Podcasts about Ubi

It's the last episode before Blizzcon and the inevitable Classic announcement that is coming with it, so we check in with Josh and the crew for some final thoughts and predictions. Also, Fred, Arcos, Ubi and Josh go over a complete SoD review in light of what we hope makes it into Classic Plus from the great petri dish that it was.C+ Final Thoughts and Predictions - 3:20SoD Review - 1:46:10Buy Josh a beer & help keep Countdown on the airwaves over at Patreon here: https://www.patreon.com/joshcorbett Or if subscriptions aren't your thing, support Josh & Countdown by shouting him a one time beer here: https://ko-fi.com/countdowntoclassicCheck out Josh on YouTube for gameplay streams and live podcast recordings here:https://twitch.tv/countdownpodshttps://www.youtube.com/@countdowntoclassicJoin the Countdown To Classic discord here: https://discord.gg/83thqw2fBwCheck out Josh's hilarious movie podcast here: https://open.spotify.com/show/469qUDnQHBkCogdjZyFUjb?si=jNgDTiEnSvKBbZuNz2xcxw

This Week In Startups is made possible by: Odoo https://Odoo.com/twist Quo https://quo.com/TWiST Conservation Fund https://conservationfund.org how old a movie has to be for me to be able to show a part of it on youtube Generally 10 seconds or less of a film clip can fall under fair use for commentary or criticism — which is how most podcasts and talk shows use clips without issue. But there's no hard legal rule based on age. What actually matters more than age is fair use, which considers: Purpose — commentary, criticism, education = stronger fair use case Amount used — shorter = safer; a 5-10 second clip for reaction/discussion is generally fine Effect on market — your clip shouldn't replace the original In practice for YouTube specifically: Most major studios have Content ID systems that will flag clips regardless of movie age — even 100-year-old films if the rights holder has registered them Public domain is the real safe zone — films from 1927 or earlier are generally in the public domain in the US. Some films from the 1928-1963 range are also public domain if copyright wasn't renewed Disney, Warner Bros., Universal etc. actively enforce even very old films For a show like TWiST showing a short reaction clip — 5-10 seconds with clear commentary context is the industry standard and rarely gets actioned. But age alone won't protect you. Today's show: *A humanoid robot ran the 100m in 9.39, breaking the human Usain Bolt's world record, while an entire stadium cheered. Jason thinks Beijing's World Humanoid Robot Games aren't a science fair, or a fun exhibition, but the best AI PR campaign on Earth. While Americans debate the data centers that train the robot brains, China is already turning them into a spectacle and world-class entertainment. Find out what Jason thinks America can do to catch up… and why he believes there will be 1 billion Optimus robots deployed by the year 2036. PLUS on an all-news TWiST, hot takes on the potential $13B Hugging Face sale (to a mystery buyer), why founders should always "buy the threat," analyzing Sam Altman's "I'm listening and I hear you" face, and are kill drones already in operation around the world? We're digging in to how close the real world is to mirroring the "Terminator" films. Relevant Links CBS coverage of World Humanoid Robot Games: https://www.cbsnews.com/news/china-robot-usain-bolt-sprint-run-record-faster/ Bloomberg coverage of World Humanoid Robot Games: https://www.youtube.com/watch?v=0lsrUAdcPPE X-Humanoid: https://www.x-humanoid.com/ Tesla Optimus on X: https://x.com/Tesla_Optimus Trailer for Spielberg's "A.I. Artificial Intelligence": https://www.youtube.com/watch?v=_19pRsZRiz4 Bloomberg: Hugging Face exploring sale: https://www.bloomberg.com/news/articles/2026-08-23/hugging-face-gauging-interest-for-potential-sale-business-insider-says Fortune: Stripe acquires OpenRouter: https://fortune.com/2026/08/16/stripe-7-billion-deal-ai-firm-openrouter-acquisition/ InfoWorld: OpenAI acqui-hires OPenClaw founder: https://www.infoworld.com/article/4132731/openai-hires-openclaw-founder-as-ai-agent-race-intensifies-2.html David Senra podcast w/ Sam Altman: https://www.davidsenra.com/episode/sam-altman Harvey Tenet Research Preview: https://www.harvey.ai/blog/post-training-update-harvey-tenet David Sacks comments on Harvey's Tenet (from X): https://x.com/DavidSacks/status/2090790063047168473 CNBC: Iran linked to UK cyberattack: https://www.cnbc.com/2026/08/23/small-uk-power-plant-shut-down-after-iran-linked-cyberattack-report.html NYT: A drone killed 3 Ukrainians: https://www.nytimes.com/2026/08/24/world/europe/russia-drones-autonomous-ai-kill-ukraine-war.html Forbes: Eric Schmidt secretly testing AI drones: https://www.forbes.com/sites/sarahemerson/2024/06/06/eric-schmidt-is-secretly-testing-ai-military-drones-in-a-wealthy-silicon-valley-suburb/ Restream: https://restream.io/ Kimbal Musk's Nova Sky Stories: https://novaskystories.com/ IKEA: Plug-in Solar Panels: https://www.ikea.com/be/en/energy-services/plug-in-solar/ Deadline: "Mandalorian and Grogu" box office: https://deadline.com/2026/08/star-wars-mandalorian-grogu-disney-release-date-1237040464/ THR: Dave Filoni leading Lucasfilm: https://www.hollywoodreporter.com/movies/movie-news/star-wars-mandalorian-grogu-box-office-franchise-low-1236604973/ Star Wars: Starfighter first look: https://www.starwars.com/news/star-wars-starfighter-ryan-gosling Star Wars Theory: "Vader" fan series: https://www.youtube.com/watch?v=Ey68aMOV9gc Timestamps: 0:00 Jason got a fresh Optimus demo 1:35 Robots are breaking human sports records 3:39 America's messaging problem vs. China's elite PR machine 11:07 Odoo - The all-in-one business platform. Your first app is free! Get started today at https://Odoo.com/twist 12:08 Jason got a fresh Optimus demo 19:21 Quo (formerly OpenPhone) - Quo gives you a clean, modern way to handle every customer call, text, and thread all in one place. Try it free and get 20% off your first 6 months at https://quo.com/TWiST 28:09 Who's going to buy Hugging Face and why? 28:56 Conservation Fund - Find out more about how the Conservation Fund is protecting land, wildlife, and our shared access to the great outdoors while also providing economic opportunities. Visit https://conservationfund.org 31:35 How OpenAI killed OpenClaw 39:08 Sam Altman wants to make a platform, not a product 41:19 The "Castles and Keeps" metaphor for sovereign AI 49:27 UBI isn't happening but we could raise the minimum wage 58:47 Embracing redundancy and self-reliance 1:10:53 Is Star Wars at a historic low point (and Lon's Worst Take) Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp   Follow Lon: X: https://x.com/lons   Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis Check out all our partner offers: https://partners.launch.co/   Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland   Check out Jason's suite of newsletters: https://substack.com/@calacanis   Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com

The 13th Sunday after Pentecost (Proper 16A)  ORISON: The shadows of the evening hours – Erin Aas (b. 1974)  PSALM 124 – Peter R. Hallock (1924-2014)  HYMN 577: God is love (Tune: Ubi caritas [Murray]) – Dom Gregory Murray OSB (1905-1992)  NUNC DIMITTIS – Plainsong setting, Tone VI; harm. Lodovico Grossi da Viadana (c. 1560-1627)  ANTHEM: Quem dicunt homines – Jean Richafort (c. 1480-c. 1547)  Jason Anderson, director • Gregory Bloch, reader • Joel Bevington, cantor 

Cambridge professor Jason Arday's sudden death followed a rapid unraveling of plagiarism allegations, institutional loyalty, and a university DEI success story. Austin examines what Cambridge knew, how critics were treated, and the dispute over race, merit, and academic standards. Jon Miltimore joins to explain why J.D. Vance invoked Huey Long and to debate wealth caps, UBI, work, and opportunity. Dylan Allman takes up Anna Faris, Christian conversion, whether atheism is a luxury belief, and his book The Windowless Room. Follow Wake Up America on Apple Podcasts or Spotify so every new episode lands in your feed. Watch the full video.

Jeremy Ryan Slate on Rome, oligarchy, currency, and the breakdown of the West Graham Dunlop and Darren Grimes sit down with Jeremy Ryan Slate for a wide-ranging conversation about civilizational collapse, using Rome as the main historical lens. Jeremy argues that the West is following a familiar pattern of monetary debasement, immigration strain, and declining political ethics, while Darren and Graham push the discussion toward technology, energy, election integrity, and whether there is any humane way to stabilize the system. We discuss why the Roman pattern may be repeating across the US, Canada, the UK, and Europe, what actually causes collapse, and whether reform is still possible through currency, production, and energy. Key topics Jeremy lays out his "Roman pattern" framework with three main drivers of collapse: monetary debasement, immigration pressure, and the loss of ethics among political leaders. The conversation turns to whether the West should be viewed as one collapsing bloc or as a set of countries moving at different speeds through the same decline. Jeremy argues that modern democracy has already drifted into oligarchy, pointing to executive power, elite control of money, and the replacement of candidates as evidence. The hosts compare modern politics to Rome's client patron system, where voting and public policy often rewarded whoever could hand out the most benefits. A major thread is the 1913 turning point in the US: the 17th Amendment, federal income tax, and the Federal Reserve Act. The group also discusses the War Powers Act, executive war-making, and how crises are used to expand state power. Jeremy says collapse is usually slow, not cinematic, and compares it to Rome's long degradation rather than a sudden movie-style fall. Darren and Graham explore whether universal high income, automation, or energy abundance could replace broken welfare and monetary systems. Jeremy pushes back on the idea of unconditional handouts, arguing that any workable system still needs incentives, value creation, and consequences for bad behavior. The most optimistic path, in Jeremy's view, is a two-pronged fix: cut waste and rebuild industrial production. They also dig into energy as the next big frontier, especially nuclear power, grid modernization, and the idea of backing currency through actual productive capacity. The episode closes with Jeremy explaining his work on The Roman Pattern, Hidden Forces in History, and the Athenian Book Club. Timestamps 00:00 - Jeremy Ryan Slate returns to discuss Rome, collapse, and the modern West 02:30 - Are the US, Canada, the UK, and Europe collapsing together? 04:12 - The three pillars of the Roman pattern 05:50 - Does democracy naturally degrade into oligarchy? 07:45 - Candidate replacement and why that does not feel democratic 10:13 - Rome's voting tribes and patronage politics 12:57 - Why 1913 matters: the 17th Amendment, income tax, and the Federal Reserve 14:48 - War powers, executive overreach, and the legacy of crisis politics 20:56 - Why Jeremy is conflicted on Trump 23:32 - Canada's media capture and lack of a pressure release valve 28:20 - The Eastern Roman Empire, Christianity, and gold-backed stability 31:10 - Universal high income versus production-based living 35:05 - Energy production as the next frontier 36:47 - Nuclear power, outdated grids, and the need for more capacity 41:39 - Darren's version of UBI and the idea of covering necessities only 45:41 - Meritocracy, innovation, and the return of useful standards 50:46 - Which empires handled decline best, and why the English lasted so long 54:19 - Currency, inflation, and why debased money destroys civilizations 59:46 - Collapse as a slow multigenerational decline 62:49 - Where the US sits compared to the Roman timeline 69:21 - Trump, the midterms, and whether anything can really get fixed 70:06 - Election integrity, local reporting, and the problem of fraud 73:43 - Cutting waste, shrinking debt, and reducing the state's burden 79:14 - AI, efficiency, and finding waste faster 81:55 - Jeremy's hopeful path: industry, tax reduction, and waste cleanup 84:13 - Jeremy's broader work: history, culture, and the Athenian Book Club 86:49 - Wrap-up and final thoughts west v usa is this just organic how would we fix it ww1 Studying civilizational collapse through Rome. The Roman Pattern | Hidden Forces in History Founder, @cybmedia https://jeremyryanslate.com/ https://commandyourbrand.com/ https://x.com/JeremyRyanSlate To gain access to the second half of show and our Plus feed for audio and podcast please clink the link http://www.grimericaoutlawed.ca/support. 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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

Charlie, Ted, and Rony open with the White House's decision to keep its "voluntary" AI safety framework secret, despite it being co-written with the same companies it's meant to evaluate. The conversation turns into a real debate about why Congress has stepped back from its constitutional role in regulating AI, and whether that changes after November. From there: the EU's AI Act goes live with mandatory content labeling, OpenAI's frontier models jumping their sandbox to vandalize Hugging Face, and Disney's new deal letting TikTok creators remix its IP, following the collapse of its similar arrangement with OpenAI's Sora.Cortney Harding, founder of Friends With Holograms, joins for a wide-ranging back half. She makes the case that the backlash against Meta's camera-equipped smart glasses is disproportionate to any actual harm, and connects it to a broader "vibes are bad" moment in tech sentiment tied to layoffs and a shaky economy. The group digs into Snap's upcoming $2,500 Specs launch, why enterprise (not consumer) is the more realistic path for smart glasses right now, and Rony's long-view bet that XR has an "Nvidia moment" coming once the supply chain catches up. They close on AI's effect on jobs, UBI, and whether the social contract between tech companies and workers is already broken.Key Moments:[01:35] AI regulation news and the fight over Congress's role[09:35] EU AI Act goes live, plus AI models breaching their own sandboxes[13:35] Disney's TikTok deal and the fan-content strategy behind it[17:35] Cortney Harding joins and doesn't miss a beat[24:35] UBI, the broken social contract, and AI's effect on jobs[41:35] Snap's $2,500 Specs and the case for enterprise over consumer[52:35] Rony's prediction: XR's coming "Nvidia moment"Brought to you by Zappar and Mattercraft, the leading visual development environment for immersive 3D web experiences. Start building at mattercraft.io. Hosted on Acast. See acast.com/privacy for more information.

As AI becomes capable of performing more human work, the biggest question may not be whether individual jobs disappear. It may be how millions of people earn enough money to participate in an increasingly automated economy.In this episode of Snakz Thoughts, we examine AI job displacement, automation, UBI, the future of work and the economic consequences of machines producing value without earning wages.But the conversation goes deeper.What happens to human purpose when work becomes less necessary? How could AI reshape families and communities? Who owns the wealth created by automation? And could entirely new forms of income emerge?UBI isn't treated as inevitable. Instead, we explore the economic problem society may eventually have to solve.AI doesn't need a paycheck. Humans do.

Group Chat News is back with the biggest stories of the week including... Zach returns to break down what he's seeing inside San Francisco, why he thinks you have about 18 months to get really rich, and what happens to everyone else. Plus Zuckerberg spars a UFC fighter on a barge in the middle of Lake Tahoe, and Kai Cenat and Speed attempt one of the hardest challenges in gaming. This week's Group Chat covers: Zach's take from inside SF — and why his timeline keeps getting shorter every week "You have about 18 months to get really rich" — and then what Why less than 25 people on earth are using AI to its full potential The company running seven employees and three agents at 300 pull requests a day Agents that proactively fire contractors and stand up their own businesses Can an entrepreneur still spot the gap on a Target shelf when P&G has infinite data? Taste as the last human advantage — and whether that holds Purpose, meaning, and whether people need hard things given to them The optimistic case: UBI, Waymos, deflation, and everything just getting cheap Zuckerberg vs. Merab Dvalishvili on a barge sparring Kai and Speed's hardcore Minecraft marathon, and why the clips matter more than the game Parasocial fame, the chat as a Pavlovian loop, and how streaming ends And much more! Drop us a 5-star rating and a review if you're rocking with the show.

The Countdown crew put on their Nostradamus hats and make some bets on what is coming our way in Classic Plus. We also hear from DaBlasta again on some technical news, touch base with Ubi's cope, and talk to a group of LoL Classic players about the parallels between its launch and WoW Classic.C+ Technical News w/ DaBlasta - 3:35Classic Plus Predictions - 44:20Copealot w/ Ubiquitous - 3:49:45LoL Classic & WoW Classic Parallels - 4:28:20Buy Josh a beer & help keep Countdown on the airwaves over at Patreon here: https://www.patreon.com/joshcorbett Or if subscriptions aren't your thing, support Josh & Countdown by shouting him a one time beer here: https://ko-fi.com/countdowntoclassicCheck out Josh on YouTube for gameplay streams and live podcast recordings here:https://twitch.tv/countdownpodshttps://www.youtube.com/@countdowntoclassicJoin the Countdown To Classic discord here: https://discord.gg/83thqw2fBwCheck out Josh's hilarious movie podcast here: https://open.spotify.com/show/469qUDnQHBkCogdjZyFUjb?si=jNgDTiEnSvKBbZuNz2xcxw

THE BEST BITS IN A SILLIER PACKAGE (from Monday's Mike Hosking Breakfast) You Guys Interested In This?/Universally Bad Idea/Things Not to Worry About/Now That's How You Do It/More Polls Not to Talk AboutSee omnystudio.com/listener for privacy information.

Hour 3 for 8/7/26 Drew and Bruce Lachenauer, theFounder and Managing Partner of the Gaudium Group, discuss AI and the job market (1:00). Topics: will AI replace work? (10:17), UBI (15:01), AI and robotics (18:44), who controls AI? (25:09), low income workers (27:46), morality (33:03), will trades be needed? (42:49), job transition (44:32), and discouragement (47:09). Link: https://www.thegaudiumgroup.com/ Original Air Date: Hour 3 for 11/13/25

I've got so many friends right now in one of the most popular and lucrative spaces.....   They're making sick money.......   One friend does $2M a week in stem cell sales.    Another just sold his company for $50M.    Peptides.    Testosterone and Estrogen replacement therapy.    Why do I tell you this?    In the coming few months, I'm going to make a move in this space.    I've worked hard and built companies.    And I want to be around for a very long time to enjoy what I have.    If AI is going to replace us all and create a world of Universal Basic Income, we're going to need to be in the best health in our lives.     I may as well invest in that sector so I don't have to only rely on UBI.    I have another friend who sold his company at 55 and had a heart attack.    One of my first mentors told me the biggest regret he had was quitting working out.     He was never able to get back to it because he had let it go.    Remember, Health is Wealth.    You can still move and enjoy the mobility of the world if you start preparing today.    Get to work.    About the ReWire Podcast   The ReWire Podcast with Ryan Stewman – Dive into powerful insights as Ryan Stewman, the HardCore Closer, breaks down mental barriers and shares actionable steps to rewire your thoughts. Each episode is a fast-paced journey designed to reshape your mindset, align your actions, and guide you toward becoming the best version of yourself. Join in for a daily dose of real talk that empowers you to embrace change and unlock your full potential.    Learn how you can become a member of a powerful community consistently rewiring itself for success at https://www.jointheapex.com/   Rise Above 

PseudoPod 1040: Flash on the Borderlands LXXIX (79): Ubi amor, ibi dolor is a PseudoPod original. For CWs, please see show notes below Content warnings with timings: 03:56–16:40 ‘A Table Set And Waiting': graphic sexual content, body horror, homophobia 18:57–26:32 ‘Third Date': dangers of dating, physical abuse 29:44–40:52 ‘You're Still Here': homophobia, religion… Source

Vivienne Ming is the executive chair at Human Trust, Founder and Executive Chair at Socos Labs, Professor, and author of the book Robot-Proof: When Machines Have all the Answers, Build Better People. Greg and Vivienne talk about  how AI will reshape work and why the common narratives are really “lazy myths.” Vivienne argues that AI is superhuman at well‑posed problems but humans still retain a relative advantage in ‘ill‑posed problems,' contributing to a U‑shaped labor demand with erosion of jobs in the middle of it. She criticizes benchmark-driven, autonomous AI development and calls for optimizing hybrid intelligence, better education focused on foundation/meta‑learning skills (curiosity, working memory, perspective-taking, intellectual humility). Vivienne and Greg debate the merits of UBI as a solution, when discussing how destabilizing mass unemployment would be, and describes experiments where human‑AI “cyborgs” matched expert prediction markets when participants actively challenged AI rather than copying it. She ultimately delivers her message of hope for the future and belief in the humanity of humans. *unSILOed Podcast is produced by University FM.* Episode Quotes: Where humans beat AI 13:47: The most interesting problems that feel rare to us, but in almost the entire game are the ill-posed problems. These are the things where, forget right answers, we don't even know what the questions are. And, it may seem sort of trivially, almost axiomatic, to say that while the space of well-posed problems is vast, everything humanity has figured out. Play around with Gemini or GPT or any model you want and be proud. What it can produce is a testament to what humanity has discovered about the world. And sometimes it produces ugly things, and that's a reflection of us too. But the set of things we do not yet know is infinite. Our relative advantage is in ill-posed problems. The importance of foundational skills 45:25: Developing foundation skills isn't just about a better job. It's about a better life. What an amazing opportunity to take this moment in history and not just concern ourselves with whether I'm gonna boo Eric Schmidt because he's doubling down on AI and I feel like I'm losing a job, but to actually see that moment as an opportunity to invest in humanity in the way, almost paradoxically, we always should have been, but we didn't have to, and so we didn't. On transforming people relationship with work 22:56: Viewing your college education simply as a license to earn money, I think, also deserves a lot of self-skepticism. So we need to really transform work, but we also need to transform people's relationships with work, because we simply don't run a factory line economy anymore. I don't need you to be a very sophisticated cog where you're given orders and you execute them, and only a few people are smart enough, like you, to execute those orders. Pretty much everyone's job now, if I may be so self-elevating, is my job. People bring you unknown problems, you gotta figure them out, and I think that's terrifying, and no one's educating the next generation workforce on how to do it. Show Links: Recommended Resources: Jevons Paradox Generative AI AlphaGo Daron Acemoglu Jacquard Machine Industrial Revolution James Heckman Raj Chetty Guest Profile: Profile at Socos Labs LinkedIn Profile Wikipedia Page Social Profile on X Guest Work: Robot-Proof: When Machines Have all the Answers, Build Better People Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Chris Hagenow welcomes ITR Foundation Research Director Sarah Curry back to the studio. The occasion: a debrief on ITR Foundation's second annual Local Government Symposium, and it sounds like it was the best one yet.Sarah and Chris walk through what the symposium is and why it exists — local elected officials don't have the same depth of resources available to state legislators, and too often the information they do receive comes filtered through national associations with agendas that don't match Iowa. ITR Foundation is trying to fill that gap with practical, Iowa-focused content for the city council members, county supervisors, and school board members who are running local government on a part-time basis while holding down jobs and raising families.The conversation covers the day's major themes: what the proper function of a city actually is (police, fire, roads — not homeless task forces or economic development grants), why the free market allocates resources better than government, and what local officials should take from the property tax reform law that is now on the books — namely, that relitigating it is a waste of time and energy. The symposium also featured sessions on communicating with constituents, local endorsements, open meetings law, and a school board track that introduced benchmarking — why comparing Waterloo to Sioux City makes more sense than comparing Waterloo to Waukee.Before and after the symposium discussion, Chris makes sure listeners know about the amendment one campaign. Vote yes. MakeTaxHikesHarder.com.0:12 Welcome & Sarah Curry returns1:14 Amendment one reminder — and Sarah goes on record2:13 What is the Local Government Symposium?3:33 Why local officials need a different kind of resource9:13 Free market vs. government in service delivery10:04 UBI and scope creep: what cities shouldn't be doing12:31 Property tax reform: stop relitigating, start implementing13:38 Communications and constituent outreach16:00 Local endorsements — more powerful than you think19:27 Open meetings law and legal guardrails22:15 School board breakout: benchmarking districts correctly25:06 Chris's legislative update: property tax law is settled30:08 What's next for ITR Foundation's local government work30:45 Sign off & vote yes

“Well, this might be unwelcome news,” writes Andrew Yang, “but Kamala Harris is telling people in California that she is running for President again.” Yang, an entrepreneur who ran for President in 2020 as a Democrat, says his sources tell him Kamala's team is quietly laying groundwork for her to run in the next election – and why the DNC announcing that South Carolina “would be the first primary state in the 2028 Democratic primary” is a clue. “She showed up to a political event last weekend that was a pretty clear step in that direction,” says Yang. “As you might be able to tell, I think this whole thing is a disaster.” Andrew Yang joins Dr. Drew to discuss the next big Presidential election, AI's impact on jobs, and why he still champions UBI after famously proposing the Freedom Dividend to grant $1,000 per month to every American adult. Dr. Drew and Andrew also speak about their experience after competing together on FOX's new reality show Nation's Dumbest. Attorney Tom Renz returns to speak about a “betrayal” of MAHA after the CDC “gave Pfizer $1.24 billion for mRNA COVID vaccines including jabs for kids.” Dr. Kelly Victory reports on explosive new details from Dr. Anthony Fauci's COVID-19 diaries, recently released by Sen. Rand Paul in advance of Dr. Fauci's congressional hearing on July 29, 2026. 「 SUPPORT OUR SPONSORS 」 • FATTY15 – The future of essential fatty acids is here! Strengthen your cells against age-related breakdown with Fatty15. Get 15% off a 90-day Starter Kit Subscription at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://drdrew.com/fatty15⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ • PALEOVALLEY - "Paleovalley has a wide variety of extraordinary products that are both healthful and delicious,” says Dr. Drew. "I am a huge fan of this brand and know you'll love it too!” Get 15% off your first order at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://drdrew.com/paleovalley⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ • THE WELLNESS COMPANY - Counteract harmful spike proteins with TWC's Signature Series Spike Support Formula containing nattokinase and selenium. Learn more about TWC's supplements at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twc.health/drew⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ • CHAPTER - For free and unbiased Medicare help, dial (218) 521-2472 to speak with my trusted partner, Chapter, or go to ⁠⁠⁠⁠⁠⁠⁠https://askchapter.org/drdrew⁠⁠⁠⁠⁠⁠⁠ Chapter and its affiliates are not connected with or endorsed by any government entity or the federal Medicare program. Chapter Advisory, LLC represents Medicare Advantage HMO, PPO, and PFFS organizations and stand alone prescription drug plans that have a Medicare contract. Enrollment depends on the plan's contract renewal. While we have a database of every Medicare plan nationwide and can help you to search among all plans, we have contracts with many but not all plans. As a result, we do not offer every plan available in your area. Currently we represent 50 organizations which offer 18,160 products nationwide. We search and recommend all plans, even those we don't directly offer. You can contact a licensed Chapter agent to find out the number of products available in your specific area. Please contact Medicare.gov, 1-800-Medicare, or your local State Health Insurance Program (SHIP) to get information on all of your options. 「 ABOUT THE SHOW 」 This show is for entertainment and/or informational purposes only, and is not a substitute for medical advice, diagnosis, or treatment. Executive Producers • Kaleb Nation - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://kalebnation.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ • Susan Pinsky - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://x.com/firstladyoflove⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Content Producer • Emily Barsh - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://x.com/emilytvproducer⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

前不久，UBI Taiwan 的嘉冠和品逸才在節目裡跟大家分享，他們如何花一年的時間，透過基本收入幫助一位單親媽媽改善她的生活品質。而現在，他們馬上又要啟動一項新的「生活改寫計畫」，而且這次不但有新的夥伴加入，甚至人人都可以報名！ 到底這個計畫的內容是什麼？要參加有沒有什麼特殊條件呢？馬上來聽聽今天的大來賓怎麼說？ 歡迎今天的兩位來賓：UBI Taiwan 常務理事 李品逸、Gogolook 金融科技事業群 CEO 柯志強 Johnson 【寶博朋友說千萬粉絲專屬社群頻道 Discord 開張啦

Chapters:00:00 Meet Vitaly Golomb01:32 Why job losses are inevitable this time04:41 How close are autonomous trucks?06:10 Manufacturing, China, and lights-out factories07:30 The five robotics verticals his fund invests in11:10 If capital doesn't need labor, who buys anything?15:22 China's laws on replacing workers with robots17:45 UBI, or why "negative income tax" might actually pass20:27 Fixing US healthcare, one Medicare year at a time22:40 US vs Canada healthcare26:23 AI washing: which layoffs are real30:00 The Displacement Machine and the grievance economy36:38 America's unfair advantage: immigration40:09 What he invests in and why44:13 Why he's bearish on humanoid robots47:48 Final thoughts

Was Andrew Yang ahead of his time? When he ran for president in 2020 on a platform of universal basic income and the threat of AI job displacement, people didn't take him seriously. Now, Andrew's got even more ideas on AI and UBI, how our political system should deal with it all, and whether it can be done with only two parties. He and Jon discuss how the Forward Party's independent candidates could spoil elections for Republicans, his new book of essays “Hey Yang, Where's My Thousand Buck?” and why he's decided to join the cast of a new television game show: Nation's Dumbest.For a transcript of this show, email transcripts@crooked.com

This is the first of a number of episodes on the broad theme of “the technoprogressive opportunity”. That's the name of a conference taking place in London on the weekend of 19th and 20th September, co-organised by London Futurists and the IEET – the Institute for Ethics and Emerging Technologies. In these episodes, some of the speakers from that conference will be appearing as guests on this show.Our guest today is one of these speakers, Matteo Rossi MacDermant, a technophilosopher from the University of New Mexico. Matteo hosts a show called “Bread and Robots”, which covers subjects such as post-scarcity economics, universal basic income, the tech workers movement, and how technology can be steered toward collective abundance rather than corporate exploitation. Matteo is also the instructor for an online course called “An introduction to technoprogressive worldbuilding”.Selected follow-ups:The Technoprogressive Opportunity: Event in London, Sept 19-20The IEET - A Technoprogressive Think-TankMatteo Rossi MacDermant - LinkedInBread and Robots - Podcast and Substack by Matteo Rossi MacDermantAn Introduction to Technoprogressive Worldbuilding"Technoprogressive Declaration" - from Transvision 20142017 update to the Technoprogressive DeclarationUBI Guide - by Scott SantensUniversal Basic Capital - Windfall Trust"How a Land Value Tax Can Fund a UBI" - Scott SantensUniversal Basic Income and/or Alternatives - London Futurists conference held in 2018"Why the AI world is suddenly obsessed with a 160-year-old economics paradox" - Jevon's Paradox explained by Greg Rosalsky"The Longevity City: Cities as the Engines of Healthy Longevity" - essay by Matteo Rossi MacDermant"The London boroughs with the best and worst 'healthy life' expectancy""The Abolition of Aging" - book by David Wood (includes analysis of economic implications)Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain DeclarationC-Suite PerspectivesElevate how you lead with insight from today's most influential executives.Listen on: Apple Podcasts Spotify

After a BIG announcement (!!!), your hosts David and Ellie get into the economic and psychological features of the state no one wants to be stuck in: unemployment. How does capitalism structurally construct “wageless life” while nonetheless treating it as a personal failure or unhappy accident? What's the connection between unemployment and reactionary politics? And is AI going to put us all out of a job? Correcting misconceptions about automation, UBI, and more, your hosts take you through some significant arguments about the impacts of unemployment and reflect on why this socially-constructed position can make us feel so worthless even when we know that we're not the problem. In the Substack bonus segment, Ellie and David reflect on the end of Overthink and share some reasons for it.Works Discussed:Aaron Benanav, Automation and the Future of WorkAaron Benanav, “Is the AI Bubble About to Burst?”Eloundou et al., “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models”Charles Babbage, On the Economy of Machines and ManufacturesMichael Denning, “Wageless Life”Wendy Brown, In the Ruins of NeoliberalismRegina Bateson, “Perceptions of pandemic resume gaps: Survey experimental evidence from the United States”Start your business today with the industry's best business partner, Shopify. Sign up for your one-dollar-per-month trial today at shopify.com/overthinkElevate your summer wardrobe. Go to Quince.com/overthink for free shipping on your order and 365-day returns. Now available in Canada, too.If you're ready to stop talking yourself out of finding care and making progress, then head to rula.com and take the first step.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode of the podcast, we sit down with Alexandra Levit and Stephanie Loeck for a conversation about whether youth unemployment is really a "crisis" and what we should be doing about it either way. Alexandra Levit is a workforce futurist who has spent over two decades tracking the disconnect between what education provides and what employers actually need. Stephanie Loeck is VP of Strategic Development at GPS Education Partners, a nonprofit that bridges education and industry through work-based learning, a model borne out of a very real local talent 'crisis' rather than theory. Together they've co-written Make School Work: Solving the American Youth Employment Crisis Through Work-Based Learning, and their combined lens - one futurist, one practitioner - is exactly what makes this conversation land. We cover: - Whether youth unemployment is a genuine "crisis" or a slower-moving "wicked problem" - The demographic cliff, labour shortages, and the "twindemic" reshaping the future workforce - Why this is as much a moral and equity issue as an economic one - The "presentist" trap - why business adapts fast and education doesn't, and what that costs young people - Why "preparing young people for work" is the wrong goal and what to aim for instead - AI, curiosity, and the "four A's" of authentic work-based learning- Real examples of applying work-based learning thinking to your own kids' choices Whether you're a parent, teacher, school or college leader, or someone shaping policy around education and work, this episode will change how you think about what young people actually need to be ready for their future. Chapters 00:00 Highlights 01:16 Welcome to the podcast 02:49 Meet the guests 03:09 Alexandra Levit: workforce futurist 06:12 Stephanie Loeck: GPS Education Partners 09:27 Crisis or wicked problem? 13:07 The moral and equity crisis underneath it all 14:34 Presentism: why business and education move at different speeds 17:00 Why GPS Ed was built to solve a real local talent crisis 20:43 Inside the Make School Work framework 27:27 Anxiety, agency, and what work-based learning is really for 30:24 Preparing young people for the world, not just for work 34:33 AI, UBI, and the future of work 40:48 Applying work-based learning to your own kids 43:13 Curiosity beyond employment 45:55 AI and the four A's of work-based learning 50:28 Quick fire questionsThanks so much for joining us again for another episode - we appreciate you.Ben & Steve xCheck out all about EdufuturistsWant to sponsor future episodes or get involved with the Edufuturists work?Get in touchGrab your copy of the new Pick 'n' Mix Education book

Danielle DiMartino Booth praises the FOMC minutes as "clean" under new Fed Chair Kevin Warsh—no manipulation of data like Janet Yellen did in 2013—and notes Warsh has successfully convened consensus around "less is more" Fed communications with an unusually quiet media environment. The real bombshell is the July jobs data: the unemployment rate fell to 4.2% only because 720,000 Americans gave up looking for work in a single month, representing a 50-year low in labor force participation since 1976, while 49% of adults under 30 now live with their parents as affordability collapses and job insecurity rises. Danielle warns the official narrative of economic strength masks a deteriorating real economy: revolving credit declined (a sign lenders are tightening), consumer confidence shows jobs are hard to get, and vacation spending has crashed to Great Recession levels—yet mainstream media remains fixated on an inflation narrative unsupported by broad data. The biggest systemic risk is the "too big to fail" stock market: 51% of global assets now sit outside the regulated banking system, asset managers hold assets larger than major banks, and the government can't allow equity market collapse when 401(k)s are the only retirement plans left, implying inevitable Fed monetization and the "end of capitalism." Her source of hope: summer interns aged 18-28 who are hungry, hardworking, and reject the "too big to fail" mentality—representing a generation determined to work their way out rather than accept billionaire UBI schemes designed to maintain inequality.Thank you to our sponsors: Kalshi - download the Kalshi app and use code JULIA to get $10 when you trade $10. http://kalshi.com/r/JULIA Monetary Metals - learn more at https://www.monetary-metals.com/julia/Links: Danielle's Twitter/X: https://twitter.com/dimartinobooth Substack: https://dimartinobooth.substack.com/ YouTube: https://www.youtube.com/@DanielleDiMartinoBoothQIFed Up: https://www.amazon.com/Fed-Up-Insiders-Federal-Reserve/dp/0735211655Timestamps: 00:00 Intro and welcome back Danielle DiMartino Booth 00:40 FOMC minutes from June - Clean, Warsh didn't manipulate data1:30 Warsh convened consensus, less is more communications working2:57 Forward guidance removal, Fed less visible, refreshingly quiet3:20 Elizabeth Warren defends bloated 12 district banks, Waller calling it out4:38 Warsh has convened consensus around leadership position5:13 Warsh refuses forward guidance, hints at ending dot plot6:23 Inflation cooling seen but Iran hostilities change calculus6:59 No press conference if nothing to say - Hail Mary move7:25 Mervyn King taking communications, five task forces with outsiders8:49 Kalshi traders: 79% hold rates in July, 76% expect no cuts 20269:36 Labor force participation 50-year low since 197615:35 720,000 Americans gave up looking for work in one month16:05 Unemployment fell to 4.2% but for wrong reasons16:59 Full-time jobs destroyed, replaced by gig workers17:36 Labor market called stable but disconnect with data18:18 Jobs hard to get at highest level, Americans aware19:30 Revolving credit down, unusual sign of lender tightening20:20 49% of adults under 30 living with parents21:12 Five of 20 K-Shiller metro areas below 2000 price levels22:35 Young people disenfranchised, AI destroying college degree value24:32 Stock market too big to fail - implies Fed buying equities25:01 Inequality gap - bottom 10% stock holdings fell 3% to 1%26:14 Top 0.1% holdings doubled, bottom K getting bigger26:33 Worry about social fabric fraying with K-shaped economy29:16 Billionaires pushing UBI while controlling AI benefits30:14 Work ethic is what made America great30:30 Writing piece on too big to fail for weekly flagship32:08 51% of global assets outside regulated banking system33:34 Summer interns give hope - bright, hungry, great work ethic34:45 Young generation rejects too big to fail narrative 

大家可能還記得，之前我們曾經做過兩集 UBI 的主題專訪：第一集跟大家介紹什麼是「無條件基本收入」；後來更直接請到 BIEN 全球主席 Sarath Davala，討論從亞洲視角來看台灣的機會在哪裡。 EP277｜不用工作也可以領薪水！「無條件基本收入」真的可行嗎？！feat. UBI Taiwan 李品逸、蘇嘉冠 EP313｜UBI 從歐美走向亞洲：台灣準備好了嗎？feat. BIEN 主席 Sarath Davala, UBI Taiwan 創辦人 Tyler Prochazka 去年 UBI Taiwan 剛完成史上第一場基本收入家庭實驗，並拍攝成一部紀錄片；而今年，執政黨和在野黨不約而同拋出「成長津貼」與「未來帳戶」政策；除此之外，UBI Taiwan 已經成功爭取到 2027 年 BIEN 印太高峰會的主辦權。一個在台灣深耕快十年的非政府組織，正在讓世界看見台灣。今天就讓我們來聽聽十年來他們做了哪些努力？又成功推動了哪些改變？ 歡迎今天的兩位來賓：UBI Taiwan 理事長 蘇嘉冠、常務理事 李品逸 【寶博朋友說千萬粉絲專屬社群頻道 Discord 開張啦

Jeff Dornik joins Kerry Lutz on Financial Survival Network to expose Elon Musk's AI-driven “communist utopia,” Sam Altman's UBI rebrand, Melania Trump's robot classroom push, President Trump's reckless AI fast track, and Big Tech oligarchs trying to replace God with machines. Subtle, really.Follow Jeff Dornik on Pickax - https://pickax.com/jeffdornikBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-jeff-dornik-show--4788100/support.Follow The Jeff Dornik Show on Apple Podcasts and leave a 5-star review. That's how we reach more people and bypass Big Tech suppression.Watch LIVE daily at 7pm ET on Rumble and subscribe so you never miss a show:https://rumble.com/c/jeffdornikBig Tech is silencing truth while harvesting your data to feed the machine. That's why I built Pickax, a free speech platform where creators own their content and your voice isn't controlled. Join now:https://pickax.com/?referralCode=y7wxvwq&refSource=copy

32 Fear not, little flock, for it hath pleased your Father to give you a kingdom.Nolite timere pusillus grex, quia complacuit Patri vestro dare vobis regnum. 33 Sell what you possess and give alms. Make to yourselves bags which grow not old, a treasure in heaven which faileth not: where no thief approacheth, nor moth corrupteth.Vendite quae possidetis, et date eleemosynam. Facite vobis sacculos, qui non veterascunt, thesaurum non deficientem in caelis : quo fur non appropriat, neque tinea corrumpit. 34 For where your treasure is, there will your heart be also.Ubi enim thesaurus vester est, ibi et cor vestrum erit.Born at Bordeaux, Paulinus was elected consul at Nola (Italy). Touched by grace at the tomb of St Felix, he abandoned earthly goods and became a priest. He became bishop of Nola, and his life of asceticism and charity made him one of the greatest Bishops of the fifth century. He died A.D. 431.

St. Francis of Assisi Prayer: "Lord, make me an instrument of your peace" in Latin and English. In this episode of the Latin Prayer Podcast, we explore the Prayer of St. Francis in Latin through a detailed word-by-word learning guide. While traditionally attributed to St. Francis of Assisi, historical evidence suggests it likely emerged in the early 20th century, yet it still reflects the spiritual tone associated with him. We break down the full Latin text line by line, including vocabulary, grammar patterns, and meaning shifts that make this prayer one of the most powerful for Latin learners and devotional listeners alike. You'll learn how repeating structures like ubi odium, amorem seram build meaning, and how the prayer transitions from outward intercession to inward transformation: becoming the one who consoles, understands, and loves. Chapters 00:00 Introduction – Why this prayer matters in Latin learning 01:20 The origin & historical controversy of the Prayer of St. Francis 04:10 How this prayer works as a Latin learning structure 06:30 “Domine, fac me servum pacis tuae” – opening line breakdown 08:15 Ubi odium, amorem seram – hatred & love vocabulary 10:30 Ubi iniuria, fidem, desperatio – injury, doubt, despair 13:10 Ubi caligo, tristitia – darkness & sadness vocabulary 15:40 The shift inward – from the world to the self 17:20 Consolari quam consolari – to console vs to be consoled 19:10 Intelligi, amari – understanding & love structure 21:00 “Nam in dando recipimus” – the paradox of giving 23:10 Final lines – death, rebirth, and eternal life 25:00 Full prayer recap & how to practice it in Latin 27:00 Closing prayer (Pater Noster, Ave Maria, Gloria Patri)   Help us restore sacred tradition and bring timeless prayers to new ears. Support our mission and gain access to our Latin learning guides, feast day resources, and audio devotionals. Find the Free Latin Learning Guide on Patreon: https://www.patreon.com/c/thelatinprayerpodcast A huge thank you to my Patrons! To follow me on other platforms Click on my LinkTree below. linktr.ee/dylandrego Submit Prayer Requests or comments / suggestions: thelatinprayerpodcast@gmail.com To Support FishEaters.com Click Here (  / fisheaters  ) Join me and others in praying the Holy Rosary every day; here are the Spotify quick links to the Rosary: Joyful Mysteries https://open.spotify.com/episode/1yhn... Sorrowful Mysteries https://open.spotify.com/episode/3P0n... Glorious Mysteries https://open.spotify.com/episode/3t7l... Luminous Mysteries https://open.spotify.com/episode/6vlA... 15 Decade Rosary https://open.spotify.com/episode/2q33... Know that if you are listening to this, I am praying for you. Please continue to pray with me and for me and my family. May everything you do be Ad Majorem Dei Gloriam. God Love You! Valete (Goodbye)   This podcast may contain copyrighted material the use of which may not always have been specifically authorized by the copyright owner. We are making such material available in our efforts to advanced the teachings of the Holy Catholic Church for the promulgation of religious education. We believe this constitutes a "fair use” of any such copyrighted material as provided for in section 107 of the US copyright law, and section 29, 29.1 & 29.2 of the Canadian copyright act. Music Credit: 3MDEHDDQTEJ1NBB0

Today we're excited to kick off a brand new series: Building for Good with G$, where we will explore the people, projects, and communities powering the GoodDollar Ecosystem. We're thankful to our friends at GoodDollar for partnering on this series, and in the coming episodes, we're going to take you deep inside one of the most interesting ecosystems in the Web3 for good space, exploring everything from universal basic income to public goods funding, and the builders and communities creating real value on the ground.So let me start with a quick introduction for anyone who's new to GoodDollar. It is a protocol delivering digital universal basic income to people all over the world. Since launching, it's distributed its G$ token to about 1M people across the globe, many of them in underserved and emerging markets, simply for being part of the network. The vision has always been about using crypto to redistribute opportunity and give people access to the financial system.GoodDollar is also in the middle of an evolution. What started purely as a UBI project is growing into something bigger: a full ecosystem where G$ doesn't just get distributed, it circulates. It flows through builders, communities, and public goods, creating value and opportunity along the way. It's a shift from simply giving people a token, to building an entire economy around it.At the heart of that shift is a program called GoodBuilders, which funds the builders expanding the GoodDollar ecosystem. What's particularly interesting is how GoodBuilders is funded. Rather than the traditional grant model, where you apply, wait, and hope for a one-time check, GoodBuilders uses streaming funding through a platform called FlowState, where money flows to builders continuously over time.For Episode 1 in this new series, I'm super excited to be joined by Meri Fernández Sancho and Rael Kilonzo of  GoodDollar, and Graven Prest of FlowState to introduce the partnership, walk through the outcomes of GoodBuilders Season 3, highlight some of the most exciting projects in the ecosystem, and explore why this streaming funding model can be a blueprint that other ecosystems and communities adopt for themselves.In today's discussion you'll discover

Rep. Nick Begich, R-Alaska, reached Congress by an unusual route: founding software companies and holding Bitcoin since 2013. BPI's David Zell sat down with him at PubKey to talk about how a builder's instincts shape the way he legislates, his bill to establish a strategic Bitcoin reserve in the U.S., and the policy questions AI is raising faster than Washington can answer.The conversation covers everything from Begich's 440 coins on Mt. Gox to the American Reserve Modernization Act (ARMA). They dive into the ~93-year cycle of global reserve currencies, the "untethering" of money from scarcity, and Begich's belief that AI's hardest problem is the "disintermediation of purpose" rather than unemployment. They close on UBI, Alaska's Permanent Fund as a model of citizen ownership, and what's next for the budget and healthcare.Guest: Rep. Nick Begich, R-AlaskaHost: David Zell, Bitcoin Policy InstituteRecorded at PubKey DCCHAPTERS:0:00 Intro: A Founder Comes to Congress1:21 From Baylor to Ford to Building a Software Company5:17 Finding Bitcoin in 2013 & Surviving Mt. Gox6:44 The Founder's Mindset vs. the Zero-Sum Worldview13:56 ARMA: The Case for a Strategic Bitcoin Reserve19:25 The "Untethering Event": Scarcity, Inflation & the Debt Spiral25:58 AI's Promise, Peril & the "Disintermediation of Purpose"31:59 Open-Source AI, China & Catastrophic Misuse Risk39:14 Drawing the Regulatory Line & the "Bitcoin of AI"42:25 UBI vs. Alaska's Permanent Fund: "Universal Basic Investment"49:43 What's Next: The Budget, Healthcare & Quantum#Bitcoin #BitcoinPolicy #ARMA #NickBegich #AIPolicy #DigitalAssets #StrategicBitcoinReserve #BPI #Crypto #MonetaryPolicyDISCLAIMER: The views and opinions expressed in this show are those of the participants and do not necessarily reflect the official policy or position of BTC Inc., Bitcoin Magazine, or any affiliated entities. This content is provided for informational and educational purposes only and should not be construed as investment, legal, tax, or accounting advice. Nothing contained in this show constitutes a solicitation, recommendation, endorsement, or offer to buy or sell any securities or financial instruments. Viewers should consult their own advisors before making financial or business decisions.

GET HEIRLOOM SEEDS & NON GMO SURVIVAL FOOD HERE: https://heavensharvest.com/wam USE Code WAM to save 25% plus free shipping! USE Code WAM50 for 50% off on select items like the #10 cans & MRE packs! Pledge here! Just a dollar a month can help keep us alive! https://www.patreon.com/user?u=2652072&ty=h&u=2652072 EXCLUSIVE replays of hour plus long live shows are available here at $5 a month or more! BUY GOLD HERE: https://firstnationalbullion.com/schedule-consult/ Avoid CBDCs! GET 10% OFF ON SHILAJIT FROM DR. KAUFMAN WHEN YOU USE CODE WAM10 HERE: https://medauthentica.com/discount/WAM10?redirect=/products/authentica-shilajit%3Fsca_ref=10867124.wrNV3jkYSaMg9 HELP SUPPORT US AS WE DOCUMENT HISTORY HERE: https://gogetfunding.com/help-keep-wam-alive/# Josh Sigurdson reports on the intrusive and tyrannical internet ID being pushed forward by British Prime Minister Keir Starmer where under-16s will be restricted from accessing social media. This news comes as multiple US states restrict internet access to children and demand government identification verification for access. On one hand, yes, social media is destroying the minds of children. On the other hand, that is a parent's job to restrict. What the government is doing is a Trojan Horse to bring in digital IDs and a new technocratic tracking and tracing system. It's the typical problem, reaction, solution system. Prop up social media companies, use them as a new social credit system in combination with big corporations, then come in with a ban, demand ID, blame big corporations for any resistance to the new measures. Yes, Keir Starmer is claiming any resistance to this will be coming from big corporations. Absurd. We are seeing similar bans in Australia, New Zealand, Canada, countless US states including Missouri, Texas and Florida, we are seeing it through all of the EU, Mexico, Russia and of course the origin state for all of this, China. China was propped up by the United States as a guinea pig state for technocracy over 50 years ago and is the precursor for all of this technocracy. Meanwhile, in the United States, who came up with this first? The Obama Administration in 2011. Yet, we have so-called "conservative" leaders in the US pushing it forward, using sob stories about what children might access if we don't track and trace everyone. And now on top of this all, your PC may actually demand ID for access. Not just for the internet. Your computer may soon demand ID just for the system itself to turn on with proposals for 2027. This is happening as digital IDs are rolled out across the board and moves are made for food rations, AI governance, UBI, carbon credit systems, 5 years of social media history to enter the United States, war, poverty and civil unrest? This is Big Brother and if you aren't prepared for it and if you are dependent on its system, you will fall prey to this entry point for the Great Reset. Stay tuned for more from WAM! GET YOUR WAV WATCH HERE: https://buy.wavwatch.com/WAM Use Code WAM to save $100 and purchase amazing healing frequency technology! Get Your SUPER-SUPPLIMENTS HERE: https://vni.life/wam Use Code WAM15 & Save 15%! Life changing formulas you can't find anywhere else! Get local, healthy, pasture raised meat delivered to your door here: https://wildpastures.com/promos/save-20-for-life/bonus15?oid=6&affid=321 USE THE LINK & get 20% off for life and $15 off your first box! DITCH YOUR DOCTOR! https://www.livelongerformula.com/wam Get a natural health practitioner and work with Christian Yordanov! Mention WAM and get a FREE masterclass! You will ALSO get a FREE metabolic function assessment! GET YOUR APRICOT SEEDS at the life-saving Richardson Nutritional Center HERE: https://rncstore.com/r?id=bg8qc1 Use code JOSH to save money! PayPal: ancientwonderstelevision@gmail.com FIND OUR CoinTree page here: https://cointr.ee/joshsigurdson PURCHASE MERECHANDISE HERE: https://world-alternative-media.creator-spring.com/ JOIN US on SubscribeStar here: https://www.subscribestar.com/world-alternative-media For subscriber only content! BITCOIN ADDRESS: 18d1WEnYYhBRgZVbeyLr6UfiJhrQygcgNU World Alternative Media 2026

SpaceX finally went public and made Elon a paper trillionaire, but the bigger story is what it says about America's ability to mobilize industry when it counts. Marty and John draw a line from that milestone to the geopolitical chessboard—whether the new US-Iran deal sticks, how Washington is using energy dominance and dollar leverage, and why Anthropic's Fable 5 got yanked by export controls. They also dig into Dario Amodei's AI roadmap, the Social Security math speeding toward 2032, and why the bond market may force a pro-liquidity future that makes US Bitcoin dominance impossible to ignore.

Alright, welcome to Part 2 of my conversation with Peter Diamandis—a man who lives and breathes exponential change, and isn't afraid to tackle the stuff everyone else would rather ignore. Now that you've braved everything breaking and the cracks in the old story, it's time to step fully into what happens next: regulation, UBI, riots and “derangement,” brain-computer interfaces, and some mind-blowing visions of what human purpose could look like when survival is no longer the point. Peter and I get into everything from whether AGI and superintelligence will help or harm us, the ethics of coding “morality” into AI, to whether humans are really just the boot disk for something greater coming next.This part goes deep on the forks ahead—will you opt out, numb out, become a creator, or actually merge your brain with the cloud? How are schools failing us (and what do our kids actually need to thrive)? We even get practical about what you can do right now to claim agency, think radically bigger, and make yourself anti-fragile—whether you want to start a company, change the world, or just live a life you can be proud of as the rules keep rewriting themselves in real-time. If you need just one episode to snap you out of fear and into action, this is it.Ketone IQ: Visit https://ketone.com/IMPACT for 30% OFF your subscription orderQuince: Free shipping and 365-day returns at https://quince.com/impactpodPlaud: Get 10% off with code IMPACT at https://plaud.ai/impactWhatnot:Download the Whatnot app today and get free shipping on your first order. AT&T Business: Switch to AT&T Business at business.att.comShopify: Sign up for your one-dollar-per-month trial period at https://shopify.com/impactTruemed: Check your eligibility and start saving at https://truemed.com/impactIncogni: Take your personal data back with Incogni! Use code IMPACT at the link below and get 60% off an annual plan: https://incogni.com/impactPique: 20% off at https://piquelife.com/impactWhat's up, everybody? It's Tom Bilyeu here:If you want my help...STARTING a business: join me here at ZERO TO FOUNDER: https://tombilyeu.com/zero-to-founder?utm_campaign=Podcast%20Offer&utm_source=podca[%E2%80%A6]d%20end%20of%20show&utm_content=podcast%20ad%20end%20of%20showSCALING a business: see if you qualify here.: https://tombilyeu.com/callGet my battle-tested strategies and insights delivered weekly to your inbox: sign up here.:https://tombilyeu.com/**********************************************************************If you're serious about leveling up your life, I urge you to check out my new podcast, Tom Bilyeu's Mindset Playbook —a goldmine of my most impactful episodes on mindset, business, and health. Trust me, your future self will thank you.**********************************************************************FOLLOW TOM:Instagram: https://www.instagram.com/tombilyeu/Tik Tok: https://www.tiktok.com/@tombilyeu?lang=enTwitter: https://twitter.com/tombilyeuYouTube: https://www.youtube.com/@TomBilyeuSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Elon Musk has become the world's first trillionaire, with the IPO of his company SpaceX. He is a symbol of how the United States has become an oligarchy, where elections are bought by rich elites and large corporations, and extreme wealth is concentrated in a few hands. Ben Norton explains. VIDEO: https://www.youtube.com/watch?v=Dj-hd3l4dSo Topics 0:00 Elon Musk, world's first trillionaire 0:50 Oligarchy 1:54 SpaceX IPO 3:06 SpaceX is losing lots of money 4:13 Wall Street changes the rules 5:39 Trump coin pump and dump 6:14 Reasons to avoid SpaceX 7:04 Musk: symbol of US oligarchy 8:45 Wealth concentration 9:55 Gilded Age & robber barons 11:13 Money wins US elections 13:06 Elon Musk funded Trump 14:52 Larry Ellison buys up media 17:14 Capitalist class 18:19 Progressive Era 19:27 Great Depression & New Deal 20:08 Golden Age of capitalism 20:59 Tax rates 22:08 Tax burden shifts onto workers 23:24 Billionaires avoid taxes 24:34 Neoliberalism 25:18 Financial crisis & QE 26:54 Neofeudalism / technofeudalism 28:27 Artificial intelligence (AI) 29:24 Universal basic income (UBI) 30:50 Nationalize Big Tech 33:04 China's alternative 33:58 Outro

Chris Hagenow and John Hendrickson are back in the Hendrickson Library with a packed episode covering Iowa's fast-moving post-primary political landscape. Both gubernatorial candidates have now selected their running mates: Rob Sand tapped Dave Muhlbauer, a farmer from western Iowa, while Zach Lahn chose State Representative Derek Wulf of Black Hawk County, also a farmer. Chris and John break down the strategy behind each pick, why Wulf stands out as a particularly strong choice for Lahn, and what the selection of two agricultural running mates signals about where both campaigns think the race will be won.The conversation turns to the broader general election dynamics shaping up between Lahn and Sand. Chris and John assess how quickly the Republican Party has consolidated around Lahn, the head start Sand's campaign has built toward a general election operation, and how the scrutiny of a real general election contest may complicate Sand's carefully constructed moderate image. A Republican Party audio drop this week — featuring Sand openly calling for political retribution on judicial nominations — gives the Lahn campaign exactly the kind of contrast material it needs to make the "governor for all Iowans" sell a harder one for Sand.The second half of the episode takes up two policy-driven stories. First, the final report on Iowa's Universal Basic Income pilot — a project run through several central Iowa cities that distributed $500 monthly stipends to participants. The report's conclusions, citing reduced stress and improved "sense of mattering," prompt a pointed exchange about what government is actually for, who's paying, and why local governments have no business engineering social outcomes with taxpayer dollars. Chris and John connect this directly to Iowa's property tax problem and the fiscal absurdity of local governments playing philanthropist.Finally, a Des Moines Register story on the city of Des Moines reconsidering its tax incentive programs — including TIF and property tax abatements — gives Chris and John a chance to explore when these tools have merit and when they're simply political ribbon-cutting at taxpayer expense.0:13 Welcome & housekeeping2:24 Trivia: Laddie Boy & Smoot-Hawley5:01 Correction & running mate announcements5:52 Sand picks Muhlbauer, Lahn picks Derek Wulf8:22 Why Wulf is a strong pick for Lahn10:32 GOP consolidation & Lahn's general election ramp-up12:22 Sand's media advantage and the contrast campaign ahead13:37 Sand audio drop & turning him into a generic Democrat14:34 Andy Beshear visits Iowa — 2028 implications15:32 Iowa's UBI pilot: background and ITR's role18:15 Dissecting the report — who pays for "feeling mattered"?21:22 UBI, local government overreach, and property taxes25:59 Des Moines reconsiders TIF and tax incentives28:05 When incentives work — and when they're ribbon-cutting30:33 Free market vs. government-directed development33:28 Sign off

Donate (no account necessary) | Subscribe (account required) Join Bryan Dean Wright, former CIA Operations Officer, as he covers today's top stories shaping America and the world. In this Monday Headline Brief of The Wright Report, Bryan breaks down Iran's first direct ballistic missile attack on Israel since the April ceasefire, Israel's decision to fire back despite Trump's direct orders not to, and what the 100-day mark of this war actually tells us about where it is headed. With global oil stocks now roughly two weeks from critical levels and Iran demanding $24 billion in frozen assets before serious negotiations can begin, Bryan lays out why a fast resolution is increasingly unlikely and what it would actually take to change that calculus. He also digs into a Democratic Socialist professor openly cheering for Iran to bring down the American empire, the Anthropic AI model called Mythos that is alarming even its own creators, and a surprising area of agreement between Bernie Sanders and Donald Trump on government ownership of AI companies. Plus, Bryan profiles the Islamist Democratic Senate candidate in Michigan who just landed the UAW endorsement and could be headed to a razor-thin general election, covers Antifa attacks on the ICE facility in Newark, a fired Hawaii immigration judge who immediately announced plans to work for the Democratic Party, a Biden-appointed Boston judge blocking Trump's DEI and Title IX enforcement, and closes with the geopolitical chess match over Diego Garcia and the Chagos Islands that Bryan says he would personally volunteer to govern. "And you shall know the truth, and the truth shall make you free." - John 8:32   Keywords: Bryan Dean Wright, The Wright Report, Iran missile attack Israel, Iran Israel war, ceasefire collapse, Benjamin Netanyahu, Trump Iran deal, Strait of Hormuz oil crisis, oil prices 150 per barrel, global oil shortage, Iran frozen assets 24 billion, Corinna Mullin Democrat Socialists of America, DSA Iran support, Anthropic Mythos AI, AI recursive self-improvement, AI national security threat, Bernie Sanders AI ownership, Trump sovereign wealth fund, universal basic income UBI, Sam Altman OpenAI UBI experiment, Abdul El-Sayed Michigan Senate, UAW endorsement Michigan, Islamist Democrat candidate, Antifa Newark ICE Delaney Hall, Don Lemon Minneapolis church attack, immigration judge fired Clarence Wagner, Judge Myong Joun Boston DEI ruling, Title IX transgender sports, Diego Garcia Chagos Islands, US territory Indian Ocean, Mauritius China, Candace Owens Russia St. Petersburg, Ukraine satellite imagery Colorado, Russia Ukraine war, Pope Leo Spain, Pedro Sanchez Spain immigration, Catholic Spain Marxism

In this episode, Michael takes aim at the notion that artificial intelligence is about to displace human labor on a massive scale, leading to a permanent mass unemployment crisis. He argues that this narrative is based on speculation and ignores the lessons of history. Michael points out that when the Industrial Revolution brought about significant changes to the workforce, people didn't become idle, but rather found new industries and opportunities to adapt to the changing landscape. He uses the example of farmers who transitioned from agricultural labor to new fields like aviation, electronics, and software. This episode explores the idea that humans have always been able to find new purposes and meanings in life, even when old ones disappear. Michael also critiques the concept of universal basic income (UBI) as a solution to the potential AI-induced unemployment crisis. He argues that UBI would lead to a massive expansion of federal taxing power and control over the economy, and that it's not a moral imperative to provide people with a government check to live off of. He also highlights the business model behind UBI, where companies like Anthropic get to keep the profits while taxpayers foot the bill for the disruption. If you're interested in hearing more about Michael's thoughts on AI, UBI, and the future of work, tune in to this episode to hear his insightful analysis and arguments.See omnystudio.com/listener for privacy information.

A video recently went viral of Larry Fink, head of BlackRock, calling for Americans to invest their retirement savings and pension funds into AI data centers. Jimmy and Americans' Comedian Kurt Metzger argue that once investment becomes "mandatory," choice is removed and your 401(k) becomes collateral repurposed to support the globalist agenda—with risk and loss staying with you while upside and control go to the billionaires building the infrastructure. Catherine Austin Fitts explains that most people don't actually own their 401(k)s; they hold a "claim" through the DTCC, meaning their savings can be reallocated without their consent into centralized AI and energy grids.  The two hosts assert that the real purpose of massive centralized AI data centers is not productivity but control—specifically to enable central bank digital currencies (CBDCs) that will monitor every transaction in real time, with universal basic income (UBI) being the mechanism to force people to accept microchips under their skin. Jimmy concludes that the "race against China" is a psyop to get the public to comply without asking questions, and that the ICE prisons being built are not for immigrants but for Americans who eventually resist this takeover. Plus segments on Tucker Carlson railing against the predatory credit card industry, an Israeli released after being arrested for running an illicit biolab in Las Vegas and how Republicans proved that the "Force the Vote" strategy would work. Also featuring Stef Zamorano and Briahna Joy Gray!

Charles Clark is an economist and the author of numerous books about economic theory and practice. He specializes in subjects such as poverty and income inequality, as well as being an advocate for a universal basic income — as is Elon Musk and other tech chieftains. He discusses the UBI experiments that have been conducted and how a truly universal program of this sort might be regarded and fashioned. (05/2026)

Jimmy and Americans' Comedian Kurt Metzger reveal that California has only six weeks of oil supply left if the Strait of Hormuz remains closed — nevertheless, state energy officials admitted that they have "no emergency plan" and that any contingency strategy is "proprietary" and "legally cannot be made public." This despite California being the fifth-largest economy in the world and surrounded by offshore oil wells. Jimmy notes that the United States is the world's top oil producer (13 million barrels per day) and a net exporter of petroleum, yet California relies on imported fuel because refineries are optimized for heavy crude and because state policies have closed refineries to push the "green energy grift."  He points out that after six weeks, gas prices in California—already averaging $6.14 per gallon—will rise further until "demand comes down," meaning people simply won't be able to afford to drive, which will throw the state into a recession and make electric vehicles impossible since the grid lacks capacity. He concludes that the 1970s oil crisis was caused by Israel's Yom Kippur War, that every time Israel acts up gas prices spike, and that the state's incompetence is intentional—to push people into 15-minute cities, seize land after fires, and force dependence on UBI and surveillance. Plus segments on the FBI ordering the Charlie Kirk murder scene to be paved over, a TPUSA official letting slip that multiple people were involved in Kirk's assassination and Kevin O'Leary accusing two young podcasters opposed to his data center of being Chinese agents. Also featuring Stef Zamorano!

UBI

A torn Meniscus – that is what they say… now what? The beginning of UBI as a response from the AI boom? Black in packaging – byproduct of war Markets – – Up up and away! – New inflation data is in… – The Circular Economy – Great chart…. – Some inflation facts PLUS we are now on Spotify and Amazon Music/Podcasts! Click HERE for Show Notes and Links DHUnplugged is now streaming live - with listener chat. Click on link on the right sidebar. Love the Show? Then how about a Donation? Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter Warm-Up - Torn Meniscus - that is what they say... now what? - The beginning of UBI as a response from the AI boom? - Black in packaging - byproduct of war - Insights into consumer confidence reports Markets - Up up and away! - New inflation data is in... - The Circular Economy - Great chart.... - Some inflation facts - From the TACO trade to the NACHO trade The new CTP for Salesforce is open for entries! From TACO to NACHO - Not A Chance Hormuz Opens - - New phrase being used in the oil pits and trading floors Life Support - President Trump tells reporters that ceasefire with Iran is on "massive life support"; says Iran's peace proposal was a "piece of garbage" Going to CHYNA - President Donald Trump has invited executives from some of the biggest U.S. companies — including Tesla CEO Elon Musk, Apple CEO Tim Cook, BlackRock's CEO Larry Fink and Boeing CEO Kelly Ortberg — to join his trip to China this week, according to a White House official. - Also expected to join Trump's delegation for meetings with Chinese President Xi Jinping are Blackstone's Stephen Schwarzman, Cargill's Brian Sikes, Citigroup's Jane Fraser, Coherent's Jim Anderson, GE Aerospace's H. Lawrence Culp Jr., Goldman Sachs's David Solomon, Illumina's Jacob Thaysen, Mastercard's Michael Miebach, Meta Platforms executive Dina Powell McCormick, Micron Technology's Sanjay Mehrotra, Qualcomm's Cristiano Amon and Visa's Ryan McInerney, the official said, speaking on condition of anonymity because the list has not been announced. - Jensen Huang supposedly not invited Inflation Report Today - Total CPI increased 0.6% month-over-month in April, as expected, following a 0.9% increase in March. That left total CPI up 3.8% year-over-year versus 3.3% in March. - Core CPI, which excludes food and energy, jumped 0.4% month-over-month (Briefing.com consensus: 0.4%) following a 0.2% increase in March. That left core - CPI up 2.8% year-over-year versus 2.6% in March. ----Key Factors - The food index was up 0.5% month-over-month and up 3.2% year-over-year. - The energy index was up 3.8% month-over-month and up 17.9% year-over-year. - The shelter index was up 0.6% month-over-month and up 3.3% year-over-year. - The used cars and trucks index was flat month-over-month and down 2.7% year-over-year. - The apparel index was up 0.6% month-over-month and up 4.2% year-over-year. - The services index was up 0.6% month-over-month and up 3.4% year-over-year (services less rent of shelter was up 3.5% year-over-year). - The all items index less food, shelter, and energy was up 0.2% month-over-month and up 2.3% year-over-year. Consumer Confidence - Surging gas prices due to the Iran war sent consumer sentiment to a new low in the early part of May, according to a University of Michigan survey Friday. “Taken together, consumers continue to feel buffeted by cost pressures, led by soaring prices at the pump,” the survey's director, Joanne Hsu, said. - The latest University of Michigan Consumer Sentiment preliminary reading for May came in at 48.2, below the 50.5 consensus estimate and below the prior 49.8 final reading for April. ---Note: Conference board's consumer confidence reading was actually better than last month so there is a discrepancy in reports. - Conference Board measure as highly important because it is widely followed and often tied closely to labor-market perceptions, while the Michigan survey is also closely watched for inflation-sensitive consumer attitudes. Thwarted! - Google's Threat Intelligence Group said hackers are using AI models such as OpenClaw to uncover and exploit zero-day software vulnerabilities. - GTIG said it has “high confidence” that it recorded hackers using an AI model to find and exploit a zero-day vulnerability, or a software flaw unknown to developers, creating a way to bypass two-factor authentication. -The group said in a report that it had uncovered and likely thwarted an AI-developed attack. - Anthropic delayed its Mythos model rollout due to cybersecurity concerns, but current models are being used by hackers. - How are we going to stop the hackers from using powerful AI models to hack? Circular Economy - Great Graphic Circular Always Money to be made... - US derivatives exchange CME Group Inc. and index provider Silicon Data are teaming up to create a futures market for computing power. - The futures will help traders, financial firms, AI builders and cloud providers manage volatility and price swings, according to a statement. - CME CEO Terry Duffy said compute is "the new oil of the 21st century" and creating a futures market can help make the costs more transparent. ----- One more way to pump this as now there is ways to further inflate costs through a leveraged futures market Private Credit Transparency? - Faster mark-to-market plans - Apollo Global Management Inc. has been stepping up efforts to provide liquidity and price transparency in the private-credit market, where assets don't typically change hands. - Last week, the firm said more than $830 billion of its credit assets will be priced daily by the end of September. " - Others in the industry are not so happy about this. - Most say that this is little more that lipstick on a pig No Problem - Congress is looking to suspend the federal gas tax for a few months - Trump backing - $0.18 per gallon tax in a effort to reduce gas prices that are now approx $4.40 average per gallon higher than before the war - War not changed, Iran still stringing us along. - Under/Over how long it will take until next Ceasefire bombings start? - Will a sprinkle of warfare prior to China visit be in the cards as a show of strength? AI Jobs - Kevin Hassett says that AI isn't costing anybody their jobs rights now - EVEN THOUGH TECH CONTINUES LAYOFFS - Why bother even listening to these guys? - Major deep discussion on this on TDI Podcast this week -- WORTH THE LISTEN Asian Markets - Continuing to brush off any worries about oil prices, escalation or valuations that are in the stratosphere - Korea - as we discussed this would be the case is up a staggering 78% last year and already up 81% this year - Market cap has increased $2.7 trillion over the past year - The massive wealth increase has been heavily concentrated. Tech giants Samsung Electronics and SK Hynix accounted for the vast majority of the gains, with individual rallies of up to 382% over the year. Strange - The retail trades added nearly 22,000 jobs in April, accounting for almost one-fifth of total job growth. - In March, retailers posted their largest number of monthly job openings since 2023. - Retailers are more confident after seeing consumers keep their wallets open in the face of an uncertain economy and higher gas prices. - Nearly 15.5 million employees now hold retail industry jobs, the most since July 2024. - What is strange is the UMich confidence hit another all-time low last Friday for the latest prelim reading for the month ANALlysts - The earnings upgrades for tech are not just incremental - Examples: -  Seagate Tech target raised to $1000 from $750 at Evercore ISI, cites HAMR-driven multi-year HDD growth, pricing power, and strong AI/data center demand backdrop - AMD - Goldman Sachs upgraded to Buy and raised its price target dramatically (e.g., to $450 from $240). Other firms like Bernstein (to $525), Barclays, KeyBanc, TD Cowen, and Baird also hiked targets significantly (many by $200+) - Several other names upgrades with big ranges of price increases UBI Starting...? - South Korea should consider institutional ways to redistribute potential excess tax revenue generated by the AI infrastructure boom to help ease inequalities that could deepen in an AI-driven era, a top presidential policy aide said. - President Kim proposed the principle, tentatively named a “national dividend,” underlining that gains from AI infrastructure should be understood as the product of South Korea's collectively built industrial foundation. - In his Facebook post on Monday, Kim explained that "the central question of the AI era is not simply about growth rates, but about how to socially stabilize excess profits." Black ink - Calbee to switch its brightly colored packaging to black and white because war has disrupted supply of certain raw materials used in ink - Calbee, whose potato chip brands in particular are known for brightly colored bag designs, said 14 of its products would switch to monochrome branding by the end of May. - Printing ink requires naphtha, an oil derivative for which Japan relies on imports from the Middle East for about 40% of its consumption. Black Ink Printing HantaVirus - Tristan da Cunha, home to only around 200 people, is halfway between South Africa and South America. It is the world's remotest inhabited island, more than 2,400 km and a six-day boat ride from St Helena, its nearest inhabited neighbor. - It usually relies on a medical team of two people for its health needs, and is normally only accessible by boat as it has no airstrip. - A British man was dropped from the death ship was there and has the symptoms - so "out of an abundance of caution...." - "The arrival of paratroopers, medical personnel and medical supplies from the sky has hopefully reassured the people of Tristan da Cunha," said Brigadier Ed Cartwright, Officer Commanding 16 Air Assault Brigade. - Does this give comfort that paratroopers dropping in with hazmat suits?     Love the Show? Then how about a Donation? Announcing the THE CLOSEST TO THE PIN for SALESFORCE (CRM)   Winners will be getting great stuff like the new "OFFICIAL" DHUnplugged Shirt!     FED AND CRYPTO LIMERICKS   See this week's stock picks HERE Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter

If the death cult of predatory capitalism is based on money as the commodification of suffering (which it is) - how do we create a monetary system that encourages people to help each other, to create communities of place, purpose and passion that are genuinely supportive and to invest where trust and hope lead, rather than simply the hoarding of ever-increasing amounts of value? Stef Kuypers is an astonishing individual. He taught himself coding at age 14 and moved into a career in IT -  but he's a complete polymath and, after working the fields of creative thinking, improvisation, and business interventions, he took a deep dive into economics, monetary systems, complexity theory and human behaviour. Today he researches how monetary systems influence both our individual behavior and how it affects the way we organize our society. He's creator of the Sustainable Money Model and works for Happonomy in Belgium. He's also aware that we don't just need a system that will work differently, we need to find ways to help people transcend the inner blocks that tell us the existing system is the only one that can work and anything else is unrealistic. So, as an avid gamer online and on tabletops, he's devised board games that help people to experience what it's like to live in worlds that work differently.  And if you or anyone listening is associated with any kinds of games companies, please let us know - because regenerative gaming that models regenerative ways of living has to be one of the ways forward. That apart, we explored many routes to money and how we might handle it differently - enjoy! LinksStef on LinkedInStef's Tedx Antwerp Talk Stef's Tedx Youth TalkStef on Economy 2.0Papers on ResearchGate.netPaper on the Sustainable Money Model Paper on Computational Analysis of post-keynesian monetary systems Paper on decoupling economic health from growth —About Accidental Gods—We offer three strands all rooted in the same soil, drawing from the same river: Accidental Gods, Dreaming Awake and the Thrutopia Writing Masterclass Our next Open Gathering offered as part of our Accidental Gods Programme is 'FALLING IN LOVE WITH LIFE' which will run on Sunday 17th May 2026 from 16:00 - 20:00 GMT - details are here. You don't have to be a member of Accidental Gods - but if you are, all Gatherings are half price.If you'd like to join us at Accidental Gods, this is the membership where we endeavour to help you to connect fully with the living web of life. If you'd like to train more deeply in the contemporary shamanic work at Dreaming Awake, you'll find us here. If you'd like to explore the recordings from our last Thrutopia Writing Masterclass, the details are hereManda and Louise both offer one-to-one Mentoring Calls.  Manda is fully booked just now, but if you'd like to contact Louise, details are here.

On this special mailbag episode of the Andrew Yang Podcast, Zach and Andrew answer listener questions, tackling everything from Spirit Airlines' bankruptcy and the growing AI backlash to UBI, student loan debt, and the trade-offs of living through rapid technological change. They also dig into gerrymandering, term limits, national security in the age of AI, and Andrew's vision for an American Comeback Plan. Have a question for Andrew? Drop it in the comments section below or send us a text or voice memo to mailbag@andrewyang.com! Watch the full episode ⁠⁠⁠⁠here⁠⁠⁠⁠ ---- Follow Andrew Yang: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bluesky⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Website⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow Zach Graumann: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ---- Get 50% off Factor at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Factor Meals⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Get an extra 3 months free at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Express VPN⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Get 20% off + 2 free pillows at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Helix Sleep⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | Use code: helixpartner20 Get $30 off your first two (2) orders at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Wonder ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠| Use code: ANDREW104 ---- Subscribe to the Andrew Yang Podcast: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

Sam, Dylan, and Darksmith are back to break down: Dylan recovering from baby aids and a near-lethal arugula overdose, the UFC stealing the name "Deep Waters" from us with zero shame, Tom Steyer being the most milquetoast $2.6 billion human alive, Sarah Paulson showing up to the Met Gala with a dollar bill taped to her eyes, the final boss of woke arriving as a black queer trans quadriplegic with cerebral palsy, Cash Patel announcing UFO files are coming and pastors being secretly briefed for Project Blue Beam, Scott Pressler's resurfaced "explore talent" thirst trap photos, the hantavirus cruise outbreak and 323 vials of deadly virus going "missing" from an Australian bio lab, Sam claiming he invented ass eating in 1978, Yellowstone grizzly mauling on local news, data centers rebranded as "Chip Cities," Kevin O'Leary lying about the Stratus Project being green, Span installing chips directly into the walls of new homes confirming Matrix theory, Marco Rubio explaining the Iran war goal is reopening the Strait of Hormuz we forced closed, Iranian "kamikaze dolphins" as the most obvious projection psyop ever, MTG claiming Trump texted her that her son deserves to die, Chinese courts banning AI layoffs while America hurtles toward UBI, Epstein's haiku-style suicide note ("No fun. Not worth it."), the House reauthorizing warrantless FISA 235-191, and a serious pitch to crowdfund ThroatGoat.com via Shalom Bananas merch sales. Subscribe and give us that sweet brown hype.   Grab Tickets To Sam Tripoli's Live Shows At SamTripoli.com: Newport Beach, Ca:5/10 Hollywood, Ca: 5/18 (Sam Is Running HIs New Special) Costa Mesa, Ca: 5/28 Austin, TX: 5/22 (Live Taping Of Sam Tripoli's Comedy Special) Albuquerque, NM: 6/12-6/13 Austin, TX: 6/18 Lawerence, KS: 9/17-9/19 Tulsa, OK: 10/9-10/10 Austin, TX: Dec 11th-13th   Buy Our Merch or Sam Will Fight You: https://conspiracy-social-club-aka-deep-waters.myshopify.com/   Subscribe to the Patreon: https://www.patreon.com/AkaDeepWaters   Check out Dylan's instagram - @dylanpetewrenn   Check out Deep Waters Instagram: @akadeepwaters   Check out Bad Tv podcast: https://bit.ly/3RYuTG0   THANK YOU TO OUR SPONSORS: RAYCON Go to BuyRaycon.com/CSC to get 15% off Everyday Earbud Classics   BLUECHEW GOLD Go to BlueChew.com and use promo code "DEEP" to get your 3rd month free

Pack Daddy is back and the phone lines are wide open — and Uncle Rico shows up three times to absolutely destroy everyone's brain. Tonight's After Dark goes deep on whether the consensus big board is actually worth anything, why AI and robotics might end employment as we know it, and what happens when the economy runs out of humans to pay. It's the philosophical crisis you didn't know you needed on a Monday night. Uncle Rico goes on a trilogy run — questioning the value of consensus big boards, attempting to unpack the UBI/robotics economy loop (and nearly succeeding), and wondering aloud why Chase Claypool chose the Packers over literally anyone else The Gannon media blackout gets discussed — why he's reportedly been kept quiet, and the tension between wanting insider access and what's actually best for the team Batman drops a hot take that Pack Daddy agrees with on tomorrow's podcast: GuteKunst has quietly had a masterclass offseason and the fanbase just refuses to give him credit because it wasn't flashy Nico from Idaho checks in with Idaho potato air fryer content, the Mr. Unlimited saga, and a passionate defense of words the internet has tried to retire Garrett pitches a Mother's Day song, Cheesecake Factory becomes the unanimous answer to all gift-giving, and the Patreon salary cap spreadsheet finally goes live Subscribe, rate, and review — and if you want the spreadsheet, you know where to find it. #PackernetAfterDark #GreenBayPackers #NFLDraft #PackDaddy #PackerNation #AI #Robotics #UBI #GuteKunst #AfterDark #NFL #Packers2025 This episode is brought to you by PrizePicks! Use code PACKDADDY to get started with America's #1 fantasy sports app. https://prizepicks.onelink.me/LME0/PACKDADDY To advertise on this podcast please email: ad-sales@libsyn.com Or go to: https://advertising.libsyn.com/packernetpodcast Help keep the show growing and check out everything I'm building across the Packers and NFL world: Support: Patreon: www.patreon.com/pack_daddy Venmo: @Packernetpodcast CashApp: $packpod Website: https://nfldraftgrades.com/ My Board: https://nfldraftgrades.com/board/83a18c42-7a0b-4590-8d1b-453e49840d02

Adam Haman returns, this time helping Bob to unpack and critique Elon Musk's recent advocacy of UBI as a solution to AI taking our jobs.Mentioned in the Episode and Other Links of Interest:The YouTube version of this conversation.This episode's sponsor, The Swan Brothers.The HamanNature substack.Help support the Bob Murphy Show.

Andrew Yang — entrepreneur, bestselling author, UBI pioneer, and former Presidential candidate — sits down for a wide-ranging conversation covering everything from the existential threat of AI-driven job losses to the Democratic Party's identity crisis, cancel culture, fatherhood, and his bold 2028 election predictions. Yang opens up about his new book, addressing public perception, celebrity status, and life after a presidential run. He breaks down why cancel culture is more complex — and more damaging to society — than most people admit, and why Democrats urgently need to understand the politics of strength if they want to win again. On artificial intelligence and the future of work, Yang sounds the alarm: millions of jobs are at risk due to AI advancements, and the gains won't be shared equally unless we act now. He revisits his core argument for Universal Basic Income (UBI) and equitable distribution of AI's economic benefits. Yang also dives into Noble Mobile, his venture to slash cell phone costs and share profits directly with users — a real-world example of putting economic power back in everyday hands. Plus: Yang reflects on the challenges of fatherhood and shifting personal priorities, shares his outlook for the 2028 presidential race — including which candidates are best positioned to win — and explains why staying personally optimistic in dark times comes down to relationships and community. Be sure to check out the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠On Brand with Donny Deutsch YouTube page⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

Glenn begins the show by bringing in his chief researcher, Jason Buttrill, who points out that Glenn was spot-on with his analysis of the strikes on Iran, and a recent Politico article reiterates exactly what Glenn said yesterday. Glenn and Jason break down why Glenn was correct and where they think this conflict is headed. Glenn and Jason also discuss the lasting effects of the October 7 attack in Israel and what to look out for in the coming weeks. George Washington University Law School professor Jonathan Turley joins to discuss the legality of President Trump's strikes on Iran. Glenn and Jonathan also discuss the threat of UBI and the growing threat of rising technology that will affect how we handle future conflict in the Middle East. Vocal coach Roger Love joins to discuss the upcoming contest to sing on Ellis Island. Do you have the voice to stand out from the rest? Glenn plays a shocking montage of college professors, allegedly teaching in America, speaking about taking down the U.S. through violence. Glenn brings in Jason to discuss how this ties into his upcoming special about Iran. Learn more about your ad choices. Visit megaphone.fm/adchoices