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TGH Behavioral Health Hospital Launches Partial Hospitalization Program, Expanding Access to Outpatient Mental Health Care
/PRNewswire/ -- Tampa General Hospital (TGH) Behavioral Health Hospital has launched its Partial Hospitalization Program, a structured daytime treatment...
NC science panel urges caution in use of hardened structures to protect coastline
As state lawmakers consider repealing the longstanding ban on jetties on North Carolina’s coastline, some of the state’s top scientists are recommending "a cautious approach" to allowing any new hardened structures.
The “Science of Learning” Is Less Controversial Than It Used to Be
A new report tackles the challenge of integrating the science of learning into the routines, systems, and structures that shape teachers’ daily work.
SpaceX's diverse business model and future as an AI infrastructure company
Charles Payne and Dave Mazza discuss SpaceX's current revenue breakdown, with Starlink contributing 55%. Mazza highlights SpaceX's future as an AI infrastructure company, mentioning Grok and XAI, and its impact on its valuation.
Early-career scientists build national infrastructure to bridge science and policy
A new Special Report in the journal BioScience introduces the Scientist Network for Advancing Policy (SNAP), a student-led, nonpartisan, grassroots coalition founded in 2025 to empower early-career researchers ...
From Genocide to Africa’s Healthcare ‘Beacon’: Inside Rwanda's Turnaround
When global healthcare organization Partners In Health was invited to Rwanda in the early 2000s to support the country’s new universal healthcare program, it discovered a nation that had yet to recover from a 1994 genocide that killed up to 1 million citizens and decimated infrastructure. At a ...
SEC-Big Ten war is real, and Kentucky's new AD may just shift the power
J Batt was a 'game-changer' for Michigan State. Now, Kentucky's new AD may have the inside intel to change the power structure between SEC, Big Ten.
"Lost Civilizations" Linked by DNA: What Scientists Just Uncovered Changes Everything - Futura-Sciences
Megalithic Mysteries: A Shared Heritage Revealed At the closing of the Neolithic era, roughly between 4500 and 2800 BCE, vast megalithic monuments began to rise across Europe. These massive stone structures, often used as collective burial chambers, are a testament not only to strong local traditions but also hint at...
Ford has a new battery business to ride the AI wave. Here’s where Cramer stands on the stock
CNBC’s Jim Cramer said Ford's new battery storage business gives the automaker a credible way to capitalize on the AI infrastructure boom.
Rumble Announces Realignment into Two Core Business Units and Renames Newly Acquired Cloud And AI-Infrastructure Business “Quake AI”
Post-merger, Rumble Cloud Renamed Quake AI, Integrating Northern Data OfferingsRumble to Remain the Video and Media Platform’s Core Brand NameCorp...
Rumble Announces Realignment into Two Core Business Units and Renames Newly Acquired Cloud And AI-Infrastructure Business “Quake AI”
Post-merger, Rumble Cloud Renamed Quake AI, Integrating Northern Data Offerings Rumble to Remain the Video and Media Platform’s Core Brand Name Corporate Parent to be Renamed RUM Group Inc. and Will Oversee Two Business Units: Rumble and Quake AI LONGBOAT KEY, Fla., June 17, 2026 (GLOBE NEWSWIRE) -- Rumble (NASDAQ: RUM) today introduced a new business unit and legal name for the company following the closing of its acquisition of Northern Data AG (“Northern Data”). Effective June 18, 2026, under
Trade association formed for data center, AI policies
The Arkansas Connected Communities Association (ARCC) announced Tuesday (June 16) its formal establishment as a 501(c)(6) trade association dedicated to advancing data center and artificial intelligence infrastructure policy in Arkansas....
From wool sneakers to GPUs: Inside Allbirds' pivot to AI infrastructure
Allbirds announced It would no longer sell shoes and instead become an AI infrastructure provider. Business Insider talked to the new CEO.
2026 World Cup billed as ‘largest entertainment attack surface in history’
As the tournament is underway across North America, Palo Alto Networks warns that temporary supplier ecosystems, vulnerable municipal infrastructure, and geopolitical tensions are creating risks fo...
Tired of Local Politics Threatening Your Favorite Bike Lane?
Calbike is rallying behind new legislation that will protect bicycle infrastructure from being removed for purely local political reasons.
Cell and gene therapy's next challenge is not the science, it's everything else
The field's foundational biology is no longer the limiting factor. What comes next depends on whether the industry can build the manufacturing, commercial, and clinical infrastructure to match what the science already knows how to do.
Microsoft's cloud infrastructure talks with Oracle collapse, Business Insider reports
June 16 (Reuters) - Microsoft's discussions with Oracle regarding a cloud infrastructure leasing deal, potentially valued at more than $3 billion, have fallen apart due to security and compliance
Microsoft's cloud infrastructure talks with Oracle collapse, Business Insider reports
Microsoft's discussions with Oracle regarding a cloud infrastructure leasing deal, potentially valued at more than $3 billion, have fallen apart due to security and compliance concerns, Business Insider reported on Tuesday.
Microsoft was in talks to lease $3 billion in Oracle compute capacity
Microsoft was recently in talks with Oracle for cloud infrastructure, but sources say the deal fell through over security and compliance concerns.
Trump’s war on science is in the fine print
A fresh attack on the U.S. scientific infrastructure could be the most damaging yet.
Memo: Robinhood announces layoffs to flatten org chart
Robinhood's CEO said the company was "flattening" its organizational structure to avoid becoming a "heavily-layered organization."
KERRIGAN IN THE WASHINGTON TIMES: Virginia Should Not Tax Away its Data Center Advantage - Small Business & Entrepreneurship Council
“However it is framed, the practical effect would reach far beyond one industry. A tax on digital infrastructure is a tax on everyone, including working families, local governments and small businesses in every corner of Virginia.” In a June 15 Washington Times Op-ed, SBE Council President & CEO Karen Kerrigan documents the state of Virginia’s tech […]
Apptio Unveils Conversational Insights and New Suite of AI-Powered Capabilities to Translate Complex Technology Spend into Measurable Business Outcomes
Apptio announced significant updates to its solution suite designed to turn technology investment into measurable business outcomes and establish best practices for the financial governance for AI in a time of record infrastructure investments.
4 ways cities can become more marketable for business
Multinationals want to locate in cities with ample talent, venture capital, mobility infrastructure and climate resilience.
Making Sure Open Science Stays Open
Digital research infrastructure is essential to open science, but it is often overlooked and vulnerable to policy changes and funding cuts.
Common vitamin may influence brain aging in ways scientists didn't expect
New Japanese research links higher vitamin C blood levels to better-preserved brain structure and stronger neural connections in older adults.
GitHub's AI surge pushes Microsoft into Amazon's arms
Microsoft is adding AWS capacity to GitHub after AI-driven growth strained infrastructure and triggered a series of reliability issues.
Times Opinion: Trump’s war on science is being waged in the fine print
A fresh attack on the U.S. scientific infrastructure could be the most damaging yet.
Your Big Moment Fades Fast With Just a Single Press Hit — Build This Engine If You Want Coverage (and Profit) That Lasts
Reputation is built through repeated exposure over time, and the companies that treat media as long-term infrastructure consistently outperform the ones treating it like a one-off campaign.
Microsoft sued by shareholders over expenses, cloud business, AI
Microsoft has been sued by shareholders who accused the company of defrauding them and inflating its stock price by failing to disclose slowing growth in its Azure cloud business and the need to spend billions of dollars on AI infrastructure.
Microsoft sued by shareholders over expenses, cloud business, AI
Microsoft has been sued by shareholders who accused the company of defrauding them and inflating its stock price by failing to disclose slowing growth in its Azure cloud business and the need to spend billions of dollars on AI infrastructure. The proposed class action led by a Michigan pension fund was filed in Seattle federal court on Friday, after Microsoft shares fell 10% on January 29 in response to its quarterly earnings report a day earlier. About $357 billion of market value was erased, and Microsoft's stock suffered its biggest one-day decline in nearly six years.
This strange material can become strong or fall apart in seconds
Scientists have found that staple-shaped particles can tangle together to create a material that is both strong and flexible. Unlike conventional materials, these particles can be locked into a sturdy structure or rapidly unraveled using vibrations. The unusual behavior could open the door to recyclable buildings, reconfigurable structures, and even futuristic robotic technologies.
State names first round of projects funded by federal rural health program
The state this week announced its first round of federal rural health grant funding, which allocated $36 million to projects to help communities with one-time infrastructure improvements and improve technological gaps. The Nevada Health Authority on Monday named the 25 projects that were awarded funding as part of the $180 million part of the Rural […]
Formation structure in venture-backed companies: Why getting it right early matters
Choosing the right entity early can ease fundraising, reduce tax risks and help startups avoid costly restructuring later.
North Canyon Medical Center and All Access Hosted by Andy Garcia Highlight Rural Healthcare Infrastructure and Advanced Robotics
As rural medical facilities face increasing operational challenges, North Canyon Medical Center joins the award-winning series, "All Access Hosted by Andy Garcia," to explore how technical innovation and organizational agility stabilize local economies and healthcare access.
Beneath our feet lies a fungal superhighway stretching 68 quadrillion miles
Beneath our feet lies a vast hidden fungal superhighway that helps sustain much of life on Earth—and scientists have now mapped it for the first time. Researchers estimate that these underground networks stretch an astonishing 110 quadrillion kilometers, move about 4 billion tons of carbon dioxide into soils each year, and play a major role in supporting plants and regulating the climate.
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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Social Security is Facing a Political Crisis
On Tuesday the Social Security Trustees released their latest report on the system’s finances. The numbers didn’t change much: Unless something is done, the Old Age Survivors and Disability Insurance (OASDI) program, Social Security’s official name, will be unable to pay full benefits starting in either 2032 or 2034, depending on some technical issues. That’s not far away: If the Trustees are right, the prospect of a Social Security crisis will loom over the next presidential administration. It’s important to understand, however, the nature of the looming crisis. It won’t be an economic crisis. It won’t even be a serious fiscal crisis. Whatever you may have heard, Social Security isn’t in danger of going bankrupt. What we’re facing, instead, is potential political crisis. Congress and the White House could easily take action to sustain America’s retirement system. But given the current state of our politics, there’s no guarantee that they will. There is a widespread misunderstanding of how Social Security works. While Social Security was designed to look like a pension fund, it isn’t. A pension fund pays benefits out of a stock of assets it has accumulated over time. In contrast, Social Security operates as a government transfer program, like food stamps or Medicaid. Now, unlike food stamps — but like the highway trust fund — Social Security is on paper supported by a dedicated tax, the payroll tax, that is assigned to that program. But I say “on paper” because from an economic point of view assigning the payroll tax to Social Security is just an accounting convention. What matters for the U.S. economy is the overall balance between government spending and government revenue, not the difference between one type of spending and one source of revenue. So there’s no inherent economic significance to the fact that by 2034 payroll tax receipts will be insufficient to cover promised benefits. There is, however, a legislative issue. As long as the Social Security Administration can pay benefits out of payroll taxes and its cash reserve, there’s no need for Congress to vote each year to authorize benefits — they just keep going out until further notice. However, once those resources become insufficient, benefits will fall —by 17 percent according to the Trustees — unless Congress passes new legislation that “tops up” Social Security’s finances. Yet the current administration and Republican party are such extremists that there is a real risk that Social Security will be held hostage on behalf of their goals. If this should come to pass, the hostage-takers will claim that shoring up Social Security is unaffordable. Right on cue, Mike Johnson, the Trump-sycophant Speaker of the House, declared on Monday that “entitlement programs” like Social Security “have to be adjusted and fixed,” and that Republicans will introduce a plan to that effect next year. But this is a ploy, because while the cost of maintaining Social Security benefits at their promised level isn’t trivial, it is in fact affordable. According to the Trustees’ report, the actuarial balance of OASDI up through 2050 — the amount of additional funds it would need to keep paying full benefits for the next 25 years — is 1.06 percent of GDP. To put that number in perspective, the Trump administration proposes increasing military spending next year by $420 billion, equivalent to about 1.4 percent of GDP – without any discussion of whether that’s affordable Yet how did we get to the point where Social Security will need to be topped up? The main answer is that we have an aging population, with a growing ratio of retirees collecting benefits to workers paying into the system: Trump’s anti-immigration policies are making this problem worse. According to the Trustees’ report, lower immigration will deepen Social Security’s financial hole because many immigrants are working-age adults who will pay into the system for decades before they collect benefits. In fact, this problem may be much bigger than the report acknowledges: The report’s baseline assumption is that we’ll have net immigration of almost 1.2 million people a year, and even the pessimistic case assumes 750,000 a year. Meanwhile actual net immigration has already been cut far below that — and may now be negative. Moreover, Social Security is being financially damaged by growing income inequality in America. Payroll taxes are levied only on wages up to $184,500, and they don’t touch capital income. With the distribution of income increasingly shifting from labor to capital, as well as becoming more unequal among wage-earners, revenue from the Social Security payroll tax has been falling as a share of national income. From 1990 to 2024, it fell from 5.02 percent of gross domestic income to 4.46 percent. Which brings me to the question that, these days, we ask about everything: How might Social Security be affected by the advent of AI? A short answer: If, as many of us fear, AI accelerates rising income inequality, it will further reduce the payroll tax receipts that currently pay for Social Security and further endanger its finances. On the other hand, if AI, as its advocates promise, leads to faster economic growth, it will increase the potential tax base that could and should be used to support Social Security and other social insurance programs. But to take advantage of that larger base, we’ll have to get serious about taxing wealth and capital income. Is Social Security in trouble? Yes, but only because of the way its financing is currently structured — a structure that no longer works well because our society and economy have become so unequal. Moreover, Trump’s immigration policies are further endangering its already deteriorating financial condition. So don’t believe Republicans’ gaslighting that it will be necessary to cut Social Security benefits. All that is necessary to preserve Social Security is political will to raise taxes on the wealthy and a sensible immigration policy. MUSICAL CODA Actually 67 under current law.
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Scientists discover giant, fan-shaped structure deep beneath the East Antarctic Ice Sheet
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The University of Vermont Health network announced Tuesday it would cut 76 employees system-wide. Another 66 will have to reapply to restructured positions.
President’s message: Progress and opportunity for science and medicine
his is an extraordinary moment in time for the biomedical sciences. Seven decades ago, the discovery of the structure of DNA sparked a revolution in understanding life. In the decades since, the knowledge of fundamental biology we have accumulated has led to innovative treatments for many difficult-to-treat diseases, and many more are now within reach....
From Classroom to Racetrack: How Charlie Baur is Building a Motorsports Career in a Business Discipline
Charlie Baur can trace his racing story back to a Christmas morning in Michigan — the kind that starts with snow on the ground and ends with a throttle in a kid’s hands. “I remember being 3 years old and then for Christmas that year, my parents had gotten me a 120 cc snowmobile,” Baur said. Long before sprint cars, he was a Michigan kid on snowmobiles, dirt bikes, four-wheelers and jet skis — and a regular at car shows and races with his family. Photo courtesy of Charlie Baur That early exposure became a calling. At 12, he sat in a sprint car for the first time and knew he’d found the next chapter. He started in quarter midgets, moved to go-karts, and the day before he turned 16 — one day shy of the minimum age required by track insurance — he made his first sprint car start after convincing officials to let him race. Five years later, Baur is still pushing forward, now racing sprint cars on both pavement and dirt while pursuing an automotive business education at Northwood University — with a clear-eyed understanding that motorsports encompasses more than what happens under the lights on Saturday night. “It’s a business,” said Charlie, a 20-year-old who calls Fenton, Michigan, home when he’s not at a racetrack or at Northwood’s residential campus in Midland, Michigan. Photo courtesy of Charlie Baur Racing is the product — but preparation is the job To the average fan, race day begins around dinner time. For a team, race day can be a 12-hour shift — plus the work that came before it. And Baur’s racing operation is a family affair. “The team is my parents and myself,” he said, explaining they own the cars, maintain equipment and manage the business side — from sponsor relations to budgeting. On race weekends, the core group includes Baur and his parents (Eric and Stefanie), plus a couple of additional hands (Alex Photiou and Robert Flannigan) who help with the intensity and pace of trackside work. That structure has shaped how he thinks about efficiency and professionalism. “We’ve always kind of had the mentality of we’re never working on the car for maintenance at the track,” he said. “Our goal is to do as little work as possible to the car because it should be ready when that car rolls out of the trailer to go racing.” That mindset shows up in routines: bolt checks, tire procedures, safety equipment and a repeatable process designed to limit errors. “Once I fire the car up and put my helmet on, that’s when it’s go time,” he said. “You can plan as much as possible for how the race is going to play out, but until you take the green flag … you don’t know how it’s going to go.” Photo courtesy of Charlie Baur First-generation driver, student of the game In racing, “first-generation” matters — and Baur wears that label with pride. “A lot of the drivers are third or fourth generation,” he said. “My family has never been in motorsports. So for me, we’ve always looked at it as we’re new to it and we want to be as different as possible. From a marketing standpoint, it helps us stand out.” It also pushes him to seek out mentors — especially veteran drivers who have seen every scenario, solved every problem and won in multiple eras. Baur credits accomplished driver Jason Blonde, a 10-time champion, for becoming his driver coach and helping him develop as a competitor. “To me, learning from Jason helps me because he has had lots of success and knows what to do,” Baur said. “You have to humble yourself and say, ‘I don’t know what to do; can you help me out?’” That willingness to learn extends off the track, too. Baur says one of his biggest challenges is not a mechanical limitation, but credibility. “It’s my age,” he said. “Some people won’t take me seriously because I’m so young. Even though … I do know what we’re talking about and have a general idea.” His answer has been to out-prepare, out-communicate and treat the operation like a real enterprise — because it is. Photo courtesy of Charlie Baur Northwood’s role: Turning passion into a plan Baur is majoring in automotive aftermarket management and pursuing an accelerated path that pairs undergraduate study with graduate work. He’s also exploring how to align his studies with motorsports-focused coursework as Northwood advances related programs and pathways. He sees the value clearly: motorsports needs business-literate professionals who understand budgets, sponsorship, marketing, operations and long-term strategy — and who can translate between the language of the shop and the language of the boardroom. That’s why his Northwood experience isn’t limited to the classroom. He has been involved with the Northwood University International Auto Show and serves as motorsports chair — a leadership role that demands outreach, contracts, coordination and a steady professional demeanor. “There’s so much outside work that makes the auto show successful that people don’t realize,” he said. “And it is truly student led.” He’s also participated in industry experiences through Northwood at major events such as AAPEX, SEMA and PRI, along with other automotive networking events — opportunities he describes as a fast track to understanding how business really gets done. “You learn a lot in the classroom,” he said. “But if you learn it and you can’t apply it to the real world, it loses its value. Or if you learn it and can apply it, but you don’t know anyone, it is still of no value.” In his words, Northwood helps him win because it “gives me the tools to succeed” — including flexibility and faculty support as he balances travel and racing commitments with academics. Photo courtesy of Charlie Baur Racing as an ecosystem — and a talent gap waiting to be filled Baur is blunt about what he sees across the motorsports world: a deep bench of experienced professionals, and not enough young talent entering the pipeline. “I go to car shows, I go to these different events, and typically … the average age is between 45 to 55,” he said. “There’s not a lot of young people going into the motorsports world.” That reality reinforces Northwood’s communications focus: positioning motorsports not as a narrow lane, but as a wide ecosystem of business careers — sponsorship, logistics, data, media, leadership, hospitality and innovation — with real demand for prepared graduates. It also shapes how Baur talks to younger students who love racing but aren’t sure what their future looks like. “If you have a passion for motorsports, there’s gonna be a spot for you somewhere,” he said, emphasizing that driving is only one of many career paths — and that local tracks and hands-on experience are often the best first step. But if someone is interested in driving as a career path, it will take a team to make that happen. Baur’s team includes Stahl’s Motors and Music Experience, Rhino Racing, Randy Wise Buick GMC Chevrolet, Western Michigan Fleet Parts, Mother’s Polish, Schaeffer’s Oil, Hepfner Racing Products, Ameriguard Storage Centers, Justice Brothers, Race 1 Wraps, TBM Brakes, Behind Your Design, CharlieBaur.com and Northwood University. “There are so many people who invest not only in myself but in my race team and who believe in us to succeed,” he added. Pictured: Charlie Bauer’s sprint race car. Looking ahead: A headline worth chasing Ask Baur to imagine 2035, and he’ll give you two answers — one dream, one blueprint. “In a perfect world,” he said, “winning the Indy 500.” Realistically, he wants to build a life where business ownership and racing reinforce each other and where he can pay forward the help he has received by mentoring younger drivers, especially those trying to break into motorsports without a family legacy. “When I retire from full-time racing, I want to be able to provide the same opportunities that were presented to myself to the next generation of drivers,” Baur said. “I know how difficult it is to get your foot in the door.” For now, the focus is simple: keep building the operation, keep learning the business, and keep showing up prepared — because in sprint car racing, as in life, process matters. And once the helmet goes on? “It’s go time,” Baur said. Photo courtesy of Charlie Baur Learn more Students interested in motorsports careers — whether in competition, team operations, marketing, finance, sponsorship, data, or management — can explore opportunities through Northwood University’s Center for Automotive and Mobility Studies. For Charlie Baur’s latest motorsports adventures, follow Charlie_Baur and Charlie Baur Racing on social media. To learn more about Northwood’s Bachelor of Business Administration in Automotive and Mobility Studies and motorsports management concentration, visit www.northwood.edu/programs/automotive-mobility-studies/. Photo courtesy of Charlie Baur Pictured: Charlie Baur at Butler Motor Speedway on May 31, 2026. Share this page
“Tech-Driven Prosperity & Right-Wing Racist Politics”: Quinn Slobodian on Elon Musk and SpaceX IPO
Ahead of the initial public offering for SpaceX, we speak with historian Quinn Slobodian, author of Muskism: A Guide for the Perplexed. He says Elon Musk is “creating a situation where he becomes deeply reliant on state contracts” as the U.S. government then becomes reliant on Musk. “It’s not about demolishing the government,” Slobodian says of his work with DOGE, the so-called Department of Government Efficiency that Musk led for the Trump administration. “It’s about making the government more compatible, ready for the kind of products that Musk offers, and to make him then an indispensable part of the infrastructure.” Slobodian goes on to warn that Musk’s wealth is helping to fuel his anti-immigrant, racist political ideology. “We really should be worried about the possibility of those things to live together: tech-driven prosperity and right-wing racist politics.”
Scientists Discover a Vast Structure Hiding Beneath Antarctica's Ice
It's huge, ancient, and shaped like a fan.
We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
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Times Opinion: US lead in science is shrinking fast as China, India and even Iran catch up
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Croatia’s track-record on gender equality in science has been both questioned and lauded in recent months, following both bad and good news on the topic. On the one hand, a senior awards committee was announced without a single woman researcher included. On the other, the national science academy welcomed its first cohort of fellows with more women than men.Members of the committee, which gives national awards recognising scientific achievement, are proposed by the ministry of science and endorsed by the parliament. The current line-up does include a woman, but she is culture minister Nina Obuljen Koržinek rather than a researcher. Even then, she didn’t make it into the photograph released to announce the appointments on April 16. “The image speaks a thousand words,” says Tanja Rudež, a science journalist for Jutarnji List newspaper. “I am surprised that none of these men, all well-known scientists, saw a problem with the fact that there was not a single successful female scientist on the committee,” she told Science|Business.Ivan Đikić, a prominent Croatian biochemist based at Goethe University Medical School, commented on LinkedIn that the news illustrated a “sad truth” about the state of science in Croatia.“It [. . .] reveals a system where women, despite contributing a substantial share of high-quality, internationally competitive science, are systematically excluded from the very processes that determine recognition and influence,” he wrote. Some 60% of researchers in Croatia are women, according to the Croatian Bureau of Statistics, and they perform well in international competitions. For example, of the 12 European Research Council (ERC) grants awarded to Croatian researchers, seven went to women.“So how is it possible that decision-making bodies are still dominated by men over 60, while not a single female ERC laureate is involved in the selection process?” asked Đikić. “This is not a marginal oversight; it is a structural failure.” The ministry of science did not reply to a request for comment. The awards committee is not unusual. For example, a scientific advisory body announced by the ministry of demographics in April comprises 35 men and five women. The board of the Croatian Science Foundation, newly appointed by the parliament, is a little better, with five men and two women, but still unequal. “This is especially significant because such bodies do not merely distribute prestige. They shape research priorities, funding allocation and long-term scientific development,” Marija Lugarić, chair of the parliament’s committee on gender equality, told Science|Business. “Representation here matters not only symbolically, but materially.”Referring to the awards committee and the board of the science foundation, Lugarić says that while both bodies were appointed by parliament, neither met the minimum 40% gender balance threshold that Croatian law and EU guidelines expressly recommend for public bodies.Đikić wants to see EU institutions put pressure on the government to ensure basic standards of fairness, transparency and democratic governance in science policy are met. “Meaningful change will not come without pressure,” he wrote. “It must intensify both within Croatia and at the level of European institutions. Anything less undermines the credibility of Europe’s commitment to excellence and inclusion in science.”Rudež also underlined the importance of talking and writing about this topic, “because public opinion in Croatia does influence changes in the status and visibility of women in science.”The European Commission has not yet responded to a request for comment.Good news from the academyA more positive story came from the Croatian Academy of Sciences and Arts on 21 May, with the announcement of seven new women fellows and five men. This is the first time in the academy’s 165-year history that women have outnumbered men in a new intake.One of the new fellows, two-time ERC grantee Iva Tolić, credits the academy leadership with trying to be more modern and inclusive, representing the whole community better. “The academy is really aware that so far, before this election, there were very few women,” she told Science|Business. “So, they are actively trying to make this more equal.”Related articlesExplained: How the MSCA is close to achieving gender balanceCall for EU to leverage academic freedom in global talent raceRudež is more cautious, noting that despite the academy’s “breakthrough this year [. . .] it is still a predominantly male institution, with only 18% women.”This reflects a wider imbalance in Croatia’s research landscape. “The key levers of power in science are still in the hands of men,” she went on. “For example, all 14 rectors of private and public universities are men.”Similarly, Lugarić says a structural glass ceiling still exists in Croatian science, which is “most visible precisely where power is exercised.”She cites data from the Commission’s She Figures 2024 report, which shows that Croatia ranks second in the EU for gender equality in research and innovation overall, with an equality index of 79.6 out of 100. It is in the top five for women’s career advancement, participation in research; representation across all research sectors and for scientific publications.In stark contrast, it ranks second to last when it comes to women in decision-making positions in science. “This is not a coincidence,” Lugarić says. “It is a structural pattern.”Informal barriersThe persistent under-representation of women in the highest structures of Croatia’s academies, boards, award committees and leadership positions is not down to formal barriers, Lugarić says, but arises from more subtle obstacles such as “informal networks, persistent expectations regarding traditional gender roles and the unequal distribution of care responsibilities.”Tolić would like to see more support for young women in terms of affordable housing and child support, and higher salaries. Currently, she says, salaries in science stay low for the first 10-15 years, while housing and spaces in kindergarten are limited and expensive. “You have to work a lot, you get very little money, so it’s not great to have a family in this situation,” she says.But despite the challenges, Tolić is optimistic, saying that gender equality in research in Croatia is “definitely improving and going in the right direction.” The situation is particularly promising in biology, she adds, where most full professors, heads of department and deans of faculties are women. Lugarić, too, see the glass ceiling “very slowly, cracking,” and cites the academy election as an example that structural change is possible. Yet the situation in the national awards committee is a “stark reminder that without a conscious, systemic commitment to gender balance, progress will remain slow and uneven.”
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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
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