving example of French Colonial architecture is at the center of a years long dispute between local preservationists and the state, nearly three years after the two sides thought they'd worked out a compromise.
She built her own architecture firm. Now she's opening doors for others.
Katherine Serrano Sosa is growing Rooted Architecture Studio in a difficult market while helping Latina and immigrant architects navigate a profession where few see themselves represented.
Agentic coding goes hands free as OpenAI brings GPT-Live's full duplex voice control to Codex and ChatGPT on the desktop
This architecture creates a pair-programming dynamic where developers talk through problems conversationally while agents execute tasks asynchronously.
AMD Zen 7 “2028” and Zen 8 “2030” CPU Architectures Confirmed – EPYC Florence “Zen 7” To Feature Next-Gen Node, DDR6 Memory & ACE Extensions
AMD has officially confirmed its next-generation Zen 7 and Zen 8 CPU architectures, which arrive in EPYC Florence and EPYC Ravenna chips.
MLOps transformation at BBVA: delivering business value faster
In a previous article, we explored how BBVA is scaling its Machine Learning capabilities with a new architecture based on AWS technologies.
Israeli startup is using AI to shorten cloud architecture timeline
With most IT projects failing, AutoPipe believes AI can turn weeks of cloud planning into hours
iOS 26.6 Prepares Your iPhone for iOS 27
iOS 27 includes a rebuilt search architecture for Siri AI. As a result, iPhones running the iOS 27 beta go through a re-indexing process that can take many days to be completed, but iOS 26.6 should help to speed things up for users who skip the beta. iOS 26.6 "optimizes the Spotlight index to prepare for iOS 27," according to Apple's release notes for the update, which will likely be released next week.
5 Reasons Why Some Gamers Swear By Sennheiser Headphones
Sennheiser headphones match premium listener profiles, have great spatial imaging, multiple open-back architecture options, and an optimized ergonomic design.
Samsung says Galaxy Z Fold 8 crease is less visible thanks to ‘Flex Titanium’ display
The Galaxy Z Fold 8 series will get a new foldable display architecture, Samsung admits, which will improve the phone’s...
Experimental Transmission Aims To Tackle The Biggest Problems With Hybrids
Faraday Future patented the Range-Extending Hybrid Transmission System to fix PHEV complexity using a multi-clutch and multi-shaft architecture.
Knoxville team behind Dollywood projects joins new NERF, Peppa Pig attraction
Developers of the new Play District in Pigeon Forge have joined forces with the Knoxville architecture firm behind several Dollywood projects.
AMD Expands Hawk Point Lineup With Numerous New SKUs Under Ryzen 200 And 100 Series
AMD adds 11 new processors to Ryzen 200 and 100 series, featuring Zen 4 architecture with up to 8 cores and RDNA 3 graphics.
Longtime friends and business partners thrive as architecture grad students
Setareh Farashzadeh and Yasaman Ghaffarian came to Penn State as close friends and established architects in pursuit of the history and theory behind their chosen field. Originally from Iran, the duo started their own practice after earning their bachelor’s degrees and jointly decided to expand their understanding of the built environment. Their interests and underlying friendship led them to the dual master of architecture and master of science in architecture program in the College of Arts and Architecture’s Stuckeman School.
ChatGPT bot made man’s mental health worse, not better: lawsuit
“[ChatGPT’s] architecture preys upon those with mental health disabilities,” the man said in a statement.
USC Computer Science Chair Nenad Medvidović Inducted as ACM Fellow - USC Viterbi
Recognized for foundational contributions to software architecture and large-scale systems design, Medvidović reflects on research, mentorship and solving problems that matter.
Leaked A20 Pro Image Hints at iPhone 18 Pro Performance Gains
An alleged image of the iPhone 18 Pro motherboard has leaked online, showing the A20 Pro chip will use a new packaging technology that should offer notable performance gains over the previous model. The leaked image, which has been shared by the accounts "WHYLAB" and "Ice Universe" on Weibo, appears to show the A20 Pro chip integrated into TSMC's new packaging architecture, known as Wafer-Level Multi-Chip Module (WMCM) technology.
Marriott and Gensler CEOs: Good Design is Good for Business
Architecture can be a tool for culture and innovation, they say. Plus: How Opella’s chief growth officer makes the most of Cannes Lions.
10 Game-Changing Entertainment Venues Designed by Populous
A look at 10 entertainment venues by Populous, spanning esports, concerts, and more, that are redefining audience experiences worldwide.
Moment of Science: Music and Color
Dr. Stefan Wienzierl at TU Berlin studies how the architecture of a classroom or concert hall can impact the quality of music or speech.
'This is the next jump in technology': World's first sub-1nm chip keeps Moore's Law alive a little longer
IBM's NanoStack architecture has helped scientists cram 100 billion transistors onto a computer chip, delivering 50% better performance and consuming 70% less energy than the current generation.
How architecture influences political activity
A new study in Ghana shows residents in semi-communal housing are more likely to vote, attend rallies, and take part in political campaigns, compared to people in more private forms of housing.
Why healthcare CIOs must fix their foundations before scaling AI
Healthcare leaders are focused on generative AI innovation, but the organizations most likely to succeed may be those investing first in architecture, governance and interoperability.
Business Must Prepare Now for Mandatory E-Invoicing Challenges
Opinion: As mandatory e-invoicing spreads worldwide, businesses need to build flexible, central compliance architectures that can scale globally rather than react to each new mandate individually, say RSM practitioners Philip Munn and Christian Balk.
Intel’s Serpent Lake SoCs With NVIDIA RTX GPU Tiles Reportedly Arrive In Q1 2028
Intel's Serpent Lake SoCs featuring NVIDIA's RTX GPU tiles as integrated graphics are expected to roll out by Q1 2028.
A new generation of public health students
Public health is often associated with hospitals, disease outbreaks and government agencies. But health also is shaped by architecture — by sidewalks, green space, accessible design and the environments people move through every day. Teddy Basa, a rising junior at Washington University in St. Louis, is seeking to better understand those connections. Basa arrived at […]