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Yale School of Medicine On the Science of Childhood Anxiety, Say, Anxiety Disorders Are the Most Common Mental Health Challenges In Youth
Credit: Matt Bradbury October 5, 2026 - By Isabella Backman - Anxiety disorders are the most common mental health challenges in youth. Left untreated, they can disrupt the lives of both children and their families. The good news is that with intervention, most children will overcome these conditions. Across Yale School of Medicine, scientists are working to understand anxiety at every level, from common anxious behaviors to molecular biomarkers in the brain. They are using their findings to develop and disseminate new evidence-based treatments for childhood anxiety. Today, the go-to treatment for childhood anxiety disorders is cognitive behavioral therapy (CBT). This is a form of psychotherapy that teaches children skills for confronting anxious thoughts and helps them practice managing situations that cause anxiety. While it is the gold-standard treatment backed by considerable scientific evidence, up to 50% of children will not see sufficient benefit from CBT alone. Wendy Silverman, PhD, Alfred A. Messer Professor in the Child Study Center, co-directs the Anxiety and Mood Disorders Program alongside Eli Lebowitz, PhD, associate professor in the Child Study Center. One of their main interests is learning how to improve outcomes beyond CBT. “We’re developing cutting-edge, research-based knowledge that can reduce the suffering that anxiety causes children and their families,” says Silverman. Changing parent behavior YSM experts are investigating how to improve CBT outcomes for children by involving the parents. Silverman’s research has uncovered two main components of parent intervention that improve childhood anxiety: not enabling children to completely avoid the things that make them anxious and taking steps to enhance their autonomy. Parents may unintentionally reinforce their child’s anxiety by allowing them to avoid what makes them anxious. They also may unintentionally limit the children’s sense of autonomy by being overly intrusive. “One of the things that we take pride in is that we’ve focused on trying to understand what parents need to do, and to test our ideas and see if changing the parents’ behaviors is enhancing the effects of CBT,” Silverman says. “Our findings thus far suggest that they do." As much as CBT can be helpful, it can also be challenging to get one’s child to do the necessary steps for CBT to work. This is one important reason why Lebowitz created a novel intervention program for parents named Supportive Parenting for Anxious Childhood Emotions (SPACE). Existing intervention programs often teach parents to act as therapists for their child, such as training them on how to practice breathing exercises. “It makes sense, but this doesn’t work very well in practice,” says Lebowitz. “And part of the reason, I think, is that it’s not easy to get your kid to do things, especially if they’re not excited about doing it.” SPACE is unique because it focuses entirely on parent involvement and changing parental behavior—children are not involved in the sessions. For example, parents learn how to use supportive messages that convey confidence that their child can tolerate and manage distress. “It might sound like a parent saying to a child, ‘I get that this is really hard for you, but I know that you can handle it,’” Lebowitz says. The program also works with parents to identify the ways in which they are accommodating anxious behaviors. “We’ll pick one thing that parents are doing that is an accommodation and help them make a detailed plan for how they’re going to change that,” Lebowitz explains. Treating Anxiety in Children and Adolescents Much of the existing research on SPACE has focused on parents of children under age 14. Ongoing studies are now exploring its use with parents of older teens. “Both anxiety and accommodation can interfere with normative developmental shifts during the teen years, including increased autonomy and individuation from parents,” says Rebecca Etkin, PhD, associate research scientist in the Child Study Center. “In this sense, SPACE may be particularly well-suited for anxious teens and their parents. We want to see whether it is effective during this distinct developmental window.” Unfortunately, many families in need of CBT face barriers, including limited availability of services and geographic constraints. One way to improve access is digital interventions. Silverman and her team are developing an evidence-based digitized intervention program for parents that they can access directly from their computer or smart phone. “The work we’re doing will be one of the very few parent interventions that is digitized and importantly tested for effectiveness,” she says. Creating interventions for anxiety-related challenges Silverman and Lebowitz recently received a generous gift to develop and test other interventions that help parents reduce other child anxiety-related behavioral and emotional difficulties, such as restrictive eating and unexplained somatic symptoms. One of these projects in particular focuses on understanding the risks of social media and its potential benefits, including offering a sense of connection. “A lot of research has focused on the idea that social media is harmful and the less of it the better,” says Lebowitz. “But we actually see is that this is not quite right—there are negatives, but there are also positives. So, we’re trying to get a little bit more nuanced of a picture.” The team is working with teens with social anxiety and their parents over an extended period to better understand their social media habits and how their anxiety levels affect engagement with different platforms. The teens will answer questions sent to an app on their phones about mood and social media usage in an effort to capture emotional responses as they unfold. “We hope to develop interventions that can help make the best of social media while protecting against the more problematic effects,” Lebowitz says. The brain’s role in anxiety Researchers are also testing therapies to address anxiety at the neurological and molecular levels. In collaboration with Dylan Gee, PhD, professor of psychology in Yale's Faculty of Arts and Sciences, and Hilary Blumberg, MD, John and Hope Furth Professor of Psychiatric Neuroscience at Yale School of Medicine, Lebowitz and Silverman are using brain imaging to identify changes in a child’s brain before and after parents enroll in SPACE. In one study, children viewed anxiety-inducing images while undergoing magnetic resonance imaging (MRI); the researchers conducted scans with and without the children’s parents nearby. Before SPACE, children’s brains were better able to activate the regulatory circuitry needed to turn off an anxious response when their parents were nearby. But after SPACE, children could activate this circuitry even without the parent around. “As parents work on gradually reducing some of their accommodations, you can see a child get better at regulating their own fear system in the brain,” Lebowitz says. “It’s remarkable.” Research into the molecular underpinnings of anxiety can also inform new childhood anxiety treatments. In a recent study, Lebowitz, Silverman, and Flora Vaccarino, MD, Harris Professor in the Child Study Center, discovered that a protein known as fibroblast growth factor 2 (FGF2) could be protective from the toxic effects of stress. FGF2 levels in children and their parents were linked to the children’s risk of developing anxiety and depression, the study showed. The findings could help explain why some children who are exposed to early life trauma such as poverty or abuse are more prone to developing mental health disorders. And FGF2 could be a target for treatment. “This is very novel because it starts to give a clue to what is the molecular chain that connects those lived experiences to the actual psychological outcomes,” Lebowitz says. New ways to study and treat childhood anxiety Michael Crowley, PhD, associate professor in the Child Study Center, collaborates with Silverman and Lebowitz to study the role that attention plays in development and maintenance of childhood anxiety. There is a large body of research that shows that anxious children, like anxious adults, are prone to focus on threatening stimuli such as angry faces compared with neutral faces. This has led to innovative interventions that involve attention retraining. The idea is that if experts train children to look away from threatening stimuli, it may reduce their anxiety—especially children with social anxiety who are most prone to attend to threats. The researchers have just completed a clinical trial for attention retraining with 10- to 14-year-olds with social anxiety disorders and are currently analyzing the findings. Besides this innovative attention retraining research, as Crowly notes, “I like to design tasks that are engaging to kids,” Crowley says. “My goal is to assess something without the kids knowing what we are assessing.” With this goal in mind, Crowley has created a virtual game, named Boom, to study threat processing in children with anxiety disorders. The game is similar to that of hot potato—the child and three digital players take turns virtually passing a ball to each other’s gloves. At some point, the ball turns into a bomb, and as the bomb lands in each player’s glove, the outline of the glove turns yellow. The goal is to avoid having the bomb go off in your glove. What isn’t obvious is that the game is designed to measure different types of threatening stimuli, both ambiguous and direct. In the game, the direct threat occurs as the bomb is traveling towards your glove. The ambiguous threat is the yellow outlined glove as it is unknown where the bomb will travel next. While children play the game, Crowley’s team measures their brain activity using electroencephalography (EEG). The researchers have discovered that in kids with anxiety, the brain reacts more strongly to ambiguous threats than it does in their peers, but less strongly to direct threats. They believe that the initial abnormally large brain response to not knowing where the bomb is going impairs the brain's ability to properly respond when the threat is actually traveling the child’s way. “The brains of children with anxiety are unable to differentiate between threats you need to do something about and those that are not your problem.” Michael Crowley, PhD, develops innovative games for studying anxiety in children and adolescents.Credit: Matt Bradbury Based on these insights, Crowley hopes to study whether interventions like attention training could restore appropriate threat processing. Crowley’s team is also working on a test called the Balloon Risk Avoidance Task, which evaluates risk-taking and avoidance behaviors. It features a digital balloon that is full of air and about to burst. Kids have six seconds to let air out of the balloon. The more air they let out, the less risk they face that the balloon will pop. But at the same time, the balloon becomes less valuable as it deflates. “The more air you keep in the balloon, the more points you get,” Crowley explains. So far, the researchers have found that when the balloons pop unexpectedly, teens with social anxiety show distinct brain responses. Studying these responses can help psychologists understand the underlying pathways driving the various manifestations of anxiety. The genetics of anxiety When Thomas Fernandez, MD, associate professor in the Child Study Center and of psychiatry, was still in training, there was a lot of excitement about understanding the genetics associated with autism spectrum disorder. Since then, scientists have identified around 250 genes associated with autism. The genetic contribution to anxiety disorders, on the other hand, has historically received less attention. Anxiety is less heritable than autism, but the genetic contribution for anxiety disorders that emerge in childhood may be higher than for anxiety disorders overall. While scientists estimate the heritability of anxiety disorders in general to be around 30% to 50%, some studies suggest higher heritability for certain childhood-onset anxiety presentations. “I saw firsthand how researchers in the field went about finding vulnerability genes for autism,” Fernandez says. “So, we’re using a very similar playbook for childhood anxiety.” “I saw firsthand how researchers in the field went about finding vulnerability genes for autism. So, we’re using a very similar playbook for childhood anxiety.” Thomas Fernandez, MD Associate Professor In the Child Study Center and in Psychiatry Now, Fernandez and Emily Olfson, MD, PhD, assistant professor in the Child Study Center, are studying children and their parents to identify genetic changes that are present in the child but not the parents, known as “de novo mutations.” “All of us have some de novo mutations—maybe about 50 to 100 sprinkled throughout the genome,” Olfson says. “But when they occur within the coding regions of genes, they can be really disruptive.” Previous research shows that people with autism have higher rates of damaging de novo mutations than the general population. In 2022, Olfson, Lebowitz, Silverman, and Fernandez published a study on 68 children and their families that found that children with anxiety had similarly high levels of these mutations. Studying de novo mutations can help point the researchers to where they should be looking for genes associated with anxiety. “The end game is using the genes to steer us toward the right part of biology that we can then target to come up with better treatments,” Fernandez says. Building on those findings, the team is now studying more families to better understand how these de novo mutations, along with other genetic and environmental factors, shape pediatric anxiety disorders. They recently received a National Institutes of Health grant to enroll 750 new families over the next five years. Olfson is the principal investigator, with co-investigators Lebowitz, Fernandez, Silverman, and Shuangge Steven Ma, PhD, department chair and professor of biostatistics at Yale School of Public Health. Neurodevelopment, Risk Genes, and Mental Health Conditions - The Olfson Lab Joel Gelernter, MD, Foundations Fund Professor of Psychiatry, is interested in how different anxiety disorders differ genomically. “We are studying social phobia, panic disorder, and generalized anxiety disorder, working to understand why people differ in their risk to these specific anxiety disorders and evaluating various risk factors,” he says. Gelernter was recently involved in one of the largest studies to date of the genomics underlying anxiety disorder. The study included around 122,000 people with anxiety disorders and around 730,000 without. It found that some genetic differences associated with anxiety were linked to signaling that helps reduce overactivity in the nervous system. Many anxiety medications available today work by targeting this process. “It’s nice when the biology that you get out of a large genome-wide study ratifies things that we already know because that can help increase confidence in further findings from the study,” Gelernter says. Uncovering the neurobiology of psychiatric disorders John Krystal, MD, Robert L. McNeil, Jr. Professor of Translational Research and chair of the Department of Psychiatry, is interested in the neurobiology of psychiatric disorders, including post-traumatic stress disorder (PTSD), of which anxiety is a major symptom. In the late 1980s, Krystal’s team discovered that the drug yohimbine activated symptoms in patients with PTSD. “This was the first time we could link a specific biological mechanism to the symptoms of PTSD,” Krystal says. At the time, he adds, this finding was highly controversial. “A lot of people felt that PTSD was a purely psychological syndrome that didn’t have an underlying biology.” Since then, Krystal has dived deep into understanding the neurobiology of PTSD to learn how to better treat it. His team is evaluating the use of ketamine and psychedelics. These would be among the first treatments to be developed specifically for PTSD. They may also be applicable for other disorders including anxiety disorders, Krystal adds. An important gap in PTSD treatment is the absence of biological tests that inform how to best help patients manage their PTSD symptoms. A doctor doesn’t diagnose cancer based on how they feel. Rather, they use tests of the biology of the cancer to determine the best course of treatment. “And we can’t do that in any psychiatric disorder other than Alzheimer’s disease,” Krystal says. “But now we are building a foundation for this approach to the treatment of anxiety disorders for the first time.” Source: Yale School of Medicine
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How Americans’ Financial Health Is Faring In The “K-Shaped” Economy
Hey all, Jason here. Money20/20 is still two weeks away, but I’ve already started packing (not procrastinating for once!). My calendar is already filling up with various sessions I want to catch, meetings, and of course happy hours, dinners, and other side events. If you’ll be in Vegas and want to catch up, let me know by replying to this email, and we can try to find a time amidst the chaos of the Venetian. A big if not totally unexpected piece of news dropped on Friday: the Independent Community Bankers of America, a trade group that represents smaller U.S. banks, filed a lawsuit against the OCC and Comptroller Jonathan Gould, arguing that the regulator’s move to grant trust bank charters to firms seeking to use them to conduct substantial non-fiduciary activities exceeds the authorities granted to the OCC by Congress. I haven’t had time to fully digest the legal filing or speak to folks in my network about it, but you can expect to see coverage and analysis on it in next week’s newsletter. Subscribe or Support by Upgrading The NerdCon agenda just dropped. It’s stacked. Partner content: Nubank built an AI-first bank. Chime built its own banking core. Figure and Valon are rebuilding the mortgage stack. At NerdCon, you’ll hear from the people behind those bets: what they chose, what they learned, and what they’re still figuring out. The agenda is live, with leaders from Nubank, Chime, OpenAI, Remitly, Mercury, Figure, Valon and more. Follow the mainstage conversations, bring the problem your team is wrestling with to a hands-on workshop, or pull up a chair at a roundtable. Pick your quests. Meet us in San Diego, November 18–20. Fintech Business Weekly readers save 20% with code FBW20. Explore the Agenda Things To Know & Other Good Reads Federal Reserve Board finalizes changes to enhance the transparency and public accountability of its stress test and reduce volatility in its stress test-related capital requirements (Federal Reserve Board of Governors) FDIC Announces Conclusion of Independent Monitorship (FDIC) Modernizing Financial Regulation: Initial Observations from eSLR (Fed Vice Chair for Supervision Michelle Bowman) The Data Version of Godzilla versus Kong: FRED Takes on AI (Fed Governor Christopher Waller) Financing the AI buildout (Brookings) Q3’26: Rules for Banks but Not for Crypto (Fintech Takes Banking) The Inevitability of Local Stablecoins (Lombard Notes) Delusions of AI Governance: The Human-in-the-Loop Comfort Blanket (Fintech Snark Tank) Stripe agrees to acquire Parafin to expand revenue opportunities for platforms and help small businesses grow (Stripe) Listen: Fighting Fraud in the Age of AI, with SEON’s Tamas Kadar (Fintech Business Weekly) How Americans’ Financial Health Is Faring In The “K-Shaped” Economy If there is one overarching theme across the economy and politics in the U.S. at the moment, it’s “affordability.” The term is vague enough to encompass a panoply of factors shaping Americans’ day-to-day lives: rising costs for the hallmarks of being “middle class,” including healthcare, housing, childcare, and education, inflation and rising interest rates, and a job market with the specter of AI hovering over it. At the same time, policy shifts under the Trump administration have resulted in reduced subsidies to those who get health insurance under the Affordable Care Act and new restrictions on qualifying for Medicaid and SNAP, the impacts of which have yet to be fully felt. These factors are contributing to declining consumer confidence and general dissatisfaction with the economy, despite continuing GDP growth and stock market records. This divergence is encapsulated in the idea of the “K-shaped” economy, in which a small proportion of Americans have seen their wealth balloon, while the majority of American households struggle to preserve the lifestyle they have. Perhaps the greatest determinant of which branch of the “K” a household is on is whether their wealth and income are primarily derived from employment vs. from assets. Nearly two decades of low interest rates and, more recently, elevated rates of inflation have benefited asset owners, while those whose income is primarily or solely derived from labor have largely seen real purchasing power stagnate or decline. This is reflected in recent Bureau of Labor Statistics data showing that labor share of U.S. GDP — the fraction of economic output that accrues to workers as compensation in exchange for their labor — dropped to just 52.8% in Q2 2026, the lowest since the BLS began keeping records. In 1947, approximately 2/3rds of economic output accrued to workers; even as recently as 2001, labor’s share of GDP was 64.1%. Against this backdrop, we’ve seen the rise of more credit and credit-like products: cash advance apps, “no-fee” overdraft, earned wage access, and buy now pay later, which are often used as small-dollar short-term borrowing mechanisms to meet immediate consumption needs or pay other bills (eg utilities, cell phone, other debt payments). With the markers of a middle class existence and, increasingly, basic financial stability seemingly out of reach for many, it should be no surprise there’s been an increase in “financial nihilism,” something industry peers like Alex Johnson and Frank Rotman have discussed and analyzed the roots of. The explosion of gambling and gambling-adjacent products and services — often marketed under the guise of being an “investment” — is inextricably intertwined with the rise of financial nihilism. Crypto, sports betting, and, more recently, prediction markets offer an escape or even hope of sorts, like a contemporary, digital version of a scratch-off lottery ticket, while leaving the overwhelming number of people who use such products worse off. Share of Households That Are Financially “Vulnerable” Ticks Up The Financial Health Network’s 2026 Financial Health Pulse® report adds context on how American households are faring. (I linked to this report in last week’s newsletter, but wanted to take time to further unpack the data in the report this week.) The Financial Health Network publishes the report annually, providing insight into how Americans’ financial circumstances are changing over time. The 2026 report is derived from a survey fielded in April and May 2026. The report leverages survey responses to assess indicators of financial health across spending, saving, borrowing, and planning/protecting and to determine a zero to 100 “FinHealth” score. Those with scores between 0 and 39 are considered “Financially Vulnerable,” consumers with scores ranging between 40 and 79 are defined as “Financially Coping,” and those with scores of 80 to 100 are “Financially Healthy.” The report found that moderate improvements in 2025 were reversed, with the share of respondents considered financially “vulnerable” rebounding to 17%. The longitudinal data reflect the impact of pandemic-era programs, like expanded unemployment, cash stimulus payments, and pauses of federal student loan payments. The positive impacts of those programs, as reflected in the Financial Health Pulse reports, had largely disappeared by 2023. While pandemic-era inflation — to be fair, in part caused by the various support and stimulus measures — had come down from as high as 9% in 2022, it has rebounded since the start of Trump’s second term, with tariffs and energy market disruptions owing to the war in Iran pushing prices back up. Specific indicators in the survey that deteriorated from 2025 to 2026 include the share of respondents spending less than their income over the past 12 months, the share paying all bills on time over the past 12 months, the share that have a manageable amount or no debt, the share that are “moderately” or “very” confident their insurance is adequate to cover them in an emergency, and the share that “somewhat” or “strongly” agree that their household plans ahead financially. Unsurprisingly, lower- and moderate-income households are more likely to struggle to pay their bills on time and have any funds leftover to save. The share of low-income households whose income exceeded their spending dropped from 35% in 2025 to 31% in 2026, while the share of upper-income households whose income exceeded expenses remained unchanged at 63%. Households with student loans or revolving credit card debt reported having “a bit more” or “far more” debt than was manageable at rates far higher than those not carrying these types of debts. The share of those with student loan debt indicating their debt load was too high to manage increased from 50% in 2025 to 55% in 2026, while the share of those with revolving credit card debt saying the same ticked up from 51% in 2025 to 54% in 2026. Households that had student loans were markedly more likely to be financially “coping” or “vulnerable” vs. those without student loans. In 2026, 27% of households with student loans were considered financially vulnerable, a jump of 6% points vs. 2025. Households without student loans saw just a 1% point increase (not statistically significant) in those considered financially vulnerable. Households’ perception of the affordability of categories of goods and services paints a stark picture, with less than one-fourth considering higher education affordable, about a third deeming childcare affordable, and less than half viewing healthcare as affordable. Fintech Business Weekly is made possible by the generous support of paying subscribers — bringing you independent analysis of banking, fintech, and crypto without fear or favor. You can support my work by becoming a paying subscriber if you aren’t already. Paying subscribers enjoy: access to the entire archive of nearly six years of newsletters extended versions of the weekly newsletter, with additional content and analysis and (for founding member tier) quarterly personal 1:1 fintech Q&A / strategy calls with me, tapping into my unparalleled knowledge of the intersection of banking and fintech and experience working in the sector, including helping to launch Goldman Sachs’ retail bank Marcus Support Fintech Business Weekly You can also support Fintech Business Weekly by sponsoring a newsletter or podcast, putting you in front of 93,000+ of the most influential decision makers in banking, fintech, and crypto. Learn more about sponsor opportunities or request a media kit by dropping me an email. Democratic Senate Report Highlights How Iran Uses Tether to Evade U.S. Sanctions Iran uses stablecoins, specifically Tether, to evade sanctions, fund regional proxies, and to purchase military drones, a report released last week by the Senate Permanent Subcommittee on Investigations says. The report was authored by committee ranking member Senator Richard Blumenthal (D-CT) and the minority staff. Crypto and stablecoin proponents will often push back on criticism that criminals make use of these assets by arguing that, on an absolute dollar basis, far more illicit funds flow through traditional banking systems and payment rails than via crypto and stablecoins. And while that is likely true, it elides the reality presented in news story after news story: whether state actors — Russia, Iran, North Korea — or criminal groups, those looking to engage in illicit transactions increasingly favor stablecoins and, specifically, Tether (USDT). The report from the Senate Permanent Subcommittee on Investigations released last week adds context to how Iran uses Tether has a lynchpin in its shadow banking system. The report analyzed 846 wallets that have been sanctioned or targeted for seizure, finding that 84% of them transacted exclusively or primarily in Tether. The ability to create near limitless, anonymous wallets and to move funds effectively instantaneously and irreversibly have made crypto a favored financial mechanism for bad actors. Stablecoins solved key drawbacks of bitcoin, namely, the original cryptocurrency’s highly volatile value in dollars. And while Tether (the company) has the technical ability to “freeze” or destroy funds, the company is often limited and reactive in its approach to doing so, the Senate report argues. Tether, which is nominally based in crypto-friendly El Salvador, “has stated that its compliance with OFAC sanctions is ‘voluntary’ and that it follows ‘OFAC guidelines,’” the Senate report says. Owing to these favorable attributes, Tether “became a primary cryptocurrency for Iran, Hamas, Hizballah, and the Houthis beginning in 2023 and has expanded in scale since,” according to the report. Tether’s role in Iran’s shadow banking system, the report says, is enhanced by crypto exchanges like Bybit, Kyrrex, OKX, Gate, and Binance, and through “over the counter” exchanges and hawala networks. The consequences of these financial flows aren’t hypothetical. The Senate report links Tether as a funds transfer mechanism to Iranian proxies in the region, including Hizballah, the Houthis, and Hamas. Tether has also been used to make payments to secure components necessary to manufacture drones, the report says. The report concludes by arguing that “[s]tablecoin issuers with a significant nexus to the United States, particularly those that offer dollar-denominated stablecoins, should be subject to American sanctions law rather than being allowed to hide behind foreign jurisdictions.” Stablecoin issuers like Tether must be held accountable for repeated failures to prevent illicit finance and sanctions violations by law enforcement, the Department of Justice, the Securities and Exchange Commission, and OFAC, the report argues. [Paying Subscriber Exclusive] OUSD Goes Live, Make Your Own Neobank, U.S. Sanctions Russia’s A7 Network As “Transnational Criminal Organization” OpenUSD, the stablecoin issued by the Open Standard consortium that includes Stripe, Visa, Mastercard, core providers, crypto firms, and numerous banks, went live last week. Social commerce platform Whop raised eyebrows in fintech by offering creators on its platform the ability to launch their own neobanks, which the company describes as “great businesses that are easy to run,” in just 15 minutes. And OFAC and FinCEN target Russia’s “shadow banking” A7 Network — more on these stories after the paywall.
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Polk County health officials painted a somber picture of services at stake while explaining plans by Iowa HHS to regionalize public health.
I used a fake Craigslist ad to test my business idea. The reaction gave me the proof I needed.
Alison Donnally founded a business to help older adults live independently. She explains how the idea came to life.
I used a fake Craigslist ad to prove my business idea could work
Alison Donnally founded a business to help older adults live independently. She explains how the idea came to life.
Don't tell owners how to run businesses. Go where you want.
So quit complaining and trying to tell owners how to run their business. Go where you want. If the owner chooses wrong, they will go out of business.
Could psilocybin treat brain injuries? Neuroscientist explains how psychedelics could fill a gap in head trauma care
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Anthropic Is Aiming To Transform Healthcare—Here’s How
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Al Roker suddenly exits ‘Today’ during live broadcast, later explains why
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'Verity' ending explainer: What should audiences believe?
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Pau Cubarsi opens up on Barcelona pressure, mental health and life as a teenager: ‘You have to keep fighting’
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Studying Science in an Anti-Scientific Time
This Chronicle Review Deep Cut was first published in 2025. By Julianne Werlin Thanks for reading The Chronicle Review! Subscribe for free to receive new posts and support my work. Subscribe The last two decades have not been kind to science studies. Already bruised and battered by the “science wars” of the 1990s, by the 2000s sociologists of science — who had long argued that science had to be understood in its human context — were encountering the funhouse-mirror version of their own views in right-wing critiques of climate scientists. It was enough to give some prominent scholars pause. “Was I wrong to participate in the invention of this field known as science studies?” asked Bruno Latour, the most famous sociologist of science in the world, in 2003. “Should we apologize for having been wrong all along? Or should we rather bring the sword of criticism to criticism itself and do a bit of soul-searching here: What were we really after when we were so intent on showing the social construction of scientific facts?” In response, Latour proposed a change of method and attitude. Science studies, now chastened and contrite, would no longer seek to strip facts of their authority, but to enrich and enhance them. What this meant in practice was never fully clear. But his diagnosis of problems resonated, even if his solutions did not. Latour’s exercise in public hand-wringing signaled a shift in mood. By 2016, with the first election of Donald Trump, the idea that skepticism of science was a right-wing position, not a left-wing one, had become a commonplace, discussed eagerly in The New York Times. For liberals, vaccine denial during the Covid era and Trump’s second-term attacks on the funding of scientific research, covered extensively in these pages, added a new urgency to the defense of science on both practical and principled grounds. There was little appetite for a sociology or philosophy of science that emphasized the messiness and missteps of the scientific process rather than its integrity and triumphs. The distinguished historian of science Peter Dear’s new book, The World as We Know It: From Natural Philosophy to Modern Science (Princeton University Press, 2025), which seeks to tell the story of 18th- and 19th-century science for a general public, may seem comfortably remote from these bitter contests. In its 16 short chapters, Dear draws swift, sure sketches of major discoveries from Newton to Einstein, ably guiding the reader through debates on stellar nebulae, animal taxonomy, atomic chemistry, evolution, and other flashpoints in the history of science. But though a work of history, Dear’s book must still negotiate our polarized present. In a blurb on its back cover, the historian of biology Lynn K. Nyhart alludes to the “present moment, when scientific knowledge is being swamped by misinformation and its institutions are under siege.” Dear himself, in his conclusion, refers to the “yard signs” and “bumper stickers” that proclaim “Science Is Real.” One antidote to both anti-science propaganda and to reductive slogans in science’s defense is a robust history and philosophy of science: exactly what Dear’s scholarship has long offered. But his new book illustrates how difficult such work has become. Dear, an emeritus professor of history at Cornell University, is best known as a creative and philosophically minded historian of the Scientific Revolution. His edited volume The Literary Structure of Scientific Argument (1991) analyzed the rhetoric of Galileo’s thought experiments to brilliant effect. Discipline and Experience: The Mathematical Way in the Scientific Revolution (1995) was a major contribution to the analysis of scientific experiments in early modern Europe and beyond, while Revolutionizing the Sciences: European Knowledge in Transition, 1500-1700 (2001), now in its third edition, remains one of the best overviews of the profound epistemological upheavals of 16th- and 17th-century science. But Dear has also ventured into later periods. Nearly two decades ago, in The Intelligibility of Nature: How Science Makes Sense of the World (2006), he turned to the 18th and 19th centuries to sketch a history and theory of scientific knowledge. Aimed at a generalist audience, Dear’s simple prose belied serious philosophical ambitions. Science, Dear argued, has two aspects: “natural philosophy,” the quest to understand the true nature of the universe, and “instrumentality,” the applied skills and technologies that allow us to manipulate the world for human advantage. Both strands are ancient, but the innovation of the phenomenon we call “science” was to yoke them together. As Francis Bacon wrote, “Human knowledge and human power meet in one; for where the cause is not known, the effect cannot be produced,” an insight typically abridged to “knowledge is power.” Or, in Bertrand Russell’s characteristically blunt phrase, science has two functions, “1. to enable us to know things, and 2., to enable us to do things.” So far, so conventional. But Dear took issue with this familiar story in one respect. Bacon’s formula implied that theory and practice could be fused in a single method. In fact, Dear argued, the quest to understand nature and the desire to manipulate it were coupled only loosely. They could come together, but they could also pull apart. Focusing on natural philosophy, the “knowing” half of the scientific circle, he used a series of case studies from cosmology, taxonomy, chemistry, electromagnetism, and quantum theory to show how theories changed over time, in dialogue with practice, but never reducible to it. Take the case of matter, the basic substrate of the physical world. In the 18th and 19th centuries, scientists not only disagreed about what it was, but also about how well it could be known. In 1789, the chemist Antoine Lavoisier dismissed speculation about the atomic composition of chemicals as “discussions entirely of a metaphysical nature,” as he sought to orient chemistry away from philosophical abstraction and toward laboratory results. But in the 19th century, atoms returned with a vengeance in the atomic chemistry of John Dalton, who wanted to explain the underlying structure of nature. It was not just that Dalton and Lavoisier had a different set of experimental results, or even a different theory, Dear showed. What counted as an explanation had changed. In arguing that scientific theories depended on their intelligibility to human beings with complex arrays of beliefs and commitments, Dear was in sympathy with science studies. Key figures such as the sociologist Steven Shapin emphasized the importance of psychological mechanisms like trust in the formation and acceptance of scientific ideas. But in focusing on the scientific pursuit of knowledge, not its politics or economics, Dear resisted the more cynical perspective of some of his colleagues. The Intelligibility of Nature strikes a very different note than Shapin’s collection of a few years later, Never Pure: Historical Studies of Science as If It Was Produced by People With Bodies, Situated in Time, Space, Culture, and Society, and Struggling for Credibility and Authority (2010), to name just one example. Walking a very fine line, Dear depicted science as driven at once by complex social dynamics and by the intellectual power of real discoveries. It was a testament not only to his own acumen but also to the vitality of the field that he was able to do so. Nearly two decades later, The World as We Know It returns to the questions broached in The Intelligibility of Nature. Like Dear’s earlier study, it begins with the Newtonian cosmos and ends with debates between Einstein and Bohr on quantum physics. Beyond its terminal figures, many of the same characters appear in both studies, including the naturalists John Ray and the Comte de Buffon; the chemists Étienne François Geoffroy, Antoine Lavoisier, and John Dalton; as well as Charles Darwin and Michael Faraday, among others. Its subject is nearly identical: As in The Intelligibility of Nature, The World as We Know It considers natural philosophy as opposed to instrumentality, here defined as the “desire to create a picture of what the world is really like — the world as God knows it — rather than simply having instrumental or operational control over it.” The two books also overlap in insights — and sometimes even in language. The reader who has learned in The Intelligibility of Nature that Buffon’s method of classifying animals “reflected his belief in the importance of the senses and empiricism in learning about nature (a doctrine associated with Newton and the philosopher John Locke)” will experience a sense of déjà vu on reading, in The World as We Know It, that the classification “reflects Buffon’s commitment to the epistemological stance of Newton and his philosophical underlaborer John Locke, who stressed the role of the senses in creating natural knowledge.” Later in the passage, we read that, “because of its stress on understanding the ways of life of animals in their environments, Buffon’s approach could almost be labeled (anachronistically) as ‘ecological.’” And again, “an anachronistic way of putting it would be to say that Buffon recommends an ecological mode of understanding.” Likewise, in The Intelligibility of Nature we read of Dalton that he was initially drawn to meteorology, and that: He did a bit of experimental work on these things, especially having to do with the water-holding capacity of gases, but his real interest lay in understanding what, at an underlying natural-philosophical level, was really going on physically.” Whereas, in The World as We Know It: He did experimental work on these things, especially concerning the water-holding capacity of gases — although not at a very refined level quantitatively — but he was continually trying to develop a theoretical conception of what was happening physically. The final full chapter of each book closes on the same resigned note. Intelligibility: These days, most serious work on the natural philosophical underpinnings and implications of quantum mechanics is performed not by physicists but by philosophers of science. Among scientific practitioners themselves, Bohr’s attempt to use instrumentality as sufficient grounds for a respectable science has met with a high degree of success. And again, in The World as We Know It: Bohr’s and Einstein’s concerns are now questions that are mostly discussed by philosophers of science rather than by physicists themselves. The marginalization of these sorts of questions amounts to the demise of natural philosophy, at least in physics, in the 20th century. Quantum mechanics “works,” and that’s good enough for most scientists who encounter it. Examples could be multiplied. When handling the same subject matter, some echoes are inevitable. Scholars should not feel that because they have written “Isaac Newton died in 1727” in one book, in the next they must describe him as perishing, expiring, or meeting his maker. But the repetitions of language and arguments (not to mention six reproduced images) threaded throughout The World as We Know It are more extensive than that. They should have been caught by Princeton University Press or its reviewers, and revised. Because the two books have so much in common, it is easy to see where they diverge. The World as We Know It is a less theoretical work than its predecessor. The sociology of knowledge that organized The Intelligibility of Nature has been reduced in scale and folded into readings and examples. The taut interplay between history and theory, which gave the earlier study so much interest, is there, but it is subtle. In the earlier book, the case studies demonstrated theoretical claims about the character and development of science. In the later work, each narrative of discovery is an end in itself. Insights emerge from the history, but they do not control the organization of the study. With the theory muted, it can be hard to understand the logic uniting Dear’s sprawling history of more than two centuries of scientific knowledge. The 16 brisk chapters cover an enormous amount of material. But the closer the volume edges to comprehensiveness, the more obvious the missing elements become. A chapter on “Institutions and Pedagogy,” describing the birth of the research university, illustrates the problem. Inserted roughly halfway through the book, the unnumbered chapter is designed as an “entr’acte,” in acknowledgement that it does not fit the study’s organization around scientific discoveries. Yet as Dear is well aware, no general history of the development of modern science can bracket its 19th-century institutionalization and professionalization. It was the 19th-century university, after all, that gave us the “scientist,” a term coined by the Cambridge philosopher William Whewell. Dear’s history of scientific discoveries implies a wider social and institutional history, but it remains largely subterranean, only occasionally extruded under the pressure of the narrative. It must be said that the balance is not all on the side of the earlier study. There are some advantages to the more historical, less theoretical organization of The World as We Know It. Dear’s accounts of paradigm shifts are remarkably lucid, no small accomplishment given that they span fields and centuries. He makes excellent use of the wealth of new scholarship on Darwin and his influences, to which he has himself contributed. It is satisfying to see puzzle pieces from Buffon’s natural history, Georges Cuvier’s animal taxonomy, Thomas Robert Malthus’s demography, and Charles Lyell’s geology recombined seamlessly in the Darwinian jigsaw. Likewise, Dear’s discussion of stellar nebulae, divided between chapters two and 15, shows vividly how Newton, Kant, Herschel, and Hubble reimagined insights and observations as they sought to discover whether the diffuse light that filtered through early telescopes was a glowing, gaseous substance or discrete but distant stars. Yet despite such pleasures, the book remains a more modest work than The Intelligibility of Nature. Dear has clearly not changed his views about the history of science. But in the two decades that have elapsed, the discipline and the world around it have changed, making it harder to write a sociologically and philosophically ambitious history of science for the general reader. That is a shame, because in the age of Silicon Valley and biotechnology, both the reciprocity and the tensions Dear identified between the epistemological and instrumental ambitions of science are more evident than ever. In recent years, some of the most exciting new books on the history of science in early modernity have focused precisely on this pressure point. Important studies such as Pamela H. Smith’s From Lived Experience to the Written Word (2022), James Poskett’s Horizons: The Global Origins of Modern Science (2022), and Vera Keller’s The Interlopers: Early Stuart Projects and the Undisciplining of Knowledge (2023) all shed new light on how early modern science negotiated the relationship between its instrumental and epistemological ambitions, or in Russell’s phrase, knowing and doing. There is every opportunity for a philosophically informed approach to draw new insights from this wealth of material. Dear, unfortunately, has not done so in The World as We Know It. But for future scholars of the history of science, his body of work over nearly four decades may provide just the model. Julianne Werlin is an associate professor of English at Duke University. Thanks for reading The Chronicle Review! Subscribe for free to receive new posts and support my work. Subscribe
Parliamentary government and political competition are the oldest traditions in Russian politics. Where did they come from, and why did they disappear? Writer Mikhail Zygar explains in a new film.
It’s hard to believe today, but Rus’ had a parliament before Britain did and a woman with the political stature of a prime minister five centuries before Margaret Thatcher. The Novgorod Republic was a state in which the prince was a hired manager, major decisions were made by the veche or popular assembly, and political culture resembled that of Western Europe in many ways. In his new film, Mikhail Zygar explains how this system emerged, why it lost out to Moscow, and why Vladimir Putin is so fond of Alexander Nevsky.
Bishop’s computer science chair says AI is eliminating ‘grunt work’ and reshaping digital jobs
Dr. Russell Butler, chair of Bishop’s University’s Department of Computer Science, says artificial intelligence is increasingly taking over routine digital “grunt work,” changing the skills students and workers may need in the years ahead. Photo courtesy of Bishop’s University. Click here to read our platform! Get local news like this every morning. Subscribe free below. Subscribe Pulse Newsroom Artificial intelligence is already taking over some of the routine digital work once performed by entry-level employees, according to Bishop’s University Computer Science chair Dr. Russell Butler — a shift he believes will make expertise, judgement and the ability to evaluate AI-generated work increasingly important. Butler described the change as automation reaching what he calls the “bottom of the cognitive pyramid” during an interview with The Pulse about AI, university education and the future of digital work. His argument is not that humans are disappearing from the workplace. Instead, he sees their role changing as AI becomes capable of handling more of the repetitive tasks involved in computer-based work. “Everything’s converging on just AI basically doing all the grunt work,” Butler said. The bottom of the cognitive pyramid Butler is from Lennoxville, completed his undergraduate studies at Bishop’s and later earned his PhD at the Université de Sherbrooke. He joined Bishop’s in 2019 and now chairs its Department of Computer Science. He uses AI in research, teaching and other creative work, and said the technology is increasingly capable of handling routine digital tasks that once required substantial human time. “You’ve got the grunt work, like moving things around in Excel, doing basic programming tasks,” Butler said. “That’s like the bottom grunt work that the entry-level worker would have done two or three years ago.” Butler went further, saying in his view that much of this type of work has already been absorbed by AI tools. He connected that change to a shift he sees taking place in computer science, where entry-level roles have declined while experienced workers can use AI to increase what they can produce. “A senior developer now has access to essentially an unlimited amount of junior developers on demand,” Butler said. He was describing AI systems metaphorically, not literal employees. His point was that one experienced developer can now ask AI tools to produce code or other technical work that might previously have been assigned to more junior workers. That raises a broader question for universities and employers. If some of the simpler work traditionally given to newcomers can now be done by machines, how do people gain the experience needed to become senior workers themselves? Students doing work that once required years of programming Butler said he is already seeing the change inside his classroom. He created a Bishop’s course called CS208, Coding in Plain English, to show students — including those without traditional programming backgrounds — what they can accomplish using AI. Some come from disciplines such as psychology or neuroscience with no previous programming experience. “What I’m finding is that students who have zero programming experience, they come in from another discipline like psychology or neuroscience or something, they are now able to get up and running with real data sets,” Butler said. Butler’s own doctoral work involved neuroimaging, and he said he has assigned students work he considers comparable to the kinds of analysis he performed during his PhD. He described giving students a real dataset, an expected result and instructions to use AI to complete the analysis. Butler said those students have been able to carry out work at a level he associates with tasks he once performed after years of programming experience. That does not mean a student using AI instantly acquires the same knowledge as an experienced computer scientist. But Butler sees it as evidence that AI can dramatically lower the technical barrier between having a problem to solve and producing a working result. The role of the person using the system then changes. They need to know what to ask for, understand enough about the subject to judge the answer and take responsibility for what is ultimately produced. What about AI hallucinations? One persistent concern surrounding generative AI is hallucination — when a system produces information that sounds convincing but is false or unsupported. Butler said that problem has improved substantially in newer systems and is not as severe as it was with earlier models. He pointed in particular to newer AI systems’ ability to search the web, consult scientific databases and return source links rather than simply generating a citation from their internal model. Butler nevertheless said users should verify what AI produces. “You should verify,” he said while discussing citations generated with AI. His position is that hallucinations have become less common in the systems he uses, not that incorrect AI output is impossible. Human checking remains part of the process. Hours of work compressed Butler offered another example from his administrative work at Bishop’s. The university has been comparing CEGEP courses with first-year Bishop’s courses to determine where students may qualify for advanced standing. Traditionally, Butler said, that means reviewing two sets of course descriptions and manually matching one institution’s courses with another’s. As department chair, that could require hours of reading and comparison. He said AI can now be given both sets of material and asked to generate an initial mapping automatically. “It saves me hours and hours of work,” Butler said. Butler said the AI-generated mapping can also produce a better result than he would produce himself. He sees similar opportunities in businesses. A company may receive specifications, drawings, emails or other digital material from customers and then have employees convert that information into an internal format before anything can be manufactured or delivered. AI can increasingly perform parts of that intermediate processing. In Butler’s view, the employee does not necessarily disappear. Instead, the job becomes more about checking the result. “The human is still there,” he said. Judgement could become more valuable That change is central to Butler’s view of where digital work may be headed. If AI can produce a first draft, write basic code, compare documents or process large amounts of information, he argues that knowledge and judgement become increasingly important. “The real scarce resource going forward is going to be domain knowledge, expertise, and… credentials, authority, the ability to work with other people,” Butler said. He is therefore encouraging students not only to learn AI tools but also to broaden their education. He mentioned acting, fine art and photography as examples of courses that can help develop what he described as taste and intuition — the ability to recognize what is good, what is not and what should be changed. “And that’s the skill that they need now because the grunt work has been automated and now it’s about evaluating what the AI produces and telling it how to improve or expand the output,” Butler said. That does not remove the need for technical knowledge. Butler’s argument is that the emphasis may shift away from manually completing every step of a task and toward directing, checking and improving work produced with AI assistance. He also stressed that humans remain responsible for final decisions. “At the end of the day, the human has to make the final call,” Butler said, pointing to the question of accountability. Physical work may be different Butler draws a distinction between digital work and jobs requiring people to manipulate the physical world. He expects AI to continue advancing quickly in tasks performed almost entirely on computers. He is more cautious about claims that humanoid robots will soon be able to replace workers such as plumbers or electricians. Butler said those trades would not be replaced “anytime soon” in his view and described some current robotics demonstrations as overhyped. He described the situation as counterintuitive. “It’s almost like we solved the highest level first,” he said, contrasting advances in digital AI with the harder problem of building robots capable of operating reliably in the physical world. Butler’s argument is that some digital occupations could face automation sooner than trades requiring complex physical work. The wider AI shift in the Townships Butler’s comments come as local institutions are increasingly grappling with how AI should be used. The federal government recently announced up to $162 million over five years for 10,000 AI-related placements through Mitacs. The Université de Sherbrooke told The Pulse that the number of its collaborative research and innovation projects supported through Mitacs has approximately doubled since 2018-19, while the number of placements has approximately tripled. UdeS also said it already has projects involving both AI adoption and the application of AI to other fields. At Bishop’s, Butler said part of his role involves building the Computer Science department’s local presence, connecting students with regional partners and strengthening relationships with the Sherbrooke technology ecosystem. Those developments put universities in a difficult position. They have to prepare students to use the tools employers increasingly have available while also making sure graduates understand enough to recognize when those tools are wrong, incomplete or being applied to the wrong problem. The risk of losing human skills Butler does not see AI as risk-free. Later in the interview, he raised a separate concern: as AI becomes better at reading, writing, programming and other cognitive tasks, people could lose some of the underlying skills that historically helped produce new ideas. “The other danger is that we’re going to lose the human skills,” Butler said. He specifically mentioned reading and writing and argued that people could lose some of the lower-level understanding that contributes to breakthroughs. Butler said AI systems are based on existing human knowledge and argued that they become much more powerful when paired with a capable person. That tension runs through his view of the technology. AI can remove hours of routine work. It can allow beginners to perform tasks that once required substantial technical training. It can give experienced workers significant leverage. But the more capable the technology becomes, the more important it may be for people to develop the judgement needed to decide what should be done in the first place. For Butler, that is where education may have to change. Students still need to understand their field. They still need to know when something is wrong. And they still need to be able to make the final decision. But if AI increasingly handles the bottom of the cognitive pyramid, the value of a worker may depend less on how quickly they can perform the grunt work and more on whether they understand what the work is supposed to accomplish. Don’t miss the next story that affects your community. Join thousands of readers getting The Pulse every morning—plus breaking news alerts when it matters most. If you value this kind of local journalism, consider supporting it. Subscribe, share and like! Subscribe Share
Are hypernovas real?
Uneven supernova explosions may explain the unusual mix of elements in Milky Way stars once thought to be evidence of hypernovas.
White nationalism’s appeal runs deeper than partisan politics
Nearly 1 in 7 white men under 30 in the US support white nationalism, and partisanship alone doesn’t explain who’s drawn to it.
White nationalism’s appeal runs deeper than partisan politics
Nearly 1 in 7 white men under 30 in the US support white nationalism, and partisanship alone doesn’t explain who’s drawn to it.
White nationalism’s appeal runs deeper than partisan politics
Nearly 1 in 7 white men under 30 in the US support white nationalism, and partisanship alone doesn’t explain who’s drawn to it.
White nationalism’s appeal runs deeper than partisan politics
Nearly 1 in 7 white men under 30 in the US support white nationalism, and partisanship alone doesn’t explain who’s drawn to it.
Coastal forces shape building sciences practice
When Built Environments relocated to Wilmington, the firm’s partners weren’t looking for the lazy tranquility one hopes for when on vacation. Instead, they sought sultry heat. They wanted to understand whipping coastal winds and punishing precipitation – the kind that tropical storms produce. That’s because how materials degrade can reveal a lot about how they can be improved. “We specifically chose the Wilmington market because it does fall within Climate Zone 3A, which is a coastal warm-humid climate,” said Jarrett Davis, who joined Built Environments in 2023 to lead the company’s building science practice. “There’s not a lot of product research in that climate zone, but there are a lot of structures here that get weatherized pretty severely.” Built Environments has provided building science, material testing and code compliance consulting for 25 years, ever since principal materials scientist Steve Doggett founded the company in Minnesota. “There are very few manufacturers that understand fully how their own materials and products work, whereas we know their shortfalls and their benefits out in the real world on jobsites,” Doggett said. “That’s why the original Built Environments was formed, to make buildings better.” He said the 15-employee company is just as comfortable on a jobsite, retrofitting a high-end beachside property, for example, as it is recreating some of the coast’s most intense weather conditions in the great indoors. The company operates an A2LA-accredited ISO/IEC 17025 mechanical testing laboratory on Wright Street. The year-old facility has expanded Built Environments’ in-house capabilities, Doggett said. “We’re able to simulate that hurricane wind (and) rain and perform structural and impact testing on wall assemblies, roof assemblies and glazing assemblies,” he said. “There are certain requirements that those windows and doors need to meet as far as wind ratings and pressure ratings and things of that nature.” Standard Glass Co.-Glass Depots, of Wilmington, is currently testing a new glazing system using Built Environments’ stress testing, the partners said. One method involves what’s called a “large missile cannon.” “We take 8-foot two-by-fours, and we shoot them at assemblies, so they have to withstand that impact,” Davis said. “It’s pretty cool to see, I’m not going to lie.” In many cases, the lab can run a full, wall-scale test of a product in a matter of weeks or months, whereas “it might take midrange manufacturers years,” Doggett said. “We’re nimble. We can cut that time in half.” The lab also develops intellectual property. Doggett, for example, shares a patent with Sustant LLC, of Minnesota, on a fastener patch to increase the air and water tightness within building envelopes. “We have too many ideas and not enough time,” he said. But the partners noted that they or their fellow principals – of which the company has four total, including a principal engineer and a principal codes and standards expert – could be anywhere on a given day, including in the courtroom providing expert testimony in contentious insurance cases. “Let’s say there is a fire on a structure,” Davis said. “We can analyze all the specific materials within that assembly – an exterior wall or something like that – because there are loads that require certain heat release. And so, we can measure each one of those materials, if need be, to say if this was likely or not a code-approved assembly.” Despite what can be inferred in a lab, the partners said nothing beats learning from how materials change over time under real-world strain. Take, for instance, the curious case of the creaking condo. “We recently had a customer that came to us trying to assess a sound issue of a roof. It was making noises that no one could explain or account for. How do you tackle something like that?” Doggett asked rhetorically. “There’s really no generally agreed-upon methodologies.” The problem turned out to be a faulty installation of a commercial roof system. “The building in this particular case was not local,” he said. “In the initial installation, the commercial roofing contractor installed the wrong fasteners in the wrong locations into the deck, and essentially the solar radiation was causing the building to move quite substantially early in the morning when the sun initially hit that assembly after a quite cool evening, causing the threads on the fasteners to kind of jump within the material, within the metal.” Built Environments designed an effective repair methodology that was within budget, the partners said. “We get the bizarre,” Doggett said. “I will say that’s something that has stayed the same over 25 years.” The partners said they are hiring, and that Cape Fear Community College is one of their main recruiting grounds. Davis and Doggett look for talented people who can understand traditional methodologies yet are open to on-the-job training and new understandings, they said. After all, it’s not just weather that can ruin a building. “My favorite saying is,” Davis said, “‘What are the eight most expensive words in construction? That’s how my daddy used to do it.’”
Coastal forces shape building sciences practice
When Built Environments relocated to Wilmington, the firm’s partners weren’t looking for the lazy tranquility one hopes for when on vacation. Instead, they sought sultry heat. They wanted to understand whipping coastal winds and punishing precipitation – the kind that tropical storms produce. That’s because how materials degrade can reveal a lot about how they can be improved. “We specifically chose the Wilmington market because it does fall within Climate Zone 3A, which is a coastal warm-humid climate,” said Jarrett Davis, who joined Built Environments in 2023 to lead the company’s building science practice. “There’s not a lot of product research in that climate zone, but there are a lot of structures here that get weatherized pretty severely.” Built Environments has provided building science, material testing and code compliance consulting for 25 years, ever since principal materials scientist Steve Doggett founded the company in Minnesota. “There are very few manufacturers that understand fully how their own materials and products work, whereas we know their shortfalls and their benefits out in the real world on jobsites,” Doggett said. “That’s why the original Built Environments was formed, to make buildings better.” He said the 15-employee company is just as comfortable on a jobsite, retrofitting a high-end beachside property, for example, as it is recreating some of the coast’s most intense weather conditions in the great indoors. The company operates an A2LA-accredited ISO/IEC 17025 mechanical testing laboratory on Wright Street. The year-old facility has expanded Built Environments’ in-house capabilities, Doggett said. “We’re able to simulate that hurricane wind (and) rain and perform structural and impact testing on wall assemblies, roof assemblies and glazing assemblies,” he said. “There are certain requirements that those windows and doors need to meet as far as wind ratings and pressure ratings and things of that nature.” Standard Glass Co.-Glass Depots, of Wilmington, is currently testing a new glazing system using Built Environments’ stress testing, the partners said. One method involves what’s called a “large missile cannon.” “We take 8-foot two-by-fours, and we shoot them at assemblies, so they have to withstand that impact,” Davis said. “It’s pretty cool to see, I’m not going to lie.” In many cases, the lab can run a full, wall-scale test of a product in a matter of weeks or months, whereas “it might take midrange manufacturers years,” Doggett said. “We’re nimble. We can cut that time in half.” The lab also develops intellectual property. Doggett, for example, shares a patent with Sustant LLC, of Minnesota, on a fastener patch to increase the air and water tightness within building envelopes. “We have too many ideas and not enough time,” he said. But the partners noted that they or their fellow principals – of which the company has four total, including a principal engineer and a principal codes and standards expert – could be anywhere on a given day, including in the courtroom providing expert testimony in contentious insurance cases. “Let’s say there is a fire on a structure,” Davis said. “We can analyze all the specific materials within that assembly – an exterior wall or something like that – because there are loads that require certain heat release. And so, we can measure each one of those materials, if need be, to say if this was likely or not a code-approved assembly.” Despite what can be inferred in a lab, the partners said nothing beats learning from how materials change over time under real-world strain. Take, for instance, the curious case of the creaking condo. “We recently had a customer that came to us trying to assess a sound issue of a roof. It was making noises that no one could explain or account for. How do you tackle something like that?” Doggett asked rhetorically. “There’s really no generally agreed-upon methodologies.” The problem turned out to be a faulty installation of a commercial roof system. “The building in this particular case was not local,” he said. “In the initial installation, the commercial roofing contractor installed the wrong fasteners in the wrong locations into the deck, and essentially the solar radiation was causing the building to move quite substantially early in the morning when the sun initially hit that assembly after a quite cool evening, causing the threads on the fasteners to kind of jump within the material, within the metal.” Built Environments designed an effective repair methodology that was within budget, the partners said. “We get the bizarre,” Doggett said. “I will say that’s something that has stayed the same over 25 years.” The partners said they are hiring, and that Cape Fear Community College is one of their main recruiting grounds. Davis and Doggett look for talented people who can understand traditional methodologies yet are open to on-the-job training and new understandings, they said. After all, it’s not just weather that can ruin a building. “My favorite saying is,” Davis said, “‘What are the eight most expensive words in construction? That’s how my daddy used to do it.’”
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Good science requires much more than evidence
Empiricists believe that scientific theories are true only if they make correct empirical predictions: if we can't verify these predictions, then there is no reason to expect that they tell us anything about reality. However, Professor of Philosophy at King's College David Kyle Johnson argues that this is mistaken. In response to Bas van Fraassen, who has written on this subject for the IAI, he argues that we have many reasons for believing a theory to be true, such as it being parsimonious and having large scope — qualities which have been used throughout the history of science as criteria for judging theories. In 2024, Princeton philosopher Bas van Fraassen wrote a short article for IAI News entitled “Science Does not Describe Reality: The Limits and Benefits of Explanation.” At the time, I was working on two projects (a science book and a Great Course) that, when completed, would (among other things) defend science’s ability to describe reality. I wanted to write a response to van Fraassen, but the projects demanded too much of my attention. But now that those projects are complete, I am in a position to explain the objections to van Fraassen’s thesis. Summarizing van Fraassen’s argumentIn a nutshell, his argument is this: What scientists do is make predictions based on theories and then invite others to test to see whether those predictions are borne out. “If theory T is right, result R should happen in experimental condition C.” Many think that running an experiment that shows that R happens in condition C is proof that theory T is true (and should be believed). But, van Fraassen argues, that R happened in condition C is only a reason to think that R happens in condition C. That’s the only observable, or empirical, result we can actually see, so that is all that we are really justified in believing. (The underlying theory that made that prediction still may or may not be true.) That is why he calls those who accept his position “empiricists.”Before I explain what is wrong with this position, let me explain what it gets right. What van Fraassen Gets RightFirst, a theory correctly predicting the results of an experiment is not, strictly speaking, “proof” that the theory is correct. The basic line of reasoning is this:If the theory T is true, result R should happen. Result R did happen. Thus, theory T is true.In other words:“If T then R. R. Thus, T.”This is a deductively invalid, fallacious mode of reasoning, called “affirming the consequent.” Those premises do not guarantee that the conclusion follows. “If I am in Pennsylvania, then I am in the USA. I am in the USA. Thus, I am in Pennsylvania.” Clearly that argument doesn’t guarantee its conclusion; neither does the above argument.However, science is not a deductive method of reasoning; it is an inductive one. And inductive arguments merely try to raise the probability of their conclusion, not “prove” them for certain. Thus, the fact that an experimental result doesn’t prove a theory is true is irrelevant; it’s not supposed to. That is not what scientific reasoning is about.___If the hypothesis that something exists clearly provides the best explanation for what we can observe, then the belief that it exists is what is rational.___Second, it’s also true that a single experimental result doesn’t provide enough justification to believe that a theory is true. Scientists do not come to a consensus about whether a theory is true by looking at a single experiment; they look at all the evidence to determine where the preponderance of evidence points. The mere fact that I am in the USA raises the probability that I am in Pennsylvania, but not nearly enough to conclude that I am in Pennsylvania. However, if I am also regularly encountering people wearing Eagles and Phillies hats and seeing advertisements for Founding Father tours, I probably am. In the same way, a theory successfully predicting the outcome of one experiment is not enough to conclude that it is true; but if a theory makes a wide variety of predictions that are all vindicated by multiple experiments, performed by many different groups and people, all actively trying to prove the theory false (which is what good scientific experiments are designed to do)—that is a good reason to conclude that the theory is true. In other words, if you repeatedly try to prove something false but can’t, that is a very good indication that it isn’t. The problem of underdeterminationIn reply, those whom van Fraassen calls “empiricists” often point to the “problem of underdetermination.” For any given theory that makes successful predictions about what happens in experimental conditions, there are countless other possible theories that make exactly the same predictions—and since they do, the experimental evidence in question is just as much reason to accept any of those theories as it is to accept our original one. Thus, the argument goes, we have no way of knowing whether our original theory is true, or one of the other “predictively equivalent” theories is true. SUGGESTED VIEWING How Occam's Razor changed the world With Johnjoe McFadden As an example of underdetermination, philosophers often offer this example. The evidence of fossils seems to provide reason to conclude that the universe is quite old; ancient fossils are what the “ancient universe” hypothesis predicts. But it’s also possible that God created an ancient-looking universe 200 years ago, complete with a “fake” fossil record for us to find, to make it look old. The existence of fossils is what that theory predicts too. So, the argument goes, the fossil record can’t be used as evidence that the universe is ancient; the fossil record supports the “200-year-old ancient-looking universe” theory just as much. The problem of underdetermination points out that, for any theory with supporting evidence, we could invent another completely different theory that predicts exactly the same evidence, and thus undermine that evidence’s ability to support the original theory.But this “argument from underdetermination” betrays a fundamental misunderstanding of what scientific reasoning is and how it works. To explain, let me use another rather strange (but very real and commonly discussed) philosophical thought experiment called “The Grue Problem.” The “Grue” ProblemSuppose I want to know what color emeralds are, so I observe a few, see that they all appear green, and form the theory that “all emeralds are green.” I thus predict that the next 100 emeralds I find will also appear to be green. And let’s say that happens, and so I thus conclude that my theory that all emeralds are green is true. Now, one might rightly point out that I don’t know for sure that all emeralds are green—I have not seen them all. Maybe some I haven’t found yet are blue. Fair enough. After all, we used to think that all swans were white, until we found some black ones. But let’s say that I go on a quest and collect every emerald in the universe—and, for the sake of the argument, let’s say I can do this, and can even know that I have every one of them. So, I collect them all, look at them all, and verify that they all appear green. Can’t I now rightly conclude that all emeralds are green?Inspired by the problem of underdetermination, a friend might argue that I cannot.Sure, they all appear green now—but there could be another kind of color called “grue.” Something is grue if it is green up to a certain point in time, but then spontaneously turns blue. And there could be an infinite number of “shades” of grue. Something that is one shade of grue will turn blue at noon today, but something that is another shade of grue will turn blue at noon tomorrow, etc. So yes, the emeralds appear green now; and since they haven’t turned blue yet, they are not some “past” shade of grue. But they might be some “future” shade of grue (e.g., they may turn blue at noon tomorrow). And since all future shades of grue are consistent with “being green up to this point in time,” the evidence you have is completely consistent with the notion that all emeralds are some future shade of grue, rather than green. Indeed, that they would appear green until now is what the “some future shade of grue” hypothesis predicts. So, we have just as much reason to conclude that all emeralds are some shade of grue as we do to conclude that they are green.You’re probably thinking “It’s not equally possible that the emeralds are grue; they are obviously green.” You are right! But explaining why your instinctive reaction to my friend’s argument is correct will help inform why van Fraassen’s “empiricist” is wrong and science actually does describe reality.The main mistake my friend makes is in thinking that the “empirical evidence” (of all the emeralds appearing green) is (or even should be) all I am using to draw the conclusion that all emeralds are green. It is not. I am also considering known facts about how colors typically work; things don’t usually change color, and when they do, they don’t do so suddenly, without cause, and all at once. So, even though the simple evidence of the emeralds all appearing green up to this point is consistent with them being some future shade of grue—that is even what the grue hypothesis “predicts”—it is still exceedingly unlikely that they are. The hypothesis that they are green is much more likely. Understanding science refutes the problem of underdetermination The misunderstanding about science that the “empiricist” pointing to “the problem of underdetermination” makes is similar to the one that my friend made. Science is not just about considering experimental evidence; in fact, it’s not even just about considering empirical evidence. As Ernan McMullin and many other philosophers of science have pointed out, the inference that makes science is “inference to the best explanation” (sometimes called “abduction” or “retroduction” by McMullin). While, depending on the circumstances, scientists use many different kinds of reasoning, science as a whole is, simply put, the process of considering multiple competing explanations and comparing them to certain criteria—explanatory criteria (the kinds of things explanations must be and do, by definition, to be a good explanation)—to see which explanation is the best.And these criteria do not deal only with empirical results. Yes, successful predictions raise the probability of a hypothesis, but it’s also the case that the more new assumptions a theory makes, the less likely it is to be true; every new assumption presents an opportunity for the theory to be false. (The fewer assumptions a theory makes, the simpler or “more parsimonious” it is said to be.) Likewise, the more existing evidence a theory contradicts, the less likely it is, because in order for a theory to be true, every piece of evidence it contradicts has to be somehow faulty. (The fewer established theories a new theory contradicts, the more “conservative” it is said to be.) And, obviously, the more something explains, the better explanation it is. (This is called having “scope.”) If a theory raises unanswerable questions, it’s not expanding our understanding, and thus is not wide-scoping. Scientists consider all these things when deciding which theory to accept, because the theory that adheres best to these explanatory criteria is most likely to be true.___the mere fact that a theory’s predictive success is why it “sticks around” or “becomes accepted” in the scientific community is not a reason to be skeptical about its truth.___The reason the grue hypothesis is no good is because it invents an entirely new kind of color—not just a new shade, but an entirely different category of color (a different way colors work)—and thus is not parsimonious. It raises unanswerable questions about how and why emeralds would just spontaneously change from green to blue (and thus does not have wide scope). And it contradicts what is already well established about how color works (and thus is unconservative).The theories that the problem of underdetermination points to do the same thing. Yes, the theory that God created an “ancient-looking universe” (complete with an ancient-looking fossil record) two hundred years ago is also consistent with the empirical evidence (of a fossil record) that presently exists; but that theory is monumentally unparsimonious (it requires many extra assumptions about infinite entities) and not wide-scoping (it raises unanswerable questions about why God would do such a thing). So, I can rationally conclude that it is false.Indeed, the history of science includes many examples of theories that were rejected because they lacked parsimony and/or scope, even before their falsity (and the truth of their competing theory) was confirmed by observation. As I point out in my book, geocentrism was rejected by the scientific community because it lacked parsimony, almost 200 years before the observation of parallax confirmed that the Earth moves around the sun. And the continuum theory of matter was completely rejected by 1860, because it couldn’t explain chemical reactions or organize matter into a periodic table, even though the experiments that eventually confirmed atomic theory weren’t even proposed until the 1870s. Solving the best of a bad lot problemIn response, the “empiricists” are likely to object, this time pointing to the “best of a bad lot” problem. “We have no reason to conclude that an explanation that has been shown to be better than others is true because we have no guarantee that the true theory was among those that we were considering.” If you have a “lot” (a collection) of poor explanations, even the best one is not true.The responses to this problem, however, are numerous. Peter Lipton argues that the set of hypotheses that scientists consider are not just randomly selected and thus unlikely to contain a theory that is true; they use existing knowledge to home in on what is most likely to be true. A doctor trying to explain abdominal pain is not going to bother wondering whether demons are causing it; they are going to stick with things like appendicitis and kidney stones, because they are most likely. Further, despite its name, inference to the best explanation is not just the process of comparing hypotheses and accepting the best one; we also want one that is a good explanation in its own right. If it’s not, we are going to keep looking until we find an explanation that is. And while that process does not guarantee that we will come across the exact truth, it’s very likely that the theory that emerges as the best will be at least approximately true, and thus we will be justified in believing it. And lastly, as I point out in my book, although many theories not based in scientific evidence (like geocentrism and continuum theory) have been completely overturned, science almost always progresses by the refinement of scientifically established theories—not their replacement. (Although our sun is not the center of the universe, heliocentrism was our first step in understanding the Earth’s true place in the universe.) So even if a theory we accept today may not be completely right, accepting it is a step on the journey toward the complete truth. The example of atomic theoryNow, it’s worth pointing out that most empiricists are not skeptical of all scientific theories; they are only skeptical of the ones that hypothesize the existence of “unobservables.” It’s not like they think that round Earth, heliocentrism, and germ theory are just useful (but untrue) “instruments” for making predictions about how to most efficiently navigate the seas, predict planetary motion, and treat diseases. They admit that the Earth actually is round, that the planets revolve around the sun, and that germs exist and cause disease. These facts and entities can be observed; the theories that suggest they exist are true. It’s only when theories start hypothesizing the existence of unobservables—like electrons, or quantum fields—that can’t be observed directly, that we should start doubting the theories’ truth. And to be fair to van Fraassen, in this published work, he doesn’t really say that we should doubt the truth of such theories. Only that scientific reasoning does not rationally force us to accept them as true. Skepticism, or agnosticism, he argues, is warranted. SUGGESTED READING Science is based in metaphor By Andrew Reynolds But the history of atomic theory throws a monkey wrench into that argument. Atoms used to be unobservable; again, scientists first accepted the theory that they exist because it had such grandiose explanatory power. But then atoms were seen with electron microscopes. They thus moved into the category of the observable. But this didn’t substantially change anything about what scientists were justified in believing; it certainly didn’t change any “atomic skeptics’” minds. It just confirmed what everyone already knew! If the hypothesis that something exists clearly provides the best explanation for what we can observe, then the belief that it exists is what is rational. The fact that the thing itself is not technically observable does not make doubting its existence rational.What’s more, the atoms were seen with an electron microscope. But electrons are themselves unobservable. How can an electron microscope reveal an atom if electrons are not real? This leads us to what is famously known as Hilary Putnam’s “miracle argument.” It’s (not) a miraclePhilosopher Hilary Putnam’s miracle argument is somewhat famous. Basically, he argued that a scientific theory being able to repeatedly make successful predictions—even about unobservables—without being at least approximately true would require a “miracle.” In other words, it would be such an improbable occurrence that it could not rationally be believed—the better explanation for why scientific theories about unobservables make the right predictions is because the unobservable entities and structures they hypothesize exist.Van Fraassen has addressed this argument. Indeed, in his original article, where he references Alison Gopnik’s point about orgasms not being “necessary for procreation” even though the desire for orgasm “drives the creation of progeny,” he is pointing to his own argument about how organisms are not naturally selected by evolution to get true beliefs; to the extent that belief-forming processes are selected for, the ones that are selected are the ones that are useful for survival—not the ones that produce true beliefs. In the same way, van Fraassen argues, scientific theories are accepted, not because they are true, but because they aren’t falsified; they make successful predictions; that’s what makes them “stick around.” So, the fact that they make successful predictions (and thus became accepted) is not a reason to think they are true.This argument is similar to C. S. Lewis’s “argument from reason,” which he used to argue against evolution: “The truth of evolution undercuts our ability to know that it is true.” Lewis’ argument famously failed; Elizabeth Anscombe, by Lewis’s own admission, demolished Lewis in a debate on the topic, and I have explained elsewhere why modified versions of the argument fail as well. In short, it’s because it fails to recognize that a belief-forming process can make an organism more likely to survive because it reliably produces true beliefs. It is therefore not the case that beliefs generated by naturally selected belief-forming processes are automatically false or unjustified.In the same way, the reason a theory “sticks around” could be because it’s not falsified, because it “makes correct predictions.” But the reason it makes correct predictions is most likely because it is true. (In pointing this out, I am echoing the arguments of Musgrave, Lipton, Psillos, and Kitcher, who refuted van Fraassen’s argument and analogy long before his 2024 article.) It would be a huge coincidence that it could do so without being true. And that is the point of Putnam’s miracle argument. Thus, contrary to van Fraassen’s suggestion, the mere fact that a theory’s predictive success is why it “sticks around” or “becomes accepted” in the scientific community is not a reason to be skeptical about its truth. ConclusionLet me close by saying something about the fact that empiricists don’t doubt the truth of all scientific theories, just the ones that hypothesize unobservables. In today’s society, this theory is dangerous to espouse publicly. The nuance between being skeptical about all of science, and just the highly technical theories about unobservables, is guaranteed to be lost to the average reader. And there is so much unjustified skepticism and doubt about the findings of science and experts that a headline like “Science Does Not Describe Reality” could easily fuel the anti-science movement, worsen climate change denial and cause the next pandemic.This is why I believe it was important to still respond to van Fraassen’s article, a full two years after he wrote it. Not only, as I have argued, does it represent a philosophically inaccurate view of how scientific reasoning works and what scientific reasoning can do, but it is a dangerous view to boot. It should thus be rejected.
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September Monthly Review: Health MCP Guy?
For all the time I spend talking about APIs and data exchange, this newsletter has had embarrassingly little interoperability of its own. It’s old-school SaaS, baby - UI or bust. Not all of that is my fault! Substack, for all its popularity, is (as mentioned before) really frustrating as my system of record. The primary user it optimizes towards is the reader, whose attention feeds the app, the Notes feed, and the recommendation network that Substack is actually selling. Health API Guy is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe They’re falling into the same trap that Medium once sprinted into, albeit at a much more glacial speed. Younger generations may not remember (or never knew) it, but Medium once lured publications like The Ringer and Signal v. Noise onto its platform, only to lose them as it rebuilt itself around a paywall and an algorithmic feed. Substack’s original pitch circa 2020 was the antidote (you own your list), but every new feed feature chips away at it. I get it: writers are generic supply in the eyes of the Substack product team. Generic supply follows demand, so the consistent pressure on the PMs is to generate demand, which leads to every product cycle going toward discovery (Notes, recommendations, the app, live video). The end result lackluster author tools (laggy and broken analytics are truly crippling) all despite the premium price point. The archive is where it crosses from authorial gripes to actual reader impact, though. Across Substack and LinkedIn, I’ve published 1,218 posts and nearly 900,000 words since 2018. Substack’s native search can see only the 464 posts that live on Substack, and even there it’s woefully inadequate at surfacing relevant content (even for the guy who wrote it)! Readers have the same problem, writ large. Ask the Archive Fixing search (for both my readers and my own use) turned out to be extremely non-trivial, unfortunately: There’s no public API for readers to access content RSS exists for new content notification, but it only carries the 20 most recent posts and cuts paid ones off at the preview The only Substack MCP is an admin tool for analytics The export exists for authors, but it’s painfully awkward and slow. So I built something to work around that: the Health API Guy MCP. It’s a private MCP server holding the full text of everything I’ve written across Substack and LinkedIn. Add it to Claude or ChatGPT as a connector and you can ask things like: It pairs keyword search (for acronyms and case names, of which we have many) with semantic search. Every post is also labeled by topic, form, and court case, so you can pull a lawsuit’s (or multiple cases’) entire docket across Substack and LinkedIn in one query: Hail, Healthapiguyhydra This is also a bit of me eating my own cooking. I’ve spent the year arguing that headless healthcare is arriving and that developers want primitives over abstractions. Last week in Hail, Benihydra, I watched Salesforce plug its platform into Claude and bet that the records underneath outlive whatever interface sits on top: Salesforce must believe that the Hydra’s immortal head is the records, permissions, and business logic (in that they will survive whatever happens to the interface). It felt a bit hypocritical to keep my own archive behind a Substack search bar. The archive is the body. Substack is one head, and now whatever tool you might use is another. Will the EHRs follow my lead? Probably not imminently, but one can hope! Why Founding Members Only The MCP is available to Founding Member subscribers. A few reasons: It costs money and time to run. Hosting, the database, Clerk usage and AI compute for search and labeling are significant operational costs (that increase with usage). Substack gives me no way to automate access. There’s no API to check whether someone is a paid subscriber, so every user is manual operations for now. It’s a beta. We’ll need to improve things and parts will break. I’d rather they break for a small group of dedicated readers who will tell me about it. The Founding tier deserved a real benefit. Until now it was mostly a generous tip. Now it comes with something. If you’re already a Founding Member, reply to this email with the address you’d like provisioned. If you’re not and want in, upgrade here. Larger teams (such as companies) can get access too: group subscriptions at the Founding tier come with the group discount. The admin for the subscription can reply with the list of addresses to provision. Get 20% off a group subscription Pricing Founding membership is now a flat $200 a year, replacing the old suggested $150 that readers could adjust. Existing Founding Members will have access grandfathered in at their previous rate. Month in Review Articles Published Make Networks Dumb Again (Sep 11): AI is making old analog networks cheaper to use and flooding them with machine traffic in the process. A plea for agent-native rails with the intelligence at the endpoints, and for Washington to drag them to ubiquity. Interoperability, Unbuttoned (Sep 29): Interoperability is a series of workflows in a trenchcoat, and this post finally takes the trenchcoat off. A color-coded map shows which provider workflows went digital, which are fragmented, and which still run on fax. Video Content Antitrust’s System of Record Problem (Sep 3): The FTC’s investigation into Epic prompted a ninety-minute webinar, now posted with chapters and a smoothed transcript. We get into why market definition, missing B2B share data, and the SSNIP test leave antitrust poorly suited to systems of record, plus why information blocking may be doing more than any pending lawsuit. The Information Exchange: Cerner Milking Edition (Sep 18): This was so fucking fun. Ryan Tucker, Ryan Brickner, Brad Thorson, and I try an Around the Horn format, complete with points, penalties, and a mute button. We debate whether Oracle Health is dead, who would actually buy Cerner (IBM enters the chat), p(Doom), and why the front desk still hands you a clipboard. Regulatory Ask Not What Your Network Can Do (Sep 23): CDC’s RFI on public health data intermediaries lists use cases already served, unevenly, by eCR, lab reporting, IZ Gateway, and syndromic surveillance. The real opportunity sits in one question about AI: a network that can handle the next emergency without another interface project. Court cases Particle v. Epic: The Naughty Schoolteacher (Sep 4): After a summer-long vigil, the answer on Epic’s early summary judgment bid is two paragraphs and a remedial assignment. Particle advances to the next grade, and market definition gets another year of discovery. The Epic Discovery Files (Sep 8): Texas’s motions to compel arrive with a 251-page appendix of requests, objections, and meet-and-confer letters with Cravath. Inside: Epic using generative AI for document review, denied interoperability requests in scope, and Noerr-Pennington raised against lobbying discovery. Epic v. Health Gorilla: “Working as Designed” (Sep 9): Epic wants nothing to do with the data breach MDL, and explaining why puts fresh discovery on the public docket. The spiciest allegation: Health Gorilla told GuardDog to scrub its law firm business from its website instead of cutting off access. Veeva v. Epic: Legal Bypass Surgery (Sep 15): Veeva asks the Wisconsin Supreme Court to skip the Court of Appeals, while law professors and three policy groups line up as amici. Meanwhile, Epic’s lawyers apologize for another round of citation errors, including a quote that never existed. Texas v. Epic: Behind the Black Bars (Sep 17): The court portal served up Epic’s unredacted filings, revealing a $5 million document review budget and the AI workflow behind it. Also inside: the 200-plus company names Texas wants searched, and one very expensive Zoom invitation. OpenEvidence v. Doximity: Consequences Will Follow (Sep 25): OpenEvidence tells the court it will not comply with a discovery order, and its own lawyers at Quinn Emanuel say they won’t defend the choice. Add years of Slack reported unrecoverable, and Rule 37 sanctions come into view. Health Tech Keeps Choosing Violence (Sep 28): Doximity moves to end OpenEvidence’s case, Epic asks Texas to show its work, and a sleepy Audacious Inquiry patent fight sprouts antitrust and information blocking counterclaims. The main event is a long-sealed Cognizant v. Infosys ruling that hints where system of record antitrust claims can survive. EHRs One Verb for Oracle Health (Sep 14): Oracle’s earnings call quantifies every data center to the decimal point, while Oracle Health gets a single verb: “accelerate.” Panning the few healthcare mentions turns up an agentic care management system, a research-to-care ledger play, and the question of whether healthcare gets Larry’s patience. Epic’s Forretress Under Siege (Sep 30): Judy Faulkner tells a panel Epic paused hundreds of projects to harden its software and calls it a “shame,” while a spokesperson insists the roadmap hasn’t changed. Project Glasswing, a Pokémon-branded QAN sprint, and a trove of Jodel grievances reconcile the two Epics and preview the Jevons paradox coming for all software. Industry Analysis Scanning for a Network (Sep 1): Scan.com raises $220M to build the imaging network labs never got, speedrunning Zocdoc’s consumer-to-B2B pivot. Its wedge to network density may be the most American one available: personal injury litigation. The Front Desk Wants the Damn Whole Building (Sep 16): Hello Patient buys Converse Health, pushing its AI front desk into referrals, chart work, and prior auth. The copilot categories keep blurring, and your distribution partner is still your final boss. Cross-industry Comparisons A Different Patchwork Quilt (Sep 10): Healthcare loves to envy open banking, but the US version has no mandate, a toll booth at Chase, and data quality problems of its own. The grass isn’t greener; it’s another American patchwork quilt, and perhaps a worse one. Hail, Benihydra (Sep 21): Salesforce brings its platform into Claude, Slack, and its own agent, inviting the interfaces that might replace it. Cut off one head and more grow back, so long as the records underneath stay put. Living on A Trade Secret Prayer (Sep 22): Six credit unions are suing Fiserv, the Epic of core banking, by claiming their own member records as trade secrets. Banking has no Cures Act, so its lawyers reach for legal alchemy that healthcare has already tried. Other News None this month External Media Nabla Accelerate NYC: I kicked off fall conference season at Nabla’s Accelerate event in New York, where Delphine Groll opened by asking the room to picture the company it wants to become and the one it doesn’t. I liked her framework of bureaucracy, fear, and ego as the blockers. I also love vendor conferences/events in general - different vibe than industry biggies. Healthcare 101 with Nikhil Krishnan: I briefly guest lectured Out-Of-Pocket’s Healthcare 101 class. Appreciated Nikhil’s takeaways cover whether AI doctors can query HIEs, why healthcare’s data-liberation rules beat most industries’, and the hi-res graphic is in the comments. HCN: Epic Probed by the FTC: I joined the HCN guys to talk through the FTC’s investigation into Epic. Cedric’s new hairdo was an unexpected plot twist. The episode also covers MFN drug pricing, healthcare M&A and Eli Lilly’s acquisition streak. Fall Conferences: At eHealth Exchange’s Annual Meeting in Austin on Oct 27, I’ll moderate a panel on the tensions between AI, HIEs, and timely patient access with Jean Ross (Primary Record), Therasa Bell (Kno2), Byron Crowe (Doctronic), and Michael Marchant (Freenome). Here’s the rest of my schedule if you want to meet up: CommonWell (Redwood Shores, CA): Oct 13-14 Will be participating in a fun Jeopardy event Open@Epic (Madison, WI): Oct 20-23 Will be enjoying Verona, Willy Street, and other local adventures eHealth Exchange and Sequoia Project (Austin, TX): Oct 27-29 Yeehaw HLTH (Vegas): Nov 15-18 Groundhog Day, Venetian Edition RSNA (Chicago): Nov 30-Dec 2 See me at “Governing AI in Breast Imaging: What Health Systems Are Actually Doing” Digital Health Counsel 2026 AI Summit (Seattle): December 2-3 Will likely be speaking to a bunch of lawyers about information blocking Posts I Liked ChatGPT’s Epic integration: game changer or nothing burger?: Joshua Liu, MD offers five thoughts on OpenAI’s Epic launch, starting from the point that it rides the same open APIs available to anyone. He argues read-only access leaves ordering to Art and the ambient startups, and asks why HCA is piloting it alongside Commure. He’s sorta a must-follow at this point - consistently excellent, thought-provoking takes. Epic, Lilly, and the Discovery pass: Seth Chaney reads Epic’s first major Discovery deal (with Eli Lilly), as well as its revised developer terms on commercial displays, as the pipe finally opening between EHRs and pharma. Life sciences and health continue to collide. Scott Rossignol on EHR payer platforms and ePA fees: Scott argues the top ambulatory EHR vendors are turning the prior auth mandate into a per-member-per-year toll on payers, for an API whose operating cost has nothing to do with covered lives. His fix is two lines of regulatory text: put the CRD/DTR/PAS certification criteria in the Base EHR definition, and bring payer-facing ePA connectivity under § 170.404’s cost-based fee rules. It’s a good solution. Health API Guy is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe
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