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Guest column: Louisiana’s broadband strategy is helping businesses compete
Every Louisiana business depends on reliable internet service, whether it is processing payments, managing inventory, communicating with customers or supporting employees. Broadband access now directly affects productivity, customer service, operating costs and the ability to compete in an increasingly digital economy. For years, many Louisiana communities dealt with limited internet options, inconsistent service and high […]
Orlando Magic's Most Confusing Move in Free Agency
Orlando’s decision to re-sign Jonathan Isaac despite plummeting production and injury history sparks criticism, as analysts question prioritizing loyalty over finding a reliable veteran presence.
ASU scientists unveil new methods to extract reliable truth from imperfect data
Scientists at Arizona State University are advancing how they interpret complex, imperfect data — challenging long-standing assumptions in fields ranging from imaging to cellular biology. In two papers published in Nature Communications and Proceedings of the National Academy of Sciences, the team introduces fundamentally new approaches to solving “inverse problems,” where researchers work backward from noisy observations to uncover the underlying reality.
It looks like PlayStation 6 is going to cost Sony about $1,000 to make, which likely means it will cost more than that at retail
The cost to make a single unit of the PlayStation 6 has gone up, according to new information from a reliable insider.
Galaxy Z's days are apparently numbered: Accessories point to a very unusual name for Samsung's iPhone Ultra competitor
At first, it was called the Galaxy Z Wide Fold or the Galaxy Z Fold 8 Wide, then—according to generally reliable sources—simply the Galaxy Z Fold8. Now a fourth name has surfaced for the compact and wider iPhone Ultra alternative, that Samsung will unveil on July 22 alongside the updated Galaxy Z Fold 8 Ultra. However, as authentic as these images seem to be, there are doubts.
Samsung Galaxy A27 5G Brings an Immersive Display and Awesome Intelligence to More Users
Newest addition to the Galaxy A series features an Infinity-O display, combining a seamless viewing experience with reliable performance and intelligent tools for more users
Most IVF ‘add-on’ treatments have no effect on fertility or remain unproven, study says
Review shows majority of procedures offered alongside standard IVF not backed by reliable evidence
The 4 PSI Rule For Tires: What Is It, And Is It Reliable?
The 4 PSI rule compares cold and hot tire pressures to estimate proper inflation, but its reliability depends on driving conditions and tire type.
What Science Knows About Grief
After my husband’s death, I had never been more pliable, tender, open, or raw. It was then that I tried E.M.D.R. therapy.
Not TP-Link, Not Netgear: This Wi-Fi Router Gets Consumer Reports Top Rating
According to Consumer Reports, the Asus ROG Strix GS-BE12000's reliable throughput and high power makes it the best single-unit Wi-Fi router on the market.
Fewer car break-ins in Oakland means less business for repair shops
Oakland has seen a decrease in car break-ins over the past year, which is good news for car owners, but less so for some repair shops. Those businesses that specialize in replacing windows and windshields have seen a sharp decline in the once-reliable portion of their income.
“How You Like Me Now?” Hurricanes Still Without a Superstar
One of the biggest criticisms in years past by the hockey experts around the league was that the Carolina Hurricanes did not have a superstar to take them to the next level. They did not have that all-time reliable player they could count on to score a goal when they absolutely needed it. The Canes […]
A Sea Change for Camden Politics
The new leaders of the Select Board were once its most reliable dissenters.
Quality care, reliable funding are top issues for seniors
Good Samaritan leaders are meeting directly with members of Congress and staff as they advocate for quality senior care, funding and workers.
Merchants of Doubt and the attacks on Extreme Event Attribution science
Last week, I attended a meeting at Columbia University on attribution science and climate law, hosted by the Sabin Center. It was a fantastic event, bringing together scientists and legal experts working at the intersection of extreme event attribution and climate law. Subscribe For those unfamiliar with it, extreme event attribution attempts to quantify the contribution of climate change to an extreme event. For example, several groups analyzed the impact of climate change on Hurricane Harvey’s enormous rainfall totals over Houston, Texas and they found that climate change increased rainfall by 15 to 38%. One thing that came up again and again was how terrified fossil-fuel interests are of extreme event attribution science. They are acutely aware that this research could land them in court. And losing those cases would leave them legally liable for billions of dollars in climate damages. Because the legal stakes are so high, the blowback has turned ugly. I spoke with several scientists at the meeting who are facing ongoing harassment over their work. This blowback is a coordinated campaign to make the entire field look suspect. The goal is to create the impression that attribution science is too uncertain, too political, or too conflicted to be useful in court or in public policy. The strategy is not based on actual science or evidence of misconduct, but on the generation of doubt. The new Merchants of Doubt We’ve seen this before. In fact, not that long ago: We only have to go back a year to the Department of Energy (DOE) Climate Working Group (CWG) report to see an example of using doubt as the tool to push back against well-established science. This strategy is laid out in an email from a member of the CWG, Dr. Roy Spencer, that was released during litigation over the Climate Working Group process. The key quote is: About all I can hope is that what we write will provide sufficient “reasonable scientific doubt” regarding the science claims in the 2009 TSD [technical support document], based upon almost 2 decades of new science, to call into question the original reasoning for the EPA Administrator’s decision that CO2 presents a threat to human health and welfare. This statement is strong evidence that at least some members of the committee were working to support a particular policy outcome: revoking the Endangerment Finding. The email also explains how they planned to do it: by attempting to generate “reasonable doubt”. This is going to be hard, Spencer implies. Despite falsely claiming that “2 decades of new science” weakens the case, Spencer explicitly acknowledges that the actual peer-reviewed science of climate change overwhelmingly rejects his position: But if the science argument is decided upon by a vote, or by the number of published citations, we lose the science argument. We can go back even further: This CWG email shares unmistakable DNA with the infamous 1969 tobacco memo that declared: “Doubt is our product, since it is the best means of competing with the ‘body of fact’ that exists in the mind of the general public. It is also the means of establishing a controversy.” The tobacco memo also acknowledges the limit of this strategy: Like the CWG, they knew the science was not on their side. Share The new new Merchants of Doubt The people attacking the IPCC chapter on extreme event attribution are the newest iteration of the Merchants of Doubt. Their goal, like all Merchants before them, is to introduce doubt into the process. Because the report is not even out yet, they cannot attack its conclusions. So they are attacking the authors instead. Here is a press release from the House Science, Space, and Technology Committee: In the letter, the Chairmen express concerns about potential conflicts of interest involving members of the Attribution Committee, stating that “publicly available information suggests a troubling pattern” in which committee members are affiliated with nonprofits that support climate accountability lawsuits, “raising the appearance of impropriety and member bias.” To be clear, this is just innuendo. There is no actual evidence of bias. And given the robust process that these reports go through, including multiple lines of peer review, it seems very unlikely that significant bias can survive into the report. When the report comes out, critics will have the opportunity to make legitimate criticisms of the report — if any exist. If none do, however, they’ll still make criticisms, but they’ll be bogus, simply designed to generate doubt. We’ll see. A note to the press: Fix your frame To any journalists reading this: The public debate over extreme event attribution science is not going away. The science is simply too dangerous to fossil-fuel interests for them to stop fighting it. You very well might be assigned to write an article about this area of research in the future. When you do, do not automatically adopt the framing that climate misinformers want you to use. They want you to frame the story around questions like: Are climate scientists trying to put their thumb on the scale to achieve a predetermined, politically motivated result? Are climate scientists improperly letting their politics invade the science of the IPCC? That frame is a trap. Instead, you need to view this through the historical lens of the Merchants of Doubt. How does the ecosystem of doubt operate? Who funds it? What methods do they use to misrepresent science and slime researchers? What scientific results are they trying to keep people from understanding are legitimate? Ultimately, you instead need to focus your article on the generation of doubt as a way to maintain the fossil fuel industry’s social and legal license to keep burning oil, gas, and coal. If you treat the misinformers’ frame as a legitimate, good-faith scientific critique, you are helping them produce doubt. Don’t do it. Don’t be a Merchant of Doubt. Thanks for reading The Climate Brink! Subscribe for free to receive new posts and support our work. Subscribe other stuff The Staying Curious Substack has an interesting post about the different flavors of climate sensitivity. This is crucial information if you want to understand how scientists think about how much warming the Earth will experience. I’d be grateful if you could hit the like button ❤️ below! It helps more people discover these ideas and lets me know what’s connecting with readers.
2027 NFL Draft Summer Scouting Report: Braelin Moore, C, LSU
Braelin Moore is a reliable center and piece on the LSU offensive line heading into the Fall.
PETA Helps Drive Historic NIH Shift Toward Humane Science
The new office aims to advance reliable and human-relevant non-animal science.
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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Thank you for opening your Chartbook email. Anna Airy, A Shell Forge at a National Projectile Factory, Hackney Marshes, London, 1918 Bond breakout - John Authers Across the developed markets, bond markets are staging a slow-motion car wreck. Looking at the drastic moves in the US, Japan, the UK and France, Barclays’ Ajay Rajadhyaksha commented: “Last week, long bonds broke. Not in one country. In all of them. Simultaneously. Four countries. Four different political systems. Four different central banks. But the same trade — “get me out of duration!”” Each country has its own contributing political problems, but the uniformity of the shift out of long-term bonds with high duration makes clear that something broader is afoot. For Rajadhyaksha, it is “simple and uncomfortable.” The developed world “has too much debt, too little fiscal discipline, and no political appetite for fixing either.” If there’s a trigger, it has been the Iran war and the resulting shock to oil supply. Source: Bloomberg G7 countries are now borrowing at rates that were enjoyed by EM in the early 2020s For contributing subscribers only. Subscribe Trade between China and Russia has grown in recent years For contributing subscribers only. Subscribe Big effects of bad data A decade ago nearly nine in ten Americans, when approached, agreed to fill out the Current Population Survey, which is administered to about 60,000 households each month and asks about, among other things, employment. Fewer than seven in ten do so now (see chart 1). For the Consumer Expenditure Survey, which tries to capture 3,700 households monthly, the response rate is down from 68% to 40%. … When the information becomes less reliable, investment decisions get more difficult to make. Some may be delayed, creating a potential drag on the economy. A new working paper by Nicholas Bloom of Stanford University, Erica Groshen, former head of the Bureau of Labour Statistics (BLS) now at Cornell University, and Duncan Hobbs and Michael Strain of the American Enterprise Institute, a think-tank, hazards an estimate. Preserving trust in “the integrity and quality of official statistics”, the authors claim, generates economic benefits of about $25 for every $1 spent on the BLS, the agency with an annual budget of $700m that is responsible for many of these data. To arrive at their conclusion the quartet analysed an ignoble episode in the BLS’s recent history. On August 1st 2025 Donald Trump sacked Erika McEntarfer, appointed as the agency’s commissioner by his predecessor, Joe Biden. The president alleged, without evidence, that the BLS’s steep downward revision to recent jobs numbers had been “RIGGED in order to make the Republicans, and ME, look bad”. In fact, it was the wanton dismissal that looked bad in the eyes of many observers. In the following seven days there was a 50% leap, relative to the week before, in the average value of the index of Economic Policy Uncertainty (EPU), which tracks the number of articles mentioning such uncertainty that are published daily in American newspapers. This was a discernible jump even when compared to the chaos caused by Mr Trump’s trade war in April 2025 and his real one in Iran in the past two months (see chart 2). Based on Mr Bloom’s earlier study with other colleagues of the EPU’s impact on business investment, industrial production and employment, the authors estimate that the jump reduced American GDP by over $100bn (0.3%) and non-farm payrolls by 168,000 (0.1%). Source: The Economist Immigration and thermostatic politics (Sarah O’Connor in the FT) A strange thing happened a year and a half ago. No sooner had Americans elected Donald Trump as president on an explicitly anti-immigration platform than US public opinion began to swing much more favourably towards immigration. … to political scientists who study “thermostatic” politics, this development is no surprise at all. Public opinion often moves against the prevailing party, especially if they are perceived to have gone “too far” in one direction. The phenomenon was first explored in the 1990s with reference to fiscal policy, but in recent years it has been particularly evident in the realm of immigration policy. While these swings in opinion polls can make it seem as if people don’t know their own minds, they can be a useful democratic check on governments by sending bright flashing warning lights when policies stray too far from the public’s comfort zone. The problem comes when politicians misinterpret those signals. When people tell pollsters they want less immigration, or think it is bad for the country overall (or the converse), they are often expressing an opinion about how things appear to be going in that moment relative to their preferences, rather than a deep shift in their underlying views. According to Alexander Kustov, a political scientist, most Americans’ views are quite stable and moderate: they support immigration that is controlled and in the national interest, and oppose flows that are disorderly or under-enforced. In the US, he argues the Trump administration over-interpreted dissatisfaction with high immigration under Joe Biden, and implemented policies that went too far for many voters. “People don’t necessarily change their idea about their ideal immigration policy, but they can react to what the government is currently doing,” he told me. “A lot of people are not happy with what the Trump administration are doing — right now we’re not talking about border enforcement, we’re talking about people not just getting harassed, but killed.” But similarly, he added, pro-immigration advocates would be making a mistake to interpret the recent, more favourable swing towards immigration as a deep-seated ideological shift in attitudes. The recent history of UK immigration policy offers another cautionary tale. After the 2016 Brexit referendum, public concern about immigration fell. This was partly because net migration statistics declined, but also because people felt a sense of catharsis and control, says Sunder Katwala, director of British Future, a think-tank. By 2019, the public ranked immigration as only the ninth biggest issue of concern, according to Ipsos polling. But when the Conservative government liberalised visa routes for students and care workers between 2019 and 2022, that helped net migration to surge. And so, too, did public concern. Today’s Labour government has implemented another sharply restrictive turn in immigration policy. As Kustov and researcher Caitlyn Yates argue, thermostatic public opinion should be a useful check on politicians. Instead it seems to have helped to magnify the pendulum swing in the UK. The government over-interprets and overreacts to a change in public opinion, which reacts in turn, prompting a sudden swing the other way. Source: Financial Times HEY READERS, THANK YOU for opening the Chartbook email. I hope it brightens your day. I enjoy putting out the newsletter, but tbh, what keeps this flow going is the generosity of those readers who clicked the subscription button. If you are persuaded to click, please consider the annual subscription of $50. It is both better value for you and a much better deal for me, as it involves only one credit card charge. Why feed the payments companies if we don’t have to. Subscribe Anna Airy, Shop for Machining 15-Inch Shells: Singer Manufacturing Company, Clydebank, Glasgow, 1918 Racing for a vaccine … we have been here before! The deadly Ebola outbreak in two African countries driven by a virus species with no vaccine has triggered an urgent search for a potential jab, in a test of troubled global efforts to avert threats posed by emerging diseases. A World Health Organization advisory group is due to would meet on Tuesday to recommend candidate jabs to prioritise for clinical trials, the global health body said. It will assess data including an analysis by the Coalition for Epidemic Preparedness Innovations (Cepi), which was set up after failures in the international response to a previous Ebola crisis. The latest Ebola outbreak in Democratic Republic of Congo and Uganda has infected hundreds of people and killed more than 80, health authorities say. Cepi’s reaction will be a crucial indicator of its progress towards its wider goal of producing vaccines for pandemic threats in just 100 days, despite steep cuts to international health funding. “If there was ever a time that we could show the world why Cepi is needed and show the world why the 100-day mission is needed, it’s now,” Nicole Lurie, the organisation’s executive director for preparedness and response, told the FT. “We’re happy to accept that responsibility, but obviously we need help from partners — particularly financial help in the long run.” DR Congo and Uganda would make the final decision on whether to press ahead with any vaccine candidates endorsed for clinical trials by the WHO experts, the global health body said. “Other ethical and community acceptability issues will be considered,” said the WHO, which declared the latest outbreak a public health emergency of international concern on Sunday. “This will be important to make sure that the trial, if it happens, will be adequately communicated to the population.” Lurie said Cepi had provided information from a survey of research teams and companies working on drugs that might be effective against the Bundibugyo virus behind the outbreak. The organisation hoped soon to announce partners in the quest for a jab, she added, although she declined to give a timescale for when one might be developed. Cepi’s Nicole Laurie said global health bodies were ‘getting some live-fire drills’ with hantavirus and the latest Ebola outbreak. The hunt for a vaccine should be helped by scientific advances including the use of artificial intelligence to find drugs active against viruses, Lurie said. Existing jabs against the Zaire Ebola virus responsible for many previous outbreaks might provide a basis for tackling the Bundibugyo species, she added. “We’ll see how far and how fast we can get,” Lurie said, adding that Cepi had done preliminary work recently on a possible jab for the rat-borne hantavirus responsible for a recent fatal outbreak on a cruise ship. “Both with hantavirus and now with Ebola, [we] are getting some live-fire drills.” Source: Michael Peel in the Financial Times Dad-chat is coming for dad-books. (By Pamela Paul and Jeffrey A. Trachtenberg in the WSJ) They were the go-to gifts for Father’s Day: a book about some little-known chapter of World War II, the sweeping narrative of a shipwreck, perhaps the latest presidential biography. These days, dad books are a dying breed. Nonfiction book sales have been in decline for the past four years, and are now the most challenged segment of the print book market. Publishers say certain types of books still fare well—including celebrity memoirs and religious titles. But in recent years, print sales in such categories as biography, current affairs and business and economics—what publishers refer to as “serious nonfiction” and which tend to resonate especially with men—have fallen considerably. Sales of nonfiction print titles were down nearly 8% through May 9 this year, and sales of books about politics and current affairs were down 19%, according to book tracker Circana BookScan. Publishing has long been subject to cyclical changes, with trends in format and genre taking over bookshelves in grand sweeps. (Remember the adult coloring-book craze? Or the stratospheric rise of romantasy?) But many in publishing believe the decline in serious nonfiction is more existential. “The trend couldn’t be clearer,” said Jonathan Karp, the former chief executive of Simon & Schuster and publisher of the new Simon Six imprint. “This is a sea change and people should wake up and realize we’re living in a new world.” One culprit for the shift: a new media diet for fans of more serious nonfiction. … Sixty-two percent of men and 54% of women consumed a podcast in the prior month, according to a recent survey by Edison Research at SSRS, up from 46% and 39%, respectively, in 2023. “When we have internal meetings to talk about this problem, it always comes around to podcasts,” said Jonathan Burnham, president and publisher of the Harper Group at HarperCollins Publishers. “The man who wants to read American history is now tuning into one of the many good podcasts about history that lends the quiet attention to a serious subject he’s looking for. It makes the idea of sitting down with a 700-page Ron Chernow book less appealing. You’ve scratched that itch.” Source: Wall Street Journal Remarkable report by Novara from the nationalist rally in London in mid May. Anna Airy, Leopard Moth and Plums, 1915 If you’ve scrolled this far, you know you want to click: Subscribe
iOS 26.5.1 Update for iPhones is Coming Soon
Apple's software engineers are testing iOS 26.5.1, according to the MacRumors visitor logs, which have been a reliable indicator of upcoming iOS versions. iOS 26.5.1 will almost certainly be a minor update that fixes bugs and/or security vulnerabilities, and it will likely be released by the end of next week. The logs show that Apple has ramped up testing of iOS 26.6 as well, with a first beta of that update likely to be released at some point in June.
KPMG integrates Claude across its core business and workforce of more than 276,000 in strategic alliance
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
Yankees news: Could Gerrit Cole be activated on Friday?
Cole could come up for his next start; bullpen as a unit bad, Bednar particularly unreliable; retirements and callups
OpenAI and Sam Altman found not liable in Elon Musk trial verdict
A jury rendered its verdict in Elon Musk's trial against OpenAI and Sam Altman.
New MatterChat Model Helps AI to ‘See’ the Language of Science
From writing emails to generating computer code, much of the artificial intelligence prevalent in our daily lives has succeeded by mastering one domain: text. However, this leaves a major blind spot in the physical sciences, where models depend on the high-resolution, three-dimensional data of the physical world, like the intricate lattice of atoms in a crystal. Delivering on the promise of using AI for science requires teaching these data-driven text models to seamlessly “talk to” physics-based models. Now, a new AI framework from Lawrence Berkeley National Laboratory (Berkeley Lab), called MatterChat, solves this problem by creating a specialized “bridge.” It connects the conversational power of a Large Language Model (LLM) with a physics-based AI that models “interatomic potentials”: the complex physical forces between atoms. The resulting system already significantly outperforms general-purpose AI tools like GPT-4 at predicting material properties, and the team hopes it can accelerate scientific discovery by serving as a robust research partner that provides grounded insights and generates step-by-step instructions for synthesizing novel materials. A paper describing this work was recently published in Nature Machine Intelligence. “Traditional simulations can provide the physical rigor required for materials science, yet their computational cost remains prohibitive for high-throughput screening. Conversely, while LLMs excel at rapid knowledge synthesis, they inherently lack the ‘structural vision’ to interpret materials directly from their underlying atomic coordinates,” said Yingheng Tang, a postdoctoral researcher in Berkeley Lab’s Applied Math and Computational Research Division (AMCR) and lead author on the paper. “MatterChat was built to solve this dilemma, empowering LLMs with a structural ‘vision’ that allows researchers to leverage their full potential for solving complex, real-world materials challenges.” Empowering language models to solve complex challenges in materials science To build MatterChat, the Berkeley Lab team drew inspiration from technologies like Vision Question Answering (VQA) and Text-to-Image (T2I) generation. In these tasks, AI must translate high-level text concepts into visual images, or vice versa. Doing so requires developers to build tools that “bridge” two fundamentally different forms of data. The researchers adapted this concept to the physical sciences. With MatterChat, they created a “bridge model” that successfully connects an LLM’s general knowledge with the deep understanding of the atomic-scale world encoded in scientific interatomic potentials. Until now, researchers using LLMs to solve materials problems usually had to feed them raw data files as if they were just strings of text. It’s like asking an AI to understand a complex 3D engine based only on a parts list: the LLM can read the names, but it can’t “see” how the atoms fit together in space. MatterChat solves this by training a specialized AI bridge model, pre-trained on millions of crystal structures and an LLM, to align the LLM’s representation of the world with the interatomic potential’s representation of the world. The bridge model can translate physical insights into a format the LLM can actually understand. By giving the LLM these “scientific eyes”— a scientific “inductive bias” in the terminology of AI — the Berkeley Lab team has transformed it into a robust research tool capable of providing grounded scientific insights into complex materials challenges, such as predicting thermal stability or analyzing electronic band gaps. “We think of atoms as living in a physical space, but from a machine learning perspective, they are just vectors living in some very non-trivially structured manifold in a high-dimensional Euclidean space; and, of course, the same is true for the sentences and paragraphs that we use to express our ideas about those atoms,” said co-author Michael Mahoney, Berkeley Lab’s Scientific Data Division (SDD) AI Initiative Research Lead. “The bridge model basically gets those two structures to ‘talk with’ each other.” As a proof-of-concept of this general approach, the team trained their bridge model on a dataset curated by pairing nearly 143,000 stable atomic structures from the Materials Project with their corresponding physical properties. This training data was automatically assembled using the Materials Project’s API and deliberately enriched with properties fundamental to microelectronics design — like formation energy and bandgap — allowing MatterChat to learn the complex patterns connecting a material’s atomic blueprint to its functional performance. To validate their model, the researchers benchmarked MatterChat against a suite of other AI systems, from general-purpose LLMs to other specialized scientific AI methods. The results show that MatterChat consistently outperformed its competitors across a range of tasks. The model was more accurate in classifying material types and demonstrated superior precision in predicting numerical properties. For example, it excelled at predicting a material’s bandgap, a property critical for designing new electronics from high-capacity energy storage to next-generation computer chips. “Our design is significantly more efficient because we don’t have to build a massive AI model from the ground up,” said co-author Zhi (Jackie) Yao, a research scientist in Berkeley Lab’s AMCR. “Instead, we take two powerful, pre-trained models — a structural encoder for materials physics and an open-source LLM — and use them off-the-shelf. The only component we actually train is the lightweight ‘bridge model’ that translates between them. It’s the difference between building an entire car factory and simply designing a smart adapter that connects a world-class engine to a world-class navigation system. This approach is not only computationally efficient, but also makes the system modular, so we can easily upgrade components or adapt the bridge for other scientific domains in the future.” Crucially, this modular design highlights exactly how institutions like Berkeley Lab and the Department of Energy are carving out a highly valuable niche in the booming AI landscape. Rather than competing with Silicon Valley tech giants to build ever-larger language models from scratch, the lab is focusing on the specialized connective tissue that makes commercial AI useful for hardcore science. Because the bridge model approach underlying MatterChat is forward-compatible, it is perfectly positioned to leverage these parallel tracks of innovation. As Mahoney pointed out, “We expect that industry will continue to develop improved LLMs, and we expect domain scientists and facilities will continue to generate new data. An important part of scientific machine learning is not simply to solve problems on today’s data, but instead to develop general methods that will be forward-compatible with orders of magnitude more data, whether from scientific domains or from LLMs.” According to Yao, the MatterChat project, which was initially developed and enhanced with funding from a Berkeley Lab Laboratory Directed Research & Development (LDRD) Program, will now expand its capabilities. In a collaboration with Fermilab, MatterChat is already contributing to a U.S. Department of Energy Genesis Mission project — called Accelerating eXtreme Environment Specs-to-Silicon (AXESS) — that aims to speed up the development of next-generation, high-speed, radiation-hardened detectors for challenging particle physics experiments by using advanced 3D integrated circuits (chiplets) and AI-driven data analysis. In addition to the LDRD support, the team also credits supercomputing resources at the National Energy Research Scientific Computing Center (NERSC), located at Berkeley Lab, with MatterChat’s success. “We are incredibly grateful to NERSC; this research simply would not have happened without access to the Perlmutter supercomputer through their AI for Science program,” said Tang. Wenbin Xu, a NERSC postdoctoral fellow at the time, was also a major co-author of the work, as was Benjamin Erichson, a research scientist in Berkeley Lab’s SDD, highlighting the benefits of AMCR-SDD collaboration on AI for science. ### 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.
Detroit’s lunch business struggles as hybrid work, economy reshape routines
The traditional sit-down “business lunch” is no longer the reliable revenue stream it was before the pandemic began in early 2020.
Now What? With Carbon Pricing Eroded and Pipeline Politics Advancing, Here Are the Pathways for Climate Success
At the end of a whirlwind week of news on carbon pricing and electricity strategy, capping a madcap year of pretty much non-stop, dramatic change, the word across much of Canada’s climate and energy transition community is that climate policy in this country has been shredded. Eviscerated. Hit with a sledgehammer by policy rollbacks that have pushed the country’s 2050 net zero target “well out of reach”. That means it’s never been more important to look for the rays of hope and glimmers of possibility. To glimpse the seeds of the next set of strategies to get Canada’s climate pollution under control and the country into the accelerating global dash away from fossil fuels. And then, once we’ve spotted those possibilities, get on with the hard, day-to-day slog of trying to make them a reality. Nothing takes away from the frustration and the deep disappointment so many climate and energy hawks are expressing, the sense of betrayal by the author of Value(s) and the former UN special envoy for climate action and finance. But we knew two things as the week drew to a close. We’re looking at the world as it most definitely is. And we don’t get to give up finding the pathways to confront climate change and deliver faster, deeper carbon cuts that leave no one else behind. So really, the first question we all have to ask ourselves is: What’s next? Subscribe All the Oxygen in the Room The outpouring of anger and grief over the carbon pricing deal between Prime Minister Mark Carney and Alberta Premier Danielle Smith has been absolutely understandable. So much of the climate community poured years, more than a decade, into dragging the current federal pricing regime across the finish line, then defending it from a deluge of three-syllable rhymes after the Trudeau government proved utterly incapable of delivering its own message. The policy foisted on us by neo-classical economists took up all the oxygen in the room, burned through years of climate response time that we won’t get back, while cornering climate hawks into the position that the best response to the crisis of our lifetimes is a new tax…in an era of historic anti-tax sentiment. I mean, really—what could possibly have gone wrong? Against that history, let’s appreciate columnist Max Fawcett’s point that even a modest carbon pricing agreement with a government as off-the-wall libertarian/conspiracist as Danielle Smith’s—those adjectives are mine, not Max’s—is a win worth savouring (as long as Smith keeps her promises for a change). But part of living in the world as it is (no, I’m not letting that go) is to be clear-headed about what we can and can’t control. And one thing we get to decide, after seeing this week’s carbon price festivities take up all the oxygen yet again, is whether we want to keep letting that happen. Particularly when success down that road depends on a factor that no one with a climate agenda can control or influence—a provincial carbon pricing mechanism that has been manipulated and eroded beyond its lowest possible denominator. Thankfully, that’s not the way it has to go. Not when we have so many other powerful, practical tools at our disposal, including the ones still available to us through this abominable Memorandum of Understanding (MOU) between Canada and Alberta. Tools In Our Toolbox When I started roughing out this list, I didn’t expect it to grow to nearly a dozen bullet points. It’s probably still incomplete. Some of the greatest hits in today’s climate solutions toolbox include: • No Carbon Capture, No Pipeline: On Friday, Prime Minister Mark Carney explicitly stated that there’s been no change to the MOU provision that there will be no West Coast pipeline without an industry commitment to build its massive, $16.5-billion carbon capture and storage (CCUS) hub in Northern Alberta, and vice versa. The two megaprojects were joined at the hip when the MOU was signed Nov. 27, and the implementation agreement this week affirmed that they still are. A parade of independent analysts have spent years pointing to the economic and technical flaws in CCUS, while the industry kept postponing its investment decision until it could arm-twist the government for more “clarity” (by which it meant even more lavish taxpayer subsidies) on the project. Now the industry itself is running away from the CCUS hub and insisting the pipeline should proceed without it, but Carney doesn’t seem to be budging from a central tenet of the MOU to which Smith has signed on. • No Subsidies, No Investors: Climate analysts and advocates have quite rightly been hammering away at Carney and his team, insisting that any new capacity the industry wants to build must proceed without new subsidies. The government has responded with a growing collection of funding and financing mechanisms for the very wide menu of industrial development and nation-building projects they say they want to take on, and that menu always includes fossil fuel infrastructure. But it’s hard to see how even the combined fiscal clout of the federal and supportive provincial governments will deliver the financial backing private investors would need when the business case for new fossil fuel development is evaporating before their eyes. Particularly when… • No Market, No Demand, No Investors: As our friend Markham Hislop at Energi Media (our apparently sleepless friend, given the massive volume of great material he’s been churning out) points out this morning, the biggest vulnerability in any pipeline deal is the assumption that Asian buyers will want the oil or liquefied natural ga that our fossil industry is so keen to send them. Hislop writes in part: Ottawa and Alberta are effectively betting that countries like China and India will continue increasing oil imports for decades as their economies grow and energy consumption rises. That assumption underpins the entire economic rationale for expanding oil sands production and building billions of dollars in new export infrastructure. But Asian energy systems are changing rapidly… China, India, South Korea, and other major hydrocarbon importers are now investing hundreds of billions of dollars into electricity systems built around wind, solar, batteries, nuclear, hydro, and expanded transmission infrastructure. Domestic “energy sovereignty” is increasingly replacing the old energy-security model based on stable oil and gas imports. Those investments are expected to sharply slow oil demand growth and eventually reverse it, even as overall energy consumption continues rising. There are also refining constraints. Many Asian refineries and petrochemical complexes are already optimized to process discounted heavy crude from the Middle East and Latin America. That raises serious questions about how much additional Canadian bitumen Asian markets can absorb at profitable prices. Small wonder that there’s still no private sector investor for the project, despite Smith’s government desperately beating the bushes to put together a deal. For now, Alberta is acting as proponent and covering early start-up costs for a pitch to the federal Major Projects Office, which could actually turn into the next tough hurdle for Smith and her pipeline to jump. • Canada Day Looms: How ironic that Canada’s birthday this year could become a major chokepoint for the provincial premier who’s been working so diligently to tear the country apart! The July 1 target date for Alberta’s pipeline proposal is baked into the MOU, was reaffirmed by the implementation plan, but it hadn’t dawned on me that it cuts both ways. As one very smart colleague wrote in an email yesterday, “they’ve thrown the ball into Alberta’s court to get the project proposal on the table really quickly and to have to do a whole pile of work to put together a massive, complicated project that would typically take years to assemble, and they’ve given them weeks to do it.” That makes July 1 a looming deadline that Smith won’t be able to run away from politically, especially with the separatists in her party and her caucus breathing down her neck. (See previous ref to off-the-wall.) But if Alberta comes in with a sloppy, incomplete submission, even after Carney bent over backwards to simplify the application process and the rules that govern it, it may be harder for the province or the industry to blame Ottawa and insist on the faster process they claim their investors are demanding. (Oh, wait—which investors would those be??) • We’ll See You In Court (Then We’ll See You Again): If Carney did cave, that would be just one more reason for Indigenous and other affected communities to take the government and the process to court—Just as Alberta First Nations did, successfully, in response to Smith’s half-baked separation referendum. Veterans of the Harper era have been warning that a shoddy process around fossil fuel infrastructure or other major projects will be a recipe for the very delays that process seeks to avoid. And now more than ever, with renewable energy and energy storage eating fossil fuels’ lunch in import markets around the world, a delayed pipeline or LNG terminal may be as good as a cancelled one. • Charged Up, Ready to Go: With its goal of doubling Canada’s electricity supply to meet a doubling of demand by 2050, the national electricity strategy that Carney unveiled Thursday has a lot to like. With its explicit language about speeding the shift from fuels to more efficient and affordable electricity, global investment and deployment patterns that already favour that shift, an emerging bidirectional power grid, the role of front-line options like rooftop solar and heat pumps, the emergence of electric vehicles as behind-the-meter energy storage, and the domestic jobs and manufacturing to make it all happen, the document doubles down on the essential cornerstone of a faster shift off fossil fuels. The strategy envisions a more prominent role for gas and nuclear power. But some of those provisions were already in the Clean Electricity Regulations that climate hawks are now fighting so hard to defend, inserted as a compromise in a futile attempt to mollify the industry and its political representatives in Alberta and Saskatchewan. But once again, gas will be a tougher sell when it’s more expensive and now so much more unpredictable than the clean alternatives. The new nuclear technologies are still speculative and deeply vulnerable to cost overruns. Increasingly, utilities, developers, and investors know it. Carney declared Thursday that “it doesn’t do us good to be sitting in court all the time with provinces. It doesn’t do us good to be talking past each other. What does do us good is to come together with specific projects.” There are no guarantees, but so much of the language and narrative-building in the electricity strategy suggests the lion’s share of those projects will be renewable. • The Party Has Already Started: The electricity strategy only rarely digs down to specific projects, but provinces representing three-quarters of Canada’s population are already embracing a faster transition. Hydro-Québec is working on a 10-year, $185-billion renewable energy and grid buildout by 2035. Ontario recently brought 10 provinces and territories together in a National Energy Corridor Agreement to boost interprovincial and -territorial transmission infrastructure. And the MOU implementation agreement commits Alberta to facilitate investment in renewable energy projects, a big step back from its continuing, punitive restrictions on renewables development. The electricity strategy envisions much of the financing and infrastructure that will be needed to pull those advances together into an integrated whole. • Delivering on Affordability: The MOU implementation plan has little or nothing to say about affordability (are you surprised?), but the electricity strategy does, with a top-line pledge to reduce home energy costs for seven out of 10 Canadian households by 2050, a total saving of $15 billion across the board. It’s just a start, but it puts affordability on the government’s narrative map. If they’re serious and deliberate about patching together a least-cost energy strategy, it’ll have little if any room for oil, gas, or nuclear—not with powerful evidence, for example, that electricity prices are higher when those prices are set by gas. • Smith Proves that Canada Works: By signing a deal that makes it harder to claim the federal government is blocking a new pipeline, Smith has helped prove Carney’s constant anti-separatist refrain that “Canada works”—incurring the rage, no doubt, of her own political base. Canada also needs to work for anyone who depends on a clean environment, healthy and thriving ecosystems, and safe, stable climate. But with Smith and her separatist constituents setting up as a fifth column for annexation by Donald Trump’s United States, let’s set aside any notion that 51st state status for Canada is a recipe for anything other than climate disaster (and so much else). Resisting and withstanding Trump (just 170 more sleeps until mid-term elections, which is a damn sight better than the 730 we started with) doesn’t mean a free pass for a pipeline, but we can still appreciate it that keeping Canada whole and sovereign is still Carney’s Job One. • The Words Not Spoken: Apart from a commitment to export a million barrels of “low-emission” Alberta bitumen per day (memo to Smith and Carney: You realize that that’s less than the industry is already exporting??), the MOU implementation plan says nothing about the international trade and economic sovereignty deals that Carney has been travelling the world to negotiate. It didn’t have to. Carney doesn’t need permission from Danielle Smith, her American enablers, or her separatist allies to continue building the alliances that will ratchet down our economic dependency on our unstable, unreliable neighbour to the south. But every connection he builds with countries outside North America helps align our economy and our future with a faster shift off fossil fuels. Little if any of this was on our bingo cards a year or two ago. But it’s the pathway that has opened up to us. And if it gives Canada any climate or energy gains at all in an era still dominated by Trump and his increasingly ridiculous tariffs, his loutish trade negotiations and his continuing annexation threats, we have to take the wins. Not least because they’ll be the point of departure for what we can achieve in 2027, 2029, and beyond, as Trump’s grip and this era begin to loosen and then fade. What is Carney Doing? If this line of thought is right—and it’s a possibility, not a prediction—it means that Carney’s actions and decisions are largely about buying time. Time for Trump’s power to wane. Time for Alberta’s separatist/conspiracist virus to spread, mutate, die out, or at least die back, with help from a vaccine of federal steadiness and calm (appropriate enough, for a movement populated largely by vaccine deniers) that no conspiracy theory can dismiss. And time for the government’s trade and investment agenda to deliver results—even knowing that some of those results will bring impacts that front-line communities will not and should not accept. In the immediate moment, none of this changes the way the wider climate community has to respond, from campaigners to investors to front-line practitioners. All of our voices are still essential. But those voices will be stronger and more confident if we have a clearer sense of the brutal game we’ve been pulled into, and a plausible path (or two it three) for playing it to the win we need. This conversation also underscores our biggest task for the remaining years of what was supposed to be the “decisive” decade for climate response: if we can shift the energy narrative in Canada, make it a mainstream view that the country’s preoccupation with oil and gas has us falling farther behind a changing world, we’ll see far better results in the 2030s and beyond. For many veteran climate advocates, taking on that part of the fight will begin with recognizing that most of our friends and family, neighbours and colleagues already understand that climate change is real and right in front of us: they’re looking away and staying silent because they don’t have what climate communicator Katharine Hayhoe calls “efficacy”. As Hayhoe recently pointed out in a feature interview with The Energy Mix, it amounts to science denial to keep hammering away at the dire news about global warming without also helping audiences understand what they can do about it, with multiple studies telling us why that won’t work. Far better to open those conversations with the dominant crises like energy security, food security, emergency preparedness and business continuity, affordability, and financial security that have knocked climate change far lower on the list of public priorities, and will likely keep it there. We Knew This Would Happen Climate hawks have always known this day would come. For decades, we warned that everyone had to pay attention to climate change right now, because it would be much tougher to respond once the crisis itself started affecting our daily lives. We were right, and here we are. But now, thanks to decades of hard work, communities and countries have an alternative to doubling down on fossil fuels. “In 10 years everyone will have ‘always known’ that solar and batteries were going to win,” Morgan Solar Executive Chair Mike Andrade writes on LinkedIn. With the disruption in the Strait of Hormuz roiling energy priorities around the world, Canada and the United States are among the shrinking number of countries that are still very keen on fossil fuel development. Understanding that gap is a first, essential step in spotting the opportunities ahead, and averting the deep economic as well as environmental and social risk we face if we don’t seize them. Thanks for reading The Energy Mix Weekender! Subscribe for free to receive new posts and support our work. Subscribe Mitchell Beer traces his background in renewable energy and energy efficiency back to 1977, in climate change to 1997. Now he and the rest of the Energy Mix team scan 1,200 news headlines a week to pull together The Energy Mix and The Energy Mix Weekender. Chart of the Week BYD Eyes 20 Dealership Locations After Canada Greenlights Chinese EV Imports ‘Sledgehammer’ Carbon Price Deal Boosts Emissions by 230Mt, Aims for Fall 2027 Pipeline Approval ‘Worse Than Harper’: Regulatory Rollback Would Stop Canada from Assessing Project Impacts, Critics Warn Electricity Strategy Aims for Doubling by 2050, Allows ‘Flexibility’ for Gas Power Plants Opposition Mounts as O’Leary’s Data Centre Approved in Drought-Stricken Utah Diluted Carbon Price Misses ‘Reality of the World We’re In’, Could Impede Diversified Trade: Climate Institute $200B in Clean Energy Investment Will Need On-Time Delivery, Reliable Policies, Trade Association Says ‘Sovereign Pipeline’: CEO Pitches to Keep Trans Mountain in Government Hands What a Supercharged El Niño Could Mean for Canada Canada’s future is electric, and here’s how a power grid superhighway would get us there (Toronto Star) The Perilous Waters of Hecate Strait (Globe and Mail) Saudi Aramco profits jump despite conflict in Middle East (The Guardian) Farmers confront rising cost of fertilizer and fuel as spring seeding under way (Canadian Broadcasting Corporation) Clarkson forecasts 24% decline in offshore oil, gas capital expenditures (Oil & Gas Journal) Pipe Dreams: How Oil and Gas Fail to Deliver Economic Development in Africa (Oil Change International) Developing countries must hold the pen to script the fossil fuel transition (Climate Home News) Iran war threatens the dream of a post-oil economy in the Gulf (Washington Post) U.S. Oil, Gas Work Force Hits Lowest Level Since 2022 (Rigzone) Why Polestar Is Doubling Down on Net Zero as Rivals Pull Back on EVs (Bloomberg) Why this Michigan utility plans to sell its hydro dams for $13 (E&E News/Politico) Levees can no longer save New Orleans (Vox)
Anthropic forms $200 million partnership with the Gates Foundation
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
Official Xperia 1 VIII images show global colours and key features before launch day
The Xperia 1 VIII is launching on May 13. In the meantime, a reliable leaker has published a series of photos detailing Sony's new flagship. Features like a camera shutter button, a capacitive fingerprint scanner and Zeiss T* lens will return for the smartphones' eighth-generation release.
The National Academy of Sciences Elects Two Berkeley Lab Researchers
Two Lawrence Berkeley National Laboratory (Berkeley Lab) scientists have been elected to the National Academy of Sciences (NAS) in recognition of their distinguished and continuing achievements in original research. They are part of a cohort of 120 new members and 25 international members. The National Academy of Sciences is a private, nonprofit institution that was established under a congressional charter signed by President Abraham Lincoln in 1863. It recognizes achievement in science by election to membership, and — alongside the National Academy of Engineering and the National Academy of Medicine — provides science, engineering, and health policy advice to the federal government and other organizations. The 2026 electees bring the total number of active members to 2,705 with 557 international members. Gary Karpen is a biologist senior faculty scientist in Berkeley Lab’s Biological Systems and Engineering Division and a Professor of Cell and Developmental Physiology in the Department of Molecular and Cell Biology at UC Berkeley. Karpen studies how the structure, organization, and maintenance of genetic material impact inheritance and cell function. His lab focuses on chromatin — a packaged form of DNA strands wrapped around proteins that comprises our chromosomes — with a particular emphasis on centromeres, rarely expressed sequences called heterochromatin, DNA repair, and regulatory RNAs. His research reveals insights into fundamental aspects of nuclear organization and genome regulation that are shared across the tree of life, broadening our understanding of cancer and other diseases, birth defects, and aging. Kam-Biu Luk is a Professor Emeritus of physics at UC Berkeley and a retired senior faculty scientist in Berkeley Lab’s Physics Division. He is an experimental particle physicist whose research has focused on neutrinos: nearly massless, electrically neutral elementary particles that can pass through matter virtually undetected. Neutrinos are created during radioactive decay processes, including the fusion process occurring in stars and the fission process that powers nuclear reactors. Luk initiated and co-led the Daya Bay Reactor Neutrino Experiment, a project based in China with more than 200 international collaborators. In 2012, the Daya Bay experiment discovered a new form of a phenomenon called “neutrino oscillation” in which neutrinos change from one type to another. For this discovery, Luk and his collaborator Yifan Wang received the 2014 American Physical Society Division of Particles and Fields W.K.H. Panofsky Prize for outstanding work in experimental particle physics, and he was also the co-recipient of the 2016 Breakthrough Prize. Luk is currently participating in the Deep Underground Neutrino Experiment (DUNE) hosted by Fermi National Accelerator Laboratory, which will investigate any potential difference in behavior between neutrinos and antineutrinos that may shed light on the evolution of the universe, especially why antimatter is absent in the present universe. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
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‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field
‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field ‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field A field that once seemed like a direct path to stable, lucrative work is becoming less certain under AI, students and faculty told Spectator. By Ria Vasishtha and Arjun Menon May 3, 2026 Shanying Liu / Deputy Illustrations Editor Asia Genawi, SEAS ’29, first encountered artificial intelligence during her sophomore year at her high school in Indiana. Back in 2023, policies on AI varied widely—even within the same district—and some schools had no guidance at all. As Genawi began to see the effects of AI trickle into her classroom, she turned to guidance from her home state’s Department of Education, which she said she found “outdated and inaccessible.” Genawi spent the rest of her high school career dedicated to the issue, including writing a 15-page memo exploring varying AI policies across Indiana schools, which she sent to every Indiana state senator and representative whose contact information she was able to find. She later helped draft a bill that would have required public high schools to establish and share their own AI policies with students in an accessible format. But with so many other bills already awaiting referral, hers never made it to committee. When Genawi arrived at Columbia to study computer science, she found that the gaps she had spent years trying to address in Indiana had followed her there. Many students and professors were also grappling with what AI meant for the field. Across Columbia’s undergraduate schools, computer science has become the most popular major—accounting for around 11 percent of degrees awarded in Columbia College, 32 percent in the School of Engineering and Applied Science, and 12 percent in the School of General Studies in 2024. While the number of computer science majors in SEAS grew from 166 to 173 during the 2024-25 academic year, the total number across the three schools fell around 4.5 percent from 393 to 375, driven by declines at Columbia College and General Studies. At Barnard, it has grown to become the second most popular major as of 2025, up from third the year prior. The number of degrees awarded have grown fivefold since 2016. Faculty members and students in the department told Spectator that the surge in the major’s popularity has been driven in large part by its apparent promise of a secure, well-paid career path. However, they noted that the recent rise of generative AI has compromised the sense of stability that once defined the field, even as interest in technical skills among students persists. According to data from the Federal Reserve Bank of New York in 2024, out of 74 majors tracked, computer science had the fifth highest unemployment rate among majors at 7 percent, while computer engineering had the second highest at 7.8 percent. “Anecdotally, people are moving away from CS as a major,” Daniel Bauer, a senior lecturer in the computer science department, said. “They’re still taking some of our classes, right, because they think they need that, but they’re instead majoring in other things.” Bauer described a field that had shifted from what he called a “nerdy outlier discipline” to one that “everyone needs,” a sign that technical skills have become a baseline expectation across industries with the emergence of AI. “A lot of people thought, ‘Oh, it’s a guaranteed path to a stable income. You get a six-figure job right out of your undergrad,’” Bauer said. Now, companies may see AI as a way to reduce demand for entry-level programmers, especially in what he calls “code monkey jobs,” roles where a large number of employees are “just writing out code nine-to-five.” Chris Murphy, a senior lecturer in the computer science department, described the current moment as the convergence of two forces: the downturn of the job market and generative AI coming onto the scene. “Things kind of started to really pick up in 2014, 16,” he said, recalling the years after the weaker 2010 market. Murphy added that this was also a period where “students were getting more experience outside of class than they were inside of class” through internships, jobs, and side projects. Murphy suggested that the earlier momentum helped make computer science feel like both an academic program and a reliable pathway into work. That feeling is harder to sustain now as the industry reconfigures, which is a shift that students are seeing firsthand. Rebecca Yu, SEAS ’27, said that during a recent internship in Big Tech, she saw that “every single tech company was racing to integrate” large language models. She added that her manager said her intern class was the first to complete their assigned projects, which she attributed to the increased efficiency that comes with AI. Frank Liu, SEAS ’29, said that with AI lowering the barrier to coding, companies now expect everyone to write code. “A lot of companies outright say if you’re not using coding in your job, you might get fired for not being productive enough,” Liu said. Yu similarly expressed that students increasingly have to accept AI being a part of industry workflow, adding that many also “make use of it to maximize their productivity.” Richard Li, CC ’28, described a “franticness” among his peers as the pace of change accelerates. He explained how “some new tool” is released regularly, noting that tools rise and fall in prominence within weeks. He has come to believe that he doesn’t need to “match that speed all the time” and keep up with every development. Li has learned that AI tools allow him to “take a breath” and “get back into the loop much easier than before” when he needs to. While some companies specifically target Columbia when hiring, sending representatives to campus career fairs and recruiting large numbers from Columbia, a computer science degree cannot be treated as a static credential, Yu described. “You can’t just get your degree and stop learning after that. You have to always be learning,” Yu said. “That is something you basically have to expect for your job.” AI has not only affected students’ decisions to major in computer science, but also reshaped how the subject is taught. Across departments, faculty members no longer treat work done outside the classroom as a reliable measure of understanding, shifting instead toward in-person exams, quizzes, and attendance checks. While homework once made up as much as 60 to 70 percent of a student’s grade in computer science classes, Murphy said that breakdown has since flipped, with exams and quizzes now accounting for the majority. “The assumption—especially among pessimists—is that they’re using it to do their homework,” Murphy said. “They take the homework assignment. They put it into generative AI. Out comes the solution. Submit solution. The end.” Murphy said that faculty members ranged in their responses from fully prohibiting AI use to actively teaching students how to effectively work with AI. He said he shifted his approach this year after his teaching assistants called his belief that students weren’t using the technology “naïve,” and now permits students to use AI on assignments. His surveys of students in his introductory classes have told a more complicated story than the one so-called pessimists offer: Around 60 to 70 percent of students who reported using AI said they used it to have concepts explained to them, to clarify assignment instructions, to generate test cases, or to create practice questions. “Those are all ways that you might have had a tutor in the past to do that, and now you have this AI essentially as your tutor,” he said. Bauer has taken a different approach, teaching students how to use AI productively without sacrificing the core principles of the discipline. “People who are trained to actually use AI tools productively, while being in charge of designing the overall project, will be very much in demand in the next couple of years,” he said. Over the past year, a working group of computer science faculty has been redesigning the introductory and intermediate programming sequence to integrate generative AI as both a subject and a tool, representatives from SEAS wrote in a statement to Spectator. The effort is “coordinated” across the department, “not course-by-course,” with a systematic assessment of how AI integration is affecting student outcomes underway to shape the next round of changes. The goal, the department explained, is for students to be able to “read, verify, and reason about code, whether they or a model wrote it.” Tony Dear, senior lecturer in the computer science department, is piloting individualized AI feedback in a discrete mathematics course, while a machine learning course is also testing a Socratic AI dialogue system designed to “guide inquiry rather than supply answers,” the statement reads. Beyond individual courses, SEAS funded seven cross-departmental faculty projects in generative AI this past academic year, spanning biomedical engineering, chemical engineering, industrial engineering, and computer science, according to the statement. This phenomenon extends beyond the computer science department. SEAS’ new AI minor, which it announced in November 2025, reflects the University’s attempt to adapt. The minor is specifically designed for noncomputer science majors and aims to provide “essential AI knowledge” while emphasizing practical and responsible use across disciplines, according to the SEAS website. At the center of the minor is AI in Context, a course taught by a team of faculty from the departments of computer science, applied mathematics, philosophy, music, literature, and the Writing Center, the representatives told Spectator. Bauer described it as targeted at students who want to bring AI into fields like economics or biology, rather than as a path into software engineering itself. At the graduate level, SEAS announced a new Master of Science in artificial intelligence in February, with the inaugural cohort starting in the fall. While recognizing how AI has helped them navigate coursework, students also described its rise as isolating, even among members of the Application Development Initiative, one of Columbia’s largest computer science clubs. “As people start to ask AI for questions that they would normally ask students and members, it starts to isolate people” Li said. Alexa Kafka, BC ’27, echoed Li’s sentiment, pointing out that computer science has always been a collaborative discipline with code reviews and pull requests, even as AI makes independent work easier. Despite this, Kafka noted that she hasn’t been to office hours “in at least a year.” Bauer added that faculty are exploring long-term solutions to the assessment problem. “One of the things that we’re trying to do at the department or school level is to maybe move towards computerized testing facilities,” Bauer said, referring to ideas in the department to revitalize computer labs as an alternative to pen-and-paper exams. “We have to accept that this is the reality, for better or worse.” But more exams, students say, does not mean better learning, nor does it reflect how computer science is actually done. “Code is so variable and can be approached in so many directions, there’s never just going to be one correct answer. There can be multiple optimal solutions,” Kafka said. Students also expressed concern that the shift toward exam-heavy grading pulls focus away from the applied, project-based work that better reflects both discipline and industry expectations. ADI has tried to respond more quickly than the curriculum can. Through a vibe coding workshop this past semester, club members said they wanted to help students understand not just that AI exists, but how to use it deliberately. “Vibe coding” refers to the practice of prompting AI to write code for a task or project, and has become widespread enough that ADI felt the gap in formal instruction was worth addressing, according to Sophia Huang, BC ’29. The workshop introduced students to the concept of context engineering, which Shaina Sahu, SEAS ’27, described as not just writing a prompt, but optimizing “the rest of the relevant information that you would feed into the model.” Without that, students said that AI-generated code can fail at scale or introduce difficult-to-detect bugs. Genawi said the shift toward AI has changed what it means to be good at computer science. “I think the shift has kind of gone towards what can you create and what can you do, instead of, what do you know,” she said. Indeed, during ADI DevFest, its annual hackathon, Li said more teams are trying to complete their projects than in years past. While sign-ups have historically outpaced submissions, Li said this is beginning to change because teams “have a tool that completes the vision for them.” While many students see AI as an equalizer that lowers the barrier to entry for coding and technical work, Murphy said it also creates new equity concerns inside the classroom around who can afford stronger tools and who already knows how to use them. “The equity issues I worry about are students who have access to paid versions of things versus free versions of things,” he said. Although popular AI models like ChatGPT and Claude have free versions available, both have paid models that can reach over $100 per month, which output different qualities of code. Murphy also pointed to differences in prior exposure to AI literacy itself. “Students who go to high schools or K-12 education, where they’re taught AI literacy, prompt engineering, context engineering, they understand how to do this, as opposed to students who go to schools who don’t teach that,” he said. “Then they come in, and then they’re somehow expected to know how to use these tools.” For Genawi, those disparities begin long before students arrive on campus. “It’s very interesting to consider how lucky you were geographically,” she said. “To be born where you were is influencing your ability to get access to these spaces.” Students like Yu, though broadly apprehensive about the future of computer science, are not without hope. “I’m optimistic, because I think if you’re truly passionate about CS, there will definitely be a place in the field to do something you want to do,” she said. Senior Staff Writer Ria Vasishtha can be contacted at ria.vasishtha@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Staff Writer Arjun Menon can be contacted at arjun.menon@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Want to keep up with breaking news? Subscribe to our email newsletter and like Spectator on Facebook. Produced with Spectate by Emily Ta
Reliable Connectivity From Spectrum Business Helps NRG Reach Esports Fans Worldwide
Since 2024, NRG has relied on Spectrum Business to grow LA’s gaming scene, launch a nationwide tour and redefine fan experiences.
We research aging. Those at-home ‘biological age’ tests won’t work for you.
Tests promising to reveal your “biological age” are booming. But scientists say the results vary widely — and aren’t reliable enough to guide health decisions.
We research aging. Those at-home ‘biological age’ tests won’t work for you.
Tests promising to reveal your “biological age” are booming. But scientists say the results vary widely — and aren’t reliable enough to guide health decisions.
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Shaky political science: using "simulated" elections over real-world elections
[Dear DSOS readers – this article is the latest in our “Shaky Political Science” series. In our 38 page report, “Shaky political ‘science’ misses mark on ranked choice voting,” released in early December, we examined over 40 studies focused on ranked choice voting. We found that many of these studies suffered from puzzling research methodologies, poorly constructed surveys, unrealistic simulated elections, cherry picked data, and faulty analyses that often were contradicted by results from real-world elections (a link to our full paper is here). Here are links to the first three articles in our series: the first one summarizing the overall results of our report, the second one analyzing a study co-authored by University of Minnesota academic Larry Jacobs, which was one of the most error-prone of all the research we reviewed in our report, and a third article showing an admirable example of two political scientists holding two other political scientists accountable for their flawed, sloppy research]. In this fourth article in our “Shaky Political Science” series, we shine a spotlight on seven studies that were web-hosted and funded by New America’s Political Reform Program and its Electoral Reform Research Group (the ERRG is an ad hoc committee headed by New America’s Political Reform Program and joined by the American Enterprise Institute, the Unite America Institute and Stanford University’s Center on Democracy, Development, and the Rule of Law). This research consortium has been led by Lee Drutman, senior fellow with New America’s Political Reform Program. These seven studies are simply not credible from any objective standard about how political “science” is supposed to illuminate our understanding of either ranked choice voting or politics in general. The feature that six of these seven studies have in common is that they all used surveys – artificial, simulated elections -- to attempt to say something cogent or interesting about ranked choice voting (RCV) elections. None of them used real world data from actual RCV elections – either the 1000+ elections in the US or the many thousands of elections from places like Australia, Ireland and elsewhere where RCV has been used for 100 years. While a few of these studies conclude with some mixed positives about RCV, our analysis here is content neutral. The idea that, with a political reform like RCV, you can ask respondents in a mock, artificial election to vote with that method, with only the barest of explanation about how RCV works or its likely impacts (i.e. no spoiler candidates, majority winners, voters liberated to pick their favorite candidates, coalition-building), and then ask them their thoughts/reactions about voting in that system, with fake candidates and no real campaigning, no real exposure to an actual election or actual candidates, rests on some very puzzling assumptions. The findings of these seven studies actually are contradicted by the findings from many other studies based on real-world RCV elections. These New America studies also failed to list the real-world RCV studies in their own citations, instead including those studies that reinforced their conclusions. The seven studies featured in this article were chosen because they form a cohesive cohort which precisely illustrates one of the main points of our research paper, namely that using surveys, i.e. artificial elections, or mathematical/theoretical models, instead of real-world data, is in fact “shaky political science.” Below are summaries of our analysis of five of the seven New America studies. You can read our complete analysis of all seven studies at our paper, “Shaky political ‘science’ misses mark on ranked choice voting.” Choosing to “Vote As Usual” by Andre Blais, Carolina Plescia, and Semra Sevi. New America. (Blais et al., 2021). This study, web-hosted and funded by New America’s Political Reform Program and its Electoral Reform Research Group, used two surveys in which respondents completed ballots for simulated elections using plurality, approval, RCV and point (Score) voting. In both cases the authors provided the respondents with only minimal explanation of each method. For example, the entire explanation for RCV was “For this vote, rank the candidates from your first to your last choice (you do not need to rank them all). This is called the ranked vote.” That’s it. The study did not provide any context or explanation to survey respondents about why she/he might want to rank multiple candidates. Participants were not even told that ballots would be tabulated using an “instant” runoff, not to mention any description of RCVs ability to eliminate the spoiler effect or liberate voters to rank their favorite candidates without fear of helping the “greater evil” candidate to win. That’s like taking an iPhone back in time to 1970 and asking someone if they like it better than their rotary phone without explaining how it works. Their first survey targeted states that held 2020 Super Tuesday Democratic primaries and used the real candidates’ names in the survey; the second survey used fictitious candidates. In other words, they surveyed respondents who had only ever participated in plurality elections, had no history or background with using RCV or any of the other methods, had no clue about its likely impacts, and did not communicate how the alternative methods would be counted, and then asked them: “How satisfied are you with using each system?” Predictably, the winner was plurality elections, the system used for president, Congress, gubernatorial and state legislative elections – in other words, the method they have been using, year after year, all their voting lives. With so little explanation of RCV and its impacts, this study is simply not credible in discovering anything useful about real-world RCV elections. It merely shows that a basic voter education campaign is needed when adopting a new election system. Additionally, the result contradicts many other post-election surveys following real RCV elections, asking opinions from voters who had just participated in a RCV election, that showed a great deal of satisfaction with RCV (see details on that in Studies 22-25 in our paper, as well as the four studies cited in the review for Study 28). Share Other studies on “satisfaction” and “understanding” make similar mistakes, in terms of basing their surveys on inadequate explanation to respondents and lack of education about context. How could any survey ever hope to measure accurately, in essence simulate a real-world election, without actual campaigning candidates, the media writing about those candidates, organizations endorsing candidates and voters being inundated with crucial information? More than anything, what these “studies” based on surveys show is that if you ask voters about something they don’t know very much about, and fail to adequately educate them, their responses will unsurprisingly reflect their confusion. Equally puzzling is that New America’s Electoral Reform Research Group, headed by political scientist Lee Drutman, found this to be a credible enough study that they not only published it but highlighted it in Drutman’s own discussion of research related to ranked choice voting. This pattern was repeated in study after study featured by New America’s Electoral Reform Research Group. For example in Ranked-Choice Voting and Political Expression: Voter Guides Narrow the Gap between Informed and Uninformed Citizens by Cheryl Boudreau, Jonathan Colner, and Scott MacKenzie, the researchers based their analysis on a survey which found that respondents who were asked to vote in a simulated RCV election, with minimal explanation of RCV or how it works, displayed less evidence of being informed about how to use RCV effectively, with the least informed voters ranking fewer candidates. As pointed out under the study above, in a real election voters are informed by a wide variety of methods, including public education by the election administration, by interested political groups, the media, and the candidates themselves who benefit from voters being aware of how to vote in the new system. In Support for Ranked-Choice Voting across Race and Partisanship by Joseph Anthony, David C. Kimball, Jack Santucci and Jamil Scott, the researchers used respondents from different parties and races voting in simulated plurality and RCV elections, who indicated their satisfaction with each voting method. Instead of using real world election results, the authors fell back on the easy, armchair and less reliable academic research methodology of making up mock elections. As with the other studies, little explanation was given of how RCV works or what its implications might be (electing majority winners or eliminating spoilers, for example). Unsurprisingly, this study found that respondents were not very satisfied with RCV. However, when the authors added an explanation that RCV has a tendency to elect more women and people of color, they found that this greater explanation increased the preference for RCV. The more voters were familiar with it, and the more they understood potential positive impacts – surprise! -- the more they were open to switching away from the status quo. Of note, the results from this study are thoroughly contradicted by a number of other studies whose data was obtained from real RCV elections with real voters. Those studies show a great deal of satisfaction with RCV. More “fake election” research An interesting wrinkle on this “research by fake elections” approach, which only served to illustrate how unreliable such “research” can become, was taken in Ranked-Choice Voting, Runoff, and Democracy: Insights from Maine and Other U.S. States by Joseph Cerrone and Cynthia McClintock_,_ also web-hosted and funded by New America’s ERRG. It gets high marks for creativity, but an eye-roll for thinking this could possibly add anything meaningful to our understanding of real world RCV elections. This study tried to determine whether RCV, plurality or two-round runoff elections provided greater voter satisfaction and acceptance of election results. To determine that, the authors conducted a survey asking if voters were satisfied with a fictional newspaper report of an election, with three different versions given to respondents – plurality, runoff and RCV, with variations of Republican/Democrat winners and variations of “come-from-behind” winners,” i.e. candidates not initially in first place but who win the “runoff.” Again, a minimal explanation of RCV or how it works was given, and the survey showed that respondents had the greatest unfamiliarity with RCV than the other methods. The unsurprising result? Respondents were much more satisfied with “come-from-behind” victories in a traditional two-round runoff election than in RCV, even though RCV is also a runoff system but with a single round of counting. Interestingly, the dissatisfaction with RCV disappeared for those who were more familiar with RCV. In fact, among those respondents who were “very familiar” with RCV, they were equally satisfied with RCV, runoff and plurality. Surprise, surprise. Again, what this study revealed, more than anything, is not only the inadequacy of the explanation for RCV given by the authors of this study, but the failure of the research format itself, thinking that such an artificial mock election could somehow simulate what actually happens in the real world in a real RCV election. Those four studies reviewed above are fitting examples of inadequate and shaky political “science.” Other studies reviewed in our paper from the New America/Electoral Reform Research Group cohort which also used mock elections included: Ranked-Choice Voting is an Acquired Taste by Joseph Anthony and David C. Kimball. New America. (Anthony et al., 2021). Ranked-Choice Voting is No Refuge for Extreme Candidates by Melissa Baker. New America. (Baker, 2021). Also included in this New America cohort is Does Ranked-Choice Voting Reduce Racial Polarization? by Yuki Atsusaka and Theodore Landsman, which actually did use real-world election results data but applied an odd and unconventional methodology to the data which relied on shaky assumptions and non-real-world scenarios that were too divorced from reality. To determine levels of “racially polarized voting,” the researchers imagined “ethnic parties,” by which they mean political parties for each ethnic group, which was especially odd since the election data they were using came from nonpartisan RCV races. But then they made an even odder assumption, namely that since race and ethnic characteristics of studied voters were not available, the authors tried to infer that information based on who voters selected and the racial make-up of voters in each district. This amounted to little more than vague and ambiguous guesswork dressed up with a bunch of mathematical equations used to give it a veneer of scientific appearance. Indeed, it seemed to assume racial polarization in order to prove racial polarization. Based on this questionable methodology, the authors concluded that RCV doesn’t reduce racial polarization. But the premise of this study missed an obvious reality: since RCV incentivizes more candidates to run and can result in more diverse fields of candidates, one would expect more racially-polarized voting in voters’ top rankings, not less, because more voters would have someone on that ballot who represents their racial or ethnic identity and more freedom to vote for their real first choice. By encouraging more diverse candidates and eliminating the spoiler effect, RCV facilitates voting for a favorite candidate of your own race and then selecting a second choice with the best likelihood of winning. The real measure is what voters do with their additional rankings – do they consider other candidates and vote “for the best of the rest”? This study didn’t tell us anything useful about that. But readily available data shows this is common in real RCV elections, and candidates act on this knowledge by engaging with voters outside their racial group. So the authors of this study failed to consider or account for what actually happens in real-world elections, and their conclusions are tainted by this failure. New America has contributed a great deal over the past 2.5 decades to our understanding of many policy areas, including politics and political reform (full disclosure: Steven Hill, one of the authors of this article and of the “Shaky Political Science” research paper and series, is a former senior fellow and former director of New America’s political reform program, 2005-2010). So it was especially disappointing that New America funded and published such a large batch of dubious studies about ranked choice voting. The stated praiseworthy goal of its Electoral Reform Research Group was to inspire new academic research about ranked choice voting that would shed light and help the public better understand the impacts of RCV. Instead, the ERRG research did just the opposite. It produced dubious research that created more confusion and misinformation. In so doing, New America’s political reform program contributed to “shaky political science.” Steven Hill and Paul Haughey Share Upgrade to $5 subscription Thanks for reading DemocracySOS, a reader-supported digital portal for the pro-democracy movement. Subscribe for only $5 per month to receive full benefits and to support our work. Subscribe
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Advancing epidemic and pandemic preparedness through research readiness: SEAR Unity Studies Network Sites chart the way forward in 2026
On 25 March 2026, WHO Regional Office for the South‑East Asia Region (SEARO) convened the first meeting of the SEAR Chapter of the Unity Studies Network Meeting in 2026 to take stock of progress and plan next steps for strengthening epidemic and pandemic preparedness across the Region. The meeting focused on reviewing the ongoing adaptation of selected, standard Unity Study methodologies, sharing field implementation experiences from 2025, and identifying key priorities to guide collaborative work in 2026.Held virtually, the meeting brought together representatives from six Unity Studies network sites across five countries (Bangladesh, India, Nepal, Sri Lanka, and Thailand) as well as participants from all three levels of WHO.The meeting took place against the backdrop of ongoing regional efforts in South‑East Asia to strengthen early investigations and studies for epidemic and pandemic preparedness, with a focus on pandemic influenza and other emerging respiratory pathogens with epidemic and pandemic potential. In this context, and as part of preparedness for the rapid generation of evidence, the WHO Health Emergencies Programme in SEAR (WHE/SEAR) encourages Unity Studies sites to adapt prioritized, standardized Unity Study protocols to their national contexts. This approach supports the generation of timely and reliable evidence to inform public health decision‑making during epidemics and pandemics.These efforts are supported through a combination of experience sharing, participation in the global Unity Studies network webinars, capacity‑building of the national public health workforce, and regional collaboration that promotes inter‑country, inter‑agency, and inter‑ministerial action. In parallel, sites are encouraged to explore diversified funding sources to help sustain research preparedness over the long term.Discussions highlighted the importance of WHO’s catalytic financial support through the Pandemic Influenza Preparedness (PIP) Partnership Contributions (PIP‑PC). When combined with national and institutional resources, this support enables countries and sites to maintain continuity of activities at a core level and remain prepared to respond rapidly and effectively to future epidemics and pandemics.Regional and Global Updates: Opening the session, Dr Pushpa Ranjan Wijesinghe, Programme Area Manager, Pandemic and Epidemic Management (PEM) Unit, WHO Health Emergencies Programme, highlighted the progress made in operationalizing the Unity Studies Network across the Region and acknowledged the collaborative efforts of WHO at all three levels, together with country unity studies sites. He emphasized the availability of limited but catalytic funding through the Pandemic Influenza Preparedness (PIP) Partnership Contribution (PIP‑PC) workplan at SEARO to sustain network activities in 2026, while encouraging sites to leverage additional funding sources to expand implementation.Representing SEARO, Dr Ashok Basnet, consultant, Pandemic Influenza Preparedness reported that, at the regional level, the Unity Studies Network currently includes six sites across five countries: Bangladesh, India, Nepal, Sri Lanka, and Thailand. He noted that in 2025, catalytic funding supported protocol adaptation, training, and pilot implementation. In addition, a regional Unity Studies symposium held during the Global Public Health Summit in Sri Lanka strengthened and advocated collaboration between the Unity Studies site, national decision‑making forum, relevant technical units of the ministry of health, WHO and other partners.The symposium also highlighted the role of early investigations and studies as a key component of a multi‑source surveillance system needed at country level to generate a broad range of evidence. It underscored the critical role of country Unity Studies sites and similar research entities in generating evidence to support public health decision‑making during epidemics and pandemics.Participants of the WHO GISRS Unity Studies Sites Network- SEAR Chapter Meeting 2026 (Photo credit- PEM/WHE/SEARO). SEARO also updated participants on ongoing efforts to develop a regional Unity Studies webpage and to promote participation in global knowledge‑sharing platforms, including webinars and the WHO Community of Practice. Priorities for 2026 include supporting the implementation of Unity protocols, strengthening collaboration and experience sharing across sites, documenting and publishing findings, integrating Unity Studies activities as a component of multi-source national surveillance systems, and building workforce capacity.Dr Nickle Boddington of the Global Influenza Programme (GIP) at WHO Headquarters, which coordinates the Unity Studies Network globally, provided updates on global Unity Studies activities, noting that the network has expanded to 16 sites worldwide. New and updated protocols, including rapid clinical severity assessment tools and standardized data platforms, are being developed to support implementation. The global Community of Practice has also been launched to facilitate technical exchange, and continued support will be provided to sites for protocol adaptation, ethical approvals, and readiness for early investigation during outbreaks. Updates from Unity Studies sites in SEAR in 2025Unity Studies sites shared progress and experiences in implementing protocols across diverse country contexts. Sites initiated activities in 2025 using catalytic funding from SEARO, complemented by the mobilization of internal national resources. The Institute of Epidemiology, Disease Control and Research (IEDCR), Bangladesh, reported the adoption and piloting of investigation protocols on non‑seasonal influenza and emerging respiratory diseases during recent avian influenza outbreaks, demonstrating a multi‑source funding approach involving government, WHO and One Health partners.The Hamdard Institute of Medical Sciences and Research (HIMSR), New Delhi, India, highlighted the piloting of household transmission investigation studies, the expansion of the national Unity Studies network, and the contribution of study findings to global evidence through published outputs. The All-India Institute of Medical Sciences (AIIMS), New Delhi, India, shared experiences from large‑scale seroprevalence studies conducted during the COVID‑19 pandemic, recent efforts to strengthen national Unity network sites through training activities, and plans to initiate multi‑centre studies on respiratory viruses.Similarly, Prince of Songkla University, Thailand, highlighted its experience in developing integrated surveillance and diagnostic protocols for multiple respiratory pathogens, enhancing readiness for targeted investigations of emerging threats. The University of Sri Jayewardenepura, Sri Lanka, reported ongoing engagement with the Ministry of Health and exploration of additional funding sources to initiate studies. Meanwhile, the National Public Health Laboratory, Nepal, provided updates on continued coordination with national stakeholders to initiate implementation, despite delays related to the country context.Across sites, shared experiences underscored the importance of protocol adaptation, multisectoral collaboration, and sustainable funding to operationalize Unity Studies and generate evidence for public health action.Discussions and Way ForwardThe meeting reinforced key priorities for 2026, including strengthening unity studies protocol implementation, enhancing collaboration, knowledge sharing, integrating research as a component of national multi-source surveillance systems, and ensuring that generated evidence informs public health decision‑making. Participants also emphasized the importance of documentation, visibility, and leveraging multiple funding sources, and agreed on the following action points.Unity Studies Sites and WHO Country Offices:Advance adoption and implementation of at least one Unity protocol, continuing activities initiated in 2025 with catalytic funding, supplemented with other sources of funding where feasible.Document site-specific experiences, outcomes, and lessons learned; participate in regional and global knowledge-sharing platforms including WHO webinars and the Community of Practice.Work closely with ministries of health to establish unified national platforms for translating generated evidence into national public health decision-making. WHO Country Offices may facilitate technical support and complementary, synergistic funding opportunities where feasible. WHO Regional OfficeCoordinate with WHO Country Offices to support implementation of core unity studies activities by network sites through catalytic funding and provide technical guidance and virtual engagement opportunities throughout 2026.Facilitate the participation of all Unity Studies sites in the regional meeting on Unity Studies implementation in October 2026, to be held back‑to‑back with the Annual SEAR Regional PIP‑PC Meeting; develop and host a SEAR Unity Studies website; and mobilize support from academic institutions within the SEAR Unity Studies Network to support the development of regional manuscripts that showcase Unity Studies activities to scientific audiences and promote knowledge‑sharing.Explore complementary funding opportunities and partnerships to sustain and expand network activities across the Region.WHO HeadquartersProvide global technical guidance, updated protocols, and toolkits to support the implementation of Unity Studies in SEAR.Facilitate engagement of SEAR sites in global webinars, the Community of Practice, and opportunities to showcase their work.Support joint publications, inclusion of SEAR experiences in global outputs, and identification of additional funding and collaborative opportunities to strengthen Unity Studies activities.
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“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
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Glyphosate: A story of science, risk, and nuance
In the summer of 2014, a California groundskeeper named Dewayne Johnson started noticing patches of raw, weeping skin spreading across his body. He had spent years applying Roundup, Monsanto’s glyphosate-based herbicide, for a school district, sometimes drenched in it when equipment malfunctioned. By 2014, he had a diagnosis: non-Hodgkin’s lymphoma. In 2018, a jury awarded him $289 million in damages, later reduced on appeal. Tens of thousands of lawsuits followed. The story hasn’t quieted down. It’s all over the internet, swinging between “this will kill you, buy organic” and “relax, you’re fear-mongering.” Some of that recent chatter stems from an Executive Order protecting glyphosate and its production, which set off intense infighting within the MAHA movement. Some of it reflects a deeper truth: Americans genuinely care about harmful chemicals in their environment (see poll below). And the conversation isn’t going anywhere, with a Supreme Court case involving Monsanto set for this April. Glyphosate (the active ingredient in Roundup) is one of the most widely used herbicides in the world. But does glyphosate cause cancer? And how much exposure is actually risky? With so much mistrust in institutions and corporations (and some genuine conflicts of interest), making sense of the actual scientific evidence is genuinely hard. Here is a translation of what the scientific evidence actually says. Does glyphosate cause harm? Glyphosate is an herbicide that kills most plants it contacts rather than targeting specific species. It works by blocking an enzyme pathway (EPSPS) that plants and many microorganisms need to produce essential amino acids. Without those amino acids, the plant can’t grow and dies. Glyphosate was first developed by Monsanto in the 1970s and became ubiquitous after the company introduced “Roundup Ready” crops in the 1990s. In 2024, Bayer removed glyphosate from residential Roundup-branded products due to litigation pressure. But today it is still the most widely used herbicide in the world. Farmers also discovered they could spray it directly on crops like oats, lentils, beans, and peas just before harvest to dry them out faster and make harvesting easier. Because of its widespread use, it’s obviously really important to understand if it’s harmful to humans. The key principle: The dose makes the poison The most important concept in toxicology is that “the dose makes the poison.” Sixteenth-century physician Paracelsus articulated what remains the foundational principle of toxicology: sola dosis facit venenum. Water will kill you if you drink enough of it. Aspirin saves lives at 81 milligrams and causes internal bleeding at higher chronic doses. So, at what dose is glyphosate poisonous? The LD50 in rats (the dose that kills half the test animals) is around 5,600 mg/kg body weight. Table salt is roughly 3,000 mg/kg. By that classic measure, glyphosate is less acutely toxic than salt. But some people are exposed to a lot of glyphosate through their work. And among the general public, there are many questions about low-dose, cumulative exposure from multiple sources over long periods. Does glyphosate cause health problems? Cancer Correlation maps are striking, with one analysis finding that 60% of counties that spray the most glyphosate have non-Hodgkin’s lymphoma rates above the national average. But correlation alone doesn’t establish cause, which is why animal and human studies matter. In animals: Rat studies are typically what most fear is rooted in, but most people don’t know the study details on the dosage given to rats. A pooled analysis of the 13 most rigorous rat studies found evidence that very high doses, around 55,000 times greater than typical food residue exposure, can cause certain cancers. In humans: One of the strongest studies followed 55,000 herbicide users over 20 years and found no overall increased cancer risk. There was one signal: among those with the highest use, rates of acute myeloid leukemia (AML) appeared elevated. A broader meta-analysis of 18 human studies also found no overall cancer risk, though a separate meta-analysis focused specifically on non-Hodgkin lymphoma found that people with high cumulative glyphosate exposure faced a 41% higher relative risk compared to those with low or no exposure. These human studies are observational studies (randomized controlled trials would be unethical), and meta-analyses are only as reliable as the studies they pool. But the story seems consistent: at very high exposure levels, there may be some cancer risk. But that’s about as far as the science currently takes us. Emerging research: gut and reproductive health Gut: Because glyphosate targets a pathway that exists in some gut bacteria, scientists have questioned whether it could affect the microbiome. In mice, a 2023 University of Iowa study found that glyphosate altered gut bacterial composition and raised markers of intestinal inflammation. A 2024 systematic review found similar patterns: disrupted gut bacteria and problems with the mucus layer that protects the intestinal wall in mice. Whether this translates to humans is an open question. The human gut is highly variable and resistant, and we don't yet have strong studies confirming the same effects in people. Reproductive health: Animal studies have found that glyphosate-based herbicides can interfere with reproductive hormones, including disrupting egg cell development in mice and reducing the number of eggs available in sheep’s ovaries. However, the doses used in these experiments were high, so it’s not yet clear whether the same effects would occur in humans. On the human side, a 2021 pilot study led by Mount Sinai researchers tested urine samples from pregnant women across the U.S. and found glyphosate in 95% of them. The study also found a possible connection between higher glyphosate exposure during pregnancy and a subtle physical difference measured in female newborns. That said, some connections weren’t statistically reliable and this was a small study. The researchers themselves said more and larger studies are needed before drawing firm conclusions. What do regulators say about glyphosate safety? In 2015, the International Agency for Research on Cancer (IARC), part of the World Health Organization, classified glyphosate as "probably carcinogenic to humans" (Group 2A). The classification is worth taking seriously. But, there’s an important distinction: IARC classifies hazard, meaning the potential to cause harm under some conditions. It does not assess risk, which accounts for actual exposure levels and the real-world probability of harm. The U.S. EPA, the European Food Safety Authority, Health Canada, and regulatory agencies in Australia and Japan have all concluded that glyphosate is “unlikely to be carcinogenic to humans” at realistic exposure levels. They’re asking a different question: given how much of this people actually encounter, what is the probability of harm? Your actual exposure is what determines your actual risk. What does glyphosate mean for your daily life? At the grocery store: Food residues are small. The EPA’s acceptable daily intake is 1.75 mg/kg of body weight per day. To hit that threshold from oatmeal alone, a 150-pound adult would need to eat roughly 50 pounds of oats every day. But this calculation addresses only one pathway (food), and we don’t yet have robust tools to measure people’s actual total exposure. If reducing exposure matters to you, organic certification prohibits synthetic herbicides, and organic oat products generally test at lower or undetectable levels. Either way, a diet rich in whole grains, conventional or organic, is far better for your health than one that omits them out of fear of residues. On the farm: Occupational exposure is orders of magnitude higher than consumer exposure. Farmers and farm workers who mix, load, and apply glyphosate-based herbicides, especially without protective equipment, face meaningfully different risk profiles. Proper PPE isn’t optional. Integrated weed management strategies that rotate chemical modes of action are also worth taking seriously, both for health and for long-term effectiveness. If you live near a farm: Spray drift is real. Concentrations near field edges can spike during and after application, particularly on windy days. This is an area where I think public health could really step up and empower rural communities with buffer zones, rather than placing the onus on individuals. But for now, knowing your local agricultural calendar and keeping windows closed during application are the first steps. Bottom line Dewayne Johnson was exposed to glyphosate at levels most people will never encounter, and a jury decided he deserved compensation. That outcome is not evidence that the oatmeal in your pantry is dangerous. But it is a reminder that the dose, the context, and the person doing the exposing all matter enormously. Glyphosate is a chemical tool with benefits, trade-offs, and genuine open scientific questions. Learning to hold that complexity without collapsing into panic or dismissal is, honestly, one of the most useful and hardest things to do in navigating modern health science. Love, YLE Your Local Epidemiologist (YLE) is founded and operated by Dr. Katelyn Jetelina, MPH PhD—an epidemiologist, wife, and mom of two little girls. Hannah Totte, MPH, is an epidemiologist and YLE Community Manager. YLE reaches more than 425,000 people in over 132 countries with one goal: “Translate” the ever-evolving public health science so that people will be well-equipped to make evidence-based decisions. This newsletter is free to everyone, thanks to the generous support of fellow YLE community members. To support the effort, subscribe or upgrade below: Subscribe
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