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Apple Creator Studio just got better, here’s what’s new
Apple has announced a flurry of updates for Apple Creator Studio apps rolling out today, plus previewed some iOS 27...
Rethinking “Soft” Leadership Skills in Public Health
Learn how public health leaders can leverage interpersonal skills to better navigate cross-sector partnerships in this blog post.
Can the New Orleans office market rebound? New report says suburbs remain strong, city struggling
More than five years after the pandemic, New Orleans' office market remains fragile, though it is performing better than some other cities.
Community Health Workers Learn Perinatal Outreach Approaches at UAMS Training
LITTLE ROCK — Twenty community health workers (CHWs) from across Arkansas recently gathered in Springdale with a shared goal: to build connections and learn how to better support mothers across the state. Hosted by the UAMS Institute for Community Health Innovation, the three-day Perinatal CHW Upskill Training provided interactive, hands-on…
Comcast Split Shows Bigger Is No Longer Seen As Better In The Media Business
Comcast's plan to split into two smaller companies may mark the unofficial end of "all under one roof" consolidation in media.
Samsung Music Studio 7 review: A great speaker on its own, and even better in a pair
The Music Studio 7 is an excellent Sonos alternative that can pull double duty in the living room.
World Cup penalty shootouts are brutal: Is there a better way to settle tied games?
What the World Cup can steal from other sports as better ways to end games that are tied
Netflix and Superheroes Are Having a Rough Stretch on Streaming....
(Welcome to my weekly streaming ratings report, the single best guide to what’s popular in streaming TV and what isn’t. I’m the Entertainment Strategy Guy, a former streaming executive who now analyzes business strategy in the entertainment industry. If you were forwarded this email, please subscribe to get these insights each week.) I have a quick prediction before we start today’s article: You might see this data in future news articles. Not my specific charts or analysis, but the core trend I’ve been monitoring—Netflix is having a lackluster Q2—will become inescapable. Often, folks want to know “What does the data say?” Well, that’s what it says. I haven’t seen this analysis hit the trades really yet, but I have a feeling some other outlets are going to start making this case soon. Remember, you read it here first. You probably have read about the problems facing superhero films at the box office this weekend. Well, this week, I explore whether superheroes are dying on TV as well! And the data says: definitely maybe! We have a lot to cover in today’s Streaming Ratings Report, which covers the two weeks of 18-May and 25-May. Today, I’m just looking at TV, like Netflix’s disappointing Q2, superhero shows underwhelming, a few TV shows dropping off the charts, The Pitt finally ending its epic run on the viewership charts (and whether “view counts” change its status as one of the biggest shows of all time), all the flops, bombs and misses, and a whole lot more. Tomorrow, I’ve got a great rundown of the film side of things. Let’s dive right in! (Reminder: The streaming ratings report focuses on the U.S. market and compiles data from Nielsen’s weekly top ten viewership ranks, Luminate’s Top Ten Data, Showlabs, TV Time trend data, Samba TV household viewership, company datecdotes, Netflix hours viewed data, Google Trends, and IMDb to determine the most popular content. While most data points are current, Nielsen’s data covers the weeks of May 18th to May 31st, 2026. You can find a link to my terminology here.) Subscribe Television - Netflix’s Q2 Slump Continues Having analyzed streaming ratings for going on half a decade, I have learned to trust my gut, but I’m always relieved when I can find actual data to back it up. For example, after a string of very popular shows like Stranger Things, Bridgerton, The Lincoln Lawyer and The Night Agent came out, week after week, in January and February, my gut wondered if Netflix had enough new and returning TV shows to sustain them throughout the year. Since then, Netflix’s post-March slate of shows has not impressed me. This week seemed to feature a potential new hit, but then, like a classic Duffer Bros. TV show, there was a twist. I speak of The Boroughs, Netflix’s latest (and last) Duffer Bros. produced show. The pitch is “Stranger Things with older actors”. And it topped the Nielsen charts: With just two weeks of viewership, The Boroughs would have been a top 25 debut show last year: But here’s the twist: Netflix has already cancelled The Boroughs! What happened? Likely a few things. First, the show didn’t do as well globally as it did in the US. As What’s on Netflix showed, it lagged behind several other shows that got cancelled. Second, its completion rate was likely low; after an initial burst of interest, it quickly dropped off on the Samba TV and Luminate charts. Third, the Duffer Bros. left Netflix for Paramount, and that may have factored into Netflix’s decision here. (Though I tend to believe that if a show is doing well, Netflix would have kept greenlighting seasons.) Still, just because I can explain why Netflix may have cancelled this show doesn’t mean I’m justifying it. In particular, as I’ve noted before, Netflix is losing a lot of their bigger shows as they reach their natural ends, and they need to replace those shows. This show had the hallmarks of a big new genre show, and it’s their biggest hit of the last few months (in the US at least), but now it’s done. Netflix’s other show this week—The Four Seasons, the second season of the “hit” show from last year—definitely disappointed. Its sophomore season had about half the viewership of the first, only 12.7 million hours. It also only had a two week run on Samba TV, and its numbers were all down on Luminate too. In other words, even with two successes, the Q2 slump for Netflix looks real: I could add to the list of disappointments: Both Lord of the Files and Man on Fire had short runs on the Nielsen charts. Running Point’s sophomore season also opened to about half its viewership, similar to The Four Seasons. A Good Girl’s Guide to Murder’s second season—see below—didn’t even make any of the three viewership charts I track, unlike the first season. Netflix is absolutely having a dismal second three months of 2026. If this Nielsen data is any indication, and I think it is, I think this will show up in Netflix’s engagement report, and maybe even their financial outcomes. And the rest of June doesn’t look much better. We have two hit shows coming back—Sweet Magnolias, one of their quietest big shows, and Avatar: The Last Airbender, a big hit for them in 2024—but the other big swings are limited series crime thrillers i.e. no future season potential. Like I said above, sometimes when I uncover a juicy nugget like this, other outlets end up writing very similar articles mimicking the data without credit. We’ll see if that happens this time! Share Television - The State of Superhero Shows on Streaming When I first heard the pitch for Spider-Noir—a Sony TV show on Prime Video that binge-released eight episodes first on MGM+ then on Prime Video two days later—I had my doubts. It’s a live-action Spider-Man show set in the 1930s, starring Nicholas Cage, based on a cameo of a character in Spider-Man: Into the Spider-Verse. No, seriously, that’s the pitch. That could either go terrifically well or horribly wrong. Great because, Spider-Man. He’s the biggest superhero we have. I’ve seen tracking numbers that put the new _Spider-Man: Brand New Da_y as the blockbuster of the year and initial pre-sales back that up. And yet this Spider-Noir pitch feels very esoteric. (Also, the eight-episode binge release feels very “Amazon copying Netflix” again.) So what’s the data say? And what does this say about the future of superhero shows?
Will Andy Burnham’s economic vision make us all better off?
Andy Burnham has made his first major speech since returning to Westminster. He set out his vision for a No10 in the North, for more council house building and repeated his claim that Westminster was “broken”.
Better Business Bureau urges seniors to watch for scams as fraud losses quadruple since 2020
The Better Business Bureau is warning older adults to be more cautious.
Your money. Their mission. - Gary W. Rollins College of Business
Financial Literacy Ambassadors at the University of Tennessee at Chattanooga help their peers better understand finances through college.
Cheaper AI is better: Soaring bills are reshaping how businesses choose models
Silicon Valley's powerful and pricey AI models have been a necessity for businesses looking to future-proof themselves. But now a growing number of tech CEOs are arguing that cheaper options would be crucial for their wider adoption.
Comcast to split cable business from media through NBCUniversal, Sky spinoff
Comcast will split into two publicly traded companies through a spinoff of NBCUniversal and Sky, separating its cash-generating broadband arm from a media and entertainment business under pressure from streaming rivals and industry consolidation. The latest U.S. media industry shake-up follows years of cord-cutting as legacy players chase scale to better compete with Netflix while Paramount Skydance's $110 billion deal for Warner Bros Discovery is set to boost competition. Comcast, which leans on cable for much of its cash flow, is also losing broadband customers to fixed wireless offerings from T-Mobile and Verizon and to fiber rivals building out networks.
Building a culture of connection for physicians
At Sanford Health, strong relationships, listening and leadership support help physicians feel connected and better equipped to thrive.
Cognition's CEO says token leaderboards are 'directionally correct'
Cognition CEO Scott Wu said there are better ways to incentivize employees to use AI.
Queerying neuroscience: How legislation and institutions reframe LGBTQIA+ researchers’ careers
In honor of Pride Month, The Transmitter spoke with three researchers who surveyed hundreds of LGBTQIA+ neuroscientists to better understand how institutional support, harassment and policy intersect to shape their professional trajectories.
Congress passes rare bipartisan bill on housing, how manufactured housing could be crucial to affordability
Jodie Sweetin praises “Better Call Saul ”actor cast as dad in “Full House ”pilot, but Bob Saget was always 'first choice'
Saget was engaged to cohost CBS' "The Morning Program" when he got the call for "Full House," which led to his absence in the original pilot.
Jodie Sweetin praises 'Better Call Saul' actor first cast as 'Full House' dad, but Bob Saget was No. 1 choice
Bob Saget nearly missed the chance to play Danny Tanner on 'Full House,' and though he was casting's 'first choice,' Jodie Sweetin says this 'Better Call Saul' star was 'very sweet' in the original pilot.
Americans find common ground on healthcare
Americans agree that healthcare needs to be better, cheaper, and less complicated. Good ideas toward those goals are bubbling up around the country.
My son suggested the scenic route and now, he talks more in the car
My 12-year-old son suggested taking the scenic route, and now, my family has better conversations in the car. We also appreciate where we live more.
6 Ways Your Body Can Change After Starting a High-Protein Diet
Adding more protein to your diet can cause several changes in your body, such as better muscle repair and potential digestive issues.
Government overreach won’t fix youth gender medicine
The real cure for bad science is better clinical trials, not lawsuits.
Government overreach won’t fix youth gender medicine
The real cure for bad science is better clinical trials, not lawsuits.
Valleywise Health acquires system to better treat premature and critically ill infants
Valleywise Health has a new system to better treat premature and critically ill infants, becoming the first Arizona hospital to implement it.
Your health insurer shouldn’t decide your treatment plan. That’s what your doctor (and possibly AI) should be doing.
With a digital health record, clinicians and AI-driven diagnostic tools would be able to see your full health history — and then make better decisions.
Scratching that bug bite might feel good at first but science explains why it's a bad idea
Now researchers better understand why even a mildly annoying itch could put you on an itch-and-scratch cycle if you give in.
Scratching a bug bite feels good but science says it’s a bad idea
Researchers have gained a better understanding of why we have an itch to scratch.
The #1 Snack for Better Gut Health, Recommended by a Gastroenterologist
Looking to improve your gut health? Read on to discover the best gastroenterologist-recommended plant-based snack to add to your day.
Driving A New Ford Mustang Mach-E After 5 Years Feels So Much Better
The 2026 Ford Mustang Mach-E isn't drastically different from its original 2021 version, but small improvements and a price dip make it a better EV purchase.
Gen Z traders launched a trading platform to fight bad 'finfluencer' advice
Ivan Patriki and Carson Hein founded QuantMap to counter the bad info they say is all over social media and to give traders better tools to succeed.
Days Ahead to Show If Burnham Plays Politics Better Than Starmer
Standing across from Keir Starmer in the Commons chamber this week, opposition leader Kemi Badenoch drew on a particularly violent metaphor for how the Labour Party had ditched him when she conjured up an image of “400 knives stuck in the prime minister’s back.”
Falcons Roundtable: Kyle Pitts Extension Done, Is Bijan Robinson Next?
The Falcons continue to lock up the core of their roster for the future. This week's Falcons Roundtable breaks down Kyle Pitts' new extension, whether his deal or Drake London's is the better value, and why Bijan Robinson appears to be the next superstar in line for a record-setting contract.
What if the economy gets better and it doesn’t help the GOP?: From the Politics Desk
Plus, Gavin Newsom’s next move in the billionaire tax fight.
“The Cheaper the Better,” Says Valve About its Steam Machine Pricing, but Don’t Expect a Price Decrease “Any Time Soon”
"The cheaper the better" is Valve's official line for how it would like to price the Steam Machine, but don't expect it to happen soon.
Foundation Friday: Building bright futures & mental health through fitness
The Lift US Foundation and InnerCity Weightlifting empower people to strive for better lives through fitness.
Xprize founder says 'humans behave better when they’re being watched'
Peter Diamandis is the latest tech executive to argue that global surveillance will make the world a better place, following Larry Ellison's comments in 2024.
Should I Start A Business From Passion Or With The Intention To Sell?
Sara Blakely did not set out to build a billion-dollar company. She just wanted better underwear.
DJ and presenter Trevor Nelson taking a break from work due to health issues
The Radio 2 presenter said he was now "concentrating on getting better".
ESPN believes Detroit Lions have best weapons in NFL
ESPN’s Bill Barnwell believes the Detroit Lions have better offensive weapons than any other NFL team in 2026.
Neuroimaging utilization in emergency departments has increased year-over-year for a decade
Researchers believe their new data could be used to better inform staffing coverage in modern radiology departments.
Making Our Own Luck
We are cross-posting a piece that former NIH official Kris Willis wrote for Macroscience. Kris is now the President and Founder of the Woodley Park Institute, for which I’m a board member. Picking winners or hedging bets? Federal science agencies have long wrestled with the question of how to ensure their billions of dollars of grants and contracts result in maximum benefits for Americans. On the surface, it might seem that a goal-oriented, interventionist style of management would be the best policy: prioritize our most pressing problems and distribute funding accordingly.This top-down strategy creates a unique hazard, though: bad choices can send resources in the wrong direction, resulting in slow progress that impedes the delivery of tangible benefits to citizens. Moreover, if decision makers pursue an applied advance when the underlying scientific principles remain poorly understood, they risk costly failures and a loss of support for the research enterprise at large. Examples include Nixon’s 1971 War on Cancer, undertaken on the premise that a cure could be achieved in less than a decade, the drive to build an operational commercial fusion plant by the mid-1990s, and the National Plan to Address Alzheimer’s Disease, which failed to produce the effective treatments and prevention strategies promised by 2025. The contrasting laissez-faire approach is to hedge your bets. If the path to an advance is impossible to predict, then the wisest choice might be to distribute funding to as widely as possible. The main risk of this strategy is that spreading funding thinly may limit the resources available to the most promising research, resulting in the same negative outcomes as the interventionist approach: limited support for the most promising topics, slow progress, and delayed returns. In reality, at science agencies like the National Science Foundation and National Institutes of Health, program managers navigate between these two extremes, setting some funds aside to target high-priority areas and using the rest to cover the widest possible range of meritorious proposals. This middle path seems like the most rational way to proceed, but it still requires making choices. How much should be set aside for high priority topics? What should those topics be? How should we define merit? Expert opinion can provide a helpful guide, but leading experts can have remarkably different opinions about what matters and what should be prioritized. Moreover, experts and administrators alike can be risk averse, giving an edge to high-profile, well-established concepts. What’s really needed is a data-driven framework to help guide decision-making, one that doesn’t simply repackage prestige or incumbency. Recognizing breakthroughs years in advance Many breakthroughs fail to attract funding or recognition at the earliest stages of development. Katalin Karikó, for example, spent years struggling to win grants for work on mRNA that would eventually win her and Drew Weissman a Nobel Prize. Similar stories can be told about Jim Allison’s groundbreaking studies leading to the development of cancer immunotherapy, Stan Pruisner’s demonstration that prions can self-replicate without DNA, or Robin Warren and Barry Marshall’s proof that Helicobacter pylori, not stress, causes ulcers. Yet at some point, the scientific community recognizes and responds to transformative breakthroughs like these. How does that change happen? And what if science funders could identify the areas that are on the cusp of a breakthrough? Those questions led my collaborators and me to study how scientists responded to past breakthroughs. Based on patterns in that data, we developed a means of predicting future breakthroughs, using biomedicine as a proof of concept. It’s worth taking a moment to describe how our approach works. Briefly, our prediction process begins by grouping the 18 million or so peer-reviewed papers in PubMed, the authoritative database of biomedical research, into roughly 50,000 unique topics. A small subset of these topics are so widely acclaimed as breakthroughs that they have been recognized with a major prize like a Nobel or a Lasker Award. We theorized that studying the development of these outliers over time, especially before they gained acclaim, was the key to identifying future instances of them. After defining topics, we chose 21 representative breakthroughs and asked what they looked like before they won prestigious prizes. To answer that question, we re-ran our topic-mapping algorithm repeatedly, winding back the clock one year each time, stringing together the years to follow the progress of each field as it grew and advanced. We found that in each case, the years leading up to a discovery followed a pattern: a burst of papers on a new, rapidly evolving topic, many of which quickly became influential. These characteristics—the percent of papers that are new, the percentage of papers that are brought in from other topics, and the influence of each individual paper—can all be measured separately for any given topic. Together, they act as a predictive signal that is detectable an average of five years before a transformative discovery is made, and up to thirty years before the discovery receives a major prize. Scanning the current research landscape for topics that display this signal allows us to predict what work will likely produce a future breakthrough. Each year we analyzed includes four or five signals, a number that appears to have remained constant across a twenty year time frame. In their classic work on the sociology of science, Bruno Latour and Steve Woolgar established that the desire to participate in a major discovery is an important motivator for scientists as they consider whether they should take up (or abandon) a research problem. Although further work is needed, the simplest explanation for our results is that this behavior is widespread enough to detect at scale. A foundational advance, or something like it, draws the attention of scientists and causes them to change the direction of their research. They vote with their feet, staking their own reputation and careers by publishing on and citing the new idea. That upswell of interest is the foundation of our breakthrough signal. What kind of research does our model identify as a breakthrough? After building a model based on the patterns in our 21 preselected breakthroughs, we tested it by identifying all early signals of discovery from 1994 through 1997. Of the 18 signals we found in that time period, 17 can be traced forward in time to a breakthrough. Examples include super-resolution fluorescence microscopy (see the image above), the directed evolution of proteins and enzymes (Chemistry Nobel, 2018), and sequencing the human genome (National Medal of Science, 2008). The discovery of the role of leptin signaling in obesity is a particularly interesting case study. Around 1950, scientists noticed the existence of a type of lab mouse that suffered from a unique inherited form of obesity. When given the freedom to choose their own diet, these mice ate more than normal and preferred food that was high in fat. Researchers had few clues as to why the animals overate; early experiments pointed to the existence of a soluble ob factor found circulating in the bloodstream that regulated appetite, but its identity was unknown. In 1994, Jeffrey Friedman and colleagues identified and sequenced the ob gene, noting that the protein it encoded appeared to fit the profile of a circulating factor. A year later, a trio of high-profile publications demonstrated that ob was a hormone that regulated body weight and fat deposition by regulating appetite. One of these was co-authored by Friedman, who christened it leptin from the ancient Greek word for thin. Using our model, the data from 1996 make it clear this advance would become a breakthrough. Scientists had flocked to study the biology of the new hormone, generating the flurry of new papers and citations required to produce a signal. One of these demonstrated that serum leptin concentrations reflected the amount of adipose tissue in the human body, providing clinical validation of the earlier mouse studies. Before the identification of leptin, scientists were unable to point to a specific molecule that controlled appetite and adiposity, so that it was impossible to rule out non-physiological causes of obesity; its characterization fundamentally changed our concept of weight gain. In 2010, Friedman and fellow pioneer Douglas Coleman were awarded a Lasker for the discovery. Our work also identifies breakthroughs in clinical practice and behavioral and social sciences, even though these areas are less likely to draw the attention of major prize committees. One example that falls into this category is the introduction of endovascular aneurysm repair, a minimally invasive surgical procedure for the treatment of abdominal aortic aneurysm, a leading cause of death among Americans over the age of 65. Relative to the previous standard of care, it reduces in-hospital mortality by almost four-fold. In 2017, Timothy Chuter received the American College of Surgeons’ prestigious Jacobsen Innovation award for his multiple refinements to the technique. Another under-recognized breakthrough identified by our model is the development of standardized survey instruments to assess the quality of life for HIV+ patients. The antiretroviral cocktails introduced in the mid-1990s reduced AIDS mortality, but treatment came with serious side effects. The new evaluations showed that patients whose disease progressed had worse physical functioning than Americans with other chronic diseases, while those who were asymptomatic maintained a health-related quality of life on par with the overall US population. This evidence made it possible for patients and activists to argue that even in the absence of a total cure, and in spite of the side effects, alleviation of symptoms was a worthwhile priority for clinicians. My colleagues and I have failed to identify any significant recognition for the physicians and health policy experts who pioneered this advance, although in 2016, researchers called for the World Health Organization to set targets for good health-related quality of life as part of its framework to end the AIDS pandemic. From forecasting to funding How should science funders use our predictions? Any serious discussion of what we should do with this new capability needs to begin with an understanding of its limitations. We can’t (and shouldn’t) invest all of our resources in the small number of topics predicted to produce breakthroughs. Innovative new fields are born from existing ones; they rarely arise de novo. If funders don’t maintain a diverse portfolio, the breakthroughs of tomorrow will have no antecedents. Think of this as eating the seed corn, or killing the goose that lays the golden egg. There are also multiple goals of funding beyond basic scientific discovery, including translating discoveries into clinical practice, developing new technologies, supporting economic development, and training new scientists. The investments needed to accomplish these goals are likely different from those required to support emerging breakthroughs. Finally, we must be aware that we may not capture every instance of a breakthrough. The concept is fuzzy, and not every significant discovery is recognized with a major award. Further, breakthroughs are rare, meaning that our dataset is, by necessity, small. This makes formal estimates of accuracy challenging. Although our overall success rate appears to be high, any one signal may turn out to be a false positive. We found one apparent example: the development of the third generation COX-2 inhibitors, Vioxx and Celebrex, which were much celebrated in the early 2000s as effective but non-addictive pain relievers, meets all the algorithmic criteria of a breakthrough. Five years after it received FDA approval, researchers demonstrated that Vioxx was associated with serious cardiovascular complications, and the manufacturer voluntarily pulled the drug from the market. Celebrex remains available, although like other NSAIDs, it carries an FDA warning for increased cardiovascular risk. Decision makers who were overly focused on the breakthrough potential of this research might have wasted substantial resources nurturing an area that ultimately proved to be a failure—but so would those who relied on expert opinion at the time. That said, the potential benefits of funding the next mRNA vaccine or cancer immunotherapy years earlier than we might have are large. When we can’t assume the outcome of an action, it’s worth conducting an experiment. Can targeted support for areas that are poised to produce a breakthrough increase the return on scientific investment without cannibalizing future advances? With the necessary administrative infrastructure in place, conducting this experiment would be straightforward. First, identify all the breakthrough signals between 2018 and 2023, select half at random, and commit to a decade of investment. For biomedicine, a six year window should yield about 30 topics with the potential to produce a breakthrough. Providing strong support to half of these might be accomplished for $300 million a year. That’s roughly half of the recent annual budget of the NIH Common Fund or around 20% of the fiscal year 2025 budget for ARPA-H. After five years, then again at ten, compare to see which group — the one that was actively managed, or the control — produced more breakthroughs, and on what timescale. Although we used biomedicine as an example, the same experiment could just as well be run for physics, materials science, or any other discipline. Any attempt to undertake such an experiment should keep three points top of mind. First, we can predict the topic of future breakthroughs, but the people and vision for how to move the work forward are still important. In practice, this means success requires active program managers with the expertise to develop the science and the authority to do so, including by recruiting investigators. Second, we would be wise to learn from the failures of past attempts to fund transformative research. High among these are funding modestly repackaged work, confusing the genuinely novel with the merely unfamiliar, and relying too much on incumbent investigators. All of these risks can be mitigated by thoroughly and intelligently integrating high quality data into portfolio management. Finally, we need to have patience. Even with strong funding support, producing a breakthrough takes time. If supporting breakthroughs is the best way to nurture the birth of new fields, we also need to accurately describe and properly manage the other two stages, so that we invest wisely throughout the productive lifetime of ideas and divest once they reach the point of diminishing returns. We would benefit from more study of all of these phases, especially the transition from one to another. More research could answer questions like: When does a breakthrough become an established field? How do the availability of funding, the size and characteristics of the available workforce, and the accumulation of evidence that disagrees with prevailing models affect a field’s decline? Exactly how do old ideas give birth to new ones, and can we speed up the process? The more we understand about the dynamics of scientific progress, the better we can maximize the odds that our curiosity leads to results. I’d call that finding a way to make our own luck.
This Simple Trick Can Make Your Coffee Taste Way Better, Says Physics
If you love pour-over coffee it could serve you well to change how you pour.
'This is the next jump in technology': World's first sub-1nm chip keeps Moore's Law alive a little longer
IBM's NanoStack architecture has helped scientists cram 100 billion transistors onto a computer chip, delivering 50% better performance and consuming 70% less energy than the current generation.
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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