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Zac Brown's UFC performance was about 'patriotism, not politics'
Before he took the stage to sing the national anthem outside the White House to kick off UFC Freedom 250, Zac Brown stood firm despite blowback.
A Dolly Parton tribute, Madonna performs, LISA wins: What to know about the 2026 MTV VMAs
Madonna opened the starry 2026 MTV Video Music Awards and was joined by her recent collaborators Sabrina Carpenter and Charli xcx. LISA won the first award for best pop. Kacey
Exynos 2600’s Heat Pass Block Performs Better Than A Liquid-Nitrogen-Cooled Snapdragon 8 Elite Gen 5, Enabling Practical Uses For Future SoCs
A test shows that Samsung’s Heat Pass Block technology is a much more practical way of cooling an Exynos 2600
Brandon Flowers brings killer energy to Las Vegas
The rock frontman performs at Wynn on Oct. 5-6.
Julia Stiles on ‘Dancing with the Stars’ performs ‘10 Things’ tribute
Julia Stiles honored her late '10 Things' costar Heath Ledger and reunited with cast members from the movie, which was filmed in the Seattle area, during 'Dancing with the Stars'
Courtois to put Belgium career on hold
Goalkeeper Thibaut Courtois said in quotes published on Wednesday he has decided to put his Belgium career on standby until September 2027."If the goalkeeper playing in the Nations League performs well, Van Bommel might continue with him.
First Business Bank Funds $7.5MM Ledgered Line Facility for In-Store Auditing Company
First Business Bank’s accounts receivable financing team funded a $7.5 million ledgered line facility for a Massachusetts-based company that performs in-store
Zakaria: California’s private sector ‘massively overperforms’ while ‘government underperforms’
Vanderbilt Health performs nation’s first breast cancer surgery using intraoperative PET-CT scan technology
The novel imaging technology provides surgical teams with a real-time view of tumor margins in the operating room.
Entertainment: Journey performs at the Blaisdell this weekend
Journey performs at the Blaisdell this weekend as part of their farewell concert tour.
SeaWorld San Diego Summer Concert Series Returns for 2026
Ashanti, Bow Wow, Soulja Boy and more headline SeaWorld San Diego’s Summer Spectacular Concert Series through Aug. 29.
Gears of War: E-Day Performance Benchmark Review
Gears of War: E-Day is a prequel set 14 years before the original game. Built on Unreal Engine 5 with RTX Mega Geometry, DLSS 4.5, FSR 4, XeSS, and ray tracing, we examine graphics quality, VRAM usage, upscaling options, and how it performs across a wide range of modern GPUs.
In Absolutely Science Fiction! at Z-Space — Bradbury and Vonnegut, together at last
In “Absolutely Science Fiction!” at Z Space through July 19, literary theatre troupe Word for Word performs short stories by Kurt Vonnegut and Ray Bradbury.
Resident Evil 2 Deluxe Edition review for Nintendo Switch 2
Resident Evil 2 has arrived on Nintendo Switch 2 as a digital release, and we'll look at how the game performs in this review.
Microsoft AI CEO says new model beats OpenAI using less compute
Microsoft AI CEO said its latest AI model outperforms an OpenAI base model on certain pre-training metrics using a quarter of the compute.
Miami Dolphins Discussion: 2026 NFL Schedule Edition
As the Miami Dolphins enter another season under a new regime and navigate significant roster changes, there is a palpable sense of uncertainty surrounding the team. Like many of you, however, I am eager to see how the squad performs once the regular season begins. Miami faces a challenging road ahead, as the team is […]
Mariachi, heavy metal and a little blood spatter: Things to do in Merced in October
A four-time Grammy Award-winning singer-songwriter performs at the Merced Theater.
Heart surgeon makes history, performs first robotic procedure of its kind
A surgeon in Michigan has performed the world’s first single-port robotic mitral valve repair.
'State of States' analysis shows Florida outperforms in small business development
Insurance costs pose a challenge, but that's largely mitigated by low taxes.
Here's how Apple Watch Series 11 stacks up against Oura, Fitbit, and Whoop, per WSJ
Apple Watch performs favorably in The Wall Street Journal’s health and fitness tracker showdown today. Here’s how Apple Watch Series...
NYC Health + Hospitals/Kings County Successfully Performs Its First Sensation-Preserving Nipple-Sparing Mastectomy
Brooklyn, NY – NYC Health + Hospitals/Kings County successfully performed the hospital's first sensation-preserving nipple-sparing mastectomy. The
Chinese sodium battery surprised scientists by matching key Tesla benchmarks
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Draft: Arts & Entertainment Agenda, Aug. 6 to 13
Trumpeter Randy Brecker performs at the Paul JAS Center, Aug. 7 Virtuoso trumpeter and composer Randy Brecker will perform two jazz shows Aug. 7 at the Paul JAS Center, 422 E. Cooper Ave. The early...
Low Cut Connie performs at Long\'s Park [photos]
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Chartbook 464 The"China squeeze" - the politics of development counterfactuals.
There are three types of China shock discourse. Each has a distinct temporal register. China shock 1.0 was a phrase coined by US academics in the 2010s to retrospectively summarize the impact on particular US labour markets, which they identified through painstaking empirical investigation. China shock 2.0 on everyone’s lips in Europe in the mid 2020s is an alarm raised by trans-Atlantic policy thinkers warning of a coming disaster which they project onto Europe’s ailing car industry. The third type of China shock discourse - commonly known as the “China squeeze” launched most recently by Indian economists Shoumitro Chatterjee and Arvind Subramanian in the pages of Foreign Affairs - mobilizes a different, more speculative temporal register: the counterfactual realm of alternate history. The China squeeze argument asks not what has happened, or what is going to happen, but what might have been. Specifically, what options for economic development might have been open to Global South economies like India or the economies of sub-Saharan Africa, had it not been for China’s spectacular industrialization since the late 1990s and the persistent competitiveness of China’s manufacture in low-skilled segments. Chatterjee and Subramanian’s argument is that whilst China has built a powerful position in electrical machinery and other high-skill manufacturing, which is now delivering China Shock 2.0, it also continues to run a trade surplus in low-skill, labour-intensive manufacturing. This surplus is large and has remained largely unchanged in fifteen years. Indeed, in the 2010s, in trade with low-income countries, China’s surplus rapidly increased. On their reading, this squeezes low-income producers in low-skill sectors in which they “should” be able to compete. Source: “China’s Mercantilist Squeeze on Developing Countries” Shoumitro Chatterjee and Arvind Subramanian. Peterson Institute for International Economics 26-7 May 2026 Chatterjee and Subramanian’s wording is telling: (emphasis mine) The canonical trajectory of economic development in the post-World War II period is for countries to transition from agriculture to manufacturing, first in low-skilled manufacturing and then later on to high-skill manufacturing (Herrendorf, Rogerson, and Valentinyi 2013). At the global level, this implies that high-income countries (HICs) that once specialized in low-skilled activities cede manufacturing export space to poorer countries that come along to occupy that space. In 1965, advanced economies accounted for about 60 percent of global value-added exports in four key low-skilled manufacturing sectors, i.e., apparel, leather, textiles, and footwear. Over time this share declined and began to be occupied first by the East Asian tigers and, beginning in the late 1990s, by China. Today China accounts for about 50 percent of the global market. The question is whether China, with its rising prosperity, is continuing to behave like its richer counterparts and ceding export space to poorer followers or to behave unlike them and continuing to occupy space. In terms of the conceptual maneuvers it performs, of the three China shock discourses, the “China squeeze” argument is by far the most ambitious. After all, it bases its critique of China on a counterfactual imagining of the world. Like any counterfactual argument, to construct this alternative reality it must rely on a heuristic model. To make their argument Chatterjee and Subramanian mobilize two key claims. The first reference point is the actual history of rich-country industrialization, what they refer to as the “canonical path”. The idea is that countries transition from agriculture to light industry and low-skilled manufacturing and from there up the value chain. To make this useful as a standard of comparison and a means to construct a normative standard, they adjust sectoral export shares for two factors: 1. the underlying level of economic development of the exporting country, as measured by national income and 2. the degree to which the sector in question is internationalized. The idea being that for a high income country to have a high export share in low-skill sectors is anomalous and even more so if that particular product is highly internationalized and thus suitable for being “conceded” to lower-income competitors. Where should this process end? Here Chatterjee and Subramanian introduce their second benchmark. Their normative idea of a global development path is one in which, in due course, low-skill manufacturing exports are distributed in proportion to a country’s share in the global labour force. They imagine, in other words, a world of proportional, polycentric global development without much national specialization in low-skill manufacturing. A useful benchmark is that, in labor-intensive manufacturing sectors, a country’s share of world exports should be broadly commensurate with its share of the world’s low-skilled labor endowment. … In the Heckscher-Ohlin model, this would be true if value added per worker and export propensities, i.e., fraction of output that is exported, are the same across countries. It is this pleasant liberal vista of orderly global development, progressing towards a general distribution of low-skilled manufacturing against which they judge reality and conclude that the low-income countries are suffering a “China squeeze”. China is not exiting low-skill sectors fast enough Allowing for its gdp level and the degree of globalization, China’s share of low-skill manufacturing exports is far higher than it “should” be. Relative to population, its low-skill share of exports is far too high. That in turn yields the conclusion that huge numbers of jobs - the authors suggest tens of millions - are being hoarded by China. For the four sectors captured in figure 4, this excess in value-added exports is $110 billion in 2022. For all low-skilled sectors overall captured in appendix figure D.1, excess value-added exports amount to $365 billion. The crowding out of LMIC exports by China is therefore substantial. Both these claims have in common that they are strong structuralisms. The first is that development paths ought to resemble each other. The second is that broadly speaking as far as low-skilled manufacturing is concerned, all countries are alike. If the world currently diverges from the distribution of trade implied by these assumptions, if China is not following the advanced economy path and has huge exports, this must be the result of “policy”, which the authors then put in question as illegitimate or unfair. Thus the naturalistic construction of a canonical path leading to a “level” distribution of low-skilled manufacturing reveals itself to be a polemical tool. From the point of view of China shock polemics, this discourse is no doubt welcome. But as an account of economic history, it must surely be regarded as nothing short of bamboozling. Development paths do not follow a regular, natural sequence. As David Fishman put it aptly, “development is not a queue”. They are always and everywhere, through and through political. Development is not so much a process, as a project. Each such project is more or less well adjusted to its context. As Gerschenkron taught uneven and combined development implies differentiation rather than simple imitation on a single parth. It may be true that Europe and the US exited relatively early from low-skill work. But clearly technology has changed. As C&S themselves point out, China no longer enjoys a low wage environment. Do they really believe that such huge differences can be accounted for either by industrial policy, which as they admit does not operate powerfully in low-skill sectors, or by exchange rate manipulation? What is overwhelmingly dominant in explaining the persistence of “low-skill” manufacturing in China are surely the network effects of complex supply chains. Indeed the label of “low-skill” may miss the point. The degree of globalization which Chatterjee and Subramanian decide to conjure away in their model of the development path - to allow for the fact that Europe and American textiles exporters did not enjoy just-in-time logistics in the 1950s - obscures the point that the networks through which Chinese factories supply the world are themselves key parts of the low-skill industrialization project. China isn’t knocking out 1970s style “cheap”. It has redefined the entire product cycle. C&S’s casual reference to “T-shirts” is telling. In today’s “fast fashion”, there is weight on both terms. Fashion and fast. China’s success in delivering on both at unprecedentedly high speed and at unprecedentedly low cost, isn’t a low-skill operation at all. If we see development trajectories not as the serial repetition of the same, but as distinctive, crafted projects that cumulate in complex agglomerations of skill, technology and labor, then this explains why the assumption that low-skilled manufacturing should follow the distribution of population flies in the face of all experience. As countless cases attest, low-wage, low-income developed economies are not by themselves the makings of a competitive production base. As Leon Liao puts it quite fairly: “International trade has never been allocated according to population. A country’s population gives it no natural claim to an equivalent share of global exports." And the remarkable fact is that Subramanian alone and with co-authors has long been making this point, not to pillory China, as is the case of the tendentious _Foreign Affair_s and Peterson pieces, but to highlight the great counterpart to China’s industrial success, namely India’s relative failure. The much-quoted Foreign Affairs piece showed low-skill export shares only for China, not for India. If you put both countries in the picture as Chaterjee and Subramanian have done on other occasions, the picture looks like this. China is far above the line, but India, almost as large in terms of population, is no less anomalous, just in the opposite direction. As far as global populations are concerned, and the imagining of tens of millions of “squeezed” jobs, it is this Sino-Indian pair that account for a huge share of the action. What this graph reveals is not China’s cheating, but India’s “greatest development failure”, at least this is the argument made Arvind Subramanian, this time in co-authorship with Devesh Kapur, in their major book, A Sixth of Humanity. Independent India’s Development Odyssey which appeared with Oxford University Press earlier this year. In this highly critical work on India’s development, China is repeatedly evoked, not to criticize but as a benchmark against which to illuminate what India’s political economy has not delivered i.e. large-scale formal employment for hundreds of millions of citizens. Kapur and Subramanian leave no doubt, if you want to understand why China, not India was the main driver of manufacturing globalization from the 1980s, don’t look to slots opening up in the division of labour thanks to the liberal mindedness of advanced economies, look to the contrasting political economy of the Maoist PRC and India’s post-independence political economy. As Kapur and Subramian note: The great divergence between China and India did not begin in 1978 with the Deng reforms—it was well underway by then. In one of the great ironies of modern history, the conditions created by Maoism turned out to be a great launching pad for industrial globalization. As the comparison of India and China in 1980 makes clear, to imagine manufacturing would be equally distributed when populations are so differently endowed in terms of educational and nutritional is fanciful. Here is the data from Kapur and Subramanian, data which I explored back at the very beginning of Chartbook. As Kapur and Subramanian insist, the striking fact is that even as India’s growth accelerated from the 1980s, structural change has not. Formal employment in India lags woefully behind. Why? As Kapur and Subramanian note, the path-dependent impact of India’s poor early start was devastating. The list of factors they mention is long and weighty: We argue for a new five-fold explanation applicable at different stages of Indian history: in the initial stages, the legacy of two key pre-1980s policies, the neglect of agriculture and the strangling of the domestic private sector, were critical. The failure of broad-based agricultural growth militated against rural industrialization, which would have been devoted to satisfying local needs that would have necessarily been labour intensive. Then, when the boom came after 1980, the thicket of regulation and broader labour protections that were legislated prematurely in a nascent democracy, underpinned by a deep ideological aversion to private capital in general and big capital specifically, became a barrier to attaining the size that would be necessary to compete globally. Similarly, the reforms after 1991, while wide-ranging, did not quite encompass non-tradeable inputs critical to manufacturing, namely, land, logistics infrastructure and power. A combination of low investment (logistics infrastructure), regulation (land) and the continued monopoly supply by state governments (power), combined with residual socialism, kept costs at 75–100 per cent above efficient supply. These factors were compounded on the supply side by the government, which dominated the formal labour market and established exorbitantly high wages at the lower end of the skill spectrum. This made their employment in the labour-intensive sector by the private sector less attractive. Finally, when the services sector and its exports boomed, wages soared even further via a Dutch Disease effect: one successful tradeable sector killed and cannibalized another less-skilled tradable sector, namely, manufacturing. Thus, scarcity-inducing policies of the planning era, precocious democracy and a Gandhian aversion to size; inefficient, monopolistic supply of critical non-tradable inputs; the behemoth of government; and super-successful skill-intensive services successively undermined India’s unskilled labour employment opportunities. The employment failure seems to have been over-determined. … Less quantifiable but no less important are some of the sociological factors that have undermined manufacturing and factory jobs. Take the impact of the success of the IT sector—located in urban areas with better working conditions, such as air-conditioned offices, good schools for children and attractive entertainment options. It became a more attractive place for engineering and management graduates to flock to. … Consider manufacturing and female employment. Indian society makes it difficult for women to work, reflected in India’s unusually low female labour force participation (elaborated in Chapter XIII). Indian society, in particular the routine harassment of women in public spaces, makes it even more difficult for them to travel long distances to take up work.37 Combined with onerous regulations on creating hostels and dormitories for women—which, in turn, prevents multiple shifts of operation—factories need to be located in areas with a large female labour pool. While firms can take apparel plants to female labour or bring them to the plants by building large-scale dormitories, this raises costs. If there was a case for subsidies to encourage labour-intensive manufacturing, housing subsidies to build dormitories for women workers would be a good place to start. All of this meant, that when opportunities did open up, in part thanks to adjustments in China, India was in no position to take advantage. Here is the account of the 2020s from Kapur and Subramanian. It stands in dizzying contrast to the one-sided, “China squeeze” argument delivered in the Peterson and Foreign Affairs pieces: In 2022, for example, India’s share of global exports in clothing (the canonical labour-intensive sector) was 3.1 per cent compared to 32 per cent for China. This is extremely disappointing because China started vacating export space in this sector after the Global Financial Crisis as its wages started rising; it then vacated further space in the aftermath of geopolitical developments that led investors to seek alternative investment locations. Between its peak and today, China lost about 7.5 percentage points of global export share (worth about $43 billion in 2022); over this period, India’s export share remained stagnant at 3 per cent, while that of Vietnam and Bangladesh rose by 3.5 to 4 percentage points. China’s loss did not translate into any gain for India in terms of low-skill exports and employment. Put differently, when the conditions for dynamism and prosperity were established after 1991, and even more tantalizingly when the global export opportunities opened up, it was as if India was a high-wage economy and only skilled labour could benefit from the dynamism. It had converted its physically abundant unskilled workforce into an economically expensive resource, penalizing its use. It is not my mission here to argue for or against Kapur and Subramanian’s interpretation of India’s development trajectory. The point in this context is simply that the “China squeeze” such as it is can only be understood, in relation to the heavy burden of domestic constraints that Kapur and Subramanian so unsparingly enumerate. I am concentrating here on India because it is the great counterpart to China and the unspoken reference point of the “China squeeze” discourse. But the point is more general. Can anyone with a straight face suggest that the main problems afflicting the development of South Africa or Nigeria, the great centers of mass underemployment in sub-saharan Africa, are cheap competition from China driven by Beijing’s industrial policy and an undervalued RMB? Likewise, is the main problem of the American working-class the threat of Chinese competition or the manifest crisis of social reproduction in the United States itself? The point here, as in the discourse around China shock 2.0 in Europe, is to insist that we should not normalize the world and pathologize China, without actually investigating what is going on in America’s industrial wastelands, the German car industry, or India’s lopsided labour market. One-sided pathologization should be understood for what it is, a polemical gesture. Concepts like China shock and China squeeze are, what Reinhart Koselleck, following Carl Schmitt, would have described as “asymmetrische Kampfbegriffe”, or “asymmetrical counter-concepts” or “asymmetrical combat concepts”. If the function of arguments like the “China squeeze” is to alert elites in the rest of the world of the need to rejuvenate and energize their development projects, so be it. But let us see the resulting discourses as the mobilizing interventions that they are. Interestingly, a significant slice of China’s public sphere - the so-called industrial party - have been obsessed for the last twenty years with precisely these questions of counterfactual economic history. Since the mid 2000s, a massive online discourse developed around an alternative history in which Chinese techno-patriots were asked to imagine how they would proceed if they had the possibility of traveling back in time to transport modern technology into the Ming dynasty (1368-1644) thus averting the “great divergence” of the 18th and 19th centuries. It is a fantasy of course, but also a goad. The point was not directed at Westerners. The alternate history is dramatically nationalist, but few n the West had even heard of it until recently. The adherents of the industrial party are not liberal moralists. They don’t project natural ladders of international development and ask why others don’t cede space. They take the Realpolitik of global economic development for granted. Their pointe was directed against fellow Chinese. The rallying cry of the alternative narrative was this: Now that we are beginning to gain steam and push forward on our national development path, now that we are breaking out of the inferior position assigned to us even in the early years following WTO accession, do not lose yourself in humanistic imaginings about better futures. In the spirit of Deng, they called on China to embrace the hard truth of development. Perhaps one might of the Industrial Party as a cultural trend born out of desire for symbolic revenge against those who had inflicted the century of humiliation. In terms of the various temporalities of the “China shock” - retrospective, prospective and counterfactual - one might think of the Industrial Policy discourse as truly speculative, conjuring up the China shock that never was. Or perhaps one might better think of it as imagining a world in which a powerful China was no shock at all, but simply a defining fact of modern history. I love putting out Chartbook. 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Musician's hall of fame journey involves Elvis Presley, Woodstock
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Microsoft’s Mustafa Suleyman says its AI outperforms doctors on hard cases as he chases ‘medical superintelligence’
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ENTERTAINMENT: Mandolin legend set for Ozark Folk Center benefit
Mandolinist Sam Bush, a member of the Bluegrass Hall of Fame and three-time Grammy Award winner, performs with his band as headliners of a 7 p.m. concert Saturday in the Ozark Highlands Theater.
Hometown entertainment: Weekend events Aug. 15-16
Sara Evans performs in Rocky Mount Friday night — plus a car show, heritage festival, free art event and meteor shower watch this weekend
August Monthly Review: ChatGPT In Epic
Death, taxes, another frontier lab healthcare launch inspiring truly insane LinkedIn takes. As much as I want to stay away, I repeatedly am the crewman unplugging his ears whenever we pass these sirens. So it’s not the first and probably not the last time we dial up the typewriter rather than tie ourselves to the mast. The full blow-by-blow timeline: Health API Guy is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe The End of the Standalone PHR (Jan 8): ChatGPT Health arrives for consumers, at the time powered by b.well. I was bullish on the approach in that the best way for the PHR to finally solve distribution was to meet consumers were they already are (i.e. When Horizontal Meets Healthcare (Jan 9): OpenAI for Healthcare puts out the shingle for enterprises, with SharePoint ingestion standing in for the EHR integration the product actually needed. Another One: Anthropic’s Healthcare Debut (Jan 16): Claude's version, on HealthEx rails instead of b.well, with Agent Skills for FHIR development as the one true vertical-specific investment. ChatGPT for Clinicians: The Trap Sprung (Apr 24): OpenAI filled in the missing GTM middle with a free product for verified clinicians, effectively peeling the enterprise wrapper off ChatGPT for Healthcare and taking the PLG fight directly to OpenEvidence. OpenAI’s Second Attempt at Health (Jul 31): a relaunch of their patient specific app, now branded Health in ChatGPT, where the privacy partition came out, the connectors thinned, and b.well disappeared in favor of first-party FHIR work against a smaller network Beyond a Shingle So what was announced now? Today, we’re introducing a new electronic health record integration that brings authorized patient context from Epic into ChatGPT for Healthcare, along with the Healthcare Public Data plugin for direct, structured access to official healthcare datasets like PubMed, DailyMed, and CMS Coverage. Together, these capabilities bring ChatGPT closer to the systems and sources healthcare teams trust, while supporting the controls and compliance healthcare work requires. The core portfolio is unchanged from prior announcements we delineated and discussed (aside from the relaunch of patient-facing). Since trade publications still seem to be confusing them by using the wrong names: Enterprise sales motion: ChatGPT for Healthcare Protecting their core product / PLG: ChatGPT for Clinicians Consumer/patient-facing product: Health in ChatGPT So really, this is a feature release (specifically for ChatGPT for Healthcare when it comes to EHR integration). What’s cool about it, though, is that we are moving beyond just putting out a shingle. Everything in the January enterprise launch was horizontalizable: Horizontal tech companies thus generally start verticalization with their “putting out a shingle phase”. Telling the world you’re open for business as a horizontal tech company reliably attracts early inbound interest from buyers who are already trying to force-fit horizontal tools into domain-specific workflows and are eager for any signal that the vendor intends to support their use case more directly. Nothing was really vertical specific, which is exactly what you'd expect from the ultimate hypergrowth horizontal company dipping their toes into specific industries. But eventually you have to stop changing the sign on the door and start changing the product. Can you guess where the horizontal product was going to run out of road? This is the core dilemma writ large: verticalization requires differentiation, while scale economics of consumer and horizontal push toward unification. I’ve buried the lede so deep here it will be painful to some readers, but what should be shocking to no one is that my perspective is that EHR integration is the only path that meaningfully resolves this tension for ChatGPT for Healthcare. Integration with Microsoft Sharepoint is fine, but it is categorically insufficient for any product that hopes to influence clinical decision-making. SMART Money Well here we are, eight months later! Real vertical investment has begun, which surprised a few people. One friend in the industry messaged me: “Absolutely no way Epic gave ChatGPT API access.” Looking forensically at the announcements, the integration is notably read-only across appointment notes, laboratory results, medications, and specialist documentation. So while I’m not a betting man, I'd wager the house this is a SMART on FHIR launch: That sounds a lot like USCDI! The recommended path for the workflow they outline would be SMART on FHIR Their Health AI lead’s post is evocative of both standalone and EHR launch SMART, which another termed “ChartGPT” and “EHR Plugin” The implementation burden for SMART on FHIR is the lowest of the available integrative paths This is the team who literally did patient-facing SMART on FHIR with Epic last month Most importantly, if you’re a horizontal company, you generally still haven’t taken the full plunge and thus (overly) value reusability. SMART on FHIR is the logical compromise: healthcare-specific enough to matter, but standardized enough to reuse. Nobody Asked Judy Another friend asked “I thought they had to go to each health system, or did they go direct to Epic and now health systems opt in?” Given the trust paradigms in healthcare, this functionally cannot be Epic hoovering up all the data for OpenAI and shoving ChatGPT in their customers’ faces (or they would revolt). However, I do believe the aforementioned terrible coverage by TechCrunch and other outlets is probably responsible for that question, given the 325 million reference. When using FHIR as a business associate to an Epic customer, there is no EHR gatekeeping to speak of, as that isn’t how fhir.epic.com works: You register an app You pick your APIs You test against the sandbox APIs You list as “Ready for Production” Hospitals pick your app You test and go live with them Bluntly, nobody had to say yes or no in Verona (nor were they given the chance). Vendor Services (their next developer tier up that I doubt OpenAI is using quite yet in this initial release) certainly has contractual paperwork that some resent, but even there, the twin pressures of mounting antitrust and information blocking make outright gatekeeping increasingly fraught. My friend’s sentiment is, to me, representative of a broader industry neurosis, perhaps a sort of scar tissue of prior eras: people assume the gate is still there and never actually try the door. There are certainly other ways Epic (and any EHR) can put its thumb on the scale, but they are not so dumb as to stand in front of a federally mandated API and play bouncer at this exact moment in time. So I think they can and probably should go deeper, as this release is at best parity and at worst behind vertical-specific competition. OpenEvidence did a basic SMART launch with Sutter Health in February, allowing for more convenient evidence search by providers. They then add patient-context (the equivalent SMART on FHIR flow to what we see here) when Cedars-Sinai went enterprise-wide in May. UpToDate has four distinct applications across deeper workflows like patient engagement listed in Epic Showroom and announced a partnership with Epic to power Art at UGM Abridge and other ambient scribes have invested deeply into the deepest bidirectional clinical copilot workflow with integrations well beyond SMART on FHIR. In that light, the “Who is OpenAI primarily targeting here?” is clear. This release brings them into striking distance of OpenEvidence, but not the others quite yet. They are the only one of the three you can reach without building something truly Epic-specific. When will they go further? Competition is a great motivator to overcome the horizontal demons and start building things that can’t be reused: not across industries, not across EHRs, maybe not past the customer you built them for. That’s the price of actually verticalizing. The question is if and when OpenAI will be willing to pay it. Month in Review Here is the monthly review. As a reminder, this is a regular round-up of the month’s posts and other content to surface things you may have missed across regulation, litigation, interoperability, and beyond. AI assistance is used in these bullet summaries so I can focus on articles. Articles Published: None this month Video Content: The Information Exchange: Standards-based Thruple Edition (Aug 14): Back to school for us too, with Brad reporting in from the CMS Health Tech Ecosystem’s one year anniversary in DC. We get into the January CMS-0057 deadline, the fall rulemaking reading list, and why enrollment still keeps most apps off FHIR. The Information Exchange: Epic Dúnadan Edition (Aug 25): A UGM roundup with Ryan Brickner joining for the first time to check our takes against what the building actually thinks. Rangers, Ergo sitting on top of EHI, and a down-market lineup that needs some cuts. Regulatory: Stacked Deck, Bad Hand (Aug 05): ONC’s website refresh took the entire HIT Policy Committee record with it, so I rebuilt the archive and went looking for the regulatory capture story everyone assumes is buried in there. Stacking the deck and winning the hand turn out to be very different things. The Sixth Generation of Patient Access (Aug 19): A quick catalog of the five generations of patient access we’ve layered on since HIPAA, and the two candidates now competing to be the sixth. One is planned. The other is the market routing around the plan entirely. Court cases: Three Cases Walk Into a Docket (Aug 06): Two surprise settlements cleared the board in a single week, and then Judge Maddox dropped ninety-four pages on Vyne v. Henry Schein. The sleeper is a DMCA holding that makes direct-to-database a considerably riskier business model. Epic v. Health Gorilla: Into the MDL (Aug 10): Nine class actions are headed to Miami, and the Panel signaled it wants to bring the case that spawned them along too. That would leave Epic arguing the requests were obviously fraudulent in one courtroom and unknowable in the other. Amazon v. Perplexity: Agents Are Legalized! (Aug 11): The Ninth Circuit vacated the injunction against Comet, holding that the user is the one accessing the servers rather than the company that built the agent. A real win for agentic access, and a much narrower one than the headline suggests. Veeva v. Epic: Come At Me, Bro (Aug 26): Epic’s response brief wants the dismissal affirmed and, unusually, wants the opinion published as precedent. Buried in it is Epic’s own description of what its non-competes actually prohibit, which current and former employees should read closely. EHRs: The Contract Epic Would Never Sign Today (Aug 04): A 1999 SEC exhibit catches Epic licensing nearly its entire product line, Tapestry included, to the company that became TriZetto. The marketing services menu attached to it is the part that will make you blink. Forecast from Verona (Aug 07): The Epic Almanac makes one argument six different ways: the AI is only as good as the networks behind it. Emmie headlines, Art’s context stack is the most ambitious part, and Penny finally gets an autonomous coding date on the calendar. Works With Epic MyChart (Aug 17): Epic’s first new Showroom category since the death of Workshop certifies hardware instead of software, badge on the box and all. Made for iPhone, but for blood pressure cuffs, and rough news for anyone selling the RPM stack sitting in between. UGM Hot Takes 2026 (Aug 20): Everyone else covered the AI announcements, so I went after Savvy deleting the payment gateway, Rangers as Boost with the timer removed, and a down-market lineup with too many entries. Plus the Health Grid tidbits I cannot help myself on. MyChart Central Grows Up (Aug 24): Device data and Emmie turn Epic’s identity hub into a full consumer platform, which is both a logical answer to ChatGPT and a bit of a mistake. It also happens to be the best scraping target Epic has ever shipped. Industry Analysis: The Wrong Yardstick (Aug 12): Vertical software keeps getting judged against Superhuman and Notion, which is the wrong bar entirely. Your user is comparing your product to a whiteboard and forty phone calls before lunch. Primitives vs. Abstractions (Aug 27): Developers want building blocks, systems of record prefer to hand out business logic, and both sides have a real case. There’s no test from the outside that separates the engineering reason from the competitive one, which is why this keeps ending up in court. Cross-industry Comparisons: The Two Kinds of Platform Power (Aug 18): Attention power and record power are different problems that keep getting handed the same regulatory toolkit. Congress’s latest swing at Big Tech shows both what’s possible and where it falls apart. The Dogs of (Platform) War (Aug 21): A CourtListener alert turned up a property management fight with healthcare’s exact shape and none of the Cures Act. Shell prospects, ghost accounts, a notetaker bot that came back to haunt someone, and a lawyer arguing both sides of the same theory in two states. Fractals All the Way Down (Aug 31): Yardi dominates property management right up until you zoom into one segment, where AppFolio owns it outright and Yardi doesn’t appear at all. Where you draw the line around a B2B software market is about to decide an awful lot of cases. Other News: If Judy Had the Courage (Aug 13): A short eulogy for the era when software was allowed to look insane: WinAMP, bold colors, and everything the grey chatbot era has taken from us. External Media: Portland Monthly Health Tech Meetup: Erin O’Brien channeled my own feelings - maybe it’s just because it’s August and beautiful, but the monthly edition of the PDX Health community meetup was a ripper. Make sure to reach out if interested to join for the next one. STAT’s Coverage of Epic This Month: Brittany Trang of STAT did a fantastic job of breaking the FTC investigation that’s been lurking, as well as UGM related coverage, so I had to give her her wish of a meme. Fall Conferences: I’m pumped to be kicking off my fall conference season in NYC at Nabla Accelerate next month, which Chrissy provided the link to apply for. Here’s the rest of my schedule in case you want to meet up: Commonwell (Redwood Shores, CA): Oct 13-14 Open@Epic (Madison, WI): Oct 21-22 eHealthExchange (Austin, TX): Oct 27 Sequoia Project (Austin, TX): Oct 28-29 HLTH (Vegas): Nov 15-18 RSNA (Chicago): Nov 29-Dec 3 Posts I Liked: HTI-6 Should Unbundle API Certification: Josh Mandel makes the case that HTI-6 should split (g)(10) into separate authorization and data certifications, so a PACS or a genomics platform can certify only the role it actually performs. Imaging is the urgent example, but the structure solves a much bigger problem. On implementing ePrior Auth with Epic: Scott Rossignol’s field notes from a live ePrior Auth build on Epic, including the two app registrations nobody warns you about and the CPT mapping problem waiting at the end. CMS-0057 implementations are going to be such a beast. Healthcare point solutions are starting to look a lot like streaming services: Spencer Dorn runs the cable-to-streaming arc against health IT and lands squarely on the bundling half of the cycle. Bundling can be good! It can also be bad! Life is nuanced. Health API Guy is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe
Tentpole Xbox release Halo: Campaign Evolved performs better on the PlayStation 5, with the PS5 Pro providing the best experience
According to a new Digital Foundry article, the PS5 version of the game meets Xbox Series X quality, without cutscene stuttering.
What If the Coffee Grounds We Throw Away Could Help Insulate Our Homes?
The grounds left behind from your daily cup of coffee could one day help insulate buildings. Scientists transformed spent coffee grounds into biodegradable biochar insulation that performs about as well as commercial foam. The material could give mountains of coffee waste a useful second life while helping reduce energy use. Now, researchers are working to determine how well it holds up over time.
Fernando Alvarez Jr., D.O., Joins Baptist Health Primary Care as a Family Medicine Physician
September 9, 2026 – Board-certified family medicine physician Fernando Alvarez Jr., D.O., has joined Baptist Health Primary Care. Dr. Alvarez provides comprehensive care for patients of all ages, with an emphasis on preventive care and long-term health and wellness. He also performs a variety of office-based procedures, including Pap smears, contraceptive implant and intrauterine device […]
UK software sales chart for the week ending June 27, 2026 - Star Fox tops the charts, outperforms Star Fox Zero and Star Fox 64 3D
The latest UK software sales chart for the week ending June 27, 2026 is now available. Star Fox was the major release of the week, and it seems to have done pretty well. For one thing, it took the top spot. We’re also hearing that the Nintendo Switch 2 game sold almost double what Star Fox Zero did during its...
Sister’s Gift Represents Transplant First at Sutter Health
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AI outperforms standard methods of predicting organ failure in acute pancreatitis patients
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Journey brings Final Frontier tour to Heritage Bank Center. See photos
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HOMETOWN ENTERTAINMENT: A pageant finale, concerts, comedy show and New River Day
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NYC Health + Hospitals/Kings County Successfully Performs Its First Breast Tumor Cryoablation Procedure
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Prasugrel after PCI outperforms other common antiplatelet drugs
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Jenna Ortega details her most intense and specific OCD symptoms
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Madonna Turns Times Square into Night Club, Previews New Album to Kick Off Pride Month at Surprise Concert: 'C'mon, Gays!'
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Marshall Health performs first roboticassisted shoulder replacement surgery in WV
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'Today's' Craig Melvin crowned People's sexiest morning show host
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Vertu wants executives to pay $6,880 for an AI agent — here's how it actually performs
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gamescom 2026’s Steam wishlist winners
gamescom wrapped up last weekend, and it was bloody fantastic. It really felt like the industry had a spring in its step again. The show itself was BUZZING, with 368K attendees (+3% over last year), maxed-out exhibit space, and Opening Night Live pulling over 78M online views (+7.5%). Let’s have a look at the games at the event that brought in the most new Steam wishlists between August 23 and 30. One quick thing before we start, and stop me if you’ve heard this one before, but wishlists are NOT a barometer for launch sales (they’re bookmarks, not baskets is what we always say over here at Alinea). With that aside, wishlists are a nice gauge of hype, so let’s dive in. Witche(r)s and Warlocks take the top two spots The biggest game of the event (by new wishlists, anyways) wasn’t a game at all. It was an expansion. The Witcher 3: Wild Hunt’s upcoming expansion, Songs of the Past, nabbed 451K Steam wishlists by the end of the weekend. It’s a big example of something we bang on about constantly: proven IP with built-in fandom is the most powerful force in this market. But said IP can also ripple into other external success stories (more on that later!). Anyway, Songs of the Past debuted at Opening Night Live, and it’s the first Witcher 3 expansion in over a decade, landing next year after a full Witcher 3 remaster (free for existing owners!) arrives next month. The Witcher 3 has sold around 66M copies, and I’ll be one of those owners jumping back in to refresh myself on the core game (like there isn’t enough to play!), then play the expansion next year. This is all part of CDPR’s plan. The studio is clearly whetting everyone’s appetite for The Witcher 4 in 2028 via a classic bridge strategy. Want more free estimates and insights in your inbox twice a week? Subscribe below! Subscribe Releasing a free 4K remaster next month – followed by a proper expansion in 2027 – keeps IP top-of-mind across a huge gap between content (games-wise, anyway). It kind of echoes Capcom’s evergreen catalogue strategy. And outsourcing co-development to Fool’s Theory (a studio led by trusted Witcher 3 veterans) keeps CDPR’s internal headcount focused on The Witcher 4. #2 by wishlists between Aug 23 and 30 went to another giant IP: Warlock: Dungeons & Dragons, published by Wizards of the Coast and developed by Invoke Studios (a team of ex-Far Cry, Watch Dogs, and Splinter Cell devs). It’s due to launch in 2027, and it looks brilliant so far. It’s a dark-fantasy open world with third-person spell-slinging. It’s a very Witcher-ish aesthetic. As you might expect, 48% of Warlock’s Steam wishlisters have played Baldur’s Gate 3, and 46% have played The Witcher 3. One to watch. For sure! Mash-ups: Anime GTA (!?) and a city-builder for kids (and adults) Ananta took #3 with 302K new wishlists. It’s an open-world game that looks like GTA and Persona had a baby. It was published by NetEase, developed by Naked Rain, and got a trailer at Opening Night Live. I got hands-on at the show, and it wears its influences openly; there’s literal web-slinging (very Spider-Man) and a LOAD of mini-games (Like a Dragon). It’s free-to-play and out in January. For Ananta, Naked Rain is taking the free-to-play, cosmetics-only route. That’s certainly a choice for a single-player open-world game. Without gacha mechanics driving high ARPU, Ananta is betting purely on top-of-funnel reach and long-term retention. This puts pressure on skin appeal when there’s no multiplayer lobby to flex in (although, co-op is reportedly coming later). Want more free estimates and insights in your inbox twice a week? Subscribe below! Subscribe Then there’s Lego Skylines (273K wishlists between Aug 23 and 30), which does exactly what it says on the tin. It’s a cosy Lego twist on the city-builder genre, as shallow or as deep as you fancy (five min quick sesh or ten-hour no-lifer session; latter for me). There’s some family-friendly stuff in there like couch co-op, which is only really viable on console. Iceflake Studios, which Paradox acquired back in 2020 is developing this one. So Paradox is probably looking for a family-friendly onramp to its rad Cities: Skylines games. The overlap there is plain, with 33% of Lego Skylines wishlisters having played Cities: Skylines and 16% Skylines II. There’s no doubt plenty of parents here (and uncles, ahem) planning to indoctrinate the next gen of city-builders. I’ll definitely be playing with my youngest nephew. This one could be big and educational. I’m very curious to see more. Want more free estimates and insights in your inbox twice a week? Subscribe below! Subscribe Mature IP, new and old, rounds out the list As for the rest of the list of gamescom games by new wishlists between Aug 23 and 30: Heroes of Might & Magic III Remake got 189K new wishlists. It remakes the iconic 1999 turn-based-strategy classic in full 3D and 4K. It follows this year’s Heroes of Might & Magic: Olden Era (~1.8M copies sold, as per our estimates), and around a quarter of the III Remake wishlisters have played Olden Era, including 7% of them this month. Wardogs – a $40 PvP shooter – added another 146K to an already massive pile (nearly 1.6M total). It’s published by Team17 and made by Bulkhead. It plays somewhere between the tactical squad stuff of … Squad and the all-out-chaos battlefield of … Battlefield. It’s up to 100 players, and it’s out in about 10 days. It’s already pre-sold over 500K copies on Steam ($16.6M), boosted by a pre-order-only beta. It sold another 70K copies during gamescom, too. Afterworld managed 121K new wishlists. It’s a Paradox-made post-apocalyptic grand-strategy game, and it was announced at the show. It’s a departure from Paradox’s usual historical stuff, and the studio’s first new setting in over a decade. The Paradox purists are into it, with over half its wishlisters having played Hearts of Iron IV (19% this month) and 41% Crusader Kings III (17% this month). Metro 2039 got 110K during the show, tunnelling its total past 1.3M. Nearly 40% of wishlisters have played 2020’s Metro Exodus, the previous mainline title in the series. The Metro novels are Russian, dev 4A Games is Ukrainian, and it’s set in Moscow, so it’s unsurprising that – after the US (17.3% of wishlisters) – the next two countries are Russia (12.7%) and Ukraine (10.0%). The Blood of the Dawnwalker drew in (the blood of) 108K new wishlisters, now totalling an impressive 1.7M (at a $70 price point, though). It’s an open-world dark-fantasy action-RPG set in 14th-century Europe (with vampires!), made by some former Witcher devs at Rebel Wolves. Again, it shows. Over half of Blood of the Dawnwalker’s wishlisters have played The Witcher 3, and its #2 market is Poland, home of Rebel Wolves. That’s that hometown-hero effect we keep banging on about again! And Blood of the Dawnwalker plays very well, by the way. While it looks like a Witcher clone on the surface, it actually brings a lot to the table mechanics-wise. I love the whole time-as-a-currency spin on RPG gameplay. It gives the game a different, refreshing pace. Essentially, you start Blood of the Dawnwalker with a pool of 480 action points spread across 30 days and 30 nights. As for how the game performs, we’ll be posting some numbers next week, so hit subscribe below to get that in your inbox when it’s ready. Subscribe The Witcher’s legacy ripples Make no mistake: on a high level, this was The Witcher‘s gamescom. The #1 game by wishlisters was a Witcher 3 DLC. Warlock is clearly Witcher-inspired – as is Blood of the Dawnwalker, which was made by ex-Witcher devs. There was a lot of other cool stuff on the floor that we’ll no doubt be covering in subsequent newsletters, though! As always, wishlists went through the roof for sequels, remakes, DLC, and licensed IP. As always, though, these wishlists will likely convert to about 5% to 10% of launch-day buyers, with the probable exception of The Witcher DLC – the exception that proves the rule. Either way, we’ve got a nice, diverse list here – one that is very Witcher-y, Paradox-y, and – if you’re a fan of great games on Steam – a bloody good list. We even got a bunch of new IP to get excited for. Great gamescom! Even if we are still processing that unfathomable number of mini Kölsch beers! One more thing: GTA 6 broke the internet (again) Move aside, Spider-Man and The Odyssey (I love you both, really). But the decade’s defining entertainment zeitgeist is round the corner. Unless you’ve been living under a rock, you’ll know that Rockstar dropped an extended look at GTA 6 last week. It was INSANE, the most impressed I’ve ever been by a showcase like this. You can see the budget seeping through every second of the extended look, and this level of game is unobtainable for any other publisher. Everyone’s going nuts, and rightly so. GTA 6 is out in holiday 2026, and it’ll be the biggest entertainment release ever, full stop. For those without a PS5 or Xbox Series console, a $500-$900 console is a big ask in this economy, but GTA will pull a lot of last-generation-console fence sitters over the current-gen fence either way: The autumn timing puts GTA 6 on (digital) shelves for the holidays, meaning a PS5 or Xbox (let’s be honest, mostly the former) will be on many a Christmas and Hanukkah list. Demand will outweigh the struggling supply, though, and PS5 Pros are already sold out and being sold at a markup via scalpers. Every console that isn’t on a shelf is a sale deferred or lost, and worse, it hands scalpers the margin. GTA has cultural clout that reaches everyone. It’s been a mainstream fixture since 2001’s GTA III and its clout has only grown exponentially since then. There’s a touchpoint for every generation alive. The FOMO-word-of-mouth double punch does the rest. Loading... It’ll reactivate the lockdown cohort, the millions who picked up gaming in 2020-21 and churned. GTA 6 will bring them back. Remember when College Football 25 brought a bunch of US gamers to current-gen consoles? GTA will do that globally. Once they’re back (or upgraded), and all GTA’d out, the fresh current-gen-console folks will check out the rest of the current-gen catalogue they missed. GTA Online, if not free to play, will also lead to a boost for PS+ and Xbox Game Pass, another boost for the ecosystem players. Want more free estimates and insights in your inbox twice a week? Subscribe below! Subscribe Even some of the smug PCMR 60FPS crew won’t be able to resist their FOMO and will bite the bullet on a console to play this one early (then double dip). The consoles cost the same as a decent stick of RAM these days anyway lmao… Don’t get me wrong, though. GTA 6 is no silver bullet for the mature console market, but it does prime the current gen for a couple of strong swan-song years. And it’ll also give the consoles more users from which to maximise ARPU. ‘’We are in the latter half of the lifetime cycle of [the PS5],’’ said Sony President and CEO Hiroki Totoki in a Wall Street Journal interview. ‘’That means we do not need to aggressively sell the console today; rather than that, we are focusing on the current revenue from the current user base.’’ The biggest, best premium games always arrive late in a console cycle, so getting more players aboard now, ahead of that content storm (the new current-gen-only Call of Duty included), is a clear win for the platforms. On the Netflix thing, though: let’s all chill out a bit, like. Half my LinkedIn feed was going bonkers treating the reveal debuting on Netflix like it’s the future of entertainment. It wasn’t. It was a six-hour head start, and Netflix left that detail out of the “first-of-its-kind partnership” press release. The extended look hit Netflix at 3pm ET on 27 August, then went up on YouTube and Rockstar’s site for everyone six hours later. And it was all over social immediately, anyways. This reveal was going to reach an untold number of people whether it debuted on Netflix, YouTube, a Times Square screen, or a group chat. The last trailer did nearly half a billion views in a day with no gatekeeper at all. Netflix just stood next to that audience for six hours, borrowing it. It’s was a tidy deal for Netflix, sure. But every outlet on earth was breaking the thing down frame by frame by the evening anyway. Anyway. I’m hype. And we’ll be covering some GTA numbers on the newsletter soon. So hit subscribe below to get that in your inbox when it’s ready! Subscribe The last word Reply to this email – or reach out here – if you have any feedback for the newsletter – or want to request a game for us to cover. [Alinea Analytics boasts the most accurate PC and console estimates in the business. Game makers use our platform to understand their audience, keep an eye on the competition, monitor sales trends, and spot new opportunities. We equip game studios and financial institutions with accurate data and the confidence to make smarter, data-driven decisions. Want to talk about all things games market data? We’d love to chat!]
Microsoft Diagnostic AI Outperforms Generalist Physicians in Science Study
Microsoft's MAI-DxO diagnostic system outperformed participating generalist physicians on complex diagnostic cases in a peer-reviewed study published October 22, 2026 in Science, which reported 83.2% accuracy for the sys...
New York cardiologist performs first procedure in US with FDA-cleared imaging tech
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Throwback comic Jim Norton performs at the Englert
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Corewell performs world's first documented single-port robotic mitral valve repair
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Corewell Health performs world's first documented single-port robotic mitral valve repair
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Chinese team’s brain-mimicking chip ‘up to 478 times faster than Nvidia GPU’
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Is SMIC N+3’s Metal Pitch Smaller than Intel 18A’s?
Almost four years ago, we published that SMIC had started shipping 7 nm (N+1) chips. Now, SMIC is shipping its third-generation 7 nm (N+3) process in Huawei’s Kirin 9030, with a minimum metal pitch of 32.5 nm, about 10% tighter than the 36 nm minimum metal pitch shipping in Intel’s latest Panther Lake CPUs on 18A. The headline is true, but incomplete cherry picked metric. N+3 reaches the density of TSMC N6 through aggressive DUV multi-patterning and design-technology co-optimization (DTCO), but it pays for that in complexity, efficiency and process control. We found this and more in our reverse engineering and teardown where we cover SMIC’s N3 process technology, Huawei’s packaging, memory, architecture, and more. SemiAnalysis has been building a state-of-the-art teardown lab in Oregon capable of analyzing the world’s most advanced and important chips over the last year and half. We have already generated revenue on advanced datacenter chip teardowns including our recent reverse engineering of a major TSMC customer’s COUPE CPO optical engine + EIC 3D stack. This is the first public report from the SemiAnalysis Teardown Engineering & Evaluation Lab, or STEEL for short. The lab is aggressively scaling up and out and we’re excited to announce it publicly. This is a bit of inconvenient timing for TechInsights as they are private equity owned and currently being sold while having enjoyed virtually no credible competition for decades. This has led to TechInsights underinvesting in CAPEX. SemiAnalysis exceeds TechInsights in revenue despite no venture or private equity ownership and being founded only 6 years ago. Because we have no external investors and are founder led, we move faster, build faster, and we can release client chip teardowns for free regularly, while focusing on datacenter for our major clients. Here’s the first public image from our lab, the HiSilicon Kirin 9030 Pro SoC: This report will detail our teardown of the Kirin 9030 and our findings on SMIC’s N+3 process, the most advanced in China. For comparison, we’ll show our teardown of the MediaTek Helio G99, made on TSMC N6. Through this comparison, we can look at the effect of export controls – SMIC N+3 and TSMC N6 are comparable nodes, but one is heavily export-controlled, the other free to use the West’s most advanced equipment. Here we see both China’s progress and constraints. SMIC N+3 reaches TSMC N6-class logic density, but it requires far more aggressive DUV multi-patterning, so it does not match N6 on process maturity or cost. The Kirin 9030 Pro performs similarly to three-year-old Android flagships, and trails far behind the current flagship SoCs from Apple, Qualcomm, MediaTek, and Samsung. The efficiency gap is even wider. Export controls have not stopped Huawei and SMIC from shipping advanced silicon, but they have forced a different path. Without EUV, SMIC is leaning harder on DUV multi-patterning, DTCO, and increasingly complex integration. The roadmap continues forward through tighter design rules and backside power, but each step adds cost and process risk. Huawei’s τ scaling and LogicFolding show another path: stacking active logic and recovering density through advanced packaging and system-technology co-optimization (STCO). To understand the Kirin 9030, we must first understand Huawei’s SoC history. HiSilicon is Huawei’s chip design arm, responsible for the Kirin smartphone SoCs, Kunpeng server CPUs, Ascend AI accelerators, and switch/router networking silicon. Before export controls, Huawei was TSMC’s largest customer – the only customer on TSMC’s first EUV node, N7+, and among the first on N5, alongside Apple. That ended in late 2020. Huawei switched to Qualcomm SoCs in its flagship smartphones, though export controls limited them to 4G-only variants. In late 2023, Huawei returned to in-house silicon with the Kirin 9000s, a successor to the Kirin 9000, fabricated on SMIC N+2 instead of TSMC N5. In the following years, they released the Kirin 9010 and 9020 on the same N+2 process. These chips used Huawei’s in-house TaiShan CPU cores and Maleoon GPU. We have not torn down a Kirin 9020 ourselves, so the predecessor die shot is from Kurnal. The die shots show how Huawei spent its silicon budget: which functional blocks are where, and how their areas compare to the predecessor. First, a quick guide to the major blocks on the die. The total die area is nearly identical, but the 9030 uses that area more aggressively. A denser process lets Huawei fit an extra middle CPU core, more GPU and NPU cores, and larger caches into the same footprint. In contrast, the Helio G99 is a much smaller, low-cost SoC, built for budget smartphones rather than a flagship device. While the Kirin 9030 is ~140 mm², the G99 is only ~29 mm², roughly one-fifth the area. The underlying TSMC process technology, however, is directly comparable as a baseline for analyzing SMIC’s. The Kirin 9030 is an evolutionary refresh, not a clean-sheet design. Its CPU, GPU and NPU cores carry over the 9020’s families, and the gains come from three levers: the SMIC N+2-to-N+3 process step, DTCO and floorplan work, and incremental microarchitecture. Area is where the first two show up, and the 9030 scales well here. Performance and efficiency are the harder test. Huawei’s design holds up better than its node would suggest, but the chip still trails, both because N+3 sits behind the leading-edge nodes and because its cores, while competent, remain a few generations behind the newest designs. The new prime core is an incremental update. The main changes are a 10% frequency increase from 2.5 GHz to 2.75 GHz and a doubling in the L2 cache from 1 MiB to 2 MiB. Despite the increased cache, the core size decreased by 7.6%. Excluding the private L2 cache, the core size decreased by 21%. This is a large reduction for an incremental node. Compared with the TaiShan New V120 core in the Kirin 9020, the Kirin 9030’s middle core is almost unchanged architecturally, yet each core shrinks by ~22%. Most of that comes from the move from N+2 to N+3, with layout likely accounting for the rest. Visually, the most noticeable change is the increase from 3 middle cores to 4. There is also a 20% increase in the shared L3 cache of the big cluster. This helps to improve multi-core performance without sacrificing much in terms of area. Even with each core shrinking, the big CPU cluster’s total area is essentially unchanged. The per-core savings went back into an additional middle core and larger caches. The tiny cores shrank less than the prime core (excluding its L2 cache) and less than the middle cores. This is likely because fixed overhead is a larger share on a small core. We cannot resolve any architectural changes from the die shot alone, but the per-clock and efficiency gains shown below point to more than pure process and layout scaling. The area reduction was offset by a doubling of the shared L2 cache from 2 MiB to 4 MiB, leaving the total tiny CPU cluster area slightly larger. Area is the easiest improvement to see from a die shot, but it is only one part of PPA (power, performance, area). For modern logic, power and performance matter just as much, and often more. Since Dennard scaling broke down in the mid-2000s, voltage and frequency have not scaled in step with transistor dimensions, so each node has had to fight harder for gains in performance and efficiency. The starkest comparison is not Kirin 9020 versus Kirin 9030 Pro. Apple’s efficiency cores run circles around Huawei’s prime core. Apple’s low-power core delivers 20% higher integer performance while drawing only 1 W, compared with 4.5 W for Huawei’s prime core. N+3 matches TSMC N6, but N6 is several generations old. Apple and Qualcomm build on N4 and N3P, which are denser and sit on a better voltage-frequency curve, giving them a larger transistor budget and more performance per watt. The 9030’s own cores did improve. The middle and tiny cores gained 17% and 14% in per-clock integer performance over the 9020, with floating-point flat on the middle core and up 11% on the tiny. The tiny core improves cleanly, with performance rising while power falls and efficiency increases by 45% in integer and 24% in floating point. The middle core is mixed: integer performance rises but power rises faster, cutting integer efficiency by 7%, while lower power lifts floating-point efficiency 16%. Per-clock gains at the same or lower frequency are microarchitectural, so the cores are tuned, not just shrunk. Both also failed to hold their rated maximum frequencies, pointing to thermal, power, or stability limits. Per clock, the middle core sits around Arm Cortex-A720 and the tiny core near the Cortex-A520; absolute performance trails because Huawei clocks them much lower. The prime core is roughly Cortex-X2 class per clock, a 2021 design. Apple’s 2020 M1 Firestorm core is still 35% higher per clock and 57% faster in absolute integer performance at a similar 4.5 W. The current leading edge is further ahead again: the Apple M5 P-core is 60% higher per clock and 2.7× faster, the Arm C1 Ultra 45% higher and 2× faster. Matching older high-end cores per clock is a genuine design achievement. What Huawei cannot match is the voltage-frequency curve and transistor budget of leading-edge nodes, which let Apple, Qualcomm and others spend more transistors in the same area on wider cores, larger caches and deeper buffers while running at lower voltage. Huawei’s LogicFolding roadmap is one answer, stacking active logic to recover density and shorten signal paths. We return to it later. The GPU compute units (CUs) changed more visibly than the CPU cores, moving to a more rectangular layout for both the arithmetic logic unit (ALU) clusters and the CU overall. Even with ray-tracing support added, a CU shrank ~28%. However, that shrink is offset by the increase from 4 to 6 CUs and the area outside the CUs grew 33%. Overall, the GPU cluster is larger by ~10%. The GPU is where Huawei makes its biggest gains. The Maleoon 935 is not competitive with current flagships, but it is a large step up from the 920 and reaches older-flagship territory. In 3DMark it is 70% faster in Wild Life Extreme (WLE) and 79% faster in Steel Nomad Light (SNL) than the 920; with 11% higher clocks and 50% more CUs, the ~67% theoretical uplift roughly matches WLE and is beaten by SNL. It edges ahead of the Snapdragon 8+ Gen 1 in WLE and SNL, and the Dimensity 9200 and Apple A16 in WLE, but stays far behind newer parts: the Snapdragon 8 Elite Gen 5 and Dimensity 9500 are ~2.4–2.6× faster in WLE and ~3.2× faster in SNL. The Maleoon 935 is Huawei’s first GPU with hardware-accelerated ray tracing; there it lands slightly ahead of the Exynos 2200, and on par with the Apple A16, with current flagships up to 3.7× faster. The Neural Processing Unit (NPU) saw the largest structural changes of any block, moving from a Lite and a Tiny core in the Kirin 9020 to a Lite and two Tiny cores in the Kirin 9030. Both core types also show significant layout changes. This is a reversal in Huawei’s NPU design. The Kirin 9000 5G, its last flagship chip on TSMC N5, used two Lite and one Tiny core. The series of SoCs on SMIC N+2 moved to one Lite and one Tiny core, likely for area savings. With the Kirin 9030, Huawei has shifted back toward a larger multi-core NPU cluster, but with the additional area going to a Tiny core rather than a Lite core. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. Before diving into the process stack, the package and memory are worth separating from the SoC itself. The Pro variant of the Kirin 9030 carries 12 GB of Samsung DRAM, with two stacks of four dies each. The dies were identified as the K4L2E165YD, a 12 Gb LPDDR5X-9600 device fabricated on Samsung’s 1a node, the fourth generation of its 10 nm-class DRAM after 1x, 1y and 1z. 1a has shipped in volume since 2022, so this is current memory rather than older-node inventory. The 16 GB Pro Max variants we obtained were found with both CXMT and Samsung packages. The CXMT package is marked CXDD7JEDM, with two stacks of four dies, packaged in week 45 of 2025. The inferred die dimensions from X-ray computed tomography (CT) are consistent with a known density of ~0.3 Gib/mm² for the CXMT G4 process, roughly equivalent to other manufacturers’ 1z processes. The Kirin 9030 uses a typical integrated package-on-package (iPoP) stack: multiple DRAM dies in a memory package sit above an organic redistribution layer (RDL) interposer, which sits above the SoC and package substrate. The full package is then mounted to the printed circuit board (PCB) through ball-grid array (BGA) solder bumps. The memory package substrate is a thin bismaleimide-triazine (BT) laminate carrying the LPDDR5X stack. The organic RDL interposer over the SoC routes the PoP signals around the die and carries possible dummy thermal copper pillars. The package substrate, a thicker Ajinomoto Build-up Film (ABF) build-up over a BT core, fans the flip-chip bumps out to BGA pitch and embeds the power planes. The whole stack is organic. The only silicon is the SoC and the LPDDR5X dies; there is no silicon interposer. Keeping it all-organic brings the package’s coefficient of thermal expansion (CTE) close to the PCB’s, reducing board-level warpage, and avoids the cost of a silicon interposer the SoC’s bandwidth does not need. In an iPoP stack, the memory package connects to the organic RDL interposer through an array of solder bumps. Underfill fills the gap around those bumps, adding stiffness and protecting the joints from mechanical stress. The Pro and Pro Max variants differ here, which we cover behind the paywall. The die shot and architecture tell us how Huawei allocated its silicon budget. The process tells us what SMIC can manufacture. We use the Helio G99 as the process reference for TSMC N6. Both SMIC N+3 and TSMC N6 are evolutions of previous 7 nm-class nodes. We used targeted TEM cross-sections through logic and memory regions, imaged in both fin-cut and gate-cut directions. Each cross-section caption gives its horizontal field width (HFW), the real width of the imaged area. We start at the transistor fins, then move up through standard cells, local interconnect, and SRAM. SMIC has not overtaken Intel or TSMC. It uses aggressive DUV scaling and DTCO to reach N6-class density, but that density doesn’t translate into comparable performance and efficiency, for two reasons: the node gap to leading-edge nodes, and Huawei’s core designs. Fin Profile One of the most important knobs in a FinFET process is the fin profile: the shape of an individual fin and the channel where current passes from source to drain. The ideal fin is tall, narrow, and nearly vertical. A taller fin increases effective channel width, while a narrower fin improves electrostatic control by thinning the body the gate must control. Push either too far, and the process pays for it: weaker drive current, fragile fins, taper, footing and line-edge variation that hit yield and device variability. The Intel 22 nm, 14 nm, and 10 nm fin cross-sections show how FinFET nodes have improved over time. 22 nm fins were a first-generation structure, relatively short, wide and strongly tapered. The shape limits current density and reduces gate control uniformity across the height of the fin. At 14 nm and 10 nm, Intel pushed the fins taller and narrower while also making the sidewalls more vertical. Rather than shrinking the device, these changes increase the effective channel width per fin and improve electrostatic control. The trade-off is that taller fins at tighter pitches are much more difficult to manufacture. Now, let’s compare the Helio G99 on TSMC N6 with the Kirin 9030 on SMIC N+3. Both processes are in the same class, with fin pitch of 30-32 nm on N+3 and 34 nm in our N6 cross-section. The pitch for N6 is especially interesting as N7’s HD library is generally listed with a 33 nm fin pitch, and N6 did not shrink pitches directly. Its density gains came from DTCO instead of tighter pitches. The 34 nm pitch was stable across our sampled region and serves more as a comparison against the SMIC N+3 we have not investigated further. Pinning down N+3’s fin patterning scheme takes more than one core unit. The CPU cores show a dense ~32 nm pitch, with the pitch between N-P fin pairs alternating between 78 and 88 nm. Logic alone may be consistent with dual-pitch mandrels of 120 and 110 nm, but this is a complex and unusual approach. Combining the pitch from the the 8T SRAM, which has more complex repeat unit, with the CPU core sequence allows us to reverse engineer the patterning steps with more confidence. As both the logic and SRAM should share the same base grid, a single CD mandrel lithography pattern with 128 nm pitch undergoing SAQP produces a die-wide ~32 nm grid (128 nm/4), which supports the pitch sequencing seen in both logic and SRAM cells. In the sampled cross-sections, N+3 shows a taller, narrower, higher-aspect-ratio fin than N6. The measured fin aspect ratio is ~9.5:1 on N+3 versus 7.8:1 on N6. N+3 also shows less top rounding, with an estimated radius of ~2 nm, compared with 2.8 nm on N6. Even though the fin widths differ, the ratio of top rounding to fin width tells the same story, with N+3 at 0.37 and N6 at 0.44. In a geometric sense, lower is better; a perfectly rectangular fin would have no top-rounding penalty. These are single-digit-nanometer features measured from a handful of cuts, so treat the absolute numbers as approximate. The important result is the relative gap: N+3’s fins are consistently taller, narrower and less rounded than N6’s. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. Standard Cell A standard cell is the basic building block of chip layout: a fixed-height row pairing one NMOS and one PMOS transistor that share a gate, tiled in a grid to build logic blocks. The key dimensions are contacted gate pitch (CGP), cell height (CH), fin count, and the lower-metal routing grid. To measure density, we use the Bohr metric: a weighted average of NAND2 gate area (60%) and scan flip-flop area (40%). This represents a realistic mix of combinational and sequential logic. This metric has its limitations, especially for complex cell layouts like TSMC’s FinFLEX, which alternates cells with different fin counts. Even so, it is the best metric for a pure process-level comparison. Another important measurement is the fin pitch; it refers to the distance between two fins of the same transistor. In a FinFET process, multiple fins are used in each transistor to increase the drive current and thus performance. TSMC N6 ships both a high-density (HD) library with 2 PMOS and 2 NMOS fins per cell, and a high-performance (HP) library with 3 of each. More fins under the shared gate mean more effective channel width. HP cells switch harder at the cost of area. Designers mix the two on a die, primarily spending HP cells on timing-critical paths, and matching their PPA targets. In the Cortex-A55 core of the Helio G99, we found a cell height of 240 nm for the HD cell. MediaTek has used HD cells in the G99 to minimize die size and thus cost. As an SoC for budget smartphones at ~$100, this is essential. By contrast, we found only one library in the Kirin 9030, with 2 NMOS and 2 PMOS fins. This suggests a narrower library strategy than TSMC N6, where both HD and HP libraries are widely used. This likely reflects the smaller customer base and the more constrained domestic design and electronic design automation (EDA) ecosystem. In all three CPU cores of the Kirin 9030, we found cell heights of 228 nm, 5% smaller than on N6. This is also a reduction of 9.5% over SMIC N+2’s cell height of 252 nm. SMIC N+3 and TSMC N6’s HD library both feature a CGP of 57 nm. For SMIC, this is a 9.5% shrink over N+2. In the past, CGP and cell height alone may have been enough to compare transistor density. Now, however, we must consider scaling boosters and DTCO as well. SMIC’s density gain does not come from EUV. It comes from using every available DTCO booster aggressively. First is fin depopulation: reducing the number of NMOS and PMOS fins in each cell. The first FinFET nodes started with 3 or 4 fins for each transistor. SMIC N+3 and TSMC N6 HD both use only 2 fins per transistor, trading drive strength for density. Next is contact over active gate (COAG). By landing the gate contact directly over the active gate, instead of out over the isolation region, the cell height drops. N+3 integrates COAG while N6 does not. Our N+3 gate-cut cross-sections indicate COAG, with the gate contact sitting over the active region, while N6 shows an off-gate contact. Last is single diffusion break (SDB). Diffusion breaks are inserted between cells in the same row to provide electrical isolation, but they also introduce local layout effects (LLE), layout-dependent shifts in electrical characteristics. In the past, a double diffusion break was used, consuming the space of two CGPs. SMIC N+3 and TSMC N6 instead use SDB, saving area but increasing LLE sensitivity. This must be controlled at the process level and accurately modeled in the process design kit (PDK) so that EDA tools can account for it. Overall, SMIC N+3 has a transistor density of 113.4 MTr/mm², slightly above TSMC N6 at 107.7 MTr/mm². Even without EUV, SMIC has achieved density beyond TSMC’s mature N6 node which utilizes EUV. Metal Stack The smallest critical dimension in the teardown is M0; SMIC N+3 uses a 32.5 nm local metal pitch. That is smaller than the 36 nm M0 pitch on Intel 18A in Panther Lake. However, this does not mean that SMIC has a better process than Intel 18A or TSMC N3P. M0 is a local intra-cell routing layer. Its usefulness depends on the full interconnect stack: M1 and M2 pitch, track count, via and line resistance, design rules, mask count, overlay control, and routing flexibility. The 32.5 nm M0 is consistent with self-aligned quadruple patterning (SAQP), whose four-population line-width loading we read coarsely as alternating widths of 21.5 to 24 nm; M1 and M2, at 38 and 40 nm, are consistent with self-aligned double patterning (SADP), a single A/B split. On TSMC N6, M0, M2, and M3 sit at a relaxed ~40 nm and are consistent with SADP-class double patterning, with no need for quadruple patterning. That said, we measure M2 for example at ~43 nm, likely inflated by sparse routing. We do not assign any specific layer to EUV from our cross-sections; the distinction we can draw is double versus quadruple patterning, not lithography wavelength. Transistor-level density in the front-end-of-line (FEOL) sets an upper bound, but the design is ultimately limited by what the interconnect stack can route. The lowest metals are the most important for standard-cell density, but the semi-global and global layers determine how usable that density is at the block and chip level. Two axes are commonly used for chip cross-sections: the fin-cut and the gate-cut. The micrograph above is a fin-cut and shows metals 0 through 3. This axis lets us see the even-numbered metals, with M0 right above the fins. There are two kinds of M0 lines. The first are the power rails; these are wide wires for the VDD and VSS running horizontally at the top and bottom edges of each standard cell. The wide wires measure 55 nm across, more than double the other M0 lines. Their width minimizes resistance and reduces IR drop. The second kind are intra-cell wires, short segments within the cell that connect terminals to M1. These have alternating widths between 21.5 and 24 nm. The M0 pitch is 32.5 nm, a 19% reduction versus N+2 and N6. At this pitch, DUV patterning requires more aggressive multi-patterning, increasing mask count, overlay sensitivity, process complexity and cost. M0 is below what a single DUV-defined spacer (SADP) can resolve, so SMIC cascades a second spacer step (SAQP). The cross-section reflects the cost: the M0 trenches are visibly more re-entrant (narrower at the bottom than the top) than M1 or M2 on the same chip and carry a bright barrier-rich foot where the trench meets the etch-stop layer. That shape is partly the intended damascene profile, as a slightly narrow bottom helps void-free copper fill, but its magnitude at M0 is driven by the tight pitch and the higher trench aspect ratio. Intel 18A supports an M0 pitch of 32 nm, although Panther Lake has only shipped with a looser 36 nm pitch. This is due to Intel’s heavy usage of HP libraries. Among leading-edge nodes, 18A has the loosest M0 pitches due to PowerVia. With power routing moving to the backside, congestion is reduced, and the entire front-side metal stack can be used for signal routing. M2 is the first true inter-cell routing layer. It runs horizontally like M0 but spans across multiple cells to carry block-level signals. The M2 pitch sets the cell’s track height – the number of M2 tracks that fit between the VDD and VSS rails, defining what the library calls a 6-track or 7.5-track cell. This layer is the most important, limiting the routing of entire blocks. SMIC N+3 features a 5.7-track cell. The M2 pitch is 40 nm, a 5% decrease over N+2 and the same as N6. This shrink keeps the pitch at the edge of what is possible with double patterning. Future nodes will need to increase the number of masks for M2 as reducing the number of tracks is much harder due to the limitations in routing. The micrograph above is in the perpendicular direction, the gate-cut, and shows metals 0 through 4. This allows us to see and measure the odd-numbered vertical metal layers. The M1 pitch is 38 nm, 9.5% less than N+2 and 33% less than N6. The M1-to-gate ratio matters because it sets local routing flexibility. N+2 and N+3 use a 3:2 ratio, while N6 uses a 1:1 ratio, explaining the huge differences in M1 pitch. The more M1 lines there are compared with the gates, the more flexibility there is for power and signal crossing within the cell. Routing flexibility enables more complex and better cells. Clean fractional ratios are also preferred as a grid is periodic and improves layouts. The 3:2 ratio gives SMIC more local routing flexibility than a strict 1:1 grid, but it also complicates layout and patterning. This is a DTCO choice, with SMIC increasing process complexity to recover density and routability without EUV. This 3:2 ratio is not very popular in the leading-edge nodes. TSMC has only used it on N7+, the N5 family, and the short-lived N3(B). They have switched back to a 1:1 ratio for N3E. Intel has only used it on the 10 nm/Intel 7 family, with Intel 4, 3 and 18A all using a 1:1 ratio. Samsung is the only one still using a 3:2 ratio at the leading edge, using it in the SF4 and SF3 families. It remains to be seen if SMIC will remain at a 3:2 ratio or move to a 1:1 ratio with its future nodes. The industry is still actively exploring these local-routing ratios. At VLSI 2026, imec will be presenting work on even higher ratios, including a 2:1 scheme that can reduce area by up to 14%. We will be covering the conference in a future newsletter article. Subscribe We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. The final local interconnect layer for N+3 is M3, with a pitch of 44 nm. The M3 pitch is the same as on N+2 and 10% larger than on N6. The semi-global layers carry the majority of block-level signal routing. They have a coarser pitch than the lower local layers. On leading-edge nodes, they are designed to sit at the limit of DUV single patterning. M4 through M11 pitches were found divided between 80–82 nm (M4–M6), 128 nm (M7–M10), and 148 nm (M11). Given limited sampling, it is possible these are divided further in dense routing areas. At the top are two giant metal layers, M12 and M13. These have kept the same pitches as N+2 at 1920 nm and 4600 nm respectively. While the lower layers’ pitches are generally fixed by the process and library, the upper layers vary much more in pitch and count, depending on the design. Even two smartphone SoCs on the same process can have wildly different metal stacks. The Helio G99 carries fewer routing layers, reaching coarse metal pitches of 850 nm by M9, while the larger and higher-performance Kirin 9030 keeps fine pitches until M11. SRAM At the leading edge, SRAM is much more difficult to scale than logic. TSMC’s latest nodes have seen little to no bitcell scaling, while logic still has more DTCO levers to pull. While looking for other logic libraries in the GPU compute units, we stumbled upon the SRAM. The most common type of SRAM has 6 transistors (6T), but this cell had 8 transistors (8T) instead. 8T SRAM adds two transistors to form a dedicated read port. Unlike a 6T cell, where reading disturbs the storage, the decoupled read port removes read-disturb, improving read stability and letting the cell be pushed harder for performance. At first glance, the cut looked like an unusual logic library, with each cell row having 3 fins of one polarity and 5 fins of another. The rows also alternated in orientation. Energy-dispersive X-ray spectroscopy (EDS) resolved our confusion. The cut had not landed on the GPU logic, but on the SRAM macro beside it. The unusual fin pattern was due to the SRAM library. We return to EDS in the process flow analysis behind the paywall. SRAM libraries are not like traditional logic libraries. Due to the unequal number of PMOS and NMOS transistors, they require specialized rules and layout libraries. They do not need the flexibility of logic libraries, so they are hyper-optimized for one purpose: dense, reliable memory. The SRAM cell we found is a 1:2:2-2:2 cell. This means there is 1 fin per pull-up (PU) PMOS transistor, and 2 fins per pull-down (PD) and pass-gate (PG) NMOS transistor. These 2 PU, 2 PD and 2 PG transistors would usually form a single 6T high-current cell (HCC). An 8T HCC adds a read-pull-down (RPD) and a read-pass-gate (RPG) NMOS transistor, each with two fins. We measured a cell height of 406 nm, which brings the bitcell size to 0.0463 µm². That is a theoretical peak density of 21.6 Mib/mm². We estimate that a 6T HCC would have a cell height of 292 nm and a size of 0.0337 µm². This is ~12% larger than a 6T HCC on Intel 3 and 4. We also estimate the 6T high-density cell (HDC) to have a cell height of 228 nm and a size of 0.0260 µm². This is coincidentally the same as the logic standard-cell height measured earlier. The estimate puts the cell near Samsung 7LPP/5LPP and slightly below TSMC N7/N6. That is a theoretical peak density of 38.5 Mib/mm². 6T HDC is arguably the most important cell as it is used for the largest caches in a chip, the L3 caches and system-level cache (SLC). Both the Kirin 9020 and 9030 have split the SLC into 4 banks to raise total SLC bandwidth. In the Kirin 9030, the SLC increased from 2 MiB to 3 MiB per bank. Correspondingly, the number of arrays within the bank also increased by 50%, from 16 to 24. Each array can store 128 KiB and forms an orderly pattern on the die shot. From the Kirin 9020 to the Kirin 9030, the area of a 128 KiB SLC array decreased from 0.0477 mm² to 0.0392 mm², an 18% shrink. The achieved density is 25.5 Mib/mm², 66% of the theoretical maximum. While the SLC was quite similar across both chips, the L3 has seen some major changes, particularly in terms of its layout. The total capacity also went up from 10 MiB to 12 MiB. Much like the SLC, the L3 is also split into 4 banks. In the Kirin 9020, an L3 bank consisted of 16× 128 KiB arrays and 16× 32 KiB arrays. However, an L3 bank in the Kirin 9030 instead consists of 48× 64 KiB arrays. In the Kirin 9020 L3, a 128 KiB array was 0.0513 mm² and a 32 KiB array was 0.0154 mm². The size of the 128 KiB array is different on the L3 and SLC as the assist circuitry for the two arrays differs depending on their purpose. In the Kirin 9030 L3, a 64 KiB array is 0.0210 mm². Although not a like-for-like comparison, normalized for capacity, it is 18% smaller than the 9020’s 128 KiB L3 array and 31% smaller than its 32 KiB L3 array. The achieved density is slightly lower than the SLC, at 23.8 Mib/mm², 62% of the theoretical maximum. Unlike the L3 and SLC, the prime cores’ private L2 cache uses a 2-bank design. As the prime cores’ L2 is latency-critical, it likely uses 6T HCC instead of 6T HDC. The 9020 has 16 arrays in each bank while the 9030 has 32. Each array has a capacity of 32 KiB. A 32 KiB array in the L2 shrank from 0.0171 mm² to 0.0142 mm², ~17% smaller. The density is 17.6 Mib/mm², ~59% of the theoretical maximum for 6T HCC. SRAM scaled well from N+2 to N+3, shrinking by ~19%, close to the theoretical logic shrink. The caveat is that N+2’s bitcells were unusually large, bigger than comparable 7 nm-class nodes, so part of the gain is catch-up rather than true scaling. With the insights from STEEL’s teardowns, we will be doing a deep dive into SRAM in a future newsletter article. Subscribe Everything above came out of a single STEEL teardown: die annotation, block-level area analysis and TEM cross-sections through logic and SRAM. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Future Roadmap The same cross-sections that pin down N+3 also show where SMIC can go next. Although N+3 is already close to the practical limits of DUV multi-patterning in several layers, SMIC still has a few scaling levers left. A theoretical N+4 would likely start with cell height. N+3 uses 5 M0 tracks between its power rails. Moving to 4 M0 tracks, as on SMIC N+2 and TSMC N6, could reduce cell height by roughly 15%. The routing grid is only one side of the shrink; the front end also must fit into the smaller cell. One possible FEOL lever is reducing the p-to-n isolation spacing from two diffusion grid units to one. Intel used this scaling booster on Intel 4, and TSMC did so on its N3 family. This path trades layout flexibility for density. Fewer M0 tracks reduce local routing resources, while tighter p-to-n spacing raises integration and design-rule difficulty. M2 is also constrained by the cell height shrink. For SMIC to maintain a ~5.7-track cell, M2 would need to move toward ~35 nm. That would move another layer into SAQP territory. SMIC could also reduce the CGP from 57 nm to 54 nm. Intel reached a similar CGP on Intel 10 nm/Intel 7 without EUV. The local interconnect is also tougher. If SMIC keeps the 3:2 M1-to-gate ratio, M1 would need to shrink to 36 nm and would likely require SAQP as well. If SMIC moves to a 1:1 ratio, M1 could relax to 54 nm, but it would give up routing flexibility. Under this theoretical path, we estimate that SMIC N+4 could reach a cell height of 198 nm and a CGP of 54 nm, implying a Bohr density of 137.8 MTr/mm², on par with TSMC N5 or Samsung SF4. However, the difficulty is cumulative. Each step is individually plausible, but together they make N+4 harder than the transition from N+2 to N+3. It will likely take longer, cost more, and carry less process margin. A theoretical N+5 would require a larger integration shift. One possible path is backside contacts (BSCon), moving power routing and source/drain contacts to the backside, which would reduce front-side routing pressure and enable another reduction in cell height. The front-side metal pitches could relax to reduce the process complexity. M0 would likely relax slightly to ~34 nm, and M2 and M4 pitches could relax further. CGP is unlikely to shrink much further. Even with EUV, 48 nm has been the practical limit for yield and process control. This approach would allow N+5’s cell height to fall to 170 nm and its CGP to 53 nm. This implies a Bohr density of 163.6 MTr/mm², on par with Intel 18A’s HP library. However, this would not make N+5 cost-competitive with the leading edge. It would reach a similar density through a much more expensive route. The integration difficulty rises sharply, with new process flows for backside alignment, wafer thinning, contact reveal, and backside metallization. Past this point, standard density and interconnect scaling become increasingly unattractive. That is where Huawei’s roadmap stops looking like a normal foundry roadmap and starts looking like a packaging roadmap. At ISCAS 2026, Huawei unveiled its tau (τ) scaling law, reframing process scaling in the time domain. τ is the time cost of data movement and processing: switching delays in transistors, RC signal propagation delays in circuits, compute, memory, and networking latency. Outside Huawei’s terminology, this is called system-technology co-optimization. This is Huawei’s answer to its lack of EUV lithography. Without EUV, planar density cannot keep pace with TSMC, Intel, or Samsung. If transistor density cannot shrink further, Huawei’s alternative is to shorten wires, reduce buffering, and stack logic vertically. “LogicFolding”, Huawei’s implementation of this new scaling idea, is, in practice, an aggressive 3D stacking approach. AMD V-Cache places SRAM above or below a CPU die. AMD’s MI350X places active interposer dies (AIDs) underneath accelerator and compute dies (XCDs), with the AIDs handling cache, IO interfaces, the network-on-chip (NoC) and embedded metal-insulator-metal (MIM) capacitors. With LogicFolding, parts of the same logic block are split across multiple active dies bonded face-to-face at ultra-fine pitch. This allows Huawei to shorten some critical paths and reduce buffer overhead, not merely add cache capacity or offload the IO and interconnect. Shortening wires is where the higher clocks come from. A large share of a modern core’s delay and energy budget goes into driving long interconnects and the repeater buffers along them. LogicFolding distributes a block’s critical-path gates across multiple stacked tiers bonded at a very fine pitch, so the bond interface behaves like an additional metal layer and the longest paths get shorter. That is how Huawei expects to recover frequency and efficiency it cannot get from the process alone. Huawei’s roadmap shows its intent. Prime core frequency is targeted to rise from 2.75 GHz in the Kirin 9030 to roughly 5 GHz by 2031, far beyond what planar scaling alone could deliver. Prime cores with 3.1 and 3.39 GHz clocks are being tested in its labs, although their power consumption is unknown. Beyond that, chips are in the design, simulation or pathfinding phase, meaning the frequencies are targets. However, the direction matters more: LogicFolding also helps with performance, not just density. The catch is that Huawei’s density claim is not directly comparable to foundry densities. A stacked design can report more transistors per package footprint by adding active layers, even if each patterned die remains well behind TSMC or Intel in front-end density. This is how Huawei can claim to reach foundry 14A-equivalent density by 2031. This is not a like-for-like foundry comparison, with Huawei using stacked logic and measuring density per package footprint. On a normalized Bohr-density basis, SMIC N+3 is ~114 MTr/mm², 38% less than Intel 18A’s HD library. Huawei’s 3D roadmap closes the gap by stacking active logic, reaching 215 MTr/mm² by 2030. In 2031, the roadmap density jumps to 295 MTr/mm², implying either a third active layer, partial EUV insertion or aggressive planar DUV scaling. Huawei’s methodology makes other foundries look much denser as well. Applying it to AMD’s MI450X with an N2 top die on an N3P base die yields a theoretical density of 460.2 MTr/mm² in 2026, compared with Huawei’s 295 MTr/mm² in 2031. This Kirin 9030 does not use LogicFolding, remaining in a conventional mobile SoC package. Instead, it forms the baseline for how far Huawei and SMIC can push planar scaling. Future teardowns of Kirin and Ascend chips will show both planar logic density and Huawei’s hybrid bonding solutions. Export controls changed China’s optimization problem rather than ending it. EUV restrictions raised the cost and complexity of leading-edge manufacturing without freezing it. SMIC reaches N6-class logic density through DUV immersion, SAQP and DTCO, while Huawei shifts more of the burden onto architecture, packaging and system-level integration. Future nodes will be tougher. N+3 still had room to tighten local metals and reduce cell height and CGP. Further scaling without EUV leaves fewer levers. More aggressive multi-patterning adds masks and overlay error. SMIC can keep pushing DUV, but each step will get more expensive and less forgiving. The design side is just as critical. Huawei had domestic EDA tools and flows before the Kirin 9030, with the Kirin 9000s, 9010 and 9020 making that clear. Huawei was able to ship multiple consumer SoCs on SMIC N+2 and N+3 while cut off from the Western EDA stack. US export controls restricted EDA tools for advanced chips in 2022 but did not target tools for more mature chips. In 2025, the US government briefly placed much broader restrictions on EDA software from Synopsys, Cadence, and others, before lifting them less than two months later as part of a trade deal tied to rare earths. Huawei has been unable to access those tools because it remains on a US trade blacklist. That forced Huawei, SMIC and Chinese academic institutions to build their own tools and flows. Researchers at Peking University recently announced a prototype EDA tool for Huawei’s LogicFolding architecture, which requires a new flow to handle the multilayer layout and floorplan. This is not the same as replacing the full Synopsys or Cadence stack, but it shows where domestic EDA is headed: toward tighter co-optimization between architecture, process and packaging. These advances are also diffusing into the Chinese ecosystem. SMIC is licensing its N+2 and N+3 processes to HLMC/Hua Hong at the government’s direction rather than by choice. If the same process learning feeds into Ascend accelerators for AI training and inference, the choke point shifts from one named fab to an ecosystem. Alibaba’s T-Head silicon arm and Cambricon, a Chinese AI chip designer that is expected to supply ByteDance, could also be major beneficiaries. Sanctions aimed at SMIC alone become less effective once the manufacturing knowledge has spread to other fabs and design houses. China is not closing the gap with Intel, Samsung and TSMC. The teardown shows the opposite in several places: no EUV, no backside power, higher process complexity, and visible trade-offs. But China is still advancing. If domestic chips become good enough for phones, inference, networking and security-sensitive workloads, they can matter strategically without matching TSMC at the leading edge. Behind the paywall, we show what else STEEL can do, with material and process flow analysis of SMIC N+3, and analysis of the Kirin 9030 package. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline, access the full Kirin 9030 and SMIC N+3 analysis or to commission a custom teardown, contact sales@semianalysis.com.
Glencore Increases Copper Production and Maintains 2026 Guidance as Trading Business Performs Strongly
Glencore (LSE:GLEN) delivered a solid operational performance during the first half of 2026, with own-sourced copper production increasing 15% year over year to 397,000 tonnes. The improvement was driven by stronger mining rates and higher ore grades at its African Copper operations, together with improved grades at the Antamina mine in Peru.
Vikings Get Intriguing Justin Herbert Take Amid Chargers Struggles
The Minnesota Vikings don’t have a long-term solution yet at QB. That could change in the coming weeks if Kyler Murray performs well and leads the Vikings to wins. However, if that doesn’t happen and Minnesota needs to lean on Carson Wentz again this season, then general manager Nolan Teasley might need to look at […] The post Vikings Get Intriguing Justin Herbert Take Amid Chargers Struggles appeared first on HEAVY.
Sonos Arc Ultra Dolby Atmos Soundbar Drops Back to a Record Low With Hundreds Off for Labor Day
The 9.1.4 surround sound Arc Ultra overperforms its new $899 price tag with voice control and Dolby Atmos.
Microsoft is launching its first cybersecurity AI model at half the cost of rivals
MAI-Cyber-1-Flash, paired with GPT-5.4 inside Microsoft's MDASH harness, outperforms models from Anthropic, Google, and OpenAI on a key benchmark
Clinton Policy Institute outlines maternal health solutions for states
Health Care Clinton Policy Institute outlines maternal health solutions for states It’s the first major report from the project, which launched in July to foster nonpartisan policies to improve the wellbeing of children and families. Stephanie Akin 10:00 AM, Oct 15, 2026 Dr. Jacob Lenz performs an ultrasound at the Oklahoma State University obstetrics and gynecology clinic in Tulsa, Okla., on Tuesday, July 16, 2024. (AP Photo/Mary Conlon) The Clinton Policy Institute is calling on states to lead efforts to curb maternal mortality, releasing a 61-page playbook Thursday highlighting successful policies that could be adopted nationwide. The playbook is the first major report from the Clinton Foundation’s project, which launched in July to foster nonpartisan state and local policies to improve the wellbeing of children and families. “Eighty percent of maternal deaths are preventable, and we know what works,” CPI Chair Jennifer Klein said. “The solutions are well established and evidence-based.” CPI’s mission is to convene political leaders from both parties, policy experts, state officials and other stakeholders to explore solutions to longstanding problems. “What we aim to do is bring together people who are deeply engaged, but may not be meeting together in the same room,” Klein said, noting that state policymakers might not always be aware of what their peers are doing in other states. In May, CPI brought together lawmakers from Virginia and Pennsylvania, two of the 14 states that have passed bipartisan maternal health “Momnibus” packages modeled after a comprehensive federal proposal that has repeatedly stalled, called the Black Maternal Health Momnibus Act. Other participants included Dr. Mona Hanna, founder of Rx Kids, a Michigan program that provides direct cash allowances to pregnant women and infants. Pennsylvania Rep. Morgan Cephas, who was part of the group, said she learned about funding opportunities she hadn’t come across during her decade in the Legislature. “As legislators we can issue grants, we can pass legislation, we can move policy, but we want to ensure that funding opportunities and programs that are tackling this issue are sustainable,” Cephas said. The playbook outlines broad areas where state policy can make a difference, such as allowing Medicaid to pay for doulas and remote patient monitoring, and using federal and private grant funding to pay for home visits to pregnant and postpartum women. For each category, it highlights innovative programs that can serve as models, with examples of specific actions state lawmakers and governors’ offices can take. Those include the Rx Kids model, which gets funding from the Michigan state budget; Pennsylvania’s Healthy Moms Vibrant Futures strategic action plan, emphasizing equity-centered maternal care, regional collaboration, Medicaid innovation, and workforce development that was launched this year with more than 160 stakeholders; and Kansas’s Bridging Plan, which reserves three slots and provides at least $26,000 in loan forgiveness for obstetric residents who commit to practicing in rural or underserved communities for 36 months. CPI emphasizes that its work is nonpartisan. Klein, the director of the White House Gender Policy Council under former President Joe Biden, acknowledged that CPI also takes positions on abortion, Medicaid funding, and the need to address the economic and social drivers of poor health, which might rankle some conservatives. “We don’t shy away from talking about those things that we think are part and parcel of the maternal health crisis in this country,” Klein said. “That said, there are so many policies that are both evidence-based and have been tried successfully in both red and blue states, and people can see that they can have real impact.” She pointed to the federal option to extend postpartum Medicaid coverage from 60 days to 12 months that was made available during the Biden administration and has since been adopted by every state but Arkansas. Other initiatives embraced across party lines include Medicaid coverage for midwives and doulas, as well as “bundled” payments for prenatal and postpartum care. That alternative payment model, pioneered in states such as Arkansas, Colorado and Tennessee, incentivizes fixed fees to cover all healthcare services associated with pregnancy and childbirth. Advocates say it promotes more coordination among caregivers, discourages unnecessary interventions, and expands postpartum support. In addition to maternal health, the initiative is also focused on addressing family economic security, children’s well-being, and democracy and civic resilience.
Macklemore announces 'Free Palestine' tour after Ed Sheeran controversy
Macklemore performs at Rod Laver Arena on May 15, 2024, in Melbourne, Australia. (Morgan Hancock/Getty Images)
Surgery outperforms TAVR in young heart patients, new data confirm
Researchers tracked data from nearly 10,000 patients 60 years old or younger who underwent SAVR or TAVR. Propensity score matching was used to focus on two groups of approximately 1,500 patients.
Katy Perry cancels music festival performance at the last minute: 'They gave me no choice'
Katy Perry apologizes after her Werchter Boutique performance was canceled at the last minute, tells fans 'I am just as unhappy as you are.'
The Biggest Threat to Women's Sports Isn't Trans Athletes. It's the Trump Administration's Failure to Uphold Title IX's Promise of Equal Opportunity.
While the Trump administration performs protection and lets unresolved cases pile up, athletes and their lawyers are actually enforcing Title IX and expanding athletic opportunities for women and girls.
Ed Sheeran performs in Atlanta: What to know about the controversy
Ed Sheeran's tour stops at Atlanta's Mercedes-Benz Stadium Saturday. We answer some questions about his upcoming concert and controversy.
Dolly Parton honored posthumously, shares prerecorded video message
Singer Dolly Parton performs, January 24, 2014 in Rancho Mirage, Calif. (Valerie Macon/Getty Images)The late Dolly Parton is sharing a new video message, which was recorded before she died in August.The Americana Music Association released Parton’s video Wednesday, which was submitted as part of her acceptance of the Lifetime Achievement Award at the 2026 Americana Honors & Awards.“Roots music, or what is now called Americana music, is what I grew up playing back in the Smoky Mountains,” Parton said in part in the video. “It’s that same music that drew me back to East Tennessee back in 1997 to write and record my album 'Hungry Again.' And that project really opened the door to a series of bluegrass and acoustic albums that changed the course of my career in so many ways.”“Now, new fans discovered these albums, new Americana music communities embraced them, and now looking back at it all, I see this era of my career as a musical renaissance of sorts,” Parton continued.The Americana Music Association announced in August that Parton — whose songs include “I Will Always Love You,” "Coat of Many Colors" and “Jolene” — was being recognized for her “enduring contributions as one of history’s greatest songwriters and recording artists.”Parton was in good company: Emmylou Harris, Van Morrison and Willie Nelson are among the artists who have been honored with the Americana Music Association Lifetime Achievement Award.Parton, a Tennessee native, died on Aug. 25 at the age of 80 following a private battle with cancer, according to her family.Throughout her six-decade career, Parton built a legacy that expanded beyond music into entrepreneurship and philanthropy.Copyright © 2026, ABC Audio. All rights reserved.
UK Charts: Kingdom Hearts Collection Performs Well, But It's Missing The Keys To The Podium
Sora loser
RxLogic and Epidaurus Health Bring Automated Prior Authorization Live on an Independent Adjudication Platform
NASHVILLE, Tenn. and ALEXANDRIA, Va., Sept. 15, 2026 (GLOBE NEWSWIRE) -- RxLogic, a leading technology company that performs fully compliant pharmacy benefit operations with easy, rapid implementations, and Epidaurus Health, an AI software company focused on prior authorization (PA) automation, today announces that the Epidaurus PA platform is live in production on RxLogic's adjudication rail, serving a shared pharmacy benefit manager client. AI-driven prior authorization now runs directly on Rx
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.
Pope Leo XIV's Fashion Designer Celebrates 25 Years in Business
The Italian creative also has an artisanal perfume company and performs as an organist.
Miley Cyrus releases new single, 'Bass Persuades,' and music video
Miley Cyrus performs onstage during the 66th GRAMMY Awards on February 04, 2024 in Los Angeles, California. (John Shearer/Getty Images for The Recording Academy)
Who Won August and September: Original Films or Franchises?
(Welcome to the Entertainment Strategy Guy, a newsletter on the entertainment industry and business strategy. I write a weekly Streaming Ratings Report and a bi-weekly strategy column, along with occasional deep dives into other topics, like today’s article. Please subscribe.) I just heard someone say that if you’re reading a pundit or data analyst, and they don’t tell you that they got anything wrong, they’re not a pundit but an “influencer”. I agree with that. You have to tell your audience when you get things wrong, or they won’t (or shouldn’t) trust you as much. Today, we’ve got another edition of “What I Got Right, What I Got Wrong”. In general, I’m patting myself on the back (and patting very hard) about a few things like IP at the box office, LIV golf, horror films, crowdfunding movies, superheroes, and old people going to the theaters. That said, I also made a couple of data mistakes on the streaming bubble popping and HBO’s datecdotes, so I’m not perfect. But first, I need your feedback... Subscribe Follow-Ups: What Should We Call Mid-Budget Movies Which Aren’t Cinematic Enough for Theaters But Are Too Expensive to Make Money on Streaming? After I asked for feedback on what we should call straight-to-streaming films that are too big to pencil out on streaming but not really big enough to resonate in theaters, I got some great suggestions from you all, including... “Moldilocks” from John Aboud “Little Big Indie” from Travis Frick1 “Midflicks” from Jonathan Funke. “Extra-Medium” from Jona Nwuke. (Read the explanation in the footnote.) Between these and Brandon Katz—who suggested “Bermuda Budget Triangle”, “The Platform Gap” and “The Distribution Deadzone”—we’ve got some excellent suggestions. And they’re better than my suggestion, the “Streaming Budget Dead Zone”, so let’s take a poll! The winning entry becomes the new term. Loading... RIGHT: LIV Golf is Fully Bankrupt…Is Anyone Else Next? A few years ago, I was always a bit perplexed at all the articles I’d read praising LIV Golf and their strategy to disrupt the PGA. LIV Golf, a brand new professional golf league, offered PGA stars ten times what they made on the PGA Tour to join their new league. And folks praised this strategy for its initial “success” in that a lot of big names did indeed leave the PGA. Yeah, of course the players came over. LIV Golf paid them so, so, so much more! But LIV Golf hadn’t discovered a way to increase potential revenue. So think about this in basic business terms… They paid much more in costs… …but had no real way to increase revenue. That’s not a strategy! That’s deficit financing. It won’t work unless you bankrupt the competition (and then turn around and pay those same golfers much, much less). It’s not a sustainable strategy and only lasts as long as the company/person/nation backing it decides they’re cool with losing money. For LIV Golf, that meant Middle East oil wealth, in particular Saudi Arabian money. Clearly, the current Iran War has hurt Middle East finances, so they need to trim their more exorbitant spending. And LIV Golf was part of that trimming. This should be a major warning for others. TGL’s parent company, TMRW Sports, just got a $1 billion valuation, despite TGL’s (the indoor golf league) horrible TV viewership of less than half a million viewers per match. Unrivaled, the women’s basketball league, just secured a $650 valuation, despite its horrible TV viewership and a new rival (Project B) entering the scene next year. To relate this to streaming, Hollywood should ask which streamers may have wealthy patrons funding their losses. (Let’s be clear: Google, Amazon and Apple.) Could those patrons lose their appetite? For Amazon, probably not. But Apple has a new boss, so maybe! WRONG: My Analysis of the Streaming Bubble Was Missing a Week I’m going to be congratulating myself a lot today, but I make mistakes too, like this data goof. When I last compiled the data on the decline in TV shows, I was missing one week at the end of June. If you look at the first image, you can see that one week was mislabelled as “July”. Mainly, this image got updated to show a 27% decline, not 29%: This change doesn’t really impact the overall analysis, but it is two percent better. By the way, through the third quarter, the decline increased and it’s now a 30% decrease since 2022. (I’ll write/visualize this in an upcoming article.) But I want you to trust me and my data. Especially these days, when many people are using LLMs that I know are inserting faulty data into their charts, I want to keep earning my audience’s faith. So that’s the accurate data. RIGHT: IP Remains Very, Very Popular In July, I wrote a giant (and I mean giant) article on Backrooms, Obsession, and the box office, going over what we know, what we don’t know about what works in theaters, looking at YouTubers, IP, the horror genre, comic book movies, and a whole lot more. The month of August really tested a lot of my theses and, being honest, mostly supported my arguments. Let’s start with IP. Looking at 8-Aug (the weekend after Spider-Man: Brand New Day came out) to 18-Sep (the weekend that Resident Evil came out, which I think provides a nice bookend to this time period), there were seven films based on pre-existing IP: Resident Evil (2026) ($126 million) Practical Magic 2 ($65 million) _Insidious: Out of the Furthe_r ($65 million) Coyote Vs. Acme ($59 million) Paw Patrol: The Dino Movie ($53 million) Tony ($15 million) Super Troopers 3 ($7 million) Compare those to the notable original films from the past month—I actually could have included more movies, but here are just thirteen, bringing us to an even twenty films—including.... The End of Oak Street ($54 million) Buddy ($26 million) Mutiny ($15 million) By Any Means ($15 million) The Dog Stars ($14 million) Runner ($14 million) One Night Only ($11 million) Hope ($8 million) Spa Weekend ($7 million) The Uprising ($6 million) Teenage Sex and Death at Camp Miasma ($6 million) Onslaught ($3 million) Eli Roth’s Ice Cream Man ($2.8 million) Here’s that in chart form: Five of the top six films in this time period were all based on IP. I made a big chart of films that grossed over $200 million at the box office before Spider-Man: Brand New Day and The Odyssey hit theaters. Let’s update that chart! By the way, if you want to see how I categorized each film—so another bar chart—here it is: I know that many of my fellow critics/pundits/analysts dislike films based on IP and how Hollywood is making so many of them. And I’m sympathetic to this point of view. As I’ve written many, many times before, you need a balance between existing franchises, new IP, and original films. And Hollywood clearly needs to make more films like Resident Evil (a well-made film from a visionary director) and fewer Practical Magic 2’s (which didn’t get critical or customer buzz). But at some point, critics and pundits need to contend with what audiences are telling them: Movie-goers aren’t showing up to original films. Audiences are speaking with their dollars, telling you they want more IP and franchises. You can try to convince studio heads to make fewer IP-based films and franchises, but the data and numbers aren’t there. Instead, critics need to work harder to convince audiences to show up for original films. Aim your ire/concern at the average person, not studio heads.2 Because they’re just making the films that audiences are telling them to make. WRONG: Original Horror Films Didn’t Break Out I’ll be honest, even though I wrote an article casting some skepticism on the horror genre in July, if you asked me to make a prediction, I would have predicted that, in August, a new, original horror film would have blown up. No, seriously, I just assumed that Obsession and Backrooms presaged a change in audience behavior. But none of the buzzy new original horror films from August—Teenage Sex and Death at Camp Miasma, Onslaught (not an action film in spite of the ads), The End of Oak Street, Eli Roth’s Ice Cream Man, or _Buddy—_broke out. To be clear, exactly one of those films (Buddy) had good “ROI”, but again—I try to be specific in my language—none were “popular” in any broad sense of the word. None of them will be “saving” movies theaters like Backrooms or _Obsession_helped save the summer. The new Insidious film and Resident Evil, both based on IP, were far and away the biggest horror films since July. (We’ll see if this changes in October/Halloween season.) RIGHT: Stay Skeptical about Crowdfunding... I’ve long been skeptical about crowd-investing platforms as one of Hollywood’s saviors, mainly because there’s so much hype/buzz. People need to stay more skeptical about more things, explaining both the potential upside but also the downsides. In particular, the media often hypes crowdfunding at the start and never checks in on the actual results after they’ve come in later. And August gave us our first update! _Ice Cream Man—_directed by Eli Roth—grossed $6 million off of a $5.5 million budget. This is a production of The Horror Section, which was one of the first “crowd investing” studios with 2,400 investors, which means that 2,400 investors probably lost money. They certainly aren’t getting as great of returns as if they had just invested their dollars in the stock market. Hopefully Stiletto (Tagline: “Someone’s Going to Make it Rain Blood!”) does better next month. WRONG: Another Data Goof Here’s another data error. When the first episode of House of the Dragon came out, HBO put out that it had 21.5 million viewers in the first three days, and I read that to mean in the US…but no, it was global. So my US-only datecdotes charts shouldn’t have included it. We never got US-only numbers for the first episode, but for the final episode, HBO put out that it had 11 million US viewers. (And that global dropped to 21 million viewers.) Here’s the updated chart (which I’ve since used in the Streaming Ratings Report): Still, this show is absolutely huge. RIGHT: Superhero Films Remain Very, Very Popular After Supergirl flopped, I read a few takes that “comic book movies are going the way of the Western”. Post-Spider-Man: Brand New Day, that take didn’t age well. To be fair, I have a very nuanced take on the superhero genre right now; it’s down right now, for a lot of reasons. But it’s not “dead”. Maybe _Spider-_Man is just a really popular character? I saw that take, and it’s a fair counter-argument. (But pundits arguing that superhero movies were dead should have mentioned this $1.5 billion counter-argument…) But is it just Spider-Man? The next Avengers film already has $50 million in pre-sales (and I was skeptical that that film would do well) and the Avengers: End Game re-release topped the box office two weekends ago (over three original films). And I wouldn’t bet against Batman or Superman. So maybe it’s just Spider-Man, Batman, Superman and the Avengers. Oh, and Deadpool, of course. And Black Panther. And Wolverine. And probably the X-Men. Plus a well-made Wonder Woman or the Hulk film could break out. But that’s it! It’s just those ten characters/teams. Oh, what’s that? Lanterns is also doing well on HBO? (See previous section…) To be fair, I’m actually pretty sympathetic to the argument that more popular characters—like Spider-Man and Batman—anchor more popular films. In fact, I made that exact argument three years ago when I first wrote about the “Marvel-cession”. In many ways, you can blame The Guardians of the Galaxy for fooling Marvel Studios (and the rest of us) into believing that any character could pop. It turns out, the list of iconic characters is probably smaller than most people think. But it’s probably too early to say that superhero films and comic book movies are dead unless “death” means a slight decline over a longtime. RIGHT: Who Killed Theaters? Old People I get frustrated whenever I see headlines or analysis about how young people are “returning” to theaters. As I’ve detailed(for years), young people have always powered the US box office, despite narratives about “kids these days” and their “phones”. Really, what’s changed post-2020/pandemic is that old people aren’t going to the movies nearly as much. This summer, I saw a movie (from an older director) in a theater near a retirement community, and multiple older people at the theater were talking about how this was their first time seeing a movie in years. I dislike personal anecdotes, so YouGov can fill in the data, best summarized by this headline: “Who killed movie theaters? Not the youths”. According to them, 64% of people aged 18-29 have seen a movie in the last year, but only 30% of 65-and-older. 20% of 18-29 have seen a movie in theaters in the last week and 42% in the last month. Here’s the polling data: Most concerning? Many Americans (17%) think theaters are a worse or much worse experience than watching films at home. Slight WRONG: Hadestown Opens Big A live theater capture of the Broadway musical, Hadestown, made $20 million at the US box office, which begs the question: was I wrong to be skeptical about musicals a few years ago? Yes and no. On the one hand, $20 million is a far cry from being “popular”, so yeah, the genre isn’t that popular overall and Hadestown is one of the more popular musicals from recent years (i.e. the “Taylor Swift Data Fallacy” in action). On the other, I doubt filming this cost all that much, and I don’t think that they spent much on marketing, so this is a good source of ancillary revenue. Smaller Updates WRONG: As I mentioned in a Streaming Ratings Report, I underestimated the budget for Enola Holmes 3. It probably cost more like $50 million, if not more. But... I’m not sure that it really matters? At sub-10 million hours, prices have to come down to make this work. WRONG: Netflix is giving Ink a 27-day in theaters! To quote the kids/YouTubers these days, let’s go! Now I might actually have a chance to see Danny Boyle’s latest in theaters. I’d complained about this in a “Coming Soon” section, but I was heartened to read that Netflix is giving multiple films longer theatrical windows this year. RIGHT: Netflix is sending 4-5 films per year to theaters. Netflix is slowly but surely sending more and more films to theaters, as I cautiously predicted earlier this year. For now, it’s just three big films and a number of awards contenders, but still, this is great news. And they’ll be releasing box office grosses! Just this week, Ted Sarandos confirmed that KPop Demon Hunters 2 will come to theaters (and my guess is it performs in the box office top ten at a minimum). WRONG: Angel has 3 million subscribers! How do I know this? Well, they told Deadline, who reported it. I marked this as “wrong”, since they’ve doubled their subscribers in one year but, you know, they don’t really have a hit film to speak of and they’re still losing money. WRONG: Furious was only renewed for one more season. I accidentally wrote “two more seasons” in my latest “Renewals, Cancellations, Un-Orders and Removals Update”. RIGHT: House of David is ending with its third season. In July, Prime Video renewed House of David for a third season, as I just wrote in my latest “Renewals and Cancellations” report. Well, now it’s ending after that third season. Why? As I’ve been writing, its viewership wasn’t great. I got feedback that this show didn’t cost very much, but it cost enough that its limited viewership didn’t save it. WRONG: Adults was a Hulu original! So I missed Adults when it first came out last year; I saw that it aired on FX and just assumed that it was a linear-first program. Turns out, it aired three episodes on FX, but binge-released the rest of its episodes the next day on Hulu. Huh. So I should have covered it last year! But I just wrote about it. 1 “When I was a kid in the 90s, if you wore a t-shirt to school that wasn’t too big and also wasn’t too small, but somehow didn’t quite fit, we’d say you were wearing an “extra-medium” shirt.” 2 As always, a huge exception is Disney, which barely makes anything original anymore.
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