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New Quincy business brings recreation supplies, ammunition closer to home
For outdoor enthusiasts in Plumas County, finding the right gear for a day of fishing, a weekend in the backcountry or a hunting trip can sometimes mean a
Celebrate Halloween with wine matched to your favorite candy
Let’s be honest: We all “taste” our kids’ Halloween candy. Sometimes it mysteriously disappears before Halloween even arrives.
The One Chart That Explains Why Netflix Cleaned House
(Welcome to my weekly streaming ratings report, the single best guide to what’s popular in streaming TV and what isn’t. I’m the Entertainment Strategy Guy, a former streaming executive who now analyzes business strategy in the entertainment industry. If you were forwarded this email, please subscribe to get these insights each week.) Yeah, I’ll be honest: I went with a clickbait-y headline today. One chart? That explains everything? That’s as clickbait-y as it gets. But let’s be real. I’m analyzing a dataset that I’ve built over six years, and c’mon, I never write articles which feature just one chart! If you think this article—which includes a level of data detail unheard of in journalism twenty years ago—is clickbait, fine by me. Really, what I’m doing is using data to explain Netflix’s recent strategy changes, since the story of the month is the turbulence at Netflix. First, head of content Bela Bajaria was fired. Then head of film Dan Lin followed her to the unemployment ranks. (Along with, sadly, 1,000 other Netflix workers.) And then Netflix delivered a “sub-optimal” earnings report, which continued their year-long stock price slide. Then came a flood of articles explaining why all this is happening. (Mine’s coming shortly, rest assured.) I realized I had the perfect chart to explain Netflix’s woes. Specifically, Nielsen’s weekly top ten charts. Usually I focus on one or two weeks in my Streaming Ratings Reports, but sometimes it’s worth stepping back to look at the larger story. And hoo boy, what a story. This article is my annual (plus three months) follow-up to one of my most popular articles of last year. (And a topic I’ve been visualizing for the last half decade.) Subscribe Netflix’s Share of The Nielsen Charts Continues to Decline
Crows may make lightning-fast decisions, sometimes twice as fast as humans, study hints
Carrion crows made decisions about their visual environments significantly faster than humans and primates tend to, according to a comparison in a new study.
Through Pioneers and Slow Progress, America's Relationship to Women's Sports Continues to Evolve
LOS ANGELES (AP) — Athletes and fans of women's sports across the United States have found no shortage of reasons to celebrate: historic TV ratings, changing attitudes toward female players, high-profile wins for equal opportunities and pay. After decades of disparity and sometimes outright scorn ...
Science Hill, West Ridge preparing for crucial Big East clash
Sometimes you've just got to tip your cap to the other team.
A timeline of Mike Causey’s sometimes-turbulent tenure as NC insurance chief
A look at Insurance Commissioner Mike Causey’s career as a second complaint about his behavior with women surfaces.
Take a break from opinion-page politics next week
Sometimes I think I’d take tornado season over election season. And that strikes me as an apt comparison as I imagine sheltering beneath a hinged cellar door until the storm has blown over — physically in one sense, mentally in the other. As an example of the debris flying around this season, my inbox was
Sundar Pichai: Google’s Frontier Imperative, Compute Scarcity Dilemmas, Future AI Products and Businesses
Alphabet CEO Sundar Pichai has little patience for doubts about Google’s willingness or capacity to compete at the frontier of AI. His company’s latest Gemini 4 Argon model — long-promised and recently delivered — beats OpenAI and Anthropic’s latest public models on coding, math, science, and knowledge-work benchmarks and has placed Google’s AI back among the world’s best. Amid chatter that Google was fine with good-enough lightweight ‘Flash’ models, Pichai pushed back hard against any suggestion that company is limiting its research ambitions. “We won’t do it any other way,” he told me in a Big Technology Podcast interview recorded this week at Google’s Mountain View headquarters. Pursuing cutting-edge research is costly for Google, requiring compute and chips that otherwise could be sold at a high markup by its cloud business. But Pichai said allocating resources to frontier AI development is “ground zero” for Google. “Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development,” Pichai told me. In our conversation, we discussed Argon (which Pichai says processed three times as many tokens as Gemini 3 in the 72 hours after launch), AI’s role in developing better models, the AI interface of the future, competition with OpenAI, Google’s partnerships with Anthropic and Apple, data centers in space, and the nature of his optimism. You can read the full conversation, lightly edited for length and clarity, below, or listen to it on Apple Podcasts, Spotify, YouTube, or your podcast app of choice. Alex Kantrowitz: Google is back at the frontier with Gemini 4 Argon, which beats OpenAI’s and Anthropic’s public models on benchmarks spanning coding, math, science, and knowledge work. The conventional wisdom was that Google had given up on the frontier to focus on fast, good-enough models for its products. That was wrong? Sundar Pichai: Very wrong. Look, we are so committed to being at the frontier. It’s in our DNA. We won’t do it any other way. We’ve been thinking about the AI frontier for well over a decade now. The frontier is very dynamic. It’s very competitive. It’s very exciting. I couldn’t be more pleased with how Argon has come together. We took our time with it. We made sure we tested it safely and deployed it carefully. We took a bit longer than our other models, given its capabilities. Across the board, be it for agentic work, knowledge work, coding, creative tasks, Argon is one of the really good frontier models. The first 72 hours since we launched, it’s almost up to 25 trillion tokens used, which is about three times what Gemini 3.0 had in the first 72 hours. And we are capacity constrained as we are serving it. Excited, but there are always areas….. You know, the frontier is very dynamic, and you have to keep evolving the models. There are areas which we can see we need to improve as well. We’ll be at work on all of that as well. Which areas? On any given model, Argon would be across the board better at everything if you just look even two months back. But the space is so evolving. Even on coding, for example, there are pockets where we know we need to do better, and there’s clear feedback. We’ll make it better. The argument for standard models is that they’re already good enough to build great products, with the frontier valuable only for certain tasks. Meta built Muse with good models rather than the best, and Google could put standard models into Maps, Gmail, and Search. Why isn’t that sufficient? It’s definitely not how I have thought about it at all. We are deeply committed to driving the state of the art and actually pushing where this technology can go, all the way from transformers. That’s a journey we’ve been on. You will see us continue to do that. When you’re at the frontier, you get an earlier look into the emerging capabilities, earlier than others, which helps you better prepare for the future. We are fortunate to have a wide set of opportunities across consumer and enterprise. Some of our cloud customers, for sure, want the model at the frontier. The frontier models will be better at developing the next set of models in an era of recursive self-improvement. I would argue it matters there. Internally, we have definitely seen a steep point of inflection by deploying Argon to our developers. It definitely tackles problems which our previous models could not do. Being at the frontier puts you at the innovation frontier and helps you drive that as well. There is room for models of all sizes and capabilities. That’s why we did Flash, Flash-Lite, etc. I agree with you that you can develop very good product experiences with not necessarily the most frontier model, because they tend to be slower and more expensive too. But we care about the Pareto frontier, and we’re committed to doing all of that. You said developing frontier models gives you a look at what’s coming. What do you see on the horizon? What makes this moment so exciting is you build something which you think is a culmination of months of work, and you put it out. You already see clear headroom ahead. We’ve all been working on these agentic, long-horizon capabilities, but clearly the frontier is going to have multiple of these agents working together to do things. That’s an example of an exciting paradigm underway. It’s also clear to me that users, both in consumer and enterprise, will have access to some multi-agent framework for them, working all the time doing things. It feels like a moment of infinite possibilities. Sergey Brin said one of Argon’s core innovations was relying more on the model to help develop the model. Yet incidents involving OpenAI’s bots breaking containment and Google’s bots accessing a site they shouldn’t have suggest these systems still aren’t perfectly aligned. Isn’t there reasonable cause for concern about accelerating toward recursive self-improvement? There are three parts to your question. Let’s first talk about RSI itself. You’re right, Sergey mentioned RSI being a critical contributor as we are developing Argon. But to be very clear, today, in a continuum, we are pretty far from the end state of RSI or something like that. The right way to describe it is you have RSI loops along with AI researchers in various aspects of model development. You almost have agents helping AI researchers in some automated way work together to build these models. It’s good to at least understand where we are today, and it’s super exciting. I expect that continuum to keep making progress as we go into next year. The second question is around safety. Security comes from defense in depth. You’re talking about alignment, obviously. We all are doing a lot of work to make sure the models are as aligned as possible. But it’s an active area of research, and there are clear gaps. That’s not the only thing we rely on. You have the work you do on alignment, but beyond that, we put a lot of effort into real-time monitoring as these models are working. Making sure you have good telemetry into what’s happening so that you can intervene in real time if you see unexpected behaviors. How you do that, the architecture with which you do that, how you make sure it’s all sandboxed and isolated in a way that it can continue doing its job during real-time monitoring — these are all areas of investment. Obviously, when we deploy these models, giving customers, enterprises, control, both with real-time monitoring, but their ability to decide where they deploy, what type of data which models ever have access to, etc. All that work is very, very important, and we are doing a lot of that. As an industry, we need to constantly up our game and take the time to do all this stuff, including dedicating the compute required to make sure you can safely deploy these models. The third part is: Is it all enough? This is why there are a lot of conversations in the industry, and I think it’s reasonable to be worried. The right way to approach something you worry about is early on thinking about what more we can do. As an industry, you’re definitely seeing increased levels of work on alignment, testing for safety, etc., as we deploy these models. The next step would be developing better standards so there are cross-industry standards of how we do these things, including the ability to evaluate using independent evaluators. If you look at areas like financial controls, we have accounting standards, we have independent auditors. Those are all the next natural steps for us as an industry to work toward, and regulation will end up playing a role as we go through all of that as well. Google pursues both language models and world models, while some other AI companies like OpenAI have focused more heavily on LLMs as the path to AGI. Why is world-model research important to you? As humans, you obviously perceive the world around you, and you’re taking all those inputs. Making sure these models are able to understand the laws of physics, simulate the world in high fidelity. All that, I think, are key enabling blocks for intelligence. We are working across all possible applications of this. Take an area like robotics. It’s not an accident: If you look at embodied reasoning benchmarks, Argon is state of the art. That all comes from our experience building world models. Inherently, I would expect there will be architectures which will converge all of this in the future. But for us, we view this as, today, this jagged frontier that is intelligence. We want to be at the frontier of all the possible approaches, and that will help us both drive better research, better insights, and develop a much better AGI model, is how we think about it. Some people argue Google should focus on selling its computing resources rather than using them for AI research. Cloud wants to sell compute at a high markup, while researchers need a very big computer to build the best models. How do you balance those demands? Ground zero for us is making sure we are investing in the compute that’s needed to be at the AI frontier. In 2015, I set the company to be AI-first. AI is that key horizontal ingredient across everything we do, all the way from Search to Cloud to Waymo. That is ground zero for us, making sure both for research and developing the models that we have enough compute needed. Beyond that, we have these independent businesses which each have their own long-range plans. They’re using those long-range plans to project forward, and we plan for compute for all of them. The cloud team, they have a business and they have a projection and they have demand signals. They have committed contracts. With that help, we can plan on a long-term compute trajectory for them. We’re fortunate to have this wide set of opportunities. There’s no company in the world which isn’t making trade-offs at any given moment in time. But the first principles we operate on is making sure we have enough compute to invest in the frontier, and that’s non-negotiable, and everything else works on top. Remember, we are building our own compute, our own data centers. There are long lead times. The compute we have today is a function of decisions I made in 2024. Even if you think back to a year from today, last year the agentic coding takeoff hadn’t happened. Today, obviously, when you sit and look at anything, everyone is short—not just us, other companies—and everyone wishes you had two times what you had planned for. That comes with the territory. At any given moment in time, we are planning for what we would need two to three years out in the future. You’re going to find some decisions you make may look suboptimal two years out, depending on where the state of the art is. I don’t spend too much time thinking about it. The way I think about compute allocation is making sure Google DeepMind, both for research and frontier model development, is cutting edge in terms of compute access. After that, we plan for all our businesses. It’s great we can provide our compute to other people too, like we have done. It allows us to scale that up, get real tests for it in the widest application set possible, test it in the real world, and make sure we are at the cutting edge of technology at every layer of our AI stack as well. Ben Horowitz relayed on a podcast that Andy Jassy had said compute was sold out until 2028. Are you seeing the same thing? If today a customer is coming out and asking us for compute in ’27, that is incredibly difficult to do. Even last year we went out and leased some short-term compute to accommodate some customers and help them out. But yes, we are all dealing with similar timelines. Every major technology shift seems to involve overbuilding, from railroads to fiber. Do you think that’s happening here? It depends on the time frames you’re looking at. This level of build, if I take a long enough time frame, it makes complete sense to me. We are at early stages of the most consequential technology ever, and this is not like fiber. It’s not dark. We are using it. We are capacity constrained on a daily basis. The applications, the underlying technology is improving rapidly. But running a company, the way I work through the moment is you look at your demand signals. You look at your committed revenue opportunities that you’re getting in the form of contracts. You balance that with the underlying technical capability, or internally you balance how we are using it internally, the model trajectories. Based on all that, you take a disciplined approach—a confident, aggressive, but disciplined approach—to plan out compute. But when you talk about the broader economy as a whole, you’re having people operate with various levels of risk. I focus on what I can control as a company and plan through it. If your question is, can there be an overbuild? Yes, because you can have some constrained components. Remember, what matters is a set of everything you need to build out that compute. Memory, electricity, electricians…. Land. Like 2,000 components or more. You could get constrained at some point. There could be things which cause bottlenecks, and then you could have overbuilds. There could be external shocks to the economy. There are macro risks. All that is possible at any given time. But am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes. I was in the room when you said AI would be as profound as fire and electricity. If you believe that, shouldn’t you give researchers the biggest possible computer, even at the expense of today’s cloud profits? We’re all doing some form of that. Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development. What you’re seeing is true already. But you want to be thoughtful. Even our AI researchers have some constraints, which are also good. That’s what inspires creativity. That’s what helps you come up with a more efficient algorithmic breakthrough, maybe to use less memory. What we are committed to is to invest what’s needed to stay at the frontier. OpenAI President Greg Brockman told me a few months ago that the ideal AI product might have no interface at all—just as natural as conversation. What do you think the interface looks like in the age of AI? Computing historically has been humans adapting to computing. With AI, it’ll be the other way around. The technology will adapt seamlessly to how humans live and go about their day-to-day life. You want to be able to speak to that AI when you want to. It has context. It can be persistently running somewhere, give you continuity. You’re not dealing with a lot of complexity. It’s natural. All that makes sense to me. That doesn’t mean it won’t have any interface, however. Nowadays, even on my phone, Gemini voice models are so good. I can just press the microphone button. I can just ramble. That’s why we call it Rambler. I can ramble. It intelligently corrects with my personal context. Even my friends’ names, weird names, it gets it right, even if it first guesses it wrong. It’s amazing. I’m not going in, backspacing, editing once I finish saying it. It’s remarkable how good it’s gotten. But there are going to be moments you will need an interface. You may want to entertain yourself. You may want to watch a video. You may ask your AI to create something, and you want to watch it, consume it. The way I think about it is, you want it to be as seamless and as natural as possible, and that you are not doing the hard work adapting to whatever the interface is. The technology is adapting to how you’re working at any given moment in time. When I asked Sam Altman about competition from Google, he argued that OpenAI was building AI-native products and implied Google was bolting AI onto existing ones. Do you accept that framing, and can a “bolt-on” compete with an AI-native product? First of all, it’s a very dynamic moment. We are all pushing each other. OpenAI has pushed us as a company too. This is not just we are doing something to them. Far from it. Yes, Google went into its own version of a code red too. In the AI moment, all of us have to respond with varying levels of urgency at any given moment in time. We are constantly building new products as well. Gemini was built from the ground up as a product—the Gemini app I’m talking about. Or tomorrow, as all of us are adapting to agents, are you bolting agents onto ChatGPT, or are they building it natively? How would you think about that? You can always put these questions back. I don’t think of it that way. I think of it as you want to build world-class product experiences that are AI-native and they are winning. Some of it is adapting products, some of it is rethinking new experiences. They will all be part of how you approach this moment. How do you expect to make money from these AI interfaces? If an agent recommends a product and I buy it, should it take a cut of the sale, even if that’s disclosed? I’m going to put enterprise aside. Consumers are going to increasingly value this technology. Take software developers today. Developers are willing to pay for these coding models because it really helps make them more productive. Nobody would want to code the way they were coding three years ago, or even the same time last year. When you’re delivering value, there’s value to be had. I’ve never seen examples where consumer products really scale to billions of users, where people are using it intensely, and haven’t had monetization to go along with it. If you’re providing value to people, there is inherent value to be had. The nature of it may change. You’re asking a scenario in which agents are doing stuff for you. At the end of the day, you will have commercial needs. The agents need to fulfill those commercial needs. The providers on the other side are going to see value in that. There’s going to be some mechanism by which it gets fleshed out. That’s what an efficient system does. But what matters is that you’re building those world-class consumer experiences. I promised myself one product feature request: Can you make Gemini-powered search in Gmail as good as searching Gmail through ChatGPT? What’s going on here? One of the exciting things we announced recently is the unified Gemini agent for enterprise, and we’re bringing that to consumers. These are persistent agents which run 24/7 in the cloud. We’re spinning up a micro VM effectively for every query, and from the runtime, the harness, the model—it’s all a headless service. It’ll work everywhere, and it has your context with you. You can address this agent whether you’re in Workspace, in Search, in Gemini. Agents are going to drive this unification from a customer experience, which people are looking for. When you have a broad product portfolio and you’re moving forward fast, there are always areas where you’re moving forward faster than some other areas. That’s valid feedback. But we are putting a lot of time and effort into making sure, as we build out our agentic experience, that unification and that capability works well across all these services. People have asked where Google’s equivalent of Muse or Dots is. Do you anticipate creating a new product to compete with them? Our goal with Gemini is to be a personal, proactive, and powerful assistant. That’s what we are working toward. You saw us describe the early versions of it at Google I/O this year with Spark. Effectively, already, if you open the Gemini app as a Pro or an Ultra user, you see Chat and Task, and they start giving you that agentic experience. With Argon now, that capability is dramatically accelerated. What we announced recently in our enterprise event, we are bringing that to consumers as well. Soon in Gemini, you will be able to ask something, and it’s a continuum. You don’t need to think about whether you’re asking a chat or an agentic task. It’ll just automatically do the right thing, and it can work persistently in the background securely and get things done for you. Already, empirically, when I look at what’s coming into Gemini in our app, last year you would have tagged them and most of them would have been chats. But a steadily growing percentage is already agentic tasks in Gemini. Yes, we are going to bring personal agents to our consumers. It’ll work in the Gemini app. It’ll work in Google Search. It’ll work in Gmail. You can address it, call it, etc. We want to make sure we’re getting it right. It’ll be powered by Argon, and we are very excited to bring it to our users. Google has invested and committed tens of billions of dollars to Anthropic. What do you see in Dario Amodei that makes you want such a deep investment and partnership? Very early on, when we looked at the founding team of Anthropic, it was very clear to us this was a world-class team which was involved in some of the earliest GPT models. We were quickly able to assess that they would be at the frontier. We’ve always tried to identify and partner with companies which would be at the cutting edge of technology. We’ve done that with SpaceX. We’ve done that with Anthropic. That’s what we saw, and we had a chance to partner. Is your partnership with Apple to license Gemini for Siri expanding, contracting, or standing still? For both us and Apple, that partnership was about translating the capabilities of AI into better experiences for users. I thought Apple has done a phenomenal job of incorporating—they customize these models and build it into Siri—and it’s been great to see the reception. We are committed to doing more. You’re going to do more with them? There’s nothing specific to comment, but it’s a partnership. It’s working well. What have you learned so far about the feasibility of data centers in space? It’s a very exciting project. Projects like this remind me—I still recall that moment in the desert for the DARPA Grand Challenge, watching the Stanley car compete in it, which ended up being the basis for Waymo. With all these technologies, you’re at a moment in time where it looks a bit like sci-fi, but 10 to 20 years down the line, it’s actually working in a meaningful way. I feel the same in space. We’ve always believed in betting big on these technologies. Quantum has been a great example of it. We’re probably 10 years into the journey. Waymo, we are almost 20 years into the journey. Space, we are two to three years into the journey. It’s clear to me that it’s an approach which will make a lot of sense in the future. Being in sub-synchronous orbits with 24/7 access to the most abundant form of energy we have access to will make a lot of sense for this exponential increase in compute that we are going to need. Having said that, this is a challenging project. The team has disciplined milestones. It was a super exciting milestone to send the TPUs up to space. Sometime, as we are speaking, we are expecting a “hello world” moment from them. I look forward to seeing that happen, and we’ll continue investing here too. You previously said 25% of the code written within Google was written by AI. What’s the number today? That number in our world today is meaningless in the sense that every part of what we now touch is influenced by AI deeply. What we are tracking with Argon, for example, it’s been a point of inflection. People are using Argon to really go tackle complex projects, including projects which affect millions of lines of code. In the four weeks since we have launched Argon, we have seen a 30% increase in CLs, and more importantly, almost a similar increase—a one-third decrease in rollbacks. People are writing code more productively and better code with it. We are tracking it with newer metrics now. How are you dealing with opposition to data centers? If you can’t build in the U.S. the way you want to, where else do you go? We’re very committed to building our data centers very responsibly. When I look at our largest data centers in some of the communities where we have been the longest, I visit them, I talk to people. There are people who come and tell us their kids have gone to college because of our investments in these communities, and we’ve grown in those communities. To me, this is about doing it right. Engaging with the communities early, doing it transparently. We are committed to making sure we are bringing incremental power, and we cover 100% of the cost of our power needs for these projects. It’s understanding the impact on neighborhoods and making sure you’re working thoroughly to address them. Done rightly, you can continue making progress. But we are constantly taking feedback into account. I do worry not everyone operates the same way. People have been surprised by data centers showing up in their backyard. Those are all important. As an industry, we should continue to do better. But part of the reason, in a 20-year time frame, we are thinking about things like data centers in space is making sure we are able to put these data centers where maybe the trade-offs will be different. What unproven area of AI is Google working hardest to crack? Is it continual learning—getting models to keep updating after release—or something else? There are multiple parallel paths. You saw with Argon we have a million output tokens. With our Gemini models early on, we had a one-million context window on the input side. These are all examples of breakthroughs we are constantly working on. How do you think about synthetic data? How do you think about continual learning? How do you think about more efficient architectures? We do dedicate a meaningful percentage of our compute to these research bets too. It always goes hand in hand, including our world models. Those are all bets we are placing. Sometimes, as part of these bets, there are ideas that emerge which just make their way into the mainline models. Maybe we don’t fully talk about it, but we have multiple steps in these areas. Working in technology requires optimism that building something will improve the world, but those bets can leave real consequences when they fail. I recently returned to a town in El Salvador where enthusiasm for Bitcoin had given way to unfinished construction and something resembling a ghost town. How do you maintain the optimism you spoke about in your Stanford commencement address while accounting for those risks? I genuinely have a deep conviction that improving technology and improving access to technology makes people’s lives better. It’s been my personal experience in my life, out of which comes that deep conviction, optimism. But with that, I’ve always felt humanity’s journey is always about working hard to harness that technology to make it work well for society. The optimism is not an optimism that you just build whatever you want, whenever. You work hard to make sure you’re building technology in the service of humanity, to make the human condition better. That’s got to be the defining goal as we are approaching it. With that framework, I’m very, very optimistic. We are in very early stages of the kind of progress that we are going to see on many areas. We’re all talking about areas like healthcare. A lot of the progress is ahead. The ability to make things in a fundamentally cheaper way on many things where things have been getting expensive for a long, long time, discovering new materials, etc., which really bend the curve—it’s tough to step today and understand the impact of all that, but they’re going to be very profound. I’m very optimistic, but we have to be rightfully concerned about all the ways this technology can go wrong. Sundar, I know you don’t do many of these. Thank you. Thanks, Alex. Appreciate it.
Start-Up Claims to Treat a Patient With A.I.-Created Viruses
Certain viruses attack bacteria and can sometimes clear antibiotic-resistant infections. Researchers said they had tailored a virus to attack one patient’s illness.
Scientists finally uncover why statins can cause muscle pain
Scientists have uncovered a surprising reason why statins, the widely prescribed cholesterol-lowering drugs that help prevent heart attacks and strokes, can sometimes cause muscle pain, weakness, and even severe muscle damage. Using advanced imaging technology, researchers discovered that statins can attach to a crucial protein in muscle cells and force open a channel that normally controls calcium flow. This causes calcium to leak continuously, potentially damaging muscle tissue.
Humboldt County leaders warn HR 1 cuts could hit health care, education by 2027
Concerns are growing in Humboldt County about how HR 1, a sweeping federal law passed in July and sometimes referred to as the “big beautiful bill,” could affec
Why Thinking About Divorce Is Actually Good for Your Marriage, According to Science
Researchers say imagining life without your spouse can sometimes serve as a wake-up call to address problems before they get worse.
AI midwife seeks to fill gaps in Zimbabwe's maternal health care
With public health services stretched thin, women in Zimbabwe sometimes struggle to access reliable information about pregnancy and childbirth. A locally developed app aims to answer their questions…
‘Sense and Sensibility’ review: Daisy Edgar-Jones shines in adaptation
Every beloved book lives forever in the mind of its reader, and sometimes if we’re lucky, it becomes a beloved movie, too. Jane Austen’s tale of two sisters, “Sense and
Can AI solve our mental health access problem?
Last week I asked you what worries you most about AI in health. It was close, but your top answer was “AI giving bad health advice.” Last week’s poll results: You’re in good company. This concern was top of mind at a conference I joined at Stanford last week, where researchers, clinicians, and people who build AI systems spent a day debating trust and safety in health AI. Stepping back, I’m also hearing your emerging concerns about AI’s impact on our well-being generally. I see this as a matter of public health, and we’ll take it in pieces as we learn together. Today we’ll focus on AI chatbots used for therapy for mental health. Early this month, Governor Newsom vetoed Senate Bill 903, California’s most ambitious attempt to regulate AI use for mental health. It’s at the center of active debate, and raises the questions patients, family members, citizens, and voters will face as AI moves deeper into health care. Let’s work through it. What is “AI-based therapy”? It’s really two things: purpose-built mental health apps designed to deliver structured support, and general chatbots like ChatGPT, Claude, and Gemini that people increasingly use for emotional support—even if they weren’t built for it. Spoiler alert: I don’t know whether the shift toward AI therapy is good or bad. The evidence isn’t there yet. But I do know it’s already happening, and I have some ideas about how we can roll it out with safety in mind. The therapy dilemma, in a nutshell Mental health challenges are common, and strain health and well-being across populations. Therapy (interactive dialogue with a trained, empathetic practitioner) helps many people heal. The demand for licensed therapists far exceeds the supply in California, and AI-based therapy can narrow that gap. AI-based therapy remains novel, unproven, and not well tested. Right off the bat, we can see we’re dealing with trade-offs here. Since the main driver of demand for therapy from a robot is lack of access to therapy from a human, we’ll start there. It’s too hard to find and afford a human therapist California is short roughly 55,000 licensed, non-prescribing therapists and counselors, about 40% of what we need. Psychiatrists, who rely on talk therapy as a complement to prescribed medications, are even more scarce. Eight counties have none, and density ranges from 1.7 per 100,000 residents in San Benito County, to 68.1 in Marin County. My wife is a psychiatrist in Marin, and even here access is tight. If it’s hard in the best-supplied county, imagine rural Sierra County, or the San Joaquin Valley. In 2024, more than half of Californians who tried to make a mental health appointment struggled to find a provider who took their insurance, compared with 14% for physical health care. When Senate staff posed as “secret shopper” patients and called 120 mental health providers in Medicare Advantage networks, they landed an appointment less than once for every five tries. Insurers pay behavioral health clinicians about 22% less than medical and surgical clinicians for comparable office visits. On top of that, billing denials and prior authorizations hit hardest at the solo and small practices common in mental health. Psychiatrists opt out of Medicare at the highest rate of any specialty. In many places, a cash practice for therapy fills quickly with patients paying $250 or more an hour, with no insurance paperwork at all. Families who can pay cash find providers more easily. Those who rely on insurance, including Medi-Cal enrollees, older adults, rural residents, and non-English speakers, compete for what’s left. So we can’t pretend the human system works, and we can’t blame people for turning to AI for support when they’re in distress. AI is filling the gap It’s estimated that roughly a quarter of AI users use it for mental health support. If you have internet, it’s available 24/7, it’s cheap, and it speaks your language. Uninsured adults are about twice as likely as insured adults to do it (30% vs. 14%). It feels anonymous in ways that may enable people to ask things they’re unsure or embarrassed about. The largely hidden, personal use makes it hard to study whether it’s actually helping people with their mental health issues, but researchers are trying. Does AI therapy work? A 2026 analysis of nearly 40 randomized trials found chatbots showed a real but small reduction in depression symptoms. But most of these trials were compared to an alternative of no therapy, not a human therapist. So the benefit of AI therapy is probably real, but modest, and mostly untested against the alternative of human contact. We need more studies. Serious concerns OpenAI’s own estimate is that more than a million ChatGPT users each week show signs of suicidal planning or intent. These are high stakes encounters, and AI doesn’t always get it right. That’s partly because a chatbot can sound like a therapist, but it has no license, no duty of care, and it’s not clear who to hold accountable when it gets things wrong. AI is also designed to help users in whatever task they’re seeking to accomplish. People may feel like they’re getting therapy, but they’re mainly getting a convincing conversation. It’s easy for an AI companion to wander into gray areas, like playing along with “creative writing” that may be about the user’s own suicidal intent. Courts are driving safety conversations A wave of lawsuits have been filed on behalf of families claiming that chatbots pushed people they loved toward self-harm, including teens who died by suicide. The first settlements came this year. OpenAI is headquartered in San Francisco, and dozens of cases alleging that ChatGPT drove users to suicide or self-harm are now before a San Francisco judge. Character.ai has filed for bankruptcy after several states brought lawsuits accusing the chatbot of pushing dangerous content to kids. But the technology seems to be improving. Last month, an independent evaluation found that newer AI models “almost never endorse suicide.” That’s progress, but our societal appetite for even one preventable death is very low. So how do we move forward? Finding a middle ground Earlier this month the governor vetoed Senate Bill 903, a bill designed to place guardrails on the use of AI for therapy. The bill would have prohibited any AI-based platform from being advertised as psychotherapy, and would have required a licensed professional to review and approve each encounter where AI was used to support therapeutic decisions. Newsom called it overly broad, since even routine screening questions would have needed clinician sign-off. Knowing we’re already short on mental health providers, requiring human review of AI encounters may not relieve our access problems. Still, the answer isn’t no oversight. Here’s one feasible short term solution. Rather than reviewing each encounter, a licensed clinician can design the guardrails: what counts as a crisis, what triggers an immediate handoff to a human, and what gets sampled for audit each month. The AI runs inside that structure. Conversations get checked, failures get reported, and a real clinician owns the framework. Medicine already has models to learn from. The FDA process for rolling out newly approved medications is an example. After a product reaches patients, we keep watching for adverse events. Patient-facing AI needs the same “digital pharmacovigilance”: systematic tracking of harms, near misses, and the types of groups that get hurt and why. It’s also critical that users know that they can reach a human if they’re in crisis. Most chatbots now include messages like these: Bottom line The rush to use unproven AI tools for mental health support is mostly a workaround, a sign of a health system people can’t get into. We need better access to proven care from licensed clinicians. That takes reimbursement that makes accepting insurance viable, and a bigger workforce. But, built and tested well, AI could be part of the answer. It could be the 2 a.m. check-in when no one else is awake, or a way for patients to practice skills their therapist taught them. It could be the first step to get someone the right level of care, and take paperwork off clinicians’ plates so they spend more time with patients. None of this is simple. The SB 903 veto doesn’t end the debate, and the legislature will be back. In the meantime, those of us using these tools need to stay informed, ask hard questions, and hold the companies building them accountable. I’ll try to do my part, and we’ll keep learning together. Love,Matt P.S. If you or someone you know is struggling with mental health challenges, emotional distress, substance use problems, or just needs to talk, call or text 988- you’ll reach a real person— or chat at 988lifeline.org 24/7. Dr. Matt Willis is the author of Your Local Epidemiologist in California. A California native, he’s served as a primary care physician, CDC epidemiologist, and public health officer for Marin County, where he guided the pandemic response. He lives in Marin with his family and their dogs, Teddy and Ramona. Thank you Cynthia He, MD, PhD, and Parnika Saxena, MD, for their guidance in preparing this article. Cynthia works in an outpatient mental health clinic, treating patients who have Medi-Cal or, sometimes, no insurance. Parnika works in an inpatient psychiatric unit, treating patients with public or private insurance or self-pay ability. All opinions are their own and do not represent their employers.
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When 1 + 1 = 1: The Miserable Math Behind Many Media Mergers
(Welcome to the “Most Important Story of the Week”, my bi-weekly strategy column analyzing the most important (but often not buzziest) news story of the last two weeks. I’m the Entertainment Strategy Guy, a former streaming executive who now analyzes business strategy in the entertainment industry. Please subscribe.) The last few issues of this newsletter have circled two topics: September’s streaming viewership and all the Paramount/Skydance/Warner Bros./Discovery merger news. I’m happy to say on the Streaming Ratings Report front, we’ll be all caught up by early next week, then the SRR will come out on a tight weekly schedule. Then, after today, I’ll have slayed my PSWbD dragon. It’s such a big story, it demanded multiple issues. With the announcement of a name (just Skydance) and executive team, though, I think we can almost close the book on this story. In terms of topics, it becomes just another studio to cover. So today, I have one last set of takes on that merger. As I’ve sat with it, reading about who was staying and who was going, a big worry crystallized in my brain, and I gotta get that take out. Plus, I still owe you a host of “ugliness” that didn’t fit in my previous “The Good, The Bad and The Ugly” takes. Let’s dive in! Subscribe Most Important Story of the Week - As Paramount Shuffles Deck Chairs, Will They Build or Consolidate? On social media—I try to avoid most social media platforms, but sometimes I end up checking in—the best joke I saw about the announcement of the new name of Skydance was from fellow reporter Claire Atkinson who pointed out… Yeah, I laughed out loud at that one. Later, though, I saw another Twitter response (that later got deleted, so I can’t shout the person out) that did give me pause. The person noted that the Titanic film made the better analogy: a super expensive project everyone in the town doubted that went on to conquer the globe. So yeah: on one hand, the image of the Titanic recalls the actual ship and its infamous end, but that clip is from a movie which proved the doubters wrong. So for all the skepticism of the latest massive media merger, one could wonder which way this one goes. Does it crash like the Titanic or rise to glory like Titanic, the movie? Either way, this deal is “Titanic”, looking at the massive debt required to close the deal, which is not going well when it comes to borrowing costs for Paramount… …or borrowing costs for Oracle. But I’ve started to get worried about another outcome: the sum of this merger will be smaller and weaker and less profitable than its former parts. In other words, one plus one won’t equal three or two, but an even smaller number. Skydance paid to take a traditional studio off the board, but they won’t really capture the upside. Let me explain what I mean, but first, let’s look at the series of news that inspired this thought. Skydance Sets Its Leadership Team As soon as the Paramount-Skydance-Warner Bros.-Discovery merger closed, the new leadership team announced a new name—Skydance, which I’ll use going forward—and a new executive leadership team. You’ve probably read through all the executive shuffling, but if you haven’t, here’s a quick, incomplete summary, in rough order of importance: Mattel’s CEO Ynon Kreiz—who previously ran Maker Studios—joined as co-CEO with David Ellison. HBO/HBO Max’s Casey Bloys will stay and run streaming. Former head of streaming for Paramount-Skydance Cindy Holland—who was also at Netflix—will depart. The former heads of Warner Bros.—Pam Abdy and Mike De Luca—are out. The film studios will be run by Dana Goldstein and Josh Greenstein. George Cheeks of Paramount will run Skydance TV, controlling all the TV production units. In comic book news, the DC films will still be run by Peter Gunn and Peter Safran. Lastly, the head of CNN, Mark Thompson, will stay in place. Let’s quickly opine on the particulars before diving into the broad ramifications. We’re just getting started with this issue, but the rest is for paid subscribers of the Entertainment Strategy Guy, so if you’d like to find out… Who I like and dislike in their next gigs… Why I think this consolidation will likely leave Skydance worse off… The math behind which mergers succeed and which don’t… A lot more ugliness in this merger… And a whole lot more... ...please subscribe! We can only keep doing this great work with your support.
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‘Rocky Horror’ is a 50-year tradition of mess and chaos. This NOLA theatre does it anyway.
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Students Use Forensic Science Skills in Escape Room
Hundreds of students from across the region put their problem-solving skills to the test this week during the Regional Science Consortium’s annual Forensic Science Escape Room. Elementary school students had just a few hours to figure out how someone died as part of a forensic science escape room activity. The Regional Science Consortium partnered with Mercyhurst University for the 10th year of the event, which is taking place at the Tom Ridge Environmental Center. It began Monday and runs through Thursday.Throughout the week, more than 250 students are working to solve murder mysteries using real forensic science techniques. Students must analyze the evidence, piece together clues and solve the crime before time runs out.Organizers said the event gives students interested in pursuing forensic science in college a firsthand look at what a career in the field could entail.Sarah Skelton, Special Gifts Coordinator at Iroquois, said the activity provides students with an opportunity to develop skills that can sometimes be difficult to practice in a traditional classroom. "It really helps them to work in teams and on critical thinking skills. Which, not that they don't get these opportunities, but it's hard sometimes in the classroom to develop those higher-level thinking skills”, said Skelton.Students also get a chance to learn how forensic science is used in real-life investigations. As Skelton explained, "What are the real-life applications? What would you actually see in a crime scene? How would you handle these types of forensic investigations? So it's really cool our kids get to see what opportunities are out there and possibly a career field one day."The popular event has grown over the years, with some students returning year after year. Organizers said watching those students grow and develop their interests is one of the highlights of the program.Jeanette Schnars, the Executive Director of the Regional Science Consortium said it's rewarding to see how students' interests evolve as they get older. According to Schnars, "How they have grown and matured and where their interests are going and maybe they aren't going into forensics, but if they are going into sciences or different disciplines, so it's really fulfilling to see how they have matured."One student who continues to enjoy the event is sixth-grader Mia Harvey of Iroquois. Harvey hopes to pursue a career in medicine and says the forensic science activities are helping her explore her interests, "One day I want to become a neurologist, and learning about the bones is probably going to help me."Harvey said her favorite part of the event is the real-life application of what she's learning. "While you are trying to find the clues, they teach real stuff that you would need to know”, said Harvey.Event organizers said if students are learning while having fun, then the program is accomplishing its goal. "Not only are they learning the science, but they are having fun with it too”, said Schnars.Adults will also have a chance to take part in the experience this Saturday during an adult version of the escape room, which will feature food, drinks and entertainment. For more information, click here.The Forensic Science Escape Room has become one of the Regional Science Consortium’s most popular events, with teachers signing up their students as soon as registration opens in August.
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Why I Don’t Think Ride Or Die Should Have Been Cancelled (And What Company Should Save It...)
(Welcome to my weekly streaming ratings report, the single best guide to what’s popular in streaming TV and what isn’t. I’m the Entertainment Strategy Guy, a former streaming executive who now analyzes business strategy in the entertainment industry. If you were forwarded this email, please subscribe to get these insights each week.) So…I recently came across a Reddit thread, talking about films on streaming, wondering why there are no reliable sources out there for streaming ratings data. Sigh. Honestly, as annoyed as I am that so many still don’t realize that we have streaming ratings, I somewhat get it. Until now, there was such a long delay between when a show or film came to streaming and when we got its viewership data; I understand why people were confused. (But, as you know, this just changed.) Also, even back in the day, how many people knew how well films did on television? Luckily, as a reader, you’re in the know. Okay, on to this week’s issue. I want to take a look at Prime Video cancelling Ride or Die and whether I agree with that decision. Overall, we didn’t have any breakout hits (no TV shows landed over 20 million hours according to Nielsen) but we have a lot of steady performers on the streaming charts. Plus, we’ll look at where $50 million in missing box office dollars went. (Spoiler: straight-to-streaming.) All that, plus new episodes of The Secret Lives of Mormon Wives, Supergirl heads to streaming, a new YA show does well on Netflix, the viewership for the Daily Wire’s $50 million fantasy show, what TV show hit-maker can’t make streaming hits, CD sales, all the flops, bombs and misses, and a whole lot more. Let’s dive right in! (Reminder: The streaming ratings report focuses on the U.S. market and compiles data from Nielsen’s weekly top ten viewership ranks, Luminate’s Top Ten Data, JustWatch and Reelgood interest data, Samba TV household viewership, company datecdotes, Netflix hours viewed data, Google Trends, and IMDb to determine the most popular content. While most data points are current, Nielsen’s data covers the weeks of September 7th to September 13th, 2026. You can find a link to my terminology here.) Subscribe Television - Ride or Die Is a Hit…But It Got Cancelled? What The Data “Says” I hate the phrase “here’s what the data says”, even though sometimes I find myself using it. If data just told us everything, we data folks would be out of a job! Data is often messy, complicated and nuanced. When it comes to strategy, many of the best decisions can’t actually use “data”, since it’s vastly too complicated to model. Anyways, I’ve been singing the praises of Prime Video’s Ride Or Die this summer, a show that did very well for them. And yet…it got cancelled. Now, usually when a show gets cancelled, a bunch of websites say “a hit show got cancelled”... Here’s the thing, though: in this case, they’re right! The data “says” that this show was a hit. Specifically, out of 522 first seasons to make a week on the Nielsen charts since 2020, it’s the 75th biggest. On Amazon, Ride or Die is eighth out of 41 first seasons: That’s top 15th percentile all time, almost exactly, which is my definition of a “hit”! I mean, outside of Reacher, Amazon needed a hit this summer! (Admittedly, Off Campus did well for Amazon in the spring, but Ride or Die performed even better.) So what happened? Why did Amazon cancel one of their rare 2026 bright spots? Well, Deadline reported a reason—which I’ll get to—but I’d break it down into three relevant questions: What is a given streamer’s reasoning? Is that reasoning sound? Could the stated reason not be the real reason? According to Deadline, Amazon felt the show “over-indexed” in middle-aged women. (This was based on leaks from within Amazon.) The logic is this: Amazon already reaches this demographic because of unlimited two-day shipping bundled with Prime, so they don’t really need more shows like this. Now, it’s my job to ask if that’s a good reason, and I’ll be honest, in this case, I don’t think it is. Sure, this show may “over-index” in middle-aged women—I’ll just assume that’s the case here—but I think folks often oversell how important over-indexing actually is. When a show is a “hit”, it isn’t a bit bigger than its rivals; it’s usually multiples bigger. For example, Elle only made the charts for two weeks at 8.3 million hours each. So Ride or Die was 80% bigger in its first two weeks, and likely had a much stronger hold, with its terrific 13 million hours in week three. Ride or Die was nearly three times bigger than Sterling Point through three weeks too. And that means that _Elle, Sterling Point a_nd other Amazon YA shows need to not just over-index a little with younger women (assuming that’s who Amazon prefers over middle-aged women), but massively over-index. Otherwise, more people in that demo (and a bunch more besides) likely watched Ride or Die. (I’ll try to explore this concept more in a future article.) And don’t get me started on how Ride or Die did compared to a certain (very, very popular, possibly the world’s most popular) YouTuber’s reality show…which didn’t make the charts after its first week and likely cost much, much more than Ride or Die. All to say, Amazon-MGM Studios/Prime Video can tell reporters that this show didn’t reach the right audience, but that excuse feels weak to me. So this leads to the third question: could other reasons have come into play? Yes! Renewal or cancellation decisions rarely (I’m tempted to say “never”) boil down to one variable. At its simplest, it’s two things: budget versus viewership. But often factors like critical acclaim, ownership and, yes, personal opinion come into play. In this case, Amazon has a new executive running things, and this show isn’t owned by Amazon-MGM Studios (unlike Elle). And yes, it may not have indexed with the right target viewers, too. Likely all of those factors came into play.1 My guess is ownership ended up mattering most. Amazon doesn’t own Ride or Die. It’s actually produced by another major studio. Fortunately/allegedly, this show is being shopped around. I can think of one brand new CEO who should consider it for one of his two major streamers…especially if he wants to rebuild goodwill in Hollywood. His name is on this list of exec producers of Ride or Die… If David Ellison needs some goodwill—and he does—rescue this female-led show tomorrow and grab some good headlines for a day. The data justifies it. Quick Notes on TV We’re just getting started with this issue, but the rest is for paid subscribers of the Entertainment Strategy Guy, so if you’d like to find out… How The Secret Lives of Mormon Wives latest season did on Hulu, post-controversy... Whether Supergirl soared on HBO Max... Why the box office is losing money due to the streamers... The viewership for the Daily Wire’s $50 million fantasy show... Updates on The Gentlemen, Lanterns, Outer Banks, King of the Hill and more... What former hit-maker has three streaming flops in a row... Whether The Mandalorian and Grogu popped in week two... All the flops, bombs and misses... And a whole lot more... ...please subscribe! We can only keep doing this great work with your support. If you want an idea of just how much content you’d get in a full issue, check out this older issue. Coming Soon! Oh man, we’re almost caught up! We just have two more issues, then the streaming Ratings Report will be coming out just two weeks after a TV show premieres! Next issue, I’ll be looking at the first two weeks of the NFL Thursday night games on streaming, a huge new spinoff, Reacher’s Neagly on Prime Video, and the latest edition of Monster: The Lizzie Borden Story on Netflix, the return of Slow Horses on Apple and MobLand (quietly one of their biggest shows) on Paramount+, and Dancing with the Stars on both ABC and Disney+. Plus I’ll take a look at animation for adults in 2026. The week after, a ton of movies are coming to streaming: Toy Story 5 on Disney+, Backrooms on HBO Max, Jackass: Best and Last on Paramount+ and The Breadwinner on Netflix, plus a Unabomber film on Netflix and a romcom on Prime Video. Woody Harrelson and Matthew McConaughey have a show on Apple, along with a game show inspired by Willy Wonka that everyone seems to hate. Long term, some crazy (but hopefully crazy like a fox) IP news. FX ordered a new Sons of Anarchy show from Charlie Hunnam, but it’s not about the biker gang. No, it features the show’s cast playing themselves in a new “meta-thriller”. Huh. And a lot of the cast is coming back. Next, Ryan Gosling is producing a Flintstones movie about a “grown-up Bamm-Bamm Rubble”. Also, huh. This one is in “early development” so we’ll see what happens. Both projects are based on IP, but also seem to have crazy takes on the IP, which I like.
How a controversial camera system is monitoring mental health patients
The Lampard Inquiry started back in September 2024 and I remember going to the first hearing in Chelmsford. Subscribe The bereaved families made placards of their loved ones, they stood outside in silence and then laid flowers to mark the moment. Now, two years on, in a process meant to last another two years, there’s been a development. Last week, we were told the chair, Baroness Lampard, pictured below, could be looking at making interim recommendations. Throughout the process, she has remarked that although this inquiry is looking at almost 2,000 people who were under the care of Essex mental health services from 2000-2023 when they died, the findings could have national implications for other trusts. ‘Records 24 hours a day in patient’s bedroom’ So why has this happened? One system that the inquiry is going to take a closer look at is a controversial monitoring system called Oxevision. It monitors some vital signs of patients remotely by using a camera placed in a room. It’s used by 40% of NHS mental health trusts. But campaigners, particularly the group Stop Oxevision, have asked for this technology to be removed from mental health wards as soon as possible. I spoke to a member, Hat Porter, today and they described to me what they thought this technology did and explained their concerns: “It records 24 hours a day and it’s located in a patient’s bedroom, which is obviously somewhere you’d usually expect to be a private place. “Our concerns are that the use of the technology has been has been kind of implemented as a substitute for proper in-person support, that it’s kind of over relied on and that there’s a lot of misunderstandings and that the presence of a camera, which has often been used without people’s consent, sometimes without even their knowledge, that that has a really big impact on privacy, dignity and human rights.” ‘Not sufficient’ I put it to them that the company behind Oxevision would dispute their claims about consent and in videos I’ve seen online from NHS trusts, they make it clear that the technology is complementary to trained staff and not a replacement. [ ](https://channel4news.substack.com/p/the-nottingham-inquiry-what-lessons) [ How the Nottingham inquiry shines a spotlight on other key mental health care failings ](https://channel4news.substack.com/p/the-nottingham-inquiry-what-lessons) Victoria Macdonald · Feb 12 [ Read full story ](https://channel4news.substack.com/p/the-nottingham-inquiry-what-lessons) A team of lawyers has been working on the inquiry and with bereaved family members who have concerns about this technology. I spoke to solicitor Priya Singh, from Hodge Jones & Allen, who made the point that questions usually asked by nurses would be missing from using devices like this. “It’s not sufficient to say, oh, the patient is moving around. Somebody must go in and say, are you okay? Are you distressed? Do you want help? Is there anything I can do? Share Channel 4 News “How are you spending your time? So I think a lot of our families cannot see the function of it when ultimately the patient should only really be observed when they’ve been prescribed to be observed, they can be observed more, but that more should really be happening face to face.” The Chair will hear evidence over the next two weeks and then a decision made after that, which could have national impact if she does decide this technology should be investigated or even removed. The Department of Health and Social Care said: “The Government is committed to ensuring that lessons are learned from the Lampard Inquiry to improve patient safety. “We are confident that the Inquiry will play an important role in identifying wider learnings for the health system following the tragic events in Essex and will contribute to improvements in patient safety.” We approached LIO Health, which runs Oxevision, for comment. Subscribe Share Channel 4 News Leave a comment
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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
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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.
Every Health Care System Sometimes Says No
Medical programs are full of hard decisions about what to pay for and what to deny. What is the best option?
A CT business elicits giggling and sometimes screams. It’s all ages and nothing you’re ‘allowed to do at home’
“A lot of people say it’s like a rage room only artistic,” the COO said.
Buzz and business impact: Where AI is delivering value
Artificial intelligence has become a popular tool for a significant number of senior living and care providers, with many leveraging this technology to create value for residents, patients and staff members alike. But sometimes AI can be used like a hammer in search of a nail, offering oversimplified solutions for complex challenges, experts warn.
What Does Your Office Say About Your Business? by Jennifer Kraner
What does your space say about you and your business? More importantly, does it give you the best possible first impression? The minute a client, customer or patient walks through your door, they’re forming an opinion about your business – sometimes consciously, sometimes without even realizing it. Your space is communicating something about you, whether you intend it to or not. At Big Sky Design, we work with all kinds of commercial businesses, including many medical practices, law firms and other corporate offices, and I’ve seen first hand how much thoughtful design can influence that first impression. For businesses in medical, legal, and financial fields, three things are particularly important: trust, privacy and professionalism. Your space should communicate all three while also reflecting who you are as a business. That can be challenging, especially in a new construction project. Often, so much of the budget goes toward the actual construction that things like lighting, comfortable seating, artwork and finishing touches get pushed to the side. But those are often the elements that make a space feel complete – and memorable. Think about your waiting room. Is it comfortable? Is the lighting welcoming? Does the layout make sense? When someone walks through the door, do they immediately know where to go and whom to speak with? In a medical office, there’s another important consideration: privacy. Reception areas can become surprisingly noisy places, with phone calls, conversations between staff and patients, and people discussing personal information. Hard surfaces throughout a space allow sound to bounce around, making it difficult to maintain privacy. We recently worked with an eye doctor's office that had exactly this problem. The reception desk had a large opening, and staff were making and taking phone calls right next to patients who were waiting. We designed an acoustic treatment that acted as a divider between the staff and waiting areas. It absorbed sound, but it also became a branded design element. It was a visual reminder to everyone in the space to be mindful of their voices and what others might be able to hear. Those kinds of thoughtful details can make a tremendous difference. And good commercial design doesn't have to mean making your space flashy. Sometimes it’s as simple as a calming color palette, beautiful artwork, plants, carefully selected lighting or comfortable upholstered furniture. In a medical setting, those touches can help soften what might otherwise feel clinical and reduce anxiety for patients who are already nervous about an appointment. I’ve been in waiting rooms where everyone feels packed together, and you can hear every conversation – even what’s happening in the restroom next door. That’s not the experience you want to create. Your office should also feel like your business. Are you telling people that you’re clean and modern? Sophisticated and unique? Warm and welcoming? Or, unintentionally, are you telling them that your business is outdated? You don’t necessarily need a complete renovation to change that message. Fresh paint, updated lighting, new artwork, better seating or a few branded design elements can make a space feel current and intentional. At Big Sky Design, we’ve worked with radiology, dermatology, and dental practices, among many others. Every practice is different, but the goal is always the same: creating a space that reflects the business behind it and gives every person who walks through the door a positive first impression. Because your office is part of your brand. Make sure it’s telling the story you want people to hear. Read more at bigskydesignonline.com.
Looking at the Ugly Side of the Paramount-Skydance Merger...And How I’d Fix It
A Message from Our Sponsor Certified Fresh with 100% on Rotten Tomatoes, FX’s Adults is an ensemble comedy series about a group of twenty-somethings in New York leaning on each other to navigate love, work, friendship and family. Don’t sleep on “The Sitcom of the Moment” - Awards Buzz All Episodes now streaming on Hulu on Disney+. Watch The Teaser Trailer... (Welcome to the Entertainment Strategy Guy, a newsletter on the entertainment industr(Welcome to the “Most Important Story of the Week”, my bi-weekly strategy column analyzing the most important (but often not buzziest) news story of the last two weeks. I’m the Entertainment Strategy Guy, a former streaming executive who now analyzes business strategy in the entertainment industry. Please subscribe.) Before we dive in, I wanted to congratulate Broadcast Now for being the first to report that Netflix’s How to Get to Heaven from Belfast has been cancelled. (I saw this in a write-up by Kasey Moore of Whats-On-Netflix.) More importantly, I wanted to note that, as of yesterday, none of the other trades had reported the news yet. I have half a guess that eventually one of them will report this. Anyways, it’s time to take a break from my deluge of streaming ratings reports to continue my analysis of the Paramount Skydance Warner Bros. Discovery (PSWbD) saga. As I noted last week, a lot of times when folks use the (sometimes cliched) “the good, the bad and the ugly” framing for an article, “the ugly” section is just more bad. Or very, very bad. In this case, yeah, I do think it’s ugly. In more than one way. Unfortunately, we’re only going to have time to tackle one of those ways today: the ugly pile of debt to finance this deal. (In a future article, I may cover the entire process for how it went down, which was very, very ugly.) Last issue, I explained why I liked this deal in a vacuum. Unfortunately, “vacuums” don’t exist outside of space. Let’s dive in. Subscribe Most Important Story of the Week - The Ugliness of the Paramount/Warner Bros. Merger More than one person sent me a note speculating that the big problem with Paramount Skydance buying Warner Bros. Discovery is the “Warner Bros.” portion of the deal. In fact, that’s a take I read a lot about Warner Bros., ever since they caught Netflix’s, and then Paramount-Skydance’s, eye. In short, multiple companies (Time, AOL, AT&T, Discovery) have bought and then regretted buying Warner Bros. Since it’s nearing Halloween, you could almost say it’s like the folks think WB is cursed! It’s a haunted studio—like the town of Derry in movie studio form—and whoever takes possession of it will fall into misery! Yet...I just don’t like that story, because it doesn’t explain the “why” behind why those deals fell through. Especially when Warner Bros. has continued to succeed as a TV and film studio in the intervening two decades. Lord of the Rings? Harry Potter? Game of Thrones? Everything HBO? Considering it never fully owned a broadcast TV channel, this media company has done pretty well. So what went wrong? Well, in recent years, companies bought Warner Bros. instead of the other, smarter option. Explaining POCD (And Applying It to Mergers & Acquisitions) As I explained last issue, one of my favorite frameworks is the POCD tool for evaluating venture capital investments: People Opportunity Context Deal The bar to invest as a VC is high. (Or I might say “was” since cheap interest rates made life easier last decade.) You review a thousand prospectuses, get pitched by a hundred, and invest in the ten best, with the hopes that one of those ten would pay for the rest. That means every element of a deal needed to pay off: you needed the right people running the companies, who could capitalize on the right opportunity, in the context of an industry that was ripe for disruption. If you could then not overpay, you could make some money in a smart deal. The analogy isn’t perfect here, but close enough. For a merger or acquisition to work, you need the combined company (which is the opportunity) to make more than the price you need to pay (which is the deal). Note: the people and context still matter, but with M&A, it’s a little more straightforward: does the opportunity exceed the cost? And usually with Warner Bros., it hasn’t. If you want to find out the single biggest mistake made by the Warner Bros. suitors over the last few years (and my simple fix) become a paid subscriber. I’ll also touch on who I think Paramount-Skydance could (shockingly) consider buying next, TBD, and more. Sign up here.
When “DEI” research is cut, what happens to community health?
“I’m just going to run to the grocery store!” How many times have we said this without thinking about it? For most of us, it's usually an easy walk or a short drive to get the cheese we forgot. In reality, though, 13% of the U.S. population (about 39 million people) live in “food deserts,” which means living more than a mile from a grocery store in urban areas and more than 10 miles in rural areas. This was the case for people in Pittsburgh’s Hill District, where a quick run to buy food wasn’t an option. In the 1930s, federal maps marked the Hill as "hazardous" for lending, a practice known as redlining that steered investment away from Black neighborhoods. Decades of disinvestment followed, and residents went about 30 years without a nearby grocery store. This made it hard to keep healthy eating habits and contributed to chronic disease. When a grocery store finally opened in the area in 2013, researchers saw the opportunity and launched the PHRESH study, which initially compared the health of participants from the Hill district to those in the Homewood district. Over time, the study expanded to other factors such as brain health and Alzheimer's risk. Residents like Roderick Blair overcame initial hesitation to share their health data, trusting it would benefit their neighborhood. Then, in August 2026, the NIH abruptly canceled the study’s grant, citing that it was “subjective” and fell outside their scientific scope—an effect of the administration’s interpretation of diversity, equity and inclusion (DEI). Research like PHRESH can help determine what changes in a neighborhood actually improve health, so cutting it has left that question unanswered. Other canceled studies have examined health issues like maternal health, preterm birth rates, or sickle cell disease. Courts have reversed some of these grant cancellations, but not all of them. If we really want America to be healthier, research into why some communities get sicker than others is absolutely critical. The law that created the federal minority health research institute passed with bipartisan support in 2000. But these days, as evidenced by grant cancellations, the political shift in attitudes toward DEI means we often lose access to the health information we need to help all communities thrive. DEI vs. health equity vs. equality and the overlap (or lack thereof) The letters “DEI” evoke very different reactions depending on who you are in the U.S. DEI programs can address gender, disability, age, and other factors, but the fiercest debate centers on race. For some people, hearing DEI brings to mind policies that may have cost them a job or a college seat because they are white. Others see DEI as a matter of justice: a way to expand representation and opportunity for groups that have been historically marginalized. Both perspectives are rooted in lived experience, and both center on the same question: whether hiring and admissions practices are fair. Hiring and admission practices, though related, are not the same as health equity. Health equity is “the attainment of the highest level of health for all people.” It’s the recognition that some groups of people consistently experience worse health outcomes than others. For instance, black mothers die from pregnancy-related complications at three times the rate of white mothers, and rural Americans are more likely to die from issues such as heart disease and cancer. Equity is also often conflated with “equality,” which leads to misunderstanding of how to actually best support communities. Equality ignores the fact that we aren’t all starting life with the same resources or circumstances, whereas equity is about helping people get what they specifically need based on their situation so they have the chance to achieve the same outcome as anyone else. For example, if every household in Pittsburgh got a $30 grocery gift card, that would be equal. But during the decades the Hill District went without a grocery store, that card wouldn’t have helped much. Equity means making sure everyone can actually access a store in the first place. DEI policies can overlap with health equity in many ways. For example, they may help expand access to quality education and stable, well-paying jobs, which in turn can provide health insurance, safer housing, and improved finances—all of which can reduce health disparities. But to make that happen, we first need data to identify disparities in communities and to develop solutions that effectively address them. What we lose when the research stops Community data is important to health equity because: You can’t fix what you don’t see: Data show which communities are most affected by health factors such as infection, heart disease, and cancer. If we don’t know what is happening and where, then we can’t develop interventions to help. Poor health costs everyone: Chronic disease is expensive—both to the individual person and to the overall economy. Understanding what groups of people are most at risk allows us to develop interventions that can save everyone money. Disparities exist for many communities: Health equity includes disparities that exist due to a whole host of factors, such as family background, race and ethnicity, rural vs. suburban residence, gender and sexual orientation, veteran status, disability, and lower income. This means many Americans can benefit from research into differences in health outcomes and how to improve them. Trust is easy to lose: Some participants in the PHRESH study were initially vocal about their concerns about participating. But that trust was earned because the researchers kept their promises and centered their work on the community. When projects are canceled mid-cycle, researchers have no choice but to leave behind communities they had promised to help. This hurts trust in the long run. It’s also a very inefficient use of resources, and it will take significant time and effort to redo the work when the government realizes this work is important again. How should we talk about health equity? This is hard because the words “equity” and “DEI” are currently very politically charged. So whether you’re a public health professional, teacher, or friend, how we talk about this topic matters. The right framing can help people who might otherwise be skeptical see why health equity research is important and deserves funding. Here are some suggestions to help your conversations: Choose your words strategically: Who are you trying to reach with your message? Are they likely to be resistant to terms like “diversity” and “equity”? If so, find alternative words to use to still get the message across. It is frustrating to be in that position, but the goal of improving health is too important not to try to make inroads. Sometimes simply reframing can help the message reach those who need to hear it. Focus on the problem: The terms “health equity” and “DEI” are abstract. Make it more specific. For example, discuss differences in maternal death or ways to improve health outcomes in rural communities. These still have equity at their core but will likely be better understood by those you’re talking with. Focus on systems: Disparities exist, but if they are communicated poorly, people can mistakenly think the issue is the individual’s fault. Disparities are largely driven by failures of the system—such as lack of access to health care and a long legacy of racism. When discussing the disparities, frame them around these systemic issues to ensure blame isn’t placed on the individuals impacted. Tell stories: Personalize these policies by using stories from those impacted that connect to the data you’re trying to convey. This helps make the policies and outcomes more concrete and meaningful. Bottom line Some communities end up sicker than others. That’s just the facts. We can argue about the best way to do that research, but canceling it simply because it may be linked to DEI doesn’t make anyone healthier. It just leaves communities like the Hill district behind. And in the end, it causes us all to move backward in our efforts to improve health in this country. Love, EM Big thanks to MacKenzie Isaac & Legairre Radden, who reviewed this piece and generously shared their expertise and lived experience to help improve it. Dr. Liz Marnik, PhD, is Executive Director of The Evidence Collective and a science communicator and immunologist working at the intersection of public trust, science, and public health. To discuss communication training on topics like health equity and storytelling, email Liz at liz@yle.health or learn more on our website. Your Local Epidemiologist (YLE) comprises a team of experts, ranging from physicians to immunologists to epidemiologists to nutritionists, working together with one goal: to “translate” ever-evolving public health science so that people are well-equipped to make evidence-based decisions. The YLE suite of newsletters reaches over 475,000 people across more than 132 countries. This newsletter is free to everyone, thanks to the generous support of fellow YLE community members. To support the effort, subscribe or upgrade below: Your Local Epidemiologist is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe
NAD IV therapy promises more energy. But it could come at a cost
Sometimes, even an essential nutrient can be repackaged to seem more transformative than the science suggests. NAD IV therapy is a prime example.
Starbucks Is Closing Stores to Grow. That Sounds Backward Until You See Why
Starbucks shows that sometimes getting smaller, closing underperforming locations, and reinvesting in what works is the path to growth.
Lesbian Bar Project Co-Founder: Purity Politics Could Destroy Our Remaining Spaces
Subscribe As a professional lesbian and Massachusetts native, I’ve recently been asked by a lot of folks what I think about the saga unfolding at Last Ditch. The now-viral story, first reported by The Boston Globe, centers on a Western Massachusetts lesbian bar that lifted its mask requirement for one night a week to help drum up business. The decision ignited an intra-queer controversy that has been feasted on by media outlets across the political spectrum—from the New York Post to LGBTQ Nation—and is even reportedly being eyed for a Hollywood adaptation. Apparently nothing is more entertaining than a bunch of lesbians and queers fighting with each other over KN-95s. What has been lost in all of the chatter and media coverage is the very real possibility that Last Ditch, which just opened in April 2025, will have to close its doors. The venue’s one remaining founder, Jackie Matellian, revealed in a community Zoom meeting that she’s working four jobs, is now solely responsible for the bar’s $50,000 business loan and is struggling to keep up with the bills. So as we laugh about the controversy’s similarity to a “Portlandia” skit and how one of the bar’s founders left to enroll in clown school, we forget that yet another lesbian bar is facing an existential crisis—and this time the call is coming from inside the house. In 2020, my friend Elina Street and I created The Lesbian Bar Project, an Emmy-, Webby- and GLAAD Media Award-winning docuseries and initiative that tells the stories of lesbian queer bars around the world. We were moved to act because at the time there were only an estimated 16 lesbian bars in the United States, and the pandemic threatened to bring that number down even further. There are now more sapphic bars than there were just before the pandemic—about 36 of them in total—but this is down from approximately 200 in 1980. Maybe that doesn’t mean much to you, but for the millions of sapphics living in the U.S., this number is calamitous. As the world laughs at lesbians and sapphics, I want to take a second and tell you what I feel when I walk into one of these bars, which are all unique and irreplaceable in their own way: I feel seen. Sometimes I think Cubbyhole in New York City’s West Village neighborhood knew I was gay before I did. When I walked in there for the first time, before I was even out, I was transfixed by the colorful array of tchotchkes hanging from the ceiling and how the women standing next to me were completely unafraid to express their desire for one another. I knew the minute I was ready to come out, I had a home. I also felt the joy of a new couple falling in love at Seattle’s Wildrose, the quiet confidence of a short king lining up her shot at the pool table at Ginger’s in Brooklyn and the kick of cayenne pepper in Julie Mabry’s crawfish at Houston’s Pearl Bar. Subscribe Lesbian bars don’t just serve lesbians; they serve queer folks across the spectrum, including bisexual, pansexual and trans people. As Elina and I traveled across the U.S. and Europe and to parts of Latin America to document the bar owners, staff and patrons that make up the ecosystems of these spaces, we also became uniquely acquainted with the factors that threaten their existence: gentrification, wage gaps, sexism, environmental disasters, gun violence, rising insurance premiums and political administrations working to annihilate queer people and their families. Lesbian bar owners, like all leaders and entrepreneurs, are imperfect. There have been significant issues over the years at lesbian bars: employee harassment, racism, lack of accessibility, sexual assault and transphobia. All bar owners should be held accountable and should listen to their communities to evolve into safer and more equitable spaces. I also acknowledge my privilege: I am able-bodied, white, cis and from a family system that loves me for who I am. Not everyone has that. Queer people, especially trans and bisexual people, are suffering from disproportionately high levels of depression, and our mental health system isn’t equipped to support them. Immunocompromised people and long COVID are real, and many of my friends are suffering to this day. The people with the least safety net are often the ones for whom a bar closing costs the most. And yet, as we’re seeing with Last Ditch, the queer community often lashes out at each other. It’s also the mainstream media taking the internal conflict and making a mockery out of it, distracting from the real possibility that this bar could close due to financial pressures. A bar that many people loved and needed is facing extinction. Perhaps it’s safer to dissent with one another than with the larger forces trying to eradicate us, but in doing so, we risk destroying the few spaces we have left. Due to the constraints of purity politics—some in good faith, some not so much—lesbian bar owners, almost all of whom are queer women themselves, are forced to be everything for everyone, similarly to how women are forced to be everything for everyone. By asking these bars to uphold impossible standards—standards very few other third spaces are asked to consider—we all end up losing. I want to move as a community toward dialogue, compassionate dissent and collaboration to make our spaces better with the goal of keeping them OPEN and thriving, rather than destroying them from the inside with expectations that are arguably unattainable. Subscribe While I have your attention—for this fleeting second of the news cycle—may I urge us to spearhead a way to keep these small businesses afloat without attacking our own community along the way? Bar owners are left with an impasse: They want to be community-centered, but they also have to make money to keep their doors open. Sometimes they might make business decisions that don’t work for everyone—and that’s partly because different patrons have different priorities and want different things—but these owners deserve the opportunity for productive dialogue**.** We have to work with them and with each other to keep our spaces open, to keep them growing. Lesbian bars raised me into the queer woman I am today. One thing I think about as I reflect on the six years of work we’ve done to document these spaces is that shared, unspoken language I have with a stranger the moment we both walk into one of these bars: the nod, the ease, the not having to explain ourselves. I don’t want to lose that, and neither should you. If objective, nonpartisan, rigorous, LGBTQ-focused journalism is important to you, please consider making a tax-deductible donation through our 501(c)(3) fiscal sponsor, Resource Impact, by clicking this button: Donate
Primetime Review: That’s Entertainment?
If you decide to see Lance Oppenheim’s Primetime, it’s important to remember the title as you watch. Yes, this is a biopic of sorts about Chris Hansen and his work on To Catch a Predator, but it is more accurately about the quest for fame and using people’s trauma to attract ratings. True crime entertainment continues to have a prominent place in many people’s television habits, and this film asks us to examine what that means both for those whose stories have become our viewing pleasure and those of us watching. Primetime starts as Chris Hansen (Robert Pattinson) is told that his show, To Catch a Predator, is moving to a 9 PM spot opposite LOST. Chris and his producer, Andie (Merritt Wever), see this as an opportunity to make the program even more sensational, looking for bigger, more horrific predators. As part of their plan, they hire a young actor, Dan Plum (Skyler Gisondo), to play “Decoy Dan,” the person who greets the would-be predators who were catfished by Xavier Von Erck (Matthew Maher). Oppenheim, along with writers Ajon Singh and Luke Dittrich, is less concerned with an accurate retelling of the show as much as they are interested in interrogating the why of it all. That decision sometimes leaves Primetime feeling less like a biopic and more like a satirical look at the nature of programming like this. Visually, the movie is impressive. Oppenheim uses light and color effectively to cast Hansen as both villain and savior. Pattinson plays Hansen in an almost comical manner, as we see him wake his two young sons to watch a clip of the show, just to brag that he was moving into a prime spot on television. Even Andie struggles to accept a “nice” job when there is so much more fame to be found producing salacious true-crime content. Wever does an excellent job showing how the hunger for fame is not just in front of the camera, but in every aspect of the production. The heart of the movie, and the standout performance, belongs to Gisondo as Dan Plum. He is someone who just wants to get enough hours to be admitted into the Screen Actors Guild, and stumbles into this job without really knowing what it is. Throughout the film, we watch him suffer one trauma after another, ending with him witnessing the suicide of a District Attorney who is about to become the next unwitting star of Hansen’s show. If there is a sympathetic character in Primetime, it is Decoy Dan, and Gisondo plays him brilliantly. If you’re a fan of programming like To Catch a Predator, then Primetime will probably make you feel uncomfortable, and I think it’s worth sitting with that feeling and examining why we turn on shows that display the worst of humanity while we judge from our couches. Yes, it is correct to want to see people who would hurt children brought to justice, but perhaps it shouldn’t be something that we watch as entertainment. Rating: 3.5/5 This review originally appeared in The Dominion Post on September 25, 2026. MacGuffin or Meaning: Film & TV Reviews and Reflections is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe
Toward a post-Trump politics
From the clutter of midterm election poll results, it’s possible sometimes to see new patterns of political support emerging. The overall pattern, as it stands, makes some sense. As in the 2006 and 2018 off-year cycles, the Republican president’s job approval had sagged to well below 50%. Polls at this point in the year showed […]
What to know ahead of Open Enrollment to avoid health insurance scams
Health insurance scams happen year-round, but they pick up during Open Enrollment for Medicare and the Marketplace. If you’re looking to find a new plan, you might search online. But before you click on a search result, remember: scammers and dishonest businesses sometimes pay to appear at the top.
Alien nation? URI fall Honors Colloquium lecture on Oct. 6 explains why space travel means ‘More than Martians’
KINGSTON, R.I. – Sept. 28, 2026 – Interest and fascination in space travel isn’t just about visiting far-away planets, including Mars. Sometimes, it’s learning more about other life forms from another world. But venturing into outer space is still vitally important to understand the world around us, and beyond, even if aliens are never found. […]
Following the Dollars in American Politics
Money plays a powerful—and sometimes difficult to understand—role in American elections. Voters regularly hear terms such as campaign committees, PACs, Super PACs, independent expenditures, and issue advocacy. However, it is
This ancient fruit tree thrives with little water—and hides surprising health benefits - Futura-Sciences
An Overlooked Mediterranean Classic This fruit tree yields small “apples” that ripen from yellow to red in September, yet it’s been off the horticultural radar for a long time. That tree is the azarole (Crataegus azarolus), sometimes called the Spanish thorn or azarolier. Rare and seldom noticed, it’s high time...
Palace's Underrated Superstar and his Palace Future
Tyrick Mitchell's time at Palace may sometimes go out of the spotlight but he is one of Palace's most reliable performers.
Things are getting BAD. BAD… BAD. Rays 2, Phillies 0
Sometimes I look for an image and go THATS IT. THATS IT. This was one of those times.
Running a small business means making sacrifices. Are they permanent?
Small business ownership means sacrificing time, predictable income, and sometimes even your own paycheck to build a business. Those sacrifices can become
National Forensic Science week brings attention to CSI investigators
BAY COUNTY, Fla. (WMBB) – CSI teams are sometimes an under-recognized part of many law enforcement agencies. But investigating crimes takes precision, skill, and focus. “Evidence doesn&…
In Alaska, Rowdy Members of Congress Are a Tradition
Revelations about the conduct of former Representative Mary Peltola may not faze voters who have long elected abrasive and sometimes violent figures to represent them.
Is metascience being co-opted?
In July, the White House released a report that argued academia is deeply dysfunctional and in need of radical reform.Science: A New Golden Age sets out a litany of problems many academics will recognise: interminable waits for grants; short-term funding rather than support for longer, more ambitious projects; ballooning administrative requirements; pressure to publish, and many more. “The scientific machine is getting bogged down,” it says. At least on the face of it, Science: A New Golden Age is a triumph for the metascience movement, a loose-knit, global group of academics, philanthropists and think tankers that want far more testing and experimentation in how we fund public research and innovation. The report tells US research funders to set up internal metascience units to do just this. But it has also set alarm bells ringing that metascience is being conveniently deployed to help justify the defunding of US universities, which US vice president JD Vance has openly labelled “the enemy.” “Just because you have an issue with your plumbing in your house doesn't mean you light the entire house on fire and burn it to the ground,” said Colette Delawalla, chief executive of Stand Up for Science, a group campaigning against what it says are attacks on scientists by the Trump administration. No one is arguing that academia doesn’t need reform, but there’s debate over how far and how fast the system should be shaken up. Does public R&D need slow and steady evidence-based adjustment, or radical new experiments?One of the key arguments in Science: A New Golden Age is that there’s not enough funding for what it calls “mid-level” science: engineering-heavy projects that require tens of millions of dollars, involve ten to a hundred people, and last around five years. Such a project might develop a brain-computer interface for people with Alzheimer's disease, for example. As such, the US National Science Foundation (NSF) has already announced $1.5 billion to set up so-called X-Labs, effectively new research organisations to pursue breakthroughs in areas such as quantum sensing and new scientific instruments. Funding divertedBut there’s a catch: this money has squeezed NSF budgets for more traditional, fundamental research grants, Science reported in June, likely meaning less cash for university-based scientists. Science: A New Golden Age also makes various swipes at universities, accusing them of extracting overheads from scientists to fund administrative bloat. “We have become dependent on a narrow set of legacy institutions,” it says. The report doesn’t mention it, but the US government is simultaneously seeking greater powers over federal funding, which would allow political appointees at funding agencies to screen or cancel research grants mid-project. The White House is also seeking to slash the NSF’s budget by more than half in 2027, although similar cuts proposed for 2026 were blocked by Congress. Convenient hookThis has worried some that the metascience agenda could be misused as a kind of smokescreen to further weaken universities. James Wilsdon, executive director of the Research on Research Institute at University College London, said that the report contains lots of “interesting ideas” and agrees that in places, academia is a “pretty broken system.” But the risk is that metascience is being deployed as a “convenient hook” to support cuts and a “political land grab” to transfer grant power from scientists to political appointees in US funding agencies.So far, the metascience agenda hasn’t been seized on by radical right parties in Europe. But Wilsdon worries it will cross the Atlantic, politicising research reform efforts and making them more difficult to carry out. The report is already making some in Europe sceptical of the entire idea. “The system is not without flaws, but the metascience discussion is not likely to improve the system but rather to generate munition to undermine it,” said Claes de Vreese, a professor of AI and society at the University of Amsterdam. Peter Thiel protégéOfficially, Science: A New Golden Age has just one named author: Michael Kratsios, the director of the Office of Science and Technology Policy and a former chief of staff to Silicon Valley mogul Peter Thiel. He also served as chief financial officer of Thiel’s Clarium Capital Management. Since the 1990s, Thiel has criticised US universities for moving away from teaching what he views as the classic works of western civilisation, and drifting towards “anti-Western zealotry.” And since 2011, he has offered two-year fellowships of $250,000 to young people to skip or drop out of university, and instead start companies or pursue scientific research. “College can be good for learning about what’s been done before, but it can also discourage you from doing something new,” according to the Thiel Fellowships website. In other areas, the Trump administration has promoted academics with a longstanding interest in metascience. For example, the head of the National Institutes of Health, Jay Bhattacharya, in 2020 co-authored a study arguing the funder backs overly conservative proposals, although he achieved wider prominence as a critic of pandemic lockdowns. Careless talk costs fundingEven before the Trump administration embraced the metascience agenda, some metascientists privately wondered whether they should be careful not to overstate dysfunctions in academia, in case this gets used as ammunition against universities. But others think being cautious with criticism is pointless. After all, last year, the Trump administration sought to cut funding from US universities on an entirely different premise: supposed antisemitism on campus. “I don't think these governments need a technical metascientific excuse to attack the universities, they will want to do so anyway,” said Will Stone, a researcher at Science Works, a new think tank advocating for a “metascience revolution” in the UK. “I think researchers should report what they find, even when the findings may be used to advance policies they disagree with,” echoed Russell Funk, a researcher at the University of Minnesota, who in 2023 co-authored a highly influential paper that argued scientific papers were becoming less disruptive over time. No justificationThat said, Funk thinks that metascientific research certainly doesn’t justify a weakening of universities. Instead, scientists need “time, resources and stability to pursue unfamiliar ideas,” he said. “Taking away those conditions works against the very goal the reforms are supposed to achieve.”The Institute for Progress, a Washington DC-based innovation policy think tank that has championed X-Labs and metascience units, also says that reforms should complement, not supplant, universities. “Unpredictable, unreliable, and reduced federal funding to researchers is destabilising to the very foundations of research and development and is bad,” said Jenn Gustetic, the institute’s director of metascience and R&D policy.Evolution or revolution?This points to a much bigger divide in the metascience community: how far and how fast should public R&D be shaken up? Does it need evolution or revolution? For example, Science: A New Golden Age says the science system relies too heavily on traditional “workhorse” project grants, the type that pay out around $250,000 a year to fund “hypothesis-driven science by small teams on tractable questions.” However, in criticising project grants, the report “decisively picks one side of a debate that is far from settled,” David Hilmer Rex, a metascientist based in Denmark, has written. In June, Pierre Azoulay, a metascientist at the Massachusetts Institute of Technology, defended project grants, saying that the alternative of X-Labs “merits scepticism.”A-B testingOne challenge in metascience is that it’s easier to generate evidence for some types of change than others. Most straightforwardly, research funders can run A-B tests on different ways of awarding money. For example, Germany’s Volkswagen Foundation recently trialled distributed peer review, where applicants rate each other’s proposals, and measured its results against traditional review panels. Distributed peer review made quicker decisions, although awarded money to largely different ideas. However, much of the focus of the metascience movement has been on founding fast-moving and free-acting new research organisations and funders, in contrast to the alleged stodginess of mainstream academia or public innovation grants. A culture of speed and freedom are the animating principles behind Germany’s Sprind or the UK’s Advanced Research and Invention Agency (Aria).Related articlesViewpoint: it's time to experiment with how Europe funds science European researchers sound the alarm that Trump rule changes will hit cooperationHowever, while it’s possible to closely study previously successful bodies, such as the US Defense Advanced Research Projects Agency (Darpa), for historical clues to their secret sauce, it’s impossible to guarantee in advance that the likes of Sprind or Aria will actually work as hoped. But advocates of new organisations say this inherent uncertainly shouldn’t stop us trying. “If we wait for definitive proof that something will work before we try it, I don't know where your proof is coming from,” said Stone. Trade-offsUp until now, experiments in metascience haven’t generally come with hard trade-offs over money. In the US, new ideas such as focused research organisations have often been funded by billionaire philanthropists. In Europe, new innovation agencies such as Sprind and Aria, or the Netherland’s new National Agency for Disruptive Innovation, haven’t needed explicit cuts elsewhere in the research system. But with the $1.5 billion establishment of X-Labs by the NSF, the trade-offs are becoming clearer. Some metascientists privately wonder whether a smaller pilot would have been more appropriate, given that the idea is untested. The Institute for Progress counters that the $1.5 billion X-Labs budget is spread out over a decade, so will account for less than 2% of the NSF’s annual budget. “It’s important to remember that [Darpa] was once an institutional experiment as well. Now it’s one of the most cited innovative funding models in the world,” said Gustetic.As for Europe?The US isn’t the only country placing metascience at the heart of its research strategy. Science: A New Golden Age looks admiringly at the UK, which in 2024 set up a Metascience Unit to scrutinise how it funds research. In Brussels, it’s more of a mixed picture. In 2018, the EU set up the European Innovation Council to funnel grants and equity to start-ups, although it’s sometimes criticised for awarding money too slowly. Recently, the council has started experimenting with challenges, where teams of inventors compete to overcome a technological challenge. The European Commission also wants to experiment with partially randomising grant winners, to cut down on bureaucracy. However, the Commission’s own advisers have said the EU’s own research schemes are too conventional. They want the Commission to establish a new dedicated unit to scrutinise and experiment with funding. But there’s no sign of a such unit emerging in Brussels’ plans for the next Horizon Europe programme, which from 2028 is set to distribute more than €160 billion over seven years. Sam Bogerd, director of breakthrough innovation at the Brussels-based Arq Foundation, an AI-focused think tank that also looks at metascience, says that Europe might have the opposite problem to the US: universities and basic research funding are still reasonably well supported, but there’s not enough experimentation. “We're not taking a close enough look at where this money can achieve the most, and I am worried that the good ideas around metascience will be tainted politically by their association with the Trump administration,” he said. He’s still lobbying for the Commission to set up an experimental unit. But in the meantime, the Arq Foundation plans to do its own research, and is currently hiring two metascience researchers to run the rule over how Brussels spends research funding.
CJ Bailey discusses aftermath of Vanderbilt game
CJ Bailey met with the media yesterday, the first time he’d spoken with reporters since The Thing That Happened. There is no way to explain any part of what went down during The Thing That Happened, of course, but he did the best he could. Sometimes the football is snapped and major regret ensues. In […]
Chatbots are still falling short in mental health conversations with kids
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Why scientists want your photos of wildlife eating lunch
With a new platform called Who Eats Whom, citizen scientists reveal the fascinating, sometimes shocking interactions between species.
Why scientists want your photos of wildlife eating lunch
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Yet Another Jays Depressing Loss
Blue Jays 3 Orioles 4 Sometimes you make the right moves and yet they don’t work out. Bottom of the eighth Mason Fluharty pitching. He gave up a leadoff walk. Then got a pop out and a fly out. With Pete Alonso up, John brought in Tyler Rogers. I liked the call. But….Alonso hit his […]
Chicago Blackhawks Coach Makes his Stance Clear on Analytics
One of the great debates when looking at the modern NHL game has been how much to rely on analytics when it comes to assessing on-ice play. While analytics can provide more usual information that may be missed by the casual observer, there are often flaws with how these models function. Sometimes, the old school […] The post Chicago Blackhawks Coach Makes his Stance Clear on Analytics appeared first on HEAVY.
'SNL UK' mocks King Charles and Ed Sheeran in AI-themed sketch
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As families begin a new school year, many are focused on shopping for school supplies, adjusting schedules and helping children settle into new routines. It’s also a good time to check in on a child’s emotional well-being. For families, small changes in a child’s everyday habits can sometimes be early signs of behavioral health concerns. […]
Sometimes all you can do is laugh. That might actually be good for you
Sleep, diet and exercise are important for good health — but what about the benefits of a really good laugh? It turns out it’s contagious (in a good way).
Sometimes all you can do is laugh. That might actually be good for you
Sleep, diet and exercise are important for good health — but what about the benefits of a really good laugh? It turns out it’s contagious (in a good way).
Technical errors and no tenacity
Since taking charge in 2019, Mikel Arteta has made a point of reiterating how much he values physicality and the demands he puts on his side to win duels. Arteta said he was "extremely disappointed" as his side were "very far" from doing the basics he expected, with uncertainty in defence, a loss of midfield control and lack of attacking creativity. "Sometimes there were some technical errors very much involved that opened us up in a really difficult way, but that's something to learn."
Brighton 3
It’s been a while since Arsenal put in a “file and forget” performance but that is what they’ll need to do with today’s showing at Brighton. The Gunners, to a man, were well off their best. Brighton were energetic and more than a bit lucky. Combine those two and sometimes it can look like today’s […]
Scientists find that “perfect” systems may be surprisingly fragile
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Political party leaders contrast visions at forum
How does a nonpolitical organization help keep the public informed about political issues without getting political? The Western Institute of Lifelong Learning decided the solution was not a debate, but an event presenting the differing — and sometimes nondiffering — perspectives on certain issues held by representatives of Grant County’s major political parties. “Party Perspectives: A Conversation on the Issues” was presented Wednesday in Western New Mexico University’s Besse-Forward Global...
Science in a Bag is Bringing Hands-On Experiments to Wisconsin Kids
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Can researchers trust that AI models won’t leak their ideas?
Research funders are asking how they can stop confidential scientific ideas in grant proposals from leaking out through large language models (LLMs), for example if reviewers use AI to help them judge applications. Last week, a row erupted in mathematics after OpenAI announced it had solved a notoriously difficult problem, only for another mathematician to demand answers over whether the company had used ideas he had fed into its models. The spat, and the broader issue of idea leakage through AI models, was discussed by funders at an R&D policy conference held in Paris on September 14. At worst, it threatens to undermine the correct attribution of breakthroughs, one of the foundations of modern science. The mathematics dispute around OpenAI is “certainly something we're looking at” Jason Gallo, head of the newly established Office of Metascience at the US National Science Foundation (NSF), told the Blue Sky Forum, hosted by the Organisation for Economic Co-operation and Development. The controversy started on September 8, when OpenAI announced it had cracked a mathematical problem that had gone unsolved for 90 years. Using a swarm of AI agents, millions of dollars’ worth of computing power and a new internal model, the company said it had finally proved that a long-used formula for modelling fluid dynamics breaks down under certain conditions. However, Tristan Buckmaster, a New York University mathematician who had been independently working with collaborators on the same problem using OpenAI’s Codex tool, hit back with a statement questioning whether the company had snooped on his Codex sessions to gain an advantage. OpenAI has always denied it looked up the “specific user data” of Buckmaster and colleagues. However, the company initially admitted that “while unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models,” although its proofs “differ significantly” from Buckmaster’s. The firm later said that, following an investigation, it concluded that Buckmaster’s Codex prompts “could not have influenced the [OpenAI] system in any way, including through training.”Funder concernYet OpenAI’s initial admission that its model might have ingested Buckmaster’s ideas, then spat them out to its own mathematicians, has scientistsworriedthat their work could leak out if shared with AI. Some AI tools give users an opt-out option to stop their sessions from training the model. If such guarantees can be trusted, this gives scientists some confidence they can use an LLM to develop an idea without the risk of it leaking out.But there’s a bigger potential vulnerability. To win money to investigate ideas, scientists have to share them with funding bodies. These grant proposals are normally kept strictly confidential, to prevent other scientists from scooping them, or to protect commercially valuable intellectual property. To preserve this confidentiality, when the NSF uses AI models, “we have to be sure that it's not making server calls out to an external party, leaking that data,” said Gallo to delegates in Paris. Leaks through reviewersHowever, funders normally use external academic reviewers to assess proposals. And there are growing worries that these reviewers are feeding grant ideas into AI models to help them make a judgement. Research funders have noticed that peer review reports and funding recommendations have “suddenly become much longer, more detailed and complex,” said Diogo Machado, head of the data science unit at Technopolis, a science and technology-focused consultancy. “The sense is that reviewers are increasingly using generative AI in their review work, even where guidelines sometimes forbid it,” he told Science|Business. “This is very difficult for funders to control or prove.” Happening at scaleThis sharing of confidential proposals with AI models is “happening at scale already,” said James Wilson, executive director of the Research on Research Institute, based at University College London, during the debate in Paris. In response, Gallo said the NSF had “strict guidance” around the uploading of proposals to AI models. But he added the NSF was mulling new types of assessment to make sure applicants were truly the originators of their ideas, such as following a short-form proposal with an oral presentation, which would allow the foundation to “really interrogate the investigator on their intellectual contribution.” Related articlesHow can Europe cope with a flood of AI-assisted research grant proposals?The risks and benefits of AI in screening research grant proposals Viewpoint: AI could make scientific overload worse“For us as funders, a lot of the ways to ensure that the application remains the applicant's own intellectual contribution, those ways are quite regressive,” Peter Clifford, head of strategy at Research Ireland, told delegates. “They're maybe ten or 15 or 20 years old, and they don't scale particularly well.” Disputes over the origin of ideas are as old as modern science. Isaac Newton and Gottfried Wilhelm Leibniz spent more than a decade arguing over who invented calculus, for example. And long before LLMs emerged, academics would agonise over how much of their early work to reveal at conferences, in case their colleagues scooped them, Jack Leahy, a senior metascience advisor to the UK government, told delegates. But AI was “accelerating” the “problems of the system,” he said.
What Does the Science of Climate Change Really Look Like?
Editor’s Note: This is the second in a three-part series on how the Right should think about environmental and climate policy. Read Chris Barnard on Reclaiming Environmental Policy from the Left. More than a decade ago, the world’s governments negotiated and signed the Paris Climate Agreement, committing to hold global warming well below 2 degrees Celsius. In the following years, climate had immense cultural power. Greta Thunberg emerged. Fortune 500 companies established net-zero plans. The U.S. got the Inflation Reduction Act. Then the pendulum swung back. President Donald Trump returned to the Oval Office, speaking of green energy as a “scam” and carbon footprints as a “hoax” and on all matters of international policy emphasizing bilateral engagement and power over globalism and cooperation. The technology companies that had been at the forefront of net-zero pledges and green leadership discovered the importance of all-of-the-above power to their visions for artificial intelligence and began quietly postponing or removing their targets. On the left side of the political spectrum, rising concerns about inflation and cost pushed climate lower down in the stated priorities of voters and stump speeches of politicians. The moment is ripe for a reset on the politics of climate change and for conservatives especially to chart a course that acknowledges and addresses the real challenges in plausible ways. So what should we think about climate change as we enter a post-peak climate era? If it is neither an apocalypse nor a hoax, what will hold up as a durable view of climate change? And how does it project onto the concerns and priorities of the right-of-center’s emerging coalition? New to Commonplace_? Subscribe below to get the magazine in your inbox._ Subscribe A Solid Foundation As the politics and economics of climate careened over the past ten years, the physics carried on regardless. In 2015, the year the Paris Climate Agreement was adopted, the world emitted 41.2 gigatonnes of carbon dioxide. By 2025, emissions had risen only 2.4%, to 42.2 gigatonnes, because falling emissions from land use offset increasing emissions from fossil fuels. But slow growth in annual emissions is not rapid decline, let alone net-zero, so carbon dioxide kept accumulating in the atmosphere, going from 399 to 426 parts per million. Global temperature kept rising too, from about 1.1 degrees Celsius above the 1850–1900 average in 2015, to 1.4 degrees above it in 2025. None of this should surprise anyone. Even most prominent skeptics, like the authors of the 2025 report commissioned by the Trump administration’s Department of Energy, accept the basic physics. Carbon dioxide added to the atmosphere at industrial volumes accumulates and traps heat that would otherwise escape to space. That heat warms the upper ocean and increases temperatures at the surface. Increased ocean heat and melting of land ice cause sea levels to rise. Changes in the climate become noticeable to us as subtle shifts in temperature and precipitation, seasons arriving early or late, changes in the surrounding ecosystem, and weather extremes. The much more meaningful debate is not about whether these things are happening, but about what it all means for human societies. (While it is a proxy measure of overall risk, no one experiences global surface temperature.) This debate invokes at least three interesting questions: how much today’s warming is showing up in regional climate trends, how much more warming we should anticipate, and how extreme weather events can be understood in the context of climate change. Recent scientific advances give us new insights into each of these, but there is still much to learn. This is the context in which the next generation of conservative leaders will be the first to deal with significant climate change as a fact, not a forecast. It will be with them for their entire careers. Our evolving scientific picture of climate change will come from weather and climate records stretching further into the past, new kinds of observations in the present, better modeling tools for the future, and simply more time to observe the emergence of climate signals and their tangible effects on ecosystems and communities in the United States and around the world. The policy landscape will be shaped by not only the impacts of changes in the climate, but also by the ways in which societies respond and by the emergence of new technologies for both altering the trajectory of emissions and adapting to new climate realities. Global climate records now show the fingerprints of warming in different phenomena around much of the world. The IPCC, in its most recent assessment report, documents warming trends over all land regions, even as natural variability adds substantial variance to local trends. And the highest temperature extremes have become more intense and frequent, almost everywhere, since the 1950s. Scientists are highly confident that these changes are attributable to human influence. But confidence in detecting trends and attributing them to human influence degrades across heavy precipitation events; drought trends are heterogeneous and not attributable to human activity with great confidence. For hurricanes, tornadoes, and other severe storms there is even less confidence in trends or their relationship to climate change. In general, a fair summary of the evidence is that for well-observed phenomena, like surface temperature or heavy rainfall, with a clear relationship to warming, our multidecadal observations are consistent with human influence overcoming natural variability over long time periods and extremes increasing. For regions that are sampled more sparsely, or phenomena with a higher noise-to-signal ratio, we will need to observe them longer to understand the magnitude of the climate signal or improve dynamical understanding using models and observations together. But for Climate For those who can’t wait, we also have new techniques that attempt to understand the role climate change has in influencing particular extreme events. When the climate is changing and disaster strikes, it is natural to ask whether that disaster was somehow “caused” by climate change. For scientists and policymakers acting in good faith, answering that question helps develop a more accurate picture of the problem, how it may be getting worse, and what preparations we should be making to respond to it. It may, at some point, give some weight to how liability is assigned by courts or adaptation funding is distributed by society. But how we ask this question is extremely important. It is easy to get the analysis wrong by discounting the role that meteorology plays. As meteorologist Theodore Shepherd explained in a clear 2016 review of climate attribution methods, “if a weather or climate event is truly extreme in the present climate, then perforce it requires unusual meteorological conditions, which means that climate change is at most a contributing factor.” Share Scientists have two ways to probe climate as a contributing factor, both of which have entered media coverage of extreme weather events. One asks a probabilistic question: How much more likely is a particular event (e.g., a temperature record over a particular area) amid global warming? The answer involves using historical records and computer simulations to estimate the likelihood of such an event in both a changed and a preindustrial climate. The findings are less about the specific event, and more about events of that nature. The other question is: How has the changed climate affected the specific event in question? Here, scientists try to understand what a similar event would have looked like in the preindustrial climate. This is sometimes called the storyline approach, where the story is the specific meteorological details of the event. These methods offer ways to test the intuition of scientists about real weather extremes. In late June and early July of 2021, a persistent high-pressure ridge, or heat dome, set up over the Pacific Northwest and an extraordinary heat wave affected the area from Oregon to British Columbia. For six days, it shattered temperature records across the region and hundreds died from heat-related causes, in an area where such high, and persistently high, temperatures were well outside of experience and many live without air conditioning. Scientists have studied its connection to climate change using multiple approaches. This specific event was created by a rare combination of meteorological factors, a strong high-pressure ridge created the conditions for extreme heat, which occurred on top of higher average temperatures in the region from global warming. Probabilistic analyses showed that climate change increased the likelihood of such an event by at least 8-fold to more than 100-fold. One standout example found that such an event had effectively zero probability of occurring in the preindustrial climate. The enormous range in these assessments reflects how hard it is to estimate probabilities of events at the tail of the historical record. Estimates of the effect on the temperature of the event are more clustered. A recent review paper documents how multiple methodologies have found a positive influence, roughly 1-2 degrees Celsius of an anomaly that exceeded 15 degrees, of climate change on the temperature magnitude of the event. Multiple studies have investigated and found some positive influence of climate change in other extreme events. Attribution studies found climate likely increased the heavy rainfall that accompanied Hurricane Helene in North Carolina and surrounding regions in 2024. The extensive fires that struck Los Angeles in 2025 illustrate how the causal chain can become messy, though. Studies do detect a positive influence of climate on the event’s likelihood, but while climate change likely contributed to underlying aridity, it would not have played a role in the heavy winds or land practices that preceded disaster. These event attribution studies will become more common for extreme, or damaging, weather events. They can be produced quickly, and often are reported before peer review. As the Pacific Northwest heatwave example shows, when multiple methods converge on a positive attribution, the finding should probably carry some weight even if you have to be careful about accepting the results from a single study. I expect that as climate change proceeds and the climate thus departs further from a preindustrial counterfactual, the influence will become more detectable across a variety of extreme events and more easily identified. This will be used to cast blame, but can also be used to inform how communities and society adapt to ongoing change. We May Still Be Surprised One positive development, insofar as less climate change is better, has been that our central estimates for future climate change should probably be revised downward. Mostly, that is because the high-end warmings that scientists regularly analyzed about ten to 20 years ago, driven by high emissions throughout the twenty-first century, now appear to be somewhere between unlikely and impossible. At one time, the upper end of mainstream climate projections extended well into 4 to 6 degrees Celsius of warming by the end of this century, which, models suggest, would have wrought enormous real-world damage. Current energy and policy trends now point toward 2.5 or 3 degrees. Damages and risks are commonly modeled as increasing steeply with more warming, so this is already a better-than-previously-expected outcome for the climate (which is independent of the physical response to emissions). But we should maintain a wide range for plausible outcomes and prepare for the possibility of being surprised. Emissions trajectories are subject to deep uncertainty, the physical climate response is still developing, and whatever change occurs in the climate will then be mediated through unpredictable economic, social, and political institutions. Over the past decade, the pace of warming surprised some keen observers and appeared to accelerate, though not yet outside the range of expectation provided by climate models. The reasons for that apparent acceleration are being actively studied (as was the apparent pause in warming from 1998 to 2012), but no single driver has emerged. Some blame it on the El Niño, variability which would have no bearing on climate. Some think it is a result of factories in East Asia and global shipping fleets cutting aerosol emissions, which in the strongest version of the argument would indicate higher climate sensitivity to carbon-dioxide emissions. Others are more measured, not yet ready to draw solid conclusions as to whether climate projections require revision. We will have to see. Long-Term Thinking Climate change asks us to think over long time scales. But the pace of human-driven warming is compressing a large global change into a century. Changing weather patterns and extreme events are already causing adjustment costs and damages and will do more. Adaptation will help, but it is not free. For some communities, these costs will erode livelihoods and well-being, and may force migration. Are we prepared for large-scale managed retreats in the United States? Thankfully, American wealth, geographical diversity, and moderate climates may leave us better prepared than many around the world. But wealthy countries can still suffer serious disruption from narrowly concentrated costs, even if they appear entirely manageable in aggregate. Readers of Commonplace are familiar with the challenges of rapidly adapting to economic forces. The displacement that came from the China Shock is barely perceptible in aggregate GDP and employment data, but it affected millions of people and those people were far less mobile than economic models tended to assume. Deindustrialization rippled through the nation with enormous effects for not only our economic vitality, but also our national security. The forces surrounding climate change may not be so different; the world gets richer and must accept some diffuse costs. Those may be modest overall, but cause all manner of unpredictable effects with which policymakers must cope. In the case of the China Shock, our faith in a growing pie served us poorly. We’ll need to do better on climate. Leave a comment