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The Beauty Of Settled Science
Commenters convinced me I made a subtle mis-step in my framing of Psychology Research Is Mostly Fine. My framing emphasized that social priming studies had failed to replicate, but that many other subfields of psychology hadn’t. This naturally led commenters to point to failed replications in other subfields. Although none of these were as bad as social priming, few or none were entirely problem-free. Instead of emphasizing the difference between social priming and other subfields, I should have emphasized the difference between settled science and novel research. Here are some of my favorite psychology findings: The brain is sculpted by reinforcement learning, and a key driver of this learning is a prediction-error-based algorithm located in the nucleus accumbens and implemented through dopamine. Psychedelic drugs exert their effects through 5-HT2A receptors, pointing to a role for these receptors in some sort of construct like the balance between priors and new evidence. Certain cognitive functions are localized to certain brain areas (for example, impulse control to the frontal lobes; linguistic structure to Broca’s area, linguistic meaning to Wernicke’s area. But injure one of these areas, or render a certain function useless (eg a blind person having no more use for their visual cortex), and these mappings can gradually shift - suggesting that neurons are general-purpose compute whose specialization is a contingent fact about convenience and efficiency rather than a necessity. Most personality traits and general capabilities are about 50% genetic and 50% random, with little role for family environment or childhood history, with a few notable exceptions like partisan preference. Recent research complicates this story, but the further you go from this basic picture, the gnarlier the chains of indirect causation you require. Neurons fire together in “brain waves”, whose exact frequency corresponds with the character of consciousness; These divide neurons into natural subpopulations and linked graphs. The gamma rhythm corresponds surprisingly well to items in conscious awareness. GABA receptors seem to be involved in something like relaxation. By hitting different GABA receptors in different ways, you can modulate the exact type of relaxation you produce. Hitting GABA-A directly is too powerful and can cause dangerous levels of sedation, but allosteric modulation of GABA is an incredibly fruitful strategy that underlies drugs from benzos to barbituates to alcohol. If you know your GABA-A subunits, you can target specific subtypes of relaxation; for example, Ambien hits GABA-A1 and induces sleep. Memory divides somewhat fuzzily into bins of immediate-sensory, short-term, and long-term, with separate retrieval and storage functions, and separate systems for declarative vs. procedural learning. These can be harmed selectively, usually by hippocampal injury, so that some patients can remember their life stories but not form new long-term memories, or learn new skills like bike-riding but not new facts. The exact substrate of memory is unclear but seems to involve new synaptic connections between neurons in at least some way. The ability of modern psychology and psychiatry to treat mental illness is real but limited. SSRIs work for half or slightly fewer than half of patients; antipsychotics work with extreme side effects; benzodiazpines work but produce addiction and tolerance in many (not all) people. Most supplements are stuck in Poor Quality Study Replication Crisis Hell, but melatonin and l-theanine stand out as especially promising. Psychotherapy works with moderate effect size, but (with a few exceptions) seems to be more about the relationship with the provider than the details of the therapy itself. Mental hospitalization can sometimes defuse immediate crises but work less well than mental hospitals would like, with some studies showing overall negative effects. Schizophrenia is mostly genetic, but risk also increases with prenatal infection (especially rubella) and obstetric complications; there is suggestive evidence for roles of social stress and drug use (with psychedelics and marijuana making things worse, and nicotine if anything making things better). Bipolar and schizophrenia are so closely linked on a pathophysiological level that it seems like they might be the same disease; on the clinical level, they do seem to follow two separate courses/prognoses but can sometimes surprise clinicians by eg someone who seemed condemned to permanent schizophrenia unexpectedly recovering. The brain’s native reinforcement learning algorithms cash out in the phenomena of classical and operant conditioning; different reinforcement schedules reinforce at different levels, which can be exploited by bad actors to make especially compelling products (eg slot machines). None of these are immune to doubt. A few are speculative; one or two are genuinely unorthodox. But they’re all a step above one p = 0.049 paper that might not replicate. If they get overturned, they’ll be overturned by the same sort of genuine scientific shift that replaced Ptolemy with Copernicus or Lamarck with Darwin, not some sort of sordid discovery about someone entering random numbers into a spreadsheet. In 2008, Eliezer Yudkowsky wrote about The Beauty Of Settled Science: If you only care about scientific issues that are controversial, you will end up with a head stuffed full of garbage. The media thinks that only the cutting edge of science is worth reporting on. How often do you see headlines like “General Relativity still governing planetary orbits” or “Phlogiston theory remains false”? So, by the time anything is solid science, it is no longer a breaking headline. “Newsworthy” science is often based on the thinnest of evidence and wrong half the time—if it were not on the uttermost fringes of the scientific frontier, it would not be breaking news. Scientific controversies are problems so difficult that even people who’ve spent years mastering the field can still fool themselves. That’s what makes for the heated arguments that attract all the media attention. Worse, if you aren’t in the field and part of the game, controversies aren’t even fun. Don’t be embarrassed to read elementary science textbooks, either. If you want to pretend to be sophisticated, go find a play to sneer at. If you just want to have fun, remember that simplicity is at the core of scientific beauty [...] A steady diet of science news is bad for you: You are what you eat, and if you eat only science reporting on fluid situations, without a solid textbook now and then, your brain will turn to liquid. Psychology isn’t centrally the new paper in Annals Of Recently-Breaking News saying that an n=5 study showing that large portion sizes increase support for terrorism. Psychology is centrally ideas like brain waves, GABA receptors, reinforcement learning, twin studies, and connectionism. People who think differently are worrying too much about BREAKING NEWS!!!, and should stop. The key distinction isn’t “recent research in social psychology” vs. “recent research in cognitive/bio/behavioral psychology”, as my essay last week unfortunately implied. It’s recent research in general vs. the deposit of settled science, battle-tested hypotheses, and carefully-earmarked speculation. And part of the reason social priming in particular was such a disaster was that it defied that heuristic, building itself up over hundreds of studies until it approached or even entered the pantheon of settled science - before collapsing (5-HTTLPR was also sort of like this). This isn’t a claim that settled science is always right - Freud was “settled science” for a long time - just that there’s an intuitive distinction between the level of credence you should have in the existence of power posing vs. the existence of Broca’s Area. (I compounded this error by calling the last post “Psychology Research Is Mostly Fine”, which suggested I was talking about the new research rather than the settled science. I actually meant to distinguish between research-psychology-the-field vs. clinical-psychology-aka-therapy, but this framing unintentionally pushed against the point I wanted to make) Some commenters on the previous article understood this point, but challenged its weight. It’s very cool, they said, to know how many neurons are in which brain lobe, but the main way that Psychology affected their lives was through bad clickbait studies used to buttress dumb policies; some p = 0.049 study says that exposure to microaggressions makes minorities’ brains shrivel into raisins, and now their company has to implement punitive DEI or be sued for hazardous work conditions. I won’t dispute this as an accurate description of how the sausage gets made, but it’s worth examining the trains of thought that connect this observation to a campaign against psychology in general: The dumb woke psychology was wrong and bad, and I don’t have good heuristics for distinguishing dumb woke psychology from other psychology, so I’m going to assume it’s all wrong and bad. The dumb woke psychology was wrong and bad; it seems useful to punish/discredit psychology as a whole to assist my campaign against it, so for propaganda purposes I will try to discredit psychology as a whole. To people in the first camp, my response is: come on, your heuristics aren’t that bad. “Microaggressions makes minorities’ brains shrivel up like raisins” is in the woke category; “the hippocampal theta rhythm seems to be associated with both memory and pathfinding, suggesting an unexpected yet surprisingly compelling relationship between the two” is in the not-woke category, don’t play dumb here. To people in the second camp, my response is: deliberately exaggerated propaganda campaigns are bad and this is a confession, not an excuse. Inability to view science through anything but a political lens was what got us into this problem; doubling down on it is a temporary solution but a recipe for permanent disaster. If you pronounce entire fields of science to be acceptable collateral damage in your campaign against bad political actors, you will alienate not just scientists but patients, clinicians, and general lovers of truth. Instead, you can say the thing you mean - that woke partisans shouldn’t try to occupy and exploit the otherwise-noble science of psychology for their own political gain - and wage the same campaign with a halo rather than a comic-book-villain goatee. From the font of oracular wisdom that is Weird Sun Twitter: [ Vessel Of Spirit@VesselOfSpirit i love you all conditionally but i love the good part of you unconditionally. even if it becomes a very small part 2:11 AM · Jan 22, 2015 1 Reply · 5 Reposts · 29 Likes ](https://x.com/VesselOfSpirit/status/558084551772471298) This is how I feel about psychology1, too, but with a touch more optimism. The good part of psychology is enormous, a hoard of endless treasures. You just have to look past the headlines! 1 To play devil’s advocate - should one always be so magnamious? Astrology? Phrenology? These seem different in that their entire orientation was flawed from the start; they didn’t simply fail to find the correct constellation-personality connections, the whole project of searching for connections between constellations and personality was doomed from the start. More difficult might be asking the same question about education research, which is notoriously bad. I don’t know enough about education research to know if there’s some settled science of correct and enlightening results lurking in the background beyond the day-to-day froth of poorly-conducted and politically-motivated studies. I think if it’s 90% bad, 10% good, it’s correct to call it bad as a compact way of communicating a useful truth; I think psychology is better than that, but admit it’s a tough judgment call.
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Debt Digest | Federal Debt is a Health Care Problem
Welcome David Ditch to the Debt Dispatch! We are pleased to announce a new regular contributor: David Ditch will be joining the Cato Institute as a Policy Analyst for Budget and Entitlement Policy later this week. David has more than a decade of experience in fiscal policy issues, with a particular focus on federal spending. He previously worked at the Senate Budget Committee, the Heritage Foundation, and the Economic Policy Innovation Center. David’s areas of expertise include appropriations, transportation, agriculture, federalism, grantmaking, and policy options for deficit reduction. Originally from the Rochester, N.Y. area, David has a BA in Economics and Political Science from the University of Rochester and a MA in Political Management from George Washington University. David currently lives in Arlington, VA. You may have enjoyed his earlier guest posts: CBO: New Highway Bill Has More Spending, Taxes, and Deficits Trump Administration’s Proposed Changes to Federal Grants Highlight Problems of Big Government Here are this week’s reading links and fiscal facts: The federal debt is largely a health care problem. In a new report, the Congressional Budget Office and Joint Committee on Taxation find that “In 2026, federal subsidies for health insurance, net of related payments to the government, are projected to equal $2.4 trillion, or 7.4 percent of gross domestic product (GDP). In CBO and JCT’s projections, those subsidies grow by 65 percent, reaching $3.9 trillion, or 8.4 percent of GDP, in 2036. Subsidies for Medicare contribute most to that overall growth, increasing by $900 billion. Over the entire 2026–2036 period, federal subsidies for health insurance total $33.6 trillion.” Cato’s Michael Cannon emphasizes, “The long-term federal debt problem is a health care problem. […] Only two categories of federal outlays will grow faster than gross domestic product (GDP): health care subsidies and interest payments on the debt. The former is, therefore, the primary driver of the latter.” The bond market fears deficits, not the loss of tariff revenue. Responding to a New York Times essay claiming tariff revenue has grown too important for a future administration to unwind, Cato’s Kyle Handley argues: “Bond investors are not attached to customs duties as a line item revenue source. They care about the government’s overall fiscal position.” He continues, “tariff revenue is a side hustle. And the Trump administration has already promised to dole out the funds through schemes like tariff dividend rebates, farm subsidies, and pay-fors on tax cuts or other spending.” Furthermore, “tariff revenue is simply not large enough to transform the government’s fiscal trajectory.” Take net interest for example: it “reached $970 billion in fiscal year 2025, absorbing 18.5 percent of federal receipts. Customs duties accounted for only 3.7 percent of receipts—and that was before refunds (see Figure 2).” Handley concludes, “The real bond-market concern is a large and growing interest bill, persistent budget deficits, and a political system unwilling to bring spending and revenue into alignment.” Medicare’s spending growth is driven by more volume and utilization. A Congressional Research Service report finds that Medicare spending “grew at an average annual rate of 7.3%” from 1985 through 2025, and the trustees project health care expenditures will keep rising “faster than gross domestic product (GDP) in most future years.” Citing CBO, the report attributes Medicare’s projected 2026–2036 spending growth to “23% from higher enrollment, 31% from inflation, and 47% from the growth of inflation-adjusted spending per beneficiary. In other words**, the largest single driver of Medicare spending growth during the next decade is expected to be higher volume and intensity of health care services.”** Boccia and Thakur explain: “as the economy grows, Medicare spending tends to grow at least as fast—and often faster—because the program automatically pays the bill for more and more healthcare consumption by seniors, even as prices rise.” The Fed has little control over market interest rates. Cato’s Jai Kedia documents that the Fed has held its rate target steady since December, yet “nearly every rate Americans actually borrow at has climbed over the same stretch.” Kedia continues, “The Fed did nothing, and the cost of credit went up anyway. […] Markets spent these months repricing macroeconomic events such as sticky core inflation, a volatile energy market driven by the conflict in the Middle East, and global trade disrupted by tariffs, among others. None of that required a policy change to show up in borrowing costs because markets, not the FOMC, set prices.” He concludes, “Accurately priced borrowing rates will come from credible disinflation and disciplined budgets and from a Fed content to follow the economy rather than pretend it leads it.” Nearly half of US farm income comes from taxpayers. Former Reagan OMB director David Stockman notes that “in the most recently completed full year (FY 2025) farmer incomes in the US totaled $97.8 billion, but fully 42.7% or $41.8 billion of that amount came from taxpayers, not the marketplace. Nor is this some kind of one-year aberration. If we look at the most recent decade as a whole, total farm income posted at nearly $708 billion, but, as indicated, fully $322 billion or 45% of this came compliments of US taxpayers.” Cato’s Chris Edwards explains why so little of that income is market-earned: “Farmers are businesspeople, but the government shields them from just about every type of weather and market risk. Furthermore, just about every part of the agricultural industry is subsidized, including insurance, loans, marketing, research, export sales, and land improvements.” The Debt Dispatch is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe
Both Jaguars’ Coordinators Named Future Head Coaches
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Warriors Get Encouraging Moses Moody Injury Update
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Cleveland Browns Get Bad News on Potential Free Agency Target
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Trump targets Biden-era billion-dollar Smithsonian budgets amid anti-'woke' push
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Moses Moody targeting September return to on-court work
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The US ‘golden age’ science strategy: what Europeans need to know
The US has unveiled what officials claim is the most radical overhaul of the public R&D system in a generation, which if implemented could have significant implications for transatlantic research collaboration. Science: A New Golden Age derides universities as “legacy institutions” and instead wants to prioritise funding on technological challenges, new research labs and funding that follows individual scientists, rather than being linked to their institutions. “Too much of our research enterprise has come to serve itself rather than the scientists within it,” writes Michael Kratsios, director of the White House’s Office of Science and Technology Policy (OSTP), the only named author of the report. “Federal funding agencies should support a broader range of performers.” But some critics argue that the strategy is a smokescreen, using genuine problems in academia as an excuse to further weaken US universities, which US vice-president JD Vance has labelled “the enemy..”Even as it talks of a “golden age” for science, the White House has proposed halving the 2027 budget of the US National Science Foundation (NSF), and has put forward double-digit cuts for other funding bodies, such as the National Institutes for Health. It asked for similarly steep savings in 2026, but Congress stopped them going through. Science: A New Golden Age says success will be measured by the extent to which scientists are “free” to pursue the most “vital questions,,” painting academia as currently hidebound by bureaucracy, sluggishness and consensual conservatism. And yet the Trump administration is simultaneously planning new rules that will hand political appointees unprecedented powers to screen and cancel grants. The report has pretentions to be the successor to Science: The Endless Frontier, the seminal 1945 document in which presidential science advisor Vannevar Bush set out the case for post-war government funding of basic research, helping to spur the creation of funders such as the NSF. Even the title page font is the same. But it remains unclear if the latest report will leave anything like as lasting a legacy.Here, Science|Business explains some of the biggest proposed changes in Science: A New Golden Age, and how Europeans think it might change the landscape of their biggest science partner.Universities under fireA key target of the report are US universities, which it repeatedly suggests are outmoded as research institutions. It criticises the NSF for channelling resources “overwhelmingly to a single type of performer, the university-based, principal investigator-led research group..” Universities “extract” money from researchers’ grants to support “growing administrative bloat,” alongside legitimate uses. The Trump administration has been trying to cut the overhead costs in federal research grants that flow to universities. Instead, new institutions are being built. In May, the NSF announced it would spend $1.5 billion on two new so-called X-Labs, focused on scientific instrumentation and quantum systems. The idea is that they fill a niche in the landscape, tackling problems that are too big for a university lab, but too early stage for private start-up funding. Parallel with Europe?This shift to new research organisations parallels moves in Europe to set up new innovation agencies, such as Germany’s Sprind, the UK’s Advanced Research and Invention Agency, and the EU’s European Innovation Council. The Dutch and French governments are looking to set up their own entities too. There’s now an emerging consensus, evident in a number of new strategies, that public R&D needs to be shaken up, Charles McIvor, an economist at the OECD, has written in response to Kratsios’s plans. “What stands out for me across these strategies [in the US, Germany, South Korea and Japan] is that governments are realising that the old ways of delivering STI [science, technology and innovation] programming no longer works,” he said in a LinkedIn post. Metascience wins?The document is, at least on the face of it, also a big win for some policy thinkers in the US who want far more experimentation with new forms of research grant and institution.It name-checks a litany of new public R&D ideas, from focused research organisations and advance market commitments to “golden tickets,” which allow grant reviewers the power to hand applicants money without requiring the consensus of other review panellists.Related articlesEuropean researchers sound the alarm that Trump rule changes will hit cooperationFocused research organisations: what are they, and how do you set them up? NIH turmoil risks reciprocal EU-US access to health research funding In addition, every federal research funding agency should set up a metascience unit, the strategy says, “with authority to run controlled experiments on review and funding mechanisms.” The UK has already set one up, the strategy points out. This mirrors recommendations to the European Commission. In 2024, a report led by former Portuguese science minister Manuel Heitor also advised the EU to set up an “experimental unit” to test out new methods of funding research. So far, however, Brussels hasn’t put forward any plans to do so. Bypass universitiesBut there’s a worry that the Trump administration is using the metascience movement to justify an overhaul of public R&D that punishes universities, which it sees as centres of political resistance. Last year, it threatened to cut Harvard University’s federal funding unless it hired and admitted a “critical mass” of new faculty and students to create “viewpoint diversity” across campus. It’s also attempted to strip academics of funding for working on politically forbidden areas, like LGBTIQ people’s health.The new strategy appears to be a “move to bypass the power of universities full stop and allow funders and political bureaucracies to handpick the projects and people who will execute them,” said Jean-Claude Burgelman, a former senior official in the Commission’s research directorate, on LinkedIn.The EU’s science counsellor in Washington, Florent Bernard, wrote that the plans risk diverting public research funding so that it “solely serves private interests.” “Who, if not independent universities, will do research that demonstrates the impact of tobacco on lung cancer or the role of human activities in our current climate crisis?” he wrote.Should we take it seriously?“I would view the overall document as a bad faith argument,” said Cole Donovan, director of science policy at the campaigning group Stand Up for Science, and a former OSTP official. “I don’t see the project itself as something that can be isolated from the broader attack on the system.” He thinks the argument that academia has become dysfunctional and in need of radical reform is being deployed as part of a “PR argument” to legitimate moves against US scientists. The strategy presents ideas like metascience units as new, whereas there were moves to set them up at the NSF during the previous administration of Joe Biden, Donovan said. The other caveat here is that the OSTP has limited power to carry out any of these recommendations. While Kratsios has issued a memo to research agencies telling them to implement the recommendations in the report, he doesn’t have direct budgetary control. “As a former OSTPer myself, this does not seem like a document that is realistic,” said Donovan.
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One Vikings Receiver Stole the Show at Wednesday’s Practice
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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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