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Chartbook 464 The"China squeeze" - the politics of development counterfactuals.
There are three types of China shock discourse. Each has a distinct temporal register. China shock 1.0 was a phrase coined by US academics in the 2010s to retrospectively summarize the impact on particular US labour markets, which they identified through painstaking empirical investigation. China shock 2.0 on everyone’s lips in Europe in the mid 2020s is an alarm raised by trans-Atlantic policy thinkers warning of a coming disaster which they project onto Europe’s ailing car industry. The third type of China shock discourse - commonly known as the “China squeeze” launched most recently by Indian economists Shoumitro Chatterjee and Arvind Subramanian in the pages of Foreign Affairs - mobilizes a different, more speculative temporal register: the counterfactual realm of alternate history. The China squeeze argument asks not what has happened, or what is going to happen, but what might have been. Specifically, what options for economic development might have been open to Global South economies like India or the economies of sub-Saharan Africa, had it not been for China’s spectacular industrialization since the late 1990s and the persistent competitiveness of China’s manufacture in low-skilled segments. Chatterjee and Subramanian’s argument is that whilst China has built a powerful position in electrical machinery and other high-skill manufacturing, which is now delivering China Shock 2.0, it also continues to run a trade surplus in low-skill, labour-intensive manufacturing. This surplus is large and has remained largely unchanged in fifteen years. Indeed, in the 2010s, in trade with low-income countries, China’s surplus rapidly increased. On their reading, this squeezes low-income producers in low-skill sectors in which they “should” be able to compete. Source: “China’s Mercantilist Squeeze on Developing Countries” Shoumitro Chatterjee and Arvind Subramanian. Peterson Institute for International Economics 26-7 May 2026 Chatterjee and Subramanian’s wording is telling: (emphasis mine) The canonical trajectory of economic development in the post-World War II period is for countries to transition from agriculture to manufacturing, first in low-skilled manufacturing and then later on to high-skill manufacturing (Herrendorf, Rogerson, and Valentinyi 2013). At the global level, this implies that high-income countries (HICs) that once specialized in low-skilled activities cede manufacturing export space to poorer countries that come along to occupy that space. In 1965, advanced economies accounted for about 60 percent of global value-added exports in four key low-skilled manufacturing sectors, i.e., apparel, leather, textiles, and footwear. Over time this share declined and began to be occupied first by the East Asian tigers and, beginning in the late 1990s, by China. Today China accounts for about 50 percent of the global market. The question is whether China, with its rising prosperity, is continuing to behave like its richer counterparts and ceding export space to poorer followers or to behave unlike them and continuing to occupy space. In terms of the conceptual maneuvers it performs, of the three China shock discourses, the “China squeeze” argument is by far the most ambitious. After all, it bases its critique of China on a counterfactual imagining of the world. Like any counterfactual argument, to construct this alternative reality it must rely on a heuristic model. To make their argument Chatterjee and Subramanian mobilize two key claims. The first reference point is the actual history of rich-country industrialization, what they refer to as the “canonical path”. The idea is that countries transition from agriculture to light industry and low-skilled manufacturing and from there up the value chain. To make this useful as a standard of comparison and a means to construct a normative standard, they adjust sectoral export shares for two factors: 1. the underlying level of economic development of the exporting country, as measured by national income and 2. the degree to which the sector in question is internationalized. The idea being that for a high income country to have a high export share in low-skill sectors is anomalous and even more so if that particular product is highly internationalized and thus suitable for being “conceded” to lower-income competitors. Where should this process end? Here Chatterjee and Subramanian introduce their second benchmark. Their normative idea of a global development path is one in which, in due course, low-skill manufacturing exports are distributed in proportion to a country’s share in the global labour force. They imagine, in other words, a world of proportional, polycentric global development without much national specialization in low-skill manufacturing. A useful benchmark is that, in labor-intensive manufacturing sectors, a country’s share of world exports should be broadly commensurate with its share of the world’s low-skilled labor endowment. … In the Heckscher-Ohlin model, this would be true if value added per worker and export propensities, i.e., fraction of output that is exported, are the same across countries. It is this pleasant liberal vista of orderly global development, progressing towards a general distribution of low-skilled manufacturing against which they judge reality and conclude that the low-income countries are suffering a “China squeeze”. China is not exiting low-skill sectors fast enough Allowing for its gdp level and the degree of globalization, China’s share of low-skill manufacturing exports is far higher than it “should” be. Relative to population, its low-skill share of exports is far too high. That in turn yields the conclusion that huge numbers of jobs - the authors suggest tens of millions - are being hoarded by China. For the four sectors captured in figure 4, this excess in value-added exports is $110 billion in 2022. For all low-skilled sectors overall captured in appendix figure D.1, excess value-added exports amount to $365 billion. The crowding out of LMIC exports by China is therefore substantial. Both these claims have in common that they are strong structuralisms. The first is that development paths ought to resemble each other. The second is that broadly speaking as far as low-skilled manufacturing is concerned, all countries are alike. If the world currently diverges from the distribution of trade implied by these assumptions, if China is not following the advanced economy path and has huge exports, this must be the result of “policy”, which the authors then put in question as illegitimate or unfair. Thus the naturalistic construction of a canonical path leading to a “level” distribution of low-skilled manufacturing reveals itself to be a polemical tool. From the point of view of China shock polemics, this discourse is no doubt welcome. But as an account of economic history, it must surely be regarded as nothing short of bamboozling. Development paths do not follow a regular, natural sequence. As David Fishman put it aptly, “development is not a queue”. They are always and everywhere, through and through political. Development is not so much a process, as a project. Each such project is more or less well adjusted to its context. As Gerschenkron taught uneven and combined development implies differentiation rather than simple imitation on a single parth. It may be true that Europe and the US exited relatively early from low-skill work. But clearly technology has changed. As C&S themselves point out, China no longer enjoys a low wage environment. Do they really believe that such huge differences can be accounted for either by industrial policy, which as they admit does not operate powerfully in low-skill sectors, or by exchange rate manipulation? What is overwhelmingly dominant in explaining the persistence of “low-skill” manufacturing in China are surely the network effects of complex supply chains. Indeed the label of “low-skill” may miss the point. The degree of globalization which Chatterjee and Subramanian decide to conjure away in their model of the development path - to allow for the fact that Europe and American textiles exporters did not enjoy just-in-time logistics in the 1950s - obscures the point that the networks through which Chinese factories supply the world are themselves key parts of the low-skill industrialization project. China isn’t knocking out 1970s style “cheap”. It has redefined the entire product cycle. C&S’s casual reference to “T-shirts” is telling. In today’s “fast fashion”, there is weight on both terms. Fashion and fast. China’s success in delivering on both at unprecedentedly high speed and at unprecedentedly low cost, isn’t a low-skill operation at all. If we see development trajectories not as the serial repetition of the same, but as distinctive, crafted projects that cumulate in complex agglomerations of skill, technology and labor, then this explains why the assumption that low-skilled manufacturing should follow the distribution of population flies in the face of all experience. As countless cases attest, low-wage, low-income developed economies are not by themselves the makings of a competitive production base. As Leon Liao puts it quite fairly: “International trade has never been allocated according to population. A country’s population gives it no natural claim to an equivalent share of global exports." And the remarkable fact is that Subramanian alone and with co-authors has long been making this point, not to pillory China, as is the case of the tendentious _Foreign Affair_s and Peterson pieces, but to highlight the great counterpart to China’s industrial success, namely India’s relative failure. The much-quoted Foreign Affairs piece showed low-skill export shares only for China, not for India. If you put both countries in the picture as Chaterjee and Subramanian have done on other occasions, the picture looks like this. China is far above the line, but India, almost as large in terms of population, is no less anomalous, just in the opposite direction. As far as global populations are concerned, and the imagining of tens of millions of “squeezed” jobs, it is this Sino-Indian pair that account for a huge share of the action. What this graph reveals is not China’s cheating, but India’s “greatest development failure”, at least this is the argument made Arvind Subramanian, this time in co-authorship with Devesh Kapur, in their major book, A Sixth of Humanity. Independent India’s Development Odyssey which appeared with Oxford University Press earlier this year. In this highly critical work on India’s development, China is repeatedly evoked, not to criticize but as a benchmark against which to illuminate what India’s political economy has not delivered i.e. large-scale formal employment for hundreds of millions of citizens. Kapur and Subramanian leave no doubt, if you want to understand why China, not India was the main driver of manufacturing globalization from the 1980s, don’t look to slots opening up in the division of labour thanks to the liberal mindedness of advanced economies, look to the contrasting political economy of the Maoist PRC and India’s post-independence political economy. As Kapur and Subramian note: The great divergence between China and India did not begin in 1978 with the Deng reforms—it was well underway by then. In one of the great ironies of modern history, the conditions created by Maoism turned out to be a great launching pad for industrial globalization. As the comparison of India and China in 1980 makes clear, to imagine manufacturing would be equally distributed when populations are so differently endowed in terms of educational and nutritional is fanciful. Here is the data from Kapur and Subramanian, data which I explored back at the very beginning of Chartbook. As Kapur and Subramanian insist, the striking fact is that even as India’s growth accelerated from the 1980s, structural change has not. Formal employment in India lags woefully behind. Why? As Kapur and Subramanian note, the path-dependent impact of India’s poor early start was devastating. The list of factors they mention is long and weighty: We argue for a new five-fold explanation applicable at different stages of Indian history: in the initial stages, the legacy of two key pre-1980s policies, the neglect of agriculture and the strangling of the domestic private sector, were critical. The failure of broad-based agricultural growth militated against rural industrialization, which would have been devoted to satisfying local needs that would have necessarily been labour intensive. Then, when the boom came after 1980, the thicket of regulation and broader labour protections that were legislated prematurely in a nascent democracy, underpinned by a deep ideological aversion to private capital in general and big capital specifically, became a barrier to attaining the size that would be necessary to compete globally. Similarly, the reforms after 1991, while wide-ranging, did not quite encompass non-tradeable inputs critical to manufacturing, namely, land, logistics infrastructure and power. A combination of low investment (logistics infrastructure), regulation (land) and the continued monopoly supply by state governments (power), combined with residual socialism, kept costs at 75–100 per cent above efficient supply. These factors were compounded on the supply side by the government, which dominated the formal labour market and established exorbitantly high wages at the lower end of the skill spectrum. This made their employment in the labour-intensive sector by the private sector less attractive. Finally, when the services sector and its exports boomed, wages soared even further via a Dutch Disease effect: one successful tradeable sector killed and cannibalized another less-skilled tradable sector, namely, manufacturing. Thus, scarcity-inducing policies of the planning era, precocious democracy and a Gandhian aversion to size; inefficient, monopolistic supply of critical non-tradable inputs; the behemoth of government; and super-successful skill-intensive services successively undermined India’s unskilled labour employment opportunities. The employment failure seems to have been over-determined. … Less quantifiable but no less important are some of the sociological factors that have undermined manufacturing and factory jobs. Take the impact of the success of the IT sector—located in urban areas with better working conditions, such as air-conditioned offices, good schools for children and attractive entertainment options. It became a more attractive place for engineering and management graduates to flock to. … Consider manufacturing and female employment. Indian society makes it difficult for women to work, reflected in India’s unusually low female labour force participation (elaborated in Chapter XIII). Indian society, in particular the routine harassment of women in public spaces, makes it even more difficult for them to travel long distances to take up work.37 Combined with onerous regulations on creating hostels and dormitories for women—which, in turn, prevents multiple shifts of operation—factories need to be located in areas with a large female labour pool. While firms can take apparel plants to female labour or bring them to the plants by building large-scale dormitories, this raises costs. If there was a case for subsidies to encourage labour-intensive manufacturing, housing subsidies to build dormitories for women workers would be a good place to start. All of this meant, that when opportunities did open up, in part thanks to adjustments in China, India was in no position to take advantage. Here is the account of the 2020s from Kapur and Subramanian. It stands in dizzying contrast to the one-sided, “China squeeze” argument delivered in the Peterson and Foreign Affairs pieces: In 2022, for example, India’s share of global exports in clothing (the canonical labour-intensive sector) was 3.1 per cent compared to 32 per cent for China. This is extremely disappointing because China started vacating export space in this sector after the Global Financial Crisis as its wages started rising; it then vacated further space in the aftermath of geopolitical developments that led investors to seek alternative investment locations. Between its peak and today, China lost about 7.5 percentage points of global export share (worth about $43 billion in 2022); over this period, India’s export share remained stagnant at 3 per cent, while that of Vietnam and Bangladesh rose by 3.5 to 4 percentage points. China’s loss did not translate into any gain for India in terms of low-skill exports and employment. Put differently, when the conditions for dynamism and prosperity were established after 1991, and even more tantalizingly when the global export opportunities opened up, it was as if India was a high-wage economy and only skilled labour could benefit from the dynamism. It had converted its physically abundant unskilled workforce into an economically expensive resource, penalizing its use. It is not my mission here to argue for or against Kapur and Subramanian’s interpretation of India’s development trajectory. The point in this context is simply that the “China squeeze” such as it is can only be understood, in relation to the heavy burden of domestic constraints that Kapur and Subramanian so unsparingly enumerate. I am concentrating here on India because it is the great counterpart to China and the unspoken reference point of the “China squeeze” discourse. But the point is more general. Can anyone with a straight face suggest that the main problems afflicting the development of South Africa or Nigeria, the great centers of mass underemployment in sub-saharan Africa, are cheap competition from China driven by Beijing’s industrial policy and an undervalued RMB? Likewise, is the main problem of the American working-class the threat of Chinese competition or the manifest crisis of social reproduction in the United States itself? The point here, as in the discourse around China shock 2.0 in Europe, is to insist that we should not normalize the world and pathologize China, without actually investigating what is going on in America’s industrial wastelands, the German car industry, or India’s lopsided labour market. One-sided pathologization should be understood for what it is, a polemical gesture. Concepts like China shock and China squeeze are, what Reinhart Koselleck, following Carl Schmitt, would have described as “asymmetrische Kampfbegriffe”, or “asymmetrical counter-concepts” or “asymmetrical combat concepts”. If the function of arguments like the “China squeeze” is to alert elites in the rest of the world of the need to rejuvenate and energize their development projects, so be it. But let us see the resulting discourses as the mobilizing interventions that they are. Interestingly, a significant slice of China’s public sphere - the so-called industrial party - have been obsessed for the last twenty years with precisely these questions of counterfactual economic history. Since the mid 2000s, a massive online discourse developed around an alternative history in which Chinese techno-patriots were asked to imagine how they would proceed if they had the possibility of traveling back in time to transport modern technology into the Ming dynasty (1368-1644) thus averting the “great divergence” of the 18th and 19th centuries. It is a fantasy of course, but also a goad. The point was not directed at Westerners. The alternate history is dramatically nationalist, but few n the West had even heard of it until recently. The adherents of the industrial party are not liberal moralists. They don’t project natural ladders of international development and ask why others don’t cede space. They take the Realpolitik of global economic development for granted. Their pointe was directed against fellow Chinese. The rallying cry of the alternative narrative was this: Now that we are beginning to gain steam and push forward on our national development path, now that we are breaking out of the inferior position assigned to us even in the early years following WTO accession, do not lose yourself in humanistic imaginings about better futures. In the spirit of Deng, they called on China to embrace the hard truth of development. Perhaps one might of the Industrial Party as a cultural trend born out of desire for symbolic revenge against those who had inflicted the century of humiliation. In terms of the various temporalities of the “China shock” - retrospective, prospective and counterfactual - one might think of the Industrial Policy discourse as truly speculative, conjuring up the China shock that never was. Or perhaps one might better think of it as imagining a world in which a powerful China was no shock at all, but simply a defining fact of modern history. I love putting out Chartbook. I am particularly pleased that it goes out for free to thousands of subscribers around the world. But, writing this newsletter takes a lot of work. If you are enjoying it and would like to support the project, sign up for one of the subscription options here. It is the equivalent of a fancy cup of coffee per month! Subscribe
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The Science Behind the AI Panic Is Shakier Than You Think
A column by Sascha Lobo Is AI making us dumb? So asked DER SPIEGEL on its cover a while ago. After a few weeks of recovery and a therapeutic intervention with the help of my AI, I feel able to respond. That’s a bit unfair, of course; the article itself is more nuanced than the loud cover, the image of a schoolkid with dull prompt questions, or the subtitle: “AI is conquering the schools, and educators fear a disaster.” The short answer to “Is AI making us dumb?” is: no. The long answer is more interesting, and even carries a preliminary glimmer of the spectacular. Unfortunately, it requires a detour into the sphere of artificial intelligence, into the mogul-field terrain of technology assessment, the attention economy, and the destructive virus of cultural pessimism. Along with narrowing the scope to AI’s effects on education and work rather than on intelligence. Because the honest version of the question would be: Does AI help us learn and work, or not? Generative AI, which set off the still-ongoing hype with ChatGPT, has only existed in relevant distribution since late 2022, early 2023. For a technology-driven transformation of people and society, that is extremely short. For that reason alone, research is still in its infancy. And technology assessment is not a fast discipline anyway. Prominent voices from Elizabeth Eisenstein to RAND to Tom Wheeler say we still cannot conclusively judge the consequences of the printing press. Its invention is a mere 580 years back, and some scholars would rather not circulate assessments that might have to be retracted in two or three hundred years. We’re Going Through Growing Pains A phenomenon that already caused upheaval in social media research makes serious assessment of AI’s consequences even harder: the “moving target” problem of LLM-ology, as the linguist Sean Trott called it in 2025. AI develops so quickly and so unpredictably that researchers often end up studying snapshots of the past. There are concepts, from morphological analysis to anticipatory governance, for keeping research, planning, and regulation halfway meaningful anyway. But even with those, universally valid statements are rare. DER SPIEGEL - The German View is a reader-supported publication. To receive new posts and support our work, consider becoming a free or paid subscriber. Subscribe We should therefore treat findings about the interplay between humans and AI as far more procedural and situational. Unfortunately, this challenge comes at the worst possible time. We are going through growing pains: AI’s everyday impact has grown very large very fast, and with it the longing for guidance. Annoyingly, the body of solid knowledge has not exploded along with it. Experts cannot be moved to broad consensus on even banal statements about AI. Sascha Lobo, born in 1975, is an author and strategy consultant focusing on the internet and digital technologies. Together with Jule Lobo, he explores the debates of the day in the podcast “Feel the News – Was Deutschland bewegt” (”What Moves Germany”). Nvidia CEO Jensen Huang says software developers are dying out. Bill Gates considers coders one of only three professions that will survive the AI tsunami. Top tech economists find virtually irrefutable arguments both for and against an AI bubble. The same naturally applies to the structurally youth-hostile debate over whether AI is good or bad for young people. The gap between the demand for insight and the supply of solid knowledge creates an orientation vacuum. This vacuum of AI cluelessness is filled, as the attention economy described by Georg Franck in 1998 would have it, with half-knowledge (funnily enough, often AI-generated), marketing, and self-marketing. After all, infinite money is at stake, along with academia’s best current shot at global fame. But this iridescent froth of interpretation operates more by the algorithmic principles of the digital public sphere than by seriously earned insight. That is why dramatized, fear-feeding claims about AI are so promising. It tempts parts of academia into distorted drivel, and parts of the media then sensationalize the published drivel once more. Was the Research Aimed at a Desired Result? How badly this can influence academia and public perception is demonstrated by one of the most-discussed studies on AI’s effects: “Your Brain on ChatGPT.” The MIT study by Nataliya Kosmyna also gets plenty of space in the SPIEGEL cover story. What was measured and interpreted: lower brain activity in people who write with AI assistance, compared to people who only google or write without any tools. As is common with preprints, the study has not yet been peer-reviewed, and it contains substantial weaknesses that experts have sharply criticized. The first round had 54 participants in three groups, the second only 18, who reportedly also came from MIT’s own orbit. There was no preregistration (committing in advance to what you want to find out with which data). In their analysis, Stanković et al. even discovered an opposite effect that the study does not further explain. Some measurement data has not (yet) been published. And the EEG analysis method used is considered too imprecise to support such far-reaching conclusions. The biggest criticism, though, is the suspicion of an activism-driven study. In an interview with Time, lead author Kosmyna said she published the study immediately, rather than after full peer review, because she feared politicians might otherwise decide to put ChatGPT in kindergartens. She considers that absolutely harmful. It is legitimate to hold such a view. It just seems unscientific to approach a study with it, and then to design the setup so that the chance of a fitting desired result remains as high as possible. Ideologies Collide It is not easy to separate genuine AI insights from studies that trivialize and weaken well-founded AI criticism. The same goes for the opposing, AI-enthusiastic narratives, because the pro-AI side works against it with billions in resources. Ideologies collide, fed by egos, PR strategies, moral posturing, and once-in-a-lifetime opportunities. Doomsday marketing makes the search even harder: AI companies can profit from warnings about the dangers of AI, whether through the notoriety of apocalypse narratives or because, in some target groups, danger translates into power. Some seemingly negative findings are therefore quite welcome in the AI industry. If you try to get at the findings presumably less influenced by interest groups, the picture in education seems relatively clear: AI can improve educational outcomes when the right AI is used the right way, namely pedagogically. Simply dumping an AI chatbot into a classroom, by contrast, accomplishes little. Huge surprise. But something else is also obvious: whether they are allowed to or not, kids use AI for school, which is why the controlled integration of AI tools into education is, in the medium term, entirely without alternative. In the world of work, the landscape of findings is more complex and requires more differentiation. A series of serious studies points to an AI effect that suits neither the alarmists, regulation fetishists, and AI opponents, nor the trillion-dollar corporations with their armada of paid scientists, PR professionals, and ecosystems: working with AI helps the inexperienced (at least for now) considerably more than it helps the pros, in certain fields of work, and provided they know how to use it correctly. Missing Domain Knowledge Among the experienced, the professionals, and the high flyers, by contrast, despite frequent time savings, partially negative effects and new problems are emerging. Take the jagged frontier problem: AI solves task A brilliantly and fails completely at task B, even though both look similar even to professionals. Or the verification-cost problem, in which AI’s greater productivity gets eaten up by the enormous effort of checking its output. Or the illusion of competence, because AI makes new domains seem easily penetrable, until you painfully discover that deep domain knowledge is missing. Taken as a whole, the effects of working with AI do lean positive. But the promise that AI works as a turbocharger for everyone and boosts practically everything simply refuses to materialize. The AI magic the world has been feeling since ChatGPT arrived in late 2022 has so far barely shown up in most companies’ numbers. The consulting firm McKinsey has coined a term for this, the “gen AI paradox,” and believes that work processes and corporate structures would have to be rebuilt quite fundamentally to realize AI’s benefits at scale. Reuters just reported that only 3.3 percent of Microsoft’s customers have subscribed to the AI package Copilot. That may be down to the product; in other areas we continue to see soaring successes and new AI triumphs. But after the first quarter of 2026, disillusionment is the word for all too many AI projects at traditional companies: because the promised revolution of AI agents has so far failed to arrive or been postponed, and because of the leveling effect that many of the studies cited here suggest. Against Inequality The societal effects, on the other hand, appear rather encouraging. Many previous technologies operated on the Matthew principle, named for the Gospel verse about giving to those who already have: they tended to reward the more competent and more privileged. AI, however, seems more often to have an equalizing effect once a threshold of access and education is crossed. Some anecdotal insights point in this direction, for example, how an unemployed man in Leipzig successfully defended himself in court in 2025 against fraud accusations from Germany’s federal employment agency — without a lawyer, armed only with ChatGPT. The AI made mistakes and cited invented court rulings, but in the end the man walked away unpunished. And a key capability of artificial intelligence is its capacity to learn: set up correctly and fed with data, it can keep getting better. It is a question of time, or more precisely of AI innovations and training data, until even free AI chatbots write better legal briefs than law graduates with perfect exam scores. Whether that will make anyone dumb is something the world’s press will surely inform us of in due time. Subscribe Leave a comment
Former Cowboys 81-Tackle Starter Flirts With NFC East Rival
As the Dallas Cowboys prepare for training camp, one of their former players is trying to return to the NFL.Most teams start their training camps this week, with some hosting workouts. One of the Cowboys’ biggest rivals, the New York Giants, has been hosting multiple workouts.They had an interesting one on Monday that will have […] The post Former Cowboys 81-Tackle Starter Flirts With NFC East Rival appeared first on HEAVY.
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Nintendo explains why it's coming out with Splatoon Raiders instead of Splatoon 4
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Vikings Expected to Release Injury-Riddled TE
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Less sleep = less health; Led Zeppelin in the OR; dangers of people-pleasing; plus other stories with Vanderbilt Health sources
Prevention interviewed Beth Malow, MD, Professor of Neurology and Pediatrics in the Vanderbilt Sleep Division, for a story about two randomized trials published in Annals of Internal Medicinethat found decreasing sleep by just 1.5 hours each night was linked with increases in body weight, waist circumference, and sedentary time in adults with elevated cardiometabolic risk. Yahoo Healthalso picked up the story. WTVF News Channel 5 reporter Nikki Hauser interviewed Ashish Shah, MD, Professor and Chair of Cardiac Surgery, for a new video podcast series called “Why Nashville” where she interviews interesting people doing interesting work about why they came to Nashville and how it is going. Among the revelations: his go-to OR music includes Led Zeppelin and AC/DC. Everyday Health interviewed Aaron Brinen, PsyD, Assistant Professor of Psychiatry and Behavioral Sciences, for a story about the TikTok trend that encourages women to speak up about their concerns or they’ll develop an autoimmune disease. Overall, people suggest that suppressing feelings of anger, minimizing your own needs, or people-pleasing raises your risk. Telemundo Nashville broadcast a story on cyclosporiasis that quotes both William Schaffner, MD, professor of Preventive Medicine, and Juan C. Salazar, MD, MPH, chair of the Department of Pediatrics and system Pediatrician-in-Chief for Monroe Carell Jr. Children’s Hospital at Vanderbilt. Schaffner also spoke to WKRN News 2 about measles and The Cutabout shingles and long distance running. WREG (Memphis) and others picked up the measles story as well. CNN reporter Deidre McPhillips interviewed John Trahanas, MD, Assistant Professor of Cardiac Surgery, and Director of Thoracic Aortic Surgery, for a story about aortic dissection in last week’s death of U.S. Sen. Lindsey Graham. The Nashville Post published a story by freelance reporter Leah Hunter on robotic surgery, including a patient story and numerous quotes from Sekhar Padmanabhan, MD, Assistant Professor of Surgery in the Division of Surgical Oncology.
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47 revelatory hours with Assassin’s Creed Black Flag Resynced
To review this month’s supposed remake of 2013’s Assassin’s Creed IV: Black Flag, I must first correct the record. Assassin’s Creed Black Flag Resynced isn’t a game about pirates (and assassins) in the 18th century Caribbean. Sure, sure, that’s what the trailers would have you believe. They’ve go their clips of Edward Kenway swinging his swords and steering his pirate ship into cannon-blasting combat against British frigates. Is Resynced truly a pirate game? Of the 47 hours I spent playing Resynced over the past few weeks, I will admit I spent maybe 44 (?) of them collecting plunder with Blackbeard, drinking rum with Anne Bonny and debating the pros and cons of the lawless pirate-run settlement Nassau with Benjamin Hornigold. I may in fact have spent dozens of hours sailing to remote islands, reading treasure maps, opening messages left in bottles, sinking the odd Man o’ War with a well-timed broadside, collecting sea shanties for my crew, befriending a pet monkey, and hunting whales. I am nevertheless neither so Gullible nor so Narrow-Minded as to call the magnificent Resynced a “pirate remake.” Oh no. Forget any reviews of Assassin’s Creed Black Flag Resynced you may have read. Forget what you yourself may have played, if you’ve spent any time with the game since its launch. What Assassin’s Creed Black Flag Resynced is really about I’m here to give you the scoop (spoilers for Resynced’s Animus menu lore, which many players are probably not going to see): Assassin’s Creed Black Flag Resynced, the new game whose development was led by Ubisoft Singapore, is set in the year 2096. It’s about a societal split, about revolution, about an old Assassin’s Creed adventure repurposed in the series’ distant future as propaganda or samizdat. It’s about the overheated future of 2096, essentially conquered by the series’ well-established nefarious Templar-run company Abstergo. (In this future, Abstergo is, among other things, remaking a 2013 pirate movie called Devils of the Caribbean, but this time in VR. The VR version will be backed by a marketing plan that involves AI influencers, a 10,000-avatar “metaverse premiere event” and that manufacturing of memes aimed at “50 million VR nodes.” See what I mean about Abstergo being evil?) Resynced is about a late 21st-century population mollified by the widespread use of Abstergo’s Animus devices which let people experience the lives of historical figures culled from “genetic memories.” It’s about an insurgent faction of Assassins. And it’s about a plan to wake the population into revolutionary fervor by showing them the memories of 18th century pirate Edward Kenway, who—as depicted in the original Black Flag and again in Resynced—gradually comes to an understanding about the Assassins of his day and their centuries old fight for freedom. Resynced is also about an AI developed within the late 21st-century Animus, called Ego. It has found a way to alter genetic memories, to create and show Animus users false ones. The game is about Ego’s efforts to tell the people experiencing Edward Kenway’s memories that Kenway was wrong, that fighting against order is folly, that we should crave a world where “compliance is rewarded with power.” Who are we in this game? We’re an unnamed Animus user in 2096 who experiences Kenway’s journey virtually. At four moments during Kenway’s exploits in the Caribbean our virtual jaunt can be interrupted for 10-15 minutes, as we explore Animus “rifts” where Ego explicitly tries to tell us how wrong Kenway was. None of this is what 2013’s Assassin’s Creed IV: Black Flag was about, of course. That was a different game, even if you also spent dozens of hours pirating as Kenway in that game, across missions and a map that’s been faithfully reintroduced and rebuilt for Resynced. The 2013 Black Flag was about you playing as an unnamed researcher newly hired in 2013 by Abstergo Entertainment, the video game development wing of the Templars’ evil-corp. In that game, you were assigned to use the Animus to dive into the genetic memories of Desmond Miles, a man who died in Assassin’s Creed III and who was descended from a long line of Assassins (and at least one Templar) whose adventures were featured across the prior AC games. In 2013’s Black Flag, your mission was to plumb the Edward Kenway experience for material that Abstergo could use in productions such as the motion picture Devils of the Caribbean, though the Templars had some ulterior motives for the assignment. Resynced is therefore not a remake of 2013’s Black Flag. It’s a sequel. It is a new game about a new exploration of Edward Kenway’s pirate/assassin journey, set 83 years after the framing sequences of the original game. Resynced provides a new narrative framework that, thanks to the concept of an AI that wants to alter historical memories, arrives with creative justification to tweak a now-classic 13-year old game. That makes Resynced, already a hot seller, a tantalizing hint of where Ubisoft could go with a line of Assassin’s Creed remakes. They could remake the first game, the Ezio games, even the recent ones set in ancient Egypt and Greece. And for each, they could not just modernize the graphics and controls but expand and alter their sequences of events to use them as reference points for a conflict set in the late 21st century, as rival factions try to weaponize historical memories. Game File is a reader-supported publication. To receive new posts and support my work, even when that work consists of ravings about Assassin’s Creed’s modern day plot, please consider becoming a free or paid subscriber. Subscribe A story most players will probably miss If you didn’t know any of this, I’m not surprised. If you played the game and still didn’t catch it, I’m not a bit shocked. If Ubisoft has no plans to do more of this, I won’t fall down. Almost all of what I’ve described is optional and missable in Black Flag Resynced. I nearly missed it myself, as I planned for a breezy playthrough of an old Assassin’s Creed favorite. Instead, I found myself in a 40+ hour hunt for the “real” story, buried deeper in Resynced than any gold-filled chest. The story of 2096 is Resynced’s meta-story, told in two remote pockets of the game: 1) four optional playable “rifts” that players may find as they proceed through Resynced’s pirate fantasy , 2) in dozens of text files tucked three menus deep in the game’s barely-interactive Animus hub. Assassin’s Creed games have almost always had a meta-story about people in the 21st century (usually matching the game’s release year) and their connections to the main history portions you play. Those meta-stories have always been squeezed into the margins, though seldom as severely and as, uh, graphiaclly simple, as in recent games. Back in 2013, the modern-day exploits of you as an Abstergo Entertainment employee were prominent. They were portrayed via first-person portions that occasionally interrupted the third-person Kenway experience and could be explored for hours, complete with their own missions, side quests and puzzles. Resynced’s 2096 story—of Assassin “Watchers” vs. Abstergo/Tempar goons, of rogue AIs, of panicking scientists, of a woman wondering why her Animus-addicted husband is muttering about Mary Read (“What does he mean by Assassins?”), of a fight for the population’s hearts and minds via varying interpretations of Edward Kenway’s life choices—is mostly told through unlockable text files. The files are interesting. Offered via diary entries, intercepted chats and news reports, they present varied perspectives of a world in crisis. One set works as a narrative puzzle as it reveals the Rashomonic interpretations of a massacre, or was that a successful Assassin hit? Another favorite reveals the rising bitterness of a person who, through Edward Kenway’s journey, now shakes at the ignored evil around him. One surmises that Ubisoft is averse to putting the Assassin’s Creed’s meta-story in the way of more marketable pirate playgrounds and ninja/samurai epics. Instead, we get a better-than-nothing text-in-menus approach introduced in 2025’s Assassin’s Creed Shadows. In Shadows, players unlocked the meta-story text files largely via time-gated missions that called on the user to kill X number of enemy type Y, clear three enemy castles, re-win a three-phase fighting tournament, and other grinds. Time restrictions on accessing these missions and unlocking the files meant it could take players months to access the full story in-game. But even in the most hardcore Assassin’s Creed hangouts, the fanbase instead seemed to throw up their hands rather than grind to keep up. In Resynced, Ubisoft has shown some mercy, diminishing the amount of new text files that are tied to timed grinds. Now, most of the text-based 2096 lore are associated with “data file” collectibles that players can find in the world. That includes a batch of written entries that explain a historical link between the feudal Japan era in Shadows and the piracy adventures in Black Flag. Players who explore a lot will find dozens of data files, though the game does not appear to offer any in-game tracker for finding each one. You just need to keep looking for them. As of this writing, I have most of them, but not all. I can’t find any full guide for them online. And even Ubisoft’s optional $5 microtransaction that reveals lots of items in-game does not appear to reveal the locations of the text files (at least the official description omits them from its collectibles list). That’s not a huge bother, because tracking the data files down compelled me to re-explore the waves and islands of Black Flag. That was a thoroughly pleasant task to do for the first time in a dozen years. I expect many players, though, will be content to wait for the files to be reproduced in an online AC wiki somewhere, the easier to read on a web page. It may not seem enticing to play a video game in order to gradually unlock pages of text to read. Why not, you may ask, just read a book, where they put the pages in order? For one, they’ve not tucked this latest installment of Assassin’s Creed’s meta saga in a book. I also enjoy the unordered delivery, the dawning comprehension of a story about 2096’s desperate people and questionable tech, which I glimpsed first at its middle, then the end, then the front, then other moments in between1. I am also enamored with the idea of the Assassin’s Creed games I’ve previously played, including Black Flag, being repurposed for an information war. The ideas in Resynced’s meta-story, which builds off characters and ideas in Shadows’ chunks of text, are strong. The potential of where they could go with it is exciting. As for the pirate stuff? Looks great, controls pretty well, is fun to play and oh so speedy in terms of how much of a good time you can have with each sit-down. It never gets old to chase a new shanty page across a rooftop, bombard a fort with upgraded mortars or use a diving bell to explore sunken wrecks (hats off to the team at Ubisoft Barcelona that worked on the underwater portions in Resynced; that team is now facing layoffs, such is the misery of modern gaming and oft-troubled Ubisoft). New pirate missions in Resynced involving new ally characters are brief, dramatic and add more to Edward’s story, even if the gameplay perks they provide barely enhance the gameplay. A new denouement for high-class pirate Stede Bonnet is affecting; a new hunt for Blackbeard’s treasure is a welcome extra moment. A new epilogue to hunt down a pirate killer, on the other hand, over-complicates Kenway’s character. The ending credits remain my favorite of any game I’ve ever played. Black Flag provides great source material. The revisit is well-made and a great sign of what could be. It is offered in a different wrapper that hints at an opportunity for more interesting remakes to come, should Ubisoft choose to pursue such treasures—and as we brace to resist or comply with an Abstergo future. 1 The messages that Edward Kenway finds in bottles tell another out-of-order story, one about a man reckoning with the strange memories and voices he’s heard in his head since he was a boy. That tale was also in the 2013 game and develops into one of Black Flag’s most compelling character arcs. In Resynced, it has lost its climax, because the bottle story previously paid off in the halls of the first-person Abstergo Entertainment, which is exclusive to the older game. An altered resolution, written by the 2013 game’s lead writer is fine, doesn’t invalidate the 2013 version, but makes for an inferior final note.
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Scottie Barnes Sends ‘Very Dangerous’ Declaration About Kawhi Leonard’s Raptors
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Explaining The Comcast and NBCUniversal Divorce
(Welcome to the “Most Important Story of the Week”, my bi-weekly strategy column analyzing the most important (but often not buzziest) news story of the last two weeks. I’m the Entertainment Strategy Guy, a former streaming executive who now analyzes business strategy in the entertainment industry. Please subscribe.) I hope everyone had a happy Fourth of July in America, celebrating the country’s 250th birthday. Frankly, I am bummed the 250th wasn’t a bigger celebration, but that’s a different topic for a different Substack. While it was generally a quiet week on the entertainment/streaming/media business fronts, we still had some interesting news stories. Actually, that kinda undersells one of the stories, a huge, huge, huge divestment that could upend the entertainment landscape... So let’s get right to it. In today’s column, I’ll cover Comcast spinning off the rest of NBCUniversal, an entertainment company investing in an LLM company and an LLM-owner investing in a movie studio, the latest round of threats of digital tariffs, and more. Let’s dive in. Subscribe Most Important Story of the Week - Most: Comcast/NBCU Breakup!!!! If I had to describe my reaction to Comcast spinning out NBCUniversal, it feels like a relationship ending, either breaking up or divorcing. Comcast’s decision to spin off the rest of NBCUniversal, after spinning off some of its cable assets into Versant last year, really does feel like a family breaking up under “irreconcilable differences”. Of my stereotypes of the entertainment moguls, my go-to for Comcast CEO Brian Roberts—heir to the Comcast fortune, but a man who’s been at the helm for 24 years now, and president for twelve years before that—is that he likes building empires. He cut his teeth acquiring cable rivals in the 1990s and tried to buy Disney repeatedly, then settled for NBCUniversal after a long court battle. (Contrast this with Rupert Murdoch, a man who also loved to buy, but would also sell when the price is right. Murdoch made Fox out of nothing, but then sold 20th Century Fox to Disney, arguably at the top of the market. So I understood why he sold.) But I don’t understand Roberts’ motivation to sell now. I have some explanations but no single, super-satisfying explanation. Strategically, it would just make more sense to have kept NBCUniversal in-house, especially for a one-time empire builder like Comcast. Yet one powerful force—arguably the decider in modern capitalism—likely overwhelmed all other considerations. At first, I thought I’d arrange my thoughts as a “Five Why” analysis, digging through each layer of the puzzle. But instead, I found myself asking, “But…why?” (And I only ended up with four sections.) So let’s look at Comcast from that angle. Then, at the end, I’ll give my biggest strategic recommendation for every company in Hollywood. It’s obvious, but sometimes, the obvious strategy is the most ignored. But…Why? The Stock Market, Conglomerations, and Sum of the Parts Analysis We’re just getting started with this issue, but the rest is for paid subscribers of the Entertainment Strategy Guy, so if you’d like to find out… Why Comcast could never get a “tech company” valuation The not-so-secret motivation for Comcast What Versant has to do with all of this The key strategic advice I think every entertainment exec should follow (though it’s really obvious) Some big AI investment news… Whether we’ll see digital tariffs… And more… ...please subscribe! We can only keep doing this great work with your support.
Trump told interesting stories about George Washington and the Panama Canal. They’re both fake
Last week, President Donald Trump’s annual financial disclosure showed he made more than $1 billion from cryptocurrency ventures in 2025. On Monday, after a reporter asked him a question related to crypto, he eventually pivoted to a colorful anecdote about the nation’s first president.
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Making Our Own Luck
We are cross-posting a piece that former NIH official Kris Willis wrote for Macroscience. Kris is now the President and Founder of the Woodley Park Institute, for which I’m a board member. Picking winners or hedging bets? Federal science agencies have long wrestled with the question of how to ensure their billions of dollars of grants and contracts result in maximum benefits for Americans. On the surface, it might seem that a goal-oriented, interventionist style of management would be the best policy: prioritize our most pressing problems and distribute funding accordingly.This top-down strategy creates a unique hazard, though: bad choices can send resources in the wrong direction, resulting in slow progress that impedes the delivery of tangible benefits to citizens. Moreover, if decision makers pursue an applied advance when the underlying scientific principles remain poorly understood, they risk costly failures and a loss of support for the research enterprise at large. Examples include Nixon’s 1971 War on Cancer, undertaken on the premise that a cure could be achieved in less than a decade, the drive to build an operational commercial fusion plant by the mid-1990s, and the National Plan to Address Alzheimer’s Disease, which failed to produce the effective treatments and prevention strategies promised by 2025. The contrasting laissez-faire approach is to hedge your bets. If the path to an advance is impossible to predict, then the wisest choice might be to distribute funding to as widely as possible. The main risk of this strategy is that spreading funding thinly may limit the resources available to the most promising research, resulting in the same negative outcomes as the interventionist approach: limited support for the most promising topics, slow progress, and delayed returns. In reality, at science agencies like the National Science Foundation and National Institutes of Health, program managers navigate between these two extremes, setting some funds aside to target high-priority areas and using the rest to cover the widest possible range of meritorious proposals. This middle path seems like the most rational way to proceed, but it still requires making choices. How much should be set aside for high priority topics? What should those topics be? How should we define merit? Expert opinion can provide a helpful guide, but leading experts can have remarkably different opinions about what matters and what should be prioritized. Moreover, experts and administrators alike can be risk averse, giving an edge to high-profile, well-established concepts. What’s really needed is a data-driven framework to help guide decision-making, one that doesn’t simply repackage prestige or incumbency. Recognizing breakthroughs years in advance Many breakthroughs fail to attract funding or recognition at the earliest stages of development. Katalin Karikó, for example, spent years struggling to win grants for work on mRNA that would eventually win her and Drew Weissman a Nobel Prize. Similar stories can be told about Jim Allison’s groundbreaking studies leading to the development of cancer immunotherapy, Stan Pruisner’s demonstration that prions can self-replicate without DNA, or Robin Warren and Barry Marshall’s proof that Helicobacter pylori, not stress, causes ulcers. Yet at some point, the scientific community recognizes and responds to transformative breakthroughs like these. How does that change happen? And what if science funders could identify the areas that are on the cusp of a breakthrough? Those questions led my collaborators and me to study how scientists responded to past breakthroughs. Based on patterns in that data, we developed a means of predicting future breakthroughs, using biomedicine as a proof of concept. It’s worth taking a moment to describe how our approach works. Briefly, our prediction process begins by grouping the 18 million or so peer-reviewed papers in PubMed, the authoritative database of biomedical research, into roughly 50,000 unique topics. A small subset of these topics are so widely acclaimed as breakthroughs that they have been recognized with a major prize like a Nobel or a Lasker Award. We theorized that studying the development of these outliers over time, especially before they gained acclaim, was the key to identifying future instances of them. After defining topics, we chose 21 representative breakthroughs and asked what they looked like before they won prestigious prizes. To answer that question, we re-ran our topic-mapping algorithm repeatedly, winding back the clock one year each time, stringing together the years to follow the progress of each field as it grew and advanced. We found that in each case, the years leading up to a discovery followed a pattern: a burst of papers on a new, rapidly evolving topic, many of which quickly became influential. These characteristics—the percent of papers that are new, the percentage of papers that are brought in from other topics, and the influence of each individual paper—can all be measured separately for any given topic. Together, they act as a predictive signal that is detectable an average of five years before a transformative discovery is made, and up to thirty years before the discovery receives a major prize. Scanning the current research landscape for topics that display this signal allows us to predict what work will likely produce a future breakthrough. Each year we analyzed includes four or five signals, a number that appears to have remained constant across a twenty year time frame. In their classic work on the sociology of science, Bruno Latour and Steve Woolgar established that the desire to participate in a major discovery is an important motivator for scientists as they consider whether they should take up (or abandon) a research problem. Although further work is needed, the simplest explanation for our results is that this behavior is widespread enough to detect at scale. A foundational advance, or something like it, draws the attention of scientists and causes them to change the direction of their research. They vote with their feet, staking their own reputation and careers by publishing on and citing the new idea. That upswell of interest is the foundation of our breakthrough signal. What kind of research does our model identify as a breakthrough? After building a model based on the patterns in our 21 preselected breakthroughs, we tested it by identifying all early signals of discovery from 1994 through 1997. Of the 18 signals we found in that time period, 17 can be traced forward in time to a breakthrough. Examples include super-resolution fluorescence microscopy (see the image above), the directed evolution of proteins and enzymes (Chemistry Nobel, 2018), and sequencing the human genome (National Medal of Science, 2008). The discovery of the role of leptin signaling in obesity is a particularly interesting case study. Around 1950, scientists noticed the existence of a type of lab mouse that suffered from a unique inherited form of obesity. When given the freedom to choose their own diet, these mice ate more than normal and preferred food that was high in fat. Researchers had few clues as to why the animals overate; early experiments pointed to the existence of a soluble ob factor found circulating in the bloodstream that regulated appetite, but its identity was unknown. In 1994, Jeffrey Friedman and colleagues identified and sequenced the ob gene, noting that the protein it encoded appeared to fit the profile of a circulating factor. A year later, a trio of high-profile publications demonstrated that ob was a hormone that regulated body weight and fat deposition by regulating appetite. One of these was co-authored by Friedman, who christened it leptin from the ancient Greek word for thin. Using our model, the data from 1996 make it clear this advance would become a breakthrough. Scientists had flocked to study the biology of the new hormone, generating the flurry of new papers and citations required to produce a signal. One of these demonstrated that serum leptin concentrations reflected the amount of adipose tissue in the human body, providing clinical validation of the earlier mouse studies. Before the identification of leptin, scientists were unable to point to a specific molecule that controlled appetite and adiposity, so that it was impossible to rule out non-physiological causes of obesity; its characterization fundamentally changed our concept of weight gain. In 2010, Friedman and fellow pioneer Douglas Coleman were awarded a Lasker for the discovery. Our work also identifies breakthroughs in clinical practice and behavioral and social sciences, even though these areas are less likely to draw the attention of major prize committees. One example that falls into this category is the introduction of endovascular aneurysm repair, a minimally invasive surgical procedure for the treatment of abdominal aortic aneurysm, a leading cause of death among Americans over the age of 65. Relative to the previous standard of care, it reduces in-hospital mortality by almost four-fold. In 2017, Timothy Chuter received the American College of Surgeons’ prestigious Jacobsen Innovation award for his multiple refinements to the technique. Another under-recognized breakthrough identified by our model is the development of standardized survey instruments to assess the quality of life for HIV+ patients. The antiretroviral cocktails introduced in the mid-1990s reduced AIDS mortality, but treatment came with serious side effects. The new evaluations showed that patients whose disease progressed had worse physical functioning than Americans with other chronic diseases, while those who were asymptomatic maintained a health-related quality of life on par with the overall US population. This evidence made it possible for patients and activists to argue that even in the absence of a total cure, and in spite of the side effects, alleviation of symptoms was a worthwhile priority for clinicians. My colleagues and I have failed to identify any significant recognition for the physicians and health policy experts who pioneered this advance, although in 2016, researchers called for the World Health Organization to set targets for good health-related quality of life as part of its framework to end the AIDS pandemic. From forecasting to funding How should science funders use our predictions? Any serious discussion of what we should do with this new capability needs to begin with an understanding of its limitations. We can’t (and shouldn’t) invest all of our resources in the small number of topics predicted to produce breakthroughs. Innovative new fields are born from existing ones; they rarely arise de novo. If funders don’t maintain a diverse portfolio, the breakthroughs of tomorrow will have no antecedents. Think of this as eating the seed corn, or killing the goose that lays the golden egg. There are also multiple goals of funding beyond basic scientific discovery, including translating discoveries into clinical practice, developing new technologies, supporting economic development, and training new scientists. The investments needed to accomplish these goals are likely different from those required to support emerging breakthroughs. Finally, we must be aware that we may not capture every instance of a breakthrough. The concept is fuzzy, and not every significant discovery is recognized with a major award. Further, breakthroughs are rare, meaning that our dataset is, by necessity, small. This makes formal estimates of accuracy challenging. Although our overall success rate appears to be high, any one signal may turn out to be a false positive. We found one apparent example: the development of the third generation COX-2 inhibitors, Vioxx and Celebrex, which were much celebrated in the early 2000s as effective but non-addictive pain relievers, meets all the algorithmic criteria of a breakthrough. Five years after it received FDA approval, researchers demonstrated that Vioxx was associated with serious cardiovascular complications, and the manufacturer voluntarily pulled the drug from the market. Celebrex remains available, although like other NSAIDs, it carries an FDA warning for increased cardiovascular risk. Decision makers who were overly focused on the breakthrough potential of this research might have wasted substantial resources nurturing an area that ultimately proved to be a failure—but so would those who relied on expert opinion at the time. That said, the potential benefits of funding the next mRNA vaccine or cancer immunotherapy years earlier than we might have are large. When we can’t assume the outcome of an action, it’s worth conducting an experiment. Can targeted support for areas that are poised to produce a breakthrough increase the return on scientific investment without cannibalizing future advances? With the necessary administrative infrastructure in place, conducting this experiment would be straightforward. First, identify all the breakthrough signals between 2018 and 2023, select half at random, and commit to a decade of investment. For biomedicine, a six year window should yield about 30 topics with the potential to produce a breakthrough. Providing strong support to half of these might be accomplished for $300 million a year. That’s roughly half of the recent annual budget of the NIH Common Fund or around 20% of the fiscal year 2025 budget for ARPA-H. After five years, then again at ten, compare to see which group — the one that was actively managed, or the control — produced more breakthroughs, and on what timescale. Although we used biomedicine as an example, the same experiment could just as well be run for physics, materials science, or any other discipline. Any attempt to undertake such an experiment should keep three points top of mind. First, we can predict the topic of future breakthroughs, but the people and vision for how to move the work forward are still important. In practice, this means success requires active program managers with the expertise to develop the science and the authority to do so, including by recruiting investigators. Second, we would be wise to learn from the failures of past attempts to fund transformative research. High among these are funding modestly repackaged work, confusing the genuinely novel with the merely unfamiliar, and relying too much on incumbent investigators. All of these risks can be mitigated by thoroughly and intelligently integrating high quality data into portfolio management. Finally, we need to have patience. Even with strong funding support, producing a breakthrough takes time. If supporting breakthroughs is the best way to nurture the birth of new fields, we also need to accurately describe and properly manage the other two stages, so that we invest wisely throughout the productive lifetime of ideas and divest once they reach the point of diminishing returns. We would benefit from more study of all of these phases, especially the transition from one to another. More research could answer questions like: When does a breakthrough become an established field? How do the availability of funding, the size and characteristics of the available workforce, and the accumulation of evidence that disagrees with prevailing models affect a field’s decline? Exactly how do old ideas give birth to new ones, and can we speed up the process? The more we understand about the dynamics of scientific progress, the better we can maximize the odds that our curiosity leads to results. I’d call that finding a way to make our own luck.
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Merchants of Doubt and the attacks on Extreme Event Attribution science
Last week, I attended a meeting at Columbia University on attribution science and climate law, hosted by the Sabin Center. It was a fantastic event, bringing together scientists and legal experts working at the intersection of extreme event attribution and climate law. Subscribe For those unfamiliar with it, extreme event attribution attempts to quantify the contribution of climate change to an extreme event. For example, several groups analyzed the impact of climate change on Hurricane Harvey’s enormous rainfall totals over Houston, Texas and they found that climate change increased rainfall by 15 to 38%. One thing that came up again and again was how terrified fossil-fuel interests are of extreme event attribution science. They are acutely aware that this research could land them in court. And losing those cases would leave them legally liable for billions of dollars in climate damages. Because the legal stakes are so high, the blowback has turned ugly. I spoke with several scientists at the meeting who are facing ongoing harassment over their work. This blowback is a coordinated campaign to make the entire field look suspect. The goal is to create the impression that attribution science is too uncertain, too political, or too conflicted to be useful in court or in public policy. The strategy is not based on actual science or evidence of misconduct, but on the generation of doubt. The new Merchants of Doubt We’ve seen this before. In fact, not that long ago: We only have to go back a year to the Department of Energy (DOE) Climate Working Group (CWG) report to see an example of using doubt as the tool to push back against well-established science. This strategy is laid out in an email from a member of the CWG, Dr. Roy Spencer, that was released during litigation over the Climate Working Group process. The key quote is: About all I can hope is that what we write will provide sufficient “reasonable scientific doubt” regarding the science claims in the 2009 TSD [technical support document], based upon almost 2 decades of new science, to call into question the original reasoning for the EPA Administrator’s decision that CO2 presents a threat to human health and welfare. This statement is strong evidence that at least some members of the committee were working to support a particular policy outcome: revoking the Endangerment Finding. The email also explains how they planned to do it: by attempting to generate “reasonable doubt”. This is going to be hard, Spencer implies. Despite falsely claiming that “2 decades of new science” weakens the case, Spencer explicitly acknowledges that the actual peer-reviewed science of climate change overwhelmingly rejects his position: But if the science argument is decided upon by a vote, or by the number of published citations, we lose the science argument. We can go back even further: This CWG email shares unmistakable DNA with the infamous 1969 tobacco memo that declared: “Doubt is our product, since it is the best means of competing with the ‘body of fact’ that exists in the mind of the general public. It is also the means of establishing a controversy.” The tobacco memo also acknowledges the limit of this strategy: Like the CWG, they knew the science was not on their side. Share The new new Merchants of Doubt The people attacking the IPCC chapter on extreme event attribution are the newest iteration of the Merchants of Doubt. Their goal, like all Merchants before them, is to introduce doubt into the process. Because the report is not even out yet, they cannot attack its conclusions. So they are attacking the authors instead. Here is a press release from the House Science, Space, and Technology Committee: In the letter, the Chairmen express concerns about potential conflicts of interest involving members of the Attribution Committee, stating that “publicly available information suggests a troubling pattern” in which committee members are affiliated with nonprofits that support climate accountability lawsuits, “raising the appearance of impropriety and member bias.” To be clear, this is just innuendo. There is no actual evidence of bias. And given the robust process that these reports go through, including multiple lines of peer review, it seems very unlikely that significant bias can survive into the report. When the report comes out, critics will have the opportunity to make legitimate criticisms of the report — if any exist. If none do, however, they’ll still make criticisms, but they’ll be bogus, simply designed to generate doubt. We’ll see. A note to the press: Fix your frame To any journalists reading this: The public debate over extreme event attribution science is not going away. The science is simply too dangerous to fossil-fuel interests for them to stop fighting it. You very well might be assigned to write an article about this area of research in the future. When you do, do not automatically adopt the framing that climate misinformers want you to use. They want you to frame the story around questions like: Are climate scientists trying to put their thumb on the scale to achieve a predetermined, politically motivated result? Are climate scientists improperly letting their politics invade the science of the IPCC? That frame is a trap. Instead, you need to view this through the historical lens of the Merchants of Doubt. How does the ecosystem of doubt operate? Who funds it? What methods do they use to misrepresent science and slime researchers? What scientific results are they trying to keep people from understanding are legitimate? Ultimately, you instead need to focus your article on the generation of doubt as a way to maintain the fossil fuel industry’s social and legal license to keep burning oil, gas, and coal. If you treat the misinformers’ frame as a legitimate, good-faith scientific critique, you are helping them produce doubt. Don’t do it. Don’t be a Merchant of Doubt. Thanks for reading The Climate Brink! Subscribe for free to receive new posts and support our work. Subscribe other stuff The Staying Curious Substack has an interesting post about the different flavors of climate sensitivity. This is crucial information if you want to understand how scientists think about how much warming the Earth will experience. I’d be grateful if you could hit the like button ❤️ below! It helps more people discover these ideas and lets me know what’s connecting with readers.
Is SMIC N+3’s Metal Pitch Smaller than Intel 18A’s?
Almost four years ago, we published that SMIC had started shipping 7 nm (N+1) chips. Now, SMIC is shipping its third-generation 7 nm (N+3) process in Huawei’s Kirin 9030, with a minimum metal pitch of 32.5 nm, about 10% tighter than the 36 nm minimum metal pitch shipping in Intel’s latest Panther Lake CPUs on 18A. The headline is true, but incomplete cherry picked metric. N+3 reaches the density of TSMC N6 through aggressive DUV multi-patterning and design-technology co-optimization (DTCO), but it pays for that in complexity, efficiency and process control. We found this and more in our reverse engineering and teardown where we cover SMIC’s N3 process technology, Huawei’s packaging, memory, architecture, and more. SemiAnalysis has been building a state-of-the-art teardown lab in Oregon capable of analyzing the world’s most advanced and important chips over the last year and half. We have already generated revenue on advanced datacenter chip teardowns including our recent reverse engineering of a major TSMC customer’s COUPE CPO optical engine + EIC 3D stack. This is the first public report from the SemiAnalysis Teardown Engineering & Evaluation Lab, or STEEL for short. The lab is aggressively scaling up and out and we’re excited to announce it publicly. This is a bit of inconvenient timing for TechInsights as they are private equity owned and currently being sold while having enjoyed virtually no credible competition for decades. This has led to TechInsights underinvesting in CAPEX. SemiAnalysis exceeds TechInsights in revenue despite no venture or private equity ownership and being founded only 6 years ago. Because we have no external investors and are founder led, we move faster, build faster, and we can release client chip teardowns for free regularly, while focusing on datacenter for our major clients. Here’s the first public image from our lab, the HiSilicon Kirin 9030 Pro SoC: This report will detail our teardown of the Kirin 9030 and our findings on SMIC’s N+3 process, the most advanced in China. For comparison, we’ll show our teardown of the MediaTek Helio G99, made on TSMC N6. Through this comparison, we can look at the effect of export controls – SMIC N+3 and TSMC N6 are comparable nodes, but one is heavily export-controlled, the other free to use the West’s most advanced equipment. Here we see both China’s progress and constraints. SMIC N+3 reaches TSMC N6-class logic density, but it requires far more aggressive DUV multi-patterning, so it does not match N6 on process maturity or cost. The Kirin 9030 Pro performs similarly to three-year-old Android flagships, and trails far behind the current flagship SoCs from Apple, Qualcomm, MediaTek, and Samsung. The efficiency gap is even wider. Export controls have not stopped Huawei and SMIC from shipping advanced silicon, but they have forced a different path. Without EUV, SMIC is leaning harder on DUV multi-patterning, DTCO, and increasingly complex integration. The roadmap continues forward through tighter design rules and backside power, but each step adds cost and process risk. Huawei’s τ scaling and LogicFolding show another path: stacking active logic and recovering density through advanced packaging and system-technology co-optimization (STCO). To understand the Kirin 9030, we must first understand Huawei’s SoC history. HiSilicon is Huawei’s chip design arm, responsible for the Kirin smartphone SoCs, Kunpeng server CPUs, Ascend AI accelerators, and switch/router networking silicon. Before export controls, Huawei was TSMC’s largest customer – the only customer on TSMC’s first EUV node, N7+, and among the first on N5, alongside Apple. That ended in late 2020. Huawei switched to Qualcomm SoCs in its flagship smartphones, though export controls limited them to 4G-only variants. In late 2023, Huawei returned to in-house silicon with the Kirin 9000s, a successor to the Kirin 9000, fabricated on SMIC N+2 instead of TSMC N5. In the following years, they released the Kirin 9010 and 9020 on the same N+2 process. These chips used Huawei’s in-house TaiShan CPU cores and Maleoon GPU. We have not torn down a Kirin 9020 ourselves, so the predecessor die shot is from Kurnal. The die shots show how Huawei spent its silicon budget: which functional blocks are where, and how their areas compare to the predecessor. First, a quick guide to the major blocks on the die. The total die area is nearly identical, but the 9030 uses that area more aggressively. A denser process lets Huawei fit an extra middle CPU core, more GPU and NPU cores, and larger caches into the same footprint. In contrast, the Helio G99 is a much smaller, low-cost SoC, built for budget smartphones rather than a flagship device. While the Kirin 9030 is ~140 mm², the G99 is only ~29 mm², roughly one-fifth the area. The underlying TSMC process technology, however, is directly comparable as a baseline for analyzing SMIC’s. The Kirin 9030 is an evolutionary refresh, not a clean-sheet design. Its CPU, GPU and NPU cores carry over the 9020’s families, and the gains come from three levers: the SMIC N+2-to-N+3 process step, DTCO and floorplan work, and incremental microarchitecture. Area is where the first two show up, and the 9030 scales well here. Performance and efficiency are the harder test. Huawei’s design holds up better than its node would suggest, but the chip still trails, both because N+3 sits behind the leading-edge nodes and because its cores, while competent, remain a few generations behind the newest designs. The new prime core is an incremental update. The main changes are a 10% frequency increase from 2.5 GHz to 2.75 GHz and a doubling in the L2 cache from 1 MiB to 2 MiB. Despite the increased cache, the core size decreased by 7.6%. Excluding the private L2 cache, the core size decreased by 21%. This is a large reduction for an incremental node. Compared with the TaiShan New V120 core in the Kirin 9020, the Kirin 9030’s middle core is almost unchanged architecturally, yet each core shrinks by ~22%. Most of that comes from the move from N+2 to N+3, with layout likely accounting for the rest. Visually, the most noticeable change is the increase from 3 middle cores to 4. There is also a 20% increase in the shared L3 cache of the big cluster. This helps to improve multi-core performance without sacrificing much in terms of area. Even with each core shrinking, the big CPU cluster’s total area is essentially unchanged. The per-core savings went back into an additional middle core and larger caches. The tiny cores shrank less than the prime core (excluding its L2 cache) and less than the middle cores. This is likely because fixed overhead is a larger share on a small core. We cannot resolve any architectural changes from the die shot alone, but the per-clock and efficiency gains shown below point to more than pure process and layout scaling. The area reduction was offset by a doubling of the shared L2 cache from 2 MiB to 4 MiB, leaving the total tiny CPU cluster area slightly larger. Area is the easiest improvement to see from a die shot, but it is only one part of PPA (power, performance, area). For modern logic, power and performance matter just as much, and often more. Since Dennard scaling broke down in the mid-2000s, voltage and frequency have not scaled in step with transistor dimensions, so each node has had to fight harder for gains in performance and efficiency. The starkest comparison is not Kirin 9020 versus Kirin 9030 Pro. Apple’s efficiency cores run circles around Huawei’s prime core. Apple’s low-power core delivers 20% higher integer performance while drawing only 1 W, compared with 4.5 W for Huawei’s prime core. N+3 matches TSMC N6, but N6 is several generations old. Apple and Qualcomm build on N4 and N3P, which are denser and sit on a better voltage-frequency curve, giving them a larger transistor budget and more performance per watt. The 9030’s own cores did improve. The middle and tiny cores gained 17% and 14% in per-clock integer performance over the 9020, with floating-point flat on the middle core and up 11% on the tiny. The tiny core improves cleanly, with performance rising while power falls and efficiency increases by 45% in integer and 24% in floating point. The middle core is mixed: integer performance rises but power rises faster, cutting integer efficiency by 7%, while lower power lifts floating-point efficiency 16%. Per-clock gains at the same or lower frequency are microarchitectural, so the cores are tuned, not just shrunk. Both also failed to hold their rated maximum frequencies, pointing to thermal, power, or stability limits. Per clock, the middle core sits around Arm Cortex-A720 and the tiny core near the Cortex-A520; absolute performance trails because Huawei clocks them much lower. The prime core is roughly Cortex-X2 class per clock, a 2021 design. Apple’s 2020 M1 Firestorm core is still 35% higher per clock and 57% faster in absolute integer performance at a similar 4.5 W. The current leading edge is further ahead again: the Apple M5 P-core is 60% higher per clock and 2.7× faster, the Arm C1 Ultra 45% higher and 2× faster. Matching older high-end cores per clock is a genuine design achievement. What Huawei cannot match is the voltage-frequency curve and transistor budget of leading-edge nodes, which let Apple, Qualcomm and others spend more transistors in the same area on wider cores, larger caches and deeper buffers while running at lower voltage. Huawei’s LogicFolding roadmap is one answer, stacking active logic to recover density and shorten signal paths. We return to it later. The GPU compute units (CUs) changed more visibly than the CPU cores, moving to a more rectangular layout for both the arithmetic logic unit (ALU) clusters and the CU overall. Even with ray-tracing support added, a CU shrank ~28%. However, that shrink is offset by the increase from 4 to 6 CUs and the area outside the CUs grew 33%. Overall, the GPU cluster is larger by ~10%. The GPU is where Huawei makes its biggest gains. The Maleoon 935 is not competitive with current flagships, but it is a large step up from the 920 and reaches older-flagship territory. In 3DMark it is 70% faster in Wild Life Extreme (WLE) and 79% faster in Steel Nomad Light (SNL) than the 920; with 11% higher clocks and 50% more CUs, the ~67% theoretical uplift roughly matches WLE and is beaten by SNL. It edges ahead of the Snapdragon 8+ Gen 1 in WLE and SNL, and the Dimensity 9200 and Apple A16 in WLE, but stays far behind newer parts: the Snapdragon 8 Elite Gen 5 and Dimensity 9500 are ~2.4–2.6× faster in WLE and ~3.2× faster in SNL. The Maleoon 935 is Huawei’s first GPU with hardware-accelerated ray tracing; there it lands slightly ahead of the Exynos 2200, and on par with the Apple A16, with current flagships up to 3.7× faster. The Neural Processing Unit (NPU) saw the largest structural changes of any block, moving from a Lite and a Tiny core in the Kirin 9020 to a Lite and two Tiny cores in the Kirin 9030. Both core types also show significant layout changes. This is a reversal in Huawei’s NPU design. The Kirin 9000 5G, its last flagship chip on TSMC N5, used two Lite and one Tiny core. The series of SoCs on SMIC N+2 moved to one Lite and one Tiny core, likely for area savings. With the Kirin 9030, Huawei has shifted back toward a larger multi-core NPU cluster, but with the additional area going to a Tiny core rather than a Lite core. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. Before diving into the process stack, the package and memory are worth separating from the SoC itself. The Pro variant of the Kirin 9030 carries 12 GB of Samsung DRAM, with two stacks of four dies each. The dies were identified as the K4L2E165YD, a 12 Gb LPDDR5X-9600 device fabricated on Samsung’s 1a node, the fourth generation of its 10 nm-class DRAM after 1x, 1y and 1z. 1a has shipped in volume since 2022, so this is current memory rather than older-node inventory. The 16 GB Pro Max variants we obtained were found with both CXMT and Samsung packages. The CXMT package is marked CXDD7JEDM, with two stacks of four dies, packaged in week 45 of 2025. The inferred die dimensions from X-ray computed tomography (CT) are consistent with a known density of ~0.3 Gib/mm² for the CXMT G4 process, roughly equivalent to other manufacturers’ 1z processes. The Kirin 9030 uses a typical integrated package-on-package (iPoP) stack: multiple DRAM dies in a memory package sit above an organic redistribution layer (RDL) interposer, which sits above the SoC and package substrate. The full package is then mounted to the printed circuit board (PCB) through ball-grid array (BGA) solder bumps. The memory package substrate is a thin bismaleimide-triazine (BT) laminate carrying the LPDDR5X stack. The organic RDL interposer over the SoC routes the PoP signals around the die and carries possible dummy thermal copper pillars. The package substrate, a thicker Ajinomoto Build-up Film (ABF) build-up over a BT core, fans the flip-chip bumps out to BGA pitch and embeds the power planes. The whole stack is organic. The only silicon is the SoC and the LPDDR5X dies; there is no silicon interposer. Keeping it all-organic brings the package’s coefficient of thermal expansion (CTE) close to the PCB’s, reducing board-level warpage, and avoids the cost of a silicon interposer the SoC’s bandwidth does not need. In an iPoP stack, the memory package connects to the organic RDL interposer through an array of solder bumps. Underfill fills the gap around those bumps, adding stiffness and protecting the joints from mechanical stress. The Pro and Pro Max variants differ here, which we cover behind the paywall. The die shot and architecture tell us how Huawei allocated its silicon budget. The process tells us what SMIC can manufacture. We use the Helio G99 as the process reference for TSMC N6. Both SMIC N+3 and TSMC N6 are evolutions of previous 7 nm-class nodes. We used targeted TEM cross-sections through logic and memory regions, imaged in both fin-cut and gate-cut directions. Each cross-section caption gives its horizontal field width (HFW), the real width of the imaged area. We start at the transistor fins, then move up through standard cells, local interconnect, and SRAM. SMIC has not overtaken Intel or TSMC. It uses aggressive DUV scaling and DTCO to reach N6-class density, but that density doesn’t translate into comparable performance and efficiency, for two reasons: the node gap to leading-edge nodes, and Huawei’s core designs. Fin Profile One of the most important knobs in a FinFET process is the fin profile: the shape of an individual fin and the channel where current passes from source to drain. The ideal fin is tall, narrow, and nearly vertical. A taller fin increases effective channel width, while a narrower fin improves electrostatic control by thinning the body the gate must control. Push either too far, and the process pays for it: weaker drive current, fragile fins, taper, footing and line-edge variation that hit yield and device variability. The Intel 22 nm, 14 nm, and 10 nm fin cross-sections show how FinFET nodes have improved over time. 22 nm fins were a first-generation structure, relatively short, wide and strongly tapered. The shape limits current density and reduces gate control uniformity across the height of the fin. At 14 nm and 10 nm, Intel pushed the fins taller and narrower while also making the sidewalls more vertical. Rather than shrinking the device, these changes increase the effective channel width per fin and improve electrostatic control. The trade-off is that taller fins at tighter pitches are much more difficult to manufacture. Now, let’s compare the Helio G99 on TSMC N6 with the Kirin 9030 on SMIC N+3. Both processes are in the same class, with fin pitch of 30-32 nm on N+3 and 34 nm in our N6 cross-section. The pitch for N6 is especially interesting as N7’s HD library is generally listed with a 33 nm fin pitch, and N6 did not shrink pitches directly. Its density gains came from DTCO instead of tighter pitches. The 34 nm pitch was stable across our sampled region and serves more as a comparison against the SMIC N+3 we have not investigated further. Pinning down N+3’s fin patterning scheme takes more than one core unit. The CPU cores show a dense ~32 nm pitch, with the pitch between N-P fin pairs alternating between 78 and 88 nm. Logic alone may be consistent with dual-pitch mandrels of 120 and 110 nm, but this is a complex and unusual approach. Combining the pitch from the the 8T SRAM, which has more complex repeat unit, with the CPU core sequence allows us to reverse engineer the patterning steps with more confidence. As both the logic and SRAM should share the same base grid, a single CD mandrel lithography pattern with 128 nm pitch undergoing SAQP produces a die-wide ~32 nm grid (128 nm/4), which supports the pitch sequencing seen in both logic and SRAM cells. In the sampled cross-sections, N+3 shows a taller, narrower, higher-aspect-ratio fin than N6. The measured fin aspect ratio is ~9.5:1 on N+3 versus 7.8:1 on N6. N+3 also shows less top rounding, with an estimated radius of ~2 nm, compared with 2.8 nm on N6. Even though the fin widths differ, the ratio of top rounding to fin width tells the same story, with N+3 at 0.37 and N6 at 0.44. In a geometric sense, lower is better; a perfectly rectangular fin would have no top-rounding penalty. These are single-digit-nanometer features measured from a handful of cuts, so treat the absolute numbers as approximate. The important result is the relative gap: N+3’s fins are consistently taller, narrower and less rounded than N6’s. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. Standard Cell A standard cell is the basic building block of chip layout: a fixed-height row pairing one NMOS and one PMOS transistor that share a gate, tiled in a grid to build logic blocks. The key dimensions are contacted gate pitch (CGP), cell height (CH), fin count, and the lower-metal routing grid. To measure density, we use the Bohr metric: a weighted average of NAND2 gate area (60%) and scan flip-flop area (40%). This represents a realistic mix of combinational and sequential logic. This metric has its limitations, especially for complex cell layouts like TSMC’s FinFLEX, which alternates cells with different fin counts. Even so, it is the best metric for a pure process-level comparison. Another important measurement is the fin pitch; it refers to the distance between two fins of the same transistor. In a FinFET process, multiple fins are used in each transistor to increase the drive current and thus performance. TSMC N6 ships both a high-density (HD) library with 2 PMOS and 2 NMOS fins per cell, and a high-performance (HP) library with 3 of each. More fins under the shared gate mean more effective channel width. HP cells switch harder at the cost of area. Designers mix the two on a die, primarily spending HP cells on timing-critical paths, and matching their PPA targets. In the Cortex-A55 core of the Helio G99, we found a cell height of 240 nm for the HD cell. MediaTek has used HD cells in the G99 to minimize die size and thus cost. As an SoC for budget smartphones at ~$100, this is essential. By contrast, we found only one library in the Kirin 9030, with 2 NMOS and 2 PMOS fins. This suggests a narrower library strategy than TSMC N6, where both HD and HP libraries are widely used. This likely reflects the smaller customer base and the more constrained domestic design and electronic design automation (EDA) ecosystem. In all three CPU cores of the Kirin 9030, we found cell heights of 228 nm, 5% smaller than on N6. This is also a reduction of 9.5% over SMIC N+2’s cell height of 252 nm. SMIC N+3 and TSMC N6’s HD library both feature a CGP of 57 nm. For SMIC, this is a 9.5% shrink over N+2. In the past, CGP and cell height alone may have been enough to compare transistor density. Now, however, we must consider scaling boosters and DTCO as well. SMIC’s density gain does not come from EUV. It comes from using every available DTCO booster aggressively. First is fin depopulation: reducing the number of NMOS and PMOS fins in each cell. The first FinFET nodes started with 3 or 4 fins for each transistor. SMIC N+3 and TSMC N6 HD both use only 2 fins per transistor, trading drive strength for density. Next is contact over active gate (COAG). By landing the gate contact directly over the active gate, instead of out over the isolation region, the cell height drops. N+3 integrates COAG while N6 does not. Our N+3 gate-cut cross-sections indicate COAG, with the gate contact sitting over the active region, while N6 shows an off-gate contact. Last is single diffusion break (SDB). Diffusion breaks are inserted between cells in the same row to provide electrical isolation, but they also introduce local layout effects (LLE), layout-dependent shifts in electrical characteristics. In the past, a double diffusion break was used, consuming the space of two CGPs. SMIC N+3 and TSMC N6 instead use SDB, saving area but increasing LLE sensitivity. This must be controlled at the process level and accurately modeled in the process design kit (PDK) so that EDA tools can account for it. Overall, SMIC N+3 has a transistor density of 113.4 MTr/mm², slightly above TSMC N6 at 107.7 MTr/mm². Even without EUV, SMIC has achieved density beyond TSMC’s mature N6 node which utilizes EUV. Metal Stack The smallest critical dimension in the teardown is M0; SMIC N+3 uses a 32.5 nm local metal pitch. That is smaller than the 36 nm M0 pitch on Intel 18A in Panther Lake. However, this does not mean that SMIC has a better process than Intel 18A or TSMC N3P. M0 is a local intra-cell routing layer. Its usefulness depends on the full interconnect stack: M1 and M2 pitch, track count, via and line resistance, design rules, mask count, overlay control, and routing flexibility. The 32.5 nm M0 is consistent with self-aligned quadruple patterning (SAQP), whose four-population line-width loading we read coarsely as alternating widths of 21.5 to 24 nm; M1 and M2, at 38 and 40 nm, are consistent with self-aligned double patterning (SADP), a single A/B split. On TSMC N6, M0, M2, and M3 sit at a relaxed ~40 nm and are consistent with SADP-class double patterning, with no need for quadruple patterning. That said, we measure M2 for example at ~43 nm, likely inflated by sparse routing. We do not assign any specific layer to EUV from our cross-sections; the distinction we can draw is double versus quadruple patterning, not lithography wavelength. Transistor-level density in the front-end-of-line (FEOL) sets an upper bound, but the design is ultimately limited by what the interconnect stack can route. The lowest metals are the most important for standard-cell density, but the semi-global and global layers determine how usable that density is at the block and chip level. Two axes are commonly used for chip cross-sections: the fin-cut and the gate-cut. The micrograph above is a fin-cut and shows metals 0 through 3. This axis lets us see the even-numbered metals, with M0 right above the fins. There are two kinds of M0 lines. The first are the power rails; these are wide wires for the VDD and VSS running horizontally at the top and bottom edges of each standard cell. The wide wires measure 55 nm across, more than double the other M0 lines. Their width minimizes resistance and reduces IR drop. The second kind are intra-cell wires, short segments within the cell that connect terminals to M1. These have alternating widths between 21.5 and 24 nm. The M0 pitch is 32.5 nm, a 19% reduction versus N+2 and N6. At this pitch, DUV patterning requires more aggressive multi-patterning, increasing mask count, overlay sensitivity, process complexity and cost. M0 is below what a single DUV-defined spacer (SADP) can resolve, so SMIC cascades a second spacer step (SAQP). The cross-section reflects the cost: the M0 trenches are visibly more re-entrant (narrower at the bottom than the top) than M1 or M2 on the same chip and carry a bright barrier-rich foot where the trench meets the etch-stop layer. That shape is partly the intended damascene profile, as a slightly narrow bottom helps void-free copper fill, but its magnitude at M0 is driven by the tight pitch and the higher trench aspect ratio. Intel 18A supports an M0 pitch of 32 nm, although Panther Lake has only shipped with a looser 36 nm pitch. This is due to Intel’s heavy usage of HP libraries. Among leading-edge nodes, 18A has the loosest M0 pitches due to PowerVia. With power routing moving to the backside, congestion is reduced, and the entire front-side metal stack can be used for signal routing. M2 is the first true inter-cell routing layer. It runs horizontally like M0 but spans across multiple cells to carry block-level signals. The M2 pitch sets the cell’s track height – the number of M2 tracks that fit between the VDD and VSS rails, defining what the library calls a 6-track or 7.5-track cell. This layer is the most important, limiting the routing of entire blocks. SMIC N+3 features a 5.7-track cell. The M2 pitch is 40 nm, a 5% decrease over N+2 and the same as N6. This shrink keeps the pitch at the edge of what is possible with double patterning. Future nodes will need to increase the number of masks for M2 as reducing the number of tracks is much harder due to the limitations in routing. The micrograph above is in the perpendicular direction, the gate-cut, and shows metals 0 through 4. This allows us to see and measure the odd-numbered vertical metal layers. The M1 pitch is 38 nm, 9.5% less than N+2 and 33% less than N6. The M1-to-gate ratio matters because it sets local routing flexibility. N+2 and N+3 use a 3:2 ratio, while N6 uses a 1:1 ratio, explaining the huge differences in M1 pitch. The more M1 lines there are compared with the gates, the more flexibility there is for power and signal crossing within the cell. Routing flexibility enables more complex and better cells. Clean fractional ratios are also preferred as a grid is periodic and improves layouts. The 3:2 ratio gives SMIC more local routing flexibility than a strict 1:1 grid, but it also complicates layout and patterning. This is a DTCO choice, with SMIC increasing process complexity to recover density and routability without EUV. This 3:2 ratio is not very popular in the leading-edge nodes. TSMC has only used it on N7+, the N5 family, and the short-lived N3(B). They have switched back to a 1:1 ratio for N3E. Intel has only used it on the 10 nm/Intel 7 family, with Intel 4, 3 and 18A all using a 1:1 ratio. Samsung is the only one still using a 3:2 ratio at the leading edge, using it in the SF4 and SF3 families. It remains to be seen if SMIC will remain at a 3:2 ratio or move to a 1:1 ratio with its future nodes. The industry is still actively exploring these local-routing ratios. At VLSI 2026, imec will be presenting work on even higher ratios, including a 2:1 scheme that can reduce area by up to 14%. We will be covering the conference in a future newsletter article. Subscribe We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. The final local interconnect layer for N+3 is M3, with a pitch of 44 nm. The M3 pitch is the same as on N+2 and 10% larger than on N6. The semi-global layers carry the majority of block-level signal routing. They have a coarser pitch than the lower local layers. On leading-edge nodes, they are designed to sit at the limit of DUV single patterning. M4 through M11 pitches were found divided between 80–82 nm (M4–M6), 128 nm (M7–M10), and 148 nm (M11). Given limited sampling, it is possible these are divided further in dense routing areas. At the top are two giant metal layers, M12 and M13. These have kept the same pitches as N+2 at 1920 nm and 4600 nm respectively. While the lower layers’ pitches are generally fixed by the process and library, the upper layers vary much more in pitch and count, depending on the design. Even two smartphone SoCs on the same process can have wildly different metal stacks. The Helio G99 carries fewer routing layers, reaching coarse metal pitches of 850 nm by M9, while the larger and higher-performance Kirin 9030 keeps fine pitches until M11. SRAM At the leading edge, SRAM is much more difficult to scale than logic. TSMC’s latest nodes have seen little to no bitcell scaling, while logic still has more DTCO levers to pull. While looking for other logic libraries in the GPU compute units, we stumbled upon the SRAM. The most common type of SRAM has 6 transistors (6T), but this cell had 8 transistors (8T) instead. 8T SRAM adds two transistors to form a dedicated read port. Unlike a 6T cell, where reading disturbs the storage, the decoupled read port removes read-disturb, improving read stability and letting the cell be pushed harder for performance. At first glance, the cut looked like an unusual logic library, with each cell row having 3 fins of one polarity and 5 fins of another. The rows also alternated in orientation. Energy-dispersive X-ray spectroscopy (EDS) resolved our confusion. The cut had not landed on the GPU logic, but on the SRAM macro beside it. The unusual fin pattern was due to the SRAM library. We return to EDS in the process flow analysis behind the paywall. SRAM libraries are not like traditional logic libraries. Due to the unequal number of PMOS and NMOS transistors, they require specialized rules and layout libraries. They do not need the flexibility of logic libraries, so they are hyper-optimized for one purpose: dense, reliable memory. The SRAM cell we found is a 1:2:2-2:2 cell. This means there is 1 fin per pull-up (PU) PMOS transistor, and 2 fins per pull-down (PD) and pass-gate (PG) NMOS transistor. These 2 PU, 2 PD and 2 PG transistors would usually form a single 6T high-current cell (HCC). An 8T HCC adds a read-pull-down (RPD) and a read-pass-gate (RPG) NMOS transistor, each with two fins. We measured a cell height of 406 nm, which brings the bitcell size to 0.0463 µm². That is a theoretical peak density of 21.6 Mib/mm². We estimate that a 6T HCC would have a cell height of 292 nm and a size of 0.0337 µm². This is ~12% larger than a 6T HCC on Intel 3 and 4. We also estimate the 6T high-density cell (HDC) to have a cell height of 228 nm and a size of 0.0260 µm². This is coincidentally the same as the logic standard-cell height measured earlier. The estimate puts the cell near Samsung 7LPP/5LPP and slightly below TSMC N7/N6. That is a theoretical peak density of 38.5 Mib/mm². 6T HDC is arguably the most important cell as it is used for the largest caches in a chip, the L3 caches and system-level cache (SLC). Both the Kirin 9020 and 9030 have split the SLC into 4 banks to raise total SLC bandwidth. In the Kirin 9030, the SLC increased from 2 MiB to 3 MiB per bank. Correspondingly, the number of arrays within the bank also increased by 50%, from 16 to 24. Each array can store 128 KiB and forms an orderly pattern on the die shot. From the Kirin 9020 to the Kirin 9030, the area of a 128 KiB SLC array decreased from 0.0477 mm² to 0.0392 mm², an 18% shrink. The achieved density is 25.5 Mib/mm², 66% of the theoretical maximum. While the SLC was quite similar across both chips, the L3 has seen some major changes, particularly in terms of its layout. The total capacity also went up from 10 MiB to 12 MiB. Much like the SLC, the L3 is also split into 4 banks. In the Kirin 9020, an L3 bank consisted of 16× 128 KiB arrays and 16× 32 KiB arrays. However, an L3 bank in the Kirin 9030 instead consists of 48× 64 KiB arrays. In the Kirin 9020 L3, a 128 KiB array was 0.0513 mm² and a 32 KiB array was 0.0154 mm². The size of the 128 KiB array is different on the L3 and SLC as the assist circuitry for the two arrays differs depending on their purpose. In the Kirin 9030 L3, a 64 KiB array is 0.0210 mm². Although not a like-for-like comparison, normalized for capacity, it is 18% smaller than the 9020’s 128 KiB L3 array and 31% smaller than its 32 KiB L3 array. The achieved density is slightly lower than the SLC, at 23.8 Mib/mm², 62% of the theoretical maximum. Unlike the L3 and SLC, the prime cores’ private L2 cache uses a 2-bank design. As the prime cores’ L2 is latency-critical, it likely uses 6T HCC instead of 6T HDC. The 9020 has 16 arrays in each bank while the 9030 has 32. Each array has a capacity of 32 KiB. A 32 KiB array in the L2 shrank from 0.0171 mm² to 0.0142 mm², ~17% smaller. The density is 17.6 Mib/mm², ~59% of the theoretical maximum for 6T HCC. SRAM scaled well from N+2 to N+3, shrinking by ~19%, close to the theoretical logic shrink. The caveat is that N+2’s bitcells were unusually large, bigger than comparable 7 nm-class nodes, so part of the gain is catch-up rather than true scaling. With the insights from STEEL’s teardowns, we will be doing a deep dive into SRAM in a future newsletter article. Subscribe Everything above came out of a single STEEL teardown: die annotation, block-level area analysis and TEM cross-sections through logic and SRAM. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Future Roadmap The same cross-sections that pin down N+3 also show where SMIC can go next. Although N+3 is already close to the practical limits of DUV multi-patterning in several layers, SMIC still has a few scaling levers left. A theoretical N+4 would likely start with cell height. N+3 uses 5 M0 tracks between its power rails. Moving to 4 M0 tracks, as on SMIC N+2 and TSMC N6, could reduce cell height by roughly 15%. The routing grid is only one side of the shrink; the front end also must fit into the smaller cell. One possible FEOL lever is reducing the p-to-n isolation spacing from two diffusion grid units to one. Intel used this scaling booster on Intel 4, and TSMC did so on its N3 family. This path trades layout flexibility for density. Fewer M0 tracks reduce local routing resources, while tighter p-to-n spacing raises integration and design-rule difficulty. M2 is also constrained by the cell height shrink. For SMIC to maintain a ~5.7-track cell, M2 would need to move toward ~35 nm. That would move another layer into SAQP territory. SMIC could also reduce the CGP from 57 nm to 54 nm. Intel reached a similar CGP on Intel 10 nm/Intel 7 without EUV. The local interconnect is also tougher. If SMIC keeps the 3:2 M1-to-gate ratio, M1 would need to shrink to 36 nm and would likely require SAQP as well. If SMIC moves to a 1:1 ratio, M1 could relax to 54 nm, but it would give up routing flexibility. Under this theoretical path, we estimate that SMIC N+4 could reach a cell height of 198 nm and a CGP of 54 nm, implying a Bohr density of 137.8 MTr/mm², on par with TSMC N5 or Samsung SF4. However, the difficulty is cumulative. Each step is individually plausible, but together they make N+4 harder than the transition from N+2 to N+3. It will likely take longer, cost more, and carry less process margin. A theoretical N+5 would require a larger integration shift. One possible path is backside contacts (BSCon), moving power routing and source/drain contacts to the backside, which would reduce front-side routing pressure and enable another reduction in cell height. The front-side metal pitches could relax to reduce the process complexity. M0 would likely relax slightly to ~34 nm, and M2 and M4 pitches could relax further. CGP is unlikely to shrink much further. Even with EUV, 48 nm has been the practical limit for yield and process control. This approach would allow N+5’s cell height to fall to 170 nm and its CGP to 53 nm. This implies a Bohr density of 163.6 MTr/mm², on par with Intel 18A’s HP library. However, this would not make N+5 cost-competitive with the leading edge. It would reach a similar density through a much more expensive route. The integration difficulty rises sharply, with new process flows for backside alignment, wafer thinning, contact reveal, and backside metallization. Past this point, standard density and interconnect scaling become increasingly unattractive. That is where Huawei’s roadmap stops looking like a normal foundry roadmap and starts looking like a packaging roadmap. At ISCAS 2026, Huawei unveiled its tau (τ) scaling law, reframing process scaling in the time domain. τ is the time cost of data movement and processing: switching delays in transistors, RC signal propagation delays in circuits, compute, memory, and networking latency. Outside Huawei’s terminology, this is called system-technology co-optimization. This is Huawei’s answer to its lack of EUV lithography. Without EUV, planar density cannot keep pace with TSMC, Intel, or Samsung. If transistor density cannot shrink further, Huawei’s alternative is to shorten wires, reduce buffering, and stack logic vertically. “LogicFolding”, Huawei’s implementation of this new scaling idea, is, in practice, an aggressive 3D stacking approach. AMD V-Cache places SRAM above or below a CPU die. AMD’s MI350X places active interposer dies (AIDs) underneath accelerator and compute dies (XCDs), with the AIDs handling cache, IO interfaces, the network-on-chip (NoC) and embedded metal-insulator-metal (MIM) capacitors. With LogicFolding, parts of the same logic block are split across multiple active dies bonded face-to-face at ultra-fine pitch. This allows Huawei to shorten some critical paths and reduce buffer overhead, not merely add cache capacity or offload the IO and interconnect. Shortening wires is where the higher clocks come from. A large share of a modern core’s delay and energy budget goes into driving long interconnects and the repeater buffers along them. LogicFolding distributes a block’s critical-path gates across multiple stacked tiers bonded at a very fine pitch, so the bond interface behaves like an additional metal layer and the longest paths get shorter. That is how Huawei expects to recover frequency and efficiency it cannot get from the process alone. Huawei’s roadmap shows its intent. Prime core frequency is targeted to rise from 2.75 GHz in the Kirin 9030 to roughly 5 GHz by 2031, far beyond what planar scaling alone could deliver. Prime cores with 3.1 and 3.39 GHz clocks are being tested in its labs, although their power consumption is unknown. Beyond that, chips are in the design, simulation or pathfinding phase, meaning the frequencies are targets. However, the direction matters more: LogicFolding also helps with performance, not just density. The catch is that Huawei’s density claim is not directly comparable to foundry densities. A stacked design can report more transistors per package footprint by adding active layers, even if each patterned die remains well behind TSMC or Intel in front-end density. This is how Huawei can claim to reach foundry 14A-equivalent density by 2031. This is not a like-for-like foundry comparison, with Huawei using stacked logic and measuring density per package footprint. On a normalized Bohr-density basis, SMIC N+3 is ~114 MTr/mm², 38% less than Intel 18A’s HD library. Huawei’s 3D roadmap closes the gap by stacking active logic, reaching 215 MTr/mm² by 2030. In 2031, the roadmap density jumps to 295 MTr/mm², implying either a third active layer, partial EUV insertion or aggressive planar DUV scaling. Huawei’s methodology makes other foundries look much denser as well. Applying it to AMD’s MI450X with an N2 top die on an N3P base die yields a theoretical density of 460.2 MTr/mm² in 2026, compared with Huawei’s 295 MTr/mm² in 2031. This Kirin 9030 does not use LogicFolding, remaining in a conventional mobile SoC package. Instead, it forms the baseline for how far Huawei and SMIC can push planar scaling. Future teardowns of Kirin and Ascend chips will show both planar logic density and Huawei’s hybrid bonding solutions. Export controls changed China’s optimization problem rather than ending it. EUV restrictions raised the cost and complexity of leading-edge manufacturing without freezing it. SMIC reaches N6-class logic density through DUV immersion, SAQP and DTCO, while Huawei shifts more of the burden onto architecture, packaging and system-level integration. Future nodes will be tougher. N+3 still had room to tighten local metals and reduce cell height and CGP. Further scaling without EUV leaves fewer levers. More aggressive multi-patterning adds masks and overlay error. SMIC can keep pushing DUV, but each step will get more expensive and less forgiving. The design side is just as critical. Huawei had domestic EDA tools and flows before the Kirin 9030, with the Kirin 9000s, 9010 and 9020 making that clear. Huawei was able to ship multiple consumer SoCs on SMIC N+2 and N+3 while cut off from the Western EDA stack. US export controls restricted EDA tools for advanced chips in 2022 but did not target tools for more mature chips. In 2025, the US government briefly placed much broader restrictions on EDA software from Synopsys, Cadence, and others, before lifting them less than two months later as part of a trade deal tied to rare earths. Huawei has been unable to access those tools because it remains on a US trade blacklist. That forced Huawei, SMIC and Chinese academic institutions to build their own tools and flows. Researchers at Peking University recently announced a prototype EDA tool for Huawei’s LogicFolding architecture, which requires a new flow to handle the multilayer layout and floorplan. This is not the same as replacing the full Synopsys or Cadence stack, but it shows where domestic EDA is headed: toward tighter co-optimization between architecture, process and packaging. These advances are also diffusing into the Chinese ecosystem. SMIC is licensing its N+2 and N+3 processes to HLMC/Hua Hong at the government’s direction rather than by choice. If the same process learning feeds into Ascend accelerators for AI training and inference, the choke point shifts from one named fab to an ecosystem. Alibaba’s T-Head silicon arm and Cambricon, a Chinese AI chip designer that is expected to supply ByteDance, could also be major beneficiaries. Sanctions aimed at SMIC alone become less effective once the manufacturing knowledge has spread to other fabs and design houses. China is not closing the gap with Intel, Samsung and TSMC. The teardown shows the opposite in several places: no EUV, no backside power, higher process complexity, and visible trade-offs. But China is still advancing. If domestic chips become good enough for phones, inference, networking and security-sensitive workloads, they can matter strategically without matching TSMC at the leading edge. Behind the paywall, we show what else STEEL can do, with material and process flow analysis of SMIC N+3, and analysis of the Kirin 9030 package. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline, access the full Kirin 9030 and SMIC N+3 analysis or to commission a custom teardown, contact sales@semianalysis.com.
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With 22 competing theories and no consensus in sight, the science of consciousness is lost. In this exclusive IAI interview, Berkeley psychedelics researcher and bestselling author of a new book on consciousness Michael Pollan insists that even though consciousness lies beyond traditional forms of scientific investigation, we should resist becoming dualists who see it as something radically different from ordinary stuff in the world. Instead, he makes the case for reimagining science itself: it must stop reaching for objective descriptions of physical reality, and instead embrace subjectivity as the indispensable core of all inquiry. Alasdair Craig: How did your experiences with psychedelics get you interested in consciousness?Michael Pollan: One of the things that happens on psychedelics is that they foreground consciousness. What formerly seemed perfectly transparent and ever present—and yet, non-existent, as water is to fish—suddenly you see it. The metaphor I use in the book is that psychedelics sponge the windscreen. Suddenly you realize: wow, there’s a windscreen, there is this phenomenon that’s mediating my relationship to reality. This is not an unusual realization on psychedelics. I think a lot of people are called to think about consciousness after psychedelic experiences. So that was the proximate inspiration for A World Appears: A Journey Into Consciousness (2026).And then there was a more specific way in which psychedelics influence the book. That was a particular experience I had on psilocybin. I had the sensation that the plants in my garden were conscious and returning my gaze, with their own point of view and agency, and I got curious as to how to evaluate an insight like that. I mean, what’s the truth quotient of insights you have on psychedelics? And I was reading William James—who, of course, wrote beautifully about mystical experience—and he said, we can’t judge the metaphysics here, but we should pay attention to two criteria: (1) How useful is it to think this is true? He was a pragmatist, of course. And (2) can we subject it to other ways of knowing to validate it? Because maybe we should treat these insights as hypotheses. And that’s kind of how I approach psychedelics, as a great generator of hypotheses, but they need to be tested against other ways of knowing. And so that led me down this long path exploring the science of plant intelligence and plant sentience.AC: I love your phrase, “smudging the windowpane of conscious experience.” So do you now start to think, actually, even when I’m not on psychedelics, I’m just seeing a screen all the time, which is not reality, but rather is my conscious experience, behind which reality is hidden?MP: Well, you know, not when I’m brushing my teeth and making breakfast! But yes, in meditation, certainly. I mean, one of the things you realize is that my experience happens to be this way now, but it could be another way. And many things can affect it. My morning coffee changes the screen in various ways, sharpens certain things.___The only tool we have to understand consciousness is consciousness.___There’s this wonderful metaphor in Aldous Huxley’s book, The Doors of Perception, where he thinks we channel consciousness, and that what the brain does is edit it, and there’s a reducing valve. And psychedelics open the reducing valve wider. I don’t know that I’m ready to buy into the idea that consciousness exists outside brains, but I do love that metaphor because there are times when you are more conscious than others. I know I’ve switched from window pane to valve, but you know, a camera aperture can open wider or narrower. And I do have a sense of that. I have a sense of when I’m being less conscious, and when I’m being more conscious, by which I mean, you know, more present, taking in more sensory information. So when you start thinking about consciousness as a matter of degree, that changes things also.I think when we scroll on social media, obviously, we’re conscious. But minimally so, because we don’t have to generate our own thoughts. We’re basically taking these little hits from other people and thinking their thoughts.The process of writing this book made me kind of relativize everyday consciousness as just one type, which is exactly what William James said. Normal consciousness is one type, separated by the flimsiest of screens from the other types.AC: If we stick with the metaphor of the smudge on the window that you get when you take psychedelics, it seems like we’re saying that consciousness has become like an object that you’re looking at, right? You’re looking at the smudge and that just is consciousness. But sometimes we’ll talk about consciousness as a state that you’re in, rather than an object you can perceive. You’re either conscious or you’re not conscious. So it’s difficult to really know what sort of thing we’re talking about when we talk about consciousness, right?MP: Yeah. When you say that consciousness is an object, what’s the subject? Consciousness also? The only tool we have to understand consciousness is consciousness. So we’re in a closed loop and when we get a perspective on consciousness, we’re not achieving any distance from it. That’s one of the problems with studying consciousness. Science is predicated on an objective, God’s eye view of things, and you never get that. There’s no such thing. SUGGESTED VIEWING The brain filters consciousness With Àlex Gómez-Marín But, yeah, there is this sense, I think, that consciousness is something you can approach. Certainly, when you’re writing and trying to recreate a conscious experience, it’s a fiction, but you’re standing back from it, and looking at it. But there’s no real subject-object distinction.AC: That’s so interesting, because when philosophers start talking about consciousness, they often talk about what they call “qualia”, which is their word for the felt experience of, say, the color red. And I think that way of speaking often makes us think of the qualia as an object, which you can see out there: the redness of red. But, as you say, we also want to collapse the subject-object distinction when we talk about consciousness. So there’s something strange going on here…MP: Oh, yeah, there are many strange things going on! *AC: Let’s talk about the varieties of consciousness. Apart from perceptions, there are, pains, emotions, thoughts, and so on. What is it that holds all this diversity together so that they all count as instances of consciousness? What is it that they all have in common?MP: Yeah, that’s a really good question. I mean, there is sensory information, right? Which we perceive coming from the world outside ourselves. And then there’s internal information, which is what’s called interoception. And this is very important too. This is where feelings come from—what are sometimes called homeostatic feelings—that are intended to get the brain to keep us in these narrow ranges of temperature and glucose and so on. That’s a very important part of consciousness. And according to some researchers, who I think are probably right, this is where consciousness begins, with these very primal feelings, like hunger and thirst and warmth and itch, and things like that. And then there’s this third thing, which is our thinking about thinking.And what holds all this together is, I think, the sense of self. Consciousness generates a self: it is probably an organizing principle. All this stuff is happening to the self. It’s a construct, I think, but it is very useful. It can connect all these different streams of consciousness to a single being or a place, metaphorically speaking.* AC: The first half of your book is about the science of consciousness. Is consciousness just another thing, like black holes or the origin of life, which we don’t yet understand, but which we should have every hope that science will one day get to the bottom of, or is there something special about consciousness that makes this unlikely?MP: It depends how you define science. As we define science now, it’s going be very difficult. Science since Galileo has had a set of rules or assumptions, which is that the proper remit of science is to deal with the objective, quantifiable, third-person world. And the physical sciences have done amazing work under that set of limitations. Galileo—for tactical reasons, not for philosophical reasons—said science should not deal with the soul, which at the time was what consciousness was considered to be. So what is subjective, what is qualitative, what is first person, we’re going to leave to the church, he said. And this was a good agreement, and it probably kept many scientists from getting burned at the stake. But it leaves science in an awkward spot, now that it wants to venture into that territory of qualities, of first-person experience. And I don’t think it has the proper tools. I think it will have to add to its toolkit in some way, and find a way to bring in phenomenology, or lived experience and introspection, which science is usually dismissive of.The challenge—we alluded to it earlier—is that the only tool you have to study consciousness with is consciousness. And what I mean by that is, the whole scientific enterprise is a manifestation of consciousness. As much as scientists like to flatter themselves with this idea that they attain a godlike perspective on what they study, when it comes to consciousness, there’s no such thing. And it’s fine for all the things you can step outside of and study from that perspective. But two areas it won’t work are consciousness and cosmology.___It could be that we live in this anomaly of not believing nature is conscious. And maybe psychedelics are teaching us something we’ve forgotten.___With cosmology, too, you have to study the universe from inside the universe. It’s not that cosmologists haven’t made progress. We know a lot about the age of the universe and expansion and things like that. But we can’t penetrate what happened before the Big Bang. We just don’t have the tools because we can’t stand outside.The fact that there are 22 full-scale theories of consciousness tells you all you need to know: we’re kind of lost. And I was surprised to find that we are more lost than I thought we were when I started this. I thought that there’d be leading theories, or that we had experiments that we could conduct that would nail some of this down. And the fact is, we don’t.So, I don’t know if science can solve it. But, of course, we can change the rules of science. A really influential book for me was a book called The Blind Spot, by two physicists, Marcello Gleiser and Adam Frank, and a philosopher, Evan Thompson. It’s a reminder that science is a social practice. It’s organized in a certain way and it can be organized in a different way. But it has this huge blind spot at the center of it, which is its failure to recognize that it is a form of consciousness itself, and it doesn’t have a good way of taking account of lived experience.AC: On the one hand, I think you want to resist traditional dualism, which would say that consciousness is something magical, something supernatural, right? But on the other hand, you’re accepting that there is this division in reality between the stuff that you can investigate objectively and measure, and so on—the stuff that science has been good at finding out about—and consciousness, which, for some reason, you can’t objectively measure. Some would take this to suggest that the dualism is actually right. Is there a way of reconciling these two strands of your thinking?MP: To call it magical is full of value judgement. I mean, you could just say immaterial. The interesting thing is, I think, that intuitively we’re all dualists. And I don’t know whether that’s because we’re children of Descartes and other cultures are less dualist than we are. Or it may be because that’s how consciousness feels. It feels different than the body. It feels separate from the body. But I don’t think we should trust that intuition.AC: So let’s think about ways that science might be changed to get at consciousness. I suppose the starting point would be the way that you begin the second half of your book. You have this nice line from William James, where he says, “We now begin our study of the mind from within.” Could you give us some sense of the ways of studying the mind from within? What does this involve?MP: I don’t know what that science looks like. But one of the points I’m making is that it depends on the humanities. I found that I learned as much, if not more, about consciousness, from reading novels and poetry. There is a sense of the fullness and complexity of consciousness that you don’t get in any scientific account. I interviewed this stream-of-consciousness writer and I approached her as an expert on consciousness, and she is. So I think that dialogue is really important.I’m reading Ben Lerner’s new novel, Transcription. Just read the first page of this novel: it’s a man falling asleep on a train, going to conduct an interview, and there is the experience of being in the train, facing backwards, there’s the view outside, but there’s a reflection of himself through which he’s seeing outside. There’s the memory of dropping his kids off at school. All this is going on in one moment of consciousness. Scientists aren’t going anywhere near that. But I think it’s useful for them to think about. That’s where we’re trying to go: like, how does such a layered experience happen? You know, it also makes you wonder, could a computer ever do this? And it gives you some doubt.*AC: Before we get to computers, perhaps we could talk briefly about plants. How did you come to feel that they are sentient?MP: Well, it was a psychedelic experience, and it’s not unusual on psychedelics. In fact, there’ve been studies about how people become animists during psychedelic experience. Their belief in animism arises after a single psychedelic experience.You know, most scientists would say this is all a myth. But we have only 400 years of the age of enlightenment and science. Before that, we were animists, and if you go around the world, most indigenous cultures are still animists, and children are animists, by and large, until they go to school. So it could be that we live in this anomaly of not believing nature is conscious. And maybe psychedelics are teaching us something we’ve forgotten.As I said, I wanted to test this idea, and see whether there was conventional scientific evidence for it. And there’s fascinating work going on in a field that calls itself—in a rather cheeky way—plant neurobiology. These are people who fully realize there are no neurons in plants, but they’re trolling conventional botanists! And they’re doing experiments that are demonstrating that plants can learn and remember, which is kind of amazing: how do you store a memory without neurons? They’re finding plants that can solve problems, that can hear and see. In fact, they have 20 senses to our five or six. For example, a bean plant looking for something to twine, and given a pole within eight inches or so of it, knows where that pole is and will aim its spiraling reach at that pole. What is that about? Is it bouncing signals off of it?One of the spookiest things is the fact that the same anesthetics that put us out put plants out. You might think that plants are already out, but they’re not. They’re just slow. If you take a plant with a fast behavior, like a Venus flytrap, and give it an anesthetic, it will stop reacting. So you have to ask yourself, what is lost when the plant is anaesthetized? Some state of alertness, of awareness, of its environment, of reactivity. I’m not prepared to call that consciousness, but maybe it’s sentience—which is a simpler form of consciousness, and involves the ability to sense what’s going on in your environment and assess what’s good and bad for you and act appropriately. That may be a universal quality of life.AC: Let’s turn from plants to AI. You point out that LLMs are trained on the internet rather than direct interactions with reality. You put this really nicely when you say the internet is not the world so much as a shadow cast by the world. So LLMs are living in this kind of shadow world, or in Plato’s cave, as you put it. What’s the significance of this for AI consciousness and moral consideration? And, to recall the beginning of our conversation, if we’re stuck behind a veil or screen of consciousness, are we also stuck in a kind of shadow world?MP: I don’t think they’re the same shadow worlds because our world includes nature and other people. And even if it’s mediated in some ways... There is a reality out there, and we are in relation to it, through consciousness. There’s friction with it. What’s missing from the LLMs is that friction with the real world. There’s more to the world than human representation, which is what they’re dealing with. And that means that so far, AI is a cultural technology for combining thoughts we’ve already had. SUGGESTED VIEWING Cracking the code for thought With Roger Penrose, Sabrina Gonzalez Pasterski, Max Tegmark, Güneş Taylor Genuine creativity is more than collage. A lot of what AI does now is tantamount to collage, putting this with that and you know, that’s part of art, certainly. But there are also genuinely new ideas that come into the world that don’t come in through manipulating the representations we already have. So it remains to be seen whether AIs can be truly creative in that more radical sense.But my arguments against AI becoming conscious have to do with the fact that the idea of AI consciousness is based on this faulty metaphor that the brain is a computer. There are dimensions in which the brain is like a computer—it certainly does its share of computation. But computers have this sharp distinction between hardware and software, and brains don’t. In a brain, hardware and software are identical. Every memory is a physical change to the brain. Brains are not interchangeable in the way computers are. So this idea that you could take the consciousness out and somehow run it on another substrate, a silicon substrate, seems foolhardy.Anyway, it’s a more extended argument, and I may be wrong. And when we move to computers that aren’t based on silicon—neuromorphic computers and things like that—all bets may be off.AC: Regarding your point about LLMs being stuck in the world of our representations—the internet or whatever—and not being able to interact with reality, are these representations, the world of the internet, themselves part of reality? We’ve created the internet, but it’s still a real thing that exists. And so while the LLMs may be trapped within that part of reality, they’re nevertheless within reality and interacting with it and so on. So it’s not obvious that there’s a barrier there to them becoming conscious and being creative.MP: Yeah, I mean, if our consciousness is built out of human culture, which is what we’re talking about with all those representations. But I tend to think consciousness comes a bit more out of nature and friction with the natural world, but...AC: So there’s a fundamental divide between the natural world and human artefacts?MP: And we lose track of that, and especially people in this computer world. You know, the people who are creating these AI systems, my guess is they’ve been into video games since the age of three, and they didn’t go outside a lot. And their own contact with nature is somewhat limited. I could be wrong, it’d be a very interesting thing to ask, all the wizards of AI, whether they like to go fishing and hiking and do things like that. Or were they on computers from a very early age? I interviewed Blake Lemoine, the Google engineer who came to believe that LaMDA, the Google LLM out at the time, was conscious and deserved personhood. It was very clear that he didn’t see a distinction between this bubble in which he lived and the real world. And I think we’ve all been distanced from the natural world, and this is taking us even further away. But it’s still there, you know, and we still have bodies, and we’re still mortal, and we don’t live in a simulation.AC: I think that’s a great place to finish. Michael Pollan, thank you very much!
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How Science Works
The 2nd Weather and Climate Livestream is now underway. It is organized by a group of early-career researchers concerned about declining funding for the field and eager to demonstrate how weather and climate research makes a difference in our lives. Last year’s event was a great success, drawing terrific press coverage including a piece in the NY Times. This year features a strong lineup of talks aimed at a general audience — I highly recommend tuning in. Share I gave a talk yesterday about How Science Works in Three Steps. You can see the short presentation here (I’ve appended a transcript below): And please tune into the livestream here. It runs through tomorrow at 6 pm ET. Thanks for reading The Climate Brink! Subscribe for free to receive new posts and support our work. Subscribe Cleaned up transcript: Thank you, and thanks to everyone tuning in. I want to talk about something I think a lot of people don’t really understand: how science actually works. Arguments over any scientific topic — climate change, vaccines, you name it — revolve around competing claims. For climate change, the claims are: Is the Earth warming? Are humans to blame? What will the impacts be? Over the last 150 years, the scientific community has developed a remarkably robust method for sorting out which claims are true and which aren’t. That method is what I want to walk through. So how does science generate knowledge? If you ask a high schooler, they’ll give you the version they learned in ninth grade: an individual scientist forms a hypothesis, runs an experiment, and draws a conclusion. That’s more or less what happens at the individual level, though in practice it’s more iterative — you design, test, and refine all at once, landing on a slightly different idea each time until you reach a conclusion about whatever hypothesis you’ve ended up with. But this isn’t science. It’s only step one. Any individual scientist can make mistakes — scientists are human. Maybe there’s a bug in the code, maybe there’s an error in the experiment. Just because someone ran an experiment and reached a conclusion doesn’t mean it’s right, and scientists don’t treat a single result as settled truth. Step two is peer review, the first layer of quality control. The scientist writes up what they did and submits it to a journal, which sends the paper to other experts in the field. Those experts judge whether the methods are sound, whether it properly references prior work, and whether the conclusions actually follow from the data. Ask any scientist and they’ll tell you peer review is miserable — the comments come back brutal, often harsh, and you have to revise and resubmit, sometimes multiple times, before an editor finally accepts the paper. Peer review catches an immense amount of bad science, but it can’t catch all of it. Reviewers get sloppy or overworked, they miss obvious mistakes, and they can’t catch hidden errors — a bug in the code, a misread instrument. So a peer-reviewed paper making it into a journal doesn’t mean the paper is right. Nobody treats a single paper as the final word. That’s where step three comes in: the crucible of science. Important results get retested by the broader community. If you publish something interesting, it gets dissected. Sometimes people try to reproduce your work directly but more often they test its implications. If someone claims X is true, and that implies that Y is also true, then scientists will go check Y. If a claim survives this gauntlet long enough, it becomes accepted. That’s how we generate knowledge. Let me give an example: the ozone hole. In 1985 — and I doubt many people watching were around then, but I remember it well — Joe Farman and his colleagues published observations from Antarctica showing that a huge chunk of ozone was disappearing every year. This was shocking because most atmospheric chemists had expected ozone loss to occur at mid-latitudes and high altitudes, not over the South Pole. When scientists see something they don’t understand, they rush to explain it, and several competing hypotheses quickly appeared in the peer-reviewed literature. One pointed to chlorine chemistry from chlorofluorocarbons. A second blamed nitrogen chemistry, possibly driven by solar proton events — meaning the sun was responsible. A third argued it wasn’t chemistry at all but dynamics: winds were carrying naturally low-ozone air from the lower atmosphere up into the stratosphere. Then the community went to work testing which theory could survive the crucible. Aircraft flew into the ozone hole and found no nitrogen, ruling out the nitrogen theory. Measurements showed the air in the polar vortex was descending, not rising — which would bring high-ozone air down, eliminating the dynamics theory. Meanwhile, those same flights measured enormous amounts of chlorine, strongly correlated with exactly the regions where ozone was being destroyed. That became known as the “smoking gun” paper, and it established that chlorine chemistry was the cause. Through this process, a consensus emerged. And it’s worth being clear that this consensus is organic. There’s no vote, no poll, no meeting where it gets decided. Scientists go to conferences, talk to one another, and independently arrived at the same answer. It’s the ultimate free market of ideas. After the consensus was established, the peer-reviewed literature doesn’t even mention the rival theories — it simply takes chlorine chemistry as given. That’s what a consensus looks like. It doesn’t mean every single scientist agrees, but it does mean there’s no longer any legitimate debate. So where can you see what scientists actually think? It’s all in the peer-reviewed literature, which contains the entire historical record of the argument: the discovery, the competing theories, the debate, the testing, and finally the community coalescing around one answer. But there’s a lot of room for misrepresentation. You can dig up an old paper claiming the ozone hole was caused by dynamics and wave it around as if there still was a debate about the cause of the ozone hole — without mentioning that the idea was disproven long ago and that even its own authors no longer believe it. There’s a bigger problem, though: the peer-reviewed literature is written in a language I call “nerd.” It’s extremely hard to read — honestly, some of it is hard for me to follow. It takes graduate students years to work through the literature in a single field, so we can’t expect policymakers or ordinary people to do it. This is where scientific assessments come in. The most famous is the IPCC — the Intergovernmental Panel on Climate Change. The job of an assessment is to read and summarize the peer-reviewed literature, synthesizing what’s settled, what’s been disproven, and what remains genuinely debated. To ensure the assessments accurately summarize the peer-reviewed literature, assessments are written by large teams, which are far less prone to bias than individuals and which dilute the influence of any one scientist with an axe to grind. The teams are deliberately assembled to avoid stacking the deck, and the reports themselves go through multiple rounds of peer review. That rigorous process is exactly why you can trust them and why people consider the IPCC to be the gold standard of what we know about the climate system. An assessment produced through a different process may well not be credible. You can even watch the consensus evolve across assessments. Take the attribution of warming. The 1990 IPCC report said the observed warming was broadly consistent with model predictions but also comparable to natural variability — in other words, we don’t know whether humans are responsible. By 1995, it concluded that the balance of evidence suggested a discernible human influence — a sentence only a committee could write, and one that barely commits to anything. By 2001, most of the warming over the previous 50 years was judged “likely” due to greenhouse gases; by 2007, “very likely”; by 2013, “extremely likely.” The 2023 report says essentially the same as 2013 — there’s not much stronger language available. So, to wrap up: science is our ultimate tool for deciphering the universe — the most successful method we’ve ever developed for determining what’s true and what isn’t. Our understanding is written into the peer-reviewed literature, so if you want to know what scientists think, that’s where to look. And for those of us who don’t speak nerd, scientific assessments are the Rosetta Stone — they translate nerd into English. Let me open the floor to questions. Thank you.
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