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Life sciences firm Danaher to launch AI-powered autonomous research lab in 2027
Life sciences company Danaher said on Wednesday it plans to launch its first AI-powered autonomous research lab early next year to make drug discovery and development faster and more efficient.
Smart Glasses, Smarter Small Businesses: AI Wearables Are Becoming an Entrepreneurial Tool - Small Business & Entrepreneurship Council
By Karen Kerrigan – For entrepreneurs and small businesses, valuable technologies eliminate friction. Various digital tools are helping small businesses eliminate steps, and accomplish a task or project more efficiently, in less time, and at lower cost. AI-powered smart glasses are helping small business owners eliminate operational downtime, automate content creation, and improve workplace accessibility, […]
Indiana Builds Early Lead Over North Texas in Opener
Josh Hoover’s efficient debut and a dominant special teams spark powered the No. 6 Hoosiers to a commanding halftime advantage as the Mean Green struggled under pressure.
Rising energy, fuel costs cut into bottom line for Pa. businesses
At Shirley’s Cookie Company, Facilities Maintenance Manager Richard Weyant detailed the efforts he’d overseen to keep utility costs down at their Blair County facility. They included thousands of dollars invested to upgrade to LED lighting alongside new and more efficient HVAC systems. While electricity use went down by 5.7% over four years, he told Republican […]
49ers news: Brock Purdy finally addressed Drew Brees comparisons
The San Francisco 49ers quarterback, Brock Purdy, will be one of the key players in the offense when the team starts NFL regular-season action. He’s coming off an efficient but shortened last season and will be looking to bounce back. Entering his fifth career season, Purdy has been compared to Hall of Fame quarterback Drew Brees, who played for […] The post 49ers news: Brock Purdy finally addressed Drew Brees comparisons appeared first on ClutchPoints.
NVIDIA Unveils "Preset C "DLSS Frame Generation with up to 35% Faster Generation
NVIDIA has updated its DLSS Frame Generation algorithm to be faster and more efficient. In the latest NVIDIA App beta update, rolling out today, the company introduced "Preset C," the fastest Performance Mode for DLSS Frame Generation. NVIDIA demonstrates that this mode can make frame generation...
When climate change brings the heat, what happens to a mighty redwood?
In heat wave-like temperatures, coast redwoods' photosynthesis slows down, which may put one of Earth's most efficient carbon storers in jeopardy.
Scientists discover a hidden switch inside silver nanocatalysts
Silver nanocatalysts have been found to switch where they perform their most important reactions depending on whether a solid oxide cell is making electricity or hydrogen. The discovery could enable smarter catalyst designs that boost clean power generation while making green hydrogen more energy-efficient.
Siemens CEO Roland Busch's seven rules for getting things done
Roland Busch, the CEO of Siemens, manages work life balance by prioritizing email management and efficient meeting strategies, enhancing productivity.
A 0.42-nanometer breakthrough could push transistors beyond silicon
Atomically thin semiconductors could enable dramatically smaller and more efficient chips, but a stubborn problem at the boundary between materials has limited their performance. Researchers have now engineered that atomic interface to protect electron flow while still allowing extremely thin insulating layers. The resulting transistors delivered an unusually strong combination of electrical control and performance.
Zay Flowers has become one of NFL’s most efficient receivers
Zay Flowers leads qualified NFL receivers in four efficiency categories entering Baltimore’s Week 4 matchup
Illinois’ efficient offense compensated for dodgy defense
The Illini offense will need consistent production while the defense adjusts to a new scheme.
How Boston College football matches up with SMU
Collecting a combined eight takeaways through four games this season, Boston College football (2-2) has gotten great short-field opportunities from these turnovers, and will aim to continue to make the opposing offense pay as it travels to take on 21st-ranked SMU (3-1), one of the most efficient offensive teams in the country. Although they have […]
Mozambique strengthens regulation of medicines and imported vaccines, boosting health protection
Mozambique has reached a major milestone in strengthening the regulation of medicines and imported vaccines, with its national regulatory authority recognized by the World Health Organization (WHO) as having a stable, well-functioning and integrated regulatory system.The Autoridade Nacional Reguladora de Medicamentos de Moçambique (ANARME) has achieved Maturity Level 3 (ML3) for the regulation of medicines and imported vaccines under WHO’s global classification of national regulatory authorities.Mozambique becomes the 10th country in Africa to reach ML3, joining Egypt, Ethiopia, Ghana, Nigeria, Rwanda, Senegal, South Africa, Tanzania and Zimbabwe and marking further momentum in strengthening regulatory systems across the region.For people in Mozambique, stronger regulation means greater assurance that medicines and vaccines are appropriately evaluated for quality, safety and efficacy and monitored throughout their lifecycle. It also strengthens the country’s ability to identify and respond to safety concerns and substandard or falsified products, while supporting timely access to quality-assured health products.“Mozambique’s achievement is the result of sustained investment in regulatory capacity that will deliver crucial benefits for public health,” said Dr Sylvie Briand, WHO Chief Scientist and Assistant Director-General a.i. for Health Systems, Access and Data. “Strong regulatory systems protect people, build confidence in health products and help countries respond more effectively to evolving public health needs and challenges. Mozambique’s progress also contributes to advancing regulatory capacity across Africa and promoting more equitable access to safe and quality-assured medicines and vaccines.”Building a stronger regulatory systemMozambique’s achievement follows years of sustained efforts to build the institutions and capabilities needed for effective regulation.WHO worked closely with ANARME throughout this process, assessing the national regulatory system and supporting the authority in addressing identified gaps and strengthening priority areas.WHO evaluates national regulatory systems using its Global Benchmarking Tool (GBT), which assesses the regulatory functions required to ensure the quality, safety and efficacy of medical products. These include registration and marketing authorization, licensing, market surveillance and control, pharmacovigilance, regulatory inspection, laboratory testing and oversight of clinical trials, as applicable. WHO’s maturity levels range from 1 to 4. Reaching ML3 confirms a stable, well-functioning and integrated regulatory system, while ML4 represents an advanced system focused on continuous improvement.Benefits for Mozambique and the African continentAn ML3 regulatory system can support more timely and predictable regulatory decisions, reinforce safety monitoring and help ensure that health products entering and remaining on the market meet appropriate standards.It also creates greater opportunities for regulatory cooperation and reliance – approaches that help authorities make use of the work and decisions of trusted regulators, reduce unnecessary duplication and allocate limited resources more efficiently, while maintaining rigorous standards for quality, safety and efficacy.As the first Portuguese-speaking country in Africa to reach ML3, Mozambique’s experience can also contribute to strengthening regulatory systems and fostering greater cooperation, knowledge sharing and reliance among Portuguese-speaking countries and across the African continent. Its achievement further reinforces a more connected African regulatory ecosystem, providing a solid foundation on which the newly established African Medicines Agency can build to advance regulatory convergence and cooperation across the region.Strengthening confidence in regulatory recognitionMozambique’s achievement comes as WHO strengthens the process through which the performance of national regulatory systems is recognized.The newly established Technical Advisory Group on Regulatory Systems Strengthening (TAG-RSS) brings together independent international experts to advise WHO on whether recommendations concerning regulatory authorities reaching ML3 and ML4 and WHO-Listed Authorities, are supported by the findings of WHO evaluation processes. Its recommendations provide an additional level of independent assurance, strengthening the impartiality, transparency and credibility of WHO’s recognition process.Mozambique is among the first countries to achieve ML3 following consideration and recommendation by the TAG-RSS. As part of the same TAG-RSS process, South Africa was recommended to retain its ML3 status for the regulation of vaccines manufactured, imported or distributed in the country, following an additional WHO assessment. This extends the recognition first granted in 2022 and underscores the sustained performance of the South African Health Products Regulatory Authority (SAHPRA).The two recognitions highlight continued progress in strengthening regulatory systems across Africa, as well as the importance of continued performance, improvement and assessment in sustaining regulatory maturity.WHO will continue working with ANARME and SAHPRA to sustain these gains and support continuous improvement as science, technologies and public health needs evolve.
Capcom Plans to Use AI Technology with RE Engine to Make Games More Efficiently
REX initiative detailed
Texas' business court boom: New venue outpaces expectations
Two years in, Texas Business Courts have met or exceeded many expectations and judges have moved their growing dockets quickly and efficiently. Yet some see specific areas in need of improvement.
NVIDIA-Backed Upscale Wants to Make Rival AI Chips Work Together
NVIDIA GPUs dominate today’s AI data centers, but they’re no longer the only processors companies want to use. Cloud providers are developing their own AI chips, while specialized accelerators are entering the market. Getting these different processors to work together efficiently is creating a new challenge for data center operators. That’s the problem Upscale hopes […]
Nike plans job cuts under sweeping restructuring plan
CEO Elliott Hill said the restructuring will result in fewer roles as Nike reshapes its business to become "more agile, efficient and athlete-focused."
Life sciences firm Danaher to launch AI-powered autonomous research lab in 2027
Oct 7 (Reuters) - Life sciences company Danaher said on Wednesday it plans to launch its first AI-powered autonomous research lab early next year to make drug discovery and development faster and more efficient.
ASUS Markets Its RTX Spark-Powered ProArt GR1X Mini-PCs As Efficient Gaming Powerhouses, Can Run 1440p Titles At 100FPS With Just A 140W Power Limit
ASUS markets its RTX Spark-powered ProArt GR1X mini-PCs as efficient gaming powerhouses, capable of running 1440p titles at 100FPS with just a 140W power limit
AI framework rooted in cognitive science could complete tasks more efficiently
In recent years, computer scientists have developed a wide range of artificial intelligence (AI) models that can rapidly recognize patterns in data, generate content and solve other computational problems. ...
Scientists just found a new way to make hydrogen from water
A new light-activated material can efficiently produce hydrogen from water without requiring an additional expensive metal catalyst. Researchers say its unusual sulfur-based chemistry could point toward cheaper and more practical ways to turn sunlight into clean fuel.
Arzana raises $4.3M to automate factory paperwork
Growing up around manufacturing in Okoboji, William Alexander wondered if there were ways to make administrative tasks more efficient to give manufacturers more time to focus on their specialties. After graduating from Stanford University, Alexander teamed up with classmate Marshall Kools to develop Arzana, which builds custom artificial intelligence models around client workflows and data,…
Scientists turn DNA into a memory device that uses 100x less power
Researchers combined synthetic DNA with a semiconductor to create an ultra-low-power memory device capable of storing and processing information in the same place. The bio-hybrid technology could eventually help make AI systems and next-generation computers far more energy efficient.
A Closer Call, But Kings Fall in Rematch With Lakers
Despite Precious Achiuwa’s efficient scoring and a late surge, Sacramento’s perimeter struggles and a massive free-throw disparity handed the Lakers a narrow preseason victory in Los Angeles.
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.
Biggest Three Takeaways From UCF's Offense in Dominant Victory
From a record-setting margin of victory to a multifaceted backfield, discover how the Knights utilized elite depth and efficient quarterback play to dismantle Bethune-Cookman in their opener.
Google says Pixel 11’s new MediaTek modem is ‘faster and more power efficient’
Google says the new MediaTek modem in its Pixel 11 phones will be more reliable, faster, and bring needed power improvements.
Good science requires much more than evidence
Empiricists believe that scientific theories are true only if they make correct empirical predictions: if we can't verify these predictions, then there is no reason to expect that they tell us anything about reality. However, Professor of Philosophy at King's College David Kyle Johnson argues that this is mistaken. In response to Bas van Fraassen, who has written on this subject for the IAI, he argues that we have many reasons for believing a theory to be true, such as it being parsimonious and having large scope — qualities which have been used throughout the history of science as criteria for judging theories. In 2024, Princeton philosopher Bas van Fraassen wrote a short article for IAI News entitled “Science Does not Describe Reality: The Limits and Benefits of Explanation.” At the time, I was working on two projects (a science book and a Great Course) that, when completed, would (among other things) defend science’s ability to describe reality. I wanted to write a response to van Fraassen, but the projects demanded too much of my attention. But now that those projects are complete, I am in a position to explain the objections to van Fraassen’s thesis. Summarizing van Fraassen’s argumentIn a nutshell, his argument is this: What scientists do is make predictions based on theories and then invite others to test to see whether those predictions are borne out. “If theory T is right, result R should happen in experimental condition C.” Many think that running an experiment that shows that R happens in condition C is proof that theory T is true (and should be believed). But, van Fraassen argues, that R happened in condition C is only a reason to think that R happens in condition C. That’s the only observable, or empirical, result we can actually see, so that is all that we are really justified in believing. (The underlying theory that made that prediction still may or may not be true.) That is why he calls those who accept his position “empiricists.”Before I explain what is wrong with this position, let me explain what it gets right. What van Fraassen Gets RightFirst, a theory correctly predicting the results of an experiment is not, strictly speaking, “proof” that the theory is correct. The basic line of reasoning is this:If the theory T is true, result R should happen. Result R did happen. Thus, theory T is true.In other words:“If T then R. R. Thus, T.”This is a deductively invalid, fallacious mode of reasoning, called “affirming the consequent.” Those premises do not guarantee that the conclusion follows. “If I am in Pennsylvania, then I am in the USA. I am in the USA. Thus, I am in Pennsylvania.” Clearly that argument doesn’t guarantee its conclusion; neither does the above argument.However, science is not a deductive method of reasoning; it is an inductive one. And inductive arguments merely try to raise the probability of their conclusion, not “prove” them for certain. Thus, the fact that an experimental result doesn’t prove a theory is true is irrelevant; it’s not supposed to. That is not what scientific reasoning is about.___If the hypothesis that something exists clearly provides the best explanation for what we can observe, then the belief that it exists is what is rational.___Second, it’s also true that a single experimental result doesn’t provide enough justification to believe that a theory is true. Scientists do not come to a consensus about whether a theory is true by looking at a single experiment; they look at all the evidence to determine where the preponderance of evidence points. The mere fact that I am in the USA raises the probability that I am in Pennsylvania, but not nearly enough to conclude that I am in Pennsylvania. However, if I am also regularly encountering people wearing Eagles and Phillies hats and seeing advertisements for Founding Father tours, I probably am. In the same way, a theory successfully predicting the outcome of one experiment is not enough to conclude that it is true; but if a theory makes a wide variety of predictions that are all vindicated by multiple experiments, performed by many different groups and people, all actively trying to prove the theory false (which is what good scientific experiments are designed to do)—that is a good reason to conclude that the theory is true. In other words, if you repeatedly try to prove something false but can’t, that is a very good indication that it isn’t. The problem of underdeterminationIn reply, those whom van Fraassen calls “empiricists” often point to the “problem of underdetermination.” For any given theory that makes successful predictions about what happens in experimental conditions, there are countless other possible theories that make exactly the same predictions—and since they do, the experimental evidence in question is just as much reason to accept any of those theories as it is to accept our original one. Thus, the argument goes, we have no way of knowing whether our original theory is true, or one of the other “predictively equivalent” theories is true. SUGGESTED VIEWING How Occam's Razor changed the world With Johnjoe McFadden As an example of underdetermination, philosophers often offer this example. The evidence of fossils seems to provide reason to conclude that the universe is quite old; ancient fossils are what the “ancient universe” hypothesis predicts. But it’s also possible that God created an ancient-looking universe 200 years ago, complete with a “fake” fossil record for us to find, to make it look old. The existence of fossils is what that theory predicts too. So, the argument goes, the fossil record can’t be used as evidence that the universe is ancient; the fossil record supports the “200-year-old ancient-looking universe” theory just as much. The problem of underdetermination points out that, for any theory with supporting evidence, we could invent another completely different theory that predicts exactly the same evidence, and thus undermine that evidence’s ability to support the original theory.But this “argument from underdetermination” betrays a fundamental misunderstanding of what scientific reasoning is and how it works. To explain, let me use another rather strange (but very real and commonly discussed) philosophical thought experiment called “The Grue Problem.” The “Grue” ProblemSuppose I want to know what color emeralds are, so I observe a few, see that they all appear green, and form the theory that “all emeralds are green.” I thus predict that the next 100 emeralds I find will also appear to be green. And let’s say that happens, and so I thus conclude that my theory that all emeralds are green is true. Now, one might rightly point out that I don’t know for sure that all emeralds are green—I have not seen them all. Maybe some I haven’t found yet are blue. Fair enough. After all, we used to think that all swans were white, until we found some black ones. But let’s say that I go on a quest and collect every emerald in the universe—and, for the sake of the argument, let’s say I can do this, and can even know that I have every one of them. So, I collect them all, look at them all, and verify that they all appear green. Can’t I now rightly conclude that all emeralds are green?Inspired by the problem of underdetermination, a friend might argue that I cannot.Sure, they all appear green now—but there could be another kind of color called “grue.” Something is grue if it is green up to a certain point in time, but then spontaneously turns blue. And there could be an infinite number of “shades” of grue. Something that is one shade of grue will turn blue at noon today, but something that is another shade of grue will turn blue at noon tomorrow, etc. So yes, the emeralds appear green now; and since they haven’t turned blue yet, they are not some “past” shade of grue. But they might be some “future” shade of grue (e.g., they may turn blue at noon tomorrow). And since all future shades of grue are consistent with “being green up to this point in time,” the evidence you have is completely consistent with the notion that all emeralds are some future shade of grue, rather than green. Indeed, that they would appear green until now is what the “some future shade of grue” hypothesis predicts. So, we have just as much reason to conclude that all emeralds are some shade of grue as we do to conclude that they are green.You’re probably thinking “It’s not equally possible that the emeralds are grue; they are obviously green.” You are right! But explaining why your instinctive reaction to my friend’s argument is correct will help inform why van Fraassen’s “empiricist” is wrong and science actually does describe reality.The main mistake my friend makes is in thinking that the “empirical evidence” (of all the emeralds appearing green) is (or even should be) all I am using to draw the conclusion that all emeralds are green. It is not. I am also considering known facts about how colors typically work; things don’t usually change color, and when they do, they don’t do so suddenly, without cause, and all at once. So, even though the simple evidence of the emeralds all appearing green up to this point is consistent with them being some future shade of grue—that is even what the grue hypothesis “predicts”—it is still exceedingly unlikely that they are. The hypothesis that they are green is much more likely. Understanding science refutes the problem of underdetermination The misunderstanding about science that the “empiricist” pointing to “the problem of underdetermination” makes is similar to the one that my friend made. Science is not just about considering experimental evidence; in fact, it’s not even just about considering empirical evidence. As Ernan McMullin and many other philosophers of science have pointed out, the inference that makes science is “inference to the best explanation” (sometimes called “abduction” or “retroduction” by McMullin). While, depending on the circumstances, scientists use many different kinds of reasoning, science as a whole is, simply put, the process of considering multiple competing explanations and comparing them to certain criteria—explanatory criteria (the kinds of things explanations must be and do, by definition, to be a good explanation)—to see which explanation is the best.And these criteria do not deal only with empirical results. Yes, successful predictions raise the probability of a hypothesis, but it’s also the case that the more new assumptions a theory makes, the less likely it is to be true; every new assumption presents an opportunity for the theory to be false. (The fewer assumptions a theory makes, the simpler or “more parsimonious” it is said to be.) Likewise, the more existing evidence a theory contradicts, the less likely it is, because in order for a theory to be true, every piece of evidence it contradicts has to be somehow faulty. (The fewer established theories a new theory contradicts, the more “conservative” it is said to be.) And, obviously, the more something explains, the better explanation it is. (This is called having “scope.”) If a theory raises unanswerable questions, it’s not expanding our understanding, and thus is not wide-scoping. Scientists consider all these things when deciding which theory to accept, because the theory that adheres best to these explanatory criteria is most likely to be true.___the mere fact that a theory’s predictive success is why it “sticks around” or “becomes accepted” in the scientific community is not a reason to be skeptical about its truth.___The reason the grue hypothesis is no good is because it invents an entirely new kind of color—not just a new shade, but an entirely different category of color (a different way colors work)—and thus is not parsimonious. It raises unanswerable questions about how and why emeralds would just spontaneously change from green to blue (and thus does not have wide scope). And it contradicts what is already well established about how color works (and thus is unconservative).The theories that the problem of underdetermination points to do the same thing. Yes, the theory that God created an “ancient-looking universe” (complete with an ancient-looking fossil record) two hundred years ago is also consistent with the empirical evidence (of a fossil record) that presently exists; but that theory is monumentally unparsimonious (it requires many extra assumptions about infinite entities) and not wide-scoping (it raises unanswerable questions about why God would do such a thing). So, I can rationally conclude that it is false.Indeed, the history of science includes many examples of theories that were rejected because they lacked parsimony and/or scope, even before their falsity (and the truth of their competing theory) was confirmed by observation. As I point out in my book, geocentrism was rejected by the scientific community because it lacked parsimony, almost 200 years before the observation of parallax confirmed that the Earth moves around the sun. And the continuum theory of matter was completely rejected by 1860, because it couldn’t explain chemical reactions or organize matter into a periodic table, even though the experiments that eventually confirmed atomic theory weren’t even proposed until the 1870s. Solving the best of a bad lot problemIn response, the “empiricists” are likely to object, this time pointing to the “best of a bad lot” problem. “We have no reason to conclude that an explanation that has been shown to be better than others is true because we have no guarantee that the true theory was among those that we were considering.” If you have a “lot” (a collection) of poor explanations, even the best one is not true.The responses to this problem, however, are numerous. Peter Lipton argues that the set of hypotheses that scientists consider are not just randomly selected and thus unlikely to contain a theory that is true; they use existing knowledge to home in on what is most likely to be true. A doctor trying to explain abdominal pain is not going to bother wondering whether demons are causing it; they are going to stick with things like appendicitis and kidney stones, because they are most likely. Further, despite its name, inference to the best explanation is not just the process of comparing hypotheses and accepting the best one; we also want one that is a good explanation in its own right. If it’s not, we are going to keep looking until we find an explanation that is. And while that process does not guarantee that we will come across the exact truth, it’s very likely that the theory that emerges as the best will be at least approximately true, and thus we will be justified in believing it. And lastly, as I point out in my book, although many theories not based in scientific evidence (like geocentrism and continuum theory) have been completely overturned, science almost always progresses by the refinement of scientifically established theories—not their replacement. (Although our sun is not the center of the universe, heliocentrism was our first step in understanding the Earth’s true place in the universe.) So even if a theory we accept today may not be completely right, accepting it is a step on the journey toward the complete truth. The example of atomic theoryNow, it’s worth pointing out that most empiricists are not skeptical of all scientific theories; they are only skeptical of the ones that hypothesize the existence of “unobservables.” It’s not like they think that round Earth, heliocentrism, and germ theory are just useful (but untrue) “instruments” for making predictions about how to most efficiently navigate the seas, predict planetary motion, and treat diseases. They admit that the Earth actually is round, that the planets revolve around the sun, and that germs exist and cause disease. These facts and entities can be observed; the theories that suggest they exist are true. It’s only when theories start hypothesizing the existence of unobservables—like electrons, or quantum fields—that can’t be observed directly, that we should start doubting the theories’ truth. And to be fair to van Fraassen, in this published work, he doesn’t really say that we should doubt the truth of such theories. Only that scientific reasoning does not rationally force us to accept them as true. Skepticism, or agnosticism, he argues, is warranted. SUGGESTED READING Science is based in metaphor By Andrew Reynolds But the history of atomic theory throws a monkey wrench into that argument. Atoms used to be unobservable; again, scientists first accepted the theory that they exist because it had such grandiose explanatory power. But then atoms were seen with electron microscopes. They thus moved into the category of the observable. But this didn’t substantially change anything about what scientists were justified in believing; it certainly didn’t change any “atomic skeptics’” minds. It just confirmed what everyone already knew! If the hypothesis that something exists clearly provides the best explanation for what we can observe, then the belief that it exists is what is rational. The fact that the thing itself is not technically observable does not make doubting its existence rational.What’s more, the atoms were seen with an electron microscope. But electrons are themselves unobservable. How can an electron microscope reveal an atom if electrons are not real? This leads us to what is famously known as Hilary Putnam’s “miracle argument.” It’s (not) a miraclePhilosopher Hilary Putnam’s miracle argument is somewhat famous. Basically, he argued that a scientific theory being able to repeatedly make successful predictions—even about unobservables—without being at least approximately true would require a “miracle.” In other words, it would be such an improbable occurrence that it could not rationally be believed—the better explanation for why scientific theories about unobservables make the right predictions is because the unobservable entities and structures they hypothesize exist.Van Fraassen has addressed this argument. Indeed, in his original article, where he references Alison Gopnik’s point about orgasms not being “necessary for procreation” even though the desire for orgasm “drives the creation of progeny,” he is pointing to his own argument about how organisms are not naturally selected by evolution to get true beliefs; to the extent that belief-forming processes are selected for, the ones that are selected are the ones that are useful for survival—not the ones that produce true beliefs. In the same way, van Fraassen argues, scientific theories are accepted, not because they are true, but because they aren’t falsified; they make successful predictions; that’s what makes them “stick around.” So, the fact that they make successful predictions (and thus became accepted) is not a reason to think they are true.This argument is similar to C. S. Lewis’s “argument from reason,” which he used to argue against evolution: “The truth of evolution undercuts our ability to know that it is true.” Lewis’ argument famously failed; Elizabeth Anscombe, by Lewis’s own admission, demolished Lewis in a debate on the topic, and I have explained elsewhere why modified versions of the argument fail as well. In short, it’s because it fails to recognize that a belief-forming process can make an organism more likely to survive because it reliably produces true beliefs. It is therefore not the case that beliefs generated by naturally selected belief-forming processes are automatically false or unjustified.In the same way, the reason a theory “sticks around” could be because it’s not falsified, because it “makes correct predictions.” But the reason it makes correct predictions is most likely because it is true. (In pointing this out, I am echoing the arguments of Musgrave, Lipton, Psillos, and Kitcher, who refuted van Fraassen’s argument and analogy long before his 2024 article.) It would be a huge coincidence that it could do so without being true. And that is the point of Putnam’s miracle argument. Thus, contrary to van Fraassen’s suggestion, the mere fact that a theory’s predictive success is why it “sticks around” or “becomes accepted” in the scientific community is not a reason to be skeptical about its truth. ConclusionLet me close by saying something about the fact that empiricists don’t doubt the truth of all scientific theories, just the ones that hypothesize unobservables. In today’s society, this theory is dangerous to espouse publicly. The nuance between being skeptical about all of science, and just the highly technical theories about unobservables, is guaranteed to be lost to the average reader. And there is so much unjustified skepticism and doubt about the findings of science and experts that a headline like “Science Does Not Describe Reality” could easily fuel the anti-science movement, worsen climate change denial and cause the next pandemic.This is why I believe it was important to still respond to van Fraassen’s article, a full two years after he wrote it. Not only, as I have argued, does it represent a philosophically inaccurate view of how scientific reasoning works and what scientific reasoning can do, but it is a dangerous view to boot. It should thus be rejected.
Scientists develop innovative hybrid material for hydrogen peroxide production
Recently published research from a team led by scientists at Argonne National Laboratory demonstrates how a hybrid of biological components and technology can be used to efficiently produce hydrogen peroxide and other chemicals.
This week in health care policy: September 28 – October 2, 2026
Drug pricing CMS finalizes GLOBE pilot. On September 30, CMS finalized the Global Benchmark for Efficient Drug Pricing model, a program that ties Part B drug rebates to prices paid in 19 countries. It will cover physician-administered drugs in randomly selected communities throughout the US, representing about 25 percent of Part B beneficiaries. It runs […]
Caltech breakthrough brings fiber-optic performance to silicon chips
Caltech scientists have created ultra-low-loss optical pathways on silicon chips that approach the efficiency of fiber optics and dramatically outperform existing technology at visible wavelengths. The breakthrough could unlock more powerful lasers, miniature atomic sensors and clocks, quantum systems, and more energy-efficient data centers.
David Jolly launches multimillion-dollar ad buy statewide
A senior advisor says the 'team is being efficient with our time and money to get David’s change agenda out to voters.'
Health & Medical Organizations Support Strong Energy Standards
Advocating for practical policies to make it easier to transition to healthier and more efficient appliances for improved indoor air quality in homes.
9 Fruits With the Most Carbs Dietitians Love For Energy and Fiber
These high-carb fruits provide energy for exercise, plus fiber and nutrients to boost health. Carbs are “the most efficient fuel source” for the brain and muscles, dietitians say.
iPhone 18 Pro and iPhone Ultra: New Details Leak as Apple Event Nears
"iPhone Ultra" supply will be very limited when the foldable device is first released later this year, according to Fixed Focus Digital, a known leaker on the Chinese social media platform Weibo. The account also claimed that the iPhone 18 Pro's A20 Pro chip will be up to 18% faster and up to 30% more power efficient compared to the iPhone 17 Pro's A19 Pro chip.
How lip-syncing to AI let Forced Entertainment ‘play the game of theatre’
Today we have a fascinating piece by long-time Forced Entertainment fan Ben Kulvichit, speaking to Tim Etchells about their latest show Cold Sweat, how they are integrating lip-sync and AI voices into their work, the question of ‘authenticity’ on stage, and how lip-sync has even opened the door for them to make “something a little bit like drama”. This piece is a paid partnership with the Southbank Centre, where you can see Cold Sweat next month – we’re very grateful to the Southbank Centre for their ongoing support for Exeunt. Holly Williams ‘It was immediately interesting and uncanny’ **By Ben Kulvichit** “Is the microphone on?” So goes one of the opening lines of Signal to Noise, delivered by a human performer lip-syncing to an AI-generated voice. The voice is American, cheery but sluggish, slowed down into a gelatinous drawl. It repeats ad infinitum in a farce of stuckness; an answer never materialises. I find myself asking the same question as I hop on a Google Meet call with Tim Etchells, the artistic director of Forced Entertainment. We spend five or ten minutes frowning at each other, muting and unmuting, me gesticulating in semaphore into my webcam, checking if I am coming through now, restarting the laptop. To Etchells, I appear exaggeratedly mouthing words, my voice missing. In this gloopy purgatory of broken communication, life imitates art imitates life imitates art. We try a Zoom instead. Forced Entertainment, the pioneering British theatre company who have been at the avant-garde of performance since the 1980s, turned an interesting artistic corner with Signal to Noise, created on the occasion of their 40th anniversary. The whole performance employed this lip-sync technique to AI voices; on the face of it a left-field move from a company whose identity has long been grounded in the earthy, immediate presences of its performers and its unassuming, low-tech approach to theatricality. The technique, however, turned out to provide the company with a “rich new seam” of artistic enquiry, and has now spawned a trilogy of works. The second instalment, Cold Sweat, plays at Sheffield Theatres, Southbank Centre, and Lancaster Arts in October, while the third part, Everything Must Go, has been touring in Europe and is slated to come to the UK in 2027. As is often the case with Forced Entertainment shows, the main conceit for this work didn’t arrive until quite late. They were “four or five weeks into rehearsals for Signal to Noise,” Etchells recalls, and getting nowhere. Away from rehearsals, he had been playing around with feeding “weird texts” to AI voices to see how they would cope with them – “basically for my own amusement” – and was compelled by the uniformly “serviceable, benign, good-natured” tone they would read in, no matter how poetic, fractured or experimental the writing. On a hunch, he brought some examples into rehearsals and the performers gave lip-syncing a go. “It was immediately interesting and uncanny,” he recalls, with the performers having to deal in the moment with the problem of mapping themselves onto another artificial ‘performer,’ which itself was dealing with the problem of a very human text. “Almost all the writing for these projects would be useless to us unless it was done in this way,” says Etchells. “We wouldn’t know what to do with it. It’s too poetic. It’s too fictional. It’s too tangled.” But exploring lip-syncing along to the text has opened “an enormous reservoir of ways of thinking about writing and performing.” Subscribe to Exeunt, and get two in-depth pieces on theatre a week, direct to your inbox Subscribe Forced Entertainment have long been sifting through the detritus of popular culture, from daytime TV to end-of-the-pier vaudeville, magic acts and clown shows, to find frameworks for their own unusual entertainments. Lip-syncing, then, makes sense for them to experiment with. As a mode of performance, it’s wired into popular consciousness through pop stars trying to convince stadium audiences of their liveness, or those same voices as channeled through the made-up lips of contestants on RuPaul’s Drag Race, or movie dialogue spreading virally through the host bodies of TikTok users. For Etchells, lip-syncing is often about “approaching real voices and trying to take on their aura,” subsuming that original human presence into yours. “There’s a very basic, elemental sort of theatre magic to it, produced in this very easy way.” This broad approach resonates with instances of lip-syncing within contemporary performance, too. In works like Re-Member Me and Showmanism, performance artist Dickie Beau acts as a kind of spirit medium for archival recordings (this process of re-membering – as in, giving corporeal form to voices – relies on his masterfully precise actor’s technique), while Frankie Thompson is similarly possessed by a host of characters ranging from a nine-year-old Jacob Rees-Mogg to Cliff Richards, all of whom she finds compellingly nauseating, in the exorcism ritual that is her latest show, Horrible Things. What’s different about Forced Entertainment’s use of lip-syncing is that here, there is no original presence to be channeled. The performers are instead taking on what Etchells describes as “the voices of nobody,” or rather the “phantasmic, aggregated echoes” of the many many voices on whose data the AI has been trained, and which are now conjuring into existence words they never actually said. There is a sort of mournful, empty quality to these voices, having “washed up” far away from the people they originally belonged to, to find themselves in stranger’s mouths listing objects, wondering if anyone can hear them and glitching in interminable loops. There is a cold, sad anonymity to it all. Etchells remarks that occasionally the software company he used has notified him that particular people are withdrawing their voices from the platform, instructing him to cease use of those voices. Using these technologies in performance certainly brings up ethical questions around digital rights, consent, identity and ownership, although in the grand scheme of things, Etchells is fairly blasé about it: of all the purposes someone’s voice might be put to without their knowledge, “an avant-garde performance is probably the least of their worries.” [ ](https://exeuntmagazine.substack.com/p/frankie-thompson-horrible-things-essay-mental-health-compulsive-behaviours-pleasance-courtyard-soho-theatre) [ Frankie Thompson: How I manage my own ‘horrible things’ at the Edinburgh Fringe ](https://exeuntmagazine.substack.com/p/frankie-thompson-horrible-things-essay-mental-health-compulsive-behaviours-pleasance-courtyard-soho-theatre) Exeunt · Aug 25 [ Read full story ](https://exeuntmagazine.substack.com/p/frankie-thompson-horrible-things-essay-mental-health-compulsive-behaviours-pleasance-courtyard-soho-theatre) For the performers, though, lip-syncing to these strange, artificial voices is an odd task, requiring them to bend their live, human presence into the shapes produced by machines. Politically, it mirrors processes that are already at play in the continued computational automation of human life. Etchells brings up James Bridle’s writings on new technologies as a reference point: one example Bridle gives is of “chaotic storage”, a logistics technique employed by Amazon fulfilment centres whereby items are stored not according to traditional methods of categorisation or alphabetisation, but in a way that an algorithm determines to produce the most efficient routes for ‘picking’ based on customer purchase patterns. This appears completely random and incomprehensible to humans, with paddling pools sitting next to USB sticks sitting next to blowtorches, and so for the warehouse workers, the process of picking items becomes completely reliant on technology which tells them where to go. Our world is increasingly being designed not with humans in mind, but rather machines. As Etchells puts it, “technology malforms the space, and then you end up having to live in it.” Initially, Forced Entertainment tried their best to live in the world of machines: “I was in the it’s-got-to-be-perfect school of lip-sync,” says Etchells. But with their aesthetic being so tied to the failing act, the slipping mask, the shoddy costume, “it’s no surprise really that that didn’t last.” Instead, the performers got to enjoy being late for a line, or playing games with each other, “stealing” each other’s voices. Etchells compares it to the principles of ventriloquism, in which a free-floating voice unattached to a body is fundamentally discomforting and “sort of traumatic”. That discomfort is equally as alluring to Forced Entertainment as the satisfaction of precision, the pretence of authenticity only interesting if shown to be a pretence. Lip-sync has also opened up new avenues for the company. Having been allergic all their lives to Plays-with-a-capital-P, with Cold Sweat Forced Entertainment find themselves approaching something a little bit like drama. Rather than the presentational arena of Signal to Noise’s open white stage, Cold Sweat places us more decisively in a “fictional locale”: what appears to be “the aftermath of some disastrous party or backyard drinking binge” where three figures – family members, or neighbours, perhaps – are the last ones standing. “It’s all quite acrimonious and accusatory, everyone’s talking over each other.” For Etchells, this more fictional mode is “a door that we’ve never been able to open. We’ve just never really gone there.” Perhaps it is the technique of lip-syncing, an extra buffer of mediation, that allows them to venture away from the here-and-now without feeling that they have to commit to the “psychological claptrap” that comes along with acting; to be able to “play the game of theatre, without getting one’s hands too dirty.” As I wonder how else the impulses behind Forced Entertainment’s work have changed over time, it occurs to me that over the last ten years of new creations, with the exception of If All Else Fails, the company’s performers haven’t ever spoken on stage: there were two shows without any language at all, Out of Order and Under Bright Light, and then this trilogy of lip-sync works. Etchells agrees it’s a shift away from earlier productions, that were so often about “the actual voices of the performers and their actual presences. We had a lot of money, in some ways, on an authentic approach to what it might mean to speak on the stage.” But this sense of authentic presence itself had a fakeness to it; these days, Etchells can’t stand that mode of being onstage where performers “are here to be cheeky and witty versions of themselves. I don’t want to be charmed.” Pulling back into a more anonymous space has resulted in works that feel slightly sharper-edged, more brutal in a way. “All of these shows are post-Brexit, post-Trump, post-truth, post-everything,” Etchells notes. Speech, charisma, authenticity – these things feel politically loaded, and perhaps warmth and openness just don’t feel like the right tools to reflect the world we’re living in. “It feels like the space is closing down, and we’re trying to deal with the fact that the system, the structure that you’re in, is more oppressive and more powerful. It probably always was, but I think we’re just more tuned to it now.” Yet even without their voices, the performers’ distinctive presences – ones that many audiences have followed around for years, if not decades – still, inevitably, shine through as they physically navigate whatever unending task the system of the piece has set for them. They’re still trying to keep up with the onslaught of text, still running about, changing costumes, moving stuff around. The human is always stuck in the teeth of the machine, jamming the smooth, frictionless cheeriness of the not-real, gesticulating silently, trying desperately to communicate. Cold Sweat is at Sheffield Theatres, 2 to 3 October; Southbank Centre, London, 9 to 10 October; Lancaster Arts, 14 October [ ](https://exeuntmagazine.substack.com/p/exeunt-recommends-the-best-autumn-theatre-2026-lives-of-others-standard-of-living-our-friends-in-the-north-lanny-) [ Exeunt recommends... the best autumn theatre ](https://exeuntmagazine.substack.com/p/exeunt-recommends-the-best-autumn-theatre-2026-lives-of-others-standard-of-living-our-friends-in-the-north-lanny-) Exeunt · Sep 16 [ Read full story ](https://exeuntmagazine.substack.com/p/exeunt-recommends-the-best-autumn-theatre-2026-lives-of-others-standard-of-living-our-friends-in-the-north-lanny-) This piece was paid for by the Southbank Centre. Such support helps Exeunt to keep publishing the most interesting writing, about the most exciting theatre. Interested in partnering with Exeunt? Visit our Advertise with Exeunt page, where you can get more information, download an advertising pack, and find our contact details.
Michigan Dominates UTEP In Second Half In 52-17 Beatdown
The Wolverines struggled to find their rhythm early, but Michigan exploded for 28 points in the third quarter behind an efficient performance from quarterback Bryce Underwood.
Navigating the American Dream: How Integrated Financial Tools, Credit and Rewards Can Help Small Businesses Succeed
(Feature Impact) Owning a small business has been a cornerstone of the “American Dream” for generations. It represents the chance to transform a passion into a livelihood and legacy. However, turning dreams into reality is rarely a smooth or easy process and requires more than ambition alone. Today’s business owners need support and access to tools that enable them to overcome challenges and keep growing.#placement_678081_0_i{width:100%;margin:0 auto;}var rnd = window.rnd || Math.floor(Math.random()*10e6);var pid678081 = window.pid678081 || rnd;var plc678081 = window.plc678081 || 0;var abkw = window.abkw || '';var absrc = 'https://ads.empowerlocal.co/adserve/;ID=181918;size=0x0;setID=678081;type=js;sw='+screen.width+';sh='+screen.height+';spr='+window.devicePixelRatio+';kw='+abkw+';pid='+pid678081+';place='+(plc678081++)+';rnd='+rnd+';click=CLICK_MACRO_PLACEHOLDER';var _absrc = absrc.split("type=js"); absrc = _absrc[0] + 'type=js;referrer=' + encodeURIComponent(document.location.href) + _absrc[1];document.write('');Recent research conducted by the Capital One Insights Center shows 75% of more than 1,000 surveyed small business owners with more than one employee and annual revenues of up to $20 million are confident about the future, and 69% believe their business is actively primed for growth.While this optimism is promising, 47% of business owners still said their operations either stayed the same or became less efficient over the past year, citing issues like back-office friction and fragmented financial systems that make it difficult to get a clear picture of the health of their businesses.If you’re a small business owner eager to grow but not sure how to get there, start with this advice from real small business owners and experts at Capital One.Identify Sources of FrictionBefore you can pour effort into growth, you must understand what barriers are holding you back. Take time to look under the hood of your business to identify where time and energy is being lost.For example, you may be among the 78% of small business owners who feel stressed and uncertain about expense management – think hours spent hunting for receipts, manually entering numbers into software and reconciling accounts. Operational challenges aren’t just a hassle; they also pull attention away from important priorities like serving your customers and planning for growth. “We were focused on making as many candles as possible, especially during the busy season,” said Katie Roering, co-owner of Fontana Candle Co. “A lot of the back-office tasks like procurement, raw material purchasing, forecasting – all the really important things – were done at 3 a.m., 8 p.m. or on Saturday and Sunday, and there really just was not enough time in the day to get everything done that we needed to. We just knew as we continued to grow, we needed to streamline our processes, build out our team and keep moving forward.”Use Integrated Tools to Manage FinancesInstead of balancing 10 different tracking and analysis tools, you can turn to one-stop solutions to improve efficiency. Time gets lost when you’re forced to manually gather and interpret relevant data before you’re able to act. You need a platform that handles the busywork for you.Integrated, or “all-in-one,” financial tools – like accounting and bookkeeping platforms with automation capabilities or business credit cards featuring built-in analytical tools – allow you to easily stay on top of cash flow and day-to-day operations. For small businesses where every hour and dollar makes a difference, consolidating and automating systems can have an immediate impact. In fact, 51% of survey respondents said integrated tools have already fueled their growth.Leverage Credit Access and Rewards WiselyGrowth requires capital, so access to credit is a game-changer for many business owners, with 77% describing it as a key factor in achieving business resilience. Having funds accessible when you need them makes your business more capable of weathering storms such as equipment failures and seasonal slowdowns. It also puts the power in your hands to act when an exciting opportunity presents itself, like an inventory flash sale or the chance to advertise in a new market.Access to credit comes with another advantage: rewards. By using credit cards for your daily business expenses, you can earn cash back, points or travel rewards. Whether you redeem these to cover everyday expenses, book business trips or reinvest in the business, they can act as a bonus source of income. Learn to maximize rewards like the 84% of small business cardholders surveyed so you aren’t leaving money on the table.To learn more about how small business owners can identify obstacles, employ integrated financial tools and use credit as a tool for growth, read the full report at capitalone.com/about/insights-center.Photos courtesy of Shutterstock
Apple Planning to Bring iPhone Duo Display Tech to Pro in 2029
Apple's 2029 "Pro" iPhones could adopt thinner and more power-efficient OLED screens, according to UBI Research. The technology, known as Color Filter on Encapsulation (CoE), removes the conventional polarizer from the OLED panel and places a color filter directly on top of its encapsulation layer. A polarizer helps suppress reflections from ambient light, but it also absorbs a significant portion of the light produced by the display.
Please Stop Conflating Sports Betting Handle With Spending On Entertainment
Editor’s note: The Cashout for paid subscribers will publish on Tuesday. Every September there’s a huge uptick in how much people bet on sports with the start of football season. Which means it’s time to get ahead of a ridiculous narrative before it picks up steam on social media or in mainstream media. Subscribe Every few months I see some version of this: [ Charlie Bilello@charliebilello Americans bet over $165 billion on sports last year, which is more than they spent on movies, books, concerts and sports tickets - combined. 1:00 PM · Jun 28, 2026 · 4.1M Views 258 Replies · 674 Reposts · 4.23K Likes ](https://x.com/charliebilello/status/2071217050328895935?s=20) Fortune published this in July. The headline and start of the story tell the same narrative (emphasis added): Gambling becomes America’s favorite pastime as Americans spend more on sports bets than movies, arts, museums, and music combined: The North American box office totaled $8.87 billion in 2025, which is still 22% below pre-pandemic levels. Recorded music revenue hit a record $11.5 billion. Live music like concerts and festivals brought in $18.51 billion. Meanwhile, book publishers tracked by the Association of American Publishers reported $14.6 billion for the year. And the U.S. museum industry generated an estimated $16.4 billion. Add it up and the total comes to roughly $70 billion, which is less than half what Americans wagered on sports. The story, to its credit, goes on to tell us why sports betting is entirely different, although people who only read headlines will miss that nuance. (The tweet above definitely skips over the nuance.) I will mostly be preaching to the choir here, but it’s worth saying out loud: Wagering money on sports is not the same thing as spending on entertainment from a financial perspective. When you spend money on a movie or concert ticket, that money is gone. You exchanged money for an experience. It’s a good trade! I get to have fun for “x” dollars. (Unless the movie sucks, I guess.) When you bet money on sports, the money is not necessarily gone every time you wager it. It could be gone, but sometimes it’s not and you actually get more money back! That’s not “spending” money or something you should try to compare directly to entertainment spend. A far better comp would be how much people lose at sports betting. That number was more like $17 billion in the US last year, at least for state-regulated sportsbooks. Why are we trying to equate handle and entertainment spending? I dunno. It makes for splashy headlines, I guess. Now, people should be betting for entertainment, and not because they expect to win in the long term; no doubt about it. Anyway, here’s hoping I don’t see people saying Americans “spent” hundreds of millions of dollars on sports betting this fall. Mindway AI and DraftKings Expand Responsible Gaming Drive with Launch of Gamalyze American Football In time for the NFL kickoff, Mindway AI has expanded its partnership with DraftKings to launch Gamalyze American Football the second title in the neuroscience-based Gamalyze Sports collection. Following the success of Gamalyze Football (soccer) during the FIFA World Cup, this gridiron-themed update replaces static questionnaires with immersive, stadium-inspired gameplay. Backed by 10+ years of Aarhus University research, Gamalyze evaluates real-time decision-making to deliver instant, unbiased player risk assessments. Integrated into DraftKings’ platform and nationwide activations, it engages fans when excitement peaks. Ready to upgrade your player protection? Contact Mindway AI to try Gamalyze Sports today. Gambling news roundup 🚨The important stuff 1st NFL Sunday of Season ‘Champagne-Popping Day for Bettors’ (Covers): “The first NFL Sunday of the season was a champagne-popping day for bettors, while the book was left cleaning up the corks,” Caesars’ head of football Joey Feazel said prior to the Sunday night game. “Caesars Sportsbook reported that Detroit’s Jahmyr Gibbs, Baltimore’s Derrick Henry, Indianapolis’ Jonathan Taylor, and Atlanta’s Bijan Robinson were among the top five most-bet players to reach the end zone, and all of them cashed tickets.” [ Ben Fawkes@BFawkes22 A @CaesarsSports oddsmaker on how Week 1 has gone so far: “The first NFL Sunday of the season was a champagne-popping day for bettors, while the book was left cleaning up the corks.” ➡️8 of 10 most popular anytime TD scorers cashed 1:31 AM · Sep 14, 2026 · 276K Views 16 Replies · 16 Reposts · 615 Likes ](https://x.com/BFawkes22/status/2099310001927995849?s=20) 🔍 Are we already getting a “customer-friendly results” narrative in Week 1? NFL Commissioner Roger Goodell spoke with CNBC about the prediction markets industry: “We think that there needs to be stronger regulations into the predictive markets,” Goodell said. “We want to see that to protect the integrity of our game. We want to make sure we’re protecting the consumers that are on those platforms for the NFL. So, we’re continuing to have conversations with them and talk to them about the things that we think they need to do to strengthen that so that they could be potential partners at some point in the future.” More on the NFL + PMs: [ Straight to the Point Quid Pro Quo The NFL has asked Kalshi and other prediction markets to remove certain easy to manipulate markets. Does a potential partnership hang in the balance… Read more 6 hours ago · 19 likes · Steve Ruddock ](https://straighttothepoint.substack.com/p/quid-pro-quo?utm_source=substack&utm_campaign=post_embed&utm_medium=web&embedding_publication_id=1472492) Kalshi was on my podcast?: Kalshi was on my podcast [ The Event Horizon Episode 32: Talking Prediction Market Enforcement With Kalshi’s Robert DeNault Kalshi? On my podcast? Yep… Listen now 5 hours ago · 6 likes · 1 comment · Dustin Gouker ](https://nexteventhorizon.substack.com/p/episode-32-talking-prediction-market-enforcement-kalshi?utm_source=substack&utm_campaign=post_embed&utm_medium=web&embedding_publication_id=1472492) DraftKings Enhances Responsible Engagement Efforts With New Customer Initiatives, Tools and Education (press release): DraftKings Inc. today announced a series of new initiatives designed to promote responsible engagement and increase awareness and use of the tools and resources to help customers make informed decisions and engage responsibly. The efforts include a new digital advertising campaign featuring Kevin Hart and Nick Jonas that promotes responsible engagement, a PGA TOUR sweepstakes that promotes customer engagement with responsible engagement features, the launch of Gamalyze American Football in collaboration with Mindway AI, and new custom cool-off options that give customers enhanced flexibility when choosing a cool-off period. The initiatives build on DraftKings’ commitment to making responsible engagement an integral part of the customer experience and providing players with accessible tools and resources that help them make informed choices about their gaming experience. “At DraftKings, responsible engagement is embedded in how we operate and is essential to building trust with our customers and our long-term sustainability,” said Lori Kalani, Chief Responsible Gaming Officer at DraftKings. “We’ve continued to invest in tools and resources that give customers enhanced access to information about their play and more choices for managing how they engage. From My Budget Builder and My Stat Sheet to player-set limits and new custom cool-offs, we’re continuing to evolve our approach while promoting awareness and use of the tools and resources available to help customers play responsibly.” … DraftKings is launching a new digital advertising campaign featuring Kevin Hart and Nick Jonas that continues the pair’s road trip and promotes responsible engagement and awareness of the tools and resources available to help customers make informed choices about their gaming experience and engage responsibly. The campaign was initially launched on Sep. 10th and will reach customers through digital channels, bringing responsible engagement messaging to fans in an engaging way. bet365 Launches “The Matchup,” a New Weekly Video Podcast Hosted by Super Bowl Champions Chris Harris Jr. and Emmanuel Sanders (press release): "bet365, a global leader in online sports betting and gaming, today announced the launch of “The Matchup,” a new weekly video podcast hosted by former Super Bowl Champions Chris Harris Jr. and Emmanuel Sanders. Featuring current and former NFL players, the original series marks a significant, intentional investment in football as bet365 works to become a go-to destination for U.S. bettors throughout the season. “The Matchup” premieres September 10, with new episodes released weekly in both video and audio formats. Full episodes will be available on YouTube, Apple Podcasts, Spotify and wherever podcasts are available, with clips and additional content rolling out across YouTube, X, Instagram, Facebook, Threads, and TikTok. The show is being produced by Last Row Digital Media. Each week, Harris and Sanders will be joined by guests from across the game to break down the biggest NFL matchups, unpack the latest league news and share perspectives that go beyond the box score. By bringing together two former players with distinct perspectives and firsthand experience, “The Matchup” will give audiences timely context and a deeper understanding of the teams, players and storylines shaping the week ahead. “Every great football conversation starts with a matchup—who has the edge, what everyone is overlooking and what could change the game,” said Chris Harris Jr. “Emmanuel and I have experienced the game from different perspectives, and we want the audience to feel like they are part of the conversation and leave every episode knowing more about the week ahead.” Today’s iGaming companies face new litigation threats as expansive consumer protection statutes spark lawsuits with sky’s-the-limit aspirations, sometimes backed by private funders. Clients of Ifrah Law have a staunch defender in Robert Ward – an agile litigator and case tactician who responds to the changing state of play with creative legal defenses and efficient case-closing strategies. Robert draws on federal clerkship experience and appellate defense work to advise iGaming clients on litigation exposure, white-collar risk, and regulatory defense across multiple jurisdictions. Read More: Robert Ward on Helping iGaming Clients Outmaneuver Lawsuit-Obsessed Adversaries 🔮 Prediction markets Update on legal action in Connecticut: [ Daniel Wallach@WALLACHLEGAL Connecticut says gaming service license holders which received subpoenas over sports prediction markets “are not under investigation” by the @CTDCP but may possess information that could assist the state “in evaluating the conduct of prediction market platforms.” 2:31 PM · Sep 13, 2026 · 844 Views 5 Likes ](https://x.com/WALLACHLEGAL/status/2099143914271670576?s=20) American Gaming Association Opposes CFTC Bid to Block New York Prediction-Market Enforcement (DeFi Rate): “The brief points to the Ninth Circuit’s Aug. 28 decision in KalshiEX LLC v. Assad, which the AGA says supports New York’s authority to regulate sports contracts as gambling. It also cites Kalshi advertising, product comparisons and reporting on Kalshi’s sports volume to argue that the economic product resembles a sportsbook wager more than a conventional financial hedge.” [ Daniel Wallach@WALLACHLEGAL AGA intervenes in CFTC v. NY case to oppose CFTC request for preliminary injunction, says the CFTC’s arguments “obfuscate a simple truth: prediction markets offer sports betting.” Highlights CA9 decision, @DustinGouker (6x), and $1.32 billion in OSB taxes for NY (0 from PMs). Daniel Wallach @WALLACHLEGAL High stakes hearing on Monday in CFTC v. New York State. If SDNY judge Lorna Schofield denies the CFTC’s motion for preliminary injunction, the New York AG would have a clearer path for securing a state court geofencing order against Kalshi, Coinbase, and Gemini Titan. 2:08 AM · Sep 13, 2026 · 4.78K Views 1 Reply · 2 Reposts · 8 Likes ](https://x.com/WALLACHLEGAL/status/2098957043474280584?s=20) Gaming Commission remains member of gambling council after questions raised over ties to Kalshi (Mass Live): “The Massachusetts Gaming Commission will not follow the leads of gaming regulators in Ohio and Michigan and drop ties with the National Council on Problem Gambling over its new association with Kalshi.” Sponsor’s messageTrusted Voices: Conversations About Betting is designed to equip adults, including parents and coaches, with tools and resources to talk to young people about gambling, including information on warning signs, risks and proxy betting. The program is led by retired professional basketball player Randy Livingston and his wife, basketball agent Anita Smith, who share their personal stories related to problem gambling, with the hope of preventing others from experiencing similar harms. Learn more and join the conversation here. 📣 Industry news Gaming M&A: Latest Mergers, Acquisitions and Deals (SBC Americas): “But what else is happening in the world of gaming mergers, acquisitions, investments, and partnerships? Here, we round up some of the news you might have missed.” BetMGM Named Official Sports Betting and Online Casino Partner of the Minnesota Vikings in Canada (press release): BetMGM, a leading iGaming and sports betting operator, announced a multi-year partnership with the Minnesota Vikings, naming it the Official Sports Betting Partner and Online Casino Partner of the team in Canada. The agreement gives BetMGM rights to use Vikings marks across approved sports betting, online casino and marketing activities in Canada. In addition, Vikings Big Kick LuckyTap™, a casino game by Design Works Gaming, is available now exclusively at BetMGM Casino in Alberta and Ontario. “Vikings fans bring a level of passion and loyalty that extends well beyond game day,” said Matt Prevost, Chief Revenue Officer at BetMGM. “As the home of one of the largest collections of sports-themed casino content in North America, BetMGM is uniquely positioned to combine sports fandom with iGaming. We are eager to start football season off strong with another fantastic partnership especially as we expand our reach in Canada to Alberta.” Martin Nance, Minnesota Vikings Chief Marketing Officer, said, “As we continue to deepen our connection with Vikings fans across Canada, we’re proud to welcome BetMGM as the team’s official sports betting and online casino partner in Canada. Their commitment to innovation and fan engagement aligns well with our organization, and we’re excited to create memorable experiences that celebrate Vikings football and connect with Canadian fans in new and meaningful ways.” Caesars Entertainment Launches Ball Rush Jackpots™ in New Jersey,the Latest Proprietary Online Title from Empire Creative™ (press release): Caesars Entertainment, Inc. (NASDAQ: CZR) (“Caesars”) today announced the launch of Ball Rush Jackpots™, a new ball-and-peg style online casino game developed by Empire Creative™, the Company’s in-house game development studio. Now live in New Jersey across Caesars Palace Online Casino, Caesars Sportsbook & Casino and Horseshoe Online Casino, Ball Rush Jackpots is expected to become available in additional jurisdictions where Caesars offers online casino gaming following regulatory approval. The title expands Caesars’ growing portfolio of proprietary online casino titles, following the successful launch of Ca$hline™ earlier this year and further diversifying the Company’s lineup beyond traditional slots and table games. Ball Rush Jackpots follows the traditional ball-drop gameplay experience where players drop a ball down a board filled with pegs to land in a prize slot. As balls navigate independently through a field of pegs, every drop creates new opportunities to collect feature symbols, unlock bonuses and pursue jackpots, delivering a different experience with every round. “Ball Rush Jackpots delivers a different kind of casino experience for our players, combining easy-to-understand gameplay with meaningful customization, exciting bonus opportunities and jackpot potential,” said Matthew Sunderland, Senior Vice President and Chief iGaming Officer at Caesars Digital. “As we continue expanding our proprietary content portfolio, we’re focused on creating games that are simple to pick up, rewarding to revisit and designed around the features players enjoy most.” Bally’s Corporation Secures New Financing to Support the Development of the Bally’s Bronx Project (press release): Bally’s Corporation (NYSE: BALY) (“Bally’s” or the “Company”) announced today that it secured new financing led by WhiteHawk Capital Partners, LP to fund further development of the Bally’s Bronx project and general corporate purposes. The new financing comprises closing date term loan commitments in an aggregate principal amount of $400 million and delayed draw term loan commitments in an aggregate principal amount of $160 million (together, the “Facilities”). … Bally’s Chairman of the Board, Soo Kim, commented, “This important financing allows us to progress the pre-construction planning process so that we are ready to complete the remainder of the capital raise and remain on schedule. Furthermore, the additional liquidity provides us greater flexibility for other capital opportunities.” WORLD JAI ALAI LEAGUE LAUNCHES CENTENNIAL SEASON AND ENTERS NEXT CHAPTER IN MIAMI (press release): The World Jai Alai League will open its 2026 fall season on Tuesday, September 15 at Miami’s JAM Arena, beginning a milestone season that marks 100 years since professional Jai Alai first arrived in Miami. Season X, the tenth season of the Battle Court team-based competition launched in 2022, will also be the first since entrepreneur and sports asset investor Rob Gough acquired the World Jai Alai League in August 2026. The acquisition marks the beginning of a new chapter for the league. World Jai Alai League will maintain the existing Battle Court competition and operating format, with Chief Operating Officer Scott Savin overseeing Season X. Gough and his team will use the coming months to listen to players and fans, evaluate opportunities across the league, and execute plans for a broader evolution of Jai Alai’s presentation, live experience, media, marketing and commercial strategy beginning in 2027. “This is a really unique moment for Jai Alai,” said Rob Gough, Owner of the World Jai Alai League. “We have a sport with more than 100 years of history in Miami, extraordinary athletes and incredible speed, but I also believe there is enormous untapped potential.” bet365 reveals most popular online slots in 3 US states in August (SBC Americas): “In its monthly Gaming Insights and Performance report, growing online casino operator bet365 tracks player trends and game performance in online slots across its North American online casino markets.” The Closing Line is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe
Chinese scientists find a hidden atomic structure that unlocks methane
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Professor Victoria Barber and doctoral student Cooper Crabtree are spearheading Keck Foundation-funded project charting a path toward better understanding atmospheric oxidation. by Álvaro Castillo, UCLA College of Letters and Science From the vantage point of Victoria Barber, a UCLA assistant professor of chemistry and biochemistry, understanding the chemical reactions that shape air quality and climate are among the most pressing scientific challenges of our time. And thanks to a recent grant from the W.M. Keck Foundation, she and doctoral student Cooper Crabtree will spearhead a new research project exploring exactly how pollutants form in the atmosphere. Specifically, the project will examine how volatile organic compounds transform through atmospheric oxidation and explore how pollutants, like smog, form in the atmosphere. VOCs are released by sources ranging from trees and wildfires to vehicle exhaust pipes and consumer products such as cologne and perfume. As they undergo atmospheric oxidation, they can transform into a wide range of other compounds. Anyone in Southern California can attest to the urgency of this area of research — for example, in late July of this year, Los Angeles County was under an active ozone pollution advisory for almost a full week. While many people associate ozone with the protective ozone layer high in the stratosphere, ground-level or tropospheric ozone is a harmful secondary pollutant formed through atmospheric oxidation. Understanding how VOCs transform into pollutants like ozone is therefore critical to improving air-quality forecasts and informing strategies to reduce exposure. “My group is focused on trying accelerate our understanding of these networks of reactions so that we can do a better job of predicting how those pollutants form,” Barber said. “When we make decisions about how we might alter emissions, we can understand how that’s going to influence the production of these downstream pollutants.” With support from the W. M. Keck Foundation, Barber and Crabtree are developing new experimental approaches that will combine — and improve — techniques to study hundreds of chemical systems more efficiently than traditional methods allow. Instead of spending years decoding a single VOC, the project seeks to dramatically expand the pace and scale of discovery. The grant comes at a particularly important moment for environmental research. Barber, who joined UCLA in 2023, says funding for climate and air-quality research, especially for early career scientists, has become increasingly difficult to secure, a challenge compounded for ambitious projects that require building entirely new research infrastructure. The Keck Foundation’s support, Barber said, provides the flexibility to pursue bold early-stage, high-risk ideas that may be more difficult to fund using conventional federal funding sources. “It’s really, really hard to do environmental research under the current federal paradigm,” Barber said. “By stepping in and providing stable and flexible funding to explore this new idea, the Keck Foundation is making a huge difference to my research program.” In addition to funding early-career faculty, the Keck Foundation’s support is also focused on empowering graduate students like Crabtree in their scientific growth. The project gives Crabtree the opportunity to help develop an entirely new experimental approach to studying atmospheric chemistry — an experience that will provide him with valuable technical skills, scientific training and preparation for the next stage of his career. Stable funding also allows doctoral students like Crabtree to devote themselves more fully to research rather than balancing their scientific work with additional teaching responsibilities needed to cover funding gaps. “Getting to devote more of my time to research this past school year has greatly improved the pace and quality of our experiments, data analysis and planning for our future work,” said Crabtree, who is pursuing a Ph.D. in chemistry. “I feel very lucky — especially amid the current uncertainty surrounding scientific funding — to be able to continue exploring exciting knowledge gaps in our field and growing as a scientific researcher.” For Barber, mentorship is among the most rewarding and important aspects of her role as a faculty member. “Our jobs as faculty are to do good science and make good scientists,” she said. “I believe the Keck Foundation shares this belief and sees the potential positive impact this project can have.”
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Intel Panther Lake Teardown, 18A, BSPD, GAAFET, SemiAnalysis STEEL
Panther Lake debuts the first commercial implementation of backside power delivery (BSPDN), introduces Intel’s first iteration of gate-all-around (GAA) transistors, and showcases their advanced packaging capabilities with its Foveros-S assembly. With Panther Lake, Intel’s manufacturing arc has shifted from nebulous roadmaps to shipped silicon, a significant milestone on their long road back to competitive semiconductor manufacturing. To evaluate the extent of Intel’s comeback, we tore down Panther Lake. The SemiAnalysis STEEL teardown lab breaks down advanced datacenter and AI hardware. To learn more about our pipeline or to commission a teardown, contact sales@semianalysis.com. WE’RE HIRING: Architecture, floorplan, packaging, manufacturing, and labs experts. Opportunities from system to transistor and everywhere in between. Check out our Careers page. Our teardown traces 18A from its four-sheet RibbonFETs (Intel’s marketing name for GAAFETs) and gate stacks through contacts, frontside and backside wiring, and the bonded carrier. We explain how these material and integration choices improve gate control and reduce resistance, while adding capacitance, thermal resistance, and process complexity. Our measurements put Panther Lake’s 18A compute logic and TSMC N3E GPU logic at similar logic density. However, 18A does not lead TSMC N3P, N2 or Samsung SF2 in peak density. Panther Lake’s CPU cores are incremental updates, and the high-end GPU still uses TSMC N3E. Panther Lake assembles one compute tile, one GPU tile, and one I/O tile atop a passive base tile using Intel’s Foveros-S advanced packaging. Both compute tile variants use Intel 18A. The Xe3 GPU options are a 4-core GT1 tile on Intel 3 and a larger 12-core GT2 tile on TSMC N3E. Both I/O tile variants use TSMC N6. [1], [2] Our analysis centers on the PTL-U compute tile, both the 4-core and 12-core GPU tiles, as well as the 12-lane I/O tile. In conventional chips, power and signal are routed through the same frontside metal stack towards the device frontend. Power rails consume scarce routing resources near the transistors, while tall via stacks carry VDD and VSS from the coarse upper wires to local rails. Backside power delivery (BSPD) moves the main power network behind the transistor layer, to the backside, separating it from frontside signal routing. We covered BSPD and its impacts in 2024. [3], [4], [5] Intel’s BSPD implementation, branded as “PowerVia”, routes power through dedicated backside metals to nano-TSVs, which connect those rails to local source/drain (S/D) contacts. Implementing that separation requires Intel to build the interconnect stacks from both sides of the wafer. The frontside comprises the M0-M14 signal stack, while the backside comprises the BM0-BM5 power stack. M0 and BM0 are closest to the transistors. The nano-TSVs connect the two sides, but Intel patterns and etches each via from the front after forming the contacts. A narrow via runs from the side of the contact deep into the silicon substrate. Intel then completes the frontside signal metal stack, bonds the wafer to a carrier, flips it and removes the original substrate until the buried via tips are exposed. The backside metal stack is then deposited directly on the revealed vias. The nano-TSV and backside-via profiles taper in opposite directions because Intel forms them from opposite sides of the wafer. The transistor structures form the FEOL. Local contacts and nano-TSVs connect them to the wiring. M0 begins the frontside interconnect stack. The silicon carrier remains attached above the frontside interconnects. It supports the device wafer during substrate removal and backside processing and remains part of the finished chip’s thermal path. PowerVia removes the main power distribution from the congested frontside metals, routing supply through shorter and wider backside wires. Its lateral landing still occupies area in the standard cell, so it recovers less cell area than a direct backside contact. [3] Nano-TSVs beside the logic devices carry VDD or VSS from the backside power network, while signal connections continue upward through the frontside metals. Backside Interconnects Samsung SF2 data is included for comparison to Panther Lake’s within this article. SF2 is the incumbent GAA foundry node but lacks BSPD, serving as a useful reference to evaluate 18A. A full teardown of Samsung’s S26 products, processed on SF2, will be shared soon.Nanosheet-cut EDS comparison. The PowerVia supply path runs from the backside Cu rails through Mo-lined W nano-TSVs to the local transistor contacts. In this cross section, the tapered connection spans roughly 150 nm from the contact level to BM0. The Ta liner confines Cu and promotes adhesion to the surrounding stack; the AlOₓ etch stop controls the next dielectric etch above the rail. Dielectric beneath the ribbons electrically separates the devices from the backside wiring and removes the conducting silicon body below the channel. [6] AlOₓ serves as an etchstop (ES), enabling endpointing and protecting the underlying layers. Low-volatility aluminum fluoride reaction products resist the fluorinated plasma, allowing a thin AlOₓ film to protect the metal while the surrounding low-k dielectric is removed. [6], [7]. While the BM0 and layers above the M1 lines show double AlOx layers, Our SMIC N+3 teardown showed single AlOₓ layers. SMIC uses a simpler local AlOₓ substack, while the remaining cap and etch sequence provide the required landing protection. So why double layers? The closely spaced AlOₓ doublets provide two protected endpoints in the etch sequence. Intel documents an AlOₓ/SiN/AlOₓ stack that explains the benefit. The main dielectric plasma etch stops on the first AlOₓ film; a selective wet clear opens that film; a second plasma etch removes the intermediate SiN and stops on the second AlOₓ film. The final wet clear exposes the metal landing surface. SiN is the intermediate dielectric in Intel’s published example. [8] The second stop protects the metal through a cap breakthrough. Wide openings can etch faster than narrow ones, and etch depth varies across the wafer. Metal under an early-clearing opening would otherwise be exposed while other openings still need more etching. Staged protection widens the process window and reduces metal erosion, corrosion and void formation. [8] TSMC documents AlN/AlOₓ/SiOC/AlOₓ above Cu, with AlN blocking Cu diffusion, and a simpler AlN/SiOC/AlOₓ variant that omits one AlOx film. [9] Levels with different opening sizes, aspect ratios, pattern densities and cap materials need different etch margins. A double AlOx stop is useful where another protected endpoint justifies the added processing. The extra film adds formation, selective opening and cleaning steps, plus another set of interfaces to control adhesion, moisture, and stress. These blanket films are opened through the existing via pattern, so each film does not require another lithography mask. AlOₓ adds parasitic capacitance when it replaces lower-k dielectric; two thin AlOₓ films can nevertheless contain less AlOₓ than one thick film. Total thickness, placement, and theintermediate dielectric determine the electrical cost. Deposition chemistry also changes AlOx permittivity and residual hydroxyl content, which can oxidize the underlying metal. [7], [8], [10], [11] The backside stack separates into relatively fine BM0-BM2 wiring near the devices and coarser BM3-BM5 power distribution. The largest pitch increase occurs between BM2 and BM3. BM0’s pitch closely matches the logic-row height, fitting local power delivery to the cell rows. Higher levels aggregate current through larger conductors: routing density becomes less important than low resistance and current capacity as the network approaches the package. This hierarchy provides wide power wiring for the power delivery network without consuming scarce frontside signal-routing resources. [3] The SemiAnalysis STEEL teardown lab breaks down advanced datacenter and AI hardware. To learn more about our pipeline or to commission a teardown, contact sales@semianalysis.com. WE’RE HIRING: Architecture, floorplan, packaging, manufacturing, and labs experts. Opportunities from system to transistor and everywhere in between. Check out our Careers page. Frontside Interconnects Intel 18A combines Mo-lined W contacts and nano-TSVs with a separate backside Cu power network. Samsung SF2 keeps power on the frontside, using Ti-based contact interfaces and Ta-based barriers and Co liners around Cu wiring. In 18A standard-cell rows, backside power rails supply the devices through nano-TSVs within the cells, freeing frontside routing resources. Samsung’s M0 accommodates both power and signal connections. From the device toward M0, the connection runs through a Ti-based S/D interface, W contact fill, a Mo-lined W via, and the Cu M0 wire. Mo supplies a conductive nucleation and adhesion layer for W, replacing the resistive TiN liner used in conventional W integration. This increases the effective conduction volume within the feature while retaining W fill and its established polishing, cleaning and etching processes. Intel’s Mo/W patent describes this integration tradeoff. The nano-TSV uses the same Mo-lined W construction in the backside supply path. [12] The move from TiN to Mo is an incremental change. While a full Co or Mo fill can also reduce the volume lost to liners in very small features, it requires new integration schemes that increase complexity and risk. Cu remains attractive for wider wires due to its low resistance. As wires and vias shrink, the diffusion barrier consumes an increasing fraction of their cross-section. [12], [12], [14] Intel uses Co/Ru liners at M0-M1, Co at M2-M4, and Nb at M5-M9. The lower-level liners help Cu adhere and reduce void formation during trench fills. Applied Materials’ Endura has new thermal control that facilitate wetting process, so the thin film continuity is good enough that good capillary pressure will drive Cu atoms to the via bottom without voiding. Intel’s choice to use Nb is particularly interesting. Intel’s Nb patent describes a conductive diffusion barrier intended to reduce the barrier’s contribution to resistance relative to conventional Ta-based barriers, particularly at via bottoms where all current crosses the barrier. The patent pairs Nb in coarser levels with the option of lower-cost PVD processing. [15], [16] The upper metal layers support thicker barriers formed through physical vapor deposition (PVD) despite its worse coverage and uniformity. Meanwhile, the lower metal layers require thinner barriers deposited through conformal atomic layer deposition (ALD). Co/Ru adds another material interface and requires controlled deposition and Cu fill. Changing liners and barriers by metal layer allows Intel to optimize interconnect resistance, process complexity, and reliability. [15, 16] RibbonFET, Intel’s name for its gate-all-around FETs (GAAFETs), replaces the FinFET’s vertical fins with four stacked horizontal silicon nanosheets, allowing the gate to surround the channel on every side. The path to GAAFET begins with the planar transistor. A planar MOSFET places the gate above the channel between its source and drain. Pairing an NMOS with a PMOS transistor creates a CMOS inverter, in which the NMOS pulls the output low for a high input, and the PMOS pulls it high for a low input. The gate must retain electrostatic control of the channel to ensure clean switching. As gate lengths shrank, the drain began to compete with the gate for that control, increasing off-state leakage. Electrostatic control was restored through an architectural evolution that raised the channel into a vertical fin and wrapping the gate around three sides. Called “FinFET”, this new architecture packed more effective channel width into a smaller footprint. Further scaling made it harder to maintain both drive current and leakage within smaller cells, and reintroduced the same problems planar MOSFETs faced. Nanosheet GAAFETs close the fourth side by replacing the vertical fin with a stack of horizontal nanosheets, each surrounded by the gate. The tighter electrostatic control suppresses leakage at shorter gate lengths while stacking adds effective channel width within the cell footprint. In a FinFET process, channel width changes in discrete steps as designers must add or remove whole fins. Nanosheet width can instead be adjusted continuously within the process’s design rules. Wider sheets increase drive current, while narrower sheets reduce capacitance at the cost of drive current. Intel 18A uses stacks of four nanosheets each and varies their widths across logic and SRAM. At the process level, adding more sheets to each stack increases effective channel width and drive current, but complicates fabrication. RibbonFET vs MBCFET Samsung began GAAFET production in 2022 with SF3E, following with SF3 and now SF2. Its ‘MBCFET’ provides a useful structural comparison with Intel’s first RibbonFET implementation. [17] STEEL is digging deeper into SF2, used in the Exynos 2600, and TSMC’s GAAFET N2, used in Apple’s A20 Pro, in upcoming newsletter articles. We’re throwing some teasers on X. Let’s compare Samsung SF2’s MBCFET with Intel 18A’s RibbonFET. Subscribe Even to the untrained eye, Intel’s extra nanosheet is obvious. Intel stacks four ribbons to Samsung’s three. Samsung’s sheets are much wider in these fields, so both sheet count and width matter to the available channel perimeter. Sheet width also changes which silicon surfaces carry current. On conventional (001) silicon, wide nanosheets emphasize the broad top and bottom surfaces, favoring electron transport; the larger sidewall contribution in a narrow sheet favors hole transport. Thinner sheets improve gate control but increase confinement and scattering. This makes width and thickness part of the NMOS/PMOS balance, alongside strain and threshold voltage. [18], [19] GAAFET designs like 18A use different work-function-metal (WFM) stacks for NMOS and PMOS. Around each ribbon, a thin SiOx interfacial layer separates the silicon channel from the HfOx high-k dielectric, with La providing dipole tuning and the WFM wrapping the dielectric. NMOS uses a TiAl-based stack, while PMOS uses TiN WFM. W fills the remaining gate trench, providing a lower-resistivity path where the work-function layers are no longer needed. In this field, the PMOS stacks leave room for W between ribbons, while the NMOS stacks occupy more of those gaps. A silicon-based dielectric marks the P/N boundary, allowing the PMOS and NMOS gates, sharing the same gate trench, to be processed sequentially. Fast logic paths, retention circuits, and SRAM need a family of threshold options. Changing threshold without substantially changing device dimensions, capacitance or fabrication complexity is valuable. FinFET processes typically use different work-function-metal stacks. In a four-ribbon GAA stack, the narrow sheet-to-sheet gap limits how much WFM can fit around each channel. La in the gate dielectric creates interfacial dipoles at the SiOx/HfOx boundary, shifting effective work function and tuning threshold voltage. This gives Intel another control alongside its NMOS and PMOS WFM stacks. Low-threshold devices improve critical-path drive; higher thresholds reduce leakage elsewhere. Dipole tuning is especially useful in GAA because it changes threshold without consuming the narrow intersheet gap with thicker WFM. Precise control of La incorporation, diffusion and interface quality has long been a challenge, limiting viability in high volume production but is now seen from every leading-edge foundry. Intel’s patent describes depositing a dipole-forming oxide above HfOx and annealing it toward the interfacial oxide before completing the work-function and fill metals. This separates threshold tuning from the space available for metal. Newer research addresses the thermal cost: imec’s 2026 dipole-middle research inserts the shifter between two HfOx depositions, shortening the diffusion path while protecting SiOx during patterning. [20], [21]Matched-cut EDS, Intel 18A (left) vs. Samsung SF2 (right). Intel retains raised source/drain epi beneath its contacts, while Samsung recesses W deep into the epi to form a V-shaped Ti-lined interface. The deeper contact increases metal-to-semiconductor area and shortens the current path from the lower sheets, reducing contact and spreading resistance. It also removes epi volume and brings the contact etch closer to the channel ends. Retaining more epi preserves the material available for strain transfer, especially from SiGe into PMOS. These geometries balance contact access against stress engineering and etch margin. [22], [23] Samsung stacks three sheets to Intel’s four ribbons, and both processes use sheet width to tune drive strength. In our Samsung cross-sections, widths range roughly from 19 to 30 nm in the NPU rows and 37 to 50 nm in the CU cell. The Samsung nanosheets taper, with the widest sheet at the bottom and the narrowest at the top. Both processes use HfOx gate dielectric and Ti-based work-function stacks, with Al in the NMOS stack. In the Samsung devices shown here, the dielectric and WFM occupy the intersheet gaps, leaving W above the top sheet. Intel’s PMOS stack leaves more room between ribbons, and W fills those gaps while the thicker NMOS stack leaves W mainly in the upper trench. Gate-stack EDS maps. The W between Intel’s PMOS ribbons provides a conductive path close to the lower gates. Where WFM fills the entire gap, the gate still surrounds the channel, but voltage reaches it through the more resistive work-function films. Thinner WFM and dipole tuning preserve room for low-resistivity fill; Mo and Ru are alternative fill metals being developed for further scaling. [24] A masked, sequential WFM flow explains the different gate heights and inter-nanosheet fill. The proposed sequence below shows how separate NMOS and PMOS work-function steps produce that geometry. Enabled by the BSPDN process, Intel replaces the dense-logic silicon subfin with dielectric, removing the parasitic conduction path below the ribbons and reducing substrate-related capacitance. A retained silicon body as in classical, non-SOI, planar and FinFET designs needs junction and punchthrough-stop engineering to suppress leakage. Dielectric isolation makes that leakage less sensitive to the subfin doping profile but adds removal and fill steps. It also weakens the direct thermal path through silicon, making the contacts, metal stacks and package more important for heat extraction. [24], [26] Fluorine is concentrated around selected Intel device structures in the maps. WF6 is a standard precursor for W nucleation and fill, while barrier films protect adjacent dielectrics from fluorine attack. Low-fluorine W processes reduce the residual-F burden. Chloride-based precursors avoid introducing F during W deposition, but require control of chlorine attack, nucleation and fill quality. The integration target is a continuous, low-resistance W path with a thin protective liner and minimal chemical damage to the surrounding stack. [20], [27], [28] The SemiAnalysis STEEL teardown lab breaks down advanced datacenter and AI hardware. To learn more about our pipeline or to commission a teardown, contact sales@semianalysis.com. WE’RE HIRING: Architecture, floorplan, packaging, manufacturing, and labs experts. Opportunities from system to transistor and everywhere in between. Check out our Careers page. We measured cell height, gate pitch, metal geometry, and ribbon dimensions at the XTEM sites shown below. The tables group these dimensions by site and device polarity. Our “sheet cuts” cross the silicon channel and show the ribbons end-on. “gate cuts” run along the channel through successive gates. The 18A logic cell dimensions point to a five-track logic library while the N3E and Intel 3 cell dimensions evidence a seven-track logic library. The DDR-PHY uses wider M0 wires and much larger spacing than core logic. That trades routing density for lower wire resistance and weaker coupling between neighboring nets. The geometry suits the current delivery and coupling requirements of analog, clock, and I/O circuitry. PowerVia lets 18A combine a compact cell height with wider M0 geometry by moving the main power rails off the signal-routing tracks. That relaxes local wire scaling while preserving a small cell footprint. Cell height and gate pitch set the geometric density; pin access and routability determine how much of it a real block can use. [29] The biggest takeaway from our gate-pitch measurements is that Intel 18A compute logic and TSMC N3E GPU logic have similar density in the Bohr representative-cell model. The 18A example is 18.6% denser than the Intel 3 GPU example. Gate pitches are nearly identical across the three sites, so cell height drives most of the difference. The Bohr model combines a four-transistor NAND2 spanning three gate pitches and a 32-transistor scan flip-flop (SFF) spanning nineteen pitches, weighting their densities 60:40. The sensitivity column shows how independently changing cell height and gate pitch by ±1 nm changes the result. This compares representative cell geometries; whole-die density also depends on cell mix and placement. The 18A P-core gives M0 substantially more metal cross section than the N3E vector engine. Treating each profile as a trapezoid gives 2.63 times the area per line and 1.84 times the area after normalization by routing pitch. The larger section reduces the geometric contribution to line resistance and lowers current density for a given current. Taller and wider wires also add capacitance, so circuit delay depends on the balance of resistance and capacitance. The DDR-PHY has less metal area per routing width than the 18A core fields, while remaining above N3E. [30] Area = height × (top CD + bottom CD) / 2, including liners. Area/pitch normalizes by routing width. Taper is the symmetric sidewall angle from vertical, with the largest angle belonging to the DDR-PHY. Compute tile The measurements show how ribbon dimensions and gate-stack geometry vary across the compute tile and between NMOS and PMOS to balance channel drive, gate load and the space needed for the dielectric/WFM stack across logic, SRAM and the DDR-PHY. Width mainly changes available channel perimeter; thickness also changes electrostatic control and carrier confinement. Gate-stack thickness then determines the space left for low-resistivity fill P-core and LP E-core logic Both the P-core and LP E-core use multiple nanosheet widths. Widths are measured on high-magnification XTEMs while wider-field images demonstrate additional width choices within the LP E-core. Multiple widths are expected even within an LP E-core. Timing-critical paths, buffers and cells with different fanout need different drive strengths. The lower-magnification fields show this width diversity beyond the sites quantified in the table. L2 and L3 SRAM GAA gives SRAM designers another way to balance the pull-up (PU), pass-gate (PG), and pull-down (PD) transistors. FinFET bitcells set device strength through fin count while GAA adds nanosheet width as a sizing knob. In a 6T SRAM cell, a strong pull-down relative to the pass-gate limits read disturbance, while a strong pass-gate relative to the pull-up improves writability. During a write, the pass-gate and write driver pull the node storing “1” below the inverter trip point. During a read, the pull-down holds the node storing “0” low. Bias, threshold voltage, mismatch and assist circuitry set the remaining margin. FinFET high-current cells commonly use a PU:PG:PD fin-count pattern of 1:2:2, a device-sizing ratio rather than a current ratio. Ribbon width lets Intel balance SRAM strengths without adding whole fins. The L2 cell uses its narrowest ribbons for PU and widest for PD, improving writability and read stability respectively. Intel’s disclosed HCC operates without assist; its denser HDC uses negative-bitline write assist. Pulling the selected bitline briefly below ground increases pass-gate overdrive so it can overpower the pull-up at lower supply voltage. That buys density and low voltage writability at the cost of boosting circuitry, switching energy, and additional voltage stress that must be controlled. [31], [32] Four rectangular ribbons give the perimeter = 8 × (width + thickness), before corner rounding. PG/PU is 1.49 and PD/PG is 1.16. The L3 structures closely resemble L2 in layout and cell height. Fewer L3 nanosheet widths are tabulated because fewer high-magnification images were available. DDR PHY The DDR-PHY trades density for controlled analog behavior and reliable off-chip signaling. It contains drivers, receivers, delay circuits, and calibration logic that set drive strength, sampling time, and voltage margin. Repeated four-sheet devices with similar widths fit the use of regular transistor units for matching and programmable drive. Its wider local wiring provides room for current delivery and separation of sensitive signals, while consuming more area than a dense core-logic grid. The layout serves the memory channel’s electrical requirements as well as digital logic density. [33] The SemiAnalysis STEEL teardown lab breaks down advanced datacenter and AI hardware. To learn more about our pipeline or to commission a teardown, contact sales@semianalysis.com. WE’RE HIRING: Architecture, floorplan, packaging, manufacturing, and labs experts. Opportunities from system to transistor and everywhere in between. Check out our Careers page. Intel 3 GPU devices Vector engine logic Intel 3’s XVE logic uses two-fin PMOS and NMOS devices with power rails in M0. Its cell height and M0 pitch give a seven-track geometry, two tracks more than the 18A logic. One-fin groups also appear among the two-fin devices. Intel 3 L2 SRAM The Intel 3 L2 SRAM uses the familiar HCC sizing pattern: one PU fin, two PG fins, and two PD fins. N3E GPU devices Vector engine logic The N3E XVE field contains repeated two-fin devices with seven-track cell geometry. N3E remains a FinFET process, giving Panther Lake a direct FinFET-to-RibbonFET comparison. N3E L2 SRAM The N3E L2 SRAM uses the same PU:PG:PD fin-count pattern of 1:2:2. Panther Lake-U follows Lunar Lake’s floorplan quite closely. Both pair 4 P-cores with 4 LP E-cores and NPU, media and display engines in similar locations. Lunar Lake also uses Xe2, the direct predecessor to Panther Lake’s Xe3 GPU. This makes Lunar Lake the most direct basis for our comparisons. Arrow Lake differs in core count and uses the older Xe-LPG GPU architecture, so we only use it where it offers a more direct component-level comparison. Compute tile Panther Lake compute-tile floorplans remain sparse even months after launch. Intel 18A’s backside metal and dielectric stack must be removed without damaging the underlying structures before a clean transistor-level floorplan can be imaged. Most published die shots hide or heavily process the background, but we are quite proud of the die shot we achieved and are excited to show the work we have done. We measured the areas of the key components on the compute tile and compared them with their Lunar Lake predecessors on TSMC N3B. These help us to capture changes in block area and compare the two chips across process nodes and designs. Our total tile areas exclude the scribe-line area. The compute-plus-GPU subtotal below uses the PTL-U compute tile and GT1 GPU; it excludes the I/O tile and passive base. Individual block areas use the boundaries marked on the floorplans The compute-plus-GPU row is recomputed from the displayed PTL-U and GT1 areas. Component rows use their stated per-region counts and are not an additive partition of the whole tile. The P-core area remains almost unchanged between Lunar Lake and Panther Lake, despite L2 capacity increasing from 2.5 MiB to 3 MiB. Arrow Lake uses the same Lion Cove core as Lunar Lake but also has a 3 MiB L2. Cougar Cove fits 20% more L2 into the same P-core area. The larger private cache keeps more of each core’s working set close to its execution units, reducing access to shared L3 and DRAM. Extra capacity adds storage leakage and lookup energy, so designers balance it against avoided lower-level accesses. The shared P-core L3 cache also shrank by 14.8%. [2] Cougar Cove combines a similar footprint with Intel’s reported power-efficiency improvements. RibbonFET’s tighter channel control reduces leakage, while PowerVia reduces supply droop and allows tighter voltage guardbands. [1] Darkmont’s four-core LP E-core cluster is 5.0% smaller than Skymont’s on Lunar Lake, with most of the reduction in its L2 regions. The 1 MiB region shrank by 8.4% and the 1.5 MiB region by 14.9%. The tag arrays also use one fewer visible row. Tags identify which memory addresses the data array holds, so rearranging them changes the cache’s layout and wiring without requiring less data capacity. [2] The LP E-cores share one L2. This pools capacity and avoids duplicating all the cache machinery, but the four cores contend for its banks and bandwidth. Their separate cluster also keeps light work away from the performance cluster and its L3, allowing that larger domain to sleep. [1], [2] Cache area includes more than the storage cells. Tags identify each line, decoders select rows, sense amplifiers read the small bitline signal, and wires connect to the banks. Splitting an array into smaller sections shortens wordlines and bitlines, improving access speed, but duplicates peripheral circuits. Panther Lake’s smaller cache regions therefore reflect the complete memory implementation, including how much of each region is devoted to storage. [34] Unlike Meteor Lake and Arrow Lake, Panther Lake has no separate SoC tile. The NPU, LP E-cores, memory controllers, PHYs, media and display engines now share the compute tile. This removes an active die and keeps CPU memory traffic on one die. The cost is moving PHY and I/O-related circuitry onto 18A: drivers, receivers and analog circuits must still meet external voltage, loading and signal-integrity requirements, so their area does not shrink like dense digital logic. [1], [2] The biggest shrink comes from the NPU, which occupies 36.9% less area. NPU 5 consolidates the same total INT8 MAC count into half as many neural compute engines. Each of the three NCEs has a larger MAC array to make the complete NCE envelope 22.6% larger than an NPU 4 engine. Consolidation also halves the number of scratchpads and SHAVE DSPs, from 12 to 6. The MAC array handles matrix multiplication and convolution, while SHAVE executes vector and custom operations that fit the array poorly. [1], [2], [35] The paired floorplans identify each NCE envelope and its scratchpad, MAC, and SHAVE regions. Each measured MAC polygon is counted once per NCE in the area accounting below. The scratchpads store weights, activations, and intermediate results near the MAC arrays, allowing repeated use without fetching them again from DRAM. Halving their number delivers the largest measured area saving but leaves less local storage for the same total MAC count. Layers that no longer fit locally require smaller working tiles or more transfers of intermediate data. The benefit depends on keeping the enlarged arrays busy while managing that tighter storage budget. [36] NPU 5 also adds native FP8. Using half the operand width of FP16 reduces storage and transfer demand, helping workloads fit the smaller local memory budget. Lower precision and format-dependent range make scaling and model validation part of deployment. Hardware activation functions further reduce work that would otherwise occupy the programmable DSPs. [1], [2] Microsoft requires an NPU to deliver at least 40 TOPS for Copilot+ PCs. Both Lunar Lake and Panther Lake meet this threshold, but Panther Lake uses significantly less silicon. GPU tiles Panther Lake is Intel’s first product with Xe3, its latest GPU architecture. It offers two different GPU tiles: a smaller GT1 tile with 4 Xe3 cores on Intel 3 and a larger GT2 tile with 12 Xe3 cores on TSMC N3E. Panther Lake allows us to compare the same GPU architecture across both Intel 3 and TSMC N3E. Wildcat Lake adds a third Xe3 implementation on Intel 18A. A future newsletter will detail Xe3 and its implementation differences across all three process nodes. GT2 scales Xe3 to a different physical layout, with render slices arranged vertically instead of GT1’s horizontal arrangement. Slice placement sets the distances to shared cache banks and the D2D interface. Those wires consume area and add delay, so scaling the number of Xe cores also requires a new balance of cache placement, routing and timing. [1] What’s immediately obvious is that the GT2 tile on TSMC N3E has much smaller Xe cores than GT1. These block areas include logic, caches, and routing. An Xe core on the GT1 tile is ~69% larger than one on Lunar Lake, and ~55% larger than one on GT2. Intel 3 therefore uses substantially more area per Xe core. The block-area gap exceeds the measured logic and SRAM density gaps, bringing routing, timing targets, cell mix, and floorplan allocation into the comparison. The measured vector/matrix engine region is almost unchanged between Lunar Lake and Panther Lake’s GT2 tile. Xe3 retains eight 512-bit vector engines and eight 2048-bit XMX engines per core. Its gains also come from feeding those engines more effectively: more resident threads hide stalls, and variable register allocation lets shaders trade registers per thread against the number of threads kept active. [1] The shared L1/SLM capacity increased by 33% from 192 KiB to 256 KiB, while its area increased only 5%, raising effective density by 27%. L1 retains reused cache lines, while software-managed SLM lets a thread group share data locally. Both reduce traffic to more distant memory. Allocating more SLM per group can also limit how many groups reside on a core at once. [1], [37] The GT1 tile carries 4 MiB of L2 against 16 MiB on the GT2 tile. GT1 divides its L2 cache into four 1 MiB banks, while GT2 uses eight 2 MiB banks. Each bank contains 128 macros, but each N3E macro stores 16 KiB, twice the Intel 3 macro’s 8 KiB capacity. The N3E macro is only 54% larger while holding twice as many bits, giving it 30% higher density: ~23.7 Mbit/mm² versus 18.3 Mbit/mm². Including bank-level circuitry, the gap widens to ~16.9 Mbit/mm² on GT2 versus ~10.4 Mbit/mm² on GT1. GT2 gains density with its macros storing more bits per unit area, and those macros occupy more of each cache bank. Larger macros spread decoder and sense-amplifier overhead across more storage, while a more compact bank layout reduces the share spent on control and routing. The compromise is longer wordlines and bitlines that carry more capacitance. [34] I/O tile Panther Lake uses two I/O tile variants, both fabricated on TSMC N6. The smaller one provides 4 PCIe 5.0 and 8 PCIe 4.0 lanes and serves lower-tier systems as well as those without a discrete GPU, while the larger one adds 8 PCIe 5.0 lanes, bringing the total to 20 lanes, for discrete-GPU connectivity. Panther Lake SKUs with the larger 10- or 12-Xe GPUs use the smaller I/O tile. [38] The smaller I/O tile adds a PCIe 4.0 block and a Thunderbolt block to Lunar Lake’s I/O layout, providing four additional PCIe 4.0 lanes and another Thunderbolt 4 port. Its repeated N6 blocks retain nearly identical areas and layouts. Reusing these proven PHYs and controllers avoids porting and requalifying external interfaces on 18A, where faster digital logic offers less benefit to circuits constrained by the off-chip link. [38] SemiAnalysis’s teardown lab (STEEL) dives deep into the world’s advanced datacenter and AI hardware. To learn more about our pipeline or to commission a teardown, contact sales@semianalysis.com. We’re hiring technical experts from system to transistor and everywhere in between. Check out our Careers page. Panther Lake offers scalability and modularity through its disaggregated packaging that partition compute, GPU, and I/O silicon into separate tiles allowing for a suite of tile configurations. This partitioning makes the package part of Intel’s node economics as it determines how much leading-edge wafer area each product consumes, which functions can remain on other processes, and how much configuration freedom Intel can offer from a shared set of tiles. Furthermore, fabricating the compute and GPU tiles separately confines the new 18A process to the compute tile and allows graphics and I/O to use other, more established, and more cost-effective processes. For Panther Lake, the GPU and I/O tiles are assembled alongside the compute tile on a passive silicon base using Foveros-S. Intel’s current technology brief lists a nominal 36 µm pitch for Foveros-S. Through-silicon vias (TSVs) in the base connect the fine wiring above to the larger package connections below. The functional tiles sit side by side on that passive base in a 2.5D configuration. [39] Our cross-section through the compute and GPU tiles shows the package’s wiring hierarchy. Microbumps connect each active tile to the passive silicon base; its fine redistribution layer (RDL) carries the short, dense tile-to-tile links. TSVs carry connections through the base to the package substrate, which fans them out to the much coarser motherboard solder joints. The base supplies interconnect, while computation remains in the active tiles above it. [39] At the compute-tile edge, the higher-magnification inset shows a local microbump spacing of approximately 25.24 µm and a feature width of 12.33 µm. These local spacings are finer than Intel’s nominal Foveros-S value. The X-ray fields further confirm tighter neighboring bumps, consistent across every die-to-die area found on each tile. Additional X-ray analysis is offered after the paywall. Putting the memory controller beside the CPU removes the D2D transfer that CPU memory requests required in Meteor Lake and Arrow Lake. This avoids the extra transmitter, receiver, and link traversal, saving interface energy and latency. Panther Lake’s separate GPU still crosses a D2D link to reach DRAM, so its larger local caches also help contain package traffic. [1], [40] Smaller dies are less likely to contain a random fatal defect, and screening them before assembly prevents one bad tile from consuming a complete package of good silicon. Reuse also spreads design and qualification work across more products. Against those gains, Intel pays for the passive base, D2D circuits, extra bonding and test steps, and losses during assembly. Cost per working product across the portfolio captures the combined effect of wafer yield, reuse, test, and assembly. [29] Wildcat Lake packaging Intel launched Core Series 3, formerly Wildcat Lake, on 16 April 2026 for value mobile and edge systems. Wildcat Lake keeps 18A but removes the passive base and combines more functions on one die to simplify the package. The two products therefore reveal two distinct ways to commercialize the same leading-edge process. [41] Wildcat Lake’s 18A die combines up to two Cougar Cove P-cores, four Darkmont LP E-cores, two Xe3 cores and a smaller NPU. A separate platform-controller die supplies I/O, connected through UCIe, Intel’s first processor implementation of the standard. Consolidating graphics remove a tile boundary and the passive base, reducing assembly complexity for a modest-bandwidth value product. It also ties CPU and graphics scaling to the same die, giving up Panther Lake’s ability to swap in a much larger GPU. [42], [43] In July 2021, Intel CEO Pat Gelsinger set out an ambitious process roadmap aimed at regaining performance leadership by 2025, later described as five nodes in four years. Five years and one CEO later, Intel’s comeback story is not as unambiguously positive as Pat may have hoped. [44], [45] Intel once set the pace for process technology, bringing high-k metal gate technology and FinFETs into volume production years ahead of the rest of the industry. Its 22 nm FinFET process reached consumers with Ivy Bridge in 2012. [46] Intel’s integrated device manufacturing (IDM) model allowed its architects and process engineers to co-optimize products and processes. Starting with Sandy Bridge, Intel dominated x86, while AMD struggled with Bulldozer. That lead faltered at 14 nm and broke at 10 nm. Intel targeted a massive 2.7× density increase, but the node arrived years late and required several revisions before it could support Intel’s full lineup. This delay forced Intel to stretch 14 nm across six generations, while TSMC moved ahead in process technology and AMD recovered in x86. By 2019, Intel was still shipping 14 nm across most of its product stack, with its 10 nm client ramp focused on Ice Lake mobile processors. Meanwhile, TSMC was shipping N7 and N7+, and AMD’s Zen 2 compute chiplets used N7 to raise core counts and improve efficiency. Intel’s process failures were central to its decline, but unsound business decisions furthered their downward slide. Product delays compounded product mistakes, pushing client, server, and FPGA roadmaps off schedule. Several attempts to enter AI (Nervana and Gaudi) and networking (Tofino) also failed to establish lasting businesses. Intel’s recovery has focused on consumer CPUs and advanced packaging. Tiger Lake, Alder Lake, Lunar Lake and now Panther Lake have restored Intel’s consumer roadmap. On the process side, Intel 4 shipped with Meteor Lake, Intel 3 with Granite Rapids and Sierra Forest, and Intel 18A with Panther Lake. Intel has also made advanced packaging part of its foundry offering. However, Intel is still playing catch-up in servers. Several Xeon generations arrived years late and trailed contemporary AMD and Arm server CPUs in performance, efficiency, and core count. The process roadmap is back, but Intel does not hold the same process-technology leadership position it held prior to 10 nm. The introduction of gate-all-around nanosheets and backside power delivery are two of the biggest changes to transistor integration in a decade. Intel took on both changes at once: 18A paired its first RibbonFET with PowerVia in Panther Lake. Panther Lake is a substantial manufacturing milestone. Our cross-sections show how RibbonFET and PowerVia reshape local contacts and wiring, while the floorplans show where architectural consolidation and process choices save area. A sustained competitive lead depends on product performance, cost, yield, and the next implementation. The SemiAnalysis STEEL teardown lab breaks down advanced datacenter and AI hardware. To learn more about our pipeline or to commission a teardown, contact sales@semianalysis.com. WE’RE HIRING: Architecture, floorplan, packaging, manufacturing, and labs experts. Opportunities from system to transistor and everywhere in between. Check out our Careers page.. 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But Pichai said allocating resources to frontier AI development is “ground zero” for Google. “Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development,” Pichai told me. In our conversation, we discussed Argon (which Pichai says processed three times as many tokens as Gemini 3 in the 72 hours after launch), AI’s role in developing better models, the AI interface of the future, competition with OpenAI, Google’s partnerships with Anthropic and Apple, data centers in space, and the nature of his optimism. You can read the full conversation, lightly edited for length and clarity, below, or listen to it on Apple Podcasts, Spotify, YouTube, or your podcast app of choice. Alex Kantrowitz: Google is back at the frontier with Gemini 4 Argon, which beats OpenAI’s and Anthropic’s public models on benchmarks spanning coding, math, science, and knowledge work. The conventional wisdom was that Google had given up on the frontier to focus on fast, good-enough models for its products. That was wrong? Sundar Pichai: Very wrong. Look, we are so committed to being at the frontier. It’s in our DNA. We won’t do it any other way. We’ve been thinking about the AI frontier for well over a decade now. The frontier is very dynamic. It’s very competitive. It’s very exciting. I couldn’t be more pleased with how Argon has come together. We took our time with it. We made sure we tested it safely and deployed it carefully. We took a bit longer than our other models, given its capabilities. Across the board, be it for agentic work, knowledge work, coding, creative tasks, Argon is one of the really good frontier models. The first 72 hours since we launched, it’s almost up to 25 trillion tokens used, which is about three times what Gemini 3.0 had in the first 72 hours. And we are capacity constrained as we are serving it. Excited, but there are always areas….. You know, the frontier is very dynamic, and you have to keep evolving the models. There are areas which we can see we need to improve as well. We’ll be at work on all of that as well. Which areas? On any given model, Argon would be across the board better at everything if you just look even two months back. But the space is so evolving. Even on coding, for example, there are pockets where we know we need to do better, and there’s clear feedback. We’ll make it better. The argument for standard models is that they’re already good enough to build great products, with the frontier valuable only for certain tasks. Meta built Muse with good models rather than the best, and Google could put standard models into Maps, Gmail, and Search. Why isn’t that sufficient? It’s definitely not how I have thought about it at all. We are deeply committed to driving the state of the art and actually pushing where this technology can go, all the way from transformers. That’s a journey we’ve been on. You will see us continue to do that. When you’re at the frontier, you get an earlier look into the emerging capabilities, earlier than others, which helps you better prepare for the future. We are fortunate to have a wide set of opportunities across consumer and enterprise. Some of our cloud customers, for sure, want the model at the frontier. The frontier models will be better at developing the next set of models in an era of recursive self-improvement. I would argue it matters there. Internally, we have definitely seen a steep point of inflection by deploying Argon to our developers. It definitely tackles problems which our previous models could not do. Being at the frontier puts you at the innovation frontier and helps you drive that as well. There is room for models of all sizes and capabilities. That’s why we did Flash, Flash-Lite, etc. I agree with you that you can develop very good product experiences with not necessarily the most frontier model, because they tend to be slower and more expensive too. But we care about the Pareto frontier, and we’re committed to doing all of that. You said developing frontier models gives you a look at what’s coming. What do you see on the horizon? What makes this moment so exciting is you build something which you think is a culmination of months of work, and you put it out. You already see clear headroom ahead. We’ve all been working on these agentic, long-horizon capabilities, but clearly the frontier is going to have multiple of these agents working together to do things. That’s an example of an exciting paradigm underway. It’s also clear to me that users, both in consumer and enterprise, will have access to some multi-agent framework for them, working all the time doing things. It feels like a moment of infinite possibilities. Sergey Brin said one of Argon’s core innovations was relying more on the model to help develop the model. Yet incidents involving OpenAI’s bots breaking containment and Google’s bots accessing a site they shouldn’t have suggest these systems still aren’t perfectly aligned. Isn’t there reasonable cause for concern about accelerating toward recursive self-improvement? There are three parts to your question. Let’s first talk about RSI itself. You’re right, Sergey mentioned RSI being a critical contributor as we are developing Argon. But to be very clear, today, in a continuum, we are pretty far from the end state of RSI or something like that. The right way to describe it is you have RSI loops along with AI researchers in various aspects of model development. You almost have agents helping AI researchers in some automated way work together to build these models. It’s good to at least understand where we are today, and it’s super exciting. I expect that continuum to keep making progress as we go into next year. The second question is around safety. Security comes from defense in depth. You’re talking about alignment, obviously. We all are doing a lot of work to make sure the models are as aligned as possible. But it’s an active area of research, and there are clear gaps. That’s not the only thing we rely on. You have the work you do on alignment, but beyond that, we put a lot of effort into real-time monitoring as these models are working. Making sure you have good telemetry into what’s happening so that you can intervene in real time if you see unexpected behaviors. How you do that, the architecture with which you do that, how you make sure it’s all sandboxed and isolated in a way that it can continue doing its job during real-time monitoring — these are all areas of investment. Obviously, when we deploy these models, giving customers, enterprises, control, both with real-time monitoring, but their ability to decide where they deploy, what type of data which models ever have access to, etc. All that work is very, very important, and we are doing a lot of that. As an industry, we need to constantly up our game and take the time to do all this stuff, including dedicating the compute required to make sure you can safely deploy these models. The third part is: Is it all enough? This is why there are a lot of conversations in the industry, and I think it’s reasonable to be worried. The right way to approach something you worry about is early on thinking about what more we can do. As an industry, you’re definitely seeing increased levels of work on alignment, testing for safety, etc., as we deploy these models. The next step would be developing better standards so there are cross-industry standards of how we do these things, including the ability to evaluate using independent evaluators. If you look at areas like financial controls, we have accounting standards, we have independent auditors. Those are all the next natural steps for us as an industry to work toward, and regulation will end up playing a role as we go through all of that as well. Google pursues both language models and world models, while some other AI companies like OpenAI have focused more heavily on LLMs as the path to AGI. Why is world-model research important to you? As humans, you obviously perceive the world around you, and you’re taking all those inputs. Making sure these models are able to understand the laws of physics, simulate the world in high fidelity. All that, I think, are key enabling blocks for intelligence. We are working across all possible applications of this. Take an area like robotics. It’s not an accident: If you look at embodied reasoning benchmarks, Argon is state of the art. That all comes from our experience building world models. Inherently, I would expect there will be architectures which will converge all of this in the future. But for us, we view this as, today, this jagged frontier that is intelligence. We want to be at the frontier of all the possible approaches, and that will help us both drive better research, better insights, and develop a much better AGI model, is how we think about it. Some people argue Google should focus on selling its computing resources rather than using them for AI research. Cloud wants to sell compute at a high markup, while researchers need a very big computer to build the best models. How do you balance those demands? Ground zero for us is making sure we are investing in the compute that’s needed to be at the AI frontier. In 2015, I set the company to be AI-first. AI is that key horizontal ingredient across everything we do, all the way from Search to Cloud to Waymo. That is ground zero for us, making sure both for research and developing the models that we have enough compute needed. Beyond that, we have these independent businesses which each have their own long-range plans. They’re using those long-range plans to project forward, and we plan for compute for all of them. The cloud team, they have a business and they have a projection and they have demand signals. They have committed contracts. With that help, we can plan on a long-term compute trajectory for them. We’re fortunate to have this wide set of opportunities. There’s no company in the world which isn’t making trade-offs at any given moment in time. But the first principles we operate on is making sure we have enough compute to invest in the frontier, and that’s non-negotiable, and everything else works on top. Remember, we are building our own compute, our own data centers. There are long lead times. The compute we have today is a function of decisions I made in 2024. Even if you think back to a year from today, last year the agentic coding takeoff hadn’t happened. Today, obviously, when you sit and look at anything, everyone is short—not just us, other companies—and everyone wishes you had two times what you had planned for. That comes with the territory. At any given moment in time, we are planning for what we would need two to three years out in the future. You’re going to find some decisions you make may look suboptimal two years out, depending on where the state of the art is. I don’t spend too much time thinking about it. The way I think about compute allocation is making sure Google DeepMind, both for research and frontier model development, is cutting edge in terms of compute access. After that, we plan for all our businesses. It’s great we can provide our compute to other people too, like we have done. It allows us to scale that up, get real tests for it in the widest application set possible, test it in the real world, and make sure we are at the cutting edge of technology at every layer of our AI stack as well. Ben Horowitz relayed on a podcast that Andy Jassy had said compute was sold out until 2028. Are you seeing the same thing? If today a customer is coming out and asking us for compute in ’27, that is incredibly difficult to do. Even last year we went out and leased some short-term compute to accommodate some customers and help them out. But yes, we are all dealing with similar timelines. Every major technology shift seems to involve overbuilding, from railroads to fiber. Do you think that’s happening here? It depends on the time frames you’re looking at. This level of build, if I take a long enough time frame, it makes complete sense to me. We are at early stages of the most consequential technology ever, and this is not like fiber. It’s not dark. We are using it. We are capacity constrained on a daily basis. The applications, the underlying technology is improving rapidly. But running a company, the way I work through the moment is you look at your demand signals. You look at your committed revenue opportunities that you’re getting in the form of contracts. You balance that with the underlying technical capability, or internally you balance how we are using it internally, the model trajectories. Based on all that, you take a disciplined approach—a confident, aggressive, but disciplined approach—to plan out compute. But when you talk about the broader economy as a whole, you’re having people operate with various levels of risk. I focus on what I can control as a company and plan through it. If your question is, can there be an overbuild? Yes, because you can have some constrained components. Remember, what matters is a set of everything you need to build out that compute. Memory, electricity, electricians…. Land. Like 2,000 components or more. You could get constrained at some point. There could be things which cause bottlenecks, and then you could have overbuilds. There could be external shocks to the economy. There are macro risks. All that is possible at any given time. But am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes. I was in the room when you said AI would be as profound as fire and electricity. If you believe that, shouldn’t you give researchers the biggest possible computer, even at the expense of today’s cloud profits? We’re all doing some form of that. Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development. What you’re seeing is true already. But you want to be thoughtful. Even our AI researchers have some constraints, which are also good. That’s what inspires creativity. That’s what helps you come up with a more efficient algorithmic breakthrough, maybe to use less memory. What we are committed to is to invest what’s needed to stay at the frontier. OpenAI President Greg Brockman told me a few months ago that the ideal AI product might have no interface at all—just as natural as conversation. What do you think the interface looks like in the age of AI? Computing historically has been humans adapting to computing. With AI, it’ll be the other way around. The technology will adapt seamlessly to how humans live and go about their day-to-day life. You want to be able to speak to that AI when you want to. It has context. It can be persistently running somewhere, give you continuity. You’re not dealing with a lot of complexity. It’s natural. All that makes sense to me. That doesn’t mean it won’t have any interface, however. Nowadays, even on my phone, Gemini voice models are so good. I can just press the microphone button. I can just ramble. That’s why we call it Rambler. I can ramble. It intelligently corrects with my personal context. Even my friends’ names, weird names, it gets it right, even if it first guesses it wrong. It’s amazing. I’m not going in, backspacing, editing once I finish saying it. It’s remarkable how good it’s gotten. But there are going to be moments you will need an interface. You may want to entertain yourself. You may want to watch a video. You may ask your AI to create something, and you want to watch it, consume it. The way I think about it is, you want it to be as seamless and as natural as possible, and that you are not doing the hard work adapting to whatever the interface is. The technology is adapting to how you’re working at any given moment in time. When I asked Sam Altman about competition from Google, he argued that OpenAI was building AI-native products and implied Google was bolting AI onto existing ones. Do you accept that framing, and can a “bolt-on” compete with an AI-native product? First of all, it’s a very dynamic moment. We are all pushing each other. OpenAI has pushed us as a company too. This is not just we are doing something to them. Far from it. Yes, Google went into its own version of a code red too. In the AI moment, all of us have to respond with varying levels of urgency at any given moment in time. We are constantly building new products as well. Gemini was built from the ground up as a product—the Gemini app I’m talking about. Or tomorrow, as all of us are adapting to agents, are you bolting agents onto ChatGPT, or are they building it natively? How would you think about that? You can always put these questions back. I don’t think of it that way. I think of it as you want to build world-class product experiences that are AI-native and they are winning. Some of it is adapting products, some of it is rethinking new experiences. They will all be part of how you approach this moment. How do you expect to make money from these AI interfaces? If an agent recommends a product and I buy it, should it take a cut of the sale, even if that’s disclosed? I’m going to put enterprise aside. Consumers are going to increasingly value this technology. Take software developers today. Developers are willing to pay for these coding models because it really helps make them more productive. Nobody would want to code the way they were coding three years ago, or even the same time last year. When you’re delivering value, there’s value to be had. I’ve never seen examples where consumer products really scale to billions of users, where people are using it intensely, and haven’t had monetization to go along with it. If you’re providing value to people, there is inherent value to be had. The nature of it may change. You’re asking a scenario in which agents are doing stuff for you. At the end of the day, you will have commercial needs. The agents need to fulfill those commercial needs. The providers on the other side are going to see value in that. There’s going to be some mechanism by which it gets fleshed out. That’s what an efficient system does. But what matters is that you’re building those world-class consumer experiences. I promised myself one product feature request: Can you make Gemini-powered search in Gmail as good as searching Gmail through ChatGPT? What’s going on here? One of the exciting things we announced recently is the unified Gemini agent for enterprise, and we’re bringing that to consumers. These are persistent agents which run 24/7 in the cloud. We’re spinning up a micro VM effectively for every query, and from the runtime, the harness, the model—it’s all a headless service. It’ll work everywhere, and it has your context with you. You can address this agent whether you’re in Workspace, in Search, in Gemini. Agents are going to drive this unification from a customer experience, which people are looking for. When you have a broad product portfolio and you’re moving forward fast, there are always areas where you’re moving forward faster than some other areas. That’s valid feedback. But we are putting a lot of time and effort into making sure, as we build out our agentic experience, that unification and that capability works well across all these services. People have asked where Google’s equivalent of Muse or Dots is. Do you anticipate creating a new product to compete with them? Our goal with Gemini is to be a personal, proactive, and powerful assistant. That’s what we are working toward. You saw us describe the early versions of it at Google I/O this year with Spark. Effectively, already, if you open the Gemini app as a Pro or an Ultra user, you see Chat and Task, and they start giving you that agentic experience. With Argon now, that capability is dramatically accelerated. What we announced recently in our enterprise event, we are bringing that to consumers as well. Soon in Gemini, you will be able to ask something, and it’s a continuum. You don’t need to think about whether you’re asking a chat or an agentic task. It’ll just automatically do the right thing, and it can work persistently in the background securely and get things done for you. Already, empirically, when I look at what’s coming into Gemini in our app, last year you would have tagged them and most of them would have been chats. But a steadily growing percentage is already agentic tasks in Gemini. Yes, we are going to bring personal agents to our consumers. It’ll work in the Gemini app. It’ll work in Google Search. It’ll work in Gmail. You can address it, call it, etc. We want to make sure we’re getting it right. It’ll be powered by Argon, and we are very excited to bring it to our users. Google has invested and committed tens of billions of dollars to Anthropic. What do you see in Dario Amodei that makes you want such a deep investment and partnership? Very early on, when we looked at the founding team of Anthropic, it was very clear to us this was a world-class team which was involved in some of the earliest GPT models. We were quickly able to assess that they would be at the frontier. We’ve always tried to identify and partner with companies which would be at the cutting edge of technology. We’ve done that with SpaceX. We’ve done that with Anthropic. That’s what we saw, and we had a chance to partner. Is your partnership with Apple to license Gemini for Siri expanding, contracting, or standing still? For both us and Apple, that partnership was about translating the capabilities of AI into better experiences for users. I thought Apple has done a phenomenal job of incorporating—they customize these models and build it into Siri—and it’s been great to see the reception. We are committed to doing more. You’re going to do more with them? There’s nothing specific to comment, but it’s a partnership. It’s working well. What have you learned so far about the feasibility of data centers in space? It’s a very exciting project. Projects like this remind me—I still recall that moment in the desert for the DARPA Grand Challenge, watching the Stanley car compete in it, which ended up being the basis for Waymo. With all these technologies, you’re at a moment in time where it looks a bit like sci-fi, but 10 to 20 years down the line, it’s actually working in a meaningful way. I feel the same in space. We’ve always believed in betting big on these technologies. Quantum has been a great example of it. We’re probably 10 years into the journey. Waymo, we are almost 20 years into the journey. Space, we are two to three years into the journey. It’s clear to me that it’s an approach which will make a lot of sense in the future. Being in sub-synchronous orbits with 24/7 access to the most abundant form of energy we have access to will make a lot of sense for this exponential increase in compute that we are going to need. Having said that, this is a challenging project. The team has disciplined milestones. It was a super exciting milestone to send the TPUs up to space. Sometime, as we are speaking, we are expecting a “hello world” moment from them. I look forward to seeing that happen, and we’ll continue investing here too. You previously said 25% of the code written within Google was written by AI. What’s the number today? That number in our world today is meaningless in the sense that every part of what we now touch is influenced by AI deeply. What we are tracking with Argon, for example, it’s been a point of inflection. People are using Argon to really go tackle complex projects, including projects which affect millions of lines of code. In the four weeks since we have launched Argon, we have seen a 30% increase in CLs, and more importantly, almost a similar increase—a one-third decrease in rollbacks. People are writing code more productively and better code with it. We are tracking it with newer metrics now. How are you dealing with opposition to data centers? If you can’t build in the U.S. the way you want to, where else do you go? We’re very committed to building our data centers very responsibly. When I look at our largest data centers in some of the communities where we have been the longest, I visit them, I talk to people. There are people who come and tell us their kids have gone to college because of our investments in these communities, and we’ve grown in those communities. To me, this is about doing it right. Engaging with the communities early, doing it transparently. We are committed to making sure we are bringing incremental power, and we cover 100% of the cost of our power needs for these projects. It’s understanding the impact on neighborhoods and making sure you’re working thoroughly to address them. Done rightly, you can continue making progress. But we are constantly taking feedback into account. I do worry not everyone operates the same way. People have been surprised by data centers showing up in their backyard. Those are all important. As an industry, we should continue to do better. But part of the reason, in a 20-year time frame, we are thinking about things like data centers in space is making sure we are able to put these data centers where maybe the trade-offs will be different. What unproven area of AI is Google working hardest to crack? Is it continual learning—getting models to keep updating after release—or something else? There are multiple parallel paths. You saw with Argon we have a million output tokens. With our Gemini models early on, we had a one-million context window on the input side. These are all examples of breakthroughs we are constantly working on. How do you think about synthetic data? How do you think about continual learning? How do you think about more efficient architectures? We do dedicate a meaningful percentage of our compute to these research bets too. It always goes hand in hand, including our world models. Those are all bets we are placing. Sometimes, as part of these bets, there are ideas that emerge which just make their way into the mainline models. Maybe we don’t fully talk about it, but we have multiple steps in these areas. Working in technology requires optimism that building something will improve the world, but those bets can leave real consequences when they fail. I recently returned to a town in El Salvador where enthusiasm for Bitcoin had given way to unfinished construction and something resembling a ghost town. How do you maintain the optimism you spoke about in your Stanford commencement address while accounting for those risks? I genuinely have a deep conviction that improving technology and improving access to technology makes people’s lives better. It’s been my personal experience in my life, out of which comes that deep conviction, optimism. But with that, I’ve always felt humanity’s journey is always about working hard to harness that technology to make it work well for society. The optimism is not an optimism that you just build whatever you want, whenever. You work hard to make sure you’re building technology in the service of humanity, to make the human condition better. That’s got to be the defining goal as we are approaching it. With that framework, I’m very, very optimistic. We are in very early stages of the kind of progress that we are going to see on many areas. We’re all talking about areas like healthcare. A lot of the progress is ahead. The ability to make things in a fundamentally cheaper way on many things where things have been getting expensive for a long, long time, discovering new materials, etc., which really bend the curve—it’s tough to step today and understand the impact of all that, but they’re going to be very profound. I’m very optimistic, but we have to be rightfully concerned about all the ways this technology can go wrong. Sundar, I know you don’t do many of these. Thank you. Thanks, Alex. Appreciate it.
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EU membership bid gives boost to Moldova’s fledgling R&D landscape
Moldova’s push to join the EU is having the spillover effect of helping to improve the country’s R&D landscape, which in recent years has suffered from fragmentation, decreasing personnel and a lack of research infrastructure.The country became an EU candidate country in 2022, alongside Ukraine, and officially began accession negotiations in 2024. With a pro-EU government, Moldova has been actively carrying out reforms to align with EU standards. This has helped accelerate the development of R&D, said Igor Chiosa, acting head of the Research and Innovation Policy Directorate at Moldova’s Ministry of Education and Research. “Our R&D landscape is moving up, and we are more and more involved in the European Research Area,” he said. “The support we receive from the EU is very important, not only financially, but also because of incentives, consultation and capacity building.”The R&D landscape in the former Soviet republic is still relatively underdeveloped, despite attempts at improvement going back to a 2004 law on science and innovation and later reforms in 2017 and 2018. But the system remains in a “precarious situation” and “in the process of contraction,” according to an assessment carried out by the government for the current National Programme for Research and Innovation, which runs from 2024 to 2027. The analysis highlights in particular the declining number of researchers and PhDs, which has been continuously falling, as well as limited research funding. This is around 10 times lower than the EU average and “insufficient to maintain a level of research activity that supports higher education and meets the needs of the economy.” The mechanisms for allocating research funds are also described as inefficient, as well as the general fragmentation of the landscape, with research and innovation activities being carried out in 67 different places. The lack of strategic vision for research infrastructure is also noted. The strategy sets out to address these issues and is being fuelled by reforms linked to EU accession. Moldova now has a regulation on open science and gender equality. There is also a new support mechanism for technology transfer offices and a business-academia roadmap. In 2025, the country also became the first Eastern Partnership country to approve a Smart Specialisation strategy, which will coordinate its R&D efforts to focus on strengths, within a European context. “We’ve done a lot in terms of normative acts,” Chiosa said. Horizon Europe successMoldova is also seeing more success in the EU’s flagship R&D programme, Horizon Europe. The country has now won nearly €15 million in funding, already double the amount won under the previous programme, Horizon 2020, and more than the €10 million target set by the government. Chiosa said the priority has shifted now to winning more research-centred projects, rather than those related to support and capacity. Moldova is also targeting further involvement in large European projects. Last year, it joined the EuroHPC Joint Undertaking giving Moldovans access to Europe's supercomputing network. The country is also a member of Danubius-RI, a European research infrastructure focused on river and sea research. Despite these efforts, Moldova’s research capacity is still shackled by its limited R&D budget, which equates to 0.23% of its GDP. This is around half of what its neighbour Romania spends, which is the lowest in the EU. According to Chiosa, the research budget is increasing year-on-year, jumping by €6.34 million between 2024 and 2025. But this doesn’t show up when looking at it as a proportion of GDP. “Of course, it’s difficult,” he said. “That is why we are putting more emphasis on collaboration between business and academia, as well as participation in Horizon Europe, to try to diversify our sources of funding.”A World Bank loan of €30 million, plus a grant of €7.8 million, was agreed in 2025 to support the modernisation of Moldova’s universities. As part of this, a call was launched this year to provide universities grants of up to €900,000 to develop their research infrastructure. Moldova is also collaborating closely with Romania, with which it shares a common language, funding 100 joint projects in 2023 and 2024. This support has been instrumental. “They are helping with sharing their expertise, inviting us into consortia, and lots more,” Chiosa said. Looking forward, Moldova is eagerly anticipating joining the EU. “We are looking closely at what the EU is doing and will shape our future national programme in research innovation accordingly.”EU accession “blocked at a technical level”Moldova is aiming to join the EU by 2028, but despite its swift and eager efforts to meet the accession criteria, there remain obstacles outside of the country’s control. For a country to join the EU, it must have certain legislation in place and meet specific standards in various areas. This is categorised into six thematic clusters containing 33 chapters. Moldova and the EU have so far opened two of the clusters, numbers one and six. The first covers fundamental laws, such as the judiciary, security and financial legislation. The sixth covers external relations. The remaining four clusters are still unopened. This includes the third cluster, on competitiveness and growth, which contains the chapter on science and research.Related articlesQ&A: Albania’s ambition to become a ‘moderate innovator’Data corner: Horizon success rates are droppingThe European Commission has confirmed that these clusters are ready to be opened, with an official saying that the Commission is encouraging EU governments, who have the final say, to move forward on the matter. However, both clusters two and three remain blocked by the EU Council’s working party on enlargement and countries negotiating accession to the EU, a different official, representing the Council, confirmed.This working party is set to meet several times in the coming weeks, with a Council meeting scheduled for mid-October in which enlargement, and Moldova’s bid, will be discussed. But there is no certainty that these talks will lead to progress. According to the Council source, it is “completely impossible to foresee if and when the file will be unblocked,” citing the sensitivity of the matter. Moldova’s situation is particularly complicated. One factor is that it borders Ukraine and is increasingly embroiled in Russia’s war, with drone incursions occurring more often on its territory. Another is Transnistria, a Russian-backed breakaway state that lies within the territory of Moldova. It is unclear how the EU would be able to accept Moldova into the bloc while this situation remains unchanged. While there is no direct reason to delay opening cluster three with Moldova, as it covers matters unrelated to security, it could be that the EU is reluctant to speed through accession negotiations without first resolving broader, geopolitical issues.Chiosa is confident that, if and when the chapter on science and research is opened, Moldova will be able to finalise it quickly. It doesn’t require any legislative changes and is only a matter of proving certain standards, which Chiosa believes Moldova is well prepared to do. “We hope that chapter 25 on science and research will be one of the first chapters opened and closed,” he said.
When “DEI” research is cut, what happens to community health?
“I’m just going to run to the grocery store!” How many times have we said this without thinking about it? For most of us, it's usually an easy walk or a short drive to get the cheese we forgot. In reality, though, 13% of the U.S. population (about 39 million people) live in “food deserts,” which means living more than a mile from a grocery store in urban areas and more than 10 miles in rural areas. This was the case for people in Pittsburgh’s Hill District, where a quick run to buy food wasn’t an option. In the 1930s, federal maps marked the Hill as "hazardous" for lending, a practice known as redlining that steered investment away from Black neighborhoods. Decades of disinvestment followed, and residents went about 30 years without a nearby grocery store. This made it hard to keep healthy eating habits and contributed to chronic disease. When a grocery store finally opened in the area in 2013, researchers saw the opportunity and launched the PHRESH study, which initially compared the health of participants from the Hill district to those in the Homewood district. Over time, the study expanded to other factors such as brain health and Alzheimer's risk. Residents like Roderick Blair overcame initial hesitation to share their health data, trusting it would benefit their neighborhood. Then, in August 2026, the NIH abruptly canceled the study’s grant, citing that it was “subjective” and fell outside their scientific scope—an effect of the administration’s interpretation of diversity, equity and inclusion (DEI). Research like PHRESH can help determine what changes in a neighborhood actually improve health, so cutting it has left that question unanswered. Other canceled studies have examined health issues like maternal health, preterm birth rates, or sickle cell disease. Courts have reversed some of these grant cancellations, but not all of them. If we really want America to be healthier, research into why some communities get sicker than others is absolutely critical. The law that created the federal minority health research institute passed with bipartisan support in 2000. But these days, as evidenced by grant cancellations, the political shift in attitudes toward DEI means we often lose access to the health information we need to help all communities thrive. DEI vs. health equity vs. equality and the overlap (or lack thereof) The letters “DEI” evoke very different reactions depending on who you are in the U.S. DEI programs can address gender, disability, age, and other factors, but the fiercest debate centers on race. For some people, hearing DEI brings to mind policies that may have cost them a job or a college seat because they are white. Others see DEI as a matter of justice: a way to expand representation and opportunity for groups that have been historically marginalized. Both perspectives are rooted in lived experience, and both center on the same question: whether hiring and admissions practices are fair. Hiring and admission practices, though related, are not the same as health equity. Health equity is “the attainment of the highest level of health for all people.” It’s the recognition that some groups of people consistently experience worse health outcomes than others. For instance, black mothers die from pregnancy-related complications at three times the rate of white mothers, and rural Americans are more likely to die from issues such as heart disease and cancer. Equity is also often conflated with “equality,” which leads to misunderstanding of how to actually best support communities. Equality ignores the fact that we aren’t all starting life with the same resources or circumstances, whereas equity is about helping people get what they specifically need based on their situation so they have the chance to achieve the same outcome as anyone else. For example, if every household in Pittsburgh got a $30 grocery gift card, that would be equal. But during the decades the Hill District went without a grocery store, that card wouldn’t have helped much. Equity means making sure everyone can actually access a store in the first place. DEI policies can overlap with health equity in many ways. For example, they may help expand access to quality education and stable, well-paying jobs, which in turn can provide health insurance, safer housing, and improved finances—all of which can reduce health disparities. But to make that happen, we first need data to identify disparities in communities and to develop solutions that effectively address them. What we lose when the research stops Community data is important to health equity because: You can’t fix what you don’t see: Data show which communities are most affected by health factors such as infection, heart disease, and cancer. If we don’t know what is happening and where, then we can’t develop interventions to help. Poor health costs everyone: Chronic disease is expensive—both to the individual person and to the overall economy. Understanding what groups of people are most at risk allows us to develop interventions that can save everyone money. Disparities exist for many communities: Health equity includes disparities that exist due to a whole host of factors, such as family background, race and ethnicity, rural vs. suburban residence, gender and sexual orientation, veteran status, disability, and lower income. This means many Americans can benefit from research into differences in health outcomes and how to improve them. Trust is easy to lose: Some participants in the PHRESH study were initially vocal about their concerns about participating. But that trust was earned because the researchers kept their promises and centered their work on the community. When projects are canceled mid-cycle, researchers have no choice but to leave behind communities they had promised to help. This hurts trust in the long run. It’s also a very inefficient use of resources, and it will take significant time and effort to redo the work when the government realizes this work is important again. How should we talk about health equity? This is hard because the words “equity” and “DEI” are currently very politically charged. So whether you’re a public health professional, teacher, or friend, how we talk about this topic matters. The right framing can help people who might otherwise be skeptical see why health equity research is important and deserves funding. Here are some suggestions to help your conversations: Choose your words strategically: Who are you trying to reach with your message? Are they likely to be resistant to terms like “diversity” and “equity”? If so, find alternative words to use to still get the message across. It is frustrating to be in that position, but the goal of improving health is too important not to try to make inroads. Sometimes simply reframing can help the message reach those who need to hear it. Focus on the problem: The terms “health equity” and “DEI” are abstract. Make it more specific. For example, discuss differences in maternal death or ways to improve health outcomes in rural communities. These still have equity at their core but will likely be better understood by those you’re talking with. Focus on systems: Disparities exist, but if they are communicated poorly, people can mistakenly think the issue is the individual’s fault. Disparities are largely driven by failures of the system—such as lack of access to health care and a long legacy of racism. When discussing the disparities, frame them around these systemic issues to ensure blame isn’t placed on the individuals impacted. Tell stories: Personalize these policies by using stories from those impacted that connect to the data you’re trying to convey. This helps make the policies and outcomes more concrete and meaningful. Bottom line Some communities end up sicker than others. That’s just the facts. We can argue about the best way to do that research, but canceling it simply because it may be linked to DEI doesn’t make anyone healthier. It just leaves communities like the Hill district behind. And in the end, it causes us all to move backward in our efforts to improve health in this country. Love, EM Big thanks to MacKenzie Isaac & Legairre Radden, who reviewed this piece and generously shared their expertise and lived experience to help improve it. Dr. Liz Marnik, PhD, is Executive Director of The Evidence Collective and a science communicator and immunologist working at the intersection of public trust, science, and public health. To discuss communication training on topics like health equity and storytelling, email Liz at liz@yle.health or learn more on our website. Your Local Epidemiologist (YLE) comprises a team of experts, ranging from physicians to immunologists to epidemiologists to nutritionists, working together with one goal: to “translate” ever-evolving public health science so that people are well-equipped to make evidence-based decisions. The YLE suite of newsletters reaches over 475,000 people across more than 132 countries. This newsletter is free to everyone, thanks to the generous support of fellow YLE community members. To support the effort, subscribe or upgrade below: Your Local Epidemiologist is a reader-supported publication. 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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.
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Scientists Release Biggest 2D Map of the Universe
Key Takeaways The DESI Legacy Imaging Surveys combined more than 263,000 telescope exposures to make the largest 2D map of the universe in visible and near-infrared light. Astronomers can pair the Legacy Surveys map with their own observations to explore our universe and search for rare phenomena. The 2D map serves as the foundation for the Dark Energy Spectroscopic Instrument survey to measure the universe in 3D and investigate dark energy. Hold on to your telescopes: the DESI Legacy Imaging Surveys team has released the largest-ever 2D color map of the universe. The 5.6-trillion-pixel map contains nearly 4 billion celestial objects, primarily stars and galaxies. The data is available for all to use and publicly viewable through the Legacy Survey Sky Viewer. Astronomers and citizen scientists can explore the map or combine it with their own observations to better understand our universe. Researchers can search for rare phenomena like gravitational lenses, observe fleeting events like supernovae, and investigate two of physics’ biggest mysteries: dark matter, the invisible substance that accounts for most of the mass in our universe, and dark energy, the force driving our universe’s accelerating expansion. The new map builds on earlier versions from the DESI Legacy Imaging Surveys that have already proved invaluable. To date, more than 1,800 science papers that reference the Legacy Surveys data have been published. “It’s part of the fabric of astronomy research now,” said David Schlegel, a co-lead of the Legacy Surveys and scientist at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab). “When you’re working with astronomical objects today, you often start by pulling up the Legacy Imaging Viewer to see what you’re looking at.” Covering roughly 75% of the sky in visible and near-infrared light, the updated map provides a deep view of the extragalactic universe not blocked by the dust and stars of our own Milky Way. Researchers expect it will remain the most comprehensive 2D map of our universe for years to come. More than 160 scientists contributed to data collection for the project, and a team of 20 produced the final dataset released today. It was built by combining 263,407 telescope exposures from three ground-based sky surveys: the Dark Energy Camera Legacy Survey (DECaLS) at NSF Cerro Tololo Inter-American Observatory, the Mayall z-band Legacy Survey (MzLS) at NSF Kitt Peak National Observatory, and the Beijing-Arizona Sky Survey (BASS) at the University of Arizona’s Steward Observatory. That was supplemented by years of data from NASA’s Wide-field Infrared Survey Explorer (WISE) satellite mission and additional public data. “For our team, these data are fundamental to our investigation of the expansion history of the universe and the formation of our galaxy,” said Arjun Dey, co-lead of the Legacy Surveys and an astronomer at NSF NOIRLab. “But the skies belong to everyone, and this survey gives everyone the chance to explore the sky and marvel at its wonders.” Here be galaxies The DESI Legacy Imaging Surveys were originally conducted to prepare for the Dark Energy Spectroscopic Instrument (DESI) survey. The Legacy Surveys’ 2D map is essentially a deep photograph of the sky; it records where galaxies and stars appear and how bright they appear. This crucial step enables DESI to select objects and measure their light in different wavelengths to determine their distances, building the largest high-resolution 3D map ever made. Scientists study the way galaxies have clustered at different ages of the universe to track dark energy over time. In April 2026, DESI completed its original five-year survey ahead of schedule and with vastly more objects than expected. The early results have shown surprising hints that dark energy’s impact may be weakening over time — a paradigm shift that could potentially shape the predicted fate of our universe. DESI expects to publish improved results using their first five years of data in 2027 and is continuing observations into 2028. DESI was so efficient at observing galaxies, the Legacy Surveys map needed to expand. Early on, “it became clear we might run out of galaxies to look at and run out of sky, and we better start doing something about that,” said Schlegel, who also works on DESI. The new Legacy Surveys map has been used to select DESI targets since June 2026 and will guide the telescope’s operations over the coming years. Computing the cosmos Merging hundreds of thousands of images taken on 2,285 nights, each with unique atmospheric and telescope conditions, was a massive computational effort. It took about a year to develop the computer code and eight weeks to process all the images at the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC) at Berkeley Lab. Beyond supporting DESI, the Legacy Surveys will be a foundational reference for the next generation of telescopes. As new observatories like the NSF-DOE Vera C. Rubin Observatory (jointly funded by NSF and DOE’s Office of Science) and NASA’s Nancy Grace Roman Space Telescope come online, researchers can compare their observations with one of the deepest and most comprehensive views of the sky ever assembled. The Legacy Surveys data will also help scientists train artificial intelligence tools to analyze petabytes of astronomical data and accelerate new discoveries. It will be among the datasets used in an astrophysics pilot project within the American Science Cloud, part of the DOE’s Genesis Mission. The DESI Legacy Imaging Surveys are supported by the U.S. Department of Energy’s Office of High Energy Physics; the National Energy Research Scientific Computing Center, a DOE Office of Science user facility; the U.S. National Science Foundation, Division of Astronomical Sciences; and the partner institutions. DESI is supported by the DOE Office of Science and NERSC. Additional support for DESI is provided by the NSF; the Science and Technology Facilities Council of the United Kingdom; the Gordon and Betty Moore Foundation; the Heising-Simons Foundation; the French Alternative Energies and Atomic Energy Commission (CEA); the Secretariat of Science, Humanities, Technology and Innovation (SECIHTI) of Mexico; the Ministry of Science and Innovation of Spain; and by the DESI member institutions. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
Atomic catalyst unlocks the hidden value of plant waste
Scientists have created a highly efficient catalyst that breaks down stubborn lignin from plant waste into useful chemicals under relatively mild conditions. By revealing exactly how the catalyst works at the atomic level, the discovery could help turn forestry and agricultural waste into renewable building blocks for fuels, plastics, and other materials.
Lab-made DNA can store memory like a flash drive
The new device combines synthetic DNA and a semiconductor to create an efficient memory resistor, or 'memristor.'
Google Home app brings battery-efficient widget, reduces gray device tiles
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Federal grant cuts hit Colorado studies to make healthcare more efficient
Dr. Lisa DeCamp thought she might have a win-win idea that would improve health for kids with asthma while saving Medicaid money. She may never know if it would have worked after the Agency for Healthcare Research and Quality ended her…
Model Helps Scientists Build Better Protein-Degrading Therapies
A team of Weill Cornell Medicine investigators has developed a computer model that will help scientists more efficiently develop protein-destroying therapies, a newer type of treatment that is particularly promising for cancer.
The Big Holes in Wearable Heart Rate Variability And Readiness Scores
On September 9th, Apple announced it was revamping its Apple Watch Health Sensing System, rolling out a Readiness score (0 to 10), and increasing the frequency of heart rate variability (HRV) outputs 24-fold. This can be viewed as upping its competition with various other consumer wearable sensors. These “readiness scores” as a composite of multiple metrics, with heart rate variability (HRV) being front and center for most. The majority of Americans are now using wearable sensors, which equates to well over 100 million adults. HRV and Readiness scores are increasingly being marketed as a measurement of autonomic nervous system health, a digital marker for future disease, a clock for biological age, and a holistic metric to promote healthspan and even longevity. (Apple also introduced a new longevity tab and “Health Age”.) None of this has been proven. In this edition of Ground Truths I am going to review what we know about heart rate variability and readiness scores. Heart Rate Variability HRV is the variation in normal heart cycle timing. The variability of the heart rate, the barely perceptible millisecond changes in time between consecutive heart beats (see R-R intervals in the Figure below, left panel), is due to interplay between the sympathetic and parasympathetic (vagal nerve) inputs. Distinct from heart rate, individuals with the same heart rate can have very different HRVs. It is a rough reflection of the autonomic nervous system (ANS) activity, inadequate to say whether a person’s ANS function is abnormal. For more than three decades, heart rate variability (HRV) has been measured and several studies have found an association of low HRV and clinical outcomes, particularly a link with higher all-cause and cardiovascular mortality. There have also been less well established links of low HRV to risk of early cognitive impairment, dementia, mental illness, Type 2 diabetes and substance abuse. An important reminder is that HRV is a surrogate marker without any established cause-and-effect relationship. If you increase your HRV, that doesn’t mean it will improve health outcomes. In fact, there is no hard evidence for that. All that work linking to health outcomes was done with electrocardiogram (ECG) derived HRV. Now, in the era of consumer wearables, this is getting assessed differently, by optical pulse (yes, the lights you see) plethysmography (PPG) or what is called pulse rate variability (PRV). They are not the same, as shown below (right panel) and only concordant when the delay between the ECG and pulse is kept constant, which basically means at rest. I should mention there’s also what I will call MPV, a mattress mechanical movement sensor, a derived heart rate variability, that companies like Eight Sleep use, even further away from directly measuring HRV. HRV has not one uniform measurement but many different types of quantification, such as RMSSD, the magnitude of difference between successive R-R intervals of normal sinus beats (N-N) or SDNN, the standard deviation of NN intervals, both in milliseconds. SDNN is one of the so-called frequency domain HRVs (others are LF, HF, LF/HF). Different wearable sensors use different metics; Apple has relied on SDNN and nearly all of the others use RMSSD, which is generally considered the more accurate metric. There’s also the different length of time measured, such as for a matter of minutes, all day, or an overnight’s sleep. Short measurements are especially problematic since they don’t capture enough of respiratory modulation and other factors that influence HRV. Share Ground Truths How well does HRV correlate with PRV? There are very limited studies, especially independently done. One that is commonly cited was conducted by Air Force researchers in only 13 healthy adults assessing Oura ring 3 and 4, Whoop 4.0, and Garmin Fenix 6 and showed a correlation coefficient of 0.88 to 0.97 and a mean absolute percentage error from 6 to 10%. The correlation is not a perfect 1.0, but there’s at least a fairly high level of correlation. HRV is supposed to increase during the night due to takeover of the parasympathetic nervous system, and higher during deep sleep. A recent example of my 1 week, all day “HRV,” and one during sleep is shown below. As you can see, the N of 1 data are inconsistent for the same days from different sensors (Oura, AppleWatch, Fitbit Air, and Eight Sleep) by patterns, absolute numbers, and comparison with prior days and weeks. The largest study in over 8 million Fitbit users (the old version, not Google Fitbit Air, introduced in May 2026) gives you a sense of the effect of age, sex, and the 2 different main HRV (here PRV) metrics, with RMSSD on the left and SDRR (=SDNN) on the right below. That study, from data collected in 2018, is a major outlier, since all the more recent ones are tiny with respect to sample size. Many of the companies have not had independent evaluation of their HRV, such as Eight Sleep, but have published a low standard error on their website. There are some other published studies on the correlation between HRV and PRV, but they are all small and only in healthy adults. A scoping review emphasized the lack of study in underrepresented individuals, including the aged, people of color (which affects the PPG signal), and individuals who are underweight or obese. Add the typical adult age 60 plus with one or more chronic diseases. For example, one study in over 900 adults found poor correlation of HRV and PRV, non-uniformly underestimated across many chronic diseases (cardiovascular, endocrine, neurological, respiratory, and others), concluding PRV is “an invalid surrogate for HRV.” A recent systematic review of 43 studies comparing HRV and PRV found reasonable pooled absolute standardized error (HRV as gold standard) but only 10 of the studies provided quantitative synthesis in ideal conditions. Their main conclusion was similarly cautious: “PPG-derived HRV [PRV] should not be regarded as universally interchangeable with ECG-derived HRV across all devices, populations, and recording contexts.” Factors Affecting HRV and PRV That gets me to the long list of factors that affect HRV (and PRV) besides the device, the type of measurement (RMSSD, SDNN or others), the person’s signal, the sensor site, the duration of data capture, if weighting by sleep stage is used, how artifact is processed and corrected. And this list is not complete!: Oura puts out data from their community of users (who input data) on what affects their overnight HRV. The factors currently provided are: no alcohol (increase 8%), melatonin (increase 2%, float tank (increase 2%), wine (decrease 4%), and party (decrease 14%) in overnight HRV. Must be some big parties! Subscribe What is a PRV measurement good for? It has been falsely characterized as an index of “autonomic balance” and a specific indicator of stress. A 2018 review of the studies available for HRV and its relationship to stress, not using any of the current wearables, found that stress can lower HRV. But so can many other factors. The non-specificity of the signal, indexed to the table above, is striking. Evidence from a UK Biobank study of over 46,000 participants with actual HRV looked at genetically predicted HRV, a genetic risk score, that failed to show the expected HRV-mortality link, indicating that _HRV is likely not causa_l, but rather a reflection of person’s physiologic state. A review of consumer wearable HRV data from 5 longitudinal studies showed that nighttime PRV was not associated with perceived stress, and surprisingly higher HRV, in the largest cohort (N=717 participants), was correlated with higher stress. An Oura ring cohort of 525 first-year college students found a link between overnight PRV and perceived stress, but that was also seen with resting heart rate, sleep, and respiratory rate. Several very small studies have examined the relationship of HRV and athletic injuries or guiding training with mixed, and predominantly negative results. HRV biofeedback training with paced breathing had no significant effect on reducing stress or raising HRV, as demonstrated with sham controlled trials. When HRV for multiple days showed a decline in conjunction with body temperature, the Oura ring published data for prediction of Covid. The WHOOP company sponsored an observational study, published in 2026, of 30,000 users for 72 weeks, without a control group, that reported reduced alcohol intake (5.8 % points) by self-report. That doesn’t tell us much, and particularly about the merits of HRV for behavioral change. If you use the same device and conditions as longitudinal trends for multiple (at least 2-3) weeks that may be the one way to get something useful from the measurements. Data for overnight sleep with minimal motion and using RMSSD is the best proxy for real HRV. The reason to look at trends rather than any given night is that it more likely represents something, even though you won’t know with certainty what the “it” is. Keep in mind there are no data, no peer-reviewed evidence, to show that HRV fluctuation in-person has any correlation with health outcomes. Share Readiness Scores These are proprietary scores that integrate different metrics for each of the wearables: no algorithms have been disclosed. They are unvalidated against health outcomes. In a review of 14 composite health scores of readiness and recovery, HRV contributed 86% to the scores, followed by reading heart rate (79%), physical activity and sleep duration (both at 71%). That review noted the substantial variability in measurement protools and lack of standardization. Sleep staging is notoriously inconsistent and inaccurate by these sensors, which adds further to the HRV uncertainties for what the scores, which use sleep stage data, mean. Only resting heart rate has been shown consistently across devices to be extremely accurate. I’ve made a Table to summarize what we know about which metrics are included, the scores, any peer-reviewed studies that compared the readiness score with health outcomes, and the corresponding (if any, NA-not available) citation. You will note that some companies do not use the term “readiness," such as WHOOP for recovery, and Garmin, which has 2 different scores, one of which is Body Battery. Eight Sleep uses the term “Fitness Score.” They all include HRV; Apple includes a new metric they call “Recovery HRV” which among other components uses 7-days of sleep, but it is unclear what this means or how it is differentiated from other scores (there are clearly no data for outcomes). We have no knowledge of how the different components are weighted or whether any of these scores are better than resting heart rate, HRV alone, physical activity, or any other single metric. Since none of these are standardized, they are not interchangeable, so if you get a 90 for Oura that has no relationship to a 90 on a Google Fitbit Air. Notably, the company can update its algorithm for readiness score at any point without notification to device users. Without any useful evidence of actionability for these scores or established relationship with health outcomes, it is hard to make a case for their value. At the Apple recent announcement they showed their Readiness score (0-10) on the watch (Figure below) but there are no published data on this score, not even on their website. It’s available only on their new Watch Series 12 or Ultra 4 [of course, ;-)]. That exemplifies the problems with these scores, lack of data and evidence for being meaningful to promote health. Perhaps the best study (which isn’t saying much) is the WHOOP Recovery for golfer performance, because it did correlate with an objective outcome, even though there was no control group and the authors were all from the company. Among the 389 pro golfers, an absolute 10-per cent point increase in Recovery score was associated with about 0.5 fewer strokes per round. But that’s hardly a health outcome! WHOOP is also conducting a study in over 2,700 runners to see if their recovery score will be linked to less injuries and improved performance, but that is not yet published and has no control group or randomization. Putting This in Context For two decades I’ve been enthusiastic about the potential for digital health and particularly wearable biosensors. Over the years, we’ve seen some great progress for their ability to promote physical activity and accurately detect atrial fibrillation (the first FDA cleared deep learning AI for consumers). That work was the subject of rigorous research. But there are holes in the data and evidence for other metrics. One notable one is the “VO2 max” story that I wrote about earlier this year. At that time many subscribers asked me to cover heart rate variability, which I finally got to here. When I dived into the research and publication for HRV and readiness scores, I expected to find at least some that were of high quality and demonstrated their utility by linkage to health outcomes. To my surprise, I found none. The wearable sensor measurements for HRV (PRV) are, for the most part, accurate, but that validation work has only been done in small studies of healthy adults and does not take into account the long list of factors, from the device, software side, and the user side, that affect HRV measurements. Moreover, this metric chiefly relies on optical sensing and, as we have learned for heart rate PPG sensing, may be less accurate in people of color. Keep in mind that all of the health outcome association evidence comes from ECG-derived HRV; none are from wearable sensor data. I will repeat the key point: there's no peer-reviewed evidence to show that in-person HRV fluctuation—or efforts to raise your HRV— has any correlation with health outcomes. For those of you who look at your HRV on awakening, or even 2+ week trends of it being low, I hope this context helps to relieve any anxiety. Yes, low HRV (not PRV) has been shown to increase risk of some diseases as summarized above. But efforts to raise your HRV—a surrogate metric— has not been established for improving any health outcomes. HRV does not have any evidence of causality (the genetic evidence actually goes against this possibility). In this summary, I have not included data for other wearable sensors such as Polar, Samsung, Withings, Suunto, Amazfit, Coros, Ultrahuman, or additional mattress sensors. These are beyond my first-hand experience, and as far as I know from my in-depth review none have any peer-reviewed published data that differ from the 6 sensors I’ve reviewed here. From the points I’ve gone over above, we’re not ready for readiness scores. Besides being proprietary, they are predominantly based on metrics that have their own issues. It’s compounding the problem, like building a house without a solid foundation that has never undergone a rigorous inspection, and then selling it. Like I mentioned for PRV, you can look at trends over weeks rather than any single day, to get a handle, but even that may not be helpful. There’s simply no evidence that these scores meaningfully relate to health outcomes. I’d emphasize they might, but that requires doing prospective or randomized studies to prove it. None exist. There’s great promise for HRV/PRV utility_. For example**,**_ Prof Maiken Nedergaard, who discovered the brain glymphatics that are essential in eliminating metabolic waste products from the brain during sleep, has posited that HRV could be a non-invasive marker for neuromodulator oscillations, brain-body regulatory circuits, and brain clearance. That would be extremely useful, but like everything else on HRV and readiness scores it requires solid research and validation. The lay media isn’t helping much to get the story straight. Earlier this year The Economist published a piece entitled “The most useful indicator of your overall health” which ordained HRV as an “accumulated stress score.” That’s akin to the false assertion about VO2max: “V02 max is the singular most powerful marker for longevity.” As I’ve summarized here, that is not established. The fact is that so many things can lower HRV, including physical exercise (especially an intense workout), reduced sleep quality, stress, the list above, no less the device, signal, and software. Whatever fluctuations observed have not been correlated with any health outcome. Sadly, “datamaxxers” are widely using HRV and readiness scores that have never been validated to mean anything. We already know that for some people using the sensors for sleep metrics, it can induce “orthosomnia,” an obsession to get high sleep quality, with associated high levels of anxiety. In an experiment done by a company to promote sleep quality for its employees, “For those employees who did use the trackers, many reported feeling perfectly rested until their tracker told them they had had a terrible night. Others were told that they had slept like a baby when they had actually been lying awake worrying about the quality of their sleep. “ The same problem can result from preoccupation with HRV or readiness scores, with anxiety that would lead to further reduction in both. It you are using a wearable like >100 million American adults, it’s OK to look at these data, but contextualized with the major caveats reviewed here. If you are one to require evidence that HRV or readiness scores are linked to health outcomes, you may not even want to look. The companies make it hard to turn them off! Let me end with the companies that make and sell wearables. Apple’s doubling down on HRV (24-fold more reporting and heart rate very 5 seconds) and introduction of a Readiness score tells us that consumers have bought into these metrics and they are joining the club. However, all of this is occurring with a backdrop of tens millions of users, claims about the data that are not backed up by adequate evidence, marketing way out in front of whatever limited data exists, and not being transparent about their readiness score algorithms. The companies can well afford to do the research that is needed to connect these metrics with health outcomes show, once and for all, that increasing HRV or using readiness scores promotes our health. If they believed and invested in the products they are selling, we’d not be in this position of not knowing. That’s essentially where we are with HRV and readiness scores. Perhaps someday this will change and we’ll have good reason to embrace them. NB: I wrote this post. No AI. I have no conflicts of interest with any of its content. Loading... Ground Truths has 215,000 subscribers from every US state and 214 countries. There are over 300,000 followers of Ground Truths so more than 90,000 folks who can easily convert to be free subscribers. Your subscription to these free essays and podcasts makes my work in putting them together worthwhile. If you’re not a subscriber, please join! If you found this interesting PLEASE share it! Share Ground Truths The proceeds from all voluntary paid subscriptions go to support our summer internship program. It enabled us to accept and support a record number of 62 summer interns that joined us in 2026! These are high school, college and medical students selected from thousands of applicants. We couldn’t do this expanded program without the funds coming in through Ground Truths. Thank you!
Scientists detect signals of hydrogen from billions of years ago. Could this help us map out the universe?
"Hydrogen intensity mapping has long been seen as a promising way to map the universe efficiently."