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Can friends lobbying friends on politics really work? Editorial Board Roundtable
So, what does our Editorial Board Roundtable think of these approaches, paid and otherwise, of friends lobbying friends on how to vote?
Small Business Spotlight: Specialty dental practice takes root on Market St.
WHAT MADE YOU WANT TO OPEN BLUE HERON ROOT CANAL SPECIALISTS? OWNER SARAH ERMOSHKIN: “I’ve always been an entrepreneur at heart. When I was 23 years old, I owned a cafe in Seattle, where I even fought to oppose oppressive sales taxes on espresso drinks. After selling that business, I got married and started a family. Once my children were in school, I attended dental school through the Navy Health Professions Scholarship Program (HPSP), which covered my tuition and provided a stipend. From the beginning, my goal was to eventually own my own practice after completing my military service. The work I do is incredibly rewarding and being able to create an environment that puts patients first while helping employees grow professionally makes every day as a business owner meaningful.” WHAT DOES YOUR BUSINESS PROVIDE? ERMOSHKIN: “Endodontists are often called ‘pain doctors’ because our primary mission is to relieve pain and save natural teeth. We specialize in root canal therapy, retreatment of nonhealing root canals, treatment of root resorption, apicoectomies – microsurgical root-end procedures – and vital pulp therapy for children. Our goal is to preserve teeth that might otherwise need to be extracted and replaced with implants. Saving a patient’s natural tooth is almost always the best long-term solution for their oral health.” HOW DOES YOUR MILITARY TRAINING HELP YOUR BUSINESS? ERMOSHKIN: “My military service taught me how to lead, collaborate and make decisions under pressure. I worked closely with senior leadership while treating a large patient population and collaborating with specialists across multiple disciplines. During the COVID-19 pandemic, (I) served a dual role as Navy dentist and as the public affairs officer for Naval Health Clinic Annapolis, where I helped develop medical and dental communications for the midshipmen and support staff, while coordinating with the broader Navy Capital Region. We worked closely with other military treatment facilities to share best practices and deliver consistent public health messaging. Those leadership experiences continue to shape how I lead my team and serve our patients today.” WHAT CHALLENGES ARE YOU FACING RIGHT NOW IN YOUR BUSINESS? ERMOSHKIN: “One of our biggest challenges is overcoming the misinformation surrounding root canal treatment. Root canals are safe, effective and often the best way to relieve pain while preserving a natural tooth. Unfortunately, many people choose extraction because they believe it’s less expensive or because they’ve seen misleading information online. What they may not realize is that losing a tooth can lead to shifting teeth, changes in the way they bite and chew and significantly higher costs to restore their smile later.”
Think you need 10,000 steps a day? Science says otherwise
The 10,000-step goal came from a 1960s ad campaign, not science. A new study reveals the real number you need for major health benefits
The Beauty Of Settled Science
Commenters convinced me I made a subtle mis-step in my framing of Psychology Research Is Mostly Fine. My framing emphasized that social priming studies had failed to replicate, but that many other subfields of psychology hadn’t. This naturally led commenters to point to failed replications in other subfields. Although none of these were as bad as social priming, few or none were entirely problem-free. Instead of emphasizing the difference between social priming and other subfields, I should have emphasized the difference between settled science and novel research. Here are some of my favorite psychology findings: The brain is sculpted by reinforcement learning, and a key driver of this learning is a prediction-error-based algorithm located in the nucleus accumbens and implemented through dopamine. Psychedelic drugs exert their effects through 5-HT2A receptors, pointing to a role for these receptors in some sort of construct like the balance between priors and new evidence. Certain cognitive functions are localized to certain brain areas (for example, impulse control to the frontal lobes; linguistic structure to Broca’s area, linguistic meaning to Wernicke’s area. But injure one of these areas, or render a certain function useless (eg a blind person having no more use for their visual cortex), and these mappings can gradually shift - suggesting that neurons are general-purpose compute whose specialization is a contingent fact about convenience and efficiency rather than a necessity. Most personality traits and general capabilities are about 50% genetic and 50% random, with little role for family environment or childhood history, with a few notable exceptions like partisan preference. Recent research complicates this story, but the further you go from this basic picture, the gnarlier the chains of indirect causation you require. Neurons fire together in “brain waves”, whose exact frequency corresponds with the character of consciousness; These divide neurons into natural subpopulations and linked graphs. The gamma rhythm corresponds surprisingly well to items in conscious awareness. GABA receptors seem to be involved in something like relaxation. By hitting different GABA receptors in different ways, you can modulate the exact type of relaxation you produce. Hitting GABA-A directly is too powerful and can cause dangerous levels of sedation, but allosteric modulation of GABA is an incredibly fruitful strategy that underlies drugs from benzos to barbituates to alcohol. If you know your GABA-A subunits, you can target specific subtypes of relaxation; for example, Ambien hits GABA-A1 and induces sleep. Memory divides somewhat fuzzily into bins of immediate-sensory, short-term, and long-term, with separate retrieval and storage functions, and separate systems for declarative vs. procedural learning. These can be harmed selectively, usually by hippocampal injury, so that some patients can remember their life stories but not form new long-term memories, or learn new skills like bike-riding but not new facts. The exact substrate of memory is unclear but seems to involve new synaptic connections between neurons in at least some way. The ability of modern psychology and psychiatry to treat mental illness is real but limited. SSRIs work for half or slightly fewer than half of patients; antipsychotics work with extreme side effects; benzodiazpines work but produce addiction and tolerance in many (not all) people. Most supplements are stuck in Poor Quality Study Replication Crisis Hell, but melatonin and l-theanine stand out as especially promising. Psychotherapy works with moderate effect size, but (with a few exceptions) seems to be more about the relationship with the provider than the details of the therapy itself. Mental hospitalization can sometimes defuse immediate crises but work less well than mental hospitals would like, with some studies showing overall negative effects. Schizophrenia is mostly genetic, but risk also increases with prenatal infection (especially rubella) and obstetric complications; there is suggestive evidence for roles of social stress and drug use (with psychedelics and marijuana making things worse, and nicotine if anything making things better). Bipolar and schizophrenia are so closely linked on a pathophysiological level that it seems like they might be the same disease; on the clinical level, they do seem to follow two separate courses/prognoses but can sometimes surprise clinicians by eg someone who seemed condemned to permanent schizophrenia unexpectedly recovering. The brain’s native reinforcement learning algorithms cash out in the phenomena of classical and operant conditioning; different reinforcement schedules reinforce at different levels, which can be exploited by bad actors to make especially compelling products (eg slot machines). None of these are immune to doubt. A few are speculative; one or two are genuinely unorthodox. But they’re all a step above one p = 0.049 paper that might not replicate. If they get overturned, they’ll be overturned by the same sort of genuine scientific shift that replaced Ptolemy with Copernicus or Lamarck with Darwin, not some sort of sordid discovery about someone entering random numbers into a spreadsheet. In 2008, Eliezer Yudkowsky wrote about The Beauty Of Settled Science: If you only care about scientific issues that are controversial, you will end up with a head stuffed full of garbage. The media thinks that only the cutting edge of science is worth reporting on. How often do you see headlines like “General Relativity still governing planetary orbits” or “Phlogiston theory remains false”? So, by the time anything is solid science, it is no longer a breaking headline. “Newsworthy” science is often based on the thinnest of evidence and wrong half the time—if it were not on the uttermost fringes of the scientific frontier, it would not be breaking news. Scientific controversies are problems so difficult that even people who’ve spent years mastering the field can still fool themselves. That’s what makes for the heated arguments that attract all the media attention. Worse, if you aren’t in the field and part of the game, controversies aren’t even fun. Don’t be embarrassed to read elementary science textbooks, either. If you want to pretend to be sophisticated, go find a play to sneer at. If you just want to have fun, remember that simplicity is at the core of scientific beauty [...] A steady diet of science news is bad for you: You are what you eat, and if you eat only science reporting on fluid situations, without a solid textbook now and then, your brain will turn to liquid. Psychology isn’t centrally the new paper in Annals Of Recently-Breaking News saying that an n=5 study showing that large portion sizes increase support for terrorism. Psychology is centrally ideas like brain waves, GABA receptors, reinforcement learning, twin studies, and connectionism. People who think differently are worrying too much about BREAKING NEWS!!!, and should stop. The key distinction isn’t “recent research in social psychology” vs. “recent research in cognitive/bio/behavioral psychology”, as my essay last week unfortunately implied. It’s recent research in general vs. the deposit of settled science, battle-tested hypotheses, and carefully-earmarked speculation. And part of the reason social priming in particular was such a disaster was that it defied that heuristic, building itself up over hundreds of studies until it approached or even entered the pantheon of settled science - before collapsing (5-HTTLPR was also sort of like this). This isn’t a claim that settled science is always right - Freud was “settled science” for a long time - just that there’s an intuitive distinction between the level of credence you should have in the existence of power posing vs. the existence of Broca’s Area. (I compounded this error by calling the last post “Psychology Research Is Mostly Fine”, which suggested I was talking about the new research rather than the settled science. I actually meant to distinguish between research-psychology-the-field vs. clinical-psychology-aka-therapy, but this framing unintentionally pushed against the point I wanted to make) Some commenters on the previous article understood this point, but challenged its weight. It’s very cool, they said, to know how many neurons are in which brain lobe, but the main way that Psychology affected their lives was through bad clickbait studies used to buttress dumb policies; some p = 0.049 study says that exposure to microaggressions makes minorities’ brains shrivel into raisins, and now their company has to implement punitive DEI or be sued for hazardous work conditions. I won’t dispute this as an accurate description of how the sausage gets made, but it’s worth examining the trains of thought that connect this observation to a campaign against psychology in general: The dumb woke psychology was wrong and bad, and I don’t have good heuristics for distinguishing dumb woke psychology from other psychology, so I’m going to assume it’s all wrong and bad. The dumb woke psychology was wrong and bad; it seems useful to punish/discredit psychology as a whole to assist my campaign against it, so for propaganda purposes I will try to discredit psychology as a whole. To people in the first camp, my response is: come on, your heuristics aren’t that bad. “Microaggressions makes minorities’ brains shrivel up like raisins” is in the woke category; “the hippocampal theta rhythm seems to be associated with both memory and pathfinding, suggesting an unexpected yet surprisingly compelling relationship between the two” is in the not-woke category, don’t play dumb here. To people in the second camp, my response is: deliberately exaggerated propaganda campaigns are bad and this is a confession, not an excuse. Inability to view science through anything but a political lens was what got us into this problem; doubling down on it is a temporary solution but a recipe for permanent disaster. If you pronounce entire fields of science to be acceptable collateral damage in your campaign against bad political actors, you will alienate not just scientists but patients, clinicians, and general lovers of truth. Instead, you can say the thing you mean - that woke partisans shouldn’t try to occupy and exploit the otherwise-noble science of psychology for their own political gain - and wage the same campaign with a halo rather than a comic-book-villain goatee. From the font of oracular wisdom that is Weird Sun Twitter: [ Vessel Of Spirit@VesselOfSpirit i love you all conditionally but i love the good part of you unconditionally. even if it becomes a very small part 2:11 AM · Jan 22, 2015 1 Reply · 5 Reposts · 29 Likes ](https://x.com/VesselOfSpirit/status/558084551772471298) This is how I feel about psychology1, too, but with a touch more optimism. The good part of psychology is enormous, a hoard of endless treasures. You just have to look past the headlines! 1 To play devil’s advocate - should one always be so magnamious? Astrology? Phrenology? These seem different in that their entire orientation was flawed from the start; they didn’t simply fail to find the correct constellation-personality connections, the whole project of searching for connections between constellations and personality was doomed from the start. More difficult might be asking the same question about education research, which is notoriously bad. I don’t know enough about education research to know if there’s some settled science of correct and enlightening results lurking in the background beyond the day-to-day froth of poorly-conducted and politically-motivated studies. I think if it’s 90% bad, 10% good, it’s correct to call it bad as a compact way of communicating a useful truth; I think psychology is better than that, but admit it’s a tough judgment call.
Australia: NSW Health warns against handling bats due to lyssavirus
NSW Health is advising the public not to handle, or otherwise handle bats, following a recent increase in people being bitten or scratched across the state. In the two weeks from 28 September to 10 October, 43 people were assessed by NSW Health after being bitten or scratched by bats. This includes in coastal areas in Hunter New England, the Mid North Coast, Northern NSW, and the Central Coast. This is a significant increase when compared with 10 reported cases of bat bites or scratches for the same two-week period in 2025. Keira Glasgow, Director in Health Protection at NSW Health, said, “We want people to assume that any bat could carry Australian bat lyssavirus, which is a close relative to the rabies virus and very dangerous to humans.” “If you see a bat that is injured or in distress, call your local wildlife rescue service and do not touch it. Only trained, protected and vaccinated wildlife handlers should interact with bats. “It is incredibly rare for Australian bat lyssavirus to transmit to humans, but once symptoms of lyssavirus start in people who are scratched or bitten by an infected bat, sadly there is no effective treatment. “If you are bitten or scratched by a bat, urgent medical assessment is crucial.” Rabies virus and Australian bat lyssavirus (ABLV) belong to a group of viruses called lyssaviruses. These viruses transmit via a bite from an infected ("rabid") animal. They all cause a similar illness known as rabies, which affects the central nervous system and is usually fatal. Rabies and ABLV infection cause similar symptoms. The early symptoms are flu-like, including headache, fever and fatigue. The illness progresses rapidly to paralysis, delirium, convulsions and death, usually within a week or two. Rabies cases and the three known human cases of ABLV infection have shown a wide variability in the time it takes for symptoms to appear following exposure to an infected animal (from several days to several years). The best way to protect yourself from infection is to not touch bats. If you see a bat in distress, injured or trapped on the ground, do not try to rescue it. Instead, contact trained experts. Outbreak News Today is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe
Sting strips down, bashes through intimate Seattle show
Overblown drums blunted an otherwise joyous underplay from the English pop-rock giant.
Davies: Board must go
>Continued from front board fit for purpose. The economy and the game in Wales is suffering. It is time now for people to voice their opinions, ex-players, regions, and the clubs and the fans most importantly and say change is needed. “It is up to us to force the changes otherwise this is not going […] The post Davies: Board must go appeared first on The Rugby Paper.
Poor Cardinals Defense Headlines Rough Loss to Cowboys
It’s for the best that preseason football games do not count, otherwise the Arizona Cardinals‘ near nine-year streak without a loss to the Dallas Cowboys would have come to a...
Postpartum politics: What it means to run for government as a new mum
Victorian Greens candidates Nina Crawley and Lini Fernando reflect on their experiences running a political campaign just months after giving birth. From the challenges and compromises to the support networks that wrapped around them, and why showing up vulnerably in public space matters. Subscribe In 2024, when my second child was four months old, I decided to run for local government. Fast forward two years, I am again four months postpartum, with my third child, and this time running in the state election. But this time has hit differently. Going through pregnancy, birth and postpartum while being a political candidate has had a more profound and personal impact than I ever expected. I reached out to my fellow Greens candidate for the state election, Lini Fernando, who gave birth 20 days before me, and her experience closely mirrored mine. Postpartum has its own unique challenges that we’ve not seen reflected in stories about women running for office. When people discuss the “juggle”, they’re usually talking about the practical mechanics of looking after small children - childcare arrangements, pumping schedules and logistics. As mums of multiple children, we were both aware of the logistical changes and difficulties that accompany giving birth and the months after. We assumed that combining this with running for election would be hard, but that we could organise our way out of any problems. What neither of us anticipated was the intense vulnerability that comes with putting yourself into the spotlight of a state election campaign at a time when you are physically exhausted and navigating things like breastfeeding and feeling like a stranger in your own body. There have been challenges that neither of us anticipated. Firstly, needing to buy new clothes because the supportive leggings that are a staple of the postpartum wardrobe weren’t going to cut it for community events. Lini held her first fundraiser - a trivia night - just three weeks postpartum. A business-casual wardrobe, several sizes bigger than pre-baby, was needed, and fast. As women, how we look is closely scrutinised, and the internalised fatphobia is loud. We are conditioned from our earliest days to believe that our appearance is tied to our worth, and taught how we are allowed to show up in public. Women who dare to put themselves into the spotlight also receive more abuse than their male counterparts, including criticism of their appearance. To stand in front of members of your community and try to convince them that you’re the best person to represent them in parliament is a challenge in and of itself. To do it with leaking breasts, a pelvic floor that could betray you at the first sign of a sneeze, a body that still feels unfamiliar, and with half your hair falling out (hello postpartum hair loss!) is another thing altogether. It requires courage, allowing yourself the grace to be vulnerable in public, and the belief that showing up, however imperfectly, matters. And the vulnerability isn’t just physical; it also includes factors like having to ask a campaign team to accommodate the realities of having a baby. Asking for help is hard, and it’s a skill that both new parents and hopeful political candidates need to become comfortable with. Parents understand the disappointment that comes when a call for help or connection is rebuffed, like a friend cancelling a much-needed coffee. That rejection is amplified for political candidates calling dozens of people each week. Many people don’t realise running for Parliament, when you aren’t the incumbent, is a volunteer role supported largely by other volunteers who have to squeeze volunteering in around their paid work. Calls need to be made and meetings held after work hours - usually during “witching hour” (5-8 pm), that special time of night when babies cry for no reason. I found that I was struggling to make these commitments, or having to tap out and ask for them to be rearranged. When I spoke to Lini, we realised that she had had a similar experience, this push-pull of being the candidate and wanting to show our faces at meetings and help recruit new volunteers at ‘calling parties’, but also being the only people who could settle our newborns. Sometimes we could merge the two worlds; Lini and I realised that we had both attended 7:30pm meetings online, sitting in a dark room, struggling though breastfeeding and settling our babies. Although we’re both committed to breaking the stigma around breastfeeding, it can be hard to keep your composure while trying to feed a young baby as they try to latch, spew and fuss. Ultimately, Lini and I agreed that showing up vulnerably in public space matters. Leaders should possess empathy and understanding for those who feel raw and in transition; from the dad who just got evicted to the teen who acquired a disability - anyone who feels they aren’t the version of themselves they expected right now. The patriarchy tries to tell us otherwise, but women shouldn’t have to wait until they’re ‘recovered’, no longer vulnerable, before they feel entitled to participate in public life. In fact, we could all benefit from our leaders being a little more vulnerable. *This piece was cowritten, but has been presented in the first person for the sake of style. About the authors Nina Crawley is former teacher and community organiser who now works in human rights and inequality advocacy for a non-profit. She’s a mother of three keen to get back to running as soon as her pelvic floor allows! She’s contesting the seat of Ivanhoe for the Greens in this year’s state election. Lini Fernando is a lawyer working in diverse areas of law including anti corruption and public law. She’s a mother of two and a gardening and good policy enthusiast. She is currently contesting the seat of Croydon for the Greens in this year’s state election. Thanks for reading Cheek Media Co. ! This post is public so feel free to share it. Share
Lautaro
Not everything is clear from the start, as someone once said. Otherwise Vincent Van Gogh, for example, would have sold more than one painting during his lifetime. The same could be said of Inter vs. C...
The Literacy Crisis Reaches Dog-Bear Science
“New analysis of more than 300 bear encounters suggests our canine companions often increase odds of conflict and rarely serve as early warning systems many recreationists assume,” reads a subhead in Mountain Journal. The thing is, that study reaches no such conclusion. But you have to read beyond the abstract to understand that. Mountain Journal is not alone. Let’s look at a brief list of takeaways published elsewhere just to establish that none of them seem to know how to read science. Wes Siler’s Newsletter is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe Here’s CBC: “A new peer-reviewed study, The Canine Conundrum: Is a Dog a Help or Hindrance in Bear Country?, dug into conflicts similar to what the video depicts, by analyzing 326 reported human-bear conflicts from 1901 to 2023 involving dogs.” “The study found 54 per cent of conflicts were started by dogs. In the encounters researchers included in the study, five people and 56 dogs were killed, and 170 people were injured.” “Lana Ciarniello, an independent researcher who co-authored the study, said researchers found when a person goes hiking with at least one other person, the odds of a negative bear encounter decrease by 50 per cent.” “However, when a dog is involved, the chances of a negative bear encounter increase because ‘a dog really adds a chaotic factor into the mix of recreation,’ said Ciarniello.” Again, the study finds no data supporting that conclusion. “Hiking with your pet dog may make you feel safer in bear country—but a hiker’s recent encounter and a new study prove that the numbers don’t add up,” writes Backpacker, which is now part of Outside. The numbers don’t add up to support this take. There’s plenty more examples, but all are equally dumb, so I’ll stop there. How did so many people get this so wrong? Three reasons: 1) the study is published behind a paywall, with only the abstract freely available. 2) the methodology sets out to create work relevant to a hyper-specific audience, not the public. And 3) the dataset studied is almost absurdly limited and doesn’t bear (get it?!) comparison to wider information. Let’s work through all three. The Problem is Money I understand the irony, since I’m writing about this on a subscription-model newsletter, but information wants to be free. The study is published by The Wildlife Society and costs $16 to access. Reprinting it is specifically prohibited by the terms of use, so I can only share snippets and my conclusions with you here. Having worked as a journalist for 24 years, I can report with some authority that journalists are both lazy and cheap. I don’t think it’s a stretch to say that much of the material you’re seeing written about this study is drawn from the freely-available abstract (above) only, and not a deep dive into its methodology or data. And that’s a problem, because abstracts are where researchers are able to break from their numbers and editorialize their opinions about conclusions. So what’s getting reported isn’t based on numbers, it’s based on an opinion designed for a specific audience, not the general public. Hell yeah ladies, kick that bear’s ass. The Methodology is Designed to Support a Predetermined Conclusion One of the biggest issues with the science that everyone likes to cite about bear spray’s efficacy is that no one understands that study was designed for a specific audience, looking for a specific answer. And since that audience is not the public, and our safety was not the question being asked, its conclusion is rendered irrelevant to our needs. Yes, I’m talking about Efficacy of Bear Deterrent Spray in Alaska, which was created by Tom Smith and Stephen Herrero, two of the same scientists who put together this dog-bear conflict analysis. In the spray study, the purpose was to present relevant agencies with the information they needed to issue their field workers with bear spray. As such, incidents where people were unable to access the spray or similarly failed to get it deployed in time were excluded from the results, and only incidents they deemed successful were included. Could a ranger roll down their vehicle’s window and scare a bear away from a dumpster by spraying it? Turns out the answer was yes, and that was good enough for the purposes of that study. The sample size of actual bear attacks—the thing you and I would want bear spray to provide a solution to, was too small to draw conclusions from, and didn’t indicate success in that role. Is a Dog a Help or Hindrance in Bear Country? has similar limitations. “Insights provided here are intended to help wildlife managers create effective guidelines regarding the presence of domestic dogs in bear habitat,” is explained clearly by the study’s text. To conduct the study, Smith and Herrero went looking for publicly available reports of dog-bear conflicts: “All incidents included in our analysis involved at least one bear, one dog, and one person who was at immediate risk of injury (e.g., not in a protective structure such as a cabin or vehicle), injured, or killed,” explains the report’s methodology. Why is that a problem? “We recognize that data used in this study are biased in that people generally only report adverse interactions with bears, not benign ones. Consequently, we assume that for the most part, people and their dogs pass through bear country without incident.” As was the problem with the bear spray study, people are drawing conclusions here from a study that was never intended to be relevant to the general public. The Smith-Herrero database famously excludes Alaska’s Defense of Life and Property records, which are elsewhere considered the largest and most authoritative source on human-bear conflict. The Dataset is Absurdly Limited A 2009 study conducted by the Washington Department of Fish & Wildlife estimates that each year there are at least 43,000 documented complaints of human-bear conflict across North America. Is a Dog a Help or Hindrance in Bear Country? Examined those reports for years ranging from 1901 to 2023, and only managed to find 326 such incidents that met the criteria explained above. Let’s do some quick math, poorly. 43,000 incidents x 122 years = 5,246,000 total incidents of documented human-bear conflict. Smith and Herrero were only able to find 326 incidents that met their criteria for dog+human+bear+risk or occurrence of injury. That means there’s a 0.006 percent chance that the study’s conclusion—that dogs make bear encounters more dangerous—even gets a chance of occurring. And remember, that’s only in documented conflicts. There’s no way to know how many people across North America run into a bear in any given year without any conflict, but it’s probably safe to assume that the total number is well in excess of 43,000. And, dig deeper into the study and you can find further restrictions that skew its conclusions even more. “While the people involved in these incidents did not report the level of training their dogs had received, it is our assumption that most were untrained companion dogs as opposed to well-trained bear dogs, such as Karelians and livestock guardian breeds that have proven to be significant aids in bear country,” the study’s methodology explains. And that’s a problem, because an ongoing study here in Montana is finding that the simple presence of a Kangal like our youngest dog Teddy can reduce visits to private property from grizzly bears by 87.8 percent. In addition to being a badass, Teddy is also a companion animal. And in my admittedly limited sample size of the three dogs currently present in our living room, she’s no more effective than her mutt brothers. Plenty of people own a dog capable of deterring bears, even if they didn’t set out to own a bear dog. The real purpose of this study appears to be a recommendation that leashes are mandated and recreationists pursue training in their deterrent of choice. But that doesn’t make for sensational headlines. Are There Any Real Takeaways Here? Let’s throw some numbers around, just to try and get our hands around what the larger picture might look like for bears and dogs here. The American Veterinary Medical Association estimates that 42.6 percent of American households own a dog. The Canadian Animal Health Institute says roughly 35 percent of households in Canuckistan own a dog. In an attempt to create the worst possible result for dogs, let’s run with that 35 percent number. And given that outdoorsy and rural people are more likely to both own a dog, and have bear encounters, let’s just make a giant assumption and apply that number to those 43,000 conflicts documented annually. If 35 percent of 43,000 human-bear encounters involve a dog, then we can assume that at least 15,050 dog-bear conflicts are occurring annually in North America. 15,050 x 122 = 1,836,100. Smith and Herrero were only able to find 326 incidents that met their human, dog, chance or occurrence of injury criteria across 122 years. And only 170 documented human injuries. So, with all the obvious caveats about these numbers in no way describing reality beyond narrow study parameters, what they actually found is that your risk of injury if your dog tangles with a bear is only 0.009 percent. I’ll take those odds next time I’m hiking my dogs in bear country. Top photo: “Superintendent Horace Albright, eating pancakes with bears, at Lake Lodge, Yellowstone National Park.” NPS. A journalist with more than two decades of experience working around the world, Wes Siler is here to cut through the outrage and disinformation to bring you the factual, insightful, actionable reporting you need to understand what’s going on. Upgrading to a paid subscription supports this reporting, and buys personal access to Wes, who will help you save money on gear, plan outdoor adventures, and prepare for real life, and who promises he’s less salty in real life than he sometimes comes across as on the Internet. Wes Siler’s Newsletter is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe
Democrats accuse Susan Collins of 'corruption' following report on alleged pay-to-play scheme
Collins' office as well as FBI officials have said the allegations — contained in an investigative report from ProPublica — are completely false. But Democrats clearly hope to convince voters otherwise in the final six weeks of the fall election campaign.
Industrial real estate in south Louisiana is hot, while other sectors are more sluggish
Louisiana’s industrial construction boom is fueling demand for warehouse space across the region, including the Greater New Orleans area — a welcome trend in an otherwise lackluster real estate market
New Iberia’s Beneath the Balconies brings live performances, dancing to Main Street
Organizers say the event is about more than entertainment, but also an opportunity to bring visitors into New Iberia when they otherwise wouldn’t have.
Jacks look to continue playing ‘fresh’ in Week 3
SIOUX FALLS, S.D. (KELO) — SDSU fell to 1-1 on the season after a 34-18 loss at Northwestern over the weekend. The Jacks struggled to convert in the red zone but otherwise hung around with the Wildcats for the majority of the night, only trailing by two with eight minutes to go in the fourth. […]
AI needs science’s search history
“Actually, that’s where the gold is,” said Alasdair Russell, my graduate school friend who leads a pre-clinical genome editing group at Cambridge. He was talking about the winding road of science that is omitted from published papers. “When you’re discussing how to do the experiment, and why this way is better than another way, and what does the data really mean? I know what it shows, but what does it mean?” At RAAIS 2026, he described how his group has begun logging the twisting path of discovery as it happens, recording the verbal and written exchanges that normally disappear. Ideas become nodes in a living graph: they branch when a meeting produces two plausible experiments, merge when separate lines of evidence converge, and go dark when someone quietly stops pursuing them. In his implementation, one agent scores novelty, while another tries to learn “how scientists think and how they navigate through a complex world of data,” so that high-potential nodes can trigger deeper investigation. As frontier AI labs deploy agents toward scientific discovery, giving AI authentic scientific taste remains a trillion-dollar dilemma. The bottleneck is that the record we have kept for centuries of science might be insufficient to get us there. The experiments that never make the paper Peter Medawar, 1960 Nobel laureate and “father of transplantation,” asked in 1963 whether the scientific paper was a fraud, and answered yes: the form of the paper misrepresents the thinking that produced it. Sixty years on, the diagnosis is unchanged. A paper presents a clean progression from hypothesis to result to conclusion. Lost along the way are the unconventional theories, the abandoned or unaffordable methods, and the underwhelming and inconclusive data. It reads like a browser history with every dead end deleted: the ten open tabs, the four rephrased queries, the wrong turn down a subforum. All scrubbed, leaving one clean path from question to answer as the canonical path. What has changed since then is that the discarded material is now worth something. No high-impact journal wants these artifacts, but raw trial and error is the most likely source of the training data required to develop scientific intuition, or what researchers call taste. Earlier attempts to capture the discards were motivated by scientific integrity. The Journal of Negative Results in Biomedicine launched in 2002 to publish rigorous studies that disputed established models or exposed ineffective treatments. Its archive preserved negative conclusions after they had become papers, rather than the live alternatives and arguments that produced them. BioMed Central closed it in 2017, saying the mission had been served now that other journals publish null results. A less generous reading is that in fifteen years it published around 200 papers because almost nobody wants to read a negative result, let alone write one up when it will not count toward academic tenure. AI models are different. Even an uninteresting negative result can be useful, provided it is labeled. Earlier this year, Anthropic put a version of this to the test. The company pulled 129 decision points from real Claude Code sessions between January and March 2026, showed models the work up to a human detour, and asked what should happen next. Claude Mythos Preview beat the human choice 64% of the time, while Opus 4.5 managed 51%. The comparison was tilted toward the models because Anthropic deliberately chose moments where the human decision had room for improvement. On 127 further scenarios where the human action was already strong, the models improved on it only about 20% of the time. The study was possible because Anthropic’s researchers work inside a tool that logs by default. The reasoning, detours and outcome are produced in the same working environment. Biology has no equivalent. Its reasoning happens in hallways, at benches, in Slack threads and on whiteboards, while the outcome arrives weeks later somewhere else. The record has to emerge from the work itself. Ask scientists to reconstruct it afterwards and we’ll create another polished account. Subscribe A log is not a label Before any of this becomes training data, a negative result has to say what failed. The first paper published in the Journal of Negative Results in Biomedicine examined 234 negative studies across five leading medical journals. Only 30% discussed statistical power, and half clearly defined a primary outcome. Tell a model an experiment failed, without telling it whether the assay was underpowered, a reagent had degraded, or the hypothesis was simply wrong, and it will learn noise with confidence. Decision histories have a second missing label too. When a lab considers five experiments and runs one, only the chosen branch returns an outcome. The other four are experimental counterfactuals. Researchers call this the selective labels problem: the data reveal results only for the actions someone chose to take. Such a record can teach a model to imitate a lab’s taste, but it cannot establish that the taste was good. It also smuggles local constraints into general lessons. A lab that never ran cryo-EM because it did not own a microscope teaches a model that cryo-EM is rarely the right call. Alasdair’s nodes preserve the candidate set, which is the necessary first step. To be useful, each decision record needs six fields: The evidence available at that moment, and nothing that arrived later. The candidates considered, including the ones dismissed in a sentence. For each candidate: the expected result, the confidence attached to it, and the cost in time and money. The route chosen, and the reason. What happened next. How the evidence changed the scientist’s view. These must be timestamped before the outcome, because hindsight turns uncertainty into inevitability. Disagreement also has to survive. Averaging three scientists into one clean rationale reproduces exactly the information loss we are trying to fix. But there is an obvious failure mode here. Once decisions are logged and scored, people log for the record post-facto. Anyone who has watched an electronic lab notebook fill up with retrospective tidying knows how quickly a research tool becomes a compliance exercise. A record that costs a scientist ten minutes of honest reflection per decision is worth far more than one that costs an hour of performance. A publication initiative called Registered Reports offers a useful starting point. Here, researchers submit their rationale, methods and analysis plan for peer review before the data exist, and the journal commits in principle to publish if they follow the approved plan. Nature has now expanded the format across every field it covers. This approach to paper writing timestamps intent before the outcome, but it freezes one plan. A useful search history must also preserve how the plan changed, which alternatives were rejected, when, and on what evidence. Subscribe Run the runner-up Autonomous labs show what happens when decisions and outcomes are connected in a loop. Liverpool’s mobile robotic chemist ran 688 experiments over eight days in a ten-variable formulation space. A batched Bayesian search used each result to choose the next experiments and found photocatalyst mixtures six times more active than the starting formulations. Every completed experiment changed what the system did next. The robot optimized within a goal and search space that humans had already chosen. It did not decide which scientific question mattered. So what this work demonstrates is narrower, but still useful: a search history pays off when the options return standardized outcomes quickly. Open-ended biology is harder because branches can take weeks and the discarded alternatives may never be run. Some exploration therefore has to be bought. Where two branches are plausible and the stakes justify the cost, a lab should sometimes run the runner-up. Otherwise the record captures what today’s scientists usually chose and stays silent on what they systematically overlooked. A funder could ring-fence a small fraction of a grant for the branch not taken, provided that the decision record and the outcome are both deposited. That costs money, but so does having every lab rediscover the same abandoned path. Test whether taste transfers When it comes time to evaluate if our new AI system exhibits taste, the comparison should run across laboratories and fields. At selected decision points in a live research campaign, we should freeze the six fields: the evidence, the candidates, expected results, confidence, costs and the choice made. Experts then rank the options before the outcomes are known, and those outcomes are checked independently later. We could give models one of two diets - papers alone, or papers plus decision histories - and ask them to rank the next experiment on unfamiliar projects. Then, we score information gained per dollar and week, calibration, and how quickly weak branches are abandoned. If histories improve choices only inside the lab that produced them, they amount to useful organizational memory. If they improve choices on unfamiliar problems elsewhere, Alasdair’s group will have captured something science has never managed to write down: a transferable record of taste. Until that happens, a scientific search history is a promising record, not yet a training set.
If Not AOC, Then Who?
What do we do if Alexandria Ocasio-Cortez decides not to run for president in 2028? It makes sense that many leftists are currently focused on calling on her to take the plunge. But the absence of a Plan B, or even much discussion about one, is worrisome. The US Left can’t afford to sit out the 2028 Democratic primary. Doing so in 2024 was a disaster: our abstention self-marginalized anti-billionaire politics from the national conversation and thereby gave space for Harris to pivot to the “center” (i.e. the policy preferences of corporate donors, establishment hacks, and AIPAC). Abstaining again in 2028 would be especially counterproductive since democratic socialists now have so much wind in our sails after our big electoral wins in NYC and across the country. It’s time to deepen our momentum, organization, and profile — not to retreat to the margins during America’s single most important political battle. Even more urgently, keeping Trump’s heir out of the White House may depend on whether leftists and the party’s unprecedentedly disenchanted base can force the Democrats in 2028 to take meaningful steps towards a very angry electorate and away from the corporate-consultant blob that has helped get us into this mess. If AOC (or Abdul El-Sayed) decides to run, we should enthusiastically go all in — DSA, fighting unions, progressive organizations, anybody who wants to see a better country and world. But, as far as I can tell from the outside, there’s a real chance that she doesn’t run. And we can’t just wait and see where she lands before discussing alternatives, since AOC’s decision might not come for quite a while, giving us little time to scramble at the last minute for an alternative. So we should start working now on getting a Plan B into motion, and recruiting a good candidate for the role. Here’s one idea. A Worker Candidate — Healthcare Not Warfare 2028 Run a union worker Leftists don’t need to always run experienced, well-known socialist politicians for high-profile contests. Lula, the current president of Brazil, began his political career as a metalworker militant who ran for governor of São Paulo in 1982. Especially in an era of institutional distrust, it’s possible to lean hard into making a virtue out of a working-class comrade’s distance from traditional politics. Experience abroad shows this clearly. Rachel Kéké — an immigrant hotel housekeeper — ran as the candidate of the leftist La France Insoumise for the National Assembly in 2022, defeating a former minister in the Macron government. The Belgian Workers’ Party in 2024 won its first Flemish seat in the European Parliament by running Rudi Kennes, a three-decade veteran of the Opel Antwerp auto factory. And the South Korean left in 2024 elected auto factory militant Yoon Jong-oh to the National Assembly. If a tiny French Trotskyist organization could make such a big splash in 2002 and 2007 by running a well-spoken postal worker cadre for office — the LCR’s Olivier Besancenot received about 1.5 million first-round votes — it’s not far-fetched to expect that a much larger organization like DSA, together with a broader coalition of the willing, could run this playbook to much greater effect. This is especially the case now that almost everything DSA does immediately becomes a national news story. We don’t need someone famous. We just need a charismatic, well-vetted comrade who works in a relatable working-class occupation — e.g. a nurse, or a worker in auto or construction, or at Amazon. Ideally, they have a compelling personal story or have been part of a significant strike or organizing campaign, but even those criteria don’t necessarily have to be deal-breakers. The main lesson of the Graham Platner fiasco isn’t that we should never run outsiders for office (the momentum he generated proves the exact opposite), but that they need proper vetting and roots in democratic membership organizations like DSA or a union. In any case, there are plenty of ways to signal working-class authenticity without resorting to Platner’s or Fetterman’s performative macho white dude affect. Better to have someone of any gender or race who is actually working class, with firm socialist politics, who can speak from experience about the day-to-day realities of trying to make it in America. There are surely at least a dozen such worker leaders in our movement nationwide; we just need to sit down and convince one that their personal sacrifice for the greater good (i.e. running for president) will be worth it. If we don’t have a frontrunner with a clear path to victory like AOC (or El-Sayed, were he to win Michigan), it’s better to embrace someone whose decision to run in itself tells an important story about US politics: workers have been iced out of our political system and are now fighting hard to break back in. The novelty factor, in itself, would generate press. And it would help democratic socialists signal to the American public that our movement’s defining characteristic is that we represent workers vs. bosses, not that we’re “the furthest left” force in American politics — a framework that suggests we’re extremists and that helps our opponents isolate us from non-college-educated voters by highlighting extremely minoritarian (and misguided) stances included in DSA’s platform like prison abolition. We don’t get many opportunities to speak to tens of millions of Americans; we need to use these opportunities to broaden our appeal beyond those already in our orbit. Run to polarize the 2028 primary and general election around “Medicare for All” and “End All Aid to Israel” Why should democratic socialists use our scarce time and volunteer energy to run in a race we’re unlikely to win? Running in the 2028 primary will not only recruit us scores of members (imagine having a DSA member intervening in eleven nationally televised primary debates!), but — more importantly — it has the potential to be a decisive leap forward towards an America that finally starts meeting the needs of working people instead of HMO and AIPAC billionaire donors. Polarizing litmus tests are good, actually — as long as they’re fought for around popular demands in a thoughtful and strategic way. Democratic socialists have an opportunity and responsibility to make the 2028 primary about two of the most widely and deeply felt issues of our moment: domestically, America’s unaffordable, family-breaking private healthcare system; internationally, America’s bankrolling of the genocidal Israeli government. A candidacy relentlessly demanding Medicare for All and No Aid to Israel would pose a sharp question to the American public, the Democratic Party, and other 2028 contenders: Why can’t working-class Americans afford their healthcare bills at the same time that our government is spending at least $3.8 billion yearly to fund an Israeli regime eager to keep dragging us and the world into more forever wars? Our candidate can agitate around a clear message: I’m running to give voice to all those working-class Americans who are struggling to get by. No matter what state you live in, who you voted for in 2024, or your background, it’s just plain common sense that we should make healthcare more affordable instead of spending billions to enable the Israeli government’s crimes against humanity. Every American who agrees with me when I say ‘Medicare for All — Not a Dime for Israel’ should make their voice heard through our campaign. Racking up a high number of votes for this agenda is a crucial way to show the Democratic Party and the country as a whole that we can’t keep supporting a life-destroying status quo in our healthcare system and in the Middle East. If we want to defeat Trump’s handpicked crony this coming November, we need to speak to ordinary people’s frustrations with the status quo — we can’t keep running the same tired establishment playbook and expecting a different result. For those highly engaged voters (or skeptical media pundits) who raise concerns about wasting votes, the candidate can take the time to explain why this isn’t the case: It's true I'm a long shot. But Zohran showed anything can happen — and voting for me is a pragmatic decision, not a wasted vote. In a heavily divided primary with a large number of candidates and a very discredited establishment, my campaign and the democratic socialist movement I’m part of are positioned to be the balance of power if we can get a large enough number of voters to loudly demand Healthcare Not Warfare. Since there’s a high chance no candidate will get to 50% in the 2028 primaries, the Democrats could very well be heading towards a brokered convention, where a disciplined, principled current committed to breaking from corporate donors can play an outsized role in making sure whoever ends up with the nomination raises the banner of single-payer healthcare and cutting aid to Israel. How exactly could running this sort of primary campaign be translated into actual leverage? The candidate replies: I’m aiming to win the nomination, but if I don’t achieve that goal, my delegates and I will pledge our support in the primary only for a candidate who agrees in the general election to call for ending US aid to Israel and establishing Medicare for All at home. These are both winning planks for November against the Republicans, and the only reason I can see that other candidates haven’t yet raised these is that they’re still more scared of the donor class than they are of the people. That needs to change. If Democratic leaders want young people, workers, and those sick of the status quo to show up in big numbers this November, they need to meet us halfway. In this way, whether or not we win the nomination, democratic socialists could still play a decisive role in 2028. The approach outlined here is similar to what radical left parties periodically do in parliamentary democracies like Denmark or Sweden when they find themselves as a junior partner electorally: they dictate to center-left allies what their terms would be for enabling, from the outside, the formation of a new government. Running a Healthcare Not Warfare candidate will make it significantly more likely that the 2028 Democratic candidate stands with ordinary people instead of donors on these two pivotal planks — a shift that will, in turn, make it easier to achieve the existentially important task of defeating MAGA’s heir in November. Look at how much popular traction the Uncommitted movement got with a similar strategy in 2024 with so little on-the-ground organization — surely we can go much further with a working-class candidate, a much bigger DSA, and a Democratic Party base that has turned against Israel, billionaires, and the hacks in the party who prop them up. Objections and Responses There are quite a few objections to this proposal, many of which are very reasonable. Let me briefly address each here. Socialists shouldn’t waste their scarce time and resources on a candidate unlikely to win. This is a good rule in general, but there are important exceptions, and presidential elections are the main one. There’s no other political contest that gets anything like this level of attention and engagement in the US, so if you want to be a serious force in nationwide politics, you basically can’t afford not to have a candidate. Far from being an overall drain on our resources, running a dynamic 2028 campaign would likely result in a major net increase in members and capacity. In that spirit, the very pragmatic Milwaukee sewer socialists — who mostly ran to win at home in Wisconsin — supported Eugene Debs’s propagandistic presidential campaigns, which they correctly saw as a crucial means of recruiting to the Socialist Party. And now that the (post-1972) Democratic Party gives more weight to voters than party operatives in determining its presidential nomination, it’s become feasible to run as a socialist in primaries without risking a split in the vote against the Republicans in November. We should just support Ro Khanna, who also supports M4A and ending aid to Israel. I like Ro Khanna and would enthusiastically vote and canvass for him for president were he to get the nomination. But there’s a real chance that Khanna’s bid for office doesn’t catch on; he lacks the grassroots organizational apparatus of DSA, and, because he’s not really of the Left or labor (despite his current very good positions), he’s unlikely to generate the type of buy-in and enthusiasm that a DSA candidate would. Moreover, Khanna would be running as an individual, not as someone emerging from and building an organized movement — his campaign would not likely focus on building sustained power from below, nor would it likely polarize the 2028 primary around M4A and Israel aid. Also, and not inconsequentially, the poor electoral results for Tom Steyer and Saikat Chakrabarti this spring suggest that there’s perhaps not much of a popular appetite in the Democrats’ base to support multi-millionaire class traitors. Much better, then, to run a democratic socialist worker. If by Super Tuesday it’s clear that our campaign has captured the main left lane in the primary, Khanna should drop out and endorse us; conversely, if he’s managed to capture this lane, DSA could at that point transform its Healthcare Not Warfare campaign into an independent campaign to elect Ro Khanna focused on winning (and holding him accountable to) those two critical planks. An openness to consolidating an anti-corporate candidate by Super Tuesday is a necessary component of the strategy I’m proposing, because otherwise we risk splitting the primary votes (per DNC rules, you need at least 15% to get any delegates to convention). We should convince Shawn Fain or Sara Nelson to run for president instead. I agree it’d be great if either ran for president, but my impression is that there’s basically no chance that will happen in 2028, because both are firmly focused on their union leadership duties (and, in the case of Fain, beating back a federal witch-hunt). This plan could have DSA end up endorsing a non-socialist, non-DSAer for president. That’s against our principles. It’s a myth, and bad politics, that socialists can’t ever support candidates who aren’t members of their organization and/or who don’t call themselves socialists. Bernie, after all, was not a DSAer. And socialists abroad regularly make tactical decisions about if and when supporting a non-socialist candidate makes sense from the standpoint of promoting working-class interests, defending democracy, and building a socialist current in the process. If your “principles” prevent you and DSA from helping achieve the momentous stride forward of having a Democratic candidate in 2028 run on Medicare for All and ending aid to Israel, those “principles” are not very good. In fact, they actively undermine the causes they’re aiming to support. We shouldn’t confuse principles (working-class independence) with tactics (the best way to advance this in a given context). In my recent article in Catalyst on Milwaukee sewer socialism (I’ve uploaded the full text here), I have a section showing how similarly rigid rules about endorsements had to be eventually discarded in the old Socialist Party nationwide after WWI because they tended to lead socialists towards a sectarian abstention from broader anti-corporate electoral insurgencies beyond their party’s control. For what it’s worth, Lenin’s current under Tsarism frequently backed bourgeois candidates in the second round of elections and Lenin himself advocated that British Communists support Labour Party candidates; for his part, Kautsky in 1912 defended the German Social Democracy’s electoral pact with the Progressives in the runoffs. If our predecessors could be that tactically flexible in an era before the limits of their underlying strategic assumptions about intransigent class politics in capitalist democracies were made clear, then there’s no need to insist on being more tactically rigid today, after a century’s worth of accumulated experience. As long as you maintain your own political profile and organization while engaging in broader coalitional efforts, there’s zero inherent contradiction with the principle of political independence. More specifically: DSA and allied organizations should enthusiastically back Michigan’s Abdul El-Sayed if AOC declines to run and he throws his hat in the presidential ring. (I have no idea whether he’s even considering this path, but there’ll surely be chatter about it if he wins his Senate race this November.) Though he doesn’t call himself a socialist, El-Sayed is a principled Berniecrat, a consistent fighter for Palestine, and would have a clear path to victory. Using the “s” word is far less important than whether a campaign fights for the interests of working people at home and abroad — and whether actively participating in it can grow the organized working class and the Left. And the same holds true if a less anti-corporate candidate were, under pressure, to adopt our two Healthcare Not Warfare planks. Why focus only on M4A and ending US aid to Israel? There are so many other important issues to fight for! Obviously there are many pivotal issues beyond healthcare and Israel, and our candidate — and especially our organizations involved in the campaign — should be prepared to speak on all of these. But one of the crucial lessons of the Zohran campaign is that you have to relentlessly stay on message if you want to cut through the noise. CNN and Fox News will want to talk about DSA’s views on Venezuela and abolishing the police; we need to polarize the debate on the widely and deeply felt issues that are most favorable for growing the Left, isolating our opponents, and winning urgently needed changes. One recent poll found 63% of Americans support M4A, even after respondents were told it would raise taxes and eliminate most private insurance. Another found that 74% of Democrats opposed “providing additional economic and military support to Israel.” And focusing on two demands sets the campaign up to be able to leverage its mandate to make clear demands upon other candidates (which we can’t do if we run with a laundry list of demands). Talking about a Plan B is a distraction from the more urgent work of building a groundswell of calls on AOC to run. It’s good for DSAers, fightback unionists, and progressive organizations to try to convince AOC to run. This proposal is meant as an addendum to pushes to get AOC to run and to line DSA up for that eventuality, not as a substitute for those efforts. But I’m worried that, like in 2024, we might eventually find ourselves with no time to find a viable alternative unless we start now. DSA, with its abundant deliberative processes, moves slowly. The approach you’re suggesting seems risky. If a DSA candidate runs but doesn’t receive a lot of votes, it’ll undercut our current national momentum. This is a reasonable worry. All big initiatives always carry risks, and the path I’m advocating is certainly not free of these. That said, if a Healthcare Not Warfare campaign doesn’t catch on like we envisioned, it’s a pretty simple pivot in March 2028 to explain that in the future we really need our standard-bearers like AOC, or our growing bench of national electeds, to step up in presidential primaries. Moreover, it’s worth noting that the risk of my Plan B proposal is on the whole significantly less than the risk entailed by an AOC run. We should all back her if she runs, since she’d fire up the base, provide huge openings for organizing, have a real shot at defeating a Republican (especially given MAGA’s unpopularity), be a great standard-bearer for our issues, particularly M4A and ending aid to Israel, and be less willing than mainstream candidates to capitulate to any anti-democratic machinations from Trump and co. But it’s also true that AOC could lose, which would be a devastating setback to the country and the Left. Or she could win and find herself unable to pass much, if any, of her (and our) program, a demoralizing prospect. So if we’re willing to take these risks with AOC (and we should), it follows that we should be able to take lower-level risks if she doesn’t throw her hat in the ring. What’s Your Plan B? The US Left is facing its biggest opening for growth and influence since the 1930s. Initiatives that would have been impossible a few months ago are now potentially feasible. A President AOC is now easier to imagine, but if she ultimately assesses that the risks aren’t worth the potential reward in 2028, we can’t be left flat-footed. So even if you’re not convinced by the specifics of my Plan B, I hope you can at least agree that it’d be a disaster for us not to have one — and not to start getting a Plan B into motion. Hopefully other organizers and political currents will put forward their responses and alternative ideas ASAP. The stakes are too high to just wait and see. More As always, please share this article widely. This newsletter depends on all you lovely people to get the word out. Thank you! I uploaded the full text of my Catalyst article on Milwaukee sewer socialism here (it’s updated and 3x as long as the version published earlier on this substack). If you haven’t yet, please make sure to subscribe to Catalyst here; I read every issue front to back, highly recommend. 🌹🎵 Ivan Lins (arranged by Arthur Verocai) — “General da Banda” 🌹🎵
Business Restructuring Review Vol. 25 No. 4 July-August 2026
In This Issue New York Bankruptcy Court: Premature Allegations of Creditor Mistreatment Insufficient to Deny Chapter 15 Recognition of Foreign Restructuring Proceeding In In re ARD Finance, S.A., No. 25-12794 (MG), 2026 WL 817458 (Bankr. S.D.N.Y. Mar. 25, 2026), the U.S. Bankruptcy Court for the Southern District of New York was presented with a petition seeking chapter 15 recognition of Luxembourg's recently enacted bankruptcy law governing judicial reorganization proceedings. The court granted the petition, turning aside challenges to recognition voiced by a creditor group arguing that recognition should be denied based upon allegations of unfairness to creditors in the Luxembourg proceeding that would make recognition manifestly contrary to U.S. public policy. According to the bankruptcy court, recognition was warranted because: (i) the rules and procedures governing the proceeding under Luxembourg law were fundamentally fair; (ii) the petition otherwise satisfied chapter 15's recognition requirements; and (iii) the creditor group's allegations were premature, given the fact that the Luxembourg court presiding over the debtor's restructuring proceeding had not confirmed a restructuring plan, and all creditors had the right to participate fully and voice their objections in the proceeding (including the right to appeal an order approving a plan). [read more …] Florida Bankruptcy Court Examines Scope of Discovery in Chapter 15 Cases In In re Banco Master, S.A., No. 25-24568-SMG, 2026 WL 931729 (Bankr. S.D. Fla. Apr. 6, 2026), the U.S. Bankruptcy Court for the Southern District of Florida granted in part and denied in part post-recognition motions filed by an insider of a foreign debtor seeking to bar or limit discovery sought by the debtor's foreign representative concerning allegedly voidable transfers involving the debtors' U.S. real property. According to the court: (i) although chapter 15 includes its own discovery provisions among the relief that may be granted to a foreign representative, the expansive discovery available in cases under other chapters of the Bankruptcy Code is also available in chapter 15 cases; (ii) the discovery sought by the foreign representative was permissible; (iii) discovery regarding matters that were the subject of a pending adversary proceeding had to be undertaken under the more restrictive rules applicable to such proceedings; and (iv) a discovery request would be barred to the extent that it sought production of documents or testimony from a subpoenaed entity that exceeded the geographic limitations specified in such rules. [read more …] Delaware District Court Affirms Chapter 15 Recognition Order Enforcing Nonconsensual Third-Party Releases In U.S. Int'l Dev. Fin. Corp. v. Credito Real S.A.B. de C.V. SOFOM, E.N.R. (In re Credito Real S.A.B. de C.V. SOFOM, E.N.R.), 677 B.R. 192 (D. Del. 2026), appeal filed, No. 26-2356 (3d Cir. June 5, 2026), the U.S. District Court for the District of Delaware affirmed a bankruptcy court order granting chapter 15 recognition to a Mexican "concurso mercantile" proceeding and enforcing nonconsensual third-party releases approved by the Mexican court. Agreeing with the bankruptcy court that the releases were not manifestly contrary to U.S. public policy, the district court concluded: "It is well settled that U.S. bankruptcy courts can give effect to foreign orders in recognized foreign proceedings pursuant to the authority granted in Chapter 15, even when those orders contain relief unavailable under U.S. law. [The appellant's] arguments misconstrue the Supreme Court's narrow holding in Purdue, applicable in Chapter 11 cases, as well as the statutory framework of Chapter 15 and its core policy objectives. Accordingly, I will affirm the Enforcement Order." The ruling has been appealed to the U.S. Court of Appeals for the Third Circuit. [read more …] Fifth Circuit: Bankruptcy Court, Not State Court, Had Jurisdiction to Allow or Disallow Administrative Expense Claim In Storey Minerals Ltd. v. EP Energy E&P Co., LP (Matter of EP Energy E&P Co., LP), 169 F.4th 609 (5th Cir. 2026), the U.S. Court of Appeals for the Fifth Circuit affirmed lower courts' rulings disallowing a creditor's administrative expense claim. It also rejected the creditor's argument that, because the claims were predicated on state law, a state court should adjudicate the merits of the state law causes of action before the bankruptcy court could allow or disallow the claim as an administrative expense. According to the Fifth Circuit, even though the causes of action were based on state law, the bankruptcy court had jurisdiction to determine whether the creditor's claims should be conferred with administrative expense priority in the debtors' chapter 11 case. [read more …] New York Bankruptcy Court: Dismissal of Involuntary Chapter 11 Case Filed Prior to Commencement of Foreign Restructuring Proceeding Unwarranted In In re Xinyuan Real Estate Co. Ltd., No. 25-10745 (PB), 2026 WL 592250 (Bankr. S.D.N.Y. Mar. 3. 2026), the U.S. Bankruptcy Court for the Southern District of New York considered the bona fides of an involuntary chapter 11 petition filed by certain holders of U.S. dollar-denominated notes against a holding company for a group of Chinese property development companies that filed a restructuring proceeding in the Cayman Islands after the noteholders filed the involuntary chapter 11 petition. The court denied the debtor's motion to dismiss the chapter 11 case. Given the timing of the filing of the Cayman Islands restructuring proceeding, the absence of any progress in the proceeding for seven months, the debtor's need to restructure notes governed by U.S. law, and the debtor's lack of any meaningful contacts to the Cayman Islands, dismissal of the chapter 11 case was unwarranted under the applicable provision of the Bankruptcy Code, international comity, or the doctrine of forum non conveniens. [read more …] Bankruptcy Court Evaluates Trustee's Authority to Sell Avoidance Actions and Right of Target Defendants to Object In In re Law Enf't Officers Sec. Union, No. 24-70277-JAD, 2026 WL 560452 (Bankr. W.D. Pa. Feb. 27, 2026), a chapter 7 trustee sought court approval to sell certain avoidance actions to a creditor of the debtor's estate. Potential targets of the subject avoidance actions objected, arguing that such claims were nontransferable powers of the trustee rather than saleable estate property and urging the court to approve their competing settlement with the trustee. The U.S. Bankruptcy Court for the Western District of Pennsylvania disagreed with the potential defendants, holding that they lacked standing to object to the proposed sale, that avoidance actions are indeed estate property, and that the sale—rather than the settlement—best served the estate's interests. [read more …] U.S. Supreme Court Bankruptcy Roundup in Brief In Keathley v. Buddy Ayers Construction Inc., No. 25-6, 2026 WL 1686028 (U.S. June 11, 2026), the U.S. Supreme Court overturned a Fifth Circuit decision judicially estopping a chapter 13 debtor from asserting a personal injury claim against a construction company because the debtor failed to disclose the claim during his bankruptcy case. In so ruling, the Court held that "[t]he Fifth Circuit erred by artificially narrowing its inquiry to whether the debtor had knowledge of the underlying facts or a potential motive to conceal the claim." It represents the first time that the Court has applied judicial estoppel in the bankruptcy context, although it has suggested in other contexts that it may be inappropriate where an inconsistent legal position resulted from "inadvertence or mistake." Also in June, the Court denied petitions for certiorari in a few notable bankruptcy cases. [read more …] Newsworthy Heather Lennox (Cleveland/New York) (Star Individual), Bruce Bennett (Los Angeles) (Senior Statesperson), Kevyn D. Orr (Washington) (Eminent Practitioner), Gregory M. Gordon (Dallas) (Band 1), Carl E. Black (Cleveland), Corinne Ball (New York) (Senior Statesperson), Brad B. Erens (Chicago), Jeffrey B. Ellman (Atlanta), T. Daniel Reynolds (Cleveland), and Dan T. Moss (Washington/New York) were recognized as leading lawyers in the practice area Bankruptcy/Restructuring in the 2026 edition of Chambers USA: The World's Leading Lawyers for Business. The 2026 edition of Lawdragon 500 Leading Bankruptcy and Restructuring Lawyers ranked the following lawyers: Corinne Ball (New York) (Hall of Fame), Bruce Bennett (Los Angeles), Carl E. Black (Cleveland), Jeffrey B. Ellman (Atlanta), Brad B. Erens (Chicago), Gregory M. Gordon (Dallas), Heather Lennox (Cleveland/New York), Joshua M. Mester (Los Angeles), and Kevyn D. Orr (Washington). Fabienne Beuzit (Paris) and Jasper Berkenbosch (Amsterdam) were among the "Leading Individuals" ranked in the 2026 edition of The Legal 500 EMEA guide in the field of Restructuring and Insolvency or Insolvency. Rodolphe Carrière (Paris) was named a "Next Generation Partner" in the practice area Insolvency. Sid Pepels (Amsterdam) was selected by Global Restructuring Review's 40 under 40 2026. He was identified as a Rising Star within the global cross-border restructuring community. Global Restructuring Review is a leading digital subscription service, news outlet, and information platform dedicated exclusively to cross-border insolvency and corporate restructuring law. Fabienne Beuzit (Paris), Rodolphe Carrière (Paris), and Isabelle Maury (Paris) were recognized in the 2027 edition of The Best Lawyers in France™ in the practice area Insolvency and Reorganization Law. An article written by Corinne Ball (New York) titled "Comity and Fairness are Critical to Granting Additional Assistance under Chapter 15: A Canadian Reverse Vesting Order and Related Releases in Proceedings Involving Canadian and U.S.-Affiliated Companies Are Approved" was published in the June 24, 2026, edition of the New York Law Journal. An article written by Caitlin K. Cahow (Detroit) titled "New York Bankruptcy Court Weighs In on Adequate Assurance of Future Performance under Assigned Leases and Chapter 11 Plan Exculpation Provisions" was published on June 8, 2026, in Lexis Practical Guidance. An article written by Trisha L. Mowbray (Real Estate; Chicago) and Ryan Sims (Washington) titled "Chapter 11 Plan that Abridged Non-Debtor Lessee's Rights to Remain in Possession After Rejection Unconfirmable as Having Been Proposed in Bad Faith" was published on June 16, 2026, in the Harvard Law School Bankruptcy Roundtable. An article written by Dan T. Moss (Washington/New York), Corinne Ball (New York), Isel M. Perez (Miami), and David S. Torborg (Washington) titled "Texas Bankruptcy Court Adopts Barnet Rule Requiring Foreign Debtor to Have U.S. Assets to Be Eligible for Chapter 15 Relief" was published on June 10, 2026, in Lexis Practical Guidance. An article written by Daniel J. Merrett (Atlanta) and Nathaniel Parr (Atlanta) titled "Southern District of Texas Clarifies Post-Purdue Course for Consensual Third-Party Releases and Gatekeeping Provisions" was published on June 11, 2026, in Lexis Practical Guidance. An article written by Corinne Ball (New York) titled "LMEs, Sophisticated Investors and Equitable Mootness: The Fifth and Third Circuits Warn Against Testing the Limits of Chapter 11" was published in the June 2026 issue of the American Bankruptcy Institute Journal. Nicholas J. Morin (New York) was quoted in an article titled "Serta Ruling Offers Damages Road Map in Lender-on-Lender Disputes," published by Bloomberg Law on July 7, 2026. The article discussed U.S. Bankruptcy Judge Christopher Lopez's July 7, 2026, ruling that participating lenders in Serta Simmons Bedding's 2020 non-pro rata uptier exchange breached the credit agreement's ratable-sharing provision, and awarding excluded lenders more than $261 million in damages. The opinion resolves six years of litigation related to Serta's 2020 transaction, where it raised $200 million in new financing and exchanged $1.2 billion of term loans for $875 million in superpriority debt. Judge Lopez found that the debt-for-debt exchange constituted a "payment" under the credit agreement, requiring proportional sharing among lenders, and dismissed various defenses raised by the participating lenders. The case is Serta Simmons Bedding LLC v. AG Centre Street Partnership (In re Serta Simmons Bedding LLC), 2026 WL 1968535 (Bankr. S.D. Tex. Jul. 7, 2026). The New York Law Journal has recognized Andrew M. Butler (New York) as a 2026 "Rising Star" in Restructuring/Bankruptcy as part of the New York Legal Awards. The Rising Star category recognizes New York's most promising lawyers age 40 and under who have distinguished themselves through exceptional legal work, leadership, and contributions to the profession. Lawyer Spotlight: Daniel J. Merrett Dan Merrett, a partner in the Atlanta Office, has spent nearly 20 years representing all manner of stakeholders in complex corporate restructurings and chapter 11 reorganizations. His practice extends to related transactions and disputes involving acquisitions; divestitures; credit facilities; securities; environmental obligations; and vendor, employee, and governance matters. Dan has substantial experience managing mass tort liabilities, including the restructurings and related chapter 11 cases of affiliates of Georgia Pacific and Johnson & Johnson, implemented to resolve asbestos and talc liabilities. He also represents key parties in energy and infrastructure matters, including facility owners, EPC contractors, and joint-venture parties. Dan was a key player in the restructuring teams in the historic bankruptcies of the City of Detroit and Chrysler. His other significant representations in chapter 11 cases include AFA Foods, Alpha Natural Resources, Dana Corporation, M&G Chemicals, McDermott International, Monster/CareerBuilder, Transtar Industries, and Westinghouse. He is licensed in both the United States and England & Wales and recognized in restructuring by Chambers USA and Best Lawyers.
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[This article is the latest in our “Shaky Political Science” series. In our 38 page report, “Shaky political ‘science’ misses mark on ranked choice voting,” released in December 2025, we examined over 40 studies focused on ranked choice voting (RCV). Many of these studies suffered from puzzling research methodologies, poorly constructed surveys, unrealistic simulated elections, cherry picked data and faulty analyses that often were contradicted by results from real-world elections (a link to our full paper is here). Here are links to the first four articles in our series: the first one summarizing the overall results of our report; the second one analyzed a study co-authored by University of Minnesota academic Larry Jacobs, which was one of the most error-prone of all the research we reviewed in our report; a third article showing an admirable example of two political scientists holding two other political scientists accountable for their flawed, sloppy research; and a fourth article that analyzed seven studies sponsored by New America’s political reform program that deployed flawed methodology based on “simulated” elections instead of real-world elections, which when combined with some basic misunderstandings of RCV, produced odd and misleading results]. In this fifth article in our “Shaky Political Science” series, we examine two flawed studies on the impact of RCV on voter turnout authored by San Francisco State University professor Jason McDaniel. McDaniel’s research in these two papers stands out for its puzzling methodology, inadequate and cherry-picked data, and faulty analyses that not only misunderstands ranked choice voting but reflects a basic misunderstanding of politics in general. The first of the McDaniel studies actually passed through a peer review process and then was subsequently published by the Journal of Urban Affairs, despite its obvious errors. It is perplexing that a jury of peer political scientists did not catch the errors and send it back to McDaniel for a rewrite or outright rejection. This calls into question the credibility of the peer review process itself. The peer review process is supposed to represent the intellectual pinnacle of the profession – a way for academics to certify the quality of each other’s work, and to act as a check against sloppy research. In the case of the McDaniel study, the peer review process failed. Yet his flawed study has been cited dozens of times by other political scientists in their own flawed works — an unvirtuous circle of academic research. Here is the first Jason McDaniel study that we analyzed: “Writing the Rules to Rank the Candidates: Examining the Impact of Instant-Runoff Voting on Racial Group Turnout in San Francisco Mayoral Elections,” by Jason McDaniel, 2016, Journal of Urban Affairs. This study covered San Francisco’s use of ranked choice voting (RCV) in mayoral elections, purporting to show that RCV reduces voter turnout overall, increases voter errors, and has a negative impact on “marginal populations,” i.e. racial minorities, mainly as the result of the alleged complexity of RCV asking voters to rank candidates and having to be familiar with multiple candidates and related dynamics. McDaniel reached these conclusions by examining five San Francisco mayoral elections taking place in odd years (i.e. where there are no federal or state races on the ballot) from 1995 to 2011, comparing three earlier non-RCV races (in 1995, 1999 and 2003) with the later two RCV races (in 2007 and 2011). Only two RCV races were studied, and as McDaniel acknowledged, only the contest in 2011 was “competitive” (that contest in 2007 was the first mayoral election using RCV, and incumbent mayor Gavin Newsom won easily in the first round of counting, earning 74% of first choices and far ahead of the second place candidate who had just 6.3% of first choices). Yet even the allegedly competitive contest in 2011was won by an incumbent with a landslide margin of nearly 20 points. No other elections were studied by McDaniel, even though San Francisco elects 17 other offices besides mayor with RCV, including the 11 seats on the Board of Supervisors (the name for the city council) in even years, and six other citywide offices in odd years. Previous academic research has found that competitive elections can stimulate greater voter involvement and higher turnout. For unexplained reasons, this study ignored at least 34 other RCV races between the years 2004 to 2011, many of which were very competitive and had high voter turnout. Instead, McDaniel’s analysis based its conclusions on data from only a single, marginally-competitive mayor’s race using RCV. These 2007 and 2011 mayoral elections were the only two RCV elections that McDaniel used as the sole basis for his small data set to determine the impact of RCV on voter turnout. Despite having no meaningful data to inform his study, McDaniel nonetheless concluded that RCV lowered turnout when compared to the previous three mayoral non-RCV elections using two-round runoffs. Then, using the same inadequate database of San Francisco elections, McDaniel also found that “Instant-runoff voting is associated with a significant decline in Black voter turnout and White voter turnout…On average, Black voter turnout decreases by 18 points and White voter turnout decreases by 16 points …” But how could he credibly arrive at that conclusion when his study was based on voter turnout in two RCV contests that were not even remotely competitive? Beyond that, McDaniel tacitly admits that the key determining factor driving Black voter turnout was not RCV vs non-RCV elections, but the presence of a viable Black candidate. McDaniel wrote, “Black voter turnout was about 17 percentage points higher for the two elections that featured an African American candidate compared to the three elections that did not” — but one of those three elections did not use RCV. The same dynamic was present for White voter turnout, in which higher turnout was not correlated with RCV vs non-RCV elections, but the presence of a viable White candidate. Beyond that, McDaniel’s voter turnout results, whether correlated with the race of the candidates or RCV vs non-RCV elections, showed no clear patterns. In his Figure 1, he shows low Asian voter turnout in the non-RCV elections in 1999 and 2003 when there was no viable Asian candidate running, but in the 2007 mayoral election using RCV, there is still no viable Asian candidate, yet Asian turnout increases dramatically. For Latinos, voter turnout is virtually the same in 1999 (no viable Latino candidate, non-RCV elections), 2003 (leading Latino candidate, non-RCV elections), 2007 (no viable Latino candidate, RCV elections) and 2011 (leading Latino candidate, RCV elections). Meanwhile White voter turnout increased dramatically from a low in the 2007 mayoral election, in which the leading candidate was a white incumbent, to the 2011 election in which there were no leading white candidates that finished any higher than sixth place with 5.6% of the vote. Clearly other factors were determining voter turnout than either the race of the candidates or RCV vs non-RCV elections. Despite these inconsistent findings, McDaniel boldly marches forth to render conclusions about the impact of RCV on different groups of racial voters, even though his conclusions are not remotely supported by his data. This looks like the worst kind of “data cherry picking” to cover up a puzzling conclusion based on a poor data set involving only two RCV elections, neither of which were remotely competitive. McDaniel’s cherry picking didn’t stop there. In calculating voter turnout for the three non-RCV mayoral elections in 1995, 1999 and 2003, he failed to mention that in each of these years there was a two-round runoff election, with a first round election in November in which no candidate garnered a winning majority of the vote, followed by the decisive runoff in December. So there were two non-RCV elections in each election year. But McDaniel only decided to include one of the two elections in his data set, apparently the December runoff in each year (which had higher turnout in two out of the three mayor’s races, and lower turnout in the third). So he ignored the November 1995 election, the November 1999 election when turnout was only 45 percent, and the November 2003 election when turnout was only 45.7 percent. This is flawed methodology based on blatant cherry picking, since McDaniel simply omitted three elections worth of data that did not fit within his frame. His study should never have made it through a peer review process, and it is an embarrassment to political science that not only was it approved for publication, but it has been uncritically cited by dozens of other political scientists. Share A “study” without wider context or credible data Lacking a data set that included even a single competitive RCV mayoral race, McDaniel declared that RCV lowered voter turnout in 2011, when turnout was 42.5 percent. But he also ignored -- or did not realize -- that voter turnout in mayoral elections during 2011 had sharply declined in all big cities, including far more steeply in nearby Los Angeles and other cities. In fact, San Francisco’s election in 2011 had the second highest turnout of any mayoral election in the nation’s 22 largest cities from 2008-2011. Was 2011 just a particularly low turnout year all across the country, perhaps because the country was still reeling from the aftermath of the housing market collapse in 2008-2010? McDaniel ignored this factor and did not search for alternative explanations beyond his flawed thesis. Furthermore, while McDaniel suggested that RCV leads to more “overvotes” – which is an invalidated ballot in which a voter, in either a non-RCV or RCV race, makes a mistake and selects two candidates for a single choice – he failed to address that in the June 2012 US Senate primary contest, which used the typical “one-choice” plurality method and did not use RCV, there was more than five times as many overvotes in San Francisco and Oakland than in the RCV mayoral elections in those cities. Additionally, turnout had been significantly higher and more representative in RCV elections for the San Francisco Board of Supervisors races -- that McDaniel didn’t even bother to study -- as compared to previous “delayed two-round runoffs” for those offices, often higher by as much as 40% in the RCV races. Perhaps the most surprising irony was that an earlier study co-authored by McDaniel (Overvoting and the Equality of Voice under Instant-Runoff Voting in San Francisco by Francis Neely and Jason McDaniel, 2015, California Journal of Politics and Policy) found that overvotes are often more common in lower-income precincts, but that the pattern of overvoting is similar in both RCV and non-RCV contests. Wrote Neely and McDaniel: “[Overvote] errors appear to be a function of complexity in general and not IRV per se…. the pattern of overvoting is similar in both IRV and non-IRV contests…Even among votes cast in a top-of-the-ticket race like U.S. Senate, overvoting occurs in an uneven fashion” (IRV, or ‘instant runoff voting,’ is another name for ranked choice voting in single-winner contests). So McDaniel not only contradicted his own conclusion in his earlier study with Neely, but the earlier study had been an important corrective for one of the most frequently made mistakes made by anti-RCV political scientists – a continual failure to provide crucial context by pairing a comparative analysis of real world RCV elections with an analysis of real world plurality or other electoral methods. Unfortunately McDaniel’s earlier study does not get cited much by other researchers, and McDaniel did not even cite it in his own flawed study analyzed above. Incredibly, McDaniel cherrypicked McDaniel, even as his flawed study has been cited dozens of times. Despite being a textbook example of poor political “science,” featuring both inadequate as well as cherrypicked data, the shelf life of McDaniel’s study has been extended endlessly by other political scientists and media outlets who have cited it uncritically. Equally troubling, the Journal of Urban Affairs put this paper through its peer review process and published it despite its numerous flaws. McDaniel gets it wrong, Part II Not content with producing one shoddy, confused paper, McDaniel produced another one a few years later, here is the link: Electoral Rules and Voter Turnout in Mayoral Elections: An Analysis of Ranked-Choice Voting by Jason McDaniel. San Francisco State Univ. (McDaniel, 2019). In this paper, McDaniel extended his previous flawed research focused on San Francisco, about voter turnout and voter impact, to multiple RCV cities through the 2018 elections. In his data set he included San Francisco, Minneapolis, St Paul, Oakland, Berkeley, San Leandro and Santa Fe. He then compared their turnout in mayoral elections to non-RCV cities, and to mayoral elections in those cities held before first RCV usage, and concluded “a significant decrease in voter turnout of approximately 3–5 percentage points in RCV cities after the implementation of RCV.” But he replicated many of the same mistakes as his first study presented above. While in this study McDaniel attempted to distinguish between various factors, such as odd year vs. even year elections, competitive races vs. noncompetitive, contests with open seats and other factors, the analysis is superficial and inadequate. He attempts to incorporate so many often-conflicting variables that his conclusions are once again unsupported by the actual data, and ultimately unconvincing (this second study never underwent a peer review process). For example, McDaniel declared that for RCV cities “in elections that are more competitive than average (margin of victory less than 20%), there is no significant RCV effect on voter turnout.” Yet McDaniel is a professor in a city, San Francisco, in which the 2018 special election using RCV to fill a mayoral vacancy was exceedingly close, decided by a margin of only one percentage point, and resulted in the second highest turnout in San Francisco history for a mayoral election. Illustrating the complexity involved in assessing the critical factors in turnout, that special election was held on the same election day as federal and state offices, which tends to boost turnout. Yet more voters participated in that RCV mayoral election than voted on the same day in the non-RCV primary for governor in which former San Francisco mayor Gavin Newsom was running. Even more puzzling, in this study McDaniel inaccurately cited his own previous study from 2015 with Neely. McDaniel wrote, “Neely and McDaniel (2015) examine individual ballot image files within a natural experiment, and find higher rates of ballot errors in RCV elections compared to non-RCV elections” (emphasis added). However, we quoted from that 2015 study several paragraphs above, and in actual fact Neely and McDaniel actually concluded the opposite -- that the pattern of overvoting – which is the most common form of ballot error – is similar in both RCV and non-RCV contests. McDaniel also ignored the fact that in Bay Area RCV elections held in even years – at the same time as federal and state elections, when turnout is highest -- the “undervote” (that is, the dropoff in ballots cast for governor or mayor) has declined, which contradicts his thesis that the act of ranking ballots in RCV elections is too complex and presents a barrier for certain demographics of voters. To his credit, McDaniel actually acknowledged another contradiction in his own paper, which found a negative RCV turnout effect in some odd-year elections but not others, admitting that “may present a challenge to the theory that increased complexity [from RCV] will have a marginal negative effect on voter turnout.” But he tried to wallpaper over that contradiction by blaming it on an unlikely source -- bad election administration. McDaniels wrote, “This would suggest that the negative impact of RCV on voter participation may be alleviated by high quality election administration,” without presenting any data or even anecdotes to support such an unfounded theory. While competent election administration of any election, whether RCV or non-RCV, is crucially important, there is no credible research that supports the far-fetched notion that the quality of election administration has much of an impact on voter turnout in US elections, whether using RCV or otherwise. Upgrade to a $5 subscription The unvirtuous circle of academic research It is puzzling how McDaniel’s first study could have survived the peer review process for the Journal of Urban Affairs. The mistakes in McDaniel’s first study above should have been obvious to anyone with a basic understanding of ranked choice voting as well as elections in general, and elections in San Francisco specifically. It undermines faith in the credibility of the peer review process to think that a jury of peer political scientists did not catch the egregious errors and reject McDaniel’s sloppy research and paper. And it remains a black mark on political science that this thoroughly discredited study has been cited dozens of times by other researchers. Indeed, in our research paper it became apparent that many political scientists cite each other’s flawed research to pad their list of citations and to legitimize their own flawed research, creating an unvirtuous circle of political science. More broadly, McDaniel’s voter turnout studies, like other studies evaluating RCV’s impact on turnout, lacked a recognition that no credible opinion or research can claim that RCV increases voter turnout merely because so many voters are excited to vote for their favorite candidates instead of “lesser evil” candidates without worrying about spoilers. Instead, with respect to turnout, the advantage of RCV has always been that, in most places where it has been used, it has eliminated a pre-November primary or post-November runoff, which usually saw about half the turnout of the November general election. This is especially true during even years when there are state and federal elections on the ballot which substantially drive turnout more than local contests. Any study that does not adequately account for this non-November primary/runoff vs November general election dynamic, or odd year vs even year election factors, is simply not credible. Both of McDaniel’s studies largely failed on those grounds. Steven Hill and Paul Haughey Share Upgrade to a $5 subscription Thanks for reading DemocracySOS…your digital portal for the pro-democracy movement. Subscribe for only $5 per month to receive full benefits and to support our work. Subscribe
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Science Vs on the 49ers Substation: Anatomy of a Hatchet Job
In August, Pablo Torre Finds Out and Science Vs released an episode about my theory that electromagnetic fields from the electrical substation beside the 49ers facility may be contributing to the team’s run of soft-tissue injuries. As the originator of the theory, I was also a subject of the episode. I sat was interviewed by Wendy Zukerman and Rose Rimler of Sciene Vs. I sent them research, connected them with scientists, and spent hours digging through the City of Santa Clara’s and Silicon Valley Power’s records to help clarify critical details. Both Wendy and Rose came off as affable and professional, showing curiosity during the interviews, with Wendy going out of her way to question the “conspiracy theory” framing itself. Then they titled the episode We Solve the 49ers Viral Conspiracy Theory. I was familiar enough with their catalog that I never expected them to agree with my theory, but I did expect them to give it a fair hearing and to deliver their findings honestly. On both counts they came up short. They were not directly dishonest in anything they said, but rather in what they left out, in how what remained was cut, and in the picture painted by those two things in combination. They asked me to recorded the interview on my phone for better audio quality, and I held on to my copy for reference later. That turned out to be a fortuitous decision. Here is the raw audio of my first interview, unedited*. Only my side was recorded, so their questions aren’t on it, but you can generally infer them from the answers, and every claim I make below about what I said can be checked against my statements in this clip. (Note: Wendy had connection trouble at the beginning of the interview and was cutting in and out, which accounts for the false starts.) 0:00 -1:19:13 Audio playback is not supported on your browser. Please upgrade. Science Vs is a fun show. It is pitched at roughly a high-school understanding of science, for an audience that is skeptical, Snopes-oriented, and worried about misinformation. Take a claim circulating online, interview a supporter of the claim, interview consensus scientists, and then tell people which side of the line it lands on. I have listened to their episodes on raw milk, seed oils, 5G and other contested issues in the wellness space. I have problems with all of them, but each at least made an honest attempt to represent the actual range of scientific opinion by talking to credentialed scientists with differing perspectives. Dissenting researchers were heard from, answered, and often given a chance to respond. This episode did not do that. This article is not a relitigation of the science. I have written extensively about that already. This post is for the record. It is simply an accounting of what was wrong with their episode. Contents Is there a power plant next to the 49ers practice facility? — the clearest single example of getting something objectively wrong For the record — a high-level overview of all of the problems with their investigation Draw your own conclusions — why they had to make this episode at all What they left out about childhood leukemia — the exposure threshold, and playing games with statistics to minimize sick children What they left out about the mechanism — the body of literature they refused to cover, and evidence for the mechanism used against it The scientists Science Vs interviewed and did not air — four credential scientists who support myhe theory, recorded and cut, or not even called How they misrepresented me and my case — an answer severed from its question, and three telling edits How you would actually study the injuries — it doesn’t actually matter which team is the most injured, overall The theory: magnetic fields, oxidative stress, and collagen Since the episode never states my theory, here it is below. Those who are familiar with my work can skip this section. Electrical equipment produces magnetic fields, and those fields induce faint electrical currents in anything that passes through them, including people. My argument goes further than induced current. Mitochondria — the structures that generate a cell’s energy — run on continuous flows of electrons and protons, and those flows carry faint electromagnetic fields of their own. Maxwell’s principle of superposition shows that when two fields share the same space, they add together. An external field does not need to be strong to alter an internal field, it only needs to be present. The electron transport chain inside the mitochondria leaks a small fraction of its electrons by design, and those become reactive oxygen species — free radicals, which healthy cells use as signals. External magnetic fields cause the rate of leakage to change, increasing free radicals. When free radicals accumulate faster than the cell’s antioxidant systems can clear them, the result is oxidative stress. Oxidative stress is a recognized driver of chronic illness, including cardiovascular disease, neurodegeneration, diabetes and cancer, and it is in every medical school curriculum. One of its ordinary effects in connective tissue is that it switches on matrix metalloproteinases — MMPs, the enzymes that break collagen apart — while suppressing new collagen synthesis. Tendons are made of collagen. An athlete loading his soft tissue heavily six days a week is already creating oxidative stress in the tendons, so the addition of magnetic field exposure compounds the damage and inhibits repair, making him more susceptible to injury when enormous force lands on a tendon in a fraction of a second. That is the theory. We know that EMF causes oxidative stress, and we know that oxidative stress degrades collagen. The only thing missing is a study showing that direct connection. I have never claimed certainty that the substation is contributing to the elevated injury rate. I do believe it is likely, and I believe it warrants caution, and that a revenue machine like a major NFL team can easily afford to remove the exposure out of precaution. Is there a power plant next to the 49ers practice facility? Here is the clearest single instance of the problem with this episode. It requires no knowledge of electromagnetic fields at all. This story first came to me through Jon Feliciano, a former 49er, in an interview on Willard & Dibs on 95.7 The Game. Asked about why the 49ers are always injured, he said players used to joke that it was the power plant behind the practice fields. Wendy Zukerman corrects him on air. “It’s important to say that it’s not a power plant near the 49ers site.” Torre then asks her to explain the difference between a substation and a power plant. Her answer: a power plant generates electricity and transmits it to a substation, which converts the voltage into something your house can use and sends it on to your home or your business. There are two problems with that. The first is that there is a power plant on that site. The Gianera Generating Station is a natural-gas-fired peaker plant owned by Silicon Valley Power, and it has been there since 1986. I have published aerial photographs of the location from 1987 in which the gas plant is the only thing standing, before the practice facility was even there. I even mentioned the gas plant in my first interview. (Feliciano has since gone on record about this issue, and it is clear he had the substation in mind rather than the gas plant.) The second is that her definition describes a distribution substation, and the facility beside the 49ers is not that. It is the Northern Receiving Station, which takes in bulk power from outside the city’s grid and delivers it to distribution substations. Silicon Valley Power’s own map distinguishes receiving stations from substations, and Rose Rimler even reads the label out loud in the episode when she walks through that map on air. A few minutes before the map segment, Zukerman tells Torre: “like I said, we fact check everything.” Well, maybe not everything. I made a mistake On the morning of July 6th, at 10:20, I sent Rose and Wendy two studies. During the interview I had been asked which human studies show oxidative stress from electromagnetic fields, and I had not been able to name one on the spot. Rose emailed asking for the studies, I quickly searched for a couple, and I wrote that I thought these were the most relevant from that search. Unfortunately, I was busy and did not read either one closely at first. I picked them because they were topically relevant — one on substation workers, one on power plant workers. Neither were great studies, and their problems go past the ones raised in the episode. That was careless on my part. There is context that did not make the episode, though. Before that email I had been on a Zoom call with Rose talking about EMF and oxidative stress, and I pointed her to Dr. Henry Lai at the University of Washington, who has spent years aggregating the literature on this question. His tallies show that roughly 90% of studies since the late 1990s find oxidative effects from electromagnetic field exposure — hundreds of studies, systematically counted. Rose told me she was already aware of his work. So when she asked for studies, I understood her to have the aggregate view already and that she would report anything within that context. Not reading those two carefully was still a mistake, but the larger mistake was assuming the episode would be based on a thorough review of the actual body of literature, rather than on whatever I happened to send that morning. The second email At 1:49 that same afternoon — three hours and twenty-nine minutes after the first — I sent them a second email that I had been preparing for a while. It was a structured document in six numbered sections, written in response to a request they had made two weeks earlier, laying out my entire argument with citations attached to every link in the chain. The episode aired on August 13th, five weeks later, and not a single item from the second email is in it. Here is how it opens: The full email is included at the bottom of this post in Appendix B. Nearly 1,500 words in total. It contains two named mechanisms with the physics behind them. Occupational studies in humans. The literature on oxidative stress and collagen. A section addressing the strongest counter-evidence against my own theory, which I raised myself. And a closing section stating plainly what the theory cannot yet show. My entire argument, in writing, with detailed citations for every claim, provided to them five weeks before broadcast, and none of it made it into the episode. That is the single largest omission, but it is not an isolated one. The whole episode is riddled with similar problems, and the conclusion they came to is not merely a difference of opinion about the scientific evidence. Through omission and through strategic editing, Science Vs misinformed its audience — about what the evidence shows, about what I argued, and about who they spoke to. That is a notable outcome for a show whose stated purpose is correcting misinformation. For the record I am not going to tell you that Science Vs set out to discredit me, or that they had come to their conclusion before they reported the story, or that they used misleading editing to make me look like a man retreating from his own theory. I cannot know what was in anyone’s head, and I am not going to pretend otherwise, either. What I can tell you is what is on the record. They asked me for evidence and aired the weakest thing I sent. They interviewed three scientists who supported my theory and aired none of them. They discussed childhood leukemia without ever saying the exposure level at which it appears. And they took an answer I gave weeks before Ken Karipidis, the one EMF scientist who was put on air, was interviewed, and placed it directly after his critique, where it comes off as a major concession. A friend of mine who has worked as a journalist for thirty years read that last one and called it sleazy. I agree with her. Those are just the four issues that are impossible to defend as pure editorial decision, but they are not the only issues. The most glaring issues stem from what they left out: The exposure level at which the childhood leukemia association actually appears — three to four milligauss, a number the episode never states despite it being much lower than what I measured. The mechanism itself: The episode gets as far as “oxidative stress could weaken collagen,” which leaves it sounding like speculation, rather than a known pathway. The second email, and all the studies in it. Dr. Paul Héroux at McGill, whose work my proposed mechanism rests on, whose contact details I supplied, and who was never called. Dr. Beatrice Golomb at UC San Diego, who runs a research group on oxidative stress and mitochondrial function and has specific expdertice in collagen, interviewed and not aired. Dr. David Carpenter at Albany, the most prominent academic voice on the harm from magnetic field exposure, interviewed and not aired. Dr. Joel Moskowitz at Berkeley, interviewed and not even credited. The specific, testable condition I put in writing that would make me abandon the theory. My own unprompted statement of the theory’s evidentiary gap. My caveats about my own measurements. My ranking of EMF as one factor among several rather than the only one. The new equipment installed at those facilities between 2010 and 2014, and the sharp rise in system load from 2013 onward. But that is not all. What they kept, they selectively edited to serve a narrative that was not served by the raw transcripts of the interviews. A description of thousands of commercial routers in a workplace, reduced to the Wi-Fi in your house. My own acknowledgment, multiple times, that "I'm not a scientist" clipped in an episode that spends considerable energy pointing out that I am not a scientist. And a qualification I volunteered about my own measurements, cut from my answer and delivered later by their EMF expert as a correction. Together their edit produce a portrait of a wellness guy with Wi-Fi phobia and two bad studies, backing away from his theory the moment a “real” scientist looked at it. A practice facility where nothing changed because no new substation was built. Injuries written off as random because the 49ers were not quite the single most injured team in the league over the period in question. That portrait is false even though almost no individual statement made in the episode is. Draw your own conclusions At the top of the episode, Pablo Torre says his show had been flooded with requests to investigate the substation — that it was the thing they were asked to look into over and over again. Neither show went hunting for this story. It arrived from their own audience, in volume, and it could not be waved off. A thread I posted on X reached twenty-two million people. A guy with a gaussmeter and a brand-new Substack obligated a Pultizer prize winning program to spend an hour on a question about player health, that is considered fringe enough that most media operations won’t touch it. Pablo Torre Finds Out could not ignore it. So Science Vs came in and ran a “debunking” operation instead of an investigation. Everything below is what had to be left on the cutting room floor to deliver the “debunking”. They could not ignore it. So they tried debunked it instead. The demand was strong enough to force an episode, and since the episodes release the 49ers have sustained more injuries, including season ending ones, and interest has only grown. Demand from fans and playets is strong enough to force a better investigation from someone else. An omission is not a lie until you find out what was omitted. What follows is the detailed record so you can see the full picture. Draw your own conclusions. I have drawn mine. I am going to keep publishing. A note about Pablo Torre Finds Out After the episode aired I posted publicly, calling out Pablo Torre Finds Out for concerns I had about the integrity of their investigation, and about an interview directly with Pablo Torre which had been offered but never happened. I have since spoken with a producer at PTFO. The offer had rested on a misunderstanding about where things stood between us, and it fell away as the Science Vs part of the production widened past the science. He apologized for the miscommunication and I accepted. The producer also confirmed to me that they did essentially hand off the entire investigation to Science Vs for narrative purposes — so Pablo would be genuinely curious or potentially surprised by their findings — while still taking responsibility for what went out under their name. I appreciated the candor and the accountability, and I am comfortable with where we left things. So this post is not about PTFO. My disagreement is with Science Vs. What they left out about childhood leukemia Childhood leukemia appears at three milligauss. They gave no number at all. 1. They built an EMF ruler, then discussed childhood cancer without it. The sequence on air went like this: First I measured almost 9 mG, then I measured up to 50 milligauss. Torre reacts with concern — “that sounds high.” Cut to Karipidis: the public limit is 2,000 milligauss. Torre reacts again — “What happens at 2,000?” Karipidis explains that nothing happens at 2,000, that safety factors are built in. Then: at more than 50,000 milligauss you get flickers of light in the retina, and higher still, nerve stimulation. The listener now has a ruler (their ruler) to measure these numbers with. Fifty is nothing by their ruler. Then the episode turns to childhood leukemia and states, accurately, that there is a real association — but tellingly, they never give the exposure level at which that association appears. Childhood leukemia appears at three milligauss. They conveniently left that out. The pooled analyses that produced the World Health Organization’s carcinogen classification found roughly a doubling of childhood leukemia risk above three to four milligauss. That is the number that belongs in that segment, and it is more than ten times below the reading that had just been dismissed as trivial. I gave them that number myself, on tape, in the interview. Describing my own threshold, I said my comfort level for long-term chronic exposure is three to four milligauss, but preferably one. 2. Six children in thirteen million. The episode reports that these fields might explain one to four percent of childhood leukemias, then reframes it as a German estimate of perhaps six cases among thirteen million children. Torre, to his credit, is uncomfortable. What never gets explained is why that number is small. Almost no children lives above three milligauss in Europe. Fewer than two children in a hundred, in the Italian data. So even if the risk to those children doubles, the number of additional cases across an entire country stays small — not because the hazard is small, but because hardly anyone is exposed to it. Count the whole population and the few who are exposed disappear. And the reason hardly anyone is exposed is that siting rules keep housing away from transmission lines. In Europe, at least. The Dutch government has a standing precautionary policy against building new homes in any zone where the annual average magnetic field exceeds 4 milligauss — the same number the episode never mentioned. Denmark and Norway treat 4 milligauss as the level that triggers a look at mitigation. That is five hundred times below the ICNIRP figure quoted on air. The United States has no federal equivalent. That is evidence of infrastructure siting policy working. It is not evidence that there is no hazard. It is evidence that we already act as though there is one. If you listen to the raw audio you will hear that my proposed solution is EMF exposure is to build better infrastructure. What they left out about the mechanism 3. They accused me of cherry-picking, and then cherry-picked themselves. On air, Zukerman tells Torre that people who worry about electromagnetic fields dismiss the studies showing null results and collect the ones showing harm, and that this is why you have to be careful not to cherry-pick. They had two papers I had rattled off in a hurry, and a second email containing a carefully chosen set. They ran the two weakest, graded them on air, and presented the result as the state of the evidence for not only my theory, but the whole question of oxidative stress from magnetic fields. That is cherry-picking from my emails. The bigger picture is what they did with the literature as a whole. Zukerman acknowledges it exists, dismisses it, and disposes of it in a single stroke. You can go online right now, she tells Torre, and find scores of studies showing these fields increase oxidative stress. The issue, she says, is that they are not studies in people. Many of them are in people. The occupational studies in my second email are in people. That email had been in her inbox for five weeks. She waved off the literature on a factual claim that the documents in her possession contradict. (I have written separately about the evidentiary standard at work here). The effect was that they essentially baited me: ask the subject to supply the evidence, take the weakest thing he sends, and let that stand as a proxy for the whole argument. Wendy has been doing this show for ten years and clearly knows how to extract what she needs for her desired narrative. 4. They presented the collagen-improvement research as a gotcha, but I had already addressed it. The episode treats the existence of studies showing that EMF can improve collagen as a surprise that undercuts me, but I had pre-emptively addressed that five weeks earlier in the email they ignored. Those studies use measured, high-intensity pulsed dosing. The mechanism by which they work is oxidative signalling. The cell reads a controlled rise in reactive oxygen species as a warning, upregulates its antioxidant defenses, mounts a repair response, and the therapy stops before the system is overwhelmed. The key point is that it induces an oxidative response, which means those studies are actually evidence for my proposed mechanism, not against it. A field that produces a therapeutic effect cannot be biologically inert (which is the implication when they say “Nothing happens at 2,000 milligauss). Chronic ambient exposure is the same pathway with no off switch, and it is most consequential during the evening and while sleeping, when the body is in repair mode and the exposure does not stop. Section 5 of my email is titled What about the study showing improved collagen synthesis. I raised the counter-evidence myself, explained why I did not think it refuted the theory, and wrote that I was disappointed the question had not come up in the interview. That is worth holding against the cherry-picking charge in the item above: the studies they presented as inconvenient for me were studies I had gone out of my way to put in front of them. 5. Section 1 of my email stated a critical argument that was never put to the expert directly responsible for it. My email says the exposure limits exist to prevent acute effects and do not address chronic exposure at all, and that my dispute is with the entire framework. Zukerman gestures at this once. After Karipidis gives the 2,000 milligauss figure, she tells Torre that I would object because those levels do not capture “more subtle effects.” That is not my argument, and “subtle” is doing a lot of work here. My argument is that the limits are built to prevent acute effects — nerves firing, muscles twitching, etc — and were not designed to address chronic exposure at all. In fact, ICNIRP’s own documents state as much themselves. Mine is a structural objection to what the standard measures, reduced as a concern about subtlety, it becomes a matter of degree, rather than a categorical difference. They interviewed Ken Karipidis, vice chair of ICNIRP — the body that writes those limits. He was asked what the limit is, but he was never asked how it applies to chronic exposure. The scientists Science Vs interviewed and did not air 6. They interviewed four officials of the standards body. The episode credits roughly a dozen researchers. Four of them are associated with ICNIRP. Ken Karipidis is the vice chair. Rodney Croft chaired ICNIRP from 2020 to 2024 and led the development of its current guidelines. Dan Baaken is ICNIRP’s scientific secretary. Frank de Vocht sits on the Main Commission. One of them speaks in the episode. Their institution’s position is more than adequately represented — the 2,000 milligauss figure, the safety factors, the assurance that nothing happens below the line. 7. The four who were left out. Dr. Beatrice Golomb is Professor of Medicine at UC San Diego — a physics undergraduate degree, a PhD in biology, an MD. She directs a research group whose stated focus is the relationship between oxidative stress, mitochondrial function and health. That is the mechanism the episode spent its first half dismissing. Her published work includes the fluoroquinolone case series behind the FDA’s black box warning on an antibiotic class that ruptures tendons, where the accepted mechanism is oxidative stress and mitochondrial toxicity — the back half of my chain, already on a drug label. She is in the credits. She is not in the episode. She has told me she finds my theory biologically plausible, and that she has a recording of her interview. Dr. David Carpenter directed the Institute for Health and the Environment at the University at Albany for decades and is the most prominent academic voice arguing these fields cause harm. Childhood leukemia is the reason these fields carry a WHO classification at all, and Carpenter is the person you call if you want that case made by someone who has spent a career on it. He is in the credits. He is not in the episode. He has told me that when he was interviewed they could not have been less interested in what he had to say. Dr. Joel Moskowitz directs the Center for Family and Community Health at UC Berkeley’s School of Public Health. He told me directly that they interviewed him and that he thought it went well. He does not appear in the episode, and unlike Golomb and Carpenter, he does not even appear in the credits. Dr. Paul Héroux at McGill is the researcher whose work provides the foundation for the oxidative stress mechanism my theory rests on. I gave Rose his email address and phone number. He was never interviewed. They never even called him. 8. “He’s not a scientist.” The episode leans hard on the fact that I am not a credentialed scientist. Zukerman says it. Torre repeats it. “At all.” Having built a segment on that, they then left out the physicist-biologist-physician, the professor of public health, the director of a university environmental health institute, and the McGill researcher whose work the primary mechanism comes from. They spent the episode telling their audience I am not a scientist, and then kept four actual scientists who support the theory off because their argument does not survive otherwise. How they misrepresented me and my case 9. They played my words as a response to an interview I had never heard. Near the end of the hour, Zukerman says she went to me and told me the evidence was lacking. Then she plays me saying I am not wedded to it being an EMF, that I have staked my reputation on it needing more study, and that I am not certain of anything. That segment was placed immediately after Karipidis had finished taking the cherry-picked papers apart. It plays as a man folding under expert scrutiny. Commenters have used it against me since, but it is incorrect. Here is the sequence as it happened. I was interviewed. Karipidis was interviewed weeks later. I was never told what he said, or even who he was. I was never asked to respond to it. I did not know his name until the episode aired. That is not a simple editing decision. That is a quote from one interview, moved to follow a critique from a different interview it was not a response to, so that it would land as a concession. A friend of mine who has spent thirty years as a journalist read that sequence and called it sleazy. She is right. The full passage is reproduced in Appendix A, and it is worth reading. What airs is the preamble. Four paragraphs later I say “to directly answer your question” — and that answer, about what would change my mind if the team relocated, is not in the episode. Neither is the sentence two paragraphs on, where I say the EMF might turn out to be ten percent of the problem. I will say the underlying point again, though, because I meant it and I still do. I am not wedded to my theory that magnetic fields from the substation are contributing to the 49ers injuries. I could be wrong. What I am wedded to is the process of scientific inquiry, and the fact that we should be cautious around these exposures and we need more independently funded studies. 10. Three telling edits. They cut “I’m not a scientist” mid-sentence. What I said: “...and I am not certain of anything. I’m not a scientist. To me, I find the mechanisms compelling. I will go back to the fact that I got into this not because of the 49ers, but because of clients I was working with who suffer from EDS...” What aired: “...and that I am not certain of anything. I’m not.” The clip ends on the first two words of the sentence in which I say I am not a scientist, in an episode that spends considerable energy establishing that I am not a scientist. The full sequence is in Appendix A. They cut my own description of my work. Asked what I do, I said I run a wellness business, that I am focused on writing software to help people navigate environmental stressors, that environmental health is my focus, and that I also do independent journalism and research. What aired is the wellness business and the environmental health, with the software and the journalism removed from my own answer. The software engineering does come up later — narrated by them, as a hacker who grew up to write firmware — but my current work, in my own words, arrives as “wellness guy.” Thousands of commercial routers became home Wi-Fi. What I described: working at a wireless networking company surrounded by thousands of Wi-Fi routers, and a stress-induced health collapse afterward that working in that environment contributed to. What aired: Wi-Fi, the kind you have at home — reinforced by a clip of Christian McCaffrey talking about turning his router off at night. Three cuts, and every one of them makes me seem less credible. That is not a coincidence. 11. I gave them my own caveats and the test that would disprove me. In the first email I wrote that a well-designed chronic, ambient-level exposure study in tendon showing no collagen degradation, run over many months, would undercut my confidence in the theory. That is a specific, testable condition, stated in advance and in writing. There was a second interview, weeks later, which was not used in the episode, but in that I asked again for the falsification criterion to be included. What the episode presents instead is a man saying “we need more study,” which reads as a hedge, an open-ended request for the benefit of the doubt. It is the opposite of a hedge. Section 6 of my second email says, unprompted, that as far as I am aware there is not a single study showing EMF-driven oxidative stress degrading collagen in a human in vivo or in human tendon in a lab. I wrote that because it is true and because it is the first thing anyone should check. Under their standard it becomes the hinge of the entire first half, framed as something they had to surface without my cooperation. I had handed it to them five weeks earlier. And in the interview I said that my 25 to 50 milligauss readings were taken at knee level, and that these fields attenuate quickly, so by the time it reaches your head it is not that high anymore. That did not air either. The qualification arrives later, from Karipidis, as a correction. How you would actually study the injuries 12. They measured the wrong thing. The episode spends its final act on whether the 49ers are the most injured team in the NFL, finds no clean ranking, and lands on “injuries are random”. That is the wrong test. You would define the exposure — measured field levels at the facility — and the outcome narrowly: soft-tissue injuries, not a single MSK injury count that a broken finger and a ruptured Achilles both land in. Then you would compare the 49ers to themselves before and after the exposure changed, and compare that change against every other team over the same window. That controls for league-wide shifts in rules, turf and training load. It is a difference-in-differences design, and it is standard. I sent them that argument on July 28th, in writing, along with the fact that in eleven of the past twelve seasons the 49ers had finished top ten in Adjusted Games Lost. And by the episode’s own reporting, the 49ers are second in games lost to injury over the past decade, with FTN’s Aaron Schatz confirming that once you adjust for starters they come out second worst in the league — after having been one of the healthiest teams for years before that. A finding like that is where an investigation starts, not where it stops. 13. I did a lot of substation research for the episode that was never aired. I had reported that the substation next to the facility was expanded in 2014. That was wrong, and Rose Rimler and I sorted it out together. They gave me credit on air, which I appreciated, but framed it as a concession on my part to something they had found. In fact I had been investigating it myself, was uncertain before they asked, but was waiting for clarification from Silicon Valley Power. The uncertainty centered around a substation named “Esparanca” that was proposed right behind the facilities as part of the stadium construction, but now appears to never have been built. I spent days in Silicon Valley Power’s records, and did eventually reach Silicon Valley Power for confirmation and indentified the mistake myself. I also gave them highly detailed documents on what we do know about those facilities: new equipment installed between 2010 and 2014, and load growth across the system with an inflection around 2013–2014, with the Northern Receiving Station carrying the highest capacity in the system and sitting closest to the data center corridor. None of that is in the episode. I have a separate piece about it coming out soon. *I cut 2 seconds from the raw audio to remove personal information that had no connection to anything in the episode. Appendix A: the passage the clip was taken from This is the sequence from my first interview, in full. The episode uses one fragment of it. It’s tricky because my understanding is that there’s something like a 15-year half-life on collagen remodeling issues. They’re going to get a new crop of players. What I really need, though, is for the injuries to be broken down so we can see the soft tissue injuries. Right now, they’re categorized as MSK injuries, but let’s say we can actually identify them more specifically. Is that right? He was able to do that? Oh, cool. I wasn’t able to find that information. Interesting — I didn’t know that data existed. I tried so hard to get it, and I didn’t know it was available. Maybe you could connect me with that person. That would be amazing. I’m not wedded to it being an EMF, honestly. I’m not. I would say that, to a certain extent, I’ve staked my reputation on the fact that it needs more study. It’s important to look into it, and I am not certain of anything. I’m not a scientist. To me, I find the mechanisms compelling. I will go back to the fact that I got into this not because of the 49ers, but because of clients I was working with who suffer from EDS, Ehlers-Danlos Syndrome, a tendon hypermobility issue. This condition is documented to be partially caused by oxidative stress. I’ve worked with them to mitigate their EMF exposure and have seen people improve. I think it’s a bigger story. In a way, the 49ers represent an occupational cluster with specific exposure risks. If someone would conduct a study on this, I believe we could learn a lot. Perhaps we would discover that it’s like, okay, maybe it’s 10% of the problem. I don’t know the exact number, but I think either way, conducting the study would provide valuable insights. To directly answer your question, it’s challenging for me because it’s part of a bigger story. If the 49ers move and stop having injuries, I might conclude that it was related to the substation. However, if they move and still experience injuries, I would want to analyze the nature of those injuries. Are they primarily soft tissue injuries? I don’t think it’s necessarily a case closed, but the story might lose traction at that point regardless. Three things are worth noting. The aired fragment stops immediately before the sentence in which I say I am not a scientist — the cut described in item 10. It also stops four paragraphs before the words “to directly answer your question.” What the episode plays is the throat-clearing. The actual answer, about what would change my mind, never airs. And two paragraphs in, I say the EMF might turn out to be ten percent of the problem. That is the ranking the episode says I never gave. Appendix B: the full email, July 6th, 1:39 PM Hi Rose, You asked me to send you some studies a couple of weeks ago. I apologize for the delay. I have been incredibly busy. I also wanted to give you a chance to dig in yourself, and get your own perspective before I influenced you too much with mine. That said, I do want to make sure that the actual argument is abundantly clear, so here it is! One note before I begin, there are really two separate questions here: Does the substation cause the soft-tissue injuries? That was my specific claim, and what the podcast is about. Is the theory biologically sound? Can 50/60 Hz fields at these levels do anything biological below the safety limits? Question two is why people have been calling me a conspiracy theorist. The regulatory and advisory bodies (ICNIRP, etc) maintain that anything below their exposure limits is biologically inert, and that position is broadly accepted within the mainstream, but the actual science paints a different picture. To defend my theory I’ve had to defend a much larger claim: that the advisory bodies have the biology wrong. I can’t honestly answer question 1 without first addressing that. Here’s is a simplified outline of my theory, with links to studies: 1. Advisory body thresholds for biological effects The ELF magnetic-field limits (ICNIRP’s ~2,000 mG public / 10,000 mG occupational) exist to prevent acute effects from exposure. If a magnetic field is strong enough, it makes nerves fire and muscles contract. Muscles twitch. You can see faint flashes of light in your vision. People can even feel tingling or even a shock. That happens due to Faraday’s law, which states that a time-varying magnetic field induces an electric field inside the body via the creation of Eddy currents.The standards assume that those currents do nothing below the twitch threshold. They do not consider chronic effects at all. My argument is that they are biologically active and do have chronic effects. 2. There are multipe ELF-EMF mechanisms for inducing oxidative stress. This is basic biophysics. Below are the two primary mechanisms. Simplest first. Dr. Paul Héroux of McGill has the most intuitive explanation, which rests on Maxwell’s principle of superposition: because Maxwell’s equations are linear, two sets of electromagnetic waves sharing the same space simply add together. In the interview I pictured a pebble dropped into a pond, sending out clean ripples. Then a boat motors past at distance, sending larger waves, and where they meet the ripples, the two add together and the original pattern gets distorted. In our cells, the mitochondria run on continuous currents of electrons and protons that carry their own faint electromagnetic fields. An external field from a substation, by the principle of superposition, adds to those internal currents and shifts the pattern. The electron transport chain in the mitochondria leaks a fraction of its electrons by design as reactive oxygen species (ROS), which act as signalling molecules in a healthy cell. Distorting that field increases that electron leakage, creating oxidative stress. A tiny electromagnetic interference results in a large downstream effect. https://www.sciencedirect.com/science/article/pii/S2405844025006474 The more complex pathway is Dimitris Panagopoulos’s Ion Forced-Oscillation / Voltage-Gated Ion Channel (IFO-VGIC) mechanism. Weak, polarized, low-frequency fields force the ions (calcium, potassium, sodium) next to an ion channel’s voltage sensor to oscillate; because that force scales with the inverse cube of the distance involved, fields far below the stimulation threshold can still force the channel open, disrupting the cell’s ion balance and driving ROS overproduction (Nitric oxide and superoxide interact and produce peroxynitrite, a highly unstable cytotoxin that breaks down into free radicals). https://www.researchgate.net/publication/366088934 https://www.spandidos-publications.com/ijo/59/5/92 https://www.mdpi.com/1422-0067/22/18/10041 https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1585441/full https://pmc.ncbi.nlm.nih.gov/articles/PMC3772193/ 3. Evidence of oxidative stress, in vivo, in humans from ELF-EMF There are many studies, and the strongest are occupational, which applies to the 49ers situation. 115 power-plant workers vs. 145 office controls. Serum malondialdehyde (MDA, the standard lipid-peroxidation marker), superoxide dismutase, and catalase were all significantly higher in the exposed group, and the markers rose with increasing field exposure. https://journals.sagepub.com/doi/abs/10.3233/WOR-203244 A follow-up where they reversed the oxidative stress in power plant workers. A double-blind randomized controlled trial: 91 power-plant workers given vitamin E, vitamin C, both, or placebo for 90 days. The antioxidant groups showed reduced lipid peroxidation and raised antioxidant capacity versus controls. https://pubmed.ncbi.nlm.nih.gov/32191586/ A spot-welder study: 46 welders occupationally exposed at 50 Hz, showed significantly altered red-blood-cell antioxidant enzyme activity and concluded ELF-MF “might act as an oxidative stressor… even at the recommended levels of exposure.” https://pubmed.ncbi.nlm.nih.gov/18504600/ A substation-specific study found oxidative stress and DNA damage https://pubmed.ncbi.nlm.nih.gov/24460415/ 4. Oxidative stress degrades collagen This half isn’t controversial at all. Oxidative stress activates the matrix metalloproteinases (MMPs) that break collagen down and suppresses its synthesis. Here are three example studies: Siwik et al. 2001: Oxidative stress raises MMPs and lowers collagen synthesis in cardiac fibroblasts. https://pubmed.ncbi.nlm.nih.gov/11121376/ Fisher et al. 2009: Collagen fragmentation drives oxidative stress and elevates MMP-1 in aged human skin. https://pmc.ncbi.nlm.nih.gov/articles/PMC2631323/ Varani et al. 2006: Decreased collagen production in oxidatively aged skin. https://pubmed.ncbi.nlm.nih.gov/16723701/ 5. What about the study showing improved collagen synthesis I’m glad you asked this question, and I’m bummed we didn’t get to it in the interview because it is a great question, and very important to understand. Superficially it may appear to be a refutation of my theory, but I would argue that it actually supports the underlying argument. That study used a short, high-intensity pulsed dose: therapeutic-PEMF, the same type of field used clinically to stimulate bone and tissue healing. The reason this works is because it triggers the very same biological pathways that can lead to oxidative stress. The exposure still stimulates ROS production, but done at a controlled dosage, for a limited duration it has a different effect. The cell reads the ROS as a warning signal and it upregulates its antioxidant defenses, and mounts a repair/proliferation response. The therapy is then stopped before the cell gets overwhelmed. That is essentially a hormetic response: a small dose of a stressor producing a beneficial adaptation. Two things follow: At the very least, this means the field is biologically active, which by itself undercuts the regulatory claim that these fields are inert below their thresholds. There is a wealth of literature on PEMF (pulsed EMF) bone regeneration and calcium channel signalling (which is the second mechanims in section one). Here is one. https://www.nature.com/articles/srep13856 At best, it leads us straight to my actual concern, which is that under chronic, chaotic and unregulated exposure, the cell’s antioxidant capacity gets overwhelmed, resulting in oxidative stress. There are plentiful examples of things that are beneficial at controlled doses, but harmful at uncontrolled doses. Excecise is one obvious, uncontroversial example. Botox is perhaps a more interesting example. 6. What about a study that shows EMF causing collagen degradation in humans? As far as I am aware there is not a single study showing EMF-driven oxidative stress degrading collagen in a human, in vivo, or in human tendon in a lab. There are studies involving isolated fibroblasts though, which secrete collagen. The most relevant one showed that human fetal scleral fibroblasts exposed to 50 Hz ELF-EMF showed a significant decrease in collagen synthesis and that MMP-2 expression was upregulated, which is the exact mechanism I described in section 2. https://pmc.ncbi.nlm.nih.gov/articles/PMC3626379/ That’s it! Thanks again for the interview. Best, Peter Anthony Cowan Environmental Health · Wellness Tech · Researcher & Journalist Publisher & Editor, Living Energy Bio: petercowan.comSocial: X · Instagram · TikTokApp: sunlightis.lifePhone: [redacted]
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Intel Panther Lake Teardown, 18A, BSPD, GAAFET, SemiAnalysis STEEL
Panther Lake debuts the first commercial implementation of backside power delivery (BSPDN), introduces Intel’s first iteration of gate-all-around (GAA) transistors, and showcases their advanced packaging capabilities with its Foveros-S assembly. With Panther Lake, Intel’s manufacturing arc has shifted from nebulous roadmaps to shipped silicon, a significant milestone on their long road back to competitive semiconductor manufacturing. To evaluate the extent of Intel’s comeback, we tore down Panther Lake. The SemiAnalysis STEEL teardown lab breaks down advanced datacenter and AI hardware. To learn more about our pipeline or to commission a teardown, contact sales@semianalysis.com. WE’RE HIRING: Architecture, floorplan, packaging, manufacturing, and labs experts. Opportunities from system to transistor and everywhere in between. Check out our Careers page. Our teardown traces 18A from its four-sheet RibbonFETs (Intel’s marketing name for GAAFETs) and gate stacks through contacts, frontside and backside wiring, and the bonded carrier. We explain how these material and integration choices improve gate control and reduce resistance, while adding capacitance, thermal resistance, and process complexity. Our measurements put Panther Lake’s 18A compute logic and TSMC N3E GPU logic at similar logic density. However, 18A does not lead TSMC N3P, N2 or Samsung SF2 in peak density. Panther Lake’s CPU cores are incremental updates, and the high-end GPU still uses TSMC N3E. Panther Lake assembles one compute tile, one GPU tile, and one I/O tile atop a passive base tile using Intel’s Foveros-S advanced packaging. Both compute tile variants use Intel 18A. The Xe3 GPU options are a 4-core GT1 tile on Intel 3 and a larger 12-core GT2 tile on TSMC N3E. Both I/O tile variants use TSMC N6. [1], [2] Our analysis centers on the PTL-U compute tile, both the 4-core and 12-core GPU tiles, as well as the 12-lane I/O tile. In conventional chips, power and signal are routed through the same frontside metal stack towards the device frontend. Power rails consume scarce routing resources near the transistors, while tall via stacks carry VDD and VSS from the coarse upper wires to local rails. Backside power delivery (BSPD) moves the main power network behind the transistor layer, to the backside, separating it from frontside signal routing. We covered BSPD and its impacts in 2024. [3], [4], [5] Intel’s BSPD implementation, branded as “PowerVia”, routes power through dedicated backside metals to nano-TSVs, which connect those rails to local source/drain (S/D) contacts. Implementing that separation requires Intel to build the interconnect stacks from both sides of the wafer. The frontside comprises the M0-M14 signal stack, while the backside comprises the BM0-BM5 power stack. M0 and BM0 are closest to the transistors. The nano-TSVs connect the two sides, but Intel patterns and etches each via from the front after forming the contacts. A narrow via runs from the side of the contact deep into the silicon substrate. Intel then completes the frontside signal metal stack, bonds the wafer to a carrier, flips it and removes the original substrate until the buried via tips are exposed. The backside metal stack is then deposited directly on the revealed vias. The nano-TSV and backside-via profiles taper in opposite directions because Intel forms them from opposite sides of the wafer. The transistor structures form the FEOL. Local contacts and nano-TSVs connect them to the wiring. M0 begins the frontside interconnect stack. The silicon carrier remains attached above the frontside interconnects. It supports the device wafer during substrate removal and backside processing and remains part of the finished chip’s thermal path. PowerVia removes the main power distribution from the congested frontside metals, routing supply through shorter and wider backside wires. Its lateral landing still occupies area in the standard cell, so it recovers less cell area than a direct backside contact. [3] Nano-TSVs beside the logic devices carry VDD or VSS from the backside power network, while signal connections continue upward through the frontside metals. Backside Interconnects Samsung SF2 data is included for comparison to Panther Lake’s within this article. SF2 is the incumbent GAA foundry node but lacks BSPD, serving as a useful reference to evaluate 18A. A full teardown of Samsung’s S26 products, processed on SF2, will be shared soon.Nanosheet-cut EDS comparison. The PowerVia supply path runs from the backside Cu rails through Mo-lined W nano-TSVs to the local transistor contacts. In this cross section, the tapered connection spans roughly 150 nm from the contact level to BM0. The Ta liner confines Cu and promotes adhesion to the surrounding stack; the AlOₓ etch stop controls the next dielectric etch above the rail. Dielectric beneath the ribbons electrically separates the devices from the backside wiring and removes the conducting silicon body below the channel. [6] AlOₓ serves as an etchstop (ES), enabling endpointing and protecting the underlying layers. Low-volatility aluminum fluoride reaction products resist the fluorinated plasma, allowing a thin AlOₓ film to protect the metal while the surrounding low-k dielectric is removed. [6], [7]. While the BM0 and layers above the M1 lines show double AlOx layers, Our SMIC N+3 teardown showed single AlOₓ layers. SMIC uses a simpler local AlOₓ substack, while the remaining cap and etch sequence provide the required landing protection. So why double layers? The closely spaced AlOₓ doublets provide two protected endpoints in the etch sequence. Intel documents an AlOₓ/SiN/AlOₓ stack that explains the benefit. The main dielectric plasma etch stops on the first AlOₓ film; a selective wet clear opens that film; a second plasma etch removes the intermediate SiN and stops on the second AlOₓ film. The final wet clear exposes the metal landing surface. SiN is the intermediate dielectric in Intel’s published example. [8] The second stop protects the metal through a cap breakthrough. Wide openings can etch faster than narrow ones, and etch depth varies across the wafer. Metal under an early-clearing opening would otherwise be exposed while other openings still need more etching. Staged protection widens the process window and reduces metal erosion, corrosion and void formation. [8] TSMC documents AlN/AlOₓ/SiOC/AlOₓ above Cu, with AlN blocking Cu diffusion, and a simpler AlN/SiOC/AlOₓ variant that omits one AlOx film. [9] Levels with different opening sizes, aspect ratios, pattern densities and cap materials need different etch margins. A double AlOx stop is useful where another protected endpoint justifies the added processing. The extra film adds formation, selective opening and cleaning steps, plus another set of interfaces to control adhesion, moisture, and stress. These blanket films are opened through the existing via pattern, so each film does not require another lithography mask. AlOₓ adds parasitic capacitance when it replaces lower-k dielectric; two thin AlOₓ films can nevertheless contain less AlOₓ than one thick film. Total thickness, placement, and theintermediate dielectric determine the electrical cost. Deposition chemistry also changes AlOx permittivity and residual hydroxyl content, which can oxidize the underlying metal. [7], [8], [10], [11] The backside stack separates into relatively fine BM0-BM2 wiring near the devices and coarser BM3-BM5 power distribution. The largest pitch increase occurs between BM2 and BM3. BM0’s pitch closely matches the logic-row height, fitting local power delivery to the cell rows. Higher levels aggregate current through larger conductors: routing density becomes less important than low resistance and current capacity as the network approaches the package. This hierarchy provides wide power wiring for the power delivery network without consuming scarce frontside signal-routing resources. [3] The SemiAnalysis STEEL teardown lab breaks down advanced datacenter and AI hardware. To learn more about our pipeline or to commission a teardown, contact sales@semianalysis.com. WE’RE HIRING: Architecture, floorplan, packaging, manufacturing, and labs experts. Opportunities from system to transistor and everywhere in between. Check out our Careers page. Frontside Interconnects Intel 18A combines Mo-lined W contacts and nano-TSVs with a separate backside Cu power network. Samsung SF2 keeps power on the frontside, using Ti-based contact interfaces and Ta-based barriers and Co liners around Cu wiring. In 18A standard-cell rows, backside power rails supply the devices through nano-TSVs within the cells, freeing frontside routing resources. Samsung’s M0 accommodates both power and signal connections. From the device toward M0, the connection runs through a Ti-based S/D interface, W contact fill, a Mo-lined W via, and the Cu M0 wire. Mo supplies a conductive nucleation and adhesion layer for W, replacing the resistive TiN liner used in conventional W integration. This increases the effective conduction volume within the feature while retaining W fill and its established polishing, cleaning and etching processes. Intel’s Mo/W patent describes this integration tradeoff. The nano-TSV uses the same Mo-lined W construction in the backside supply path. [12] The move from TiN to Mo is an incremental change. While a full Co or Mo fill can also reduce the volume lost to liners in very small features, it requires new integration schemes that increase complexity and risk. Cu remains attractive for wider wires due to its low resistance. As wires and vias shrink, the diffusion barrier consumes an increasing fraction of their cross-section. [12], [12], [14] Intel uses Co/Ru liners at M0-M1, Co at M2-M4, and Nb at M5-M9. The lower-level liners help Cu adhere and reduce void formation during trench fills. Applied Materials’ Endura has new thermal control that facilitate wetting process, so the thin film continuity is good enough that good capillary pressure will drive Cu atoms to the via bottom without voiding. Intel’s choice to use Nb is particularly interesting. Intel’s Nb patent describes a conductive diffusion barrier intended to reduce the barrier’s contribution to resistance relative to conventional Ta-based barriers, particularly at via bottoms where all current crosses the barrier. The patent pairs Nb in coarser levels with the option of lower-cost PVD processing. [15], [16] The upper metal layers support thicker barriers formed through physical vapor deposition (PVD) despite its worse coverage and uniformity. Meanwhile, the lower metal layers require thinner barriers deposited through conformal atomic layer deposition (ALD). Co/Ru adds another material interface and requires controlled deposition and Cu fill. Changing liners and barriers by metal layer allows Intel to optimize interconnect resistance, process complexity, and reliability. [15, 16] RibbonFET, Intel’s name for its gate-all-around FETs (GAAFETs), replaces the FinFET’s vertical fins with four stacked horizontal silicon nanosheets, allowing the gate to surround the channel on every side. The path to GAAFET begins with the planar transistor. A planar MOSFET places the gate above the channel between its source and drain. Pairing an NMOS with a PMOS transistor creates a CMOS inverter, in which the NMOS pulls the output low for a high input, and the PMOS pulls it high for a low input. The gate must retain electrostatic control of the channel to ensure clean switching. As gate lengths shrank, the drain began to compete with the gate for that control, increasing off-state leakage. Electrostatic control was restored through an architectural evolution that raised the channel into a vertical fin and wrapping the gate around three sides. Called “FinFET”, this new architecture packed more effective channel width into a smaller footprint. Further scaling made it harder to maintain both drive current and leakage within smaller cells, and reintroduced the same problems planar MOSFETs faced. Nanosheet GAAFETs close the fourth side by replacing the vertical fin with a stack of horizontal nanosheets, each surrounded by the gate. The tighter electrostatic control suppresses leakage at shorter gate lengths while stacking adds effective channel width within the cell footprint. In a FinFET process, channel width changes in discrete steps as designers must add or remove whole fins. Nanosheet width can instead be adjusted continuously within the process’s design rules. Wider sheets increase drive current, while narrower sheets reduce capacitance at the cost of drive current. Intel 18A uses stacks of four nanosheets each and varies their widths across logic and SRAM. At the process level, adding more sheets to each stack increases effective channel width and drive current, but complicates fabrication. RibbonFET vs MBCFET Samsung began GAAFET production in 2022 with SF3E, following with SF3 and now SF2. Its ‘MBCFET’ provides a useful structural comparison with Intel’s first RibbonFET implementation. [17] STEEL is digging deeper into SF2, used in the Exynos 2600, and TSMC’s GAAFET N2, used in Apple’s A20 Pro, in upcoming newsletter articles. We’re throwing some teasers on X. Let’s compare Samsung SF2’s MBCFET with Intel 18A’s RibbonFET. Subscribe Even to the untrained eye, Intel’s extra nanosheet is obvious. Intel stacks four ribbons to Samsung’s three. Samsung’s sheets are much wider in these fields, so both sheet count and width matter to the available channel perimeter. Sheet width also changes which silicon surfaces carry current. On conventional (001) silicon, wide nanosheets emphasize the broad top and bottom surfaces, favoring electron transport; the larger sidewall contribution in a narrow sheet favors hole transport. Thinner sheets improve gate control but increase confinement and scattering. This makes width and thickness part of the NMOS/PMOS balance, alongside strain and threshold voltage. [18], [19] GAAFET designs like 18A use different work-function-metal (WFM) stacks for NMOS and PMOS. Around each ribbon, a thin SiOx interfacial layer separates the silicon channel from the HfOx high-k dielectric, with La providing dipole tuning and the WFM wrapping the dielectric. NMOS uses a TiAl-based stack, while PMOS uses TiN WFM. W fills the remaining gate trench, providing a lower-resistivity path where the work-function layers are no longer needed. In this field, the PMOS stacks leave room for W between ribbons, while the NMOS stacks occupy more of those gaps. A silicon-based dielectric marks the P/N boundary, allowing the PMOS and NMOS gates, sharing the same gate trench, to be processed sequentially. Fast logic paths, retention circuits, and SRAM need a family of threshold options. Changing threshold without substantially changing device dimensions, capacitance or fabrication complexity is valuable. FinFET processes typically use different work-function-metal stacks. In a four-ribbon GAA stack, the narrow sheet-to-sheet gap limits how much WFM can fit around each channel. La in the gate dielectric creates interfacial dipoles at the SiOx/HfOx boundary, shifting effective work function and tuning threshold voltage. This gives Intel another control alongside its NMOS and PMOS WFM stacks. Low-threshold devices improve critical-path drive; higher thresholds reduce leakage elsewhere. Dipole tuning is especially useful in GAA because it changes threshold without consuming the narrow intersheet gap with thicker WFM. Precise control of La incorporation, diffusion and interface quality has long been a challenge, limiting viability in high volume production but is now seen from every leading-edge foundry. Intel’s patent describes depositing a dipole-forming oxide above HfOx and annealing it toward the interfacial oxide before completing the work-function and fill metals. This separates threshold tuning from the space available for metal. Newer research addresses the thermal cost: imec’s 2026 dipole-middle research inserts the shifter between two HfOx depositions, shortening the diffusion path while protecting SiOx during patterning. [20], [21]Matched-cut EDS, Intel 18A (left) vs. Samsung SF2 (right). Intel retains raised source/drain epi beneath its contacts, while Samsung recesses W deep into the epi to form a V-shaped Ti-lined interface. The deeper contact increases metal-to-semiconductor area and shortens the current path from the lower sheets, reducing contact and spreading resistance. It also removes epi volume and brings the contact etch closer to the channel ends. Retaining more epi preserves the material available for strain transfer, especially from SiGe into PMOS. These geometries balance contact access against stress engineering and etch margin. [22], [23] Samsung stacks three sheets to Intel’s four ribbons, and both processes use sheet width to tune drive strength. In our Samsung cross-sections, widths range roughly from 19 to 30 nm in the NPU rows and 37 to 50 nm in the CU cell. The Samsung nanosheets taper, with the widest sheet at the bottom and the narrowest at the top. Both processes use HfOx gate dielectric and Ti-based work-function stacks, with Al in the NMOS stack. In the Samsung devices shown here, the dielectric and WFM occupy the intersheet gaps, leaving W above the top sheet. Intel’s PMOS stack leaves more room between ribbons, and W fills those gaps while the thicker NMOS stack leaves W mainly in the upper trench. Gate-stack EDS maps. The W between Intel’s PMOS ribbons provides a conductive path close to the lower gates. Where WFM fills the entire gap, the gate still surrounds the channel, but voltage reaches it through the more resistive work-function films. Thinner WFM and dipole tuning preserve room for low-resistivity fill; Mo and Ru are alternative fill metals being developed for further scaling. [24] A masked, sequential WFM flow explains the different gate heights and inter-nanosheet fill. The proposed sequence below shows how separate NMOS and PMOS work-function steps produce that geometry. Enabled by the BSPDN process, Intel replaces the dense-logic silicon subfin with dielectric, removing the parasitic conduction path below the ribbons and reducing substrate-related capacitance. A retained silicon body as in classical, non-SOI, planar and FinFET designs needs junction and punchthrough-stop engineering to suppress leakage. Dielectric isolation makes that leakage less sensitive to the subfin doping profile but adds removal and fill steps. It also weakens the direct thermal path through silicon, making the contacts, metal stacks and package more important for heat extraction. [24], [26] Fluorine is concentrated around selected Intel device structures in the maps. WF6 is a standard precursor for W nucleation and fill, while barrier films protect adjacent dielectrics from fluorine attack. Low-fluorine W processes reduce the residual-F burden. Chloride-based precursors avoid introducing F during W deposition, but require control of chlorine attack, nucleation and fill quality. The integration target is a continuous, low-resistance W path with a thin protective liner and minimal chemical damage to the surrounding stack. [20], [27], [28] The SemiAnalysis STEEL teardown lab breaks down advanced datacenter and AI hardware. To learn more about our pipeline or to commission a teardown, contact sales@semianalysis.com. WE’RE HIRING: Architecture, floorplan, packaging, manufacturing, and labs experts. Opportunities from system to transistor and everywhere in between. Check out our Careers page. We measured cell height, gate pitch, metal geometry, and ribbon dimensions at the XTEM sites shown below. The tables group these dimensions by site and device polarity. Our “sheet cuts” cross the silicon channel and show the ribbons end-on. “gate cuts” run along the channel through successive gates. The 18A logic cell dimensions point to a five-track logic library while the N3E and Intel 3 cell dimensions evidence a seven-track logic library. The DDR-PHY uses wider M0 wires and much larger spacing than core logic. That trades routing density for lower wire resistance and weaker coupling between neighboring nets. The geometry suits the current delivery and coupling requirements of analog, clock, and I/O circuitry. PowerVia lets 18A combine a compact cell height with wider M0 geometry by moving the main power rails off the signal-routing tracks. That relaxes local wire scaling while preserving a small cell footprint. Cell height and gate pitch set the geometric density; pin access and routability determine how much of it a real block can use. [29] The biggest takeaway from our gate-pitch measurements is that Intel 18A compute logic and TSMC N3E GPU logic have similar density in the Bohr representative-cell model. The 18A example is 18.6% denser than the Intel 3 GPU example. Gate pitches are nearly identical across the three sites, so cell height drives most of the difference. The Bohr model combines a four-transistor NAND2 spanning three gate pitches and a 32-transistor scan flip-flop (SFF) spanning nineteen pitches, weighting their densities 60:40. The sensitivity column shows how independently changing cell height and gate pitch by ±1 nm changes the result. This compares representative cell geometries; whole-die density also depends on cell mix and placement. The 18A P-core gives M0 substantially more metal cross section than the N3E vector engine. Treating each profile as a trapezoid gives 2.63 times the area per line and 1.84 times the area after normalization by routing pitch. The larger section reduces the geometric contribution to line resistance and lowers current density for a given current. Taller and wider wires also add capacitance, so circuit delay depends on the balance of resistance and capacitance. The DDR-PHY has less metal area per routing width than the 18A core fields, while remaining above N3E. [30] Area = height × (top CD + bottom CD) / 2, including liners. Area/pitch normalizes by routing width. Taper is the symmetric sidewall angle from vertical, with the largest angle belonging to the DDR-PHY. Compute tile The measurements show how ribbon dimensions and gate-stack geometry vary across the compute tile and between NMOS and PMOS to balance channel drive, gate load and the space needed for the dielectric/WFM stack across logic, SRAM and the DDR-PHY. Width mainly changes available channel perimeter; thickness also changes electrostatic control and carrier confinement. Gate-stack thickness then determines the space left for low-resistivity fill P-core and LP E-core logic Both the P-core and LP E-core use multiple nanosheet widths. Widths are measured on high-magnification XTEMs while wider-field images demonstrate additional width choices within the LP E-core. Multiple widths are expected even within an LP E-core. Timing-critical paths, buffers and cells with different fanout need different drive strengths. The lower-magnification fields show this width diversity beyond the sites quantified in the table. L2 and L3 SRAM GAA gives SRAM designers another way to balance the pull-up (PU), pass-gate (PG), and pull-down (PD) transistors. FinFET bitcells set device strength through fin count while GAA adds nanosheet width as a sizing knob. In a 6T SRAM cell, a strong pull-down relative to the pass-gate limits read disturbance, while a strong pass-gate relative to the pull-up improves writability. During a write, the pass-gate and write driver pull the node storing “1” below the inverter trip point. During a read, the pull-down holds the node storing “0” low. Bias, threshold voltage, mismatch and assist circuitry set the remaining margin. FinFET high-current cells commonly use a PU:PG:PD fin-count pattern of 1:2:2, a device-sizing ratio rather than a current ratio. Ribbon width lets Intel balance SRAM strengths without adding whole fins. The L2 cell uses its narrowest ribbons for PU and widest for PD, improving writability and read stability respectively. Intel’s disclosed HCC operates without assist; its denser HDC uses negative-bitline write assist. Pulling the selected bitline briefly below ground increases pass-gate overdrive so it can overpower the pull-up at lower supply voltage. That buys density and low voltage writability at the cost of boosting circuitry, switching energy, and additional voltage stress that must be controlled. [31], [32] Four rectangular ribbons give the perimeter = 8 × (width + thickness), before corner rounding. PG/PU is 1.49 and PD/PG is 1.16. The L3 structures closely resemble L2 in layout and cell height. Fewer L3 nanosheet widths are tabulated because fewer high-magnification images were available. DDR PHY The DDR-PHY trades density for controlled analog behavior and reliable off-chip signaling. It contains drivers, receivers, delay circuits, and calibration logic that set drive strength, sampling time, and voltage margin. Repeated four-sheet devices with similar widths fit the use of regular transistor units for matching and programmable drive. Its wider local wiring provides room for current delivery and separation of sensitive signals, while consuming more area than a dense core-logic grid. The layout serves the memory channel’s electrical requirements as well as digital logic density. [33] The SemiAnalysis STEEL teardown lab breaks down advanced datacenter and AI hardware. To learn more about our pipeline or to commission a teardown, contact sales@semianalysis.com. WE’RE HIRING: Architecture, floorplan, packaging, manufacturing, and labs experts. Opportunities from system to transistor and everywhere in between. Check out our Careers page. Intel 3 GPU devices Vector engine logic Intel 3’s XVE logic uses two-fin PMOS and NMOS devices with power rails in M0. Its cell height and M0 pitch give a seven-track geometry, two tracks more than the 18A logic. One-fin groups also appear among the two-fin devices. Intel 3 L2 SRAM The Intel 3 L2 SRAM uses the familiar HCC sizing pattern: one PU fin, two PG fins, and two PD fins. N3E GPU devices Vector engine logic The N3E XVE field contains repeated two-fin devices with seven-track cell geometry. N3E remains a FinFET process, giving Panther Lake a direct FinFET-to-RibbonFET comparison. N3E L2 SRAM The N3E L2 SRAM uses the same PU:PG:PD fin-count pattern of 1:2:2. Panther Lake-U follows Lunar Lake’s floorplan quite closely. Both pair 4 P-cores with 4 LP E-cores and NPU, media and display engines in similar locations. Lunar Lake also uses Xe2, the direct predecessor to Panther Lake’s Xe3 GPU. This makes Lunar Lake the most direct basis for our comparisons. Arrow Lake differs in core count and uses the older Xe-LPG GPU architecture, so we only use it where it offers a more direct component-level comparison. Compute tile Panther Lake compute-tile floorplans remain sparse even months after launch. Intel 18A’s backside metal and dielectric stack must be removed without damaging the underlying structures before a clean transistor-level floorplan can be imaged. Most published die shots hide or heavily process the background, but we are quite proud of the die shot we achieved and are excited to show the work we have done. We measured the areas of the key components on the compute tile and compared them with their Lunar Lake predecessors on TSMC N3B. These help us to capture changes in block area and compare the two chips across process nodes and designs. Our total tile areas exclude the scribe-line area. The compute-plus-GPU subtotal below uses the PTL-U compute tile and GT1 GPU; it excludes the I/O tile and passive base. Individual block areas use the boundaries marked on the floorplans The compute-plus-GPU row is recomputed from the displayed PTL-U and GT1 areas. Component rows use their stated per-region counts and are not an additive partition of the whole tile. The P-core area remains almost unchanged between Lunar Lake and Panther Lake, despite L2 capacity increasing from 2.5 MiB to 3 MiB. Arrow Lake uses the same Lion Cove core as Lunar Lake but also has a 3 MiB L2. Cougar Cove fits 20% more L2 into the same P-core area. The larger private cache keeps more of each core’s working set close to its execution units, reducing access to shared L3 and DRAM. Extra capacity adds storage leakage and lookup energy, so designers balance it against avoided lower-level accesses. The shared P-core L3 cache also shrank by 14.8%. [2] Cougar Cove combines a similar footprint with Intel’s reported power-efficiency improvements. RibbonFET’s tighter channel control reduces leakage, while PowerVia reduces supply droop and allows tighter voltage guardbands. [1] Darkmont’s four-core LP E-core cluster is 5.0% smaller than Skymont’s on Lunar Lake, with most of the reduction in its L2 regions. The 1 MiB region shrank by 8.4% and the 1.5 MiB region by 14.9%. The tag arrays also use one fewer visible row. Tags identify which memory addresses the data array holds, so rearranging them changes the cache’s layout and wiring without requiring less data capacity. [2] The LP E-cores share one L2. This pools capacity and avoids duplicating all the cache machinery, but the four cores contend for its banks and bandwidth. Their separate cluster also keeps light work away from the performance cluster and its L3, allowing that larger domain to sleep. [1], [2] Cache area includes more than the storage cells. Tags identify each line, decoders select rows, sense amplifiers read the small bitline signal, and wires connect to the banks. Splitting an array into smaller sections shortens wordlines and bitlines, improving access speed, but duplicates peripheral circuits. Panther Lake’s smaller cache regions therefore reflect the complete memory implementation, including how much of each region is devoted to storage. [34] Unlike Meteor Lake and Arrow Lake, Panther Lake has no separate SoC tile. The NPU, LP E-cores, memory controllers, PHYs, media and display engines now share the compute tile. This removes an active die and keeps CPU memory traffic on one die. The cost is moving PHY and I/O-related circuitry onto 18A: drivers, receivers and analog circuits must still meet external voltage, loading and signal-integrity requirements, so their area does not shrink like dense digital logic. [1], [2] The biggest shrink comes from the NPU, which occupies 36.9% less area. NPU 5 consolidates the same total INT8 MAC count into half as many neural compute engines. Each of the three NCEs has a larger MAC array to make the complete NCE envelope 22.6% larger than an NPU 4 engine. Consolidation also halves the number of scratchpads and SHAVE DSPs, from 12 to 6. The MAC array handles matrix multiplication and convolution, while SHAVE executes vector and custom operations that fit the array poorly. [1], [2], [35] The paired floorplans identify each NCE envelope and its scratchpad, MAC, and SHAVE regions. Each measured MAC polygon is counted once per NCE in the area accounting below. The scratchpads store weights, activations, and intermediate results near the MAC arrays, allowing repeated use without fetching them again from DRAM. Halving their number delivers the largest measured area saving but leaves less local storage for the same total MAC count. Layers that no longer fit locally require smaller working tiles or more transfers of intermediate data. The benefit depends on keeping the enlarged arrays busy while managing that tighter storage budget. [36] NPU 5 also adds native FP8. Using half the operand width of FP16 reduces storage and transfer demand, helping workloads fit the smaller local memory budget. Lower precision and format-dependent range make scaling and model validation part of deployment. Hardware activation functions further reduce work that would otherwise occupy the programmable DSPs. [1], [2] Microsoft requires an NPU to deliver at least 40 TOPS for Copilot+ PCs. Both Lunar Lake and Panther Lake meet this threshold, but Panther Lake uses significantly less silicon. GPU tiles Panther Lake is Intel’s first product with Xe3, its latest GPU architecture. It offers two different GPU tiles: a smaller GT1 tile with 4 Xe3 cores on Intel 3 and a larger GT2 tile with 12 Xe3 cores on TSMC N3E. Panther Lake allows us to compare the same GPU architecture across both Intel 3 and TSMC N3E. Wildcat Lake adds a third Xe3 implementation on Intel 18A. A future newsletter will detail Xe3 and its implementation differences across all three process nodes. GT2 scales Xe3 to a different physical layout, with render slices arranged vertically instead of GT1’s horizontal arrangement. Slice placement sets the distances to shared cache banks and the D2D interface. Those wires consume area and add delay, so scaling the number of Xe cores also requires a new balance of cache placement, routing and timing. [1] What’s immediately obvious is that the GT2 tile on TSMC N3E has much smaller Xe cores than GT1. These block areas include logic, caches, and routing. An Xe core on the GT1 tile is ~69% larger than one on Lunar Lake, and ~55% larger than one on GT2. Intel 3 therefore uses substantially more area per Xe core. The block-area gap exceeds the measured logic and SRAM density gaps, bringing routing, timing targets, cell mix, and floorplan allocation into the comparison. The measured vector/matrix engine region is almost unchanged between Lunar Lake and Panther Lake’s GT2 tile. Xe3 retains eight 512-bit vector engines and eight 2048-bit XMX engines per core. Its gains also come from feeding those engines more effectively: more resident threads hide stalls, and variable register allocation lets shaders trade registers per thread against the number of threads kept active. [1] The shared L1/SLM capacity increased by 33% from 192 KiB to 256 KiB, while its area increased only 5%, raising effective density by 27%. L1 retains reused cache lines, while software-managed SLM lets a thread group share data locally. Both reduce traffic to more distant memory. Allocating more SLM per group can also limit how many groups reside on a core at once. [1], [37] The GT1 tile carries 4 MiB of L2 against 16 MiB on the GT2 tile. GT1 divides its L2 cache into four 1 MiB banks, while GT2 uses eight 2 MiB banks. Each bank contains 128 macros, but each N3E macro stores 16 KiB, twice the Intel 3 macro’s 8 KiB capacity. The N3E macro is only 54% larger while holding twice as many bits, giving it 30% higher density: ~23.7 Mbit/mm² versus 18.3 Mbit/mm². Including bank-level circuitry, the gap widens to ~16.9 Mbit/mm² on GT2 versus ~10.4 Mbit/mm² on GT1. GT2 gains density with its macros storing more bits per unit area, and those macros occupy more of each cache bank. Larger macros spread decoder and sense-amplifier overhead across more storage, while a more compact bank layout reduces the share spent on control and routing. The compromise is longer wordlines and bitlines that carry more capacitance. [34] I/O tile Panther Lake uses two I/O tile variants, both fabricated on TSMC N6. The smaller one provides 4 PCIe 5.0 and 8 PCIe 4.0 lanes and serves lower-tier systems as well as those without a discrete GPU, while the larger one adds 8 PCIe 5.0 lanes, bringing the total to 20 lanes, for discrete-GPU connectivity. Panther Lake SKUs with the larger 10- or 12-Xe GPUs use the smaller I/O tile. [38] The smaller I/O tile adds a PCIe 4.0 block and a Thunderbolt block to Lunar Lake’s I/O layout, providing four additional PCIe 4.0 lanes and another Thunderbolt 4 port. Its repeated N6 blocks retain nearly identical areas and layouts. Reusing these proven PHYs and controllers avoids porting and requalifying external interfaces on 18A, where faster digital logic offers less benefit to circuits constrained by the off-chip link. [38] SemiAnalysis’s teardown lab (STEEL) dives deep into the world’s advanced datacenter and AI hardware. To learn more about our pipeline or to commission a teardown, contact sales@semianalysis.com. We’re hiring technical experts from system to transistor and everywhere in between. Check out our Careers page. Panther Lake offers scalability and modularity through its disaggregated packaging that partition compute, GPU, and I/O silicon into separate tiles allowing for a suite of tile configurations. This partitioning makes the package part of Intel’s node economics as it determines how much leading-edge wafer area each product consumes, which functions can remain on other processes, and how much configuration freedom Intel can offer from a shared set of tiles. Furthermore, fabricating the compute and GPU tiles separately confines the new 18A process to the compute tile and allows graphics and I/O to use other, more established, and more cost-effective processes. For Panther Lake, the GPU and I/O tiles are assembled alongside the compute tile on a passive silicon base using Foveros-S. Intel’s current technology brief lists a nominal 36 µm pitch for Foveros-S. Through-silicon vias (TSVs) in the base connect the fine wiring above to the larger package connections below. The functional tiles sit side by side on that passive base in a 2.5D configuration. [39] Our cross-section through the compute and GPU tiles shows the package’s wiring hierarchy. Microbumps connect each active tile to the passive silicon base; its fine redistribution layer (RDL) carries the short, dense tile-to-tile links. TSVs carry connections through the base to the package substrate, which fans them out to the much coarser motherboard solder joints. The base supplies interconnect, while computation remains in the active tiles above it. [39] At the compute-tile edge, the higher-magnification inset shows a local microbump spacing of approximately 25.24 µm and a feature width of 12.33 µm. These local spacings are finer than Intel’s nominal Foveros-S value. The X-ray fields further confirm tighter neighboring bumps, consistent across every die-to-die area found on each tile. Additional X-ray analysis is offered after the paywall. Putting the memory controller beside the CPU removes the D2D transfer that CPU memory requests required in Meteor Lake and Arrow Lake. This avoids the extra transmitter, receiver, and link traversal, saving interface energy and latency. Panther Lake’s separate GPU still crosses a D2D link to reach DRAM, so its larger local caches also help contain package traffic. [1], [40] Smaller dies are less likely to contain a random fatal defect, and screening them before assembly prevents one bad tile from consuming a complete package of good silicon. Reuse also spreads design and qualification work across more products. Against those gains, Intel pays for the passive base, D2D circuits, extra bonding and test steps, and losses during assembly. Cost per working product across the portfolio captures the combined effect of wafer yield, reuse, test, and assembly. [29] Wildcat Lake packaging Intel launched Core Series 3, formerly Wildcat Lake, on 16 April 2026 for value mobile and edge systems. Wildcat Lake keeps 18A but removes the passive base and combines more functions on one die to simplify the package. The two products therefore reveal two distinct ways to commercialize the same leading-edge process. [41] Wildcat Lake’s 18A die combines up to two Cougar Cove P-cores, four Darkmont LP E-cores, two Xe3 cores and a smaller NPU. A separate platform-controller die supplies I/O, connected through UCIe, Intel’s first processor implementation of the standard. Consolidating graphics remove a tile boundary and the passive base, reducing assembly complexity for a modest-bandwidth value product. It also ties CPU and graphics scaling to the same die, giving up Panther Lake’s ability to swap in a much larger GPU. [42], [43] In July 2021, Intel CEO Pat Gelsinger set out an ambitious process roadmap aimed at regaining performance leadership by 2025, later described as five nodes in four years. Five years and one CEO later, Intel’s comeback story is not as unambiguously positive as Pat may have hoped. [44], [45] Intel once set the pace for process technology, bringing high-k metal gate technology and FinFETs into volume production years ahead of the rest of the industry. Its 22 nm FinFET process reached consumers with Ivy Bridge in 2012. [46] Intel’s integrated device manufacturing (IDM) model allowed its architects and process engineers to co-optimize products and processes. Starting with Sandy Bridge, Intel dominated x86, while AMD struggled with Bulldozer. That lead faltered at 14 nm and broke at 10 nm. Intel targeted a massive 2.7× density increase, but the node arrived years late and required several revisions before it could support Intel’s full lineup. This delay forced Intel to stretch 14 nm across six generations, while TSMC moved ahead in process technology and AMD recovered in x86. By 2019, Intel was still shipping 14 nm across most of its product stack, with its 10 nm client ramp focused on Ice Lake mobile processors. Meanwhile, TSMC was shipping N7 and N7+, and AMD’s Zen 2 compute chiplets used N7 to raise core counts and improve efficiency. Intel’s process failures were central to its decline, but unsound business decisions furthered their downward slide. Product delays compounded product mistakes, pushing client, server, and FPGA roadmaps off schedule. Several attempts to enter AI (Nervana and Gaudi) and networking (Tofino) also failed to establish lasting businesses. Intel’s recovery has focused on consumer CPUs and advanced packaging. Tiger Lake, Alder Lake, Lunar Lake and now Panther Lake have restored Intel’s consumer roadmap. On the process side, Intel 4 shipped with Meteor Lake, Intel 3 with Granite Rapids and Sierra Forest, and Intel 18A with Panther Lake. Intel has also made advanced packaging part of its foundry offering. However, Intel is still playing catch-up in servers. Several Xeon generations arrived years late and trailed contemporary AMD and Arm server CPUs in performance, efficiency, and core count. The process roadmap is back, but Intel does not hold the same process-technology leadership position it held prior to 10 nm. The introduction of gate-all-around nanosheets and backside power delivery are two of the biggest changes to transistor integration in a decade. Intel took on both changes at once: 18A paired its first RibbonFET with PowerVia in Panther Lake. Panther Lake is a substantial manufacturing milestone. Our cross-sections show how RibbonFET and PowerVia reshape local contacts and wiring, while the floorplans show where architectural consolidation and process choices save area. A sustained competitive lead depends on product performance, cost, yield, and the next implementation. The SemiAnalysis STEEL teardown lab breaks down advanced datacenter and AI hardware. To learn more about our pipeline or to commission a teardown, contact sales@semianalysis.com. WE’RE HIRING: Architecture, floorplan, packaging, manufacturing, and labs experts. Opportunities from system to transistor and everywhere in between. Check out our Careers page.. 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Sundar Pichai: Google’s Frontier Imperative, Compute Scarcity Dilemmas, Future AI Products and Businesses
Alphabet CEO Sundar Pichai has little patience for doubts about Google’s willingness or capacity to compete at the frontier of AI. His company’s latest Gemini 4 Argon model — long-promised and recently delivered — beats OpenAI and Anthropic’s latest public models on coding, math, science, and knowledge-work benchmarks and has placed Google’s AI back among the world’s best. Amid chatter that Google was fine with good-enough lightweight ‘Flash’ models, Pichai pushed back hard against any suggestion that company is limiting its research ambitions. “We won’t do it any other way,” he told me in a Big Technology Podcast interview recorded this week at Google’s Mountain View headquarters. Pursuing cutting-edge research is costly for Google, requiring compute and chips that otherwise could be sold at a high markup by its cloud business. But Pichai said allocating resources to frontier AI development is “ground zero” for Google. “Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development,” Pichai told me. In our conversation, we discussed Argon (which Pichai says processed three times as many tokens as Gemini 3 in the 72 hours after launch), AI’s role in developing better models, the AI interface of the future, competition with OpenAI, Google’s partnerships with Anthropic and Apple, data centers in space, and the nature of his optimism. You can read the full conversation, lightly edited for length and clarity, below, or listen to it on Apple Podcasts, Spotify, YouTube, or your podcast app of choice. Alex Kantrowitz: Google is back at the frontier with Gemini 4 Argon, which beats OpenAI’s and Anthropic’s public models on benchmarks spanning coding, math, science, and knowledge work. The conventional wisdom was that Google had given up on the frontier to focus on fast, good-enough models for its products. That was wrong? Sundar Pichai: Very wrong. Look, we are so committed to being at the frontier. It’s in our DNA. We won’t do it any other way. We’ve been thinking about the AI frontier for well over a decade now. The frontier is very dynamic. It’s very competitive. It’s very exciting. I couldn’t be more pleased with how Argon has come together. We took our time with it. We made sure we tested it safely and deployed it carefully. We took a bit longer than our other models, given its capabilities. Across the board, be it for agentic work, knowledge work, coding, creative tasks, Argon is one of the really good frontier models. The first 72 hours since we launched, it’s almost up to 25 trillion tokens used, which is about three times what Gemini 3.0 had in the first 72 hours. And we are capacity constrained as we are serving it. Excited, but there are always areas….. You know, the frontier is very dynamic, and you have to keep evolving the models. There are areas which we can see we need to improve as well. We’ll be at work on all of that as well. Which areas? On any given model, Argon would be across the board better at everything if you just look even two months back. But the space is so evolving. Even on coding, for example, there are pockets where we know we need to do better, and there’s clear feedback. We’ll make it better. The argument for standard models is that they’re already good enough to build great products, with the frontier valuable only for certain tasks. Meta built Muse with good models rather than the best, and Google could put standard models into Maps, Gmail, and Search. Why isn’t that sufficient? It’s definitely not how I have thought about it at all. We are deeply committed to driving the state of the art and actually pushing where this technology can go, all the way from transformers. That’s a journey we’ve been on. You will see us continue to do that. When you’re at the frontier, you get an earlier look into the emerging capabilities, earlier than others, which helps you better prepare for the future. We are fortunate to have a wide set of opportunities across consumer and enterprise. Some of our cloud customers, for sure, want the model at the frontier. The frontier models will be better at developing the next set of models in an era of recursive self-improvement. I would argue it matters there. Internally, we have definitely seen a steep point of inflection by deploying Argon to our developers. It definitely tackles problems which our previous models could not do. Being at the frontier puts you at the innovation frontier and helps you drive that as well. There is room for models of all sizes and capabilities. That’s why we did Flash, Flash-Lite, etc. I agree with you that you can develop very good product experiences with not necessarily the most frontier model, because they tend to be slower and more expensive too. But we care about the Pareto frontier, and we’re committed to doing all of that. You said developing frontier models gives you a look at what’s coming. What do you see on the horizon? What makes this moment so exciting is you build something which you think is a culmination of months of work, and you put it out. You already see clear headroom ahead. We’ve all been working on these agentic, long-horizon capabilities, but clearly the frontier is going to have multiple of these agents working together to do things. That’s an example of an exciting paradigm underway. It’s also clear to me that users, both in consumer and enterprise, will have access to some multi-agent framework for them, working all the time doing things. It feels like a moment of infinite possibilities. Sergey Brin said one of Argon’s core innovations was relying more on the model to help develop the model. Yet incidents involving OpenAI’s bots breaking containment and Google’s bots accessing a site they shouldn’t have suggest these systems still aren’t perfectly aligned. Isn’t there reasonable cause for concern about accelerating toward recursive self-improvement? There are three parts to your question. Let’s first talk about RSI itself. You’re right, Sergey mentioned RSI being a critical contributor as we are developing Argon. But to be very clear, today, in a continuum, we are pretty far from the end state of RSI or something like that. The right way to describe it is you have RSI loops along with AI researchers in various aspects of model development. You almost have agents helping AI researchers in some automated way work together to build these models. It’s good to at least understand where we are today, and it’s super exciting. I expect that continuum to keep making progress as we go into next year. The second question is around safety. Security comes from defense in depth. You’re talking about alignment, obviously. We all are doing a lot of work to make sure the models are as aligned as possible. But it’s an active area of research, and there are clear gaps. That’s not the only thing we rely on. You have the work you do on alignment, but beyond that, we put a lot of effort into real-time monitoring as these models are working. Making sure you have good telemetry into what’s happening so that you can intervene in real time if you see unexpected behaviors. How you do that, the architecture with which you do that, how you make sure it’s all sandboxed and isolated in a way that it can continue doing its job during real-time monitoring — these are all areas of investment. Obviously, when we deploy these models, giving customers, enterprises, control, both with real-time monitoring, but their ability to decide where they deploy, what type of data which models ever have access to, etc. All that work is very, very important, and we are doing a lot of that. As an industry, we need to constantly up our game and take the time to do all this stuff, including dedicating the compute required to make sure you can safely deploy these models. The third part is: Is it all enough? This is why there are a lot of conversations in the industry, and I think it’s reasonable to be worried. The right way to approach something you worry about is early on thinking about what more we can do. As an industry, you’re definitely seeing increased levels of work on alignment, testing for safety, etc., as we deploy these models. The next step would be developing better standards so there are cross-industry standards of how we do these things, including the ability to evaluate using independent evaluators. If you look at areas like financial controls, we have accounting standards, we have independent auditors. Those are all the next natural steps for us as an industry to work toward, and regulation will end up playing a role as we go through all of that as well. Google pursues both language models and world models, while some other AI companies like OpenAI have focused more heavily on LLMs as the path to AGI. Why is world-model research important to you? As humans, you obviously perceive the world around you, and you’re taking all those inputs. Making sure these models are able to understand the laws of physics, simulate the world in high fidelity. All that, I think, are key enabling blocks for intelligence. We are working across all possible applications of this. Take an area like robotics. It’s not an accident: If you look at embodied reasoning benchmarks, Argon is state of the art. That all comes from our experience building world models. Inherently, I would expect there will be architectures which will converge all of this in the future. But for us, we view this as, today, this jagged frontier that is intelligence. We want to be at the frontier of all the possible approaches, and that will help us both drive better research, better insights, and develop a much better AGI model, is how we think about it. Some people argue Google should focus on selling its computing resources rather than using them for AI research. Cloud wants to sell compute at a high markup, while researchers need a very big computer to build the best models. How do you balance those demands? Ground zero for us is making sure we are investing in the compute that’s needed to be at the AI frontier. In 2015, I set the company to be AI-first. AI is that key horizontal ingredient across everything we do, all the way from Search to Cloud to Waymo. That is ground zero for us, making sure both for research and developing the models that we have enough compute needed. Beyond that, we have these independent businesses which each have their own long-range plans. They’re using those long-range plans to project forward, and we plan for compute for all of them. The cloud team, they have a business and they have a projection and they have demand signals. They have committed contracts. With that help, we can plan on a long-term compute trajectory for them. We’re fortunate to have this wide set of opportunities. There’s no company in the world which isn’t making trade-offs at any given moment in time. But the first principles we operate on is making sure we have enough compute to invest in the frontier, and that’s non-negotiable, and everything else works on top. Remember, we are building our own compute, our own data centers. There are long lead times. The compute we have today is a function of decisions I made in 2024. Even if you think back to a year from today, last year the agentic coding takeoff hadn’t happened. Today, obviously, when you sit and look at anything, everyone is short—not just us, other companies—and everyone wishes you had two times what you had planned for. That comes with the territory. At any given moment in time, we are planning for what we would need two to three years out in the future. You’re going to find some decisions you make may look suboptimal two years out, depending on where the state of the art is. I don’t spend too much time thinking about it. The way I think about compute allocation is making sure Google DeepMind, both for research and frontier model development, is cutting edge in terms of compute access. After that, we plan for all our businesses. It’s great we can provide our compute to other people too, like we have done. It allows us to scale that up, get real tests for it in the widest application set possible, test it in the real world, and make sure we are at the cutting edge of technology at every layer of our AI stack as well. Ben Horowitz relayed on a podcast that Andy Jassy had said compute was sold out until 2028. Are you seeing the same thing? If today a customer is coming out and asking us for compute in ’27, that is incredibly difficult to do. Even last year we went out and leased some short-term compute to accommodate some customers and help them out. But yes, we are all dealing with similar timelines. Every major technology shift seems to involve overbuilding, from railroads to fiber. Do you think that’s happening here? It depends on the time frames you’re looking at. This level of build, if I take a long enough time frame, it makes complete sense to me. We are at early stages of the most consequential technology ever, and this is not like fiber. It’s not dark. We are using it. We are capacity constrained on a daily basis. The applications, the underlying technology is improving rapidly. But running a company, the way I work through the moment is you look at your demand signals. You look at your committed revenue opportunities that you’re getting in the form of contracts. You balance that with the underlying technical capability, or internally you balance how we are using it internally, the model trajectories. Based on all that, you take a disciplined approach—a confident, aggressive, but disciplined approach—to plan out compute. But when you talk about the broader economy as a whole, you’re having people operate with various levels of risk. I focus on what I can control as a company and plan through it. If your question is, can there be an overbuild? Yes, because you can have some constrained components. Remember, what matters is a set of everything you need to build out that compute. Memory, electricity, electricians…. Land. Like 2,000 components or more. You could get constrained at some point. There could be things which cause bottlenecks, and then you could have overbuilds. There could be external shocks to the economy. There are macro risks. All that is possible at any given time. But am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes. I was in the room when you said AI would be as profound as fire and electricity. If you believe that, shouldn’t you give researchers the biggest possible computer, even at the expense of today’s cloud profits? We’re all doing some form of that. Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development. What you’re seeing is true already. But you want to be thoughtful. Even our AI researchers have some constraints, which are also good. That’s what inspires creativity. That’s what helps you come up with a more efficient algorithmic breakthrough, maybe to use less memory. What we are committed to is to invest what’s needed to stay at the frontier. OpenAI President Greg Brockman told me a few months ago that the ideal AI product might have no interface at all—just as natural as conversation. What do you think the interface looks like in the age of AI? Computing historically has been humans adapting to computing. With AI, it’ll be the other way around. The technology will adapt seamlessly to how humans live and go about their day-to-day life. You want to be able to speak to that AI when you want to. It has context. It can be persistently running somewhere, give you continuity. You’re not dealing with a lot of complexity. It’s natural. All that makes sense to me. That doesn’t mean it won’t have any interface, however. Nowadays, even on my phone, Gemini voice models are so good. I can just press the microphone button. I can just ramble. That’s why we call it Rambler. I can ramble. It intelligently corrects with my personal context. Even my friends’ names, weird names, it gets it right, even if it first guesses it wrong. It’s amazing. I’m not going in, backspacing, editing once I finish saying it. It’s remarkable how good it’s gotten. But there are going to be moments you will need an interface. You may want to entertain yourself. You may want to watch a video. You may ask your AI to create something, and you want to watch it, consume it. The way I think about it is, you want it to be as seamless and as natural as possible, and that you are not doing the hard work adapting to whatever the interface is. The technology is adapting to how you’re working at any given moment in time. When I asked Sam Altman about competition from Google, he argued that OpenAI was building AI-native products and implied Google was bolting AI onto existing ones. Do you accept that framing, and can a “bolt-on” compete with an AI-native product? First of all, it’s a very dynamic moment. We are all pushing each other. OpenAI has pushed us as a company too. This is not just we are doing something to them. Far from it. Yes, Google went into its own version of a code red too. In the AI moment, all of us have to respond with varying levels of urgency at any given moment in time. We are constantly building new products as well. Gemini was built from the ground up as a product—the Gemini app I’m talking about. Or tomorrow, as all of us are adapting to agents, are you bolting agents onto ChatGPT, or are they building it natively? How would you think about that? You can always put these questions back. I don’t think of it that way. I think of it as you want to build world-class product experiences that are AI-native and they are winning. Some of it is adapting products, some of it is rethinking new experiences. They will all be part of how you approach this moment. How do you expect to make money from these AI interfaces? If an agent recommends a product and I buy it, should it take a cut of the sale, even if that’s disclosed? I’m going to put enterprise aside. Consumers are going to increasingly value this technology. Take software developers today. Developers are willing to pay for these coding models because it really helps make them more productive. Nobody would want to code the way they were coding three years ago, or even the same time last year. When you’re delivering value, there’s value to be had. I’ve never seen examples where consumer products really scale to billions of users, where people are using it intensely, and haven’t had monetization to go along with it. If you’re providing value to people, there is inherent value to be had. The nature of it may change. You’re asking a scenario in which agents are doing stuff for you. At the end of the day, you will have commercial needs. The agents need to fulfill those commercial needs. The providers on the other side are going to see value in that. There’s going to be some mechanism by which it gets fleshed out. That’s what an efficient system does. But what matters is that you’re building those world-class consumer experiences. I promised myself one product feature request: Can you make Gemini-powered search in Gmail as good as searching Gmail through ChatGPT? What’s going on here? One of the exciting things we announced recently is the unified Gemini agent for enterprise, and we’re bringing that to consumers. These are persistent agents which run 24/7 in the cloud. We’re spinning up a micro VM effectively for every query, and from the runtime, the harness, the model—it’s all a headless service. It’ll work everywhere, and it has your context with you. You can address this agent whether you’re in Workspace, in Search, in Gemini. Agents are going to drive this unification from a customer experience, which people are looking for. When you have a broad product portfolio and you’re moving forward fast, there are always areas where you’re moving forward faster than some other areas. That’s valid feedback. But we are putting a lot of time and effort into making sure, as we build out our agentic experience, that unification and that capability works well across all these services. People have asked where Google’s equivalent of Muse or Dots is. Do you anticipate creating a new product to compete with them? Our goal with Gemini is to be a personal, proactive, and powerful assistant. That’s what we are working toward. You saw us describe the early versions of it at Google I/O this year with Spark. Effectively, already, if you open the Gemini app as a Pro or an Ultra user, you see Chat and Task, and they start giving you that agentic experience. With Argon now, that capability is dramatically accelerated. What we announced recently in our enterprise event, we are bringing that to consumers as well. Soon in Gemini, you will be able to ask something, and it’s a continuum. You don’t need to think about whether you’re asking a chat or an agentic task. It’ll just automatically do the right thing, and it can work persistently in the background securely and get things done for you. Already, empirically, when I look at what’s coming into Gemini in our app, last year you would have tagged them and most of them would have been chats. But a steadily growing percentage is already agentic tasks in Gemini. Yes, we are going to bring personal agents to our consumers. It’ll work in the Gemini app. It’ll work in Google Search. It’ll work in Gmail. You can address it, call it, etc. We want to make sure we’re getting it right. It’ll be powered by Argon, and we are very excited to bring it to our users. Google has invested and committed tens of billions of dollars to Anthropic. What do you see in Dario Amodei that makes you want such a deep investment and partnership? Very early on, when we looked at the founding team of Anthropic, it was very clear to us this was a world-class team which was involved in some of the earliest GPT models. We were quickly able to assess that they would be at the frontier. We’ve always tried to identify and partner with companies which would be at the cutting edge of technology. We’ve done that with SpaceX. We’ve done that with Anthropic. That’s what we saw, and we had a chance to partner. Is your partnership with Apple to license Gemini for Siri expanding, contracting, or standing still? For both us and Apple, that partnership was about translating the capabilities of AI into better experiences for users. I thought Apple has done a phenomenal job of incorporating—they customize these models and build it into Siri—and it’s been great to see the reception. We are committed to doing more. You’re going to do more with them? There’s nothing specific to comment, but it’s a partnership. It’s working well. What have you learned so far about the feasibility of data centers in space? It’s a very exciting project. Projects like this remind me—I still recall that moment in the desert for the DARPA Grand Challenge, watching the Stanley car compete in it, which ended up being the basis for Waymo. With all these technologies, you’re at a moment in time where it looks a bit like sci-fi, but 10 to 20 years down the line, it’s actually working in a meaningful way. I feel the same in space. We’ve always believed in betting big on these technologies. Quantum has been a great example of it. We’re probably 10 years into the journey. Waymo, we are almost 20 years into the journey. Space, we are two to three years into the journey. It’s clear to me that it’s an approach which will make a lot of sense in the future. Being in sub-synchronous orbits with 24/7 access to the most abundant form of energy we have access to will make a lot of sense for this exponential increase in compute that we are going to need. Having said that, this is a challenging project. The team has disciplined milestones. It was a super exciting milestone to send the TPUs up to space. Sometime, as we are speaking, we are expecting a “hello world” moment from them. I look forward to seeing that happen, and we’ll continue investing here too. You previously said 25% of the code written within Google was written by AI. What’s the number today? That number in our world today is meaningless in the sense that every part of what we now touch is influenced by AI deeply. What we are tracking with Argon, for example, it’s been a point of inflection. People are using Argon to really go tackle complex projects, including projects which affect millions of lines of code. In the four weeks since we have launched Argon, we have seen a 30% increase in CLs, and more importantly, almost a similar increase—a one-third decrease in rollbacks. People are writing code more productively and better code with it. We are tracking it with newer metrics now. How are you dealing with opposition to data centers? If you can’t build in the U.S. the way you want to, where else do you go? We’re very committed to building our data centers very responsibly. When I look at our largest data centers in some of the communities where we have been the longest, I visit them, I talk to people. There are people who come and tell us their kids have gone to college because of our investments in these communities, and we’ve grown in those communities. To me, this is about doing it right. Engaging with the communities early, doing it transparently. We are committed to making sure we are bringing incremental power, and we cover 100% of the cost of our power needs for these projects. It’s understanding the impact on neighborhoods and making sure you’re working thoroughly to address them. Done rightly, you can continue making progress. But we are constantly taking feedback into account. I do worry not everyone operates the same way. People have been surprised by data centers showing up in their backyard. Those are all important. As an industry, we should continue to do better. But part of the reason, in a 20-year time frame, we are thinking about things like data centers in space is making sure we are able to put these data centers where maybe the trade-offs will be different. What unproven area of AI is Google working hardest to crack? Is it continual learning—getting models to keep updating after release—or something else? There are multiple parallel paths. You saw with Argon we have a million output tokens. With our Gemini models early on, we had a one-million context window on the input side. These are all examples of breakthroughs we are constantly working on. How do you think about synthetic data? How do you think about continual learning? How do you think about more efficient architectures? We do dedicate a meaningful percentage of our compute to these research bets too. It always goes hand in hand, including our world models. Those are all bets we are placing. Sometimes, as part of these bets, there are ideas that emerge which just make their way into the mainline models. Maybe we don’t fully talk about it, but we have multiple steps in these areas. Working in technology requires optimism that building something will improve the world, but those bets can leave real consequences when they fail. I recently returned to a town in El Salvador where enthusiasm for Bitcoin had given way to unfinished construction and something resembling a ghost town. How do you maintain the optimism you spoke about in your Stanford commencement address while accounting for those risks? I genuinely have a deep conviction that improving technology and improving access to technology makes people’s lives better. It’s been my personal experience in my life, out of which comes that deep conviction, optimism. But with that, I’ve always felt humanity’s journey is always about working hard to harness that technology to make it work well for society. The optimism is not an optimism that you just build whatever you want, whenever. You work hard to make sure you’re building technology in the service of humanity, to make the human condition better. That’s got to be the defining goal as we are approaching it. With that framework, I’m very, very optimistic. We are in very early stages of the kind of progress that we are going to see on many areas. We’re all talking about areas like healthcare. A lot of the progress is ahead. The ability to make things in a fundamentally cheaper way on many things where things have been getting expensive for a long, long time, discovering new materials, etc., which really bend the curve—it’s tough to step today and understand the impact of all that, but they’re going to be very profound. I’m very optimistic, but we have to be rightfully concerned about all the ways this technology can go wrong. Sundar, I know you don’t do many of these. Thank you. Thanks, Alex. Appreciate it.
No, Siri AI is not coming to the original HomePod mini
Siri AI will be exclusive to HomePod mini 2, it‘s not coming to the original HomePod mini despite a report temporarily claiming otherwise.
New money and political comebacks punctuate otherwise sleepy Clearwater City Council races
2 candidates are making another go of it, while 2 others are scooping up conservative credentials and cash.
The government is digging into who’s really doing work under small-business contracts
They're looking for, what is the lived reality on the ground, not what the corporate structure might otherwise suggest," said William Hart.
What Does the Science of Climate Change Really Look Like?
Editor’s Note: This is the second in a three-part series on how the Right should think about environmental and climate policy. Read Chris Barnard on Reclaiming Environmental Policy from the Left. More than a decade ago, the world’s governments negotiated and signed the Paris Climate Agreement, committing to hold global warming well below 2 degrees Celsius. In the following years, climate had immense cultural power. Greta Thunberg emerged. Fortune 500 companies established net-zero plans. The U.S. got the Inflation Reduction Act. Then the pendulum swung back. President Donald Trump returned to the Oval Office, speaking of green energy as a “scam” and carbon footprints as a “hoax” and on all matters of international policy emphasizing bilateral engagement and power over globalism and cooperation. The technology companies that had been at the forefront of net-zero pledges and green leadership discovered the importance of all-of-the-above power to their visions for artificial intelligence and began quietly postponing or removing their targets. On the left side of the political spectrum, rising concerns about inflation and cost pushed climate lower down in the stated priorities of voters and stump speeches of politicians. The moment is ripe for a reset on the politics of climate change and for conservatives especially to chart a course that acknowledges and addresses the real challenges in plausible ways. So what should we think about climate change as we enter a post-peak climate era? If it is neither an apocalypse nor a hoax, what will hold up as a durable view of climate change? And how does it project onto the concerns and priorities of the right-of-center’s emerging coalition? New to Commonplace_? Subscribe below to get the magazine in your inbox._ Subscribe A Solid Foundation As the politics and economics of climate careened over the past ten years, the physics carried on regardless. In 2015, the year the Paris Climate Agreement was adopted, the world emitted 41.2 gigatonnes of carbon dioxide. By 2025, emissions had risen only 2.4%, to 42.2 gigatonnes, because falling emissions from land use offset increasing emissions from fossil fuels. But slow growth in annual emissions is not rapid decline, let alone net-zero, so carbon dioxide kept accumulating in the atmosphere, going from 399 to 426 parts per million. Global temperature kept rising too, from about 1.1 degrees Celsius above the 1850–1900 average in 2015, to 1.4 degrees above it in 2025. None of this should surprise anyone. Even most prominent skeptics, like the authors of the 2025 report commissioned by the Trump administration’s Department of Energy, accept the basic physics. Carbon dioxide added to the atmosphere at industrial volumes accumulates and traps heat that would otherwise escape to space. That heat warms the upper ocean and increases temperatures at the surface. Increased ocean heat and melting of land ice cause sea levels to rise. Changes in the climate become noticeable to us as subtle shifts in temperature and precipitation, seasons arriving early or late, changes in the surrounding ecosystem, and weather extremes. The much more meaningful debate is not about whether these things are happening, but about what it all means for human societies. (While it is a proxy measure of overall risk, no one experiences global surface temperature.) This debate invokes at least three interesting questions: how much today’s warming is showing up in regional climate trends, how much more warming we should anticipate, and how extreme weather events can be understood in the context of climate change. Recent scientific advances give us new insights into each of these, but there is still much to learn. This is the context in which the next generation of conservative leaders will be the first to deal with significant climate change as a fact, not a forecast. It will be with them for their entire careers. Our evolving scientific picture of climate change will come from weather and climate records stretching further into the past, new kinds of observations in the present, better modeling tools for the future, and simply more time to observe the emergence of climate signals and their tangible effects on ecosystems and communities in the United States and around the world. The policy landscape will be shaped by not only the impacts of changes in the climate, but also by the ways in which societies respond and by the emergence of new technologies for both altering the trajectory of emissions and adapting to new climate realities. Global climate records now show the fingerprints of warming in different phenomena around much of the world. The IPCC, in its most recent assessment report, documents warming trends over all land regions, even as natural variability adds substantial variance to local trends. And the highest temperature extremes have become more intense and frequent, almost everywhere, since the 1950s. Scientists are highly confident that these changes are attributable to human influence. But confidence in detecting trends and attributing them to human influence degrades across heavy precipitation events; drought trends are heterogeneous and not attributable to human activity with great confidence. For hurricanes, tornadoes, and other severe storms there is even less confidence in trends or their relationship to climate change. In general, a fair summary of the evidence is that for well-observed phenomena, like surface temperature or heavy rainfall, with a clear relationship to warming, our multidecadal observations are consistent with human influence overcoming natural variability over long time periods and extremes increasing. For regions that are sampled more sparsely, or phenomena with a higher noise-to-signal ratio, we will need to observe them longer to understand the magnitude of the climate signal or improve dynamical understanding using models and observations together. But for Climate For those who can’t wait, we also have new techniques that attempt to understand the role climate change has in influencing particular extreme events. When the climate is changing and disaster strikes, it is natural to ask whether that disaster was somehow “caused” by climate change. For scientists and policymakers acting in good faith, answering that question helps develop a more accurate picture of the problem, how it may be getting worse, and what preparations we should be making to respond to it. It may, at some point, give some weight to how liability is assigned by courts or adaptation funding is distributed by society. But how we ask this question is extremely important. It is easy to get the analysis wrong by discounting the role that meteorology plays. As meteorologist Theodore Shepherd explained in a clear 2016 review of climate attribution methods, “if a weather or climate event is truly extreme in the present climate, then perforce it requires unusual meteorological conditions, which means that climate change is at most a contributing factor.” Share Scientists have two ways to probe climate as a contributing factor, both of which have entered media coverage of extreme weather events. One asks a probabilistic question: How much more likely is a particular event (e.g., a temperature record over a particular area) amid global warming? The answer involves using historical records and computer simulations to estimate the likelihood of such an event in both a changed and a preindustrial climate. The findings are less about the specific event, and more about events of that nature. The other question is: How has the changed climate affected the specific event in question? Here, scientists try to understand what a similar event would have looked like in the preindustrial climate. This is sometimes called the storyline approach, where the story is the specific meteorological details of the event. These methods offer ways to test the intuition of scientists about real weather extremes. In late June and early July of 2021, a persistent high-pressure ridge, or heat dome, set up over the Pacific Northwest and an extraordinary heat wave affected the area from Oregon to British Columbia. For six days, it shattered temperature records across the region and hundreds died from heat-related causes, in an area where such high, and persistently high, temperatures were well outside of experience and many live without air conditioning. Scientists have studied its connection to climate change using multiple approaches. This specific event was created by a rare combination of meteorological factors, a strong high-pressure ridge created the conditions for extreme heat, which occurred on top of higher average temperatures in the region from global warming. Probabilistic analyses showed that climate change increased the likelihood of such an event by at least 8-fold to more than 100-fold. One standout example found that such an event had effectively zero probability of occurring in the preindustrial climate. The enormous range in these assessments reflects how hard it is to estimate probabilities of events at the tail of the historical record. Estimates of the effect on the temperature of the event are more clustered. A recent review paper documents how multiple methodologies have found a positive influence, roughly 1-2 degrees Celsius of an anomaly that exceeded 15 degrees, of climate change on the temperature magnitude of the event. Multiple studies have investigated and found some positive influence of climate change in other extreme events. Attribution studies found climate likely increased the heavy rainfall that accompanied Hurricane Helene in North Carolina and surrounding regions in 2024. The extensive fires that struck Los Angeles in 2025 illustrate how the causal chain can become messy, though. Studies do detect a positive influence of climate on the event’s likelihood, but while climate change likely contributed to underlying aridity, it would not have played a role in the heavy winds or land practices that preceded disaster. These event attribution studies will become more common for extreme, or damaging, weather events. They can be produced quickly, and often are reported before peer review. As the Pacific Northwest heatwave example shows, when multiple methods converge on a positive attribution, the finding should probably carry some weight even if you have to be careful about accepting the results from a single study. I expect that as climate change proceeds and the climate thus departs further from a preindustrial counterfactual, the influence will become more detectable across a variety of extreme events and more easily identified. This will be used to cast blame, but can also be used to inform how communities and society adapt to ongoing change. We May Still Be Surprised One positive development, insofar as less climate change is better, has been that our central estimates for future climate change should probably be revised downward. Mostly, that is because the high-end warmings that scientists regularly analyzed about ten to 20 years ago, driven by high emissions throughout the twenty-first century, now appear to be somewhere between unlikely and impossible. At one time, the upper end of mainstream climate projections extended well into 4 to 6 degrees Celsius of warming by the end of this century, which, models suggest, would have wrought enormous real-world damage. Current energy and policy trends now point toward 2.5 or 3 degrees. Damages and risks are commonly modeled as increasing steeply with more warming, so this is already a better-than-previously-expected outcome for the climate (which is independent of the physical response to emissions). But we should maintain a wide range for plausible outcomes and prepare for the possibility of being surprised. Emissions trajectories are subject to deep uncertainty, the physical climate response is still developing, and whatever change occurs in the climate will then be mediated through unpredictable economic, social, and political institutions. Over the past decade, the pace of warming surprised some keen observers and appeared to accelerate, though not yet outside the range of expectation provided by climate models. The reasons for that apparent acceleration are being actively studied (as was the apparent pause in warming from 1998 to 2012), but no single driver has emerged. Some blame it on the El Niño, variability which would have no bearing on climate. Some think it is a result of factories in East Asia and global shipping fleets cutting aerosol emissions, which in the strongest version of the argument would indicate higher climate sensitivity to carbon-dioxide emissions. Others are more measured, not yet ready to draw solid conclusions as to whether climate projections require revision. We will have to see. Long-Term Thinking Climate change asks us to think over long time scales. But the pace of human-driven warming is compressing a large global change into a century. Changing weather patterns and extreme events are already causing adjustment costs and damages and will do more. Adaptation will help, but it is not free. For some communities, these costs will erode livelihoods and well-being, and may force migration. Are we prepared for large-scale managed retreats in the United States? Thankfully, American wealth, geographical diversity, and moderate climates may leave us better prepared than many around the world. But wealthy countries can still suffer serious disruption from narrowly concentrated costs, even if they appear entirely manageable in aggregate. Readers of Commonplace are familiar with the challenges of rapidly adapting to economic forces. The displacement that came from the China Shock is barely perceptible in aggregate GDP and employment data, but it affected millions of people and those people were far less mobile than economic models tended to assume. Deindustrialization rippled through the nation with enormous effects for not only our economic vitality, but also our national security. The forces surrounding climate change may not be so different; the world gets richer and must accept some diffuse costs. Those may be modest overall, but cause all manner of unpredictable effects with which policymakers must cope. In the case of the China Shock, our faith in a growing pie served us poorly. We’ll need to do better on climate. Leave a comment
‘The Dink’: An uneven rally
Some bad calls mar this otherwise enjoyable dramedy
South Carolina Republicans line up to run in special primary to replace late Sen. Lindsey Graham
WASHINGTON (AP) — The field of Republican candidates vying to replace the late Sen. Lindsey Graham on November ballots is nearly set, with at least 10 hopefuls filing their paperwork and launching into a two-week sprint toward the Aug. 11 special GOP primary. WATCH LIVE: Sen. Lindsey Graham remembered in funeral service at Washington National Cathedral It's been a jumbled and frenetic period during what would have otherwise been Graham's expected glide path toward a
Engrams Embedding Entendre: Codesign for Efficient DRAM/SSD Offloading
Engram extends standard token embeddings with learned multi-token lookups. Recurring local patterns retrieve vectors directly, reducing the need to reconstruct them through attention and feed-forward layers. With Engram model architecture optimization, it allows for lower HBM capacity to be needed for models at the same quality. This does not mean there won’t be an insane demand for HBM but it just means that model architecture will continue to innovate around constraints. This model architecture design is naturally codesigned for parameter offloading: each token accesses a few embedding rows whose addresses depend on token IDs, not hidden states. The runtime can prefetch those rows from host DRAM while earlier layers compute, keeping the table outside HBM without transferring entire weight matrices. Our Memory model contains our latest estimates of quarter by quarter HBM, DRAM, & NAND supply and demand. Offloading frees HBM for model weights and KV cache, potentially supporting larger batches or more concurrent sessions. When DRAM becomes the next constraint, NVMe offers another tier. Recommendation systems already cache frequently or recently accessed embedding rows in faster memory while backing colder rows with SSDs. After NVIDIA roadmap had to change due to massively despec’ing Rubin Ultra from 1024GB to now ~200GB of HBM per chip, model architecture optimizations like emgram maybe helpful. Our DeepSeek-V4.1-Flash configuration uses roughly 189 GiB of memory for Engram. We replace it with a memory-mapped (mmap) file and measure serving performance with offload to SSD. Later on in our report, we will show our Engram offloading experiments along with the official InferenceX agentic inference serving results on Engram models like DeepSeekv4.1 Flash across all 6 NVIDIA GPU SKUs along with MI355X. Unsurprisingly, the CUDA Moat is still mogging MI355X on the ultra popular DeepSeekV4.1 Flash model. We also show how even on high capacity HBM SKUs, offloading emgrams to DRAM could result in even better performance for most of the pareto than keeping the emgram in HBM. Our benchmark has been widely reproduced, validated and/or supported by almost every major buyer of compute from Google Cloud to Microsoft Azure to Oracle, to Meta and many more. Furthermore, it has the support of the ML community including from vLLM, LMCache, SGLang, PyTorch, Huggingface and the support of major labs like OpenAI, MiniMax, ZAI, Qwen, Moonshot Kimi, etc. Star the InferenceX GitHub repository if you find the open-source benchmark and data useful!. InferenceX is the only inference benchmark in the world to have TPUv7, Jalapeño, Nvidia Rubin NVL72, AMD, and soon, SambaNova and Trainium. Due to how realistic AgentX scenario is to real world agentic inference workloads, AMD has committed to collaborating on MI455X UALoE72 too. DeepSeek did not release the original paper’s two trained Engram models. We replicated its setup on fineweb-edu using the released code and training hyperparameters, at an estimated 6E18 FLOPs per run. We observed the same U-shape scaling: Engram improved performance over pure MoE baselines. We also reproduced DeepSeek’s results, where earlier-layer representations with Engram resembled those of later layers. Like the original Engram paper, it is possible to probe Engram’s gate scores to see what n-grams DeepSeek-V4.1-Flash makes the most use of. Our gate scan finds names, code fragments, relational phrasing, and boilerplate. These examples prioritize interesting-ness over gate strength. One unexpected result was Wright : Ace Attorney. These examples suggest learned memory optimizes the training objective, not a judgment of which facts deserve storage. Licenses, bibliography fragments, API scaffolding, and website furniture can provide prediction shortcuts, so the value of additional Engram capacity may depend on what survives data preparation. This does not show that table capacity is “wasted”: the evaluation-corpus scan establishes neither training exposure nor the capacity occupied by each category. For offloading, strong gates do not identify cache-hot rows. Low gates do not automatically save reads either: computing the gate requires the retrieved key and undoes the performance gain of a fused kernel. Skipping reads would require a separate usefulness predictor before retrieval. In the original paper’s inference-time ablation, factual-knowledge benchmarks retained just 29–44% of their original performance, while reading comprehension retained 81–93%. This is due to the training–inference mismatch. The resulting degradation therefore measures this trained model’s dependence on Engram, not the performance difference between models trained with and without it. In our ablations, suppressing Engram worsens token likelihood across all evaluated domains, especially encyclopedia text and several code corpora. Surprisingly, GSM8K accuracy stays within measured run-to-run variation and removing Engram has no effect. Engram is not a detachable dictionary beside an unchanged MoE. Removing it changes downstream features and expert selection. We tested whether rerouting hurts or compensates by holding tokens fixed in a teacher-forced experiment on CRUXEval, a code-reasoning benchmark of small Python functions where the model predicts a function’s output from its code and an input, and scoring the reference answer. Removing Engram raised answer loss from 0.2848 to 0.3093 bits/token. Forcing the ablated model to use the original Engram-on expert choices made it worse still, at 0.3375 bits/token. Rerouting partially compensates for the missing memory. Memory features and expert selection work together, rather than following a clean “memory stores facts; experts reason” division. On the same CRUXeval, removing Engram during either phase reduced accuracy and increased generated tokens; removing it throughout produced the largest changes. Keeping Engram for prefill leads to more correct answers than keeping for decode likely due to semantically richer KV cache transferred to decode workers, allowing it to mitigate some of the performance loss. Engram’s table is large, but each lookup is small. DeepSeek-V4.1-Flash requests 24 rows at each of two Engram layers, about 12.4 KiB per processed token position across the model, or 3.1 KiB per GPU when split across four GPUs. Currently as of Day 7 since Model Release, MI355X is still 2-4x worse performance per dollar compared to B200 even when normalized by Mi355X’s lower TCO. Our full total cost of ownership breakdown comes from our AI Cloud TCO Model along with monthly market surveys of over 100+ gpu clouds & gpu cloud customers. On the Day 0 release of DeepSeekv4.1 Flash, NVIDIA vLLM works out of the box with zero issues across all 6 SKUs: H100, H200, B200, B300, GB200, GB300! This was thanks to the amazing work by the NVIDIA & Interact teams! In comparison, AMD vLLM did not work on day 0 for DeepSeekv4.1 Flash.Source: SemiAnalysis InferenceX AMD’s vLLM documentation points to using vllm/vllm-openai-rocm:deepseekv41-flash-0909, but from hour 0 of the model release to hour 23, AMD has not publicly released the image. AMD claims, “SPEED IS THE MOAT,” yet it has still not released it by the 23rd hour. We wish that, going forward, the AMD team has a better process for hour 0 model releases. Eventually when they did publicly release for “day 0” image support, performance-wise, it is currently up to 14.8x worse perf per dollar than H200 and up to 42x worse perf per dollar than B200/B300. The power of the CUDA MOAT is NVIDIA’s collaboration with its massive 6 million-developer community ecosystem including most of the vLLM & SGLang & Tokenspeed maintainers which means that CUDA is optimized on day 0. Overall, AMD did make significant improvements but the performance per dollar is still currently 2-4x worse than B200. AgentX Engram DRAM Offloading Improving Performance The HBM and DRAM offload use the same GPU kernel to select and dequantize rows. With HBM, it reads GPU memory; with Unified Virtual Addressing (UVA), it reads pinned host memory directly. Both support full decode graphs. Moving the table into HBM accelerates only the sparse lookup, leaving decoder computation and communication unchanged, resulting in little overall benefit while consuming memory otherwise available to KV cache. Another benefit of Engram offloaded to DRAM which means you can reduce the communication overhead by using less HBM GPUs per replica. For example, when enabling Engram offloading on B300, we are able to switch from TP4 to now TP2 which improves the pareto curve by up to 1.6x. When iso-model quality, less HBM is required as the engrams could be offloaded to host DRAM. Thus HBM bandwidth matters way more than HBM capacity. For inference workloads where memory bandwidth matters the most, 4-hi HBM provides the best $/bandwidth and therefore lowest cost per token. If China continues to make more and more revolutionary model architecture innovations, soon it could potentially 0Hi HBM stacks. [ ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) [ Long Live the Short King: Why 4-hi HBM Wins ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) Myron Xie, Bryan Shan, and 3 others · Sep 13 [ Read full story ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) Moreover, on B300 and week-0 stack, moving the Engram table back to HBM did not improve results and stay within run-to-run variance. This is a result of the work optimizing DRAM offload, such as async, and overlap. SSD Offloading On B200, we created a unoptimized vLLM fork and stored the Engram tables in memory-mapped files on local SSDs. File backing lets the OS reclaim table pages when other applications need RAM. Pages already cached in memory can be served without reading the SSD again. Furthermore, note that we were unable to turn on GDS The unoptimized SSD implementation changes how rows reach the GPU. It copies row IDs to the CPU, deduplicates them, gathers the requested rows into pinned buffers, copies those rows back to the GPU and dequantizes them. This work runs between segments of the GPU execution graph. Native UVA performs row selection and dequantization directly on the GPU, avoiding the CPU round trip. A warm filesystem cache removes physical SSD reads, but leaves the coordination, row gathering and transfers. This is why a file already cached in RAM can still perform worse than a pinned DRAM table. The comparison measures the whole serving path; it does not separate the time spent on each of these operations. B200 DRAM dominates both measured SSD serving curves in total tokens per dollar and P90 interactivity. Near 125 tokens/s/user, DRAM delivers 121 million total tokens per dollar versus 52 million for SSD. For production serving, SSD offloading is likely not worth the tradeoff. On the B200 configurations we measured, SSD offloading loses on both measures: every observed SSD point has a DRAM alternative that delivers higher P90 interactivity and more total tokens per dollar. The only points where Cheaper storage does not automatically produce a cheaper inference service. Moving Engram to SSD leaves the same four expensive GPUs and the rest of the server in place. Reclaiming RAM only creates an economic benefit if it enables a cheaper server configuration or additional useful capacity. The current unoptimized path provides neither benefit, and the filesystem cache still consumes RAM when table pages are resident. Next we will look at the mechanisms and specific implementation of ngrams from DeepSeekv4.1 Flash, LongCat, Qwen3.8 Flash Next.
Why I Don’t Think Ride Or Die Should Have Been Cancelled (And What Company Should Save It...)
(Welcome to my weekly streaming ratings report, the single best guide to what’s popular in streaming TV and what isn’t. I’m the Entertainment Strategy Guy, a former streaming executive who now analyzes business strategy in the entertainment industry. If you were forwarded this email, please subscribe to get these insights each week.) So…I recently came across a Reddit thread, talking about films on streaming, wondering why there are no reliable sources out there for streaming ratings data. Sigh. Honestly, as annoyed as I am that so many still don’t realize that we have streaming ratings, I somewhat get it. Until now, there was such a long delay between when a show or film came to streaming and when we got its viewership data; I understand why people were confused. (But, as you know, this just changed.) Also, even back in the day, how many people knew how well films did on television? Luckily, as a reader, you’re in the know. Okay, on to this week’s issue. I want to take a look at Prime Video cancelling Ride or Die and whether I agree with that decision. Overall, we didn’t have any breakout hits (no TV shows landed over 20 million hours according to Nielsen) but we have a lot of steady performers on the streaming charts. Plus, we’ll look at where $50 million in missing box office dollars went. (Spoiler: straight-to-streaming.) All that, plus new episodes of The Secret Lives of Mormon Wives, Supergirl heads to streaming, a new YA show does well on Netflix, the viewership for the Daily Wire’s $50 million fantasy show, what TV show hit-maker can’t make streaming hits, CD sales, all the flops, bombs and misses, and a whole lot more. Let’s dive right in! (Reminder: The streaming ratings report focuses on the U.S. market and compiles data from Nielsen’s weekly top ten viewership ranks, Luminate’s Top Ten Data, JustWatch and Reelgood interest data, Samba TV household viewership, company datecdotes, Netflix hours viewed data, Google Trends, and IMDb to determine the most popular content. While most data points are current, Nielsen’s data covers the weeks of September 7th to September 13th, 2026. You can find a link to my terminology here.) Subscribe Television - Ride or Die Is a Hit…But It Got Cancelled? What The Data “Says” I hate the phrase “here’s what the data says”, even though sometimes I find myself using it. If data just told us everything, we data folks would be out of a job! Data is often messy, complicated and nuanced. When it comes to strategy, many of the best decisions can’t actually use “data”, since it’s vastly too complicated to model. Anyways, I’ve been singing the praises of Prime Video’s Ride Or Die this summer, a show that did very well for them. And yet…it got cancelled. Now, usually when a show gets cancelled, a bunch of websites say “a hit show got cancelled”... Here’s the thing, though: in this case, they’re right! The data “says” that this show was a hit. Specifically, out of 522 first seasons to make a week on the Nielsen charts since 2020, it’s the 75th biggest. On Amazon, Ride or Die is eighth out of 41 first seasons: That’s top 15th percentile all time, almost exactly, which is my definition of a “hit”! I mean, outside of Reacher, Amazon needed a hit this summer! (Admittedly, Off Campus did well for Amazon in the spring, but Ride or Die performed even better.) So what happened? Why did Amazon cancel one of their rare 2026 bright spots? Well, Deadline reported a reason—which I’ll get to—but I’d break it down into three relevant questions: What is a given streamer’s reasoning? Is that reasoning sound? Could the stated reason not be the real reason? According to Deadline, Amazon felt the show “over-indexed” in middle-aged women. (This was based on leaks from within Amazon.) The logic is this: Amazon already reaches this demographic because of unlimited two-day shipping bundled with Prime, so they don’t really need more shows like this. Now, it’s my job to ask if that’s a good reason, and I’ll be honest, in this case, I don’t think it is. Sure, this show may “over-index” in middle-aged women—I’ll just assume that’s the case here—but I think folks often oversell how important over-indexing actually is. When a show is a “hit”, it isn’t a bit bigger than its rivals; it’s usually multiples bigger. For example, Elle only made the charts for two weeks at 8.3 million hours each. So Ride or Die was 80% bigger in its first two weeks, and likely had a much stronger hold, with its terrific 13 million hours in week three. Ride or Die was nearly three times bigger than Sterling Point through three weeks too. And that means that _Elle, Sterling Point a_nd other Amazon YA shows need to not just over-index a little with younger women (assuming that’s who Amazon prefers over middle-aged women), but massively over-index. Otherwise, more people in that demo (and a bunch more besides) likely watched Ride or Die. (I’ll try to explore this concept more in a future article.) And don’t get me started on how Ride or Die did compared to a certain (very, very popular, possibly the world’s most popular) YouTuber’s reality show…which didn’t make the charts after its first week and likely cost much, much more than Ride or Die. All to say, Amazon-MGM Studios/Prime Video can tell reporters that this show didn’t reach the right audience, but that excuse feels weak to me. So this leads to the third question: could other reasons have come into play? Yes! Renewal or cancellation decisions rarely (I’m tempted to say “never”) boil down to one variable. At its simplest, it’s two things: budget versus viewership. But often factors like critical acclaim, ownership and, yes, personal opinion come into play. In this case, Amazon has a new executive running things, and this show isn’t owned by Amazon-MGM Studios (unlike Elle). And yes, it may not have indexed with the right target viewers, too. Likely all of those factors came into play.1 My guess is ownership ended up mattering most. Amazon doesn’t own Ride or Die. It’s actually produced by another major studio. Fortunately/allegedly, this show is being shopped around. I can think of one brand new CEO who should consider it for one of his two major streamers…especially if he wants to rebuild goodwill in Hollywood. His name is on this list of exec producers of Ride or Die… If David Ellison needs some goodwill—and he does—rescue this female-led show tomorrow and grab some good headlines for a day. The data justifies it. Quick Notes on TV We’re just getting started with this issue, but the rest is for paid subscribers of the Entertainment Strategy Guy, so if you’d like to find out… How The Secret Lives of Mormon Wives latest season did on Hulu, post-controversy... Whether Supergirl soared on HBO Max... Why the box office is losing money due to the streamers... The viewership for the Daily Wire’s $50 million fantasy show... Updates on The Gentlemen, Lanterns, Outer Banks, King of the Hill and more... What former hit-maker has three streaming flops in a row... Whether The Mandalorian and Grogu popped in week two... All the flops, bombs and misses... And a whole lot more... ...please subscribe! We can only keep doing this great work with your support. If you want an idea of just how much content you’d get in a full issue, check out this older issue. Coming Soon! Oh man, we’re almost caught up! We just have two more issues, then the streaming Ratings Report will be coming out just two weeks after a TV show premieres! Next issue, I’ll be looking at the first two weeks of the NFL Thursday night games on streaming, a huge new spinoff, Reacher’s Neagly on Prime Video, and the latest edition of Monster: The Lizzie Borden Story on Netflix, the return of Slow Horses on Apple and MobLand (quietly one of their biggest shows) on Paramount+, and Dancing with the Stars on both ABC and Disney+. Plus I’ll take a look at animation for adults in 2026. The week after, a ton of movies are coming to streaming: Toy Story 5 on Disney+, Backrooms on HBO Max, Jackass: Best and Last on Paramount+ and The Breadwinner on Netflix, plus a Unabomber film on Netflix and a romcom on Prime Video. Woody Harrelson and Matthew McConaughey have a show on Apple, along with a game show inspired by Willy Wonka that everyone seems to hate. Long term, some crazy (but hopefully crazy like a fox) IP news. FX ordered a new Sons of Anarchy show from Charlie Hunnam, but it’s not about the biker gang. No, it features the show’s cast playing themselves in a new “meta-thriller”. Huh. And a lot of the cast is coming back. Next, Ryan Gosling is producing a Flintstones movie about a “grown-up Bamm-Bamm Rubble”. Also, huh. This one is in “early development” so we’ll see what happens. Both projects are based on IP, but also seem to have crazy takes on the IP, which I like.
Think you need 10,000 steps a day? Science says otherwise
The 10,000-step goal came from a 1960s ad campaign, not science. A new study reveals the real number you need for major health benefits
OC Jim Bob Cooter discusses health at wide receiver | 2026 Training camp media availability
While unfortunate to have veteran players out for a day or two, the Colts offensive coordinator appreciates the chance to better evaluate younger players who now get a chance to run routes they might not otherwise.
When “DEI” research is cut, what happens to community health?
“I’m just going to run to the grocery store!” How many times have we said this without thinking about it? For most of us, it's usually an easy walk or a short drive to get the cheese we forgot. In reality, though, 13% of the U.S. population (about 39 million people) live in “food deserts,” which means living more than a mile from a grocery store in urban areas and more than 10 miles in rural areas. This was the case for people in Pittsburgh’s Hill District, where a quick run to buy food wasn’t an option. In the 1930s, federal maps marked the Hill as "hazardous" for lending, a practice known as redlining that steered investment away from Black neighborhoods. Decades of disinvestment followed, and residents went about 30 years without a nearby grocery store. This made it hard to keep healthy eating habits and contributed to chronic disease. When a grocery store finally opened in the area in 2013, researchers saw the opportunity and launched the PHRESH study, which initially compared the health of participants from the Hill district to those in the Homewood district. Over time, the study expanded to other factors such as brain health and Alzheimer's risk. Residents like Roderick Blair overcame initial hesitation to share their health data, trusting it would benefit their neighborhood. Then, in August 2026, the NIH abruptly canceled the study’s grant, citing that it was “subjective” and fell outside their scientific scope—an effect of the administration’s interpretation of diversity, equity and inclusion (DEI). Research like PHRESH can help determine what changes in a neighborhood actually improve health, so cutting it has left that question unanswered. Other canceled studies have examined health issues like maternal health, preterm birth rates, or sickle cell disease. Courts have reversed some of these grant cancellations, but not all of them. If we really want America to be healthier, research into why some communities get sicker than others is absolutely critical. The law that created the federal minority health research institute passed with bipartisan support in 2000. But these days, as evidenced by grant cancellations, the political shift in attitudes toward DEI means we often lose access to the health information we need to help all communities thrive. DEI vs. health equity vs. equality and the overlap (or lack thereof) The letters “DEI” evoke very different reactions depending on who you are in the U.S. DEI programs can address gender, disability, age, and other factors, but the fiercest debate centers on race. For some people, hearing DEI brings to mind policies that may have cost them a job or a college seat because they are white. Others see DEI as a matter of justice: a way to expand representation and opportunity for groups that have been historically marginalized. Both perspectives are rooted in lived experience, and both center on the same question: whether hiring and admissions practices are fair. Hiring and admission practices, though related, are not the same as health equity. Health equity is “the attainment of the highest level of health for all people.” It’s the recognition that some groups of people consistently experience worse health outcomes than others. For instance, black mothers die from pregnancy-related complications at three times the rate of white mothers, and rural Americans are more likely to die from issues such as heart disease and cancer. Equity is also often conflated with “equality,” which leads to misunderstanding of how to actually best support communities. Equality ignores the fact that we aren’t all starting life with the same resources or circumstances, whereas equity is about helping people get what they specifically need based on their situation so they have the chance to achieve the same outcome as anyone else. For example, if every household in Pittsburgh got a $30 grocery gift card, that would be equal. But during the decades the Hill District went without a grocery store, that card wouldn’t have helped much. Equity means making sure everyone can actually access a store in the first place. DEI policies can overlap with health equity in many ways. For example, they may help expand access to quality education and stable, well-paying jobs, which in turn can provide health insurance, safer housing, and improved finances—all of which can reduce health disparities. But to make that happen, we first need data to identify disparities in communities and to develop solutions that effectively address them. What we lose when the research stops Community data is important to health equity because: You can’t fix what you don’t see: Data show which communities are most affected by health factors such as infection, heart disease, and cancer. If we don’t know what is happening and where, then we can’t develop interventions to help. Poor health costs everyone: Chronic disease is expensive—both to the individual person and to the overall economy. Understanding what groups of people are most at risk allows us to develop interventions that can save everyone money. Disparities exist for many communities: Health equity includes disparities that exist due to a whole host of factors, such as family background, race and ethnicity, rural vs. suburban residence, gender and sexual orientation, veteran status, disability, and lower income. This means many Americans can benefit from research into differences in health outcomes and how to improve them. Trust is easy to lose: Some participants in the PHRESH study were initially vocal about their concerns about participating. But that trust was earned because the researchers kept their promises and centered their work on the community. When projects are canceled mid-cycle, researchers have no choice but to leave behind communities they had promised to help. This hurts trust in the long run. It’s also a very inefficient use of resources, and it will take significant time and effort to redo the work when the government realizes this work is important again. How should we talk about health equity? This is hard because the words “equity” and “DEI” are currently very politically charged. So whether you’re a public health professional, teacher, or friend, how we talk about this topic matters. The right framing can help people who might otherwise be skeptical see why health equity research is important and deserves funding. Here are some suggestions to help your conversations: Choose your words strategically: Who are you trying to reach with your message? Are they likely to be resistant to terms like “diversity” and “equity”? If so, find alternative words to use to still get the message across. It is frustrating to be in that position, but the goal of improving health is too important not to try to make inroads. Sometimes simply reframing can help the message reach those who need to hear it. Focus on the problem: The terms “health equity” and “DEI” are abstract. Make it more specific. For example, discuss differences in maternal death or ways to improve health outcomes in rural communities. These still have equity at their core but will likely be better understood by those you’re talking with. Focus on systems: Disparities exist, but if they are communicated poorly, people can mistakenly think the issue is the individual’s fault. Disparities are largely driven by failures of the system—such as lack of access to health care and a long legacy of racism. When discussing the disparities, frame them around these systemic issues to ensure blame isn’t placed on the individuals impacted. Tell stories: Personalize these policies by using stories from those impacted that connect to the data you’re trying to convey. This helps make the policies and outcomes more concrete and meaningful. Bottom line Some communities end up sicker than others. That’s just the facts. We can argue about the best way to do that research, but canceling it simply because it may be linked to DEI doesn’t make anyone healthier. It just leaves communities like the Hill district behind. And in the end, it causes us all to move backward in our efforts to improve health in this country. Love, EM Big thanks to MacKenzie Isaac & Legairre Radden, who reviewed this piece and generously shared their expertise and lived experience to help improve it. Dr. Liz Marnik, PhD, is Executive Director of The Evidence Collective and a science communicator and immunologist working at the intersection of public trust, science, and public health. To discuss communication training on topics like health equity and storytelling, email Liz at liz@yle.health or learn more on our website. Your Local Epidemiologist (YLE) comprises a team of experts, ranging from physicians to immunologists to epidemiologists to nutritionists, working together with one goal: to “translate” ever-evolving public health science so that people are well-equipped to make evidence-based decisions. The YLE suite of newsletters reaches over 475,000 people across more than 132 countries. This newsletter is free to everyone, thanks to the generous support of fellow YLE community members. To support the effort, subscribe or upgrade below: Your Local Epidemiologist is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe
Think you need 10,000 steps a day? Science says otherwise
The 10,000-step goal came from a 1960s ad campaign, not science. A new study reveals the real number you need for major health benefits
Time lost to political haggling means lost momentum for research
While it’s not up to universities to set deadlines for EU institutions, “lost time” in the run-up to the launch of the next Horizon Europe would translate into “lost momentum” for universities, according to Dag Rune Olsen, president of the Young European Research Universities Network.“I think it's important to have [the EU budget] in place in order to be able to work on the research funding mechanisms, because we cannot afford to lose more momentum than we already have,” Olsen told Science|Business in an interview in September, just after EU research lobbies gathered in Brussels to push for a €200 billion budget for the next Horizon Europe programme.A funding gap would mean ramping down research activities, said Olsen. “You can do that quite swiftly, that’s not a problem, but ramping up again is much more difficult.”The last-minute push for an ambitious budget, and for a timely adoption of the research programme, comes at a time when the richest EU states have teamed up to call for significant cuts to European Commission’s proposal for the next multiannual budget, which allocates €175 billion for Horizon Europe. At the same gathering of research associations in September, the European Parliament’s budget rapporteur warned that the EU will need to raise its own taxes, otherwise political pressure and tighter national budgets will translate into a limited EU budget with a less ambitious envelope for research. The research sector hopes EU institutions will reach timely agreement on the budget and on Horizon Europe, but progress has been slow. Legislative progress has been delayed, partly because the European Parliament’s budget committee is still haggling over how to apply new EU-wide spending rules, known as the Performance Framework Regulation, and partly because EU governments have yet to agree on the size of the next EU budget. EU research Commissioner Ekaterina Zaharieva told Science|Business in July that she was hoping for an agreement on Horizon Europe by the end of 2026, but the timelines have been pushed back significantly, with the Parliament now aiming to announce its official position on Horizon Europe only in November. Avoid the funding gapAccording to Olsen, delayed EU funds could prompt universities and researchers to move on and do something else, potentially jeopardising years of work building consortia and collaborations. “If funding is interrupted, the networks fall apart in just a few months,” he said. Olsen also noted that an interruption would affect junior researchers first. PhD students and postdocs are on shorter term contracts and universities risk losing them if funding for EU projects is interrupted. Related articlesEU own resources ‘essential’ for future research budget, says leading MEPParliament haggle over EU-wide spending rules delays vote on Horizon EuropeZaharieva hopes for a Horizon Europe deal in 2026Universities usually have more employees, permanent and short-term staff, than they can cover with national funding, and external funding is essential to keep them all on the payroll. In case of an EU funding gap, “there will be a huge number of people that might end up without any contracts,” he said. Apart from the money issue, Olsen said EU projects forces universities to collaborate more which, in turn, increases the quality of teaching and research. In case of a funding dip, people will look around for opportunities elsewhere, in the private sector or abroad. “And then then you have to recruit again, and it's a tedious business,” said Olsen. “A gap is not a good idea, it's actually a waste of money.”According to Olsen, the difficult political context should encourage universities to make a stronger case for the EU increasing research funding, as universities can deliver on competitiveness, technological sovereignty, security and defence. “The reason why we ask for substantial investments in research is exactly what the Commission, what Europe, the Parliament have been discussing,” he said. “You really need to invest in order to thrive in the longer run.”Focus on the member statesBut EU and national R&D expenditure hasn't kept up with the times. EU governments have failed to reach a spending target set almost 25 years ago. Only six EU countries spend 3% or more of their GDP on R&D. Asked whether universities should put more effort into convincing the governments to ramp up their own spending, but also to support a strong budget for Horizon Europe in the ongoing budget negotiations, Olsen said “the answer is yes.”“We are very actively encouraging our members to talk to their ministries in order to enlighten them about the joint effort,” he said. The network is made up of 25 universities from across Europe, all established in the past 60 years or so.Who will call the shots?Budget and timing issues aside, the Parliament’s research committee has been locked in behind-the-scenes negotiations over the future structure of Horizon Europe, with MEPs disagreeing on the degree to which its collaborative research should be governed by councils of experts.The European Parliament’s lead rapporteur on Horizon Europe wants councils of independent experts to decide on the funding agenda, but the Commission, the Council, and parts of the Parliament disagree. For Olsen, it is normal for politicians to point at the big challenges, but when they start detailing how those challenges should be solved, the endeavour “fails utterly.” He does not necessarily think that “bureaucrats who don’t know the field” would be “much better at it” either. “There has to be a combination” of broad political directions and bottom-up implementation, Olsen said.
The politics and possibilities of ‘AI could kill us all’
I found myself fielding a lot of questions from folks who don’t usually talk much about AI this week. They were mostly along the lines of “do you think AI is going to kill us all?” or “how worried should I be about this human extinction stuff?” Motivating this wave of existential anxiety was the highly dramatic resignation of now-former Anthropic employee Jacob Coxon, who quit his job while publicly sharing his concerns about safety practices at the leading AI companies. More specifically, he declared that almost everyone at OpenAI and Anthropic, the leading AI labs, believes that there’s a decent chance the AI systems they’re producing could “kill us all” by the end of the decade, and the companies aren’t “acting responsibly.” Other AI safety researchers, including those still employed at Anthropic and OpenAI, all confirmed that yes, Coxon is correct, they’re all worried that their company’s products might prove fatal for the entire human race as well. Two more AI employees quit and joined the chorus. Now, the warning shot has been covered everywhere from the Wall Street Journal to Fox News to WIRED to the Guardian. I understand why all this sounds scary, but my response to those who have asked, has generally been: No, you don’t need to worry that AI will kill go rogue and kill us all. But that does not mean we shouldn’t worry, period. Far from it. The companies building AI products are now among the wealthiest and most powerful entities in the world. And they very much are building enormous engines of mass surveillance, extraction, and automation. They can and have allowed their AI systems to carry out felony hacks of other companies, provided the government with autonomously generated kill lists, and sold enterprise technology to firms around the world that is explicitly intended to replace jobs. But “AI” has done these things not because they have all-powerful minds of their own but, ultimately, because people have programmed, calibrated, and directed them to. In other words, it’s not AI we need to worry the most about. It’s the people and corporations working to profit from and gain power by deploying it. The technology is sophisticated, sure, and, especially in cybersecurity contexts, potentially disruptive to plenty of existing systems. But if we want to stop this predatory, profiteering, and extractive form of AI—and it’s worth noting that at this point, most people do—we absolutely can. A quick note/plea: I can only do all of this with the support of readers like you, so if you find value in independent, critical tech journalism and analysis, please consider becoming a paid subscriber. I want to keep as much of this un-paywalled as absolutely possible, and I need your support to do so. Many thanks to all who continue to chip in, it’s very much appreciated. Onward. Subscribe First, some context: AI industry folks like Coxon have been discussing the prospect that AI will destroy humanity for years. Even before the beginning of the AI boom, many working in the industry, from the executive staff to the rank and file researchers, have obsessed over ‘x-risk’, or the possibility that AI will become sentient or semi-sentient and move one way or another to exterminate humanity. This is ostensibly why Elon Musk bankrolled OpenAI in the first place, and is the grounds for his lawsuit alleging OpenAI betrayed its charter by abandoning its nonprofit status to become a commercial enterprise. Many AI engineers and startup founders have calculated their own personal p(doom), or the probability that the tech product they are working on will initiate an actual humanity-extinguishing apocalypse. 10-25% p(doom)s are pretty common. (I wrote about a long weekend I spent among these folks at one of their top conferences here, to give you an idea of their ideas and belief systems.) Sam Altman himself drove the narrative of the imminent rise of a super-powerful AI, or artificial general intelligence, to prominence by declaring that the technology he was building posed an existential risk to humanity. This idea is in other words baked into the DNA of modern AI companies and into the products they engineer. It’s also why the industry is routinely accused, by myself and others, of “doom marketing,” ie, using such proclamations and framings to impart the sheer power of the technology behind your product onto investors, partners, consumers, and the state. Some ambient understanding of this tendency may have helped prevent the press from repeating the claim every time one of the x-risk folks made it over the years, including when they quit their jobs at AI companies while doing so. Yet Coxon’s AI x-risk alarm has broken containment, as they say, and has exposed the idea to the mainstream media once again. This has happened, I think, for a number of reasons. First, when AI CEOs were going around saying AI might kill everyone in 2023, their companies were well-capitalized, but not nearly to the extent they are today. They were more easily dismissed as eccentric geeks of the sort that Silicon Valley is supposed to produce. Now, not only do their products have much greater market saturation, but people are more aware of a) the often unethical ways the executives and companies comport themselves b) the other adverse impacts of AI that have already arrived, and c) the physical cost of building these systems, particularly when it comes to data centers. Finally, toss in the constant stream of headlines in 2026 about AI models going rogue or potentially upending the world’s infrastructure. [ ](https://www.bloodinthemachine.com/p/with-the-backlash-to-data-centers) [ With the backlash to data centers, Flock and AI glasses, a mass opposition to big tech is underway. Silicon Valley is in denial. ](https://www.bloodinthemachine.com/p/with-the-backlash-to-data-centers) Brian Merchant · Aug 27 [ Read full story ](https://www.bloodinthemachine.com/p/with-the-backlash-to-data-centers) As a result, general sentiment was already overwhelmingly skeptical of AI and its overlords, so the table was pretty well set for someone to come along and say ‘just a reminder, the companies that are building the product that promises to take your job and leave a data center in your neighborhood also think that it might exterminate the human race’ and get a strong reaction from the media and the public. The question is: Should we understand this stuff as hype, strategically fanned to feed already swelling valuations? Is it doom marketing? Genuine cause for alarm that the infrastructure that undergirds modern life is facing a novel threat vector? Or reason to be sincerely worried that AI companies are amassing too much power, and that their products are genuinely dangerous? The answer, I’m afraid, is “yes.” Let me try to untangle all of this a bit, because arguments over how to navigate this moment are extremely heated right now, as they are essentially arguments over how to approach the politics of AI at a moment where there may be a unique opening for action. Those sympathetic to AI alignment concerns and x risk arguments—which increasingly includes a faction on the left who want to shut down AI over safety concerns—argue that people who dismiss AI as all hype have their heads in the sand. Meanwhile, critics of the x-risk narrative—which I might note includes a cadre of investors and tech folks on the right, too—argue that it is hype and can be used to further inflate valuations or for pursuing regulatory capture. The challenge is that many feel that dismissing AI as just hype or purely marketing also diminishes the urgency of taking on the AI industry as the novel and threat to labor, civil society, and the public sphere it is; others that by accepting the industry’s own terms, we imbue it with undue power and enable it. But let’s be clear: Declarations that AI is so powerful it might end the world are a kind of marketing, and anyone who says otherwise is just as off base as those who say that’s all that it is. If you don’t think that these sub-trillion dollar companies have higher-ups who noticed that when their founders talked about super-powerful dangerous AI they got spikes in media interest and phone calls from investors, or that those founders didn’t notice that themselves, and lean into certain narratives to advantage their products, then I don’t know what to tell you. Not all marketing is a Pepsi ad campaign with a celebrity spokesperson. Yet confronting the AI industry should certainly be considered an urgent project. Do I share the x-riskers’ fear that out-of-control, super-intelligent AI agents might end the world? I do not. I’ve read a good deal of AI extinction literature over the last couple of years and I have not come across a credible, step-by-step documentation of how exactly AI might move from self-recursively improving AI to killing every single human on the planet. Most of what I’ve read seems predicated on a sort of reverse Ian Malcolmian logic: Eventually, it becomes smart enough and AI, uh, finds a way (to exterminate humanity). Are the AI companies also overstating and dramatizing what their products can do? Yes, pretty much all the time. They’re in a knife fight for market share and are trying to win customers and government contracts before their investors come knocking. And yet. They have concentrated enormous amounts of compute, have extensive data collection and surveillance capacities, have inserted themselves into the state in numerous capacities, including military kill chains, and they are being run by egomaniacal billionaires who are rushing to monopolize an entire new market. The way I think about their most recent frightening-seeming exploits is not as a “swarm of rogue AI agents building a new civilization” or whatever, but as corporations with a hitherto-unimaginable amount of compute at their disposal recklessly experimenting with automation programs, completely unafraid of any repercussions because we have been unwilling or unable to impose any on them. So I think we should welcome any newfound imperative to hold them accountable. I also think that ultimately, the AI critics, Pause AI folks, leftists, doomers, effective altruists, and, honestly, most of the public, want a lot of the same things: legitimate checks and limits on AI companies’ power, actual transparency into what the companies are doing, and for more of AI to be under public control. (Ultimately, a lot of what animates the x-risk fears are rooted in just that: a fear of losing control.) How we move towards all this, exactly, is the question of the day, and while there’s much to be argued over in terms of building a viable political platform to challenge the AI industry’s power, I do want to cast a vote against one particular approach. Not to pick on Chris here, as there are a lot of folks out there proposing stuff like this, and the policy ideas actually stem from AI safety groups. But not only would this be challenging to land politically (I can already hear the ‘China will beat us to AGI’ calls) and all but unworkable in practice, this approach is a) far too limiting, b) fails to address the current harms caused by AI and c) would likely only wind up serving Anthropic and OpenAI; it’s what regulatory capture looks like in action. Full model transparency is the floor for what should be on offer here. If these companies believe their products will kill us, the very least we deserve is to know how. It’s in the public interest. I would also argue we might get a lot of the way to stopping any currently envisioned robot apocalypse simply by forcing AI companies to assume liability, and to submit to the full brunt of the law. If Sam Altman were liable to be charged with a felony for approving the hack on Hugging Face1, if Google was forced to pay defamation fines each time it published a false and injurious AI Overviews, these companies might suddenly find cause to restrain their models’ output, and we might hear less about rogue AI. We would need to introduce new frameworks and available tools for tracking automated lawbreaking, to be sure, but I’d wager that little would frighten the AI companies more than actually being held accountable for what their products enable. To that end, Illinois has passed an AI liability law, which the industry fought tooth and nail, and Florida is considering one. The next steps would be towards moving AI under public control; again, it’s only reasonable, since everyone, not just AI insiders, should get a say over how a technology whose developers say might kill us is developed and deployed. Dismantling the whole industrial project as currently structured should be on the table, too, pending democratic input. But that’s for another post. For now, suffice to say that while I won’t be losing any sleep about an AI-enabled extinction event, Mr. Coxon and co. have given us an opportunity to underscore the urgency of the clear and present damage AI companies are doing, the extreme power they wield and the extent to which they hold themselves unaccountable, and the abundance of political capital there is out there for taking them on. Subscribe 1 Purely speculative! I do not know if he did or not.
Think you need 10,000 steps a day? Science says otherwise
Bodog reports that the 10,000-step goal originates from a 1960s marketing campaign, with research suggesting 7,000 steps may provide significant health benefits.
Trump hails U.S. exceptionalism before veering into darkly political speech to usher in America's 250th anniversary
The president's speech capped an Independence Day eve otherwise most notable for a brutal heat wave that gripped much of the eastern part of the country.
Think 8 Hours of Sleep Is the Best for Your Cognitive Health? A New Study Suggests Otherwise
Research reveals a hidden genetic link that dictates exactly how your nighttime routine impacts your brain.
Curt Cignetti talks like college football's alpha, but Indiana's schedule says otherwise
The reigning national champion Hoosiers are not scheduled to face a Power Four opponent or Notre Dame in non-conference until 2030
Apple Watch Series 12 to Get Fitness Tracking Upgrades
Apple's new Apple Watch models will feature upgrades to fitness tracking, according to Bloomberg's Mark Gurman, who otherwise characterized this year's changes to the lineup as "minor". Apple's new Apple Watch models will feature upgrades to fitness tracking, according to Bloomberg's Mark Gurman, who otherwise characterized this year's changes to the lineup as "minor".
Bickering Over Healthcare Coverage Disrupts House Panel Hearing
Finger pointing over the number of Americans losing health insurance roiled what otherwise would have been a technical House hearing over employee health coverage.
Apollo 15 astronauts collected tiny green glass beads from the Moon in 1971; more than 35 years later scientists found evidence of water inside, changing what we knew about the lunar interior
In 1971, Apollo 15 astronauts David Scott and James Irwin noticed something unusual on the Moon: tiny green flecks scattered across the otherwise grey surface. They collected samples and brought them back to Earth, unaware that the material would decades later challenge a long-held belief about the Moon.
What Does Your Office Say About Your Business? by Jennifer Kraner
What does your space say about you and your business? More importantly, does it give you the best possible first impression? The minute a client, customer or patient walks through your door, they’re forming an opinion about your business – sometimes consciously, sometimes without even realizing it. Your space is communicating something about you, whether you intend it to or not. At Big Sky Design, we work with all kinds of commercial businesses, including many medical practices, law firms and other corporate offices, and I’ve seen first hand how much thoughtful design can influence that first impression. For businesses in medical, legal, and financial fields, three things are particularly important: trust, privacy and professionalism. Your space should communicate all three while also reflecting who you are as a business. That can be challenging, especially in a new construction project. Often, so much of the budget goes toward the actual construction that things like lighting, comfortable seating, artwork and finishing touches get pushed to the side. But those are often the elements that make a space feel complete – and memorable. Think about your waiting room. Is it comfortable? Is the lighting welcoming? Does the layout make sense? When someone walks through the door, do they immediately know where to go and whom to speak with? In a medical office, there’s another important consideration: privacy. Reception areas can become surprisingly noisy places, with phone calls, conversations between staff and patients, and people discussing personal information. Hard surfaces throughout a space allow sound to bounce around, making it difficult to maintain privacy. We recently worked with an eye doctor's office that had exactly this problem. The reception desk had a large opening, and staff were making and taking phone calls right next to patients who were waiting. We designed an acoustic treatment that acted as a divider between the staff and waiting areas. It absorbed sound, but it also became a branded design element. It was a visual reminder to everyone in the space to be mindful of their voices and what others might be able to hear. Those kinds of thoughtful details can make a tremendous difference. And good commercial design doesn't have to mean making your space flashy. Sometimes it’s as simple as a calming color palette, beautiful artwork, plants, carefully selected lighting or comfortable upholstered furniture. In a medical setting, those touches can help soften what might otherwise feel clinical and reduce anxiety for patients who are already nervous about an appointment. I’ve been in waiting rooms where everyone feels packed together, and you can hear every conversation – even what’s happening in the restroom next door. That’s not the experience you want to create. Your office should also feel like your business. Are you telling people that you’re clean and modern? Sophisticated and unique? Warm and welcoming? Or, unintentionally, are you telling them that your business is outdated? You don’t necessarily need a complete renovation to change that message. Fresh paint, updated lighting, new artwork, better seating or a few branded design elements can make a space feel current and intentional. At Big Sky Design, we’ve worked with radiology, dermatology, and dental practices, among many others. Every practice is different, but the goal is always the same: creating a space that reflects the business behind it and gives every person who walks through the door a positive first impression. Because your office is part of your brand. Make sure it’s telling the story you want people to hear. Read more at bigskydesignonline.com.
Think you need 10,000 steps a day? Science says otherwise
The 10,000-step goal came from a 1960s ad campaign, not science. A new study reveals the real number you need for major health benefits
Think you need 10,000 steps a day? Science says otherwise
Bodog reports that the 10,000-step goal originates from a 1960s marketing campaign, with research suggesting 7,000 steps may provide significant health benefits.
KSU, Technovative AI sign pilot agreement with Moultrie Police Department for AI-powered behavioral health detection tool
Technovative AI, Inc. and Kennesaw State University today announced a pilot agreement with the Moultrie Police Department to deploy CaseFinder, an AI-powered behavioral health detection platform that analyzes police incident reports and 911 call transcripts to identify individuals in behavioral health crisis who would otherwise go undetected.
The Science Behind the AI Panic Is Shakier Than You Think
A column by Sascha Lobo Is AI making us dumb? So asked DER SPIEGEL on its cover a while ago. After a few weeks of recovery and a therapeutic intervention with the help of my AI, I feel able to respond. That’s a bit unfair, of course; the article itself is more nuanced than the loud cover, the image of a schoolkid with dull prompt questions, or the subtitle: “AI is conquering the schools, and educators fear a disaster.” The short answer to “Is AI making us dumb?” is: no. The long answer is more interesting, and even carries a preliminary glimmer of the spectacular. Unfortunately, it requires a detour into the sphere of artificial intelligence, into the mogul-field terrain of technology assessment, the attention economy, and the destructive virus of cultural pessimism. Along with narrowing the scope to AI’s effects on education and work rather than on intelligence. Because the honest version of the question would be: Does AI help us learn and work, or not? Generative AI, which set off the still-ongoing hype with ChatGPT, has only existed in relevant distribution since late 2022, early 2023. For a technology-driven transformation of people and society, that is extremely short. For that reason alone, research is still in its infancy. And technology assessment is not a fast discipline anyway. Prominent voices from Elizabeth Eisenstein to RAND to Tom Wheeler say we still cannot conclusively judge the consequences of the printing press. Its invention is a mere 580 years back, and some scholars would rather not circulate assessments that might have to be retracted in two or three hundred years. We’re Going Through Growing Pains A phenomenon that already caused upheaval in social media research makes serious assessment of AI’s consequences even harder: the “moving target” problem of LLM-ology, as the linguist Sean Trott called it in 2025. AI develops so quickly and so unpredictably that researchers often end up studying snapshots of the past. There are concepts, from morphological analysis to anticipatory governance, for keeping research, planning, and regulation halfway meaningful anyway. But even with those, universally valid statements are rare. DER SPIEGEL - The German View is a reader-supported publication. To receive new posts and support our work, consider becoming a free or paid subscriber. Subscribe We should therefore treat findings about the interplay between humans and AI as far more procedural and situational. Unfortunately, this challenge comes at the worst possible time. We are going through growing pains: AI’s everyday impact has grown very large very fast, and with it the longing for guidance. Annoyingly, the body of solid knowledge has not exploded along with it. Experts cannot be moved to broad consensus on even banal statements about AI. Sascha Lobo, born in 1975, is an author and strategy consultant focusing on the internet and digital technologies. Together with Jule Lobo, he explores the debates of the day in the podcast “Feel the News – Was Deutschland bewegt” (”What Moves Germany”). Nvidia CEO Jensen Huang says software developers are dying out. Bill Gates considers coders one of only three professions that will survive the AI tsunami. Top tech economists find virtually irrefutable arguments both for and against an AI bubble. The same naturally applies to the structurally youth-hostile debate over whether AI is good or bad for young people. The gap between the demand for insight and the supply of solid knowledge creates an orientation vacuum. This vacuum of AI cluelessness is filled, as the attention economy described by Georg Franck in 1998 would have it, with half-knowledge (funnily enough, often AI-generated), marketing, and self-marketing. After all, infinite money is at stake, along with academia’s best current shot at global fame. But this iridescent froth of interpretation operates more by the algorithmic principles of the digital public sphere than by seriously earned insight. That is why dramatized, fear-feeding claims about AI are so promising. It tempts parts of academia into distorted drivel, and parts of the media then sensationalize the published drivel once more. Was the Research Aimed at a Desired Result? How badly this can influence academia and public perception is demonstrated by one of the most-discussed studies on AI’s effects: “Your Brain on ChatGPT.” The MIT study by Nataliya Kosmyna also gets plenty of space in the SPIEGEL cover story. What was measured and interpreted: lower brain activity in people who write with AI assistance, compared to people who only google or write without any tools. As is common with preprints, the study has not yet been peer-reviewed, and it contains substantial weaknesses that experts have sharply criticized. The first round had 54 participants in three groups, the second only 18, who reportedly also came from MIT’s own orbit. There was no preregistration (committing in advance to what you want to find out with which data). In their analysis, Stanković et al. even discovered an opposite effect that the study does not further explain. Some measurement data has not (yet) been published. And the EEG analysis method used is considered too imprecise to support such far-reaching conclusions. The biggest criticism, though, is the suspicion of an activism-driven study. In an interview with Time, lead author Kosmyna said she published the study immediately, rather than after full peer review, because she feared politicians might otherwise decide to put ChatGPT in kindergartens. She considers that absolutely harmful. It is legitimate to hold such a view. It just seems unscientific to approach a study with it, and then to design the setup so that the chance of a fitting desired result remains as high as possible. Ideologies Collide It is not easy to separate genuine AI insights from studies that trivialize and weaken well-founded AI criticism. The same goes for the opposing, AI-enthusiastic narratives, because the pro-AI side works against it with billions in resources. Ideologies collide, fed by egos, PR strategies, moral posturing, and once-in-a-lifetime opportunities. Doomsday marketing makes the search even harder: AI companies can profit from warnings about the dangers of AI, whether through the notoriety of apocalypse narratives or because, in some target groups, danger translates into power. Some seemingly negative findings are therefore quite welcome in the AI industry. If you try to get at the findings presumably less influenced by interest groups, the picture in education seems relatively clear: AI can improve educational outcomes when the right AI is used the right way, namely pedagogically. Simply dumping an AI chatbot into a classroom, by contrast, accomplishes little. Huge surprise. But something else is also obvious: whether they are allowed to or not, kids use AI for school, which is why the controlled integration of AI tools into education is, in the medium term, entirely without alternative. In the world of work, the landscape of findings is more complex and requires more differentiation. A series of serious studies points to an AI effect that suits neither the alarmists, regulation fetishists, and AI opponents, nor the trillion-dollar corporations with their armada of paid scientists, PR professionals, and ecosystems: working with AI helps the inexperienced (at least for now) considerably more than it helps the pros, in certain fields of work, and provided they know how to use it correctly. Missing Domain Knowledge Among the experienced, the professionals, and the high flyers, by contrast, despite frequent time savings, partially negative effects and new problems are emerging. Take the jagged frontier problem: AI solves task A brilliantly and fails completely at task B, even though both look similar even to professionals. Or the verification-cost problem, in which AI’s greater productivity gets eaten up by the enormous effort of checking its output. Or the illusion of competence, because AI makes new domains seem easily penetrable, until you painfully discover that deep domain knowledge is missing. Taken as a whole, the effects of working with AI do lean positive. But the promise that AI works as a turbocharger for everyone and boosts practically everything simply refuses to materialize. The AI magic the world has been feeling since ChatGPT arrived in late 2022 has so far barely shown up in most companies’ numbers. The consulting firm McKinsey has coined a term for this, the “gen AI paradox,” and believes that work processes and corporate structures would have to be rebuilt quite fundamentally to realize AI’s benefits at scale. Reuters just reported that only 3.3 percent of Microsoft’s customers have subscribed to the AI package Copilot. That may be down to the product; in other areas we continue to see soaring successes and new AI triumphs. But after the first quarter of 2026, disillusionment is the word for all too many AI projects at traditional companies: because the promised revolution of AI agents has so far failed to arrive or been postponed, and because of the leveling effect that many of the studies cited here suggest. Against Inequality The societal effects, on the other hand, appear rather encouraging. Many previous technologies operated on the Matthew principle, named for the Gospel verse about giving to those who already have: they tended to reward the more competent and more privileged. AI, however, seems more often to have an equalizing effect once a threshold of access and education is crossed. Some anecdotal insights point in this direction, for example, how an unemployed man in Leipzig successfully defended himself in court in 2025 against fraud accusations from Germany’s federal employment agency — without a lawyer, armed only with ChatGPT. The AI made mistakes and cited invented court rulings, but in the end the man walked away unpunished. And a key capability of artificial intelligence is its capacity to learn: set up correctly and fed with data, it can keep getting better. It is a question of time, or more precisely of AI innovations and training data, until even free AI chatbots write better legal briefs than law graduates with perfect exam scores. Whether that will make anyone dumb is something the world’s press will surely inform us of in due time. Subscribe Leave a comment
Jobs in health care. Jobs for women. The US labour market is segmenting. Chernobyl’s radiation and Lenin’s love of literature.
Thank you for opening your Chartbook email. Elmer Bischoff, Two Figures at the Seashore, 1957 The only really dynamic job-creator in the US economy is health care: The largest crunch has come in Federal government, which has shed 2.7 m jobs. Source: tchouvkov on Twitter Tech is struggling. [ Joey Politano 🏳️🌈@JosephPolitano The US tech sector continues bleeding jobs in official data released today—overall, employment decreased by 17k last month and is down 69k over the last year That's nearly as bad as the worst of the 2024 tech-cession, and much worse than the 2022 boom or even pre-2020 norms 12:42 PM · Oct 2, 2026 · 15.2K Views 15 Replies · 47 Reposts · 390 Likes ](https://x.com/JosephPolitano/status/2106001807847940232?s=20) The new jobs are all for women. [ Justin Wolfers@JustinWolfers I'm not sure who saw this coming, but so far nearly all the jobs created during the second Trump administration have gone to women. 1:09 PM · Oct 2, 2026 · 33K Views 70 Replies · 146 Reposts · 644 Likes ](https://x.com/JustinWolfers/status/2106008723533533601?s=20) Meanwhile, real wages are being eaten up by modest over-target inflation. [ Heather Long@byHeatherLong The financial pain is real: Wage growth fell to 3% (y/y) in September. That's the lowest in 5 years. Wage growth: 3% Inflation: ~3.4% Inflation has eaten up all wage gains for the average worker since April. Many people are having to make hard choices about what to buy and what … 1:24 PM · Oct 2, 2026 · 37K Views 24 Replies · 275 Reposts · 533 Likes ](https://x.com/byHeatherLong/status/2106012367712620728?s=20) HEY READERS, THANK YOU for opening the Chartbook email. I hope it brightens your day. I enjoy putting out the newsletter, but tbh, what keeps this flow going is the generosity of those readers who clicked the subscription button. If you are persuaded to click, please consider the annual subscription of $60. It is both better value for you and a much better deal for me, as it involves only one credit card charge. Why feed the payments companies if we don’t have to. Subscribe Elmer Bischoff, Bathers, 1960 Americans are disillusioned with higher ed. For contributing subscribers only. Subscribe Where the Chernobyl radiation went. h/t Simon Kuestenmacher aka simongerman600 How Lenin’s love of literature shaped the Russian Revolution - Tariq Ali This, then, was the intellectual atmosphere in which Lenin came of age. His father, a highly cultured conservative, was the chief inspector of schools in his region and much respected as an educationalist. At home, Shakespeare, Goethe and Pushkin, among others, were read aloud on Sunday afternoons. It was impossible for the Ulyanov family – “Lenin” was a pseudonym adopted to outwit the tsarist secret police – to escape high culture. At high school, Lenin fell in love with Latin. His headteacher had high hopes that he might become a philologist and Latin scholar. History willed otherwise, but Lenin’s passion for Latin, and taste for the classics, never left him. He read Virgil, Ovid, Horace and Juvenal in the original, as well as Roman senatorial orations. He devoured Goethe during his two decades in exile, reading and rereading Faust many times. Lenin put his knowledge of the classics to good use in the time leading up to the October revolution of 1917. In April of that year, he broke with Russian social-democratic orthodoxy and, in a set of radical theses, called for a socialist revolution in Russia. A number of his own close comrades denounced him. In a sharp riposte, Lenin quoted Mephistopheles from Goethe’s masterwork: “Theory, my friend, is grey, but green is the eternal tree of life.” Source: The Guardian Elmer Bischoff, The River, 1953 If you’ve scrolled this far, you know you want to click: Subscribe
First rule of politics: Make no enemies
Politics is often described as the art of winning. History suggests otherwise. The greatest political leaders understood that success is not measured merely by defeating opponents but by preserving relationships. Elections are won through competition. Governments are sustained through coalition. That is why the first rule of politics is remarkably simple: Make no unnecessary enemies. […]
In New York, a growing Latino population seeks greater political clout
Outside of lieutenant governor, no Latino or Latina has otherwise held statewide office, nor been elected mayor of New York City.
Scrolling for science: How a Twitter post discovered a new wasp in Fukuoka, Japan
Researchers at Kyushu University recently identified a wasp species, Eupelmus curvator, in Japan for the first time after citizen scientists posted photos of it on Twitter (now X), marking both a new national record and the first formal description of the male of the species. The discovery highlights the growing role of social media in biodiversity research, allowing scientists to tap into observations from the public that would otherwise go unnoticed.
Yale scientists found a hidden network inside the eye
Researchers have discovered that the retina uses an unexpected communication network that lets separate visual pathways cooperate instead of working alone. A newly identified "commander" cell appears to coordinate this system, helping the eye detect faint details that might otherwise be missed.
Meta wants its AI glasses to seem less creepy. Its AI strategy says otherwise.
Meta is adding a new safeguard to stop people from secretly recording others with its AI glasses. But the update comes as the company continues to expand how much personal data its AI products collect and use.
Domestic Abuse Allegations Rock Max Miller’s Ohio House Race
Representative Max Miller, a Republican, is facing accusations of abuse from his ex-wife in a bitter family feud that might put his otherwise safe seat in play.
Ukrainian defence tech seeks new ways to scale up
Since Russia's full-scale invasion in February 2022, Ukraine's defence sector has expanded several-fold, becoming a significant source of military innovation, especially in unmanned systems, counter-drone technologies, electronic warfare and AI-driven combat systems. But finding the funds and personnel to scale up and to reach international markets remains a challenge.Spurred on by the demands of the front-line, Ukrainian defence companies say they can develop a minor component in a couple of weeks, or major hardware or software solutions within a year. This is several times faster than the usual development cycles in the sector. Tencore, founded in 2024, is now one of Ukraine’s largest providers of unmanned ground vehicles, well exceeding the production of NATO members in this category. Odd Systems, a Ukrainian combat cameras developer founded in 2023, launched its first thermal camera in 2024. Less than two years later, it is developing the fourth generation of the product, one that has evolved into a full vision system.According to Michael Singer, managing partner at Verne Capital, a venture capital fund supporting defence companies in Europe, this speed is down to the short feedback loop between the developer and the end user. “Ukrainian companies develop technologies in close interaction with soldiers and military units, test them under real combat conditions and rapidly incorporate feedback into the next generation of the product,” he says. “Sometimes we need to react to feedback and deploy an upgrade within hours,” says Polina Tymchenko, the spokesperson for Himera, which develops electronic warfare-resistant walkie-talkies. “It means that, as a company, you have to have a higher tolerance for risk, and also design your products in a way that fosters those quick changes.”Getting a product deployed by a combat command involves no paperwork other than a purchase order, says Perry Boyle, co-founder and chief executive of Mits Capital, a Ukrainian-US investment group focused on attracting capital for Ukraine's defence tech sector. “New weapons can be developed and deployed based largely on handshakes.”Nevertheless, the speed of development is largely subject to the level of funding, says Iryna Sukhomlyn, head of business development at RMachine, a developer of unmanned ground systems. “Financial support needs to start much earlier, particularly at technology readiness level 3-5, not only when most of the technological risk has already been removed,” she says.In 2025, Ukrainian defence tech companies attracted about $129 million in investment, with the true figure likely higher given the share of undisclosed security-sensitive deals, according to a report published by the Kyiv School of Economics. These investments were primarily concentrated in the AI and software segment, as they are considered a lower financial risk.Scaling production is a major barrierAccording to Iryna Supruniuk, business development director at Odd Systems, proving a defence product in Ukraine is not the end of the development process, but the beginning of a continuous evolution, since operational requirements change rapidly. “Continuous iteration remains our highest priority,” she says.It's also when production must scale up, which represents a major hurdle. “Scaling production requires capital, manufacturing capacity, components, certification, procurement access and reliable supply chains,” Singer says. Scale-up is also hampered by demographic pressures, especially a shortage of young specialists needed to expand engineering teams. Companies have reported struggling to replace specialists lost due to Russian attacks, mobilisation and emigration. In order to meet this scale-up challenge and finance arms production, begun allowing defence technologies to be exported, assuming it has an excess capacity. It has also announced the creation of ten defence and weapons export centres across Europe by the end of 2026.According to Singer, a technology that has demonstrated its value under real combat conditions has a fundamentally different credibility than a product that has only been tested in a laboratory or training environment. But combat validation alone doesn’t automatically create an international business. “Companies still need access to foreign defence ministries and procurement organisations, international certification, financing for production scale-up and, in many cases, local industrial partners,” he says.Already in 2026, the EU has signed several agreements with Ukraine that aim at joint defence technology development and production. These include allowing Ukraine to join the European Defence Fund, signing a financing agreement under the European Defence Industry Programme, and setting up the EU-Ukraine Drone Alliance.For Singer, it is important that European defence companies don’t just see their Ukrainian partners as technology suppliers, but rather as potential innovation partners. “Ukrainian companies bring speed, operational experience and an exceptional ability to iterate. European partners can provide industrial capacity, capital, established supply chains and access to European procurement markets.”Procurement possibilitiesWhen it comes to exports, the procurement and compliance processes involved require time, taking months or even years, and money. In other words, resources that small defence tech companies need if they are to improve and scale up their technologies. According to Sukhomlyn of RMachine, if the priority is to rapidly increase available defence capabilities, then the processes need to reflect that urgency. “If Europe wants defence innovation at wartime speed, its administrative processes also need to operate much closer to wartime speed,” she says, highlighting a need for support with certification, market access and mutual recognition of relevant testing. “Otherwise, we risk having excellent technology available but spending years making it administratively eligible to be purchased.”Related articlesRussia widens targeting of European research centresEU must support research managers in central and eastern Europe, says Visegrad fund headThis is a lesson Europe’s defence sector would do well to learn, Singer says. “Defence innovation needs to be organised around the speed and requirements of the end user, not around the speed of the bureaucracy.”Supruniuk says the European defence sector should rethink the balance between quality and quantity. “In many cases, a good-enough system available in thousands of units creates greater operational value than a perfect system available in dozens.”“For decades, everyone has been looking for that all-powerful, exquisite system that can protect everyone,” adds Tymchenko. “We see in crime that only scalable, dedicated, affordable solutions can actually defend democracies.”
Trump hails US exceptionalism before veering into darkly political speech to usher in America 250
The president’s speech capped an Independence Day eve that was otherwise most notable for a brutal heat wave the gripped much of the eastern portion of the country.
Dinosaurs became giants — so why did they never become tiny?
Dinosaurs conquered the land at enormous sizes, but strangely, they almost never evolved into truly tiny animals. New research suggests that physiology alone can’t explain this missing miniature end of the dinosaur spectrum—and that early mammals may have been partly responsible. While dinosaurs kept mammals from becoming large, small mammals may have returned the favor by occupying ecological niches that could otherwise have supported mouse-sized dinosaurs.
I thought I'd never pay $1,000 for a Googlebook, but Google convinced me otherwise
An Android laptop at that price sounds ridiculous until it doesn't.
Why Twins Must Re-Sign Veteran Hitter
Josh Bell provided a beacon of light during an otherwise disappointing season for the Minnesota Twins, hitting .255 and knocking in 96 RBI.
Saber's newest game trailer takes a shot at Gearbox's CEO, if you look closely
The first hint that something might be up with the recent debut trailer for Saber Interactive’s Rideshare Stimulator is an unusual choice of words—aside from that second one in the game’s title. “Don’t take your passengers too seriously,” the trailer’s narrator says, as he describes this upcoming game. It a car game about the gig work of driving eccentric people to their destinations, a riff on Crazy Taxi. And then, he says: “When I think I’ve met the randiest of passengers, someone even stranger gets in.” “Randiest”? That suggests “randy,” which usually means having strong sexual desire. Yet none of the oddball backseat occupants featured in the trailer seem particularly lustful. One is an old man who considers his grandson a failure, another shares the theory that birds might control traffic lights, another says he’s banned from an aquarium. A little later, there’s a license plate, shown 63 seconds into the trailer, on the front of an SUV: USB DRV It’s a Texas plate. The symbol between those letters is a cog wheel. If you know, you know. That’s a reference to Texas-based Borderlands game development studio Gearbox Software (their logo features a cog wheel). More specifically, it’s a reference to Gearbox’s CEO Randy Pitchford, who in 2018 found himself explaining the nature of video he’d saved on a USB drive that was found at a Medieval Times restaurant before being returned to the studio: “It was ‘barely legal’ porn. This girl’s handle was ‘Only 18.’” The “girl” in the video had committed a solo sex act that Pitchford, an avid practitioner of magic, found remarkable. “I realized, this is not a sex worker…This is a fucking magician.” Back then, the USB drive was central to competing lawsuits against Gearbox by the studio’s former lawyer and by Gearbox against him. A settlement in late 2019 “exonerated” Pitchford, per the Hollywood Reporter. The USB story has resurfaced at times, now in a trailer from a company that not long ago was corporate brethren with Gearbox. Saber vs. Gearbox The head of Saber told Game File that the reference is payback. “The dig is because Randy took shots at Embracer in Borderlands 4,” Saber CEO Matt Karch told me when I asked him about the license plate. “Without Embracer, Randy is performing magic tricks at birthday parties for a living.” Having interviewed Karch during a cross-country flight in late 2024, I can confirm that Karch was unimpressed with Pitchford during their time both working in the Embracer group. I’d approached him not out of surprise that there was animosity but that it was manifesting publicly now. Gearbox was founded in 1999, worked on a port of Halo early on, and developed hit series such as the loot-based sci-fi shooter Borderlands before it was purchased by The Embracer Group in early 2021 for $363 million (with a possible earn-out of over $1 billion, if certain targets were hit by 2027). Saber was founded in 2001, worked on a remaster of Halo early on, had a big hit with zombie shooter World War Z and was purchased by Embracer in early 2020 for $150 million, with a potential earn-out of up to $375 million. For a few years, the Embracer group was a voracious acquirer of game studios. Then, as investment dried up, it choked. In March 2024, Embracer sold Saber to an investment company controlled by Saber CEO Matt Karch, and it sold Gearbox in an all-stock deal to long-time Borderlands publisher Take Two Interactive. Other studios were shut down. Hundreds of developers were laid off. Gearbox didn’t say much about their Embracer experience, nothing too direct, at least. In the summer 2024, Pitchford told reporter and occasional Game File contributor Nicole Carpenter that Borderlands 4’s story was in some way thematically connected to Gearbox’s experience at Embracer: “There’s this cultural and emotional shift in me, personally, and at the studio. What does it mean to trade some autonomy for organization?” When Borderlands 4 came out, there was something stranger: the matter of “Embracer’s Bluff,” a location in the game featuring an AI-powered toilet and a large trash dump, according to a detailed Kotaku account of the place. Gearbox never commented on it; Take Two never got back to me about it when I asked them last year. By contrast, since Saber exited Embracer, Karch has been one of Embracer’s biggest defenders, casting their management as well-meaning and supportive despite the layoffs and studio closures that resulted from their excessive expansion. Like others, Saber’s Karch saw Embracer’s as a shot. With Rideshare Stimulator’s trailer, he’s returned fire. “It’s amazing to me how fast people forget those who went above and beyond for them,” Karch told Game File. A rep for Gearbox did not comment by press time. Game File is a reader-supported publication. Your backing makes my reporting—including any coverage of trailer-based beefs—possible. Good news! I’m currently running Game File’s first big sale of 2026. You can get a one-year subscription for 23% off. Click here to take advantage of the limited-time offer. And thank you for your support! Item 2: In brief… 🚫 Wal-Mart has laid off the staff of Restart, a games media outlet launched in 2024, Aftermath reports. 🟥 A GTA VI preview is on the way, at last, via an “extended look” that will premiere, unusually, on Netflix at 3pm ET on August 27, then officially go live elsewhere six hours later. Last week, Game File noted that Take Two label Rockstar Games is overdue for showing off GTA VI, based on the timing of the initial hands-off previews of GTA V and Red Dead Redemption 2. During a call with investors, Take Two CEO Strauss Zelnick said pre-orders for GTA VI were “unprecedented and astonishing,” but added the company can’t predict how many of those pre-orders represent bonus sales vs. sales that otherwise would have been made at launch. During the same call, Zelnick was noticeably bullish on the future of playing games over streaming cloud connections (predicting wider adoption in three or so years). He sounded unbothered by Sony’s plans to stop making discs for new games (he said the business is 90% digital already and predicted that there could still be limited edition physical editions of games “in the same way there’s still vinyl in the recorded music business.”) 👀 Take Two wrote off $43 million in their spring quarter after deciding “not to proceed with further development of an unannounced title in its pipeline from a third-party developer.” 🤔 Some rare age-demographics from a big publisher: Capcom recently told investors that “users in their 20s account for approximately half of the users of both Pragamata and Resident Evil Requiem.” Item 3: The week ahead Tuesday, August 11 Game releases: The Elder Scrolls IV: Oblivion Remastered (Switch 2; already out for PC, PS5, Xbox) continues the Bethesda strategy of putting Elder Scrolls games on everything; Royalty Free-For-All is a Smash Bros.-style fighting game featuring public domain characters such as Mother Goose brawling with Oliver Twist in a stage based on the original Little Shop of Horrors movie. Wednesday, August 12 Game release: Pro Jank Footy (PC, console) is an arcade-style Australian Rules Football game with increasingly ridiculous rules, the more you play (I enjoyed the Steam demo) Thursday, August 13 Game release: Super Mario Sunshine (Switch 2, originally released in 2002 on the GameCube) is released for Switch 2’s Nintendo Switch Online service. This game is the black sheep of 3D Mario games. It’s the successor to the breakthrough Super Mario 64 and the rare Mario game built around a single (vacation resort) theme. Mario’s got a water-spraying backpack, a Yoshi pal and a lot of blue coins to collect.