Podcasts about Nobel Prize

The man who open-sourced the most-used AI image model in history sat down with the man who has spent a decade proving superintelligence cannot be controlled. Brian set up a debate. What emerged was something more unsettling than any debate. Roman Yampolskiy is the computer scientist who coined the term AI safety and author of AI: Unexplainable, Unpredictable, Uncontrollable. Emad Mostaque is the co-founder of Stable Diffusion and the only AI CEO who signed the pause letter. He now says he doesn't know how a pause could work. Yampolskiy thinks that is the only option left. The question underneath everything is simple: if you have a 50% chance of wiping out civilization and you build it anyway, what are you actually doing? We cover what AI safety researchers actually think the danger is, why nobody has published a paper, filed a patent, or shipped a prototype for controlling a superintelligence, what the Qwen weights being out means for the pause argument, and why Mostaque thinks swarm intelligence is the most dangerous and most unpredictable risk vector we have. What you'll hear: -Why both guests think P(doom) tells you less than you'd hope -What it means that no company, no lab, and no team has a patent on controlling superintelligence -Why the models the public receives are slightly lobotomized -The difference between an AI swarm and the ASI everyone is debating -Why giving every psychopath access to a cutting-edge intelligence weapon is incoherent safety strategy -What it would actually take to update Yampolskiy's assessment “We either do it, or we die. There is nothing for you to gain by doing it.” — Roman Yampolskiy CHAPTERS 00:00 A debate that wasn't a debate 00:36 Turing test, AGI, superintelligence: where are we? 02:02 The open source argument nobody wins 03:32 Pause frontier AI forever. Which button? 05:04 P(doom): parameterizing our ignorance 09:02 Nukes are inefficient. AI isn't. 11:46 The lobotomized model problem 18:04 Decade-old problems solved weekly now 26:18 We either do it or we die 31:10 Stop the training or stop the funders 33:34 Lipstick on a Shoggoth 35:50 No paper. No patent. No framework. 39:42 Train only on what you need 44:14 What lowers Mostaque's p(doom)? 52:00 Same future. Two perspectives. Get the transcript, fascinating bonus content, and my Monday M.A.G.I.C. Message: https://briankeating.com/yt Have a .edu email and live in the USA? You automatically win a meteorite: https://BrianKeating.com/edu Subscribe: https://www.youtube.com/DrBrianKeating?sub_confirmation=1 Support Into the Impossible on Patreon, get my weekly M.A.G.I.C. Message, unfiltered bonus content, and live monthly Office Hours with me: https://www.patreon.com/drbriankeating Join this channel for perks, monthly Office Hours, and your name in the Member Roster at the end of every episode: https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join Featured Guests: Roman Yampolskiy on Twitter/X: https://x.com/romanyam?lang=en AI: Unexplainable, Unpredictable, Uncontrollable (book): https://www.amazon.com/dp/103257626X MIRI: https://intelligence.org Emad Mostaque on Twitter/X: https://x.com/EMostaque I.I.I. Inc.: https://ii.inc/ Stable Diffusion: https://stability.ai/ My books: Losing the Nobel Prize (memoir): http://amzn.to/2sa5UpA Think Like a Nobel Prize Winner: https://a.co/d/03ezQFu Focus Like a Nobel Prize Winner: https://a.co/d/hi50U9U Galileo's Dialogue (first-ever audiobook): https://a.co/d/iZPi9Un Twitter/X: https://x.com/BrianKeating Substack: https://briankeating.substack.com Blog: https://briankeating.com/blog Audio-only: https://briankeating.com/podcast #AIrisk #aisafety #stablediffusion #RomanYampolskiy #EmadMostaque #briankeating #intotheimpossible Learn more about your ad choices. Visit megaphone.fm/adchoices

Coming up: how chemistry is transforming 21st-century healthcare. Jeremy Nicholson from the Hong Kong University of Science and Technology talks about phenomics. Gemma Sharp shares how the world's largest menstrual fluid biobank was created. David Nutt from Imperial College explains drug mechanisms, and Nobel Prize winner David Baker discusses the role of AI in treating diseases. Leilani Arrow-Smith, a chemistry student at the University of Cambridge, produced this episode. Like this podcast? Please help us by supporting the Naked Scientists

BBC Newsnight presenter Paddy O'Connell speaks to Nobel Prize-winning economist Daron Acemoglu about why he thinks liberal democracy is in crisis, and how artificial intelligence could make it worse.Daron argues that liberal democracy worked because people were given a say in how they were governed and then benefited from their country's economy as it prospered. But western governments have made major decisions on issues like immigration without first building public agreement, something which was once important.In an interview with BBC Newsnight, he says that together, these changes have left many working people feeling that politicians no longer listen to them and have helped to fuel a rise in populism.Now Daron warns that the way artificial intelligence is developed and used, so far without consensus, could make it worse, by widening inequality and putting people out of work. “AI is going to transform every aspect of our lives, and we're not being asked. We have no say in how AI is going to shape our society. I mean people in the UK, people in the US. Even worse for 6 billion people who are outside of the US, UK, China. Their lives are going to be completely reshaped by AI and they have zero say whatsoever,” he says. The Interview brings you conversations with people shaping our world, from all over the world. The best interviews from the BBC, including episodes with Indian activist Sonam Wangchuk, South African minister Gayton McKenzie and New York Times White House correspondent Maggie Haberman. You can listen on the BBC World Service on Mondays, Wednesdays and Fridays at 0800 GMT. Or you can listen to The Interview as a podcast, out three times a week on BBC Sounds or wherever you get your podcasts. Presenter: Paddy O'Connell Producer: Osman Iqbal Editor: Damon Rose(Image: Daron Acemoglu. Credit: Europa Press News via Getty Images)

In this episode, Nobel Prize-winning physicist Dr. John Martinis reveals how his breakthrough in superconducting qubits made quantum physics real at macroscopic scale and what it means for the future of technology. The former lead of Google's Quantum AI lab explains why quantum computing is so fragile, why a lot of hype has a low chance to work, and why his fabless company Qolab could be the Nvidia of quantum computingIn this conversation, Dr. Martinis joins Tech Surge to explain the science behind macroscopicquantum coherence, the engineering challenges of scaling quantum computers, and how hybrid quantum-classical computing will shape the future of technology.The conversation covers:✅ How the superconducting qubit breakthrough won the Nobel Prize in Physics✅ Why Nature wants to destroy quantum coherence and why quantum is fragile✅ From academic physics to building Google's quantum computer✅ The engineering challenge of scaling quantum computing beyond the lab✅ Why a lot of quantum computing hype has a low chance to work✅ How Qolab's fabless model could scale quantum hardwareGuest Links:John Martinis: 2025 Nobel Prize laureate in Physics, superconducting-qubit pioneer, formerGoogle quantum-hardware researcher, and founder and CTO of Qolab.Nobel Prize profile: https://www.nobelprize.org/prizes/physics/2025/martinis/Qolab: https://qolab.ai/Further Reading and ResourcesGoogle Sycamore Quantum Processor - Google's 2019 experiment used a 53-qubitsuperconducting processor to perform a specific random-circuit-sampling task substantiallyfaster than the then-known classical approach.Nature research paper:https://www.nature.com/articles/s41586-019-1666-5Google Research explanation:https://research.google/blog/quantum-supremacy-using-a-programmable-superconducting-processor/Artificial Intelligence and Transformers – The Transformer architecture discussed in thepodcast was introduced in the paper “Attention Is All You Need.”Original paper:https://arxiv.org/abs/1706.03762AlphaFold and Protein Structure Prediction – AlphaFold demonstrated how classical AI canpredict protein structures with high accuracy, illustrating the distinction between present-day AIand potential future quantum applications.Nature paper:https://www.nature.com/articles/s41586-021-03819-2Google DeepMind – AlphaFold:https://deepmind.google/science/alphafold/Quantum Computing Hardware Approaches – The podcast compares superconductingqubits, semiconductor spin qubits, neutral atoms, trapped ions and photonic systems.Google Quantum AI:https://quantumai.google/Intel Quantum Computing:https://www.intel.com/content/www/us/en/research/quantum-computing.htmlQuEra – Neutral-atom quantum computing:https://www.quera.com/Atom Computing:https://atom-computing.com/Quantum Manufacturing and Scaling – Qolab is focused on improving the fabrication, wiring and scalability of superconducting quantum processors through industrial partnerships.Qolab:https://qolab.ai/Qolab and Applied Materials collaboration:https://thequantuminsider.com/2025/03/18/qolab-secures-investment-from-applied-ventures-and-announces-collaboration-to-advance-quantum-computing-manufacturing/Applied Materials:https://www.appliedmaterials.com/Quantum–Optical Networking – The podcast discusses the challenge of convertingmicrowave signals used by superconducting qubits into optical signals suitable for fiber-optic communication.Microwave-to-optical conversion research:https://www.nature.com/articles/s41567-019-0650-1Chapters:00:00 – The Quantum Computing Hype: Physics vs Engineering04:06 – Introducing Nobel Prize Winner John Martinis12:09 – Schrödinger's Cat Explained13:12 – Can Quantum Effects Exist at a Macroscopic Scale?17:45 – The Experiment That Changed Quantum Computing34:33 – The Biggest Challenge: Scaling Quantum Computers43:21 – John Martinis on Google's Quantum Supremacy45:51 – AI vs Quantum Computing01:01:45 – Can Quantum and Classical Computers Work Together?01:07:36 – The NVIDIA Model for Quantum ComputingAbout TechSurge:TechSurge Podcast shares the latest insights directly from legendary Silicon Valley leaders,daring new founders, and visionary technologists.Subscribe for weekly conversations into the intersection of technology advancement, market dynamics, and founder journeys.#quantumcomputing #quantumphysics #nobelprize #technology

Linda Pastam wrote a marvelous poem called “Driving West” in which these lines appear:We are the pioneersof our own histories, drawnto the horizon as if it waited just for usthe way the young are drawnto the future, the old to the past.“…the way the young are drawn to the future, the old to the past.”I need you to hold those words tightly in your hands, even though they wriggle and thrash like slippery fishes anxious to escape your unfamiliar fingers and return to the quiet darkness where they can breathe water and feel entirely at home.Hold on to those words.Dr. Roger Sperry was awarded the Nobel Prize in 1981 for his documentation of the differences between the two hemispheres of our brains.One half of your brain puts you in touch with this Present Moment in this Real World.The other half puts you in touch with the Past and the Future in that mystical – magical – hallucinatory unreliable World of Imagination.Dr. Sperry discovered that what appears to a brain divided into two halves could more accurately be described as two separate, competing brains.In his Nobel Prize acceptance speech, Sperry said,“Each brain half appeared to have its own, largely separate, cognitive domain with its own private perceptual, learning and memory experiences, all of which were seemingly oblivious of corresponding events in the other hemisphere.”Dr. Sperry later said,“Each hemisphere of the brain is indeed a conscious system in its own right, perceiving, thinking, remembering, reasoning, willing, and emoting, all at a characteristically human level, and both the left and the right hemisphere may be conscious simultaneously in different, even in mutually conflicting,mental experiences that run along in parallel.”Regardless of which half you prefer, both halves are equally important.One half of your brain puts you in touch with This world.The other half puts you in touch with Possible worlds.One half puts you in touch with the sciences.The other half puts you in touch with the arts.Now let's get back to those wriggling fish that I asked you to hold.The Future to which the young are drawn, and the Past to which the old are attracted, are different time zones in the world of imagination.The young live in the brightness of the noonday sun. The old live in the twilight and remember the light of day.Most Americans attempt to persuade each other with facts. We do this because we trust Science and we worship Mathematics, and that is why we write such terrible ads.Most of us, most of the time, trust our instincts.We trust our intuitive hearts.This is what Nobel laureate Daniel Kahneman described as System 1 or “Thinking Fast” in his book, “Thinking Fast and Slow.”Behavioral scientists estimate that roughly 90% to 95% of our daily decisions are made automatically and unconsciously using System 1 thinking.Most people don't want to admit it.In fact, most people refuse to accept it.But the scientific truth is that we make at least nine-tenths our decisions in that half of the brain where the darkwater fish glimmer effortlessly and thoughtlessly, silver and gold in the moonlight.Study the arts. Learn to win the heart.If you win the heart, the mind will follow. The mind will always create logic to justify what the heart has already decided.Roy H. WilliamsPaul Kesig uses the acronym L.O.V.E. to encapsulate his approach to sales.L is for celebrating Little wins.O is for asking Open-ended questions.V is for creating and communicating Value.E is for building an Emotional connection.Paul says that pairing love with sales strikes many people as contradictory. This is usually because their experiences with bad salespeople have taught them to truly hate the sales process.Listen in as Paul explains to roving reporter Rotbart that those who have adopted his approach have quickly discovered that L.O.V.E. really can spell success; MondayMorningRadio.com

What did Dr. Deming really mean when he said to "cease dependence on mass inspection"?    In this episode, Balaji Reddie and host Andrew Stotz unpack why one Ford manager's decision to fire his inspection team missed the point entirely. They explain how inspection can help leaders understand and improve the process, rather than simply sort good from bad.    They also explore why Deming's 14 Points must be understood as a system, including his evolving call for cooperation and win-win thinking among employees, customers, suppliers, and even competitors. Whether you are new to Deming or have studied his work for years, this conversation offers a sharper way to think about quality, systems, and improvement.   TRANSCRIPT 0:00:02.0 Andrew Stotz: My name is Andrew Stotz and I'll be your host as we dive deeper into the teachings of Dr. W. Edwards Deming. Today I'm continuing my discussion with Balaji Reddie, an educator and trainer in the teachings of Dr. Deming and quality management generally. Balaji, how are you doing today?   0:00:25.1 Balaji Reddie: Oh, I'm doing good. We're meeting after a small little gap, and in the interim I think a few things happened. One of the major things, I think you and I were really happy to see that Bill Scherkenbach sent us a lovely photo. So Bill, if you're listening to this, thank you so much, of course we thank you for the photo. We mentioned the last time or rather when we were speaking that I said that there used to be a group of people that used to meet over the weekend. I don't know what the actual protocol was, but all of the so-called core Deming people, you know, like Gipsie Ranney and Nida Backaitis, I hope I'm getting the names right, Barbara Lawton. And I remember Henry Neave saying he was a part of that. So I didn't see that in the photo. So, maybe one of the weekends he was there over in America and so he was there because he mentioned this. And so Bill very kindly said what it was christened. It was christened the Cosmos Club. And then he shared a photo. So Bill, thank you so much. And also helping us identify most of the people in that photograph. If sometime we could share it with the Deming Institute, if they could show it as part of the... If ever we convert this into some kind of video later on with some slides and things like that, they could add that in.   0:01:53.0 Andrew Stotz: Yeah, it's still wonderful.   0:01:53.7 Balaji Reddie: That was wonderful. Yes.   0:01:54.9 Andrew Stotz: It's a great picture. In fact, I'm gonna just make sure that I download it for today's episode.   0:02:00.9 Balaji Reddie: Yes, yes.   0:02:01.7 Andrew Stotz: Because I'll supply it to the Institute and ask Bill if we can use it. Let's see.   0:02:08.4 Balaji Reddie: Yeah, I mean, I presume that he would want us to ask, but he is very big-hearted. I think he wouldn't mind at all.   0:02:14.7 Andrew Stotz: He looks like a spring chicken in that picture. [laughter]   0:02:17.2 Balaji Reddie: Oh, yeah, all of them. There was Joyce Orsini too there. It was nice to see the whole jing-bang gang, as they call them, [laughter] the Cosmos Club. But wonderful. So, yes, so last time we were discussing point number one and we saw his interpretation in the broadest sense of constancy of purpose. I shared also what he was saying towards the end of his life that he said, "Create and publish the statement of the aims and purposes of the company or other organization." So he was envisioning that it's no longer one company. It's a family of companies working together. And so there has to be something that binds all these companies together because today's business is very complex. The family is actually globally dispersed. And so there has to be a common thread that links all of these organizations together and that has to be the statement of purpose. Why do we exist? Each could have their own, you know. Because that's what Deming said in the word interdependent components of a system. And interdependent means being independent and mutually dependent simultaneously. So independent does not mean isolated. It means autonomous, that means you go by yourself. You could have your own purpose, but you need to align it with the purpose of the companies you're actually contributing to, which is amazing. So he asks us to look at it that way. And he said that the purpose should be to impact people in the broadest sense of the term. And you, in fact, brought that up, that quality, and we saw what he meant by quality. And then I think we discussed the purpose of educational institutions, et cetera, et cetera. What should they be?   0:04:07.5 Balaji Reddie: So that was point one. Now we get to point two. And I'll start with the original wordings which he wrote in 1986. So let me just pull that up here. He says here that, "Adopt the new philosophy. We are in a new economic age." And then he very specifically states, "Western management must awaken to the challenge, must learn their responsibilities and take on leadership for change." Now, let's look at it both ways because in 1990 he reworded this completely. But let's get back to what he meant in 1986, the new philosophy. Now, what was the new philosophy? What did he mean then? Recently I heard, or I think I saw, Dr. Joseph DeFeo, that is the current CEO of Juran Institute, release a version of the 14 points, or rather saying that these were Dr. Deming's 14 points. And I saw what he wrote there about point number two, which was his interpretation. Now, when I look deep, actually, it looked more like Dr. Juran's interpretation of the 14 points. Incidentally, talking about Dr. Juran and the 14 points, it's no secret that Dr. Juran always said that Deming did not speak much about management, he spoke only about statistics, blah, blah, blah. But there was, if you know, part of the inner circle of Dr. Deming was Dr. Myron Tribus. And Myron Tribus, if you know, was a director at MIT for some time, right? And around the time Deming was there at the Center for Advanced Engineering Study. And when... He, of course, went and met Dr. Juran to speak to him about the 14 points, and then they had a discussion for half a day on the 14 points, and Dr. Juran agreed with every single one of them. He said, "Yeah." He said, "He's spot on." But then he said that he never spoke of management then, and so he stuck to his version that he taught statistics to the Japanese. Anyway, let's leave that aside.   0:06:19.0 Balaji Reddie: But when he spoke about point two, and that's what Juran's interpretation was, that the new philosophy was quality should be the basis for running a business. That was the new philosophy, right? And he said that top management, of course, Deming always said that top management should be involved because unless that happens, nothing happens inside the company. So the new philosophy, what now we interpret is what Dr. Deming was trying to say in 1986, was "quality is the basis for running an organization," right? And quality, again, what we discussed last time, in the broadest sense of the term, not just of a product or a process, but the way you conduct yourself as a business, as a people. So that was the new philosophy. And he said that you should take on leadership for change, and he aimed it at Western management at that point in time, right? So that was the 1986, and he goes on to explain that, saying that we are living with commonly accepted... Because he was trying to shock the Americans at that time, right? If you try to realize when they woke up to quality in the 1980s, primarily to be... I mean, to their credit, to the American businesses, that they were doing a lot of course correction. They realized that they had done things wrong for 20 years, and a lot of course correction started happening, and they started seeing benefits. So they started interpreting that as improvement when actually it was just a correction. And I think Deming wanted to bring in that shock treatment to them, and he said improvement is not enough, right? So at that point in time, that new philosophy was basically that, okay, you're doing it right, but it's not enough. And that's why it has to come from the top. So top people have to be involved. And that was how they interpreted this.   0:08:12.1 Balaji Reddie: And we also looked at it that way. But in 1990, he changed the wordings of the point two, and that was in light of what he had started professing, which was a System of Profound Knowledge. So I'll just read out what he wrote in 1990. He says now, "Adopt the new philosophy of cooperation and win-win, in which everybody wins. Put it into practice and teach it to your employees, your customers, your suppliers, and" why not "your competitors." So that was, I think, the new philosophy. Much of the stuff, if you start seeing, Andrew, when I look at all the 14 points, the 14 points have to be interpreted through the 14 points.   0:09:06.7 Andrew Stotz: Right.   0:09:07.8 Balaji Reddie: You need to understand the purpose of each point. You need to understand that they're a system. And that's why you can't just read them sentence by sentence and want to implement them, if you know what I'm trying to say here. They're a system by themselves. So you need to interpret them the right way and through the 14 points... I don't know if I'm making sense to you, but that's exactly how these are. He wanted it to be that way. So you can never tell that you've understood it completely. You're getting me? You're learning something new about it every single day. And as you start looking at things around you through the lens of Profound Knowledge, you will see things differently. And sometimes you see some gaps with the sentences that Dr. Deming was uttering, and then you say, "Okay, okay, this, this, this, I understood it now. I need to make this correction." Right? And so you can never say that I've completely understood them. I mean, after all these years, still reading Out of the Crisis, still doing the series with you, has been a catharsis for me. It's revisiting these things all over again. This, despite the fact that I've been teaching this regularly to my students for the last 20-plus years, it's still new for me.   0:10:24.4 Andrew Stotz: And so could I describe what you just said about interpreting it within the system is that really, that's the thing about systems thinking, is that you can no longer look at an individual part. Every time you get a deeper meaning or understanding of an individual part, that changes the way you view the overall system and the interconnectedness of everything.   0:10:48.6 Balaji Reddie: Exactly.   0:10:48.9 Andrew Stotz: Where life would be much more easy if we could just, "Okay, I understand that one thing deeper and deeper. I can make a control chart better and better and better."   0:11:01.1 Balaji Reddie: So, it's crazy. And this realization came on me way back in, I think around '98 or '99, when I was teaching this, the 14 points, for the third or fourth time. I think I mentioned this in episode one and two, where I told you that it was very easy for me to teach the works of Philip Crosby, very easy to teach the work of Dr. Juran, because they gave methods, they gave steps. But Dr. Deming didn't give anything. You just couldn't show the wording on the slide and get away with it. You had to explain what he wanted us to do. And then, I mentioned this before, you can go back and listen to that episode, but I said that it suddenly struck me, "God damn it, these points are a system." And when I was describing this and I explained this to Hazel Cannon, and she said, "We call this in Deming-speak an 'aha' moment. You had your 'aha' moment." She said, "That's the beauty." She told me once that in the middle of the night, at 3:00 in the morning, she understood what he was trying to say, and she called up Dr. Deming.   0:12:11.3 Balaji Reddie: And he was very, very patient. He listened to her. And she said, "I'm sorry. I..." He said, "I knew you'd understand." That's all he said. So he used to be very happy when people used to call back and suddenly say, "We realized it now, what you were trying to say then." So it's a moment of realization. So this point to... If you read some of the notes that he gave to the Japanese in 1950, and incidentally, it's they who made the notebook and then he added on to it, if you know, Elementary Principles of the Statistical Control of Quality, first edition and then the second edition. And when they made the first edition, they had actually done this without his permission. But when they told him that we made these notes, they thought that he would be a little upset because of copyrights, blah, blah. But instead he was so happy that they'd done that, he actually helped them with the second edition. And if you read those notes, a lot of things there where he was speaking about cooperation, about understanding people. So that may not have been explicitly stated by him in his lectures in Japan, they understood it. Right? And that's a very... I don't want to sound communal here, but a very Asian way of looking at it. Because our languages are very metaphorical, so we always look for different meanings to the statements being uttered. And we always look out for what has this person actually not said, which I... Or rather, yeah, has not said, which I have not heard, but I need to hear. Right?   0:13:46.5 Balaji Reddie: So that's amazing, how they looked at what he was saying. I'll give an instance here where he talks about the use of the control chart, and he says that you can use it for training a worker. And then if you see that before and after training, there's been no change in the control chart, well, then the worker's attained his optimum. Then use that worker somewhere else. He talks in a very positive way about understanding people. And then going back to what we spoke in our second episode about the principles of leadership, it comes back there, that choose people in the right way and optimize all of their aims, hopes, et cetera. So coming back here, the new philosophy, basically, now we can interpret it as what he intended it to be in 1990. He said, "Win-win." But the original wordings are also... You don't replace it, you add on to it, right? So we look at it this way, that he meant that quality should be the center point of running your business. People at the top should be involved. And now we talk about cooperation, win-win. And he says this, "Teach and practice this." Now, all these things are easier said than done. We know that. But we should make an attempt. So employees, your customers, your suppliers, and your competitors. Sometimes it could be funny, right? You go up to your competitor and you say, "Look, I think we've done enough of this, going at each other's throat. Let's get together to do something." And obviously the other person will say, "What's the agenda?" They'll think you have some hidden agenda. So it's gonna be difficult when you start doing this. But I think that's become... Slowly people are warming up to that idea of being close to your competitors and learning with them and from them. Right? We don't look at them as a rival in the true sense, trying to cut the other guy down and things like that. But that's the way it is. And if you say that it's a cutthroat thing, but we need to have some things in common before we decide to branch out and go our own way. Right? So there has to be some kind of a cooperation so that win-win... But main thing is to win, that is, everyone wins. He says, "Some may win less than others, but we all win just the same." So the whole purpose was that. Yeah.   0:16:02.4 Andrew Stotz: It's interesting because I remember hearing that when I was younger and the first thing is cooperating just within a team. The second one is cooperating maybe within a department. Another one is cooperating within a business. And then... That's already hard enough to get to. And then he talked about cooperating within an industry. And of course, there are anti-trust laws in America, which he wasn't talking about colluding on prices to take advantage of the position against the customer. But he was also... I think if we look at AI right now, the development of artificial intelligence, and to what extent could the industry work together to safeguard, for instance, or to understand the development of the energy needed to do this, you know.   0:16:54.4 Balaji Reddie: Yes.   0:16:55.3 Andrew Stotz: Or how do countries across the world work together to make sure that one doesn't have an unfair advantage with AI or with energy or that type of thing? So, yeah, it was definitely interesting when I first heard it. But as I look at it now, I get what he's saying about the industry cooperation.   0:17:15.3 Balaji Reddie: Yes, I saw this in our country, in India, where you had the rival mobile service providers getting together and realizing that the hardware needed was the same, the software would be different. So they got together and created a company, you heard that right, a company, that set up mobile communication towers. So there are no multiple towers everywhere for each mobile provider. There's one set of towers, and it was a separate company. And by the way, that company went on to win the Deming Prize.   0:17:48.0 Andrew Stotz: Interesting, interesting. It's interesting that the concept for point number two is... And sometimes I read it and I think, "Adopt the new philosophy." It's kind of obvious, isn't it?   0:18:04.1 Balaji Reddie: Yeah. [laughter]   0:18:05.8 Andrew Stotz: And that's where part of what you're talking about, about understanding all of it through the Deming 14 points lens.   0:18:13.7 Balaji Reddie: Right. Yes.   0:18:14.9 Andrew Stotz: But I can also think... I wanted to highlight back for those people that weren't around at that time. I graduated from university in 1989. I went to work at Pepsi. And so Out of the Crisis came out about 1986. And I remember during my years at university, the Japanese were just killing the American car manufacturers. And the motorcycle companies were killing... Honda and the others were killing Harley-Davidson.   0:18:43.2 Balaji Reddie: Right.   0:18:43.5 Andrew Stotz: Even to the extent that Harley-Davidson and others went to the government to try to get Reagan to give them tariff protection, which he did, in fact. But there was a huge debate I had in class, I remember, about protection versus competition. And it just highlights what was going on. And so Out of the Crisis, as you mentioned, the title, just the idea of shocking people to say, "We're in a crisis," I think at that time it was really apropos. And also the other part is he's talking about Western management.   0:19:21.1 Balaji Reddie: Yes.   0:19:21.4 Andrew Stotz: Western management. And now you look at... Japan was really doing great at that time. And now we have Japan and China that's also made huge strides. And so it's just so fascinating that he was directing it at Western management for sure, that we've got to fix things. And sometimes I look back and I think, "Oh yeah, Western management learned and improved," because we've got so much innovation going on in America as an example and all of that.   0:19:52.6 Balaji Reddie: Right. Right.   0:19:53.4 Andrew Stotz: But sometimes I look at the developments of the Chinese or Japan and I think, "Hmmm...Did we learn? Did we learn?" Look at the trade balance, look at the amount of debt, look at that in the US and you think, "I don't know." I'm curious, how would you score it?   0:20:09.5 Balaji Reddie: Yeah, I mean, that's just what I had to say. So the whole approach towards this whole thing being holistic, not just looking at it from a very narrow point of view and realizing that we need each other, we can't do without... We call them competitors, but they are also helping us do a better job. They're pushing me to look out for what I'm good at and I don't have to put somebody else down to show myself as big. And if I contribute to the industry in a very big way, not only do I gain, others gain, and if I share, then they would also share with me, right? So that realization, I think, is coming in. And if you think about this, it was very interesting, the mathematician by the name John Nash, if you remember him, he actually postulated this in terms of mathematics in 1950, and he got a PhD from Princeton for this. Now, I would give full credit to the jury listening to his dissertation, defending his thesis, that you had to do this because, interestingly, he gets a Nobel Prize for that in 1974. It took 24 years for the world to figure out what he was trying to say. And you want to go read that, he was thinking win-win.   0:21:41.7 Balaji Reddie: So I often wondered if Dr. Deming, John Nash, did they know each other? Obviously, they didn't, but they were thinking and saying the same thing in two different parts of the world. He was doing that here in the US and Deming was talking about this there in Japan, and he was talking about telling the Japs that Japan must see itself as a system. And so all their companies actually work together. We see them as versus; they don't see each other as versus. You're interestingly saying this about the two-wheeler companies, the bikes... In India, we had just one or two bikes before the four Japanese companies came in. It was Suzuki, Honda, Kawasaki, and Yamaha. And they tied up with different Indian companies. Now, of course, after a long time, they've all gone their separate ways. But as a teenager, I remember these. And the funny thing was all four motorcycles were different and all four sold well. All four. And they sold in different parts of the country.   0:22:43.8 Balaji Reddie: I can tell you this firsthand because just after I completed my engineering, I was looking for a job. So I had some time to myself and my mother's friend, she ran a market research agency and she just called up one fine day and said, "Would Balaji care to do this market research for me? It's about bikes." So I had to travel north, south, east, west and tape a conversation with bikers asking them which motorcycles they liked among these four. And these four, the plants were set up at four different places in our country. So north, south, east, west, typically, you know how vast India is in that sense. And the one... It was very interesting. The bikes that were made in the north were sold very well in the south, and some made in the south were sold very well in the west. It was crazy. But there was, of course, one motorcycle that would always stand out and people would say that, "Yeah, this is the one which really..." But that would always be the case. But by and large, everyone did well. Looking back, I say that, wow, they were practicing this without us knowing it.   0:23:51.6 Balaji Reddie: And even now, if you know, I don't know whether this happens outside of India, but Suzuki and Toyota in India have collaborated with each other to use the car body, if you know what I mean. The insides would be their own inside. Suzuk... Maruti is the name of the Indian company that's tied up with Suzuki Motor. So you have Maruti Suzuki and Toyota. They have common bodies, names are different, but the inside is Toyota technology and Suzuki technology. So they've decided, they just realized that they need each other, right? And they're doing exceedingly well, both. Just both.   0:24:33.7 Andrew Stotz: Sharing the platform.   0:24:36.0 Balaji Reddie: Yeah. It's crazy. And you don't start bothering so much about trying to defend yourself, right? You go ahead and focus on what you're good at. So this point two, the new philosophy, what it meant before and what it means now, I would not say it replaced it, but it's just added on to it, where he said that top management should be involved. That was the new philosophy, that quality has to come from the top. Western management waking up, et cetera. The Western managers to see quality is the basis for running your organization. It should be your strategy. And he just says as an extension of that strategy, we need to get together and cooperate and think win-win. So that's the new philosophy. It takes us, he always says, a new reward system would come in. It would be a better applied science. You can read that in The New Economics, that what is the purpose of this entire new philosophy, The New Economics, where he explains this.   0:25:39.2 Andrew Stotz: Yeah.   0:25:41.4 Balaji Reddie: In a nutshell is point two. So the purpose was to shock the Western management at that point in time, but also in a different sense, try to open the eyes of people even now that please don't think small, think big, right? The new philosophy tells us look beyond the boundaries that you've artificially created and see what you can do with the others, including your competitors. And don't look at them as adversaries, rather than partners in something that both of you will grow. Maybe not evenly, but you will grow. So that was...   0:26:18.7 Andrew Stotz: Yeah. One last thing for me on that is just the idea that he reinforces the focus that quality is, the key is the customer in that process of quality.   0:26:33.8 Balaji Reddie: That's right. That's right.   0:26:34.4 Andrew Stotz: As opposed to it's not about quality, quality, quality, control charts and QC circles and all that. It's about what is quality in the eyes of the customer and how do we better deliver that. So that always gave me so much comfort when I learned what he was teaching, that he wasn't just... It wasn't all about quality and tools. It was about quality in the eyes of the customer.   0:27:00.8 Balaji Reddie: Right. So that's why he said tools are just 3%. And you can be 100% excellent at the 3% and still run out of business, right? He always said that. So you need to apply this in the broader sense of the term. So that was...   [overlapping conversation]   0:27:08.5 Andrew Stotz: Yeah. And the problem is if you don't understand the system and you don't understand the whole teaching, then even if you're good at the tools, you may misinterpret from the tools.   0:27:25.6 Balaji Reddie: Yes. So, right, so that was point two.   0:27:30.4 Andrew Stotz: Whoo!   0:27:31.3 Balaji Reddie: So now we come to point three. [laughter] Now point three... Yeah, we just got I think 15-odd minutes, but I'll cover some part of it and I think we'll continue with this.   0:27:34.0 Andrew Stotz: Yeah, yeah. We good. I got time.   0:27:42.5 Balaji Reddie: Point three. Now this again has been so crazily misinterpreted. All right, so I'll start with the wordings once again, I'm referring to the document what he wrote. And he says now, "Cease dependence on mass inspection to achieve quality. Eliminate the need for inspection on a mass basis by building quality into the product in the first place." Now if you look at that, he was talking much about the act of inspecting and the entire activity-based and saying don't depend. The keyword was "cease dependence," because if you hear what happened in one funny case where the manager from Ford went back and just sacked all his inspectors saying Dr. Deming said we don't need inspection. But that was, I think, overstating it. But nevertheless, the keyword is dependence. Don't depend on inspection. And then he said, build quality into the product in the first place. And then he quotes Harold Dodge who said you cannot inspect quality into a product. Quality is already there. Inspection just reports what's happening. It doesn't tell you where... You know.   0:29:00.4 Andrew Stotz: Yep.   0:29:03.4 Balaji Reddie: Andrew, can you hear me?   0:29:05.8 Andrew Stotz: Yes. Keep going. So what you're talking about is the idea of thinking about quality not from the perspective...   0:29:20.2 Balaji Reddie: Yeah, we got disconnected.   0:29:21.7 Andrew Stotz: Yeah. What I was saying you're talking about is this idea of cease dependence on inspection, that it doesn't mean inspection's completely gone, but it means starting at the beginning of the process and thinking about how do we improve things from there. So continue on.   0:29:37.3 Balaji Reddie: Yeah, so I just said here that he was not so much at that time when he wrote this, like I said here, it was misinterpreted because, yes, he did start with saying the activity, the activity-based thing about inspection, that we should not depend on it. And I think I mentioned that Ford... One of the managers of Ford, misinterpreted that and sacked his inspectors and things like that. But he said that eliminate the need for inspection by building quality into the process and the product in the first place. So the word was dependence. And so he did not say that you need to eliminate the act of inspecting. In fact, it's very interesting what he actually spoke about this in the workbook which you get when you go a four-day seminar on video which was created by General Motors, right?   0:30:30.6 Balaji Reddie: So those video cassettes, and then the workbook that came with that had a very interesting take on inspection. So he said that it doesn't mean that we're gonna stop. He said you'd be a fool to keep yourself in the dark about what's happening in the process. You need to know what's happening in the process. So he's not saying that do away, he's saying use inspection. So now he actually said this, and here's where the interpretation comes in: understand the purpose of inspection. The purpose of inspection is to give yourself more and more knowledge of the process, not for sorting bad from good, because good and bad product come from the same process. We need to fix the process. And this is to be interpreted if you talk about how he looked at this, because if you read what he spoke about the nine heavy losses, when we discuss those, he says that one of the losses was performance appraisal. So I want to ask those people who say that this interpretation, isn't performance appraisal quality by inspection? You're sorting bad from good based on some arbitrary measure that you created in your head. You decide what's good, you decide what's bad. You're God, is it?   0:31:52.9 Balaji Reddie: I mean, Douglas McGregor actually wrote an article on this saying that managers don't like to play God, right? And he's the one who advocated, no, we don't need performance appraisal. It actually harms. And Dr. Deming just said the same thing. So if you read Douglas McGregor's book, The Human Side of Enterprise, there's a new version, the annotated version, where they've given commentary of what some experts or some the interpretations of the text. And I was amazed to see Dr. Deming's name come up there and saying that Dr. Deming also concurred with this and said it in very plain words that do not carry out performance appraisal. And he likened it to using inspection to create quality, whereas we know it doesn't. So fix the process. Right? And then people again take this in piecemeal and start quoting Deming: "A bad system will beat a good person anytime." I don't know what to say here, really. I mean, he obviously he said... He was trying to explain that you cannot judge that person based on the output of the system. I mean, it's an output of the system, the person. So you need to look at it that way. I don't know whether I'm making sense here, but that's the way to look at this point number three. Right? And he says that don't depend. Use the inspection to understand the process. Now, he says here, and I think I've got the text in that, he says, "There will always be inspection. We must never deliberately leave ourselves devoid of information on how the process is doing. Is it still in statistical control? Is there a trend? Were our efforts towards shrinkage of variation or change of level successful? The function of inspection is optimization of the whole system, including suppliers of materials and services and the ultimate consumer." So he very clearly stated this, that this is a system. And this was the system he was talking about, right? And he says here that it's not about just manufacturing, service. It's if you are really, really interpreting this, then you'd also remove performance appraisal in the process, right? Because that's quality by inspection. Uh-oh.   0:34:21.2 Andrew Stotz: The Human Side of Enterprise, the annotated edition, is available on Amazon. Just looking at it right now, this annotated edition came out in 2023. So I haven't read it, so I'm gonna check it out myself.   0:34:42.1 Balaji Reddie: Yeah, I think I lost you again.   0:34:44.5 Andrew Stotz: Yep. I just went through the details about The Human Side of Enterprise and that latest version or edition that's just come out that you've mentioned. It came out in 2023.   0:34:57.0 Balaji Reddie: Right, right. So you could get that, read that book, you'll get to see it. And he very clearly... Did you record that bit where I read out the text?   0:35:09.4 Andrew Stotz: I don't remember that.   0:35:10.8 Balaji Reddie: Okay, I'll just read it out again so you can edit it later. So, yeah, he says here, "There will always be inspection. We must never deliberately leave ourselves devoid of information on how the process is doing. Is it still in statistical control? Is there a trend? Were our efforts towards shrinkage of variation or change of level successful? The function of inspection is optimization of the whole system, including suppliers of materials and services and the ultimate consumer." Did you get that? Did that get recorded?   0:35:45.5 Andrew Stotz: Yeah.   0:35:46.0 Balaji Reddie: Yeah. So that's exactly what he meant. He said that he was not talking about the act, and he said you would have some inspection. Now, interestingly, a very, very different take on this that, yeah, I told you Myron Tribus was talking to Dr. Juran about this, and Dr. Juran gave his interpretation. Because if you know, he invented this badly misunderstood and abused term called cost of quality, right? And if you know why he invented it and what happened later, I don't even want to get into it. He said, "The only way I could grab management's attention was to present my problem in terms of money. If I explained to them and said to them that there is... This process running at 90% efficiency, they would be happy. They said, "Great." He said, "No, it's not great." And then they just wouldn't listen. They said, "90% is great." So he said, "How do I talk to them?" And so he went back to them and said, "Okay, your process is running at 90% efficiency." They said, "Yes." He said, "But you're paying 100% salary to this guy to do 90% good work, and then you're paying 100% salary to another guy to remove the 10% bad work, and then you're paying 100% salary to a third guy to correct that bad work." And that's how he invented cost of quality.   0:37:11.8 Balaji Reddie: Now people have gone and overindulged in this and they start having arguments about what is the category and where should it fit in and... Anyway, what he was trying to say is there are some activities where the more money you spend, the better it is. And so he came up with that cost of prevention and of course, I mean, conformance and non-conformance. In conformance, there were two categories. One was called prevention, where the more money you spend, obviously the better it is, but you get... You know, the returns on them will come much, much later, like training and spending money on the right things like maintaining your equipment, blah, blah, blah, research and development. He also gave a category called appraisal. And appraisal, he says, these are all activities which are necessary evils. [laughter] That means you can't eliminate them, at the same time you should not overtly depend on them to create quality. And one of them was inspection. And he says you should not spend more than necessary on inspection, just enough.   0:38:16.1 Balaji Reddie: Now here's the question I get, how much do we spend? [laughter] And the trick which I read somewhere and I saw that because I interpreted these points both ways. So when you start doing this, you walk into a company, make them calculate how much money they're spending on inspection right now. So let's say it's $100,000 or rupees or whatever. Now you start implementing the improvement processes, you start understanding the process and you start, well, the works, control charts, blah, blah, blah, blah. And then you start seeing that the process is getting better and so your defects are coming down and things are getting better generally. And so you're spending lesser money on inspection, you're spending lesser time on inspection, your resources are going in the right direction. And so that amount starts coming down and you keep doing that and you keep doing that and then you reach a point where you say, "Okay, this is it. I need this much of inspection at a bare minimum to keep myself," like Dr. Deming said, "not devoid of any information." I need to know what's happening. So that need-to-know basis, just enough, now that becomes the cap, right? And you say now, "If after this I see an increase in the money being spent, then... It's not that I'm going to go and just eliminate. I'll try to find out why." And if you look at it, that's exactly what Dr. Deming said, understand the purpose of inspection is to make the process better. The product, of course, will get better and you use the inspection intelligently to understand because good and bad products come from the same process. This is absolutely true for both manufacturing as well as services. You always can look at the activities and see which are the ones which are prone to a lot of mistakes that can happen and then you try to help the person carrying out the process. "Can I eliminate this? Can I reduce this?"   0:40:14.0 Balaji Reddie: And talking about AI, I think AI can help a lot in that, in much of the so-called mundane activities which are repeatable and being done on a regular basis. You can bring in AI over there, use it in the right way. Right? Even for the inspection thing, they talk about it, but yeah, like I said, we need to keep our eye and just keep glancing at it, but not going and standing in front of the process all the time. You don't need to do that. Just... So like Dr. Deming said, watch that... Look at the trend, the impact of my action that I need to inspect. So in that sense, like I said, the act of inspecting will never go away, but our reliance on that will come down drastically. And once in a while, just glancing through, just letting us know that things are going according to plan is the right way of looking at this. Anyway, I think that's all we have time for today, Andrew.   0:41:10.2 Andrew Stotz: Yeah. So I'm gonna wrap up by saying 290 years ago, Benjamin Franklin said, "An ounce of prevention is worth a pound of cure."   0:41:23.2 Balaji Reddie: Wow.   0:41:24.2 Andrew Stotz: Well, it's a lot of what you've just described is the idea of starting at the beginning and trying to reduce the need to depend on inspection. I love the stuff that you talked about about... He didn't say eliminate inspection, he said reduce the dependence. And that really reminded me that there's a purpose. And as you've described, the purposes of inspection isn't only just, "Okay, we don't want something bad going out to the customer," but what it really is about is understanding how are we doing. How have the upstream preventative or improvements that we've done in the upstream resolved or reduced what's happening at the downstream? I think that was a major thing for me as a young guy when I first heard Deming, to understand that, start at the beginning and try to get things right from the beginning, that will reduce the amount of trouble that you have towards the end. So, fantastic.   0:42:22.2 Balaji Reddie: That's true. That's true. All right, then.   0:42:24.5 Andrew Stotz: Well...   0:42:25.1 Balaji Reddie: We meet again next week or week after that to continue this.   0:42:27.0 Andrew Stotz: Yeah, I look forward to it. And for the listeners out there, remember to go to deming.org and jump into DemingNext to continue your journey.   0:42:36.5 Balaji Reddie: Yes.   0:42:37.0 Andrew Stotz: This is your host, Andrew Stotz, and I'll leave you with one of my favorite quotes from Dr. Deming, which is, "People are entitled to joy in work."   0:42:45.3 Balaji Reddie: "Joy in work."

Use code: PINKc60 https://shopc60.com/ Could a Nobel Prize–winning carbon molecule hold the secret to optimal cellular health, enhanced energy, and anti-aging? In this episode of The Better Life, host Dr. Pinkston is joined by Ken Swartz, founder of C60 Power, to explore the remarkable science behind Carbon 60 (C60). Discover how this unique molecule acts as a powerful superoxide dismutase (SOD) and catalase mimic within the mitochondria—the energy powerhouses of our cells. Ken shares his personal journey with C60, from protecting his research crew against radiation to witnessing incredible health turnarounds in retinal health, energy, and overall vitality. Tune in to learn how target mitochondrial support can help neutralize damaging free radicals, combat chronic inflammation, and boost your body’s natural capacity to heal and thrive.See omnystudio.com/listener for privacy information.

I joined Peter McCormack to dig into one of the deepest unanswered questions in physics: what is time, and why does it only seem to move in one direction? My answer might surprise you. Time may not be a fundamental feature of reality at all, and understanding space, matter, and energy still doesn't mean we understand the universe itself. We cover: - What may have existed before the Big Bang - Why something can't truly come from nothing - How my work with the Simons Observatory could detect gravitational waves from the universe's earliest moments - What dark matter actually means - Why even a film as detail-obsessed as Interstellar still gets the physics wrong. The conversation then turns from cosmic time to human time: mortality, family, meaning, and attention. We get into UFO disclosure, the collapse of institutional trust, and whether AI can ever reproduce the embodied human insight that let Einstein transform physics. My take: AI remains a tool built to serve humanity, and it's time we stop apologizing for humanity's greatness. ———

A new week means new questions! Hope you have fun with these!Which US state has the only American diamond field open to the public?The Piano is the most popular musical instrument in the world. In which country was it invented?In the musical Grease, Danny Zuko, Kenickie, Sonny, Doody and Putzie are all members of what group?Saffron is s spice derived from which flower?In the NATO phonetic alphabet what word represents the letter G?Which war lasted from 1936-1939?Who recently made headlines saying they'll be returning to the U.K. by the end of the month?Erik Larson's book "The Splendid and the Vile" is a biography of what person during 1940?Popular in the 70s and 80s, what is the edible term for the device used for amateur non-commercial communication?Who was the first American woman to win the Nobel Peace Prize in 1931?In Norse mythology, Fenrir is destined to break free and devour Odin during Ragnarok; what type of creature is Fenrir?In I dream of Jeannie, besides the obvious, what part of Barbara Eden's body did censors demand her genie costume cover up?Cartoonist Gary Larson won 4 awards in 10 years from the National Cartoonist Society for what surrealist single-panel strip?In the film version of Little Shop of Horrors, Audrey II (the carnivorous plant) is voiced by Levi Stubbs, lead vocalist of which legendary Motown Group?The stars Elnath and Tianguan represents the horns in which constellation?On the periodic table, Element 111 is named after which German physicist, the first to win the Nobel Prize in Physics?MusicHot Swing, Fast Talkin, Bass Walker, Dances and Dames, Ambush by Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 3.0 http://creativecommons.org/licenses/by/3.0/Don't forget to follow us on social media:Patreon – patreon.com/quizbang – Please consider supporting us on Patreon. Check out our fun extras for patrons and help us keep this podcast going. We appreciate any level of support!Website – quizbangpod.com Check out our website, it will have all the links for social media that you need and while you're there, why not go to the contact us page and submit a question!Facebook – @quizbangpodcast – we post episode links and silly lego pictures to go with our trivia questions. Enjoy the silly picture and give your best guess, we will respond to your answer the next day to give everyone a chance to guess.Instagram – Quiz Quiz Bang Bang (quizquizbangbang), we post silly lego pictures to go with our trivia questions. Enjoy the silly picture and give your best guess, we will respond to your answer the next day to give everyone a chance to guess.Twitter – @quizbangpod We want to start a fun community for our fellow trivia lovers. If you hear/think of a fun or challenging trivia question, post it to our twitter feed and we will repost it so everyone can take a stab it. Come for the trivia – stay for the trivia.Ko-Fi – ko-fi.com/quizbangpod – Keep that sweet caffeine running through our body with a Ko-Fi, power us through a late night of fact checking and editing!

In this episode we discuss the work of 2025 literature laureate Lázsló Krasznahorkai with literary translator Daniel Gustaffson. Having translated many of Krasznahorkai's novels from Hungarian to Swedish, Gustafsson is an expert on his depictions of apocalyptic terror and long, winding sentences. Selections from Krasznahorkai's Nobel Prize lecture, interview and banquet speech are examined by Gustafsson and host Adam Smith to gain some understanding of this mysterious writer, his creative process and influences. For a quick introduction to 2025's awarded work in literature, check out our Crash Course on László Krasznahorkai or Anders Olsson's eloquent speech from the Nobel Prize award ceremony.Read a complete profile of László Krasznahorkai, explore the 2025 Nobel Prize in Literature and read his Nobel Prize lecture at our website, nobelprize.org.See the announcement of the Nobel Prize in Literature 2025 and the moment László Krasznahorkai was awarded his medal.This podcast was a production of Nobel Prize Outreach and Filt, and created in cooperation with Fundación Ramón Areces. Hosted on Acast. See acast.com/privacy for more information.

Adhesion GPCRs are the largest receptors in the human genome — and until recently, no one was certain they coupled to G proteins at all. Boucard is working to change that, one synapse at a time.Antony Boucard didn't plan to be a scientist. He was heading toward medical school — fresh from social work in Nicaragua, where he built wood-burning ovens for women's cooperatives, and years of service in the Canadian Navy Reserve — when a summer in a biochemistry lab changed his trajectory entirely. He turned down his medical school acceptance and never looked back.After graduate training at the Université de Sherbrooke, a postdoctoral fellowship in Thomas Südhof's Nobel Prize-winning lab (first at UT Southwestern in Dallas, then at Stanford) opened a new research direction: the molecular code governing synapse formation. A chance experiment — testing whether a cell adhesion molecule he was studying might bind to a GPCR — yielded a result that Südhof himself didn't believe at first. Both proteins, it turned out, were independently known to bind alpha-latrotoxin, the toxin from black widow spider venom. No one had thought to ask whether they interacted with each other. That question has defined Boucard's lab ever since.Now at UNAM in Mexico City — where no lab was working on adhesion GPCRs when he arrived — he is building a research program that connects these colossal, largely orphan receptors to synapse specificity, addiction, autism, schizophrenia, bipolar disorder, and cancer. The conversation covers the science, the serendipitous path behind it, and what it looks like to pioneer a research field in a place no one expected.Why adhesion GPCRs are structurally unlike any other GPCR family — sprawling N-terminal domains, autoproteolytic processing, up to 1,000 amino acids — and what made them so difficult to work with for so longHow alpha-latrotoxin from black widow spider venom became the unexpected clue connecting a cell adhesion molecule and a GPCR into the same intercellular complexWhat synapse formation reveals about adhesion GPCR function — and how addiction, autism, schizophrenia, and cancer all converge on the same receptor biologyWhy Boucard left Stanford and UT Southwestern to build a lab at UNAM, and what it means to recruit from scientific communities that larger institutions overlookThe assays the lab uses to probe adhesion GPCR biology: BRET, FRET, microscopy, flow cytometry, and custom protein engineering strategies to solubilize membrane-anchored ligandsThe dream tool he can't build yet — a nanoscale real-time camera navigating the cell surface — and why cryo-EM snapshots still miss the most important partDr. GPCR Ecosystem: https://www.ecosystem.drgpcr.com/Membership & Pricing: https://www.ecosystem.drgpcr.com/university-pricingWeekly News: https://www.ecosystem.drgpcr.com/gpcr-weekly-news

A Nobel Prize-winning discovery from 1931 described a cellular “fermentation switch” tied to cancer. That same switch shows up — in a much milder form — in millions of exhausted, foggy, always-cold people who've never had the right tests run.TLDR: Fatigue that sleep doesn't fix. Brain fog. Always cold. Muscle heaviness. Crashing after a workout. These aren't personality traits or “just aging”; they're often signs that your mitochondria, the tiny power plants inside every cell, aren't running the way they should. In this episode, I walk through:* What mitochondria actually do (in plain English)* The 6 physical warning signs your body sends when they're struggling* The 5 lab markers that reveal mitochondrial stress — most of which your standard panel isn't interpreting correctly* Why your body's pH and proton gradient might be the most underrated marker you've never heard of* 8 ways to start rebuilding mitochondrial function this week, starting todayWhat Mitochondria Actually Are (In Plain English)This is a very rudimentary explanation…Every one of your roughly 37 trillion cells contains tiny structures called mitochondria. Their job is to take the food you eat and the oxygen you breathe and convert them into a usable form of energy called ATP (adenosine triphosphate).Think of it like currency exchange. Food is your paycheck. But you can't pay your bills with a paycheck directly. You have to convert it into cash first. That's what mitochondria do. Your heart, brain, and liver cells are packed with hundreds to thousands of them, because those organs demand the most energy.So the real question is: what happens when those mitochondria get damaged?The Warburg Connection And What It Actually Tells UsIn 1931, German biochemist Otto Warburg won the Nobel Prize in Physiology or Medicine for his work on cellular respiration. Part of what made him famous was an earlier observation: many cancer cells generate energy primarily through fermentation (a process called aerobic glycolysis) rather than through the oxygen-based process healthy cells typically rely on - even when plenty of oxygen is available. Warburg's Nobel Prize was officially awarded for his discovery of the respiratory enzyme, and part of what he demonstrated along the way was that cancerous cells can live and develop even in the absence of oxygen. This pattern is now known as the Warburg effect.Here's where I want to be careful, because this is the kind of claim that's easy to oversimplify. It's tempting to say “damaged mitochondria cause cancer,” full stop. The real picture is messier and still debated. Researchers have gone back and forth for decades on whether this metabolic shift is a cause of cancer or a consequence of it, and more importantly, later research found that in most cancers, the mitochondria aren't actually broken. Subsequent research has shown that mitochondrial function is not impaired in most cancer cells, even though those cells still preferentially ferment glucose. The “why” behind that switch remains one of the more actively studied questions in cancer metabolism.So what does this mean for you, a person who almost certainly doesn't have cancer but does feel exhausted all the time? It means the fermentation-under-stress pattern Warburg first described is a real, well-documented phenomenon in cell biology, but I'm not going to tell you that your fatigue is “pre-cancer” or that a milder version of the Warburg effect is definitively what's causing your brain fog. What the broader mitochondrial dysfunction research does support is more modest and, frankly, more useful: when mitochondria are chronically stressed by poor diet, chronic stress, poor sleep, toxin exposure, or simply aging, cells generate less usable energy and more oxidative byproducts, and that shows up in the body long before it shows up on a standard lab panel.The Physical Warning Signs Most Doctors MissI hear a version of this sentence constantly: “My doctor ran labs and everything came back normal.” I believe them - the numbers usually do fall within standard reference ranges. But standard panels weren't built to catch mitochondrial strain. The body, on the other hand, tends to tell you well before a lab does. As you read this list, take honest mental inventory:* Brain fog and poor concentration. Your brain uses roughly 20% of your total energy output despite being about 2% of your body weight, so when brain-cell mitochondria underperform, mental clarity is often the first thing to go.* Fatigue that sleep doesn't fix. Not “I'm tired,” but “I slept eight hours, and I'm still exhausted.” This is one of the most common complaints I hear in practice.* Always feeling cold, or a low body temperature. Body heat is a byproduct of mitochondrial activity, so a consistently low waking temperature (roughly below 97.8°F) can point to a slower metabolic rate.* Muscle weakness or heaviness without a clear cause. Muscle cells are mitochondria-dense and need constant energy for contraction.* Exercise intolerance. You work out, and instead of feeling good afterward, you're wiped out for days without properly recovering.* Sensitivity to light or sound. Sensory processing is energy-expensive, and when cellular energy runs low, the nervous system loses some of its buffering capacity.None of these symptoms are proof of mitochondrial dysfunction on their own; fatigue and brain fog have a long list of possible causes, from thyroid issues to iron deficiency to depression to sleep apnea, and a thorough workup should rule those out first. But if several of these are chronic and unexplained, mitochondrial strain deserves a place on the list.The Lab Markers That Tell the Real StoryThese are markers you can request from a standard blood draw, though a few need to be ordered specifically. I want to flag something important up front: the “optimal” ranges I use in my own practice are tighter than the standard reference range most labs will flag as normal. That's intentional. A value can sit inside the reference range and still not be where I'd want to see it for someone chasing energy and longevity, rather than just ruling out overt disease. That distinction matters, and it's worth discussing with your own physician rather than self-diagnosing off a lab printout.* Fasting insulin. Most labs allow up to 25 µIU/mL as “normal.” I typically look for something closer to 2–5. Insulin creeping up into the high single digits or beyond can be an early sign that cells are struggling to use glucose efficiently.* Fasting glucose. Optimal is generally lower than most people assume, closer to 72–85 mg/dL rather than the upper 90s that many labs will still call normal.* High-sensitivity CRP (hs-CRP). This measures systemic inflammation. Levels of CRP less than 1 mg/L are generally considered low cardiovascular risk, 1 to 3 mg/L moderate risk, and above 3 mg/L elevated risk. Damaged mitochondria leak more free radicals, and that oxidative debris is one of several things that can trigger this kind of low-grade inflammatory signal.* CO2 (bicarbonate). Part of a standard basic metabolic panel, usually reported somewhere in the low-to-high 20s (mEq/L). When it trends toward the lower end, it can be a signal of mild metabolic acidosis; worth flagging and discussing with your doctor rather than a red-alert finding on its own.* Lactate-to-pyruvate ratio. This is a more advanced, specialist-ordered marker. Normally pyruvate flows into the mitochondria and gets converted into usable energy; when mitochondrial function is impaired, pyruvate backs up and converts to lactate instead. It's worth being precise here about what the actual research supports: a lactate-to-pyruvate ratio above 20 has been shown to distinguish patients with primary mitochondrial disease from those with other conditions in clinical studies, and this marker's best-documented use is in diagnosing rare inherited mitochondrial disorders or evaluating critically ill patients not as a general screening tool for everyday fatigue. If you're curious about your own ratio, this is a conversation to have with a clinician who can interpret it in the context of your full picture, not something to self-order and self-interpret.The Proton Gradient: Why pH Might Be an Underrated MarkerHere's a mental picture that helped this concept click for me: imagine Niagara Falls. Water crashes from a height, and that force turns a turbine that generates electricity at the base. Mitochondria do something similar, except instead of water, they're moving protons (hydrogen ions) across a membrane, building up a pressure gradient. When those protons flow back through a molecular structure called ATP synthase, that “turbine” spins and produces ATP.Now imagine you lower the height of the falls. Less force, less electricity. That's roughly what happens when your body's internal environment shifts toward acidity; the proton gradient shrinks, and energy production slows with it.Blood pH is tightly regulated and normally stays between 7.35 and 7.45, it doesn't swing wildly, and if you ever see it trend outside that narrow range on a lab, that's a signal your body's buffering systems are genuinely taxed, not a subtle wellness finding. Because blood pH is so tightly controlled and hard to use as an early, everyday signal, a more practical (though far less precise) proxy some practitioners use is first-morning urine pH, tested with an inexpensive strip. Consistently low first-morning urine pH is sometimes used as a rough indicator that the body is buffering more acid overnight than ideal; though it's a screening tool at best, not a diagnostic one, and it can be influenced by diet, hydration, and other unrelated factors.A quick, honest aside on “structured water.” You may hear claims - including sometimes in wellness spaces I respect - that water inside your cells exists in a special “fourth phase” or “exclusion zone” structure that's central to mitochondrial function, an idea popularized by bioengineer Gerald Pollack. I want to be straight with you about where this stands scientifically: it's a genuinely contested hypothesis, not a fact. Independent researchers have proposed alternative physical explanations for the phenomena Pollack describes, and some of his specific claims - like light-driven charge separation in water - are considered difficult to reconcile with basic physical chemistry. The underlying biology of cellular hydration and mitochondrial function is real and important; the specific “structured water” framework is an interesting but unproven idea layered on top of it, and I don't want to present it to you as settled science.8 Ways to Rebuild Mitochondrial Function (Mitochondrial Biogenesis)The good news is that mitochondria aren't fixed in number or function. Your body can build new ones; a process researchers call mitochondrial biogenesis. Here are eight inputs that support it, roughly in the order I'd actually prioritize them with a patient. Notice that supplements are last, not first.* Morning sunlight. Getting outside within about 30 minutes of waking supports circadian alignment, which in turn supports metabolic function.* Zone 2 cardio. This is exercise intensity where you can still hold a conversation roughly 3–4 sessions per week, 30–45 minutes. It's popularly described as uniquely effective for mitochondrial biogenesis through a signaling pathway called PGC-1α. I want to add a fair caveat here: while Zone 2 has been widely positioned in popular media as the optimal intensity for improving mitochondrial and fatty-acid oxidative capacity, a 2025 narrative review found the evidence for that specific claim is more mixed than commonly portrayed, and some research suggests higher-intensity training activates these same pathways just as strongly, if not more so. Zone 2 is still a well-supported, low-risk, sustainable way to build an aerobic base; I just don't want to oversell it as the only path to mitochondrial adaptation.* Cold exposure. This is a stressor, so I don't recommend starting here if you haven't built a foundation first. In animal studies, chronic cold exposure increases expression of mitochondrial proteins and the master regulator of mitochondrial biogenesis, PGC-1α, in brown adipose tissue - human data on the same mechanism is still developing. However, functional brown fat has been confirmed to exist and activate with cold exposure in adult humans. If you're adapted to it, starting with 30–60 seconds at the end of a shower is a reasonable entry point.* Circadian alignment. Consistent sleep/wake timing supports the same systems that morning light and cold exposure influence.* Structured hydration. Beyond simply drinking more water, this includes grounding and infrared light exposure - inputs that (independent of the more speculative “structured water” claims above) are reasonably tied to circadian and metabolic health.* Metabolic flexibility. This is your body's ability to switch between burning glucose and burning fat for fuel. Time-restricted eating is one of the more accessible tools for improving this, but it's a second-phase intervention, not a starting point, especially if your body isn't metabolically ready for it yet.* Red and near-infrared light therapy. Red to near-infrared light is absorbed by cytochrome c oxidase, an enzyme in the mitochondrial electron transport chain, and this interaction is associated with increased ATP synthesis by mitochondria. Most of the research uses roughly 10–20 minutes of targeted exposure, often over the head and torso.* Mitochondrial cofactors. This is where most people want to start and where I'd ask you to start last. CoQ10, magnesium, and B vitamins (especially B1, B2, B3) function as electron carriers within the Krebs cycle, and alpha-lipoic acid is a mitochondria-specific antioxidant. On top of these, I use Nion to support the body's pH and hydration balance, and Protandim NrF2 to help address the oxidative load that builds up when mitochondria are already under stress, but these work best layered on top of the lifestyle inputs above, not as a substitute for them.Quick Reference: What the Markers MeanThese "optimal" figures reflect the ranges I personally use with patients pursuing proactive metabolic health, not universal diagnostic cutoffs. Always interpret your own labs with a clinician who has your full history.This Week's ProtocolThree simple steps, none of which should cost much or take more than a few minutes a day:* Tomorrow morning: get outside within 30 minutes of waking for a few minutes of natural light.* Pick up pH test strips (inexpensive, available almost anywhere) and test your first-morning urine for three consecutive days. Write the numbers down.* Pull your last basic metabolic panel and find your CO2/bicarbonate value. If you don't have a recent one, get one drawn.Where to Go From HereIf you recognized yourself in several of the symptoms above, the first move isn't panic - it's data. We walk through exactly this kind of cellular assessment with patients every week inside the Thrive 120 framework. If you want a professional to look at your labs and symptom picture and tell you the top three things to address first: Disclaimer: this article is educational, not medical advice. Talk to your doctor before making changes to your diet, exercise routine, or supplement regimen, especially if you're pregnant, nursing, on blood thinners, or managing a chronic condition — including cancer. Some links below are affiliate links — if you buy through them, I may earn a commission at no extra cost to you. These statements have not been evaluated by the FDA, and none of these products are intended to diagnose, treat, cure, or prevent any disease.)References* Nobel Prize in Physiology or Medicine 1931 — Otto Warburg, biographical: https://www.nobelprize.org/prizes/medicine/1931/warburg/biographical/* Warburg effect(s) — a biographical sketch of Otto Warburg and his impacts on tumor metabolism — PMC: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4784299/* Warburg Effect — a Consequence or the Cause of Carcinogenesis? — J Cancer: https://www.jcancer.org/v07p0817.htm* Genome-Scale Metabolic Modeling: mitochondrial function is not impaired in most cancer cells — PMC: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3053319/* C-Reactive Protein and cardiovascular risk categories — Circulation (AHA): https://www.ahajournals.org/doi/10.1161/01.cir.0000093381.57779.67* Clinical usefulness of hs-CRP across Framingham risk scores — Circulation (AHA): https://www.ahajournals.org/doi/10.1161/01.cir.0000125690.80303.a8* Diagnostic values of lactate-to-pyruvate ratio in mitochondrial disease — PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC9334250/* Lactate and lactate:pyruvate ratio in pediatric acute liver failure — PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC5328928/* Mitochondrial dysfunction and ischemia in critical illness (lactate:pyruvate ratio) — PMC: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4470667/* Much Ado About Zone 2: A Narrative Review — PubMed: https://pubmed.ncbi.nlm.nih.gov/40560504/* Chronic cold exposure induces mitochondrial biogenesis in brown adipose tissue — iScience: https://www.cell.com/iscience/fulltext/S2589-0042(21)00402-8* Effect of habitual cold exposure on brown adipose tissue activity — systematic review: https://www.tandfonline.com/doi/full/10.1080/22423982.2025.2545059* Photobiomodulation of cytochrome c oxidase by chronic transcranial laser — PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC8971717/* Brain Photobiomodulation Therapy: A Narrative Review — PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC6041198/* Exclusion Zone Phenomena in Water — A Critical Review of Experimental Findings and Theories: https://arxiv.org/pdf/1909.06822Links mentioned:* Metabolic Blueprint Session: https://calendly.com/tripleplaydoc/complimentary-consult * Nion: https://www.nionhealth.com/tripleplaydoc* Protandim NrF2: https://tripleplaydoc.lifevantage.com/us-en/shop/protandim-nrf2 This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit tripleplaydoc.substack.com/subscribe

The same mathematics that explains why a fridge magnet sticks to your refrigerator also explains why neural networks work when they have no right to. His group did the calculation. Nobody else had. Subscribe if you want science with evidence, not speculation. Goldenfeld is a Professor of Physics at UC San Diego, a Fellow of the Royal Society, and a Member of the National Academy of Sciences who spent 36 years at the University of Illinois applying condensed matter physics to problems everyone else had given up on: why the genetic code is optimal, why early life evolved impossibly fast, and why AI works despite being overparameterized beyond anything classical statistics can explain. The thread connecting all three is one idea: that what emerges from many things together is qualitatively different from the sum of its parts. That idea explains magnetism, AI, the origin of the genetic code, and why life may be inevitable wherever the laws of physics apply. He also argues that what is happening to science right now is not a disagreement about facts but a fracture in how people decide what is true, and that is a more dangerous problem. What you'll hear: -Why the same phase transition that explains magnetism also explains why AI works at all -Why Francis Crick concluded life must have come from outer space and what Goldenfeld found instead -What horizontal gene transfer has to do with how libraries work -Why Goldenfeld thinks Enceladus is a better bet for life than Europa -The purpose of life, stated as a thermodynamics problem -Why “different is more” is more useful than “more is different” “The impact you make is the ratio of what you do divided by what everybody else does. Minimize the denominator.” — Nigel Goldenfeld CHAPTERS 00:00 AI shouldn't work. It does. 00:56 What is a phase transition? 03:10 What the renormalization group does 08:54 Ising gave up. Wrong dimension. 13:32 Nigel almost met Ising. 40 minutes away. 26:38 AI is the best example of more is different 33:52 Bardeen won two Nobels. The transistor looked like chewing gum. 37:24 The three mysteries Crick couldn't solve 46:20 The genetic code can't evolve. And yet it did. 49:00 How early life evolved like a library 57:06 The purpose of life as a physics problem 01:00:04 Life is physics, not chemistry 01:05:56 Anti-science age. Not because of opinions. 01:13:00 20 seconds with your 20-year-old self 01:17:14 Different is more Get the transcript, fascinating bonus content, and my Monday M.A.G.I.C. Message: https://briankeating.com/yt Have a .edu email and live in the USA? You automatically win a meteorite: https://BrianKeating.com/edu Subscribe: https://www.youtube.com/DrBrianKeating?sub_confirmation=1 Support Into the Impossible on Patreon, get my weekly M.A.G.I.C. Message, unfiltered bonus content, and live monthly Office Hours with me: https://www.patreon.com/drbriankeating Join this channel for perks, monthly Office Hours, and your name in the Member Roster at the end of every episode: https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join Featured Guest: Nigel Goldenfeld website: https://guava.physics.ucsd.edu/~nigel/ Lectures on Phase Transitions and the Renormalization Group: https://www.amazon.com/dp/0201554097?lv=shuf&channelId=500&plpRedirect=mhFallbackNigel Goldenfeld on Twitter/X: https://x.com/NigelGoldenfeld My books: Losing the Nobel Prize (memoir): http://amzn.to/2sa5UpA Think Like a Nobel Prize Winner: https://a.co/d/03ezQFu Focus Like a Nobel Prize Winner: https://a.co/d/hi50U9U Galileo's Dialogue (first-ever audiobook): https://a.co/d/iZPi9Un Twitter/X: https://x.com/BrianKeating Substack: https://briankeating.substack.com Blog: https://briankeating.com/blog Audio-only: https://briankeating.com/podcast #intotheimpossible #briankeating #NigelGoldenfeld #physics #AI #originoflife #condensedmatterphysics #podcast Learn more about your ad choices. Visit megaphone.fm/adchoices

What can we do when doing the morally “right” thing produces worse outcomes?And what if some of society's strongest moral instincts are preventing us from solving problems we all agree are terrible?Alvin Roth is a Stanford professor and Nobel Prize-winning economist whose work has helped redesign real-world systems, including kidney exchanges and the way doctors are matched with hospitals. His new book, Moral Economics, tackles a harder category of problems: markets and behaviors we find morally objectionable, even when banning them may create consequences we don't want.He discusses:Why nearly 100,000 Americans can be waiting for a kidney What prohibitions of alcohol and heroin can teach us What happened when Rhode Island accidentally legalized indoor prostitutionWhether paying kidney donors could save lives without creating an ethically unacceptable marketWhy evidence becomes especially important when reasonable people disagree about moralityWhether performance-enhancing drugs could eventually become as ordinary as coffeeSome policies sound obviously right when judged by their intentions. But what happens when you judge them by their actual consequences?Roth pushes into the uncomfortable territory between those two questions: when banning something creates a black market, when changing a rule may save more lives than inventing a new technology, and what we should do when the outcome we believe is morally necessary turns out to be something we cannot actually achieve.

It's Monday, August 24th, A.D. 2026. This is The Worldview in 5 Minutes heard on 140 radio stations and at www.TheWorldview.com.  I'm Adam McManus. (Adam@TheWorldview.com) By Adam McManus Urge Chinese ambassador to allow Chinese pastor to come to U.S. ChinaAid, an international group supporting persecuted Christians inside China, has launched a petition and advocacy campaign to bring Pastor John Cao back to his home in the U.S, reports International Christian Concern. Pastor John has been imprisoned in China for seven years for his missionary work on the China-Myanmar border, helping establish 16 schools that serve thousands of local children and families. While he was “released” in 2024, he is still blocked from returning to the United States, where his wife and sons -- all of whom are American citizens -- live. To make matters worse, Pastor John now has advanced prostate cancer, and his situation is even more critical. ChinaAid's campaign and petition, which I've signed, call for Pastor John's immediate release, particularly urging President Donald Trump to personally raise his situation in his meeting with Chinese President Xi Jinping on September 24th. Write a polite and urgent 4-sentence note to the Chinese Ambassador to America, Xie Feng, urging him to persuade Chinese President Jinping to release Pastor John Cao so he can reunite with his wife and children here in America. Send it to the Chinese Embassy, 3505 International Place NW, Washington, DC 20008. You can find that address in our transcript today at www.TheWorldview.com. Wolves in White Coats: How doctors profited from “gender medicine” Appearing on Epoch TV, Admiral Brian Christine, who serves as the Health and Human Services Assistant Secretary, discussed a disturbing new report entitled “Wolves in White Coats: How Doctors and Hospitals Pushed and Profited from the Fraud of so-called ‘Gender Medicine.'” He laid significant blame on the Biden administration and his predecessor, a man born Richard Levine in 1957, who pretended to be a woman named “Rachel.” CHRISTINE: “The gender ideology pushed on the American people, and these unfortunate children, by the Biden administration, the last administration, and by my predecessor ‘Rachel' Levine, he really pushed gender ideology on the country and even suggested removing age limitations on performing mutilating surgeries or castrating chemicals.” Admiral Christine defined what a sex-rejecting procedure was. CHRISTINE: “Sex rejecting procedures: What we mean is either castrating chemicals, cross-sex hormones, or even surgeries to change the external appearance of the body. These mutilating surgeries that don't do anything to change gender, but radically change the appearance of the body, and can certainly have complications along the way.” He revealed the alarming number of surgeries that were tragically performed on confused minors. CHRISTINE: “From 2019 up to 2023, $120 million spent on sex-rejecting procedures in this country. Over 5,500 surgeries, sex-rejecting surgeries on minors.” Not surprisingly, fraud abounded. CHRISTINE: “There was $11 million billed for ‘precocious' puberty in kids who were 13 to 17 years old. That's not precocious puberty. That's just puberty!” The financial incentive for the ethically-challenged doctors is obvious. CHRISTINE: “If you take a child who has gender dysphoria and you get them to agree to sex-rejecting procedures, you've created a patient for life. So, there can be a tremendous financial incentive for these practitioners and these clinics to have these patients who come back again and again and again.” Psalm 82:4 says, “Rescue the weak and the needy; deliver them from the hand of the wicked.” ‘Influencer' tries to justify her 26-week abortion A social media fitness and lifestyle influencer in Australia with 480,000 followers, named Danielle Mitchell, has publicly documented the details of her abortion at 26 weeks after discovering her baby had severe defects and might have died before birth, reports LifeSiteNews.com. MITCHELL: “We had our fetal medicine growth scan today. And they also had our results for the amnio[centesis], the chromosomal abnormalities, and some new findings on her brain, being that she won't survive.” She went viral for sharing a video on X in which she explains that she had scheduled an abortion. Oddly enough, she entitled it, “Our sweet baby girl goes to sleep with the angels today at 26 weeks.” Through tears, she said this. MITCHELL: “I feel like I'm at peace with my decision. I didn't know how late-term terminations work. “You lay on a bed, awake, and they have doctors and midwives in the room. Under ultrasound, [they put] a needle into the baby's heart, and inject it to stop it from beating. And then you slowly feel all the movements stop from the baby.  No more kicks. Get sent home for another 24 hours, back up to the hospital, and they induce you, and give birth to a sleeping baby.” Mitchell became extremely emotional, saying that she needed to “let out” the grief before she explained to her young daughter that her baby would not be here anymore. MITCHELL: “Baby sister is gonna go live in the clouds today with Nanny Sandy. Okay?” DAUGHTER: “I love you.” MITCHELL: “I love you too, baby. Cuddle?” DAUGHTER: (giggles) On X, Mitchell's video of herself explaining the abortion was shared without the context of the child's condition. She was lambasted there in a flurry of outrage, while on her Instagram page she was met with more sympathy by followers who perceived that she felt she had “no choice” and was sparing her baby suffering overall. Some social media users pointed out that the diagnoses and prognoses of doctors for unborn children are frequently false. LifeSiteNews noted, “A high likelihood that a baby will not survive birth is never a justification for the direct killing of innocent life.” In response, veteran pro-life activist Frank Pavone shared a prayer for women considering abortion, and for the protection of unborn children. He wrote, “Dear Lord, send Your heavenly angels to women thinking about abortion. Protect their unborn children, soften the hearts of their parents, and open their eyes to the truth that abortion takes an innocent human life. Amen.” Young brothers address science and the Bible And finally, Beau, age 11, and Gray Reusch, age 8, are young brothers who have gone viral on social media for creating Christian faith and apologetics content through their family's brand, Armor Up Kids. Their father is Will Reusch, the founder of Armor Up Academy. In one of their videos, they explore the Bible and science. GREY: “People say, ‘It's Bible versus science.'” BEAU: “But what if they've been saying the same thing the whole time?” GREY: “Watch this.” BEAU: “I'm Beau.” GREY: “And I'm Grey.” BEAU AND GREY: “Let's show you something crazy!” GREY: “The Bible says a grateful heart is good medicine. Proverbs 17:22.” BEAU: “Science says gratitude reduces depression and improves mental health.” GREY:  “Be transformed by the renewing of your mind. That's Romans 12:2.” BEAU: “Science calls that, here we go, neuroplasticity. It means your brain can literally change.” GREY: “Jesus said some things only come from prayer and fasting. That's Matthew 17:21.” BEAU: “Science says fasting helps your body heal and reset itself. It's called cellular autophagy. And it actually won the Nobel Prize.” GREY: “But a peaceful heart gives life, but envy rots the bones. That's Proverbs 14:30.” BEAU: “Science shows anger increases disease and stress in your body.” GREY: “Sing and make music for the Lord. That's Ephesians 5:19.” BEAU: “Science says music releases dopamine and reduces stress.” GREY: “So, the Bible isn't outdated.” BEAU: “It's ahead of its time.” GREY: “God designed your mind …” BEAU: “and science is just catching up.” GREY AND BEAU: “So, it's not Bible versus science. It's truth confirming truth! ” GREY: “Don't ignore what God already said.” BEAU: “You might be missing what actually works.” Psalm 111:2 says, "Great are the works of the Lord; they are pondered by all who delight in them." Close And that's The Worldview on this Monday, August 24th, in the year of our Lord 2026. Subscribe for free by Spotify, Amazon Music, or by iTunes or email to our unique Christian newscast at www.TheWorldview.com.  Plus, you can get the Generations app through Google Play or The App Store. I'm Adam McManus (Adam@TheWorldview.com). Seize the day for Jesus Christ.

On this week's episode of the RealClearInvestigations Podcast, RCI Editor J. Peder Zane and RCI Senior Reporter James Varney speak with Atlantic writer Tyler Austin Harper about his widely discussed, gracefully written articles – including a piece addressing the recent suicide of Cambridge Professor Jason Arday and another detailing why Harper left his tenure track job at Bates College – reporting how identity politics is undermining academic freedom. On the news round-up, Zane and Varney discuss Wall Street Journal and Free Press articles on the pushback against data centers and AI, a ProPublica piece on some of the problem when public money is used to support private schools and a New Yorker article on a Nobel Prize-winning researcher who sexually abused dozens of the young people from Papua New Guinea that he studied. 00:00 Curriculum and Social Justice Initiatives 03:05 The Pushback Against AI and Data Centers 06:13 The Debate on Academic Freedom and Private Schools 09:08 The Case of Jason Arday and Academic Integrity 12:03 Navigating Cultural Sensitivities in Academia 14:47 The Role of Media in Shaping Academic Discourse 33:02 The Evolution of Academic Discourse 37:04 Marxism and the Academy 41:01 The Assault on Academic Freedom 46:10 The Crisis in the Humanities 50:08 The Value of Humanities in Society 54:31 Navigating Political Discourse in Academia   Articles & Books Discussed in This Podcast: ·       Tyler Austin Harper/Atlantic: The Truths That Failed Jason Arday ·       Tyler Austin Harper/Atlantic: Why I Quit the Tenure Track ·       Tyler Austin Harper/Atlantic: What Is the Mellon Foundation Doing to Higher Education? ·       “Words For the Taking: The Hunt for a Plagiarist” by Neal Bowers ·       Wall Street Journal: The 'Country Hicks' Who Refused $26M from AI Data Center ·       Free Press: A City's War on Surveillance Cameras ·       ProPublica: Private Voucher Schools Face Little Accountability ·       The New Yorker: The Nobel Laureate Who Experimented on Children   Sign up for the RealClearInvestigations Newsletter. Watch each episode on the RealClearPolitics YouTube ChannelContact us with your thoughts and feedback: jpederzane@realclearinvestigations.com

What if one of cancer's greatest vulnerabilities is hiding in plain sight?On this episode of Sugar Crush: The Rest of the Story, Dr. Rick Jacoby tackles one of the biggest subjects of the series: cancer and metabolism—and explores the long history behind scientists' understanding of how many cancer cells use glucose.The journey begins nearly a century ago with Nobel Prize-winning scientist Otto Warburg, whose research revealed that cancer cells frequently rely heavily on glucose metabolism even when oxygen is available—a phenomenon that became known as the Warburg effect. Dr. Rick then brings the conversation forward to the work of Thomas Seyfried and the modern debate over viewing cancer, at least in part, through a metabolic lens. But Dr. Rick takes the discussion in his own direction.Drawing on decades of experience with diabetic neuropathy, nerve decompression and microsurgery, he connects the Double Crush Theory to his own broader Global Compression Theory and introduces the concept of the “estuary”—areas where he believes impaired circulation, inflammation, metabolic dysfunction and tissue damage may converge.Using the Mississippi River's dead zone as his metaphor, Dr. Rick asks whether similar biological “dead zones” could develop throughout the body—and whether those environments might contribute to disease.

What if one of the most intriguing molecules in longevity research began with something that looks surprisingly simple—a tiny carbon structure shaped like a soccer ball? In this episode of Light Body Radio, Dr. Lara May welcomes research engineer, inventor, author, and longevity advocate Chris Burres to explore the fascinating world of C60 and ESS60, two terms increasingly appearing in conversations about healthy aging, biohacking, and longevity science. Chris takes us back to the discovery of C60, a molecule composed of 60 carbon atoms arranged in a spherical structure. Discovered in 1985, C60 became the subject of groundbreaking scientific research and ultimately contributed to a Nobel Prize-winning discovery. The conversation also explores the research surrounding C60 and longevity, including a study in which a particular formulation was associated with significantly extended lifespan in laboratory rats. Chris emphasizes an important distinction: these findings were observed in animal research and should not be interpreted as proof of lifespan extension in humans. Dr. Lara and Chris also break down what C60 actually is, how ESS60 differs from industrial C60, why processing matters, and how researchers have been studying these unique carbon molecules for decades. Whether you're interested in longevity science, biohacking, healthy aging, emerging wellness research, or simply curious about one of chemistry's most fascinating molecules, this episode offers an accessible introduction to the science behind C60 and ESS60. Tune in for a thought-provoking conversation about scientific discovery, longevity research, and what we still have to learn about the possibilities of living younger and better. © Light Body Radio-Podcast, 2026. All rights reserved. This podcast features background music by ScottHolmes Music. We have obtained the necessary licenses for the use of this music. Our license was renewed on May 7, 2024, and we have been using ScottHolmes Music since 2017. Unauthorized use or distribution of this podcast, including but not limited to the background music, is strictly prohibited and may result in legal action. For more information or to request permissions, please contact scott@scottholmesmusic.com.

Try TrueDark glasses: https://truedark.comTry Danger Coffee: https://dangercoffee.com/discount/davetubeTry Suppgrade Labs: https://shopsuppgradelabs.com/Try Longevity Gummies: https://www.timeline.com/partners/dave-aspreyMost people see the physical decline of aging as an inevitable process that can only be managed with medications, but this video cuts through that assumption by explaining that the smartest minds in science, including multiple Nobel Prize winners, have identified a single root cause of aging tied to a specific molecule called NAD+. You'll also learn:Why your body actively destroys NAD+ faster as you age, and why that's the real driver of fatigue and brain fogHow the gap between NAD+ production and destruction widens every year, quietly draining your cellular energyThe early signs your NAD+ levels are already too low, from slow-healing skin to workouts that take days to recover fromWhat actually flips your body's NAD+ production back on, and the exact order of diet, fasting, exercise, and supplementation that makes it workTry Danger Creatine: https://dangercoffee.com/products/danger-creatineTry TrueDark glasses: https://truedark.comTry Danger Coffee: https://dangercoffee.com/discount/davetubeTry Suppgrade Labs: https://shopsuppgradelabs.com/Thank you to our sponsors!ECHO Water | Go to http://echowater.com/daveand use code DAVE10 for 10% off your ECHO Flask.Viome | Check it out at viome.com and use code 10DAVE for 10% off. It's time to stop guessing and start knowing your body.iRestore | Reverse hair loss at www.irestore.com/DAVE and get exclusive savings on the iRestore Elite, use code DAVETimestamps:00:00 – The Aging Molecule01:03 – The War Within Your Cells03:28 – Builder vs. Demolition Crew04:34 – NAD+ and Brain Function06:00 – Root Cause Over Symptoms08:31 – Fixing the Imbalance10:10 – Fasting for NAD+11:30 – The REHIT Protocol12:37 – The Supplementation StepConnect with Dave Asprey!Website: https://daveasprey.comTikTok: https://www.tiktok.com/@daveaspreyofficialInstagram: https://www.instagram.com/dave.asprey/Facebook: https://www.facebook.com/Daveaspreyofficial/X: https://x.com/daveaspreyYouTube: https://www.youtube.com/c/daveaspreybprThe Human Upgrade Podcast: https://www.instagram.com/TheHumanUpgradePodcast/ https://m.facebook.com/Thehumanupgrade/Dave Asprey's BEYOND Conference: https://beyondconference.com/Dave Asprey's New Book - Heavily Meditated: https://daveasprey.com/heavily-meditated/Dave's favorite supplements: https://www.shopsuppgradelabs.com/discount/DAVE15Upgrade Labs: https://upgradelabs.com40 Years of Zen: https://40yearsofzen.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Today on the show, Trump cut short military drills between the US and South Korea this week, saying that they sent the wrong signal to the "unthreatening and respectful" North Korea. Fareed asks former NSA official and scholar Victor Cha if Trump is throwing over one longtime US ally to court a rogue state...again? Then, China spends a fraction of what the US does on data centers and lacks the most advanced AI chips. Why, then, does its AI industry appear to be catching up, and rivaling Silicon Valley? Fareed discusses with Evan Osnos, staff writer at the New Yorker. Later, Fareed speaks to climate scientist Katharine Hayhoe about this summer of record heat and raging wildfires in many parts of the world, and climate change's role in all of it. Finally, the V-Dem Institute's latest Democracy Report has downgraded the US from a liberal democracy to an electoral democracy. Why is American democracy in decline? Fareed asks Nobel Prize winning economist Daron Acemoglu. GUESTS: Victor Cha (@VictorDCha); Evan Osnos (@eosnos); Katharine Hayhoe (@KHayhoe); Daron Acemoglu (@DAcemogluMIT) Learn more about your ad choices. Visit podcastchoices.com/adchoices

If you've ever wondered whether there's a practical route to more consistent energy, deeper sleep, and improved well-being in midlife, you're not alone. So many women in their 40s, 50s, and 60s are searching for grounded ways to support their health, from hormones to brain fog to the daily realities of menopause symptoms. Today's episode explores one scientist's journey from research lab to real-world solutions, centered on a Nobel Prize-winning molecule and the hard-earned lessons behind it. Chris Burris, mechanical engineer, entrepreneur, and author of "Live Longer and Better," steps out from behind the lab bench to offer a candid, evidence-based look at the daily realities of longevity, sleep, headaches, and navigating the supplement industry. His experience spans decades of manufacturing, product safety, and the often-overlooked realities women face as hormone shifts and sleepless nights arrive. This conversation unpacks what's possible with science, what still demands more answers, and how the right information can lead to meaningful day-to-day change. By listening, you'll learn: • What a Nobel Prize-winning molecule actually is, how it works in the body, and why it matters for women in midlife • How sleep, energy, and hormone balance are connected and what the latest research reveals about practical steps forward • Why so many midlife women experience new headaches or fatigue, and how to approach these changes with clarity, not confusion • Real-life insight into the supplement industry, including quality concerns, what to look for, and how to make informed choices • How scientific discovery translates (or doesn't) to trustworthy options for women navigating menopause and perimenopause This episode delivers clarity, possibility, and validation for any woman feeling stuck between headlines, hormone shifts, and a changing body. You'll walk away with a deeper understanding grounded in science and lived experience about supporting your energy, sleep, and long-term health in ways that feel both reasonable and real. Resources: Episode website: https://www.natalietysdal.com/post/menopause-headaches-sleep https://myvitalc.com/nataliet https://www.natalietysdal.com https://www.instagram.com/ntysdal https://www.tiktok.com/@ntysdal https://www.facebook.com/NatalieTysdal

Send us Fan MailOnce upon a time, people blamed tuberculosis on vampires.Seriously.In this Public Health Is Weird episode, we head to 1892 Rhode Island, where the deaths of several members of one family led to an exhumation, a suspected vampire, and a truly horrifying attempt at a cure. The real culprit was tuberculosis. We explore the strange connection between TB and vampire folklore, the discovery and treatment of tuberculosis, a messy Nobel Prize controversy, and a bigger question: Why do humans reach for supernatural explanations when something frightening happens that we can't explain? Vampires, tuberculosis, human psychology, and somehow, Dr. Eeks' great grandfather and groundhog grease.Welcome to Public Health Is Weird. Be sure to check out the other Public Health is Weird episodes too! Work with Eeks? Perhaps you are a good match. Keep Causes or Cures Ad-Free with Listener SupportYou can contact Dr. Eeks at bloomingwellness.com.Follow Eeks on Instagram here.Follow on X. Or Facebook here.On Youtube.Or TikTok.SUBSCRIBE to the Eeks Weekly here! (the bits not posted on socia media)Sources used in the podcast are listed below and in my blog here. Food for the Dead, by Michael BellMysterious, medieval child vampire, The IndependentConsumptive Chic, by Carolyn DaySmithsonian Magazine, Kat Eschner, March, 2017CDC MMWR, 1982Streptomycin, American Chemical Society, 2014World's Top Infectious Killer, Science Alert, 2025Support the show

This Friday, I had to play back an all-time favorite. I was honored and grateful to bring you a conversation that I recorded in person with Gurudev Sri Sri Ravi Shankar at the Art of Living Center in Los Angeles. Gurudev Sri Sri Ravi Shankar is a leader of an international movement with more than 50 million followers who do breathwork every single morning. He has worked for 40 years as a global ambassador for peace at the highest levels. I did his breathwork exercises every morning without fail for five years while fixing my mind, body and nervous system, and I have a huge amount of respect for his work in the world. It was a joy to chat with him in person and his childlike curiosity, enthusiasm for teaching, and deep understanding of the world are echoed in every word of this episode. Gurudev is a true visionary, so I am grateful that he set aside time to talk with me so I could share his wisdom with you. Thank you to our sponsors! -Qualia | If you want to take the guesswork out of maintaining high NAD+ levels as you age, go to www.qualialife.com/daveNAD to get clinically proven Qualia NAD+ backed by a 100 day money back guarantee and code DAVENAD at checkout gets you an extra 15% off.-Essentia | Go to https://myessentia.com/dave and use code DAVE for $100 off The Dave Asprey Upgrade.-iRestore | Reverse hair loss at www.irestore.com/DAVE and get exclusive savings on the iRestore Elite, use code DAVEDave Asprey is a four-time New York Times bestselling author, founder of Bulletproof Coffee, and the father of biohacking. With over 1,000 interviews and 1 million monthly listeners, The Human Upgrade brings you the knowledge to take control of your biology, extend your longevity, and optimize every system in your body and mind. Each episode delivers cutting-edge insights inhealth, performance, neuroscience, supplements, nutrition, biohacking, emotional intelligence, and conscious living. New episodes are released every Tuesday, Thursday, Friday, and Sunday (BONUS). Dave asks the questions no one else will and gives you real tools to become stronger, smarter, and more resilient. Keywords: Sri Sri Ravi Shankar, Dave Asprey, Art of Living, Sudarshan Kriya, breathwork, pranayama, meditation, non-dual consciousness, gut feeling intuition, biohacking, quantum physics consciousness, Nobel Prize physics, curiosity and childlike wonder, skepticism vs cynicism, scientific temper, dogmatism, veterans PTSD breathwork, SKY campus happiness program, violence prevention, community building, dating apps romance, Elon Musk Mars, computer chip brain implant, duality and consciousness, equanimity, ego and inner voice, spirituality and science, yoga philosophy, guru interview, positive energy radiation, transcending fear Resources: • Get My 2026 Clean Nicotine Roadmap | Enroll for free at https://daveasprey.com/2026-clean-nicotine-roadmap/ • Dave Asprey's Latest News | Go to https://daveasprey.com/ to join Inside Track today. • Danger Coffee: https://dangercoffee.com/discount/dave15? • My Daily Supplements: SuppGrade Labs (15% Off) • Favorite Blue Light Blocking Glasses: TrueDark (15% Off) • Dave Asprey's BEYOND Conference: https://beyondconference.com • Dave Asprey's New Book – Heavily Meditated: https://daveasprey.com/heavily-meditated • Join My Substack (Live Access To Podcast Recordings): https://substack.daveasprey.com/ • Upgrade Labs: https://upgradelabs.com Timestamps: 00:00 - Trailer01:11 - Curiosity & Skepticism03:42 - Reality, Physics & Illusion06:34 - Non-Dual Consciousness08:37 - Origins of Breathwork13:36 - Trusting Your Gut Feeling14:28 - Violence, Peace & Veterans19:54 - Romance & Dating Apps23:44 - Science Behind Meditation27:27 - Fear of the UnknownSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

When we first dicsussed the Summer of Simulative AI in 2024 we knew it would be a brief summer, but it has recently come back with a vengeance with SimGym in April and now Simile AI's $2B Series B, backed by GreenOaks and Index Ventures with prominent backers like Fei-Fei Li and Andrej Karpathy, running tens of millions of simulations for Fortune 100 clients like CVS and 85–99% accuracy vs human focus groups. Time to catch up on why this Second Summer of simulation is working!From creating Smallville, the landmark 2023 paper on Generative Agents that showed AI characters could remember, plan, socialize, and develop emergent behaviors, to now building foundation models of human behavior, Joon Sung Park is trying to answer a much bigger question: what if we could simulate the world before making decisions in it? In this episode, the Simile co-founder and CEO joins us to unpack the path from generative agents to digital twins, why today's frontier models still fail to capture how humans actually behave, and what it would take to eventually simulate all 8 billion people on Earth.We go deep on Simile's approach to modeling human behavior: long-form interviews, observational and transaction data, randomized controlled trials, population-level and individual-level models, and post-training on the causal mechanisms behind why people make decisions. Joon explains how his research created digital twins that reproduced human behavior and attitudes 85% as accurately as people reproduced their own responses, why models optimized to be rational can be bad simulations of irrational humans, and why understanding “social physics” may require changing model weights rather than simply prompting frontier LLMs.We also explore the much larger ambition behind simulation: testing products and policies before deploying them, finding counterintuitive paths toward desired outcomes, modeling emergent behavior across entire societies, and potentially tackling problems like climate change, democratic instability, and UBI. Joon reflects on scaling laws for simulation, the economics of data-center-scale simulated worlds, the connection to Thomas Schelling and psychohistory, why simulation is surprisingly similar to painting, and whether we might already be living in one.We discuss:* How Smallville and Generative Agents led to Simile* Why Joon's team asked: “What if we can just recreate the world that we live in?”* Why useful personal agents require deep models of their users* Memory architectures, Markdown files, and the limits of prompting* “Social physics” and behavioral foundation models* Why web data captures what people say more than what they actually do* Interviews, transactions, observational data, and randomized controlled trials* Why predicting the future matters less than understanding how to shape it* How Simile creates representative simulated populations* Simulation versus prediction and the connection to Foundation's psychohistory* How to evaluate simulations instead of simply stacking LLM hallucinations* Creating digital twins of 1,000 real people and reaching 85% behavioral accuracy* Why frontier models can struggle to reproduce real human behavior* Why good simulations need to reproduce human biases and mistakes* Post-training models on randomized controlled trials* Population-level versus individual-level simulation* Scaling laws for human simulation* The long-term ambition to simulate all 8 billion people on Earth* Whether simulations could help solve climate change or detect collapsing democracy* Thomas Schelling and the history of agent-based modeling* Why future simulations could require an entire data center* Multi-agent simulations and what happens when simulated people interact* Replacing expensive human panels with synthetic populations* Why market research is only the starting point for simulation* Why Joon sees simulation as surprisingly similar to painting* Using simulation to study questions like UBI* Whether we are already living in a simulation* Why AGI and simulation may be the twin technologies of advanced civilizationsJoon Sung Park* LinkedIn: https://www.linkedin.com/in/joonspark* X: https://x.com/joon_s_pk* Website: https://www.joonsungpark.com* Simile: https://www.simile.comTimestamps00:00:00 Introduction and Joon's Path from Art to AI00:01:46 Smallville, Generative Agents, and the Origins of Simulation00:05:03 “Let's Just Create a World” and the Future of Personal Agents00:09:53 Social Physics and Behavioral Foundation Models00:14:08 Prediction vs. Simulation: How Do You Shape the Future?00:16:59 How Simile Models Real People and Populations00:25:35 Evaluating Simulations, Digital Twins, and 85% Accuracy00:30:23 Post-Training Models to Reproduce Human Behavior00:40:04 Scaling Laws and Simulating 8 Billion People00:43:10 From Schelling to Society-Scale Agent Simulations00:46:13 The Cost and Economics of Simulating the World00:52:05 Real-World Use Cases, Synthetic Populations, and the Market00:57:27 The Future of Simulation, Painting, and UBI01:04:23 Are We Already Living in a Simulation?01:06:08 Building Simile and HiringTranscriptIntroduction: Joon Sung Park, Simile, and the Story So FarVibhu [00:00:00]: Today, we have Joon in the podcast. Excited to kick this one off. Very exciting company. I wanna kick off and ask you the question, talk us through the story of your life. How have you gotten here?Joon [00:00:13]: Yeah, for sure. I'm really excited to be here. A story of my life. So I was born in Korea, and I lived there for a good 11 years or so of my life, and then my family moved to Boston. So we moved when I was 11, and my parents were doctors, so they were going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years at the Boston Children's Hospital. So I grew up there, not too close to tech. I was very much a music and artsy, painting kind of guy.Vibhu [00:00:49]: Painting.Joon [00:00:49]: Exactly. I got into painting a little bit later, in high school, but that's what I used to do. And then I grew up mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania. And I got into more of this tech scene, in college. So I was originally trained to be an artist. I thought that would be my professional career. So it wasn't a hobby. It was like, “Hey, let's make a living out of this.” And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium, and the best medium that we had available today was in computation. So I decided to go deeper into that, and one thing led to another, and we can go deeper into this, but I decided that research was something that I gradually got interested in, and here I am.Smallville, Generative Agents, and the 2023 Breakout PaperSwyx [00:01:46]: So there's a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.Swyx [00:01:58]: Feel free to call back to anything else that you mentioned, but most people would have heard of you from this. Do you have any statistics on how many people have, like, read it? arXiv gives you something, right? Some stats.Joon [00:02:10]: Yeah, it's a good question. How many people have read it, I'm not sure.Joon [00:02:14]: I know we do keep track of citations, and they are going up quite fast.Swyx [00:02:23]: Yeah, Google Scholar has 7,200 citations.Vibhu [00:02:25]: I feel like it made a bigger hit than that, and it was a pretty instrumental paper. It got cited so many times.Swyx [00:02:34]: It is frequently the answer when people ask, “What is the best paper you've read recently?” It's this one.Vibhu [00:02:39]: I thought the memory component was pretty underrated. It was a very good early memory system, and one of the biggest papers.Foundation Models and the Search for Killer ApplicationsJoon [00:02:47]: Yeah, so maybe I can talk a little bit about how this particular paper came together. So when I got into research, it was back in 2020 when I started my PhD program at Stanford, and that was the year, when we were about to get GPT-3 to be available. So we already had GPT-2, and you could sense that there was this new class of models that was just becoming available in the market, and the team got very intrigued. And the general consensus was, “Well, is this model going to be useful for anything?” “It's really strange that these models are not trained to do any particular task.” But we decided to take a bet. So a large group of scholars at Stanford, and it was led by one of my co-founders, Percy Liang, and we came togetherSwyx [00:03:35]: Who coined foundation models.Joon [00:03:36]: Who coined the term foundation models. We wrote this paper, where that term came from called Opportunities and Risks of Foundation Models. And during that process, really the thing that I started to think deeply about was, here is a model that is fundamentally new in our ecosystem. The reason why this was new was it wasn't, again, trained to do anything in particular, but its premise was it could do anything and everything. It was like a stem cell, if you were to take a biology analogy. And I got really interested in this idea that, well, if we were to really think about what are the killer applications that this particular technology would enable, what would that be? Many of my colleagues were using this for simple classification, simple generations. Interesting that these models can do that, but from an interaction perspective, not that interesting. We've known how to do that for many decades. And what we came down to was these models are trained on this very broad data from the web, right? So these are human behavioral data. It's social media, Wikipedia, all these data. So if you poke at the right angle, then you could see human behavior that would just pop out that's quite realistic, and we've never seen that before.The Time Machine Game and Recreating the WorldJoon [00:04:45]: So that got us really interested. The exercise that we decided to do, with this particular group of colleagues, Michael Bernstein, Percy Liang, and myself, who ended up becoming my co-founder at Simile, we sat down and we played this game that we call the time machine game.Joon [00:05:03]: Imagine we were to get on a time machine and fast-forward 10 years and look back. What would have been the single application that will have mattered that would be the most interesting and inspiring? And when we thought, “Well, what if we can just recreate the world that we live in?” it's really hard to get more ambitious than that. Like, let's just create a world.Joon [00:05:24]: And that's where we started. And initially, we had this paper that was a precursor to the generative agents paper called Social Simulacra.Swyx [00:05:32]: Before you go further, were there other candidates for the most ambitious thing in the time machine exercise? What was number two or number three?Personal Agents, User Models, and Why Simulation Came FirstJoon [00:05:44]: There is a close second that we were considering, which ended up becoming more of these automation tools, especially the vision around really personalized agents that would do things for you.Swyx [00:05:59]: That's also happening.Joon [00:06:00]: It's also happening. But it was interesting for us, right, in that the reason why, we decided to go with the idea of simulation, one, I was a huge science fiction nerd, and this idea of creating simulation, I was personally really just fascinated. I loved the idea. It's really cool to see, like, a game town like this and just see these agents live in it. But at the same time, my bet was if you were to create a really amazing personal assistant out of this technology, what you need first is an amazing model of your users. So I told a model, “Hey, can you go buy late dinner for me?” And it orders Hawaiian pizza, and I do not like pineapples on my pizza. Then it totally failed. The way for it to not make that mistake is only by having a deep understanding of who I am. And I gave a very simple and dumb example here, but you can imagine how this core understanding of people is instrumental. This is how, if we have our family and closest friends, they have a good mental model of who we are. That's the basis of our social connection. So our bet also was this technology around simulation, creating accurate representation of people ought to precede the more complex agents that would automate the world that we live in. So that was the bet. But that was a very close second, and I'm still very much fascinated by it. I think there's a lot of interesting work that's going around. My hot take here, though, is I don't think we've seen a true personal assistant that's useful, in ways that meet the ambition of that particular line of work. I think there are early applications that are interesting, and if you talk to even ChatGPT nowadays or Claude, they know a lot about us. So a lot of the generation it's doing, I do think it's much more tailored, but I think the ambition is quite large in that field, and I don't think we quite have all the right ingredients just yet.Swyx [00:08:01]: So OpenClaw and these personal agents, what do you want to see from them that they don't currently have?Memory, Markdown, and the Limits of PromptingJoon [00:08:09]: I do think it's slowly getting there, but I do generally want them to have much deeper understanding of the person. Right now, you look at the models. OpenClaw, what it's leveraging is a Markdown file, and I think it's quite clever, right? So if you look at the generative agents paper, this was the same intuition that we had, where initially when we were creating the memory architecture for the generative agents, and, like, this is, like, back in 2022, so we didn't really quite have the idea of even agentive architecture or the term agent. But the intuition that we shared with some of the work that's coming out today was we initially thought, “Well, do we want to make the memory into, let's say, knowledge graph? Do we want to train a bespoke model?” All of these things. And what we decided to do was, “No. Just forget about all this.” These language models are quite good at modeling text and understanding and reasoning about text. So just put everything in a Markdown file or a text file. You're done. I thought that was quite interesting that we could do that, and there's a lot of strength in doing that. But also, there are limitations. It's the way you retrieve and make sense of data that's extremely large, it takes a lot of work. So I think that technology is getting better. I also do, however, think, there are certain things you just cannot shape just by prompting the model. So to some degree, you do need to touch the parameters of the model itself. So there is this work that I do think does need to happen, and it is happening. The question is, how far can we take it? How do we source data, and how do you also create an ecosystem where people are continuously feeding data to this model so it's learning about you?Vibhu [00:09:50]: What's the intuition between why you need to do it in the model?Social Physics and Behavior Foundation ModelsJoon [00:09:53]: My intuition behind the actual when do you train or even post-train a model versus just prompt a model is if the model has to learn the underlying physics of the world that it's operating in. So it has to learn new social physics. The places where it doesn't have to train are the places where it already has the physics. We trust the physics. It already has the base statistics, but it's just trying to react to an environment. Then I think you can just prompt your way into getting the actions out of it. I don't think the models that are out in the open have yet learned the complete mapping of social physics of humanity. This is one of the core theses of Simile, right? And one of the core reasons why that is the case is if you look at the data that the model was trained on, these models were trained on the web data, like, whatever was available on the web. And these are really interesting data sets, but they are fundamentally the self-exposed attitudinal data with some behavior data that's sprinkled around here and there. And it has yet to learn the really deep behavioral nature of people, not just what people say they do online, but what they do in real life. And this is one of what I would consider to be the dark knowledge of humanity that we haven't quite captured. And it's these data that would also need to get factored into the model creation.Vibhu [00:11:21]: You call it behavior foundation model.Vibhu [00:11:23]: There's a good one-liner here, but outside of that, what type of data do you need? What are you changing on the model level? How do you go about modeling, doing a behavior foundation model?The Three Data Buckets: Interviews, Behavior, and CausalityJoon [00:11:35]: We think about data in three buckets. So one bucket is interview data. It's quite interesting. Rich qualitative data is interesting. It's not behavioral, but we would literally ask people, “Hey, tell me the story of your life.”Vibhu [00:11:53]: It's just what we're doing here exactly.Joon [00:11:54]: The question that you all asked at the beginning of this interview literally is the question we also ask. And we ask our participants to go a little bit deeper, than how far I went. Maybe I can give more of my life story in lieu of this. But the reason why that data is interesting is by learning about this very long-tail information about people, you get a lot of texture around this model, like, this person as a model. So even understanding their childhood memory or even their trauma, their first love, these things, quite informative in ways that's really hard to predict. So that's one. Then there are two tranches of what I would consider to be the behavioral data. One kind of behavioral data is observational. So these might be like transaction data, or these might be data that you can get by scraping the web, right? So you can imagine why these data sets would be interesting, right, because they give you the base statistics of people's behavior.Joon [00:12:55]: But then there is the last category of data, that I personally think is perhaps the most important, which is the data that describes the causal mechanism, the whys of people. Some of this is covered by the interview data, the qualitative, because people talk about why they made certain decisions. But really, where you get to see the most behavioral aspect of this is in randomized controlled trials, like RCTs. Imagine you have the same setup, but you have a few different variables that you are trying to tweak. Can you get realistic human behavior out of it in ways where, imagine you had this particular option. Imagine you're even trying to choose whether you're going to drink coffee or not. The day you drink coffee versus the day you didn't drink coffee, does your behavior change? That's a data set that describes a causal mechanism. This is quite important in modeling people. The reason why this is important is oftentimes when people come to us, or not just to us, but the reason why people are interested in simulation isn't because they want to predict the future. If you're trying to win against the stock market, predicting the future is interesting.Prediction vs. Simulation: Shaping the FutureJoon [00:14:08]: But most people, most decision-makers, what they want to know is, how can we shape the future? It doesn't really help you to hear that your sales are going to tank in two quarters. They're just gonna say, “Wow, that sucks.” What they want to know is, well, what do we need to do now to avoid that future? That's the causal mechanism. And this is also very hard data to come by, right, because the world is our ground truth, but it happens once. So in a very controlled setup where everything is equal except for one variable, this kind of data set rarely happens. So this is a reason why this data set is both hard to come by and quite important if you're trying to model human behavior.Swyx [00:14:50]: So behavior, I think, is the hardest data set to acquire. What is out there? What is even possible? You're not going to know a lot of details about my life. I don't even have data for myself on my own health or habits, and I just don't log everything. So how can you have that data?Joon [00:15:14]: So we run a lot of randomized controlled trials.Swyx [00:15:17]: But you put people in the lab, they watch them sleep, or what?Joon [00:15:20]: We do care a lot about the consent process. People know that we invite them to be a member of this community to both share data and have themselves represented in different forms. But we bring a lot of people to the lab, or virtual lab, where we design experiments that would pose them real behavioral decisions. And often in these experimental setups, what makes the difference between what is attitudinal versus behavioral is whether the stake in your decision is real. That's ultimately what makes it behavioral. So in these setups, we are inspired by our colleagues in social sciences, psychology, and so forth. So when they run studies, the techniques they utilize is imagine there's an online store that you're inviting people to come by. Then whatever they purchase in this experiment, they actually get that item delivered. Like, these are the things that make the stakes real. So we run a lot of these experiments, and we also do partner with firms. Right now, we also have customers who are quite excited to at least give us a glimpse of the behaviors that their users exhibit so that we can get a little bit deeper understanding of how people behave in these different platforms.How Customers Use Simile: Populations, Queries, and ExperimentsVibhu [00:16:39]: I think on the customer side, they have a lot of data about their users, who has bought. They have the action data.Vibhu [00:16:47]: Can you walk us through an example of what someone comes to you for? What questions would they want solved? Do you customize a model for them? Do you have something off the shelf? What does that look like?Joon [00:16:59]: Today, when people leverage our models, it's often to better understand the population of their interest. So usually, the start of the relationship, we come together and hear about what population they want us to model, right? So it might be that if you're a CPG company that's selling to all of the US, then maybe it's fairly straightforward. You want to model the gen pop of the US. But at the same time, if there is a vertical or if there's a market that they're trying to go into, imagine, they want to better understand, let's say, people in their 20s and 30s living in California. That's a much more specific population. So we hear about this population, and we go recruit these people, with consent, and with incentives, and we collect some of their data and create a model of these people. Then what our product allows you to do is query them. So it can take as input a filter that is a description of the population that you want to talk to, just like the one I just mentioned, and an environment. The environment can literally be survey questions, behavioral experiments, It can be A/B testing. Oftentimes, the core use cases are things like concept testing, to start with. But also, people sometimes want to do focus groups or one of the fun use cases that we also serve is even modeling things like earnings calls for public companies.Joon [00:18:21]: So these are the use cases that we often start with.Swyx [00:18:23]: Concept testing, is that an established term? I've never heard of concept testing.Concept Testing, Gallup, and PoliticsJoon [00:18:27]: Yeah. So it has to do with they have, let's say, different messaging, different products, different ideas.Swyx [00:18:32]: It's like a marketing exercise.Swyx [00:18:33]: Okay, got it. Got it. Politics?Joon [00:18:36]: We do, have a strategic partnership with Gallup, and of course, Gallup is deep into policy space and so forth. Right now, we have not worked deeply with politics, like that area just yet, however.Swyx [00:18:49]: I'm curious if there is demand or if they really would have different needs that somehow fundamentally don't mix with your existing, users or people.Joon [00:19:00]: I think there's certainly demand.Joon [00:19:02]: But we are very much mindful of how this technology gets adopted and the societal impact that we'll end up having with this technology. And I do see politics as an area where a company has to be particularly thoughtful about the way they operate and make impact. So this is where we also want to make sure that we form enough of guardrail and perspective on how to leverage this technology before we go on to serve markets like the politics.Swyx [00:19:29]: I'll give people an example. one of my favorite shows is The West Wing. I don't know if people have watched.Swyx [00:19:34]: One of the key storylines is, like, the president has, multiple sclerosis, but they haven't. they need to figure out how to disclose it. So they run a poll with a fake governor and ask people to respond on the poll,Counterfactuals, Polling, and When Simulation Is UsefulSwyx [00:19:47]: They try to make decisions based on the results of that poll on, like, how well they'll be received, like where, how should we play this?Swyx [00:19:54]: And I'm like, well, I think those counterfactual things, I would use a simulation for this if I could trust it.Joon [00:20:01]: For sure.Joon [00:20:02]: In that show, how'd it go?Swyx [00:20:04]: In that show, it was, like a foregone conclusion. They were like, “We know it's bad. We just don't know how bad.” And then the poll came back. It was like, “It's really bad.” And then they just did it anyway.Joon [00:20:14]: Part of it is to show, right? So you're, you're looking at the ideaSwyx [00:20:17]: Maximizing drama.Joon [00:20:18]: How bad could it be? Oh, it's horrible.Swyx [00:20:20]: And to some extent, I think that is part of the trick of the, or the challenge or with being a customer of yours, which is that if I know it's. if I roughly know and can intuitSwyx [00:20:35]: What the effect is going to be, do I need you? What sensitivity of it, of effect do I need in order to make a decision, right? So for example, if I, my approval rating is 50%Swyx [00:20:48]: And I, they have this negative piece, news item comes out, and it drops to 30.Swyx [00:20:52]: If it drops to 20, if it drops to 40, do I care? No. It, I know it drops. It's negative. So when do I care about simulations?Joon [00:21:01]: You do something that's clearly bad, that's not popular, and people don't like you, like, yeah, it's likeSwyx [00:21:05]: You don't need a simulation.Joon [00:21:07]: Yeah. Well, so there are a couple of things. one is, there are use cases where, like every day, developers, designers, policymakers, marketers, every single day, they create assets. They create new products. And turns out, it's many of the decisions in hindsight is obvious. Yes, of course this is bad, but we still run those studies because understanding the magnitude and understanding how acute something is quite difficult, even if, we feel like, of course, like this makes sense. this is the reason why we make so many mistakes. Like, every time somebody goes online and say something that has huge backlash, you look at that and like, “What an idiot.” However, it's tough. That's one. There's also another aspect here, which is, again, this is the reason why simulation is different from prediction. In simulation, in the ideal case scenario. So what simulation is trying to show is it's trying to show each step of the way or each step that we need to take to get to a certain outcome, right? So in the most advanced simulations, sometimes the next step that we're suggesting might be quite counterintuitive. The analogy that I sometimes give, and I ground it in a more realistic example, but, I, as I mentioned, I'm a huge fan of science fiction, and I don't know how, many of the audience members have read, like, things like the Foundation series by Asimov.Simulation as a Path, Not Just a PredictionSwyx [00:22:37]: Oh, yeah. We've mentioned psychohistory a number of times.Joon [00:22:39]: Okay, fantastic. So I might be, talking to the right crew. If you read Foundation series, literally the first act is there's a group of scientists who have found out that, “Oh, our galactic empire is going to collapse, and we're going to have 30,000 years of unrest.” And they run psychohistory, the simulator that tries to teach them, “Okay, how can we keep this unrest to a 1,000 years?” And they plan this out, and the first step of that plan is to get the scientists who say, “Okay, this is coming,” exiled into this random place in this, galax- galaxy.Swyx [00:23:18]: Terminus.Joon [00:23:19]: Exactly. And that's so counterintuitive. Like, what a strange move that you literally sent the group of scientists who was raising voice around this potential collapse of galactic empire into nowhere. How is that the right first move? Well, it turns out in this particular simulation, that was the move.Joon [00:23:40]: It's these things, right? And the reason why these reasoning is possible is because you're showing the step function or each step that results in a particular outcome. So really what simulation allows you to do in its highest form is you give it not a problem or question, like what would people answer to the survey? That's not what we do. What we tell it is, “Here is a goal that we have. In the context of foundation, we want to keep the unrest to a 1,000 years. What is the path that we need to take now to get to that particular future?” And that's what simulation allows you to do. Now, translating that into real market, imagine you're a automobile company and you're about to release a, EV, and you're trying to understand, well, how do we market EV, to make sure that our stock price goes up? But what if the answer comes down that, well, you can market your EV in XYZ way, but that might change people's perception around the cars that's not EV and make your overall sales to go down. Not very intuitive, especially all you're trying to optimize is EV salesss, and that's the only thing that you're tracking, then that might result in a completely wrong solution, or at least different solution than what you would have expected, whether it's right or wrong.Joon [00:24:57]: That's the power of simulation.Swyx [00:24:58]: For listeners, we covered a similar topic with Mikhail Parakhin from Shopify, where they are working on SimGym. I don't know if he ever talked to you about it. it's very similar.Joon [00:25:07]: ISwyx [00:25:07]: The goal is increased conversion, but then the journey is very unusual.Joon [00:25:12]: Journey is unusual.Swyx [00:25:12]: Yeah. The-- He's trying to look for interventions on a shopping trajectory, which is similar to what you're saying. Like, it's not about the attitudinal, is your word for it.Swyx [00:25:24]: It's about behavior.Joon [00:25:25]: It's about behavior.Swyx [00:25:25]: And that's exactly the difference, right? It's, like, not about the near-term direction about-- but it's more about, like, how do you affect multiple turns of interactions.Vibhu [00:25:35]: You had a good quote at the start about this as well. It's not about people wanting to know the outcome. It's about how they can change it, change the way to get there, something like that. But I wanna take it back to how do we know this is grounded? LikeGrounding and Evaluating Digital TwinsVibhu [00:25:47]: How do you run evals? How do you test that simulations come through? if I was to do the same thing that you described with, say, your favorite LLM, Opus, GPT-5.6, have some agent to map out these thingsVibhu [00:26:02]: How different are the answers we would get if I give it the same goal, the same objective, make a decent system? You're saying that you need to change the model weight. You have your own solution to this. But how far off are we, and how do you check if it's grounded? you have some interesting stuff on your site that points to how you run real evals, but if you could take us through that side. I think that's one of the big concerns that people have. They're like, “LLMs hallucinate.”Vibhu [00:26:27]: “You're just hallucinating layer after layer,” right?Joon [00:26:30]: The way we do this, and this is the paper that we worked on after the generative agents paper that really became the, at least for Simile and also the field of simulation and synthetic panels, really became the foundation. Yeah, this is the paper. the paper is called Generative Agent Simulations of 1000 People. Here's what we've done. For this paper, we brought 1,000 people that's representatively sampled from the US to a virtual lab. And what we have done was we spent two hours collecting fairly wide-ranging data. In this particular study, we focused a lot on this interview data, that was, whose script was taken from this project called American Voices Project. And then we would also pair that with a lot of behavior data and so forth, whatever we can collect within two hours. And then we would send these people away for a couple of weeks. And during that time, I would use this data to create their digital twins. And I would bring the humans, participants back after 2 weeks and have them complete a battery of surveys, experiments, behavior studies. So we have the list here, which included things like behavioral economics games. We would run literally, like, Big Five personality test, General Social Survey. We would also go ahead and run the randomized controlled trials that were published on PNAS. And we would have their digital twins predict how the source individuals would have acted in these studies and surveys. And this is where we could replicate people's behaviors and attitudes 85 percent as accurately as people would replicate their own. So that was the first really paper that gave this validated results that we can model individuals in an accurate way. And what we ended up finding now, of course, in AI space, so this paper came out at the end of 2024. AI space, a year and a half, 2 years, that's a lifetime.85% Accuracy and Why Frontier Models Miss Human BehaviorSwyx [00:28:24]: Yeah. Just, for listeners who are not seeing the YouTube, I just wanna say, like, the headline figure is 85 percent accuracy, like, which is a big improvement over all the otherSwyx [00:28:34]: Methods that you showed.Joon [00:28:36]: But the part that was particularly striking to us, especially as we improved this technology even further, was the generative AI models like ChatGPT, Claude that's coming out, it does give you the right foundation. However, what they do not consider is the true attitudinal and behavioral aspect of people, especially in the population that you care about. So what these models are really good at today is they're trying to become the super rational, objective machines, right? So you go get their data from places like Mercor, Scale. You talk to professional programmers, scientists to create model that's amazing at reasoning. That's what they do. Simile doesn't care about any of this. The models that we're talking about here, what we're trying to create are models that are as dumb as I am, right? So if I make some mistakes, the model has to make the same mistake.Swyx [00:29:34]: Oh, that's very hard.Joon [00:29:35]: That's very hard.Swyx [00:29:36]: You're solving Murphy's paradox.Joon [00:29:37]: That's exactly. And this is a completely different data and training objective. This is also where we see quite a bit of discrepancy in the performance in human behavior prediction between the frontier models, Simile's model, and the models being created in this space, where in some cases, the model performance of frontier models go all the way down to 20, 30 percent, especially if you go into that more niche population on topics that our customers would care about. On more gen pop, it might be around 50 to 60 percent. So it's not very robust. Like, you wouldn't want to make your decision off of these and these findings. If you can bring that up to 85 percent, that is ultimately what people end up getting very excited about.Swyx [00:30:20]: Yeah. Do we wanna keep going on the paper, routes?Joon [00:30:23]: Yeah, for sure. So the last one, was an interesting one. So this, paper was the follow-up paper that we had, to the 1000 agents paper, where the idea was now can we augment the models even further and post-train a model based on a lot of randomized controlled trials? So this was an interesting one. The data is always the most interesting part of modeling in many ways. The data that we got here was there's this, there's this platform called Open Science Framework. So some, the audience might be familiar with this. And there has been, especially in the social sciences over the past 5 years or so, there has been this concern around replicability of studies. And so it was a bit of a crisis, the scientists acknowledged, where we rerun the study and we don't see the same finding.Post-Training on RCTs and Replication StudiesVibhu [00:31:12]: Oof.Joon [00:31:12]: It's tough. And the reason why it's there-- that was often the case was there's this survival bias where the papers that get published often need to maintain what we call the value of less than 0.05 in the experiments that we ran. That suggests that only-- there's only 5% chance that the results that we saw is false positive. But the tricky part was all the papers that were not published, and there's still a 5% chance that whatever we publish is totally just randomly generated. Like, there's a 5% chance that, hey, this effect is not real, but it just happened to be real because of the sampling bias. So because of that, what scientists started to do was they started to register their studies. So before running an experiment, they would go to this platform and say, “Here is the data. Here is the population that we're collecting, and here's the hypotheses.” And they would just say, “Here is our hypothesis.” Like, “This is what we believe.” And you cannot retroactively change those hypotheses. This is what gives us more scientific statistical confidence that whatever effect that you ended up seeing is true. So that ended up creating this really interesting platform where there's one platform that has now contains tens of thousands of real-world experiments and hypotheses. And a lot of these are really high-quality, like, professionally designed behavior studies and random- randomized controlled trials. So we got the data and the studies from this platform and used that to make a point. And this particular, model is not, something that we're serving commercially because this was a part of the open science. But this particular data set, helped us make a point that by collecting a lot of these randomized controlled trials, that are really well-designed, we can make significant improvement in model's capability to predict human behaviors. So that's what this paper was about.Vibhu [00:33:10]: Is this stuff done on a individual level? Like, do I need to tune the model per individual, per company? Is there foundation model changes and then some slight post-training? Anything you can share there?Population-Level vs. Individual-Level ModelsJoon [00:33:21]: So this particular model was trained. the data we had at the level of individuals, but this particular model was trained. We experimented with both. And this is what we end up doing at Simile too. We always train 2, distinct model. One is what we call the population-level model. The other is what we call the individual-level model. And both take very similar input, which is the description of a subpopulation or individual and a stimuli. In this particular work, we've done the same. Here, the results that we are reporting are much more geared towards individuals because we do think that is a harder task in many ways, but that's what we have done.Vibhu [00:34:02]: You seen anything on the questions that humans can solve that models can't solve? So likeHuman Biases, Mundane Choices, and What Models MissVibhu [00:34:09]: Currently, it's, I live 5 minutes walk away from a car wash. It's a 10-minute drive. Should I walk or drive?Joon [00:34:16]: Huh.Vibhu [00:34:16]: The model will say, “Oh, walk to the car wash.” And, you don't have your car.Vibhu [00:34:20]: Is anything like this a problem in simulation? You would assume, like, very simple for human to think about, but if the model is saying you should walk to the car wash, anything here?Joon [00:34:32]: It's less, what can we solve, but I think it's more about what biases or mistakes do people make that models miss. Like, imagine that you are, like the. When I was still at Stanford, I lived in Palo Alto. So it's about, I would say, 40-minute walk from the campus. You ask the model, “Okay, let's go home. What can I, what can I do?” It would likely call an Uber or, give me, the bus time. But for the longest time, I really liked walking back. And the reason why I wanted to do that was not for efficiency. It really helped me think. And I like to walk for, half an hour or 40 minutes or so a day, where I just get to, just think about ideas, research, just get lost in my thoughts. That's very human activity. Unless the model has seen that and understands the importance of that activity, it would miss these kinds of features. So that I think, is fundamentally what we're trying to model. Like, what is fundamentally human might not be the most efficient thing to do, might not be the right thing to do, but things that make us who we are.Swyx [00:35:43]: I'm curious if, there are some data sets that you really want that would materially help you. One version of this may be interesting, which is more valuable to you to acquire as a data set, all of LinkedIn, all of Twitter, all of Facebook?What Data Matters: Social Media, Transactions, and FacebookJoon [00:35:57]: It's a little bit hard to rank, in part because, there's, there's this product saying where no feedback is wrong because it teaches you something about your users. Doesn't matter what feedback.Joon [00:36:11]: I think it's a little bit like that.Swyx [00:36:12]: So just whatever is bigger.Vibhu [00:36:13]: What about a different domain? Say it was. What about all of Amazon data?Joon [00:36:17]: Oh, yeah.Vibhu [00:36:18]: Shopping data, right?Joon [00:36:18]: Shopping data. So Amazon data is interesting in that it's very much behavioral, although, like, what people do on social media, you could squint and say that is also behavioral. But the transaction data is always interesting. It is also most commonly available, however.Joon [00:36:33]: If we were to look at purely social media, like if you really, if I were, if I had to really pick, Facebook likely is interesting because I do think it is most a default version of people. Because you go to LinkedIn, it's very much professional environment. So people put up their, they have their guards up, right? And that still is interesting because that is true human attitude and behavior, but it is not your base state. you go to Twitter- Twitter, people have their own crazy personas, or depending on who you are. Like, my Twitter profile and, persona is very much, initially was I was very much an academic. “Hey, I'm here to share my studies.” Now, I share, things that's related to Simile. But Facebook is one of those more private space where people just connect with their friends. In that way, I do think it shows you a little bit more about who that person is. So if I had to pick, I'd likely pick, Facebook.Swyx [00:37:30]: Yeah. And you're interested in, like, the whole person and their background and philosophy. I, is it too clinical or too machine learning-oriented to just say this is just ways to inject variance and biases? The broad question, is, like, is this any better than a randomized, like, combinatorial explosion version? So we have a link to the TencentBillion Personas, Synthetic Demographics, and Bespoke DataSwyx [00:37:54]: Billion persona paper, where they did not do any of the groundwork that you are doing.Swyx [00:37:59]: They just did like a cross matrix of here's all the professions in the world, here's all the people, possible backgrounds in the world, do a dot product across all of them, and that's it. That's your prompt for a billion people.Swyx [00:38:12]: This will do something. I don't know if it'll do what you do, but it gets you some way, some percent of the way there.Joon [00:38:18]: So this was an interesting paper. Like, what I admired about this paper when it came out was the scale. And you do gradually want to be able to simulate really large societies and interactions. So the scale is definitely admirable. it is relying heavily on the known statistics that went into training the model. So to the extent that you believe that statistics is correct, this is not a bad way to go about this. But the thesis here, and this is something that we also have seen in the market, like if this works, then we have solved simulation.Joon [00:38:54]: It,Swyx [00:38:55]: Because I survey, like, okay, 5% of the US population is in construction.Swyx [00:39:01]: The other 5% is in medicine, whatever, right? And then you just keep going down the list, and then you do the other side. 5% has, like, the big 5 personalitySwyx [00:39:08]: Of, like, neurotic or whatever. That's it.Joon [00:39:11]: That's it. So if you believe that the underlying data set and the platform that we're leveraging has all the right statistics, then this will have solved it. you're at that point merely retrieving the knowledge that is already embedded in the model, in the model parameters. That's not, unfortunately, what we see, where there is such detailed and also niche knowledge about people that if you just take one example, it might feel very mundane, but it's quite rich when you put together, that you do need to do a lot of bespoke data collection to better understand people. And this is also, I think what makes this particular, job fun, which you want to deeply understand people, and the process of deeply understanding them requires a lot of attention to the details. And you do need to pay attention to and pay respect to the daily lives that people lead.Scaling Simulation: From Thousands to SocietiesVibhu [00:40:04]: I wanna talk about scaling simulation.Vibhu [00:40:07]: So what can't we simulate, what can we simulate, and how does scaling affect this? So how big are the models? What if we go from, 8B, like, couple 100 billionVibhu [00:40:18]: Like billion000 parameters, billion000? Do we get scaling? Any interesting emergence? Like, at a certain scale, at a certain amount of training, you uncover anything unusual and any learnings from that?Joon [00:40:31]: What we are seeing is at Simile, so we do post-train our own model. The thing that we're seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you start to get predictive and predictable gains of the model performance in simulating it, simulating people.Vibhu [00:40:51]: Ooh. We need a scaling law curve.Joon [00:40:52]: It's scaling law. Whenever you find it's a beautiful thing. And we're starting to see the glimpse of it, which is quite exciting. But if you talk about the ambition of simulation as a whole, it's not merely about building a model. It's about building a model, then creating the agents that become the individuals in a much larger ecosystem. So they're creating this multi-agent simulation. Down the line, you want these multi-agent simulation to also live in a very rich environment, right? What we are really trying to get to at that point is, hey, can we create. All right, let's do a time machine game again, and 5 years, 10 years into the future, can we create a simulation of 8 billion people living on Earth? I think that's quite interesting. And that really is the vision. And once you get to that state, the questions that you can help answer for the society also start to change from my perspective. The answers are fundamentally about emergence of the emergent behavior of society and large groups of people.Joon [00:41:53]: So the questions that I get excited by, and maybe this is a stodgy- a bit. I have my, academic side of me.Joon [00:42:01]: And for me, it's questions like, can we help solve climate change? If you look at climate change as a problem space, this is what we, like social scientists would often call it the wicked problems, problem where you have many actors with competing incentives for trying to make a very complex decision and coordinating that coordination decision. Very difficult to really solve in real life, which is also the reason why we couldn't solve it. Can simulation help us solve that? Another one is, can we understand the signals for collapsing democracy, or can we understand or can we uncover the origin story of the monetary system? These are societal questions that we never really had a good way of answering. If we can create simulations of our society, you have to believe that these are the problems that we can solve. So that's really the ambition of this field. And, I also think, yes, I think there's a Nobel Prize to be won there, which wouldn't be surprising. And I think there's some amazing societal impact that we can have to help people make better decisions.Climate Change, Democracy, and Societal SimulationSwyx [00:43:04]: Nobel Prize in economics?Joon [00:43:06]: In economics.Swyx [00:43:06]: Oh, I see. I see. Rooting for you to write that paper.Joon [00:43:10]: One of these days. But, one of the scholars that I was deeply inspired by, When I was coming into the space of simulation, is this scholar, named Thomas Schelling.Schelling, Agent-Based Models, and the Nobel PrizeSwyx [00:43:23]: Schelling point?Joon [00:43:24]: So the canonical example of the work that he's done was he was one of the creators of agent-based modeling. So this was, like, in the 1970s and 80s. It's very early days, but this was truly one of the first exemplars of simulations. And one of the canonical model from that time, and of course many of these simulations are trying to tackle the societal problems that's most relevant for their era, it was called the model of segregation. So racial segregation was a big topic, that, we cared about. And what they've done was they created this grid world where they had red dots and blue dots. And these dots were, back in the day, like, they were the agents, and they had a simple rule that governed their behavior. If certain percentage of your neighbors are of different color and if that goes above certain threshold, then you move to a new location at random.Joon [00:44:21]: One of the striking finding of this paper or this agent-based model was for the longest time, people thought the segregation within society was caused by explicit and overt racism.Joon [00:44:34]: But if you look at this model, people's preference towards living with people of the same color, that preference can be very minute.Joon [00:44:42]: But the very small difference causes the society to segregate completely over time. This was very counterintuitive for a lot of people. And this particular work ended up informing housing policies. Mixed income housing, got really inspired by this work. And Thomas Schelling ends up winning the Nobel Prize for having laid the groundwork for very early versions of simulations. The opportunity that I do see here in the more scientific terms, is agent-based models for the longest, had impact in the 1980s, 90s, to some extent, early 2000s, but it has now gotten forgotten by the community a little bit. Because as you can imagine, red dots and blue dots is not really a rich description of people.Joon [00:45:31]: But with the emergence of things like generative AI and, in particular, generative agents, we do have an opportunity to create these agent-based models that are high fidelity enough to help us make really complex decisions. And that's the opportunity that I see. If that truly works, then yes, that is the work that will result in a Nobel Prize.Swyx [00:45:53]: Yeah. For what it's worth, and I grew up in Singapore. 80% of Singapore is in public housing, and public housing has, enforced racial quotas for exactly that reason, which is very interesting. okay, so we talk about scaling, we talk about all these, the agent possible applications.Cost, Reuse, and the Economics of SimulationSwyx [00:46:13]: I'm scared about the cost. if you even-- let's just keep it to the US, about 8 billion people.Swyx [00:46:21]: But, how much does it cost to model so many hundreds of millions of people?Joon [00:46:26]: Oftentimes today, we don't start at that scale, this stage of the, of industry and simulation as technology. But we can get our users extremely rich and meaningful insights even by modeling thousands, tens of thousands of people. And today what we do is every week we are collecting data on the scale of tens of thousands people's data, and we have panel partnerships that gets us to tens of millions of people globally. So that's what we do today.Swyx [00:46:55]: And just as a side note once you've collected one person for one studySwyx [00:46:59]: Can you reuse that same person for all the subsequent studies?Joon [00:47:03]: That's exactly right.Swyx [00:47:03]: Okay.Joon [00:47:04]: The beauty of this model and these agents is the fact that they are domain-agnostic.Joon [00:47:08]: That what you're really trying to understand is what is the fundamental nature of these people? What's their social physics? And there are a lot of, a lot of, people that does change over time. Like, even, like, even things like, how many times have you gone have you been to, like, CVS the past week? that will change. But there's so many traits about people that are also known to never change. Like, your risk tolerance doesn't really change over time. It's very consistent. So it's these things that we're trying to learn. But the scale we are operating is right now hundreds or, tens of thousands to hundreds of thousands. And in many of the core use cases that we are deployed in, and this is more than enough population, to cover those. Really, at that point, what you care about is less the number of people, but more do you have the right subpopulation of interest covered? And this is also the reason why people want a larger sample. It's not because they want, stronger statistical guarantees. It's more that can they filter down to any population of their interest. However, you can also imagine in 10 years, if we truly believe that the compute is going to scale, that we'll have much more availability for compute, and our ambition for simulation is also going to scale accordingly, there's definitely a reason for us to create an entire data center worth of simulations.Joon [00:48:35]: Or in my hunch here is I do think in the next some number of years, we will start creating simulations that will cost as much as training a foundation model. But perhaps it's going to be so valuable to the society that it would be a no-brainer. Right now, even today, like, we are training bunch of new foundation model just so we can say we trained one and we spent tens of millions. But if we can create a simulation at the level of society that would solve climate change, I would run that today. I would raise the money right now just to run that.Multi-Agent Simulation and Social InfluenceSwyx [00:49:10]: Amazing. the follow-up question is, does it also compound if you let the simulations talk to each other?Swyx [00:49:18]: Or do they already do that today? They don't, right, as far as I understand?Joon [00:49:22]: It depends on what simulation you're trying to run.Joon [00:49:24]: In the multi-agent simulation setup, the agents do talk to each other.Swyx [00:49:28]: Right, which is exactly Smallville, right?Joon [00:49:29]: That's right.Swyx [00:49:30]: But a lot of times, for example, in commerce, you're just by yourself, so there's no point talking. which is way cheaper.Vibhu [00:49:37]: But they use all these levels, right? Like, you decide what you will buy based on what other people around you buy and talk about, right?Swyx [00:49:43]: It depends.Vibhu [00:49:44]: It depends.Swyx [00:49:45]: Again, I'm, I'm coming at this from a cost point of view. I'm like, “Oh my God.” LikeVibhu [00:49:48]: I thinkSwyx [00:49:49]: If there is, like, some combinatorial thing of, like, thousands of people talking to thousands of people, then that one million X's might cost.Vibhu [00:49:56]: I have a very different view as the cost point aside. Like, running these studies in reality is a lot more expensive, right? Running any study like this is you gotta have people do it, you gotta sign people up. It's very expensive and sometimes, like, not feasible to run the study.Vibhu [00:50:14]: But the outcome or the decisions you make are very expensive on them, right? So spend X million on something that, the overall process costs 100 million might as well, right? There's, there's a lot of value to be had there. It's a small cost, but I'm excited on the cost side.Joon [00:50:33]: To some extent, and when you deploy technology, you often want to deploy in a way where you can replace existing budget or you can make things more efficient, and that is the best way to deploy. However, the way you capture the long-term value of the technology is making the argument that, no, it's the upside, that by making this better decision using simulation, you have saved yourself or made yourself hundreds of millions or even billions of dollars, and that's a case to be made.Vibhu [00:51:06]: Random tangent question. So if you're doing a lot of inference, a lot of model multi-agent stuff, are you at the point where it makes sense to, train a model that' very sparse? You're expecting to do multi-million dollar runs. Are you thinking about this in model architecture standpoint or inference efficiency, or, you're still at the research phase of it works, we're not super there yet?Joon [00:51:34]: Efficiency, we do think quite a bit about. this is technology that is deployed now in some of the largest enterprise companies in the world, and we do process significant number of queries, that are trying to, simulate the populations in the world. So efficiency is a consistent thing. we don't want to over-optimize too early, so I wouldn't say, like, this is the higher bid Right now, but this is definitely something that we think pretty carefully about.Swyx [00:52:05]: Yeah. Are there other case studies? So we, you talked about CVS, talked about Gallup, Deloitte, Wealthfront.Efficiency, Enterprise Use, and Real-World Case StudiesJoon [00:52:12]: Wealthfront is an interesting one, because one of the things they were trying to do, they were one of the first customers that wanted to do product testing that goes beyond just asking people what they think about, let's say, behavior experiments and so forth. So there, really what we had to do was reason about multimodal input, so images, but also you can also imagine, like, these agents traversing through Figma mockups or websites. So some of the things that our agents can also do is it can be given a domain, like, or, like, a website URL and go use it for a while. It's these things. And Wealthfront was one of the first, customers, that was very excited about this possibility.Vibhu [00:52:53]: What have people been asking? Like, is there any demand that we have not covered? Like, UI testing, right?Vibhu [00:52:59]: I wanna try a new. I wanna ship a new feature, test the UI, simulate how people will do it. Any interesting things that you're seeing demand for?Product Testing, Websites, and Synthetic PanelsJoon [00:53:08]: Today, a lot of the demand does come from like, the places where people have historically used human panels, we can now replace with agents, and these synthetic populations. And this is not replacing human panel. in many ways, the simulation that Simile is building is grounded. So the way that I think about this is we are trying to represent humanity at scale. And in that way, the use cases are what we would expect, but it's the scale of deployment that surprises me.Joon [00:53:44]: Turns out there are so many decisions that people make every day in these organizations, groups, and we want to be able to say, “We listen to people. We have consulted our users.” But in reality, that is rarely the case because getting to people and asking them many questions, it's difficult. It's both costly, time-consuming, but most importantly, people are just not available. If I had to answer 1000 survey questions for this one particular, vendor, even if I wanted to do that, like, I would never do it. And that's very much the case. What simulation can do is ensure that the voices of people are always represented in rooms where the decisions for them is made, right? So all the stakeholders of this particular product launch, ideally they're consulted. That's what this technology really is trying to enable.Market Size, TAM, and Human Decision-MakingSwyx [00:54:39]: In my mind, that means it skews towards more consumer focus, right? Like, anything with a wide enough customer base where you do benefit from the diversity that you represent. What are some rough statistics, just for people who are not familiar with this market in general, what's the market size that. I'm sure you have some, like, rough numbers. market size is, like, a vague questionSwyx [00:55:01]: But, like, how much do people spend?Joon [00:55:03]: So market research is a $100 billion industry.Joon [00:55:06]: But the thing about simulation is not a tool for market research. Simulation is a tool for human decision-making. So the question around what is a TAM here is quite tricky, right? Because it's easy to say, “Well, market research TAM is roughly 100 million or 100 billion.” so is it a TAM? And not really, right? Because in many ways, you're trying to inform all human decision-making. You're trying to inform every decision that are made about humans for humans. What is a TAM for that? It's really unclear. And I'll be honest. Like, I have a scientific background, I have a research background, so I didn't come into the field calculating, oh, what is the TAM for human decision-making? But I just had to assume, well, if we can inform every decision that is made about human for human, that has to be big.Swyx [00:55:58]: Some- something valuable.Joon [00:55:59]: Exactly.Swyx [00:55:59]: To some extent, you are a unicorn founder now, and you have to care as a CEO. But, like, I do think, like, yeah, when you go into these boardrooms with people that you're quoting millions of dollars of contracts for, like, you have to say, “Well, here's what you spend on humans-”Swyx [00:56:15]: “. And here's what we save you, and it's 85% similar.”Joon [00:56:19]: And certainly, the value case, is something that we care deeply about. Like, what is the value that we provide to the users and the decision-makers? But this is also where, like, as a founder, I think valuation only tells one very superficial aspect of the story, and I try not to think too much about valuation, in general, because that's not what also motivates a team or certainly doesn't. I'm, I-- Again, the interesting thing about researchers is we are happy living in academia, getting paid next to. we get paid okay. we don't get paid that much, as a researcher here in academia, but it's the impact and it's the, it's the value that we can provide to the individuals and the society that really drives us. And in that way, ultimately what drives us is the impact. Does the simulation we provide have a real impact in people's decision-making in ways that progresses our society forward? If the answer is yes, then yes. that has to be great business, and we see that in numbers, and we do care deeply about that upside story, but that's the heart of it.Where Simulation Goes NextVibhu [00:57:27]: Do you have any timeline predictions? So we talked about scaling laws of simulations.Vibhu [00:57:33]: You brought up, okay, maybe one day we can simulate how to solve climate change.Vibhu [00:57:38]: Where are we now?Vibhu [00:57:40]: If that's not the end state, what is an end state, and what does progress look like?Joon [00:57:45]: So what I sometimes tell people is simulation as industry, it feels a lot like where GPT-3.5, GPT-4 was, for the AGI saga, which is we have now technology that is powerful enough to do real damage on the verticals that we are tackling. At the same time, there's a lot of progress that is yet to come. And that's, I think, where this is. So the way I see it, I do think there will continue to be breakthroughs both in data, in algorithms, and there will be much more aggressive scaling that will also happen over the next few years. But I think that's roughly where we are.Swyx [00:58:27]: I think that was about the ro

"Do I need a trust or just a will?" might be the single most common estate planning question there is, and estate attorney Tim Semro says most people are asking it backwards. The real question isn't trust versus will, it's how do you avoid probate, and a trust is just one of several ways to get there. Tim returns to answer a full mailbag of real Stacker questions, covering everything from a $200,000 mistake buried in a lady bird deed to the exact reason so many families accidentally disqualify a parent from Medicaid.What You'll Walk Away WithWhy "trust versus will" is the wrong question, and the three-column framework that actually determines what you needWhat a lady bird deed is, when it makes sense, and the family conflict it can quietly set up down the roadThe tax detail buried in gifting property early that can cost your heirs tens of thousands of dollars they didn't expectWhy naming a power of attorney without having an honest conversation first is one of the most common and costly mistakes families makeThe five-year Medicaid look-back rule explained clearly, including what happens if you don't quite make it to five yearsHow debt actually works after someone dies, including a real statute of limitations window most people don't know existsA special needs trust structuring tip that can protect a family member's government benefits without giving up their inheritanceWhy This Matters NowEstate planning tends to get pushed to "someday" because it feels complicated, uncomfortable, or like it only matters once you're wealthy. But the actual decisions, who has power of attorney, how property transfers, what happens if a parent needs long-term care, apply to nearly every family, regardless of net worth. Getting the structure right isn't about predicting the future perfectly. It's about making sure the people you love aren't left guessing, fighting, or losing money to easily avoidable mistakes during an already difficult time.From the BasementA birthday trivia detour into the surprising origin of the Nobel Prize reveals it was born from a very specific kind of reputation crisis, proof that it's never too late to actively shape how you'll be remembered.Resources MentionedYour Money, Your Way by Tim Semro — Tim's book on estate planning, free to downloadSemro Henry Ltd. — Tim's estate planning law firmStacking Benjamins Field Kit — the all-in-one financial organization toolSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Our website - www.perksofbeingabooklover.com. Instagram - @perksofbeingabookloverpod Facebook - Perks of Being a Book Lover. To send us a message go to our website and click the Contact button. For more information about Ohio Celebrates Toni Morrison, visit their website www.OhioCelebratesToniMorrison.org or on Insta at @ohiocelebratesmorrison.   This week we have created a motley episode of different segments all about or set in OH in some way.  Think of it as the Frankenstein's monster episode. First we will be chatting with Britt Lovett, who is the Project Manager for the Ohio Celebrates Toni Morrison Project.  This year-long celebration running from February 2026-February 2027 focuses on the life, literature, and legacy of Toni Morrison, a native of Ohio and the first African American woman to win the Nobel Prize for Literature.   While Amy has gone to Ohio this year to visit Hocking Hills and the Columbus Book Festival, Carrie has driven to Ohio three times because her youngest son had surgery in June at Cincinnati Children's Hospital. Because this year feels like it has been “All Ohio, all the time,” we're giving you recommendations for books set in the Buckeye State.   Books Mentioned in This Episode: 1- Assassination Nation by Sarah Vowell  2- The Bluest Eye by Toni Morrison  3- Sula by Toni Morrison  4- Beloved by Toni Morrison  5- Paradise by Toni Morrison  6- I'll Watch Your Baby by Nina Viel  7- Clutch by Emily Nemens  8- Murder Your Darlings by Jenna Blum  9- Penelope's Bones: A New History of Homer's World Through the Women Written Out of It by Emily Houser  10- The Night Hunter by Natalie Moss  11- Guess Again by Charlie Donlea  12- A Harlem Wedding by Tiffany L. Warren  13- A Hunger to Kill by Kim Mager with Liza Pulitzer  14- Love is a Contact Sport by Frederick Smith  15- The French Winemaker's Daughter by Loretta Ellsworth  16- The Jilted Countess by Loretta Ellsworth  17-We Were Promised: How an Appalachian Grandmother Fought a Corporate Giant by Julia Flint  18- Mosquitoland by David Arnold  19- The Ghosts of Eden Park: The Bootleg King, The Woman Who Pursued Him, and the Murder that Shocked Jazz-Age America by Karen Abbott  20- Buckeye by Patrick Ryan  21- The Fertile Earth and the Ordered Cosmos by M. Elizabeth Weiser  22- Kent State by Deborah Wiles  23- To Slip the Bonds of Earth by Amanda Flower  24- The Wright Sister by Richard Maurer  25- Harvey Pekar's Cleveland by Harvey Pekar    Media Mentioned: 1- Death by Lightening (Netflix, 2026) 2- Ohio Celebrates Toni Morrison - https://ohiocelebratestonimorrison.org

How do we revive living standards and restore confidence in liberal democracy? Is the link between productivity and wages permanently broken? What is working-class liberalism? Why should Andy Burnham's first budget raise taxes on capital while cutting taxes on employment? And why are tech giants like Google and Amazon twice as wealthy as the British Empire at its peak? Robert investigates how to safeguard our way of life with Nobel Prize–winning economist Daron Acemoglu. Together, they discuss whether the prescriptions in Acemoglu's influential new book are practical enough to prevent liberal democracy from eroding further. The Rest is Money is brought to you by Octopus Energy, Britain's smart energy pioneer. This episode is brought to you by Accenture. https://Accenture.com/Spotify-UK Buy tickets for The Rest is Fest: https://www.southbankcentre.co.uk/whats-on/the-rest-is-money-live/ Email: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠the⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠restismoney@goalhanger.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ X: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@TheRestIsMoney⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@TheRestIsMoney⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ TikTok: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@RestIsMoney⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Advertise with us: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Partnerships@goalhanger.com⁠ For more Goalhanger Podcasts, head to ⁠www.goalhanger.com⁠ Video Editor: Dylan Bonham Producer: Isabelle Bougeard Exec Producers: Bella Soames and Tom Whiter Learn more about your ad choices. Visit podcastchoices.com/adchoices

"There's very few cases of more intelligent things being controlled by less intelligent things. Once they're a lot smarter than us, I don't think they'll put up with that." – Geoffrey HintonArtificial intelligence (AI) is rapidly developing and changing the world around us. In this special bonus episode we speak with Nobel Prize laureates to examine AI's growing influence on our society. From various disciplines and perspectives, they share their thoughts about AI's impact on the job market, medical research opportunities, existential threats and the need for government regulation. Discover more from the laureates featured in this episode and explore the discoveries and ideas behind their awarded work. At nobelprize.org, you'll find a wealth of content dedicated to each of our guests – Omar M. Yaghi, Mary E. Brunkow, Peter Howitt, Philippe Aghion, Joel Mokyr, Geoffrey Hinton and John Jumper – featuring interviews and descriptions of their prize-awarded work. You can also explore the 2025 Nobel Prize in Chemistry, the 2025 Nobel Prize in Physiology or Medicine, the 2025 prize in economic sciences, the 2024 Nobel Prize in Physics and the 2024 Nobel Prize in Chemistry. This podcast was a production of Nobel Prize Outreach and Filt, and created in cooperation with Fundación Ramón Areces. Hosted on Acast. See acast.com/privacy for more information.

Nobel Prize-winning economist Daron Acemoglu argues liberalism has lost touch with the working people it once championed. He makes the case for what it will take to rebuild a politics of shared prosperity and defeat anti-democratic forces. Plus: Sportscaster Hazel Mae on her journey from convincing her immigrant parents her career choice would work out to being inducted into Canada's baseball hall of fame -- and how she really feels about all those Gatorade showers from the Blue Jays.

Aug 18, 2026; 6pm: New polling from Reuters shows President Trump's approval rating at 33 percent. MS NOW's Ari Melber reports and is joined by Nobel Prize-winning economist Paul Krugman. Plus, ABC is suing the FCC over threats to force its stations off the air. Melber breaks down the lawsuit with NPR's David Folkenflik. To listen to this show and other MS podcasts without ads, sign up for MS NOW Premium on Apple Podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The system that produces the world's most important discoveries runs on metrics that nobody agrees on, odds that would stop most people, and mentorship that sometimes looks like cruelty in blue ink. Subscribe if you want science with evidence, not speculation. Lipomi is Professor and Chair of Chemical and Sustainability Engineering at the University of Rochester and the author of Science Nonfiction: Behind the Scenes in University Research. His lab works on organic electronics, materials science, recyclable polymers, and the science of human touch. He trained under George Whitesides at Harvard, one of the most cited chemists who has never won the Nobel Prize. The conversation covers the H-index, what elite mentorship actually looks like when your advisor writes “This is illiterate” in blue ink on your outline, and what the future of academic research versus industry careers looks like in an era of AI, demographic headwinds, and graduate school debt that can't be discharged in bankruptcy. We also get into homochirality, OLED displays, and why Lipomi thinks the nanotechnology revolution already happened, we just didn't call it that. What you'll hear: -Whether the academic system is designed to produce scientists or to filter them out -What is Baumol's cost disease and why it explains rising tuition costs -Why the nanotechnology revolution already happened and nobody called it that -How nanotechnology is used in cancer drug delivery today versus what science fiction promised -What it actually takes to build a PhD career at the intersection of chemistry, mechanics, and neuroscience -Whether engineering students should be reading Plato — and what countries that skip it are getting right “Find a skill at the intersection of three or more interests that no one else is working on.” — Darren Lipomi CHAPTERS 00:00 He says don't go to college. He runs the department. 01:10 The H-index: imperfect, irreplaceable 02:04 1 in 20 PhDs gets the job 05:42 The meteorite and the origin of life 08:34 Did life's asymmetry come from space? 12:00 Who is an organic materials chemist at 3am? 13:52 The collaboration that started at a coffee shop 15:22 The nanobot revolution already happened 18:24 Nanotechnology inside your body right now 19:08 The $70 billion invention Kodak gave away 22:12 Kodak, Xerox, Bausch + Lomb: what Rochester built 26:06 Working under Whitesides at Harvard 28:54 What he wrote on the outline in blue ink 30:38 Who you work with matters more than what you work on 31:56 Department chair: hostage negotiator, not boss 34:36 Why the department changed its name after 110 years 36:00 The headwinds have never been stronger 38:44 Should engineers read Plato? 39:38 The debt you can't discharge in bankruptcy 40:06 Do you buy Baumol's cost disease? 43:14 Why he had to write the book 48:16 The Fisher-Price camcorder experiment 51:06 Teaching in one sentence. Research in one. 53:42 Losing Darren to Rochester: the Fernando Tatis analogy Get the transcript, fascinating bonus content, and my Monday M.A.G.I.C. Message: https://briankeating.com/yt Have a .edu email and live in the USA? You automatically win a meteorite: https://BrianKeating.com/edu Subscribe: https://www.youtube.com/DrBrianKeating?sub_confirmation=1 Support Into the Impossible on Patreon, get my weekly M.A.G.I.C. Message, unfiltered bonus content, and live monthly Office Hours with me: https://www.patreon.com/drbriankeating Join this channel for perks, monthly Office Hours, and your name in the Member Roster at the end of every episode: https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join Learn more about your ad choices. Visit megaphone.fm/adchoices

This week we talk about the liver, viral infections, and the NHS.We also discuss blood scandals, needle usage, and Nobel Prizes.Recommended Book: A World Appears by Michael PollanTranscriptThe term “hepatitis” refers to the inflammation of the liver, which can result from all kinds of things, including environmental toxins, the consumption of alcohol, or autoimmune diseases. It can also result from viral infections, and the most prominent liver-inflaming viruses are called viral hepatitis.There are five types of viral hepatitis, A, B, C, D, and E, and each of these viruses are distinct, not part of the same viral family, they're just similarly named because they impact the same organ.Hepatitis A and E are primarily spread through contaminated food and water, and generally resolve on their own, untreated, and cause relatively mild symptoms. Hepatitis B and C are spread through blood and other bodily fluids, and can linger in a host's body for decades before even showing symptoms. Hepatitis D is a parasite of Hepatitis B, and thus only infects people who carry Hepatitis B.Now again, these are all different conditions that just happen to inflame the liver, so impact and treatment also vary quite a lot. As I mentioned, A and E generally present with mild symptoms and tend to go away on their own, while B and C can stick around a long time. There's a vaccine for B, but no cure; you can treat it, but that treatment involves suppressing it, and keeping it suppressed, forever. Hep C, in contrast, is curable, and has been since 2014 using what are called direct-acting antiviral pills, but these pills, which are taken for 8 to 12 weeks, are expensive—ranging from $22-95k without insurance, though that price is often reduced substantially for those with insurance, down to as low as $5. This category of drug coverage is often rejected by insurance companies, though, in part because they're so expensive, that expense the result of little competition in this space; few companies make this type of drug, so those that do can charge more or less whatever they like.Some people with Hepatitis C clear it on their own; about 30% of people who contract it, in fact, clear it within a few months, medication-free. Which is good, because our understanding of this virus is relatively new. Up until 1989, Hep C didn't even have its own name: it was established as its own thing, not Hep A and not Hep B, back in the 1970s, and doctors knew that something that wasn't those two viruses, that was being spread by transfusions, was causing hepatitis symptoms, but they didn't know any real specifics, so they just called it “non-A, non-B hepatitis,” and that name stuck for more than a decade.In 1989 the virus was cloned using molecular techniques (as opposed to simply growing the virus, which wasn't proving fruitful in trying to isolate and identify the thing), and the folks who managed that cloning, and the person who later proved that the genome they cloned, alone, caused the disease, received a Nobel Prize in Medicine for their efforts in 2020.By 1991, antibody tests were available for Hep C, and many countries began screening donated blood for this virus, to ensure it wasn't working its way into their blood supply.And one instance of that screening process, or I suppose, an event that led up to mass screening, and the consequences that followed, are what I'd like to talk about today. The UK's efforts in trying to eliminate Hep C, and England's recently announced near-success in that pursuit.—Hepatitis C is an RNA virus with high genetic variability that makes developing a reliable vaccine difficult. And though somewhere between a quarter and a third of all cases clear on their own, those that don't clear on their own become chronic, lying in wait for twenty to thirty years, slowly accumulating fibrosis—thick scar tissue in the liver—which eventually results in cirrhosis, which means a liver that's so heavily scarred that the organ is no longer fully functional and the damage is permanent. From there, infected people often experience liver failure or hepatocellular (huh-pah-toe) carcinoma, liver cancer.So this virus is a sleeper, and unless it's caught by accident somewhere along the way, it slowly causes damage over time until the damage is too severe to reverse. About 80% of people who have it don't know they have it, and in some parts of the world medical injections are the most common transmitter, but in higher-income areas, it's usually transmitted by injectable drugs.Pre-2014 treatments for Hep C were pretty horrible, involving a combination antiviral therapy called pegylated interferon plus ribavirin that was injected weekly for six months to a year, and this was terribly tolerated by pretty much everyone, causing anemia, depression, and flu-like symptoms for the duration. It also only cured about 50% of people who received the full treatment, and a lot of people had to stop because it caused such ridiculous side effects.Another antiviral called Sofosbuvir (so-FAS-buh-vir), which kept Hep C from replicating in its host, hit the market in late-2013, and that led to a series of direct-acting antivirals that reduced the treatment period dramatically, allowing most people, 95%, to cure their Hep C entirely by taking generally well-tolerated pills for 8 to 12 weeks.These pills were staggeringly expensive from the get-go, with an entire treatment course initially costing about $84,000, or $1,000 a pill. This led to rationing, and saving these pills for the worst-impacted people who already had severe liver damage. There were also pretty stringent requirements attached to their distribution, including that people who received them could no longer drink alcohol, because it was considered a waste to give these crazy expensive, liver-saving drugs to people who would just go and hurt their liver more, anyway.In the UK, the demand for this treatment type was different than in most other countries, in large part because of something that happened back in the 1970s and 80s.The UK's publicly funded healthcare system, the NHS, was in the midst of a shortage of clotting factor, which are plasma proteins and ions that help blood clot and which are used for medical purposes. So they imported a bunch of plasma products from the US, and those products were sourced from the blood of paid donors—and that donor pool included prisoners and people who used injectable drugs. Just one Hep C contaminated blood donation could contaminate an entire batch of blood, and remember, they only started screening the blood supply for Hep C in 1991, and they didn't start treating their blood supply for Hep C until a little before that, 1985, so this was well before they had any idea what was in those blood products they were importing and administering.Consequently, between 1970 and the early 1990s, more than 30,000 NHS patients received transfusions or other blood product treatments contaminated with Hep B, Hep C, or HIV, and about a tenth of those people, around 3,000 patients, have since died of those conditions.The UK government leaned on denial and a refusal to look into the details of this for years, but in 2017 it announced an independent public inquiry into the matter, and in May of 2024, that inquiry concluded that this whole scandal was avoidable, that patients were knowingly exposed to “unacceptable risks,” and that there was a big cover up by government officials, doctors, and other people working with the NHS.As of mid-2026, only a little over 3,200 people of the more than 18,500 who registered claims, demanding compensation from the government because they were impacted by this scandal, have been paid out. The expected total expense for the UK government is on the order of 12.8 billion pounds, but a lot of people who are probably due a payout, and who are in poor and deteriorating health as a consequence of all this, don't yet have a sense of when they'll receive their payment.Back in 2016, before all that came to a head, the UK set itself an aggressive goal: to eliminate Hep C by the WHO's 2030 target, or before. It then ran a competitive tender for antivirals, inviting medical suppliers to submit competing bids, resulting in the largest single medicine procurement program in the NHS' history. The pharmaceutical companies that won their bids were also obliged, as part of the agreement, to help fund efforts to identify undiagnosed but infected patients, in addition to supplying antiviral pills, and this combination of investment and application led to the deployment of new tests and scanning machines, free postal test kits, the hiring of specialists, and services that focused on prisons and drug users.The impact of all this has been significant: a more than 61% decline in infections from 2015 to 2024, nearly half of all drug users with Hep C had cleared the virus in that time, and deaths from Hep C are down 36% over the past decade.The WHO treatment-coverage target—the percentage of people who are diagnosed getting treatment—was 80%, and England has hit 81.5%, which was recently announced to much fanfare. It hasn't yet hit the diagnosis target, however, which is to diagnose 90% of people who are estimated to have Hep C; they've hit 84.6%, which is still quite a lot of progress, even if they're not yet where they'd like to be. That's all based on models, of course, as are the assumed number of infections among people who use injectable drugs, which is also a spot where England is currently flagging; there's no centralized system in England to monitor needle and syringe provisions, and reinfection rates are around 8.8 per 100 person-years among people who had injected within three years of receiving treatment, and that rate is even higher for people who have ever been to prison, around 9.4 per 100.What that means in practice is that the English government overall has done a pretty astounding and effective job at negotiating their relationships with pharma companies and getting detection on track at that scale, but on more ground-level issues that are, interestingly, a lot cheaper to implement, but at times more politically complicated because of public sentiment about drug use and drug users, they're doing a lot less well—and important to note here is that these outcomes vary a bit across the four programs being run across the UK. Scotland and Wales are doing relatively better and worse in some regards compared to England, for instance.Also worth noting here that while England is broadly doing a great job with Hep C diagnosis and treatment, they aren't the first to achieve those WHO-set goals: Egypt reached Gold tier status according to the WHO's Hep C guidelines in October of 2023, at that point having diagnosed 87% of people who have the virus, and treating 93% of those who were diagnosed. They managed to cut incidence of the virus by 97% in just 8 years, leaning on a system of high-yield testing—they tested more than 60 million people during those 8 years—alongside a production scheme that included local manufacturing of antivirals, making them more available and affordable.All of which are generally good signs about where Hep C testing and treatment is going, at least in these regions. And it paints a optimistic picture for other countries that might want to replicate some of what's working within their own borders.Show Noteshttps://www.bbc.com/news/articles/c75gk620r22ohttps://en.wikipedia.org/wiki/Infected_blood_scandal_in_the_United_Kingdomhttps://en.wikipedia.org/wiki/Hepatitis_Chttps://en.wikipedia.org/wiki/Viral_hepatitishttps://en.wikipedia.org/wiki/Hepatitis_Bhttps://www.healthline.com/health/hepatitis-c/treatment-costshttps://www.who.int/news-room/fact-sheets/detail/hepatitis-chttps://publichealthscotland.scot/publications/surveillance-of-hepatitis-c-in-scotland/surveillance-of-hepatitis-c-in-scotland-progress-on-elimination-of-hepatitis-c-as-a-major-public-health-concern-2025-update/https://www.emro.who.int/media/news/egypt-becomes-the-first-country-to-achieve-who-validation-on-the-path-to-elimination-of-hepatitis-c.htmlhttps://www.gov.uk/government/publications/hepatitis-c-in-england-and-the-uk/hepatitis-c-in-england-2025https://www.england.nhs.uk/2026/08/100000-people-receive-treatment-to-cure-deadly-hep-c-virus-on-nhs-in-just-ten-years/https://www.hepctrust.org.uk/blog/2019/04/hepatitis-c-trust-welcomes-elimination-deal-hepatitis-c-and-calls-government-backed/https://commonslibrary.parliament.uk/research-briefings/cbp-10099/https://www.who.int/teams/global-hiv-hepatitis-and-stis-programmes/hepatitis/reports/global-hepatitis-report-2026 This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit letsknowthings.substack.com/subscribe

This week on the Stay Tuned with Preet podcast, Nobel Prize-winning economist Daron Acemoglu joins Preet to discuss his latest book, What Happened to Liberal Democracy? Remaking a Politics of Shared Prosperity. Acemoglu explains why he thinks American politics is in  crisis and why a return to the foundational principles of liberalism—with shared prosperity at the forefront—is the best solution. They also discuss the Democratic socialist movement, the reasons for its rise, and whether “socialism” is even the right term to describe it. Then, Preet and Acemoglu turn to AI and its potential to become a pro-worker tool.  After the interview, Preet answers listener questions about FEMA emergency funding and whether the attorney general should be an elected position. In the bonus for Insiders, Acemoglu defines some of the key terms from his book and explains how concepts like liberalism can have different meanings. Join the Insider community for access to bonus content from Stay Tuned and weekly episodes of the Insider podcast hosted by Preet and Joyce Vance. Visit staytuned.substack.com to sign up. Thank you for supporting our work. Photo by Costas Baltas/Anadolu via Getty Images  Show notes and a transcript of the episode are available on our website.  Watch this episode on our Youtube channel. Shop Stay Tuned merch and featured books by our guests in our Amazon storefront. Have a question for Preet? Ask @PreetBharara on BlueSky, or Twitter with the hashtag #AskPreet. Email us at staytuned@cafe.com, or call 833-997-7338 to leave a voicemail. Stay Tuned with Preet is brought to you by CAFE and the Vox Media Podcast Network. Learn more about your ad choices. Visit podcastchoices.com/adchoices

For most of the last century liberal democracies offered a simple deal: the economy grows and, if you work hard, you get a fair shot at the rewards. According to 2024 Nobel Prize-winning economist Daron Acemoglu, that deal is broken. Acemoglu joins Bethany McLean and Luigi Zingales to discuss his new book, What Happened to Liberal Democracy?, and why the system that delivered unprecedented freedom and prosperity abandoned the working class in favor of the college-educated elite.  Acemoglu makes the case that things will only get worse as AI is being designed to replace human labor, driving wage stagnation and economic anxiety. But he thinks we can still turn things around, and he brought his solutions to our podcast. Connect with us:

Academic and critic Maria Delgado and writer and producer Jake Cunningham join Tom Sutcliffe to discuss the film Tony, a film about the early career of food writer Anthony Bourdain, who is played by Dominic Sessa. The film also stars Antonio Banderas, who becomes an unexpected mentor for the young chef.They also talk about Death Note: The Musical. Based on the best-selling Manga series of the same name, the Barbican show tells the story of a student who discovers a notebook which gives him the power to kill. The final item for review is Nobel Prize in Literature winner Mario Vargas Llosa's final novel, I Give You My Silence, where a music journalist is on a quest to tell the story of an unforgettable musician. The book is also a love letter to Peruvian culture. Sarah Ditum discusses Jason Arday's memoir, Great and Unfortunate Things. This programme was broadcast before the announcement of Jason Arday's death. Presenter: Tom Sutcliffe Producer: Claire Bartleet

It's time to be honest: Jonah Goldberg can't remember if he's ever had a Nobel Prize-winning guest on The Remnant. If that shocks you, calm down. It's called getting old. After today, however, Jonah can say with certainty that he has, as he is joined by Nobel laureate in economics Daron Acemoglu. Listen in as Jonah and Daron light up the Remnant bingo card like a Hanukkah bush, covering liberal democracy, community, subsidiarity, the welfare state, status, prosperity gaps, AI, automation, China, abundance, the working class, Zohran Mamdani, and the Democratic Party. Show Notes: —Why Nations Fail: The Origins of Power, Prosperity, and Poverty —What Happened to Liberal Democracy?: Remaking a Politics of Shared Prosperity —Daron Acemoglu in Financial Times: “Liberalism can win back the working class. Here's how” —Violence and Social Orders: A Conceptual Framework for Interpreting Recorded Human History —Jonah's book Suicide of the West The Remnant is a production of ⁠The Dispatch⁠, a digital media company covering politics, policy, and culture from a nonpartisan perspective. To access all of The Dispatch's offerings—including the Saturday Ruminant, audio versions of all our articles and newsletters, and Jonah's twice-weekly G-File—⁠click here⁠. Instructions on how to set up your members-only feed can be found here, and if you'd like to remove all ads from your podcast experience, consider becoming a premium Dispatch member ⁠by clicking here⁠.  Learn more about your ad choices. Visit megaphone.fm/adchoices

Here's the Spotify description for this episode: In Episode 384 of The Real Jason Duncan Podcast, think about the last time a bill showed up that you couldn't pay. The anger. The fear. That knot in your stomach at 2 in the morning. Now imagine the money to cover it just landed in your account. Tell me you didn't just feel happier. Money can buy happiness. And whoever told you it can't was probably broke. In this solo Wednesday episode drawn from his What's Real newsletter, Jason dismantles one of the most repeated lies in the world — a coping mechanism dressed up as wisdom, passed down from person to person until everybody just repeats it without ever checking it. And then he hands you the research to defend it the next time someone hits you with the old line. In this episode, Jason covers: Why “money can't buy happiness” is a coping mechanism — and what it's actually covering up The 2010 Nobel Prize study that put a $75,000 ceiling on happiness — and why it became gospel for a decade What happened in 2021 when a researcher pinged 1.5 million people on their phones in real time and asked how they actually felt What Kahneman and Killingsworth found when they sat down together in 2023 — and what they landed on after starting out on opposite sides The one exception where money stops moving the needle — and why your gut already knew this The critical difference between “money can't fix everything” and “money can't buy happiness” — and why people use the true one to make the false one sound reasonable What Proverb 10:22 actually says about riches — and why the idea that money and misery are a package deal was never ancient wisdom What to do if more money would relieve the pressure you're under — and why there's no shame in just saying so Money can buy happiness. Go build a life you actually want and stop apologizing for wanting it.

Can one molecule almost double our lifespan?Chris Burres is the founder and chief scientist at MyVitalC, the oldest and longest manufacturer of the Nobel Prize-winning ESS60 molecule. He came here to explain why almost nobody has heard of it.The story gets stranger the further in we go. Rice University scientists discovered this ball-shaped carbon molecule in 1985. It performs better than most existing materials in batteries, tires, and inks, and its antioxidant properties are far more powerful than vitamin C. Then a French toxicity study produced unexpected longevity results.Chris outlines the manufacturing process, the liver recovery data, the cancer cell study, the sleep testimonials, and the pet results that leave placebo out of the argument.If you've ever taken C60 and wondered whether you were drinking expensive pee, this one is for you.Visit myvitalc.com/lukestorey and use code STOREY for $30 off your first order.You'll learn:[0:00] Introduction[5:38] The rat study that found 90% longer lifespans[14:15] The graphite-vaporizing, oxygen-free reactor process behind manufacturing pure ESS60[21:18] Adding "not for human consumption" while biohackers kept calling about their 275-pound rats[34:14] The multi-billion-person sleep study we run every daylight savings time[48:47] Why five ampoules a flight became part of my air travel arsenal[55:29] Exploring whether C60 can detox your brain the way activated charcoal can't[1:27:50] The gossip behind the Nobel Prize and the grad students who discovered it but got nothing[1:37:50] Why manually stacking the youth peptide and ESS60 beats using an emulsifier[1:42:45] The Sesame Street test for spotting C60 products that contain zero C60[1:50:39] Three teachers who shaped ChrisResources Mentioned:Wizard Sciences: Use code LUKE for 15% off all products! (except subscriptions) | WebsiteSens.AI | ProductThe prolongation of the lifespan of rats by repeated oral administration of [60]fullerene by Baati et al. | ArticleAX3 Bio-Pure Astaxanthin | ProductPresence and Quantity of Botanical Ingredients With Purported Performance-Enhancing Properties in Sports Supplements by Cohen et al. | ArticleFullerene C60 Protects Against Intestinal Injury from Deoxynivalenol Toxicity by Improving Antioxidant Capacity by Liao et al. | ArticleC60 Fullerene Reduces the Development of Post-Traumatic Dysfunction in Rat Soleus Muscle by Prylutskyy et al. | ArticleREAD: Why We Sleep: Unlocking the Power of Sleep and Dream by Matt Walker | BookREAD: The Most Beautiful Molecule: The Discovery of the Buckyball by Hugh Aldersey-Williams | BookFull show notes at lukestorey.com/c60Related The Life Stylist Episodes:C-60: The Miracle Molecule for Biohacking Pets, Hair Loss, EMF, & Cancer W/ Ian Mitchell | PodcastHolon Brain Training: Transcend Trauma & Master the Flow State w/ Drs. Drew Pierson & Amy Albright | PodcastDave Asprey – Smarter Not Harder: Top Biohacks for Vitality, Longevity & Maximum Brain Power | PodcastFind more from Chris:MyVitalC | Website | Instagram | Facebook | X | TikTok | YouTubeRead: Live Longer and Better: Your Journey to Living Longer and Better Has Never Been More Achievable Than Today by Chris Burres and Jerome R. Corsi, PhD here.Read: The Longevity Molecule: The Secret to Doubling Lifespan to 152 Years (and Beyond) by Chris Burres here.Find more from Luke:Luke Storey | Instagram | Facebook | X | YouTube | LinkedInThe Life Stylist is Brought To You By:ACTIVATION PRODUCTS | Visit activationproducts.com/luke and use code LUKE for 15% off your order.LEELA QUANTUM | Go to lukestorey.com/leelaq and use code LUKE10 for 10% off your first order.BIOPTIMIZERS | Visit bioptimizers.com/luke and use code LUKE15 to save 15% off sitewide. Plus, get a free bottle of MassZymes while supplies last.ACTIVE SKIN REPAIR | Visit lukestorey.com/skinrepair and use code LUKE for 20% off your order.

Nobel Prize-winning MIT economist Daron Acemoglu joins Nick and Goldy to discuss his new book, What Happened to Liberal Democracy? Remaking a Politics of Shared Prosperity. They explore why so many people have lost faith in democracy, how inequality and weakened worker power have reshaped politics, and why democracy has to deliver in people's daily lives. They also dig into AI, automation, and whether new technology will concentrate even more wealth and power — or help build a more prosperous, democratic future. Daron Acemoglu is a Nobel Prize-winning economist and Institute Professor at MIT. He is one of the world's leading thinkers on political economy, institutions, inequality, technology, and the relationship between democracy and shared prosperity. His books include Why Nations Fail, The Narrow Corridor, and Power and Progress. His new book is What Happened to Liberal Democracy? Remaking a Politics of Shared Prosperity. Social Media: @dacemoglumit.bsky.social @DAcemogluMIT Further reading:  What Happened to Liberal Democracy? Remaking a Politics of Shared Prosperity New York Times - Nearly 200 Economists and Tech Leaders Warn of A.I. Threats NBER - Automation and Repression Check out THE BILLIONAIRE AGE on IDEAS  Website: http://pitchforkeconomics.com Facebook: Pitchfork Economics Podcast Bluesky: @pitchforkeconomics.bsky.social Instagram: @pitchforkeconomics Threads: pitchforkeconomics TikTok: @pitchfork_econ YouTube: @pitchforkeconomics LinkedIn: Pitchfork Economics Twitter: @PitchforkEcon, @NickHanauer Substack: The Pitch

John Martinis won the 2025 Nobel Prize in Physics for proving macroscopic quantum tunneling is real. Less than a year later he's telling why he left Google's quantum computing team to start over. Subscribe if you want the physics and the politics of building the impossible. Martinis is the co-founder of Colab and one of the physicists who proved ordinary quantum rules apply to macroscopic systems. He built his career on the same Josephson-junction hardware his Nobel is built on, then led Google's superconducting-qubit effort, the same team behind Google's 2019 quantum supremacy claim, before an internal reorg pushed him out. We go into Anthony Leggett's challenge to Schrödinger's cat, what decoherence in quantum mechanics really means, and whether quantum computing is the first technology ever born from pure theory rather than experiment. We also cover the internal Google reorg that pushed Martinis out and what he learned about building something new inside a large institution. Whether quantum mechanics applies to the macroscopic world and what it took to prove it Why Martinis thinks quantum computing may be the first technology born from pure theory, not experiment What negative authority means and why it matters for anyone building something new inside a large institution Whether the US can win the quantum computing race against China How Martinis thinks about quantum mechanics interpretations after spending a career inside the math “Always be on the lookout for the impossible, right?” John Martinis CHAPTERS 00:00  The Nobel call that almost wasn't 01:01  How his wife found out before he did 03:32  Anthony Leggett's challenge to Schrödinger's cat 05:53  Why a Josephson junction, not a quantum dot or trapped ion 07:45  Quantized oscillations: the “smoking gun” and Balmer's ghost 08:18  Measuring the system: the resonance experiment 11:21  Systematic effects: what separates a good scientist from a lucky one 13:42  The Ed Ohm story: the man who found the CMB and doubted it 15:01  Wigner's “unreasonably effective” math and the weirdest thing about QM 16:54  The transistor myth: chewing gum, coat hangers, and germanium 17:34  Microwave engineering meets quantum mechanics 19:26  Decoherence: the friction you can't live without 24:06  The heretical claim: does theory ever precede technology? 28:22  The “paper qubit” problem 29:34  What quantum computers are actually good for 32:27  Should quantum computing be regulated like AI? 33:35  US vs. China: the quantum computing race 35:44  Collapse, Copenhagen, or many worlds? Martinis's answer 37:43  Leaving Google: “essentially demoted” 40:48  Why Colab exists and what “negative authority” means 42:50  The real bottleneck: funding, not physics 43:31  Final advice: always be on the lookout for the impossible Qolab: https://qolab.ai/ Get the transcript, fascinating bonus content, and my Monday M.A.G.I.C. Message: https://briankeating.com/yt Have a .edu email and live in the USA? You automatically win a meteorite: https://BrianKeating.com/edu Subscribe: https://www.youtube.com/DrBrianKeating?sub_confirmation=1 Support Into the Impossible on Patreon, get my weekly M.A.G.I.C. Message, unfiltered bonus content, and live monthly Office Hours with me: https://www.patreon.com/drbriankeating Join this channel for perks, monthly Office Hours, and your name in the Member Roster at the end of every episode: https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join My books: Losing the Nobel Prize (memoir): http://amzn.to/2sa5UpA Think Like a Nobel Prize Winner: https://a.co/d/03ezQFu Focus Like a Nobel Prize Winner: https://a.co/d/hi50U9U Galileo's Dialogue (first-ever audiobook): https://a.co/d/iZPi9Un Twitter/X: https://x.com/BrianKeating Substack: https://briankeating.substack.com Blog: https://briankeating.com/blog Audio-only: https://briankeating.com/podcast Landing page: https://awake-mill-k25t.here.now #intotheimpossible #briankeating #JohnMartinis #NobelPrize #quantumcomputing #physics #podcast Learn more about your ad choices. Visit megaphone.fm/adchoices

"If you want to know what life's like when you're not the apex of intelligence, ask a chicken."That's the line that Geoffrey Hinton, the Nobel Prize-winning "Godfather of AI" dropped on Steven Bartlett's podcast. It stopped Cal cold. Along with a lot of other people. One of them was a digital anthropologist and best-selling author named Brian Solis, who's spent decades watching technology unfold. To Solis, Hinton's statement felt in the moment like we had reached The End. But when Cal caught up with Brian a year later, he found that the anthropologist had adapted and made a case for what's possible. It's all in the book he co-wrote with Dave Wright that's titled: Infinite. Cal's conversation with Brian did something he never expected. It made him think about himself, for the first time, as a leader. Stay tuned . . .

“If you really focus on the reader and creating something meaningful and impactful and excellent... it has longevity and shelf life and continued work to do out in the world well beyond you.” — Stacy EnnisIn this episode of the Sunlight Tax Podcast, I sit down with Stacy Ennis to explore what it really takes to write and publish a nonfiction book that grows your business, strengthens your brand, and creates lasting impact. We discuss the publishing process, book coaching, traditional publishing vs. self-publishing, and why integrity, strategy, and a clear message matter more than simply getting a book into the world.Stacy shares practical insights from years of helping authors craft books that not only sell, but also make a meaningful difference—offering valuable guidance for anyone considering writing a business book or becoming a published author.Also mentioned in today's episode:00:10 Introduction to Stacy Ennis and her background01:00 Stacy's journey from childhood reading to publishing expert02:36 Her experience in publishing and ghostwriting03:24 Working with authors with a bigger mission04:49 The ROI of writing a book and trust building05:56 Trusting Stacy's expertise after a trust-breaking experience07:43 The importance of mission and impact in writing09:04 The strategic value of a book for influence and marketing10:33 Shelf life and longevity of well-done work12:24 Reflections on the author journey and publishing routes14:36 Exploring traditional, self, and hybrid publishing16:36 The evolving landscape of publishing and author education18:24 Why hybrid and self-publishing are viable options20:28 The role of a book in business, trust, and influence24:18 The transformative process of organizing ideas27:37 The impact of writing on personal and business growth28:58 The importance of creative health and deep thought31:16 Rebuilding self-trust through the writing process32:44 The societal and professional importance of authorship33:01 Stacy's resources and podcastIf you enjoyed this episode, please rate, review and share it! Every review makes a difference by telling Apple or Spotify to show the Sunlight Tax podcast to new audiences.About Stacy Ennis:Stacy Ennis is a best-selling author, book coach, and speaker on a mission to help leaders clarify their ideas and harness their unique story to make an impact. Her background includes impacting more than 100 books in her 17-plus years in publishing; ghostwriting for a Nobel Prize winner in medicine; and leading as executive editor of Sam's Club's Healthy Living Made Simple, a publication that reached around 11 million readers. Stacy's work and writing have been featured in Yahoo!, Inc., Insider, Publisher's Weekly, Katie Couric, and the TEDx stage. Stacy is also the host of the podcast Beyond Better and holds a master's in writing and editing from the University of Cincinnati.Check Out Stacy Ennis' Work:* 10 Things You Should Know Before Writing a Nonfiction Book free resource* Author Influence Circle* Direct link to book a call with Stacy to chat about their book* Instagram @stacyennis* LinkedIn: Stacy Ennis* Study mentioned on the episodeEpisode Links:Check out my program, Money BootcampGet my Tax Help on SubstackGet your FREE visual guide to tax deductionsOrder my book: Taxes for Humans: Simplify Your Taxes and Change the World When You're Self-Employed Get full access to Taxes For Humans at sunlighttax.substack.com/subscribe

Drew Forsyth is an award-winning portrait photographer and director based in the North West of England, working with commercial and advertising clients across the UK and internationally. His work centres on people who've given everything to be extraordinary at what they do — from dancers with English National Ballet and musicians of the BBC Philharmonic and the Hallé Orchestras, to Nobel Prize-winning scientists, politicians, and performers at the height of their careers. His photography has been featured by BBC News, The Guardian, The Times, and Rolling Stone, and recognised by the Royal Society of the Arts. He's an AOP Accredited Photographer and Fellow of the RSA, and speaks internationally at events like Photo North, Pas de Deux Photo Conference, and the Royal Photographic Society.In this episode, Drew and Nikki trace his path from a JCPenney-style family portrait studio to photographing world leaders, Nobel laureates, and major corporate campaigns — and he breaks down exactly how he prices that work today. They dig into setting boundaries on set (and how to say yes to more without losing your mind), why those early "conveyor belt" studio years became his real technical training, and why he believes his biggest competition isn't other photographers — it's being forgotten. Drew also walks through how he actually quotes corporate jobs (day rates, itemized breakdowns, and the cautionary tale of a client who blamed him for "cheap-looking" dresses), how personal passion projects like his "Breaking Ground" series and a helicopter shoot over Manhattan became his best marketing, and the "Helsinki Bus Station Theory" — his framework for why so many photographers give up on their style right before it becomes distinctive.Topics covered:Setting boundaries with clients without losing bookingsWhy "conveyor belt" studio work builds indispensable technical skillsThe accidental path from individual sessions to corporate/organizational clientsPricing corporate shoots: day rates, itemized quotes, and scope questionsWhy past-client relationships and personal introductions beat cold outreachMarketing through consistent presence, not constant hard-sellingPassion projects as a client-acquisition strategyThe Helsinki Bus Station Theory: staying the course creativelyIf you're building a photography business, want to grow your portrait photography income, or are curious about how to make money from photography online, this conversation is packed with actionable advice.

Marie Curie was one of the most remarkable scientists in history. She was the first woman to win a Nobel Prize, the first person to win two, and the only person ever to earn Nobel awards in separate fields of science. Today we explore the life and legacy of Marie Curie with the science writer Dava Sobel. Dava is the author of prominent and best-selling science history books, including Longitude, Galileo's Daughter, The Planets, A More Perfect Heaven, and The Glass Universe. Today's conversation is based on Dava's latest book, entitled, The Elements of Marie Curie, How the Glow of Radium Lit a Path for Women in Science.

For most of human history, seeing inside a living body meant surgery. Newt tells how Dr. Raymond Damadian and physicist Paul Lauterbur turned 1940s nuclear magnetic resonance research into the MRI scanner—a machine that images soft tissue in astonishing detail without radiation or a single incision. From Damadian's hand-built "Indomitable" scanner to today's 40 million annual U.S. procedures, this episode traces how basic physics research, conducted with no medical purpose in mind, became one of medicine's most powerful diagnostic tools—and examines the controversial Nobel Prize snub that still stings decades later.See omnystudio.com/listener for privacy information.

Scott Dodelson spent ten years on the Dark Energy Survey testing the standard model of cosmology. It passed and missed by 2.5 sigma. The Director of Fermilab's Cosmic Physics Division on why the theory he helped build may be wrong, why nobody can find dark matter, and what it takes to change a scientific consensus. Subscribe if you want science with evidence, not speculation. Dodelson is Director of the Cosmic Physics Division at Fermilab, Professor of Astronomy and Astrophysics at the University of Chicago, and author of Modern Cosmology, the textbook a generation of cosmologists learned the field from. Lambda-CDM predicts how the early universe's tiny fluctuations grew into the structure we see today. The Dark Energy Survey was designed to check that prediction. The answer came back two and a half sigma off. That is both the most precise measurement ever made of how the universe grew, and a crack he cannot stop looking at. His question is not whether the model is close. It is whether close is enough to trust. We get into the Sigma-8 tension and what it actually takes for a scientific community to change its mind, the Dodelson-Widrow mechanism and his 1994 proposal that sterile neutrinos produced in the early universe could constitute dark matter, and the Neptune vs. Vulcan history of getting dark matter right and wrong. Asked which of those two situations we are in now, Dodelson's answer is: I have no idea. What you'll hear: Why the April 24, 1992 CMB discovery may have been the only science story ever to lead the New York Times front column The Dodelson-Widrow mechanism: how ordinary neutrinos in the early universe may have oscillated into the dark matter we see today Why Brian told Neil deGrasse Tyson to his face that we have already detected dark matter What it means when a theory can accommodate any result and whether inflation has that problem Why the particle physics community still does not trust cosmology's neutrino mass measurements What cosmology looks like in 2036 if Lambda-CDM breaks Killing the model is my dream. 0:00 None of it has been found in a lab. How many free passes do we get? 0:44 Dodelson helped build Lambda-CDM. His dream is to kill it. 2:10 10 years. One prediction. Two and a half sigma off. 5:42 "You're in charge" — what mentorship in science actually looks like 6:56 April 24, 1992: cosmology stopped being speculation 9:48 Geoff Burbidge went to his grave a steady-state believer 11:04 What a neutrino is and why it barely interacts with anything 12:36 Brian told Neil deGrasse Tyson we already detected dark matter 18:08 How ordinary neutrinos may have become dark matter 23:24 Lambda-CDM predicts Manhattan's density 13.7 billion years later 26:42 Two and a half sigma: technically a 1% chance the theory is right 28:04 Neptune was dark matter. Vulcan wasn't. Which one are we in now? 31:20 Dark matter, inflation, dark energy: none found in a lab 36:46 If both cracks are the same crack, Lambda-CDM is finished Get the transcript, fascinating bonus content, and my Monday M.A.G.I.C. Message: https://briankeating.com/yt Have a .edu email and live in the USA? You automatically win a meteorite: https://BrianKeating.com/edu Subscribe: https://www.youtube.com/DrBrianKeating?sub_confirmation=1 Support Into the Impossible on Patreon, get my weekly M.A.G.I.C. Message, unfiltered bonus content, and live monthly Office Hours with me: https://www.patreon.com/drbriankeating Join this channel for perks, monthly Office Hours, and your name in the Member Roster at the end of every episode: https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join Featured Guest: Scott Dodelson Substack: https://scottdodelson.substack.com/about Scott Dodelson on LinkedIn: https://www.linkedin.com/in/scott-dodelson-b6ba429/ Scott Dodelson on Twitter/X: https://x.com/ScottDodelson Modern Cosmology (book): https://www.sciencedirect.com/book/monograph/9780128159484/modern-cosmology Dark Energy Survey final results: https://www.darkenergysurvey.org/news-and-results/darchives/ Dodelson and Widrow 1994, Sterile neutrinos as dark matter: https://arxiv.org/abs/hep-ph/9303287 Previous ITI episode with Kyle Dawson on DESI: https://youtu.be/LPx4oiwGp2k?si=u_ZJWfJG5AhvBAEv  My books: Losing the Nobel Prize (memoir): http://amzn.to/2sa5UpA Think Like a Nobel Prize Winner: https://a.co/d/03ezQFu Focus Like a Nobel Prize Winner: https://a.co/d/hi50U9U Galileo's Dialogue (first-ever audiobook): https://a.co/d/iZPi9Un Twitter/X: https://x.com/BrianKeating Substack: https://briankeating.substack.com Blog: https://briankeating.com/blog Audio-only: https://briankeating.com/podcast #intotheimpossible #briankeating #cosmology #darkmatter #darkenergy #physics #LambdaCDM #podcast Learn more about your ad choices. Visit megaphone.fm/adchoices

In nature, enzymes are the catalysts that make much of biology work. They jumpstart chemical reactions that either wouldn't happen, or would happen super slowly. They break down food, build other molecules, extract energy, and more. What if we could harness evolution to engineer designer enzymes that do other specific jobs that benefit us?  Putting that idea into practice changed the game for chemistry, and earned Frances Arnold the Nobel Prize prize in 2018. She called it “directed evolution.” Today, thousands of labs use her methods to coax enzymes into doing things no one ever thought of. She joined Host Flora Lichtman in March 2026 to talk about where she sees this approach going in the future, and the personal evolution that brought her into science. Guest:  Dr. Frances Arnold is the Linus Pauling Professor of Chemical Engineering, Bioengineering and Biochemistry at the California Institute of Technology in Pasadena, California. Other episodes you may enjoy: Enzymes Are Taking On Our Plastic Problem Even Nobel Prize Winners Deal With Imposter Syndrome The transcript for this episode is available at sciencefriday.com. Subscribe to this podcast. Follow our show on Instagram, TikTok, Facebook, and Bluesky @scifri and sign up for our newsletters. Got a science question that's keeping you up at night? Call us: 877-472-4374 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Erin Ryan and Sami Sage (Betches Media) dig into the Trump administration's move to terminate millions of dollars of federal grants for teen pregnancy prevention and what that says about the administration's vision of women. Then, they dive into Emily Wilson's scathing review of the Odyssey. Plus, Annie Andrews, Democratic Candidate for U.S. Senate in South Carolina, stops by to discuss her race and how the death of Lindsay Graham is shaking things up. And as always, we end with sanipetty.Emily Wilson: An Uncomplicated Man Government Overhauls Teen Pregnancy Program to Focus on Marriage and Starting Families (NYT 7/22 + NPR 7/7)A Nobel Prize winner decodes why people aren't having kids (NYT OPED 2025 + Claudia's Original Research & Ruben Gallego 7/23)

Huxley is an example of having a good attitude about publicly acknowledging errors and learning from them. He earned the nickname “Darwin’s bulldog” for his very vocal support of Darwin’s work, and he wanted to promote science and education, particularly to people that didn’t normally have access to those things. Research: Bernoulli, D., Jenkyns, H.C. “Thomas Henry Huxley, a stone tablet, coccoliths, and deep-sea sediments in the high Alps.” Int J Earth Sci (Geol Rundsch) 112, 1661–1669 (2023). https://doi.org/10.1007/s00531-023-02330-5 Bowen, Keith. “Thomas Henry Huxley.” Ebsco. 2024. https://www.ebsco.com/research-starters/history/thomas-henry-huxley Burkhardt, Richard W.. "Jean-Baptiste Lamarck". Encyclopedia Britannica, 14 Dec. 2025, https://www.britannica.com/biography/Jean-Baptiste-Lamarck Darwin, Charles. “On the Origin of Species.” JOHN MURRAY, ALBEMARLE STREET. London. 1859. https://www.gutenberg.org/files/1228/1228-h/1228-h.htm Desmond, Adrian J. "Thomas Henry Huxley". Encyclopedia Britannica, 25 Jun. 2026, https://www.britannica.com/biography/Thomas-Henry-Huxley Dolan, John R. “The famous and lesser-known illustrations of Thomas Huxley’s Bathybius.” History of Oceanography. International Commission of the History of Oceanography. https://oceansciencehistory.com/2020/07/13/the-famous-and-lesser-known-illustrations-of-thomas-huxleys-bathybius/ “Freshwater Bathybius.” Nature 4, 49–50 (1871). https://doi.org/10.1038/004049b0 “The Great Debate.” University of Oxford Museum of Natural History. https://oumnh.ox.ac.uk/great-debate “The History of Evolutionary Thought.” Understanding Evolution. Berkeley University. https://evolution.berkeley.edu/the-history-of-evolutionary-thought/1800s/early-concepts-of-evolution-jean-baptiste-lamarck/ Huxley, T.H. “Agnosticism.” K. Paul, Trench. London. 1889. https://archive.org/details/a622650700huxluoft/page/169/mode/1up Huxley, T.H. “Aphorisms and Reflections. From the Works of T. H. HUXLEY, Selected byHenrietta A. Huxley. Macmillan and Co. London. 1907. Accessed online: http://aleph0.clarku.edu/huxley/Book/Aphor.html Huxley, T.H. “Autobiography and Selected Essays.” Riverside College Classics. 1909. Accessed online: https://www.gutenberg.org/files/1315/1315-h/1315-h.htm#link2H_4_0007 Huxley, T.H. “Collected Essays, Volume V: Science and Christian Tradition.” https://www.gutenberg.org/files/15905/15905-h/15905-h.htm Huxley, T.H. “Evidence as to man's place in nature.” D. Appelton and Co. New York. 1863. https://archive.org/details/evidenceastomans00huxl/page/n1/mode/2up Huxley, T.H. “On Some Organisms Living at Great Depths in the North Atlantic Ocean.” Quarterly Journal of Microscopical Science. viii., new Series, 1868, pp. 203-212. http://aleph0.clarku.edu/huxley/SM3/bathy.html Huxley, T.H. “On the Relations of Man to the Lower Animals.” Project Gutenberg eBook. 2001. https://www.gutenberg.org/cache/epub/2932/pg2932-images.html Joshi RS. “The Inner Root Sheath and the Men Associated with it Eponymically.” Int J Trichology. 2011 Jan;3(1):57-62. https://pmc.ncbi.nlm.nih.gov/articles/PMC3129131/ Lamarck, Jean-Baptiste. “Zoological Philosophy.” Macmillan. London. 1914. https://archive.org/details/ZoologicalPhilosophy Lyons, Sherrie L. “Thomas Henry Huxley : the evolution of a scientist.” Prometheus Books. New York. 1999. McGraw, Donald J. “Bye-Bye Bathybius: The Rise and Fall of a Marine Myth.” Bios, vol. 45, no. 4, 1974, pp. 164–71. JSTOR, http://www.jstor.org/stable/4607257 “Nobel Prize in Physiology or Medicine 1963.” The Nobel Prize. https://www.nobelprize.org/prizes/medicine/1963/summary/ Pearce, JMS. “Thomas Henry Huxley.” Hektoen International. Sept. 18, 2020. https://hekint.org/2020/09/18/thomas-henry-huxley/ Schwartz, Joel S. “Darwin, Wallace, and Huxley, and ‘Vestiges of the Natural History of Creation.’” Journal of the History of Biology, vol. 23, no. 1, 1990, pp. 127–53. JSTOR, http://www.jstor.org/stable/4331120 “Thomas Henry Huxley.” Darwin Correspondence Project. University of Cambridge. https://www.darwinproject.ac.uk/thomas-henry-huxley “Thomas Henry Huxley (1825-1895).” Humanist Heritage. https://heritage.humanists.uk/thomas-henry-huxley/ Tunstad, Erik. “Darwin's Bulldog: Thomas Huxley 200 Years.” Prosa. July 10, 2025. https://prosa.no/artikler/aktuelt/darwins-bulldog-thomas-huxley-200-ar Wyhe, John van. “It ain't necessarily so ...” The Guardian. Feb 9, 2008. https://www.theguardian.com/science/2008/feb/09/darwin.myths See omnystudio.com/listener for privacy information.