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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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(Welcome to the Entertainment Strategy Guy, a newsletter on the entertainment industry and business strategy. I write a weekly Streaming Ratings Report and a bi-weekly strategy column, along with occasional deep dives into other topics, like today’s article. Please subscribe.) One of my rules is “never bet against PE [private equity]”. This isn’t to say that I agree with their methods or tactics or strategies or financial innovations (aka shenanigans). Just that in the long arc of financial history, they’ve tended to return very good returns to their shareholders. That said, their forays into entertainment have left me...skeptical. I’ve written before about PE’s production company/celebrity production company roll ups, and those haven’t really worked out. Some PE groups tried to “roll up” an industry that isn’t “roll-up-able” for lack of a better term—since the barrier to entry for starting a production company is very low—often paying top dollar for celebrity production companies at the height of streaming valuations. So yeah maybe we can bet against PE when they venture into filmed entertainment. I’ve also noted that studio lots seemed to be a bubble back in 2022. Sure enough… Yikes! That sure sounds like some private equity guys lost some money. The only caveat being that holding a lot of land in Los Angeles is always valuable. But that wasn’t the pitch and likely real estate investment companies overpaid for said land. I mean, Goldman Sachs had to repossess it after all. (Technically, I’m not sure these buyers were traditional private equity, but PE was buying LA studio lots earlier this decade.) All to say: I call out bubbles when I see them, and they often pop. Anywho, my most famous bubble call was the “streaming bubble”, specifically that the major streamers were making too many shows and films. And that it could all come crashing down. Notably, I made this call before the strikes of 2023, but those strikes potentially accelerated the popping. And now it has indeed popped. One of my most popular articles of last year made that exact case. A year later, it’s time to update that analysis. The bottom line is that the streaming contraction continues. Credit where credit is due, Luminate—an analytics company whose data I use weekly in my streaming ratings report—called this out in a recent report I saw highlighted in both Bloomberg and The Hollywood Reporter. But I’m adding my data to their look, including streaming films and kids TV shows. I’ll also provide some other data that all tells the same picture. Let’s dive in! Subscribe The Number of Streaming TV Shows, Films and Specials Is Declining AGAIN in 2026 Let’s get right to the data, starting with my dataset. Specifically, each week I track every “notable” title to come out on streaming. I try to grab every title on every major streamer, meaning if it’s a first run or original or exclusive, I track it. This helps me call out the “dogs not barking” and the misses/flops each week. So that will be our first series of charts, my weekly collection data. Let’s start with the raw TV show, film and special data, by week. Note: As the years have gone by, I have actually added more sources (going from just one source to five sources) to find new titles, meaning that this data collection is, likely, more accurate now than in the past. Which means my team and I are more likely to have undercounted titles in the past rather than today. I like to cut the data several different ways. Here’s that same look, by quarter, to more sharply show the trends: And here’s the data just looking at the first half of each year. That’s the same data, just cut three different ways with three different time periods. Note that films, TV shows, and specials are all down by 25%, 42% and 17%, respectively, from 2022. The good news is that the number of films and specials actually increased year-over-year from 2025 to 2026, but not enough to offset the 25% decline in TV shows. Now, I also look at kids shows, and here they are pulled out on their own: And here’s that by quarter: This really seems to be a notable area of pullback by the streamers. Kids shows are down 80% from 2022. This reflects some formerly Disney+ shows going to cable TV first, but also pullbacks from HBO Max, Prime Video and Netflix. I also categorize the TV shows by English language and non-English language, and that also reveals a lot of where the drop comes from: And here is by the half-year: In this look, the good news is the drop for English language shows seems to have stabilized in the last year. But the bad news is the long term decline from the 2022 peak. Other Data So I wasn’t the only person to notice this decline in the number of shows. I have a few other data sources for this, though most only go through the end of 2025. As I said, Luminate beat me to the punch, so let’s look at their data. 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… What TV loss streaming didn’t make up for... How Netflix has changed their programming slate over time… Ten more images charting the decline of production... What type of films aren’t being made anymore... The good news (for movie theaters)… And more... ...please subscribe! We can only keep doing this great work with your support.
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"A rep for Daniel Theis, after initially showing …
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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
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Denver's initial offers to restricted free agent …
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