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Antarctica’s sudden sea ice loss is one of the most extreme and confusing events in the modern climate record. Scientists now know why it's happening.
In 2015, after decades of relative stability, Antarctica's sea ice suddenly began to disappear. Sea ice extent reached a record low in 2023, and scientists have now figured out what happened in that period.
NASA’s Roman Space Telescope could reveal millions of invisible neutron stars
NASA’s Roman Space Telescope could expose a vast hidden population of neutron stars lurking unseen across the Milky Way. By detecting subtle shifts in starlight caused by gravity, the mission may identify and even weigh isolated neutron stars that are otherwise impossible to see. Scientists hope the discoveries will reveal how these extreme objects are born and why they are blasted through space at incredible speeds.
'Extreme' crystal that formed in 1945 nuclear bomb test is unlike anything scientists have seen
Samples of "trinitite" created during the world’s first nuclear bomb test in 1945 contain unique crystals never seen before.
England fans at World Cup opener at risk of health complications due to extreme heat - academics
England fans in Texas for next month's FIFA World Cup opener against Croatia are among those most at risk of health complications caused by extreme heat, academics have warned.
Humid Heat: A Growing Health Risk in a Warming Climate
The combination of extreme heat and humidity is dangerous and becoming more common in our warming climate.
Extreme heat is worsening faster for Black Americans
Bill Cassidy's can't-win conundrum, what RFK Jr. gets right and wrong about antidepressants, and more health news
As El Niño Approaches, Scientists Predict Fierce Heatwaves, Wildfires and Floods
Emerging Pacific Ocean heat, combined with ongoing human-caused global warming, is a grim recipe for deadly climate extremes. Heat alone already kills more than 500,000 annually.
80 years after the Trinity nuclear test, scientists identify new molecule-trapping crystal formed in the blast
Matter behaves strangely under extreme conditions, and often, remnants of these behaviors are left behind even when conditions return to normal. The Trinity nuclear test in 1945 left behind such remnants, ...
Scientists Subjected Fruit Flies To Hypergravity And Their Behavior Became Counterintuitive
Some of the flies were subjected to extreme hypergravity for 10 whole generations, living out their lives in conditions found around a super-Jupiter.
Locking up key contributor named Eagles' most underrated offseason move
It has been an extremely eventful offseason for the Philadelphia Eagles. The Eagles made several major additions over the offseason, including edge rusher Jonathan Greenard and wide receivers Hollywood Brown, Dontayvion Wicks and rookie first-round pick Makai Lemon. Locking up defensive tackle Jordan Davis on a three-year, $78 million extension was named Philadelphia's most underrated offseason move by Jeff Kerr of Sports Illustrated.
Billy Bob Thornton reveals the lifelong health battle behind his extreme diet
Billy Bob Thornton opens up about his restricted diet, rare blood type and food allergies that have influenced his unusual eating habits for decades.
Beware: Smart Glasses Are the New Tool for Extortion
One woman was recently told to pay up if she wanted clandestine footage removed from social media.
“Windows 11 Still Relies on Code From the 1990s…” Microsoft Stock (NASDAQ:MSFT) Slips on Revelation of Windows 11 Code
The old saying goes, if it ain’t broke, don’t fix it. But tech giant Microsoft ($MSFT) may be taking that to an unexpected extreme as it revealed that Windows 11
Trump's Truth Social lays bare narrow obsessions of an extremely online president
The bulk of the president's social media posts don't make news. But taken together they show what's on his mind as he leads the nation through war and domestic turmoil.
The night sky could get three times brighter as new satellites launch — all but ruining the Vera C. Rubin Observatory's survey of the universe
Extremely bright satellites and megaconstellations could make the night sky up to three times brighter than it is now, a new study warns. This would seriously hinder astronomical imaging devices, like the enormous camera at the Vera C. Rubin Observatory.
'Food insecurity is no longer just about low-income countries': Environmental economist explains how climate change is pushing agricultural systems to the brink
As a U.N. report warns that extreme temperature swings are disrupting crops and endangering agricultural workers, we spoke with environmental economist Shouro Dasgupta about farming in an overheating world.
Roku and TCL Accused of Bricking Smart TVs Through Software Updates
A class action lawsuit argues that marketing materials promise reliable performance and ongoing improvements through updates that actually ruined consumers' TVs.
How States Are Navigating the Intersection of Disasters and Public Health
As state officials seek to address the full scope of extreme weather impacts, they must look beyond physical damage metrics—washed-out roads, downed power lines, and destroyed buildings—to broader health effects that are increasingly evident in emergency rooms, public health data, and community well-being.
Caleb Downs Is the Cowboys’ Best Pick in Years
Caleb Downs very clearly has “it.” It’s extremely premature to make a claim as bold as “best in a decade” before a draft pick touches the field in training camp, let alone at game speed. However, some people have “it”—an aura people can see on film and in real game time. Every draft has a […] The post Caleb Downs Is the Cowboys’ Best Pick in Years appeared first on The Lead.
Trump keeps saying the quiet part out loud on changes to the 2026 election
President Donald Trump’s latest gerrymandering push is an extremely desperate partisan power grab that would invalidate votes already cast.
Greenland ice melt has surged sixfold and scientists are alarmed
Greenland’s ice sheet is now melting in ways never seen before, with extreme events becoming more frequent, widespread, and intense. Since 1990, meltwater production has skyrocketed, and most record-breaking events have occurred in recent years. Scientists say warming temperatures are supercharging these episodes beyond natural climate patterns.
Democrats slammed for 'dehumanizing' rhetoric amid concerns about political-fueled violence
Political analyst Gianno Caldwell discusses dangerous political rhetoric and its consequences, including an assassination attempt on President Donald Trump. He also examines how extreme language and socialist policies impact the country.
Scientists built a memory chip that breaks the rules of miniaturization
A new kind of memory device may finally solve the problem of overheating and battery drain in electronics. By shrinking components to an extreme scale and redesigning their structure, researchers found a way to reduce energy loss instead of increasing it. The result is a tiny memory unit that improves as it gets smaller—something once thought impossible. This could pave the way for ultra-efficient smartphones, wearables, and AI systems.
Comey Indictment Shows Justice Dept. Got the Message From Bondi’s Firing
In naming only an interim successor as acting attorney general, President Trump has established even greater incentives to execute his most extreme demands, current and former officials say.
Museum of Science and Mystic River Watershed Association Honored with 2026 Boston Climate Leader Award for Research and Innovation
BOSTON, April 30, 2026--The Museum of Science, in partnership with the Mystic River Watershed Association, has been selected as a recipient of the 2026 Boston Climate Leader Awards in the Community Organization category for Research and Innovation. The award recognizes the organizations’ groundbreaking work engaging communities in climate research and advancing understanding of extreme heat as an environmental justice issue.
A New Jersey Republican hasn’t voted in weeks, sparking questions over lengthy absence on Capitol Hill
Republican Rep. Tom Kean Jr. of New Jersey has not voted in Congress in weeks, sparking questions over when he will return to Capitol Hill as he faces a competitive race for reelection back home and Speaker Mike Johnson oversees an extremely narrow House majority in Washington.
Brain scans reveal 3 ADHD subtypes, including a more extreme form
A study analyzing 1,154 children and adolescents breaks new ground in how to think about the growing diagnosis.
Democrats Regret Creating Independent Redistricting Commissions After Supreme Court Ruling
A decade ago, the party sought independent redistricting commissions. Now, in an era of extreme gerrymandering, such efforts could slow Democrats as they try to keep up with Republicans.
Viewpoint: A career in science is possible, and worth it
Starting a career in science is exciting, but also demanding, with researchers often faced with intense competition, long working hours and high levels of stress. A pioneer in the field of mass spectrometry, Carol Robinson has been there. She speaks with Science|Business on the importance of creating a supportive environment, particularly for women.Robinson did not wake up one day thinking that she would become a scientist. It was only late in school that she developed an interest in chemistry. “The more you work at something, the more you think, actually, this is fascinating. So, I went much further and deeper than I ever needed to pass the exam. And then I loved it,” she says. “I thought, I’m going to give it my all. I’m going to enjoy it while I’m here. But I didn’t think this would be my career for 50 odd years.”She left school at 16 to become a lab technician with the drug company Pfizer, which is where she first worked with mass spectrometry. Fascinated, she was encouraged to take a part-time degree and then a PhD. She went on to do pioneering work in native mass spectrometry, a technique that is used to study protein structures in their natural state, paving the way for breakthroughs in drug discovery. She also broke new ground by becoming the first female professor of chemistry at the University of Cambridge, and then the first female professor of chemistry at the University of Oxford, her current home.She now wants to show young researchers that a career in science is worth it. “Sometimes, I believe the culture isn’t very encouraging, and that’s something I have worked on,” Robinson says.In 2021, she launched the Kavli Institute for Nanoscience Discovery at Oxford, whose acronym gives a sense of what she hopes it will achieve. “Maybe it is corny,” she says. “It’s just a reminder that I would like it if we could all be kind to each other and supportive, especially for early-career researchers of either gender, because there’s quite a big attrition rate in science, and maybe it just doesn’t feel like a very friendly place to work.”And yet, a career in science comes with a lot of flexibility. “There are few careers where you have that amount of freedom, to go to conferences, to the library. Just because you didn’t see me didn’t mean I wasn’t working,” she says. “And I’m excited to come in every day. So, I don’t want people to think it’s a terrible place.”Gender equality is also important when it comes to the quality of science as a career. “The more women we have, the better it becomes,” Robinson adds.Imposter syndromeAccording to EU data, women outnumber men in university education but account for just about a third of graduates in science, technology, engineering and mathematics (Stem). And while the number of women working as scientists and engineers in the 27-member bloc is on the rise, their representation remains below that of men, at 41%. This discrepancy is even more pronounced in top-tier functions across these fields, where women fill just 20% of positions.Having supervised many young scientists, Robinson sees that women continue to have a greater propensity than men to doubt their abilities, a feeling she once shared. “When I was doing my PhD at the University at Cambridge, I really felt like an imposter,” she says. “I don’t think I ever felt I deserved to be there or owned it and was proud. I just thought I was lucky.” Later on, she adds, “I was always that person who said, I can’t apply yet because my CV is not strong enough.”She has now made it her job to encourage women to break with this imposter syndrome. For example, rather than calling for volunteers to give a lecture in the groups that she mentors or teaches, which nearly always results in men stepping forward, she goes straight to the women, “because I know they won’t push themselves forward.”But the gender gap in science is not just down to imposter syndrome. The “obvious” reason, Robinson says, and the reason that is on everybody’s mind when discussing workplace gender equality, is the balance between work and raising a family. Her own path was to choose between the two, taking an eight-year break after completing a postdoctoral training fellowship to bring up three children. And she continues to advise women not to work part-time while they raise a family. “You’ll just get half the pay and continue to work full-time because you are not able to fit everything in,” she says. “You should be able to have a family and come back to have the career that you want.”The solution is for men to also take career breaks. “That kind of evens it up, so that it’s not such a big thing for women,” Robinson says. “I always encourage men in my groups to do that. I think it’s important to take your share of the duties.”Support produces resultsAccording to the Women in Stem Network, a UK advocacy group, countries with strong childcare systems and equal pay policies retain more women in technical careers. In Europe, these are primarily found in the Nordic region and parts of central Europe.At the EU level, the European Commission asks all Horizon Europe applicant organisations to have a gender action plan in place. Since this criterion was introduced in 2021, the share of women leading consortia has increased to 31%, against 23% in Horizon 2020. Their overall participation, however, has risen just 1%, to 38%. After trying to weaken the requirement for gender action plans in 2025, the Commission now intends to adopt a plan for supporting women in research and innovation.Related articlesCommission scraps plan to water down gender requirements in HorizonHow gender balance can swing who wins Horizon Europe fundingRobinson herself has benefited from three Advanced Grants from the European Research Council. “I was extremely honoured and privileged to get those, because they really allowed me to fuel my passion,” she says. “Without that, it would have been a much harder slog.”Beyond applying gender rules, she says that more work is needed on translational enterprise, where women are, once again, largely underrepresented. The European Patent Office found that only 13.8% of applications were filed by women in 2022. “We have so many more women doing amazing science; why do they feel it’s not a thing to have a company?” she asks.She founded her own spin-out company, OMass Technologies, in 2016, and remains chair of its scientific advisory board now that it trades as OMass Therapeutics. As for research, Robinson is now studying how changes in the brain may be linked to depression, using port-mortem tissue. “We’re hoping to publish soon,” she says. “We had to consult with a lot of people who knew a lot more about the brain than we did. So, we’ve learnt a lot, and that’s always a lot of fun to learn a new thing. That’s what keeps you going.”
Significant rise in valley fever cases in El Paso linked to extreme weather, dust, study finds
A new study by researchers at The University of Texas at El Paso has identified a significant rise in Valley fever cases in El Paso over the past decade and found strong connections between the disease ...
Learn How to Stay Safe in Extreme Heat at Citywide Keep Austin Cool Day Events
The City of Austin is getting ahead of the heat this summer by hosting Keep Austin Cool Day. The day aims to raise awareness of the dangers of heat and its health impacts, and to distribute resources to help keep Austinites cool.
James Harden says Cavs 'extremely confident' after losses to Raptors
James Harden considers the Cavaliers' problems in Game 3 and 4 losses to the Toronto Raptors as "self-inflicted and things that we can control."
We need AI, but the political backlash cannot be ignored
The rapid development of data centers to support AI has sparked a grass-roots political backlash, threatening to result in extreme measures such as the ban on data center development imposed by Mai…
Texas' Top Criminal Court Has Stymied the State's 'Junk Science' Law. Will SCOTUS Intervene?
If the U.S. Supreme Court renders a decision in favor of Charles Flores and others, it would likely force the Court of Criminal Appeals to relax its extremely strict handling of appeals based on shoddy and outdated science.
Why Sustainability In Business Is Entering A More Operational Phase
Sustainability is still embedded across many corporations, which are now grappling with extreme weather events, driven by climate change.
How Trump’s America Produces Normie Assassins
The only extremism suspected would-be assassins like Cole Tomas Allen share is an extreme response to Trump’s deranging politics.
Why Doesn't Star Trek Ever Leave The Milky Way Galaxy?
Throughout the many adventures of Star Trek, it's extremely rare that the Enterprise (or any other ship) leaves the Milky Way galaxy. But why is that?
Five Formula 1 drivers who have taken on extreme sporting challenges
Tomorrow is the London Marathon, so there’s no better time to look at some drivers who’ve taken on some extreme sporting challenges.
Pokemon Z development build found, footage released
Brand new footage of Pokemon Z – which was never released – has now been shared. It goes without saying, but this was an extremely early development build. MDTravisYT posted about the discovery on social media. It’s noted that the source code has an “upper_fake” branch that compiles as VERSION_Z. This has a “unique” title screen alongside some “new” mega...
Scientists revive ancient 24,000-year-old ‘zombie worm’ from Arctic ice
Scientists revived a 24,000-year-old microscopic organism frozen in Siberian permafrost, offering new insights into how life endures extreme conditions.
New York City is at major risk of flooding that could leave 4.4 million people exposed to extreme damage, study finds
The new flood risk index identified eight cities along the U.S. East Coast that are at high or very high risk of floods causing extreme damage.
'A completely new reality': Bolder measures are needed to prevent extreme water shortages in cities like Phoenix and Las Vegas that depend on the Colorado River
Cities fed by the Colorado River have taken huge steps to reduce their water consumption over the past few decades, yet water shortages are projected to grow more intense. What can be done?
El Niño could be here by May, new forecast reveals — here's what it means for summer weather
The climate event is poised to supercharge weather extremes and push global temperatures to new highs.
Fires, floods and extreme weather will imperil a third of all life on land in the next 60 years
This forecast does not look good.
Could an Earthly Fungus Contaminate Mars? NASA May Have Found One Hardy Enough.
Researchers identified a species that can survive radiation, extreme heat and simulated Martian soil, posing a new challenge for protocols intended to keep the red planet pristine.
Focusing in on human health research at annual space meeting
Long-duration spaceflight can chip away at an astronaut’s health, prompting scientists to find new ways to make living in space easier on the body. The journey to outer space is incredibly dangerous, but crews must also face day-to-day hazards, such as exposure to cancer-causing radiation, microgravity and extreme isolation. Understanding and ...
Christian nationalists more likely to oppose gun regulations and support political violence, study finds
A new article finds that for Christian nationalists with a high gun-owner identity, there is an instilled culture of personal and community protection that makes them more likely to endorse extreme actions like violence.
Diablo 4: Lord of Hatred Review
An extremely satisfying conclusion to Diablo's latest demonic saga.
Shaky political science: using "simulated" elections over real-world elections
[Dear DSOS readers – 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 early December, we examined over 40 studies focused on ranked choice voting. We found that 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 three articles in our series: the first one summarizing the overall results of our report, the second one analyzing 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, and a third article showing an admirable example of two political scientists holding two other political scientists accountable for their flawed, sloppy research]. In this fourth article in our “Shaky Political Science” series, we shine a spotlight on seven studies that were web-hosted and funded by New America’s Political Reform Program and its Electoral Reform Research Group (the ERRG is an ad hoc committee headed by New America’s Political Reform Program and joined by the American Enterprise Institute, the Unite America Institute and Stanford University’s Center on Democracy, Development, and the Rule of Law). This research consortium has been led by Lee Drutman, senior fellow with New America’s Political Reform Program. These seven studies are simply not credible from any objective standard about how political “science” is supposed to illuminate our understanding of either ranked choice voting or politics in general. The feature that six of these seven studies have in common is that they all used surveys – artificial, simulated elections -- to attempt to say something cogent or interesting about ranked choice voting (RCV) elections. None of them used real world data from actual RCV elections – either the 1000+ elections in the US or the many thousands of elections from places like Australia, Ireland and elsewhere where RCV has been used for 100 years. While a few of these studies conclude with some mixed positives about RCV, our analysis here is content neutral. The idea that, with a political reform like RCV, you can ask respondents in a mock, artificial election to vote with that method, with only the barest of explanation about how RCV works or its likely impacts (i.e. no spoiler candidates, majority winners, voters liberated to pick their favorite candidates, coalition-building), and then ask them their thoughts/reactions about voting in that system, with fake candidates and no real campaigning, no real exposure to an actual election or actual candidates, rests on some very puzzling assumptions. The findings of these seven studies actually are contradicted by the findings from many other studies based on real-world RCV elections. These New America studies also failed to list the real-world RCV studies in their own citations, instead including those studies that reinforced their conclusions. The seven studies featured in this article were chosen because they form a cohesive cohort which precisely illustrates one of the main points of our research paper, namely that using surveys, i.e. artificial elections, or mathematical/theoretical models, instead of real-world data, is in fact “shaky political science.” Below are summaries of our analysis of five of the seven New America studies. You can read our complete analysis of all seven studies at our paper, “Shaky political ‘science’ misses mark on ranked choice voting.” Choosing to “Vote As Usual” by Andre Blais, Carolina Plescia, and Semra Sevi. New America. (Blais et al., 2021). This study, web-hosted and funded by New America’s Political Reform Program and its Electoral Reform Research Group, used two surveys in which respondents completed ballots for simulated elections using plurality, approval, RCV and point (Score) voting. In both cases the authors provided the respondents with only minimal explanation of each method. For example, the entire explanation for RCV was “For this vote, rank the candidates from your first to your last choice (you do not need to rank them all). This is called the ranked vote.” That’s it. The study did not provide any context or explanation to survey respondents about why she/he might want to rank multiple candidates. Participants were not even told that ballots would be tabulated using an “instant” runoff, not to mention any description of RCVs ability to eliminate the spoiler effect or liberate voters to rank their favorite candidates without fear of helping the “greater evil” candidate to win. That’s like taking an iPhone back in time to 1970 and asking someone if they like it better than their rotary phone without explaining how it works. Their first survey targeted states that held 2020 Super Tuesday Democratic primaries and used the real candidates’ names in the survey; the second survey used fictitious candidates. In other words, they surveyed respondents who had only ever participated in plurality elections, had no history or background with using RCV or any of the other methods, had no clue about its likely impacts, and did not communicate how the alternative methods would be counted, and then asked them: “How satisfied are you with using each system?” Predictably, the winner was plurality elections, the system used for president, Congress, gubernatorial and state legislative elections – in other words, the method they have been using, year after year, all their voting lives. With so little explanation of RCV and its impacts, this study is simply not credible in discovering anything useful about real-world RCV elections. It merely shows that a basic voter education campaign is needed when adopting a new election system. Additionally, the result contradicts many other post-election surveys following real RCV elections, asking opinions from voters who had just participated in a RCV election, that showed a great deal of satisfaction with RCV (see details on that in Studies 22-25 in our paper, as well as the four studies cited in the review for Study 28). Share Other studies on “satisfaction” and “understanding” make similar mistakes, in terms of basing their surveys on inadequate explanation to respondents and lack of education about context. How could any survey ever hope to measure accurately, in essence simulate a real-world election, without actual campaigning candidates, the media writing about those candidates, organizations endorsing candidates and voters being inundated with crucial information? More than anything, what these “studies” based on surveys show is that if you ask voters about something they don’t know very much about, and fail to adequately educate them, their responses will unsurprisingly reflect their confusion. Equally puzzling is that New America’s Electoral Reform Research Group, headed by political scientist Lee Drutman, found this to be a credible enough study that they not only published it but highlighted it in Drutman’s own discussion of research related to ranked choice voting. This pattern was repeated in study after study featured by New America’s Electoral Reform Research Group. For example in Ranked-Choice Voting and Political Expression: Voter Guides Narrow the Gap between Informed and Uninformed Citizens by Cheryl Boudreau, Jonathan Colner, and Scott MacKenzie, the researchers based their analysis on a survey which found that respondents who were asked to vote in a simulated RCV election, with minimal explanation of RCV or how it works, displayed less evidence of being informed about how to use RCV effectively, with the least informed voters ranking fewer candidates. As pointed out under the study above, in a real election voters are informed by a wide variety of methods, including public education by the election administration, by interested political groups, the media, and the candidates themselves who benefit from voters being aware of how to vote in the new system. In Support for Ranked-Choice Voting across Race and Partisanship by Joseph Anthony, David C. Kimball, Jack Santucci and Jamil Scott, the researchers used respondents from different parties and races voting in simulated plurality and RCV elections, who indicated their satisfaction with each voting method. Instead of using real world election results, the authors fell back on the easy, armchair and less reliable academic research methodology of making up mock elections. As with the other studies, little explanation was given of how RCV works or what its implications might be (electing majority winners or eliminating spoilers, for example). Unsurprisingly, this study found that respondents were not very satisfied with RCV. However, when the authors added an explanation that RCV has a tendency to elect more women and people of color, they found that this greater explanation increased the preference for RCV. The more voters were familiar with it, and the more they understood potential positive impacts – surprise! -- the more they were open to switching away from the status quo. Of note, the results from this study are thoroughly contradicted by a number of other studies whose data was obtained from real RCV elections with real voters. Those studies show a great deal of satisfaction with RCV. More “fake election” research An interesting wrinkle on this “research by fake elections” approach, which only served to illustrate how unreliable such “research” can become, was taken in Ranked-Choice Voting, Runoff, and Democracy: Insights from Maine and Other U.S. States by Joseph Cerrone and Cynthia McClintock_,_ also web-hosted and funded by New America’s ERRG. It gets high marks for creativity, but an eye-roll for thinking this could possibly add anything meaningful to our understanding of real world RCV elections. This study tried to determine whether RCV, plurality or two-round runoff elections provided greater voter satisfaction and acceptance of election results. To determine that, the authors conducted a survey asking if voters were satisfied with a fictional newspaper report of an election, with three different versions given to respondents – plurality, runoff and RCV, with variations of Republican/Democrat winners and variations of “come-from-behind” winners,” i.e. candidates not initially in first place but who win the “runoff.” Again, a minimal explanation of RCV or how it works was given, and the survey showed that respondents had the greatest unfamiliarity with RCV than the other methods. The unsurprising result? Respondents were much more satisfied with “come-from-behind” victories in a traditional two-round runoff election than in RCV, even though RCV is also a runoff system but with a single round of counting. Interestingly, the dissatisfaction with RCV disappeared for those who were more familiar with RCV. In fact, among those respondents who were “very familiar” with RCV, they were equally satisfied with RCV, runoff and plurality. Surprise, surprise. Again, what this study revealed, more than anything, is not only the inadequacy of the explanation for RCV given by the authors of this study, but the failure of the research format itself, thinking that such an artificial mock election could somehow simulate what actually happens in the real world in a real RCV election. Those four studies reviewed above are fitting examples of inadequate and shaky political “science.” Other studies reviewed in our paper from the New America/Electoral Reform Research Group cohort which also used mock elections included: Ranked-Choice Voting is an Acquired Taste by Joseph Anthony and David C. Kimball. New America. (Anthony et al., 2021). Ranked-Choice Voting is No Refuge for Extreme Candidates by Melissa Baker. New America. (Baker, 2021). Also included in this New America cohort is Does Ranked-Choice Voting Reduce Racial Polarization? by Yuki Atsusaka and Theodore Landsman, which actually did use real-world election results data but applied an odd and unconventional methodology to the data which relied on shaky assumptions and non-real-world scenarios that were too divorced from reality. To determine levels of “racially polarized voting,” the researchers imagined “ethnic parties,” by which they mean political parties for each ethnic group, which was especially odd since the election data they were using came from nonpartisan RCV races. But then they made an even odder assumption, namely that since race and ethnic characteristics of studied voters were not available, the authors tried to infer that information based on who voters selected and the racial make-up of voters in each district. This amounted to little more than vague and ambiguous guesswork dressed up with a bunch of mathematical equations used to give it a veneer of scientific appearance. Indeed, it seemed to assume racial polarization in order to prove racial polarization. Based on this questionable methodology, the authors concluded that RCV doesn’t reduce racial polarization. But the premise of this study missed an obvious reality: since RCV incentivizes more candidates to run and can result in more diverse fields of candidates, one would expect more racially-polarized voting in voters’ top rankings, not less, because more voters would have someone on that ballot who represents their racial or ethnic identity and more freedom to vote for their real first choice. By encouraging more diverse candidates and eliminating the spoiler effect, RCV facilitates voting for a favorite candidate of your own race and then selecting a second choice with the best likelihood of winning. The real measure is what voters do with their additional rankings – do they consider other candidates and vote “for the best of the rest”? This study didn’t tell us anything useful about that. But readily available data shows this is common in real RCV elections, and candidates act on this knowledge by engaging with voters outside their racial group. So the authors of this study failed to consider or account for what actually happens in real-world elections, and their conclusions are tainted by this failure. New America has contributed a great deal over the past 2.5 decades to our understanding of many policy areas, including politics and political reform (full disclosure: Steven Hill, one of the authors of this article and of the “Shaky Political Science” research paper and series, is a former senior fellow and former director of New America’s political reform program, 2005-2010). So it was especially disappointing that New America funded and published such a large batch of dubious studies about ranked choice voting. The stated praiseworthy goal of its Electoral Reform Research Group was to inspire new academic research about ranked choice voting that would shed light and help the public better understand the impacts of RCV. Instead, the ERRG research did just the opposite. It produced dubious research that created more confusion and misinformation. In so doing, New America’s political reform program contributed to “shaky political science.” Steven Hill and Paul Haughey Share Upgrade to $5 subscription Thanks for reading DemocracySOS, a reader-supported digital portal for the pro-democracy movement. Subscribe for only $5 per month to receive full benefits and to support our work. Subscribe
'Everyone's nervous': Wells Fargo CEO on how businesses and consumers feel about the economy
Wells Fargo CEO Charles Scharf said on Monday that the US economy right now is “extremely strong,” but he also acknowledged how bad sentiment is among Americans consumers and businesses.
2025 major champion puts 30-gram driver shaft into play
Miyu Yamashita opts for an extremely lightweight shaft profile to make some performance gains this week at the JM Eagle Championship.
Savannah named among Top 12 U.S. cities to watch on environmental health
Savannah has been named one of 12 cities to watch on environmental health through a national initiative recognizing work on extreme heat, air quality and infrastructure.
The extreme positions Trump proposes
President Donald Trump this month threatened to strike Iranian infrastructure in ways that might well be war crimes. It’s his latest extreme move that polls show his GOP base has embraced.
Dimensity 9600 Pro Beats A19 Pro And Snapdragon 8 Elite Gen 5 By 25% In Benchmarks, But Extreme Heat May Erase That Gap
A tipster shared the first single-core and multi-core scores of the Dimensity 9600 Pro, revealing it’s much faster than the A19 Pro and Snapdragon 8 Elite Gen 5
Rewriting political memory in Chile
Chile has elected extreme-right candidate José Antonio Kast as its new president. His victory, argues Anitta Kynsilehto, may reflect a broader process of rewriting political memory in the post-dictatorship era, suggesting that Chileans' memories of 'never again' moments are beginning to fade
Windows 11 Speeds Up Storage, Lifts 32GB FAT32 Limit After 3 Decades
This should help storage management feel faster and more user-friendly.
Ocean eddies are amplifying climate extremes in coastal seas, study finds
New research reveals a powerful yet overlooked driver of climate change: Intensifying ocean eddies.
95% of people carry this virus and scientists may have just found how to stop it
Scientists have taken a major step toward stopping Epstein-Barr virus (EBV), an extremely common infection linked to cancer and chronic disease. By using mice engineered with human antibody genes, researchers created powerful human-like antibodies that block the virus from attaching to and entering immune cells. One of these antibodies completely prevented infection in lab models with human immune systems, marking a breakthrough after years of difficulty tackling EBV’s ability to invade nearly all B cells.
Asking Eric: ‘Extremely online’ girlfriend shares too much
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Public Health Actions for Extreme Heat Protections
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Trump’s Erratic Behavior and Extreme Comments Revive Mental Health Debate
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Trump’s Erratic Behavior and Extreme Comments Revive Mental Health Debate
As the president threatens to wipe out Iran and attacks the pope, even some former allies and advisers are questioning whether he has grown increasingly unbalanced, describing him as “lunatic” and “clearly insane.”
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Astronomers detect mega-laser beam signal from 8 billion light-years away
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“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
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Gemma 4 and what makes an open model succeed
Having written a lot of model release blog posts, there’s something much harder about reviewing open models when they drop relative to closed models, especially in 2026. In recent years, there were so few open models, so when Llama 3 was released most people were still doing research on Llama 2 and super happy to get an update. When Qwen 3 was released, the Llama 4 fiasco had just gone down, and a whole research community was emerging to study RL on Qwen 2.5 — it was a no brainer to upgrade. Today, when an open model releases, it’s competing with Qwen 3.5, Kimi K2.5, GLM 5, MiniMax M2.5, GPT-OSS, Arcee Large, Nemotron 3, Olmo 3, and others. The space is populated, but still feels full of hidden opportunity. The potential of open models feels like a dark matter, a potential we know is huge, but few clear recipes and examples for how to unlock it are out there. Agentic AI, OpenClaw, and everything brewing in that space is going to spur mass experimentation in open models to complement the likes of Claude and Codex, not replace them. Especially with open models, the benchmarks at release are an extremely incomplete story. In some ways this is exciting, as new open models have a much higher variance and ability to surprise, but it also points at some structural reasons that make building businesses and great AI experiences around open models harder than the closed alternatives. When a new Claude Opus or GPT drops, spending a few hours with them in my agentic workflows is genuinely a good vibe test. For open models, putting them through this test is a category error. Something else to be said about open models in the era of agents is that they get out of the debate of integration, harnesses, and tools and let us see close to the ground on what exactly is the ability of just a model. Of course, we can’t test some things like search abilities without some tool, but being able to measure exactly the pace of progress of the model alone is a welcome simplification to a systematically opaque AI space. Share The list of factors I’d use to assess a new open-weight model I’m considering investing in includes: Model performance (and size) — how this model performs on benchmarks I care about and how it compares to other models of a similar size. Country of origin — some businesses care deeply about provenance, and if a model was built in China or not. Model license — if a model needs legal approval for use, uptake will be slower at mid-sized and large companies. Tooling at release — many models release with half-broken, or at least substantially slower, implementations in popular software like vLLM, Transformers, SGLANG, etc due to pushing the envelope of architectures or tools. Model fine-tunability — how easy or hard it is to modify the given model to your use-case when you actually try and use it. The core problem is that some of these are immediately available at release, e.g. general performance, license, origin, etc. but others such as tooling take day(s) to week(s) to stabilize, and others are open research questions — with no group systematically monitoring fine-tunability. In the early era of open models, the days of Llama 2 or 3 and Qwen pre v3.5, the architectures were fairly simple and the models tended to work out of the box. Some of this was due to the extremely hard work of the Llama, Qwen, Mistral, etc. developer teams. Some is due to the new models being genuinely harder to work with. When it comes to something like Qwen 3.5 or Nemotron 3, with hybrid models (either gated delta net or mamba layers), the tooling is very rough at release. Things you would expect to “just work” often don’t. I’ve been following this area closely since we released Olmo Hybrid with a similar architecture, and Qwen 3.5 is just starting to work well in the various open-source tools that need to all play nice together for RL research. That’s 1.5 months after the release date! This is just to start really investing more into understanding the behavior of the models. Of course, others started working on these models sooner by investing more engineering resources or relying on partially closed software. The fully open and distributed ecosystem takes a long time to get going on some new models. All of this is lead-in for the most important question for open models — how easy is it to adapt to specific use-cases? This is a different problem for different model sizes. Large MoE open-weight models may be used by entities like Cursor who need complex capabilities in their domain, e.g. Composer 2 trained on Kimi K2.5. Other applications can be built on much smaller models, such as Chroma’s Context-1 model for agentic search, built on GPT-OSS 20B. The question of “which models are fine-tunable” is largely background knowledge known by engineers across the industry. There should be a thriving research area here to support the open ecosystem model. The first step is to understand characteristics of different base and post-trained models to understand what they look like. The second step is to tune pretraining recipes for open models so they’re more flexible. Interconnects AI is a reader-supported publication. Consider becoming a subscriber. Subscribe For The ATOM Project and other Interconnects endeavors, we’ve put in substantial effort to measuring adoption trends in the open ecosystem. Everything takes a long time to unfold after a model is first publicly available — and adaptability is why. What we know for sure now, when Qwen has been going from strength to strength with its releases, is that technical staff across the industry has gotten comfortable working with Qwen models. Countless research methods and datasets were made to work with Qwen. It’ll take patience for any other model family to get to this point — a patience I’m not sure many open model builders have. This takes us to Gemma 4, Google’s latest open models. Gemma 3 was released more than a year ago, in March of 2025, and is a bit underrated. Gemma 4 comes in 4 sizes for now, with a bigger, MoE model of over 100B total parameters rumored but not released yet. The models we have today come in sizes of ~5B dense, 8B dense, 26B total 4B active MoE, and 31B dense. I’m most excited that they’re finally adopting a standard Apache 2.0 open source license. This’ll massively boost adoption. The standard of better licenses for strong open-weight LLMs was set by mostly Chinese open model labs in the last 1-2 years, and now U.S. companies are following suit. I will personally be so happy if the horrible Llama licenses and Gemma terms of service were an ~18-month transient dynamic of the industry being nervous about releasing strong open models. The Gemma 4 scores look very solid, the small models have incredible benchmark scores (especially in general domains like LMArena) and the 31B model rivals the recent Qwen 3.5 27B, which is the leading member of that class. The ~30B size range is an important one, as it’s accessible both to researchers and to enterprises looking to deploy the model in real use-cases. Where the 7B model scale is the default for tinkering and research, a 30B model is the default for seeing if an open model can unlock substantial value in your specific workflow — a good mix of intelligence, low price, tractability for downstream training, etc. This takes us back to the above adoption criteria I mentioned for open models and the bigger question — do I think Gemma 4 will be an overwhelming success? Previous Gemma models have been plagued by tooling issues and poorer performance when being finetuned. Gemma 4’s success is going to be entirely determined by ease of use, to a point where a 5-10% swing on benchmarks wouldn’t matter at all. It’s strong enough, small enough, with the right license, and from the U.S., so many companies are going to slot it in. I’m cautiously optimistic that Gemma 4 is going to work better here. Winds are shifting for open models built in America. We saw GPT-OSS go through a bumpy launch to become an overwhelming success. There’s a collective energy around the likes of Reflection, Arcee, Nemotron, Gemma, Olmo, and peers that show substantial demand for building new stacks around open models. There’s capital to be spent on AI stacks across the economy by those who want more ownership of everything, including the model. After launching The ATOM Project 240 days ago, the conversation is shifting into the next stage. Summer of 2025 was a crisis moment where the U.S. AI scene realized it can’t wait and figure out open models after building AGI. The two markets will capture different areas and proceed in parallel. Now that more companies in the U.S. are releasing strong models, we need to improve the ecosystem so that these models are easy to use, understand, and build value around. It’s the hard work to build another inflection point in these adoption plots I’ve been updating consistently, but that’s the work to be done. Join me in it. More data coming soon! Here’s a sneak peek:
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In a speech from the White House, the US president vowed two to three weeks of "extremely hard" strikes against Iran
Extreme wildfires, droughts and storms could happen even under moderate global warming, study finds
New research suggests devastating climate outcomes that are typically associated with extreme global warming could hit even we limit heating to 3.6 F above preindustrial levels.
Fires Tear Through Nebraska Grasslands
Dry, warm, and windy conditions across the U.S. Great Plains led to extreme fire activity in March 2026.
How energy volatility is rewriting the rules of business operations
Energy is no longer a predictable overhead—it’s becoming a core strategic lever for businesses navigating an increasingly volatile market, Harvard Business Review writes. Driven by geopolitics, extreme weather, grid constraints and surging demand from AI and electrification, energy costs are rising and behaving less like a fixed expense and more like a dynamic risk. That […]