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Global Health & WASH: May 2026 Funding Opportunities (14 new opportunities!)
The May update for Global Health & WASH brings new calls that cluster around three distinct shifts: LMIC research leadership moving from funder encouragement into a hard eligibility constraint, pharmaceutical and corporate-linked funders running structurally independent education and use-inspired research portfolios at substantial scale, and AI adoption shifting decisively from speculative tool to operational capability across health and WASH systems alike. In LMIC Research Leadership as Eligibility, Not Encouragement, Wellcome anchors the dominant signal of the month with three separate major calls that all require or strongly center LMIC leadership in their basic eligibility architecture. Wellcome’s ESIC Hubs (£1.5-£1.9M per hub over 3-5 years) explicitly requires an LMIC-based lead applicant, treating Global South leadership as a design constraint rather than a participation goal. Wellcome’s Infectious Disease Clinical Trial Development Award funds transdisciplinary teams whose lead must be a mid-career or established researcher based at an organization in Africa, South Asia, or Southeast Asia. Wellcome’s larger Infectious Disease Clinical Trial Award (£1M-£8M for optimizing licensed interventions) requires the administering organization to be in eligible LMIC regions and at least 50% of applicants based there. Around Wellcome, the same pattern shows up at smaller scale and across funders: CHINNOVA channels $1M into West and Central African research institutions for climate-health work; IBRO’s Neuroscience Training Grants explicitly tier ceilings by region of residence with Africa receiving the highest amount ($5,000); IHME’s GBD Emerging Researcher Award reserves at least one of its two annual awards for an LMIC researcher; Sidaction’s HIV Cure call funds research teams across France, the Netherlands, and eligible African countries with a minimum two-country collaboration requirement. Taken together, these calls signal a meaningful shift in how research equity is being operationalized — not as encouragement language at the bottom of a call, but as a structural feature of who can lead, where the work must be administered, and what proportion of the team must be regionally rooted. In Pharma-Funded Independent Medical Education and Use-Inspired Research, the lineup this month is unusually deep. Pfizer alone runs four parallel calls under independent education and quality improvement frameworks: a Migraine and Women’s Health RFP (200K x 2-year, $500K pool), Migraine IME ($200K, $1M pool), JAK Inhibitor evidence-based education ($100K), and Maternal Vaccination HCP Education in Saudi Arabia ($75K), plus a separate Pediatric Pneumococcal Surveillance research call in Saudi Arabia ($400K per project). Novo Nordisk Foundation runs four parallel calls of its own at substantial scale: Infectious Diseases Catalyst Grants (DKK 60M pool, up to DKK 7M collaborative), Non-Diabetic Endocrinology Collaborative Grants (DKK 53M pool, DKK 5-10M per project), Pioneer Innovator Grant Health (DKK 1.1M), and Distinguished Innovator Grant Health (DKK 6.8M). LEO Foundation deploys DKK 2-4M per project for dermatology research excellence. Pfizer’s framing across calls deliberately separates the funding from product promotion, emphasizing measurable practice-relevant change and structural distance between scientific outputs and commercial activity. Novo Nordisk’s “use-inspired” hard filter pushes researchers toward a credible translation pathway from mechanism to deployable tool. The pattern: industry-aligned funders are running structurally independent portfolios at meaningful $ that fund implementation evidence, not promotion. In AI as Operational Capability, Not Speculation, several calls this month explicitly treat AI adoption as a near-term operational question with real evidence and governance requirements, rather than a speculative trend to be watched. Wellcome’s ESIC Hubs frames AI as “the accelerator” for evidence synthesis, asking hubs to translate technical innovation into adoption-ready workflows that can match real-world policy timelines. UNICEF Venture Fund’s new Climate Tech for Children’s Health call funds for-profit startups deploying AI, machine learning, and blockchain in low-resource environments, with a strict requirement that solutions must already have a working prototype with promising pilot results. The Water Research Foundation has launched two simultaneous AI calls treating reproducibility, cybersecurity guardrails, and human-in-the-loop deployments as core design constraints rather than nice-to-haves: one on GenAI and Agentic AI in water utilities ($200K), one on alternatives to water shutoffs that explicitly screens for implementation economics evidence. Nordic Innovation’s quantum technology call (NOK 4.5M) extends the same logic into life science and healthcare, demanding consortium-stage projects mature enough to test with real users. TEF-Health offers €300K in subsidized testing infrastructure for European healthcare AI and robotics SMEs. The pattern: funders are converting AI pilots into playbooks, requiring transfer evidence and risk controls before AI can move from experimentation to sector-wide operational practice. For LMIC-based researchers in infectious disease or evidence synthesis, the three Wellcome calls together represent the deepest concentration of LMIC-led research capital in recent memory — pair the Infectious Disease Clinical Trial Development Award (£200K, deadline May 19) as a strategic pipeline feeder into the larger Infectious Disease Clinical Trial Award (£1-£8M, deadline June 2), since the development award functions explicitly as a track into the larger trial scheme. For Nordic researchers, the four Novo Nordisk Foundation calls plus LEO Foundation and Nordic Innovation Quantum represent ~$30M in regional research capital this cycle, all with deadlines clustered in May-August. For WASH practitioners, the three Water Research Foundation calls together (each up to $200K) are a cluster worth reading across — one on AI deployment, one on shutoff alternatives, one rewarding applied innovation — and the strongest applicants will articulate how their work bridges utility operations to policy-relevant evidence. For US-based clinical researchers and educators, the four Pfizer calls plus the APF Portfolio’s 80+ programs form an unusually accessible recurring pipeline; APF’s prohibition on indirect costs is a meaningful structural feature for early-career applicants whose institutions might otherwise absorb significant overhead. Snapshot of New Opportunities Total Estimated Funding Pool: $100 Million+ USD The grants are organized into three categories: Open Calls: Current grant and opportunities with a deadline. Grants are listed by closing date. 39 open opportunities- 14 new! Rolling Applications: current grant and opportunities with rolling applications (but it’s still best to submit as early as possible). 14 rolling opportunities- 1 new opportunity! Long term planning: Grants that have closed their current rounds, but are expected to open new windows. 4 long term opportunities! A quick tip for returning readers: if you want to jump straight to the newest additions, use CTRL F to search for “New!” and navigate quickly to the latest funding opportunities This post is for paid subscribers. This helps support the time and effort it takes to curate and organize these opportunities. Subscribe To keep this accessible to everyone who needs it, we’re happy to offer pay what you can rates. You can find more details here. Open Calls: Migraine Competitive Grant Program: Migraine and Women’s Health (Quality Improvement and/or Research RFP), Pfizer.*Closing soon!* Pfizer, in collaboration with the American Headache Society, is seeking independent quality improvement and research projects that close persistent gaps in migraine care for women across the United States. The funding logic centers on measurable, practice-relevant change: proposals should generate actionable evidence, tools, or system improvements that can be shared and used widely, not just within a single site. Priority areas span menstrual migraine and screening in women’s health settings, the role of hormonal fluctuations across the lifespan, sex-specific risk factors, comorbidities and quality-of-life burden, and disparities in outcomes by race, ethnicity, and socioeconomic status. The call is structured as a two-step competitive process with an initial Letter of Intent, with the strongest concepts expected to translate scientific insight into implementable improvements in diagnosis, management, and equity. Geographies: United States. Who can apply: U.S.-based organizations (not individuals), including professional schools, healthcare institutions, professional organizations, and other entities focused on healthcare improvement. Funding amount: Up to USD $200,000 total over 2 years; total pool USD $500,000. Targeted Sectors / SDGs: Health; Focus areas: migraine, women’s health, quality improvement, clinical research, menstrual migraine, health disparities. Deadline: LOI: May 5, 2026; Full proposal (by invitation): August 28, 2026. Learn more and apply here. This RFP rewards “shareable impact”: the most competitive submissions will pair rigorous methods with a clear plan for system uptake and dissemination beyond the originating institution. ReSSARC: Strengthening the Health Sector and Food Security in the Central African Republic (AID 013381/01/0) Call for Proposals, Italian Agency for Development Cooperation (AICS). *Closing soon!* AICS is seeking a limited set of high-capacity humanitarian partners to deliver an integrated package that stabilizes essential health services while reducing acute malnutrition and strengthening food security resilience in priority areas of the Central African Republic. The funder’s logic is explicitly nexus-based: proposals should meet urgent, life-saving needs while reinforcing local systems, coordination mechanisms, and community-level capacity so results persist beyond the emergency window. AICS emphasizes inclusion and protection outcomes, expecting gender-sensitive design and explicit measures for groups facing compounded vulnerability, including women, children under five, internally displaced people, and people with disabilities. The call also signals a localization pathway, encouraging stronger collaboration with national NGOs and balanced partnerships, alongside clear coordination with existing cluster and humanitarian actors to minimize duplication and improve coverage. Geographies: Central African Republic (Bangui, Ombella-Mpoko, Ouham, Ouham-Pende, Lim-Pende, Lobaye). Who can apply: Non-profit organizations registered with AICS, plus eligible non-profits without an office in Italy that have a pre-existing collaboration agreement with an AICS-listed organization. Funding amount: Total pool EUR €1,800,000; max EUR €800,000 (single applicant) or EUR €1,000,000 (ATS). Targeted Sectors / SDGs: Health; Focus areas: malnutrition prevention and treatment, maternal and child health, community health services, food security and resilience, localization partnerships Deadline: May 5, 2026. Learn more and apply here. This call is structured to reward partners who can link frontline service delivery to system durability and localization, not just short-term coverage gains. Contracts for Innovation in drug and alcohol addiction healthcare, Innovate UK. *Closing soon!* Innovate UK, on behalf of the Office for Life Sciences Addiction Healthcare Goals program, is procuring R&D to accelerate innovations that can improve treatment outcomes, strengthen recovery, and reduce harm and deaths linked to drug and alcohol addiction. The competition is positioned as a market-facing readiness push: selected projects are expected to advance solutions toward later-stage validation, generate evidence of user acceptability and UK market fit, and map credible routes through regulatory and certification requirements. Innovate UK signals a preference for innovations that can be field-tested in relevant UK settings and progressed to TRL 6 or 7, with practical plans for commercialization after contract completion. This structure favors applicants that can execute most work in-house, co-develop with service providers and people with lived experience, and translate technical progress into deployable tools for addiction healthcare delivery. Geographies: United Kingdom. Who can apply: Single organizations of any size (including EU, EEA, or international) leading delivery, with most work and key deliverables carried out in the UK; subcontractors for specialist skills only. Funding amount: GBP £200,000–1.5 million per project (inclusive of VAT); Total pool: GBP £20 million (across two strands). Targeted Sectors / SDGs: Health; Focus areas: Mental Health, Health Systems Strengthening, Research & Development, Technology Access. Deadline: May 6, 2026 (11:00am). Learn more and apply here. This call uses procurement to pull near-market addiction innovations toward operational proof and adoption pathways in UK services.
Quantum Batteries Could Run For Years Off Of A Single One-Minute Charge
Quantum batteries have been a theoretical concept since 2013, but Australian scientists have just created a working prototype with insane charging speeds.
Global Health & WASH: October 2026 Funding Opportunities (26 new opportunities)
41 opportunities on the Global Health & WASH page this cycle, 26 of them new. The health money on this page is mostly research money, and much of it is aimed at people early in their careers. Funder after funder, from the TANGO2 Research Foundation’s Research Pathways grants to the South African Medical Research Council’s Early Investigators Programme to the National Ataxia Foundation and Alex’s Lemonade Stand Foundation, is funding early-career investigators and the institutions that host them, with a clear tilt toward researchers based in or from the Global South, from AMBSO in Uganda to Kenya’s CONNECT-VAX network to Ersilia’s AI Incubator for African drug discovery. The money is organized by disease as much as by need: narrow foundations, the Parkinson’s Foundation, NTAP for neurofibromatosis, the RUNX1 Research Program for an inherited blood disorder, each run their own grant, so the question for an applicant is less which health problem you work on than which foundation owns your disease. The largest European cheques go to consortia only, with the European Innovation Council putting up to EUR €4 million behind a single healthy-ageing project and UNITE up to EUR €1 million behind each cross-border digital health team. Water sits apart, almost entirely in applied utility research: the Water Research Foundation’s cluster of USD $250,000 to $500,000 projects, each built around a utility problem from sewer laterals to reuse treatment. And the biggest single pot is public: HRSA is renewing community health center service areas with awards of up to USD $10 million each. This post is for paid subscribers. This helps support the time and effort it takes to curate and organize these opportunities. Subscribe To keep this accessible to everyone who needs it, we’re happy to offer pay what you can rates. You can find more details here. Open Opportunities 2027 Building the Field of Health Advocacy Grant, Georgia Health Initiative. *New!* *Closing soon!* Georgia Health Initiative, a nonprofit private foundation focused on health equity, is opening its 2027 Building the Field of Health Advocacy Grant to strengthen the organizations that push for healthier systems across the state. The Initiative treats advocacy as infrastructure, backing one-year projects that range from urgent responses to shifting federal policy to longer arc campaigns on housing, behavioral health, care access, and food security. It wants coalitions that elevate community voice, apply an equity lens, and arrive ready to deploy concrete strategies, and it deliberately seeks variety in issues, communities, and organizational size. The optional webinar and required lobbying training signal a funder investing in a durable field, not one grant cycle. The strongest fits are Georgia nonprofits and public agencies ready to turn advocacy readiness into systems change. Geographies: United States. Who can apply: Tax-exempt 501(c)(3) public charities, state or local government agencies, or coalitions/collaboratives using a 501(c)(3) fiscal sponsor, that work in Georgia on behalf of Georgians and align with the mission, vision, and values of Georgia Health Initiative. Only one application per organization, though an organization may be listed as a partner or subcontractor on multiple applications. Funding amount: Organizations can apply for up to USD $80,000 per project; the Initiative anticipates investing a total of USD $1.36 million. Targeted Sectors / SDGs: Governance, Policy & Advocacy · SDG3; SDG10. Deadline: October 2, 2026. Learn more and apply here. TDD Research Pathways Grant, TANGO2 Research Foundation. *New!* *Closing soon!* The TANGO2 Research Foundation, working with a founding gift from Tango2UK, is opening its TDD Research Pathways Grant to fund early-career investigators anywhere in the world who study TANGO2 Deficiency Disorder. The foundation is using this money to seed pilot work and to build a research community around a rare disorder that few labs currently study. It wants to back emerging scientists, clinicians, and trainees who will stay with this field, not one-off projects, which is why it requires a committed mentor and real institutional support and asks each applicant for a plan for future contributions. The design rewards promise and persistence over track record. The strongest fits are students, residents, postdoctoral fellows, and new investigators ready to commit to TANGO2 research. Geographies: Global. Who can apply: Early-career investigators worldwide, including undergraduate, graduate, and medical trainees, postdoctoral fellows, residents, and clinical or research fellows. Early-career investigators must hold a PhD, MD, or equivalent doctoral degree and be within 10 years of that degree or within 5 years of initiating rare disease research, engaged in postdoctoral research at an academic institution, teaching hospital, or nonprofit research institute. Currently enrolled trainees and transitional PhD candidates beginning a postdoctoral appointment at a nonprofit institution by July 1, 2026 are also eligible. All applicants must identify a qualified mentor with relevant TDD or rare disease research experience and demonstrate institutional support. Funding amount: Up to USD $15,000. Targeted Sectors / SDGs: Innovation, Science & Technology. Deadline: October 2, 2026. Learn more and apply here. Call for Expression of Interest (EOI): AMBSO Impact Research Initiative, Africa Medical and Behavioral Sciences Organization (AMBSO). *Closing soon!* The Africa Medical and Behavioral Sciences Organization is inviting expressions of interest for its Impact Research Initiative, which funds early-stage pilot studies and proof of concept work on priority health challenges. AMBSO is a Ugandan research organization backing bold ideas that can grow into larger studies, and its tracks span antimicrobial resistance, epidemic preparedness, artificial intelligence for diagnostics, HIV prevention, climate and health, cancer, maternal and child health, and non-communicable diseases. The small award size and direct-cost-only rule point to a funder that wants evidence rather than overhead, and that favors projects able to attract follow-on money or use AMBSO’s own research infrastructure. Eligibility runs worldwide, welcoming researchers and institutions inside and outside Africa. The strongest fits are researchers with a feasible pilot that generates policy-ready evidence. Geographies: Global. Who can apply: Open worldwide to researchers, academic institutions, and research organizations whose work aligns with generating high-impact health evidence. Independent researchers are eligible if affiliated with an academic or research institution. Doctoral students are encouraged to apply as part of a research team led by a faculty Principal Investigator. Community-based organizations and other eligible entities may apply. Projects may be implemented inside or outside Uganda. Funding amount: Up to USD $10,000 per project, with no minimum funding threshold. Targeted Sectors / SDGs: SDG3. Deadline: October 2, 2026. Learn more and apply here. Entrepreneurs for Resilience 2027 (E4R27), Swiss Re Foundation. *Closing soon!* Swiss Re Foundation is opening the tenth edition of Entrepreneurs for Resilience, which backs healthcare social enterprises expanding affordable, quality primary care for low-income communities across low- and middle-income countries. The Foundation is using catalytic, blended funding plus multi-year advisory to take risk out of scaling, helping proven models reach more people sustainably rather than stay stuck at pilot size. It looks to back brick-and-click enterprises that pair physical care with digital tools and build affordability through savings, payments, or microinsurance, ideally integrated with public health systems. Requiring earned revenue, real customer scale, and gender-inclusive teams signals that this is growth capital for commercially serious ventures, not seed money. The strongest fits are established, revenue-generating healthcare enterprises ready to scale access for underserved populations. Geographies: Global. Who can apply: For-profit and hybrid healthcare social enterprises serving low-income and underserved populations in low- and middle-income countries, using combined physical and digital (brick-and-click) healthcare delivery models. Applicants must demonstrate measurable social impact, generate at least 50% of income through earned revenue, have been established no later than December 2023, already serve at least 50,000 people with potential to scale, and have at least 30% low-income customers. Preference for gender-inclusive enterprises with at least one female founder or at least 30% women in management. Funding amount: Pilot funding of up to USD $45,000 for the 6-8 shortlisted social enterprises; up to USD $1 million in blended funding (up to USD $600,000 unconditional plus up to USD $400,000 conditional) shared among 3-4 finalists over a two-year implementation period; total programme funding approximately USD $1.3 million. Targeted Sectors / SDGs: Innovation, Science & Technology · SDG3; SDG5. Deadline: October 4, 2026. Learn more and apply here. Horizon HealthTech 2026, IIITD Innovation & Incubation Center. *Closing soon!* IIITD Innovation and Incubation Center is opening Horizon HealthTech 2026, a nine-month MedTech incubation cohort run under the aegis of BIRAC EDGE for founders building medical devices, diagnostics, surgical and interventional tools, or remote monitoring. The center is backing teams that already hold a validated proof of concept and want to reach a tested prototype, pairing each founder with a named expert and clinical validation through a hospital partner. What they seek to back is Indian health technology that can survive real clinical scrutiny and regulatory review, not slideware. The structure, with lab access, CDSCO guidance, IP support, and investor connects, signals a program built to make startups ready for follow-on capital. The strongest fits are early-stage MedTech teams with defensible IP and a working path from bench to bedside. Geographies: India. Who can apply: Early-stage teams/startups with a validated idea/proof of concept (TRL 3 onwards) working on medical devices, diagnostics, surgical/interventional technologies, or remote monitoring, holding IP or with potential to generate IP. Funding amount: No cash award amount is stated on the official program page. Benefits are in-kind: prototyping lab and equipment access, clinical/hospital validation (Max Healthcare), expert mentorship, CDSCO regulatory guidance, IP filing support, workspace, cloud credits and tools, and investor connects. Run under the aegis of BIRAC EDGE and builds a path to follow-on Government/VC/Angel funding. Targeted Sectors / SDGs: Innovation, Science & Technology · SDG3; SDG9. Deadline: October 5, 2026. Learn more and apply here. OpenAI AI and Teen Development Research Grant Program, OpenAI. *New!* *Closing soon!* OpenAI is opening its AI and Teen Development Research Grant Program to fund independent studies of how generative AI shapes the lives of young people ages 13 to 17. OpenAI wants a stronger evidence base on how teens actually use these tools and what that use does to their social and emotional development, so that product teams, policymakers, and regulators can act on findings rather than assumptions. It seeks work on emotion regulation, relationships, belonging, and the safeguards that keep teens safer, welcoming interdisciplinary teams and any methodology. That it will fund research whose conclusions may not favor AI providers, and prioritizes studies touching ChatGPT, signals a genuine appetite for credible outside scrutiny. The strongest fits are research groups grounded in adolescent development, well-being, and AI safety. Geographies: Global. Who can apply: Researchers aged 18 or older who are affiliated with a research institution or organization, and/or have significant experience relevant to child or adolescent development, well-being, human-computer interaction, AI impacts, or a closely related field. Multiple institutions may collaborate. OpenAI is seeking to fund research rather than for-profit initiatives and will not prioritize for-profit organizations. Funding amount: Individual research grants up to USD $1 million, totaling up to USD $5 million; maximum 10% of each grant for overhead or indirect costs. Targeted Sectors / SDGs: Social Inclusion & Community Wellbeing; Innovation, Science & Technology. Deadline: October 6, 2026. Learn more and apply here.
We tried Google’s AI glasses and they’re almost there
Google demoed prototype Android XR glasses that overlay Gemini-powered translation, navigation, and other information directly into your field of view.
SpaceX has an AI device prototype, and it sure sounds phone-ish
SpaceX reportedly showed investors a "handset-like" AI device before going public. It could be another signal SpaceX wants to expand into wireless.
Celebrating Women in Science Day 2026 at CERN
Every year, on 11 February, CERN celebrates the International Day of Women and Girls in Science. In an effort to amplify diverse voices in STEM, six women scientists from CERN open up about their careers and the lessons they hope to pass on to the next generation. Francesca is an Italian biomedical engineer pursuing her PhD at CERN, working in the robotics team of the Beams Department. She’s currently working on the MARCHESE project, in affiliation with Campus Bio-Medico University of Rome, focusing on contactless monitoring of physiological parameters. Despite choosing a scientific career and often volunteering in scientific outreach initiatives, Francesca continues to nurture her passion for the humanities and arts. “I firmly believe that collaboration and inclusion are the right paths to improve the world together. I’m driven by a strong curiosity about everything around me.” Berare is a Turkish experimental physicist who worked with the ATLAS experiment during her MSc and PhD on searches for physics beyond the Standard Model. During her PhD, she became a Support Scientist for the Beamline for Schools (BL4S) competition, which marked the beginning of her journey combining research with science communication. She now works for both BL4S and ELISA, the mini proton accelerator at CERN’s Science Gateway. She fondly remembers the moment when the BL4S 2022 winners arrived at the experimental zone: “I felt incredibly lucky to share my passion with young, curious and really passionate people. We spent two weeks of beam time together watching their ideas turn into reality. All the intricate details of running an experiment, we accomplished them as a team.” Mia is a Canadian research fellow in the Accelerator Systems Department, working on beam developments for fundamental physics with radioactive molecules. Mia pursued a bachelor’s degree in mechanical engineering before moving to Vancouver for a master’s degree in engineering physics. Her interest in radioactive ion beam production brought her to CERN, where she studied actinide beams for three years as a PhD student in the Marie Curie European Training network “LISA” (Laser Ionisation and Spectroscopy of Actinides) at ISOLDE – a facility at CERN where scientists study the properties of atomic nuclei, with further applications in fundamental studies, astrophysics, and material and life sciences. Laura is a German mechanical engineer at CERN working on research and development to produce cavities for future accelerators. She studied mechanical and aerospace engineering at the Technical University of Munich. She joined CERN four years ago as a technical student for her master’s thesis and continued as a fellow working on collimators. Laura says that “as a child, I took part in girls’ day initiatives, which sparked my fascination for astrophysics and engineering. Later, it felt natural for me to give something back by organising workshops for young students to make STEM seem less intimidating and more accessible.” She particularly enjoys being close to the CERN Main Workshop and understanding how design ideas become real components. Paraskevi is a Greek physicist working at CERN’s HiRadMat (High-Radiation to Materials) facility, designed to deliver high-intensity beams to an irradiation area where material samples, as well as accelerator component assemblies, can be tested. She studied physics at NKUA, specialising in nuclear and particle physics, and is now pursuing her PhD at HiRadMat in collaboration with the University of Liverpool, studying novel materials for high-intensity beam diagnostics. “Continuous learning is one of my biggest motivators, and I am looking forward to exploring the limits of our knowledge on beam-material interactions,” says Paraskevi. Silvia is a Spanish physicist and deputy project manager for the Muon Drift Tube of the CMS detector, a general-purpose detector at the Large Hadron Collider (LHC). She started her career at CERN more than 20 years ago as a technical student, working on prototypes of the first dipoles for the LHC. “My first motivation came in high school, from my teacher,” says Silvia about what made her pursue a career in STEM. “My favourite part about my job is learning. You can learn something new every day.” In parallel to this initiative, like every year since 2017, from 2 to 6 February 66 women scientists and engineers visited schools in Geneva, Switzerland, and in the Haute-Savoie and Ain regions of France to meet with students. Organised in collaboration with CERN, the University of Geneva, EPFL, LAPP and LAPTh, this event allowed more than 4200 students between 6 and 17 years old to meet a woman scientist, discuss their profession and career path, ask questions and participate in classroom activities.
Zelda Art Director Receives Backlash For AI Prototype Test
Takaya Imamura was surprised that he was 'getting slammed this hard' for sharing a prototype made with the help of AI
Call of Duty studio Sledgehammer reportedly worked on a Halo multiplayer prototype before it got canned
The next Halo game may be developed by an unlikely studio.
From invention to impact: Hong Kong Science Fair gives young innovators a wider stage
Organised by the Hong Kong Innovation Foundation, with Sino Group as Principal Patron and the Innovation, Technology and Industry Bureau as Supporting Bureau, the Fifth Hong Kong Science Fair has drawn to a close. Students are already looking beyond their prototypes, with international exchanges and future applications now firmly in sight.
Proposed Space Station Could Be Deployed in a Single Launch
A prototype of Max Space's Thunderbird could launch as early as 2027.
How LuGus Studios Turned a Viral Prototype Into a Successful Business
LuGus Studios discusses building Liftoff, growing a niche drone sim audience, and sustaining a 15-year indie studio through community-driven development, iterative workflows, and a focus on impact over scale.
Casio previews a portable standalone sampler at NAMM 2026
NAMM 2026: Casio previews a new portable standalone sampler with 16 pads, real-time recording, a sequencer, and more.
Leaker: Foldable 'iPhone Ultra' Will Feature Liquid Metal Hinge
Apple's first foldable iPhone will feature an innovative liquid metal hinge and has now shipped prototype units to carriers around the world for testing, the leaker known as "Fixed Focus Digital" today said. In a new post on Weibo, Fixed Focus Digital said development and production related to the foldable are now "progressing rapidly". The claim arrives one day after the leaker reported that the foldable iPhone would feature vapor chamber cooling.
Senphonix Receives National Science Foundation Grant to Advance SleeveSense, a Continuous, Long-Term Vitals Monitoring Platform
Senphonix, Inc., a medical-technology startup developing SleeveSense™, a wearable that will continuously monitor patients' vital signs over days, weeks, or months, today announced it has received a Small Business Technology Transfer (STTR) Phase I award from the U.S. National Science Foundation (NSF) through America's Seed Fund. The award supports engineering to strengthen the patent-pending device's wireless and radio-frequency systems and to move it from prototype toward a manufacturable produ
Apple Pencil for iPhone Ultra Was Tested
Apple has internally prototyped, tested, and considered launching an Apple Pencil for the upcoming foldable iPhone, according to Bloomberg's Mark Gurman. However, he said it is unlikely that the accessory will be released. In his Power On newsletter today, Gurman said the Apple Pencil tested for the foldable iPhone is stubbier than the versions for iPads. He said the Apple Pencil for the foldable iPhone was designed to magnetically attach and charge on the side of the device.
New Richland Lab Could Transform How Scientists Study Microbes and Boost Regional Jobs
The BriefGroundbreaking held Tuesday: PNNL officially began construction on the M2PC lab, a 30,800-square-foot facility funded by the U.S. Department of Energy's Office of Science that will study soil microbes to improve crop productivity and develop new biotechnology applications. The facility will be open to researchers from around the nation and the world.Research timelines could shrink dramatically: The facility's automation and AI capabilities could reduce experiments that currently take six months to just 72 hours, allowing scientists to explore more possibilities faster than ever before.New jobs coming to the Tri-Cities: The project is expected to create positions for automation engineers, AI experts, roboticists, technicians, and skilled craftspeople, with the facility opening in 2029.RICHLAND, Wash. — Pacific Northwest National Laboratory broke ground Tuesday on a first-of-its-kind facility that could accelerate scientific discoveries about the microscopic organisms living in soil—organisms that may hold the key to growing more food and developing new energy technologies.The Microbial Molecular Phenotyping Capability, or M2PC, represents a significant investment in automation and artificial intelligence to speed up research that traditionally takes years to complete. The 30,800-square-foot facility will house more than 100 connected automated instruments designed to study microbes at a pace and scale never before possible.Reshaping Agriculture's FutureThe M2PC facility will not put a new tool directly into a farmer's hands tomorrow morning, but it is actively reshaping the commercial pipeline to solve agriculture's biggest headache with biologicals: inconsistent field performance. For years, PNNL researchers have explored how microbes work and how they might help produce useful materials, advance energy technologies, and address challenges involving critical materials and minerals. But those experiments have been limited by what researchers could accomplish by hand."They can be used to provide nutrients to a plant so that the plant would grow more. So, like increasing your crop productivity, for example," said Douglas Mans, PNNL's Associate Laboratory Director for Integrated Discovery Sciences.PNNL's own research shows soil microbes can help plants access nutrients and withstand drought—benefits that could prove crucial as farmers face growing environmental pressures. The M2PC facility will allow scientists to identify and optimize useful microbes faster than ever before, accelerating the development of biological solutions that actually work consistently in the field.What Makes M2PC UniqueThe Microbial Molecular Phenotyping Capability differs from traditional research labs because it operates as a first-of-its-kind, AI-enabled autonomous laboratory designed to shrink experimental timelines from years down to months or weeks. The M2PC lab will connect more than 100 automated instruments to grow microbes, prepare samples, measure their behavior, and link observations to the molecular processes behind them. Artificial intelligence will help analyze results and guide the next experiments, allowing scientists to explore more questions faster.Deb Gracio, PNNL Director, emphasized the transformative potential during Tuesday's groundbreaking ceremony: "Research that has taken years will become possible in months and hopefully in weeks."That acceleration is already being demonstrated. PNNL's smaller automated lab, called the Anaerobic Microbial Phenotyping Platform or AMP2, completed three experimental cycles in just 72 hours—a process that normally would have taken about six months."That normally would have taken us about six months … and we were able to do that in 72 hours," Mans said.The AMP2 laboratory is not being closed or replaced. Instead, the current 1,800-square-foot laboratory serves as an active prototype and precursor for the much larger M2PC facility. AMP2 will continue operating as a separate lab designed to run anaerobic experiments—those conducted in the absence of oxygen—while M2PC will focus on aerobic research.Broader Applications Beyond AgricultureWhile improving crop productivity is one important application, the M2PC facility is designed to support research into multiple areas critical to national priorities. The lab will advance research into biofuels, manufacturing materials, and energy technologies. It will also help researchers understand how microbes respond to environmental changes and how to design biological systems for biotechnology applications.As a flagship component of the Department of Energy's Genesis Mission, M2PC represents a significant investment in using artificial intelligence and automation to accelerate U.S. scientific discovery. Dorothy Koch, Associate Director of the DOE Office of Science Biological and Environmental Research program, highlighted the facility's importance during the groundbreaking."M2PC will accelerate biological discovery and design," Koch said, noting that the facility is expected to conduct a minimum of 500 strain experiments per week, 100 microbiome experiments per week, and take a million measurements per month.Economic Opportunity for the RegionBeyond the scientific breakthroughs, the M2PC project represents significant economic opportunity for the Tri-Cities region. The facility will require hiring automation engineers, AI experts, technicians skilled in operating robotics, and specialized craftspeople to build and maintain the sophisticated equipment."We're anticipating that type of hiring," Mans said when asked about job creation.PNNL has already begun recruiting talent nationally and internationally for positions related to the smaller AMP2 lab, bringing automation engineers from cities like Boston and San Francisco to work in the region. The M2PC facility is expected to accelerate that trend."This capability won't really exist anywhere else in the world," Mans said, suggesting the facility could become a draw for scientific talent seeking to work on cutting-edge biotechnology research.Timeline and Next StepsThe M2PC facility is expected to open in 2029. Between now and then, construction crews will level and prepare the ground, while the building takes shape. Simultaneously, Ginkgo Bio Works, the automation contractor, will build the facility's automation components off-site so they can be installed as the building is completed.In the interim, PNNL researchers and the broader scientific community will begin planning the first experiments to be conducted in the facility, ensuring research is lined up and ready to begin as soon as the doors open.The groundbreaking ceremony included representatives from the Department of Energy, PNNL leadership, and elected officials at the federal, state, and local levels, underscoring the project's significance to the region and the nation's biotechnology future. News The U.S. Department of Energy Secretary tours Pacific Northwest National Laboratory Dan Hanson, Apple Valley News Now News Beyond DNA: Scientists Explore New Frontiers in Biology Research Dan Hanson, Apple Valley News Now News TRIDEC optimistic that Hanford, PNNL will see funding in national budget Robert Desaulniers News Big picture science: monitoring the Yakima River Basin News PNNL research could shape the future of fighting fentanyl COPYRIGHT 2026 BY APPLE VALLEY NEWS NOW. ALL RIGHTS RESERVED. THIS MATERIAL MAY NOT BE PUBLISHED, BROADCAST, REWRITTEN OR REDISTRIBUTED.
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The approach uses lasers to hold a tiny bead containing radioactive atoms; when one decays, the system measures the bead’s motion and the energy of any particles emitted. The experiment’s initial goal is to search for heavy sterile neutrinos, hypothetical particles predicted by some extensions of the Standard Model. The system could also help measure the mass of the neutrino, study the way particles interact through the electromagnetic and weak forces, and hunt for low-mass dark matter — all unsolved mysteries in particle physics. Callum Wilkinson: Using machine learning to demystify neutrinos Callum Wilkinson is a staff scientist in the Physics Division and part of the international Deep Underground Neutrino Experiment (DUNE) hosted by Fermi National Accelerator Laboratory. DUNE will create an intense beam of neutrinos that interact in detectors in Illinois and, after an 800-mile journey through the earth, in massive detectors in South Dakota. By precisely measuring neutrinos and how they change, researchers will get a better understanding of their fundamental properties and whether they are responsible for why there is so much more matter than antimatter in our universe. Wilkinson’s ECRP project will apply two machine learning techniques to improve the analysis of DUNE’s data and enhance the experiment’s precision. One approach will look for hidden patterns in how neutrinos interact and produce low-energy particles, while the other will help correct for distortions in what the detectors record. The methods will be tested on one of DUNE’s prototype detectors and could be used in other neutrino experiments, improving their measurements and helping scientists build better models. Aditi Krishnapriyan: Machine learning methods that scale as datasets grow Aditi Krishnapriyan is a faculty scientist in Berkeley Lab’s Applied Mathematics and Computational Research Division (AMCR) and assistant professor at UC Berkeley. Her project, awarded through UC Berkeley, will develop machine learning methods that scale with dataset size for modeling complex, multi-scale phenomena. The resulting tools can be applied in a wide range of research, including the study of new electrolytes for batteries or the examination of molecular interactions. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
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Get E-International Relations delivered to your inbox, free of charge. As you sign up, consider becoming a paid subscriber, or make a donation, to support our work. Subscribe This is an excerpt from Global Politics in a Post-Truth Era. You can download the book free of charge from E-International Relations. The term post-truth is by now so ubiquitous in describing the state of politics in the Western world and even beyond that the most pressing issue of the day appears to be about making democracy work in the era of post-truth rather than questioning the normalisation of the term itself in the first place. The critical turning point appeared to have occurred in 2016 when the term was chosen by the Oxford English Dictionary as ‘The Word of the Year.’ On its website, the Dictionary notes the transformation of post-truth from ‘being a peripheral term to being a mainstay in political commentary and connects ‘the spike in frequency’ directly with the Brexit referendum in the United Kingdom and the election of Donald J. Trump in the United States – both occurring in 2016. Paired with the noun ‘politics,’ post-truth is defined as an adjective ‘relating to or denoting circumstances in which objective facts are less influential in shaping public opinion than appeals to emotion and personal belief.’ The Oxford English Dictionary traces the first use of the term to a 1992 essay in The Nation by the playwright Steve Tesich as he reflected on the 1980s Iran-Contra scandal and the 1990–91 Gulf War: We are rapidly becoming prototypes of a people that totalitarian monsters could only drool about in their dreams. All the dictators up to now have had to work hard at suppressing the truth. We, by our actions, are saying that this is no longer necessary, that we have acquired a spiritual mechanism that can denude truth of any significance. In a very fundamental way we, as a free people, have freely decided that we want to live in some post-truth world (Tesich 1992, 13; emphasis added). He then continued, The Gulf War is over but the war at home goes on. The gulf between rich and destitute widens – between those of us who live in a modern post-industrial nation and those of us who live in the Third World countries of our inner cities. The present administration’s response to this internal crisis has shifted from benign to malignant neglect. The current levels of misery and decomposition of our cities and the economic gulags of our ghettos are acceptable. Since there is only so much hope to go around, there is a freeze on hope. The have-nots have now been reclassified as never-will-haves (Tesich 1992, 13). In the context of Tesich’s essay, truth includes both facts, as in what happens, and ‘moral absolutes,’ as in the ‘self-evident truths mentioned in our Constitution’ (Tesich 1992, 14). Together, these two notions of truth entail the obligation to both act and react. The two passages cited above show that even in the early post-Cold War years, Tesich was already deeply concerned that the American people are neither prepared to act on truth nor react to truth that is disconcerting, or what he calls ‘bad news’ (Tesich 1992, 12). Accordingly, Tesich’s insight as captured by the term post-truth is not that truth is irrelevant to what we know, but rather it is irrelevant to how we act. In contrast, ‘post-truth politics,’ as defined by Oxford as a term that pitches ‘objective facts’ against ‘emotion and personal belief’ such that the latter appears to have overtaken the former in shaping ‘public opinion’ and thereby, democratic politics. When Tesich’s post-truth politics and its current usage are juxtaposed, the two are in fact not identical in terms of what truth is and how it relates to politics. Against such consideration, this chapter provides a critical survey of selected historical events and theoretical considerations for the purpose of gaining a more nuanced understanding of the relation between truth and politics. Truth and Language One notable impact of post-truth politics is that language is being overtly deployed as a political weapon not only by politicians and those in power, but among the general population. It seems that once facts are irrelevant to the validity of what is being said, language becomes the platform for an all-out political contest in which what is said is no more than an act of will expressed as an assertion of power and/or an exercise in defiance. As early as in the writings of Thucydides (460–400 BCE), the vulnerability of logos – which is the human capacity for rational discourse in tumultuous times – was already noted. Thucydides belonged to a school of Greek thought which maintained that language is conventional and that the association between name and things is a result of ‘human use and habit’ rather than ‘nature or divine dispensation’ (Connor 1984, 99). The conventional nature of language means that it is incapable of providing its users with any independent criteria for judgement. In Thucydides’ study of the Peloponnesian War (431–405 BCE), the subversion of logos as seen in the manipulation of language was introduced through his account of the civil war in Corcyra (Thucydides 1972, 236-245). The war first broke out in 427 BCE between the pro-Athenian democratic faction and the pro-Spartan oligarchic faction before it spread to the rest of the Greek world. There was a general breakdown of law and order, resulting in extreme violence and death. Language underwent changes that were reflective of the events of the time. The English philosopher Thomas Hobbes (1588–1679) translated Thucydides’ description of the situation as follows: The received value of names imposed for signification of things, was changed into arbitrary. For inconsiderate boldness, was counted true-hearted manliness: provident deliberation, a handsome fear: modesty, the cloak of cowardice: to be wise in everything, to be lazy in everything... he that had been so provident as not to need to do the one or the other, was said to be a dissolver of society, and one that stood in fear of his adversary. In brief, he that could outstrip another in the doing of an evil act, or that could persuade another thereto that never meant it, was commended (Hobbes 1839–1845, 8: 348; Thucydides 1972, 242-243). What concerned Thucydides most was that the meanings of words are no more than linguistic conventions that require logos to support them. The Corcyrean civil war was a reminder that such capacity cannot be assumed. This is why history understood as an authentic account of what happens matters. Guided by logos, the historian’s task is ‘the search after truth’, which is established through ‘the absence of the fabulous,’ including ‘the rejection of myth and unverifiable stories’ (de Jonge 2017, 2). Thus, history plays an important role in facilitating the stability of language – without which distinctions and judgements cannot be rendered communicable. Just like Thucydides, Hobbes was a nominalist who subscribed to the view that there is no inherent meaning to words. Hobbes’ state of nature, where there is no common authority, is precisely one in which distinctions cannot be made. As Hobbes put it, there is ‘no Mine and Thine distinct’ nor is there distinction between ‘the notions of Right and Wrong, Justice and Injustice’ (1968, 188; emphasis in original). This is clearly an unsustainable condition for everyone. Behind Hobbes’ famous words that life in the state of nature is ‘solitary, poor, nasty, brutish, and short’ is his uncompromising argument for the ‘Leviathan’, which is described by the late American political theorist, Sheldon Wolin (1922–2015), as the ‘sovereign definer’ (Hobbes 1968, 186; Wolin 2004, 238–243). Accordingly, the social contract is importantly a ‘linguistic contract’ that binds all parties to ‘general names agreed upon’ (Peters 1979, 125; Hobbes 1968, 111). Through Thucydides and Hobbes, we can identify a line of thinking that sees politics as a contestation over the use of language, which is based on shared conventions and norms. Thus understood, politics is not a collective quest for the true nature of our being or an epistemological exercise in pursuit of true knowledge. Rather, politics is about humans living with one another by finding a shared language to communicate with one another on matters that affect them all. Truth is in that sense contingent on what is being said as opposed to setting the standard for it. In other words, truth is the embodiment of shareability among us. While truth as such ensures the viability of a political entity, truth by no means represents a normative standard to adjudicate among contesting actors in what they say, let alone in what they do. During Thucydides’ times, ancient China was also experiencing a tumultuous time as the ruling Zhou dynasty deteriorated into the period of the Warring States (481–221 BCE). This was when the rulers of Zhou no longer presided over the territories brought under its control through conquest. Instead of professing allegiance to the Zhou royalty, the lords of these territories became kings themselves to rule over their own land. The period was one marked by endless military campaigns as each of the seven major states aimed to conquer the rest (Lewis 1999). Yet, the period was also known for its intellectual vibrancy as different thinkers challenged the restoring of order among these warring states – including offering ideas and advice to rulers on good governance. Among these thinkers was Confucius (551–479 BCE). His thought, which came to be known as Confucianism, went on to become the state ideology of China from the Han dynasty (206 BCE–222 CE) until the end of the Qing dynasty in 1911 when China became a republic. The founding text of Confucianism was the Analects, which is a collection of dialogues between Confucius and his students as recorded by them. One key concept Confucius advanced is the ‘rectification of names’. Though the term only appears in one exchange in the Analects, it is considered a major aspect of Confucius’ thought. Generations of Confucian scholars from past and present, including Western academics, have debated over its interpretation (Makeham 2003). The passage in question reads as follows: Zi Lu said: ‘The monarch of the state of Wei wants you to govern the country, what is the first thing you plan on doing?’ Confucius said: ‘First it is necessary to rectify the names.’Zi Lu said: ‘Is that really what has to be done? You are being too pedantic, aren’t you now? How will you rectify these names?’Confucius said: ‘Zhong You, you are too unrefined. A gentleman, faced with a matter that he does not understand, takes a skeptical attitude. If names are not correct, one cannot speak smoothly and reasonably, and if one cannot speak smoothly and reasonably, affairs cannot be managed successfully. If affairs cannot be managed successfully, rites and music will not be conducted. If rites and music are not conducted, punishments will not be suitable. And if punishments are not suitable, the common people will not know what to do. So, when the gentleman uses names, it is necessary to be able to speak so that people understand. If one can say it, one can definitely do it. A gentleman should not be careless with words’ (Analects 13:3 in Cao 2016, 148). This translation is offered by the contemporary philosopher Cao Feng. As Cao notes, the passage is what started the whole debate over ‘exactly what kind of names did Confucius... wish to rectify’ (Cao 2016, 148). Of the three major groups of interpretation examined by Cao, beginning with classical Confucian scholarship, one is arguably the most influential and certainly most pertinent to the issue of language and politics. This is the interpretation that was first associated with the Neo-Confucian master, Zhu Xi (1130–1200), who argued that the ‘rectification of names is simply the rectification of politics’ (Cao 2016, 149). Within this interpretive framework, rectification has the dual functions of correction and prescription by ensuring that predetermined hierarchical roles are strictly adhered to through following ‘the system of rites and propriety’ (Cao 2016, 151). In addition, the rectification of names entails ‘using names to rectify actuality’ (Cao 2016, 151). Yet Cao rejects this whole body of interpretive works by arguing that these were ideas developed later. Instead, Cao suggests the following: The original meaning of Confucius’ rectification of names may, then, be quite simple. Confucius did not mean to establish a concrete, normative system of “names”; rather, Confucius was simply the first person in history to realize or remark upon the importance of language in politics. As a politician, he noticed and foresaw the impact that the indeterminacy, ambiguity, and arbitrariness of names would have on politics. Confucius recognized the political consequences of language’s not accurately expressing meaning or not being accurately received by people. He realized the great role that names, as a means of distinguishing right and wrong and establishing norms, could play in society and politics (Cao 2016, 168). Scholarly debates aside, Cao’s view has the advantage of identifying an interpretation of this controversial exchange in the Analects that enables its readers to extrapolate from it the observation that the meaning of words depends as much on their users as on their interpreters; neither of which can be predetermined. Accordingly, a good ruler is someone who can speak without ambiguity to his subjects and make good his words through actions. Stated differently, Cao’s view on what concerned Confucius then, who was living through times of protracted political upheaval, is that language is an indispensable political tool for those in power and can be deployed by both good and bad rulers alike. To sum up, this historical survey shows that politics as a war of words is by no means a distinctive marker of contemporary politics nor the Western world. Be it ancient or current, east or west, language is subject to manipulation to serve political purposes and when there is an outright war of words that defies existing norms, it is indicative of a political situation in which people no longer share a language and truth becomes irrelevant. Accordingly, political order entails stability in language use. Truth and Politics As previously noted, current use of the term post-truth as an adjective to the term ‘politics’ suggests that once there was a time when politics aligned with truth. Indeed, if we start with Plato’s Republic as the key founding text of Western political thought, we see that the ideal polis (city-state of ancient Greece) is where truth and politics converge, and justice is achieved. Written around 380 BCE when Athens was still coping with its defeat by the Spartans in the Peloponnesian War, the Republic is a defence of the rule of the ‘philosopher king’, defined as someone who is ‘guided by the truth’ and is ‘always and in every way’ in pursuit of the truth (Plato 1974, 490a). For Plato, truth converges with virtues and hence, letting the philosopher take charge of politics is to let the wisest and the most virtuous lead the less wise and the less virtuous even if it requires compulsion (Plato 1974, 499b). In Plato’s words, ‘…until the philosophers attain power in a city there will be no respite from evil for either city or citizens…’ (Plato 1974, 501e). The uncompromising rule of philosopher-kings is made palatable by the claim that we are all ‘earth- born brothers’ with random mixtures of gold, silver, bronze and iron, but only the gold can rule (Plato 1974, 414–415). Understandably, academic debates over the interpretation of Plato’s Republic revolve around the question of whether truth as attained in philosophy can be realised in politics as action and whether such a connection is even desirable. Without getting into the details of these scholarly exchanges, I suggest that they all share the concern that truth acquired through the rigorous process of philosophical reasoning is not without problem when applied to the real world of living humans. This is because not all humans are philosophers – and indeed, in Plato’s view, not everyone is capable of becoming a philosopher given the protracted and arduous training that one needs to endure to become one. Hannah Arendt (1906–1975), a highly influential political philosopher of the twentieth century, notes that ‘Plato was the first to introduce the division between those who know and do not act and those who act and do not know’ and that such a separation has remained ‘at the root of all theories of domination’ (Arendt 1958, 223, 225). What Arendt points out in this remark is not the gap between theory and practice, but rather that Plato provides the normative ground for those who know the truth to tell those who do not know what to do. Arendt herself took on the challenge of understanding the relationship between truth and politics through her analysis of Nazism and Stalinism. Arendt’s insight, which was controversial when The Origins of Totalitarianism was published in 1951, was to identify Nazism and Stalinism as representative of a new form of political rule – totalitarianism – making it distinct from ‘other forms of political oppression... such as despotism, tyranny and dictatorship’ (Arendt 1973, 460). Arendt defines totalitarianism as a self- contained system of ideas driven by their ‘logic’ and marked by a mode of reasoning called ‘ideological thinking.’ Ideological thinking as such is a distinctive form of political reasoning defined by three characteristics. First, ideological thinking claims to provide a ‘total explanation’ of all historical happenings, encompassing the past, the present, and the future. Second, while ideological thinking is an attempt to account for ‘factual reality’ in its totality, this mode of thinking is paradoxically divorced from reality and experience. This is because experience, being rooted in specificity, is clearly limited in its claim to totality. It follows that as its third characteristic, ideological thinking aims to emancipate thought from the limitation imposed by experience through a ‘specific method of demonstration.’ The method involves arranging facts into an ‘absolutely logical procedure’ that starts from an ‘axiomatically accepted premise’. It is, in short, a ‘process of deduction from a premise’ – which is the only possible movement in the realm of logic that leads to one definitive conclusion. This is how the ‘logicality of ideological thinking’ provides the basis for total explanation and the ‘principle of action’ for totalitarian movement (Arendt 1973, 468–474). Stated differently, totalitarianism is a form of uncompromising idealism that exists in one’s head irrespective of shared reality as experienced. It is the perverse form of what Arendt later refers to as ‘rational truth’, which is the product of the ‘speculative mind’ that belongs to philosophy, mathematics and the sciences. The ‘speculative mind’ is the mind working on its way to generate axioms and theories by relying on the cognitive capacity of the human brain (Arendt 1977, 231). The presence of others has no bearing whatsoever on rational truth. In contrast, ‘factual truth’ is what happens when humans are ‘living and acting together’ (Arendt 1977, 231). Factual truth is therefore particular and temporal by definition. But more importantly, factual truth is ‘political by nature’ because it can only be validated in the context of the human community (Arendt 1977, 238). In other words, factual truth is about humans in their plurality rather than in their singularity. For factual truth to be sustainable, we need what Arendt calls ‘common sense’ – understood as ‘community sense’ (Arendt 1982, 72). There is also another distinction about truth that Arendt makes which is pertinent to understanding how totalitarianism works. The issue is whether the so-called totalitarian ideology is simply an unnecessarily cumbersome way of saying that it is an all-round and polished lie. Here, Arendt calls totalitarian ideology the ‘modern lie’ – in contrast to the ‘traditional lie’. The latter is restricted by two conditions: that it is a lie about the ‘particulars’ and that it deceives only the ‘enemy’ – which means that the liar is not engaged in self- deception. The modern lie, however, involves a ‘complete rearrangement of the whole factual texture’. It also intends to deceive everyone alike, to the extent that those who initiate the lie eventually ‘fall victims to their own falsehood’ (Arendt 1977, 249–254). This distinction between the traditional and modern lie is important. It indicates that not only is totalitarianism a lie, but the lie is in fact too big to fit into the standard meaning of the word. To tell a lie from truth, we need a common standard. In the case of a traditional lie, the common standard is precisely ‘the fabric of factuality’. As such, a lie always appears as a ‘tear’ to that fabric. Yet, modern lies require making a surrogate reality with a completely different context for facts to fit ‘without seam, crack, or fissure, exactly as the facts fitted into their original context’. Arendt notes that as long as those who concoct the surrogate reality are prepared to engage in self- deception to ‘create a semblance of truthfulness’, there is nothing to prevent ‘these new stories, images, and non-facts from becoming an adequate substitute for reality and factuality’ (Arendt 1977, 253–254). Totalitarianism therefore requires a person to stand outside of the system itself in order to make sense of it. Arendt points out that it is futile for us to argue with (for example) a Nazi or a Stalinist on race or class. To confront totalitarianism requires more than just a confrontation with specific facts. It is a more fundamental confrontation between reality as experienced and its total rejection. This means that Nazism and Stalinism are more than radical ways of conducting racial conflict or class struggle. Rather: Totalitarian politics – far from being simply antisemitic or racist or imperialist or communist – use and abuse their own ideological and political elements until the basis of factual reality, from which the ideologies originally derived their propaganda value– the value of struggle, for instance, or the interest conflicts between Jews and their neighbours – have all but disappeared (Arendt 1973, xv). Accordingly: The ideal subject of totalitarian rule is not the convinced Nazi or the convinced Communist, but people for whom the distinction between fact and fiction (that is, the reality of experience) and the distinction between true and false (that is, the standards of thought) no longer exist (Arendt 1973, 474). Who then is the ‘ideal subject’ of totalitarianism? Although there is a revival of interest in Arendt’s observation on truth and politics since the onset of post- truth politics, not enough has been said about her reply to the question (Hyvönen 2018; Klinkler 2018; Lee 2019; Zerilli 2020). Yet if we take a look at Arendt’s portrait of this ideal subject, her view is even more pertinent to our current challenge. As a self-contained system of ideas that is detached from experience, totalitarian thinking offers consistency in ways that reality cannot. Arendt notes that this kind of consistency is especially appealing to those who have lost a sense of bearing in the world (Arendt 1973, 353). They constitute what she calls the ‘masses’. The ‘mass man’ is thus a highly isolated and atomised individual, who is ‘obsessed by a desire to escape from reality’ (Arendt 1973, 318, 352). In his ‘essential homelessness’ the mass man can ‘no longer bear its accidental, incomprehensible aspects’ (Arendt 1973, 352). This longing for escape from reality is a ‘verdict against the world’ in which one is ‘forced to live’ but ‘cannot exist’ (Arendt 1973, 352). Such a situation is ripe for the ‘revolt of the masses’ against ‘common sense,’ which is ‘the result of their atomisation, of their loss of social status’ and with it, the ‘whole sector of communal relationships in whose framework common sense makes sense’ (Arendt 1973, 352). In short, the masses are people who are considered to be ‘superfluous or can be spared … [who] cannot be integrated into any organisation based on common interest’ (Arendt 1973, 311). Whether one agrees with Arendt’s analysis of the social conditions in the interwar years that led to the formation of the ‘masses’, her portrait of the ‘mass man’ is a powerful account of what can happen to an individual who has become disposable to the society that they used to belong to. Hand in hand with this phenomenon is indeed the collapse of ‘common sense’ that leaves the dislodged individual scrambling for reconnection. By offering the masses a substitute reality validated by the ‘logic of an idea’ rather than by experience, totalitarianism makes it possible for them to live in a world where there is no condition of plurality. Between ‘facing the anarchic growth and total arbitrariness of decay’ and ‘bowing down before the most rigid, fantastically fictitious consistency of an ideology’, the masses will likely ‘choose the latter’ (Arendt 1973, 352). A world created by ideology, then, is more attractive to the masses not because ‘they are stupid and wicked, but because in the general disaster this escape grants them a minimum of self- respect’ by conjuring up ‘a lying world of consistency which is more adequate to the needs of the human mind than reality itself’ (Arendt 1973, 353). Moreover, it is precisely this ability to guarantee consistency that makes it possible for the leaders of totalitarian movements to demand ‘total, unrestricted, unconditional and unalterable loyalty of the individual member’ (Arendt 1973, 323). While this chapter is not a study of the social factors that led to specific contemporary developments such as the result of the Brexit referendum or the election of Donald Trump in 2016, Arendt’s ‘mass man’ seem to have found their counterparts among the supporters of Brexit and Trumpism. Studies of voters’ profiles in both events point to a disproportionate share of supporters from white male lower income backgrounds without university educations. These are individuals who have either already lost their jobs to globalisation or who are at high risk of losing their job with very little capacity to be retrained for the twenty-first century market economy of advanced industrialised states (BBC 2016; Pew Research Center 2018). In short, these displaced workers are part of the tapestry forming the modern equivalent of Arendt’s ‘mass man’ – ready to be the recruits of totalitarianism. Conclusion If we step back to take a more comprehensive approach to the relationship between truth and politics, both historically and theoretically, it is not so obvious that 2016 is a defining moment. From ancient China and ancient Greece to our so-called post-truth age, the indeterminacy of language appears to be a persistent political challenge. Language is always open to manipulation by its users to serve power. Nonetheless, the tendency these days is to single out the ubiquity of social media and the proliferation of Internet platforms as the incubator for the countless cyberwars of words that in turn fuel the real world of politics. In this context, language is no longer the conveyor of objective facts and accurate information, but rather the medium for expressing subjective opinions and feelings, as captured by the Oxford Dictionary’s definition of the term ‘post-truth politics’. In contrast, the historical approach used in this chapter shows that it is in fact too easy to point the figure at the Internet and digital communication for the political challenge that we face today. What is being suggested here is that with or without the Internet, language, be it used to convey facts or emotions, is simply a tool for communication that humans have invented. While language per se cannot adjudicate what is true or not true, what is right or wrong, it embodies such capacity when language is a shared norm. That shared norm cannot be sustained without some semblance of cohesion in any given community. Any war of words, irrespective of its platform, is indicative of the breakdown of such cohesiveness. Returning to the original context in which the term post-truth politics was first used by Steve Tesich helps to identify the important distinction between truth as the standard for knowledge and truth as the standard for action. The two do not necessarily coincide. Placed in the context of the history of political thought, this distinction is in fact at the heart of Plato’s Republic. Yet when the philosopher as the quintessential bearer of truth becomes king, the polis that he rules over is far from the ideal of a just state that it purports to be. Instead, it is one that is held together by lies and coercion. What Arendt reveals through her study of Nazism and Stalinism is that it is dangerous for the kind of truth that philosophy generates – ‘rational truth’ – to become the guide to politics. Politics guided by rational truth can only be uncompromising and follows a logic of its own that cannot afford the ambiguity and indeterminacy of reality as lived experiences of humans. A world of post-truth politics can be an opportunity for a new kind of politics if it is indeed about dismantling the idea that truth is the ideal that politics should strive for. But as it stands, the post-truth politics of today is more about invoking a past that never exists to judge the presence as a crisis in naming when the real crisis is about rebuilding a shared space that has room for all – be they of the left, right or centre; cosmopolitan or parochial; civic-minded or self-centred; liberal-minded or bigoted. Politics is and always will be about striving for that space, without which no human can thrive. References Arendt, Hannah. 1958. The Human Condition. Chicago: The University of Chicago Press. Arendt, Hannah. 1973. The Origins of Totalitarianism. Rev. ed. New York: Harcourt Brace Jovanovich. Arendt, Hannah. 1977. “Truth and Politics.” In Between Past and Future, 227–264. New York: Penguin Books. Arendt, Hannah. 1982. Lectures on Kant’s Political Philosophy, edited by Ronald Beiner. Chicago: University of Chicago Press. British Broadcasting Corporation. 2016. “Brexit vote: The breakdown.” BBC News, December 7, 2016. https://www.bbc.com/news/uk-politics-38227674 Cao, Feng. 2016. “A New Examination of Confucius’ Rectification of Names.” Journal of Chinese Humanities 2: 147–171. https://doi-org.subzero.lib. uoguelph.ca/ doi/ 10.1163/23521341-12340032. Connor, W. Robert. 1984. Thucydides. Princeton: Princeton University Press. de Jonge, Casper C. 2017. “Dionysius of Halicarnassus on Thucydides.” in The Oxford Handbook of Thucydides, edited by Sara Forsdyke, Edith Foster, and Ryan Balot. Oxford University Press. DOI: 10.1093/ oxfordhb/9780199340385.013.17 https://www-oxfordhandbooks-com.subzero. lib.uoguelph.ca/view/10.1093/oxfordhb/9780199340385.001.0001/oxfordhb-9780199340385 Hyvönen, Ari-Elmeri. 2018. “Careless Speech: Conceptualizing Post-Truth Politics.” New Perspectives 26 (3): 31–55. https://www.proquest.com/ scholarly-journals/careless-speech-conceptualizing-post-truth/ docview/2204515132/se-2?accountid=11233. Hobbes, Thomas. 1839–1845. The English Works of Thomas Hobbes, edited by Sir William Molesworth. London: John Bohn. Vol. 8. Klinkner, Melanie. 2018. “Reasonable Truth.” International Journal of Media & Cultural Politics 14 (3): 393–99. doi:10.1386/macp.14.3.393_7. Lee, Haiyan. 2019. “When Nothing Is True, Everything Is Possible: On Truth and Power by Way of Socialist Realism.” PMLA/Publications of the Modern Language Association of America 134 (5). Cambridge University Press: 1157–64. https://doi.org/10.1632/pmla.2019.134.5.1157 Lewis, Mark E. 1999. “Warring States Political History.” In The Cambridge History of Ancient China: From the Origins of Civilization to 221 BC, edited by Michael Loewe and Edward Shaughnessy, 587–650. Cambridge: Cambridge University Press. Makeham, John. 2003. “Zheng ming (Rectification of names).” In RoutledgeCurzon Encyclopedia of Confucianism, edited by Xinzhong Yao. Vol. 2: 813-814. New York: Routledge. Oxford Languages, n. d. “Word of the Year 2016.” Accessed 19 August 2017. https://languages.oup.com/word-of-the-year/2016/ Peters, Richard. 1979. Hobbes. Westport: Greenwood Press. Pew Research Center. 2018. “An examination of the 2016 electorate, based on validated voters.” Report, August 9, 2018. https://www.pewresearch.org/ politics/2018/08/09/an-examination-of-the-2016-electorate-based-on- validated-voters/ Plato. 1974. Republic. Translated by G. M. A. Grube. Indianapolis: Hackett. Tesich, Steve. 1992. “Watergate Syndrome: A Government of Lies.” The Nation: 12-14. January 6/13, 1992. Thucydides. 1972. The Peloponnesian War. Translated by Rex Warner. Harmondsworth, England: Penguin Books. Wolin, Sheldon. 2004. Politics and Vision: Continuity and Innovation in Westen Political Thought. Expanded Edition. Princeton: Princeton University Press. Zerilli, Linda M. G. 2020. ‘Fact-Checking and Truth-Telling in an Age of Alternative Facts.’ Le foucaldien 6, no. 1: 2, 1–22. Theresa Man Ling Lee is an Associate Professor of Political Science at the University of Guelph (Canada). Lee is a political theorist who specializes in contemporary political thought. Her research and teaching areas include continental philosophy, Marxism and critical theory, feminism, postmodernism, philosophy of social science, psychoanalysis, multiculturalism, human rights, disability studies, modern Chinese political thought and comparative political theory. Lee has published on a wide range of subjects in these areas, including intercultural pedagogy and the challenges of teaching as a person with multiple minority designations in a Canadian university classroom.
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The new "science golden age" looks suspiciously like a grift
This edition of PN is made possible by paid subscribers. Become one ⬇️ 🚀 Subscribe to PN 🚀 The Trump regime is trying to usher in a “Golden Age in Science” where scientific research is run like a Trump family business, artificial intelligence is all that matters, profit is everything, and expertise is for chumps. Or at least that’s the vision emerging from a new 123-page “Report to the President” from Michael Kratsios, the Director of the White House Office of Science and Technology Policy. In a normal administration, you could probably skip over learning about a science policy document — but this is no normal administration, and this is no normal science policy document. Instead, it’s Trump’s attempt to end the actual golden age of science that was kicked off in 1945 with the publication of '“The Endless Frontier” by Vannevar Bush. [ ](https://www.publicnotice.co/p/harmeet-dhillon-portland) [ Harmeet Dhillon demonstrates danger of posting through it ](https://www.publicnotice.co/p/harmeet-dhillon-portland) Liz Dye · Jul 29 [ Read full story ](https://www.publicnotice.co/p/harmeet-dhillon-portland) Bush, at President Franklin Delano Roosevelt’s request, laid out a vision of federal support for science that centered on funding universities to do basic research, saying that as long as those institutions were “vigorous and healthy and their scientists are free to pursue the truth wherever it may lead, there will be a flow of new scientific knowledge to those who can apply it to practical problems in Government, in industry, or elsewhere.” Kratsios’s report is supposed to be the successor to Bush’s, and even apes the format, but this isn’t really a document about science policy. It’s one about ending science research as we know it in favor of shoveling cash at big tech companies. While it might be temping to wave this away as an aspirational policy document, Trump has already begun to shift billions in research funds to AI projects and has taken a hatchet to science funding across the board. He’s teed up the shift Kratsios proposes here, and since neither Congress nor the Supreme Court seem inclined to stop him from doing whatever he wants with taxpayer money, it isn’t doomering or hyperbole to say we are staring down the barrel of a world where the only “science” the government intends to support is for-profit tech and defense advancements. Science for dummies Part of how you can tell this isn’t a document about science is that you are immediately faced with an absolute wall of business-speak of the worst sort. Did you know we must “tightly couple our science and technology enterprises to ensure that groundbreaking ideas invented in the United States are rapidly prototyped, tested, manufactured, and scaled domestically”? You do now! That tone isn’t surprising, because Kratsios has no background in science or research. He has a bachelor’s in political science and spent most of his career working for Peter Thiel at Thiel Capital. After a stint in the first Trump administration as Chief Technology Officer, where he also did not do any science, Kratsios was tasked in 2020 with overseeing the Pentagon’s research budget for a brief time, a position that, as the Wall Street Journal put it, “carries a great deal of power in terms of dictating investment allocations.” He spent the Biden years at Scale AI as “head of strategy,” which you will also note is neither a science nor a research job. But Scale AI has contracts with the Department of Defense and, as the Washington Post wrote in 2023, “has been aggressively pitching itself as the company that will help the US military in its existential battle with China.” And now you have the necessary background for our new golden age, which is going to be a golden age of taking money away from scientists and universities and giving it to companies like Scale AI. A note from Aaron: Working with brilliant contributors like Lisa takes resources. If you aren’t already a paid subscriber, please become one to support our work. 👇 Subscribe In a genuinely galling move, Katsios spends a lot of time slamming universities because of “administrative burdens on researchers” and the “well-documented inefficiencies” in academia, the message being that those damn universities waste the time of amazing researchers with paperwork. Except it’s the administration that is attempting to impose inefficient, unnecessary burdens on those researchers with a proposed Office of Management and Budget rule that would require senior political appointees to approve every science grant and ensure it aligns with Trump’s personal priorities. Researchers would also have to seek prior government approval to attend conferences or publicize their research. And political appointees could terminate any grant at any time if they feel like it. [ ](https://www.publicnotice.co/p/omb-rule-science-funding) [ The murder of expertise ](https://www.publicnotice.co/p/omb-rule-science-funding) Lisa Needham · Jun 11 [ Read full story ](https://www.publicnotice.co/p/omb-rule-science-funding) But when it comes to AI, we’re going to “restore permissionless innovation.” The report proposes to give lucky ducky innovators a pile of cash up front for grants lasting five years or longer, saying “awards should be fully-funded in year one, with all resources earmarked upfront, to minimize administrative burdens and reduce pressure for researchers to generate intermediate results to secure continued funding.” Yeah, wouldn’t want to have to make the Very Special Tech Bros show results or get money parceled out year by year like the plebes at universities. The report also proposes that we should have “fast grants” with “simplified applications requiring just a few pages of writing” because that is definitely how science works, just writing a bit off the dome and getting some cash. [ ](https://www.publicnotice.co/p/trump-ai-action-plan-altman-llms) [ Tech elites are turning AI into ChatGPTrump ](https://www.publicnotice.co/p/trump-ai-action-plan-altman-llms) Paul Waldman · July 29, 2025 [ Read full story ](https://www.publicnotice.co/p/trump-ai-action-plan-altman-llms) Oh, and we should have prizes and challenges, where multiple teams compete. The proposed prizes include incentives like “advance procurement commitments,” which means the federal government agrees to buy whatever is being developed. There’s also “regulatory fast-tracking,” making sure all those great AI thingies don’t have to wait too long to be unleashed on a market. These aren’t incentives to do science. These are incentives to develop things with tax dollars that you can then sell to the government for more tax dollars. In the suggestion that might be the most unhinged, the most very opposite of how both government and research work, our new age will include “golden tickets.” That’s where an individual reviewer in an agency — yes, just one person — can “recommend unconventional proposals that may not pass consensus-driven review panels” because somehow this will “surface valuable ideas too divisive for committees and attract higher quality reviewers by empowering them to exercise independent scientific judgment.” Yes, that’s saying that a random person in an agency should be able to shovel money at some idea even if it can’t pass a review panel, and that the opportunity to do so will attract better employees because apparently all the best minds in science are chomping at the bit to be allowed to unilaterally fund AI research based on their personal vibes. But that isn’t at all how actual science works, and actual scientists aren’t unhappy about review panels. They work collaboratively, not siloed in some secret race to create the next AI chatbot. A 2020 study by the National Bureau of Economic Research examined whether peer review panels succeed in helping agencies predict which research ideas will best advance science. Peer review was better at finding “diamond in the rough” applicants than a single government program official. How about that. Trump always wants a taste It’s painfully clear that what Katsios is trying to do here is turn the whole of government science research and funding into the sort of “move fast and break things” paradigm that is the hallmark of big tech. That method works to line the pockets of tech executives, but it isn’t science. There’s a reason the government funds different kinds of research than private tech companies do. What Katsios either fails to understand or deliberately ignores is that the federal government is the main funder of basic research precisely because it can take a long time for it to bear fruit and is not intended to be profitable as such. “Basic” here does not mean rudimentary or simple, but rather research that is designed to advance scientific knowledge instead of achieving a specific outcome or product. Big tech companies don’t widely fund that sort of research, because it doesn’t make them money. Industry dollars account for only about 20 percent of basic research funding, while the government contributes around 60 percent. Big tech companies and startups do, however, rely on and benefit from that basic research. A 2019 study found that 35 percent of all patents obtained by venture-backed startups from 1976 to 2016 cited federally supported research. Basic research funded by the NIH contributed to every single drug the FDA approved from 2010 to 2016. [ ](https://www.publicnotice.co/p/trump-saudi-nuclear-deal) [ The Saudi nuclear farce and governance by whim and meme ](https://www.publicnotice.co/p/trump-saudi-nuclear-deal) Noah Berlatsky · Jul 27 [ Read full story ](https://www.publicnotice.co/p/trump-saudi-nuclear-deal) Unlike inventing the next ChatGPT, actual science is slow and iterative. Government funding of research that, on its face, would not appear to lead to any big new invention is actually what underpins those big new inventions. If the government hadn’t funded research into desert lizard venom, we wouldn’t know about semaglutide, and we wouldn’t have Ozempic. If research scientists hadn’t studied how bees optimize nectar foraging within a colony, we wouldn’t have the algorithm that assigns internet traffic across computer servers. The report assiduously ignores that a symbiotic, profitable relationship between the federal government, universities, and private companies already exists. Each federal research dollar generates about $1.50 to $3.00 in long-term economic benefits. The $36 billion the government awarded in NIH research funding in fiscal year 2025 generated $94 billion in new economic activity. Trump’s proposed new golden age is intended to strip those billions from universities, resulting in the dismantling of departments and the shuttering of labs. But Katsios has a plan for that: Universities should just become trade schools to churn out no-show STEM degrees! “Require universities and community colleges to embed practical technical training and externships into STEM curricula. Let hands-on experience and industry credentials count toward degrees.” It’s quite the feat to, on the one hand, strip schools of funding for scientific research while simultaneously demanding to control what is taught and, oh, also, colleges need to give degrees to employees at big tech companies just for being employees at big tech companies. The new golden age would also exacerbate the brain drain Trump’s policies have already caused. Nature found that in the first quarter of 2025, US scientists submitted 32 percent more job applications abroad than in 2024. A March 2025 poll by Nature of roughly 1,600 scientists found that over 1,200 said they were considering leaving the country. Europe is investing $565 million to make it a “magnet for researchers” and provide funding for scientists who relocate. The new golden age is also about embedding Trump’s xenophobia into the fabric of American science. The report complains of an “over-reliance on foreign students” that “creates a security challenge our institutions are ill-equipped to address” and that a “reliance on foreign talent sidelines American students.” This is, to put it mildly, nonsense. International students pay far higher tuition and often receive no financial aid. Their tuition dollars are what make it possible for American students to get financial aid. In the 2023-2024 school year alone, international students contributed $43.8 billion to the US economy and supported over 378,000 jobs. Those 1.1 million international students accounted for only six percent of the total number of students in higher education that year, but were such an economic engine that for every three international students, one job was created or supported. It’s impossible not to wonder whether part of Trump’s loathing for universities and their leading role in scientific research is that there is no way for him to corrupt the process and make money off it in the same way he can by giving millions and billions to Silicon Valley types who, in turn, give him millions in cash. There’s no way for his children to invest in universities that the government then gives no-bid contracts to, fattening the family’s bottom line, which is pretty much the Trump sons’ current venture capital practice. Trump and Katsios’s view of science is narrow, cramped, and wrong. We’re not in a crisis, we’re not a failed state. We’ve been a science superpower for decades, and the only thing that threatens that is Donald Trump. Tech bros are already living high off the hog in this administration. They don’t need any more of our tax dollars. Hopefully this new golden age never comes to pass. Thanks for reading Public Notice. This post is public so feel free to share it. Share We’ll be back with more tomorrow. If you appreciate today’s PN, please do your part to keep us free by signing up for a paid subscription. 🚀 Subscribe to PN 🚀 Thanks for reading, and for your support.
Fish named after Blackpink’s Jennie; China’s 6G smart city: 7 science highlights
From two Chinese scientists leaving the US to a desalination prototype, here are some highlights from SCMP’s recent science reporting.
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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