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Cowboys expect defense to be 'significantly improved' under Christian Parker
The job for new Dallas Cowboys defensive coordinator Christian Parker will be to resurrect the worst defense in franchise history.
The 15 Worst Foods for Your Heart Health, According to Cardiologists and Dietitians
Experts say you should avoid them as much as possible.
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
Cowboys Defender Subtly Threw Former Coach Under the Bus
Last year was a nightmare for the Dallas Cowboys and their defense, which never got going under defensive coordinator Matt Eberflus.The unit was one of the NFL‘s worst, finishing last in points allowed per game and passing yards per game. Their total and run defense stats were no better, proving to be the thing holding […] The post Cowboys Defender Subtly Threw Former Coach Under the Bus appeared first on HEAVY.
Ebola cases in Congo top 3,200, nearing record of the country’s worst outbreak
The Ebola outbreak in eastern Congo has surpassed 3,200 cases.
Texas ranks as 4th worst state for healthcare in 2026, WalletHub study finds
Texas ranked as the fourth-worst state in the nation for healthcare in a new WalletHub study that compared all 50 states and the District of Columbia on healthcare costs, access and outcomes.
New Orleans Pelicans' Best, Worst Duos From Last Season
These two trios had the highest and lowest net rating changes when they were on the court for the New Orleans Pelicans last season.
International Business Machines Corporation (IBM) Survived One AI Scare in February. This One Might Be Different.
International Business Machines Corporation (NYSE:IBM)‘s stock dropped 25% on July 14. That’s the worst single day for the stock since records began in 1968, even worse than its previous record drop on October 19, 1987. By the end of the week, IBM’s total value was just under $200 billion. The drop happened because IBM warned […]
The Curious Case of Benjamin Button
Movie slumps are the worst. You know when you’re looking through the titles on your streaming services, but nothing sounds quite right? And as you continue to look with little success, you get
There are already more U.S. measles cases this year than in all of 2025
This year has easily become the U.S.’s worst year for measles since 1991, as major outbreaks in South Carolina, Utah and Arizona have sickened hundreds and smaller clusters of cases sparked in other states.
Hope Returns: Why Sunderland Fans Can Dream Again
For years, Sunderland supporters expected the worst. But has the club finally reached a point where hope outweighs fear and anything feels possible again?
'It got higher, and higher': Businesses hit hard by severe flooding in Gallup
"This is one of the worst floods I've ever seen in my life," New Mexico Pottery Company owner Thomas Piano told KOAT.
Ranking all 191 first-round NFL Draft picks since 2020: Best, worst selections of the decade
Five QBs rank among the top seven first-round selections since 2020, while Caleb Williams lands in the bottom third of our list
States with the best and worst school systems, ranked
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2026 NFL Odds: Which Division Loser Can Go Worst To First?
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Twins All-Star Joe Ryan surrenders 6 home runs in start vs. Guardians, the AL's worst home-run hitting team
G.O.P. Boxed In on Iran War as Trump Sidelines Congress
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Shaky political "science": breakdown of the peer review process
[This article is the latest in our “Shaky Political Science” series. In our 38 page report, “Shaky political ‘science’ misses mark on ranked choice voting,” released in December 2025, we examined over 40 studies focused on ranked choice voting (RCV). Many of these studies suffered from puzzling research methodologies, poorly constructed surveys, unrealistic simulated elections, cherry picked data and faulty analyses that often were contradicted by results from real-world elections (a link to our full paper is here). Here are links to the first four articles in our series: the first one summarizing the overall results of our report; the second one analyzed a study co-authored by University of Minnesota academic Larry Jacobs, which was one of the most error-prone of all the research we reviewed in our report; a third article showing an admirable example of two political scientists holding two other political scientists accountable for their flawed, sloppy research; and a fourth article that analyzed seven studies sponsored by New America’s political reform program that deployed flawed methodology based on “simulated” elections instead of real-world elections, which when combined with some basic misunderstandings of RCV, produced odd and misleading results]. In this fifth article in our “Shaky Political Science” series, we examine two flawed studies on the impact of RCV on voter turnout authored by San Francisco State University professor Jason McDaniel. McDaniel’s research in these two papers stands out for its puzzling methodology, inadequate and cherry-picked data, and faulty analyses that not only misunderstands ranked choice voting but reflects a basic misunderstanding of politics in general. The first of the McDaniel studies actually passed through a peer review process and then was subsequently published by the Journal of Urban Affairs, despite its obvious errors. It is perplexing that a jury of peer political scientists did not catch the errors and send it back to McDaniel for a rewrite or outright rejection. This calls into question the credibility of the peer review process itself. The peer review process is supposed to represent the intellectual pinnacle of the profession – a way for academics to certify the quality of each other’s work, and to act as a check against sloppy research. In the case of the McDaniel study, the peer review process failed. Yet his flawed study has been cited dozens of times by other political scientists in their own flawed works — an unvirtuous circle of academic research. Here is the first Jason McDaniel study that we analyzed: “Writing the Rules to Rank the Candidates: Examining the Impact of Instant-Runoff Voting on Racial Group Turnout in San Francisco Mayoral Elections,” by Jason McDaniel, 2016, Journal of Urban Affairs. This study covered San Francisco’s use of ranked choice voting (RCV) in mayoral elections, purporting to show that RCV reduces voter turnout overall, increases voter errors, and has a negative impact on “marginal populations,” i.e. racial minorities, mainly as the result of the alleged complexity of RCV asking voters to rank candidates and having to be familiar with multiple candidates and related dynamics. McDaniel reached these conclusions by examining five San Francisco mayoral elections taking place in odd years (i.e. where there are no federal or state races on the ballot) from 1995 to 2011, comparing three earlier non-RCV races (in 1995, 1999 and 2003) with the later two RCV races (in 2007 and 2011). Only two RCV races were studied, and as McDaniel acknowledged, only the contest in 2011 was “competitive” (that contest in 2007 was the first mayoral election using RCV, and incumbent mayor Gavin Newsom won easily in the first round of counting, earning 74% of first choices and far ahead of the second place candidate who had just 6.3% of first choices). Yet even the allegedly competitive contest in 2011was won by an incumbent with a landslide margin of nearly 20 points. No other elections were studied by McDaniel, even though San Francisco elects 17 other offices besides mayor with RCV, including the 11 seats on the Board of Supervisors (the name for the city council) in even years, and six other citywide offices in odd years. Previous academic research has found that competitive elections can stimulate greater voter involvement and higher turnout. For unexplained reasons, this study ignored at least 34 other RCV races between the years 2004 to 2011, many of which were very competitive and had high voter turnout. Instead, McDaniel’s analysis based its conclusions on data from only a single, marginally-competitive mayor’s race using RCV. These 2007 and 2011 mayoral elections were the only two RCV elections that McDaniel used as the sole basis for his small data set to determine the impact of RCV on voter turnout. Despite having no meaningful data to inform his study, McDaniel nonetheless concluded that RCV lowered turnout when compared to the previous three mayoral non-RCV elections using two-round runoffs. Then, using the same inadequate database of San Francisco elections, McDaniel also found that “Instant-runoff voting is associated with a significant decline in Black voter turnout and White voter turnout…On average, Black voter turnout decreases by 18 points and White voter turnout decreases by 16 points …” But how could he credibly arrive at that conclusion when his study was based on voter turnout in two RCV contests that were not even remotely competitive? Beyond that, McDaniel tacitly admits that the key determining factor driving Black voter turnout was not RCV vs non-RCV elections, but the presence of a viable Black candidate. McDaniel wrote, “Black voter turnout was about 17 percentage points higher for the two elections that featured an African American candidate compared to the three elections that did not” — but one of those three elections did not use RCV. The same dynamic was present for White voter turnout, in which higher turnout was not correlated with RCV vs non-RCV elections, but the presence of a viable White candidate. Beyond that, McDaniel’s voter turnout results, whether correlated with the race of the candidates or RCV vs non-RCV elections, showed no clear patterns. In his Figure 1, he shows low Asian voter turnout in the non-RCV elections in 1999 and 2003 when there was no viable Asian candidate running, but in the 2007 mayoral election using RCV, there is still no viable Asian candidate, yet Asian turnout increases dramatically. For Latinos, voter turnout is virtually the same in 1999 (no viable Latino candidate, non-RCV elections), 2003 (leading Latino candidate, non-RCV elections), 2007 (no viable Latino candidate, RCV elections) and 2011 (leading Latino candidate, RCV elections). Meanwhile White voter turnout increased dramatically from a low in the 2007 mayoral election, in which the leading candidate was a white incumbent, to the 2011 election in which there were no leading white candidates that finished any higher than sixth place with 5.6% of the vote. Clearly other factors were determining voter turnout than either the race of the candidates or RCV vs non-RCV elections. Despite these inconsistent findings, McDaniel boldly marches forth to render conclusions about the impact of RCV on different groups of racial voters, even though his conclusions are not remotely supported by his data. This looks like the worst kind of “data cherry picking” to cover up a puzzling conclusion based on a poor data set involving only two RCV elections, neither of which were remotely competitive. McDaniel’s cherry picking didn’t stop there. In calculating voter turnout for the three non-RCV mayoral elections in 1995, 1999 and 2003, he failed to mention that in each of these years there was a two-round runoff election, with a first round election in November in which no candidate garnered a winning majority of the vote, followed by the decisive runoff in December. So there were two non-RCV elections in each election year. But McDaniel only decided to include one of the two elections in his data set, apparently the December runoff in each year (which had higher turnout in two out of the three mayor’s races, and lower turnout in the third). So he ignored the November 1995 election, the November 1999 election when turnout was only 45 percent, and the November 2003 election when turnout was only 45.7 percent. This is flawed methodology based on blatant cherry picking, since McDaniel simply omitted three elections worth of data that did not fit within his frame. His study should never have made it through a peer review process, and it is an embarrassment to political science that not only was it approved for publication, but it has been uncritically cited by dozens of other political scientists. Share A “study” without wider context or credible data Lacking a data set that included even a single competitive RCV mayoral race, McDaniel declared that RCV lowered voter turnout in 2011, when turnout was 42.5 percent. But he also ignored -- or did not realize -- that voter turnout in mayoral elections during 2011 had sharply declined in all big cities, including far more steeply in nearby Los Angeles and other cities. In fact, San Francisco’s election in 2011 had the second highest turnout of any mayoral election in the nation’s 22 largest cities from 2008-2011. Was 2011 just a particularly low turnout year all across the country, perhaps because the country was still reeling from the aftermath of the housing market collapse in 2008-2010? McDaniel ignored this factor and did not search for alternative explanations beyond his flawed thesis. Furthermore, while McDaniel suggested that RCV leads to more “overvotes” – which is an invalidated ballot in which a voter, in either a non-RCV or RCV race, makes a mistake and selects two candidates for a single choice – he failed to address that in the June 2012 US Senate primary contest, which used the typical “one-choice” plurality method and did not use RCV, there was more than five times as many overvotes in San Francisco and Oakland than in the RCV mayoral elections in those cities. Additionally, turnout had been significantly higher and more representative in RCV elections for the San Francisco Board of Supervisors races -- that McDaniel didn’t even bother to study -- as compared to previous “delayed two-round runoffs” for those offices, often higher by as much as 40% in the RCV races. Perhaps the most surprising irony was that an earlier study co-authored by McDaniel (Overvoting and the Equality of Voice under Instant-Runoff Voting in San Francisco by Francis Neely and Jason McDaniel, 2015, California Journal of Politics and Policy) found that overvotes are often more common in lower-income precincts, but that the pattern of overvoting is similar in both RCV and non-RCV contests. Wrote Neely and McDaniel: “[Overvote] errors appear to be a function of complexity in general and not IRV per se…. the pattern of overvoting is similar in both IRV and non-IRV contests…Even among votes cast in a top-of-the-ticket race like U.S. Senate, overvoting occurs in an uneven fashion” (IRV, or ‘instant runoff voting,’ is another name for ranked choice voting in single-winner contests). So McDaniel not only contradicted his own conclusion in his earlier study with Neely, but the earlier study had been an important corrective for one of the most frequently made mistakes made by anti-RCV political scientists – a continual failure to provide crucial context by pairing a comparative analysis of real world RCV elections with an analysis of real world plurality or other electoral methods. Unfortunately McDaniel’s earlier study does not get cited much by other researchers, and McDaniel did not even cite it in his own flawed study analyzed above. Incredibly, McDaniel cherrypicked McDaniel, even as his flawed study has been cited dozens of times. Despite being a textbook example of poor political “science,” featuring both inadequate as well as cherrypicked data, the shelf life of McDaniel’s study has been extended endlessly by other political scientists and media outlets who have cited it uncritically. Equally troubling, the Journal of Urban Affairs put this paper through its peer review process and published it despite its numerous flaws. McDaniel gets it wrong, Part II Not content with producing one shoddy, confused paper, McDaniel produced another one a few years later, here is the link: Electoral Rules and Voter Turnout in Mayoral Elections: An Analysis of Ranked-Choice Voting by Jason McDaniel. San Francisco State Univ. (McDaniel, 2019). In this paper, McDaniel extended his previous flawed research focused on San Francisco, about voter turnout and voter impact, to multiple RCV cities through the 2018 elections. In his data set he included San Francisco, Minneapolis, St Paul, Oakland, Berkeley, San Leandro and Santa Fe. He then compared their turnout in mayoral elections to non-RCV cities, and to mayoral elections in those cities held before first RCV usage, and concluded “a significant decrease in voter turnout of approximately 3–5 percentage points in RCV cities after the implementation of RCV.” But he replicated many of the same mistakes as his first study presented above. While in this study McDaniel attempted to distinguish between various factors, such as odd year vs. even year elections, competitive races vs. noncompetitive, contests with open seats and other factors, the analysis is superficial and inadequate. He attempts to incorporate so many often-conflicting variables that his conclusions are once again unsupported by the actual data, and ultimately unconvincing (this second study never underwent a peer review process). For example, McDaniel declared that for RCV cities “in elections that are more competitive than average (margin of victory less than 20%), there is no significant RCV effect on voter turnout.” Yet McDaniel is a professor in a city, San Francisco, in which the 2018 special election using RCV to fill a mayoral vacancy was exceedingly close, decided by a margin of only one percentage point, and resulted in the second highest turnout in San Francisco history for a mayoral election. Illustrating the complexity involved in assessing the critical factors in turnout, that special election was held on the same election day as federal and state offices, which tends to boost turnout. Yet more voters participated in that RCV mayoral election than voted on the same day in the non-RCV primary for governor in which former San Francisco mayor Gavin Newsom was running. Even more puzzling, in this study McDaniel inaccurately cited his own previous study from 2015 with Neely. McDaniel wrote, “Neely and McDaniel (2015) examine individual ballot image files within a natural experiment, and find higher rates of ballot errors in RCV elections compared to non-RCV elections” (emphasis added). However, we quoted from that 2015 study several paragraphs above, and in actual fact Neely and McDaniel actually concluded the opposite -- that the pattern of overvoting – which is the most common form of ballot error – is similar in both RCV and non-RCV contests. McDaniel also ignored the fact that in Bay Area RCV elections held in even years – at the same time as federal and state elections, when turnout is highest -- the “undervote” (that is, the dropoff in ballots cast for governor or mayor) has declined, which contradicts his thesis that the act of ranking ballots in RCV elections is too complex and presents a barrier for certain demographics of voters. To his credit, McDaniel actually acknowledged another contradiction in his own paper, which found a negative RCV turnout effect in some odd-year elections but not others, admitting that “may present a challenge to the theory that increased complexity [from RCV] will have a marginal negative effect on voter turnout.” But he tried to wallpaper over that contradiction by blaming it on an unlikely source -- bad election administration. McDaniels wrote, “This would suggest that the negative impact of RCV on voter participation may be alleviated by high quality election administration,” without presenting any data or even anecdotes to support such an unfounded theory. While competent election administration of any election, whether RCV or non-RCV, is crucially important, there is no credible research that supports the far-fetched notion that the quality of election administration has much of an impact on voter turnout in US elections, whether using RCV or otherwise. Upgrade to a $5 subscription The unvirtuous circle of academic research It is puzzling how McDaniel’s first study could have survived the peer review process for the Journal of Urban Affairs. The mistakes in McDaniel’s first study above should have been obvious to anyone with a basic understanding of ranked choice voting as well as elections in general, and elections in San Francisco specifically. It undermines faith in the credibility of the peer review process to think that a jury of peer political scientists did not catch the egregious errors and reject McDaniel’s sloppy research and paper. And it remains a black mark on political science that this thoroughly discredited study has been cited dozens of times by other researchers. Indeed, in our research paper it became apparent that many political scientists cite each other’s flawed research to pad their list of citations and to legitimize their own flawed research, creating an unvirtuous circle of political science. More broadly, McDaniel’s voter turnout studies, like other studies evaluating RCV’s impact on turnout, lacked a recognition that no credible opinion or research can claim that RCV increases voter turnout merely because so many voters are excited to vote for their favorite candidates instead of “lesser evil” candidates without worrying about spoilers. Instead, with respect to turnout, the advantage of RCV has always been that, in most places where it has been used, it has eliminated a pre-November primary or post-November runoff, which usually saw about half the turnout of the November general election. This is especially true during even years when there are state and federal elections on the ballot which substantially drive turnout more than local contests. Any study that does not adequately account for this non-November primary/runoff vs November general election dynamic, or odd year vs even year election factors, is simply not credible. Both of McDaniel’s studies largely failed on those grounds. Steven Hill and Paul Haughey Share Upgrade to a $5 subscription Thanks for reading DemocracySOS…your digital portal for the pro-democracy movement. Subscribe for only $5 per month to receive full benefits and to support our work. Subscribe
The best and worst moments from the 2026 World Cup halftime show
Justin Bieber, the Muppets, and Madonna step out for an overstuffed 2026 World Cup final halftime show. EW breaks down the best and worst moments.
Hawai‘i comes in first — at being worst state for business
The fix for a bad business climate isn’t more bureaucracy. It’s less. Reducing taxes and regulations help revitalize the economy. If only our lawmakers would embrace those policies more often.
Hawai‘i comes in first — at being worst state for business
The fix for a bad business climate isn’t more bureaucracy. It’s less. Reducing taxes and regulations help revitalize the economy. If only our lawmakers would embrace those policies more often.
Eagles Can Only Trade Jalen Carter Under One Condition
If the Philadelphia Eagles planned to trade Jalen Carter, though I don't want to say that it absolutely would be the worst move ever, I think it's important to recognize that first-round picks and draft assets aren't always as promising as they seem.
Here’s where MS ranks among worst states for business in 2026. Did it get better from last year?
See how Mississippi’s ranking compares to previous year and other states across the country.
Caitlin Clark sets career high with 45 points as Fever eke out win over WNBA-worst Storm
Clark is the first player in WNBA history to post a 40-point, 10-assist game.
Wildfire Smoke Prompts Health Warnings
WASHINGTON — Scorching temperatures are contributing to wildfires that are erupting faster than usual this year in the United States and Canada while also prompting health warnings about air pollution. In the United States, some of the worst wildfires are burning this week in Colorado, California, Minnesota and other parts of the Upper Midwest. They … Continue reading "Wildfire Smoke Prompts Health Warnings"
Taco Bell has a diarrhea problem
Taco Bell has been the shining star of the fast-food business for several years. But now the company is contending with a restaurant’s worst nightmare: an explosive diarrhea outbreak.
Pittsburgh Steelers offensive trio ranked among worst in the NFL
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Sports without politics is a fantasy and the World Cup proves it
From ancient Greece to Nazi Germany, sport and politics have long been intertwined. Nevertheless, we still imagine that international sport can be kept above the fray and serve as a neutral arena where nations compete on merit alone. In this article, diplomacy scholar Geoffrey Allen Pigman argues the 2026 FIFA World Cup shows this is a dangerous illusion. When international sport is left entirely to revenue-maximizing private bodies like FIFA, it doesn't escape politics, but rather surrenders to its worst expressions. With the 2028 Los Angeles Olympics fast approaching, the IOC must decide whether to learn that lesson or repeat the same mistakes. The largest and most lucrative FIFA World Cup ever draws toward its climactic conclusion amidst a swirl of controversy. Team members, staff, fans, and spectators have been denied their right to enter the United States to participate, and US President Donald Trump intervened with FIFA boss Gianni Infantino to overturn a referee’s call that Trump believed disadvantaged the US national team. It is a moment to step back and reflect on what the purposes of international sport mega-events like the World Cup, and international sport more broadly, are, and what they should be. Does international sport serve public purposes? Is there a public interest? Or is it primarily a private endeavor organized by commercial enterprises to entertain the global public (and generate profit in the process), which is occasionally used by governments and leaders for raisons d’etat? Central to the idea of sport is the principle of fair play, which in the international context demands that games be free from political interference. However, it is impossible to separate sport from politics, all claims to the contrary notwithstanding. The most positive political use of international sport through the millennia has been to facilitate diplomacy. The use of international sport for diplomatic objectives has existed since at least the time of the ancient Olympic Games, around which was instituted the custom of the Ekecheiria, or Olympic Truce, between participating states to facilitate fair competition and diplomatic achievements by ensuring that participants could travel safely to the Olympiad. In his seminal work On Diplomacy (1987), diplomacy scholar James Der Derian characterized the objectives of diplomacy as overcoming alienation and mediating estrangement between governments and between peoples. One increasingly important diplomatic tool in the technology-infused media environment over the past century has been public diplomacy, under which governments and non-state diplomatic actors (e.g. transnational corporations) communicate to global publics about their international policy objectives and to shape their global brand image. Political scientist Michał Kobierecki, in his important book Sports Diplomacy (2020), views sports diplomacy as falling broadly within the ambit of public diplomacy, as international sport intimately involves the participation of global publics as spectators and consumers of global media. Diplomacy scholar Stuart Murray and this author, in a 2013 article, distinguish between international sport used for diplomatic purposes and international sport-as-diplomacy, which encompasses the range of diplomacy that occurs as teams and competitors, spectators and fans, and government officials all converge for international sporting events like the Olympic Games and FIFA World Cup. Diplomacy scholar H.E. Chehabi, in a 2001 article on US-Iran sports diplomacy, describes the interaction between spectators and fans at international sporting events as “people-to-people diplomacy,” in which relationships develop independently of governments and their objectives. Diplomacy by the aforementioned definitions is often unsuccessful. Moreover, all too often the tools of diplomacy may be employed by governments and other actors to thwart or oppose the objectives of diplomacy as characterized by Der Derian. Governments routinely use the tools of public diplomacy to disseminate propaganda, spreading misinformation in the attempt to deceive global publics about their objectives and to create misleading impressions about their brand identity. Within sports diplomacy, the term “sportswashing” has been coined recently to describe how governments invest in and promote international sport to create positive global public impressions of their nations and, in doing so, to distract the global public from their deficient records in areas such as human rights and democratic governance. Murray, in his 2018 book Sports Diplomacy: Origins, Theory and Practice, introduces the concept of sports anti-diplomacy, wherein governments intentionally use international sport to advance aggressive and nationalistic objectives. Obvious historical examples include Hitler’s use of the 1936 Berlin Olympics to glorify his Third Reich and the 1969 El Salvador-Honduras football “war.” SUGGESTED READING The beautiful game made ugly By Emily Ryall FIFA argues that it is and seeks to be apolitical in its stewardship of international Association Football (soccer). FIFA policies governing bids to host the World Cup require uninhibited and non-discriminatory access for players, team staff, and spectators of all participating countries to host nations and venues. It became clear soon after Trump returned to power in the United States in January 2025 that Trump’s political intentions and objectives to limit non-US citizens from living and working in the United States and, in numerous cases, even visiting the country, would collide with FIFA’s host nation policies. Beginning soon after the start of the second Trump administration, US federal authorities imposed restrictions on entry to the United States of citizens of between 20 and 40 countries for any purpose, with only limited exceptions. Federal Immigration and Customs Enforcement (ICE) began arresting non-US citizens in the United States, including persons with and without legal status, holding them in jails, concentration camps, and other detention facilities until they were deported to their home or third countries, where they faced substantive risks to their personal safety. FIFA boss Gianni Infantino, in an effort to ensure the most successful and lucrative World Cup in history, engaged diplomatically with President Trump in an attempt to facilitate entry to the US for World Cup participants. Infantino did all in his power to ingratiate himself with Trump, to the extent of creating a FIFA “Peace Prize,” which he awarded to Trump in December 2025 at a ceremony in the then Trump-controlled John F. Kennedy Center for the Performing Arts in Washington, DC, on the occasion of the World Cup draw. The prize was an ersatz consolation for someone who aspired in vain to win the rather more prestigious and well-known Nobel Peace Prize. And in the run-up to the World Cup, conditions became worse. In February 2026, Trump and Israeli Prime Minister Benjamin Netanyahu launched a war of aggression against Iran, illegal under international law, under the false pretence that Iran’s government was preparing to attack the United States and Israel. During the US aerial bombardment of Iran, US planes bombed Azadi Stadium, the facility where Iran’s national football team trained. As the 2026 FIFA World Cup began, it was clear that Infantino’s efforts had been for naught: the Trump administration would not adhere to FIFA policies governing access to World Cup host nations. World Cup referee Omar Artan, a citizen of Somalia, was denied a US visa to enter the United States to participate in the World Cup. Trump’s government only granted US visas to Iran’s football team ten days before they were scheduled to arrive for the tournament. Numerous members of the support staff for Iran’s team were denied US visas. Iran’s team, which had been scheduled to train in Arizona, was directed to shift its training site to Tijuana, Mexico, notwithstanding that all three of their group stage matches took place at US venues. The US Department of Homeland Security only permitted the Iranian team to enter the US on the day before each scheduled match and then to return to Mexico immediately after playing. Members of Iraq’s and Switzerland’s football teams were delayed for hours by US officials at the US border, and an Iraqi team photographer was refused entry. Spectators and fans from Haïti and Iran were denied entry to the United States altogether. A large number of fans from a range of countries had their applications for US visas rejected. There were multiple off-ramps through which participants could have avoided the negative consequences of Trump’s decisions for the 2026 World Cup. Once it was clear that the Trump government intended to violate the established expectations for World Cup hosts, FIFA could have shifted the matches scheduled to take place in the United States to Mexico and Canada. The three nations were already designated as co-hosts, and host cities and venues had been selected. Once it was clear that FIFA, governed by FIFA boss Gianni Infantino’s dependent and dysfunctional relationship with Trump, would never consider this option on its own, it fell to the national football federations to act. The national federations, particularly some combination of federations of the leading contenders (France, Spain, Brazil, Argentina, Morocco, England, Germany, etc.), could have refused to participate under the US-imposed conditions or, at the least, demanded an ironclad agreement with the Trump government that FIFA policies on access would be respected. ___ International sport is inextricably entangled with diplomacy of different sorts, for good or ill, it cannot be considered to be in the purely private realm and left to revenue-maximizing private actors. ___ Why did neither FIFA nor the national football federations act to protect the interests of players, team staff, referees, fans, and spectators? In essence, the relentless engines of capitalism prevailed over norm observance, ethics, and fair play. The revenue interests of FIFA, sponsors, global media, and host cities won out. FIFA’s managers viewed hosting another World Cup principally in the United States as a veritable pot of gold for FIFA and its collaborators. FIFA has estimated its own revenue over the 2023-26 budget cycle, which includes the 2026 World Cup, at $13 billion, revised up from an initial projection of $11 billion, and 72% higher than the previous revenue cycle. Of that total, FIFA is estimated to have generated $8.9 billion from the Cup itself in revenue from TV rights ($3.92 billion), sponsorships ($2.69 billion), ticket sales ($3.1 billion), licensing rights, and other sources. A joint study by FIFA and the World Trade Organization estimates that the World Cup will contribute over $40 billion to global GDP, including nearly $14 billion in direct visitor spending in the three host countries (but principally in the United States, which hosted the lion’s share of the matches). The FIFA World Cup is now reportedly “the single most lucrative recurring commercial property in global sport.” And yet, notwithstanding the Trump government’s uses of the World Cup for sports anti-diplomacy, and the climate of anxiety that this created for participants and spectators, diplomacy that did not depend upon governments was able to thrive at the World Cup. Examples of successful people-to-people diplomacy were legion, as citizens of towns and cities across the States, as well as in Mexico and Canada, welcomed team members, their families and supporters, to training sites across North America. Fans of different teams joined together in person in stadiums for matches, at watch parties and bars, and at welcome events sponsored by host cities. The joy and camaraderie shared between the citizenry of Boston and Scotland’s “Tartan Army” of fans is but one example of how the “Beautiful Game” brings people together. Friendships forged across continents are likely to endure well beyond the Cup final. Even those achievements cannot be seen as unalloyed, however. As South African football fan Byron Pillay commented to Al Jazeera, “Football is called the Beautiful Game for a reason, for its ability to unite people. But it’s hard to believe in that magic with the politics and war rhetoric off the field of play, especially when one of the tournament hosts is central to that.” Ultimately, the experience of the 2026 FIFA World Cup illustrates that, as international sport is inextricably entangled with diplomacy of different sorts, for good or ill, it cannot be considered to be in the purely private realm and left to revenue-maximizing private actors. As long as there is the possibility of diplomatic progress in mediating estrangement between governments or between peoples, there is a broad and overarching public interest in international sport. Likewise, as it would be difficult to put on an international sport mega-event without international sporting organizations like FIFA and the International Olympic Committee (IOC), global media firms, and companies purchasing sponsorships, public and private interests in international sport must coexist, most likely somewhere on a shifting spectrum between partnership and rivalry or cross purposes. As the World Cup wraps up, the IOC needs to consider its lessons before the scheduled 2028 Los Angeles Olympics if they want to avoid a media-hyped Trump version of the 1936 Berlin Olympiad. The IOC and the National Olympic Committees need to set conditions on and make demands of Trump’s government with clearly articulated consequences for non-compliance, up to and including cancellation of the Games. Time is running out for the IOC, as unlike with the FIFA World Cup, there is no at-the-ready alternate venue for Los Angeles.
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Long Reads for the Long Weekend
(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.) Happy Fourth of July weekend! As has been a tradition of mine going back to the beginning of the EntStrategyGuy website, I’m sharing my favorite long reads of the last year, like I did in 2019, 2020, 2021, 2024, and 2025. Let’s dive right in! Subscribe “Different Prices for the Same Ride: How Uber and Lyft Use AI to Get More Money Out of You“ by Derek Kravitz in Consumer Reports I don’t think surveillance pricing, like the kind described in this brilliant Consumer Reports article, is long for the world—it’s toxically unpopular—but we’ll see if either party takes on this issue. If I ran a political party focused on affordability, I would focus on a few pro-consumer and pro-market policies: Banning non-compete agreements. Ending surveillance pricing Eliminating junk fees (read more here...) Ensuring “right to repair” for consumer (and military!) products Capping excessive app store fees Stopping digital/algorithmic price-fixing tools. Preventing automatic subscription price increases. And more. If you want to fight inflation, there are a lot of great ways to do it quickly. Speaking of higher prices... “Secret Documents Show Pepsi and Walmart Colluded to Raise Food Prices Across the Economy“ by Matt Stoller in BIG Again, I think a political party should focus on lowering prices for consumers. And deals like this—Walmart making a deal/forcing Pepsi to offer the lowest prices at its stores and nowhere else—hypothetically lower prices at Walmart...but raise them everywhere else. “15 DUIs, still driving: California’s failure to take repeat drunk drivers off the road“ by Robert Lewis and Lauren Hepler in CalMatters CalMatters does terrific reporting in California, something the Golden State desperately needs more of, and this article is a great example of that. “PugLips: The next insanely popular thing you’ve never heard of…” by Simon Carless in Game Discover Co I thought that this parody article (and yes, it’s a parody, but I was definitely unsure at first) was hilarious and totally nailed how much tech/business journalism feels these days. Actually, three of my favorite articles this year were on hype in the video game industry, including Georg Zoller’s “The Video Game Industries Very Dark Night”—which I linked to recently and wrote a follow-up here—and Owen Mahoney’s “Derek Zoolander, Videogame Exec”. In particular, I like Mahoney’s focus on user experience as a guide to what trends will actually grab hold of customers. “The Matrix Lies“ by Evan Shapiro in Media War & Peace Evan Shapiro’s first person account of his experience with AI was hilarious. I think LLMs capabilities have significantly grown from last summer, and yet...my editor/researcher just had an abysmal experience using an LLM to do a quite basic task (which our LLM normally does each week) and the LLM made a mistake on every single data entry...along with seven different mistakes affecting twelve of the twenty or so entries, including mistakes that are explicitly spelled out in the prompt. That’s a brutal failure rate. Again, if you use AI/LLM for data collection, you need to take a lot of extra steps to verify and check the data. “Ghosts in the Balcony: A Cross-Country Trip to 58 Theaters Fighting to Survive“ by Matthew Frank in The Ankler I thought Matthew Frank’s pitch to head across America to visit theaters sounded insane aggressive at the time—at least it felt that way to someone a decade older than him; man, to be in your twenties again, am I right?—but I knew the result would be great. And it was, a terrific feature by a great young writer. Also, I loved Matthew’s recent article on who’s getting the precious few entry level jobs in Hollywood. (Hint: nepotism.) “Disney erased FiveThirtyEight“ and “Did Las Vegas get too greedy?” by Nate Silver in Silver Bulletin I love media history and analysis, and there’s no one better to get the history of FiveThirtyEight than from Nate Silver, who wrote about his personal experience in one of my favorite pieces from this year. I also loved Nate’s breakdown of what’s causing Vegas’ woes, but I’ll offer one criticism, summarized as “Might I suggest antitrust?” Many centrists/center-right/libertarian thinkers (Nate is self-described as the latter) often don’t consider consolidation or antitrust as factors in policy issues like housing, America’s military readiness, or, in this case, Las Vegas. Even if libertarians want to debunk antitrust/consolidation concerns, I’d love to read that debunking. In this case, Vegas is incredibly consolidated (though it also has been for a long time) and I’d have loved to read Nate at least contending with this factor, since he addresses almost every other potential factor for Vegas’ decline. “Why Microsoft’s Carbon Removal Pullback Is Such a Big Deal“ by Robinson Meyer in Heatmap News I’m a big supporter of carbon removal efforts, but this nascent industry faces a lot of headwinds, including this big move most recently. I think it’s clear that solar panels and batteries should be able to get the world to its carbon footprint goals, but effective carbon renewal remains necessary to prevent the worst impacts of global warming. “AI is killing the cheap smartphone“ by David Oks LLMs and AI will impact the world in ways we’re not ready for, and already, third world countries are seeing the prices of cheap cellphones skyrocket due to the cost of chips skyrocketing. This article by David Oks is a terrific breakdown. “100 Days of Madness: Netflix, Paramount, Warner and the Future of Hollywood“ by Wade Major at Hollywood Heretic Wade Major dove deep into Hollywood/streaming’s financials in this must read piece. Not that I agree with everything in this article (read my take on Warner Bros./Paramount here) but the analysis is top notch. I think many people discount how quality thinking, regardless of the argument/conclusion, will make you a better thinker. “Erased by the UFC: Frank Shamrock, The MMA GOAT of the 20th Century“ by Nate Wilcox in The MMA Draw Newsletter Growing up playing the UFC fighting game on the Dreamcast, Frank Shamrock was my favorite MMA fighter, and this history/tribute to him was great. Other Fun Reads “The Hardest Part Of History To Tell Is How It Felt“ by Craig Fehrman at Defector “Unmasking the Sea Star Killer“ by Craig Welch at bioGraphic “What Could Save the Industry? Fin-Syn“ by Richard Rushfield in The Ankler Alex Rollins Berg in Underexposed on movie bumpers, pillow shots, Disney trash cinema, cult films, and Casablanca. David H. Montgomery in YouGov’s The Surveyor on punctuation marks, condiments, dinosaurs, and comic strips and kids media. “How Medium finally pivoted its way to profitability“ and “Why the best journalists on YouTube are all former Vox employees“ by Simon Owens in Simon Owens’s Media Newsletter “It’s Not Just You, Netflix Shows Have Gotten Slightly Shorter: Here’s What the Data Says“ by Kasey Moore at What’s on Netflix “Numlock Awards: How Diane Warren became the biggest loser in Oscar history“ by Nathaniel Rakich and “Numlock Awards: The Oscar Bait era is over. The Oscar Chum era is here.” by Walt Hickey & Michael Domanico at Numlock Awards
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How should European scientists respond to the US AI kill switch?
Earlier in June, Europe’s worst fears of technological dependence were confirmed when the White House ordered the AI firm Anthropic to stop non-US citizens using its leading models. This kill-switch moment crystalised anxieties in Brussels that the continent, lagging the US on the creation of leading-edge AI models, is now a technological of vassal of Washington. The shock has specific implications for European scientists and universities. Some governments are now worried that with AI being integrated into labs, Donald Trump now has his finger hovering over an off switch for European science. “Having restrictions on this tool, it can have an impact on our own research,” said France’s research minister, Philippe Baptiste, at the VivaTech conference in Paris on June 18.Baptiste, himself a former academic, said that colleagues in mathematics had recently warned him they were now cut off from leading-edge US models, risking their work. “This model is not available anymore now because of the US restriction, and for the first time this had an impact on mathematics,” a field where large language models (LLMs) are making a significant contribution, said Baptiste. So, should European scientists avoid relying on US models in the future? Or would this be a self-harming overreaction? And how is AI being used in science, anyway? What happened?On June 12, it emerged that Washington, citing cybersecurity concerns, had ordered Anthropic to stop non-US citizens using two models: Claude Mythos 5, and Claude Fable 5, a version of Mythos but with guardrails to prevent abuse. Anthropic said Washington’s concerns were overblown. But forced to follow the order, yet with no way to check who was using the models, the company suddenly shut down access for everyone, US citizens included. There’s still no agreement on how to restore broad access. But last week, Bloomberg reported that select US companies, and the US government, still had access to Mythos despite the ban, but the EU’s cybersecurity agency Enisa had been shut out. Class division of researchersIn the longer term, access outside the US should be restored, said Marcel Salathé, co-director of the AI centre at the Federal Polytechnic School of Lausanne (EPFL), because a permanent restriction would be financially ruinous to US AI firms. “Even Trump will eventually budge to economic pressure,” he said.However, US AI firms might end up operating rather like banks, doing due diligence on users of their leading-edge AI models, he went on.European scientists are unlikely to fall foul of such checks, he said. But having a Chinese postgraduate in their lab, or doing a type of research the US disapproved of, could potentially result in access being denied. “It will create a sort of class division of researchers,” he said. More than frontier modelsHowever, successfully deploying AI in science is about far more than just having access to leading LLMs such as Mythos.Instead, many science-specific tools, most famously Alphafold, which predicts protein folding structures, require “far less” computing power to train than frontier models, well within the capabilities of European supercomputers, according to a recent analysis by the Arq Foundation, a Brussel-based AI think tank. Instead, tools such as Alphafold are possible because certain fields, like protein folding, weather prediction or astronomy, have large, clean, standardised datasets that can be used to train predictive models. Rather than compute, “data is the central constraint for AI for science,” the report says. For example, a less powerful physics model trained on CERN’s data “will be more valuable than the best model without certain data,” said Bengüsu Özcan, author of the report. European advantage?Europe could therefore steal a march by using its scientific data and infrastructure to create models US digital giants cannot. “They might actually match or go even beyond what these big [US] companies are doing,” said Özcan.Indeed, some European start-ups hope to create automated testing labs to create the kind of standardised data that could speed up the discovery of new materials. Materials scientists say there’s currently too little data to train models. AI scientistsHowever, even if the bottleneck for AI in science is often data, rather than compute, being at the mercy of the US for LLM access could still become a problem for European scientists. A new class of “AI scientists” based on leading LLMs is also emerging, Özcan’s report says.Unlike domain-specific tools like Alphafold, these try to automate day-to-day scientific work, by providing an overarching tool that coordinates specific models to synthesise existing literature, generate hypotheses or run analysis of data. The problem for Europe, however, is that these tools are sometimes based on US leading models. Google Deepmind’s AI Co-Scientist, for example, is built on Google’s Gemini LLM. Systematic failureThese scientific automation tools are still in their early stages, and still exhibit “systematic failures on scientific tasks,” Özcan’s report says. One of the problems is that the messy, day-to-day process of scientific discovery is largely not documented, meaning LLMs don’t have a good source of data to learn how scientists actually work. Meanwhile, some tasks, such as literature synthesis, don’t typically require access to leading edge models.However, the performance of AI scientific assistants could improve as companies such as Google pour resources into developing them. Rely on US“Unfortunately, for scientific assistants, there is not much we can do right now except rely on US frontier AI,” said Antoine Bosselut, a natural language processing expert based at EPFL. Chinese-built open models, which can run on local computers, lessening the risk of a shutdown, are touted as one alternative to US dependence. There’s also Mistral, once seen as Europe’s main AI hope, but the Paris-based company has seen its recent models fall behind in performance. But the Chinese alternatives are generally not as capable as US frontier AI models, said Bosselut, unless paired with fiddly, custom tools that allows them to act as independent agents. “Most scientists do not have the time,” he said. Don’t jump ship yetAlthough the kill switch moment is seen a warning shot for European scientists, it doesn’t mean labs should abandon US AI tools immediately. “I do not think the right response is simply to abandon US-based models and systems under the banner of sovereignty or independence,” said Mattias Björnmalm, secretary general of the Cesaer university association. “That would be an overreaction.”Related articlesEuropean sovereignty in AI requires ‘ugly trade-offs,’ say experts Robotic lab start-up makes case for €500M EU-funded materials testing facilityAI in science: is it useful? Instead, European researchers need to ask themselves whether they’re becoming dependent on technology that could be turned off in Washington, said Özcan. “They might not see this today, but they should really start asking this question,” she said. European researchers need a plan B, said Salathé. “I would recommend to every researcher, very proactively explore alternatives.”Own toolsIn addition, Björnmalm and others want European research institutions to develop their own scientific AI tools.Instead of signing AI deals with US digital giants, European universities should be using this money to create their own models, which may end up being superior for research, said Sabine Leonelli, an expert on the use of AI in science at the Technical University of Munich. “Universities in Europe seem to be happy to sign commercial contracts with US companies on the assumption [their AI tools] are better,” she said. But US frontier LLMs are typically opaque, and have been developed without independent scientific input, she said. “This is not good enough for science.”Last year, Swiss researchers released Apertus, an LLM designed to be completely transparent, from its development methods to its training data. The idea is that this makes scientific tools built on top of the model far more reproducible than with closed, commercial alternatives.“Europe needs more of this: serious capacity-building that gives researchers, universities and companies credible options,” said Björnmalm.
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