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Making Our Own Luck
We are cross-posting a piece that former NIH official Kris Willis wrote for Macroscience. Kris is now the President and Founder of the Woodley Park Institute, for which I’m a board member. Picking winners or hedging bets? Federal science agencies have long wrestled with the question of how to ensure their billions of dollars of grants and contracts result in maximum benefits for Americans. On the surface, it might seem that a goal-oriented, interventionist style of management would be the best policy: prioritize our most pressing problems and distribute funding accordingly.This top-down strategy creates a unique hazard, though: bad choices can send resources in the wrong direction, resulting in slow progress that impedes the delivery of tangible benefits to citizens. Moreover, if decision makers pursue an applied advance when the underlying scientific principles remain poorly understood, they risk costly failures and a loss of support for the research enterprise at large. Examples include Nixon’s 1971 War on Cancer, undertaken on the premise that a cure could be achieved in less than a decade, the drive to build an operational commercial fusion plant by the mid-1990s, and the National Plan to Address Alzheimer’s Disease, which failed to produce the effective treatments and prevention strategies promised by 2025. The contrasting laissez-faire approach is to hedge your bets. If the path to an advance is impossible to predict, then the wisest choice might be to distribute funding to as widely as possible. The main risk of this strategy is that spreading funding thinly may limit the resources available to the most promising research, resulting in the same negative outcomes as the interventionist approach: limited support for the most promising topics, slow progress, and delayed returns. In reality, at science agencies like the National Science Foundation and National Institutes of Health, program managers navigate between these two extremes, setting some funds aside to target high-priority areas and using the rest to cover the widest possible range of meritorious proposals. This middle path seems like the most rational way to proceed, but it still requires making choices. How much should be set aside for high priority topics? What should those topics be? How should we define merit? Expert opinion can provide a helpful guide, but leading experts can have remarkably different opinions about what matters and what should be prioritized. Moreover, experts and administrators alike can be risk averse, giving an edge to high-profile, well-established concepts. What’s really needed is a data-driven framework to help guide decision-making, one that doesn’t simply repackage prestige or incumbency. Recognizing breakthroughs years in advance Many breakthroughs fail to attract funding or recognition at the earliest stages of development. Katalin Karikó, for example, spent years struggling to win grants for work on mRNA that would eventually win her and Drew Weissman a Nobel Prize. Similar stories can be told about Jim Allison’s groundbreaking studies leading to the development of cancer immunotherapy, Stan Pruisner’s demonstration that prions can self-replicate without DNA, or Robin Warren and Barry Marshall’s proof that Helicobacter pylori, not stress, causes ulcers. Yet at some point, the scientific community recognizes and responds to transformative breakthroughs like these. How does that change happen? And what if science funders could identify the areas that are on the cusp of a breakthrough? Those questions led my collaborators and me to study how scientists responded to past breakthroughs. Based on patterns in that data, we developed a means of predicting future breakthroughs, using biomedicine as a proof of concept. It’s worth taking a moment to describe how our approach works. Briefly, our prediction process begins by grouping the 18 million or so peer-reviewed papers in PubMed, the authoritative database of biomedical research, into roughly 50,000 unique topics. A small subset of these topics are so widely acclaimed as breakthroughs that they have been recognized with a major prize like a Nobel or a Lasker Award. We theorized that studying the development of these outliers over time, especially before they gained acclaim, was the key to identifying future instances of them. After defining topics, we chose 21 representative breakthroughs and asked what they looked like before they won prestigious prizes. To answer that question, we re-ran our topic-mapping algorithm repeatedly, winding back the clock one year each time, stringing together the years to follow the progress of each field as it grew and advanced. We found that in each case, the years leading up to a discovery followed a pattern: a burst of papers on a new, rapidly evolving topic, many of which quickly became influential. These characteristics—the percent of papers that are new, the percentage of papers that are brought in from other topics, and the influence of each individual paper—can all be measured separately for any given topic. Together, they act as a predictive signal that is detectable an average of five years before a transformative discovery is made, and up to thirty years before the discovery receives a major prize. Scanning the current research landscape for topics that display this signal allows us to predict what work will likely produce a future breakthrough. Each year we analyzed includes four or five signals, a number that appears to have remained constant across a twenty year time frame. In their classic work on the sociology of science, Bruno Latour and Steve Woolgar established that the desire to participate in a major discovery is an important motivator for scientists as they consider whether they should take up (or abandon) a research problem. Although further work is needed, the simplest explanation for our results is that this behavior is widespread enough to detect at scale. A foundational advance, or something like it, draws the attention of scientists and causes them to change the direction of their research. They vote with their feet, staking their own reputation and careers by publishing on and citing the new idea. That upswell of interest is the foundation of our breakthrough signal. What kind of research does our model identify as a breakthrough? After building a model based on the patterns in our 21 preselected breakthroughs, we tested it by identifying all early signals of discovery from 1994 through 1997. Of the 18 signals we found in that time period, 17 can be traced forward in time to a breakthrough. Examples include super-resolution fluorescence microscopy (see the image above), the directed evolution of proteins and enzymes (Chemistry Nobel, 2018), and sequencing the human genome (National Medal of Science, 2008). The discovery of the role of leptin signaling in obesity is a particularly interesting case study. Around 1950, scientists noticed the existence of a type of lab mouse that suffered from a unique inherited form of obesity. When given the freedom to choose their own diet, these mice ate more than normal and preferred food that was high in fat. Researchers had few clues as to why the animals overate; early experiments pointed to the existence of a soluble ob factor found circulating in the bloodstream that regulated appetite, but its identity was unknown. In 1994, Jeffrey Friedman and colleagues identified and sequenced the ob gene, noting that the protein it encoded appeared to fit the profile of a circulating factor. A year later, a trio of high-profile publications demonstrated that ob was a hormone that regulated body weight and fat deposition by regulating appetite. One of these was co-authored by Friedman, who christened it leptin from the ancient Greek word for thin. Using our model, the data from 1996 make it clear this advance would become a breakthrough. Scientists had flocked to study the biology of the new hormone, generating the flurry of new papers and citations required to produce a signal. One of these demonstrated that serum leptin concentrations reflected the amount of adipose tissue in the human body, providing clinical validation of the earlier mouse studies. Before the identification of leptin, scientists were unable to point to a specific molecule that controlled appetite and adiposity, so that it was impossible to rule out non-physiological causes of obesity; its characterization fundamentally changed our concept of weight gain. In 2010, Friedman and fellow pioneer Douglas Coleman were awarded a Lasker for the discovery. Our work also identifies breakthroughs in clinical practice and behavioral and social sciences, even though these areas are less likely to draw the attention of major prize committees. One example that falls into this category is the introduction of endovascular aneurysm repair, a minimally invasive surgical procedure for the treatment of abdominal aortic aneurysm, a leading cause of death among Americans over the age of 65. Relative to the previous standard of care, it reduces in-hospital mortality by almost four-fold. In 2017, Timothy Chuter received the American College of Surgeons’ prestigious Jacobsen Innovation award for his multiple refinements to the technique. Another under-recognized breakthrough identified by our model is the development of standardized survey instruments to assess the quality of life for HIV+ patients. The antiretroviral cocktails introduced in the mid-1990s reduced AIDS mortality, but treatment came with serious side effects. The new evaluations showed that patients whose disease progressed had worse physical functioning than Americans with other chronic diseases, while those who were asymptomatic maintained a health-related quality of life on par with the overall US population. This evidence made it possible for patients and activists to argue that even in the absence of a total cure, and in spite of the side effects, alleviation of symptoms was a worthwhile priority for clinicians. My colleagues and I have failed to identify any significant recognition for the physicians and health policy experts who pioneered this advance, although in 2016, researchers called for the World Health Organization to set targets for good health-related quality of life as part of its framework to end the AIDS pandemic. From forecasting to funding How should science funders use our predictions? Any serious discussion of what we should do with this new capability needs to begin with an understanding of its limitations. We can’t (and shouldn’t) invest all of our resources in the small number of topics predicted to produce breakthroughs. Innovative new fields are born from existing ones; they rarely arise de novo. If funders don’t maintain a diverse portfolio, the breakthroughs of tomorrow will have no antecedents. Think of this as eating the seed corn, or killing the goose that lays the golden egg. There are also multiple goals of funding beyond basic scientific discovery, including translating discoveries into clinical practice, developing new technologies, supporting economic development, and training new scientists. The investments needed to accomplish these goals are likely different from those required to support emerging breakthroughs. Finally, we must be aware that we may not capture every instance of a breakthrough. The concept is fuzzy, and not every significant discovery is recognized with a major award. Further, breakthroughs are rare, meaning that our dataset is, by necessity, small. This makes formal estimates of accuracy challenging. Although our overall success rate appears to be high, any one signal may turn out to be a false positive. We found one apparent example: the development of the third generation COX-2 inhibitors, Vioxx and Celebrex, which were much celebrated in the early 2000s as effective but non-addictive pain relievers, meets all the algorithmic criteria of a breakthrough. Five years after it received FDA approval, researchers demonstrated that Vioxx was associated with serious cardiovascular complications, and the manufacturer voluntarily pulled the drug from the market. Celebrex remains available, although like other NSAIDs, it carries an FDA warning for increased cardiovascular risk. Decision makers who were overly focused on the breakthrough potential of this research might have wasted substantial resources nurturing an area that ultimately proved to be a failure—but so would those who relied on expert opinion at the time. That said, the potential benefits of funding the next mRNA vaccine or cancer immunotherapy years earlier than we might have are large. When we can’t assume the outcome of an action, it’s worth conducting an experiment. Can targeted support for areas that are poised to produce a breakthrough increase the return on scientific investment without cannibalizing future advances? With the necessary administrative infrastructure in place, conducting this experiment would be straightforward. First, identify all the breakthrough signals between 2018 and 2023, select half at random, and commit to a decade of investment. For biomedicine, a six year window should yield about 30 topics with the potential to produce a breakthrough. Providing strong support to half of these might be accomplished for $300 million a year. That’s roughly half of the recent annual budget of the NIH Common Fund or around 20% of the fiscal year 2025 budget for ARPA-H. After five years, then again at ten, compare to see which group — the one that was actively managed, or the control — produced more breakthroughs, and on what timescale. Although we used biomedicine as an example, the same experiment could just as well be run for physics, materials science, or any other discipline. Any attempt to undertake such an experiment should keep three points top of mind. First, we can predict the topic of future breakthroughs, but the people and vision for how to move the work forward are still important. In practice, this means success requires active program managers with the expertise to develop the science and the authority to do so, including by recruiting investigators. Second, we would be wise to learn from the failures of past attempts to fund transformative research. High among these are funding modestly repackaged work, confusing the genuinely novel with the merely unfamiliar, and relying too much on incumbent investigators. All of these risks can be mitigated by thoroughly and intelligently integrating high quality data into portfolio management. Finally, we need to have patience. Even with strong funding support, producing a breakthrough takes time. If supporting breakthroughs is the best way to nurture the birth of new fields, we also need to accurately describe and properly manage the other two stages, so that we invest wisely throughout the productive lifetime of ideas and divest once they reach the point of diminishing returns. We would benefit from more study of all of these phases, especially the transition from one to another. More research could answer questions like: When does a breakthrough become an established field? How do the availability of funding, the size and characteristics of the available workforce, and the accumulation of evidence that disagrees with prevailing models affect a field’s decline? Exactly how do old ideas give birth to new ones, and can we speed up the process? The more we understand about the dynamics of scientific progress, the better we can maximize the odds that our curiosity leads to results. I’d call that finding a way to make our own luck.
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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.
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The work you can't always see: Beer is political, part 2
Recently I published a piece about beer, politics, and the question of who speaks up and who doesn’t. I meant every word of it. I still do. And then my inbox opened up. Cleveland Prost is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe I’ve been writing about beer since 2014. For most of that time, I kept things deliberately light — taproom profiles, new releases, the people behind the pints. Gradually, over years of building this thing, you gave me something I didn’t expect: permission. Permission to go deeper. To say what I actually saw in this industry and what I actually believed about it. That trust isn’t something I take lightly. It’s why that piece existed at all, and it’s why this one does too. I’m not walking anything back. But I want to go further. In that piece, I wrote that beer has never existed outside the system. That immigration policy affects who staffs breweries and harvests ingredients. That labor policy affects taproom workers. That civil rights policy affects who feels safe walking into a taproom. That “beer does not exist in a vacuum. The idea that it does is comforting. It is also false.” I believe that as much now as I did when I wrote it. But one idea came back at me, in different forms, over and over (from reader emails, comments, and conversations): that beer itself isn’t political — that it only becomes political when someone decides to make it that way. That what we’re really seeing is a choice by individuals to inject division into something that doesn’t inherently require it. It’s a comforting idea. It’s also wrong — and I think it’s worth being precise about why. That framing assumes that breweries operate outside the conditions shaping everything else. That politics is something you choose to bring into a room that was, until then, clean and neutral. That if you stay quiet, you stay clear of it. But the room was never neutral. It was never clean. Politics is already in the room. It’s in who walks through the door comfortably and who hesitates. It’s in whether a space can be used for organizing without fear. It’s in whether helping someone quietly might carry consequences you have to think carefully about in advance. It’s in who staffed the brewery, who harvested the grain, who can afford to be a regular. The idea that you opt out of all that by staying quiet is an illusion. You only change how visible your response to it is. This is, I realize, a hard thing to hear if you came to a beer newsletter for something lighter. I wrote recently that “beer, for me, has always felt like a refuge — a third place in an increasingly fractured world.” I meant that. I still do. But a refuge is not an escape from reality. It’s a place to gather strength to face it. Here’s what I actually missed — and what I want to correct. Multiple people in the industry reached out to me. I’m not naming them and I’m not identifying their businesses, because that’s not my call. If and when they want to be loud about what they’re doing, that agency belongs to them. What I can share is the substance of what they said, because it complicated something I presented too cleanly. The overall message: *You don’t know what we all are doing.* And they’re right. I don’t. One example involved a collaboration brewed quietly to support another business facing serious challenges in a whole different region, with proceeds directed toward solidarity, community, and keeping operations afloat. No announcement. It could have been broadcast widely. The point was never publicity. Another example involved logistical support for community efforts: organizing safe transportation, providing space for meetings on otherwise closed days, facilitating gatherings under difficult conditions. All of it happening while balancing real financial pressure and genuine concern for personal safety. None of what they described is a brewer “deciding to make beer political.” It’s people responding to the conditions around them — conditions that are already shaping who feels safe, who has access, who can gather, who needs help, and who has the capacity to offer it. Politics, again, already in the room. Already shaping choices. Whether anyone announced it or not. I’ve spent decades in taprooms, at barrel releases, at festivals, in back offices and brewing floors, talking to the people who built this industry from nothing. I know how much happens that never makes it onto social media. I know the owner who quietly comp’d a tab for someone having a crisis. The brewer who drove an hour to pick up a stranger who needed a safe ride. The taproom that stayed dark on a Tuesday so a community group could use the space without it becoming A Whole Thing. None of that gets a post. Most of it never will. So when I wrote recently about silence — when I said that “silence right now doesn’t feel neutral, it feels like complicity” — I was writing about what I could see. And I was right about what I saw. The public silence of breweries that have the platform, the stability, and frankly the safety to say something and choose not to? That still troubles me. I’m not softening that. But some of what I read as silence is actually something else entirely. It’s people doing the work with their hands instead of their phones. It’s people who are genuinely afraid — not of being canceled, but of real consequences for their families, their employees, their ability to keep the doors open at all. That deserves more than a footnote. It deserves to be named clearly. Here’s the tension I want to sit with honestly: public statements matter. They matter because people can’t rally around something they can’t see. Visibility creates awareness and solidarity. It helps people find each other. It tells someone standing outside your taproom door, uncertain whether they’ll be welcomed inside, something they need to know — or whether it’s even safe for them to be in public at all. That’s not performative. I wrote recently that the craft beer movement “was built on collective defiance. It was built on rejecting consolidation. On pushing back against homogeneity.” I believe that too. And I’ll extend it: breweries should be at least as loud about their values as they are when they drop a new seasonal. Your hazy IPA gets a full rollout — the label art, the tap handle photo, the countdown post. Human dignity deserves the same energy. Be as loud about human rights as you are about your beer. There is no reason those two things should require different levels of courage from you. But visibility isn’t the only form of courage. And not everyone can be visible right now without real consequences — financial, personal, sometimes physical. There are people in this industry doing hard, unglamorous, sometimes risky work in the background, and announcing it would compromise it or paint a target on someone who’s already stretched thin. That’s not silence as complicity. That’s operational discipline under pressure. What I want to push back on — clearly, not gently — is the idea that behind-the-scenes action and public visibility are in competition with each other. The person organizing quietly in the back room and the person posting a statement of solidarity on a Tuesday morning are both doing something real. We need both. We need the quiet infrastructure and we need the visible signal. We need people to know that this industry — at its best, at its most independent, and human — gives a fuck. I closed that piece with a question: “Reactive and fearful? Or clear and values-driven?” That question still stands. But having heard from you, I’d add a corollary: the values-driven path isn’t always the loudest one. Sometimes it’s a phone call and a recipe. Sometimes it’s a room made available on a closed Tuesday. Sometimes it’s a drive across town for someone who needed a safe ride home. All of it is political. All of it was always political. You don’t get to opt out of that — you only get to decide what you do with it. I’ve spent more than a decade writing about this industry because I believe it can be exactly that. I’ve been lucky enough to build an audience that lets me say so plainly. That’s a privilege I intend to use. The work is happening. Some of it you’ll see. Some of it you won’t. Support it either way. Cleveland Prost is a reader-supported publication covering beer and brewing across New York state. To receive new posts and support this work, consider becoming a free or paid subscriber. Cleveland Prost is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe
KC officials urge Costco to consider consequences of business center conversion
All but one member of the City Council signed a letter to Costco echoing residents’ concerns about the midtown store’s proposed conversion.
Science Central celebrates Animal Day on May 30
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SRAM vs the UCI: How a dispute over gear ratios could change global sport forever
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The Pentagon Knows the True Threat of Climate Denialism. Too Bad They Can’t Do Anything About It.
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Scientists propose recommendations to G7 leaders over Arctic warming
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Bonus 228: The $1.776B Political Question
Every so often a story comes along that is so brazen, so cleanly emblematic of the moment we are living through, that staying quiet about it feels like a kind of complicity unto itself. Monday’s announcement by the Department of Justice that the Acting Attorney General has unilaterally created a $1.776 billion “Anti-Weaponization Fund” to compensate so-called “victims of lawfare” is one of those stories. And the reason I want to write about it here, in a newsletter ostensibly devoted to the Supreme Court and the law and politics surrounding it, is that I’m increasingly worried that we’re asking the wrong institution to save us from one of the most offensive, alarming, and anti-constitutional things we’ve seen from any president in American history. As I explain below the fold, the Anti-Weaponization Fund is not principally a legal problem awaiting a judicial fix. It is a shockingly egregious political abuse—the President suing his own government, purporting to settle with himself, and routing nearly $2 billion from the Treasury to his own supporters as a not-so-subtle (and not-so-formal) reward, if not an incentive, for political violence. And the Constitution’s answer to political abuses of this kind and magnitude is a political remedy, not a legal one. In his majority opinion in NFIB v. Sebelius (the Affordable Care Act case from 2012), Chief Justice Roberts defended the Court’s endorsement of Congress’s power to adopt the individual mandate by noting that it is “not our job to protect the people from the consequences of their political choices.” As much of a judicial supremacist as I am, that sentiment is, for better or worse, an entirely correct (and, in the long term, normatively desirable) institutional assessment. One can believe in substantial judicial power without believing that literally every political dispute in our country can and should be resolved by unelected judges. The real upshot of this latest headline (among so, so many other similar headlines) is that it is past time to reinvigorate impeachment—not because conviction is realistic in this Senate, or even because impeachment itself is feasible in this House, but because forcing every member of Congress to vote on the record whether this brazen, corrosive, and affirmatively dangerous corruption is impeachable is itself a point worth fighting for (and fighting with our friends over). Assuming the courts will save us absolves our political leaders of both responsibility and accountability for allowing this kind of mischief to continue, and it absolves all of us of our responsibility to refuse to tolerate those who would just as quickly shrug their shoulders and move on. For those who aren’t paid subscribers, we’ll be back (no later than) Monday with our continuing coverage of the Supreme Court. For those who are, please read on.
Researchers say the Trump administration is finding new ways to punish science
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Scientists improve knowledge on sea level rise—and confirm it has been accelerating since 1960
Sea level rise is a direct consequence of human-induced climate change: global warming. It is relentless and very hard to stop. It arises from human-induced warming and the consequential expansion of ...
2026 National Defense Science and Engineering Graduate (NDSEG) Fellowship Award
Undergraduate senior biochemistry student Lillian Ott (Mohanty group) has been awarded the 2026 National Defense Science and Engineering Graduate (NDSEG) Fellowship, one of the country’s most prestigious honors for graduate students in the fields of Science, Technology, Engineering and Math (STEM). The program, jointly sponsored by the U.S. Army, Navy, and Air Force, supports doctoral students across a broad range of scientific and technological fields of strategic importance. This highly competitive fellowship provides full tuition, fees, and a monthly stipend for three years. In 2026, only 102 fellowships were awarded nationwide. Lillian also received an honorable mention for the prestigious National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP). Studying biochemistry at UCLA, Lillian became excited about how her physical science background could be applied to solve environmental issues. She joined the Subsurface Engineering Analysis Laboratory (SEA Lab) led by Professor Sanjay Mohanty in the Samueli’s Department of Civil and Environmental Engineering. In the SEA lab, she has studied the fate and transport of per and polyfluoroalkyl substances (PFAS). PFAS are ubiquitous “forever chemicals” and known carcinogens that impact water quality worldwide. This fall, she will begin her Ph.D. in Civil and Environmental Engineering at Stanford University with Professor Nina Zhao. Her research will be focused on metabolomics and the discovery of unrecognized chemical determinants of environmental and human health. “I have seen the serious and unforeseen environmental and health consequences from man-made chemicals like PFAS and microplastics, which are often only appreciated after the damage has been done. I hope that my research can lead to more sustainable chemical design and inform policy and solutions-based innovation in public health and water quality engineering,” Lillian said. Penny Jennings, UCLA Department of Chemistry & Biochemistry, penjen@g.ucla.edu.
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Modeling context-dependent RNA splicing by deep learning
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Tom Brady gives commencement speech to Georgetown University business school graduates
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'Knights of the Old Republic' Animatic Reimagines Endar Spire - Star Wars News Net
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Analysis: Fulham's European hopes peter out
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Rochester Regional Health graduates first class of peace officers
Officials believe the new peace officer program will significantly strengthen the hospital system’s ability to respond to emergencies in real-time.
Chartbook 449: Voldemort on Threadneedle street. The Bank of England and the "haunted house" of English politics.
Imagine the scene: In some Harry Potter version of 21st-century London, a group of deathless UK Treasury officials gathered from across the last century huddle over warm pints and glasses of Chardonnay. Around the table there are officials from the 1920s, the 1930s, the 1970s, 2022 and today. Inevitably, in May 2026, the subject of conversation is the disintegration of the current Labour government. Despite its huge majority in parliament, it has been reduced to tatters by catastrophic local election result. These exposed the fact that rather than having a government with a clear popular majority, the UK is like most other modern societies divided into 5 segments each of which commands between 26 and 16 percent of the vote. Perhaps unsurprisingly, the leadership at Nos 10 and 11 Downing Street - where the Prime Minister and the Chancellor (Treasury Secretary) work - seem paralyzed. They are caught between mounting discontent over unfairness and the cost of living, and tough fiscal strictures that push them into making unpopular spending decisions. In the London pub, the Treasury officials from across the ages, can barely hide their satisfaction at the latest display of democratic politics caught in the vice-grip of financial logic. “Remember the mur d’argent and the banker’s ramp?” “Just like yesterday!” There are smirks around the table. Then the representative of 2026 pipes up: “One thing you have to say about the current lot (referring to the latest hapless Labour government), you don’t have to teach them about the bond market!” There is a murmur of ascent from the Treasury Mandarins. The trauma of the fiscal crisis of September-October 2022 sits deep. Back then, in the final stages of Tory decay, a ‘mini budget’ that challenged fiscal orthodoxy led to a brief but fierce panic in one of the oldest government bond markets in the world (aka gilt market). Now all you have to do is to mention the “bond market” to hush debate. [ Paul Mason@paulmasonnews In the name of public service, and as ex-economics editor of BBC Newsnight, I offer to do a zoom call, tonight, with any Labour MP who wants to understand why bond markets do not "fall into line" with governments. 1/ 3:57 PM · May 12, 2026 · 466K Views 224 Replies · 674 Reposts · 3.93K Likes ](https://x.com/paulmasonnews/status/2054229532404519082?s=20) “And the funny thing is”, the young man in the huddle continues, “they don’t even want to talk about … ”. There is a sudden hush. Significant looks are exchanged around the table. About? One of the senior figures puts down his glass with a decisive “clink”. He-Who-Must-Not-Be-Named has entered their thoughts. Meanwhile, a few miles away in a grubby pub in Bloomsbury, two conspirators from the camp of Critical Macrofinance huddle over cans of low-alcohol IPA. They look at each other and blurt out loud: “The Bank!” “Everyone in the bloody Labour party is talking about the bond market. But why aren’t they even mentioning … the Bank of England!?” “Why is no one talking about Voldemort?” In Paul Mason’s blogpost, after much chest-thumping about basic fiscal logic, the basic point about the Bank of England is buried in the fine print at #3 in his list of suggestions as to what a radical Labour government might actually be able to do! [ Conflict & Democracy Down with the bond market! Down with gravity! It may seem unfair to focus on Labour MP Paula Barker for her off-the-cuff assertion that the bond market would have to “fall in line” with Andy Burnham, should he replace Keir Starmer as prime minister. But Burnham himself told the New Statesman that Labour had to “get beyond this thing of being in hock to the bond markets… Read more 2 days ago · 100 likes · 3 comments · Paul Mason ](https://htsf.substack.com/p/down-with-the-bond-market-down-with?utm_source=substack&utm_campaign=post_embed&utm_medium=web) Back in Bloomsbury they shake their heads and ask: “Didn’t we learn anything from the Eurozone crisis?” Meanwhile, as if summoned to the conversation by some higher force, the “pink pages” announce: “The days of ultra-cheap government borrowing have come to an end” and Martin “Whizzard” Wolf publishes the graph of the season Look at the UK in 2026, perilously positioned on the upper edge of the pack. No wonder no one in London needs any lessons about the “bond market”. But, look again. Imagine how they feel in Rome. What is London’s nightmare in 2026? The nightmare is that you end up like Italy in the early 2010s during the Eurozone crisis. Once comfortably situated in the middle of the pack, watching with horror as your ten-year yields explode to 7 percent and “spreads” become a national nightmare. One of the largest economies in Europe, a founding member of the euro, stigmatized. But the message of the Italian experience is ambiguous. After all, the stigma didn’t last. In 2026, Italy borrows at rates lower not only than the UK but the US. The US, which, as everyone knows, enjoys the “exorbitant privilege” of being the issuer of reserve, safe asets. What kind of magic is this? How did Italy, in the space of fifteen years, go from being good, to bad and back again? Which brings us back to our dissident friends in their Bloombury boozer and their memories of the Eurozone crisis. Italy never was Britain - with its ongoing fiscal deficits. Without interest payments, on primary balance, from 2000 to 2020 Italy ran a tight fiscal ship. Even in 2008 it barely went into deficit and when it was being put on the rack in the early 20210s its primary surplus matched that of austere Germany. So, what happened? How did it end up stigmatized? Of course Italian politics didn’t help. Berlusconi was at the helm and he had legal issues. But amongst friends in the Bloomsbury pub, Berlusconi is a sideshow, the name we whisper to each other is “Trichet”. From 2004 to 2011 the ECB was headed by Jean-Claude Trichet, ex of the French Treasury, more German than the Germans. After 2009 as the news of the Greek fiscal disaster broke, the European bond markets wento into panic mode and Trichet began a brutal game of cat and mouse. Between 2009 and 2011, Trichet intervened in the bond market to help, only when he thought he had compliance with austerity. When he thought they were uncooperative, he withdrew. In the summer of 2011, he even made direct suggestions to his Italian counterparts in Rome, as to how Italy should be governed. It was this cat and mouse game that drove the bond markets crazy and exposed Italy to a violent squeeze. When Mario Draghi took over the ECB at the end of 2011 and declared “whatever it takes”, the pressure on Italy, gradually eased. It is that starring role that has made Draghi into the towering figure in European politics that he remains today. So now you see why voices in London hush when it comes to the Bank of England. “The bond market” isn’t an irresistible objective force like the weather or an avalanche. It can appear like that. But that depends on the way it is being handled, or not handled by the central bank. Which is why the silence about the Bank of England in London right now is so significant. If you think this all sounds a bit fishy, coming from a left-liberal Keynesian like myself, well, let me introduce you to Robin Brooks, who, to put it mildly, plays on a different quidditch team to me. Here, from the position of a fiscal hawk is Robin’s celebratory account of the Bank of England’s role since 2022; In October 2022, at the height of the bond market blow-up in the UK, something incredible happened. Governor Andrew Bailey told British pension funds the Bank of England would end its support program for the country’s fragile gilt market, drawing a line under how much help the central bank was willing to give to exposed pension funds and - implicitly - the government. By the end of that week, Kwasi Kwarteng - the finance minister - had resigned. Prime Minister Liz Truss would soon follow. This episode is so incredible because it contrasts with what happened in the Euro zone earlier that year. Bond markets in Spain and Italy were under pressure amid high inflation and rising interest rates. In the middle of this, Mario Draghi - Italy’s Prime Minister - resigned, compounding pressure on bond markets of high-debt countries on the Euro periphery. Unlike the Bank of England, the ECB caved. It bought Italian and Spanish government bonds in an effort to cap yields and introduced a new tool that signaled to markets it might do such caps more frequently going forward. Faced with pressure on bond markets, the ECB capitulated to political pressure from high-debt countries like Italy and Spain. The Bank of England stood its ground and toppled the Truss government. This kind of thing has long-lasting consequences. It means markets price a higher probability of yield caps in the Euro zone, which keeps Italian and Spanish yields artificially low, while markets feel free to push up UK yields without fear of central bank intervention. As I flagged in yesterday’s post, this means you really can’t compare UK yields to those of other high-debt countries. The UK looks worse, but only because its central bank isn’t interfering with markets. There’s more to this than just that 2022 episode. The Bank of England has also been much more aggressive in its quantitative tightening (QT) than other central banks. If one motivation for quantitative easing (QE) is to lower bond yields - especially longer-dated ones - then it stands to reason QT pushes yields up. The left chart above shows a four-quarter moving average of UK government debt issuance (black line) in percent of GDP and which sectors absorb this issuance, including the Bank of England (blue bars). The right chart above compares the Bank of England’s accumulation or run-off of government bonds (blue line) with that of other central banks, where this blue line is the same as the blue bars in the left chart. The Bank of England was quicker and more aggressive than other central banks in downsizing its bond holdings, so UK yields will have risen more than elsewhere also for this reason, not just because of Governor Andrew Bailey’s remarks in October 2022. Back in October 2025, after a conversation in New York with friends in British politics, my sense of dysphoria was such that I raised the issue of the Bank of England openly in the FT Since 2008, the UK has slid into a deep economic and political predicament. Growth has stagnated to a degree far worse even than in the 1970s. The Tory leadership’s wager on the Brexit referendum spectacularly backfired, leaving the UK isolated from Europe and at the mercy of its special relationship with the US, many of whose political class regard the UK as a basket case. After years of austerity, the Labour party lurched to the left and then back to the centre. The Tory party has been eclipsed by the populist nationalism of Reform. Any British government should be worrying first and foremost about how to restart growth. Instead, Keir Starmer is beset by shades of the gilt market panic triggered by the Liz Truss government in 2022. The debt level is higher than in recent years, but there is no cause for panic. UK debt is at far from critical levels and of long duration. Whether you want to “take back control” or launch progressive economic policy, a democracy dogged by fear of bond market vigilantes is unhealthy. In a country that is a monetary sovereign, this climate points to one thing: a disconnect between the government and the central bank. Since 1997, the independence of the Bank of England has become a sacred cow. But independence is what you make of it. And in the wake of the gilt market crisis of September 2022, relations between the Bank of England and the UK government need a reset. In normal times, there is no doubt that an inflation-targeting independent central bank can serve a country well. But since 2008, the UK and other advanced economies have not had the luxury of such simple problems. To their credit, central bankers around the world have not stood on orthodoxy. After 2008, the US Federal Reserve rescued the global banking system and, through quantitative easing, it stabilised the Treasury market in the face of huge deficits. In Europe, things were trickier. Both the Bank of England under Mervyn King and Jean-Claude Trichet’s European Central Bank played cat and mouse with national governments. This was a high stakes wager that between 2010 and 2012 escalated bond market tension and triggered a disastrous turn to austerity. Shaken by the impact of those shocks, their successors, Mark Carney and Mario Draghi adopted a more protective stance (assuming you were not the government of Greece). Recall that Draghi’s legendary slogan, “Whatever it takes”, was uttered to face down the City of London’s bond vigilantes. In 2015, Draghi launched QE — financial repression by another name. In 2016, Carney used the authority of the Bank of England to stabilise markets following the vote for Brexit, an exemplary demonstration of how central banks can underpin democratic choice, even when those choices are wrong headed. All these were independent central bankers steeped in the best practice of the 1990s, who recognised that historic crises demanded radical action. It was the price surge of 2021-2023 that poisoned the atmosphere. Though the shock came mainly from the supply side, central banks ended up in the dock. Nowhere more so than in the UK. The Bank of England reacted with aggressive normalisation. Faced with a cost of living crisis, the governor called for workers to accept real wage cuts. In early 2022, as gas prices were at record levels and Russia’s army was poised to invade Ukraine, the Bank adopted a stringent programme of quantitative tightening, elevating interest rates. When the Truss government got itself in trouble, the Bank took almost a week to come to the rescue. Rather than enacting a charade of normality, it is time to recognise that in 2025 the UK faces a period of historic transition. To meet this challenge, what is needed is a new concordat with the Bank. This should not ignore inflationary pressures or the long-run trajectory of debt, but should affirm the priority of reviving investment-led growth. One could follow Donald Trump in demanding interest rate cuts. The Bank could shadow the ECB in its commitment to capping Eurozone yields. But the top priority should be to lay the ghost of September 2022. The Bank’s slowing of QT last month was a concession. An immediate and complete end would send the right signal. Such a concordat will not by itself restore sanity to politics, solve the housing problem or rebuild the NHS. But it would help to lift the fear of bond market crisis, which is paralysing the effort by democratic politicians to answer the fundamental questions facing the nation. Now, that call for a new concordat between government and Bank of Englands seems more urgent than ever. The problem is that in October 2025, Starmer still had some authority. Today the government is in tatters. And when you are the “under the harrow” - to echo a phrase of Montagu Norman’s from the 1920s - it is the worst possible time to consider institutional change. To make general arguments at a time of crisis can only seem like special pleading rather than a general argument for long-term change. The same, unfortunately, is also true for the other basic institutional reform that Britain urgently needs i.e. the abolition of the out-dated first past the post electoral system and its replacement by a moderated form of proportional representation. And thirdly - whilst we are saying the quiet parts out loud - Britain needs to reopen the question of EU membership. What the local election results of 2026 show is that this remains a key issue. And what they also show is that there is a clear majority that would favor a return. Until these basic features of Britain’s political economy are addressed the country will carry on its politics as though in a haunted house, governed by governments that lack actual majorities, conjuring up false constraints whilst ignoring the real ones. This one is for DG and MI. I love writing the newsletter. If you enjoy it too and fancy buying me a coffee once a month, you know what to do. Click below! Subscribe
'Outlander' boss breaks down the series finale, that post-credits scene, and the stories they never got to (exclusive)
The 'Outlander' series finale provided a few answers and a few questions. Showrunner Matthew B. Roberts breaks down that post-credits sequence and the stories they never got to write.
Craig Venter obituary
Pioneering and controversial geneticist who was one of the first to sequence the human genome, in part by using his own DNA
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Homo erectus genetic material sequenced for the first time, and it shows 'deep genetic links' with modern humans
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South Carolina Republicans defy Trump’s demands for redistricting
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Biggest VAR call ever?
The most consequential decision in the history of the video assistant referee in the Premier League was played out over four minutes 11 seconds of nerve-shredding drama inside London Stadium. Darren England had to pore over the most significant domestic VAR decision since its inception in 2019 before sending referee Chris Kavanagh to the screen. VAR decisions have been important before - but none have been quite so heavy, with the consequences potentially giving Arsenal a decisive push towards their first Premier League title in 22 years, while at the same time sending West Ham towards the Championship.
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Hantavirus: the risks, the science and what you need to know
On May 2, the World Health Organization got an alarming report: People aboard a ship in the Atlantic Ocean were falling ill. The culprit is now confirmed as hantavirus, a pathogen that some rodents carry that can infect humans in rare, but often deadly, instances. Multiple passengers have died, and more people are showing symptoms. So, we’re talking to Emily Abdoler, an infectious disease doctor at the University of Michigan, about the news – how common is hantavirus in humans, what are the consequences of getting it, and how can at-risk people protect themselves from it? If you found this episode interesting, listen to our episode on Rocky Mountain spotted fever.Interested in more science in the news? Email us your question at shortwave@npr.org.Listen to every episode of Short Wave sponsor-free and support our work at NPR by signing up for Short Wave+ at plus.npr.org/shortwave.
The National Academy of Sciences Elects Two Berkeley Lab Researchers
Two Lawrence Berkeley National Laboratory (Berkeley Lab) scientists have been elected to the National Academy of Sciences (NAS) in recognition of their distinguished and continuing achievements in original research. They are part of a cohort of 120 new members and 25 international members. The National Academy of Sciences is a private, nonprofit institution that was established under a congressional charter signed by President Abraham Lincoln in 1863. It recognizes achievement in science by election to membership, and — alongside the National Academy of Engineering and the National Academy of Medicine — provides science, engineering, and health policy advice to the federal government and other organizations. The 2026 electees bring the total number of active members to 2,705 with 557 international members. Gary Karpen is a biologist senior faculty scientist in Berkeley Lab’s Biological Systems and Engineering Division and a Professor of Cell and Developmental Physiology in the Department of Molecular and Cell Biology at UC Berkeley. Karpen studies how the structure, organization, and maintenance of genetic material impact inheritance and cell function. His lab focuses on chromatin — a packaged form of DNA strands wrapped around proteins that comprises our chromosomes — with a particular emphasis on centromeres, rarely expressed sequences called heterochromatin, DNA repair, and regulatory RNAs. His research reveals insights into fundamental aspects of nuclear organization and genome regulation that are shared across the tree of life, broadening our understanding of cancer and other diseases, birth defects, and aging. Kam-Biu Luk is a Professor Emeritus of physics at UC Berkeley and a retired senior faculty scientist in Berkeley Lab’s Physics Division. He is an experimental particle physicist whose research has focused on neutrinos: nearly massless, electrically neutral elementary particles that can pass through matter virtually undetected. Neutrinos are created during radioactive decay processes, including the fusion process occurring in stars and the fission process that powers nuclear reactors. Luk initiated and co-led the Daya Bay Reactor Neutrino Experiment, a project based in China with more than 200 international collaborators. In 2012, the Daya Bay experiment discovered a new form of a phenomenon called “neutrino oscillation” in which neutrinos change from one type to another. For this discovery, Luk and his collaborator Yifan Wang received the 2014 American Physical Society Division of Particles and Fields W.K.H. Panofsky Prize for outstanding work in experimental particle physics, and he was also the co-recipient of the 2016 Breakthrough Prize. Luk is currently participating in the Deep Underground Neutrino Experiment (DUNE) hosted by Fermi National Accelerator Laboratory, which will investigate any potential difference in behavior between neutrinos and antineutrinos that may shed light on the evolution of the universe, especially why antimatter is absent in the present universe. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
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‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field
‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field ‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field A field that once seemed like a direct path to stable, lucrative work is becoming less certain under AI, students and faculty told Spectator. By Ria Vasishtha and Arjun Menon May 3, 2026 Shanying Liu / Deputy Illustrations Editor Asia Genawi, SEAS ’29, first encountered artificial intelligence during her sophomore year at her high school in Indiana. Back in 2023, policies on AI varied widely—even within the same district—and some schools had no guidance at all. As Genawi began to see the effects of AI trickle into her classroom, she turned to guidance from her home state’s Department of Education, which she said she found “outdated and inaccessible.” Genawi spent the rest of her high school career dedicated to the issue, including writing a 15-page memo exploring varying AI policies across Indiana schools, which she sent to every Indiana state senator and representative whose contact information she was able to find. She later helped draft a bill that would have required public high schools to establish and share their own AI policies with students in an accessible format. But with so many other bills already awaiting referral, hers never made it to committee. When Genawi arrived at Columbia to study computer science, she found that the gaps she had spent years trying to address in Indiana had followed her there. Many students and professors were also grappling with what AI meant for the field. Across Columbia’s undergraduate schools, computer science has become the most popular major—accounting for around 11 percent of degrees awarded in Columbia College, 32 percent in the School of Engineering and Applied Science, and 12 percent in the School of General Studies in 2024. While the number of computer science majors in SEAS grew from 166 to 173 during the 2024-25 academic year, the total number across the three schools fell around 4.5 percent from 393 to 375, driven by declines at Columbia College and General Studies. At Barnard, it has grown to become the second most popular major as of 2025, up from third the year prior. The number of degrees awarded have grown fivefold since 2016. Faculty members and students in the department told Spectator that the surge in the major’s popularity has been driven in large part by its apparent promise of a secure, well-paid career path. However, they noted that the recent rise of generative AI has compromised the sense of stability that once defined the field, even as interest in technical skills among students persists. According to data from the Federal Reserve Bank of New York in 2024, out of 74 majors tracked, computer science had the fifth highest unemployment rate among majors at 7 percent, while computer engineering had the second highest at 7.8 percent. “Anecdotally, people are moving away from CS as a major,” Daniel Bauer, a senior lecturer in the computer science department, said. “They’re still taking some of our classes, right, because they think they need that, but they’re instead majoring in other things.” Bauer described a field that had shifted from what he called a “nerdy outlier discipline” to one that “everyone needs,” a sign that technical skills have become a baseline expectation across industries with the emergence of AI. “A lot of people thought, ‘Oh, it’s a guaranteed path to a stable income. You get a six-figure job right out of your undergrad,’” Bauer said. Now, companies may see AI as a way to reduce demand for entry-level programmers, especially in what he calls “code monkey jobs,” roles where a large number of employees are “just writing out code nine-to-five.” Chris Murphy, a senior lecturer in the computer science department, described the current moment as the convergence of two forces: the downturn of the job market and generative AI coming onto the scene. “Things kind of started to really pick up in 2014, 16,” he said, recalling the years after the weaker 2010 market. Murphy added that this was also a period where “students were getting more experience outside of class than they were inside of class” through internships, jobs, and side projects. Murphy suggested that the earlier momentum helped make computer science feel like both an academic program and a reliable pathway into work. That feeling is harder to sustain now as the industry reconfigures, which is a shift that students are seeing firsthand. Rebecca Yu, SEAS ’27, said that during a recent internship in Big Tech, she saw that “every single tech company was racing to integrate” large language models. She added that her manager said her intern class was the first to complete their assigned projects, which she attributed to the increased efficiency that comes with AI. Frank Liu, SEAS ’29, said that with AI lowering the barrier to coding, companies now expect everyone to write code. “A lot of companies outright say if you’re not using coding in your job, you might get fired for not being productive enough,” Liu said. Yu similarly expressed that students increasingly have to accept AI being a part of industry workflow, adding that many also “make use of it to maximize their productivity.” Richard Li, CC ’28, described a “franticness” among his peers as the pace of change accelerates. He explained how “some new tool” is released regularly, noting that tools rise and fall in prominence within weeks. He has come to believe that he doesn’t need to “match that speed all the time” and keep up with every development. Li has learned that AI tools allow him to “take a breath” and “get back into the loop much easier than before” when he needs to. While some companies specifically target Columbia when hiring, sending representatives to campus career fairs and recruiting large numbers from Columbia, a computer science degree cannot be treated as a static credential, Yu described. “You can’t just get your degree and stop learning after that. You have to always be learning,” Yu said. “That is something you basically have to expect for your job.” AI has not only affected students’ decisions to major in computer science, but also reshaped how the subject is taught. Across departments, faculty members no longer treat work done outside the classroom as a reliable measure of understanding, shifting instead toward in-person exams, quizzes, and attendance checks. While homework once made up as much as 60 to 70 percent of a student’s grade in computer science classes, Murphy said that breakdown has since flipped, with exams and quizzes now accounting for the majority. “The assumption—especially among pessimists—is that they’re using it to do their homework,” Murphy said. “They take the homework assignment. They put it into generative AI. Out comes the solution. Submit solution. The end.” Murphy said that faculty members ranged in their responses from fully prohibiting AI use to actively teaching students how to effectively work with AI. He said he shifted his approach this year after his teaching assistants called his belief that students weren’t using the technology “naïve,” and now permits students to use AI on assignments. His surveys of students in his introductory classes have told a more complicated story than the one so-called pessimists offer: Around 60 to 70 percent of students who reported using AI said they used it to have concepts explained to them, to clarify assignment instructions, to generate test cases, or to create practice questions. “Those are all ways that you might have had a tutor in the past to do that, and now you have this AI essentially as your tutor,” he said. Bauer has taken a different approach, teaching students how to use AI productively without sacrificing the core principles of the discipline. “People who are trained to actually use AI tools productively, while being in charge of designing the overall project, will be very much in demand in the next couple of years,” he said. Over the past year, a working group of computer science faculty has been redesigning the introductory and intermediate programming sequence to integrate generative AI as both a subject and a tool, representatives from SEAS wrote in a statement to Spectator. The effort is “coordinated” across the department, “not course-by-course,” with a systematic assessment of how AI integration is affecting student outcomes underway to shape the next round of changes. The goal, the department explained, is for students to be able to “read, verify, and reason about code, whether they or a model wrote it.” Tony Dear, senior lecturer in the computer science department, is piloting individualized AI feedback in a discrete mathematics course, while a machine learning course is also testing a Socratic AI dialogue system designed to “guide inquiry rather than supply answers,” the statement reads. Beyond individual courses, SEAS funded seven cross-departmental faculty projects in generative AI this past academic year, spanning biomedical engineering, chemical engineering, industrial engineering, and computer science, according to the statement. This phenomenon extends beyond the computer science department. SEAS’ new AI minor, which it announced in November 2025, reflects the University’s attempt to adapt. The minor is specifically designed for noncomputer science majors and aims to provide “essential AI knowledge” while emphasizing practical and responsible use across disciplines, according to the SEAS website. At the center of the minor is AI in Context, a course taught by a team of faculty from the departments of computer science, applied mathematics, philosophy, music, literature, and the Writing Center, the representatives told Spectator. Bauer described it as targeted at students who want to bring AI into fields like economics or biology, rather than as a path into software engineering itself. At the graduate level, SEAS announced a new Master of Science in artificial intelligence in February, with the inaugural cohort starting in the fall. While recognizing how AI has helped them navigate coursework, students also described its rise as isolating, even among members of the Application Development Initiative, one of Columbia’s largest computer science clubs. “As people start to ask AI for questions that they would normally ask students and members, it starts to isolate people” Li said. Alexa Kafka, BC ’27, echoed Li’s sentiment, pointing out that computer science has always been a collaborative discipline with code reviews and pull requests, even as AI makes independent work easier. Despite this, Kafka noted that she hasn’t been to office hours “in at least a year.” Bauer added that faculty are exploring long-term solutions to the assessment problem. “One of the things that we’re trying to do at the department or school level is to maybe move towards computerized testing facilities,” Bauer said, referring to ideas in the department to revitalize computer labs as an alternative to pen-and-paper exams. “We have to accept that this is the reality, for better or worse.” But more exams, students say, does not mean better learning, nor does it reflect how computer science is actually done. “Code is so variable and can be approached in so many directions, there’s never just going to be one correct answer. There can be multiple optimal solutions,” Kafka said. Students also expressed concern that the shift toward exam-heavy grading pulls focus away from the applied, project-based work that better reflects both discipline and industry expectations. ADI has tried to respond more quickly than the curriculum can. Through a vibe coding workshop this past semester, club members said they wanted to help students understand not just that AI exists, but how to use it deliberately. “Vibe coding” refers to the practice of prompting AI to write code for a task or project, and has become widespread enough that ADI felt the gap in formal instruction was worth addressing, according to Sophia Huang, BC ’29. The workshop introduced students to the concept of context engineering, which Shaina Sahu, SEAS ’27, described as not just writing a prompt, but optimizing “the rest of the relevant information that you would feed into the model.” Without that, students said that AI-generated code can fail at scale or introduce difficult-to-detect bugs. Genawi said the shift toward AI has changed what it means to be good at computer science. “I think the shift has kind of gone towards what can you create and what can you do, instead of, what do you know,” she said. Indeed, during ADI DevFest, its annual hackathon, Li said more teams are trying to complete their projects than in years past. While sign-ups have historically outpaced submissions, Li said this is beginning to change because teams “have a tool that completes the vision for them.” While many students see AI as an equalizer that lowers the barrier to entry for coding and technical work, Murphy said it also creates new equity concerns inside the classroom around who can afford stronger tools and who already knows how to use them. “The equity issues I worry about are students who have access to paid versions of things versus free versions of things,” he said. Although popular AI models like ChatGPT and Claude have free versions available, both have paid models that can reach over $100 per month, which output different qualities of code. Murphy also pointed to differences in prior exposure to AI literacy itself. “Students who go to high schools or K-12 education, where they’re taught AI literacy, prompt engineering, context engineering, they understand how to do this, as opposed to students who go to schools who don’t teach that,” he said. “Then they come in, and then they’re somehow expected to know how to use these tools.” For Genawi, those disparities begin long before students arrive on campus. “It’s very interesting to consider how lucky you were geographically,” she said. “To be born where you were is influencing your ability to get access to these spaces.” Students like Yu, though broadly apprehensive about the future of computer science, are not without hope. “I’m optimistic, because I think if you’re truly passionate about CS, there will definitely be a place in the field to do something you want to do,” she said. Senior Staff Writer Ria Vasishtha can be contacted at ria.vasishtha@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Staff Writer Arjun Menon can be contacted at arjun.menon@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Want to keep up with breaking news? Subscribe to our email newsletter and like Spectator on Facebook. Produced with Spectate by Emily Ta
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Australia becomes the 30th country to eliminate trachoma as a public health problem
The World Health Organization (WHO) has validated Australia for eliminating trachoma as a public health problem, marking a significant milestone in the health of indigenous peoples and in global efforts to combat neglected tropical diseases (NTDs). Trachoma, the world’s leading infectious cause of blindness, no longer represents a public health problem in the country.Australia is among a growing number of countries that have successfully eliminated trachoma, contributing to global progress towards the targets set out in the WHO road map for NTDs 2021–2030.Trachoma is caused by the bacterium Chlamydia trachomatis and spreads through close contact with infected individuals, contaminated surfaces, and flies that carry eye and nose discharge. Repeated infections can lead to scarring of the eyelids, turning eyelashes inward, and ultimately causing blindness if untreated.“WHO congratulates Australia on this important achievement,” said Dr Tedros Adhanom Ghebreyesus, WHO Director-General. “This success reflects sustained commitment, strong partnerships, and a focus on reaching populations most affected by health inequities. It brings us closer to a world free from the suffering caused by trachoma.”Australia’s journey to eliminationAustralia’s achievement reflects decades of targeted public health action, particularly in remote Aboriginal and Torres Strait Islander communities, where trachoma persisted despite its earlier disappearance from the rest of the country.National efforts intensified with the establishment of the National Trachoma Management Programme in 2006, which implemented the WHO-recommended SAFE strategy: surgery for trichiasis, antibiotics to treat infection, promotion of facial cleanliness, and environmental improvement. Regular screening of all communities classified as at-risk of trachoma by teams of qualified health workers formed an important part of the Programme. Interventions were delivered through coordinated partnerships between federal and state governments, Aboriginal community-controlled health services, and local communities.Over time, sustained screening, treatment, and prevention activities, including improvements in housing, water, sanitation and hygiene, led to a steady decline in trachoma prevalence. Australia’s approach included adaptations to reflect its context, such as targeted treatment based on community-level data rather than mass drug administration, and strong integration with environmental health programmes.“Elimination of trachoma is a win for the eye health of communities across Australia, particularly those whose lives have been impacted by a disease that is entirely preventable,” said Mark Butler, Minister for Health and Ageing, Australia. “This major milestone is thanks to Aboriginal and Torres Strait Islander leadership, community commitment and sustained investment over many decades.“The lessons from this work will inform how we approach other preventable health conditions in remote and regional Australia. Aboriginal Community Controlled Health Organisations and local health workers have been central to this success, delivering culturally safe care and community-led solutions.”“This recognition from the World Health Organization reflects decades of work led by Aboriginal Community Controlled Health Organisations, alongside local health workers in remote First Nations communities,” said Malarndirri McCarthy, Minister for Indigenous Australians. “Their work has been critical to eliminating trachoma as a public health problem in Australia.”Advancing global efforts against neglected tropical diseases (NTDs)Trachoma is one of 21 diseases and disease groups that are regarded by WHO as NTDs. Together, NTDs affect more than 1 billion people worldwide, primarily in underserved populations with limited access to essential services such as clean water, sanitation, and health care.Australia’s elimination of trachoma as a public health problem highlights the importance of sustained political commitment and cross-sectoral collaboration in addressing the underlying determinants of health. It also underscores the feasibility of eliminating trachoma even in geographically challenging settings.In addition to trachoma, Australia has several endemic NTDs, including Buruli ulcer, leprosy and scabies. Validation of elimination of trachoma as a public health problem marks the first time that WHO has confirmed the elimination of an NTD in Australia, which becomes the 63rd country globally and 16th in the Western Pacific Region to have eliminated at least one NTD.“Tackling neglected tropical diseases in the Western Pacific Region has long been a challenge for countries across the socioeconomic spectrum, given the complexities in reaching the most vulnerable communities, including in remote areas,” said Dr Saia Ma’u Piukala, WHO Regional Director for the Western Pacific. “As a doctor from Tonga, I’ve experienced these challenges for myself. But I also know that with strategic commitment underpinned by optimal resources and partnerships in health, success is possible as other countries in our region have also demonstrated. I commend Australia on eliminating trachoma as a public health problem and urge all involved to remain vigilant to ensure this status is maintained.”WHO continues to support countries working to eliminate trachoma and other NTDs, ensuring that progress reaches those most in need and that gains are sustained through strong surveillance, appropriately integrated into national health systems, and mainstreamed within the wider health sector and beyond.Editor’s notesNeglected tropical diseases and trachoma elimination effortsNeglected tropical diseases are associated with devastating health, social and economic consequences. Their burden is mainly felt among impoverished communities in tropical areas.Public health targets for the control, elimination and eradication of these conditions were set in the road map for neglected tropical diseases 2021–2030 (https://www.who.int/publications/i/item/9789240010352).In 1996, WHO launched the WHO Alliance for the Global Elimination of Trachoma by 2020 (GET2020), creating a network of governments, non-governmental organizations and academic institutions dedicated to the fight against trachoma. WHO continues to support endemic countries to accelerate progress towards the global target of eliminating trachoma as a public health problem worldwide; 2030 is the new target date.Elimination of trachoma as a public health problem is defined as: (i) a prevalence of trachomatous trichiasis (TT) “unknown to the health system” of < 0.2% in ≥15-year-olds; and (ii) a prevalence of trachomatous inflammation—follicular (TF) in children aged 1–9 years of < 5%, in each formerly endemic district, plus (iii) the existence of a system to identify and manage incident cases of TT.Other countries validated by WHO as having eliminated trachoma as a public health problem are: Algeria, Benin, Burundi, Cambodia, China, Egypt, Fiji, Gambia, Ghana, India, Iraq, Islamic Republic of Iran, Lao People’s Democratic Republic, Libya, Malawi, Mali, Mauritania, Mexico, Morocco, Myanmar, Nepal, Oman, Pakistan, Papua New Guinea, Saudi Arabia, Senegal, Togo, Vanuatu and Viet Nam.
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