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Brain scans reveal 'number line' that's used to mentally compare values
Although whole numbers and fractions are processed differently in the brain, when you compare their values, a single "mental map" akin to a number line lights up.
Dodgers Free Agent Skubal Revealed He Rejected Traditional Starter Rituals
Ditching the isolation of pitchers like Los Angeles Dodgers legend Clayton Kershaw, Tarik Skubal embraced a mentally freeing pre-game routine without silent superstitions.
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
You are who you vote for: Identity politics in Texas midterms
While the campaigns in Texas have been focused fundamentally on the affordability crisis, the Texas Senate race between Republican Ken Paxton and Democrat James Talarico remains highly contested due to the engagement of identity politics from both candidates.
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.
He Wants AI Benchmarking To Be a Science, Not a Dark Art
Moritz Hardt, author of a new book on benchmarks, believes these “standardized tests” for artificial intelligence are fundamentally broken — but not unfixable.
Single 2D crystal transforms into transistor with built-in insulation and electrical contacts
A transistor normally requires several fundamentally different materials—a semiconductor to carry current, an insulating dielectric to control it and metallic contacts through which current enters and ...
Fanatics CEO Michael Rubin: I fundamentally believe in the sports betting business long term
Fanatics founder and CEO Michael Rubin joins 'Squawk Box' to discuss the company's sports betting business, the increase in marketing spend on sports betting, his thoughts on prediction markets, staying private vs. going public, and more.
Oct. 7 reshaped Israel. Three years on, its political map remains largely unchanged.
As Israelis head to the polls, analysts say Oct. 7 and the war that followed have hardened existing political divisions rather than fundamentally changing them.
Arctic Wolf CEO: AI Has Changed Cybersecurity Forever
Arctic Wolf CEO Nick Schneider joins the Twin Cities Business Show to talk about how artificial intelligence has fundamentally changed cybersecurity. According to Schneider, we are already past the point where humans can defend against agents, and need agents of their own to keep themselves safe. Schneider explains how AI is giving cybercriminals new capabilities,
Sitcom king Chuck Lorre says Roseanne Barr ‘fundamentally hated’ her show’s writers: ‘It was malice’
Lorre, who created hits like “Two and a Half Men” and “The Big Bang Theory,” got his start writing for the tempestuous stand-up on “Roseanne.”
Chuck Lorre says Roseanne Barr ‘fundamentally hated’ her show’s writers: ‘It was malice’
Chuck Lorre, who got his start writing on ‘Roseanne’ in the ’80s, says Roseanne Barr ‘fundamentally hated’ her sitcom’s writers, because as a stand-up, ‘she made herself a superstar based on her words.’
Rethinking discovery: how powerful screening tools are redefining the search for new medicines
In medicine discovery, identifying the right biological target has always been a critical and complex step. Today, the rise of powerful screening tools is fundamentally reshaping this process, creating a step-change in how we move from scientific hypotheses to transformative medicines. Advances in high-throughput screening, genomics and AI-driven analytics are enabling scientists to interrogate disease biology at unprecedented scale, bringing forward opportunities that were previously beyond reach.
Small Business Administration Update on New Rules for Small Business Size Standards - Law Offices of Snell & Wilmer
By Brett W. Johnson, Carrie Schaffer, and Hannah Wolf1 Recently, the U.S. Small Business Administration (SBA) provided notice of a proposed rule, Small Business Size Standards, that would fundamentally restructure how the federal government defines a “small business.” The rule proposes new size standards for 338 industry groups and industries.2 On that same day, the […]
Do dying people know the end is near? This is what science has discovered
People who work around death often describe an unsettling phenomenon: some dying patients seem to know the end is approaching before anyone explicitly tells them. They may start saying goodbye, giving instructions, withdrawing from ordinary routines or talking about deceased relatives. Research suggests that awareness of approaching death does occur in many patients—but it is far from universal, and scientists still cannot explain every way that awareness appears. Some people remain mentally cle
‘I Was Dealing With a Lot,’ ‘It Was Hard’ — Browns QB Deshaun Watson Opens Up About Bouncing Back From Mental Health Struggles
Deshaun Watson worked his way back from tearing his right Achilles twice to earn his starting quarterback job and lead the Browns to a surprising 2-1 start in 2026. Watson spoke with Taylor Rooks about how he overcame these obstacles mentally and bounced back from potentially career-altering injuries.
The Enemies Project: WashU father-daughter duo fight against political extremism
With over 90000 subscribers on YouTube, senior Sadie Rosen and her father Larry Rosen (B.A. ‘88) reveal the power of empathy through their channel, The Enemies Project. The pair bring together strangers with fundamentally opposed political beliefs for a sit-down conversation. Their docuseries aims to break online echo chambers through vulnerability. It’s working, as across their 16 uploaded episodes, all the enemies have left connected.
How Washington Funded the Politicization of Science
Over the last half-decade, the National Science Foundation (NSF) has spent hundreds of millions of dollars on projects designed to influence academic policies, both in the sciences and other disciplines. The aim was to foster a progressive conception of social justice in these fields. In pursuit of this end, the foundation earmarked nearly a million dollars for a project titled “Creating Inclusive Scientific Societies through Policies and Practices,” over a million for a project on “Faculty Online Learning Communities for Gender Equity,” and over $2 million to foster “racial equity focused departmental change.” Through a public records request, I’ve obtained the proposals and progress reports for dozens of NSF grants designed to promote social justice. Although funding for these grants was eventually halted by the Trump administration, the projects illustrate an important facet of recent higher-education policy. Many of the university policies that promised “social transformation” didn’t arise in a vacuum. They were actively encouraged by outside funders—often by the federal government itself. Subscribe Accreditation—the peer-review process which enables universities to receive federal grants and student loans—has recently become a policy battleground, with accreditors wielding significant power over both universities and academic disciplines. In 2020, when Oregon Health and Science University’s medical school issued an extensive diversity action plan—which promised “consequences” for employees who failed to support “DEI, anti-racism and social justice”—it specifically pointed to the Liaison Committee on Medical Education (LCME), the medical-school accrediting body, to justify the overhaul. The NSF actively promoted DEI-focused accreditation policies. The proposal for a $1.2 million program on promoting “community engagement” in STEM, which I obtained through a records request, notes that the Accreditation Board for Engineering and Technology was exploring changes to its criteria “that would incorporate JEDI [justice, equity, diversity, and inclusion].” The proposal promised to “support broader JEDI efforts in the general criteria for engineering.” Another grant funded a conference designed to “identify and share best practices and strategies to integrate IDEA principles in accreditation criteria”—referring to an acronym that stands for “inclusion, diversity, equity, and accessibility.” Scholarly societies have become another flashpoint in debates over higher education. Critics argue that many of these societies, which have the power to shape entire disciplines, have become increasingly politicized. Again, through its giving, the NSF played a role in promoting this politicization. In one million-dollar grant that received funding in 2024, a group of psychologists proposed a series of social-justice reforms to the Society for the Psychological Study of Social Issues. The proposal, which I obtained via a records request, asserts that “[r]edistributing resources (such as moving different people into leadership roles) is not enough” and that the society’s “governance structures do not yet reflect its stated values of diversity, equity, and inclusion.” Its solution: review and rework the society’s criteria for leadership development, academic awards, conferences, and publications. Adjusting review criteria informed by “intersectionality” designed to fight “epistemic exclusion” is an easy way to introduce de facto ideological litmus tests into the peer-review process. For many of the grants I obtained, such litmus tests seem to be the point. “Academic departments are racialized social systems that produce and sustain the byproducts of systemic racism,” a proposal for a $2.7 million project funded in 2024 asserts, promising to develop “departmentally-focused change efforts that cultivate values, assumptions, and behaviors that advance racial equity.” The University of Illinois Urbana Champaign’s million-dollar “I-ADVANCE” project, which effectively created a small DEI bureaucracy on campus, promised to Embed 28 ADVANCE faculty fellows in STEM departments” to amplify its equity goals. Another $1.2 million project promised to create “faculty online learning communities,” made up of scientists at universities around the country, that would eventually build department-level diversity action plans. “Ultimately,” the proposal asserts, perhaps more perceptively than its authors realized, “both organizational change and intersectionality frameworks deal with power.” Indeed, such grants confer a great deal of power to administrators and scholar-activists, who can even use the mere availability of funding as a tool to advance their causes. That history is worth remembering amid today’s debate over federal involvement in higher education: Washington has been shaping university priorities for years, including by financing efforts to instill progressive ideas about race, gender, and equity into academic institutions—and many of the policies those grants encouraged remain embedded within universities long after the federal money has stopped flowing. John D. Sailer is the director of higher education policy and a senior fellow at Manhattan Institute. Share
Good science requires much more than evidence
Empiricists believe that scientific theories are true only if they make correct empirical predictions: if we can't verify these predictions, then there is no reason to expect that they tell us anything about reality. However, Professor of Philosophy at King's College David Kyle Johnson argues that this is mistaken. In response to Bas van Fraassen, who has written on this subject for the IAI, he argues that we have many reasons for believing a theory to be true, such as it being parsimonious and having large scope — qualities which have been used throughout the history of science as criteria for judging theories. In 2024, Princeton philosopher Bas van Fraassen wrote a short article for IAI News entitled “Science Does not Describe Reality: The Limits and Benefits of Explanation.” At the time, I was working on two projects (a science book and a Great Course) that, when completed, would (among other things) defend science’s ability to describe reality. I wanted to write a response to van Fraassen, but the projects demanded too much of my attention. But now that those projects are complete, I am in a position to explain the objections to van Fraassen’s thesis. Summarizing van Fraassen’s argumentIn a nutshell, his argument is this: What scientists do is make predictions based on theories and then invite others to test to see whether those predictions are borne out. “If theory T is right, result R should happen in experimental condition C.” Many think that running an experiment that shows that R happens in condition C is proof that theory T is true (and should be believed). But, van Fraassen argues, that R happened in condition C is only a reason to think that R happens in condition C. That’s the only observable, or empirical, result we can actually see, so that is all that we are really justified in believing. (The underlying theory that made that prediction still may or may not be true.) That is why he calls those who accept his position “empiricists.”Before I explain what is wrong with this position, let me explain what it gets right. What van Fraassen Gets RightFirst, a theory correctly predicting the results of an experiment is not, strictly speaking, “proof” that the theory is correct. The basic line of reasoning is this:If the theory T is true, result R should happen. Result R did happen. Thus, theory T is true.In other words:“If T then R. R. Thus, T.”This is a deductively invalid, fallacious mode of reasoning, called “affirming the consequent.” Those premises do not guarantee that the conclusion follows. “If I am in Pennsylvania, then I am in the USA. I am in the USA. Thus, I am in Pennsylvania.” Clearly that argument doesn’t guarantee its conclusion; neither does the above argument.However, science is not a deductive method of reasoning; it is an inductive one. And inductive arguments merely try to raise the probability of their conclusion, not “prove” them for certain. Thus, the fact that an experimental result doesn’t prove a theory is true is irrelevant; it’s not supposed to. That is not what scientific reasoning is about.___If the hypothesis that something exists clearly provides the best explanation for what we can observe, then the belief that it exists is what is rational.___Second, it’s also true that a single experimental result doesn’t provide enough justification to believe that a theory is true. Scientists do not come to a consensus about whether a theory is true by looking at a single experiment; they look at all the evidence to determine where the preponderance of evidence points. The mere fact that I am in the USA raises the probability that I am in Pennsylvania, but not nearly enough to conclude that I am in Pennsylvania. However, if I am also regularly encountering people wearing Eagles and Phillies hats and seeing advertisements for Founding Father tours, I probably am. In the same way, a theory successfully predicting the outcome of one experiment is not enough to conclude that it is true; but if a theory makes a wide variety of predictions that are all vindicated by multiple experiments, performed by many different groups and people, all actively trying to prove the theory false (which is what good scientific experiments are designed to do)—that is a good reason to conclude that the theory is true. In other words, if you repeatedly try to prove something false but can’t, that is a very good indication that it isn’t. The problem of underdeterminationIn reply, those whom van Fraassen calls “empiricists” often point to the “problem of underdetermination.” For any given theory that makes successful predictions about what happens in experimental conditions, there are countless other possible theories that make exactly the same predictions—and since they do, the experimental evidence in question is just as much reason to accept any of those theories as it is to accept our original one. Thus, the argument goes, we have no way of knowing whether our original theory is true, or one of the other “predictively equivalent” theories is true. SUGGESTED VIEWING How Occam's Razor changed the world With Johnjoe McFadden As an example of underdetermination, philosophers often offer this example. The evidence of fossils seems to provide reason to conclude that the universe is quite old; ancient fossils are what the “ancient universe” hypothesis predicts. But it’s also possible that God created an ancient-looking universe 200 years ago, complete with a “fake” fossil record for us to find, to make it look old. The existence of fossils is what that theory predicts too. So, the argument goes, the fossil record can’t be used as evidence that the universe is ancient; the fossil record supports the “200-year-old ancient-looking universe” theory just as much. The problem of underdetermination points out that, for any theory with supporting evidence, we could invent another completely different theory that predicts exactly the same evidence, and thus undermine that evidence’s ability to support the original theory.But this “argument from underdetermination” betrays a fundamental misunderstanding of what scientific reasoning is and how it works. To explain, let me use another rather strange (but very real and commonly discussed) philosophical thought experiment called “The Grue Problem.” The “Grue” ProblemSuppose I want to know what color emeralds are, so I observe a few, see that they all appear green, and form the theory that “all emeralds are green.” I thus predict that the next 100 emeralds I find will also appear to be green. And let’s say that happens, and so I thus conclude that my theory that all emeralds are green is true. Now, one might rightly point out that I don’t know for sure that all emeralds are green—I have not seen them all. Maybe some I haven’t found yet are blue. Fair enough. After all, we used to think that all swans were white, until we found some black ones. But let’s say that I go on a quest and collect every emerald in the universe—and, for the sake of the argument, let’s say I can do this, and can even know that I have every one of them. So, I collect them all, look at them all, and verify that they all appear green. Can’t I now rightly conclude that all emeralds are green?Inspired by the problem of underdetermination, a friend might argue that I cannot.Sure, they all appear green now—but there could be another kind of color called “grue.” Something is grue if it is green up to a certain point in time, but then spontaneously turns blue. And there could be an infinite number of “shades” of grue. Something that is one shade of grue will turn blue at noon today, but something that is another shade of grue will turn blue at noon tomorrow, etc. So yes, the emeralds appear green now; and since they haven’t turned blue yet, they are not some “past” shade of grue. But they might be some “future” shade of grue (e.g., they may turn blue at noon tomorrow). And since all future shades of grue are consistent with “being green up to this point in time,” the evidence you have is completely consistent with the notion that all emeralds are some future shade of grue, rather than green. Indeed, that they would appear green until now is what the “some future shade of grue” hypothesis predicts. So, we have just as much reason to conclude that all emeralds are some shade of grue as we do to conclude that they are green.You’re probably thinking “It’s not equally possible that the emeralds are grue; they are obviously green.” You are right! But explaining why your instinctive reaction to my friend’s argument is correct will help inform why van Fraassen’s “empiricist” is wrong and science actually does describe reality.The main mistake my friend makes is in thinking that the “empirical evidence” (of all the emeralds appearing green) is (or even should be) all I am using to draw the conclusion that all emeralds are green. It is not. I am also considering known facts about how colors typically work; things don’t usually change color, and when they do, they don’t do so suddenly, without cause, and all at once. So, even though the simple evidence of the emeralds all appearing green up to this point is consistent with them being some future shade of grue—that is even what the grue hypothesis “predicts”—it is still exceedingly unlikely that they are. The hypothesis that they are green is much more likely. Understanding science refutes the problem of underdetermination The misunderstanding about science that the “empiricist” pointing to “the problem of underdetermination” makes is similar to the one that my friend made. Science is not just about considering experimental evidence; in fact, it’s not even just about considering empirical evidence. As Ernan McMullin and many other philosophers of science have pointed out, the inference that makes science is “inference to the best explanation” (sometimes called “abduction” or “retroduction” by McMullin). While, depending on the circumstances, scientists use many different kinds of reasoning, science as a whole is, simply put, the process of considering multiple competing explanations and comparing them to certain criteria—explanatory criteria (the kinds of things explanations must be and do, by definition, to be a good explanation)—to see which explanation is the best.And these criteria do not deal only with empirical results. Yes, successful predictions raise the probability of a hypothesis, but it’s also the case that the more new assumptions a theory makes, the less likely it is to be true; every new assumption presents an opportunity for the theory to be false. (The fewer assumptions a theory makes, the simpler or “more parsimonious” it is said to be.) Likewise, the more existing evidence a theory contradicts, the less likely it is, because in order for a theory to be true, every piece of evidence it contradicts has to be somehow faulty. (The fewer established theories a new theory contradicts, the more “conservative” it is said to be.) And, obviously, the more something explains, the better explanation it is. (This is called having “scope.”) If a theory raises unanswerable questions, it’s not expanding our understanding, and thus is not wide-scoping. Scientists consider all these things when deciding which theory to accept, because the theory that adheres best to these explanatory criteria is most likely to be true.___the mere fact that a theory’s predictive success is why it “sticks around” or “becomes accepted” in the scientific community is not a reason to be skeptical about its truth.___The reason the grue hypothesis is no good is because it invents an entirely new kind of color—not just a new shade, but an entirely different category of color (a different way colors work)—and thus is not parsimonious. It raises unanswerable questions about how and why emeralds would just spontaneously change from green to blue (and thus does not have wide scope). And it contradicts what is already well established about how color works (and thus is unconservative).The theories that the problem of underdetermination points to do the same thing. Yes, the theory that God created an “ancient-looking universe” (complete with an ancient-looking fossil record) two hundred years ago is also consistent with the empirical evidence (of a fossil record) that presently exists; but that theory is monumentally unparsimonious (it requires many extra assumptions about infinite entities) and not wide-scoping (it raises unanswerable questions about why God would do such a thing). So, I can rationally conclude that it is false.Indeed, the history of science includes many examples of theories that were rejected because they lacked parsimony and/or scope, even before their falsity (and the truth of their competing theory) was confirmed by observation. As I point out in my book, geocentrism was rejected by the scientific community because it lacked parsimony, almost 200 years before the observation of parallax confirmed that the Earth moves around the sun. And the continuum theory of matter was completely rejected by 1860, because it couldn’t explain chemical reactions or organize matter into a periodic table, even though the experiments that eventually confirmed atomic theory weren’t even proposed until the 1870s. Solving the best of a bad lot problemIn response, the “empiricists” are likely to object, this time pointing to the “best of a bad lot” problem. “We have no reason to conclude that an explanation that has been shown to be better than others is true because we have no guarantee that the true theory was among those that we were considering.” If you have a “lot” (a collection) of poor explanations, even the best one is not true.The responses to this problem, however, are numerous. Peter Lipton argues that the set of hypotheses that scientists consider are not just randomly selected and thus unlikely to contain a theory that is true; they use existing knowledge to home in on what is most likely to be true. A doctor trying to explain abdominal pain is not going to bother wondering whether demons are causing it; they are going to stick with things like appendicitis and kidney stones, because they are most likely. Further, despite its name, inference to the best explanation is not just the process of comparing hypotheses and accepting the best one; we also want one that is a good explanation in its own right. If it’s not, we are going to keep looking until we find an explanation that is. And while that process does not guarantee that we will come across the exact truth, it’s very likely that the theory that emerges as the best will be at least approximately true, and thus we will be justified in believing it. And lastly, as I point out in my book, although many theories not based in scientific evidence (like geocentrism and continuum theory) have been completely overturned, science almost always progresses by the refinement of scientifically established theories—not their replacement. (Although our sun is not the center of the universe, heliocentrism was our first step in understanding the Earth’s true place in the universe.) So even if a theory we accept today may not be completely right, accepting it is a step on the journey toward the complete truth. The example of atomic theoryNow, it’s worth pointing out that most empiricists are not skeptical of all scientific theories; they are only skeptical of the ones that hypothesize the existence of “unobservables.” It’s not like they think that round Earth, heliocentrism, and germ theory are just useful (but untrue) “instruments” for making predictions about how to most efficiently navigate the seas, predict planetary motion, and treat diseases. They admit that the Earth actually is round, that the planets revolve around the sun, and that germs exist and cause disease. These facts and entities can be observed; the theories that suggest they exist are true. It’s only when theories start hypothesizing the existence of unobservables—like electrons, or quantum fields—that can’t be observed directly, that we should start doubting the theories’ truth. And to be fair to van Fraassen, in this published work, he doesn’t really say that we should doubt the truth of such theories. Only that scientific reasoning does not rationally force us to accept them as true. Skepticism, or agnosticism, he argues, is warranted. SUGGESTED READING Science is based in metaphor By Andrew Reynolds But the history of atomic theory throws a monkey wrench into that argument. Atoms used to be unobservable; again, scientists first accepted the theory that they exist because it had such grandiose explanatory power. But then atoms were seen with electron microscopes. They thus moved into the category of the observable. But this didn’t substantially change anything about what scientists were justified in believing; it certainly didn’t change any “atomic skeptics’” minds. It just confirmed what everyone already knew! If the hypothesis that something exists clearly provides the best explanation for what we can observe, then the belief that it exists is what is rational. The fact that the thing itself is not technically observable does not make doubting its existence rational.What’s more, the atoms were seen with an electron microscope. But electrons are themselves unobservable. How can an electron microscope reveal an atom if electrons are not real? This leads us to what is famously known as Hilary Putnam’s “miracle argument.” It’s (not) a miraclePhilosopher Hilary Putnam’s miracle argument is somewhat famous. Basically, he argued that a scientific theory being able to repeatedly make successful predictions—even about unobservables—without being at least approximately true would require a “miracle.” In other words, it would be such an improbable occurrence that it could not rationally be believed—the better explanation for why scientific theories about unobservables make the right predictions is because the unobservable entities and structures they hypothesize exist.Van Fraassen has addressed this argument. Indeed, in his original article, where he references Alison Gopnik’s point about orgasms not being “necessary for procreation” even though the desire for orgasm “drives the creation of progeny,” he is pointing to his own argument about how organisms are not naturally selected by evolution to get true beliefs; to the extent that belief-forming processes are selected for, the ones that are selected are the ones that are useful for survival—not the ones that produce true beliefs. In the same way, van Fraassen argues, scientific theories are accepted, not because they are true, but because they aren’t falsified; they make successful predictions; that’s what makes them “stick around.” So, the fact that they make successful predictions (and thus became accepted) is not a reason to think they are true.This argument is similar to C. S. Lewis’s “argument from reason,” which he used to argue against evolution: “The truth of evolution undercuts our ability to know that it is true.” Lewis’ argument famously failed; Elizabeth Anscombe, by Lewis’s own admission, demolished Lewis in a debate on the topic, and I have explained elsewhere why modified versions of the argument fail as well. In short, it’s because it fails to recognize that a belief-forming process can make an organism more likely to survive because it reliably produces true beliefs. It is therefore not the case that beliefs generated by naturally selected belief-forming processes are automatically false or unjustified.In the same way, the reason a theory “sticks around” could be because it’s not falsified, because it “makes correct predictions.” But the reason it makes correct predictions is most likely because it is true. (In pointing this out, I am echoing the arguments of Musgrave, Lipton, Psillos, and Kitcher, who refuted van Fraassen’s argument and analogy long before his 2024 article.) It would be a huge coincidence that it could do so without being true. And that is the point of Putnam’s miracle argument. Thus, contrary to van Fraassen’s suggestion, the mere fact that a theory’s predictive success is why it “sticks around” or “becomes accepted” in the scientific community is not a reason to be skeptical about its truth. ConclusionLet me close by saying something about the fact that empiricists don’t doubt the truth of all scientific theories, just the ones that hypothesize unobservables. In today’s society, this theory is dangerous to espouse publicly. The nuance between being skeptical about all of science, and just the highly technical theories about unobservables, is guaranteed to be lost to the average reader. And there is so much unjustified skepticism and doubt about the findings of science and experts that a headline like “Science Does Not Describe Reality” could easily fuel the anti-science movement, worsen climate change denial and cause the next pandemic.This is why I believe it was important to still respond to van Fraassen’s article, a full two years after he wrote it. Not only, as I have argued, does it represent a philosophically inaccurate view of how scientific reasoning works and what scientific reasoning can do, but it is a dangerous view to boot. It should thus be rejected.
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Quick hitters: O-line health, safety depth and Dan Campbell’s first days in Detroit with Glenn
Allen Park — Detroit Lions coach Dan Campbell and quarterbacks coach Mark Brunell met with the media on Friday. Here are the noteworthy items from those conversations. Healthier up front, banged up in the back It’s good news, bad news on the injury front. First, the offensive line is going to be far closer to full strength with starters Blake Miller (knee) and Christian Mahogany (hip) on track to return to the lineup. That provides stability the offense lacked against Buffalo, particularly at right tackle, where backup Larry Borom allowed 11 quarterback pressures and committed a trio of penalties. Subscribe However, with that same unit, veteran Ben Bartch (foot) is likely to miss Sunday’s matchup with the Jets. The left guard competition that’s been ongoing since May has effectively been whichever option is healthy that week. In the back end, safety Thomas Harper (ankle) is also unlikely to be available on Sunday. That leaves the team precariously thin at the position following Avonte Maddox going on injured reserve this week. Christian Izien is expected to get the start alongside Chuck Clark. “Izzy's been working a lot back there and had a full week,” Campbell said. “And he's gotten a ton of work this week, a ton of reps, full-speed reps. ‘This is what you're going to do, this is the calls’ and just dousing him with full-speed reps and he's done a good job this week.” Safety depth is the bigger concern. The team is expected to count on some combination of Jalen Mills, who signed to the practice squad on Monday, Bishop Fitzgerald, the undrafted rookie poached off the Steelers’ practice squad on Tuesday, and rookie cornerback Keith Abney, who doesn’t have experience at safety. “I'll say this about Abney, man, for a rookie, he's a pretty dang smart, conscientious, aware football player that's got a ton of room to really take off,” Campbell said. “…He's young. He's another one, he just needs reps and reps. But, if we need it, that'll be a guy who may have to go in for us.” You can check here for the full injury report, with game designations, later this afternoon. Reflecting on the past Responding to comments made by Jets coach Aaron Glenn this week, Campbell reflected on his earliest days with the Lions, before the coaching staff had been fleshed out and it was just he and Glenn at the facility working through what they wanted things to look like. “It was just he and I in the lab,” Campbell said. The coach said the two would fill the whiteboard that remains in current coordinator Kelvin Sheppard’s office top to bottom with notes about philosophy, coaching style, team identity and the type of players they wanted to target. “He looks different than me,” Campbell said. “I'm taller, I'm whatever. He's quicker. All these different things, man, but we're really the same. I mean we're so much the same. And he's the defensive version, I'm the offensive version. He was my right hand man. …It was fun, man. Those were good days.” Campbell said it was one of the best and worst days of his life when the Jets hired Glenn to be their coach. “AG means the world to me,” Campbell said. “He's one of my dearest friends because I know what he's made of. “…It's because you know you're losing a little piece of you. That's what he meant to me and to us. So, that's hard.” Traits under center A couple of weeks back, Sheppard noted that Brunell had identified Saints quarterback Tyler Shough as the best prospect at the position ahead of the 2025 draft. On Friday, Brunell confirmed his evaluation and explained what he looks for when studying a young quarterback. Here’s the full answer: “Young quarterbacks, you have to look at everything, you know? I look at guys coming out of college just like I look at any free agents that we might be looking at, or guys that we might add to our roster. Do they take care of the ball? Are they on time? How is their decision-making? How are their feet? “It's tough because you look at a college player, you don't understand what they're trying to do offensively, what their reads are, but you can identify when a guy is accurate, obviously, and when the ball's getting there on time. If there are a lot of interceptions, it raises a red flag. And I think with a lot of the spread offenses of what you're seeing, when you see a young player and they're running some pro concepts and he's having to make protection calls, and he's getting the right play out of the run game, and when there's a lot on his plate and he's executing it very well, and he's managing all of that, that really jumps out at you. It’s really hard to find. “That's one thing we saw with Luke Altmeyer. He had a lot of experience. We call it, ‘Hey, he's playing quarterback.’ And what does that mean? It's not just completing passes. It's getting guys lined up, getting the right play, getting the protection call right, just fixing some problems, being able to progress (through reads). And there's still some young guys out there that can do that at a high level, which is good to see.” Regarding Altmyer, who competed for the team’s backup job through the offseason and currently resides on the practice squad, Brunell acknowledged physical development slows once the season starts, because there aren’t reps to go around. However, there’s still extensive mental development that’s ongoing. “The key for him is approach the game as if you're the starter, mentally,” Brunell said. “And that's really all you can do. Approach the game as if you're the starter and be good in the (class)room. You come in alongside Jared (Goff), and you come in alongside Josh (Dobbs) and (do) whatever they need. And if you get an opportunity to talk to Luke, he'll tell you he does have a lot of responsibilities besides just getting donuts for us on Saturday. “For him, it's mentally staying focused, and even though there's really no chance this Sunday of him playing, he has to approach it as if he's taking the first snap and playing the whole game. So what's our game plan? What are our checks? What are we anticipating? And then during the game, we keep him involved as far as looking at coverages and pressures. And so, really, for him, it's just a mental game. And for now, that's all we can do.” Reason for more zone The Lions have significantly increased their usage of zone coverage on defense this season. I asked Campbell if that was a reaction to their personnel or driven by the focus to generate more turnovers, given it can be easier to produce interceptions when you can keep eyes on the quarterback while playing zone. “I think it was a little bit of both,” Campbell said. “We just felt like this would suit us a little bit better up to this point, and then be disruptive and get takeaways, which worked well Week 1. We had two (opportunities) last week against Buffalo we weren’t able to get, but they’re there.” I followed up by asking how much the combination of heavy blitzing with the back end playing zone has contributed to some of the early-season coverage woes, since adding extra players to the pass rush means leaving the coverage short-staffed. “I mean, it’s challenging, and that’s why a lot of the things we’ve done in zone, for the most part, have been to reduce that stress level back there,” Campbell said. “But that also requires the front getting there. And if you don’t get there, we already kind of talked about that, but there (has to be) a trust there.” The alternative, Campbell explained is sending four and it taking longer for the rush to get home, which can create different problems for a defense. “You can bleed yards,” Campbell said about the strategy. “It gets a little longer and it’s dink-and-dunk and run after catch. …So, we do what we think is right by what we feel like our personnel (does well). But also the game plan, who you’re going in to play, the matchups, their offense. “We’re not stuck on one way.” Detroit Football Network is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe
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How lip-syncing to AI let Forced Entertainment ‘play the game of theatre’
Today we have a fascinating piece by long-time Forced Entertainment fan Ben Kulvichit, speaking to Tim Etchells about their latest show Cold Sweat, how they are integrating lip-sync and AI voices into their work, the question of ‘authenticity’ on stage, and how lip-sync has even opened the door for them to make “something a little bit like drama”. This piece is a paid partnership with the Southbank Centre, where you can see Cold Sweat next month – we’re very grateful to the Southbank Centre for their ongoing support for Exeunt. Holly Williams ‘It was immediately interesting and uncanny’ **By Ben Kulvichit** “Is the microphone on?” So goes one of the opening lines of Signal to Noise, delivered by a human performer lip-syncing to an AI-generated voice. The voice is American, cheery but sluggish, slowed down into a gelatinous drawl. It repeats ad infinitum in a farce of stuckness; an answer never materialises. I find myself asking the same question as I hop on a Google Meet call with Tim Etchells, the artistic director of Forced Entertainment. We spend five or ten minutes frowning at each other, muting and unmuting, me gesticulating in semaphore into my webcam, checking if I am coming through now, restarting the laptop. To Etchells, I appear exaggeratedly mouthing words, my voice missing. In this gloopy purgatory of broken communication, life imitates art imitates life imitates art. We try a Zoom instead. Forced Entertainment, the pioneering British theatre company who have been at the avant-garde of performance since the 1980s, turned an interesting artistic corner with Signal to Noise, created on the occasion of their 40th anniversary. The whole performance employed this lip-sync technique to AI voices; on the face of it a left-field move from a company whose identity has long been grounded in the earthy, immediate presences of its performers and its unassuming, low-tech approach to theatricality. The technique, however, turned out to provide the company with a “rich new seam” of artistic enquiry, and has now spawned a trilogy of works. The second instalment, Cold Sweat, plays at Sheffield Theatres, Southbank Centre, and Lancaster Arts in October, while the third part, Everything Must Go, has been touring in Europe and is slated to come to the UK in 2027. As is often the case with Forced Entertainment shows, the main conceit for this work didn’t arrive until quite late. They were “four or five weeks into rehearsals for Signal to Noise,” Etchells recalls, and getting nowhere. Away from rehearsals, he had been playing around with feeding “weird texts” to AI voices to see how they would cope with them – “basically for my own amusement” – and was compelled by the uniformly “serviceable, benign, good-natured” tone they would read in, no matter how poetic, fractured or experimental the writing. On a hunch, he brought some examples into rehearsals and the performers gave lip-syncing a go. “It was immediately interesting and uncanny,” he recalls, with the performers having to deal in the moment with the problem of mapping themselves onto another artificial ‘performer,’ which itself was dealing with the problem of a very human text. “Almost all the writing for these projects would be useless to us unless it was done in this way,” says Etchells. “We wouldn’t know what to do with it. It’s too poetic. It’s too fictional. It’s too tangled.” But exploring lip-syncing along to the text has opened “an enormous reservoir of ways of thinking about writing and performing.” Subscribe to Exeunt, and get two in-depth pieces on theatre a week, direct to your inbox Subscribe Forced Entertainment have long been sifting through the detritus of popular culture, from daytime TV to end-of-the-pier vaudeville, magic acts and clown shows, to find frameworks for their own unusual entertainments. Lip-syncing, then, makes sense for them to experiment with. As a mode of performance, it’s wired into popular consciousness through pop stars trying to convince stadium audiences of their liveness, or those same voices as channeled through the made-up lips of contestants on RuPaul’s Drag Race, or movie dialogue spreading virally through the host bodies of TikTok users. For Etchells, lip-syncing is often about “approaching real voices and trying to take on their aura,” subsuming that original human presence into yours. “There’s a very basic, elemental sort of theatre magic to it, produced in this very easy way.” This broad approach resonates with instances of lip-syncing within contemporary performance, too. In works like Re-Member Me and Showmanism, performance artist Dickie Beau acts as a kind of spirit medium for archival recordings (this process of re-membering – as in, giving corporeal form to voices – relies on his masterfully precise actor’s technique), while Frankie Thompson is similarly possessed by a host of characters ranging from a nine-year-old Jacob Rees-Mogg to Cliff Richards, all of whom she finds compellingly nauseating, in the exorcism ritual that is her latest show, Horrible Things. What’s different about Forced Entertainment’s use of lip-syncing is that here, there is no original presence to be channeled. The performers are instead taking on what Etchells describes as “the voices of nobody,” or rather the “phantasmic, aggregated echoes” of the many many voices on whose data the AI has been trained, and which are now conjuring into existence words they never actually said. There is a sort of mournful, empty quality to these voices, having “washed up” far away from the people they originally belonged to, to find themselves in stranger’s mouths listing objects, wondering if anyone can hear them and glitching in interminable loops. There is a cold, sad anonymity to it all. Etchells remarks that occasionally the software company he used has notified him that particular people are withdrawing their voices from the platform, instructing him to cease use of those voices. Using these technologies in performance certainly brings up ethical questions around digital rights, consent, identity and ownership, although in the grand scheme of things, Etchells is fairly blasé about it: of all the purposes someone’s voice might be put to without their knowledge, “an avant-garde performance is probably the least of their worries.” [ ](https://exeuntmagazine.substack.com/p/frankie-thompson-horrible-things-essay-mental-health-compulsive-behaviours-pleasance-courtyard-soho-theatre) [ Frankie Thompson: How I manage my own ‘horrible things’ at the Edinburgh Fringe ](https://exeuntmagazine.substack.com/p/frankie-thompson-horrible-things-essay-mental-health-compulsive-behaviours-pleasance-courtyard-soho-theatre) Exeunt · Aug 25 [ Read full story ](https://exeuntmagazine.substack.com/p/frankie-thompson-horrible-things-essay-mental-health-compulsive-behaviours-pleasance-courtyard-soho-theatre) For the performers, though, lip-syncing to these strange, artificial voices is an odd task, requiring them to bend their live, human presence into the shapes produced by machines. Politically, it mirrors processes that are already at play in the continued computational automation of human life. Etchells brings up James Bridle’s writings on new technologies as a reference point: one example Bridle gives is of “chaotic storage”, a logistics technique employed by Amazon fulfilment centres whereby items are stored not according to traditional methods of categorisation or alphabetisation, but in a way that an algorithm determines to produce the most efficient routes for ‘picking’ based on customer purchase patterns. This appears completely random and incomprehensible to humans, with paddling pools sitting next to USB sticks sitting next to blowtorches, and so for the warehouse workers, the process of picking items becomes completely reliant on technology which tells them where to go. Our world is increasingly being designed not with humans in mind, but rather machines. As Etchells puts it, “technology malforms the space, and then you end up having to live in it.” Initially, Forced Entertainment tried their best to live in the world of machines: “I was in the it’s-got-to-be-perfect school of lip-sync,” says Etchells. But with their aesthetic being so tied to the failing act, the slipping mask, the shoddy costume, “it’s no surprise really that that didn’t last.” Instead, the performers got to enjoy being late for a line, or playing games with each other, “stealing” each other’s voices. Etchells compares it to the principles of ventriloquism, in which a free-floating voice unattached to a body is fundamentally discomforting and “sort of traumatic”. That discomfort is equally as alluring to Forced Entertainment as the satisfaction of precision, the pretence of authenticity only interesting if shown to be a pretence. Lip-sync has also opened up new avenues for the company. Having been allergic all their lives to Plays-with-a-capital-P, with Cold Sweat Forced Entertainment find themselves approaching something a little bit like drama. Rather than the presentational arena of Signal to Noise’s open white stage, Cold Sweat places us more decisively in a “fictional locale”: what appears to be “the aftermath of some disastrous party or backyard drinking binge” where three figures – family members, or neighbours, perhaps – are the last ones standing. “It’s all quite acrimonious and accusatory, everyone’s talking over each other.” For Etchells, this more fictional mode is “a door that we’ve never been able to open. We’ve just never really gone there.” Perhaps it is the technique of lip-syncing, an extra buffer of mediation, that allows them to venture away from the here-and-now without feeling that they have to commit to the “psychological claptrap” that comes along with acting; to be able to “play the game of theatre, without getting one’s hands too dirty.” As I wonder how else the impulses behind Forced Entertainment’s work have changed over time, it occurs to me that over the last ten years of new creations, with the exception of If All Else Fails, the company’s performers haven’t ever spoken on stage: there were two shows without any language at all, Out of Order and Under Bright Light, and then this trilogy of lip-sync works. Etchells agrees it’s a shift away from earlier productions, that were so often about “the actual voices of the performers and their actual presences. We had a lot of money, in some ways, on an authentic approach to what it might mean to speak on the stage.” But this sense of authentic presence itself had a fakeness to it; these days, Etchells can’t stand that mode of being onstage where performers “are here to be cheeky and witty versions of themselves. I don’t want to be charmed.” Pulling back into a more anonymous space has resulted in works that feel slightly sharper-edged, more brutal in a way. “All of these shows are post-Brexit, post-Trump, post-truth, post-everything,” Etchells notes. Speech, charisma, authenticity – these things feel politically loaded, and perhaps warmth and openness just don’t feel like the right tools to reflect the world we’re living in. “It feels like the space is closing down, and we’re trying to deal with the fact that the system, the structure that you’re in, is more oppressive and more powerful. It probably always was, but I think we’re just more tuned to it now.” Yet even without their voices, the performers’ distinctive presences – ones that many audiences have followed around for years, if not decades – still, inevitably, shine through as they physically navigate whatever unending task the system of the piece has set for them. They’re still trying to keep up with the onslaught of text, still running about, changing costumes, moving stuff around. The human is always stuck in the teeth of the machine, jamming the smooth, frictionless cheeriness of the not-real, gesticulating silently, trying desperately to communicate. Cold Sweat is at Sheffield Theatres, 2 to 3 October; Southbank Centre, London, 9 to 10 October; Lancaster Arts, 14 October [ ](https://exeuntmagazine.substack.com/p/exeunt-recommends-the-best-autumn-theatre-2026-lives-of-others-standard-of-living-our-friends-in-the-north-lanny-) [ Exeunt recommends... the best autumn theatre ](https://exeuntmagazine.substack.com/p/exeunt-recommends-the-best-autumn-theatre-2026-lives-of-others-standard-of-living-our-friends-in-the-north-lanny-) Exeunt · Sep 16 [ Read full story ](https://exeuntmagazine.substack.com/p/exeunt-recommends-the-best-autumn-theatre-2026-lives-of-others-standard-of-living-our-friends-in-the-north-lanny-) This piece was paid for by the Southbank Centre. Such support helps Exeunt to keep publishing the most interesting writing, about the most exciting theatre. Interested in partnering with Exeunt? Visit our Advertise with Exeunt page, where you can get more information, download an advertising pack, and find our contact details.
Jiang Feng: The Unravelling of German & European Politics
A state election in eastern Germany has become a revealing test of the changing political landscapes of Germany and Europe, writes Jiang Feng, Professor and Chairman of the Council of the Shanghai Academy of Global Governance and Area Studies (SAGGAS) at Shanghai International Studies University. His analysis follows the decisive victory of the Alternative for Germany (AfD) (which has been classified as right-wing extremist by German authorities)in the state of Saxony-Anhalt on 6 September, in which it finished far ahead of its mainstream rivals. Jiang previously served as Minister-Counsellor at the Chinese Embassy in Berlin. The following article was published on 8 September 2026 on huanqiu.com, the online platform of the Global Times. On 6 September, the results of the state election in Saxony-Anhalt, Germany, were announced. The Alternative for Germany (AfD), classified as far-right by German authorities, made history by becoming the largest party in the state parliament, winning 43.8 per cent of the vote — twice its share in the previous election. The Christian Democratic Union (CDU), which had governed the state continuously for 24 years, won just 17.2 per cent, its worst result ever in Saxony-Anhalt. On the night the results were announced, the atmosphere at the CDU gathering was unusually calm. There was neither shock nor argument, as if the defeat had long been expected. Several veteran party members who had spent years working in the state government said: “Defeat is not a bad thing. It is time to make changes.” This state election has not only upended Saxony-Anhalt’s political landscape, but also laid bare the deep tensions between Germany’s traditional party structure and model of governance, on the one hand, and an increasingly polarised society on the other. Its repercussions are already being felt at the federal level, making Saxony-Anhalt a useful case study of the broader trend towards political polarisation and realignment in Europe. Governing Difficulties after Electoral Victory The most immediate consequence of the election is a historic setback for the mainstream party order that took shape in post-war Germany. In Saxony-Anhalt, the AfD has leapt from a peripheral party to the largest political force in the state. Its electoral base has expanded beyond the traditional category of “marginal protest voters” to take on the characteristics of a cross-class coalition. The party has not only consolidated its support in rural areas, but also attracted many middle-class voters and even members of the social elite with stable jobs, their own homes, and substantial incomes. It has thus evolved from a “protest party” into one with the potential to govern. Despite its commanding lead in the vote, however, the AfD faces an institutional obstacle to governing the state: the mainstream parties’ long-standing “firewall” against it. Neither of the two potential routes to forming a government appears likely to break the current political deadlock. The first would be a coalition of establishment parties, led by the CDU and bringing together the Social Democratic Party (SPD), the Greens, the Left Party (Die Linke) and the Sahra Wagenknecht Alliance (BSW) in a cross-camp government. In terms of parliamentary arithmetic, such a coalition could prevent the AfD from forming a government and preserve the outward stability of the traditional political order. Yet these parties are fundamentally divided over key issues including immigration, the energy transition, economic management, and cultural and education policy. Coalition negotiations would therefore be extremely difficult, and even if a government could be formed, it would most likely prove short-lived. More importantly, an alliance of establishment parties aimed at keeping the largest party out of government would play directly into the AfD’s narrative that “political elites are suppressing public opinion”. It could deepen perceptions of political unfairness, further strengthening the AfD’s political capital, and creating a dynamic in which “the more it is suppressed, the more people vote for it”. Ulrich Siegmund, the AfD’s candidate for state premier, has said that if efforts to form a government fail because other parties refuse to cooperate, the AfD will push for a fresh election, which he believes could deliver the party an absolute majority of seats. This points to more intense political battles ahead. The second option would be a coalition between the AfD and the BSW. Together, the two parties would command a simple majority in parliament, making this, on paper, the quickest route to forming a government. The BSW is the only elected party that has not ruled out cooperation with the AfD, and the two parties share some common ground, including on policy towards Russia. There is therefore a degree of political basis for cooperation. Were such a coalition to materialise, Germany would see its first state government jointly formed by a party defined as far-right and one with a far-left background, creating what might be called a “bipolar anti-establishment alliance”. Such an outcome would fundamentally break with the ideological boundaries of post-war German party politics. It is worth noting that even if the AfD ultimately fails to enter the state government, its roughly 47 per cent share of parliamentary seats would give it a powerful blocking minority. Without its consent, major decisions requiring a two-thirds majority — including constitutional amendments, significant budgetary adjustments, and state-level institutional reforms — would be extremely difficult to pass. Therefore, whoever ultimately forms the government, the AfD will be an unavoidable force in Saxony-Anhalt politics, with a direct influence on the state’s legislative process. Systemic Failures in Germany’s Governance Model The Saxony-Anhalt election result reflects a systemic failure of Germany’s post-war mainstream-party model of governance to adapt to rapidly changing socioeconomic conditions. It stems from multiple, mutually reinforcing structural tensions and highlights the existing political system’s difficulty in adapting to “an increasingly polarised society First, collective anxiety has replaced individual material hardship as a central driver of political mobilisation. The rise of the AfD can no longer be explained simply by poverty or social marginalisation. Its increasingly cross-class electoral base suggests that what is driving voters towards the party is, above all, concern about the broader direction of society. This anxiety about the future of the political community goes beyond individual gains and losses. In political discourse, economic anxiety has become closely intertwined with immigration, while immigration and refugee issues are, in essence, questions of social fairness. This perception turns conflicts over the distribution of social resources into negative assessments of governing parties, providing a broad social basis for far-right political narratives. Second, regional disparities and East German identity have undermined the strategy of addressing eastern Germany’s structural challenges at the regional level. More than three decades after German reunification, eastern states such as Saxony-Anhalt have experienced periods of economic growth, but their underlying economic structures remain weak, and household incomes and wealth remain significantly below those in western Germany. At the same time, they continue to face population outflow, an ageing population, labour shortages, and other structural challenges. At a deeper level, historical experiences — including the collapse of the former East German economic system, mass unemployment during the transition, and the westward shift of decision-making power over major issues — have left some residents with a sense of being “second-class citizens” and of being excluded. For years, the German government treated the “syndrome” of reunification as a regional problem to be gradually addressed at the local level. That approach is becoming increasingly unsustainable, as regional development disparities emerge as a core structural factor shaping national politics. Third, the erosion of institutional trust is perhaps the most decisive factor. AfD supporters tend to distrust establishment institutions, including parliament, government, and the mainstream media. Their concerns about economic security, pensions, and social fairness are significantly greater than the social average. Increasingly, their support for the AfD reflects not an expectation that the existing system can be improved, but a search for an alternative to the current political order. Such a systemic deficit of trust will be extremely difficult for the traditional parties to repair in the short term. Fourth, a generational shift in political mobilisation has amplified political polarisation. Unlike traditional parties, which rely heavily on offline rallies and mainstream media coverage, the AfD has developed a social-media mobilisation strategy built around “raising an issue — identifying who is responsible — offering a simplified solution — building a shared identity”. It turns complex social tensions into concise political narratives, rapidly channelling public sentiment into political support. The contrast on the eve of the election was particularly telling. The establishment parties, together with trade unions and churches, organised an offline march attended by more than 10,000 people, but its reach was largely confined to those present and to traditional media coverage. The AfD held a much smaller indoor event, yet livestreaming on TikTok, YouTube, and other platforms, combined with the involvement of online influencers, gave it a reach and mobilising capacity far beyond that of the march. The contrast illustrates how the traditional parties’ methods of political mobilisation are increasingly struggling to keep pace with a political environment transformed by changes in media technology. A Microcosm of Structural Political Change in Europe The shockwaves from the Saxony-Anhalt election quickly travelled beyond the state to the federal level. Calls for political accountability followed immediately after the results were announced. The governing CDU and SPD are now under enormous pressure, while Chancellor Friedrich Merz and Vice Chancellor Lars Klingbeil face an unprecedented test. Political polarisation in Germany is no longer simply a regional phenomenon. The post-war model of multiparty government by establishment parties is breaking down and being reshaped. Viewed in a broader European context, the Saxony-Anhalt election is part of a continent-wide wave of party polarisation, mainstream-party decline, and rising right-wing forces. Far from an isolated phenomenon, it is a microcosm of the structural political changes unfolding across Europe. Weak economic growth, cultural tensions over immigration, and declining trust in institutions are fuelling the rise of right-wing forces in several countries, challenging the liberal internationalist order established in post-war Europe. As a core member state of the European Union, Germany’s ability to adapt its political system and the direction of its politics will have profound implications for both European integration and the evolution of the international order. Subscribe [ Pekingnology China’s Germany watchers take stock of Berlin’s uneasy turn On 7 December 2025, nearly 50 Chinese scholars and policy researchers gathered in Beijing for the 2025年德国形势年终研讨会 “2025 Year-End Seminar on the Situation in Germany… Read more 9 months ago · 31 likes · 1 comment · YIRUI LI and Yuxuan JIA ](https://www.pekingnology.com/p/chinas-germany-watchers-take-stock?utm_source=substack&utm_campaign=post_embed&utm_medium=web&embedding_publication_id=1151841) [ Jiang Feng on Germany's 2025 and beyond: no easy exit ](https://www.eastisread.com/p/jiang-feng-on-germanys-2025-and-beyond) Yirui LI and Yuxuan JIA · Jan 24 The following is an op-ed by Jiang Feng, published on 29 December 2025 at Shanghai Observer 上观新闻, the digital platform of Jiefang Daily, the official newspaper of the Shanghai Municipal Committee of the Communist Party of China (CPC). [ Read full story ](https://www.eastisread.com/p/jiang-feng-on-germanys-2025-and-beyond)
Ukrainian defence tech seeks new ways to scale up
Since Russia's full-scale invasion in February 2022, Ukraine's defence sector has expanded several-fold, becoming a significant source of military innovation, especially in unmanned systems, counter-drone technologies, electronic warfare and AI-driven combat systems. But finding the funds and personnel to scale up and to reach international markets remains a challenge.Spurred on by the demands of the front-line, Ukrainian defence companies say they can develop a minor component in a couple of weeks, or major hardware or software solutions within a year. This is several times faster than the usual development cycles in the sector. Tencore, founded in 2024, is now one of Ukraine’s largest providers of unmanned ground vehicles, well exceeding the production of NATO members in this category. Odd Systems, a Ukrainian combat cameras developer founded in 2023, launched its first thermal camera in 2024. Less than two years later, it is developing the fourth generation of the product, one that has evolved into a full vision system.According to Michael Singer, managing partner at Verne Capital, a venture capital fund supporting defence companies in Europe, this speed is down to the short feedback loop between the developer and the end user. “Ukrainian companies develop technologies in close interaction with soldiers and military units, test them under real combat conditions and rapidly incorporate feedback into the next generation of the product,” he says. “Sometimes we need to react to feedback and deploy an upgrade within hours,” says Polina Tymchenko, the spokesperson for Himera, which develops electronic warfare-resistant walkie-talkies. “It means that, as a company, you have to have a higher tolerance for risk, and also design your products in a way that fosters those quick changes.”Getting a product deployed by a combat command involves no paperwork other than a purchase order, says Perry Boyle, co-founder and chief executive of Mits Capital, a Ukrainian-US investment group focused on attracting capital for Ukraine's defence tech sector. “New weapons can be developed and deployed based largely on handshakes.”Nevertheless, the speed of development is largely subject to the level of funding, says Iryna Sukhomlyn, head of business development at RMachine, a developer of unmanned ground systems. “Financial support needs to start much earlier, particularly at technology readiness level 3-5, not only when most of the technological risk has already been removed,” she says.In 2025, Ukrainian defence tech companies attracted about $129 million in investment, with the true figure likely higher given the share of undisclosed security-sensitive deals, according to a report published by the Kyiv School of Economics. These investments were primarily concentrated in the AI and software segment, as they are considered a lower financial risk.Scaling production is a major barrierAccording to Iryna Supruniuk, business development director at Odd Systems, proving a defence product in Ukraine is not the end of the development process, but the beginning of a continuous evolution, since operational requirements change rapidly. “Continuous iteration remains our highest priority,” she says.It's also when production must scale up, which represents a major hurdle. “Scaling production requires capital, manufacturing capacity, components, certification, procurement access and reliable supply chains,” Singer says. Scale-up is also hampered by demographic pressures, especially a shortage of young specialists needed to expand engineering teams. Companies have reported struggling to replace specialists lost due to Russian attacks, mobilisation and emigration. In order to meet this scale-up challenge and finance arms production, begun allowing defence technologies to be exported, assuming it has an excess capacity. It has also announced the creation of ten defence and weapons export centres across Europe by the end of 2026.According to Singer, a technology that has demonstrated its value under real combat conditions has a fundamentally different credibility than a product that has only been tested in a laboratory or training environment. But combat validation alone doesn’t automatically create an international business. “Companies still need access to foreign defence ministries and procurement organisations, international certification, financing for production scale-up and, in many cases, local industrial partners,” he says.Already in 2026, the EU has signed several agreements with Ukraine that aim at joint defence technology development and production. These include allowing Ukraine to join the European Defence Fund, signing a financing agreement under the European Defence Industry Programme, and setting up the EU-Ukraine Drone Alliance.For Singer, it is important that European defence companies don’t just see their Ukrainian partners as technology suppliers, but rather as potential innovation partners. “Ukrainian companies bring speed, operational experience and an exceptional ability to iterate. European partners can provide industrial capacity, capital, established supply chains and access to European procurement markets.”Procurement possibilitiesWhen it comes to exports, the procurement and compliance processes involved require time, taking months or even years, and money. In other words, resources that small defence tech companies need if they are to improve and scale up their technologies. According to Sukhomlyn of RMachine, if the priority is to rapidly increase available defence capabilities, then the processes need to reflect that urgency. “If Europe wants defence innovation at wartime speed, its administrative processes also need to operate much closer to wartime speed,” she says, highlighting a need for support with certification, market access and mutual recognition of relevant testing. “Otherwise, we risk having excellent technology available but spending years making it administratively eligible to be purchased.”Related articlesRussia widens targeting of European research centresEU must support research managers in central and eastern Europe, says Visegrad fund headThis is a lesson Europe’s defence sector would do well to learn, Singer says. “Defence innovation needs to be organised around the speed and requirements of the end user, not around the speed of the bureaucracy.”Supruniuk says the European defence sector should rethink the balance between quality and quantity. “In many cases, a good-enough system available in thousands of units creates greater operational value than a perfect system available in dozens.”“For decades, everyone has been looking for that all-powerful, exquisite system that can protect everyone,” adds Tymchenko. “We see in crime that only scalable, dedicated, affordable solutions can actually defend democracies.”
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Thank heaven for small favors: Senators on the Appropriations Committee on Sunday released a stopgap funding bill to avert a government shutdown by Oct. 1 and temporarily block a White House proposal that would fundamentally change how the federal government awards and manages grants. In late May, the Office of Management and Budget released … Continue reading "Science Wins A Skirmish"
Business Restructuring Review Vol. 25 No. 4 July-August 2026
In This Issue New York Bankruptcy Court: Premature Allegations of Creditor Mistreatment Insufficient to Deny Chapter 15 Recognition of Foreign Restructuring Proceeding In In re ARD Finance, S.A., No. 25-12794 (MG), 2026 WL 817458 (Bankr. S.D.N.Y. Mar. 25, 2026), the U.S. Bankruptcy Court for the Southern District of New York was presented with a petition seeking chapter 15 recognition of Luxembourg's recently enacted bankruptcy law governing judicial reorganization proceedings. The court granted the petition, turning aside challenges to recognition voiced by a creditor group arguing that recognition should be denied based upon allegations of unfairness to creditors in the Luxembourg proceeding that would make recognition manifestly contrary to U.S. public policy. According to the bankruptcy court, recognition was warranted because: (i) the rules and procedures governing the proceeding under Luxembourg law were fundamentally fair; (ii) the petition otherwise satisfied chapter 15's recognition requirements; and (iii) the creditor group's allegations were premature, given the fact that the Luxembourg court presiding over the debtor's restructuring proceeding had not confirmed a restructuring plan, and all creditors had the right to participate fully and voice their objections in the proceeding (including the right to appeal an order approving a plan). [read more …] Florida Bankruptcy Court Examines Scope of Discovery in Chapter 15 Cases In In re Banco Master, S.A., No. 25-24568-SMG, 2026 WL 931729 (Bankr. S.D. Fla. Apr. 6, 2026), the U.S. Bankruptcy Court for the Southern District of Florida granted in part and denied in part post-recognition motions filed by an insider of a foreign debtor seeking to bar or limit discovery sought by the debtor's foreign representative concerning allegedly voidable transfers involving the debtors' U.S. real property. According to the court: (i) although chapter 15 includes its own discovery provisions among the relief that may be granted to a foreign representative, the expansive discovery available in cases under other chapters of the Bankruptcy Code is also available in chapter 15 cases; (ii) the discovery sought by the foreign representative was permissible; (iii) discovery regarding matters that were the subject of a pending adversary proceeding had to be undertaken under the more restrictive rules applicable to such proceedings; and (iv) a discovery request would be barred to the extent that it sought production of documents or testimony from a subpoenaed entity that exceeded the geographic limitations specified in such rules. [read more …] Delaware District Court Affirms Chapter 15 Recognition Order Enforcing Nonconsensual Third-Party Releases In U.S. Int'l Dev. Fin. Corp. v. Credito Real S.A.B. de C.V. SOFOM, E.N.R. (In re Credito Real S.A.B. de C.V. SOFOM, E.N.R.), 677 B.R. 192 (D. Del. 2026), appeal filed, No. 26-2356 (3d Cir. June 5, 2026), the U.S. District Court for the District of Delaware affirmed a bankruptcy court order granting chapter 15 recognition to a Mexican "concurso mercantile" proceeding and enforcing nonconsensual third-party releases approved by the Mexican court. Agreeing with the bankruptcy court that the releases were not manifestly contrary to U.S. public policy, the district court concluded: "It is well settled that U.S. bankruptcy courts can give effect to foreign orders in recognized foreign proceedings pursuant to the authority granted in Chapter 15, even when those orders contain relief unavailable under U.S. law. [The appellant's] arguments misconstrue the Supreme Court's narrow holding in Purdue, applicable in Chapter 11 cases, as well as the statutory framework of Chapter 15 and its core policy objectives. Accordingly, I will affirm the Enforcement Order." The ruling has been appealed to the U.S. Court of Appeals for the Third Circuit. [read more …] Fifth Circuit: Bankruptcy Court, Not State Court, Had Jurisdiction to Allow or Disallow Administrative Expense Claim In Storey Minerals Ltd. v. EP Energy E&P Co., LP (Matter of EP Energy E&P Co., LP), 169 F.4th 609 (5th Cir. 2026), the U.S. Court of Appeals for the Fifth Circuit affirmed lower courts' rulings disallowing a creditor's administrative expense claim. It also rejected the creditor's argument that, because the claims were predicated on state law, a state court should adjudicate the merits of the state law causes of action before the bankruptcy court could allow or disallow the claim as an administrative expense. According to the Fifth Circuit, even though the causes of action were based on state law, the bankruptcy court had jurisdiction to determine whether the creditor's claims should be conferred with administrative expense priority in the debtors' chapter 11 case. [read more …] New York Bankruptcy Court: Dismissal of Involuntary Chapter 11 Case Filed Prior to Commencement of Foreign Restructuring Proceeding Unwarranted In In re Xinyuan Real Estate Co. Ltd., No. 25-10745 (PB), 2026 WL 592250 (Bankr. S.D.N.Y. Mar. 3. 2026), the U.S. Bankruptcy Court for the Southern District of New York considered the bona fides of an involuntary chapter 11 petition filed by certain holders of U.S. dollar-denominated notes against a holding company for a group of Chinese property development companies that filed a restructuring proceeding in the Cayman Islands after the noteholders filed the involuntary chapter 11 petition. The court denied the debtor's motion to dismiss the chapter 11 case. Given the timing of the filing of the Cayman Islands restructuring proceeding, the absence of any progress in the proceeding for seven months, the debtor's need to restructure notes governed by U.S. law, and the debtor's lack of any meaningful contacts to the Cayman Islands, dismissal of the chapter 11 case was unwarranted under the applicable provision of the Bankruptcy Code, international comity, or the doctrine of forum non conveniens. [read more …] Bankruptcy Court Evaluates Trustee's Authority to Sell Avoidance Actions and Right of Target Defendants to Object In In re Law Enf't Officers Sec. Union, No. 24-70277-JAD, 2026 WL 560452 (Bankr. W.D. Pa. Feb. 27, 2026), a chapter 7 trustee sought court approval to sell certain avoidance actions to a creditor of the debtor's estate. Potential targets of the subject avoidance actions objected, arguing that such claims were nontransferable powers of the trustee rather than saleable estate property and urging the court to approve their competing settlement with the trustee. The U.S. Bankruptcy Court for the Western District of Pennsylvania disagreed with the potential defendants, holding that they lacked standing to object to the proposed sale, that avoidance actions are indeed estate property, and that the sale—rather than the settlement—best served the estate's interests. [read more …] U.S. Supreme Court Bankruptcy Roundup in Brief In Keathley v. Buddy Ayers Construction Inc., No. 25-6, 2026 WL 1686028 (U.S. June 11, 2026), the U.S. Supreme Court overturned a Fifth Circuit decision judicially estopping a chapter 13 debtor from asserting a personal injury claim against a construction company because the debtor failed to disclose the claim during his bankruptcy case. In so ruling, the Court held that "[t]he Fifth Circuit erred by artificially narrowing its inquiry to whether the debtor had knowledge of the underlying facts or a potential motive to conceal the claim." It represents the first time that the Court has applied judicial estoppel in the bankruptcy context, although it has suggested in other contexts that it may be inappropriate where an inconsistent legal position resulted from "inadvertence or mistake." Also in June, the Court denied petitions for certiorari in a few notable bankruptcy cases. [read more …] Newsworthy Heather Lennox (Cleveland/New York) (Star Individual), Bruce Bennett (Los Angeles) (Senior Statesperson), Kevyn D. Orr (Washington) (Eminent Practitioner), Gregory M. Gordon (Dallas) (Band 1), Carl E. Black (Cleveland), Corinne Ball (New York) (Senior Statesperson), Brad B. Erens (Chicago), Jeffrey B. Ellman (Atlanta), T. Daniel Reynolds (Cleveland), and Dan T. Moss (Washington/New York) were recognized as leading lawyers in the practice area Bankruptcy/Restructuring in the 2026 edition of Chambers USA: The World's Leading Lawyers for Business. The 2026 edition of Lawdragon 500 Leading Bankruptcy and Restructuring Lawyers ranked the following lawyers: Corinne Ball (New York) (Hall of Fame), Bruce Bennett (Los Angeles), Carl E. Black (Cleveland), Jeffrey B. Ellman (Atlanta), Brad B. Erens (Chicago), Gregory M. Gordon (Dallas), Heather Lennox (Cleveland/New York), Joshua M. Mester (Los Angeles), and Kevyn D. Orr (Washington). Fabienne Beuzit (Paris) and Jasper Berkenbosch (Amsterdam) were among the "Leading Individuals" ranked in the 2026 edition of The Legal 500 EMEA guide in the field of Restructuring and Insolvency or Insolvency. Rodolphe Carrière (Paris) was named a "Next Generation Partner" in the practice area Insolvency. Sid Pepels (Amsterdam) was selected by Global Restructuring Review's 40 under 40 2026. He was identified as a Rising Star within the global cross-border restructuring community. Global Restructuring Review is a leading digital subscription service, news outlet, and information platform dedicated exclusively to cross-border insolvency and corporate restructuring law. Fabienne Beuzit (Paris), Rodolphe Carrière (Paris), and Isabelle Maury (Paris) were recognized in the 2027 edition of The Best Lawyers in France™ in the practice area Insolvency and Reorganization Law. An article written by Corinne Ball (New York) titled "Comity and Fairness are Critical to Granting Additional Assistance under Chapter 15: A Canadian Reverse Vesting Order and Related Releases in Proceedings Involving Canadian and U.S.-Affiliated Companies Are Approved" was published in the June 24, 2026, edition of the New York Law Journal. An article written by Caitlin K. Cahow (Detroit) titled "New York Bankruptcy Court Weighs In on Adequate Assurance of Future Performance under Assigned Leases and Chapter 11 Plan Exculpation Provisions" was published on June 8, 2026, in Lexis Practical Guidance. An article written by Trisha L. Mowbray (Real Estate; Chicago) and Ryan Sims (Washington) titled "Chapter 11 Plan that Abridged Non-Debtor Lessee's Rights to Remain in Possession After Rejection Unconfirmable as Having Been Proposed in Bad Faith" was published on June 16, 2026, in the Harvard Law School Bankruptcy Roundtable. An article written by Dan T. Moss (Washington/New York), Corinne Ball (New York), Isel M. Perez (Miami), and David S. Torborg (Washington) titled "Texas Bankruptcy Court Adopts Barnet Rule Requiring Foreign Debtor to Have U.S. Assets to Be Eligible for Chapter 15 Relief" was published on June 10, 2026, in Lexis Practical Guidance. An article written by Daniel J. Merrett (Atlanta) and Nathaniel Parr (Atlanta) titled "Southern District of Texas Clarifies Post-Purdue Course for Consensual Third-Party Releases and Gatekeeping Provisions" was published on June 11, 2026, in Lexis Practical Guidance. An article written by Corinne Ball (New York) titled "LMEs, Sophisticated Investors and Equitable Mootness: The Fifth and Third Circuits Warn Against Testing the Limits of Chapter 11" was published in the June 2026 issue of the American Bankruptcy Institute Journal. Nicholas J. Morin (New York) was quoted in an article titled "Serta Ruling Offers Damages Road Map in Lender-on-Lender Disputes," published by Bloomberg Law on July 7, 2026. The article discussed U.S. Bankruptcy Judge Christopher Lopez's July 7, 2026, ruling that participating lenders in Serta Simmons Bedding's 2020 non-pro rata uptier exchange breached the credit agreement's ratable-sharing provision, and awarding excluded lenders more than $261 million in damages. The opinion resolves six years of litigation related to Serta's 2020 transaction, where it raised $200 million in new financing and exchanged $1.2 billion of term loans for $875 million in superpriority debt. Judge Lopez found that the debt-for-debt exchange constituted a "payment" under the credit agreement, requiring proportional sharing among lenders, and dismissed various defenses raised by the participating lenders. The case is Serta Simmons Bedding LLC v. AG Centre Street Partnership (In re Serta Simmons Bedding LLC), 2026 WL 1968535 (Bankr. S.D. Tex. Jul. 7, 2026). The New York Law Journal has recognized Andrew M. Butler (New York) as a 2026 "Rising Star" in Restructuring/Bankruptcy as part of the New York Legal Awards. The Rising Star category recognizes New York's most promising lawyers age 40 and under who have distinguished themselves through exceptional legal work, leadership, and contributions to the profession. Lawyer Spotlight: Daniel J. Merrett Dan Merrett, a partner in the Atlanta Office, has spent nearly 20 years representing all manner of stakeholders in complex corporate restructurings and chapter 11 reorganizations. His practice extends to related transactions and disputes involving acquisitions; divestitures; credit facilities; securities; environmental obligations; and vendor, employee, and governance matters. Dan has substantial experience managing mass tort liabilities, including the restructurings and related chapter 11 cases of affiliates of Georgia Pacific and Johnson & Johnson, implemented to resolve asbestos and talc liabilities. He also represents key parties in energy and infrastructure matters, including facility owners, EPC contractors, and joint-venture parties. Dan was a key player in the restructuring teams in the historic bankruptcies of the City of Detroit and Chrysler. His other significant representations in chapter 11 cases include AFA Foods, Alpha Natural Resources, Dana Corporation, M&G Chemicals, McDermott International, Monster/CareerBuilder, Transtar Industries, and Westinghouse. He is licensed in both the United States and England & Wales and recognized in restructuring by Chambers USA and Best Lawyers.
US Stand Up for Science movement expands to Europe
A grassroots movement by scientists in the US pushing back against threats to academic freedom is setting up a Swiss outpost to take the movement global. Stand Up for Science started in February 2025 to protest against politically-motivated grant cancellations by the Trump administration. Since then, it has organised numerous rallies by scientists and turned into a broader campaigning organisation, supporting “pro-science” political candidates and attempting to impeach Robert F. Kennedy Jr, the health and human services secretary the group accuses of advancing “quack” science. Now, Stand Up for Science is establishing a Swiss office to take some of its campaigning beyond the US, and its leader is urging European academics to organise similar pro-science groups in case they face similar threats in Europe. There will likely be “spillover” to Europe from what the Trump administration is attempting in the US, said Colette Delawalla, chief executive of Stand Up for Science, who last month returned from a tour of Europe. “Are we going to see a Trump-style scientific [. . .] take over in the same way?” she asked.Delawalla, who is doing a PhD in clinical psychology, said she was driven to help found the organisation after the Trump administration started cancelling health research grants in politically taboo topics, such as transgender people, diversity and HIV, in early 2025. Since then, the administration has introduced several other measures that infringe academic and institutional autonomy.It threatened to cut Harvard University’s federal funding unless the university accepted a “critical mass” of new faculty and students to create “viewpoint diversity.” And this year, it proposed explosive new rules that would allow political appointees at research funders to screen grants to make sure they “advance the president's policy priorities.”US scientists have been at times stunned by the volume of announcements, and the country’s researchers were relatively unprepared. “We sit in front of the fire hose every day,” said Delawalla. For this reason, European scientists need to prepare now for similar assaults. “If you've got a little bit of extra time to start thinking through, and making those preparations and building community and solidarity,” then European researchers will “start five steps ahead of where we were.” She plans to return to Paris in September to share experiences with concerned French academics. Is Le Pen the same?During her European tour, Delawalla said that French academics were beginning to worry about a National Rally (RN) victory in presidential elections next year. She said there were “chilling similarities” between Trump and Marine Le Pen, the RN’s likely candidate. However, so far, the RN hasn’t proposed anything nearly as radical as the Trump administration has carried out. Its current manifesto is all but silent on university and research policy. Le Pen’s 2022 manifesto bemoaned a lack of French R&D spending, including in fundamental research, and promised to modernise labs and strip away grant bureaucracy. The party’s 2024 European election manifesto explicitly supported the Erasmus+ student exchange programme, scientific exchange and joint European technology projects. However, a Le Pen victory next year could see France drastically cut its contribution to the EU, which would hit the budget for science and innovation. Meanwhile, more restrictive immigration policies could hit university recruitment of faculty and students. Climate science denial?As heatwaves and wildfires have ravaged France, the RN has sought to distance itself from previous comments casting doubt on climate science.In the UK, the radical-right party Reform UK, now second in the polls, denied climate research in a draft of its 2024 election manifesto, but its leader, Nigel Farage, then appeared to quietly U-turn and said he was “not arguing the science.” By contrast, Trump has repeatedly and explicitly denied climate change science, in 2025 calling it “the greatest con job.” Like the RN, Reform UK has been light on research and university policy. However, it did say in its 2024 manifesto that universities should suffer “heavy financial penalties” for allowing “political bias or cancel culture” on campus, building on laws brought in by a previous Conservative government. Ahead of September state elections in Saxony-Anhalt in Germany, the poll-leading Alternative for Germany has proposed abolishing gender studies and postcolonial studies, and promised money for “critical” climate research and studies of Islam. Starts with rhetoricEven if European radical right parties aren’t currently planning the draconian policy measures of the Trump administration, Delawalla said that academics should not be complacent, and fight even small funding restrictions or anti-science talk from the start. “It starts off with sowing distrust about institutions,” she said. “It starts off with these small things, and then all of a sudden it's there.” There are other, more established science and university advocacy groups in the US aside from Stand Up for Science. But the group stands out because of Delawalla’s refusal to attempt to appear politically bipartisan or conciliatory. “I firmly believe that the US is experiencing fascism,” she said. “We’re not trying to win over the super Maga people,” she said, “and we never have been.” The lesson of the US is that even if universities or other institutions capitulate to government demands, this doesn’t save them from cuts and restrictions. “It doesn’t matter what people do,” she said. Global “pseudoscience”Another aim of Stand Up for Science’s push into Europe is to better coordinate a campaign to protect people in poorer countries from what the group describes as “pseudoscientific exploitation,” trials outside the US used to build a case against things like vaccines. Earlier this year, the group sent an investigator to Guinea-Bissau to investigate a controversial, Danish-run Hepatitis B vaccine project funded by the US Centers for Disease Control and Prevention.The study would have withheld a proven vaccine from newborns, Stand Up for Science argued in a briefing it presented to Congress, risking their health. Significantly, the research group based in Guinea-Bissau, known as Bandim, focuses on the “non-specific effects” of vaccines: impacts on health beyond preventing the target disease. Related articlesEuropean researchers sound the alarm that Trump rule changes will hit cooperationData corner: which fields of health science is the US curbing? The US ‘golden age’ science strategy: what Europeans need to know The suspicion is that the study was funded as part of an attempt to shop around for results that could help build a case against vaccines in the US, said Delawalla. “This is just code for vaccines cause autism,” she said.Bandim has put out a rebuttal of Stand Up for Science’s criticisms, arguing its work has uncovered a number of vaccine side effects, both positive and negative, which need to be taken into account in public health. However, Congressional Democrats took up the Stand Up for Science cause, and in February, Guinea-Bissau said it had permanently halted the study. WatchdogBut for Delawalla, this is not the end of the fight. Kennedy has made a number of health claims that scientists think lack evidence, such as the idea that seed oils cause inflammation, for example. Delawalla worries that more studies could be launched in poorer countries to fish around for evidence to support this agenda. Stand Up for Science is talking to African science organisations to create a kind of “watchdog” to scrutinise any further trials. “Outsourcing the investigation to some of the most vulnerable children in the world who are desperate [. . .] is fundamentally wrong,” she said.
In Pursuit of Precision: Remembering John Barker
John L. Barker, who passed away on July 6, 2026, at age 88, was a seminal force who fundamentally transformed Landsat into a rigorous science mission through his relentless pursuit of precision remote sensing calibration standards.
Structural alignments to design functional RNAs
Nature Computational Science - A new method designs RNA sequences by learning from alignments of structurally similar molecules, producing aptamers and ribozymes that are experimentally validated.
The Science Behind the AI Panic Is Shakier Than You Think
A column by Sascha Lobo Is AI making us dumb? So asked DER SPIEGEL on its cover a while ago. After a few weeks of recovery and a therapeutic intervention with the help of my AI, I feel able to respond. That’s a bit unfair, of course; the article itself is more nuanced than the loud cover, the image of a schoolkid with dull prompt questions, or the subtitle: “AI is conquering the schools, and educators fear a disaster.” The short answer to “Is AI making us dumb?” is: no. The long answer is more interesting, and even carries a preliminary glimmer of the spectacular. Unfortunately, it requires a detour into the sphere of artificial intelligence, into the mogul-field terrain of technology assessment, the attention economy, and the destructive virus of cultural pessimism. Along with narrowing the scope to AI’s effects on education and work rather than on intelligence. Because the honest version of the question would be: Does AI help us learn and work, or not? Generative AI, which set off the still-ongoing hype with ChatGPT, has only existed in relevant distribution since late 2022, early 2023. For a technology-driven transformation of people and society, that is extremely short. For that reason alone, research is still in its infancy. And technology assessment is not a fast discipline anyway. Prominent voices from Elizabeth Eisenstein to RAND to Tom Wheeler say we still cannot conclusively judge the consequences of the printing press. Its invention is a mere 580 years back, and some scholars would rather not circulate assessments that might have to be retracted in two or three hundred years. We’re Going Through Growing Pains A phenomenon that already caused upheaval in social media research makes serious assessment of AI’s consequences even harder: the “moving target” problem of LLM-ology, as the linguist Sean Trott called it in 2025. AI develops so quickly and so unpredictably that researchers often end up studying snapshots of the past. There are concepts, from morphological analysis to anticipatory governance, for keeping research, planning, and regulation halfway meaningful anyway. But even with those, universally valid statements are rare. DER SPIEGEL - The German View is a reader-supported publication. To receive new posts and support our work, consider becoming a free or paid subscriber. Subscribe We should therefore treat findings about the interplay between humans and AI as far more procedural and situational. Unfortunately, this challenge comes at the worst possible time. We are going through growing pains: AI’s everyday impact has grown very large very fast, and with it the longing for guidance. Annoyingly, the body of solid knowledge has not exploded along with it. Experts cannot be moved to broad consensus on even banal statements about AI. Sascha Lobo, born in 1975, is an author and strategy consultant focusing on the internet and digital technologies. Together with Jule Lobo, he explores the debates of the day in the podcast “Feel the News – Was Deutschland bewegt” (”What Moves Germany”). Nvidia CEO Jensen Huang says software developers are dying out. Bill Gates considers coders one of only three professions that will survive the AI tsunami. Top tech economists find virtually irrefutable arguments both for and against an AI bubble. The same naturally applies to the structurally youth-hostile debate over whether AI is good or bad for young people. The gap between the demand for insight and the supply of solid knowledge creates an orientation vacuum. This vacuum of AI cluelessness is filled, as the attention economy described by Georg Franck in 1998 would have it, with half-knowledge (funnily enough, often AI-generated), marketing, and self-marketing. After all, infinite money is at stake, along with academia’s best current shot at global fame. But this iridescent froth of interpretation operates more by the algorithmic principles of the digital public sphere than by seriously earned insight. That is why dramatized, fear-feeding claims about AI are so promising. It tempts parts of academia into distorted drivel, and parts of the media then sensationalize the published drivel once more. Was the Research Aimed at a Desired Result? How badly this can influence academia and public perception is demonstrated by one of the most-discussed studies on AI’s effects: “Your Brain on ChatGPT.” The MIT study by Nataliya Kosmyna also gets plenty of space in the SPIEGEL cover story. What was measured and interpreted: lower brain activity in people who write with AI assistance, compared to people who only google or write without any tools. As is common with preprints, the study has not yet been peer-reviewed, and it contains substantial weaknesses that experts have sharply criticized. The first round had 54 participants in three groups, the second only 18, who reportedly also came from MIT’s own orbit. There was no preregistration (committing in advance to what you want to find out with which data). In their analysis, Stanković et al. even discovered an opposite effect that the study does not further explain. Some measurement data has not (yet) been published. And the EEG analysis method used is considered too imprecise to support such far-reaching conclusions. The biggest criticism, though, is the suspicion of an activism-driven study. In an interview with Time, lead author Kosmyna said she published the study immediately, rather than after full peer review, because she feared politicians might otherwise decide to put ChatGPT in kindergartens. She considers that absolutely harmful. It is legitimate to hold such a view. It just seems unscientific to approach a study with it, and then to design the setup so that the chance of a fitting desired result remains as high as possible. Ideologies Collide It is not easy to separate genuine AI insights from studies that trivialize and weaken well-founded AI criticism. The same goes for the opposing, AI-enthusiastic narratives, because the pro-AI side works against it with billions in resources. Ideologies collide, fed by egos, PR strategies, moral posturing, and once-in-a-lifetime opportunities. Doomsday marketing makes the search even harder: AI companies can profit from warnings about the dangers of AI, whether through the notoriety of apocalypse narratives or because, in some target groups, danger translates into power. Some seemingly negative findings are therefore quite welcome in the AI industry. If you try to get at the findings presumably less influenced by interest groups, the picture in education seems relatively clear: AI can improve educational outcomes when the right AI is used the right way, namely pedagogically. Simply dumping an AI chatbot into a classroom, by contrast, accomplishes little. Huge surprise. But something else is also obvious: whether they are allowed to or not, kids use AI for school, which is why the controlled integration of AI tools into education is, in the medium term, entirely without alternative. In the world of work, the landscape of findings is more complex and requires more differentiation. A series of serious studies points to an AI effect that suits neither the alarmists, regulation fetishists, and AI opponents, nor the trillion-dollar corporations with their armada of paid scientists, PR professionals, and ecosystems: working with AI helps the inexperienced (at least for now) considerably more than it helps the pros, in certain fields of work, and provided they know how to use it correctly. Missing Domain Knowledge Among the experienced, the professionals, and the high flyers, by contrast, despite frequent time savings, partially negative effects and new problems are emerging. Take the jagged frontier problem: AI solves task A brilliantly and fails completely at task B, even though both look similar even to professionals. Or the verification-cost problem, in which AI’s greater productivity gets eaten up by the enormous effort of checking its output. Or the illusion of competence, because AI makes new domains seem easily penetrable, until you painfully discover that deep domain knowledge is missing. Taken as a whole, the effects of working with AI do lean positive. But the promise that AI works as a turbocharger for everyone and boosts practically everything simply refuses to materialize. The AI magic the world has been feeling since ChatGPT arrived in late 2022 has so far barely shown up in most companies’ numbers. The consulting firm McKinsey has coined a term for this, the “gen AI paradox,” and believes that work processes and corporate structures would have to be rebuilt quite fundamentally to realize AI’s benefits at scale. Reuters just reported that only 3.3 percent of Microsoft’s customers have subscribed to the AI package Copilot. That may be down to the product; in other areas we continue to see soaring successes and new AI triumphs. But after the first quarter of 2026, disillusionment is the word for all too many AI projects at traditional companies: because the promised revolution of AI agents has so far failed to arrive or been postponed, and because of the leveling effect that many of the studies cited here suggest. Against Inequality The societal effects, on the other hand, appear rather encouraging. Many previous technologies operated on the Matthew principle, named for the Gospel verse about giving to those who already have: they tended to reward the more competent and more privileged. AI, however, seems more often to have an equalizing effect once a threshold of access and education is crossed. Some anecdotal insights point in this direction, for example, how an unemployed man in Leipzig successfully defended himself in court in 2025 against fraud accusations from Germany’s federal employment agency — without a lawyer, armed only with ChatGPT. The AI made mistakes and cited invented court rulings, but in the end the man walked away unpunished. And a key capability of artificial intelligence is its capacity to learn: set up correctly and fed with data, it can keep getting better. It is a question of time, or more precisely of AI innovations and training data, until even free AI chatbots write better legal briefs than law graduates with perfect exam scores. Whether that will make anyone dumb is something the world’s press will surely inform us of in due time. Subscribe Leave a comment
Why Did This Man Avoid Alzheimer’s? Scientists Reveal Their Astonishing Theory - Futura-Sciences
A Family Plagued by Early-Onset Alzheimer’s Doug Whitney inherited a rare genetic mutation that’s devastated much of his family: many relatives experienced significant cognitive decline as early as their 50s. Yet Doug himself has stayed mentally alert well past the age at which illness struck in the family. According to...
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MAGA's attack on science is even worse than it looks
In my post on America’s 250-year anniversary, I argued that respect for the individual was the “secret sauce” in the U.S.’ long-lasting preeminence among nations. But it was certainly not the only factor. There were plenty of institutional innovations that helped the U.S. stay on top — economically, militarily, and in terms of the attractiveness of its society. One of these was American science. Today we take things like the modern research university, government grants for science, public-to-private research spinoffs, etc. for granted, but a lot of that infrastructure wasn’t there before World War 2. It was either invented or scaled up massively by the U.S. government in the postwar period, led by far-sighted scientist-bureaucrats like Vannevar Bush (pictured above). If you want to read about this history, a good place to start would be Jonathan Gruber and Simon Johnson’s excellent book Jump-Starting America. Those scientific institutions didn’t exist in a vacuum, however. They were backed by the U.S. government’s abiding faith in the power of science — and, even more fundamentally, by deep popular trust in the scientific enterprise. Science gave us radar and the atom bomb in wartime, and in peacetime it gave us plastics, vaccines, cheap food, and a thousand other things that made our lives easier. Science was also the driver behind American industry — chemicals, aerospace, telecommunications, computers, electronics, and so on. We owed science our jobs, our livelihoods, our comfortable living standards, and our greatness and power as a nation. It’s little wonder that both political parties, even as they fought viciously over other issues, were steadfast in their support of science. For a long time, the only people who distrusted science were a hippie fringe on the left who disliked modernity (or thought they did, anyway), who also disliked American industry and American power, and who subscribed to an early version of degrowth environmentalism. America’s scientific enterprise is still strong, especially compared to the systems in Europe, Japan, Korea, and other developed countries. It has lost a lot of ground to China in a relative sense, but a lot of that is because of China’s incredible growth; as China has poured untold amounts of money and talent into its research labs, spending and output have overtaken the U.S. by some measures. There are a few ways in which China’s rise creates problems for American science — for example, top scientists can choose to work in China instead of in the U.S. — and of course there’s the concern that China’s technological strength will help its military to reign supreme. But overall, China’s rise in science should be good for American science, since American scientists can use Chinese discoveries for free and build on them. The much bigger problem is that the scientific enterprise America built during and after World War 2 is now being threatened with absolute decline. The biggest problem, of course, is that much of the country — the Republican half — has basically lost faith in the scientific enterprise. To what degree this loss of faith is justified is an open question, and deserves to be discussed openly. But the larger point — that the system that powered American dominance is under threat — is true either way. Most Americans still trust science, but MAGA Republicans don’t There’s a myth, popular in right-wing circles, that scientists have lost the trust of regular Americans — either due to the increasingly left-wing composition of academic departments, or to misbehavior during Covid, or to DEI-related research taking over science, etc. This also fits with a wider narrative that Americans are losing trust in all of our institutions. But it just isn’t true. Poll after poll shows that on the whole, Americans still trust scientists and want to spend more on science. For example, here’s a Pew poll from late 2025 showing that although about a fifth of Republicans did lose confidence in science in 2020, two thirds still have at least “a fair amount” of confidence: That same poll found that scientists are among America’s most trusted groups — even better trusted than the military! In fact, Americans trust scientists more than people in most other countries do. Other polls show that although Republican trust in science has dropped somewhat, Republican support for spending more on science remains very strong: A Pew poll in 2023 found the exact same thing: So when MAGA types tell you about scientists losing their credibility, or a drop in trust in science, they’re only talking about themselves. Whatever left-wing politicization of science happened during the Biden administration — and there was definitely some of that — it was not enough to make most Republicans lose faith in the scientific enterprise, or favor research cuts in order to purge unwanted ideology from the system. That doesn’t mean I think progressives should continue down the path of politicizing scientific research. They should not, and Biden made real missteps in this area. Objectivity clearly matters for public trust of science in the long term. But as of right now, there’s no crisis of trust in science, except among the smallish minority of people who are running around screaming that there’s a crisis of trust in science. But despite science’s overwhelming popularity and public trust, Trump and his administration are launching an unprecedented and devastating attack on American science — cutting funding, and forcing science projects to undergo ideological review by government commissars. Trump’s comprehensive attack on science
Socialists launch radical platform to abolish the US Senate in bid to fundamentally transform America
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AMERICA AT 250: COMPETING CELEBRATIONS REFLECT NATIONAL POLARIZATION, SAYS SMU POLITICS EXPERT
DALLAS (SMU) – As the nation marks its 250th anniversary with separate celebrations in Washington, D.C., and Los Angeles, SMU political scientist Matthew Wilson says the split reflects a deeper reality about modern America: the absence of a shared national narrative. Wilson argues that what should have been a unifying moment of national pride has instead become a symbol of the country's growing political polarization, with rival groups promoting fundamentally different visions of Amer
How a new White House proposal could fundamentally change American science
In this webinar, we discuss the potential impacts of a Trump admin plan to overhaul how federal research grants are awarded and administered.
Medicare's health tech spending test
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ASU scientists unveil new methods to extract reliable truth from imperfect data
Scientists at Arizona State University are advancing how they interpret complex, imperfect data — challenging long-standing assumptions in fields ranging from imaging to cellular biology. In two papers published in Nature Communications and Proceedings of the National Academy of Sciences, the team introduces fundamentally new approaches to solving “inverse problems,” where researchers work backward from noisy observations to uncover the underlying reality.
Professor, Mentor, Researcher, and Data Pioneer: Celebrating Lynn Blewett’s Retirement
I first met Lynn Blewett as a graduate student at the University of Minnesota School of Public Health, where her courses on the U.S. health care system fundamentally shaped how I understand policy and evidence. What struck me most was her ability to translate complex health systems into their human dimensions and to remind us […]
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Making Our Own Luck
We are cross-posting a piece that former NIH official Kris Willis wrote for Macroscience. Kris is now the President and Founder of the Woodley Park Institute, for which I’m a board member. Picking winners or hedging bets? Federal science agencies have long wrestled with the question of how to ensure their billions of dollars of grants and contracts result in maximum benefits for Americans. On the surface, it might seem that a goal-oriented, interventionist style of management would be the best policy: prioritize our most pressing problems and distribute funding accordingly.This top-down strategy creates a unique hazard, though: bad choices can send resources in the wrong direction, resulting in slow progress that impedes the delivery of tangible benefits to citizens. Moreover, if decision makers pursue an applied advance when the underlying scientific principles remain poorly understood, they risk costly failures and a loss of support for the research enterprise at large. Examples include Nixon’s 1971 War on Cancer, undertaken on the premise that a cure could be achieved in less than a decade, the drive to build an operational commercial fusion plant by the mid-1990s, and the National Plan to Address Alzheimer’s Disease, which failed to produce the effective treatments and prevention strategies promised by 2025. The contrasting laissez-faire approach is to hedge your bets. If the path to an advance is impossible to predict, then the wisest choice might be to distribute funding to as widely as possible. The main risk of this strategy is that spreading funding thinly may limit the resources available to the most promising research, resulting in the same negative outcomes as the interventionist approach: limited support for the most promising topics, slow progress, and delayed returns. In reality, at science agencies like the National Science Foundation and National Institutes of Health, program managers navigate between these two extremes, setting some funds aside to target high-priority areas and using the rest to cover the widest possible range of meritorious proposals. This middle path seems like the most rational way to proceed, but it still requires making choices. How much should be set aside for high priority topics? What should those topics be? How should we define merit? Expert opinion can provide a helpful guide, but leading experts can have remarkably different opinions about what matters and what should be prioritized. Moreover, experts and administrators alike can be risk averse, giving an edge to high-profile, well-established concepts. What’s really needed is a data-driven framework to help guide decision-making, one that doesn’t simply repackage prestige or incumbency. Recognizing breakthroughs years in advance Many breakthroughs fail to attract funding or recognition at the earliest stages of development. Katalin Karikó, for example, spent years struggling to win grants for work on mRNA that would eventually win her and Drew Weissman a Nobel Prize. Similar stories can be told about Jim Allison’s groundbreaking studies leading to the development of cancer immunotherapy, Stan Pruisner’s demonstration that prions can self-replicate without DNA, or Robin Warren and Barry Marshall’s proof that Helicobacter pylori, not stress, causes ulcers. Yet at some point, the scientific community recognizes and responds to transformative breakthroughs like these. How does that change happen? And what if science funders could identify the areas that are on the cusp of a breakthrough? Those questions led my collaborators and me to study how scientists responded to past breakthroughs. Based on patterns in that data, we developed a means of predicting future breakthroughs, using biomedicine as a proof of concept. It’s worth taking a moment to describe how our approach works. Briefly, our prediction process begins by grouping the 18 million or so peer-reviewed papers in PubMed, the authoritative database of biomedical research, into roughly 50,000 unique topics. A small subset of these topics are so widely acclaimed as breakthroughs that they have been recognized with a major prize like a Nobel or a Lasker Award. We theorized that studying the development of these outliers over time, especially before they gained acclaim, was the key to identifying future instances of them. After defining topics, we chose 21 representative breakthroughs and asked what they looked like before they won prestigious prizes. To answer that question, we re-ran our topic-mapping algorithm repeatedly, winding back the clock one year each time, stringing together the years to follow the progress of each field as it grew and advanced. We found that in each case, the years leading up to a discovery followed a pattern: a burst of papers on a new, rapidly evolving topic, many of which quickly became influential. These characteristics—the percent of papers that are new, the percentage of papers that are brought in from other topics, and the influence of each individual paper—can all be measured separately for any given topic. Together, they act as a predictive signal that is detectable an average of five years before a transformative discovery is made, and up to thirty years before the discovery receives a major prize. Scanning the current research landscape for topics that display this signal allows us to predict what work will likely produce a future breakthrough. Each year we analyzed includes four or five signals, a number that appears to have remained constant across a twenty year time frame. In their classic work on the sociology of science, Bruno Latour and Steve Woolgar established that the desire to participate in a major discovery is an important motivator for scientists as they consider whether they should take up (or abandon) a research problem. Although further work is needed, the simplest explanation for our results is that this behavior is widespread enough to detect at scale. A foundational advance, or something like it, draws the attention of scientists and causes them to change the direction of their research. They vote with their feet, staking their own reputation and careers by publishing on and citing the new idea. That upswell of interest is the foundation of our breakthrough signal. What kind of research does our model identify as a breakthrough? After building a model based on the patterns in our 21 preselected breakthroughs, we tested it by identifying all early signals of discovery from 1994 through 1997. Of the 18 signals we found in that time period, 17 can be traced forward in time to a breakthrough. Examples include super-resolution fluorescence microscopy (see the image above), the directed evolution of proteins and enzymes (Chemistry Nobel, 2018), and sequencing the human genome (National Medal of Science, 2008). The discovery of the role of leptin signaling in obesity is a particularly interesting case study. Around 1950, scientists noticed the existence of a type of lab mouse that suffered from a unique inherited form of obesity. When given the freedom to choose their own diet, these mice ate more than normal and preferred food that was high in fat. Researchers had few clues as to why the animals overate; early experiments pointed to the existence of a soluble ob factor found circulating in the bloodstream that regulated appetite, but its identity was unknown. In 1994, Jeffrey Friedman and colleagues identified and sequenced the ob gene, noting that the protein it encoded appeared to fit the profile of a circulating factor. A year later, a trio of high-profile publications demonstrated that ob was a hormone that regulated body weight and fat deposition by regulating appetite. One of these was co-authored by Friedman, who christened it leptin from the ancient Greek word for thin. Using our model, the data from 1996 make it clear this advance would become a breakthrough. Scientists had flocked to study the biology of the new hormone, generating the flurry of new papers and citations required to produce a signal. One of these demonstrated that serum leptin concentrations reflected the amount of adipose tissue in the human body, providing clinical validation of the earlier mouse studies. Before the identification of leptin, scientists were unable to point to a specific molecule that controlled appetite and adiposity, so that it was impossible to rule out non-physiological causes of obesity; its characterization fundamentally changed our concept of weight gain. In 2010, Friedman and fellow pioneer Douglas Coleman were awarded a Lasker for the discovery. Our work also identifies breakthroughs in clinical practice and behavioral and social sciences, even though these areas are less likely to draw the attention of major prize committees. One example that falls into this category is the introduction of endovascular aneurysm repair, a minimally invasive surgical procedure for the treatment of abdominal aortic aneurysm, a leading cause of death among Americans over the age of 65. Relative to the previous standard of care, it reduces in-hospital mortality by almost four-fold. In 2017, Timothy Chuter received the American College of Surgeons’ prestigious Jacobsen Innovation award for his multiple refinements to the technique. Another under-recognized breakthrough identified by our model is the development of standardized survey instruments to assess the quality of life for HIV+ patients. The antiretroviral cocktails introduced in the mid-1990s reduced AIDS mortality, but treatment came with serious side effects. The new evaluations showed that patients whose disease progressed had worse physical functioning than Americans with other chronic diseases, while those who were asymptomatic maintained a health-related quality of life on par with the overall US population. This evidence made it possible for patients and activists to argue that even in the absence of a total cure, and in spite of the side effects, alleviation of symptoms was a worthwhile priority for clinicians. My colleagues and I have failed to identify any significant recognition for the physicians and health policy experts who pioneered this advance, although in 2016, researchers called for the World Health Organization to set targets for good health-related quality of life as part of its framework to end the AIDS pandemic. From forecasting to funding How should science funders use our predictions? Any serious discussion of what we should do with this new capability needs to begin with an understanding of its limitations. We can’t (and shouldn’t) invest all of our resources in the small number of topics predicted to produce breakthroughs. Innovative new fields are born from existing ones; they rarely arise de novo. If funders don’t maintain a diverse portfolio, the breakthroughs of tomorrow will have no antecedents. Think of this as eating the seed corn, or killing the goose that lays the golden egg. There are also multiple goals of funding beyond basic scientific discovery, including translating discoveries into clinical practice, developing new technologies, supporting economic development, and training new scientists. The investments needed to accomplish these goals are likely different from those required to support emerging breakthroughs. Finally, we must be aware that we may not capture every instance of a breakthrough. The concept is fuzzy, and not every significant discovery is recognized with a major award. Further, breakthroughs are rare, meaning that our dataset is, by necessity, small. This makes formal estimates of accuracy challenging. Although our overall success rate appears to be high, any one signal may turn out to be a false positive. We found one apparent example: the development of the third generation COX-2 inhibitors, Vioxx and Celebrex, which were much celebrated in the early 2000s as effective but non-addictive pain relievers, meets all the algorithmic criteria of a breakthrough. Five years after it received FDA approval, researchers demonstrated that Vioxx was associated with serious cardiovascular complications, and the manufacturer voluntarily pulled the drug from the market. Celebrex remains available, although like other NSAIDs, it carries an FDA warning for increased cardiovascular risk. Decision makers who were overly focused on the breakthrough potential of this research might have wasted substantial resources nurturing an area that ultimately proved to be a failure—but so would those who relied on expert opinion at the time. That said, the potential benefits of funding the next mRNA vaccine or cancer immunotherapy years earlier than we might have are large. When we can’t assume the outcome of an action, it’s worth conducting an experiment. Can targeted support for areas that are poised to produce a breakthrough increase the return on scientific investment without cannibalizing future advances? With the necessary administrative infrastructure in place, conducting this experiment would be straightforward. First, identify all the breakthrough signals between 2018 and 2023, select half at random, and commit to a decade of investment. For biomedicine, a six year window should yield about 30 topics with the potential to produce a breakthrough. Providing strong support to half of these might be accomplished for $300 million a year. That’s roughly half of the recent annual budget of the NIH Common Fund or around 20% of the fiscal year 2025 budget for ARPA-H. After five years, then again at ten, compare to see which group — the one that was actively managed, or the control — produced more breakthroughs, and on what timescale. Although we used biomedicine as an example, the same experiment could just as well be run for physics, materials science, or any other discipline. Any attempt to undertake such an experiment should keep three points top of mind. First, we can predict the topic of future breakthroughs, but the people and vision for how to move the work forward are still important. In practice, this means success requires active program managers with the expertise to develop the science and the authority to do so, including by recruiting investigators. Second, we would be wise to learn from the failures of past attempts to fund transformative research. High among these are funding modestly repackaged work, confusing the genuinely novel with the merely unfamiliar, and relying too much on incumbent investigators. All of these risks can be mitigated by thoroughly and intelligently integrating high quality data into portfolio management. Finally, we need to have patience. Even with strong funding support, producing a breakthrough takes time. If supporting breakthroughs is the best way to nurture the birth of new fields, we also need to accurately describe and properly manage the other two stages, so that we invest wisely throughout the productive lifetime of ideas and divest once they reach the point of diminishing returns. We would benefit from more study of all of these phases, especially the transition from one to another. More research could answer questions like: When does a breakthrough become an established field? How do the availability of funding, the size and characteristics of the available workforce, and the accumulation of evidence that disagrees with prevailing models affect a field’s decline? Exactly how do old ideas give birth to new ones, and can we speed up the process? The more we understand about the dynamics of scientific progress, the better we can maximize the odds that our curiosity leads to results. I’d call that finding a way to make our own luck.
SBA’s Proposed Update to the 8(a) Business Development Program: A New Approach to Social Disadvantage
On June 11, 2026, the Small Business Administration (“SBA”) published a proposed rule that would fundamentally alter how individuals qualify as “socially
How a new White House proposal could fundamentally change American science
Join us for a discussion on the potential impacts of a Trump admin plan to overhaul how federal research grants are awarded and administered.
Rice in 'good space' after 'mentally tough' season
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Would you return a favor? Scientists say it depends on the relationship
People generally only engage in reciprocal generosity with others of equal or unknown status, MIT researchers experimentally demonstrated. During repeated interactions between people of different social status, generosity typically flows only one way, either up or down.
New OMB rule could break science in the United States
The drug that saved your mother. The treatment your doctor recommended. The clinical trial that bought someone you love more time. None of it was inevitable. It required a system built to operate outside politics and sustained over decades, because science doesn’t operate on the timeline of a politician. That system is now under direct threat. The Office of Management and Budget (OMB) has proposed a new rule to regulate all federal grants, not just science, but housing, education, defense, NASA and everything in between, that would fundamentally change how research is conducted in the United States. There have been a lot of fires in the past 16 months, and if everything is an emergency, nothing is an emergency. But this is a very big deal. These rules would strip away the independence that has kept science from becoming a political tool. The good news is there’s still something we can all do. How the grant system was designed to work When I was a new faculty member, I was full of millions of ideas, so I sat down and wrote an NIH proposal. I worked on it for months and thought it was so good. Then I sent it to my mentor. It came back covered in comments. Places where my reasoning wasn’t as airtight as I’d thought, methods that could be stronger, gaps in my thinking that I had been too close to the work to see. It stung, as honest feedback always does, but peer review almost always makes the work better. In fact, it’s the backbone of how American science has functioned for generations. At NIH, submitted proposals go to a panel of scientists who have experience in the field. These are the people who can read research methods and spot their weaknesses, who understand when an experiment is designed well and when it isn’t. Applications are scored rigorously, and often only the top 3-20% of proposals receive funding. The people making funding decisions are required to justify them based on these reviews. To be clear: this is not a perfect system, and good science still goes unfunded more often than any of us would like due to limited resources. There is also criticism that peer review may undervalue bold ideas that are risky but could have high payoffs. But the peer review system is nonetheless a meaningful part of the grant selection process. While thoughtful and careful changes could improve the system, the changes being proposed by OMB will not make things better. The rule that could change everything Through this OMB proposal, the system that helps decide what gets funded is being dismantled. There are many troubling elements buried in this proposal, but three provisions stand out in particular. Each would be damaging alone. Together, they would change what kind of science gets done in America, and who it serves. 1. Political appointees would decide which science gets funded, and peer review would be explicitly sidelined or ignored. The guidelines your doctor follows, the standards that determine what’s in your air and water, the drugs that get approved for your family: all of it starts with a funding decision. Right now, that decision is made by scientists who can evaluate whether the work is actually sound and whether it could move the needle forward. Under this proposal, funding decisions could be made by political appointees. The careful, expertise-driven review process that shapes grant funding would be effectively sidelined. That means that a poorly designed study could get funded simply because someone in a political office liked the sound of it, while rigorous, important work doesn’t get funded because it no longer aligns with the administration’s priorities. In other words, critical work that benefits all of us could now be missed just because it has become politically inconvenient. 2. Grants could be canceled at any point, without warning, in the middle of ongoing work. Say your tax dollars have been paying for a cancer study for two years. The grant was awarded for five years, so the team is mid-experiment. There are three additional years’ worth of answers that could change how the disease is treated for hundreds of thousands of people. With this new rule, the federal government could cancel the remaining funding tomorrow, walk away from the commitment it made with your money, and have no legal obligation to finish what it started. This is a huge waste of money. There are rarely other sources of funding for this work, which means the team, the graduate students, postdocs, and research staff who have dedicated years of their careers to this project could lose their jobs. Patients who have contributed their time to studies may not see it turn into useful learnings for others. Every hour of work already completed could result in nothing useful. Science can take decades to get to tangible outcomes. It needs a stable funding system to flourish. 3. Researchers would have a harder time communicating their findings without pre-approval from the federal government. There is a saying from Sir Mark Walport: “Science isn’t finished until it’s communicated.” Science is no use to anyone if it is kept locked away in the labs that produced the data. The proposed rule could make communicating research findings harder in a number of ways: Make it harder for scientists to widely share their findings in scientific journals. Publication costs, including journal fees that allow for broad public access, could not be paid for by federal funds which is often how scientists pay these costs. Restrict researchers from engaging publicly about how their findings should inform policy or public health guidelines. This would restrict scientists from doing the work that transforms years of lab work into decisions that actually affect people’s lives. Make it harder for scientists to attend conferences. Conferences would now need to be approved by the federal agency at the time of grant award. This disadvantages conferences or publications that are not politically aligned. Conferences are a key way in which scientists learn about work happening in their field so they can collaborate and build on each other’s findings. What good is science with a muzzle? The long-term impact These changes could (and hopefully would) be reversed under the next administration. But by then, damage has been done. Once projects are halted or denied funding by political appointees, people leave their science careers for other paths or even leave the country to do their amazing science elsewhere. You cannot pause a scientific study the way you pause a subscription you decided isn’t useful anymore. You cannot freeze a research team that depends on salaries to feed their families, causing a scramble for replacement funding that, in most cases, simply does not exist. When the money stops, everything stops, and all the work in progress and its potential are lost. Despite all of the language about reducing waste, fraud, and abuse, this new proposed rule increases the administrative burden tremendously. Every researcher watching this unfold will make a rational decision: don’t start anything that will take a decade to finish. This includes not building a team around funding that could vanish, or asking the big questions that need patience and stability to answer. The most consequential science, the kind that produces the treatments and the breakthroughs that reach your doctor’s office twenty years from now, is exactly the science that would be abandoned first. This is still a proposal, so we can all do something Some worry this rule is already a done deal. But by law, OMB is required to open a public comment period. Those comments matter because they inform members of Congress, they can be used in litigation, and they create a public record. So here’s what you can do: Submit a public comment. The public comment period on this proposed change is open until July 13. Anyone can submit a comment, and OMB is required to respond to substantial ones. Each comment must be different to count as distinct; identical comments will be counted only once. Comment as an individual, not on behalf of your organization. Here’s a full resource to help you, with useful examples, but in general: Share a story about how science or federal funding has benefitted you or is involved in your work. Are you or someone you loved a cancer survivor? Do you know someone with Alzheimer’s? Have you received NIH/NSF or other federal funding? All of these can be stories you use to highlight why you care. Explain why you’re worried about these proposed changes. You can use the wording found here or in other resources to help you. Ask them to withdraw this proposed rule. Contact your elected representatives. Congress allocates money to federal agencies to spend, and it has oversight of how that process works. You can let your representatives know this proposed change concerns you and ask them to intervene. There are levers they can push that can help at this moment. Let others know why federal funding matters. This isn’t just about science, but ALL federal grants, including for education systems, defense projects, and more. The process, including the importance of federal funding, is invisible to many. We can all make it more visible by sharing how science benefits us and why we value it. You can also share this article, or any of the resources linked here, to help spread awareness! Bottom line We are quickly losing ground in scientific research in the U.S. More changes, like the proposed rule by OMB, would decimate our ability to conduct research here, leaving us all worse off: fewer treatments for pancreatic cancer, less research on pollution impacts, fewer answers for patients with rare disease, and more unaddressed health needs. The impact will be very real for all of us, regardless of where we stand politically. Using our voices for science is more important than ever. Love, LM Big thanks to Dr. Megan Ranney for reading this over and providing brilliant insights, as always. Dr. Liz Marnik, PhD, is Executive Director of The Evidence Collective at YLE and a science communicator and immunologist working at the intersection of public trust, science, and public health. You can find her on Substack, LinkedIn, Instagram and Threads. 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: Subscribe
A Science Based Look at Fluorination and PFAS.
Are all fluorinated packaging technologies created equal? Or are we oversimplifying risk? Greif examines how process chemistry, particularly oxygen control, fundamentally shapes PFAS outcomes. Backed by validated data and scientific rigor, this paper reframes barrier selection for regulators, engineers, and decision-makers seeking compliant, high-performance packaging solutions.This Technical Paper is brought to you by Greif.
11th Class Graduates Favarh’s Project SEARCH at UConn Health
Prepares intellectually or developmentally disabled for workforce
Partisan Politics Trumps Peer Review
On May 29, the Office of Management and Budget published a proposed rule that would fundamentally reshape how the federal government funds scientific research.
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Experts question modern gut health and supplement culture, as Professor Paul Clayton of LYMA warns many probiotics and wellness formulas may be fundamentally incomplete.
Advancing high-threat pathogen preparedness in WHO South-East Asia Region: 2025 in review
Effective preparedness for high-threat pathogens demands more than technical tools — it requires a coherent strategy, sustained country engagement, and the institutional infrastructure to translate evidence into action. In 2025, the Pandemic and Epidemic Management (PEM) Unit advanced all three, working across the full spectrum of high threat pathogen (HTP) preparedness: from methodology development and evidence generation to country-level technical support, emergency response, and regional workforce strengthening.At the core of the PEM unit's work is building the regional evidence and policy architecture needed to prioritize and respond to HTPs systematically. A major milestone was the development of the HTP Prioritization Methodology — a structured, multi-criteria approach to ranking pathogens by epidemic potential, severity, and national context — and its national pilot in Bangladesh, where SARS-CoV-2, Nipah, dengue, and cholera emerged as the highest-priority threats, informing the PEM unit's contribution to 2026–27 biennium prioritization and workplan development (Access link here).Underpinning this work, the PEM unit advanced the peer-reviewed evidence base for HTP preparedness in the South-East Asia Region (SEAR), with three publications in international journals in 2025: Development of a zoonotic influenza distribution assessment and ranking system (ZIDAR): Technical application in Nepal to support cross-sectoral risk-based surveillance in One Health; Implications for influenza A virus surveillance in Southeast Asian Region countries: a scoping review of approaches for the surveillance of swine influenza viruses at human-swine interfaces in BMJ Public Health; and Operationalizing multisector partnerships: a Theory of Action and Reflection tool for zoonotic influenzas in Health Policy and Planning. Together, these contributions move the Region’s HTP preparedness agenda from practice to science — generating the transferable methodological foundations that will inform evidence-based capacity strengthening in Member States.Country-level engagement in 2025 reflected the breadth of contexts in which HTP preparedness must function. Technical support was provided for Mpox and cholera events across the Region, including coordination of cholera and acute watery diarrhea prevention and control in Myanmar in the wake of the March 2025 earthquake. In Myanmar, the PEM unit contributed technical leadership to the earthquake emergency response — one of the most complex humanitarian emergencies in the SEAR — as Health Operations and Technical Support lead within the Incident Management Support Team. This included strengthening emergency response planning, supporting operational and after-action reviews, and providing monitoring and evaluation support for both acute and protracted crisis response. The breadth of HTP support provided to emergency operations across the SEAR was documented in the PEM unit’s contribution to the regional report for the Health Security Interface Technical Advisory Group.In Sri Lanka, technical collaboration with the WHO Country Office and the Ministry of Health laid the groundwork for the National Steering Committee's 2026 annual meeting, development of Standard Operating Procedures for specialized emergency response, and a national training programme — all structured around evidence-based risk assessment for specialized hazards including HTPs.Across the SEAR, the unit contributed to strengthening the workforce architecture that underpins HTP response. Technical inputs shaped the WHO South-East Asia Regional Health Emergency Workforce Strategy 2025–2030, and the PEM unit supported conceptual development of the health emergency workforce framework for the Pandemic Fund Round 3 proposal.Alongside this, the unit's sustained engagement on the meningitis agenda culminated in the launch of the Regional Implementation Framework to Defeat Meningitis in South-East Asia by 2030 at the Seventy-eighth Session of the Regional Committee, ensuring the Region's priorities were reflected in the Global Technical Task Force and expanding the unit's HTP engagement beyond outbreak-prone zoonotic pathogens (Regional Implementation Framework to Defeat Meningitis in South-East Asia by 2030).Taken together, 2025 demonstrated that meaningful HTP preparedness is built incrementally — through methodology, engagement, and the steady accumulation of institutional capacity across Member States.Bangladesh piloted the HTP Prioritization Methodology and identified the highest-priority pathogens in the country context (Photo credit: WHO Country Office, Bangladesh).
One type of sitting may pose greater dementia risk than another, study suggests
New research suggests the type of sitting matters for dementia risk, with mentally engaging activities linked to a significant reduction in risk.
FINN Partners and WifOR Institute Launch Global Initiative to Show Health Spending as an Economic Investment
For decades, ministries of health, finance leaders and policymakers have needed to defend health spending as a necessary cost to be managed. FINN Partners and WifOR Institute believe this framing is fundamentally outdated and increasingly harmful to public health in a world confronting aging populations, workforce shortages, rising chronic disease and widening health inequities.
Biscayne Bay's health shows little improvement, report says
The bay's health is vital for Miami-Dade County, both economically and environmentally.
Judge orders mental health evaluation for the woman charged with the attempted murder of Rihanna
A judge has paused the prosecution of a woman charged with the attempted murder of Rihanna while it is determined whether she is mentally competent to stand trial
Judge orders mental health evaluation for woman accused of trying to murder Rihanna
A judge has paused the prosecution of a woman charged with the attempted murder of Rihanna while it is determined whether she is mentally competent to stand trial
New MatterChat Model Helps AI to ‘See’ the Language of Science
From writing emails to generating computer code, much of the artificial intelligence prevalent in our daily lives has succeeded by mastering one domain: text. However, this leaves a major blind spot in the physical sciences, where models depend on the high-resolution, three-dimensional data of the physical world, like the intricate lattice of atoms in a crystal. Delivering on the promise of using AI for science requires teaching these data-driven text models to seamlessly “talk to” physics-based models. Now, a new AI framework from Lawrence Berkeley National Laboratory (Berkeley Lab), called MatterChat, solves this problem by creating a specialized “bridge.” It connects the conversational power of a Large Language Model (LLM) with a physics-based AI that models “interatomic potentials”: the complex physical forces between atoms. The resulting system already significantly outperforms general-purpose AI tools like GPT-4 at predicting material properties, and the team hopes it can accelerate scientific discovery by serving as a robust research partner that provides grounded insights and generates step-by-step instructions for synthesizing novel materials. A paper describing this work was recently published in Nature Machine Intelligence. “Traditional simulations can provide the physical rigor required for materials science, yet their computational cost remains prohibitive for high-throughput screening. Conversely, while LLMs excel at rapid knowledge synthesis, they inherently lack the ‘structural vision’ to interpret materials directly from their underlying atomic coordinates,” said Yingheng Tang, a postdoctoral researcher in Berkeley Lab’s Applied Math and Computational Research Division (AMCR) and lead author on the paper. “MatterChat was built to solve this dilemma, empowering LLMs with a structural ‘vision’ that allows researchers to leverage their full potential for solving complex, real-world materials challenges.” Empowering language models to solve complex challenges in materials science To build MatterChat, the Berkeley Lab team drew inspiration from technologies like Vision Question Answering (VQA) and Text-to-Image (T2I) generation. In these tasks, AI must translate high-level text concepts into visual images, or vice versa. Doing so requires developers to build tools that “bridge” two fundamentally different forms of data. The researchers adapted this concept to the physical sciences. With MatterChat, they created a “bridge model” that successfully connects an LLM’s general knowledge with the deep understanding of the atomic-scale world encoded in scientific interatomic potentials. Until now, researchers using LLMs to solve materials problems usually had to feed them raw data files as if they were just strings of text. It’s like asking an AI to understand a complex 3D engine based only on a parts list: the LLM can read the names, but it can’t “see” how the atoms fit together in space. MatterChat solves this by training a specialized AI bridge model, pre-trained on millions of crystal structures and an LLM, to align the LLM’s representation of the world with the interatomic potential’s representation of the world. The bridge model can translate physical insights into a format the LLM can actually understand. By giving the LLM these “scientific eyes”— a scientific “inductive bias” in the terminology of AI — the Berkeley Lab team has transformed it into a robust research tool capable of providing grounded scientific insights into complex materials challenges, such as predicting thermal stability or analyzing electronic band gaps. “We think of atoms as living in a physical space, but from a machine learning perspective, they are just vectors living in some very non-trivially structured manifold in a high-dimensional Euclidean space; and, of course, the same is true for the sentences and paragraphs that we use to express our ideas about those atoms,” said co-author Michael Mahoney, Berkeley Lab’s Scientific Data Division (SDD) AI Initiative Research Lead. “The bridge model basically gets those two structures to ‘talk with’ each other.” As a proof-of-concept of this general approach, the team trained their bridge model on a dataset curated by pairing nearly 143,000 stable atomic structures from the Materials Project with their corresponding physical properties. This training data was automatically assembled using the Materials Project’s API and deliberately enriched with properties fundamental to microelectronics design — like formation energy and bandgap — allowing MatterChat to learn the complex patterns connecting a material’s atomic blueprint to its functional performance. To validate their model, the researchers benchmarked MatterChat against a suite of other AI systems, from general-purpose LLMs to other specialized scientific AI methods. The results show that MatterChat consistently outperformed its competitors across a range of tasks. The model was more accurate in classifying material types and demonstrated superior precision in predicting numerical properties. For example, it excelled at predicting a material’s bandgap, a property critical for designing new electronics from high-capacity energy storage to next-generation computer chips. “Our design is significantly more efficient because we don’t have to build a massive AI model from the ground up,” said co-author Zhi (Jackie) Yao, a research scientist in Berkeley Lab’s AMCR. “Instead, we take two powerful, pre-trained models — a structural encoder for materials physics and an open-source LLM — and use them off-the-shelf. The only component we actually train is the lightweight ‘bridge model’ that translates between them. It’s the difference between building an entire car factory and simply designing a smart adapter that connects a world-class engine to a world-class navigation system. This approach is not only computationally efficient, but also makes the system modular, so we can easily upgrade components or adapt the bridge for other scientific domains in the future.” Crucially, this modular design highlights exactly how institutions like Berkeley Lab and the Department of Energy are carving out a highly valuable niche in the booming AI landscape. Rather than competing with Silicon Valley tech giants to build ever-larger language models from scratch, the lab is focusing on the specialized connective tissue that makes commercial AI useful for hardcore science. Because the bridge model approach underlying MatterChat is forward-compatible, it is perfectly positioned to leverage these parallel tracks of innovation. As Mahoney pointed out, “We expect that industry will continue to develop improved LLMs, and we expect domain scientists and facilities will continue to generate new data. An important part of scientific machine learning is not simply to solve problems on today’s data, but instead to develop general methods that will be forward-compatible with orders of magnitude more data, whether from scientific domains or from LLMs.” According to Yao, the MatterChat project, which was initially developed and enhanced with funding from a Berkeley Lab Laboratory Directed Research & Development (LDRD) Program, will now expand its capabilities. In a collaboration with Fermilab, MatterChat is already contributing to a U.S. Department of Energy Genesis Mission project — called Accelerating eXtreme Environment Specs-to-Silicon (AXESS) — that aims to speed up the development of next-generation, high-speed, radiation-hardened detectors for challenging particle physics experiments by using advanced 3D integrated circuits (chiplets) and AI-driven data analysis. In addition to the LDRD support, the team also credits supercomputing resources at the National Energy Research Scientific Computing Center (NERSC), located at Berkeley Lab, with MatterChat’s success. “We are incredibly grateful to NERSC; this research simply would not have happened without access to the Perlmutter supercomputer through their AI for Science program,” said Tang. Wenbin Xu, a NERSC postdoctoral fellow at the time, was also a major co-author of the work, as was Benjamin Erichson, a research scientist in Berkeley Lab’s SDD, highlighting the benefits of AMCR-SDD collaboration on AI for science. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
Phonon-polaritonic skyrmions: transition from bubble- to Néel-type
Light: Science & Applications - We demonstrate wavelength-tunable surface phonon-polariton skyrmions on silicon carbide membranes. Leveraging strong material dispersion, we experimentally...
New AI tool developed by Stowers Institute and Helmholtz Munich scientists predicts how cells choose their future — helping uncover hidden drivers of development
Scientists at the Stowers Institute for Medical Research and Helmholtz Munich have developed RegVelo, a new AI framework that predicts how cells acquire their identities and identifies the genetic regulators guiding those changes. Published in Cell, the study used zebrafish neural crest development to show RegVelo can uncover early drivers of cell fate, including regulators of pigment cell formation, and then support those predictions experimentally. The researchers also applied the framework across multiple biological systems, suggesting its value extends beyond neural crest cells as a broadly useful tool for studying how cells change over time. The team says the new model could pave the way for future cell therapy treatments.
Pophouse Entertainment Rewriting the Rules of Music Licensing
Pophouse Entertainment is doing something fundamentally different in music licensing, and the industry is starting to pay attention.
Petaluma businesses earn California Green Business Certification; community invited to celebrate May 12
A cohort of 16 forward-thinking Petaluma businesses has successfully achieved or are working to achieve California Green Business Certification through a collaborative program led by local climate action nonprofit, Cool Petaluma. The initiative supports businesses in adopting environmentally responsible practices that reduce waste, conserve resources, and improve operational efficiency.
Scientists finally solve 40-year-old physics puzzle about how things grow
In a major breakthrough, scientists have experimentally confirmed a universal growth law in two dimensions using a quantum system of fleeting light–matter particles. The finding strengthens the idea that wildly different processes—from crystals to living systems—may all follow the same hidden rules.