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Black Book: Australia's 2027 Health IT Market Shifts From Pilots to Proof
Post-HIC 2026, provider study finds interoperability, clinical cyber continuity and statewide EHR execution will dominate the next buying cycle NORTH SYDNEY, AU / ACCESS Newswire / August 9, 2026 / Black Book Research today announced the publication ...
The Chinese Smart Glasses Proving That Smart Glasses Can Be Repairable
It’s often claimed that wearable devices cannot have user-replaceable batteries or even be serviceable at all due to how compact and waterproof they must be, yet there are plenty of examples …
Growing the Game Has Never Felt More Meaningful
Fast-forward almost 30 years, and here I am playing a major role in influencing the next generation of golfers: Being named a national ambassador for Youth on Course. From that gritty Leisure Time course to playing some of the top-ranked golf courses in the country, working alongside the top golf coaches in the world, and even hitting balls next to Tiger Woods and his son Charlie (seriously, here's proof!), this sport has opened so many doors for me. Just like I did nearly 30 years ago with my dad and Uncle Chris, I can create experiences that, hopefully, stick with them for the rest of their lives, too.
GOP lawmakers keep trying to defang voters in their own states
The big headline out of Tuesday’s primaries was the slim victory of former public health official Abdul El-Sayed in Michigan’s Democratic Senate primary — proof that progressives are ascendant in the party, even in a key swing state.
Fall sports Senior Media Day
Seniors, mark your calendars for our fall sports Senior Media Day photo shoot, which will take place on Monday, Sept. 14, at 3:30 p.m.! All seniors playing a fall sport should report to the third-floor balcony area at Albany High promptly at 3:30 p.m. to get their photograph taken by John Wong Photography. Photographs taken on our fall sports Senior Media Day will be utilized for the creation of large, weatherproof senior banners. Each senior will be featured in their own individual banner. We extend our heartfelt thanks to the Albany Booster Club for funding our senior banners, which will be proudly displayed at our senior night events. Thanks to our booster club's support, each senior will get to keep their banner as a memento of their hard work and dedication as a Falcon student-athlete. There is no sitting fee for the Senior Media Day photo shoot. Students and families will be given the opportunity to purchase photos separately if they so choose. Senior Media Day photo shoots for winter and spring sports will be publicized leading up to each season. In addition to our Senior Media Day photo shoots, seniors will also be asked to participate in our sports photo sessions each season for all student-athletes in grades 9-12, facilitated by Upstate Images. Upstate Images provides our sports photography for our Albany High yearbook and students and families will be given the opportunity to purchase individual and/or team photos if they so choose. More details will be available at the start of the fall sports season.
MrBeast Sales Slow, Angel Stock Falls, MUBI and Neon Look for Hits, and Will an NBA Franchise Set a Sales Record?
(Welcome to the Entertainment Strategy Guy, a newsletter on the entertainment industry and business strategy. I write a weekly Streaming Ratings Report and a bi-weekly strategy column, along with occasional deep dives into other topics, like today’s article. Please subscribe.) You probably remember egg prices spiking a few years ago. This led to a lot of debate between the pro-market/pro-competition/antitrust/Neo-Brandesian crowd and the center/center-right/center-left/abundance/libertarian crowd over the cause, whether it was the avian flu outbreak or whether industry consolidation led to price fixing, using avian flu as a convenient cover. Basel Musharbash wrote a whole investigation at BIG looking at it. Well, two years later, we have our answer: it was price fixing. The Justice Department found proof, via emails, of price fixing. Indeed, just the threat of an investigation brought prices down two years ago: I bring this up because at least half a dozen of my favorite political writers and pundits, who were skeptical about price fixing at the time, haven’t written any mea culpas on this issue. It’s really dispiriting and makes it much, much harder to trust their work on other issues. If you take a hard stand on an issue, then you get it wrong, you need to explain how and why you got it wrong. Or update your priors. This is how epistemological bubbles are formed! All of which is to say, it’s time for another installment of “What I Got Right, What I Got Wrong”. Last issue, I mostly focused on the “wrong” side of the equation, so today’s is a bit more balanced, but with one big mea culpa (though I still stand by my original concerns). Let’s dive right in! Subscribe CORRECTION: UFC Didn’t Release Subscriber Data (But Seems to Have Bragged About It Internally) In my analysis of the viewership of UFC Freedom 250, I wrote... “Paramount has also released datecdotes bragging about their subscriber numbers. Earlier this year, they claimed that the first UFC fight on Paramount+ brought in 1 million subscribers.” Well, that datecdote did not come from a PR rep, but from great reporting from James Faris at Business Insider. The actual quote was this: “Paramount’s flagship streaming service generated about a million new subscribers on the day of its first-ever UFC event, Paramount product chief Dane Glasgow told employees in a town hall on Tuesday morning, three staffers who attended the meeting told Business Insider.“ This wasn’t the biggest goof in the world; even Faris emphasized to me that this is more a “clarification” than a correction. Still, it sounds like, internally, the folks at Paramount were very happy about this result—and I’m guessing they were happy with this news getting out—even though the gains didn’t last through the quarter, dropping to 700K adds. RIGHT: MrBeast’s Struggles Continue... I’m very skeptical about MrBeast’s burgeoning media “empire” and, in particular, its $5 billion valuation, as I wrote earlier this year and last year. Sure enough, according to Business Insider, regarding its “booming” consumer products division_:_ “US sales volume grew 13% year-over-year in 2025, following a 33% leap the previous year, according to a presentation deck dated May 2026....A February 2025 investor deck viewed by Business Insider showed Feastables’ net revenue more than doubled in 2024 from 2023, to $215 million, and was forecast to grow 74% in 2025, to $375 million.” [Emphasis mine.] If you told investors that you projected 74% growth but delivered 13%,1 that’s not ideal. But it’s not just MrBeast. Logan Paul and KSI’s Prime had sales drop in half for their energy drinks. It’s one thing to build a successful entertainment company and an entirely different thing to succeed in the heavily-consolidated-but-also-very-competitive consumer packaged goods space! CPGs are dominated by a few firms with very wide moats compared to YouTube, which has few barriers to entry. These businesses require different skill sets and have completely different business environments. WRONG: Does the NBA Have an $8 Billion Bidder? Almost as soon as I published my article about the NBA’s finances, casting skepticism on NBA expansion, I saw this Front Office Sports headline: One group “claims to have already raised $8 billion” for the Vegas expansion team. I mean, wow. That’s just $2 billion less than what the Los Angeles Lakers were valued at when they were sold just last year. And they’re arguably the most popular team in the NBA. (Side note: the new owner is now under an investigation for fraud in how he went about financing the Lakers and the Dodgers purchases.) So much for the “dried up” language I used in that article. (This is why I usually stick to my “Entertainment Nuance Guy” tone in articles...) I went off old information—from April, when I read/heard about the NBA downplaying expansion efforts—instead of doing a quick double-check for new news. That said...if you read the article, the market for Seattle is “slow”, with just one reported bidder. So I wasn’t entirely off. This goof has annoyed me since I first published it, mainly because it undercut that article’s real point, which I stand by: NBA franchises are really overvalued right now. Now, I get why NBA teams are overvalued. When it comes to owning sports teams, it’s basically never wrong to assume that some rich person somewhere will care more about owning a team (or just being part of a group since they’re so expensive) than the actual financials. There are only so many teams to own, and opportunities to buy in preferred markets don’t come along very often. This explains why the teams sell for such high multiples (usually above a 10 to 12.5 revenue multiple, which is very high). You only need one person to overpay! Beyond the ultra-wealthy luxury good explanation, I worry NBA teams are becoming speculative assets. More and more private equity investors are buying into NBA teams. But the value, in owning a team, comes from selling it later, not any meaningful relationship to revenue or profit growth. In other words, the newer NBA owners are increasingly expecting someone later to buy their inflated asset. That’s not sound economics; that’s the “greater fool” theory at work.2 At some point, actually making money has to matter. Especially since revenue growth is now below the growth of the stock market. (And this matters because the NBA doesn’t have easy levers to pull to drastically lower their costs—half of their revenue has to go to players and then you actually have to run a team, including coaching, training, travel, media expenses, and more—or raise revenues aside from one-off expansions in America and overseas.) I just don’t see how investors make their money back buying NBA teams, except for selling at a higher price later on down the line. But if revenue/profit growth continues to slow, at some point, someone will be left holding the bag. RIGHT: Baseball and Basketball are about equally popular. Related to the above, I wanted to cite two quick pieces of evidence for one of my other theories: that the NBA and MLB are about as popular as each other, but the NBA gets much, much better media coverage. One YouGov survey has that fans prefer basketball to baseball by 52% to 48%, which I’m guessing is within the margin of error for that poll. That’s virtually even, meaning they’re about the same popularity. Over on NBC, a recent Red Sox-Yankees game averaged 4 million viewers, higher than Sunday Night Basketball’s average of 3.4 million on the same network. A Yankees-Dodgers game averaged 3.9 million two weeks ago. Not every game averages this much, but it shows you that baseball isn’t far off from basketball, depending on the matchup. Altogether, the NBA is just slightly more popular than baseball, but NBC is paying MLB a fraction of what they pay the NBA, a point I’ve made before. RIGHT: Angel, MUBI and Neon Updates... I’ve long been skeptical about many of Hollywood’s buzziest startups/smaller indie studios. It’s not that they don’t make great movies—films like Anora and Everything Everywhere All at Once are amazing—or that they’re not (rightfully) pushing the majors to make better films, but...they get a ton of hype, and that hype often doesn’t live up to the reality or difficulty in scaling up. Starting with MUBI, well, The Wall Street Journal wrote a feature on MUBI’s struggles last April, best summarized by the subheader “After Sequoia valued Mubi at $1 billion, a left-wing revolt sent the indie film company into a tailspin”. MUBI lost $7.3 million on $200 million in revenue in Q4 2025, but reportedly hit a record 1.7 million subscribers at the end of Q1. As for Neon, Department M purchased a “significant stake” in the company earlier this year. How much is that “stake” worth? According to Deadline, “No financial figures for the Neon-Department M deal were disclosed.” So...that’s not great! If it was a big valuation, we’d hear about. Also, I found this curious: “Both Neon and Department M already have separate partnerships with Qatar. The Qatar Film Committee has a slate deal in place with the former, and a biopic production pact with the latter.” Color me skeptical about much Middle Eastern investment dollars. See: LIV Golf. Finally, as for Angel (formerly Angel Studios), their stock very, very slightly popped last month after Young Washington ($20 million budget) which made $45 million in the US, but their stock is still way down from its opening. Smaller Updates RIGHT: The WGA should focus on antitrust concerns! I’ve been on this for years. Post-WGA/AMTPT deal in 2023, I wrote an article for the Ankler calling on the WGA to focus and organize against industry consolidation. Sure enough, the WGA filed a lawsuit against the Paramount-WBD merger, and people are organizing around this issue now. I wish this energy had coalesced earlier (specifically to lobby Washington much earlier), but better late than never! WRONG: Actually, the chip shortage might be due to market manipulation. In my last “Long Reads for the Long Summer” article, I shared an article about how LLMs are causing memory chip prices to spike, which will make all electronics more expensive. Well, turns out, three RAM manufacturers control 90% of the market, and now a private lawsuit accuses them of price-fixing. So yeah. WRONG/UPDATE: The Odyssey will hold! Two weeks ago, I wrote, “As of this moment, we haven’t had a super-blockbuster, though The Odyssey, Spider-Man: Brand New Day and Avengers: Doomsday could all cross the $500 million mark. (At this point, The Odyssey would need a very strong hold to get there.)” Well, it’s safe to say it’s going to have that hold! It only dropped 30% in weekend two and IMAX screenings are sold out all month. Spider-Man: Brand New Day is also off to a great start. RIGHT: People are rushing to get shows out yearly. I’ve long been bullish on yearly, weekly-released shows, and Leslie Goldberg at The Ankler wrote about how many people in town are now listening. WRONG: Mark Carney reverses Canada’s stance on homegrown production. After writing about Canada installing domestic production quotas, Canada has backed off. WRONG: I don’t trust Spider-Man: Brand New Day’s online “views”. When I originally wrote about the latest Spider-Man film’s record-breaking trailer, I thought this was just YouTube views, but after watching this Corridor Crew video and doing more research, well, it’s all social views of the trailer globally. Really? That stat includes TikTok, Reels and YouTube Shorts views. Basically, I don’t trust it anymore. (This film will probably set record, but I trust the pre-sales data more.) RIGHT: Celebrity Production companies are still struggling. I’ve written about this for years, but the company that I didn’t track was Kevin Hart’s HartBeat, which was valued at $650 million but is struggling now and might just end soon, especially after Kevin Hart pulled his endorsement deals. And Bad Robot is moving to NYC. CORRECTION: SpongeBob is a “second run” title. In my 2025 recap article, I had _SpongeBob SquarePants_labelled as a “library” title, when it is still a “second run” show, with new episodes still coming out on Nickelodeon. CORRECTION: In my last “What I Got Right, What I Got Wrong” update, I offered a correction from Doug Creutz and misspelled TD Cowen. Thanks, Grammarly! 1 Though he could have increased prices to make up for the gap, he couldn’t have increased them that much. 2 There’s a theory, which I think that Daryl Morey created and Bill Simmons popularized, that, to be successful building your team, you only need a handful of bad GMs in the league to take advantage of. To be clear, Daryl Morey’s theory was also for signing bad contracts, that you could always get out of bad contracts. Basically, there’s always someone who will make a bad trade. It’s not the same exact thing, but similar enough to the Greater Fool Theory that I really like it.
2026 Fall Preview: Diamond Football
Share on Facebook Share on Twitter By Chad Hayworth (for OzarksSportsZone.com) Nearly every man who has coached the Diamond football team in the past two decades has pointed to Brad Hocker’s tenure there as proof the Wildcats
Acoustirats and Jordan Lauterbach to Perform at LI Music & Entertainment Hall of Fame on Aug 23rd
As part of the Long Island Music and Entertainment Hall of Fame Soundtrack to Summer Series, the band Acoustirats will perform LIVE music and Comedian Jordan Lauterbach will open with comedy set at the Long Island Music and Entertainment Hall of Fame in Stony Brook Village (97 Main Street, Stony Brook, NY) on Aug. 23th from 3-4pm. The event is free for members or with general admission ticket purchase or $10 at the door concert ticket purchase. For details on this and upcoming events please visit https://www.limusichalloffame.... About AcoustiRats AcoustiRats is an acoustic duo dedicated to stripping Good Rats classics down to their raw, essential form. Fronted by Stefan Marchello, lead singer and 36-year veteran of the Good Rats, alongside guitarist Matt McCarthy, who also did a run on guitar with the band. Between them, they've got decades of Good Rats DNA in their blood. After years of playing these songs loud and electric, Stefan and Matt are thrilled to break them down to the bare bones. No frills, no amps cranked to 11 — just voice, acoustic guitar, and the songwriting that made the Good Rats legendary. It's the same attitude and stories fans know, delivered in a raw, intimate format. For diehard Good Rats fans, it's a new way to hear the classics. For first-timers, it's proof that great songwriting doesn't need to be loud to hit hard. About Jordan Lauterbach Jordan Lauterbach is a fast-rising comedian who features prominently in clubs around the country. His autobiographical style and supreme wit keep audiences laughing for hours after they leave the club. Lauterbach is likable, relatable, and tremendously funny. He has opened for some of comedy's best and is quickly carving out an audience of his own.
A CT health care system is sending people home with new technology. Why it’s called a ‘game changer’
The CEO said such technology is part of the “future of health care,” and a “game changer,” an “early proof of what we will see to help people live their healthiest life.”
Hockwold farmer swaps onions for grapes to future-proof business
The third-generation farmer says his chalk-rich soil is similar to that of France's Champagne region.
Kentucky governor presses McConnell on legacy-'ruining' health
Andy Beshear, a Democrat, has used the Republican senator's weekslong absence to demand proof of his capacity to serve.
Fashion IPOs Became a Recipe for Disaster. Reformation Thinks Its Business Model Can Break the Curse
Reformation’s IPO shows investors may be willing to buy fashion again, but only if the story comes with profits, cash flow, and proof customers keep coming back.
LinkedIn adds a button to report AI-generated 'slop'
LinkedIn is introducing new ways to reduce low-quality AI-generated posts, including a “seems like AI slop” reporting option. It's also replacing its own AI writing feature with a proofreading tool.
Conservatives tout Biden tapes as proof of his decline. The audio is more nuanced.
Audio of Joe Biden's interviews with a ghostwriter in 2016 and 2017 show him referencing classified materials and occasionally struggling to recall details.
15 years ago, 4 letters changed Magic forever — and one new ability backfired
Magic 2012’s designers reveal how “dies” streamlined the game and why hexproof eventually gave way to ward
'Show me how this ends.' Thune responds to Trump’s escalated calls to pass SAVE America Act
Senate Majority Leader John Thune responded to President Donald Trump's escalating calls Monday to cancel the August recess until the Senate passes a proof-of-citizenship voting bill by again challenging the president.
Trump demands Senate cancel August break until it passes the SAVE America Act
President Donald Trump is calling on Senate Majority Leader John Thune to keep senators in Washington until they pass a strict proof-of-citizenship voting bill.
Soil carbon effectively measured by new, efficient AI model
A new model offers a proof-of-principle for how AI might be used to shed light on obscure biological processes.
Fact-checking Mullin’s claim that foreign adversaries can access voting machines in U.S. elections
Mullin’s comments about foreign adversaries accessing voting machines reflect concerns that security and election technology experts have long had. But so far, there’s no proof of such attempts.
Industry and D.C. Are Racing Ahead on Psychedelics. They Should Let Science Lead.
President Trump and pharmaceutical companies are betting big on psychedelics. But hype isn't the same as scientific proof, and urgency must be coupled with caution.
Is AMC Entertainment’s (AMC) Record Revenue Proof That the Theater Recovery Is Finally Real?
AMC Entertainment Holdings, Inc. (NYSE:AMC) jumped 16% in premarket trading on Monday after reporting record second-quarter revenue and a surprise adjusted profit. Revenue reached $1.60 billion, compared with the $1.47 billion expected by analysts, while adjusted earnings came in at 14 cents per share instead of the expected 6-cent loss. The quarter provides the strongest […]
We treat steady eye contact as proof of honesty, but the science is messier: it can signal dominance as much as sincerity, and liars often hold your gaze more, not less
Ask almost anyone how to spot a liar and you will get a version of the same answer. Watch the eyes. A dishonest person cannot hold your gaze. The idea is
Priyanka Chopra says Nick Jonas needed proof it was actually her during first text exchange
While appearing on the ‘Hey Jonas’ podcast, Priyanka Chopra shared that Nick Jonas wanted proof that she was who she said she was while initially texting him.
China-proofing European cleantech is a tricky business
Crude tariffs won’t be enough to promote EU renewable energy
How Beauty Brands Can Build Intuitive Customer Journeys
At The Business of Beauty Global Forum 2026, BoF’s Annabel Bolton sat down with Klaviyo’s vice president of product marketing Kelly Thacker to unpack the ever-evolving customer path to purchase, and how brands can future-proof their strategies for both discoverability and, ultimately, loyalty.
Scientists build tiny 'diving suit' for cockroaches, turning them into search-and rescue cyborgs
Researchers in Singapore and Japan have built a waterproof shell for cyborg cockroaches that could be deployed in disaster zones to investigate flooded areas.
GOP leaders tried to quell McConnell health questions. It’s not working
As online speculation swirls over Sen. Mitch McConnell’s hospitalization and scant public updates from his staff, the Kentucky Republican’s top allies spoke up this week, insisting he remains alert. But those assurances have done little to quash demands for proof of life, including from the state’s governor. The episode has reignited a debate over lawmakers’ […]
It's Indie-Penance Day! Have 10 Incredible Indie Games To Wishlist
Video games are amazing! Here's a lot of proof
Bonus 236: A Term of Self-Inflicted Wounds
Within minutes of the Supreme Court handing down the birthright citizenship ruling on Tuesday and rising for its summer recess, its defenders were pointing to that decision (alongside the late-arriving mail-in ballots ruling on Monday and the tariffs ruling from February) as decisive proof that the Court really is “above politics,” and that criticisms of the justices for behaving more like politicians in robes than like judges are thus unfair. For a particularly thoughtful version of this claim, see Professor Will Baude’s opening response to the latest New York Times written roundtable about the Court with Baude, Professor Kate Shaw, and me. In that roundtable, I already offered one of the two major arguments against this thesis—that it’s an assessment of the numerator without regard to the denominator, and that a full account of the Court’s behavior throughout OT2025 suggests that the Court behaved even more politically during the current term than during even the previous (highly politicized) one. Just to take one of many data points, last term (OT2024), there were six rulings in argued cases in which all six Republican-appointed justices were in the majority and all three of the Democratic appointees were in dissent (along with a slew of such rulings on the emergency docket). This term, there were … 13. (Along with two 6-3 summary reversals and 10 rulings on the emergency docket from which the three Democratic appointees all dissented.) Today’s bonus issue addresses the flip side of that coin. In particular, below the fold, I identify four different moments in which the justices could have acted in ways to specifically defuse charges of political behavior—and opted instead to lean all the way in. As much as end-of-term narratives focus on what the Court did, and on the takeaways from individual rulings, consider today’s post an attempt to identify opportunities that the Court missed, as well. At least in the examples discussed below, by opting for the outcome that made the Court look more transparently political, the Court inflicted a series of (unnecessary) credibility wounds on itself. For those who aren’t paid subscribers, we’ll be back Monday with our regular coverage of the Court. For those who are, please read on.
Millennials Are Buying Blue Collar Small Businesses To AI-Proof Their Future
Young entrepreneurs are snapping up baby boomer-owned plumbing, construction, manufacturing and HVAC companies. Plus: Tips for would-be buyers and sellers.
They say Andy Burnham is ‘good at politics’ – but the proof will only come in time
The PM-in-waiting brings the promise of likability and a mission voters can believe in. He’ll need all that and more to survive, says Guardian columnist Zoe Williams
Trump unloads after Supreme Court upholds late mail-in ballots in Mississippi
Trump slams Supreme Court ruling allowing late mail-in ballots, demands Congress pass the SAVE Act requiring voter ID and proof of citizenship.
Supreme Court won't revive Alan Dershowitz's $300 million suit against CNN
Dershowitz had urged the court to reconsider New York Times Co. v. Sullivan. The landmark First Amendment case that made it harder for public figures to win libel lawsuits because it requires proof that an outlet knowingly published something false, or showed a reckless disregard for the truth.
Supreme Court will weigh Trump-backed Republican push to revive Arizona voting laws
WASHINGTON (AP) — The Supreme Court said Monday it will consider a Republican push to enforce strict Arizona voting laws passed in the swing state after the 2020 election. The high court has allowed some similar rules to take effect temporarily before, including Arizona's proof-of-citizenship requirement for state and local elections and a Virginia purge of voter rolls that the state said was aimed at keeping noncitizens from voting. READ MORE: Supreme Court rejects Trump's
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.
Career coach recommends 4-hour burnout-proof job application routine
A career coach advises job seekers against spending all day applying to jobs. She says a four-hour routine is burnout-proof and more effective.
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.
Samsung’s new budget phone costs $50 more despite downgrades
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Trump abruptly cancels signing housing bill, blindsiding Republicans
Trump is pressuring senators to focus on his proof-of-citizenship voting bill even though it doesn't have the votes to pass
Federal judge bars Trump from implementing proof of citizenship requirement to vote
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Trump refuses to sign landmark housing bill, demanding Congress pass voter ID law
Trump demanded that Congress pass the SAVE America Act, the proof-of-citizenship voting bill, before he would consider the housing legislation.
Trump abruptly cancels signing housing bill, blindsiding Republicans
Trump is pressuring senators to focus on his proof-of-citizenship voting bill even though it doesn't have the votes to pass
Trump abruptly cancels signing housing bill, blindsiding Republicans
Trump is pressuring senators to focus on his proof-of-citizenship voting bill even though it doesn't have the votes to pass
Frances Crawford’s bold, brilliant crime debut
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Jimmy Kimmel has proof world leaders ‘hate’ Trump: ‘Saddest photo I’ve ever seen’
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Support Local, Get Rewarded: Portland entrepreneur offers 50% discount incentive
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"I've watched USAID help hold the line against epidemics," writes Monik Bhatta. "Gutting it was premature — and DRC's Ebola crisis is the proof."
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Chesterfield-based entertainment agency forms music education nonprofit
“(It’s) basically a proof of concept ... what we’re doing, who we want to help,” Harry said. “We’re going to lean preferably to nonprofit organizations in this space that are focused on kids. Getting them exposed to music at an early age.”
We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
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In this age of seeming permacrisis, how might countries across Asia and the Pacific collectively anticipate, prepare for and respond to public health emergencies – including possible pandemics – that lie ahead? Key answers could be found from the outcomes of a regional mapping exercise launched by the World Health Organization (WHO) Regional Office for the Western Pacific during the WHO-led Asia Pacific Health Security Action Framework (APHSAF) Stakeholders Meeting held on 11 June 2026 in Malaysia. For the exercise, WHO mobilized an extensive network of contributors, synthesizing 228 distinct inputs from 11 Member States - including Australia, Japan, the Philippines and Viet Nam from the Western Pacific Region - alongside leading academic institutions and regional partner organizations, as well as WHO regional and country offices. The exercise identified a diverse range of opportunities to boost regional operational readiness, transitioning the focus on health security investment in individual countries to a unified, interoperable health security collective for the entire Asia-Pacific region.
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'SAVE' America Act raises confusion as Republicans continue push
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Health Alert: U.S. Embassy in Rwanda (May 23, 2026) - U.S. Embassy in Rwanda
Health Alert: U.S. Embassy in Rwanda (May 23, 2026) Health Alert: U.S. Embassy in Rwanda (May 23, 2026) Location: Rwanda, countrywide Event: Entry and Exit Requirements Due to the Ebola outbreak, Rwanda has implemented new entry and exit requirements. On May 22, 2026, the Ministry of Health of Rwanda announced that all foreign nationals who have traveled to, or transited through, the Democratic Republic of the Congo (DRC) within 30 days of their travel to Rwanda will be denied entry. Rwandan nationals and foreign residents with valid proof of residency in Rwanda can enter and are subject to mandatory quarantine procedures. Travel to the U.S. Review the U.S. Centers for Disease Control and Prevention (CDC) information on what travelers need to know about returning to the United States from DRC, Uganda, and South Sudan. Actions to Take: Do not travel to the Democratic Republic of Congo (DRC). Monitor the Ministry of Health of Rwanda webpage for local updates. If you are planning to travel to the United States, contact your airline to confirm your flight and route. Review the CDC webpage on the Ebola Current Situation. Review Consular Information for Americans Regarding the Ebola Outbreak. Review the CBP webpage for information on entry restrictions Notify friends and family of your safety. Have a plan to depart in case of emergency. Have travel documents up to date and easily accessible. Review the CDC Travelers’ Health Page for Rwanda. Continue to take malaria prophylaxis as recommended, as the symptoms of malaria can mimic symptoms of Ebola, leading to preventable health scares. Monitor your health for 21 days following travel in an area with an Ebola outbreak. If at risk for Ebola, alert your healthcare provider if you are experiencing symptoms consistent with Ebola to receive guidance prior to presenting for care. Review the Department of State’s Rwanda Travel Advisory for general health information. Refer to the U.S. Embassy’s list of medical providers. Check with your travel insurance provider about medical evacuation options should you fall ill while overseas. Enroll in the Smart Traveler Enrollment Program (STEP) to receive safety and security updates. Assistance: U.S. Embassy Kigali, Rwanda 30 KG 7 Avenue (Kacyiru) +250-252-596-400 ACSKigali@state.gov https://rw.usembassy.gov State Department – Consular Affairs 888-407-4747 or 202-501-4444 Rwanda Country Information Enroll in Smart Traveler Enrollment Program (STEP) to receive security updates. Follow us on Facebook and X
Jon Batiste and Paul McCartney give Stephen Colbert a ‘Late Show’ goodbye
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As companies gather mountains of information, they need business intelligence analysts working alongside AI to make sense of it all.
DNA floating in seawater is now enough to let scientists monitor the health of America’s dolphin populations
Sequencing environmental DNA in seawater shows the local presence or absence of species, but until now could give little information about those measures of biodiversity that are the most useful in conservation. As a proof-of-principle, scientists in the US have now shown that mitochondrial DNA in water sampled near schools of dolphins contains enough information to measure their local effective population size and monitor the health of these populations.
Every Pokémon Is Someone's Favorite And Now There's Proof
Someone out there loves Goldeen, I promise
Epic gains ground as acute care EHR purchasing slows
The recent KLAS U.S. Acute Care EHR Market Share report showed Epic adding hospitals among smaller health systems, and researchers said Oracle's new AI-enabled EHR would be a "pivotal proofpoint."
New MatterChat Model Helps AI to ‘See’ the Language of Science
From writing emails to generating computer code, much of the artificial intelligence prevalent in our daily lives has succeeded by mastering one domain: text. However, this leaves a major blind spot in the physical sciences, where models depend on the high-resolution, three-dimensional data of the physical world, like the intricate lattice of atoms in a crystal. Delivering on the promise of using AI for science requires teaching these data-driven text models to seamlessly “talk to” physics-based models. Now, a new AI framework from Lawrence Berkeley National Laboratory (Berkeley Lab), called MatterChat, solves this problem by creating a specialized “bridge.” It connects the conversational power of a Large Language Model (LLM) with a physics-based AI that models “interatomic potentials”: the complex physical forces between atoms. The resulting system already significantly outperforms general-purpose AI tools like GPT-4 at predicting material properties, and the team hopes it can accelerate scientific discovery by serving as a robust research partner that provides grounded insights and generates step-by-step instructions for synthesizing novel materials. A paper describing this work was recently published in Nature Machine Intelligence. “Traditional simulations can provide the physical rigor required for materials science, yet their computational cost remains prohibitive for high-throughput screening. Conversely, while LLMs excel at rapid knowledge synthesis, they inherently lack the ‘structural vision’ to interpret materials directly from their underlying atomic coordinates,” said Yingheng Tang, a postdoctoral researcher in Berkeley Lab’s Applied Math and Computational Research Division (AMCR) and lead author on the paper. “MatterChat was built to solve this dilemma, empowering LLMs with a structural ‘vision’ that allows researchers to leverage their full potential for solving complex, real-world materials challenges.” Empowering language models to solve complex challenges in materials science To build MatterChat, the Berkeley Lab team drew inspiration from technologies like Vision Question Answering (VQA) and Text-to-Image (T2I) generation. In these tasks, AI must translate high-level text concepts into visual images, or vice versa. Doing so requires developers to build tools that “bridge” two fundamentally different forms of data. The researchers adapted this concept to the physical sciences. With MatterChat, they created a “bridge model” that successfully connects an LLM’s general knowledge with the deep understanding of the atomic-scale world encoded in scientific interatomic potentials. Until now, researchers using LLMs to solve materials problems usually had to feed them raw data files as if they were just strings of text. It’s like asking an AI to understand a complex 3D engine based only on a parts list: the LLM can read the names, but it can’t “see” how the atoms fit together in space. MatterChat solves this by training a specialized AI bridge model, pre-trained on millions of crystal structures and an LLM, to align the LLM’s representation of the world with the interatomic potential’s representation of the world. The bridge model can translate physical insights into a format the LLM can actually understand. By giving the LLM these “scientific eyes”— a scientific “inductive bias” in the terminology of AI — the Berkeley Lab team has transformed it into a robust research tool capable of providing grounded scientific insights into complex materials challenges, such as predicting thermal stability or analyzing electronic band gaps. “We think of atoms as living in a physical space, but from a machine learning perspective, they are just vectors living in some very non-trivially structured manifold in a high-dimensional Euclidean space; and, of course, the same is true for the sentences and paragraphs that we use to express our ideas about those atoms,” said co-author Michael Mahoney, Berkeley Lab’s Scientific Data Division (SDD) AI Initiative Research Lead. “The bridge model basically gets those two structures to ‘talk with’ each other.” As a proof-of-concept of this general approach, the team trained their bridge model on a dataset curated by pairing nearly 143,000 stable atomic structures from the Materials Project with their corresponding physical properties. This training data was automatically assembled using the Materials Project’s API and deliberately enriched with properties fundamental to microelectronics design — like formation energy and bandgap — allowing MatterChat to learn the complex patterns connecting a material’s atomic blueprint to its functional performance. To validate their model, the researchers benchmarked MatterChat against a suite of other AI systems, from general-purpose LLMs to other specialized scientific AI methods. The results show that MatterChat consistently outperformed its competitors across a range of tasks. The model was more accurate in classifying material types and demonstrated superior precision in predicting numerical properties. For example, it excelled at predicting a material’s bandgap, a property critical for designing new electronics from high-capacity energy storage to next-generation computer chips. “Our design is significantly more efficient because we don’t have to build a massive AI model from the ground up,” said co-author Zhi (Jackie) Yao, a research scientist in Berkeley Lab’s AMCR. “Instead, we take two powerful, pre-trained models — a structural encoder for materials physics and an open-source LLM — and use them off-the-shelf. The only component we actually train is the lightweight ‘bridge model’ that translates between them. It’s the difference between building an entire car factory and simply designing a smart adapter that connects a world-class engine to a world-class navigation system. This approach is not only computationally efficient, but also makes the system modular, so we can easily upgrade components or adapt the bridge for other scientific domains in the future.” Crucially, this modular design highlights exactly how institutions like Berkeley Lab and the Department of Energy are carving out a highly valuable niche in the booming AI landscape. Rather than competing with Silicon Valley tech giants to build ever-larger language models from scratch, the lab is focusing on the specialized connective tissue that makes commercial AI useful for hardcore science. Because the bridge model approach underlying MatterChat is forward-compatible, it is perfectly positioned to leverage these parallel tracks of innovation. As Mahoney pointed out, “We expect that industry will continue to develop improved LLMs, and we expect domain scientists and facilities will continue to generate new data. An important part of scientific machine learning is not simply to solve problems on today’s data, but instead to develop general methods that will be forward-compatible with orders of magnitude more data, whether from scientific domains or from LLMs.” According to Yao, the MatterChat project, which was initially developed and enhanced with funding from a Berkeley Lab Laboratory Directed Research & Development (LDRD) Program, will now expand its capabilities. In a collaboration with Fermilab, MatterChat is already contributing to a U.S. Department of Energy Genesis Mission project — called Accelerating eXtreme Environment Specs-to-Silicon (AXESS) — that aims to speed up the development of next-generation, high-speed, radiation-hardened detectors for challenging particle physics experiments by using advanced 3D integrated circuits (chiplets) and AI-driven data analysis. In addition to the LDRD support, the team also credits supercomputing resources at the National Energy Research Scientific Computing Center (NERSC), located at Berkeley Lab, with MatterChat’s success. “We are incredibly grateful to NERSC; this research simply would not have happened without access to the Perlmutter supercomputer through their AI for Science program,” said Tang. Wenbin Xu, a NERSC postdoctoral fellow at the time, was also a major co-author of the work, as was Benjamin Erichson, a research scientist in Berkeley Lab’s SDD, highlighting the benefits of AMCR-SDD collaboration on AI for science. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
Exploit available for new DirtyDecrypt Linux root escalation flaw
A recently patched local privilege escalation vulnerability in the Linux kernel's rxgk module now has a proof-of-concept exploit that allows attackers to gain root access on some Linux systems.
DeerTV Reports Surge in Demand for Outdoor TV Enclosures Ahead of Summer 2026 Outdoor Entertainment Boom
Changan, China--(Newsfile Corp. - May 18, 2026) - DeerTV, a manufacturer of weatherproof outdoor TV and projector protection systems, is...
New Windows 'MiniPlasma' zero-day exploit gives SYSTEM access, PoC released
A cybersecurity researcher has released a proof-of-concept exploit for a Windows privilege escalation zero-day dubbed "MiniPlasma" that lets attackers gain SYSTEM privileges on fully patched Windows systems.
AI Is Forcing Businesses to Adapt. Here’s What Actually Works
Here are six ways founders can be defensive and AI-proof their revenue streams and future business growth.
MBAs are on sale: Business schools slash tuition by up to 50% as workers scramble to future-proof careers against AI
Save tens of thousands of dollars on an MBA while learning AI-specific skills.
Windows BitLocker zero-day gives access to protected drives, PoC released
A cybersecurity researcher has published proof-of-concept (PoC) exploits for two unpatched Microsoft Windows vulnerabilities named YellowKey and GreenPlasma, which are a BitLocker bypass and a privilege-escalation flaw.
New York’s Most Powerful Entertainment Attorneys 2026
Celebrities, studios and criminal minds alike turn to the Big Apple’s top legal eagles — for profit or proof of innocence.
Small business expert discusses impact of AI on new grad hiring
Small business expert Gene Marks discusses President Donald Trump's celebration of small businesses and the April 2026 jobs report. He highlights AI-proof jobs like field managers and service technicians.
Martin Brundle reacts as Sky Sports extends F1 deal until 2034
Martin Brundle has praised Sky Sports’ renewed F1 deal as a major responsibility and proof of the broadcaster’s long-term commitment to the championship
Xenon Reports Q1 2026 Financial Results and Provides Business Update
Reported positive topline data from Phase 3 X-TOLE2 study of azetukalner in FOS in March and anticipate NDA submission in Q3 2026Five additional Phase 3 studies of azetukalner continue to enroll in epilepsy and depression indications with Phase 3 X-NOVA2 MDD topline data expected in H1 2027 Phase 1 studies of novel NaV1.7 (XEN1701) and KV7 (XEN1120) candidates expected to complete H2 2026 to support Phase 2 proof-of-concept studies in pain Cash, cash equivalents and marketable securities of $1.3
Republicans alone on filibuster-proof immigration enforcement bill
Democrats score no wins five months after insisting on use of body cameras, judicial warrants and a racial profiling ban
CISA says ‘Copy Fail’ flaw now exploited to root Linux systems
CISA has warned that threat actors have started exploiting the "Copy Fail" Linux security vulnerability in the wild, one day after Theori researchers disclosed it and shared a proof-of-concept (PoC) exploit.
Scott Galloway: Top skills for AI-proofing kids' future careers
Parents should tell their kids to prioritize storytelling skills in the AI era, Scott Galloway told Steven Bartlett on "The Diary of a CEO."
Global Health & WASH: May 2026 Funding Opportunities (14 new opportunities!)
The May update for Global Health & WASH brings new calls that cluster around three distinct shifts: LMIC research leadership moving from funder encouragement into a hard eligibility constraint, pharmaceutical and corporate-linked funders running structurally independent education and use-inspired research portfolios at substantial scale, and AI adoption shifting decisively from speculative tool to operational capability across health and WASH systems alike. In LMIC Research Leadership as Eligibility, Not Encouragement, Wellcome anchors the dominant signal of the month with three separate major calls that all require or strongly center LMIC leadership in their basic eligibility architecture. Wellcome’s ESIC Hubs (£1.5-£1.9M per hub over 3-5 years) explicitly requires an LMIC-based lead applicant, treating Global South leadership as a design constraint rather than a participation goal. Wellcome’s Infectious Disease Clinical Trial Development Award funds transdisciplinary teams whose lead must be a mid-career or established researcher based at an organization in Africa, South Asia, or Southeast Asia. Wellcome’s larger Infectious Disease Clinical Trial Award (£1M-£8M for optimizing licensed interventions) requires the administering organization to be in eligible LMIC regions and at least 50% of applicants based there. Around Wellcome, the same pattern shows up at smaller scale and across funders: CHINNOVA channels $1M into West and Central African research institutions for climate-health work; IBRO’s Neuroscience Training Grants explicitly tier ceilings by region of residence with Africa receiving the highest amount ($5,000); IHME’s GBD Emerging Researcher Award reserves at least one of its two annual awards for an LMIC researcher; Sidaction’s HIV Cure call funds research teams across France, the Netherlands, and eligible African countries with a minimum two-country collaboration requirement. Taken together, these calls signal a meaningful shift in how research equity is being operationalized — not as encouragement language at the bottom of a call, but as a structural feature of who can lead, where the work must be administered, and what proportion of the team must be regionally rooted. In Pharma-Funded Independent Medical Education and Use-Inspired Research, the lineup this month is unusually deep. Pfizer alone runs four parallel calls under independent education and quality improvement frameworks: a Migraine and Women’s Health RFP (200K x 2-year, $500K pool), Migraine IME ($200K, $1M pool), JAK Inhibitor evidence-based education ($100K), and Maternal Vaccination HCP Education in Saudi Arabia ($75K), plus a separate Pediatric Pneumococcal Surveillance research call in Saudi Arabia ($400K per project). Novo Nordisk Foundation runs four parallel calls of its own at substantial scale: Infectious Diseases Catalyst Grants (DKK 60M pool, up to DKK 7M collaborative), Non-Diabetic Endocrinology Collaborative Grants (DKK 53M pool, DKK 5-10M per project), Pioneer Innovator Grant Health (DKK 1.1M), and Distinguished Innovator Grant Health (DKK 6.8M). LEO Foundation deploys DKK 2-4M per project for dermatology research excellence. Pfizer’s framing across calls deliberately separates the funding from product promotion, emphasizing measurable practice-relevant change and structural distance between scientific outputs and commercial activity. Novo Nordisk’s “use-inspired” hard filter pushes researchers toward a credible translation pathway from mechanism to deployable tool. The pattern: industry-aligned funders are running structurally independent portfolios at meaningful $ that fund implementation evidence, not promotion. In AI as Operational Capability, Not Speculation, several calls this month explicitly treat AI adoption as a near-term operational question with real evidence and governance requirements, rather than a speculative trend to be watched. Wellcome’s ESIC Hubs frames AI as “the accelerator” for evidence synthesis, asking hubs to translate technical innovation into adoption-ready workflows that can match real-world policy timelines. UNICEF Venture Fund’s new Climate Tech for Children’s Health call funds for-profit startups deploying AI, machine learning, and blockchain in low-resource environments, with a strict requirement that solutions must already have a working prototype with promising pilot results. The Water Research Foundation has launched two simultaneous AI calls treating reproducibility, cybersecurity guardrails, and human-in-the-loop deployments as core design constraints rather than nice-to-haves: one on GenAI and Agentic AI in water utilities ($200K), one on alternatives to water shutoffs that explicitly screens for implementation economics evidence. Nordic Innovation’s quantum technology call (NOK 4.5M) extends the same logic into life science and healthcare, demanding consortium-stage projects mature enough to test with real users. TEF-Health offers €300K in subsidized testing infrastructure for European healthcare AI and robotics SMEs. The pattern: funders are converting AI pilots into playbooks, requiring transfer evidence and risk controls before AI can move from experimentation to sector-wide operational practice. For LMIC-based researchers in infectious disease or evidence synthesis, the three Wellcome calls together represent the deepest concentration of LMIC-led research capital in recent memory — pair the Infectious Disease Clinical Trial Development Award (£200K, deadline May 19) as a strategic pipeline feeder into the larger Infectious Disease Clinical Trial Award (£1-£8M, deadline June 2), since the development award functions explicitly as a track into the larger trial scheme. For Nordic researchers, the four Novo Nordisk Foundation calls plus LEO Foundation and Nordic Innovation Quantum represent ~$30M in regional research capital this cycle, all with deadlines clustered in May-August. For WASH practitioners, the three Water Research Foundation calls together (each up to $200K) are a cluster worth reading across — one on AI deployment, one on shutoff alternatives, one rewarding applied innovation — and the strongest applicants will articulate how their work bridges utility operations to policy-relevant evidence. For US-based clinical researchers and educators, the four Pfizer calls plus the APF Portfolio’s 80+ programs form an unusually accessible recurring pipeline; APF’s prohibition on indirect costs is a meaningful structural feature for early-career applicants whose institutions might otherwise absorb significant overhead. Snapshot of New Opportunities Total Estimated Funding Pool: $100 Million+ USD The grants are organized into three categories: Open Calls: Current grant and opportunities with a deadline. Grants are listed by closing date. 39 open opportunities- 14 new! Rolling Applications: current grant and opportunities with rolling applications (but it’s still best to submit as early as possible). 14 rolling opportunities- 1 new opportunity! Long term planning: Grants that have closed their current rounds, but are expected to open new windows. 4 long term opportunities! A quick tip for returning readers: if you want to jump straight to the newest additions, use CTRL F to search for “New!” and navigate quickly to the latest funding opportunities This post is for paid subscribers. This helps support the time and effort it takes to curate and organize these opportunities. Subscribe To keep this accessible to everyone who needs it, we’re happy to offer pay what you can rates. You can find more details here. Open Calls: Migraine Competitive Grant Program: Migraine and Women’s Health (Quality Improvement and/or Research RFP), Pfizer.*Closing soon!* Pfizer, in collaboration with the American Headache Society, is seeking independent quality improvement and research projects that close persistent gaps in migraine care for women across the United States. The funding logic centers on measurable, practice-relevant change: proposals should generate actionable evidence, tools, or system improvements that can be shared and used widely, not just within a single site. Priority areas span menstrual migraine and screening in women’s health settings, the role of hormonal fluctuations across the lifespan, sex-specific risk factors, comorbidities and quality-of-life burden, and disparities in outcomes by race, ethnicity, and socioeconomic status. The call is structured as a two-step competitive process with an initial Letter of Intent, with the strongest concepts expected to translate scientific insight into implementable improvements in diagnosis, management, and equity. Geographies: United States. Who can apply: U.S.-based organizations (not individuals), including professional schools, healthcare institutions, professional organizations, and other entities focused on healthcare improvement. Funding amount: Up to USD $200,000 total over 2 years; total pool USD $500,000. Targeted Sectors / SDGs: Health; Focus areas: migraine, women’s health, quality improvement, clinical research, menstrual migraine, health disparities. Deadline: LOI: May 5, 2026; Full proposal (by invitation): August 28, 2026. Learn more and apply here. This RFP rewards “shareable impact”: the most competitive submissions will pair rigorous methods with a clear plan for system uptake and dissemination beyond the originating institution. ReSSARC: Strengthening the Health Sector and Food Security in the Central African Republic (AID 013381/01/0) Call for Proposals, Italian Agency for Development Cooperation (AICS). *Closing soon!* AICS is seeking a limited set of high-capacity humanitarian partners to deliver an integrated package that stabilizes essential health services while reducing acute malnutrition and strengthening food security resilience in priority areas of the Central African Republic. The funder’s logic is explicitly nexus-based: proposals should meet urgent, life-saving needs while reinforcing local systems, coordination mechanisms, and community-level capacity so results persist beyond the emergency window. AICS emphasizes inclusion and protection outcomes, expecting gender-sensitive design and explicit measures for groups facing compounded vulnerability, including women, children under five, internally displaced people, and people with disabilities. The call also signals a localization pathway, encouraging stronger collaboration with national NGOs and balanced partnerships, alongside clear coordination with existing cluster and humanitarian actors to minimize duplication and improve coverage. Geographies: Central African Republic (Bangui, Ombella-Mpoko, Ouham, Ouham-Pende, Lim-Pende, Lobaye). Who can apply: Non-profit organizations registered with AICS, plus eligible non-profits without an office in Italy that have a pre-existing collaboration agreement with an AICS-listed organization. Funding amount: Total pool EUR €1,800,000; max EUR €800,000 (single applicant) or EUR €1,000,000 (ATS). Targeted Sectors / SDGs: Health; Focus areas: malnutrition prevention and treatment, maternal and child health, community health services, food security and resilience, localization partnerships Deadline: May 5, 2026. Learn more and apply here. This call is structured to reward partners who can link frontline service delivery to system durability and localization, not just short-term coverage gains. Contracts for Innovation in drug and alcohol addiction healthcare, Innovate UK. *Closing soon!* Innovate UK, on behalf of the Office for Life Sciences Addiction Healthcare Goals program, is procuring R&D to accelerate innovations that can improve treatment outcomes, strengthen recovery, and reduce harm and deaths linked to drug and alcohol addiction. The competition is positioned as a market-facing readiness push: selected projects are expected to advance solutions toward later-stage validation, generate evidence of user acceptability and UK market fit, and map credible routes through regulatory and certification requirements. Innovate UK signals a preference for innovations that can be field-tested in relevant UK settings and progressed to TRL 6 or 7, with practical plans for commercialization after contract completion. This structure favors applicants that can execute most work in-house, co-develop with service providers and people with lived experience, and translate technical progress into deployable tools for addiction healthcare delivery. Geographies: United Kingdom. Who can apply: Single organizations of any size (including EU, EEA, or international) leading delivery, with most work and key deliverables carried out in the UK; subcontractors for specialist skills only. Funding amount: GBP £200,000–1.5 million per project (inclusive of VAT); Total pool: GBP £20 million (across two strands). Targeted Sectors / SDGs: Health; Focus areas: Mental Health, Health Systems Strengthening, Research & Development, Technology Access. Deadline: May 6, 2026 (11:00am). Learn more and apply here. This call uses procurement to pull near-market addiction innovations toward operational proof and adoption pathways in UK services.
Behind Supreme Court Voting Rights Ruling, a Clash Over the Reality of Racism
The Supreme Court ruling said there must be proof that a racial group was “intentionally” disadvantaged. The dissent called it “well-nigh impossible.”
The politics of Trump's ballroom
WEST PALM BEACH, Florida — President Trump was about a half-hour into his second speech of the day, musing about the danger he faced last week at the White House Correspondents’ Dinner. “Saturday night, it was good for one thing. People are loving my ballroom now,” Trump told a crowd last night. “That’s the only thing. They love my ballroom.” Ever since he was hustled off stage at the Washington Hilton last week, after an attempted shooter breached the dinner’s security perimeter and sent guests diving to the floor, the president has argued that the incident is proof for why his White House ballroom is needed. Only his planned $400 million, 90,000-square-foot project, Trump has reasoned, can keep a president truly safe at major events. It’s true that a bunch of GOP lawmakers have jumped on board with new legislation and words of support for Trump’s project. House Speaker Mike Johnson, Sen. Rand Paul and other prominent Republicans went on TV to talk about the need for a ballroom. But as we reported at The Washington Post this week, “people” aren’t really changing their minds on Trump’s ballroom. A new Post-ABC News-Ipsos poll found that most Americans still oppose the ballroom project: 56 percent against it versus 28 percent in favor. The numbers are the same as a poll from last year, the Post’s polling whiz Scott Clement found. Other new polls have found similar margins, too. “I’m just not a giant fan, especially of extravagance,” one independent voter told me. “I mean, in the era we’re in right now, given how much people are struggling, it just doesn’t make a lot of sense to me to spend $400 million on a ballroom.” How we got here The Washington Post has followed Trump’s ballroom project very closely. (My colleague Jonathan Edwards may have written the most total words of any reporter about the project; I may be second.) The project exploded as a national issue after Trump tore down the White House’s East Wing after initially promising not to touch it.1 Historic preservationists sued, and about a month ago, a federal judge ordered a halt to ballroom construction.2 The only way the ballroom could proceed, U.S. District Judge Richard Leon ruled, was if Congress explicitly authorized Trump’s plans. (An appeals court has stayed Leon’s order for now.) The judge’s ruling raised an obvious question: would Congress authorize the project? Keep in mind, “authorization” doesn’t mean that Congress needs to necessarily fund the planned $400 million project; they could just sign off on the private donations that Trump has solicited to pay for the ballroom.3 But GOP lawmakers really didn’t want to talk about the ballroom, we reported at The Post just a few weeks ago. One major reason was the ballroom’s bad polling — not a small detail with many of their own midterm elections looming. The White House Correspondents’ Dinner incident, and Trump’s advocacy, may have changed the political conversation around the ballroom. As this week’s polling shows, it doesn’t seem to have affected the fundamental questions surrounding the project. To put it another way, voters still say that cost-of-living concerns are top-of-mind heading into the midterms. Some Republican and independent voters have told me that they see the ballroom as a vanity project that will help Trump but do nothing for their own lives. It might not help Republicans that Trump has dismissed some of the cost-of-living concerns. He’s described “affordability” as a buzzword manufactured by Democrats whose policies helped cause inflation. “They’ve got one good line of bullshit,” Trump said in his first speech yesterday, delivered at a Florida retirement community known as The Villages. Thanks for reading Dan Diamond's newsletter! Subscribe for free to stay up-to-date on my work. Subscribe Speaking of Trump’s speeches… I’m down in Florida because it’s my “duty week” as a White House reporter — basically, I follow the president wherever he goes. (And then hand the baton off to one of my Washington Post colleagues next week.) As part of my duty week, I’ve spent a few days covering the White House’s reaction to Saturday’s attempted shooting, including officials’ review of the security protocols to protect the president. Trump was restrained in the wake of the incident, addressing the nation from the White House briefing room about the need for peaceful dialogue and trying to lower the temperature. But he’s clearly been itching to get back out of the White House, and I was curious to hear how his Florida speeches would go. And — to my ears — it was an unburdened Trump. Not the one we saw Saturday night in the White House briefing room, but the one we witnessed on the campaign trail in 2024. Loose, dropping profanities, mocking everyone, including members of his team. “We have a man here who knows more about Medicaid, Medicare, medical crap than any human being. Where’s Dr. Oz? Where the hell are you, stand up,” Trump said, referring to Mehmet Oz, the administrator of the Centers for Medicare and Medicaid Services. “It’s the most boring trip I’ve ever made. He’s telling me about Medicare, Medicaid. All I want to do is take care of you, I don’t care. I said, ‘You work out the details.’” I wrote up the speech, as Trump headed to West Palm Beach to deliver more remarks a few hours later. The White House didn’t like my story. Their rapid-response team called it the work of a “total loser”4 and “a miserable TDS-ridden moron.” You can be the judge of whether it’s a fair story or the product of someone afflicted with “TDS.” (The White House’s shorthand for “Trump Derangement Syndrome.”) I’ve pasted the story below with a gift link, meaning that you don’t need to be a Washington Post subscriber to read it. It might prompt you for an email address, though. Meanwhile, I’ll be down in Florida for the rest of the weekend, covering the president. It’s TBD whether those stories will be deemed TDS-afflicted, too. 1 Jonathan details the past six months of ballroom fallout, reactions and the implications for executive authority here. I also had a great time talking about Trump’s ballroom and other projects with Australia’s ABC News Daily on a podcast this week. 2 The judge allowed underground construction and national security-related work to continue. But that hasn’t been enough for Trump, who says the ballroom itself is essential for his protection. 3 The ballroom donors include companies like Palantir, Google and Amazon. For transparency, Amazon founder Jeff Bezos owns The Washington Post, where I work. No one has ever interfered with our team’s coverage of the ballroom or our mentions of Amazon or Bezos, and if that changed, I’d speak out. 4 To be fair, I’ve spent the morning working on stories and writing this Substack post rather than enjoying the beautiful Florida weather. So they may be onto something about who’s winning and who’s losing.
Forget Big Tech: Small businesses will hire nearly 1 million grads in 2026—and some of the hottest roles are gloriously AI-proof
While financial analysts and software engineers have suffered the biggest decline in share of the new grad job market, AI engineers and service technicians are at the top.