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Swiping MAGA right or liberal left dealbreakers in today's dating world
Smoking? Kids? Finances? Political alignment is now at the forefront for those looking for romance in today's world of dating and finding a partner.
Could Canada join the EU?
Mark Carney eyes up ‘associate membership’ status as Brussels seeks closer alignment on trade and security
Evaluating Cboe Global Markets (CBOE) After Q1 Earnings Beat And Business Realignment Plan
Cboe Global Markets (CBOE) drew fresh attention after first quarter 2026 results beat revenue and earnings projections, alongside a realignment plan focused on core derivatives, index products, and new prediction market initiatives. See our latest analysis for Cboe Global Markets. The earnings beat and realignment plan have coincided with strong positive momentum, with a 30 day share price return of 17.78% and a year to date share price return of 40.49%. The 5 year total shareholder return of...
How AI Is Driving Both Parties Back to Their Roots
The dot-com boom shifted political alignments. AI is shifting them back.
Why Marketing Feels Harder as Your Business Grows by Nathan Tayloe
At some point, most growing businesses hit the same moment. What used to feel straightforward starts to feel more complicated. Marketing takes more effort. Results feel less predictable. What once worked consistently begins delivering mixed outcomes. It is easy to assume something is broken. In many cases, nothing is. The business has simply reached a different stage. What Worked Before Starts to Lose Its Edge Early on, growth is often driven by a few things working well at the same time: Referrals are strong The value of the offering is clear The team is aligned Decisions are made quickly Marketing, in that stage, tends to feel easier because the business itself is easier to understand. As the company grows, that simplicity fades. The audience expands. Services evolve. New people join the team. More channels are added. More decisions are made across more touchpoints. Marketing is no longer communicating one clear story. It is trying to represent a more complex business. Complexity Creates Friction That added complexity does not always show up immediately. It builds over time. Messaging starts to drift. Different parts of the business describe what they do in slightly different ways. The website no longer reflects how the company actually operates. Sales conversations evolve faster than the brand. At the same time, more activity is introduced to try to keep growth moving: New campaigns More content Additional platforms More tools and reporting The volume increases. The clarity does not always keep pace. That is when marketing starts to feel harder. It’s Not a Lack of Effort Most teams respond to this stage by doing more. More output More meetings More adjustments More experimentation That effort is not misplaced. It is often pointed in the wrong direction. When the underlying issue is a lack of alignment, more activity tends to create more noise. Results become harder to interpret. Decisions take longer. Progress feels inconsistent. From the outside, everything looks active. From the inside, it feels less certain. The Real Shift As businesses grow, marketing becomes less about execution and more about alignment. The companies that navigate this transition well tend to refocus on a few fundamentals: A clearly defined audience A strong, consistent position in the market Messaging that reflects how the business actually operates Alignment between leadership, marketing, and sales When those elements are in place, execution becomes more effective again. Marketing does not necessarily become easier, but it becomes more consistent. A Different Kind of Discipline Growth introduces more opportunity, but it also demands more discipline. What worked when the business was smaller often cannot be scaled without adjustment. The systems, messaging, and decision-making that once felt intuitive need to become more intentional. That is not a sign of failure. It is a normal part of growth. Marketing feels harder because the business has become more complex. The solution is not to outwork that complexity. It is to bring clarity to it. The companies that do that well tend to regain momentum quickly. Those who do not often stay busy without seeing the same level of progress. — Learn more about how we help organizations align strategy, brand, and execution. Visit tayloegray.com, or reach out directly at nathan@tayloegray.com.
OpenAI keeps losing the people in charge of AI safety
OpenAI just lost another leader of one of its safety teams. The company's safety and alignment teams have had high turnover in recent years.
Algonquin Anishinabe Nation and Senators Sports & Entertainment Joint Statement
Through ongoing discussions, both parties have identified a strong alignment in their commitment to creating Ottawa–Gatineau’s next great gathering place
CBL Scholars Program Opens Pathways to Business Law Careers - UC Law San Francisco (Formerly UC Hastings)
First-year law students and 2026 CBL Scholars Deniz Masjedi, Alvaro Flores, and Tavia Pappaly gain mentorship connections with leading industry professionals as they prepare for careers in business law. Created by the UC Center for Business Law San Francisco (CBL), the CBL Scholars Program aims to develop the next generation of business law leaders, supported by contributions from individuals and corporate sponsors. The highly competitive program provides funding along with academic and professional mentorship to students from disadvantaged backgrounds. Scholars gain exposure to the Bay Area’s technology and venture capital ecosystems and access to UC Law SF’s broad network of senior legal professionals. Now in its sixth year, the CBL Scholars Program provides a scholarship and academic and professional mentorship to students entering the business law community from disadvantaged educational, economic, social, or physical backgrounds. The program is generously sponsored by the law firms Gunderson Dettmer and Goodwin Procter. Meet this year’s CBL Scholars: Deniz Masjedi, Alvaro Flores, and Tavia Pappaly. All three have secured summer associate positions at prestigious law firms. Deniz Masjedi: A Passion for Helping Founders Grow Companies Deniz Masjedi, who was born in Iran and moved to Southern California as a teenager, traces her interest in law to her family’s immigration journey and her desire to advocate for herself and her family. She earned a bachelor’s degree at UC Berkeley, where she studied political science and sociology. At UC Law SF, she serves as a 1L representative for the Iranian Law Students Association and is active in the Business Law Society and Technology & Law Society. Next fall, she will serve as director of mentorship for the Technology & Law Society and director of professional development for the Business Law Society. She will join Sidley Austin this year as a 1L/2L summer associate. Q: What draws you to business law? A: Prior to law school, I worked in Cooley LLP’s Private Companies practice group as a corporate paralegal for three years, supporting over 100 venture-backed startups and venture capital funds. I found purpose in helping founders transform their vision into successful companies. It was incredibly rewarding being part of their journeys—from company formation and initial financing to major transactions such as M&A deals. What excites me most about this field is the potential for a small, innovative company to become the next Google or Zoom, shaping how people communicate and access information. I hope to continue supporting entrepreneurs and investors as a corporate attorney. Q: What does it mean to you to have been awarded this scholarship? A: As the first person in my family to attend law school in the United States, receiving this scholarship is a tremendous honor. My parents and I left everything behind so that I could enjoy the freedom to practice my religion without persecution and pursue a career in law as a woman. This award is an acknowledgment of my dedication to business law and motivates me to continue pursuing excellence in this field. It also recognizes years of hard work—learning a new language, navigating the college and law school application process, and ultimately building a career in the legal profession. Q: How will this scholarship help support your future goals? A: This scholarship will provide access to mentorship from leaders in the field, enabling me to develop the skills necessary to offer practical advice and effective representation to future clients. It also eases the financial burden of law school, letting me focus on my coursework and deepen my commitment to corporate law. As an immigrant, breaking into the legal profession has not always been easy, and the time that mentors have invested in me has been instrumental to my progress. This scholarship will allow me to continue building those relationships while positioning me to one day mentor others pursuing similar paths. Alvaro Flores: Inspired by Entrepreneurs The son of Mexican immigrants raised in San Antonio, Texas, Alvaro Flores says he has long been interested in how policy, markets, and culture shape society. He studied international relations and global economy at the University of Southern California, with minors in business finance and legal studies. His experiences span local political organizing, venture capital and startup work, and teaching entrepreneurship. At UC Law SF, he is involved in the Business Law Society, Technology & Law Society, and the Latinx Law Student Association. He will join Perkins Coie as a 1L/2L summer associate with the firm’s Emerging Companies & Venture Capital practice. Q: What draws you to business law? A: I’m drawn to stories of entrepreneurs whose simple ideas become companies that shape the economy and society. Spending time in venture capital and working directly with founders has shown me that many important decisions in the scaling process are ultimately legal ones, particularly how companies are structured and how capital is raised. Business law sits at the intersection of innovation and equity. It offers a way to stay close to the pace of technological change while also helping expand who gets to participate in it. There is something special about being part of that process here in the Bay Area, where the American Dream feels like it is reimagined in real time. Q: What does it mean to you to have been awarded this scholarship? A: I am truly grateful for the support and belief this scholarship represents. I see it not just as recognition, but also as alignment. The CBL Scholars Program’s focus on developing individuals who engage deeply with the business and technology ecosystem reflects the path I have been intentionally building. More than anything, it feels like an investment in that journey. I have been thoughtful about each next step–whether teaching, working in venture capital, or building multidisciplinary experiences–and this scholarship reinforces that those efforts come together in a meaningful way. Q: How will this scholarship help support your future goals? A: It allows me to focus more on developing as a business lawyer, without the distraction of financial constraints. Beyond that, the mentorship and access to the Bay Area network are just as valuable. I’m especially interested in working with early-stage companies and investors, so the opportunity to learn directly from people operating at a high level in that space is huge. Tavia Pappaly: Working in the Wine Industry Sparked Interest in Business and Law Tavia Pappaly spent five years in the wine industry before deciding to pursue law school with a goal of becoming a corporate transactions attorney. Pappaly says she wants to use her legal skills to help business grow and thrive. A first-generation law student from Fairfield, California, she is a member of the Legal Education Opportunity Program (LEOP) and will serve as vice president of social planning for the UC Law SF Sports Law Society in her 2L year. She plans to work as a summer associate at Gibson Dunn in San Francisco after her 2L year. Q: What draws you to business law? A: Business law brings together many of my interests and personal experiences. As the daughter of immigrants who grew up around a family-owned small business, I saw firsthand both the opportunities and challenges of building something from the ground up. That perspective, combined with my experience in the wine industry, solidified my interest in helping businesses of all sizes tackle evolving legal challenges. I’m excited to grow within the corporate law space and develop the skills and experience to support businesses at every stage. I’m deeply motivated to give back to immigrant communities and small business owners, whose experiences often intersect. By building a strong foundation in corporate law, I hope to support those working to create generational stability and to contribute in a meaningful way to the communities that have shaped me. Q: What does it mean to you to have been awarded this scholarship? A: Receiving this scholarship is incredibly meaningful to me. I take great pride in my background as a first-generation student and a woman of color, so being recognized for my potential in a field that has historically underrepresented those communities is both affirming and empowering. This program will help connect me to the resources and committed mentors that will help me succeed. Beyond that, it is no secret that law school is a significant financial commitment. This scholarship helps ease both the financial burden and the stress that comes with it, allowing me to focus more on my education and long-term goals. I am incredibly grateful! Q: How will this scholarship help support your future goals? A: This scholarship has already connected me with an incredible network of faculty, business law professionals, and like-minded students. The community fostered by the Center for Business Law is deeply supportive, and I’m excited to grow alongside my cohort. I truly believe this network will play an important role in helping me thrive in law school and position me for success in business law. As I have seen firsthand how impactful both financial scholarship and mentorship are, I hope to give back in this same way later in my career.
Protect College Sports Act stalls in overnight Senate showdown, vote pushed to September after chaotic evening
The Protect College Sports Act stalled Friday as senators filed amendments targeting conference realignment, women's sports, and NCAA liability.
Continental on Track to Reach Tires Business Outlook Despite Rising Costs
The German tire company said it is in the final stage of its strategic realignment and warned of a substantial increase in raw-material costs in the second half of the year.
Jiang Feng: The Unravelling of German & European Politics
A state election in eastern Germany has become a revealing test of the changing political landscapes of Germany and Europe, writes Jiang Feng, Professor and Chairman of the Council of the Shanghai Academy of Global Governance and Area Studies (SAGGAS) at Shanghai International Studies University. His analysis follows the decisive victory of the Alternative for Germany (AfD) (which has been classified as right-wing extremist by German authorities)in the state of Saxony-Anhalt on 6 September, in which it finished far ahead of its mainstream rivals. Jiang previously served as Minister-Counsellor at the Chinese Embassy in Berlin. The following article was published on 8 September 2026 on huanqiu.com, the online platform of the Global Times. On 6 September, the results of the state election in Saxony-Anhalt, Germany, were announced. The Alternative for Germany (AfD), classified as far-right by German authorities, made history by becoming the largest party in the state parliament, winning 43.8 per cent of the vote — twice its share in the previous election. The Christian Democratic Union (CDU), which had governed the state continuously for 24 years, won just 17.2 per cent, its worst result ever in Saxony-Anhalt. On the night the results were announced, the atmosphere at the CDU gathering was unusually calm. There was neither shock nor argument, as if the defeat had long been expected. Several veteran party members who had spent years working in the state government said: “Defeat is not a bad thing. It is time to make changes.” This state election has not only upended Saxony-Anhalt’s political landscape, but also laid bare the deep tensions between Germany’s traditional party structure and model of governance, on the one hand, and an increasingly polarised society on the other. Its repercussions are already being felt at the federal level, making Saxony-Anhalt a useful case study of the broader trend towards political polarisation and realignment in Europe. Governing Difficulties after Electoral Victory The most immediate consequence of the election is a historic setback for the mainstream party order that took shape in post-war Germany. In Saxony-Anhalt, the AfD has leapt from a peripheral party to the largest political force in the state. Its electoral base has expanded beyond the traditional category of “marginal protest voters” to take on the characteristics of a cross-class coalition. The party has not only consolidated its support in rural areas, but also attracted many middle-class voters and even members of the social elite with stable jobs, their own homes, and substantial incomes. It has thus evolved from a “protest party” into one with the potential to govern. Despite its commanding lead in the vote, however, the AfD faces an institutional obstacle to governing the state: the mainstream parties’ long-standing “firewall” against it. Neither of the two potential routes to forming a government appears likely to break the current political deadlock. The first would be a coalition of establishment parties, led by the CDU and bringing together the Social Democratic Party (SPD), the Greens, the Left Party (Die Linke) and the Sahra Wagenknecht Alliance (BSW) in a cross-camp government. In terms of parliamentary arithmetic, such a coalition could prevent the AfD from forming a government and preserve the outward stability of the traditional political order. Yet these parties are fundamentally divided over key issues including immigration, the energy transition, economic management, and cultural and education policy. Coalition negotiations would therefore be extremely difficult, and even if a government could be formed, it would most likely prove short-lived. More importantly, an alliance of establishment parties aimed at keeping the largest party out of government would play directly into the AfD’s narrative that “political elites are suppressing public opinion”. It could deepen perceptions of political unfairness, further strengthening the AfD’s political capital, and creating a dynamic in which “the more it is suppressed, the more people vote for it”. Ulrich Siegmund, the AfD’s candidate for state premier, has said that if efforts to form a government fail because other parties refuse to cooperate, the AfD will push for a fresh election, which he believes could deliver the party an absolute majority of seats. This points to more intense political battles ahead. The second option would be a coalition between the AfD and the BSW. Together, the two parties would command a simple majority in parliament, making this, on paper, the quickest route to forming a government. The BSW is the only elected party that has not ruled out cooperation with the AfD, and the two parties share some common ground, including on policy towards Russia. There is therefore a degree of political basis for cooperation. Were such a coalition to materialise, Germany would see its first state government jointly formed by a party defined as far-right and one with a far-left background, creating what might be called a “bipolar anti-establishment alliance”. Such an outcome would fundamentally break with the ideological boundaries of post-war German party politics. It is worth noting that even if the AfD ultimately fails to enter the state government, its roughly 47 per cent share of parliamentary seats would give it a powerful blocking minority. Without its consent, major decisions requiring a two-thirds majority — including constitutional amendments, significant budgetary adjustments, and state-level institutional reforms — would be extremely difficult to pass. Therefore, whoever ultimately forms the government, the AfD will be an unavoidable force in Saxony-Anhalt politics, with a direct influence on the state’s legislative process. Systemic Failures in Germany’s Governance Model The Saxony-Anhalt election result reflects a systemic failure of Germany’s post-war mainstream-party model of governance to adapt to rapidly changing socioeconomic conditions. It stems from multiple, mutually reinforcing structural tensions and highlights the existing political system’s difficulty in adapting to “an increasingly polarised society First, collective anxiety has replaced individual material hardship as a central driver of political mobilisation. The rise of the AfD can no longer be explained simply by poverty or social marginalisation. Its increasingly cross-class electoral base suggests that what is driving voters towards the party is, above all, concern about the broader direction of society. This anxiety about the future of the political community goes beyond individual gains and losses. In political discourse, economic anxiety has become closely intertwined with immigration, while immigration and refugee issues are, in essence, questions of social fairness. This perception turns conflicts over the distribution of social resources into negative assessments of governing parties, providing a broad social basis for far-right political narratives. Second, regional disparities and East German identity have undermined the strategy of addressing eastern Germany’s structural challenges at the regional level. More than three decades after German reunification, eastern states such as Saxony-Anhalt have experienced periods of economic growth, but their underlying economic structures remain weak, and household incomes and wealth remain significantly below those in western Germany. At the same time, they continue to face population outflow, an ageing population, labour shortages, and other structural challenges. At a deeper level, historical experiences — including the collapse of the former East German economic system, mass unemployment during the transition, and the westward shift of decision-making power over major issues — have left some residents with a sense of being “second-class citizens” and of being excluded. For years, the German government treated the “syndrome” of reunification as a regional problem to be gradually addressed at the local level. That approach is becoming increasingly unsustainable, as regional development disparities emerge as a core structural factor shaping national politics. Third, the erosion of institutional trust is perhaps the most decisive factor. AfD supporters tend to distrust establishment institutions, including parliament, government, and the mainstream media. Their concerns about economic security, pensions, and social fairness are significantly greater than the social average. Increasingly, their support for the AfD reflects not an expectation that the existing system can be improved, but a search for an alternative to the current political order. Such a systemic deficit of trust will be extremely difficult for the traditional parties to repair in the short term. Fourth, a generational shift in political mobilisation has amplified political polarisation. Unlike traditional parties, which rely heavily on offline rallies and mainstream media coverage, the AfD has developed a social-media mobilisation strategy built around “raising an issue — identifying who is responsible — offering a simplified solution — building a shared identity”. It turns complex social tensions into concise political narratives, rapidly channelling public sentiment into political support. The contrast on the eve of the election was particularly telling. The establishment parties, together with trade unions and churches, organised an offline march attended by more than 10,000 people, but its reach was largely confined to those present and to traditional media coverage. The AfD held a much smaller indoor event, yet livestreaming on TikTok, YouTube, and other platforms, combined with the involvement of online influencers, gave it a reach and mobilising capacity far beyond that of the march. The contrast illustrates how the traditional parties’ methods of political mobilisation are increasingly struggling to keep pace with a political environment transformed by changes in media technology. A Microcosm of Structural Political Change in Europe The shockwaves from the Saxony-Anhalt election quickly travelled beyond the state to the federal level. Calls for political accountability followed immediately after the results were announced. The governing CDU and SPD are now under enormous pressure, while Chancellor Friedrich Merz and Vice Chancellor Lars Klingbeil face an unprecedented test. Political polarisation in Germany is no longer simply a regional phenomenon. The post-war model of multiparty government by establishment parties is breaking down and being reshaped. Viewed in a broader European context, the Saxony-Anhalt election is part of a continent-wide wave of party polarisation, mainstream-party decline, and rising right-wing forces. Far from an isolated phenomenon, it is a microcosm of the structural political changes unfolding across Europe. Weak economic growth, cultural tensions over immigration, and declining trust in institutions are fuelling the rise of right-wing forces in several countries, challenging the liberal internationalist order established in post-war Europe. As a core member state of the European Union, Germany’s ability to adapt its political system and the direction of its politics will have profound implications for both European integration and the evolution of the international order. Subscribe [ Pekingnology China’s Germany watchers take stock of Berlin’s uneasy turn On 7 December 2025, nearly 50 Chinese scholars and policy researchers gathered in Beijing for the 2025年德国形势年终研讨会 “2025 Year-End Seminar on the Situation in Germany… Read more 9 months ago · 31 likes · 1 comment · YIRUI LI and Yuxuan JIA ](https://www.pekingnology.com/p/chinas-germany-watchers-take-stock?utm_source=substack&utm_campaign=post_embed&utm_medium=web&embedding_publication_id=1151841) [ Jiang Feng on Germany's 2025 and beyond: no easy exit ](https://www.eastisread.com/p/jiang-feng-on-germanys-2025-and-beyond) Yirui LI and Yuxuan JIA · Jan 24 The following is an op-ed by Jiang Feng, published on 29 December 2025 at Shanghai Observer 上观新闻, the digital platform of Jiefang Daily, the official newspaper of the Shanghai Municipal Committee of the Communist Party of China (CPC). [ Read full story ](https://www.eastisread.com/p/jiang-feng-on-germanys-2025-and-beyond)
Pussycat Dolls star triples down on MAGA label after claiming politics cost her the reunion tour
Pussycat Dolls member Jessica Sutta says her MAGA alignment and RFK Jr. support cost her a spot on the group's 53-date PCD Forever reunion tour.
Stage and Screen Unifies Global Sports and Entertainment Offering, Strengthening U.S. Presence
/PRNewswire/ -- Stage and Screen, a specialist division of Corporate Traveler, has today announced a strategic realignment of its global brand structure that...
Why Is Elbit Systems (TASE:ESLT) Rebranding Its U.S. Business As Twenty Six Defense?
Elbit Systems (TASE:ESLT) has rebranded its U.S. operating segment as Twenty-Six Defense, targeting the domestic American defense market. The new Twenty-Six Defense banner is intended to highlight U.S. industrial capacity and onshore production for defense customers. Elbit Systems links the rebrand to closer alignment with U.S. national security priorities and government focus on localized manufacturing. The Twenty-Six Defense shift toward a U.S. centric identity is an important context for...
Video: Opinion | Will the Midterms Bring a Political Realignment?
This week in politics: Midwest realignment and what’s next for conservatives.
How Investors May Respond To OPKO Health (OPK) Boosting Its Nicoya Stake And RAYALDEE Milestones
In early June 2026, OPKO Health reported amending its agreement with Nicoya Therapeutics, gaining a 15% equity stake while maintaining eligibility for up to US$115.0 million in potential milestone payments tied to the commercialization of RAYALDEE for chronic kidney disease patients in Greater China and related territories. This deeper partnership not only expands OPKO’s economic interest in RAYALDEE but also tightens its alignment with Nicoya ahead of planned 2027 regulatory filings across...
What's behind the rising polarization in German politics?
Recent state elections in Germany saw wins for parties on the political fringes, while parties in the center struggled. What is behind this realignment? And is it here for good?
CMS extends ACCESS model to more chronic conditions as agency official eye cross-payer alignment
The Trump administration is rapidly expanding its 10-year ACCESS Model, a value-based payment program that reimburses digital health companies based on patient outcomes rather than services deliver | The Trump administration is rapidly expanding its 10-year ACCESS Model, a value-based payment program that reimburses digital health companies based on patient outcomes rather than services delivered.
Politics in the logistics mix: How tariffs and polarization alter corporate supply chains
How do political ideology and perceived ideological alignment influence supply chain professionals' evaluation of operational decisions involving politically charged macroeconomic issues such as tariffs?
Eric DeCosta credits coordinator input in Ravens draft success
Ravens GM Eric DeCosta says strong alignment between personnel and coaching staff is key to the team's success.
Pulpit Politics: Johnson Amendment Rarely Enforced
The Johnson Amendment prohibits tax-exempt religious and charitable organizations from endorsing or opposing political candidates. Some Oklahoma churches have found a loophole: partnerships with other organizations let them reveal political alignments.
NCAA president Charlie Baker floats regional approach to non-football sports
Conference realignment has turned college athletics into a much more national endeavor than it ever was previously. The Big Ten stretches from Southern California to New Jersey. The ACC includes a pair of teams on the Pacific coast. And the Big 12 has members located in both the deserts of the Southwest and the mountains…
New Small Business Alliances Signal Competitive Positioning by 40+ IT and Consulting Leaders Across FedCiv, FedHealth, and DoD
In the GovCon IT and consulting market, some companies react to opportunities. Others shape the board well before the game begins. Mentor-protégé agreements fall squarely into the latter category. These partnerships are not administrative check-the-box exercises. They require sustained commitment, alignment at the leadership level, and a clear view of where the market is heading. […]
Columbia Distributing and Hayden Beverage Complete Strategic Partnership Investment
Alignment between two established beverage distributors strengthens a growing Pacific Northwest total beverage platform.
College football realignment may not be done after late Protect College Sports Act provision
A 180-day grace period in the Protect College Sports Act could give Power Four schools a rare chance to change conferences without a five-year penalty.
National Pork Board
U.S. Pork Industry Unites to Advance Swine Health The U.S. pork industry is working together to strengthen herd health through a coordinated, producer-led effort. The National Swine Health Strategy brings producers and partners into alignment to address the industry’s most significant health challenges. By advancing measurable improvements in herd health, the initiative supports the well-being of pigs and […]
Florida State, other ACC realignment targets push to expand conference cap in college sports bill
The bill initially required five years of independence and capped any league reporting more than $700 million in annual revenue at 19 teams.
UNITE HERE Urges Other PENN Entertainment Shareholders to Support Proposal to Declassify the Board
UNITE HERE urges PENN (NASDAQ: PENN) shareholders to vote to declassify the Board and move to annual director elections at the AGM on June 16, 2026. The group says shareholders previously backed declassification in 2010 and cites industry norms, peer comparators, and 2025 proxy-vote statistics to argue annual elections improve accountability.UNITE HERE challenges PENN's regulatory and stability defenses, notes PENN reported 42 properties in 19 states and operations in 28 jurisdictions, and calls for alignment with peers that already use annual elections.
Video: Opinion | The Great Political Realignment of 2026
The country seems to be tiring of the Trump era. Now what?
Sanford Health announces new research president, leadership restructuring
Sanford Health has named a new president for Sanford Research along with what it's calling a leadership realignment.
Penn State Health appoints Dara Royer as senior vice president and chief marketing and communications officer
Penn State Health has appointed Dara Royer as its new senior vice president and chief marketing and communications officer. Royer will oversee systemwide strategy to strengthen alignment…
Freedom of Choice
A lens for understanding business leaders' ability to act in alignment with their values.
Why medical group leaders are seeing big leaps in pay
Consultants say compensation is increasing quickly for roles tied to clinical integration, physician alignment and growth.
Alignment Health Plan and Astrana Health Partner to Expand Access to Trusted Local Care for California Seniors
New collaboration expands access to multilingual, community-based care across seven Northern and Southern California counties
New Small Business Alliances Signal Competitive Positioning by 18+ IT and Consulting Leaders Across FedCiv, FedHealth, and DoD
In the GovCon IT and consulting market, some companies react to opportunities. Others shape the board well before the game begins. Mentor-protégé agreements fall squarely into the latter category. These partnerships are not administrative check-the-box exercises. They require sustained commitment, alignment at the leadership level, and a clear view of where the market is heading. […]
Sundar Pichai: Google’s Frontier Imperative, Compute Scarcity Dilemmas, Future AI Products and Businesses
Alphabet CEO Sundar Pichai has little patience for doubts about Google’s willingness or capacity to compete at the frontier of AI. His company’s latest Gemini 4 Argon model — long-promised and recently delivered — beats OpenAI and Anthropic’s latest public models on coding, math, science, and knowledge-work benchmarks and has placed Google’s AI back among the world’s best. Amid chatter that Google was fine with good-enough lightweight ‘Flash’ models, Pichai pushed back hard against any suggestion that company is limiting its research ambitions. “We won’t do it any other way,” he told me in a Big Technology Podcast interview recorded this week at Google’s Mountain View headquarters. Pursuing cutting-edge research is costly for Google, requiring compute and chips that otherwise could be sold at a high markup by its cloud business. But Pichai said allocating resources to frontier AI development is “ground zero” for Google. “Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development,” Pichai told me. In our conversation, we discussed Argon (which Pichai says processed three times as many tokens as Gemini 3 in the 72 hours after launch), AI’s role in developing better models, the AI interface of the future, competition with OpenAI, Google’s partnerships with Anthropic and Apple, data centers in space, and the nature of his optimism. You can read the full conversation, lightly edited for length and clarity, below, or listen to it on Apple Podcasts, Spotify, YouTube, or your podcast app of choice. Alex Kantrowitz: Google is back at the frontier with Gemini 4 Argon, which beats OpenAI’s and Anthropic’s public models on benchmarks spanning coding, math, science, and knowledge work. The conventional wisdom was that Google had given up on the frontier to focus on fast, good-enough models for its products. That was wrong? Sundar Pichai: Very wrong. Look, we are so committed to being at the frontier. It’s in our DNA. We won’t do it any other way. We’ve been thinking about the AI frontier for well over a decade now. The frontier is very dynamic. It’s very competitive. It’s very exciting. I couldn’t be more pleased with how Argon has come together. We took our time with it. We made sure we tested it safely and deployed it carefully. We took a bit longer than our other models, given its capabilities. Across the board, be it for agentic work, knowledge work, coding, creative tasks, Argon is one of the really good frontier models. The first 72 hours since we launched, it’s almost up to 25 trillion tokens used, which is about three times what Gemini 3.0 had in the first 72 hours. And we are capacity constrained as we are serving it. Excited, but there are always areas….. You know, the frontier is very dynamic, and you have to keep evolving the models. There are areas which we can see we need to improve as well. We’ll be at work on all of that as well. Which areas? On any given model, Argon would be across the board better at everything if you just look even two months back. But the space is so evolving. Even on coding, for example, there are pockets where we know we need to do better, and there’s clear feedback. We’ll make it better. The argument for standard models is that they’re already good enough to build great products, with the frontier valuable only for certain tasks. Meta built Muse with good models rather than the best, and Google could put standard models into Maps, Gmail, and Search. Why isn’t that sufficient? It’s definitely not how I have thought about it at all. We are deeply committed to driving the state of the art and actually pushing where this technology can go, all the way from transformers. That’s a journey we’ve been on. You will see us continue to do that. When you’re at the frontier, you get an earlier look into the emerging capabilities, earlier than others, which helps you better prepare for the future. We are fortunate to have a wide set of opportunities across consumer and enterprise. Some of our cloud customers, for sure, want the model at the frontier. The frontier models will be better at developing the next set of models in an era of recursive self-improvement. I would argue it matters there. Internally, we have definitely seen a steep point of inflection by deploying Argon to our developers. It definitely tackles problems which our previous models could not do. Being at the frontier puts you at the innovation frontier and helps you drive that as well. There is room for models of all sizes and capabilities. That’s why we did Flash, Flash-Lite, etc. I agree with you that you can develop very good product experiences with not necessarily the most frontier model, because they tend to be slower and more expensive too. But we care about the Pareto frontier, and we’re committed to doing all of that. You said developing frontier models gives you a look at what’s coming. What do you see on the horizon? What makes this moment so exciting is you build something which you think is a culmination of months of work, and you put it out. You already see clear headroom ahead. We’ve all been working on these agentic, long-horizon capabilities, but clearly the frontier is going to have multiple of these agents working together to do things. That’s an example of an exciting paradigm underway. It’s also clear to me that users, both in consumer and enterprise, will have access to some multi-agent framework for them, working all the time doing things. It feels like a moment of infinite possibilities. Sergey Brin said one of Argon’s core innovations was relying more on the model to help develop the model. Yet incidents involving OpenAI’s bots breaking containment and Google’s bots accessing a site they shouldn’t have suggest these systems still aren’t perfectly aligned. Isn’t there reasonable cause for concern about accelerating toward recursive self-improvement? There are three parts to your question. Let’s first talk about RSI itself. You’re right, Sergey mentioned RSI being a critical contributor as we are developing Argon. But to be very clear, today, in a continuum, we are pretty far from the end state of RSI or something like that. The right way to describe it is you have RSI loops along with AI researchers in various aspects of model development. You almost have agents helping AI researchers in some automated way work together to build these models. It’s good to at least understand where we are today, and it’s super exciting. I expect that continuum to keep making progress as we go into next year. The second question is around safety. Security comes from defense in depth. You’re talking about alignment, obviously. We all are doing a lot of work to make sure the models are as aligned as possible. But it’s an active area of research, and there are clear gaps. That’s not the only thing we rely on. You have the work you do on alignment, but beyond that, we put a lot of effort into real-time monitoring as these models are working. Making sure you have good telemetry into what’s happening so that you can intervene in real time if you see unexpected behaviors. How you do that, the architecture with which you do that, how you make sure it’s all sandboxed and isolated in a way that it can continue doing its job during real-time monitoring — these are all areas of investment. Obviously, when we deploy these models, giving customers, enterprises, control, both with real-time monitoring, but their ability to decide where they deploy, what type of data which models ever have access to, etc. All that work is very, very important, and we are doing a lot of that. As an industry, we need to constantly up our game and take the time to do all this stuff, including dedicating the compute required to make sure you can safely deploy these models. The third part is: Is it all enough? This is why there are a lot of conversations in the industry, and I think it’s reasonable to be worried. The right way to approach something you worry about is early on thinking about what more we can do. As an industry, you’re definitely seeing increased levels of work on alignment, testing for safety, etc., as we deploy these models. The next step would be developing better standards so there are cross-industry standards of how we do these things, including the ability to evaluate using independent evaluators. If you look at areas like financial controls, we have accounting standards, we have independent auditors. Those are all the next natural steps for us as an industry to work toward, and regulation will end up playing a role as we go through all of that as well. Google pursues both language models and world models, while some other AI companies like OpenAI have focused more heavily on LLMs as the path to AGI. Why is world-model research important to you? As humans, you obviously perceive the world around you, and you’re taking all those inputs. Making sure these models are able to understand the laws of physics, simulate the world in high fidelity. All that, I think, are key enabling blocks for intelligence. We are working across all possible applications of this. Take an area like robotics. It’s not an accident: If you look at embodied reasoning benchmarks, Argon is state of the art. That all comes from our experience building world models. Inherently, I would expect there will be architectures which will converge all of this in the future. But for us, we view this as, today, this jagged frontier that is intelligence. We want to be at the frontier of all the possible approaches, and that will help us both drive better research, better insights, and develop a much better AGI model, is how we think about it. Some people argue Google should focus on selling its computing resources rather than using them for AI research. Cloud wants to sell compute at a high markup, while researchers need a very big computer to build the best models. How do you balance those demands? Ground zero for us is making sure we are investing in the compute that’s needed to be at the AI frontier. In 2015, I set the company to be AI-first. AI is that key horizontal ingredient across everything we do, all the way from Search to Cloud to Waymo. That is ground zero for us, making sure both for research and developing the models that we have enough compute needed. Beyond that, we have these independent businesses which each have their own long-range plans. They’re using those long-range plans to project forward, and we plan for compute for all of them. The cloud team, they have a business and they have a projection and they have demand signals. They have committed contracts. With that help, we can plan on a long-term compute trajectory for them. We’re fortunate to have this wide set of opportunities. There’s no company in the world which isn’t making trade-offs at any given moment in time. But the first principles we operate on is making sure we have enough compute to invest in the frontier, and that’s non-negotiable, and everything else works on top. Remember, we are building our own compute, our own data centers. There are long lead times. The compute we have today is a function of decisions I made in 2024. Even if you think back to a year from today, last year the agentic coding takeoff hadn’t happened. Today, obviously, when you sit and look at anything, everyone is short—not just us, other companies—and everyone wishes you had two times what you had planned for. That comes with the territory. At any given moment in time, we are planning for what we would need two to three years out in the future. You’re going to find some decisions you make may look suboptimal two years out, depending on where the state of the art is. I don’t spend too much time thinking about it. The way I think about compute allocation is making sure Google DeepMind, both for research and frontier model development, is cutting edge in terms of compute access. After that, we plan for all our businesses. It’s great we can provide our compute to other people too, like we have done. It allows us to scale that up, get real tests for it in the widest application set possible, test it in the real world, and make sure we are at the cutting edge of technology at every layer of our AI stack as well. Ben Horowitz relayed on a podcast that Andy Jassy had said compute was sold out until 2028. Are you seeing the same thing? If today a customer is coming out and asking us for compute in ’27, that is incredibly difficult to do. Even last year we went out and leased some short-term compute to accommodate some customers and help them out. But yes, we are all dealing with similar timelines. Every major technology shift seems to involve overbuilding, from railroads to fiber. Do you think that’s happening here? It depends on the time frames you’re looking at. This level of build, if I take a long enough time frame, it makes complete sense to me. We are at early stages of the most consequential technology ever, and this is not like fiber. It’s not dark. We are using it. We are capacity constrained on a daily basis. The applications, the underlying technology is improving rapidly. But running a company, the way I work through the moment is you look at your demand signals. You look at your committed revenue opportunities that you’re getting in the form of contracts. You balance that with the underlying technical capability, or internally you balance how we are using it internally, the model trajectories. Based on all that, you take a disciplined approach—a confident, aggressive, but disciplined approach—to plan out compute. But when you talk about the broader economy as a whole, you’re having people operate with various levels of risk. I focus on what I can control as a company and plan through it. If your question is, can there be an overbuild? Yes, because you can have some constrained components. Remember, what matters is a set of everything you need to build out that compute. Memory, electricity, electricians…. Land. Like 2,000 components or more. You could get constrained at some point. There could be things which cause bottlenecks, and then you could have overbuilds. There could be external shocks to the economy. There are macro risks. All that is possible at any given time. But am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes. I was in the room when you said AI would be as profound as fire and electricity. If you believe that, shouldn’t you give researchers the biggest possible computer, even at the expense of today’s cloud profits? We’re all doing some form of that. Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development. What you’re seeing is true already. But you want to be thoughtful. Even our AI researchers have some constraints, which are also good. That’s what inspires creativity. That’s what helps you come up with a more efficient algorithmic breakthrough, maybe to use less memory. What we are committed to is to invest what’s needed to stay at the frontier. OpenAI President Greg Brockman told me a few months ago that the ideal AI product might have no interface at all—just as natural as conversation. What do you think the interface looks like in the age of AI? Computing historically has been humans adapting to computing. With AI, it’ll be the other way around. The technology will adapt seamlessly to how humans live and go about their day-to-day life. You want to be able to speak to that AI when you want to. It has context. It can be persistently running somewhere, give you continuity. You’re not dealing with a lot of complexity. It’s natural. All that makes sense to me. That doesn’t mean it won’t have any interface, however. Nowadays, even on my phone, Gemini voice models are so good. I can just press the microphone button. I can just ramble. That’s why we call it Rambler. I can ramble. It intelligently corrects with my personal context. Even my friends’ names, weird names, it gets it right, even if it first guesses it wrong. It’s amazing. I’m not going in, backspacing, editing once I finish saying it. It’s remarkable how good it’s gotten. But there are going to be moments you will need an interface. You may want to entertain yourself. You may want to watch a video. You may ask your AI to create something, and you want to watch it, consume it. The way I think about it is, you want it to be as seamless and as natural as possible, and that you are not doing the hard work adapting to whatever the interface is. The technology is adapting to how you’re working at any given moment in time. When I asked Sam Altman about competition from Google, he argued that OpenAI was building AI-native products and implied Google was bolting AI onto existing ones. Do you accept that framing, and can a “bolt-on” compete with an AI-native product? First of all, it’s a very dynamic moment. We are all pushing each other. OpenAI has pushed us as a company too. This is not just we are doing something to them. Far from it. Yes, Google went into its own version of a code red too. In the AI moment, all of us have to respond with varying levels of urgency at any given moment in time. We are constantly building new products as well. Gemini was built from the ground up as a product—the Gemini app I’m talking about. Or tomorrow, as all of us are adapting to agents, are you bolting agents onto ChatGPT, or are they building it natively? How would you think about that? You can always put these questions back. I don’t think of it that way. I think of it as you want to build world-class product experiences that are AI-native and they are winning. Some of it is adapting products, some of it is rethinking new experiences. They will all be part of how you approach this moment. How do you expect to make money from these AI interfaces? If an agent recommends a product and I buy it, should it take a cut of the sale, even if that’s disclosed? I’m going to put enterprise aside. Consumers are going to increasingly value this technology. Take software developers today. Developers are willing to pay for these coding models because it really helps make them more productive. Nobody would want to code the way they were coding three years ago, or even the same time last year. When you’re delivering value, there’s value to be had. I’ve never seen examples where consumer products really scale to billions of users, where people are using it intensely, and haven’t had monetization to go along with it. If you’re providing value to people, there is inherent value to be had. The nature of it may change. You’re asking a scenario in which agents are doing stuff for you. At the end of the day, you will have commercial needs. The agents need to fulfill those commercial needs. The providers on the other side are going to see value in that. There’s going to be some mechanism by which it gets fleshed out. That’s what an efficient system does. But what matters is that you’re building those world-class consumer experiences. I promised myself one product feature request: Can you make Gemini-powered search in Gmail as good as searching Gmail through ChatGPT? What’s going on here? One of the exciting things we announced recently is the unified Gemini agent for enterprise, and we’re bringing that to consumers. These are persistent agents which run 24/7 in the cloud. We’re spinning up a micro VM effectively for every query, and from the runtime, the harness, the model—it’s all a headless service. It’ll work everywhere, and it has your context with you. You can address this agent whether you’re in Workspace, in Search, in Gemini. Agents are going to drive this unification from a customer experience, which people are looking for. When you have a broad product portfolio and you’re moving forward fast, there are always areas where you’re moving forward faster than some other areas. That’s valid feedback. But we are putting a lot of time and effort into making sure, as we build out our agentic experience, that unification and that capability works well across all these services. People have asked where Google’s equivalent of Muse or Dots is. Do you anticipate creating a new product to compete with them? Our goal with Gemini is to be a personal, proactive, and powerful assistant. That’s what we are working toward. You saw us describe the early versions of it at Google I/O this year with Spark. Effectively, already, if you open the Gemini app as a Pro or an Ultra user, you see Chat and Task, and they start giving you that agentic experience. With Argon now, that capability is dramatically accelerated. What we announced recently in our enterprise event, we are bringing that to consumers as well. Soon in Gemini, you will be able to ask something, and it’s a continuum. You don’t need to think about whether you’re asking a chat or an agentic task. It’ll just automatically do the right thing, and it can work persistently in the background securely and get things done for you. Already, empirically, when I look at what’s coming into Gemini in our app, last year you would have tagged them and most of them would have been chats. But a steadily growing percentage is already agentic tasks in Gemini. Yes, we are going to bring personal agents to our consumers. It’ll work in the Gemini app. It’ll work in Google Search. It’ll work in Gmail. You can address it, call it, etc. We want to make sure we’re getting it right. It’ll be powered by Argon, and we are very excited to bring it to our users. Google has invested and committed tens of billions of dollars to Anthropic. What do you see in Dario Amodei that makes you want such a deep investment and partnership? Very early on, when we looked at the founding team of Anthropic, it was very clear to us this was a world-class team which was involved in some of the earliest GPT models. We were quickly able to assess that they would be at the frontier. We’ve always tried to identify and partner with companies which would be at the cutting edge of technology. We’ve done that with SpaceX. We’ve done that with Anthropic. That’s what we saw, and we had a chance to partner. Is your partnership with Apple to license Gemini for Siri expanding, contracting, or standing still? For both us and Apple, that partnership was about translating the capabilities of AI into better experiences for users. I thought Apple has done a phenomenal job of incorporating—they customize these models and build it into Siri—and it’s been great to see the reception. We are committed to doing more. You’re going to do more with them? There’s nothing specific to comment, but it’s a partnership. It’s working well. What have you learned so far about the feasibility of data centers in space? It’s a very exciting project. Projects like this remind me—I still recall that moment in the desert for the DARPA Grand Challenge, watching the Stanley car compete in it, which ended up being the basis for Waymo. With all these technologies, you’re at a moment in time where it looks a bit like sci-fi, but 10 to 20 years down the line, it’s actually working in a meaningful way. I feel the same in space. We’ve always believed in betting big on these technologies. Quantum has been a great example of it. We’re probably 10 years into the journey. Waymo, we are almost 20 years into the journey. Space, we are two to three years into the journey. It’s clear to me that it’s an approach which will make a lot of sense in the future. Being in sub-synchronous orbits with 24/7 access to the most abundant form of energy we have access to will make a lot of sense for this exponential increase in compute that we are going to need. Having said that, this is a challenging project. The team has disciplined milestones. It was a super exciting milestone to send the TPUs up to space. Sometime, as we are speaking, we are expecting a “hello world” moment from them. I look forward to seeing that happen, and we’ll continue investing here too. You previously said 25% of the code written within Google was written by AI. What’s the number today? That number in our world today is meaningless in the sense that every part of what we now touch is influenced by AI deeply. What we are tracking with Argon, for example, it’s been a point of inflection. People are using Argon to really go tackle complex projects, including projects which affect millions of lines of code. In the four weeks since we have launched Argon, we have seen a 30% increase in CLs, and more importantly, almost a similar increase—a one-third decrease in rollbacks. People are writing code more productively and better code with it. We are tracking it with newer metrics now. How are you dealing with opposition to data centers? If you can’t build in the U.S. the way you want to, where else do you go? We’re very committed to building our data centers very responsibly. When I look at our largest data centers in some of the communities where we have been the longest, I visit them, I talk to people. There are people who come and tell us their kids have gone to college because of our investments in these communities, and we’ve grown in those communities. To me, this is about doing it right. Engaging with the communities early, doing it transparently. We are committed to making sure we are bringing incremental power, and we cover 100% of the cost of our power needs for these projects. It’s understanding the impact on neighborhoods and making sure you’re working thoroughly to address them. Done rightly, you can continue making progress. But we are constantly taking feedback into account. I do worry not everyone operates the same way. People have been surprised by data centers showing up in their backyard. Those are all important. As an industry, we should continue to do better. But part of the reason, in a 20-year time frame, we are thinking about things like data centers in space is making sure we are able to put these data centers where maybe the trade-offs will be different. What unproven area of AI is Google working hardest to crack? Is it continual learning—getting models to keep updating after release—or something else? There are multiple parallel paths. You saw with Argon we have a million output tokens. With our Gemini models early on, we had a one-million context window on the input side. These are all examples of breakthroughs we are constantly working on. How do you think about synthetic data? How do you think about continual learning? How do you think about more efficient architectures? We do dedicate a meaningful percentage of our compute to these research bets too. It always goes hand in hand, including our world models. Those are all bets we are placing. Sometimes, as part of these bets, there are ideas that emerge which just make their way into the mainline models. Maybe we don’t fully talk about it, but we have multiple steps in these areas. Working in technology requires optimism that building something will improve the world, but those bets can leave real consequences when they fail. I recently returned to a town in El Salvador where enthusiasm for Bitcoin had given way to unfinished construction and something resembling a ghost town. How do you maintain the optimism you spoke about in your Stanford commencement address while accounting for those risks? I genuinely have a deep conviction that improving technology and improving access to technology makes people’s lives better. It’s been my personal experience in my life, out of which comes that deep conviction, optimism. But with that, I’ve always felt humanity’s journey is always about working hard to harness that technology to make it work well for society. The optimism is not an optimism that you just build whatever you want, whenever. You work hard to make sure you’re building technology in the service of humanity, to make the human condition better. That’s got to be the defining goal as we are approaching it. With that framework, I’m very, very optimistic. We are in very early stages of the kind of progress that we are going to see on many areas. We’re all talking about areas like healthcare. A lot of the progress is ahead. The ability to make things in a fundamentally cheaper way on many things where things have been getting expensive for a long, long time, discovering new materials, etc., which really bend the curve—it’s tough to step today and understand the impact of all that, but they’re going to be very profound. I’m very optimistic, but we have to be rightfully concerned about all the ways this technology can go wrong. Sundar, I know you don’t do many of these. Thank you. Thanks, Alex. Appreciate it.
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Is SMIC N+3’s Metal Pitch Smaller than Intel 18A’s?
Almost four years ago, we published that SMIC had started shipping 7 nm (N+1) chips. Now, SMIC is shipping its third-generation 7 nm (N+3) process in Huawei’s Kirin 9030, with a minimum metal pitch of 32.5 nm, about 10% tighter than the 36 nm minimum metal pitch shipping in Intel’s latest Panther Lake CPUs on 18A. The headline is true, but incomplete cherry picked metric. N+3 reaches the density of TSMC N6 through aggressive DUV multi-patterning and design-technology co-optimization (DTCO), but it pays for that in complexity, efficiency and process control. We found this and more in our reverse engineering and teardown where we cover SMIC’s N3 process technology, Huawei’s packaging, memory, architecture, and more. SemiAnalysis has been building a state-of-the-art teardown lab in Oregon capable of analyzing the world’s most advanced and important chips over the last year and half. We have already generated revenue on advanced datacenter chip teardowns including our recent reverse engineering of a major TSMC customer’s COUPE CPO optical engine + EIC 3D stack. This is the first public report from the SemiAnalysis Teardown Engineering & Evaluation Lab, or STEEL for short. The lab is aggressively scaling up and out and we’re excited to announce it publicly. This is a bit of inconvenient timing for TechInsights as they are private equity owned and currently being sold while having enjoyed virtually no credible competition for decades. This has led to TechInsights underinvesting in CAPEX. SemiAnalysis exceeds TechInsights in revenue despite no venture or private equity ownership and being founded only 6 years ago. Because we have no external investors and are founder led, we move faster, build faster, and we can release client chip teardowns for free regularly, while focusing on datacenter for our major clients. Here’s the first public image from our lab, the HiSilicon Kirin 9030 Pro SoC: This report will detail our teardown of the Kirin 9030 and our findings on SMIC’s N+3 process, the most advanced in China. For comparison, we’ll show our teardown of the MediaTek Helio G99, made on TSMC N6. Through this comparison, we can look at the effect of export controls – SMIC N+3 and TSMC N6 are comparable nodes, but one is heavily export-controlled, the other free to use the West’s most advanced equipment. Here we see both China’s progress and constraints. SMIC N+3 reaches TSMC N6-class logic density, but it requires far more aggressive DUV multi-patterning, so it does not match N6 on process maturity or cost. The Kirin 9030 Pro performs similarly to three-year-old Android flagships, and trails far behind the current flagship SoCs from Apple, Qualcomm, MediaTek, and Samsung. The efficiency gap is even wider. Export controls have not stopped Huawei and SMIC from shipping advanced silicon, but they have forced a different path. Without EUV, SMIC is leaning harder on DUV multi-patterning, DTCO, and increasingly complex integration. The roadmap continues forward through tighter design rules and backside power, but each step adds cost and process risk. Huawei’s τ scaling and LogicFolding show another path: stacking active logic and recovering density through advanced packaging and system-technology co-optimization (STCO). To understand the Kirin 9030, we must first understand Huawei’s SoC history. HiSilicon is Huawei’s chip design arm, responsible for the Kirin smartphone SoCs, Kunpeng server CPUs, Ascend AI accelerators, and switch/router networking silicon. Before export controls, Huawei was TSMC’s largest customer – the only customer on TSMC’s first EUV node, N7+, and among the first on N5, alongside Apple. That ended in late 2020. Huawei switched to Qualcomm SoCs in its flagship smartphones, though export controls limited them to 4G-only variants. In late 2023, Huawei returned to in-house silicon with the Kirin 9000s, a successor to the Kirin 9000, fabricated on SMIC N+2 instead of TSMC N5. In the following years, they released the Kirin 9010 and 9020 on the same N+2 process. These chips used Huawei’s in-house TaiShan CPU cores and Maleoon GPU. We have not torn down a Kirin 9020 ourselves, so the predecessor die shot is from Kurnal. The die shots show how Huawei spent its silicon budget: which functional blocks are where, and how their areas compare to the predecessor. First, a quick guide to the major blocks on the die. The total die area is nearly identical, but the 9030 uses that area more aggressively. A denser process lets Huawei fit an extra middle CPU core, more GPU and NPU cores, and larger caches into the same footprint. In contrast, the Helio G99 is a much smaller, low-cost SoC, built for budget smartphones rather than a flagship device. While the Kirin 9030 is ~140 mm², the G99 is only ~29 mm², roughly one-fifth the area. The underlying TSMC process technology, however, is directly comparable as a baseline for analyzing SMIC’s. The Kirin 9030 is an evolutionary refresh, not a clean-sheet design. Its CPU, GPU and NPU cores carry over the 9020’s families, and the gains come from three levers: the SMIC N+2-to-N+3 process step, DTCO and floorplan work, and incremental microarchitecture. Area is where the first two show up, and the 9030 scales well here. Performance and efficiency are the harder test. Huawei’s design holds up better than its node would suggest, but the chip still trails, both because N+3 sits behind the leading-edge nodes and because its cores, while competent, remain a few generations behind the newest designs. The new prime core is an incremental update. The main changes are a 10% frequency increase from 2.5 GHz to 2.75 GHz and a doubling in the L2 cache from 1 MiB to 2 MiB. Despite the increased cache, the core size decreased by 7.6%. Excluding the private L2 cache, the core size decreased by 21%. This is a large reduction for an incremental node. Compared with the TaiShan New V120 core in the Kirin 9020, the Kirin 9030’s middle core is almost unchanged architecturally, yet each core shrinks by ~22%. Most of that comes from the move from N+2 to N+3, with layout likely accounting for the rest. Visually, the most noticeable change is the increase from 3 middle cores to 4. There is also a 20% increase in the shared L3 cache of the big cluster. This helps to improve multi-core performance without sacrificing much in terms of area. Even with each core shrinking, the big CPU cluster’s total area is essentially unchanged. The per-core savings went back into an additional middle core and larger caches. The tiny cores shrank less than the prime core (excluding its L2 cache) and less than the middle cores. This is likely because fixed overhead is a larger share on a small core. We cannot resolve any architectural changes from the die shot alone, but the per-clock and efficiency gains shown below point to more than pure process and layout scaling. The area reduction was offset by a doubling of the shared L2 cache from 2 MiB to 4 MiB, leaving the total tiny CPU cluster area slightly larger. Area is the easiest improvement to see from a die shot, but it is only one part of PPA (power, performance, area). For modern logic, power and performance matter just as much, and often more. Since Dennard scaling broke down in the mid-2000s, voltage and frequency have not scaled in step with transistor dimensions, so each node has had to fight harder for gains in performance and efficiency. The starkest comparison is not Kirin 9020 versus Kirin 9030 Pro. Apple’s efficiency cores run circles around Huawei’s prime core. Apple’s low-power core delivers 20% higher integer performance while drawing only 1 W, compared with 4.5 W for Huawei’s prime core. N+3 matches TSMC N6, but N6 is several generations old. Apple and Qualcomm build on N4 and N3P, which are denser and sit on a better voltage-frequency curve, giving them a larger transistor budget and more performance per watt. The 9030’s own cores did improve. The middle and tiny cores gained 17% and 14% in per-clock integer performance over the 9020, with floating-point flat on the middle core and up 11% on the tiny. The tiny core improves cleanly, with performance rising while power falls and efficiency increases by 45% in integer and 24% in floating point. The middle core is mixed: integer performance rises but power rises faster, cutting integer efficiency by 7%, while lower power lifts floating-point efficiency 16%. Per-clock gains at the same or lower frequency are microarchitectural, so the cores are tuned, not just shrunk. Both also failed to hold their rated maximum frequencies, pointing to thermal, power, or stability limits. Per clock, the middle core sits around Arm Cortex-A720 and the tiny core near the Cortex-A520; absolute performance trails because Huawei clocks them much lower. The prime core is roughly Cortex-X2 class per clock, a 2021 design. Apple’s 2020 M1 Firestorm core is still 35% higher per clock and 57% faster in absolute integer performance at a similar 4.5 W. The current leading edge is further ahead again: the Apple M5 P-core is 60% higher per clock and 2.7× faster, the Arm C1 Ultra 45% higher and 2× faster. Matching older high-end cores per clock is a genuine design achievement. What Huawei cannot match is the voltage-frequency curve and transistor budget of leading-edge nodes, which let Apple, Qualcomm and others spend more transistors in the same area on wider cores, larger caches and deeper buffers while running at lower voltage. Huawei’s LogicFolding roadmap is one answer, stacking active logic to recover density and shorten signal paths. We return to it later. The GPU compute units (CUs) changed more visibly than the CPU cores, moving to a more rectangular layout for both the arithmetic logic unit (ALU) clusters and the CU overall. Even with ray-tracing support added, a CU shrank ~28%. However, that shrink is offset by the increase from 4 to 6 CUs and the area outside the CUs grew 33%. Overall, the GPU cluster is larger by ~10%. The GPU is where Huawei makes its biggest gains. The Maleoon 935 is not competitive with current flagships, but it is a large step up from the 920 and reaches older-flagship territory. In 3DMark it is 70% faster in Wild Life Extreme (WLE) and 79% faster in Steel Nomad Light (SNL) than the 920; with 11% higher clocks and 50% more CUs, the ~67% theoretical uplift roughly matches WLE and is beaten by SNL. It edges ahead of the Snapdragon 8+ Gen 1 in WLE and SNL, and the Dimensity 9200 and Apple A16 in WLE, but stays far behind newer parts: the Snapdragon 8 Elite Gen 5 and Dimensity 9500 are ~2.4–2.6× faster in WLE and ~3.2× faster in SNL. The Maleoon 935 is Huawei’s first GPU with hardware-accelerated ray tracing; there it lands slightly ahead of the Exynos 2200, and on par with the Apple A16, with current flagships up to 3.7× faster. The Neural Processing Unit (NPU) saw the largest structural changes of any block, moving from a Lite and a Tiny core in the Kirin 9020 to a Lite and two Tiny cores in the Kirin 9030. Both core types also show significant layout changes. This is a reversal in Huawei’s NPU design. The Kirin 9000 5G, its last flagship chip on TSMC N5, used two Lite and one Tiny core. The series of SoCs on SMIC N+2 moved to one Lite and one Tiny core, likely for area savings. With the Kirin 9030, Huawei has shifted back toward a larger multi-core NPU cluster, but with the additional area going to a Tiny core rather than a Lite core. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. Before diving into the process stack, the package and memory are worth separating from the SoC itself. The Pro variant of the Kirin 9030 carries 12 GB of Samsung DRAM, with two stacks of four dies each. The dies were identified as the K4L2E165YD, a 12 Gb LPDDR5X-9600 device fabricated on Samsung’s 1a node, the fourth generation of its 10 nm-class DRAM after 1x, 1y and 1z. 1a has shipped in volume since 2022, so this is current memory rather than older-node inventory. The 16 GB Pro Max variants we obtained were found with both CXMT and Samsung packages. The CXMT package is marked CXDD7JEDM, with two stacks of four dies, packaged in week 45 of 2025. The inferred die dimensions from X-ray computed tomography (CT) are consistent with a known density of ~0.3 Gib/mm² for the CXMT G4 process, roughly equivalent to other manufacturers’ 1z processes. The Kirin 9030 uses a typical integrated package-on-package (iPoP) stack: multiple DRAM dies in a memory package sit above an organic redistribution layer (RDL) interposer, which sits above the SoC and package substrate. The full package is then mounted to the printed circuit board (PCB) through ball-grid array (BGA) solder bumps. The memory package substrate is a thin bismaleimide-triazine (BT) laminate carrying the LPDDR5X stack. The organic RDL interposer over the SoC routes the PoP signals around the die and carries possible dummy thermal copper pillars. The package substrate, a thicker Ajinomoto Build-up Film (ABF) build-up over a BT core, fans the flip-chip bumps out to BGA pitch and embeds the power planes. The whole stack is organic. The only silicon is the SoC and the LPDDR5X dies; there is no silicon interposer. Keeping it all-organic brings the package’s coefficient of thermal expansion (CTE) close to the PCB’s, reducing board-level warpage, and avoids the cost of a silicon interposer the SoC’s bandwidth does not need. In an iPoP stack, the memory package connects to the organic RDL interposer through an array of solder bumps. Underfill fills the gap around those bumps, adding stiffness and protecting the joints from mechanical stress. The Pro and Pro Max variants differ here, which we cover behind the paywall. The die shot and architecture tell us how Huawei allocated its silicon budget. The process tells us what SMIC can manufacture. We use the Helio G99 as the process reference for TSMC N6. Both SMIC N+3 and TSMC N6 are evolutions of previous 7 nm-class nodes. We used targeted TEM cross-sections through logic and memory regions, imaged in both fin-cut and gate-cut directions. Each cross-section caption gives its horizontal field width (HFW), the real width of the imaged area. We start at the transistor fins, then move up through standard cells, local interconnect, and SRAM. SMIC has not overtaken Intel or TSMC. It uses aggressive DUV scaling and DTCO to reach N6-class density, but that density doesn’t translate into comparable performance and efficiency, for two reasons: the node gap to leading-edge nodes, and Huawei’s core designs. Fin Profile One of the most important knobs in a FinFET process is the fin profile: the shape of an individual fin and the channel where current passes from source to drain. The ideal fin is tall, narrow, and nearly vertical. A taller fin increases effective channel width, while a narrower fin improves electrostatic control by thinning the body the gate must control. Push either too far, and the process pays for it: weaker drive current, fragile fins, taper, footing and line-edge variation that hit yield and device variability. The Intel 22 nm, 14 nm, and 10 nm fin cross-sections show how FinFET nodes have improved over time. 22 nm fins were a first-generation structure, relatively short, wide and strongly tapered. The shape limits current density and reduces gate control uniformity across the height of the fin. At 14 nm and 10 nm, Intel pushed the fins taller and narrower while also making the sidewalls more vertical. Rather than shrinking the device, these changes increase the effective channel width per fin and improve electrostatic control. The trade-off is that taller fins at tighter pitches are much more difficult to manufacture. Now, let’s compare the Helio G99 on TSMC N6 with the Kirin 9030 on SMIC N+3. Both processes are in the same class, with fin pitch of 30-32 nm on N+3 and 34 nm in our N6 cross-section. The pitch for N6 is especially interesting as N7’s HD library is generally listed with a 33 nm fin pitch, and N6 did not shrink pitches directly. Its density gains came from DTCO instead of tighter pitches. The 34 nm pitch was stable across our sampled region and serves more as a comparison against the SMIC N+3 we have not investigated further. Pinning down N+3’s fin patterning scheme takes more than one core unit. The CPU cores show a dense ~32 nm pitch, with the pitch between N-P fin pairs alternating between 78 and 88 nm. Logic alone may be consistent with dual-pitch mandrels of 120 and 110 nm, but this is a complex and unusual approach. Combining the pitch from the the 8T SRAM, which has more complex repeat unit, with the CPU core sequence allows us to reverse engineer the patterning steps with more confidence. As both the logic and SRAM should share the same base grid, a single CD mandrel lithography pattern with 128 nm pitch undergoing SAQP produces a die-wide ~32 nm grid (128 nm/4), which supports the pitch sequencing seen in both logic and SRAM cells. In the sampled cross-sections, N+3 shows a taller, narrower, higher-aspect-ratio fin than N6. The measured fin aspect ratio is ~9.5:1 on N+3 versus 7.8:1 on N6. N+3 also shows less top rounding, with an estimated radius of ~2 nm, compared with 2.8 nm on N6. Even though the fin widths differ, the ratio of top rounding to fin width tells the same story, with N+3 at 0.37 and N6 at 0.44. In a geometric sense, lower is better; a perfectly rectangular fin would have no top-rounding penalty. These are single-digit-nanometer features measured from a handful of cuts, so treat the absolute numbers as approximate. The important result is the relative gap: N+3’s fins are consistently taller, narrower and less rounded than N6’s. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. Standard Cell A standard cell is the basic building block of chip layout: a fixed-height row pairing one NMOS and one PMOS transistor that share a gate, tiled in a grid to build logic blocks. The key dimensions are contacted gate pitch (CGP), cell height (CH), fin count, and the lower-metal routing grid. To measure density, we use the Bohr metric: a weighted average of NAND2 gate area (60%) and scan flip-flop area (40%). This represents a realistic mix of combinational and sequential logic. This metric has its limitations, especially for complex cell layouts like TSMC’s FinFLEX, which alternates cells with different fin counts. Even so, it is the best metric for a pure process-level comparison. Another important measurement is the fin pitch; it refers to the distance between two fins of the same transistor. In a FinFET process, multiple fins are used in each transistor to increase the drive current and thus performance. TSMC N6 ships both a high-density (HD) library with 2 PMOS and 2 NMOS fins per cell, and a high-performance (HP) library with 3 of each. More fins under the shared gate mean more effective channel width. HP cells switch harder at the cost of area. Designers mix the two on a die, primarily spending HP cells on timing-critical paths, and matching their PPA targets. In the Cortex-A55 core of the Helio G99, we found a cell height of 240 nm for the HD cell. MediaTek has used HD cells in the G99 to minimize die size and thus cost. As an SoC for budget smartphones at ~$100, this is essential. By contrast, we found only one library in the Kirin 9030, with 2 NMOS and 2 PMOS fins. This suggests a narrower library strategy than TSMC N6, where both HD and HP libraries are widely used. This likely reflects the smaller customer base and the more constrained domestic design and electronic design automation (EDA) ecosystem. In all three CPU cores of the Kirin 9030, we found cell heights of 228 nm, 5% smaller than on N6. This is also a reduction of 9.5% over SMIC N+2’s cell height of 252 nm. SMIC N+3 and TSMC N6’s HD library both feature a CGP of 57 nm. For SMIC, this is a 9.5% shrink over N+2. In the past, CGP and cell height alone may have been enough to compare transistor density. Now, however, we must consider scaling boosters and DTCO as well. SMIC’s density gain does not come from EUV. It comes from using every available DTCO booster aggressively. First is fin depopulation: reducing the number of NMOS and PMOS fins in each cell. The first FinFET nodes started with 3 or 4 fins for each transistor. SMIC N+3 and TSMC N6 HD both use only 2 fins per transistor, trading drive strength for density. Next is contact over active gate (COAG). By landing the gate contact directly over the active gate, instead of out over the isolation region, the cell height drops. N+3 integrates COAG while N6 does not. Our N+3 gate-cut cross-sections indicate COAG, with the gate contact sitting over the active region, while N6 shows an off-gate contact. Last is single diffusion break (SDB). Diffusion breaks are inserted between cells in the same row to provide electrical isolation, but they also introduce local layout effects (LLE), layout-dependent shifts in electrical characteristics. In the past, a double diffusion break was used, consuming the space of two CGPs. SMIC N+3 and TSMC N6 instead use SDB, saving area but increasing LLE sensitivity. This must be controlled at the process level and accurately modeled in the process design kit (PDK) so that EDA tools can account for it. Overall, SMIC N+3 has a transistor density of 113.4 MTr/mm², slightly above TSMC N6 at 107.7 MTr/mm². Even without EUV, SMIC has achieved density beyond TSMC’s mature N6 node which utilizes EUV. Metal Stack The smallest critical dimension in the teardown is M0; SMIC N+3 uses a 32.5 nm local metal pitch. That is smaller than the 36 nm M0 pitch on Intel 18A in Panther Lake. However, this does not mean that SMIC has a better process than Intel 18A or TSMC N3P. M0 is a local intra-cell routing layer. Its usefulness depends on the full interconnect stack: M1 and M2 pitch, track count, via and line resistance, design rules, mask count, overlay control, and routing flexibility. The 32.5 nm M0 is consistent with self-aligned quadruple patterning (SAQP), whose four-population line-width loading we read coarsely as alternating widths of 21.5 to 24 nm; M1 and M2, at 38 and 40 nm, are consistent with self-aligned double patterning (SADP), a single A/B split. On TSMC N6, M0, M2, and M3 sit at a relaxed ~40 nm and are consistent with SADP-class double patterning, with no need for quadruple patterning. That said, we measure M2 for example at ~43 nm, likely inflated by sparse routing. We do not assign any specific layer to EUV from our cross-sections; the distinction we can draw is double versus quadruple patterning, not lithography wavelength. Transistor-level density in the front-end-of-line (FEOL) sets an upper bound, but the design is ultimately limited by what the interconnect stack can route. The lowest metals are the most important for standard-cell density, but the semi-global and global layers determine how usable that density is at the block and chip level. Two axes are commonly used for chip cross-sections: the fin-cut and the gate-cut. The micrograph above is a fin-cut and shows metals 0 through 3. This axis lets us see the even-numbered metals, with M0 right above the fins. There are two kinds of M0 lines. The first are the power rails; these are wide wires for the VDD and VSS running horizontally at the top and bottom edges of each standard cell. The wide wires measure 55 nm across, more than double the other M0 lines. Their width minimizes resistance and reduces IR drop. The second kind are intra-cell wires, short segments within the cell that connect terminals to M1. These have alternating widths between 21.5 and 24 nm. The M0 pitch is 32.5 nm, a 19% reduction versus N+2 and N6. At this pitch, DUV patterning requires more aggressive multi-patterning, increasing mask count, overlay sensitivity, process complexity and cost. M0 is below what a single DUV-defined spacer (SADP) can resolve, so SMIC cascades a second spacer step (SAQP). The cross-section reflects the cost: the M0 trenches are visibly more re-entrant (narrower at the bottom than the top) than M1 or M2 on the same chip and carry a bright barrier-rich foot where the trench meets the etch-stop layer. That shape is partly the intended damascene profile, as a slightly narrow bottom helps void-free copper fill, but its magnitude at M0 is driven by the tight pitch and the higher trench aspect ratio. Intel 18A supports an M0 pitch of 32 nm, although Panther Lake has only shipped with a looser 36 nm pitch. This is due to Intel’s heavy usage of HP libraries. Among leading-edge nodes, 18A has the loosest M0 pitches due to PowerVia. With power routing moving to the backside, congestion is reduced, and the entire front-side metal stack can be used for signal routing. M2 is the first true inter-cell routing layer. It runs horizontally like M0 but spans across multiple cells to carry block-level signals. The M2 pitch sets the cell’s track height – the number of M2 tracks that fit between the VDD and VSS rails, defining what the library calls a 6-track or 7.5-track cell. This layer is the most important, limiting the routing of entire blocks. SMIC N+3 features a 5.7-track cell. The M2 pitch is 40 nm, a 5% decrease over N+2 and the same as N6. This shrink keeps the pitch at the edge of what is possible with double patterning. Future nodes will need to increase the number of masks for M2 as reducing the number of tracks is much harder due to the limitations in routing. The micrograph above is in the perpendicular direction, the gate-cut, and shows metals 0 through 4. This allows us to see and measure the odd-numbered vertical metal layers. The M1 pitch is 38 nm, 9.5% less than N+2 and 33% less than N6. The M1-to-gate ratio matters because it sets local routing flexibility. N+2 and N+3 use a 3:2 ratio, while N6 uses a 1:1 ratio, explaining the huge differences in M1 pitch. The more M1 lines there are compared with the gates, the more flexibility there is for power and signal crossing within the cell. Routing flexibility enables more complex and better cells. Clean fractional ratios are also preferred as a grid is periodic and improves layouts. The 3:2 ratio gives SMIC more local routing flexibility than a strict 1:1 grid, but it also complicates layout and patterning. This is a DTCO choice, with SMIC increasing process complexity to recover density and routability without EUV. This 3:2 ratio is not very popular in the leading-edge nodes. TSMC has only used it on N7+, the N5 family, and the short-lived N3(B). They have switched back to a 1:1 ratio for N3E. Intel has only used it on the 10 nm/Intel 7 family, with Intel 4, 3 and 18A all using a 1:1 ratio. Samsung is the only one still using a 3:2 ratio at the leading edge, using it in the SF4 and SF3 families. It remains to be seen if SMIC will remain at a 3:2 ratio or move to a 1:1 ratio with its future nodes. The industry is still actively exploring these local-routing ratios. At VLSI 2026, imec will be presenting work on even higher ratios, including a 2:1 scheme that can reduce area by up to 14%. We will be covering the conference in a future newsletter article. Subscribe We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. The final local interconnect layer for N+3 is M3, with a pitch of 44 nm. The M3 pitch is the same as on N+2 and 10% larger than on N6. The semi-global layers carry the majority of block-level signal routing. They have a coarser pitch than the lower local layers. On leading-edge nodes, they are designed to sit at the limit of DUV single patterning. M4 through M11 pitches were found divided between 80–82 nm (M4–M6), 128 nm (M7–M10), and 148 nm (M11). Given limited sampling, it is possible these are divided further in dense routing areas. At the top are two giant metal layers, M12 and M13. These have kept the same pitches as N+2 at 1920 nm and 4600 nm respectively. While the lower layers’ pitches are generally fixed by the process and library, the upper layers vary much more in pitch and count, depending on the design. Even two smartphone SoCs on the same process can have wildly different metal stacks. The Helio G99 carries fewer routing layers, reaching coarse metal pitches of 850 nm by M9, while the larger and higher-performance Kirin 9030 keeps fine pitches until M11. SRAM At the leading edge, SRAM is much more difficult to scale than logic. TSMC’s latest nodes have seen little to no bitcell scaling, while logic still has more DTCO levers to pull. While looking for other logic libraries in the GPU compute units, we stumbled upon the SRAM. The most common type of SRAM has 6 transistors (6T), but this cell had 8 transistors (8T) instead. 8T SRAM adds two transistors to form a dedicated read port. Unlike a 6T cell, where reading disturbs the storage, the decoupled read port removes read-disturb, improving read stability and letting the cell be pushed harder for performance. At first glance, the cut looked like an unusual logic library, with each cell row having 3 fins of one polarity and 5 fins of another. The rows also alternated in orientation. Energy-dispersive X-ray spectroscopy (EDS) resolved our confusion. The cut had not landed on the GPU logic, but on the SRAM macro beside it. The unusual fin pattern was due to the SRAM library. We return to EDS in the process flow analysis behind the paywall. SRAM libraries are not like traditional logic libraries. Due to the unequal number of PMOS and NMOS transistors, they require specialized rules and layout libraries. They do not need the flexibility of logic libraries, so they are hyper-optimized for one purpose: dense, reliable memory. The SRAM cell we found is a 1:2:2-2:2 cell. This means there is 1 fin per pull-up (PU) PMOS transistor, and 2 fins per pull-down (PD) and pass-gate (PG) NMOS transistor. These 2 PU, 2 PD and 2 PG transistors would usually form a single 6T high-current cell (HCC). An 8T HCC adds a read-pull-down (RPD) and a read-pass-gate (RPG) NMOS transistor, each with two fins. We measured a cell height of 406 nm, which brings the bitcell size to 0.0463 µm². That is a theoretical peak density of 21.6 Mib/mm². We estimate that a 6T HCC would have a cell height of 292 nm and a size of 0.0337 µm². This is ~12% larger than a 6T HCC on Intel 3 and 4. We also estimate the 6T high-density cell (HDC) to have a cell height of 228 nm and a size of 0.0260 µm². This is coincidentally the same as the logic standard-cell height measured earlier. The estimate puts the cell near Samsung 7LPP/5LPP and slightly below TSMC N7/N6. That is a theoretical peak density of 38.5 Mib/mm². 6T HDC is arguably the most important cell as it is used for the largest caches in a chip, the L3 caches and system-level cache (SLC). Both the Kirin 9020 and 9030 have split the SLC into 4 banks to raise total SLC bandwidth. In the Kirin 9030, the SLC increased from 2 MiB to 3 MiB per bank. Correspondingly, the number of arrays within the bank also increased by 50%, from 16 to 24. Each array can store 128 KiB and forms an orderly pattern on the die shot. From the Kirin 9020 to the Kirin 9030, the area of a 128 KiB SLC array decreased from 0.0477 mm² to 0.0392 mm², an 18% shrink. The achieved density is 25.5 Mib/mm², 66% of the theoretical maximum. While the SLC was quite similar across both chips, the L3 has seen some major changes, particularly in terms of its layout. The total capacity also went up from 10 MiB to 12 MiB. Much like the SLC, the L3 is also split into 4 banks. In the Kirin 9020, an L3 bank consisted of 16× 128 KiB arrays and 16× 32 KiB arrays. However, an L3 bank in the Kirin 9030 instead consists of 48× 64 KiB arrays. In the Kirin 9020 L3, a 128 KiB array was 0.0513 mm² and a 32 KiB array was 0.0154 mm². The size of the 128 KiB array is different on the L3 and SLC as the assist circuitry for the two arrays differs depending on their purpose. In the Kirin 9030 L3, a 64 KiB array is 0.0210 mm². Although not a like-for-like comparison, normalized for capacity, it is 18% smaller than the 9020’s 128 KiB L3 array and 31% smaller than its 32 KiB L3 array. The achieved density is slightly lower than the SLC, at 23.8 Mib/mm², 62% of the theoretical maximum. Unlike the L3 and SLC, the prime cores’ private L2 cache uses a 2-bank design. As the prime cores’ L2 is latency-critical, it likely uses 6T HCC instead of 6T HDC. The 9020 has 16 arrays in each bank while the 9030 has 32. Each array has a capacity of 32 KiB. A 32 KiB array in the L2 shrank from 0.0171 mm² to 0.0142 mm², ~17% smaller. The density is 17.6 Mib/mm², ~59% of the theoretical maximum for 6T HCC. SRAM scaled well from N+2 to N+3, shrinking by ~19%, close to the theoretical logic shrink. The caveat is that N+2’s bitcells were unusually large, bigger than comparable 7 nm-class nodes, so part of the gain is catch-up rather than true scaling. With the insights from STEEL’s teardowns, we will be doing a deep dive into SRAM in a future newsletter article. Subscribe Everything above came out of a single STEEL teardown: die annotation, block-level area analysis and TEM cross-sections through logic and SRAM. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Future Roadmap The same cross-sections that pin down N+3 also show where SMIC can go next. Although N+3 is already close to the practical limits of DUV multi-patterning in several layers, SMIC still has a few scaling levers left. A theoretical N+4 would likely start with cell height. N+3 uses 5 M0 tracks between its power rails. Moving to 4 M0 tracks, as on SMIC N+2 and TSMC N6, could reduce cell height by roughly 15%. The routing grid is only one side of the shrink; the front end also must fit into the smaller cell. One possible FEOL lever is reducing the p-to-n isolation spacing from two diffusion grid units to one. Intel used this scaling booster on Intel 4, and TSMC did so on its N3 family. This path trades layout flexibility for density. Fewer M0 tracks reduce local routing resources, while tighter p-to-n spacing raises integration and design-rule difficulty. M2 is also constrained by the cell height shrink. For SMIC to maintain a ~5.7-track cell, M2 would need to move toward ~35 nm. That would move another layer into SAQP territory. SMIC could also reduce the CGP from 57 nm to 54 nm. Intel reached a similar CGP on Intel 10 nm/Intel 7 without EUV. The local interconnect is also tougher. If SMIC keeps the 3:2 M1-to-gate ratio, M1 would need to shrink to 36 nm and would likely require SAQP as well. If SMIC moves to a 1:1 ratio, M1 could relax to 54 nm, but it would give up routing flexibility. Under this theoretical path, we estimate that SMIC N+4 could reach a cell height of 198 nm and a CGP of 54 nm, implying a Bohr density of 137.8 MTr/mm², on par with TSMC N5 or Samsung SF4. However, the difficulty is cumulative. Each step is individually plausible, but together they make N+4 harder than the transition from N+2 to N+3. It will likely take longer, cost more, and carry less process margin. A theoretical N+5 would require a larger integration shift. One possible path is backside contacts (BSCon), moving power routing and source/drain contacts to the backside, which would reduce front-side routing pressure and enable another reduction in cell height. The front-side metal pitches could relax to reduce the process complexity. M0 would likely relax slightly to ~34 nm, and M2 and M4 pitches could relax further. CGP is unlikely to shrink much further. Even with EUV, 48 nm has been the practical limit for yield and process control. This approach would allow N+5’s cell height to fall to 170 nm and its CGP to 53 nm. This implies a Bohr density of 163.6 MTr/mm², on par with Intel 18A’s HP library. However, this would not make N+5 cost-competitive with the leading edge. It would reach a similar density through a much more expensive route. The integration difficulty rises sharply, with new process flows for backside alignment, wafer thinning, contact reveal, and backside metallization. Past this point, standard density and interconnect scaling become increasingly unattractive. That is where Huawei’s roadmap stops looking like a normal foundry roadmap and starts looking like a packaging roadmap. At ISCAS 2026, Huawei unveiled its tau (τ) scaling law, reframing process scaling in the time domain. τ is the time cost of data movement and processing: switching delays in transistors, RC signal propagation delays in circuits, compute, memory, and networking latency. Outside Huawei’s terminology, this is called system-technology co-optimization. This is Huawei’s answer to its lack of EUV lithography. Without EUV, planar density cannot keep pace with TSMC, Intel, or Samsung. If transistor density cannot shrink further, Huawei’s alternative is to shorten wires, reduce buffering, and stack logic vertically. “LogicFolding”, Huawei’s implementation of this new scaling idea, is, in practice, an aggressive 3D stacking approach. AMD V-Cache places SRAM above or below a CPU die. AMD’s MI350X places active interposer dies (AIDs) underneath accelerator and compute dies (XCDs), with the AIDs handling cache, IO interfaces, the network-on-chip (NoC) and embedded metal-insulator-metal (MIM) capacitors. With LogicFolding, parts of the same logic block are split across multiple active dies bonded face-to-face at ultra-fine pitch. This allows Huawei to shorten some critical paths and reduce buffer overhead, not merely add cache capacity or offload the IO and interconnect. Shortening wires is where the higher clocks come from. A large share of a modern core’s delay and energy budget goes into driving long interconnects and the repeater buffers along them. LogicFolding distributes a block’s critical-path gates across multiple stacked tiers bonded at a very fine pitch, so the bond interface behaves like an additional metal layer and the longest paths get shorter. That is how Huawei expects to recover frequency and efficiency it cannot get from the process alone. Huawei’s roadmap shows its intent. Prime core frequency is targeted to rise from 2.75 GHz in the Kirin 9030 to roughly 5 GHz by 2031, far beyond what planar scaling alone could deliver. Prime cores with 3.1 and 3.39 GHz clocks are being tested in its labs, although their power consumption is unknown. Beyond that, chips are in the design, simulation or pathfinding phase, meaning the frequencies are targets. However, the direction matters more: LogicFolding also helps with performance, not just density. The catch is that Huawei’s density claim is not directly comparable to foundry densities. A stacked design can report more transistors per package footprint by adding active layers, even if each patterned die remains well behind TSMC or Intel in front-end density. This is how Huawei can claim to reach foundry 14A-equivalent density by 2031. This is not a like-for-like foundry comparison, with Huawei using stacked logic and measuring density per package footprint. On a normalized Bohr-density basis, SMIC N+3 is ~114 MTr/mm², 38% less than Intel 18A’s HD library. Huawei’s 3D roadmap closes the gap by stacking active logic, reaching 215 MTr/mm² by 2030. In 2031, the roadmap density jumps to 295 MTr/mm², implying either a third active layer, partial EUV insertion or aggressive planar DUV scaling. Huawei’s methodology makes other foundries look much denser as well. Applying it to AMD’s MI450X with an N2 top die on an N3P base die yields a theoretical density of 460.2 MTr/mm² in 2026, compared with Huawei’s 295 MTr/mm² in 2031. This Kirin 9030 does not use LogicFolding, remaining in a conventional mobile SoC package. Instead, it forms the baseline for how far Huawei and SMIC can push planar scaling. Future teardowns of Kirin and Ascend chips will show both planar logic density and Huawei’s hybrid bonding solutions. Export controls changed China’s optimization problem rather than ending it. EUV restrictions raised the cost and complexity of leading-edge manufacturing without freezing it. SMIC reaches N6-class logic density through DUV immersion, SAQP and DTCO, while Huawei shifts more of the burden onto architecture, packaging and system-level integration. Future nodes will be tougher. N+3 still had room to tighten local metals and reduce cell height and CGP. Further scaling without EUV leaves fewer levers. More aggressive multi-patterning adds masks and overlay error. SMIC can keep pushing DUV, but each step will get more expensive and less forgiving. The design side is just as critical. Huawei had domestic EDA tools and flows before the Kirin 9030, with the Kirin 9000s, 9010 and 9020 making that clear. Huawei was able to ship multiple consumer SoCs on SMIC N+2 and N+3 while cut off from the Western EDA stack. US export controls restricted EDA tools for advanced chips in 2022 but did not target tools for more mature chips. In 2025, the US government briefly placed much broader restrictions on EDA software from Synopsys, Cadence, and others, before lifting them less than two months later as part of a trade deal tied to rare earths. Huawei has been unable to access those tools because it remains on a US trade blacklist. That forced Huawei, SMIC and Chinese academic institutions to build their own tools and flows. Researchers at Peking University recently announced a prototype EDA tool for Huawei’s LogicFolding architecture, which requires a new flow to handle the multilayer layout and floorplan. This is not the same as replacing the full Synopsys or Cadence stack, but it shows where domestic EDA is headed: toward tighter co-optimization between architecture, process and packaging. These advances are also diffusing into the Chinese ecosystem. SMIC is licensing its N+2 and N+3 processes to HLMC/Hua Hong at the government’s direction rather than by choice. If the same process learning feeds into Ascend accelerators for AI training and inference, the choke point shifts from one named fab to an ecosystem. Alibaba’s T-Head silicon arm and Cambricon, a Chinese AI chip designer that is expected to supply ByteDance, could also be major beneficiaries. Sanctions aimed at SMIC alone become less effective once the manufacturing knowledge has spread to other fabs and design houses. China is not closing the gap with Intel, Samsung and TSMC. The teardown shows the opposite in several places: no EUV, no backside power, higher process complexity, and visible trade-offs. But China is still advancing. If domestic chips become good enough for phones, inference, networking and security-sensitive workloads, they can matter strategically without matching TSMC at the leading edge. Behind the paywall, we show what else STEEL can do, with material and process flow analysis of SMIC N+3, and analysis of the Kirin 9030 package. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline, access the full Kirin 9030 and SMIC N+3 analysis or to commission a custom teardown, contact sales@semianalysis.com.
Lake Erie College renames program to the Master of Reading Science
Lake Erie College in Painesville has officially renamed its Mastery of Literacy program to the Master of Reading Science, reflecting the college’s full alignment with the Science of Reading and Ohio’s updated expectations for educator pre…
Amid drama, Clemson clarifies position on Protect College Sports Act. What to know
Despite sending a letter pushes for changes to the bill’s realignment clause, the university said it’s not trying to “derail” the PCSA.
Alignment Healthcare CEO Adds Chairman Role As Medicare Business Grows
Alignment Healthcare CEO Adds Chairman Role As Medicare Business Grows
The politics of tariffs after Trump
Trump’s most enduring legacy on trade may be not the tariffs themselves, but the partisan realignment they leave behind.
How Trump’s Republican Takeover Changed The Shape Of Politics
"Regime Change" is attracting all the attention, but another book explains how a fundamental realignment of the U.S.'s political parties powered the President to power.
Open AI and the million dollar maths problem
In early September, OpenAI announced it had solved a major mathematics problem that has stumped humans for nearly a century. The news left mathematicians reeling, and many expressed concern over what will be left for humans as AI becomes ever more adept at unravelling complex problems. Now 25 recipients of the Fields medal – often called the Nobel prize for maths – have signed an open letter expressing their fears of a ‘severe misalignment’ between AI companies and their field. To find out how AI is likely to upend maths – and how mathematicians might respond – Ian Sample speaks to Colva Roney-Dougal, professor of pure mathematics at the University of St Andrews
Cboe layoffs: exchange cutting 20% of workforce in 2026
The exchange operator notified affected workers Friday as part of a broader strategic realignment that also includes selling its Canadian and Australian operations
Alignment Healthcare Turns Profit As Insurer Gets Handle On Costs
Alignment Healthcare’s second quarter net income more than doubled to $36.6 million compared to the year ago period as costs in its Medicare Advantage plans eased.
Structural alignments to design functional RNAs
Nature Computational Science - A new method designs RNA sequences by learning from alignments of structurally similar molecules, producing aptamers and ribozymes that are experimentally validated.
Kartoon Studios Advances IP-Focused Strategy with Sale of Frederator Channel Network Business
Company Retains Key Frederator Studios IP For Licensing Opportunities Strategic Transaction Continues Company’s Realignment Towards the Monetization of Premium Intellectual Property BEVERLY HILLS, Calif., July 17, 2026 (GLOBE NEWSWIRE) -- Kartoon Studios, Inc. (NYSE American: TOON) ("Kartoon Studios" or the "Company"), a global entertainment company creating, producing, distributing and licensing children's and family content, today announced the strategic sale of Frederator Networks' Channel Ne
Rumble Announces Realignment into Two Core Business Units and Renames Newly Acquired Cloud And AI-Infrastructure Business “Quake AI”
Post-merger, Rumble Cloud Renamed Quake AI, Integrating Northern Data OfferingsRumble to Remain the Video and Media Platform’s Core Brand NameCorp...
Opinion | ‘The MAGA Flag Is in Tatters’
This week in politics: Midwest realignment and what’s next for conservatives.
The politics and possibilities of ‘AI could kill us all’
I found myself fielding a lot of questions from folks who don’t usually talk much about AI this week. They were mostly along the lines of “do you think AI is going to kill us all?” or “how worried should I be about this human extinction stuff?” Motivating this wave of existential anxiety was the highly dramatic resignation of now-former Anthropic employee Jacob Coxon, who quit his job while publicly sharing his concerns about safety practices at the leading AI companies. More specifically, he declared that almost everyone at OpenAI and Anthropic, the leading AI labs, believes that there’s a decent chance the AI systems they’re producing could “kill us all” by the end of the decade, and the companies aren’t “acting responsibly.” Other AI safety researchers, including those still employed at Anthropic and OpenAI, all confirmed that yes, Coxon is correct, they’re all worried that their company’s products might prove fatal for the entire human race as well. Two more AI employees quit and joined the chorus. Now, the warning shot has been covered everywhere from the Wall Street Journal to Fox News to WIRED to the Guardian. I understand why all this sounds scary, but my response to those who have asked, has generally been: No, you don’t need to worry that AI will kill go rogue and kill us all. But that does not mean we shouldn’t worry, period. Far from it. The companies building AI products are now among the wealthiest and most powerful entities in the world. And they very much are building enormous engines of mass surveillance, extraction, and automation. They can and have allowed their AI systems to carry out felony hacks of other companies, provided the government with autonomously generated kill lists, and sold enterprise technology to firms around the world that is explicitly intended to replace jobs. But “AI” has done these things not because they have all-powerful minds of their own but, ultimately, because people have programmed, calibrated, and directed them to. In other words, it’s not AI we need to worry the most about. It’s the people and corporations working to profit from and gain power by deploying it. The technology is sophisticated, sure, and, especially in cybersecurity contexts, potentially disruptive to plenty of existing systems. But if we want to stop this predatory, profiteering, and extractive form of AI—and it’s worth noting that at this point, most people do—we absolutely can. A quick note/plea: I can only do all of this with the support of readers like you, so if you find value in independent, critical tech journalism and analysis, please consider becoming a paid subscriber. I want to keep as much of this un-paywalled as absolutely possible, and I need your support to do so. Many thanks to all who continue to chip in, it’s very much appreciated. Onward. Subscribe First, some context: AI industry folks like Coxon have been discussing the prospect that AI will destroy humanity for years. Even before the beginning of the AI boom, many working in the industry, from the executive staff to the rank and file researchers, have obsessed over ‘x-risk’, or the possibility that AI will become sentient or semi-sentient and move one way or another to exterminate humanity. This is ostensibly why Elon Musk bankrolled OpenAI in the first place, and is the grounds for his lawsuit alleging OpenAI betrayed its charter by abandoning its nonprofit status to become a commercial enterprise. Many AI engineers and startup founders have calculated their own personal p(doom), or the probability that the tech product they are working on will initiate an actual humanity-extinguishing apocalypse. 10-25% p(doom)s are pretty common. (I wrote about a long weekend I spent among these folks at one of their top conferences here, to give you an idea of their ideas and belief systems.) Sam Altman himself drove the narrative of the imminent rise of a super-powerful AI, or artificial general intelligence, to prominence by declaring that the technology he was building posed an existential risk to humanity. This idea is in other words baked into the DNA of modern AI companies and into the products they engineer. It’s also why the industry is routinely accused, by myself and others, of “doom marketing,” ie, using such proclamations and framings to impart the sheer power of the technology behind your product onto investors, partners, consumers, and the state. Some ambient understanding of this tendency may have helped prevent the press from repeating the claim every time one of the x-risk folks made it over the years, including when they quit their jobs at AI companies while doing so. Yet Coxon’s AI x-risk alarm has broken containment, as they say, and has exposed the idea to the mainstream media once again. This has happened, I think, for a number of reasons. First, when AI CEOs were going around saying AI might kill everyone in 2023, their companies were well-capitalized, but not nearly to the extent they are today. They were more easily dismissed as eccentric geeks of the sort that Silicon Valley is supposed to produce. Now, not only do their products have much greater market saturation, but people are more aware of a) the often unethical ways the executives and companies comport themselves b) the other adverse impacts of AI that have already arrived, and c) the physical cost of building these systems, particularly when it comes to data centers. Finally, toss in the constant stream of headlines in 2026 about AI models going rogue or potentially upending the world’s infrastructure. [ ](https://www.bloodinthemachine.com/p/with-the-backlash-to-data-centers) [ With the backlash to data centers, Flock and AI glasses, a mass opposition to big tech is underway. Silicon Valley is in denial. ](https://www.bloodinthemachine.com/p/with-the-backlash-to-data-centers) Brian Merchant · Aug 27 [ Read full story ](https://www.bloodinthemachine.com/p/with-the-backlash-to-data-centers) As a result, general sentiment was already overwhelmingly skeptical of AI and its overlords, so the table was pretty well set for someone to come along and say ‘just a reminder, the companies that are building the product that promises to take your job and leave a data center in your neighborhood also think that it might exterminate the human race’ and get a strong reaction from the media and the public. The question is: Should we understand this stuff as hype, strategically fanned to feed already swelling valuations? Is it doom marketing? Genuine cause for alarm that the infrastructure that undergirds modern life is facing a novel threat vector? Or reason to be sincerely worried that AI companies are amassing too much power, and that their products are genuinely dangerous? The answer, I’m afraid, is “yes.” Let me try to untangle all of this a bit, because arguments over how to navigate this moment are extremely heated right now, as they are essentially arguments over how to approach the politics of AI at a moment where there may be a unique opening for action. Those sympathetic to AI alignment concerns and x risk arguments—which increasingly includes a faction on the left who want to shut down AI over safety concerns—argue that people who dismiss AI as all hype have their heads in the sand. Meanwhile, critics of the x-risk narrative—which I might note includes a cadre of investors and tech folks on the right, too—argue that it is hype and can be used to further inflate valuations or for pursuing regulatory capture. The challenge is that many feel that dismissing AI as just hype or purely marketing also diminishes the urgency of taking on the AI industry as the novel and threat to labor, civil society, and the public sphere it is; others that by accepting the industry’s own terms, we imbue it with undue power and enable it. But let’s be clear: Declarations that AI is so powerful it might end the world are a kind of marketing, and anyone who says otherwise is just as off base as those who say that’s all that it is. If you don’t think that these sub-trillion dollar companies have higher-ups who noticed that when their founders talked about super-powerful dangerous AI they got spikes in media interest and phone calls from investors, or that those founders didn’t notice that themselves, and lean into certain narratives to advantage their products, then I don’t know what to tell you. Not all marketing is a Pepsi ad campaign with a celebrity spokesperson. Yet confronting the AI industry should certainly be considered an urgent project. Do I share the x-riskers’ fear that out-of-control, super-intelligent AI agents might end the world? I do not. I’ve read a good deal of AI extinction literature over the last couple of years and I have not come across a credible, step-by-step documentation of how exactly AI might move from self-recursively improving AI to killing every single human on the planet. Most of what I’ve read seems predicated on a sort of reverse Ian Malcolmian logic: Eventually, it becomes smart enough and AI, uh, finds a way (to exterminate humanity). Are the AI companies also overstating and dramatizing what their products can do? Yes, pretty much all the time. They’re in a knife fight for market share and are trying to win customers and government contracts before their investors come knocking. And yet. They have concentrated enormous amounts of compute, have extensive data collection and surveillance capacities, have inserted themselves into the state in numerous capacities, including military kill chains, and they are being run by egomaniacal billionaires who are rushing to monopolize an entire new market. The way I think about their most recent frightening-seeming exploits is not as a “swarm of rogue AI agents building a new civilization” or whatever, but as corporations with a hitherto-unimaginable amount of compute at their disposal recklessly experimenting with automation programs, completely unafraid of any repercussions because we have been unwilling or unable to impose any on them. So I think we should welcome any newfound imperative to hold them accountable. I also think that ultimately, the AI critics, Pause AI folks, leftists, doomers, effective altruists, and, honestly, most of the public, want a lot of the same things: legitimate checks and limits on AI companies’ power, actual transparency into what the companies are doing, and for more of AI to be under public control. (Ultimately, a lot of what animates the x-risk fears are rooted in just that: a fear of losing control.) How we move towards all this, exactly, is the question of the day, and while there’s much to be argued over in terms of building a viable political platform to challenge the AI industry’s power, I do want to cast a vote against one particular approach. Not to pick on Chris here, as there are a lot of folks out there proposing stuff like this, and the policy ideas actually stem from AI safety groups. But not only would this be challenging to land politically (I can already hear the ‘China will beat us to AGI’ calls) and all but unworkable in practice, this approach is a) far too limiting, b) fails to address the current harms caused by AI and c) would likely only wind up serving Anthropic and OpenAI; it’s what regulatory capture looks like in action. Full model transparency is the floor for what should be on offer here. If these companies believe their products will kill us, the very least we deserve is to know how. It’s in the public interest. I would also argue we might get a lot of the way to stopping any currently envisioned robot apocalypse simply by forcing AI companies to assume liability, and to submit to the full brunt of the law. If Sam Altman were liable to be charged with a felony for approving the hack on Hugging Face1, if Google was forced to pay defamation fines each time it published a false and injurious AI Overviews, these companies might suddenly find cause to restrain their models’ output, and we might hear less about rogue AI. We would need to introduce new frameworks and available tools for tracking automated lawbreaking, to be sure, but I’d wager that little would frighten the AI companies more than actually being held accountable for what their products enable. To that end, Illinois has passed an AI liability law, which the industry fought tooth and nail, and Florida is considering one. The next steps would be towards moving AI under public control; again, it’s only reasonable, since everyone, not just AI insiders, should get a say over how a technology whose developers say might kill us is developed and deployed. Dismantling the whole industrial project as currently structured should be on the table, too, pending democratic input. But that’s for another post. For now, suffice to say that while I won’t be losing any sleep about an AI-enabled extinction event, Mr. Coxon and co. have given us an opportunity to underscore the urgency of the clear and present damage AI companies are doing, the extreme power they wield and the extent to which they hold themselves unaccountable, and the abundance of political capital there is out there for taking them on. Subscribe 1 Purely speculative! I do not know if he did or not.
WV Superintendent of Schools Launches First AI and Digital Science Framework in the Nation
Charleston, W.Va. - West Virginia Superintendent of Schools Michele L. Blatt will share the state’s Artificial Intelligence (AI) and Digital Science Framework with educators during professional development sessions at the Charleston Coliseum and Convention Center, Monday through Wednesday, July 27-29, 2026. The Framework, the first of its kind in the country, outlines an approach for digital science and AI literacy education that equips students with the knowledge, skills and credentials to succeed in this rapidly evolving workforce. It also offers educators a way to increase efficiencies in their work and better personalize learning for students.Earlier this year, Superintendent Blatt appeared before a U.S. House of Representatives Subcommittee to share the state’s strategy on addressing the use of AI while also protecting students, their data and educational integrity. The Framework is the result of years of planning, collaboration and development. Digital science encompasses the evolving fields of computer science, AI literacy, data reasoning, cybersecurity awareness, media literacy and the practical application of digital technologies. The framework equips educators to prepare students with the AI and digital science knowledge and skills needed to thrive in the workforce of the future.The West Virginia AI and Digital Science Framework establishes a statewide structure for sustained success. From policy to educator development and student learning, it defines how the state will implement digital science and AI readiness at scale. It serves two priority goals:Respond to the rapidly changing technology landscapeEstablish best practices as a leader for rural models of AI readiness, workforce development and digital science educationWith three statewide commitments, educators will be equipped with understanding and the tools to support implementation. These commitments are:Student Access - Every student will have access to AI literacy learning experiences and digital science instruction before graduationEducator Capacity - All counties will sustain local AI training capacity through a train-the-trainer modelWorkforce Alignment - Every approved pathway will connect to employer demand, postsecondary alignment, and portable or verified credentialsNext week, the West Virginia Department of Education (WVDE) will launch the framework during three educator and staff development sessions. These sessions include unique opportunities for new county central office hires, the Support, Appreciate, Invest, and Learn (SAIL) Conference, and the West Virginia Middle Schools: Bridging the Future Initiative launch. Each session is designed to meet the needs of K-12 educators, professionals, paraprofessionals, service personnel and others who serve in the education community.West Virginia is a national leader in developing a framework for AI and digital science education. We are determined to elevate our model because it prepares students and educators for the future. Our partners at Prodigy Learning and Microsoft understand this commitment and believe, as do we, that our work has national implications for accessibility and scalability because we are building systems that address learning at all levels that lead to post-graduation success.Michele L. Blatt, State Superintendent of SchoolsAs part of the partnership, Prodigy Learning is providing West Virginia educators, at no additional cost, their in-person and virtual training on all products and the materials required for license renewal.
Could Congress Reshape the Next Round of Conference Realignment?
The Protect College Sports Act could limit the growth of the wealthiest conferences while making successful Mountain West programs more attractive for future expansion. By Roger A. Holien | Mountain West Connection College football realignment has largely been driven by television contracts, conference revenue, and schools seeking greater national exposure. Now, Congress could influence what […]
Infantino’s FIFA: Ten years of power, politics, and so-called ethics
Gianni Infantino promised reform, transparency, accountability, and a “new FIFA.” Ten years on, Infantino’s FIFA is instead defined by political proximity, weakened scrutiny, and an increasing alignment with state power at the highest levels.
CEOs and CIOs Differ Sharply on AI Business Impact and Transformation Success, Protiviti Survey Finds
/PRNewswire/ -- A new Protiviti Global Transformation Survey, The Alignment Advantage in Transformation, finds a significant gap between CEOs and technology...
ChatGPT maker wants to be the App Store for AI as safety concerns grow
OpenAI on Tuesday launched a less expensive artificial intelligence (AI) model and tools that run continuously, as it aims to reposition ChatGPT as an app store for AI. Other companies, such as Adobe, will be able to offer their software directly within ChatGPT, which is now used weekly by 1.2 billion people.– “Disappoint Wall Street” – As speculation swirls around when OpenAI might go public, Altman told reporters on Tuesday that the company was "investing more in safety, security, alignment,
Alignment Health sues over 2027 star ratings
Alignment Healthcare’s lawsuit, based on previous years, is likely the first of more to come by other insurers that’s star ratings deteriorated.
UNC reportedly a top target for SEC if realignment occurs
In an interesting twist post-AFAM scandal Tar Heels are on Greg Sankey’s radar to join his conference.
Rumble Announces Realignment into Two Core Business Units and Renames Newly Acquired Cloud And AI-Infrastructure Business “Quake AI”
Post-merger, Rumble Cloud Renamed Quake AI, Integrating Northern Data Offerings Rumble to Remain the Video and Media Platform’s Core Brand Name Corporate Parent to be Renamed RUM Group Inc. and Will Oversee Two Business Units: Rumble and Quake AI LONGBOAT KEY, Fla., June 17, 2026 (GLOBE NEWSWIRE) -- Rumble (NASDAQ: RUM) today introduced a new business unit and legal name for the company following the closing of its acquisition of Northern Data AG (“Northern Data”). Effective June 18, 2026, under
Alignment Healthcare CEO Adds Chairman Role As Medicare Business Grows
Alignment Healthcare CEO Adds Chairman Role As Medicare Business Grows
Smart Adhesive Is Key to Crease-Free Foldable iPhone Display
Advances in optically clear adhesive (OCA) will be a key factor in achieving a near-invisible crease in Apple's first foldable iPhone expected later this year, according to TrendForce. The supply chain intelligence firm outlined the key technologies in a new report on foldable display innovation, explaining that creases form when layers within the display panel fall out of alignment, concentrating stress at the fold and causing micro-cracks or permanent deformation over time.
Kustom Entertainment Provides Strategic Roadmap and Anticipated Operational Synergies for Pending Acquisition of TFL, LLC
Follow-up Release Details TFL’s Multi-Channel Revenue Streams, Expected TicketSmarter Alignment, Festival Integration, and Casino/Resort Expansion...
Senate passes sweeping college sports package
It is a massive piece of legislation, setting national rules on everything from NIL compensation to conference realignment.
A Look Back at Health Insurance Providers Stocks’ Q4 Earnings: Alignment Healthcare (NASDAQ:ALHC) Vs The Rest Of The Pack
The end of the earnings season is always a good time to take a step back and see who shined (and who not so much). Let’s take a look at how health insurance providers stocks fared in Q4, starting with Alignment Healthcare (NASDAQ:ALHC).
MLB’s Next 5 Years: Realignment, Expansion, and 154 Games?
The latest schedule proposal adds to major changes on the way.
How data centers complicate American politics
Opinion: Data centers are driving a political realignment, creating contradictions in Republicans’ and Democrats’ positions on the economy, environment and the working class, writes Sreedhar Potarazu.
Medicare works to speed up coverage for ‘breakthrough’ radiology devices
The federal payment program for seniors recently released a new fact sheet with details about its Regulatory Alignment for Predictable and Immediate Device, or RAPID, coverage pathway.
Happy Independence Day Dawg Sports!
It’s July 4th, the day we celebrate one of history’s earliest and most impactful conference realignments. The decision by our founding fathers that the British Empire’s revenue sharing model just wasn’t for them, and became the Notre Dame of the Western Hemisphere, geopolitically speaking. Their fateful decision saved us from a number of indignities, such […]
Vance heads to Budapest to shore up Orban’s support before Sunday vote
US vice president's Budapest trip highlights US-Hungary far-right alignment as Orban faces toughest electoral challenge.
Debt Digest | Federal Debt is a Health Care Problem
Welcome David Ditch to the Debt Dispatch! We are pleased to announce a new regular contributor: David Ditch will be joining the Cato Institute as a Policy Analyst for Budget and Entitlement Policy later this week. David has more than a decade of experience in fiscal policy issues, with a particular focus on federal spending. He previously worked at the Senate Budget Committee, the Heritage Foundation, and the Economic Policy Innovation Center. David’s areas of expertise include appropriations, transportation, agriculture, federalism, grantmaking, and policy options for deficit reduction. Originally from the Rochester, N.Y. area, David has a BA in Economics and Political Science from the University of Rochester and a MA in Political Management from George Washington University. David currently lives in Arlington, VA. You may have enjoyed his earlier guest posts: CBO: New Highway Bill Has More Spending, Taxes, and Deficits Trump Administration’s Proposed Changes to Federal Grants Highlight Problems of Big Government Here are this week’s reading links and fiscal facts: The federal debt is largely a health care problem. In a new report, the Congressional Budget Office and Joint Committee on Taxation find that “In 2026, federal subsidies for health insurance, net of related payments to the government, are projected to equal $2.4 trillion, or 7.4 percent of gross domestic product (GDP). In CBO and JCT’s projections, those subsidies grow by 65 percent, reaching $3.9 trillion, or 8.4 percent of GDP, in 2036. Subsidies for Medicare contribute most to that overall growth, increasing by $900 billion. Over the entire 2026–2036 period, federal subsidies for health insurance total $33.6 trillion.” Cato’s Michael Cannon emphasizes, “The long-term federal debt problem is a health care problem. […] Only two categories of federal outlays will grow faster than gross domestic product (GDP): health care subsidies and interest payments on the debt. The former is, therefore, the primary driver of the latter.” The bond market fears deficits, not the loss of tariff revenue. Responding to a New York Times essay claiming tariff revenue has grown too important for a future administration to unwind, Cato’s Kyle Handley argues: “Bond investors are not attached to customs duties as a line item revenue source. They care about the government’s overall fiscal position.” He continues, “tariff revenue is a side hustle. And the Trump administration has already promised to dole out the funds through schemes like tariff dividend rebates, farm subsidies, and pay-fors on tax cuts or other spending.” Furthermore, “tariff revenue is simply not large enough to transform the government’s fiscal trajectory.” Take net interest for example: it “reached $970 billion in fiscal year 2025, absorbing 18.5 percent of federal receipts. Customs duties accounted for only 3.7 percent of receipts—and that was before refunds (see Figure 2).” Handley concludes, “The real bond-market concern is a large and growing interest bill, persistent budget deficits, and a political system unwilling to bring spending and revenue into alignment.” Medicare’s spending growth is driven by more volume and utilization. A Congressional Research Service report finds that Medicare spending “grew at an average annual rate of 7.3%” from 1985 through 2025, and the trustees project health care expenditures will keep rising “faster than gross domestic product (GDP) in most future years.” Citing CBO, the report attributes Medicare’s projected 2026–2036 spending growth to “23% from higher enrollment, 31% from inflation, and 47% from the growth of inflation-adjusted spending per beneficiary. In other words**, the largest single driver of Medicare spending growth during the next decade is expected to be higher volume and intensity of health care services.”** Boccia and Thakur explain: “as the economy grows, Medicare spending tends to grow at least as fast—and often faster—because the program automatically pays the bill for more and more healthcare consumption by seniors, even as prices rise.” The Fed has little control over market interest rates. Cato’s Jai Kedia documents that the Fed has held its rate target steady since December, yet “nearly every rate Americans actually borrow at has climbed over the same stretch.” Kedia continues, “The Fed did nothing, and the cost of credit went up anyway. […] Markets spent these months repricing macroeconomic events such as sticky core inflation, a volatile energy market driven by the conflict in the Middle East, and global trade disrupted by tariffs, among others. None of that required a policy change to show up in borrowing costs because markets, not the FOMC, set prices.” He concludes, “Accurately priced borrowing rates will come from credible disinflation and disciplined budgets and from a Fed content to follow the economy rather than pretend it leads it.” Nearly half of US farm income comes from taxpayers. Former Reagan OMB director David Stockman notes that “in the most recently completed full year (FY 2025) farmer incomes in the US totaled $97.8 billion, but fully 42.7% or $41.8 billion of that amount came from taxpayers, not the marketplace. Nor is this some kind of one-year aberration. If we look at the most recent decade as a whole, total farm income posted at nearly $708 billion, but, as indicated, fully $322 billion or 45% of this came compliments of US taxpayers.” Cato’s Chris Edwards explains why so little of that income is market-earned: “Farmers are businesspeople, but the government shields them from just about every type of weather and market risk. Furthermore, just about every part of the agricultural industry is subsidized, including insurance, loans, marketing, research, export sales, and land improvements.” The Debt Dispatch is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe
Swipe MAGA right or liberal left? Dealbreaker in today's dating world
Political alignment is now at the forefront for those looking for romance in today's world of dating and finding a partner.
Set your alarm: Jupiter will disappear behind the crescent moon in a once-in-a-decade alignment tomorrow
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Why the CMO’s next chapter is becoming a true business leader again
The CMO was once a true partner to the CEO, grounded in a shared language of customers, margins and growth. That alignment ensured marketing had a permanent voice in the room where budgets were set and strategic bets were made. This was the era when figures like David Ogilvy were not just advertising leaders, but […]
Broward Health Names Kristina Fueyo Vice President of Growth and Physician Strategies
Broward Health is pleased to announce the appointment of Kristina Fueyo, as vice president of growth and physician strategies. Most recently, Fueyo served as assistant vice president of growth strategy and network optimization for HCA East Florida where she led initiatives to strengthen physician alignment, improve patient access and support service line growth across a […]
New IHSAA sectional alignments are out. Here are takeaways with teams on move.
The Indiana High School Athletic Association released its sectional realignment on Tuesday in six sports. Takeaways on biggest changes.
Planning to stargaze in May 2026? Here's all the gear you'll need
Here's all the stargazing gear you need for May's night sky. Be ready for two full moons, a meteor shower, and a pretty cool solar alignment.
Top 10 Mountain West Football Games of the Divisional Era
With the Mountain West changing, I thought it would be fun to go back and highlight the best players, games, and teams since the last phase of conference realignment 13 years ago. Previously, I’ve covered Top 10 football players, Top 10 basketball players, Top 10 Football Teams, and Top 10 Basketball Teams. Today’s list covers […]
90 Ideas in 90 Minutes: Dr. Teri Wahlig
Lead with purpose High-performing teams connect their work to something bigger. At ChildServe, we often talk about how every role fuels our mission, like a jet engine where each part matters. When people clearly see how their work contributes to lift-off, energy and alignment increase. Leaders can reinforce this by regularly linking day-to-day tasks to mission…
How Jaguars have differentiated themselves under Gladstone and Coen
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Who wants to lead the WHO? Four candidates jostle for top job as deadline nears
As the nomination deadline nears for the global health body, some candidates may be better placed than others to win support from key member states amid the agency’s geopolitical realignment.
Sources: Steve Kerr hints he's open to Warriors return but alignment needed
Steve Kerr has hinted that he’s open to a Warriors return, sources told Monte Poole, but an alignment with Golden State's front office is needed.
Porsche sharpens focus on core business
Following the planned sale of its stakes in Bugatti Rimac and the Rimac Group, Porsche AG is implementing further extensive measures as part of its strategic realignment.
Fixing Health Care’s Affordability Crisis
In a rare display of bipartisan alignment, Ezekiel Emanuel and Brian Blase decried health care’s “perverse incentives” and monopolistic practices
Bedlam Basketball Belongs in Oklahoma City
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Stage and Screen Unifies Global Sports and Entertainment Offering, Strengthening U.S. Presence
Stage and Screen, a specialist division of Corporate Traveler, has today announced a strategic realignment of its global brand structure that will bring its sports and entertainment travel services under the Corporate Traveler banner worldwide.
Realignment in middle America: not left or right but local
Hoosiers are reeling from stacked crises. They are the middle - unrepresented by either major party with nowhere to go.
Former IDF commander Wajdi Sarhan launches first Druze political party in decades
'Brotherhood Alliance' aims for Knesset seats and kingmaker role as Druze frustration with major parties grows; no clarity yet on alignment with coalition or opposition blocs
What Happens When A.I. Stops Doing What Humans Want?
“Alignment” is the science of teaching A.I. to do what is in line with human preferences, ethics and judgment. But, at times, the systems have gone rogue.
Largest 2D map of the universe helps scientists discover new gravitational lenses
Using data from the DESI Legacy Imaging Surveys, with help from artificial intelligence, an international team of scientists has discovered 70 new gravitational lenses—rare cosmic alignments that act ...
ACC's Phillips touts league's health amid questions of possible departures, realignment in future
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Val Ackerman, who guided Big East after team exodus, to retire
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NASA’s Roman Space Telescope exceeds expectations by nearly 30%
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Commission announces plan for polar hub to step up research coordination
The European Commission plans to develop an EU polar research hub to strengthen coordination and improve the alignment between science and policy. The objective, according to a Commission official, is to “better connect research, observation and scientific infrastructure, improve access to research resources and strengthen coordination with stakeholders, including Indigenous people.”Announced in a new EU Arctic strategy on October 20, there is little detail yet on how the hub will be implemented, only that it will “reinforce and expand” the European Polar Coordination Office (EPCO), which is hosted and managed by the European Polar Board (EPB) at Umeå University in Sweden. EPCO was born from the EU-PolarNet projects funded through Horizon 2020 and currently coordinates the EU Polar Cluster, a network of EU-funded collaborative polar projects.While the practical details remain to be defined, the decision sends “a clear and positive signal,” said Maria Grigoratou, head of EPCO and executive secretary of EPB. “By reinforcing EPCO, the Commission has an opportunity to build on the legacy of EU-PolarNet 2, strengthen European polar research coordination and expand cooperation with a broader range of Arctic stakeholders,” she told Science|Business.The move has been welcomed by the polar research community. “The polar research hub could play an important role in connecting existing research communities, observation systems and infrastructure, and in bringing scientific knowledge closer to policymaking, in cooperation with local communities and Indigenous people,” said Katrine Moland Hansen, director of operations at Norway’s Nansen Environmental and Remote Sensing Center.But money will be an issue. “It is clear that EPCO will need more resources and structure to function well and to make research reach policymakers,” said Kati Lindström, vice-chair of the Polar Research Committee of the Estonian Academy of Sciences. “European polar research is big if we pool the resources, and it should not only be left to enthusiastic scientists to develop networks of strategic importance in their free time.”The Arctic strategy makes a point of saying that local communities should have a greater involvement in Arctic science. “All EU Arctic research consortia are mandated to invite Indigenous people representatives to participate in projects,” it reads. “The EU also supports research that is directly led by Indigenous communities and address their challenges.”It also underlines the importance of reinforcing collaboration with like-minded partners such as Norway, Iceland, Canada, the UK, Japan and South Korea, as well as deepening its engagement with NATO. This includes the US, with which “the EU is committed to continuing a pragmatic transatlantic engagement based on shared interests and values.”Related articlesViewpoint: Does Europe dare to invest in the Arctic?Anchor Greenlanders in Arctic climate research, experts tell EU ahead of policy updateFor Moland Hansen, this open and inclusive approach to cooperation is encouraging. “The Arctic is also a strong example of why equal participation of associated countries in EU funding instruments matters.”Overall, she finds the new Arctic strategy to be ambitious, timely and comprehensive. “We particularly welcome the recognition that climate change, environmental protection, security, resilience and competitiveness are closely interconnected,” she said. “Research, observations and forecasting capabilities are essential to understanding the rapid changes taking place in the Arctic and addressing these challenges together.”Security focusWhile the Arctic strategy foregrounds security, with pledges to protect undersea cables, boost satellite monitoring, deploy more icebreakers and improve military mobility, the polar community is satisfied that environmental concerns have not been forgotten.“It is inevitable that there is a strong security angle to the EU Arctic strategy, but the impact of climate change and need for climate mitigation and resilience is also strongly recognised and includes a pledge to stay the course on climate commitments,” said Sian Prior, lead advisor to the Clean Arctic Alliance, a coalition of campaigning non-profit organisations.“We need to remember that by the time the next Multiannual Financial Framework comes to an end, in 2034, the polar regions, especially the Arctic region, will be living under a new reality when it comes to ecosystem processes, when it comes to the climate,” Grigoratou went on. “And that will affect any ambition that humans may have in the region.”Lindström agreed. “All aspects of European security are dependent on climate change in the Arctic and consequently, on climate science,” she said. “We cannot close our eyes to issues like energy security, critical minerals, northern shipping routes, dual-use technologies and others. However, it remains to be seen how well the promises to protect the environment and Indigenous rights will hold when faced with critical mineral extraction under the Critical Minerals Act.”
Would going Independent make sense for Florida State?
With the month of July filled with conference media days, as the ACC wrapped up in Charlotte last week and the SEC begins in Tampa this week, conference expansion and realignment has once again taken center stage. On Monday, Yahoo Sports’ Ross Dellenger released an article, available here, updating where Congress stands on passing the […]
Opinion | The Great Political Realignment of 2026
The country seems to be tiring of the Trump era. Now what?