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The 3 Best Words to Say to Someone Whose Sports Team Just Lost
You definitely don't want to tell them “it’s just a game.”
In private speech, Blanche tried to mend fences with critical conservative activist
At President Donald Trump’s Miami hotel, acting Attorney General Todd Blanche recently tried to smooth things over with a conservative watchdog group whose anger at the Trump administration was building on multiple fronts.
Farm Science Review honors Hall of Fame inductees
Every year, Farm Science Review attracts over 100,000 visitors to central Ohio from across the United States and Canada to explore agricultural research, technology and education. Behind that effort are individuals whose work over decades has helped shape the event and expand its reach. Three of
Fund honoring late meteorologist to assist in global student experience
Jenni Evans, a world-renowned meteorologist whose research transformed the scientific understanding of tropical cyclones and strengthened storm forecasting worldwide, considered her students’ success her proudest accomplishment. The newly established Jenni Evans Fund in the Department of Meteorology and Atmospheric Science will ensure that those successes continue in perpetuity.
The secrets to keeping your brain sharp in old age
Neurologist Emily Rogalski studies "superagers" – people in their 80s or 90s with unusually keen memories, whose lifestyles suggest ways to slow cognitive decline
5 MLB pitchers underperforming based on their advanced metrics
Welcome back to another rundown of the league’s unluckiest arms in the 2026 MLB Season. From ERAs sitting near 9.00 to SIERAs hovering around 3.00 and expected wOBAs buried well beneath the real thing, we dig into five pitchers whose…
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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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.
Colorado Supreme Court to decide whether out-of-state defendant may be prosecuted for in-state crime
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Slick Science
Oily floors are slippery, costly, and one of the leading causes of workplace injury. Predicting just how slippery they'll be is now a good deal more reliable, thanks to a team of Swanson School of Engineering researchers whose work earned the 2025 Best Paper Award from the ASME Journal of Tribology. The winning paper, “Validation of a Multisca...
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Whoa! Netflix is Sending Narnia Heading to Theaters...What This Means for the Future of Film
(Welcome to the “Most Important Story of the Week”, my bi-weekly strategy column analyzing the most important (but often not buzziest) news story of the last two weeks. I’m the Entertainment Strategy Guy, a former streaming executive who now analyzes business strategy in the entertainment industry. Please subscribe.) Here’s a funny thing about the debate over whether streamers should release their movies in theaters first: Both sides think the other side represents the “conventional wisdom”. If you’ve been reading me for any amount of time, or even just scanned my front page, you know I think movies make more money by having a robust theatrical window compared to going straight-to-streaming. I’ve published thousands of words on this topic. And for years, I felt like I was standing up to a wave of opposition. See, while the “put your films in theaters to make money” take was obvious at any point before 2019, post-streaming revolution, most of the trendy thinkers took the opposite point of view. Big names argued that Netflix putting their film straight-to-streaming (“S-to-S” from here on out) actually made more money. Netflix was “disrupting” theaters, just as they disrupted Blockbuster Video, and since innovation is always good, this too was a great idea. This thinking then leaked into the mainstream business outlets like the Wall Street Journal and Bloomberg. I read many articles whose take boiled down to “Netflix makes tons of money from S-to-S releases” and “theaters were in structural decline” anyways. Covid-19 shutting down theaters and the 2023 strikes shutting down film production seemingly justified this point of view, as the box office struggled to return to 2017-2019 heights. Yet I’ve also read folks arguing the opposite point: the conventional wisdom says to put films in theaters! From some writers’ points of view, “everyone” actually agrees/agreed with my “send your films to theaters” take—especially the stodgy traditional studios—and folks still didn’t understand Netflix’s genius. I feel like, for the last two years, many people have argued this point as if it’s conventional wisdom. You know what? I may have been wrong. At this point, the “S-to-S strategy makes money” might be the minority viewpoint. Especially with the news this month that Netflix will put at least one film—Greta Gerwig’s Narnia film The Magician’s Nephew—in theaters in 2027 for a full 49-day run is big news. So that’s the story of the week, but I’ll also explain why they’re making this move now and why Netflix may not completely embrace theaters, along with other topics, like how I don’t buy Disney’s “super app” strategy, the latest good news out of the unions, my take on the latest attacks against free speech, and a whole lot more. Let’s get started. Subscribe Most Important Story of the Week - Netflix Goes to Theaters! Listen, I promise not to victory lap too hard today. As I just wrote, I’ve argued against sending films straight-to-streaming for—checks calendar—eight years now. Since 2018. Yikes. That’s a long time. And for years, others painted my thinking as “conventional” in that I was standing up for the status quo against trendy, cool disruption. And that’s not cool! But math is math, and I never could make the S-to-S numbers work. The only thing that ever enabled that model was Wall Street’s brief exuberance for it. It seems like the heads of Netflix now agree with me. (There were always rumors that former head of film for Netflix Scott Stuber argued internally for theatrical releases as well.) As they announced: “The Magician’s Nephew_—the first film in a Narnia series of films—will open on 12-Feb-2027, with a 49 day theatrical exclusive run.”_ I’m a little surprised at how little coverage this big change actually got. Credit to Bloomberg for leading with it and Matt Belloni’s The Town for highlighting it with the provocatively titled podcast “Hell Hath Frozen Over”. But after that? Instead of getting as much coverage as, say, the FCC’s latest egregious crackdown on free speech, this news story got crickets. But it’s massively more important for the future business of entertainment. So let’s start with the obvious question… Why Put Your Movies in Theaters? So…why should Netflix put (some!) of their movies in theaters? Well, I’ve already written that in a very, very, very long article from 2022: [ ](https://entertainment.substack.com/p/the-data-is-in-theatrical-films-massively) [ The Data Is In: Theatrical Films Massively Outperform Straight-To-Streaming Films ](https://entertainment.substack.com/p/the-data-is-in-theatrical-films-massively) Entertainment Strategy Guy · April 5, 2023 [ Read full story ](https://entertainment.substack.com/p/the-data-is-in-theatrical-films-massively) To summarize, theatrical runs make much more money, especially blockbusters, because they earn more money per viewing,1 and that helps boost home entertainment sales. Theatrical films also perform as well or better than the streaming-only films. Now, partially this is driven by increased marketing for said films, but said marketing ultimately boosts the awareness of the movies and drives improved streaming and library performance as well. Again, I’ve made this case after reviewing the economic models and streaming data for years. Just last week, Emily Horgan also made the case for kids films! Yes, it is probably conventional wisdom, but that’s also because it’s true! I’d also argue we’ve seen a lot of data in the various studios’ behavior. As I’ve noted before, the more a studio needs to make money, the likelier they are to put films in theaters. As such, Disney, Universal, Warner Bros., Paramount and Sony all send their major films to theaters. Even if they once made a big S-to-S push (*cough* Disney *cough*), they’ve since relented and returned to theaters. The tech companies don’t need to make money, per se, so it took them years to embrace theaters. Apple doesn’t need to make money because it doesn’t disclose their (likely) Quibi-sized losses each year. (They fear bad press more than losing money.) Amazon announced two years ago that they planned to make Amazon-MGM Studios/Prime Video profitable on its own, and guess what? Now they’re putting films in theaters. That leaves Netflix. They’re in between the “no need to make money at all” and the “desperately need to make money to offset linear TV declines”, so they had less pressure to put some films in theaters. Plus, for years, they were the sexy disruptor, and Wall Street rewarded them for that. (Honestly, they still do.) Yet, even Netflix likely saw the money they were leaving on the table (I’ve estimated it at an easy billion dollars before) so they changed their course. I heard one pundit argue that, because Netflix already makes so much money, they don’t need to make more money, which sort of goes against everything I’ve ever learned about business and economics. Again, to be super-nuanced, sending “films to theaters” does not mean EVERY film. TV movies have been a thing for decades. Lifetime films, Hallmark holiday films, Disney Channel Original Movies, and so on, don’t need theatrical runs. But films above a certain budget level (say $25 to 50 million) almost certainly need theaters to maximize their revenue. The Scenarios (with a Dose of Skepticism) Now time for some skepticism: it won’t surprise me at all if Netflix re-changes course. Specifically, Netflix announced that their Narnia film will go to theaters. And I’d bet anything we see KPop Demon Hunters 2: The Huntering2 also goes to theaters, cause it’s the surest hit outside of a Disney animated sequel right now. But how much does Netflix truly commit to theaters? They’ve already tried to emphasize that this is a one-off move. I see three broad scenarios:
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Power Families of Chicago: Hoffmann
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Chartbook 449: Voldemort on Threadneedle street. The Bank of England and the "haunted house" of English politics.
Imagine the scene: In some Harry Potter version of 21st-century London, a group of deathless UK Treasury officials gathered from across the last century huddle over warm pints and glasses of Chardonnay. Around the table there are officials from the 1920s, the 1930s, the 1970s, 2022 and today. Inevitably, in May 2026, the subject of conversation is the disintegration of the current Labour government. Despite its huge majority in parliament, it has been reduced to tatters by catastrophic local election result. These exposed the fact that rather than having a government with a clear popular majority, the UK is like most other modern societies divided into 5 segments each of which commands between 26 and 16 percent of the vote. Perhaps unsurprisingly, the leadership at Nos 10 and 11 Downing Street - where the Prime Minister and the Chancellor (Treasury Secretary) work - seem paralyzed. They are caught between mounting discontent over unfairness and the cost of living, and tough fiscal strictures that push them into making unpopular spending decisions. In the London pub, the Treasury officials from across the ages, can barely hide their satisfaction at the latest display of democratic politics caught in the vice-grip of financial logic. “Remember the mur d’argent and the banker’s ramp?” “Just like yesterday!” There are smirks around the table. Then the representative of 2026 pipes up: “One thing you have to say about the current lot (referring to the latest hapless Labour government), you don’t have to teach them about the bond market!” There is a murmur of ascent from the Treasury Mandarins. The trauma of the fiscal crisis of September-October 2022 sits deep. Back then, in the final stages of Tory decay, a ‘mini budget’ that challenged fiscal orthodoxy led to a brief but fierce panic in one of the oldest government bond markets in the world (aka gilt market). Now all you have to do is to mention the “bond market” to hush debate. [ Paul Mason@paulmasonnews In the name of public service, and as ex-economics editor of BBC Newsnight, I offer to do a zoom call, tonight, with any Labour MP who wants to understand why bond markets do not "fall into line" with governments. 1/ 3:57 PM · May 12, 2026 · 466K Views 224 Replies · 674 Reposts · 3.93K Likes ](https://x.com/paulmasonnews/status/2054229532404519082?s=20) “And the funny thing is”, the young man in the huddle continues, “they don’t even want to talk about … ”. There is a sudden hush. Significant looks are exchanged around the table. About? One of the senior figures puts down his glass with a decisive “clink”. He-Who-Must-Not-Be-Named has entered their thoughts. Meanwhile, a few miles away in a grubby pub in Bloomsbury, two conspirators from the camp of Critical Macrofinance huddle over cans of low-alcohol IPA. They look at each other and blurt out loud: “The Bank!” “Everyone in the bloody Labour party is talking about the bond market. But why aren’t they even mentioning … the Bank of England!?” “Why is no one talking about Voldemort?” In Paul Mason’s blogpost, after much chest-thumping about basic fiscal logic, the basic point about the Bank of England is buried in the fine print at #3 in his list of suggestions as to what a radical Labour government might actually be able to do! [ Conflict & Democracy Down with the bond market! Down with gravity! It may seem unfair to focus on Labour MP Paula Barker for her off-the-cuff assertion that the bond market would have to “fall in line” with Andy Burnham, should he replace Keir Starmer as prime minister. But Burnham himself told the New Statesman that Labour had to “get beyond this thing of being in hock to the bond markets… Read more 2 days ago · 100 likes · 3 comments · Paul Mason ](https://htsf.substack.com/p/down-with-the-bond-market-down-with?utm_source=substack&utm_campaign=post_embed&utm_medium=web) Back in Bloomsbury they shake their heads and ask: “Didn’t we learn anything from the Eurozone crisis?” Meanwhile, as if summoned to the conversation by some higher force, the “pink pages” announce: “The days of ultra-cheap government borrowing have come to an end” and Martin “Whizzard” Wolf publishes the graph of the season Look at the UK in 2026, perilously positioned on the upper edge of the pack. No wonder no one in London needs any lessons about the “bond market”. But, look again. Imagine how they feel in Rome. What is London’s nightmare in 2026? The nightmare is that you end up like Italy in the early 2010s during the Eurozone crisis. Once comfortably situated in the middle of the pack, watching with horror as your ten-year yields explode to 7 percent and “spreads” become a national nightmare. One of the largest economies in Europe, a founding member of the euro, stigmatized. But the message of the Italian experience is ambiguous. After all, the stigma didn’t last. In 2026, Italy borrows at rates lower not only than the UK but the US. The US, which, as everyone knows, enjoys the “exorbitant privilege” of being the issuer of reserve, safe asets. What kind of magic is this? How did Italy, in the space of fifteen years, go from being good, to bad and back again? Which brings us back to our dissident friends in their Bloombury boozer and their memories of the Eurozone crisis. Italy never was Britain - with its ongoing fiscal deficits. Without interest payments, on primary balance, from 2000 to 2020 Italy ran a tight fiscal ship. Even in 2008 it barely went into deficit and when it was being put on the rack in the early 20210s its primary surplus matched that of austere Germany. So, what happened? How did it end up stigmatized? Of course Italian politics didn’t help. Berlusconi was at the helm and he had legal issues. But amongst friends in the Bloomsbury pub, Berlusconi is a sideshow, the name we whisper to each other is “Trichet”. From 2004 to 2011 the ECB was headed by Jean-Claude Trichet, ex of the French Treasury, more German than the Germans. After 2009 as the news of the Greek fiscal disaster broke, the European bond markets wento into panic mode and Trichet began a brutal game of cat and mouse. Between 2009 and 2011, Trichet intervened in the bond market to help, only when he thought he had compliance with austerity. When he thought they were uncooperative, he withdrew. In the summer of 2011, he even made direct suggestions to his Italian counterparts in Rome, as to how Italy should be governed. It was this cat and mouse game that drove the bond markets crazy and exposed Italy to a violent squeeze. When Mario Draghi took over the ECB at the end of 2011 and declared “whatever it takes”, the pressure on Italy, gradually eased. It is that starring role that has made Draghi into the towering figure in European politics that he remains today. So now you see why voices in London hush when it comes to the Bank of England. “The bond market” isn’t an irresistible objective force like the weather or an avalanche. It can appear like that. But that depends on the way it is being handled, or not handled by the central bank. Which is why the silence about the Bank of England in London right now is so significant. If you think this all sounds a bit fishy, coming from a left-liberal Keynesian like myself, well, let me introduce you to Robin Brooks, who, to put it mildly, plays on a different quidditch team to me. Here, from the position of a fiscal hawk is Robin’s celebratory account of the Bank of England’s role since 2022; In October 2022, at the height of the bond market blow-up in the UK, something incredible happened. Governor Andrew Bailey told British pension funds the Bank of England would end its support program for the country’s fragile gilt market, drawing a line under how much help the central bank was willing to give to exposed pension funds and - implicitly - the government. By the end of that week, Kwasi Kwarteng - the finance minister - had resigned. Prime Minister Liz Truss would soon follow. This episode is so incredible because it contrasts with what happened in the Euro zone earlier that year. Bond markets in Spain and Italy were under pressure amid high inflation and rising interest rates. In the middle of this, Mario Draghi - Italy’s Prime Minister - resigned, compounding pressure on bond markets of high-debt countries on the Euro periphery. Unlike the Bank of England, the ECB caved. It bought Italian and Spanish government bonds in an effort to cap yields and introduced a new tool that signaled to markets it might do such caps more frequently going forward. Faced with pressure on bond markets, the ECB capitulated to political pressure from high-debt countries like Italy and Spain. The Bank of England stood its ground and toppled the Truss government. This kind of thing has long-lasting consequences. It means markets price a higher probability of yield caps in the Euro zone, which keeps Italian and Spanish yields artificially low, while markets feel free to push up UK yields without fear of central bank intervention. As I flagged in yesterday’s post, this means you really can’t compare UK yields to those of other high-debt countries. The UK looks worse, but only because its central bank isn’t interfering with markets. There’s more to this than just that 2022 episode. The Bank of England has also been much more aggressive in its quantitative tightening (QT) than other central banks. If one motivation for quantitative easing (QE) is to lower bond yields - especially longer-dated ones - then it stands to reason QT pushes yields up. The left chart above shows a four-quarter moving average of UK government debt issuance (black line) in percent of GDP and which sectors absorb this issuance, including the Bank of England (blue bars). The right chart above compares the Bank of England’s accumulation or run-off of government bonds (blue line) with that of other central banks, where this blue line is the same as the blue bars in the left chart. The Bank of England was quicker and more aggressive than other central banks in downsizing its bond holdings, so UK yields will have risen more than elsewhere also for this reason, not just because of Governor Andrew Bailey’s remarks in October 2022. Back in October 2025, after a conversation in New York with friends in British politics, my sense of dysphoria was such that I raised the issue of the Bank of England openly in the FT Since 2008, the UK has slid into a deep economic and political predicament. Growth has stagnated to a degree far worse even than in the 1970s. The Tory leadership’s wager on the Brexit referendum spectacularly backfired, leaving the UK isolated from Europe and at the mercy of its special relationship with the US, many of whose political class regard the UK as a basket case. After years of austerity, the Labour party lurched to the left and then back to the centre. The Tory party has been eclipsed by the populist nationalism of Reform. Any British government should be worrying first and foremost about how to restart growth. Instead, Keir Starmer is beset by shades of the gilt market panic triggered by the Liz Truss government in 2022. The debt level is higher than in recent years, but there is no cause for panic. UK debt is at far from critical levels and of long duration. Whether you want to “take back control” or launch progressive economic policy, a democracy dogged by fear of bond market vigilantes is unhealthy. In a country that is a monetary sovereign, this climate points to one thing: a disconnect between the government and the central bank. Since 1997, the independence of the Bank of England has become a sacred cow. But independence is what you make of it. And in the wake of the gilt market crisis of September 2022, relations between the Bank of England and the UK government need a reset. In normal times, there is no doubt that an inflation-targeting independent central bank can serve a country well. But since 2008, the UK and other advanced economies have not had the luxury of such simple problems. To their credit, central bankers around the world have not stood on orthodoxy. After 2008, the US Federal Reserve rescued the global banking system and, through quantitative easing, it stabilised the Treasury market in the face of huge deficits. In Europe, things were trickier. Both the Bank of England under Mervyn King and Jean-Claude Trichet’s European Central Bank played cat and mouse with national governments. This was a high stakes wager that between 2010 and 2012 escalated bond market tension and triggered a disastrous turn to austerity. Shaken by the impact of those shocks, their successors, Mark Carney and Mario Draghi adopted a more protective stance (assuming you were not the government of Greece). Recall that Draghi’s legendary slogan, “Whatever it takes”, was uttered to face down the City of London’s bond vigilantes. In 2015, Draghi launched QE — financial repression by another name. In 2016, Carney used the authority of the Bank of England to stabilise markets following the vote for Brexit, an exemplary demonstration of how central banks can underpin democratic choice, even when those choices are wrong headed. All these were independent central bankers steeped in the best practice of the 1990s, who recognised that historic crises demanded radical action. It was the price surge of 2021-2023 that poisoned the atmosphere. Though the shock came mainly from the supply side, central banks ended up in the dock. Nowhere more so than in the UK. The Bank of England reacted with aggressive normalisation. Faced with a cost of living crisis, the governor called for workers to accept real wage cuts. In early 2022, as gas prices were at record levels and Russia’s army was poised to invade Ukraine, the Bank adopted a stringent programme of quantitative tightening, elevating interest rates. When the Truss government got itself in trouble, the Bank took almost a week to come to the rescue. Rather than enacting a charade of normality, it is time to recognise that in 2025 the UK faces a period of historic transition. To meet this challenge, what is needed is a new concordat with the Bank. This should not ignore inflationary pressures or the long-run trajectory of debt, but should affirm the priority of reviving investment-led growth. One could follow Donald Trump in demanding interest rate cuts. The Bank could shadow the ECB in its commitment to capping Eurozone yields. But the top priority should be to lay the ghost of September 2022. The Bank’s slowing of QT last month was a concession. An immediate and complete end would send the right signal. Such a concordat will not by itself restore sanity to politics, solve the housing problem or rebuild the NHS. But it would help to lift the fear of bond market crisis, which is paralysing the effort by democratic politicians to answer the fundamental questions facing the nation. Now, that call for a new concordat between government and Bank of Englands seems more urgent than ever. The problem is that in October 2025, Starmer still had some authority. Today the government is in tatters. And when you are the “under the harrow” - to echo a phrase of Montagu Norman’s from the 1920s - it is the worst possible time to consider institutional change. To make general arguments at a time of crisis can only seem like special pleading rather than a general argument for long-term change. The same, unfortunately, is also true for the other basic institutional reform that Britain urgently needs i.e. the abolition of the out-dated first past the post electoral system and its replacement by a moderated form of proportional representation. And thirdly - whilst we are saying the quiet parts out loud - Britain needs to reopen the question of EU membership. What the local election results of 2026 show is that this remains a key issue. And what they also show is that there is a clear majority that would favor a return. Until these basic features of Britain’s political economy are addressed the country will carry on its politics as though in a haunted house, governed by governments that lack actual majorities, conjuring up false constraints whilst ignoring the real ones. This one is for DG and MI. I love writing the newsletter. If you enjoy it too and fancy buying me a coffee once a month, you know what to do. Click below! Subscribe
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The National Academy of Sciences Elects Two Berkeley Lab Researchers
Two Lawrence Berkeley National Laboratory (Berkeley Lab) scientists have been elected to the National Academy of Sciences (NAS) in recognition of their distinguished and continuing achievements in original research. They are part of a cohort of 120 new members and 25 international members. The National Academy of Sciences is a private, nonprofit institution that was established under a congressional charter signed by President Abraham Lincoln in 1863. It recognizes achievement in science by election to membership, and — alongside the National Academy of Engineering and the National Academy of Medicine — provides science, engineering, and health policy advice to the federal government and other organizations. The 2026 electees bring the total number of active members to 2,705 with 557 international members. Gary Karpen is a biologist senior faculty scientist in Berkeley Lab’s Biological Systems and Engineering Division and a Professor of Cell and Developmental Physiology in the Department of Molecular and Cell Biology at UC Berkeley. Karpen studies how the structure, organization, and maintenance of genetic material impact inheritance and cell function. His lab focuses on chromatin — a packaged form of DNA strands wrapped around proteins that comprises our chromosomes — with a particular emphasis on centromeres, rarely expressed sequences called heterochromatin, DNA repair, and regulatory RNAs. His research reveals insights into fundamental aspects of nuclear organization and genome regulation that are shared across the tree of life, broadening our understanding of cancer and other diseases, birth defects, and aging. Kam-Biu Luk is a Professor Emeritus of physics at UC Berkeley and a retired senior faculty scientist in Berkeley Lab’s Physics Division. He is an experimental particle physicist whose research has focused on neutrinos: nearly massless, electrically neutral elementary particles that can pass through matter virtually undetected. Neutrinos are created during radioactive decay processes, including the fusion process occurring in stars and the fission process that powers nuclear reactors. Luk initiated and co-led the Daya Bay Reactor Neutrino Experiment, a project based in China with more than 200 international collaborators. In 2012, the Daya Bay experiment discovered a new form of a phenomenon called “neutrino oscillation” in which neutrinos change from one type to another. For this discovery, Luk and his collaborator Yifan Wang received the 2014 American Physical Society Division of Particles and Fields W.K.H. Panofsky Prize for outstanding work in experimental particle physics, and he was also the co-recipient of the 2016 Breakthrough Prize. Luk is currently participating in the Deep Underground Neutrino Experiment (DUNE) hosted by Fermi National Accelerator Laboratory, which will investigate any potential difference in behavior between neutrinos and antineutrinos that may shed light on the evolution of the universe, especially why antimatter is absent in the present universe. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
Nevada City Chamber of Commerce 2026 Business & Community Awards, “Long Table” dinner celebration on May 16
Nevada City, CA — The Nevada City Chamber of Commerce proudly announces the recipients of its 2026 Business & Community Awards, honoring exceptional contributions to the local economy, culture, and civic life. This awards ceremony has been held for 124 years and celebrates the individuals, organizations, and businesses whose creativity, dedication, and leadership strengthen Nevada […]
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UT Health San Antonio honors those who donated their bodies to science
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The year in review: Influential people who have died in 2026
She was a beloved Bollywood singer whose voice resonated across a film-obsessed India for nearly eight decades. Asha Bhosle, who died in April, was recorded on about 12,000 songs, becoming
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Global Health & WASH: May 2026 Funding Opportunities (14 new opportunities!)
The May update for Global Health & WASH brings new calls that cluster around three distinct shifts: LMIC research leadership moving from funder encouragement into a hard eligibility constraint, pharmaceutical and corporate-linked funders running structurally independent education and use-inspired research portfolios at substantial scale, and AI adoption shifting decisively from speculative tool to operational capability across health and WASH systems alike. In LMIC Research Leadership as Eligibility, Not Encouragement, Wellcome anchors the dominant signal of the month with three separate major calls that all require or strongly center LMIC leadership in their basic eligibility architecture. Wellcome’s ESIC Hubs (£1.5-£1.9M per hub over 3-5 years) explicitly requires an LMIC-based lead applicant, treating Global South leadership as a design constraint rather than a participation goal. Wellcome’s Infectious Disease Clinical Trial Development Award funds transdisciplinary teams whose lead must be a mid-career or established researcher based at an organization in Africa, South Asia, or Southeast Asia. Wellcome’s larger Infectious Disease Clinical Trial Award (£1M-£8M for optimizing licensed interventions) requires the administering organization to be in eligible LMIC regions and at least 50% of applicants based there. Around Wellcome, the same pattern shows up at smaller scale and across funders: CHINNOVA channels $1M into West and Central African research institutions for climate-health work; IBRO’s Neuroscience Training Grants explicitly tier ceilings by region of residence with Africa receiving the highest amount ($5,000); IHME’s GBD Emerging Researcher Award reserves at least one of its two annual awards for an LMIC researcher; Sidaction’s HIV Cure call funds research teams across France, the Netherlands, and eligible African countries with a minimum two-country collaboration requirement. Taken together, these calls signal a meaningful shift in how research equity is being operationalized — not as encouragement language at the bottom of a call, but as a structural feature of who can lead, where the work must be administered, and what proportion of the team must be regionally rooted. In Pharma-Funded Independent Medical Education and Use-Inspired Research, the lineup this month is unusually deep. Pfizer alone runs four parallel calls under independent education and quality improvement frameworks: a Migraine and Women’s Health RFP (200K x 2-year, $500K pool), Migraine IME ($200K, $1M pool), JAK Inhibitor evidence-based education ($100K), and Maternal Vaccination HCP Education in Saudi Arabia ($75K), plus a separate Pediatric Pneumococcal Surveillance research call in Saudi Arabia ($400K per project). Novo Nordisk Foundation runs four parallel calls of its own at substantial scale: Infectious Diseases Catalyst Grants (DKK 60M pool, up to DKK 7M collaborative), Non-Diabetic Endocrinology Collaborative Grants (DKK 53M pool, DKK 5-10M per project), Pioneer Innovator Grant Health (DKK 1.1M), and Distinguished Innovator Grant Health (DKK 6.8M). LEO Foundation deploys DKK 2-4M per project for dermatology research excellence. Pfizer’s framing across calls deliberately separates the funding from product promotion, emphasizing measurable practice-relevant change and structural distance between scientific outputs and commercial activity. Novo Nordisk’s “use-inspired” hard filter pushes researchers toward a credible translation pathway from mechanism to deployable tool. The pattern: industry-aligned funders are running structurally independent portfolios at meaningful $ that fund implementation evidence, not promotion. In AI as Operational Capability, Not Speculation, several calls this month explicitly treat AI adoption as a near-term operational question with real evidence and governance requirements, rather than a speculative trend to be watched. Wellcome’s ESIC Hubs frames AI as “the accelerator” for evidence synthesis, asking hubs to translate technical innovation into adoption-ready workflows that can match real-world policy timelines. UNICEF Venture Fund’s new Climate Tech for Children’s Health call funds for-profit startups deploying AI, machine learning, and blockchain in low-resource environments, with a strict requirement that solutions must already have a working prototype with promising pilot results. The Water Research Foundation has launched two simultaneous AI calls treating reproducibility, cybersecurity guardrails, and human-in-the-loop deployments as core design constraints rather than nice-to-haves: one on GenAI and Agentic AI in water utilities ($200K), one on alternatives to water shutoffs that explicitly screens for implementation economics evidence. Nordic Innovation’s quantum technology call (NOK 4.5M) extends the same logic into life science and healthcare, demanding consortium-stage projects mature enough to test with real users. TEF-Health offers €300K in subsidized testing infrastructure for European healthcare AI and robotics SMEs. The pattern: funders are converting AI pilots into playbooks, requiring transfer evidence and risk controls before AI can move from experimentation to sector-wide operational practice. For LMIC-based researchers in infectious disease or evidence synthesis, the three Wellcome calls together represent the deepest concentration of LMIC-led research capital in recent memory — pair the Infectious Disease Clinical Trial Development Award (£200K, deadline May 19) as a strategic pipeline feeder into the larger Infectious Disease Clinical Trial Award (£1-£8M, deadline June 2), since the development award functions explicitly as a track into the larger trial scheme. For Nordic researchers, the four Novo Nordisk Foundation calls plus LEO Foundation and Nordic Innovation Quantum represent ~$30M in regional research capital this cycle, all with deadlines clustered in May-August. For WASH practitioners, the three Water Research Foundation calls together (each up to $200K) are a cluster worth reading across — one on AI deployment, one on shutoff alternatives, one rewarding applied innovation — and the strongest applicants will articulate how their work bridges utility operations to policy-relevant evidence. For US-based clinical researchers and educators, the four Pfizer calls plus the APF Portfolio’s 80+ programs form an unusually accessible recurring pipeline; APF’s prohibition on indirect costs is a meaningful structural feature for early-career applicants whose institutions might otherwise absorb significant overhead. Snapshot of New Opportunities Total Estimated Funding Pool: $100 Million+ USD The grants are organized into three categories: Open Calls: Current grant and opportunities with a deadline. Grants are listed by closing date. 39 open opportunities- 14 new! Rolling Applications: current grant and opportunities with rolling applications (but it’s still best to submit as early as possible). 14 rolling opportunities- 1 new opportunity! Long term planning: Grants that have closed their current rounds, but are expected to open new windows. 4 long term opportunities! A quick tip for returning readers: if you want to jump straight to the newest additions, use CTRL F to search for “New!” and navigate quickly to the latest funding opportunities This post is for paid subscribers. This helps support the time and effort it takes to curate and organize these opportunities. Subscribe To keep this accessible to everyone who needs it, we’re happy to offer pay what you can rates. You can find more details here. Open Calls: Migraine Competitive Grant Program: Migraine and Women’s Health (Quality Improvement and/or Research RFP), Pfizer.*Closing soon!* Pfizer, in collaboration with the American Headache Society, is seeking independent quality improvement and research projects that close persistent gaps in migraine care for women across the United States. The funding logic centers on measurable, practice-relevant change: proposals should generate actionable evidence, tools, or system improvements that can be shared and used widely, not just within a single site. Priority areas span menstrual migraine and screening in women’s health settings, the role of hormonal fluctuations across the lifespan, sex-specific risk factors, comorbidities and quality-of-life burden, and disparities in outcomes by race, ethnicity, and socioeconomic status. The call is structured as a two-step competitive process with an initial Letter of Intent, with the strongest concepts expected to translate scientific insight into implementable improvements in diagnosis, management, and equity. Geographies: United States. Who can apply: U.S.-based organizations (not individuals), including professional schools, healthcare institutions, professional organizations, and other entities focused on healthcare improvement. Funding amount: Up to USD $200,000 total over 2 years; total pool USD $500,000. Targeted Sectors / SDGs: Health; Focus areas: migraine, women’s health, quality improvement, clinical research, menstrual migraine, health disparities. Deadline: LOI: May 5, 2026; Full proposal (by invitation): August 28, 2026. Learn more and apply here. This RFP rewards “shareable impact”: the most competitive submissions will pair rigorous methods with a clear plan for system uptake and dissemination beyond the originating institution. ReSSARC: Strengthening the Health Sector and Food Security in the Central African Republic (AID 013381/01/0) Call for Proposals, Italian Agency for Development Cooperation (AICS). *Closing soon!* AICS is seeking a limited set of high-capacity humanitarian partners to deliver an integrated package that stabilizes essential health services while reducing acute malnutrition and strengthening food security resilience in priority areas of the Central African Republic. The funder’s logic is explicitly nexus-based: proposals should meet urgent, life-saving needs while reinforcing local systems, coordination mechanisms, and community-level capacity so results persist beyond the emergency window. AICS emphasizes inclusion and protection outcomes, expecting gender-sensitive design and explicit measures for groups facing compounded vulnerability, including women, children under five, internally displaced people, and people with disabilities. The call also signals a localization pathway, encouraging stronger collaboration with national NGOs and balanced partnerships, alongside clear coordination with existing cluster and humanitarian actors to minimize duplication and improve coverage. Geographies: Central African Republic (Bangui, Ombella-Mpoko, Ouham, Ouham-Pende, Lim-Pende, Lobaye). Who can apply: Non-profit organizations registered with AICS, plus eligible non-profits without an office in Italy that have a pre-existing collaboration agreement with an AICS-listed organization. Funding amount: Total pool EUR €1,800,000; max EUR €800,000 (single applicant) or EUR €1,000,000 (ATS). Targeted Sectors / SDGs: Health; Focus areas: malnutrition prevention and treatment, maternal and child health, community health services, food security and resilience, localization partnerships Deadline: May 5, 2026. Learn more and apply here. This call is structured to reward partners who can link frontline service delivery to system durability and localization, not just short-term coverage gains. Contracts for Innovation in drug and alcohol addiction healthcare, Innovate UK. *Closing soon!* Innovate UK, on behalf of the Office for Life Sciences Addiction Healthcare Goals program, is procuring R&D to accelerate innovations that can improve treatment outcomes, strengthen recovery, and reduce harm and deaths linked to drug and alcohol addiction. The competition is positioned as a market-facing readiness push: selected projects are expected to advance solutions toward later-stage validation, generate evidence of user acceptability and UK market fit, and map credible routes through regulatory and certification requirements. Innovate UK signals a preference for innovations that can be field-tested in relevant UK settings and progressed to TRL 6 or 7, with practical plans for commercialization after contract completion. This structure favors applicants that can execute most work in-house, co-develop with service providers and people with lived experience, and translate technical progress into deployable tools for addiction healthcare delivery. Geographies: United Kingdom. Who can apply: Single organizations of any size (including EU, EEA, or international) leading delivery, with most work and key deliverables carried out in the UK; subcontractors for specialist skills only. Funding amount: GBP £200,000–1.5 million per project (inclusive of VAT); Total pool: GBP £20 million (across two strands). Targeted Sectors / SDGs: Health; Focus areas: Mental Health, Health Systems Strengthening, Research & Development, Technology Access. Deadline: May 6, 2026 (11:00am). Learn more and apply here. This call uses procurement to pull near-market addiction innovations toward operational proof and adoption pathways in UK services.
Marc Kassen checks into political thriller ‘PH-1’
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Mercedes' Kimi Antonelli wins Miami Grand Prix
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‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field
‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field ‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field A field that once seemed like a direct path to stable, lucrative work is becoming less certain under AI, students and faculty told Spectator. By Ria Vasishtha and Arjun Menon May 3, 2026 Shanying Liu / Deputy Illustrations Editor Asia Genawi, SEAS ’29, first encountered artificial intelligence during her sophomore year at her high school in Indiana. Back in 2023, policies on AI varied widely—even within the same district—and some schools had no guidance at all. As Genawi began to see the effects of AI trickle into her classroom, she turned to guidance from her home state’s Department of Education, which she said she found “outdated and inaccessible.” Genawi spent the rest of her high school career dedicated to the issue, including writing a 15-page memo exploring varying AI policies across Indiana schools, which she sent to every Indiana state senator and representative whose contact information she was able to find. She later helped draft a bill that would have required public high schools to establish and share their own AI policies with students in an accessible format. But with so many other bills already awaiting referral, hers never made it to committee. When Genawi arrived at Columbia to study computer science, she found that the gaps she had spent years trying to address in Indiana had followed her there. Many students and professors were also grappling with what AI meant for the field. Across Columbia’s undergraduate schools, computer science has become the most popular major—accounting for around 11 percent of degrees awarded in Columbia College, 32 percent in the School of Engineering and Applied Science, and 12 percent in the School of General Studies in 2024. While the number of computer science majors in SEAS grew from 166 to 173 during the 2024-25 academic year, the total number across the three schools fell around 4.5 percent from 393 to 375, driven by declines at Columbia College and General Studies. At Barnard, it has grown to become the second most popular major as of 2025, up from third the year prior. The number of degrees awarded have grown fivefold since 2016. Faculty members and students in the department told Spectator that the surge in the major’s popularity has been driven in large part by its apparent promise of a secure, well-paid career path. However, they noted that the recent rise of generative AI has compromised the sense of stability that once defined the field, even as interest in technical skills among students persists. According to data from the Federal Reserve Bank of New York in 2024, out of 74 majors tracked, computer science had the fifth highest unemployment rate among majors at 7 percent, while computer engineering had the second highest at 7.8 percent. “Anecdotally, people are moving away from CS as a major,” Daniel Bauer, a senior lecturer in the computer science department, said. “They’re still taking some of our classes, right, because they think they need that, but they’re instead majoring in other things.” Bauer described a field that had shifted from what he called a “nerdy outlier discipline” to one that “everyone needs,” a sign that technical skills have become a baseline expectation across industries with the emergence of AI. “A lot of people thought, ‘Oh, it’s a guaranteed path to a stable income. You get a six-figure job right out of your undergrad,’” Bauer said. Now, companies may see AI as a way to reduce demand for entry-level programmers, especially in what he calls “code monkey jobs,” roles where a large number of employees are “just writing out code nine-to-five.” Chris Murphy, a senior lecturer in the computer science department, described the current moment as the convergence of two forces: the downturn of the job market and generative AI coming onto the scene. “Things kind of started to really pick up in 2014, 16,” he said, recalling the years after the weaker 2010 market. Murphy added that this was also a period where “students were getting more experience outside of class than they were inside of class” through internships, jobs, and side projects. Murphy suggested that the earlier momentum helped make computer science feel like both an academic program and a reliable pathway into work. That feeling is harder to sustain now as the industry reconfigures, which is a shift that students are seeing firsthand. Rebecca Yu, SEAS ’27, said that during a recent internship in Big Tech, she saw that “every single tech company was racing to integrate” large language models. She added that her manager said her intern class was the first to complete their assigned projects, which she attributed to the increased efficiency that comes with AI. Frank Liu, SEAS ’29, said that with AI lowering the barrier to coding, companies now expect everyone to write code. “A lot of companies outright say if you’re not using coding in your job, you might get fired for not being productive enough,” Liu said. Yu similarly expressed that students increasingly have to accept AI being a part of industry workflow, adding that many also “make use of it to maximize their productivity.” Richard Li, CC ’28, described a “franticness” among his peers as the pace of change accelerates. He explained how “some new tool” is released regularly, noting that tools rise and fall in prominence within weeks. He has come to believe that he doesn’t need to “match that speed all the time” and keep up with every development. Li has learned that AI tools allow him to “take a breath” and “get back into the loop much easier than before” when he needs to. While some companies specifically target Columbia when hiring, sending representatives to campus career fairs and recruiting large numbers from Columbia, a computer science degree cannot be treated as a static credential, Yu described. “You can’t just get your degree and stop learning after that. You have to always be learning,” Yu said. “That is something you basically have to expect for your job.” AI has not only affected students’ decisions to major in computer science, but also reshaped how the subject is taught. Across departments, faculty members no longer treat work done outside the classroom as a reliable measure of understanding, shifting instead toward in-person exams, quizzes, and attendance checks. While homework once made up as much as 60 to 70 percent of a student’s grade in computer science classes, Murphy said that breakdown has since flipped, with exams and quizzes now accounting for the majority. “The assumption—especially among pessimists—is that they’re using it to do their homework,” Murphy said. “They take the homework assignment. They put it into generative AI. Out comes the solution. Submit solution. The end.” Murphy said that faculty members ranged in their responses from fully prohibiting AI use to actively teaching students how to effectively work with AI. He said he shifted his approach this year after his teaching assistants called his belief that students weren’t using the technology “naïve,” and now permits students to use AI on assignments. His surveys of students in his introductory classes have told a more complicated story than the one so-called pessimists offer: Around 60 to 70 percent of students who reported using AI said they used it to have concepts explained to them, to clarify assignment instructions, to generate test cases, or to create practice questions. “Those are all ways that you might have had a tutor in the past to do that, and now you have this AI essentially as your tutor,” he said. Bauer has taken a different approach, teaching students how to use AI productively without sacrificing the core principles of the discipline. “People who are trained to actually use AI tools productively, while being in charge of designing the overall project, will be very much in demand in the next couple of years,” he said. Over the past year, a working group of computer science faculty has been redesigning the introductory and intermediate programming sequence to integrate generative AI as both a subject and a tool, representatives from SEAS wrote in a statement to Spectator. The effort is “coordinated” across the department, “not course-by-course,” with a systematic assessment of how AI integration is affecting student outcomes underway to shape the next round of changes. The goal, the department explained, is for students to be able to “read, verify, and reason about code, whether they or a model wrote it.” Tony Dear, senior lecturer in the computer science department, is piloting individualized AI feedback in a discrete mathematics course, while a machine learning course is also testing a Socratic AI dialogue system designed to “guide inquiry rather than supply answers,” the statement reads. Beyond individual courses, SEAS funded seven cross-departmental faculty projects in generative AI this past academic year, spanning biomedical engineering, chemical engineering, industrial engineering, and computer science, according to the statement. This phenomenon extends beyond the computer science department. SEAS’ new AI minor, which it announced in November 2025, reflects the University’s attempt to adapt. The minor is specifically designed for noncomputer science majors and aims to provide “essential AI knowledge” while emphasizing practical and responsible use across disciplines, according to the SEAS website. At the center of the minor is AI in Context, a course taught by a team of faculty from the departments of computer science, applied mathematics, philosophy, music, literature, and the Writing Center, the representatives told Spectator. Bauer described it as targeted at students who want to bring AI into fields like economics or biology, rather than as a path into software engineering itself. At the graduate level, SEAS announced a new Master of Science in artificial intelligence in February, with the inaugural cohort starting in the fall. While recognizing how AI has helped them navigate coursework, students also described its rise as isolating, even among members of the Application Development Initiative, one of Columbia’s largest computer science clubs. “As people start to ask AI for questions that they would normally ask students and members, it starts to isolate people” Li said. Alexa Kafka, BC ’27, echoed Li’s sentiment, pointing out that computer science has always been a collaborative discipline with code reviews and pull requests, even as AI makes independent work easier. Despite this, Kafka noted that she hasn’t been to office hours “in at least a year.” Bauer added that faculty are exploring long-term solutions to the assessment problem. “One of the things that we’re trying to do at the department or school level is to maybe move towards computerized testing facilities,” Bauer said, referring to ideas in the department to revitalize computer labs as an alternative to pen-and-paper exams. “We have to accept that this is the reality, for better or worse.” But more exams, students say, does not mean better learning, nor does it reflect how computer science is actually done. “Code is so variable and can be approached in so many directions, there’s never just going to be one correct answer. There can be multiple optimal solutions,” Kafka said. Students also expressed concern that the shift toward exam-heavy grading pulls focus away from the applied, project-based work that better reflects both discipline and industry expectations. ADI has tried to respond more quickly than the curriculum can. Through a vibe coding workshop this past semester, club members said they wanted to help students understand not just that AI exists, but how to use it deliberately. “Vibe coding” refers to the practice of prompting AI to write code for a task or project, and has become widespread enough that ADI felt the gap in formal instruction was worth addressing, according to Sophia Huang, BC ’29. The workshop introduced students to the concept of context engineering, which Shaina Sahu, SEAS ’27, described as not just writing a prompt, but optimizing “the rest of the relevant information that you would feed into the model.” Without that, students said that AI-generated code can fail at scale or introduce difficult-to-detect bugs. Genawi said the shift toward AI has changed what it means to be good at computer science. “I think the shift has kind of gone towards what can you create and what can you do, instead of, what do you know,” she said. Indeed, during ADI DevFest, its annual hackathon, Li said more teams are trying to complete their projects than in years past. While sign-ups have historically outpaced submissions, Li said this is beginning to change because teams “have a tool that completes the vision for them.” While many students see AI as an equalizer that lowers the barrier to entry for coding and technical work, Murphy said it also creates new equity concerns inside the classroom around who can afford stronger tools and who already knows how to use them. “The equity issues I worry about are students who have access to paid versions of things versus free versions of things,” he said. Although popular AI models like ChatGPT and Claude have free versions available, both have paid models that can reach over $100 per month, which output different qualities of code. Murphy also pointed to differences in prior exposure to AI literacy itself. “Students who go to high schools or K-12 education, where they’re taught AI literacy, prompt engineering, context engineering, they understand how to do this, as opposed to students who go to schools who don’t teach that,” he said. “Then they come in, and then they’re somehow expected to know how to use these tools.” For Genawi, those disparities begin long before students arrive on campus. “It’s very interesting to consider how lucky you were geographically,” she said. “To be born where you were is influencing your ability to get access to these spaces.” Students like Yu, though broadly apprehensive about the future of computer science, are not without hope. “I’m optimistic, because I think if you’re truly passionate about CS, there will definitely be a place in the field to do something you want to do,” she said. Senior Staff Writer Ria Vasishtha can be contacted at ria.vasishtha@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Staff Writer Arjun Menon can be contacted at arjun.menon@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Want to keep up with breaking news? Subscribe to our email newsletter and like Spectator on Facebook. Produced with Spectate by Emily Ta
The politics of Trump's ballroom
WEST PALM BEACH, Florida — President Trump was about a half-hour into his second speech of the day, musing about the danger he faced last week at the White House Correspondents’ Dinner. “Saturday night, it was good for one thing. People are loving my ballroom now,” Trump told a crowd last night. “That’s the only thing. They love my ballroom.” Ever since he was hustled off stage at the Washington Hilton last week, after an attempted shooter breached the dinner’s security perimeter and sent guests diving to the floor, the president has argued that the incident is proof for why his White House ballroom is needed. Only his planned $400 million, 90,000-square-foot project, Trump has reasoned, can keep a president truly safe at major events. It’s true that a bunch of GOP lawmakers have jumped on board with new legislation and words of support for Trump’s project. House Speaker Mike Johnson, Sen. Rand Paul and other prominent Republicans went on TV to talk about the need for a ballroom. But as we reported at The Washington Post this week, “people” aren’t really changing their minds on Trump’s ballroom. A new Post-ABC News-Ipsos poll found that most Americans still oppose the ballroom project: 56 percent against it versus 28 percent in favor. The numbers are the same as a poll from last year, the Post’s polling whiz Scott Clement found. Other new polls have found similar margins, too. “I’m just not a giant fan, especially of extravagance,” one independent voter told me. “I mean, in the era we’re in right now, given how much people are struggling, it just doesn’t make a lot of sense to me to spend $400 million on a ballroom.” How we got here The Washington Post has followed Trump’s ballroom project very closely. (My colleague Jonathan Edwards may have written the most total words of any reporter about the project; I may be second.) The project exploded as a national issue after Trump tore down the White House’s East Wing after initially promising not to touch it.1 Historic preservationists sued, and about a month ago, a federal judge ordered a halt to ballroom construction.2 The only way the ballroom could proceed, U.S. District Judge Richard Leon ruled, was if Congress explicitly authorized Trump’s plans. (An appeals court has stayed Leon’s order for now.) The judge’s ruling raised an obvious question: would Congress authorize the project? Keep in mind, “authorization” doesn’t mean that Congress needs to necessarily fund the planned $400 million project; they could just sign off on the private donations that Trump has solicited to pay for the ballroom.3 But GOP lawmakers really didn’t want to talk about the ballroom, we reported at The Post just a few weeks ago. One major reason was the ballroom’s bad polling — not a small detail with many of their own midterm elections looming. The White House Correspondents’ Dinner incident, and Trump’s advocacy, may have changed the political conversation around the ballroom. As this week’s polling shows, it doesn’t seem to have affected the fundamental questions surrounding the project. To put it another way, voters still say that cost-of-living concerns are top-of-mind heading into the midterms. Some Republican and independent voters have told me that they see the ballroom as a vanity project that will help Trump but do nothing for their own lives. It might not help Republicans that Trump has dismissed some of the cost-of-living concerns. He’s described “affordability” as a buzzword manufactured by Democrats whose policies helped cause inflation. “They’ve got one good line of bullshit,” Trump said in his first speech yesterday, delivered at a Florida retirement community known as The Villages. Thanks for reading Dan Diamond's newsletter! Subscribe for free to stay up-to-date on my work. Subscribe Speaking of Trump’s speeches… I’m down in Florida because it’s my “duty week” as a White House reporter — basically, I follow the president wherever he goes. (And then hand the baton off to one of my Washington Post colleagues next week.) As part of my duty week, I’ve spent a few days covering the White House’s reaction to Saturday’s attempted shooting, including officials’ review of the security protocols to protect the president. Trump was restrained in the wake of the incident, addressing the nation from the White House briefing room about the need for peaceful dialogue and trying to lower the temperature. But he’s clearly been itching to get back out of the White House, and I was curious to hear how his Florida speeches would go. And — to my ears — it was an unburdened Trump. Not the one we saw Saturday night in the White House briefing room, but the one we witnessed on the campaign trail in 2024. Loose, dropping profanities, mocking everyone, including members of his team. “We have a man here who knows more about Medicaid, Medicare, medical crap than any human being. Where’s Dr. Oz? Where the hell are you, stand up,” Trump said, referring to Mehmet Oz, the administrator of the Centers for Medicare and Medicaid Services. “It’s the most boring trip I’ve ever made. He’s telling me about Medicare, Medicaid. All I want to do is take care of you, I don’t care. I said, ‘You work out the details.’” I wrote up the speech, as Trump headed to West Palm Beach to deliver more remarks a few hours later. The White House didn’t like my story. Their rapid-response team called it the work of a “total loser”4 and “a miserable TDS-ridden moron.” You can be the judge of whether it’s a fair story or the product of someone afflicted with “TDS.” (The White House’s shorthand for “Trump Derangement Syndrome.”) I’ve pasted the story below with a gift link, meaning that you don’t need to be a Washington Post subscriber to read it. It might prompt you for an email address, though. Meanwhile, I’ll be down in Florida for the rest of the weekend, covering the president. It’s TBD whether those stories will be deemed TDS-afflicted, too. 1 Jonathan details the past six months of ballroom fallout, reactions and the implications for executive authority here. I also had a great time talking about Trump’s ballroom and other projects with Australia’s ABC News Daily on a podcast this week. 2 The judge allowed underground construction and national security-related work to continue. But that hasn’t been enough for Trump, who says the ballroom itself is essential for his protection. 3 The ballroom donors include companies like Palantir, Google and Amazon. For transparency, Amazon founder Jeff Bezos owns The Washington Post, where I work. No one has ever interfered with our team’s coverage of the ballroom or our mentions of Amazon or Bezos, and if that changed, I’d speak out. 4 To be fair, I’ve spent the morning working on stories and writing this Substack post rather than enjoying the beautiful Florida weather. So they may be onto something about who’s winning and who’s losing.
Yankees Birthday of the Day: Brandon Claussen
Happy 47th birthday to a man whose brief Yankees tenure still impacts the club today.
Louisiana lawmaker whose district is at heart of SCOTUS ruling speaks out
TheBody: RIP Loreen Willenberg, who gave her HIV-proof body to science and her heart to the community
Loreen Willenberg will be remembered for being one of only three known people in the world whose body seemed to eradicate HIV with no help from antiretrovirals.
Protect patient access to life-saving drugs
By Leslie Herod Since its inception, Colorado’s Prescription Drug Affordability Board (PDAB) created fear among patients around effects to life-saving medicines and the negative ramifications price controls have on affordability. Five years on, those fears persist. Patients and advocates continue to push for protections for those facing the very likely scenario drugs whose price is […]
Viewpoint: A career in science is possible, and worth it
Starting a career in science is exciting, but also demanding, with researchers often faced with intense competition, long working hours and high levels of stress. A pioneer in the field of mass spectrometry, Carol Robinson has been there. She speaks with Science|Business on the importance of creating a supportive environment, particularly for women.Robinson did not wake up one day thinking that she would become a scientist. It was only late in school that she developed an interest in chemistry. “The more you work at something, the more you think, actually, this is fascinating. So, I went much further and deeper than I ever needed to pass the exam. And then I loved it,” she says. “I thought, I’m going to give it my all. I’m going to enjoy it while I’m here. But I didn’t think this would be my career for 50 odd years.”She left school at 16 to become a lab technician with the drug company Pfizer, which is where she first worked with mass spectrometry. Fascinated, she was encouraged to take a part-time degree and then a PhD. She went on to do pioneering work in native mass spectrometry, a technique that is used to study protein structures in their natural state, paving the way for breakthroughs in drug discovery. She also broke new ground by becoming the first female professor of chemistry at the University of Cambridge, and then the first female professor of chemistry at the University of Oxford, her current home.She now wants to show young researchers that a career in science is worth it. “Sometimes, I believe the culture isn’t very encouraging, and that’s something I have worked on,” Robinson says.In 2021, she launched the Kavli Institute for Nanoscience Discovery at Oxford, whose acronym gives a sense of what she hopes it will achieve. “Maybe it is corny,” she says. “It’s just a reminder that I would like it if we could all be kind to each other and supportive, especially for early-career researchers of either gender, because there’s quite a big attrition rate in science, and maybe it just doesn’t feel like a very friendly place to work.”And yet, a career in science comes with a lot of flexibility. “There are few careers where you have that amount of freedom, to go to conferences, to the library. Just because you didn’t see me didn’t mean I wasn’t working,” she says. “And I’m excited to come in every day. So, I don’t want people to think it’s a terrible place.”Gender equality is also important when it comes to the quality of science as a career. “The more women we have, the better it becomes,” Robinson adds.Imposter syndromeAccording to EU data, women outnumber men in university education but account for just about a third of graduates in science, technology, engineering and mathematics (Stem). And while the number of women working as scientists and engineers in the 27-member bloc is on the rise, their representation remains below that of men, at 41%. This discrepancy is even more pronounced in top-tier functions across these fields, where women fill just 20% of positions.Having supervised many young scientists, Robinson sees that women continue to have a greater propensity than men to doubt their abilities, a feeling she once shared. “When I was doing my PhD at the University at Cambridge, I really felt like an imposter,” she says. “I don’t think I ever felt I deserved to be there or owned it and was proud. I just thought I was lucky.” Later on, she adds, “I was always that person who said, I can’t apply yet because my CV is not strong enough.”She has now made it her job to encourage women to break with this imposter syndrome. For example, rather than calling for volunteers to give a lecture in the groups that she mentors or teaches, which nearly always results in men stepping forward, she goes straight to the women, “because I know they won’t push themselves forward.”But the gender gap in science is not just down to imposter syndrome. The “obvious” reason, Robinson says, and the reason that is on everybody’s mind when discussing workplace gender equality, is the balance between work and raising a family. Her own path was to choose between the two, taking an eight-year break after completing a postdoctoral training fellowship to bring up three children. And she continues to advise women not to work part-time while they raise a family. “You’ll just get half the pay and continue to work full-time because you are not able to fit everything in,” she says. “You should be able to have a family and come back to have the career that you want.”The solution is for men to also take career breaks. “That kind of evens it up, so that it’s not such a big thing for women,” Robinson says. “I always encourage men in my groups to do that. I think it’s important to take your share of the duties.”Support produces resultsAccording to the Women in Stem Network, a UK advocacy group, countries with strong childcare systems and equal pay policies retain more women in technical careers. In Europe, these are primarily found in the Nordic region and parts of central Europe.At the EU level, the European Commission asks all Horizon Europe applicant organisations to have a gender action plan in place. Since this criterion was introduced in 2021, the share of women leading consortia has increased to 31%, against 23% in Horizon 2020. Their overall participation, however, has risen just 1%, to 38%. After trying to weaken the requirement for gender action plans in 2025, the Commission now intends to adopt a plan for supporting women in research and innovation.Related articlesCommission scraps plan to water down gender requirements in HorizonHow gender balance can swing who wins Horizon Europe fundingRobinson herself has benefited from three Advanced Grants from the European Research Council. “I was extremely honoured and privileged to get those, because they really allowed me to fuel my passion,” she says. “Without that, it would have been a much harder slog.”Beyond applying gender rules, she says that more work is needed on translational enterprise, where women are, once again, largely underrepresented. The European Patent Office found that only 13.8% of applications were filed by women in 2022. “We have so many more women doing amazing science; why do they feel it’s not a thing to have a company?” she asks.She founded her own spin-out company, OMass Technologies, in 2016, and remains chair of its scientific advisory board now that it trades as OMass Therapeutics. As for research, Robinson is now studying how changes in the brain may be linked to depression, using port-mortem tissue. “We’re hoping to publish soon,” she says. “We had to consult with a lot of people who knew a lot more about the brain than we did. So, we’ve learnt a lot, and that’s always a lot of fun to learn a new thing. That’s what keeps you going.”
Northwood University Celebrates 2026 Class of Outstanding Business Leaders
Gala honors achievement, raises scholarship funds, and inspires students to pursue lives of meaning, leadership, and earned success Northwood University celebrated five honorees whose vision and leadership have transformed industries and communities on Saturday, April 18, during the 46th Outstanding Business Leader Awards Gala at the Henry Ford Museum in Dearborn, Michigan. The gala brought together honorees, alumni, students, donors, and university leaders for a signature event rooted in Northwood’s mission to develop free-enterprise leaders who drive global economic and social progress. This year’s honorees represented an array of industries, including entertainment, entrepreneurship, construction, manufacturing, motorsports, and automotive retail. “Outstanding Business Leader Awards is one of the most meaningful traditions at Northwood University,” said Dr. Kent MacDonald. “It gives our students the opportunity to learn directly from leaders who have built exceptional careers through vision, perseverance, personal responsibility, and service to others. What makes these honorees special is that their impact reaches far beyond business success. They create jobs, strengthen communities, open doors for others, and lead with purpose. They remind our students that free enterprise, when guided by character and integrity, can be a powerful force for good.” Since 1981, the Outstanding Business Leader Awards have recognized exceptional business leadership while supporting student opportunity through the Outstanding Business Leader Endowed Scholarship Fund. Prior to the gala, honorees met with Northwood students during a roundtable conversation designed to give students direct access to first-hand mentorship and career insight. The student-centered experience continued that evening, as each honoree was formally introduced by a Northwood student selected through a competitive process that drew more than 50 applicants this year. The experience reflects Northwood’s emphasis on learning through experience and providing students with meaningful opportunities to build confidence, connections, and professional momentum before graduation. The following honorees and presenters were featured at this year’s Outstanding Business Leader Awards Gala: Paul A. Glantz, chairman and CEO, EH Real Estate LLC; chairman emeritus, Emagine Entertainment Inc., was introduced by Kennedy Williams, a marketing communications and automotive aftermarket management student from Farmington Hills, Michigan. They are pictured with Gala Emcee Steve Madincea (far left) and Northwood University President Kent MacDonald. Jennifer Tenaglia Panning ’92, president, Artisan Tile Inc., was introduced by Karley Baker, a finance and marketing student from Washington Township who is enrolled in Northwood’s four-year bachelor/MBA track. Danny Sullivan, 1985 Indianapolis 500 champion, 1988 IndyCar World champion, and 1994 Rolex 24 at Daytona champion, was introduced by Max Hart, an automotive marketing and management student from Cheboygan, Michigan. Reagan Cordes, a sales management student from Davison, Michigan, is pictured introducing Linzie Venegas, chief operating officer of Ideal Group, who was unable to attend the gala. Larry Zinn, executive general manager of Warren Henry Auto Group and a 2008 Northwood alumnus, was honored to accept the Outstanding Business Leader Award on behalf of his father, Warren Henry Zinn ‘77, who was unable to attend Saturday’s gala. Larry Zinn was introduced by Bella Smylie, a data analytics student from Clare, Michigan. “This gala is about more than recognizing business excellence; it’s also about investing in students and reinforcing the relationships that make Northwood special,” said Advancement Vice President Murray Kyte. “We celebrate leaders whose lives reflect The Northwood Idea while raising scholarship funds to invest in students whose ambition, character, and entrepreneurial spirit reflect the values Northwood seeks to advance.” “During my time at Northwood, I’ve been fortunate to experience things I never could have imagined,” said Baker, who added she was honored to form a meaningful connection with her Outstanding Business Leader honoree. “…Northwood builds a network that stretches far beyond these walls, one shaped by shared values, a belief in free enterprise, and a conviction that business, done right, can change lives.” To learn more about this year’s honorees and gala sponsors, visit the Outstanding Business Leaders Awards webpage at www.northwood.edu/advancement/signature-events/outstanding-business-leader-awards/. Share this page
Australia becomes the 30th country to eliminate trachoma as a public health problem
The World Health Organization (WHO) has validated Australia for eliminating trachoma as a public health problem, marking a significant milestone in the health of indigenous peoples and in global efforts to combat neglected tropical diseases (NTDs). Trachoma, the world’s leading infectious cause of blindness, no longer represents a public health problem in the country.Australia is among a growing number of countries that have successfully eliminated trachoma, contributing to global progress towards the targets set out in the WHO road map for NTDs 2021–2030.Trachoma is caused by the bacterium Chlamydia trachomatis and spreads through close contact with infected individuals, contaminated surfaces, and flies that carry eye and nose discharge. Repeated infections can lead to scarring of the eyelids, turning eyelashes inward, and ultimately causing blindness if untreated.“WHO congratulates Australia on this important achievement,” said Dr Tedros Adhanom Ghebreyesus, WHO Director-General. “This success reflects sustained commitment, strong partnerships, and a focus on reaching populations most affected by health inequities. It brings us closer to a world free from the suffering caused by trachoma.”Australia’s journey to eliminationAustralia’s achievement reflects decades of targeted public health action, particularly in remote Aboriginal and Torres Strait Islander communities, where trachoma persisted despite its earlier disappearance from the rest of the country.National efforts intensified with the establishment of the National Trachoma Management Programme in 2006, which implemented the WHO-recommended SAFE strategy: surgery for trichiasis, antibiotics to treat infection, promotion of facial cleanliness, and environmental improvement. Regular screening of all communities classified as at-risk of trachoma by teams of qualified health workers formed an important part of the Programme. Interventions were delivered through coordinated partnerships between federal and state governments, Aboriginal community-controlled health services, and local communities.Over time, sustained screening, treatment, and prevention activities, including improvements in housing, water, sanitation and hygiene, led to a steady decline in trachoma prevalence. Australia’s approach included adaptations to reflect its context, such as targeted treatment based on community-level data rather than mass drug administration, and strong integration with environmental health programmes.“Elimination of trachoma is a win for the eye health of communities across Australia, particularly those whose lives have been impacted by a disease that is entirely preventable,” said Mark Butler, Minister for Health and Ageing, Australia. “This major milestone is thanks to Aboriginal and Torres Strait Islander leadership, community commitment and sustained investment over many decades.“The lessons from this work will inform how we approach other preventable health conditions in remote and regional Australia. Aboriginal Community Controlled Health Organisations and local health workers have been central to this success, delivering culturally safe care and community-led solutions.”“This recognition from the World Health Organization reflects decades of work led by Aboriginal Community Controlled Health Organisations, alongside local health workers in remote First Nations communities,” said Malarndirri McCarthy, Minister for Indigenous Australians. “Their work has been critical to eliminating trachoma as a public health problem in Australia.”Advancing global efforts against neglected tropical diseases (NTDs)Trachoma is one of 21 diseases and disease groups that are regarded by WHO as NTDs. Together, NTDs affect more than 1 billion people worldwide, primarily in underserved populations with limited access to essential services such as clean water, sanitation, and health care.Australia’s elimination of trachoma as a public health problem highlights the importance of sustained political commitment and cross-sectoral collaboration in addressing the underlying determinants of health. It also underscores the feasibility of eliminating trachoma even in geographically challenging settings.In addition to trachoma, Australia has several endemic NTDs, including Buruli ulcer, leprosy and scabies. Validation of elimination of trachoma as a public health problem marks the first time that WHO has confirmed the elimination of an NTD in Australia, which becomes the 63rd country globally and 16th in the Western Pacific Region to have eliminated at least one NTD.“Tackling neglected tropical diseases in the Western Pacific Region has long been a challenge for countries across the socioeconomic spectrum, given the complexities in reaching the most vulnerable communities, including in remote areas,” said Dr Saia Ma’u Piukala, WHO Regional Director for the Western Pacific. “As a doctor from Tonga, I’ve experienced these challenges for myself. But I also know that with strategic commitment underpinned by optimal resources and partnerships in health, success is possible as other countries in our region have also demonstrated. I commend Australia on eliminating trachoma as a public health problem and urge all involved to remain vigilant to ensure this status is maintained.”WHO continues to support countries working to eliminate trachoma and other NTDs, ensuring that progress reaches those most in need and that gains are sustained through strong surveillance, appropriately integrated into national health systems, and mainstreamed within the wider health sector and beyond.Editor’s notesNeglected tropical diseases and trachoma elimination effortsNeglected tropical diseases are associated with devastating health, social and economic consequences. Their burden is mainly felt among impoverished communities in tropical areas.Public health targets for the control, elimination and eradication of these conditions were set in the road map for neglected tropical diseases 2021–2030 (https://www.who.int/publications/i/item/9789240010352).In 1996, WHO launched the WHO Alliance for the Global Elimination of Trachoma by 2020 (GET2020), creating a network of governments, non-governmental organizations and academic institutions dedicated to the fight against trachoma. WHO continues to support endemic countries to accelerate progress towards the global target of eliminating trachoma as a public health problem worldwide; 2030 is the new target date.Elimination of trachoma as a public health problem is defined as: (i) a prevalence of trachomatous trichiasis (TT) “unknown to the health system” of < 0.2% in ≥15-year-olds; and (ii) a prevalence of trachomatous inflammation—follicular (TF) in children aged 1–9 years of < 5%, in each formerly endemic district, plus (iii) the existence of a system to identify and manage incident cases of TT.Other countries validated by WHO as having eliminated trachoma as a public health problem are: Algeria, Benin, Burundi, Cambodia, China, Egypt, Fiji, Gambia, Ghana, India, Iraq, Islamic Republic of Iran, Lao People’s Democratic Republic, Libya, Malawi, Mali, Mauritania, Mexico, Morocco, Myanmar, Nepal, Oman, Pakistan, Papua New Guinea, Saudi Arabia, Senegal, Togo, Vanuatu and Viet Nam.
‘ShootAround,’ Zombie Webcomic, Becoming Live-Action Film From Lion Forge Entertainment and Webtoon Productions (EXCLUSIVE)
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San Francisco State University Partners with UC Law San Francisco to Expand Downtown Presence - UC Law San Francisco (Formerly UC Hastings)
SFSU’s Lam Family College of Business will use classroom and office space at 198 McAllister Street to establish a new downtown campus. San Francisco State University (SFSU) and UC Law San Francisco have reached an agreement that establishes a new downtown home for SFSU’s Lam Family College of Business at UC Law SF’s Academic Village. The agreement places SFSU business students in close proximity to leading employers, industry partners and transit hubs, enhancing opportunities for internships, networking and career advancement. The UC Law SF Academic Village is a shared-campus model designed to foster collaboration among higher education institutions and contribute to San Francisco’s ongoing revitalization. San Francisco State University (SFSU) and UC Law San Francisco have reached an agreement that establishes a new downtown home for graduate business programs from SFSU’s Lam Family College of Business at UC Law SF’s Academic Village. The agreement creates a new downtown campus location for SFSU, giving students direct access to San Francisco’s thriving business environment. Beginning in Fall 2026, graduate business students will take classes at the UC Law SF building at 198 McAllister Street, at the nexus of the Civic Center, Mid-Market and Tenderloin communities. Under the agreement, SFSU will occupy 3,200 square feet of exclusive teaching and office space and will share additional classrooms with UC Law. The move positions students in close proximity to leading employers, industry partners and transit hubs, enhancing opportunities for internships, networking and career advancement. “This partnership is an example of the growing strategic collaborations that expand opportunities for our students,” said SFSU President Lynn Mahoney. “By situating graduate business programs in the city’s urban core, we will help students build connections, gain experience and achieve their goals for meaningful careers and upward mobility.” Students enrolled in select Lam Family College of Business graduate programs will have access to the Academic Village’s modern classrooms, conference facilities, library, study spaces and resident and event support services. The site also includes lounges, on-site dining and newly built, fully furnished, apartment-style housing for students and professionals. As institutional partners, SFSU College of Business students and alumni are eligible for tuition discounts if they enroll in the UC Law SF Master of Legal Studies program, designed for professionals whose work intersects with the law. “San Francisco State University is an excellent addition to our Academic Village, and we are excited to have the Lam Family College of Business join us in the fall,” said UC Law SF Chancellor & Dean David Faigman. “The partnership advances UC Law SF’s commitment to collaborate with California’s other great public schools and adds vibrancy and energy to the heart of the city. Our students and programs will benefit from the interdisciplinary opportunities that will arise.” The UC Law SF Academic Village is a shared-campus model designed to foster collaboration among higher education institutions and contribute to San Francisco’s ongoing revitalization. The Lam Family College of Business will begin occupying its new space there in July 2026, with classes beginning in August. The move continues SFSU’s longstanding presence in downtown San Francisco and reflects a broader effort to align academic programs with the needs of both career-minded students and the region’s expanding and evolving workforce. For more than 60 years, the Lam Family College of Business has delivered high-quality, AACSB-accredited business education across the San Francisco–Silicon Valley region. Its graduate programs — including the MBAand the STEM-designated Master of Science in Business Analytics and Master of Science in Accounting and Analytics — are designed to help students develop the skills they need to navigate the complex challenges of a global, data-driven economy. About San Francisco State University San Francisco State University is a public university serving students from the San Francisco Bay Area, across California and around the world, with nationally acclaimed programs that span a broad range of disciplines. More than 20,000 students enroll at the University each year, and its more than 304,000 graduates have contributed to the economic, cultural and civic fabric of San Francisco and beyond. Through them — and more than 1,300 world-class faculty members — SFSU proudly embraces its legacy of academic excellence, community engagement and commitment to social justice. For more information, visit sfsu.edu. About UC Law SF UC Law San Francisco is a public law school of the University of California, founded in 1878 as the first law school in the UC system. Located in San Francisco’s Civic Center, it offers a comprehensive legal education with a strong emphasis on experiential learning, public service and professional preparation. Through its Academic Village, UC Law SF brings together students, faculty and partner institutions in a collaborative campus environment in the heart of the city. Other Academic Village partners include UC San Francisco, UC Davis, the University of San Francisco and the University of the Pacific Dugoni School of Dentistry. For more information, visit www.uclawsf.edu.
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Microsoft offers buyouts to thousands of US employees
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CommonSpirit Health named to ‘Dirty Dozen’ list of employers that put profits over safety
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CBL Scholars Program Opens Pathways to Business Law Careers - UC Law San Francisco (Formerly UC Hastings)
First-year law students and 2026 CBL Scholars Deniz Masjedi, Alvaro Flores, and Tavia Pappaly gain mentorship connections with leading industry professionals as they prepare for careers in business law. Created by the UC Center for Business Law San Francisco (CBL), the CBL Scholars Program aims to develop the next generation of business law leaders, supported by contributions from individuals and corporate sponsors. The highly competitive program provides funding along with academic and professional mentorship to students from disadvantaged backgrounds. Scholars gain exposure to the Bay Area’s technology and venture capital ecosystems and access to UC Law SF’s broad network of senior legal professionals. Now in its sixth year, the CBL Scholars Program provides a scholarship and academic and professional mentorship to students entering the business law community from disadvantaged educational, economic, social, or physical backgrounds. The program is generously sponsored by the law firms Gunderson Dettmer and Goodwin Procter. Meet this year’s CBL Scholars: Deniz Masjedi, Alvaro Flores, and Tavia Pappaly. All three have secured summer associate positions at prestigious law firms. Deniz Masjedi: A Passion for Helping Founders Grow Companies Deniz Masjedi, who was born in Iran and moved to Southern California as a teenager, traces her interest in law to her family’s immigration journey and her desire to advocate for herself and her family. She earned a bachelor’s degree at UC Berkeley, where she studied political science and sociology. At UC Law SF, she serves as a 1L representative for the Iranian Law Students Association and is active in the Business Law Society and Technology & Law Society. Next fall, she will serve as director of mentorship for the Technology & Law Society and director of professional development for the Business Law Society. She will join Sidley Austin this year as a 1L/2L summer associate. Q: What draws you to business law? A: Prior to law school, I worked in Cooley LLP’s Private Companies practice group as a corporate paralegal for three years, supporting over 100 venture-backed startups and venture capital funds. I found purpose in helping founders transform their vision into successful companies. It was incredibly rewarding being part of their journeys—from company formation and initial financing to major transactions such as M&A deals. What excites me most about this field is the potential for a small, innovative company to become the next Google or Zoom, shaping how people communicate and access information. I hope to continue supporting entrepreneurs and investors as a corporate attorney. Q: What does it mean to you to have been awarded this scholarship? A: As the first person in my family to attend law school in the United States, receiving this scholarship is a tremendous honor. My parents and I left everything behind so that I could enjoy the freedom to practice my religion without persecution and pursue a career in law as a woman. This award is an acknowledgment of my dedication to business law and motivates me to continue pursuing excellence in this field. It also recognizes years of hard work—learning a new language, navigating the college and law school application process, and ultimately building a career in the legal profession. Q: How will this scholarship help support your future goals? A: This scholarship will provide access to mentorship from leaders in the field, enabling me to develop the skills necessary to offer practical advice and effective representation to future clients. It also eases the financial burden of law school, letting me focus on my coursework and deepen my commitment to corporate law. As an immigrant, breaking into the legal profession has not always been easy, and the time that mentors have invested in me has been instrumental to my progress. This scholarship will allow me to continue building those relationships while positioning me to one day mentor others pursuing similar paths. Alvaro Flores: Inspired by Entrepreneurs The son of Mexican immigrants raised in San Antonio, Texas, Alvaro Flores says he has long been interested in how policy, markets, and culture shape society. He studied international relations and global economy at the University of Southern California, with minors in business finance and legal studies. His experiences span local political organizing, venture capital and startup work, and teaching entrepreneurship. At UC Law SF, he is involved in the Business Law Society, Technology & Law Society, and the Latinx Law Student Association. He will join Perkins Coie as a 1L/2L summer associate with the firm’s Emerging Companies & Venture Capital practice. Q: What draws you to business law? A: I’m drawn to stories of entrepreneurs whose simple ideas become companies that shape the economy and society. Spending time in venture capital and working directly with founders has shown me that many important decisions in the scaling process are ultimately legal ones, particularly how companies are structured and how capital is raised. Business law sits at the intersection of innovation and equity. It offers a way to stay close to the pace of technological change while also helping expand who gets to participate in it. There is something special about being part of that process here in the Bay Area, where the American Dream feels like it is reimagined in real time. Q: What does it mean to you to have been awarded this scholarship? A: I am truly grateful for the support and belief this scholarship represents. I see it not just as recognition, but also as alignment. The CBL Scholars Program’s focus on developing individuals who engage deeply with the business and technology ecosystem reflects the path I have been intentionally building. More than anything, it feels like an investment in that journey. I have been thoughtful about each next step–whether teaching, working in venture capital, or building multidisciplinary experiences–and this scholarship reinforces that those efforts come together in a meaningful way. Q: How will this scholarship help support your future goals? A: It allows me to focus more on developing as a business lawyer, without the distraction of financial constraints. Beyond that, the mentorship and access to the Bay Area network are just as valuable. I’m especially interested in working with early-stage companies and investors, so the opportunity to learn directly from people operating at a high level in that space is huge. Tavia Pappaly: Working in the Wine Industry Sparked Interest in Business and Law Tavia Pappaly spent five years in the wine industry before deciding to pursue law school with a goal of becoming a corporate transactions attorney. Pappaly says she wants to use her legal skills to help business grow and thrive. A first-generation law student from Fairfield, California, she is a member of the Legal Education Opportunity Program (LEOP) and will serve as vice president of social planning for the UC Law SF Sports Law Society in her 2L year. She plans to work as a summer associate at Gibson Dunn in San Francisco after her 2L year. Q: What draws you to business law? A: Business law brings together many of my interests and personal experiences. As the daughter of immigrants who grew up around a family-owned small business, I saw firsthand both the opportunities and challenges of building something from the ground up. That perspective, combined with my experience in the wine industry, solidified my interest in helping businesses of all sizes tackle evolving legal challenges. I’m excited to grow within the corporate law space and develop the skills and experience to support businesses at every stage. I’m deeply motivated to give back to immigrant communities and small business owners, whose experiences often intersect. By building a strong foundation in corporate law, I hope to support those working to create generational stability and to contribute in a meaningful way to the communities that have shaped me. Q: What does it mean to you to have been awarded this scholarship? A: Receiving this scholarship is incredibly meaningful to me. I take great pride in my background as a first-generation student and a woman of color, so being recognized for my potential in a field that has historically underrepresented those communities is both affirming and empowering. This program will help connect me to the resources and committed mentors that will help me succeed. Beyond that, it is no secret that law school is a significant financial commitment. This scholarship helps ease both the financial burden and the stress that comes with it, allowing me to focus more on my education and long-term goals. I am incredibly grateful! Q: How will this scholarship help support your future goals? A: This scholarship has already connected me with an incredible network of faculty, business law professionals, and like-minded students. The community fostered by the Center for Business Law is deeply supportive, and I’m excited to grow alongside my cohort. I truly believe this network will play an important role in helping me thrive in law school and position me for success in business law. As I have seen firsthand how impactful both financial scholarship and mentorship are, I hope to give back in this same way later in my career.