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Radiology vendor Nano-X Imaging warns of ‘going concern’ risk as losses deepen
The publicly traded, Israel-based company reported its quarterly earnings result on June 25, including a net loss of $14.3 million, up 8% from the previous year.
Editorial: Keep up congressional pushback against Trump’s anti-science agenda
For about a decade, hundreds of deep-sea buoys off America’s coasts have been monitoring the conditions of the Pacific and Atlantic oceans. The streams of publicly available data they produce inform weather reports, generate valuable information about the planet’s changing climate and help the maritime industry and even the military maneuver the high seas.
Everything we know about Taylor Swift and Travis Kelce’s wedding
NEW YORK — There are more people ostensibly invited to celebrate the love story of Taylor Swift and Travis Kelce than there are tidbits of publicly available information about the
Melissa Etheridge: 'We're going to rock your toes off'
Melissa Etheridge is a survivor. For starters, try coming out publicly as a lesbian in January 1992 at a time when entertainers didn’t do that kind of thing; surviving breast
Securitize and Cantor Equity Partners II Business Combination Expected to Raise Approximately $400 million in Gross Proceeds and Announce Expected Closing of Business Combination and NYSE Listing
/PRNewswire/ -- Securitize, Inc. ("Securitize" or the "Company"), and Cantor Equity Partners II, Inc. (Nasdaq: CEPT) ("CEPT"), a publicly traded special...
Elroy Air to Become Publicly Traded Company via Business Combination with Inflection Point-led SPAC
Elroy Air, Inc., a leading U.S.-based technology developer of autonomous heavy-cargo drones for defense, rapid response and commercial logistics, and Columbu...
EigenQ And Silicon Valley Acquisition Corp. Announce Definitive Business Combination Agreement to Create a Publicly Traded Quantum Technology Company
EigenQ announced an agreement with Silicon Valley Acquisition Corp. that would take the quantum technology company public on Nasdaq.
EigenQ and Silicon Valley Acquisition Corp. Announce Definitive Business Combination Agreement to Create a Publicly Traded Quantum Technology Company
Transaction expected to accelerate EigenQ's mission to develop and commercialize quantum technologies across security, artificial intelligence, communications,...
Latvia pilots citizen engagement in research grant evaluations
Latvia has introduced an element of citizen engagement to the evaluation of nationally funded research grants. The aim is to both create more connections between science and society, and to improve the quality of its evaluations. The move comes as research funders across Europe are experimenting with ways to improve evaluation processes in the face of a sharpened focus on science’s impact on society. Evaluators of grant applications submitted to Latvia’s Fundamental and Applied Research Programme (FLPP), the country’s main research funder, will this year have the option of consulting citizen feedback on challenges the public thinks science should focus on to inform their decisions. “In recent years, both public discussions and policy-level debates in Latvia have highlighted the importance of demonstrating how publicly funded research contributes to society, the economy and the resolution of real-world challenges,” said a source at the Latvian Council of Science (LCS), which manages the FLPP. “At the same time, research institutions themselves expressed interest in improving the project evaluation framework while continuing to ensure that funding is awarded to the highest-quality projects,” the source added. The citizen input comes from a survey that was conducted between 25 February and 16 March 2025, to which 1,737 people responded. It gathered information on what the public views as problem areas for Latvia, and the role of science and technology in providing solutions to these problems. A summary of these responses has been included as an annex to the FLPP 2026 call for proposals that evaluators can refer to, purely in an advisory manner, when judging proposals. Results from the survey show that the main problem areas identified by the public were in healthcare and public health, followed by the development of new treatment methods and medicines, and then digital technology, data security and cyber security. At the bottom of the list was research aimed at acquiring new knowledge about the universe, matter and the laws of nature. Further analysis looked at the public view of the possible role of science in solving these problems. The survey charted 4,167 individual responses based on open-ended questions that were derived from the participants’ answers. The analysis again found that medical and health sciences topped the scoring, with 788 responses referring to the field. Humanities and the arts were bottom of the list, with 625. Digging further into the details, the survey suggests citizens are aware of and concerned about a number of research areas. This includes technologies and innovation to improve the environment or reduce pollution, the development of modern military technologies and cybersecurity tools, and the development and application of artificial intelligence, among others. “We hope this initiative will help strengthen public trust in science and create a more meaningful dialogue between researchers and society,” the LCS source said, adding that it offers benefits to researchers, evaluators and to the general public. More space for citizen engagement in evaluationsThere is a gradual but increasing use of citizen engagement in research evaluation processes around Europe, especially in healthcare, but it remains relatively rare, said Henry Sauermann, a professor at the European School of Management and Technology in Berlin and an expert in citizen science.“Given that we are at a point where citizen involvement is extremely limited, there is presumably space for more, but it's not that we should do it just because we want to do it,” he told Science|Business. He said some research lends itself better to having citizen input, and this usually tends to be on the applied end of the science spectrum where the public can offer useful feedback based on their experiences. He added that advising scientists on certain topics to study is also a useful approach, in the way that Latvia is doing. “The public does fund a lot of science, there's a lot of tax money going into this. We of course do want to preserve academic freedom, but it's also not unreasonable to incorporate more feedback or input from citizens where possible,” he said. Related articlesCitizen science: where, when, and how to let the public contribute to researchHow can Europe cope with a flood of AI-assisted research grant proposals?Data Corner: Horizon Europe’s AI, digital and data call success rate is 5.2%There are several ways of involving citizens in science funding. The most minimal way is having a few representatives on a panel, such as members of a patient group or union. The most direct way is crowdsourcing funding for specific projects. There is an increasing number of online platforms that allow the public to chip in to support scientific proposals.Sauermann studied this in a recent paper and found that it can have its uses, but it still is not clear if there are benefits to it. There are various assumptions linked to citizen engagement, for example that it increases public trust in science, or that it provides a representative view of what the public thinks about science. These are not necessarily true, Sauermann found. People who participate in engagement activities tend to already be very trusting of science, and, on the whole, it is generally the well-off and well educated who contribute, giving a narrow view of public opinion. On whether citizen input can help manage the increasing number of proposals that are being submitted due to assistance from AI, Sauermann is more sceptical. “The obvious answer to the flood of AI-written proposals is AI-driven evaluation,” he said, adding that it doesn’t mean citizen engagement shouldn’t be a part of evaluations in a well thought-out manner. Southern Denmark’s successful approachOne successful example of citizen engagement in science evaluations was implemented in southern Denmark. Between 2019 and 2025, citizens in the region were invited to vote to fund one of five pre-selected projects in the field of healthcare. The project, called A Healthier Southern Denmark, was a collaboration between regional authorities, the University of Southern Denmark, and two media partners. Over the seven years, there were 75,000 votes and significant public engagement online and on social media. “It was a bit of a wild project, we’d never done something like that before and we were a bit nervous about what would be said in the media about it. But actually, people were very positive,” said Anne Kathrine Overgaard, one of the main organisers of the project from the University of Southern Denmark. The university and its researchers have learned a lot from the experience, particularly about engaging with society and shaping their research to match real-world needs. This is important with Europe’s increasing focus on competitiveness and bringing innovations to market faster, Overgaard said. She hopes to see more citizen engagement in evaluation processes and in science more broadly. “For many years, citizen engagement has been a bit of an add-on to proposals, just to check a box. But I think things are changing,” she said. It’s less about citizens just gathering data and more “about the dialogue between the public and the scientists,” she said. The initiative ended last year as the team wanted to regroup to find fresh momentum. The development of AI and AI-written proposals has made Overgaard reflect on how the project could be reworked to help evaluators. She hopes to bring it back in the future. “It could be a good idea to try this initiative again in a slightly re-worked format,” she said.
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.
Armie Hammer explains how 'old Jamaican guy' helped quiet his anger over famous friends' lack of support
Armie Hammer explained how an 'old Jamaican guy' helped him get over the anger of his famous friends failing to publicly support him through his abuse scandal.
Ohio bill would ban use of politicians’ names, images on publicly funded signage
COLUMBUS, Ohio (WCMH) – Two Ohio lawmakers are attempting to ban the use of taxpayer dollars for political branding on public signage, such as road signs. Reps. Mark Hiner (R-Howard) and Tex …
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Is SMIC N+3’s Metal Pitch Smaller than Intel 18A’s?
Almost four years ago, we published that SMIC had started shipping 7 nm (N+1) chips. Now, SMIC is shipping its third-generation 7 nm (N+3) process in Huawei’s Kirin 9030, with a minimum metal pitch of 32.5 nm, about 10% tighter than the 36 nm minimum metal pitch shipping in Intel’s latest Panther Lake CPUs on 18A. The headline is true, but incomplete cherry picked metric. N+3 reaches the density of TSMC N6 through aggressive DUV multi-patterning and design-technology co-optimization (DTCO), but it pays for that in complexity, efficiency and process control. We found this and more in our reverse engineering and teardown where we cover SMIC’s N3 process technology, Huawei’s packaging, memory, architecture, and more. SemiAnalysis has been building a state-of-the-art teardown lab in Oregon capable of analyzing the world’s most advanced and important chips over the last year and half. We have already generated revenue on advanced datacenter chip teardowns including our recent reverse engineering of a major TSMC customer’s COUPE CPO optical engine + EIC 3D stack. This is the first public report from the SemiAnalysis Teardown Engineering & Evaluation Lab, or STEEL for short. The lab is aggressively scaling up and out and we’re excited to announce it publicly. This is a bit of inconvenient timing for TechInsights as they are private equity owned and currently being sold while having enjoyed virtually no credible competition for decades. This has led to TechInsights underinvesting in CAPEX. SemiAnalysis exceeds TechInsights in revenue despite no venture or private equity ownership and being founded only 6 years ago. Because we have no external investors and are founder led, we move faster, build faster, and we can release client chip teardowns for free regularly, while focusing on datacenter for our major clients. Here’s the first public image from our lab, the HiSilicon Kirin 9030 Pro SoC: This report will detail our teardown of the Kirin 9030 and our findings on SMIC’s N+3 process, the most advanced in China. For comparison, we’ll show our teardown of the MediaTek Helio G99, made on TSMC N6. Through this comparison, we can look at the effect of export controls – SMIC N+3 and TSMC N6 are comparable nodes, but one is heavily export-controlled, the other free to use the West’s most advanced equipment. Here we see both China’s progress and constraints. SMIC N+3 reaches TSMC N6-class logic density, but it requires far more aggressive DUV multi-patterning, so it does not match N6 on process maturity or cost. The Kirin 9030 Pro performs similarly to three-year-old Android flagships, and trails far behind the current flagship SoCs from Apple, Qualcomm, MediaTek, and Samsung. The efficiency gap is even wider. Export controls have not stopped Huawei and SMIC from shipping advanced silicon, but they have forced a different path. Without EUV, SMIC is leaning harder on DUV multi-patterning, DTCO, and increasingly complex integration. The roadmap continues forward through tighter design rules and backside power, but each step adds cost and process risk. Huawei’s τ scaling and LogicFolding show another path: stacking active logic and recovering density through advanced packaging and system-technology co-optimization (STCO). To understand the Kirin 9030, we must first understand Huawei’s SoC history. HiSilicon is Huawei’s chip design arm, responsible for the Kirin smartphone SoCs, Kunpeng server CPUs, Ascend AI accelerators, and switch/router networking silicon. Before export controls, Huawei was TSMC’s largest customer – the only customer on TSMC’s first EUV node, N7+, and among the first on N5, alongside Apple. That ended in late 2020. Huawei switched to Qualcomm SoCs in its flagship smartphones, though export controls limited them to 4G-only variants. In late 2023, Huawei returned to in-house silicon with the Kirin 9000s, a successor to the Kirin 9000, fabricated on SMIC N+2 instead of TSMC N5. In the following years, they released the Kirin 9010 and 9020 on the same N+2 process. These chips used Huawei’s in-house TaiShan CPU cores and Maleoon GPU. We have not torn down a Kirin 9020 ourselves, so the predecessor die shot is from Kurnal. The die shots show how Huawei spent its silicon budget: which functional blocks are where, and how their areas compare to the predecessor. First, a quick guide to the major blocks on the die. The total die area is nearly identical, but the 9030 uses that area more aggressively. A denser process lets Huawei fit an extra middle CPU core, more GPU and NPU cores, and larger caches into the same footprint. In contrast, the Helio G99 is a much smaller, low-cost SoC, built for budget smartphones rather than a flagship device. While the Kirin 9030 is ~140 mm², the G99 is only ~29 mm², roughly one-fifth the area. The underlying TSMC process technology, however, is directly comparable as a baseline for analyzing SMIC’s. The Kirin 9030 is an evolutionary refresh, not a clean-sheet design. Its CPU, GPU and NPU cores carry over the 9020’s families, and the gains come from three levers: the SMIC N+2-to-N+3 process step, DTCO and floorplan work, and incremental microarchitecture. Area is where the first two show up, and the 9030 scales well here. Performance and efficiency are the harder test. Huawei’s design holds up better than its node would suggest, but the chip still trails, both because N+3 sits behind the leading-edge nodes and because its cores, while competent, remain a few generations behind the newest designs. The new prime core is an incremental update. The main changes are a 10% frequency increase from 2.5 GHz to 2.75 GHz and a doubling in the L2 cache from 1 MiB to 2 MiB. Despite the increased cache, the core size decreased by 7.6%. Excluding the private L2 cache, the core size decreased by 21%. This is a large reduction for an incremental node. Compared with the TaiShan New V120 core in the Kirin 9020, the Kirin 9030’s middle core is almost unchanged architecturally, yet each core shrinks by ~22%. Most of that comes from the move from N+2 to N+3, with layout likely accounting for the rest. Visually, the most noticeable change is the increase from 3 middle cores to 4. There is also a 20% increase in the shared L3 cache of the big cluster. This helps to improve multi-core performance without sacrificing much in terms of area. Even with each core shrinking, the big CPU cluster’s total area is essentially unchanged. The per-core savings went back into an additional middle core and larger caches. The tiny cores shrank less than the prime core (excluding its L2 cache) and less than the middle cores. This is likely because fixed overhead is a larger share on a small core. We cannot resolve any architectural changes from the die shot alone, but the per-clock and efficiency gains shown below point to more than pure process and layout scaling. The area reduction was offset by a doubling of the shared L2 cache from 2 MiB to 4 MiB, leaving the total tiny CPU cluster area slightly larger. Area is the easiest improvement to see from a die shot, but it is only one part of PPA (power, performance, area). For modern logic, power and performance matter just as much, and often more. Since Dennard scaling broke down in the mid-2000s, voltage and frequency have not scaled in step with transistor dimensions, so each node has had to fight harder for gains in performance and efficiency. The starkest comparison is not Kirin 9020 versus Kirin 9030 Pro. Apple’s efficiency cores run circles around Huawei’s prime core. Apple’s low-power core delivers 20% higher integer performance while drawing only 1 W, compared with 4.5 W for Huawei’s prime core. N+3 matches TSMC N6, but N6 is several generations old. Apple and Qualcomm build on N4 and N3P, which are denser and sit on a better voltage-frequency curve, giving them a larger transistor budget and more performance per watt. The 9030’s own cores did improve. The middle and tiny cores gained 17% and 14% in per-clock integer performance over the 9020, with floating-point flat on the middle core and up 11% on the tiny. The tiny core improves cleanly, with performance rising while power falls and efficiency increases by 45% in integer and 24% in floating point. The middle core is mixed: integer performance rises but power rises faster, cutting integer efficiency by 7%, while lower power lifts floating-point efficiency 16%. Per-clock gains at the same or lower frequency are microarchitectural, so the cores are tuned, not just shrunk. Both also failed to hold their rated maximum frequencies, pointing to thermal, power, or stability limits. Per clock, the middle core sits around Arm Cortex-A720 and the tiny core near the Cortex-A520; absolute performance trails because Huawei clocks them much lower. The prime core is roughly Cortex-X2 class per clock, a 2021 design. Apple’s 2020 M1 Firestorm core is still 35% higher per clock and 57% faster in absolute integer performance at a similar 4.5 W. The current leading edge is further ahead again: the Apple M5 P-core is 60% higher per clock and 2.7× faster, the Arm C1 Ultra 45% higher and 2× faster. Matching older high-end cores per clock is a genuine design achievement. What Huawei cannot match is the voltage-frequency curve and transistor budget of leading-edge nodes, which let Apple, Qualcomm and others spend more transistors in the same area on wider cores, larger caches and deeper buffers while running at lower voltage. Huawei’s LogicFolding roadmap is one answer, stacking active logic to recover density and shorten signal paths. We return to it later. The GPU compute units (CUs) changed more visibly than the CPU cores, moving to a more rectangular layout for both the arithmetic logic unit (ALU) clusters and the CU overall. Even with ray-tracing support added, a CU shrank ~28%. However, that shrink is offset by the increase from 4 to 6 CUs and the area outside the CUs grew 33%. Overall, the GPU cluster is larger by ~10%. The GPU is where Huawei makes its biggest gains. The Maleoon 935 is not competitive with current flagships, but it is a large step up from the 920 and reaches older-flagship territory. In 3DMark it is 70% faster in Wild Life Extreme (WLE) and 79% faster in Steel Nomad Light (SNL) than the 920; with 11% higher clocks and 50% more CUs, the ~67% theoretical uplift roughly matches WLE and is beaten by SNL. It edges ahead of the Snapdragon 8+ Gen 1 in WLE and SNL, and the Dimensity 9200 and Apple A16 in WLE, but stays far behind newer parts: the Snapdragon 8 Elite Gen 5 and Dimensity 9500 are ~2.4–2.6× faster in WLE and ~3.2× faster in SNL. The Maleoon 935 is Huawei’s first GPU with hardware-accelerated ray tracing; there it lands slightly ahead of the Exynos 2200, and on par with the Apple A16, with current flagships up to 3.7× faster. The Neural Processing Unit (NPU) saw the largest structural changes of any block, moving from a Lite and a Tiny core in the Kirin 9020 to a Lite and two Tiny cores in the Kirin 9030. Both core types also show significant layout changes. This is a reversal in Huawei’s NPU design. The Kirin 9000 5G, its last flagship chip on TSMC N5, used two Lite and one Tiny core. The series of SoCs on SMIC N+2 moved to one Lite and one Tiny core, likely for area savings. With the Kirin 9030, Huawei has shifted back toward a larger multi-core NPU cluster, but with the additional area going to a Tiny core rather than a Lite core. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. Before diving into the process stack, the package and memory are worth separating from the SoC itself. The Pro variant of the Kirin 9030 carries 12 GB of Samsung DRAM, with two stacks of four dies each. The dies were identified as the K4L2E165YD, a 12 Gb LPDDR5X-9600 device fabricated on Samsung’s 1a node, the fourth generation of its 10 nm-class DRAM after 1x, 1y and 1z. 1a has shipped in volume since 2022, so this is current memory rather than older-node inventory. The 16 GB Pro Max variants we obtained were found with both CXMT and Samsung packages. The CXMT package is marked CXDD7JEDM, with two stacks of four dies, packaged in week 45 of 2025. The inferred die dimensions from X-ray computed tomography (CT) are consistent with a known density of ~0.3 Gib/mm² for the CXMT G4 process, roughly equivalent to other manufacturers’ 1z processes. The Kirin 9030 uses a typical integrated package-on-package (iPoP) stack: multiple DRAM dies in a memory package sit above an organic redistribution layer (RDL) interposer, which sits above the SoC and package substrate. The full package is then mounted to the printed circuit board (PCB) through ball-grid array (BGA) solder bumps. The memory package substrate is a thin bismaleimide-triazine (BT) laminate carrying the LPDDR5X stack. The organic RDL interposer over the SoC routes the PoP signals around the die and carries possible dummy thermal copper pillars. The package substrate, a thicker Ajinomoto Build-up Film (ABF) build-up over a BT core, fans the flip-chip bumps out to BGA pitch and embeds the power planes. The whole stack is organic. The only silicon is the SoC and the LPDDR5X dies; there is no silicon interposer. Keeping it all-organic brings the package’s coefficient of thermal expansion (CTE) close to the PCB’s, reducing board-level warpage, and avoids the cost of a silicon interposer the SoC’s bandwidth does not need. In an iPoP stack, the memory package connects to the organic RDL interposer through an array of solder bumps. Underfill fills the gap around those bumps, adding stiffness and protecting the joints from mechanical stress. The Pro and Pro Max variants differ here, which we cover behind the paywall. The die shot and architecture tell us how Huawei allocated its silicon budget. The process tells us what SMIC can manufacture. We use the Helio G99 as the process reference for TSMC N6. Both SMIC N+3 and TSMC N6 are evolutions of previous 7 nm-class nodes. We used targeted TEM cross-sections through logic and memory regions, imaged in both fin-cut and gate-cut directions. Each cross-section caption gives its horizontal field width (HFW), the real width of the imaged area. We start at the transistor fins, then move up through standard cells, local interconnect, and SRAM. SMIC has not overtaken Intel or TSMC. It uses aggressive DUV scaling and DTCO to reach N6-class density, but that density doesn’t translate into comparable performance and efficiency, for two reasons: the node gap to leading-edge nodes, and Huawei’s core designs. Fin Profile One of the most important knobs in a FinFET process is the fin profile: the shape of an individual fin and the channel where current passes from source to drain. The ideal fin is tall, narrow, and nearly vertical. A taller fin increases effective channel width, while a narrower fin improves electrostatic control by thinning the body the gate must control. Push either too far, and the process pays for it: weaker drive current, fragile fins, taper, footing and line-edge variation that hit yield and device variability. The Intel 22 nm, 14 nm, and 10 nm fin cross-sections show how FinFET nodes have improved over time. 22 nm fins were a first-generation structure, relatively short, wide and strongly tapered. The shape limits current density and reduces gate control uniformity across the height of the fin. At 14 nm and 10 nm, Intel pushed the fins taller and narrower while also making the sidewalls more vertical. Rather than shrinking the device, these changes increase the effective channel width per fin and improve electrostatic control. The trade-off is that taller fins at tighter pitches are much more difficult to manufacture. Now, let’s compare the Helio G99 on TSMC N6 with the Kirin 9030 on SMIC N+3. Both processes are in the same class, with fin pitch of 30-32 nm on N+3 and 34 nm in our N6 cross-section. The pitch for N6 is especially interesting as N7’s HD library is generally listed with a 33 nm fin pitch, and N6 did not shrink pitches directly. Its density gains came from DTCO instead of tighter pitches. The 34 nm pitch was stable across our sampled region and serves more as a comparison against the SMIC N+3 we have not investigated further. Pinning down N+3’s fin patterning scheme takes more than one core unit. The CPU cores show a dense ~32 nm pitch, with the pitch between N-P fin pairs alternating between 78 and 88 nm. Logic alone may be consistent with dual-pitch mandrels of 120 and 110 nm, but this is a complex and unusual approach. Combining the pitch from the the 8T SRAM, which has more complex repeat unit, with the CPU core sequence allows us to reverse engineer the patterning steps with more confidence. As both the logic and SRAM should share the same base grid, a single CD mandrel lithography pattern with 128 nm pitch undergoing SAQP produces a die-wide ~32 nm grid (128 nm/4), which supports the pitch sequencing seen in both logic and SRAM cells. In the sampled cross-sections, N+3 shows a taller, narrower, higher-aspect-ratio fin than N6. The measured fin aspect ratio is ~9.5:1 on N+3 versus 7.8:1 on N6. N+3 also shows less top rounding, with an estimated radius of ~2 nm, compared with 2.8 nm on N6. Even though the fin widths differ, the ratio of top rounding to fin width tells the same story, with N+3 at 0.37 and N6 at 0.44. In a geometric sense, lower is better; a perfectly rectangular fin would have no top-rounding penalty. These are single-digit-nanometer features measured from a handful of cuts, so treat the absolute numbers as approximate. The important result is the relative gap: N+3’s fins are consistently taller, narrower and less rounded than N6’s. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. Standard Cell A standard cell is the basic building block of chip layout: a fixed-height row pairing one NMOS and one PMOS transistor that share a gate, tiled in a grid to build logic blocks. The key dimensions are contacted gate pitch (CGP), cell height (CH), fin count, and the lower-metal routing grid. To measure density, we use the Bohr metric: a weighted average of NAND2 gate area (60%) and scan flip-flop area (40%). This represents a realistic mix of combinational and sequential logic. This metric has its limitations, especially for complex cell layouts like TSMC’s FinFLEX, which alternates cells with different fin counts. Even so, it is the best metric for a pure process-level comparison. Another important measurement is the fin pitch; it refers to the distance between two fins of the same transistor. In a FinFET process, multiple fins are used in each transistor to increase the drive current and thus performance. TSMC N6 ships both a high-density (HD) library with 2 PMOS and 2 NMOS fins per cell, and a high-performance (HP) library with 3 of each. More fins under the shared gate mean more effective channel width. HP cells switch harder at the cost of area. Designers mix the two on a die, primarily spending HP cells on timing-critical paths, and matching their PPA targets. In the Cortex-A55 core of the Helio G99, we found a cell height of 240 nm for the HD cell. MediaTek has used HD cells in the G99 to minimize die size and thus cost. As an SoC for budget smartphones at ~$100, this is essential. By contrast, we found only one library in the Kirin 9030, with 2 NMOS and 2 PMOS fins. This suggests a narrower library strategy than TSMC N6, where both HD and HP libraries are widely used. This likely reflects the smaller customer base and the more constrained domestic design and electronic design automation (EDA) ecosystem. In all three CPU cores of the Kirin 9030, we found cell heights of 228 nm, 5% smaller than on N6. This is also a reduction of 9.5% over SMIC N+2’s cell height of 252 nm. SMIC N+3 and TSMC N6’s HD library both feature a CGP of 57 nm. For SMIC, this is a 9.5% shrink over N+2. In the past, CGP and cell height alone may have been enough to compare transistor density. Now, however, we must consider scaling boosters and DTCO as well. SMIC’s density gain does not come from EUV. It comes from using every available DTCO booster aggressively. First is fin depopulation: reducing the number of NMOS and PMOS fins in each cell. The first FinFET nodes started with 3 or 4 fins for each transistor. SMIC N+3 and TSMC N6 HD both use only 2 fins per transistor, trading drive strength for density. Next is contact over active gate (COAG). By landing the gate contact directly over the active gate, instead of out over the isolation region, the cell height drops. N+3 integrates COAG while N6 does not. Our N+3 gate-cut cross-sections indicate COAG, with the gate contact sitting over the active region, while N6 shows an off-gate contact. Last is single diffusion break (SDB). Diffusion breaks are inserted between cells in the same row to provide electrical isolation, but they also introduce local layout effects (LLE), layout-dependent shifts in electrical characteristics. In the past, a double diffusion break was used, consuming the space of two CGPs. SMIC N+3 and TSMC N6 instead use SDB, saving area but increasing LLE sensitivity. This must be controlled at the process level and accurately modeled in the process design kit (PDK) so that EDA tools can account for it. Overall, SMIC N+3 has a transistor density of 113.4 MTr/mm², slightly above TSMC N6 at 107.7 MTr/mm². Even without EUV, SMIC has achieved density beyond TSMC’s mature N6 node which utilizes EUV. Metal Stack The smallest critical dimension in the teardown is M0; SMIC N+3 uses a 32.5 nm local metal pitch. That is smaller than the 36 nm M0 pitch on Intel 18A in Panther Lake. However, this does not mean that SMIC has a better process than Intel 18A or TSMC N3P. M0 is a local intra-cell routing layer. Its usefulness depends on the full interconnect stack: M1 and M2 pitch, track count, via and line resistance, design rules, mask count, overlay control, and routing flexibility. The 32.5 nm M0 is consistent with self-aligned quadruple patterning (SAQP), whose four-population line-width loading we read coarsely as alternating widths of 21.5 to 24 nm; M1 and M2, at 38 and 40 nm, are consistent with self-aligned double patterning (SADP), a single A/B split. On TSMC N6, M0, M2, and M3 sit at a relaxed ~40 nm and are consistent with SADP-class double patterning, with no need for quadruple patterning. That said, we measure M2 for example at ~43 nm, likely inflated by sparse routing. We do not assign any specific layer to EUV from our cross-sections; the distinction we can draw is double versus quadruple patterning, not lithography wavelength. Transistor-level density in the front-end-of-line (FEOL) sets an upper bound, but the design is ultimately limited by what the interconnect stack can route. The lowest metals are the most important for standard-cell density, but the semi-global and global layers determine how usable that density is at the block and chip level. Two axes are commonly used for chip cross-sections: the fin-cut and the gate-cut. The micrograph above is a fin-cut and shows metals 0 through 3. This axis lets us see the even-numbered metals, with M0 right above the fins. There are two kinds of M0 lines. The first are the power rails; these are wide wires for the VDD and VSS running horizontally at the top and bottom edges of each standard cell. The wide wires measure 55 nm across, more than double the other M0 lines. Their width minimizes resistance and reduces IR drop. The second kind are intra-cell wires, short segments within the cell that connect terminals to M1. These have alternating widths between 21.5 and 24 nm. The M0 pitch is 32.5 nm, a 19% reduction versus N+2 and N6. At this pitch, DUV patterning requires more aggressive multi-patterning, increasing mask count, overlay sensitivity, process complexity and cost. M0 is below what a single DUV-defined spacer (SADP) can resolve, so SMIC cascades a second spacer step (SAQP). The cross-section reflects the cost: the M0 trenches are visibly more re-entrant (narrower at the bottom than the top) than M1 or M2 on the same chip and carry a bright barrier-rich foot where the trench meets the etch-stop layer. That shape is partly the intended damascene profile, as a slightly narrow bottom helps void-free copper fill, but its magnitude at M0 is driven by the tight pitch and the higher trench aspect ratio. Intel 18A supports an M0 pitch of 32 nm, although Panther Lake has only shipped with a looser 36 nm pitch. This is due to Intel’s heavy usage of HP libraries. Among leading-edge nodes, 18A has the loosest M0 pitches due to PowerVia. With power routing moving to the backside, congestion is reduced, and the entire front-side metal stack can be used for signal routing. M2 is the first true inter-cell routing layer. It runs horizontally like M0 but spans across multiple cells to carry block-level signals. The M2 pitch sets the cell’s track height – the number of M2 tracks that fit between the VDD and VSS rails, defining what the library calls a 6-track or 7.5-track cell. This layer is the most important, limiting the routing of entire blocks. SMIC N+3 features a 5.7-track cell. The M2 pitch is 40 nm, a 5% decrease over N+2 and the same as N6. This shrink keeps the pitch at the edge of what is possible with double patterning. Future nodes will need to increase the number of masks for M2 as reducing the number of tracks is much harder due to the limitations in routing. The micrograph above is in the perpendicular direction, the gate-cut, and shows metals 0 through 4. This allows us to see and measure the odd-numbered vertical metal layers. The M1 pitch is 38 nm, 9.5% less than N+2 and 33% less than N6. The M1-to-gate ratio matters because it sets local routing flexibility. N+2 and N+3 use a 3:2 ratio, while N6 uses a 1:1 ratio, explaining the huge differences in M1 pitch. The more M1 lines there are compared with the gates, the more flexibility there is for power and signal crossing within the cell. Routing flexibility enables more complex and better cells. Clean fractional ratios are also preferred as a grid is periodic and improves layouts. The 3:2 ratio gives SMIC more local routing flexibility than a strict 1:1 grid, but it also complicates layout and patterning. This is a DTCO choice, with SMIC increasing process complexity to recover density and routability without EUV. This 3:2 ratio is not very popular in the leading-edge nodes. TSMC has only used it on N7+, the N5 family, and the short-lived N3(B). They have switched back to a 1:1 ratio for N3E. Intel has only used it on the 10 nm/Intel 7 family, with Intel 4, 3 and 18A all using a 1:1 ratio. Samsung is the only one still using a 3:2 ratio at the leading edge, using it in the SF4 and SF3 families. It remains to be seen if SMIC will remain at a 3:2 ratio or move to a 1:1 ratio with its future nodes. The industry is still actively exploring these local-routing ratios. At VLSI 2026, imec will be presenting work on even higher ratios, including a 2:1 scheme that can reduce area by up to 14%. We will be covering the conference in a future newsletter article. Subscribe We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. The final local interconnect layer for N+3 is M3, with a pitch of 44 nm. The M3 pitch is the same as on N+2 and 10% larger than on N6. The semi-global layers carry the majority of block-level signal routing. They have a coarser pitch than the lower local layers. On leading-edge nodes, they are designed to sit at the limit of DUV single patterning. M4 through M11 pitches were found divided between 80–82 nm (M4–M6), 128 nm (M7–M10), and 148 nm (M11). Given limited sampling, it is possible these are divided further in dense routing areas. At the top are two giant metal layers, M12 and M13. These have kept the same pitches as N+2 at 1920 nm and 4600 nm respectively. While the lower layers’ pitches are generally fixed by the process and library, the upper layers vary much more in pitch and count, depending on the design. Even two smartphone SoCs on the same process can have wildly different metal stacks. The Helio G99 carries fewer routing layers, reaching coarse metal pitches of 850 nm by M9, while the larger and higher-performance Kirin 9030 keeps fine pitches until M11. SRAM At the leading edge, SRAM is much more difficult to scale than logic. TSMC’s latest nodes have seen little to no bitcell scaling, while logic still has more DTCO levers to pull. While looking for other logic libraries in the GPU compute units, we stumbled upon the SRAM. The most common type of SRAM has 6 transistors (6T), but this cell had 8 transistors (8T) instead. 8T SRAM adds two transistors to form a dedicated read port. Unlike a 6T cell, where reading disturbs the storage, the decoupled read port removes read-disturb, improving read stability and letting the cell be pushed harder for performance. At first glance, the cut looked like an unusual logic library, with each cell row having 3 fins of one polarity and 5 fins of another. The rows also alternated in orientation. Energy-dispersive X-ray spectroscopy (EDS) resolved our confusion. The cut had not landed on the GPU logic, but on the SRAM macro beside it. The unusual fin pattern was due to the SRAM library. We return to EDS in the process flow analysis behind the paywall. SRAM libraries are not like traditional logic libraries. Due to the unequal number of PMOS and NMOS transistors, they require specialized rules and layout libraries. They do not need the flexibility of logic libraries, so they are hyper-optimized for one purpose: dense, reliable memory. The SRAM cell we found is a 1:2:2-2:2 cell. This means there is 1 fin per pull-up (PU) PMOS transistor, and 2 fins per pull-down (PD) and pass-gate (PG) NMOS transistor. These 2 PU, 2 PD and 2 PG transistors would usually form a single 6T high-current cell (HCC). An 8T HCC adds a read-pull-down (RPD) and a read-pass-gate (RPG) NMOS transistor, each with two fins. We measured a cell height of 406 nm, which brings the bitcell size to 0.0463 µm². That is a theoretical peak density of 21.6 Mib/mm². We estimate that a 6T HCC would have a cell height of 292 nm and a size of 0.0337 µm². This is ~12% larger than a 6T HCC on Intel 3 and 4. We also estimate the 6T high-density cell (HDC) to have a cell height of 228 nm and a size of 0.0260 µm². This is coincidentally the same as the logic standard-cell height measured earlier. The estimate puts the cell near Samsung 7LPP/5LPP and slightly below TSMC N7/N6. That is a theoretical peak density of 38.5 Mib/mm². 6T HDC is arguably the most important cell as it is used for the largest caches in a chip, the L3 caches and system-level cache (SLC). Both the Kirin 9020 and 9030 have split the SLC into 4 banks to raise total SLC bandwidth. In the Kirin 9030, the SLC increased from 2 MiB to 3 MiB per bank. Correspondingly, the number of arrays within the bank also increased by 50%, from 16 to 24. Each array can store 128 KiB and forms an orderly pattern on the die shot. From the Kirin 9020 to the Kirin 9030, the area of a 128 KiB SLC array decreased from 0.0477 mm² to 0.0392 mm², an 18% shrink. The achieved density is 25.5 Mib/mm², 66% of the theoretical maximum. While the SLC was quite similar across both chips, the L3 has seen some major changes, particularly in terms of its layout. The total capacity also went up from 10 MiB to 12 MiB. Much like the SLC, the L3 is also split into 4 banks. In the Kirin 9020, an L3 bank consisted of 16× 128 KiB arrays and 16× 32 KiB arrays. However, an L3 bank in the Kirin 9030 instead consists of 48× 64 KiB arrays. In the Kirin 9020 L3, a 128 KiB array was 0.0513 mm² and a 32 KiB array was 0.0154 mm². The size of the 128 KiB array is different on the L3 and SLC as the assist circuitry for the two arrays differs depending on their purpose. In the Kirin 9030 L3, a 64 KiB array is 0.0210 mm². Although not a like-for-like comparison, normalized for capacity, it is 18% smaller than the 9020’s 128 KiB L3 array and 31% smaller than its 32 KiB L3 array. The achieved density is slightly lower than the SLC, at 23.8 Mib/mm², 62% of the theoretical maximum. Unlike the L3 and SLC, the prime cores’ private L2 cache uses a 2-bank design. As the prime cores’ L2 is latency-critical, it likely uses 6T HCC instead of 6T HDC. The 9020 has 16 arrays in each bank while the 9030 has 32. Each array has a capacity of 32 KiB. A 32 KiB array in the L2 shrank from 0.0171 mm² to 0.0142 mm², ~17% smaller. The density is 17.6 Mib/mm², ~59% of the theoretical maximum for 6T HCC. SRAM scaled well from N+2 to N+3, shrinking by ~19%, close to the theoretical logic shrink. The caveat is that N+2’s bitcells were unusually large, bigger than comparable 7 nm-class nodes, so part of the gain is catch-up rather than true scaling. With the insights from STEEL’s teardowns, we will be doing a deep dive into SRAM in a future newsletter article. Subscribe Everything above came out of a single STEEL teardown: die annotation, block-level area analysis and TEM cross-sections through logic and SRAM. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Future Roadmap The same cross-sections that pin down N+3 also show where SMIC can go next. Although N+3 is already close to the practical limits of DUV multi-patterning in several layers, SMIC still has a few scaling levers left. A theoretical N+4 would likely start with cell height. N+3 uses 5 M0 tracks between its power rails. Moving to 4 M0 tracks, as on SMIC N+2 and TSMC N6, could reduce cell height by roughly 15%. The routing grid is only one side of the shrink; the front end also must fit into the smaller cell. One possible FEOL lever is reducing the p-to-n isolation spacing from two diffusion grid units to one. Intel used this scaling booster on Intel 4, and TSMC did so on its N3 family. This path trades layout flexibility for density. Fewer M0 tracks reduce local routing resources, while tighter p-to-n spacing raises integration and design-rule difficulty. M2 is also constrained by the cell height shrink. For SMIC to maintain a ~5.7-track cell, M2 would need to move toward ~35 nm. That would move another layer into SAQP territory. SMIC could also reduce the CGP from 57 nm to 54 nm. Intel reached a similar CGP on Intel 10 nm/Intel 7 without EUV. The local interconnect is also tougher. If SMIC keeps the 3:2 M1-to-gate ratio, M1 would need to shrink to 36 nm and would likely require SAQP as well. If SMIC moves to a 1:1 ratio, M1 could relax to 54 nm, but it would give up routing flexibility. Under this theoretical path, we estimate that SMIC N+4 could reach a cell height of 198 nm and a CGP of 54 nm, implying a Bohr density of 137.8 MTr/mm², on par with TSMC N5 or Samsung SF4. However, the difficulty is cumulative. Each step is individually plausible, but together they make N+4 harder than the transition from N+2 to N+3. It will likely take longer, cost more, and carry less process margin. A theoretical N+5 would require a larger integration shift. One possible path is backside contacts (BSCon), moving power routing and source/drain contacts to the backside, which would reduce front-side routing pressure and enable another reduction in cell height. The front-side metal pitches could relax to reduce the process complexity. M0 would likely relax slightly to ~34 nm, and M2 and M4 pitches could relax further. CGP is unlikely to shrink much further. Even with EUV, 48 nm has been the practical limit for yield and process control. This approach would allow N+5’s cell height to fall to 170 nm and its CGP to 53 nm. This implies a Bohr density of 163.6 MTr/mm², on par with Intel 18A’s HP library. However, this would not make N+5 cost-competitive with the leading edge. It would reach a similar density through a much more expensive route. The integration difficulty rises sharply, with new process flows for backside alignment, wafer thinning, contact reveal, and backside metallization. Past this point, standard density and interconnect scaling become increasingly unattractive. That is where Huawei’s roadmap stops looking like a normal foundry roadmap and starts looking like a packaging roadmap. At ISCAS 2026, Huawei unveiled its tau (τ) scaling law, reframing process scaling in the time domain. τ is the time cost of data movement and processing: switching delays in transistors, RC signal propagation delays in circuits, compute, memory, and networking latency. Outside Huawei’s terminology, this is called system-technology co-optimization. This is Huawei’s answer to its lack of EUV lithography. Without EUV, planar density cannot keep pace with TSMC, Intel, or Samsung. If transistor density cannot shrink further, Huawei’s alternative is to shorten wires, reduce buffering, and stack logic vertically. “LogicFolding”, Huawei’s implementation of this new scaling idea, is, in practice, an aggressive 3D stacking approach. AMD V-Cache places SRAM above or below a CPU die. AMD’s MI350X places active interposer dies (AIDs) underneath accelerator and compute dies (XCDs), with the AIDs handling cache, IO interfaces, the network-on-chip (NoC) and embedded metal-insulator-metal (MIM) capacitors. With LogicFolding, parts of the same logic block are split across multiple active dies bonded face-to-face at ultra-fine pitch. This allows Huawei to shorten some critical paths and reduce buffer overhead, not merely add cache capacity or offload the IO and interconnect. Shortening wires is where the higher clocks come from. A large share of a modern core’s delay and energy budget goes into driving long interconnects and the repeater buffers along them. LogicFolding distributes a block’s critical-path gates across multiple stacked tiers bonded at a very fine pitch, so the bond interface behaves like an additional metal layer and the longest paths get shorter. That is how Huawei expects to recover frequency and efficiency it cannot get from the process alone. Huawei’s roadmap shows its intent. Prime core frequency is targeted to rise from 2.75 GHz in the Kirin 9030 to roughly 5 GHz by 2031, far beyond what planar scaling alone could deliver. Prime cores with 3.1 and 3.39 GHz clocks are being tested in its labs, although their power consumption is unknown. Beyond that, chips are in the design, simulation or pathfinding phase, meaning the frequencies are targets. However, the direction matters more: LogicFolding also helps with performance, not just density. The catch is that Huawei’s density claim is not directly comparable to foundry densities. A stacked design can report more transistors per package footprint by adding active layers, even if each patterned die remains well behind TSMC or Intel in front-end density. This is how Huawei can claim to reach foundry 14A-equivalent density by 2031. This is not a like-for-like foundry comparison, with Huawei using stacked logic and measuring density per package footprint. On a normalized Bohr-density basis, SMIC N+3 is ~114 MTr/mm², 38% less than Intel 18A’s HD library. Huawei’s 3D roadmap closes the gap by stacking active logic, reaching 215 MTr/mm² by 2030. In 2031, the roadmap density jumps to 295 MTr/mm², implying either a third active layer, partial EUV insertion or aggressive planar DUV scaling. Huawei’s methodology makes other foundries look much denser as well. Applying it to AMD’s MI450X with an N2 top die on an N3P base die yields a theoretical density of 460.2 MTr/mm² in 2026, compared with Huawei’s 295 MTr/mm² in 2031. This Kirin 9030 does not use LogicFolding, remaining in a conventional mobile SoC package. Instead, it forms the baseline for how far Huawei and SMIC can push planar scaling. Future teardowns of Kirin and Ascend chips will show both planar logic density and Huawei’s hybrid bonding solutions. Export controls changed China’s optimization problem rather than ending it. EUV restrictions raised the cost and complexity of leading-edge manufacturing without freezing it. SMIC reaches N6-class logic density through DUV immersion, SAQP and DTCO, while Huawei shifts more of the burden onto architecture, packaging and system-level integration. Future nodes will be tougher. N+3 still had room to tighten local metals and reduce cell height and CGP. Further scaling without EUV leaves fewer levers. More aggressive multi-patterning adds masks and overlay error. SMIC can keep pushing DUV, but each step will get more expensive and less forgiving. The design side is just as critical. Huawei had domestic EDA tools and flows before the Kirin 9030, with the Kirin 9000s, 9010 and 9020 making that clear. Huawei was able to ship multiple consumer SoCs on SMIC N+2 and N+3 while cut off from the Western EDA stack. US export controls restricted EDA tools for advanced chips in 2022 but did not target tools for more mature chips. In 2025, the US government briefly placed much broader restrictions on EDA software from Synopsys, Cadence, and others, before lifting them less than two months later as part of a trade deal tied to rare earths. Huawei has been unable to access those tools because it remains on a US trade blacklist. That forced Huawei, SMIC and Chinese academic institutions to build their own tools and flows. Researchers at Peking University recently announced a prototype EDA tool for Huawei’s LogicFolding architecture, which requires a new flow to handle the multilayer layout and floorplan. This is not the same as replacing the full Synopsys or Cadence stack, but it shows where domestic EDA is headed: toward tighter co-optimization between architecture, process and packaging. These advances are also diffusing into the Chinese ecosystem. SMIC is licensing its N+2 and N+3 processes to HLMC/Hua Hong at the government’s direction rather than by choice. If the same process learning feeds into Ascend accelerators for AI training and inference, the choke point shifts from one named fab to an ecosystem. Alibaba’s T-Head silicon arm and Cambricon, a Chinese AI chip designer that is expected to supply ByteDance, could also be major beneficiaries. Sanctions aimed at SMIC alone become less effective once the manufacturing knowledge has spread to other fabs and design houses. China is not closing the gap with Intel, Samsung and TSMC. The teardown shows the opposite in several places: no EUV, no backside power, higher process complexity, and visible trade-offs. But China is still advancing. If domestic chips become good enough for phones, inference, networking and security-sensitive workloads, they can matter strategically without matching TSMC at the leading edge. Behind the paywall, we show what else STEEL can do, with material and process flow analysis of SMIC N+3, and analysis of the Kirin 9030 package. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline, access the full Kirin 9030 and SMIC N+3 analysis or to commission a custom teardown, contact sales@semianalysis.com.
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A UAP Science Advisory Council to the U.S.
On June 12, 2026, Steven Spielberg publicly released his film Disclosure Day while on the same day the U.S. Government disclosed the third…
Trump and Hegseth Broadcast U.S. Military Strikes Before They Happen
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BOXABL and FG Merger II Corp. Stockholders Approve Business Combination
BOXABL Inc. ("BOXABL") and FG Merger II Corp. (Nasdaq: FGMC) ("FGMC"), a publicly traded special purpose acquisition company, today announced that stockholders of FGMC and BOXABL voted to approve the previously announced business combination between FGMC and BOXABL (the "Business Combination") at their respective special meetings of stockholders held on June 9, 2026 (collectively, the "Special Meetings").
BOXABL and FG Merger II Corp. Stockholders Approve Business Combination
/PRNewswire/ -- BOXABL Inc. ("BOXABL") and FG Merger II Corp. (Nasdaq: FGMC) ("FGMC"), a publicly traded special purpose acquisition company, today announced...
New OMB rule could break science in the United States
The drug that saved your mother. The treatment your doctor recommended. The clinical trial that bought someone you love more time. None of it was inevitable. It required a system built to operate outside politics and sustained over decades, because science doesn’t operate on the timeline of a politician. That system is now under direct threat. The Office of Management and Budget (OMB) has proposed a new rule to regulate all federal grants, not just science, but housing, education, defense, NASA and everything in between, that would fundamentally change how research is conducted in the United States. There have been a lot of fires in the past 16 months, and if everything is an emergency, nothing is an emergency. But this is a very big deal. These rules would strip away the independence that has kept science from becoming a political tool. The good news is there’s still something we can all do. How the grant system was designed to work When I was a new faculty member, I was full of millions of ideas, so I sat down and wrote an NIH proposal. I worked on it for months and thought it was so good. Then I sent it to my mentor. It came back covered in comments. Places where my reasoning wasn’t as airtight as I’d thought, methods that could be stronger, gaps in my thinking that I had been too close to the work to see. It stung, as honest feedback always does, but peer review almost always makes the work better. In fact, it’s the backbone of how American science has functioned for generations. At NIH, submitted proposals go to a panel of scientists who have experience in the field. These are the people who can read research methods and spot their weaknesses, who understand when an experiment is designed well and when it isn’t. Applications are scored rigorously, and often only the top 3-20% of proposals receive funding. The people making funding decisions are required to justify them based on these reviews. To be clear: this is not a perfect system, and good science still goes unfunded more often than any of us would like due to limited resources. There is also criticism that peer review may undervalue bold ideas that are risky but could have high payoffs. But the peer review system is nonetheless a meaningful part of the grant selection process. While thoughtful and careful changes could improve the system, the changes being proposed by OMB will not make things better. The rule that could change everything Through this OMB proposal, the system that helps decide what gets funded is being dismantled. There are many troubling elements buried in this proposal, but three provisions stand out in particular. Each would be damaging alone. Together, they would change what kind of science gets done in America, and who it serves. 1. Political appointees would decide which science gets funded, and peer review would be explicitly sidelined or ignored. The guidelines your doctor follows, the standards that determine what’s in your air and water, the drugs that get approved for your family: all of it starts with a funding decision. Right now, that decision is made by scientists who can evaluate whether the work is actually sound and whether it could move the needle forward. Under this proposal, funding decisions could be made by political appointees. The careful, expertise-driven review process that shapes grant funding would be effectively sidelined. That means that a poorly designed study could get funded simply because someone in a political office liked the sound of it, while rigorous, important work doesn’t get funded because it no longer aligns with the administration’s priorities. In other words, critical work that benefits all of us could now be missed just because it has become politically inconvenient. 2. Grants could be canceled at any point, without warning, in the middle of ongoing work. Say your tax dollars have been paying for a cancer study for two years. The grant was awarded for five years, so the team is mid-experiment. There are three additional years’ worth of answers that could change how the disease is treated for hundreds of thousands of people. With this new rule, the federal government could cancel the remaining funding tomorrow, walk away from the commitment it made with your money, and have no legal obligation to finish what it started. This is a huge waste of money. There are rarely other sources of funding for this work, which means the team, the graduate students, postdocs, and research staff who have dedicated years of their careers to this project could lose their jobs. Patients who have contributed their time to studies may not see it turn into useful learnings for others. Every hour of work already completed could result in nothing useful. Science can take decades to get to tangible outcomes. It needs a stable funding system to flourish. 3. Researchers would have a harder time communicating their findings without pre-approval from the federal government. There is a saying from Sir Mark Walport: “Science isn’t finished until it’s communicated.” Science is no use to anyone if it is kept locked away in the labs that produced the data. The proposed rule could make communicating research findings harder in a number of ways: Make it harder for scientists to widely share their findings in scientific journals. Publication costs, including journal fees that allow for broad public access, could not be paid for by federal funds which is often how scientists pay these costs. Restrict researchers from engaging publicly about how their findings should inform policy or public health guidelines. This would restrict scientists from doing the work that transforms years of lab work into decisions that actually affect people’s lives. Make it harder for scientists to attend conferences. Conferences would now need to be approved by the federal agency at the time of grant award. This disadvantages conferences or publications that are not politically aligned. Conferences are a key way in which scientists learn about work happening in their field so they can collaborate and build on each other’s findings. What good is science with a muzzle? The long-term impact These changes could (and hopefully would) be reversed under the next administration. But by then, damage has been done. Once projects are halted or denied funding by political appointees, people leave their science careers for other paths or even leave the country to do their amazing science elsewhere. You cannot pause a scientific study the way you pause a subscription you decided isn’t useful anymore. You cannot freeze a research team that depends on salaries to feed their families, causing a scramble for replacement funding that, in most cases, simply does not exist. When the money stops, everything stops, and all the work in progress and its potential are lost. Despite all of the language about reducing waste, fraud, and abuse, this new proposed rule increases the administrative burden tremendously. Every researcher watching this unfold will make a rational decision: don’t start anything that will take a decade to finish. This includes not building a team around funding that could vanish, or asking the big questions that need patience and stability to answer. The most consequential science, the kind that produces the treatments and the breakthroughs that reach your doctor’s office twenty years from now, is exactly the science that would be abandoned first. This is still a proposal, so we can all do something Some worry this rule is already a done deal. But by law, OMB is required to open a public comment period. Those comments matter because they inform members of Congress, they can be used in litigation, and they create a public record. So here’s what you can do: Submit a public comment. The public comment period on this proposed change is open until July 13. Anyone can submit a comment, and OMB is required to respond to substantial ones. Each comment must be different to count as distinct; identical comments will be counted only once. Comment as an individual, not on behalf of your organization. Here’s a full resource to help you, with useful examples, but in general: Share a story about how science or federal funding has benefitted you or is involved in your work. Are you or someone you loved a cancer survivor? Do you know someone with Alzheimer’s? Have you received NIH/NSF or other federal funding? All of these can be stories you use to highlight why you care. Explain why you’re worried about these proposed changes. You can use the wording found here or in other resources to help you. Ask them to withdraw this proposed rule. Contact your elected representatives. Congress allocates money to federal agencies to spend, and it has oversight of how that process works. You can let your representatives know this proposed change concerns you and ask them to intervene. There are levers they can push that can help at this moment. Let others know why federal funding matters. This isn’t just about science, but ALL federal grants, including for education systems, defense projects, and more. The process, including the importance of federal funding, is invisible to many. We can all make it more visible by sharing how science benefits us and why we value it. You can also share this article, or any of the resources linked here, to help spread awareness! Bottom line We are quickly losing ground in scientific research in the U.S. More changes, like the proposed rule by OMB, would decimate our ability to conduct research here, leaving us all worse off: fewer treatments for pancreatic cancer, less research on pollution impacts, fewer answers for patients with rare disease, and more unaddressed health needs. The impact will be very real for all of us, regardless of where we stand politically. Using our voices for science is more important than ever. Love, LM Big thanks to Dr. Megan Ranney for reading this over and providing brilliant insights, as always. Dr. Liz Marnik, PhD, is Executive Director of The Evidence Collective at YLE and a science communicator and immunologist working at the intersection of public trust, science, and public health. You can find her on Substack, LinkedIn, Instagram and Threads. Your Local Epidemiologist (YLE) comprises a team of experts, ranging from physicians to immunologists to epidemiologists to nutritionists, working together with one goal: to “Translate” ever-evolving public health science so that people are well-equipped to make evidence-based decisions. The YLE suite of newsletters reaches over 475,000 people across more than 132 countries. 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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We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
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What It Takes To Be A Top Tier Entertainment Lawyer in 2026
Share I’ve recently run several popular posts breaking down data related to sought after legal jobs. General Counsel Big Law Appellate Attorney Here is another in this series. Two Lawyers, One Elite Market Entertainment law can sound like a single professional category. In practice, the lawyers who reach the top of the Hollywood market often look quite different from one another. Consider two attorneys from the 2025 top-entertainment-law universe. Lesley Freeman represents one route: the company-side entertainment lawyer. She is chief legal officer of AGBO, where she oversees company-wide business and legal affairs across film, television, video game and interactive experiences, and technological innovation, after senior legal roles at MGM, Amazon MGM Studios, HBO, and O’Melveny. Daniel Petrocelli represents another route: the outside-counsel trial lawyer. He is chair of O’Melveny’s trial practice and a vice chair of the firm, with a national litigation practice that includes entertainment, sports, and media disputes. Their careers differ by platform, role, practice area, and professional identity. One sits inside an entertainment company; the other practices from a major law firm. One is associated with corporate and governance work; the other with litigation. Yet both fall within the same publicly recognized top tier of Hollywood lawyers. That contrast points to the article’s central question. If there is no single mold for a top entertainment lawyer, are the paths simply idiosyncratic, or do they follow recurring patterns? The data suggest the latter. The attorneys recognized on The Hollywood Reporter and Variety’s 2025 top-attorney lists do not follow one career template, but their paths are not random. They cluster around several defined routes: entertainment-focused private practice, company-side or operator experience, corporate and finance conversion, litigation conversion, public-service-to-litigation pathways, and smaller specialized lanes in labor, employment, and celebrity family law. Across those routes, the recurring ingredients are platform strength, educational capital or Los Angeles market proximity, long career tenure, specialized practice identity, and entertainment-industry fluency. The article examines five dimensions of the top-attorney sample: route into entertainment law, firm or organizational platform, education pipeline, seniority, and practice specialization. Data and Methodology The dataset includes 103 attorneys drawn from The Hollywood Reporter and Variety’s 2025 lists of top entertainment or Hollywood attorneys. For each attorney, the dataset tracks current organization, law school, J.D. year where available, first-known bar admission year, practice area, prior roles, platform type, and route into entertainment law. The sample is best understood as a recognized-attorney dataset, not a census of all entertainment lawyers. It captures lawyers who reached public top-tier recognition in 2025, rather than every lawyer practicing in the entertainment sector. Multiple but Defined Routes Into Entertainment Law The first question is how these lawyers entered the entertainment-law market. Because every attorney in the dataset is entertainment-related by definition, the key distinction is not whether they practice entertainment law. It is how they got there. The largest group followed what might be called the entertainment private-practice route. These attorneys appear to have built their practices around entertainment, media, talent, music, rights, production, intellectual property, or adjacent industry work relatively early. That route accounts for 40 of the 103 attorneys in the sample, or 38.8%.
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Hundreds of people in Camden County publicly rejected a proposal to build a new data center near their communities. Opponents of the proposed data center said t
State auditor calls for delay on vote for Atrium to take control of WakeMed
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RadNet expands into its 11th state with new $30M Trinity Health joint venture
The publicly traded imaging center operator is teaming with Saint Alphonsus Health System to jointly operate five facilities in Idaho.
RadNet expands into its 11th state with new $30M Trinity Health joint venture
The publicly traded imaging center operator is teaming with Saint Alphonsus Health System to jointly operate five facilities in Idaho.
Can LIV Golf work without the Saudi billions? Sports investors predict a ‘free fall’
The inner workings of LIV Golf’s business model are a lot bleaker than what the league is boasting publicly.
Can LIV Golf work without the Saudi billions? Sports investors predict a ‘free fall’
The inner workings of LIV Golf’s business model are a lot bleaker than what the league is boasting publicly.
‘Stretched thin’: Secret Service faces renewed scrutiny after White House Correspondents’ Dinner attack
President Donald Trump, the acting attorney general and other top administration officials all publicly say the Secret Service responded as intended when a gunman tried to force his way into the White House Correspondents’ Association Dinner.
Having a religious affiliation doesn’t prevent betting on sports
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Republicans pressure Ron DeSantis to redistrict in Florida after Virginia Democrats’ win
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With acting AG at his side, FBI Director Patel publicly addresses Atlantic allegations
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Kentucky governor blasts UK athletics' 'decision-making'
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These candidates for governor support single-payer health care in California
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Berkshire Hathaway’s Big Utility Business Could Be Worth Close to $100 Billion
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Trump to Nominate Doctor Who Has Publicly Supported Vaccines as C.D.C. Director
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La. Senate panel OKs bill to indoctrinate teens in conservative 'success' ideology
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Golden Entertainment takes next step toward deregistration
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California Democrats Rushed to Back Eric Swalwell — and Quickly Abandoned Him
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NFL publicly defends media deal amid reports of DOJ investigation
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Golden Entertainment takes next step toward going private
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Terry Crews' Wife, Rebecca, Reveals Parkinson's Diagnosis, Had New Treatment: EXCLUSIVE
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Gemma 4 and what makes an open model succeed
Having written a lot of model release blog posts, there’s something much harder about reviewing open models when they drop relative to closed models, especially in 2026. In recent years, there were so few open models, so when Llama 3 was released most people were still doing research on Llama 2 and super happy to get an update. When Qwen 3 was released, the Llama 4 fiasco had just gone down, and a whole research community was emerging to study RL on Qwen 2.5 — it was a no brainer to upgrade. Today, when an open model releases, it’s competing with Qwen 3.5, Kimi K2.5, GLM 5, MiniMax M2.5, GPT-OSS, Arcee Large, Nemotron 3, Olmo 3, and others. The space is populated, but still feels full of hidden opportunity. The potential of open models feels like a dark matter, a potential we know is huge, but few clear recipes and examples for how to unlock it are out there. Agentic AI, OpenClaw, and everything brewing in that space is going to spur mass experimentation in open models to complement the likes of Claude and Codex, not replace them. Especially with open models, the benchmarks at release are an extremely incomplete story. In some ways this is exciting, as new open models have a much higher variance and ability to surprise, but it also points at some structural reasons that make building businesses and great AI experiences around open models harder than the closed alternatives. When a new Claude Opus or GPT drops, spending a few hours with them in my agentic workflows is genuinely a good vibe test. For open models, putting them through this test is a category error. Something else to be said about open models in the era of agents is that they get out of the debate of integration, harnesses, and tools and let us see close to the ground on what exactly is the ability of just a model. Of course, we can’t test some things like search abilities without some tool, but being able to measure exactly the pace of progress of the model alone is a welcome simplification to a systematically opaque AI space. Share The list of factors I’d use to assess a new open-weight model I’m considering investing in includes: Model performance (and size) — how this model performs on benchmarks I care about and how it compares to other models of a similar size. Country of origin — some businesses care deeply about provenance, and if a model was built in China or not. Model license — if a model needs legal approval for use, uptake will be slower at mid-sized and large companies. Tooling at release — many models release with half-broken, or at least substantially slower, implementations in popular software like vLLM, Transformers, SGLANG, etc due to pushing the envelope of architectures or tools. Model fine-tunability — how easy or hard it is to modify the given model to your use-case when you actually try and use it. The core problem is that some of these are immediately available at release, e.g. general performance, license, origin, etc. but others such as tooling take day(s) to week(s) to stabilize, and others are open research questions — with no group systematically monitoring fine-tunability. In the early era of open models, the days of Llama 2 or 3 and Qwen pre v3.5, the architectures were fairly simple and the models tended to work out of the box. Some of this was due to the extremely hard work of the Llama, Qwen, Mistral, etc. developer teams. Some is due to the new models being genuinely harder to work with. When it comes to something like Qwen 3.5 or Nemotron 3, with hybrid models (either gated delta net or mamba layers), the tooling is very rough at release. Things you would expect to “just work” often don’t. I’ve been following this area closely since we released Olmo Hybrid with a similar architecture, and Qwen 3.5 is just starting to work well in the various open-source tools that need to all play nice together for RL research. That’s 1.5 months after the release date! This is just to start really investing more into understanding the behavior of the models. Of course, others started working on these models sooner by investing more engineering resources or relying on partially closed software. The fully open and distributed ecosystem takes a long time to get going on some new models. All of this is lead-in for the most important question for open models — how easy is it to adapt to specific use-cases? This is a different problem for different model sizes. Large MoE open-weight models may be used by entities like Cursor who need complex capabilities in their domain, e.g. Composer 2 trained on Kimi K2.5. Other applications can be built on much smaller models, such as Chroma’s Context-1 model for agentic search, built on GPT-OSS 20B. The question of “which models are fine-tunable” is largely background knowledge known by engineers across the industry. There should be a thriving research area here to support the open ecosystem model. The first step is to understand characteristics of different base and post-trained models to understand what they look like. The second step is to tune pretraining recipes for open models so they’re more flexible. Interconnects AI is a reader-supported publication. Consider becoming a subscriber. Subscribe For The ATOM Project and other Interconnects endeavors, we’ve put in substantial effort to measuring adoption trends in the open ecosystem. Everything takes a long time to unfold after a model is first publicly available — and adaptability is why. What we know for sure now, when Qwen has been going from strength to strength with its releases, is that technical staff across the industry has gotten comfortable working with Qwen models. Countless research methods and datasets were made to work with Qwen. It’ll take patience for any other model family to get to this point — a patience I’m not sure many open model builders have. This takes us to Gemma 4, Google’s latest open models. Gemma 3 was released more than a year ago, in March of 2025, and is a bit underrated. Gemma 4 comes in 4 sizes for now, with a bigger, MoE model of over 100B total parameters rumored but not released yet. The models we have today come in sizes of ~5B dense, 8B dense, 26B total 4B active MoE, and 31B dense. I’m most excited that they’re finally adopting a standard Apache 2.0 open source license. This’ll massively boost adoption. The standard of better licenses for strong open-weight LLMs was set by mostly Chinese open model labs in the last 1-2 years, and now U.S. companies are following suit. I will personally be so happy if the horrible Llama licenses and Gemma terms of service were an ~18-month transient dynamic of the industry being nervous about releasing strong open models. The Gemma 4 scores look very solid, the small models have incredible benchmark scores (especially in general domains like LMArena) and the 31B model rivals the recent Qwen 3.5 27B, which is the leading member of that class. The ~30B size range is an important one, as it’s accessible both to researchers and to enterprises looking to deploy the model in real use-cases. Where the 7B model scale is the default for tinkering and research, a 30B model is the default for seeing if an open model can unlock substantial value in your specific workflow — a good mix of intelligence, low price, tractability for downstream training, etc. This takes us back to the above adoption criteria I mentioned for open models and the bigger question — do I think Gemma 4 will be an overwhelming success? Previous Gemma models have been plagued by tooling issues and poorer performance when being finetuned. Gemma 4’s success is going to be entirely determined by ease of use, to a point where a 5-10% swing on benchmarks wouldn’t matter at all. It’s strong enough, small enough, with the right license, and from the U.S., so many companies are going to slot it in. I’m cautiously optimistic that Gemma 4 is going to work better here. Winds are shifting for open models built in America. We saw GPT-OSS go through a bumpy launch to become an overwhelming success. There’s a collective energy around the likes of Reflection, Arcee, Nemotron, Gemma, Olmo, and peers that show substantial demand for building new stacks around open models. There’s capital to be spent on AI stacks across the economy by those who want more ownership of everything, including the model. After launching The ATOM Project 240 days ago, the conversation is shifting into the next stage. Summer of 2025 was a crisis moment where the U.S. AI scene realized it can’t wait and figure out open models after building AGI. The two markets will capture different areas and proceed in parallel. Now that more companies in the U.S. are releasing strong models, we need to improve the ecosystem so that these models are easy to use, understand, and build value around. It’s the hard work to build another inflection point in these adoption plots I’ve been updating consistently, but that’s the work to be done. Join me in it. More data coming soon! Here’s a sneak peek:
A Look At Six Flags Entertainment (FUN) Valuation After Jana Partners Activism And Leadership Shake Up
Six Flags Entertainment (FUN) is back in focus after activist investor Jana Partners publicly urged the company to consider a sale and overhaul its board leadership, prompting a rapid shake up in the chairman role. See our latest analysis for Six Flags Entertainment. The recent governance shake up comes against a mixed backdrop, with a 10.52% 90 day share price return and a 7.17% year to date share price return set against a 1 year total shareholder return decline of 53.46%. This suggests...
Publicly, NH never joined Northeast Public Health Collaborative. Privately, it’s deeply involved.
When 10 Northeastern U.S. states came together in September to announce a new public health collaborative amid turmoil within federal health agencies, New Hampshire was the only state in the region not included. However, behind closed doors, New Hampshire was directly involved in the group from an early stage and continues to be involved today, internal emails obtained by the Bulletin reveal.
Steven Fulop on Fighting for Business Interests in Zohran Mamdani's New York
Steven Fulop, the new President and CEO of the Partnership for New York City, the city’s leading big business advocacy group, spoke with The New York Editorial Board on the morning of March 19, 2026. Fulop recently began in the role after 12 years as mayor of Jersey City and a 2025 run for governor of New Jersey. He replaced longtime Partnership President/CEO Kathryn Wylde. (photo by Liena Žagare) Participating journalists: Nicole Gelinas, Josh Greenman, Alyssa Katz, Ben Max, Akash Mehta, Harry Siegel, Ben Smith, Liena Žagare Full Transcript **Ben Max**Good morning. Thanks for being here. So start us off: The people who hired you for this role, what did they make clear were their top priorities for you coming into it, and for the Partnership, and what did you sort-of pitch as the direction you thought things could go? Give us a little of that bifurcation of where the minds met in terms of the priorities and the leadership for the new era. **Steven Fulop**Let me explain to you how I ended up in the job, OK? That’s probably a good starting point, because I don’t think anybody initially thought that I was the pick that they coveted or thought that would be an ideal fit for this position. I’d run for [New Jersey] governor, as you guys know, and I was very deliberate in the lane that I was taking as a candidate for governor. The same candidate when I ran for mayor and city council — it was outside the establishment. It was building on issues, single-issue voters — very, very concentrated on detailed policy. And I thought that if I could build an organization, outside of the typical political organization, of single-issue voters, you could become viable because they’re passionate people generally, OK? And one is mayor, outside the establishment doing that. You won two races in the country that Obama actually engaged in as president. Rahm Emanuel and Jeremiah Healy, my predecessor, was a ten-year incumbent. So we won based on this structure. Run for governor, kind of in that same model. And I thought at the time that 550,000 people would turn out in the election. That was based on history in a similar election, when Chris Christie left and it was Trump’s first-year primary, you know, changeover in governor. There was five hundred or four eighty-seven [thousand]. So I thought five-fifty was a bump up. Same numbers Sherrill had, Ras Baraka had. I thought five hundred and fifty thousand people in the election, six candidates running that are viable — two congresspeople, two mayors, senate president, and the teachers’ union president — that if I got to a hundred and thirty thousand votes, single-issue passionate voters, I would get in through the primary with twenty, twenty-five percent, and then put it back together even though I was kind of the outside establishment guy. I think I did everything perfect during that election, OK? With the exception of winning. Which is the big deal. But I miscalculated one thing. And I don’t know in hindsight how I could have changed anything. But I miscalculated that the turnout ended up spiking to eight-fifty, OK? So I said five-fifty. We all said five-fifty. Going into Election Day on Tuesday, and I’m a pretty pragmatic person. Going into Election Day on Tuesday, if you said, Steve, are you gonna win? I would have told you, we caught them and we’re gonna win, OK? We have more volunteers. I mean, to give you perspective on it: Uber, which is a data company, like, the week before, they wrote a million-dollar check to my super PAC, five hundred [thousand] to Sherrill, five hundred [thousand] to Ciattarelli. Election Day, the New York Times embedded one reporter, photographer for most of the day with only one of us, me. CNN, the day before, me, right? The trend was there. I miscalculated that there was an additional 300,000 people that came out. I saw, similar to here [in New York City], the voter numbers surging early, and I just thought it was a displaced Election-Day voter. I thought you were so excited to vote that you were voting early. I never imagined that there were 300,000 additional people voting. And if you tuned in late, then you would have ultimately– I had the wrong message. My message was, Trenton was broken, these are the things we would fix. And Sherrill, for example, was: I’m a helicopter pilot to fight Trump, right? Her message was better for that. It was over in five minutes. So why do I tell you that? I was shell-shocked, didn’t really know what happened, and the way this business works is when you lose, your phone goes dead. Nobody calls. It’s just, that’s it. Silence. And so, I’m trying to think about what happened and two weeks after the fact, I get a phone call by this guy. His name is Jim Simpson. He was Christie’s DOT chair, OK? Commissioner. And he worked on the widening of the Pulaski Skyway with me as the mayor of Jersey City. And Christie went after me with Bridgegate, and I was a government witness during Bridgegate. But even when nobody in the Christie administration could talk to me, Jim Simpson was always like, I have this major project going through Hudson County, predominantly Jersey City. I’m gonna deal with the guy. And he would tell you that Steve was a professional, good guy to deal with. I think that’s what Jim Simpson would say. I didn’t talk to Jim Simpson for 12 years. I lose the election. Two weeks after, I get a phone call. He leaves a voicemail. Hey, Steve, it’s Jim Simpson. Can you give me a call back? And I was kind of like fuck the world at that point. I didn’t want to call anybody back. And I called Jim Simpson back, and I was like, Hey, Jim. How’s it going? And he says to me, he says, Look, I’m very thankful you remember who I am, blah, blah, blah, blah. He said, Look, I had an issue in a warehouse I own in Jersey City. Didn’t know really how to navigate the building department. I figured it out, right? But thanks for calling me back. That was the conversation, OK? And I’m like, OK, great. You know? Whatever. So the conversation ended. He says, You know, I moved to Pennsylvania, but I’m sometimes up in New York. If I’m up there again, why don’t we just get together? I said Sure. I got nothing but time, right? And so he calls again about a week-and-a-half later, says, I’m gonna be in New York tomorrow. Wanna grab breakfast? Sure. So we meet for breakfast, and he knew nothing about me. When I got there, he was googling me, OK? Like, he knew nothing about my deeds. He’s like, Oh, you were in the military. That’s amazing. Like, it was a weird conversation. And, you know, towards the end of the conversation, he says, What are you gonna do next? And I said, I honestly don’t know. You know, I think maybe this, maybe that. And he says, You know who you should call? I said, No idea. Who, Jim? And he says, I don’t get along with her, he said. I used to be a Rockefeller fellow. I do not get along with her. We do not see eye to eye, but you should call this woman named Kathy Wylde. And I said, I met Kathy, ten years ago once. I don’t know if I would call her, and I don’t…That’s a weird conversation, just to call a stranger and ask for help with a job, right? And he says, No, you should figure out how to call her. We’re gonna figure out how to get you in touch with her again. I gotta think about it. I said, OK. We left. I never thought anything of it. He calls me four days later, and he says, I was giving it some thought. I was googling and I saw she’s leaving [as President and CEO of the Partnership for New York City]. You should apply for the job. And I said, I don’t know anybody. It’s a weird dynamic. And he said, We gotta figure it out. You know? Good guy. A couple weeks later, I was going to a conference in Montana, and three years earlier, I had met Rob Speyer at the conference. Spoke to Rob probably twice in my life since then, OK? But, we had a night we sat next to each other at a dinner, had a nice conversation. So I texted him. I said, Are you going to this conference by chance, in a couple weeks? He said, I am going, right? I said, Oh, can I grab you when you’re there for a couple minutes? He said, Sure. I go with my wife. He comes over, says hello. He knew my wife from a couple years ago, pleasantries. I said, Hey, Rob. you know, I texted you, if I could speak to you. He said, Yeah, yeah. What’s up? And I was, like, nervous at this point. I’m, like, sweating. Like, I’m gonna give a pitch. I said, I see you’re very involved with the Partnership. I think I’d be killer at that job. And he says, Why? And I gave him a pitch, which I’ll tell you in a second. And he said, Honestly, he said, Steve, there’s 12 people in the search committee. He says, I couldn’t even help you if I wanted to, nor would I. But what I can do when people ask me is just get in touch with Heidrick & Struggles and, go through the process. I said, That’s all I could ask. It’s fine. So I send him a text two days later with my resume, and I said I was serious about this. Can you connect me to the person? He says, Sure. Heidrick & Struggles would tell you that they did the interview as a courtesy. And that they thought that it was going nowhere, largely because when the search committee, which is a who’s who of business, outlined who they wanted, they said they wanted somebody from New York City, and they wanted somebody who was not in elected office. So here I am. I would go through these interviews, and I would outline what I thought the Partnership was doing wrong, and how politics has changed, and where I thought the potential was. Largely that the model of shuttle diplomacy, of relationships in the way that it worked 20, 30 years ago, doesn’t work anymore. And today you see, both with Mamdani and Trump— The assets that they [Partnership member companies] have are that they have 800,000 employees. They have the ability to communicate at scale, and they have a general centrist message, and none of those things are leveraged in any meaningful way, is what I would outline. And I really believe, and I continue to believe, that if the Partnership is perceived as a voice of very wealthy people and the CEOs, it will no longer exist in five or ten years. And the reason I say that is because one of the assets that people value is this convening component. And it’s a convening component because you have everybody from, you know, the CEO of JP Morgan and Goldman Sachs towards mid-level to low-level CEOs, and they want to be in the room with the next person in that food chain, so to speak. And when people view it as not good use of their time, because it’s not effective, you start seeing deterioration and you no longer get that involvement. And I would explain this to them, and I would say, This is how we’re going to organize employees, and I think is the most effective way to do it. It’s something that I did as a [mayor] or a candidate when I was running these campaigns, so it’s something I know. And they would ask a lot of questions, and we would go through this model in detail, and they would ask, and they would just move me along in the campaign in, in this situation. And then at the end, probably ten interviews in, the woman from, you know, Heidrick says, You know, I think, you know, as crazy it is, you might land this plane. And I said I knew I was going to land the plane. But— **Josh Greenman**Can I ask? So you mentioned— **Steven Fulop**Sorry to take so much time on that, but— **Josh Greenman**Three hundred thousand, or I forget the number. **Steven Fulop**Eight hundred thousand. **Josh Greenman**Eight hundred thousand employees. And correct me if any of the assumptions embedded in the question are wrong. But you’re a membership organization, you’re supposed to be speaking for the large employers. **Steven Fulop**Yes. Large employees. Employers and employees. **Josh Greenman**Employers and employees. But you don’t canvas them every time an issue comes up. You described it as centrist. OK. An issue comes up. First of all, how do you, as the head of the Partnership, decide whether to get engaged and involved in that particular issue? And second of all, how do you decide what the posture of the Partnership is, if it’s not predefined? How much of it is your instinct? How much of it is, Let me take the temperature of the employees and the employers first? **Steven Fulop**The honest answer is I think it’s a hybrid. At the last board meeting, I spoke about being more narrow in the things that we’re going to be involved in. I think it’s very important we’re very deliberate and specific on issues that we care about and being less involved in kind of broad things that don’t necessarily touch 250 of the different companies. **Josh Greenman**Even though you made a statement about antisemitism. **Steven Fulop**I made a statement on antisemitism because it touches a lot of our companies, if not all of them, because they have Jewish employees, and they’re all very invested here. **Ben Smith**What’s something that you think the Partnership should get out of? **Steven Fulop**We had a lot of time last year in the fashion industry. I think the fashion industry is important. I don’t know who reads that report ultimately or how you move the needle ultimately on the fashion industry. We did some work very publicly on short-term rentals. Not really sure that that’s the best place to be spending a lot of time, because I don’t think it pertains to every single person here. I think that there are things that do pertain, quality-of-life issues that touch everybody. How do those things come to light? So I spoke broadly at the executive board meeting about things that I thought were important. They had lots of questions, Why this, that, and I outlined things both in a framework of government and politics: This is what I think. This is where the opportunity is. This is where the vulnerability is, et cetera, and we went through some of that stuff. When we’re working, for example, now, I gotta review it. We’ve been going back and forth on a blueprint of things to change for New York City that I think, or our team thinks, are meaningful, practical, and tangible things that you could change that would impact the budget in a meaningful way. And a normal person would say, You’re probably right about that. And the politics could be tough for those. So put a litany of, let’s say, 330 of them together. A lot of it’s our team’s ideas. Some of them are a little bit maybe venturing into a place that the Partnership historically has felt maybe uncomfortable. Those are broader conversations because I don’t yet have 20 years of trust with those people. So I say, Here’s what I’m looking to do. Here’s the reasons why. And we have those conversations with the executive board. **Liena Žagare**Does universal childcare fit into that? **Steven Fulop**Universal childcare is a part of it ‘cause— universal childcare is a part of it. It’s definitely in there. And I’ll tell you this. We polled our employee base because we were curious. It’s something again that touches, I believe, the affordability crisis. I think Mamdani and Hochul are right on this. It touches everybody who lives here in some way or another. Roughly two-thirds of our employers provide some sort of childcare today. Some is on-site, some is off-site, some is subsidies for low-income, some is subsidies for everybody, some is using a tax credit. And I’ve said this to the mayor before, that our knowledge of this space is vast because we have experience, practical experience on this sort of thing that others don’t have. They may have abstract experience or be ideological in a certain way. We experience it. We can tell you what works and what doesn’t work in the Building Department, what the challenges are in the bureaucracy, what the issues are in accomplishing this in a different way, because a lot of us have tried to provide that, right? So how do we communicate that publicly in a way that makes sense from our objective, which is improving New York, and at the same time in a way that’s value-added to the mayor or the administration, is kind of where we are. But it’s a hundred-percent part of that blueprint. There is a section on this. **Akash Mehta**It’s interesting to hear you emphasize that you represent the employees, not just the employers. Are there formal structures that put that into practice? Are there elected board positions from the employee base, or anything like that? **Steven Fulop**What we’re starting to do now— So it’s a shift, OK? And what we’re doing now, the short answer is no. What we’re doing now is starting a lot of these town hall-type conversations with the CEOs and me. It is an April, May, June, two, three-year initiative, where we’re talking about civic involvement, the importance of New York, and people can opt to sign up for information from us. So we’re communicating in that way. People will opt in, and then we’ll have the ability to better communicate with the employee base with regards to what we’re doing. You know, there’s no secrets to what I’m saying. I mean— **Ben Smith**But you think the opportunity is to mobilize employees? **Steven Fulop**The opportunity is to better communicate because I think there’s a disconnect between a lot of the things that happen, and how and when we communicate to employees things that may impact them. Because you have all sorts of legalities, and general counsels, obviously, that put obstacles in place on this stuff. And we need to figure out a way, because people are invested here in a meaningful way, so they should have knowledge of this. I don’t believe– Of course, our employee base is different than working for the Hotel Trade Council or Carpenters [union] or whatever, right? They’re monolithic, told to do something, they’re gonna do it. Employee bases aren’t that. I think that’s an asset. Having a generally college-educated employee base, if you lay out an issue in a reasonable way, they could make an informed decision whether that works for them or doesn’t work for them. The— **Akash Mehta**Wait, the carpenters can’t make an informed decision? **Steven Fulop**I didn’t say they can’t make an informed decision. I’m gonna say they’re more monolithic in what they do, and it’s driven by the decision of a leader, specifically in the Carpenters. The Carpenters would say from their executive board, maybe in Washington, maybe in New York, maybe in New Jersey, say, Directionally, we are supporting Ben. And then we support Ben. We don’t have that luxury. That’s not the way we’re structured. **Akash Mehta**What does that have to do with them being college-educated? **Steven Fulop**Well, it doesn’t have to. I mean, I think that you don’t have to necessarily be college-educated, it’s a fair question, to weigh something. But I think generally somebody that’s gone through some sort of rigorous education and training on pragmatic thinking has a better chance because they’ve been educated to think that way. I don’t think it’s a referendum on, you only could understand an issue if you’re college-educated. **Alyssa Katz**But I want to dig in a little more to that when you’re talking about pragmatic thinking, because it’s not an elephant exactly, but the sort-of power in the room is the mayor, who’s elected with actually a lot of college-educated support. And also based on a sort of different way of looking at pretty much everything in some ways, right? Like around– OK, we’re gonna start with principles, we’ll figure out the practicalities later. And also around mobilizing large numbers of people who have interests potentially at odds with capitalism, right? Tenants, people who generally think that money and politics and billionaires are the core problem, or a core problem to address, and on and on. So I’m interested in your efforts to mobilize people in that light. I mean, are you looking to build a counterforce? How does that factor in? **Steven Fulop**I don’t use a “counter force.” I don’t think people are generally sympathetic or upset with capitalism or sympathetic to socialism. I view it more as a couple of things were true in the November election. A, you had an alternative to Mamdani that was tremendously flawed. So they had a ceiling. It was an exact situation that happened in Jersey City, where you had a former governor who resigned in disgrace running for mayor, my old seat, same exact outcome. So it wasn’t, it wasn’t a reflection of, everybody’s sympathetic to socialism. I think elections are choices ultimately, and these are the four choices that you ultimately had. Do I think that people are frustrated and more fiscally liberal than they’ve ever been before? Yeah, absolutely. I think that when people say that I, people say, a twenty-year model ago would say, I am, you know, fiscally conservative and socially liberal, that model has flipped really. People are fiscally liberal because what’s going on today isn’t working for them. They’re not necessarily sympathetic to socialism, but they’re open to trying things because there’s an acknowledgement that what’s happening isn’t working. And we see that too. We’re not blind to that. So do I think that you can organize a counterweight, was the question? Is that what it is? I don’t know if I would frame it as a counterweight. I think that you need to organize something that has a voice that is relevant in the political atmosphere, definitely. **Josh Greenman**Can you just explain both the number and percentage of people the Partnership represents of employees, versus the employment base of the city as a whole? **Steven Fulop**Yeah, I don’t know the— **Josh Greenman**What’s happened to that over time? I’m curious, like, is it— **Steven Fulop**I can get that to you. I don’t know. I don’t know that— **Josh Greenman**And then the other is inside the Partnership. What are the constituent companies and groups, and…Are there a couple of companies that have a highly disproportionate share of Partnership members? **Steven Fulop**No. **Josh Greenman**So how broad-based is it? Can you just describe it? **Steven Fulop**I mean, there’s 350 companies. They range in size from– I mean, they would be significant companies, maybe 50 employees, OK, to several thousand employees. But on the executive board, you can see from the site and the broader board, they vary in size and background. Most of the companies you would have some familiarity with. But, they vary in size. **Josh Greenman**How is it determined whether you’re part of the Partnership as a company in New York City? **Steven Fulop**As part of the Partnership, is generally we go through a process of meetings. I meet with them initially, provide them some information. They express that they’re interested, I express that we’re interested. I take it to the board to make sure that we’re moving in that direction, and then the board validates that. This year we’ve had, I mean, a bunch more have joined, and we’ve restructured some things so some have left intentionally. It’s not intentionally, but I guess– **Nicole Gelinas**You’re running pretty hard against the two income-tax hike proposals that the mayor ran for office and won on. Do you see the employee base as part of the battle against those tax increases? Would Partnership company employees go up to Albany to make their point known against that? **Steven Fulop**Yeah. But I don’t think you’re gonna have that same sort of advocacy group that the DSA does. It’s a totally different type of person that would engage, number one. Do I think that a lot of the employees would be opposed to an increased tax structure in New York? I think without clear parameters on what that tax structure looks like, I think the answer is yes. And it’s not only the income taxes that we’re concerned about. So we’ll go into that, too. The reality on this is, if you look at it from a reasonable point of view, if you added the income tax, the payroll, the PTET tax, which is one that we care a lot about and we’re concerned about, the corporate tax, the estate tax, and the property taxes: You put all of those together, right? And you said, OK, we’re gonna do them all. And without some structural changes to the budget, you will be at the trough again in three years. And I think a reasonable person who’s even supportive of, maybe, the highest earners paying more would say, Does it make sense to do this every two or three years, and at what point is there a tripwire? So I think, yeah, the people would be sympathetic. It depends how and what information you provide and how those taxes are. But we don’t have that structure in place yet. I just got started. So we’re just building that stuff out, which I’ve told you, but it’s new days on this. **Nicole Gelinas**So there’s, there’s a reasonable person, and then there’s Albany. Now that the budget is— **Steven Fulop**No, I gotta be careful ‘cause I gotta work with people. So I can’t say they’re unreasonable. **Nicole Gelinas**Now that the budget is just a week-and-a-half away, what do you see as your prospects for stopping those tax hikes? And if you don’t stop them, does that hurt your power early on? **Steven Fulop**No. I don’t think any reasonable person would say that there’s a structure in place today, after two months of me being there, that is gonna push back against everything in Albany from a political standpoint. I don’t think that’s a fair thing to say. I don’t think the Partnership leadership think of it that way, and I think that most people wouldn’t think of it that way. I think the governor has been very strong [against tax increases], so that’s good. That pass-through tax that was put in place when the SALT reduction was eliminated, and now there’s a proposal to eliminate that, that would be an income tax increase on not only the wealthiest people, it would be an income tax increase on business owners throughout the city. And that is one that we’re keeping track of right now that I think doesn’t get the scrutiny that it deserves. Everybody focuses on the income tax and the corporate tax, which the governor is fairly clear, at least now, that she’s not supportive of, but we’ll see. **Josh Greenman**On the corporate tax, Mamdani has repeatedly said it shouldn’t harm the climate here in New York City because it applies to anyone who does business in the state, not if you’re headquartered here, so it shouldn’t lead to corporate flight of any kind. Is that how you see it? **Steven Fulop**No. So first of all, it’s challenging when it’s depicted as corporate flight, because then it becomes a conversation around, Well, you didn’t leave. You didn’t leave. So I know what I did is OK because you didn’t leave. The reality of how this works is that it’s attrition over time, and decisions on where to scale outside of New York. That’s generally how this works. And because the immediacy isn’t there, people don’t view it as, Well, I made this decision, and tomorrow you left. And that’s a challenge for us, because the argument we lose, even though what we’re saying happens to be true, that people constantly are making decisions to scale [businesses] elsewhere. You see it. You see it. So I think that his argument is flawed, although it’s a political argument ultimately is what he’s making, and I understand that. But if you do change the tax rate as he proposes, you will be one hundred percent higher than New Jersey, right, which is at 11 percent, and you would be at 22 percent, according to the CBC, when you layer in all these other things. So at that point, you start saying, Well, I have the same talent pool that I’m pulling from here in the New York system. I’m no longer making the decision to go to Texas or Florida or Tennessee, where it’s a geographic total distance, but at a hundred percent premium to be in New York City, is that worth it, and should I be elsewhere? Your competition is elsewhere at that point in proximity. That’s the dangerous thing to what he’s saying. **Ben Smith**Do you think even more broadly, there is just this sense among— ’cause I think you’re right. You talk to business leaders, and most of the really big businesses in New York don’t care about New York anymore. They’re not New York businesses. They’re global businesses that happen to be here, and New York taxes are a diminishing share of the thing they worry about. Because they’re so global now, they don’t have the kind of commitment they did when David Rockefeller was running the Partnership. And I think a lot of them and a lot of people just think, long-term, New York is cooked. There’s a flight not to Jersey, but to the Sun Belt — to Houston, to Dallas, to Atlanta, to Miami — that’s kind of unstoppable at this point. And you see just the growth of those cities demographically. You see it in terms of their businesses. You know, maybe that’s not this year or next, but I think if you just literally look at the lines on paper of New York, Philadelphia, Boston…these northeastern cities, versus the Sun Belt. Just feels like an irreversible trend: these places are in decline, these places are rising. This is probably beyond our professional careers even, we’ll be fine. But do you think long-term that it’s true, that New York is just sort of irreversibly declining? **Steven Fulop**I think it’s fragile. There’s a lot of indicators and pressure to push people away, and there isn’t an easy solution to it. First, let’s just talk about last week. Regardless of what you say about the corporate tax conversation, we could disagree or agree, regardless. You know, your job isn’t to agree or disagree. But using the estate tax that was proposed last week, OK, or two weeks ago, nobody above the age of 60 would live in New York City if that happened to be approved. Period. End of story. You would see flight, because no sane person would do that? Because the tax rates, that you’re not talking about the one percent, you’re talking about somebody who has a $750,000 home or apartment, OK? You know who that is. So you have pressure because of the dialogue that’s happening there for sure. You definitely have gradual attrition and people moving jobs elsewhere because they’re more portable. There’s no question about that. You have pressure at the bottom because it is very hard for entry-level people to find jobs, and at the same time to find housing. It’s a true thing here in New York, the pressure. So you have a lot of difficult situations going on and absence of a tough political solution, meaning willing to make decisions that are not popular, you won’t be able to change the trajectory. And that is only one that the mayor can actually drive, OK? So the property tax reform stuff that de Blasio and Adams both tried to do, it’s necessary. It’s really necessary, but it’s gotta be driven by the mayor leaning into it, making that a number one priority. Use things like FHEPS, OK? You guys have all written about that. **Ben Smith**I don’t know what that is, sorry. **Steven Fulop**It’s the rental assistance program, OK? It’s growing at four percent per month. It’s a billion-dollar-plus program. Four percent per month! It’s not sustainable. And if the mayor was sitting here, he would say, Well, I didn’t approve the expansion of the program. That’s what he would say. Because I had a conversation with one of his people last night on this, and I said, Yeah, but the expansion would have made it worse. The existing program is four percent per month, right? We need to solve for that. How do you solve for that? That program is a byproduct of the de Blasio administration when Cuomo got rid of the rental assistance program at the state, and so the city inherited that. There isn’t another city in the country that has a similar entitlement program. It’s not sustainable. That program belongs at the state or at the federal government, but it is a politically tough thing to do. So you gotta be able to make decisions like the property tax reform, that. Those are not easy things to do. But outside of that, your trajectory for the city is very, very dangerous. It’s a fact, you know? [Note: After this interview occurred but before it was published, the Mamdani administration continued the appeal of a court ruling requiring the city to expand the FHEPS program as dictated in a law passed by the City Council, while the two sides of City Hall negotiate a potential settlement.] **Akash Mehta**Do you broadly agree with Mamdani’s analysis that Cuomo shafted New York City — that Albany shafted New York City under Cuomo? **Steven Fulop**Honestly, it predates me, so it’s hard for me to kind of give you a fair assessment on that. I would tell you that I was mayor for 12 years in Jersey City, economic backbone for New Jersey. We drive jobs, we drive the statewide economy, we drive every number. It’s disproportionately housing growth. Everything is Jersey City. And I used to say, We don’t get what we put in. We don’t get that back. It’s true, OK. I know New Jersey and New York both say to the federal government, you know, they both pay much more to the federal government than what they get back. I mean, this is a narrative that people say. It’s probably true. Does it get corrected? I don’t know. You know, it’s on the governor’s situation. I have no idea, is the short answer, ‘cause I’m not in the weeds on that. **Akash Mehta**One quick question on the tax battle. The Partnership has a [501] C4 that was seeded with $10 million. What are your plans for the group? Are you planning to spend that money on this battle? **Steven Fulop**Um…unclear. And, conversations this week on that. You know— **Josh Greenman**Do you run the C4? **Steven Fulop**We hired an executive director this week. There is a willingness, we had a call this week and there is a willingness to spend real money to make sure that New York City stays competitive. So this was literally this week. There was a call. I would tell you that there is a serious appetite there. It’s literally a ripe conversation that’s happening in the last 24 hours. Good question, though. **Nicole Gelinas**Large donors proved last year that they’re very good at spending a lot of money — but it didn’t work. In fact, it may have worked against them. Are you confident that beyond the money, you understand local and state politics well enough that some of these people are actually listening? I mean, is [State Assembly Speaker] Carl Heastie, is [State Senate Majority Leader] Andrea Stewart-Cousins listening to these arguments and saying, We didn’t realize that the potential for attrition in the corporate side was this great. Because the money hasn’t worked before. **Steven Fulop**The short answer is, I’ve been there two months. I doubt at this stage, we don’t have that relationship. I mean, I’ve met everybody. I doubt that I have the standing or credibility with them to make, or the structure in place to make that argument today more compelling than it was made six months ago. It’s just the reality of the situation. I think that you have two types of people that exist in any political sphere. You have ideologues and opportunistic people, OK, that just think about their next election. Those are the two types only. And absent somebody that has an alternative, the opportunistic person will only always be sympathetic to the ideologue, because they view that as their best route to get reelected because that’s where the gravity of energy is. **Josh Greenman**What were you? **Steven Fulop**It’s a good question. I was a hybrid. **Josh Greenman**Oh, the only one. **Steven Fulop**[Laughing] That’s right. I was unique. I think that when you look at Albany, you have a growing DSA base for sure. The question that I would have as I get into it is how much of those— the rest of the people that aren’t pure ideologues are there because there’s not an alternative that they see, can be supportive, and helpful to a pragmatic view. So I don’t know what they’ll listen to — Carl and Andrea — in the next week-and-a-half. I mean, who knows? **Josh Greenman**Should we charge more for street parking? **Steven Fulop**It’s a good question. I happen to be a supporter of some of those proposals on street safety and better streets. I don’t think it’s a broader position that the Partnership has taken, and I don’t think it’s something, going back to what I said earlier, narrowing view and going back to your question earlier on how we approve things. I’ll tell you that, as I wrote out this idea of a blueprint of things that, pragmatically, that are politically difficult to change that I believe are the right things to do, I included it in there. And going back to what you said and questioned earlier in how we get these things approved, because the nature of what we wrote, you know, once we get it to a place, I will circulate that a little bit more to the executive board and get some more feedback on whether we wanna go down this road at this point or not, or not at this time. So there’s a process on this stuff. **Ben Max**What are a few other examples? So property tax reform, some streetscape stuff, universal childcare, what else? **Steven Fulop**If you can wait till next week, it’ll be better. OK. Like– **Josh Greenman**Are you fast enough for the political world? What I’m saying is, if you’ve got to go through that process, and these conversations are happening, the budget’s gonna be cooked in ten days, are you gonna be behind the curve? **Steven Fulop**I am obsessed with the political world. I mean, I, I lived in this— Ben MaxHe means the Partnership. **Josh Greenman**The Partnership. Is the Partnership behind the curve if it’s got to deliberate on all this? **Steven Fulop**Look, I think the answer is yes. The short answer is, I don’t know, right? Like, I don’t know. I’ve not been in long enough to have a gauge on all of this. But my gut tells me yes, because the leadership there is hyper-engaged in New York City and the Partnership, it’s not something where they just kind of parachute in once every three months and think about, kind of the Partnership and what’s happening in Albany and happening in New York City. They happen to be very engaged here on a regular basis. So I think the short answer is yes. They will be able to turn stuff around relatively quickly, and I feel pretty good about that. It’s not where I think about obstacles. **Ben Max**So big picture, I’m still trying to figure out right now: What does the Partnership care about, big picture. I know you’re working on a blueprint. I know you’re rethinking some of this, but is it first and foremost, the business climate and economic growth? **Steven Fulop**Right now, I wouldn’t say this is in perpetuity. I mean, obviously, in this moment in time, it’s competitiveness and the business climate. But when the C4 was launched initially last year, they spent money on one initiative, OK? Spent a couple million dollars on the discovery bill. It had nothing to do with finances, taxes. It had to do with public safety and what they viewed as in the best interest of quality of life. So their interests aren’t necessarily only about taxes. We happen just to be in this fire drill at this moment that I got thrown into, where there’s a new tax proposal every other day. So trying to deal with that is where we are right now. I think broadly speaking, the place of the Partnership is really wanting to have a relationship where we’re kind of helping bridge the gap between the public and private sector, and helping find solutions. We have a venture fund, a $200 million venture fund, which I don’t think gets the recognition it deserves. It’s been involved in creating 37,000 jobs, seeding very early-stage New York companies that do a lot of good here. We have a tech lab that partners with DOT, with the Port Authority, with New Jersey Transit, experimenting on technologies to make government more efficient. It is a collaboration that has happened for 15 or 20 years. A lot of companies are a part of that, new companies helping on that on-ramp. It is a unique and special thing. We actually, I think, at the end of this month, have kind of that shark tank at our office where everybody comes in and pitches, and the board of that is like a who’s who. So, I mean, there’s a lot of things beyond what you guys see on the C4 and the advocacy and the lobbying sort-of side. **Nicole Gelinas**So on the Transit Tech Lab, some of the ideas that have come out of there have been very good ideas. But more broadly, for the MTA or the state and local government to actually save enough money so that we don’t have what you described with them coming back for new revenue every couple years, you have to confront the unions on some big issues, whether it’s work rules on the construction side, whether it’s two people on every single subway train, whether it’s Department of Education rules that mean they can’t be as flexible as charter schools. Do you see the Partnership taking on the unions in any sense? **Steven Fulop**Going back to the pragmatic that you said, I would use the word pragmatic and then you asked me about kind of a little bit more about that. You brought up the conductors on the trains and the proposals on that. It is very expensive. There isn’t a city in the country that has that structure. If you outline that to an employee of XYZ Corporation, and you said to them, Here’s the landscape, OK, We need help with XYZ, I would venture to say that they would be sympathetic to our point of view. That’s what I was getting at, just to give that to you earlier. You know, the New York Post called me the day that Mamdani entered into the— he said at the end of a press conference, I wasn’t there, obviously, but he mentioned that he wasn’t gonna do the sweeps of the homeless encampment. And it was probably my second or third day on the job. And the Post called and said, Do you want to comment on this? And I said, Yeah. And I said something critical of him at the time. Prior to doing that, because it was like my second or third day, I did say to some of the people on the team, going back to how nimble the organization is, I said, Hey, I would like to comment on this, OK? Because I think it’s wrong, his approach, and I think he’s gonna have to move off of where he is, ultimately. And some people said, Well, is that really the hill you wanna climb? Is that really that important to us? And I said, It absolutely is. Because, A, he’s wrong on the issue, and B, it’s a quality-of-life issue. Going back to the fact that it ended up being right, he ended up moving on, and you could see it from, like, far away, just based on being a legislator versus an executive and accountability. But I think our job is to call balls and strikes and just be fair about it. So do I think– Let me say it this way better. If you are gonna grow housing, OK, ‘cause he says it’s a priority. During his election, when he was a candidate, he would tell you if he was sitting here, he would tell you, you look at his CBS, the debates, he would talk about Jersey City and Tokyo, Jersey City and Tokyo as housing growth models. He would tell you that if he was sitting here — he said it on TV multiple times. Now, he followed that about what we did in Jersey City ‘cause we grew a lot. How are we able to grow? OK, A, you had layers of government here that we do not have there, so you need to clean that up. We upzoned, of course, more aggressively than the City of Yes around transit centers, which you have to do here for sure. And then the third part about it is that if you mandate affordable housing, community givebacks, the union labor, et cetera, et cetera, the projects financially cannot handle that. They don’t pencil out. So what we would generally do is that, on the union labor front, because the people that get squeezed the most were the ones that pressure the building trades better, because their jobs are usually the ones that get–are most competitive, are the less skilled parts of the building trades. So when you build a high-rise building, the electricians, the plumbers, the operating engineers, the ironworkers are generally gonna be union, because it’s a more complicated thing. Carpenters, laborers, painters generally are the ones that have competition from non-union, and that’s really where it is ultimately. And we would say in Jersey City, on the projects that are important to us, if they were coming to the city for some sort of subsidies or big zoning changes, and it was an important project, we would work with the developer to give the unions a last look to be competitive with the open-shop bid, and some sort of grace between five and ten percent to be competitive. Now, absent that, there is no way to get it built in union labor. So if the mayor continues to be strident that everything is gonna be a hundred percent building trades, you’re never gonna meet the objective of whatever he has a hundred, two hundred thousand units. So I think that our voice is gonna be that you need some practical reforms to these incentives, and practical reforms means that you cannot pay a 30 percent premium for the carpenters. **Nicole Gelinas**So something very simple on that, though. Two people on a train. Would you see yourself making a statement similar to the encampments that, you know, Hey, intuitively, it doesn’t make a lot of sense to leave people in encampments. Intuitively, it doesn’t make a lot of sense to mandate two people on a train. Would you just call up the Post and say that, or that’s a different— **Steven Fulop**I don’t think that— I think if you called me and you said to me, What do you think? I’d probably just answer you, that I think it’s not something that any other city does, and in a financial crisis, you probably gotta be reasonable about that. **Ben Max**This is what you got hired in part for, right? To be out there more and to be a voice pushing back. **Steven Fulop**I think that it’s just my personality. I don’t know if it’s necessarily, it’s just like this is— You’d ask me, and I would say it. I wouldn’t go call up the Post and say, Let me-- Carl [Campanile], let me comment on this, right? I wouldn’t do that. Like, that’s not how I would think about it, right? I’d be going with my day, and then Carl would say, Do you care to comment on this? Or he would call, and I would just be like, Yeah, I think so and so. And he would say, Is that on the record? And generally, I’m not gonna say, This is off the record, on anything. I generally just talk. OK? So— **Josh Greenman**You talked about incentives, government incentives. I want to talk about literal government incentives, like, Amazon, right? Remember how many years ago, half of Amazon’s HQ2 was gonna be here. **Steven Fulop**Yeah, I remember. We tried to pitch it too. **Josh Greenman**It didn’t happen. You tried to pitch it, too. What do you think of the state’s current corporate incentives? Do they make sense as currently structured? To try to attract businesses to New York State, to try and attract businesses to New York City, there are various programs. There’s been a lot of talk about restructuring those programs to make them more rational, and there’s a larger question of, should we have this environment in which cities chase corporations by throwing money at them or tax abatements at them, or should there be some kind of compact where cities agree that they don’t do this to each other? **Steven Fulop**Yeah. I generally think — putting on my mayor’s hat — that the structure exists today is a race to zero, and it’s not healthy from the public side. But you cannot unilaterally disarm, is the unfortunate part of the conversation. And so absent governors getting together and having some sort of détente where they’re no longer doing that, I do not see how a state doesn’t participate in this. That said, I think that the competitiveness conversation in New York and New York City is more complicated than just incentives. I think that, you think about other places in the country, where people have mobility to get to, and there’s opportunity, there’s more of a deliberate effort to attract beyond just the incentives. Texas reordered their entire court system to replicate Delaware, to make it more friendly for corporations. They’re having a Texas stock exchange to have better access to capital markets. It’s more of a deliberate strategy than just saying, Hey, we’re gonna give incentives. Now, it is not helpful– you know, people, absent certainty on communication, they try to interpret a lot of what somebody means or what they say. So, governor or mayor says X, Y, Z in a statement, they try to interpret, what does that ultimately mean? How should I envision that to manifest itself for what I’m trying to do? And the thing that we are trying to calibrate for, and I say this publicly and privately, is that a lot of the rhetoric out of City Hall, which is consistent tax, tax, tax on any front that’s possible, whether you make $500,000, whether you have a house that’s $750,000, whether it’s a pass-through and you have a small business, it creates a very volatile environment. And that’s the problem ultimately. So I think you need certainty on that. **Josh Greenman**I have one other topic that we haven’t talked about that I just wanna make sure we discuss, which is AI. You represent a lot of employees. And AI is going to be increasingly a large employer, in theory in the city, but it’s also coming for some of those employees’ jobs, maybe many of those employees’ jobs. **Ben Smith**And the employers are very excited about it. **Josh Greenman**And some of the employers, the CEOs, are excited about being able to save dollars and innovate in various ways. **Steven Fulop**Well, they don’t communicate that with me, but just go ahead. **Josh Greenman**So how do you, how do you balance those tensions, which are very, very real, among employees who might be excited and employers who might be excited about AI, and those who might say, Holy shit, my job is gonna be gone in 10 years, or five? **Steven Fulop**I think that that is— I said this, maybe I touched on it a little bit earlier, I think that young people not being able to find jobs and the housing crisis was pressure from the bottom that is one of the most at-risk situations for New York. AI is part of that conversation, potentially — so you have macro environment, why people aren’t hiring, and then AI might be part of that too. You know, though they don’t clarify that with me. They don’t say, Here’s the reason I’m doing this or doing that. I do think that one of the biggest risks over the next 12 or 18 months is the AI impact on jobs. I agree. And I don’t think anybody has a good solution, both in the Partnership or in City Hall, yet on that. And in order to kind of figure out how to do that and solve for that, I think that you need to have some sort of broader conversation on implications. I will tell you that one of the things that we outlined in kind of what we were talking about on this blueprint was that, I do think that part of the procurement process for city government services needs to factor in who is training and who is factoring in the AI conversation, and those scores should be appropriately weighted, beneficial for companies that are thinking about training or have a training program as part of it. So we’re trying to think of ways to kinda think about training people and getting people into another space and using the leverage of City Hall to do that. I don’t think there’s a broad enough plan yet to solve for it. It is the biggest risk for New York, because New York City relies on white-collar jobs disproportionately for its budget. And where AI is coming is directly at the white-collar jobs as opposed to the blue-collar jobs. And so New York City is poised to get hit in a different sort-of way than anybody else. Short answer is, I don’t think anybody has a clear-eyed solution on that. Some of that’s gotta be set by the mayor, you know, that’s the truth too. I said earlier in the conversation, we had a roundtable on childcare in December with the mayor. And the mayor, at the end of it, I said to his staff, I said, You know, we outlined some of the inefficiencies and bureaucracies to getting these childcare centers open, which we’re familiar with. I told you earlier. We can help you with that from a technology standpoint, okay? And this goes back to making tough decisions. You’ve seen employee headcount since COVID skyrocket at City Hall. Whole, another conversation. I mean, that’s a fair thing to say. We said we’d help you with some efficiencies on that from a technology standpoint. And they were nice in their response but there was really no follow-up on that. And, and my point on that is we can only do so much from the Partnership. You need a dance partner in order to dance, right? Like, that’s really the reality of this. So, I do not know the solution, and I push back. People aren’t— They don’t, at least they don’t express to me they’re happy about, you know, not hiring jobs. You know, maybe to you, Ben. But, and AI displacement, I think people are very concerned about it in the Partnership too. **Harry Siegel**You said maybe without some changes, the Partnership won’t be there in five to ten years. We just did have a big turnout election, that led to an avowedly socialist mayor. Can you just step back and say— **Steven Fulop**No, I think the Partnership— I think I clarified that on the, that current trajectory of how it positions itself needed to change. So I think that’s part of the reason we’re here, to make sure it’s relevant for the next ten years, but— **Harry Siegel**But the business community seems increasingly less relevant to the political direction of the city. The Partnership seems less relevant. This is a bit of an inside conversation. For people who maybe vaguely know what it is or just spot its name in headlines or whatever, what is the point of the Partnership? And frankly, what is the good and utility that the business community brings to the city, if you just want to explain that? **Steven Fulop**What does the Partnership add to the city? **Harry Siegel**Sure. **Steven Fulop**Or the politics shifting left. **Harry Siegel**However you want to take it. **Steven Fulop**I think, forget about the Partnership as an organization for a second. I think the big corporations add a disproportionate amount of philanthropy, revenue, budget help, employee help to the ecosystem here in New York City. If those larger corporations migrate out, you would have a massive budget problem, you would have significant more unemployment, and the amount of philanthropy, which is tens of millions of dollars a year across, which sometimes is underreported or undershared, is a good thing ultimately, helps private institutions or public institutions, culture and arts institutions disproportionately, hospitals disproportionately. So I think that the business community here has a huge impact. The Partnership acts as a conduit from them to the public sector, and has multiple layers to it as I touched on earlier, where we help incubate early companies, facilitate connection to New York City, help advocate for policy change, et cetera, et cetera. **Harry Siegel**Outside of real estate, the business community, as you and Ben [Smith] were talking about, is less and less rooted here, and the problem is sort of a slow trickle to other regions, as opposed to people just pushing out and moving. Lots more people are voting. They seem less interested in the priorities of the business community. You’re talking about employees as a sort of base for the organization, 800,000 of them. I’m just trying to understand how business makes itself more relevant. **Steven Fulop**That’s a little bit of a process that I touched on earlier, a little bit of the transition that I touched on earlier on why we’re here. I think that people, as you saw in this last election, are struggling, an affordability crisis is real. There’s a lot of pressure, whether it’s jobs or housing. I think that’s all real. And I think that people have become, as I said earlier, fiscally more liberal because what’s working for them today and not working today has shifted from 10 years ago. So in order to be relevant, we need to be cognizant of that. **Alyssa Katz**People mentioned earlier that antisemitism is a priority for you, fighting it, and you know, I know you— **Steven Fulop**It’s a priority for us, yeah. **Alyssa Katz**Yeah, and we had a couple incidents that may have coincided with right when you arrived at the Partnership, right? The most visible one was, you know, this Israeli drone company. **Steven Fulop**Oh, the Israeli drone company, Easy Aerial? Yeah. OK. **Alyssa Katz**Yeah. What’s the other one? **Steven Fulop**It was the Park East Synagogue situation. **Alyssa Katz**Oh, right. Well, listen, but I’m talking purely on the business side. We can talk about it more broadly. But you know, there’s a company that was in a city-owned property. The city says it was a lease issue but it had been the target of a campaign saying, Well, you’re providing drones to the Israeli military, and we need you out of here. And then sure enough, that’s exactly what happened. And I’m just kind of curious about how you are approaching that kind of pressure on Israeli individuals and their businesses to get out of town, basically. How do you address that? **Steven Fulop**I think it’s a new conversation, obviously. We do think that antisemitism is rising, and it’s a significant concern. The BDS conversation and singling out one country to different standards than other countries that the U.S. or the public agrees with or disagrees with, or that has human rights violations or doesn’t have human rights violations, is a totally different conversation that we don’t agree with — singling out a single country. And the reality is that that BDS conversation, building on that, is not acceptable to us. And as you see elected officials more vocal in this space, unfortunately, if that ends up being, I think that is a place you’d definitely see us spend money. **Liena Žagare**Do you think Mayor Mamdani is doing a good job, and where would you— **Steven Fulop**Doing a good job? **Liena Žagare**Yeah. Doing a good job in the city and— **Steven Fulop**I think— Yeah, look, I think— **Liena Žagare**Where should he be more careful? **Steven Fulop**I think, I think there is— The short answer is, it’s been two-and-a-half months, so it’s hard to tell. I think that there’s a lot of mixed signals between things he says sometimes in smaller meetings versus what he says publicly. I think that, there’s been some missteps along the way, like for example the real estate tax proposal I thought was a misstep. I think that there’s been some really encouraging things. How he’s handled the president has been terrific. And I think that the city is in a better place than anybody would have thought post-election with regards to the president as a result of his communication there. I think that he has shown some willingness to move away from an extreme left position that he came in. That transition from a legislator to an executive is a transition, and from being a backbench Assemblyperson to a prominent front and center mayor, that transition is always complicated. Homeless encampments, the library funding, the FHEPS situation, he’s moderated some of those views, which are good. And at the same time, I think sometimes going back to how we interpret things that he puts out there, sometimes he’ll put something out that we’ll interpret in a way that I – ‘cause you have no choice but to interpret this stuff – you’re concerned about. You know, like, and I’ve said to them, Look, when American Express announced their headquarters, right? It’s a big deal. You know, a big American brand establishing a significant presence again here, recommitting. It’s good for his narrative ultimately, right? You know, that was in the works for some time. Or the Bank of America situation. If you look at the kind of statements that came out as a result of that, they never acknowledged the corporate side. They talked about the building trades. And that signals to us, A, questions about the housing conversation, right? And B, whether you understand the ecosystem that American Express employs thousands of people here, and they’re hugely involved philanthropically and committed to the city, and what that means. So I would say it’s mixed. **Nicole Gelinas**One thing that’s been missing from the ecosystem is a person like Dick Ravitch, who passed a...
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Partisanship and science
I recently participated in a symposium of the Denver-based Institute for Science & Policy, during which I had a “fireside chat” with Tony Mills of the American Enterprise Institute. Mills has written extensively on the politicization of science, and the two of us had a very enjoyable hour-long conversation on the topic. You can watch it below. The main questions we dealt with are why there is such a vast partisan gap in trust of science, and to what extent this is a unique moment. Mills helpfully brings a good deal of data to the discussion, as he did in this useful piece at Issues in Science & Technology. Tusk depends on reader support. To support this work, please become a paid subscriber. Subscribe Note this graph, drawing on a range of survey data to demonstrate partisan attitudes toward science since 1970s. As it demonstrates, there’s been a very sharp partisan divergence in confidence in science during the Trump Era, starting prior to the Covid pandemic. At least in part, we can attribute this to a rise in populist anti-science rhetoric embraced by Trump, his appointees, and those in his media orbit. But as the graph also shows, the partisan divide has existed in the past, and it’s not always in the same direction. The largest divergence in party views toward science prior to 2016 was in the 1970s, when liberals equated scientific achievement with the military-industrial complex and thus trusted it less. That era of scientific mistrust is often overlooked. Indeed, as Mills notes, the Moon landings (1969-72), which today are considered one of the last great moments of national unity, were experienced nothing like that at the time. There was sharp debate back then over whether the Apollo missions were worth the vast expenditures when the country had so much else to deal with. It’s outside the dataset, and we don’t have the partisanship lens for it, but the 1950s was another deeply anti-science decade, with critics of intellectuals and college professors alleging their ties to communism and their lack of commitment to basic American moral beliefs. This was the subject of Richard Hofstadter’s 1962 book Anti-Intellectualism in American Life, in which he quotes religious leaders like Franklin Graham: [In place of the Bible] we substituted reason, rationalism, mind culture, science worship, the working power of government, Freudianism, naturalism, humanism, behaviorism, positivism, materialism, and idealism. [This is the work of] so-called intellectuals. Thousands of these “intellectuals” have publicly stated that morality is relative—that there is no norm or absolute standard.… This fervor was heavily whipped up by Sen. Joe McCarthy (R-Wisconsin), although it’s clear that not all his fellow Republicans were on board with his red-baiting. What ended that era? The simple answer is the 1957 launch of Sputnik. Americans became afraid that they were losing to Soviet scientists, and suddenly university researchers and government projects were awash with funding. In our conversation, I asked Mills about the possibility of us having a Sputnik moment again that would rally the American public in favor of science today. Mills argued that the COVID-19 pandemic was essentially a Sputnik moment — a terrifying threat to all Americans that required scientific advances to address. But thanks in part to political polarization and the populist and conspiratorial leanings of Donald Trump, we got the opposite of a unifying moment. Many Americans turned against science, despite science actually stepping up in that moment. As we discussed in the chat, it’s not clear what scientists could or should be doing in this moment to increase faith in their work. As Mills notes, one of the chief complaints against scientists and professors is that they’ve become too political; scholars becoming even more political might not help them achieve their goals. This (mainly Republican-driven) anti-science era will end, of course, but it’s not clear what event will bring that about. Share
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