Podcasts about West Wing

Michael Cohen challenges mainstream political narratives while defending Donald Trump's influence, policies, and movement. Joined by K.T. McFarland and Laura Loomer, Cohen explores Trump's strategy toward Iran, Middle East geopolitics, West Wing tensions, Ukraine, and the growing divide within American politics. He also calls for accountability, forgiveness, independent thinking, and an end to partisan “cult mentalities.”

Laura Loomer joins Michael Cohen for an exclusive interview only on WABC Radio. Here's a sneak peek of what's to come!

Dan and Maureen are back and ready to rocket into the autumn of 2026! It's been a spicy summer, and it's we're going into a fall that will break the Scoville scale. In the meantime, let's catch up and hear about the totally normal and not at all weird person who follows Trump around with a printer in a backpack and feels normal things for him. We're in this together, SaysWhovia. Says Who is made possible by you, through your support of our Patreon at patreon.com/sayswho

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

Rob Lowe is best known for his decades in Hollywood, from The West Wing to Parks and Recreation and beyond. In this episode of Next Question, I spoke with him about his career, his long marriage, and his family's experience with cancer — including how a clinical trial helped extend his grandmother's life after her breast cancer diagnosis. We also discussed why he's become an advocate for helping people better understand what cancer clinical trials are and how they work in cancer care.Presented in partnership with @EliLillyandCompany #LillyPartner#sponsored Hosted on Acast. See acast.com/privacy for more information.

Kelsi is joined by theologian, Amy Orr-Ewing, to discuss the unique gift of Christian forgiveness which Amy writes about in her most recent book, Forgiveness: Reclaiming its Power in a Culture of Outrage and Fear.Amy Orr-Ewing (DPhil, Oxford University) is an international speaker, theologian, and apologist. She is the author of multiple books, including Where Is God in All the Suffering? and Why Trust the Bible? Orr-Ewing speaks at churches and on university campuses around the world and has spoken in the UK Parliament, the United States Capitol, and the West Wing of the White House. She previously served as president of the Oxford Centre for Christian Apologetics and is an honorary lecturer at the University of Aberdeen. She lives near Oxford, England, with her husband and their three sons.Show Notes:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Support 1517 Podcast Network⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠1517 Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠1517 on Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠1517 Podcast Network on Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠1517 Events Schedule⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠1517 Academy - Free Theological Education⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠More from Kelsi:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Kelsi Klembara⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow Kelsi on Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow Kelsi on Twitter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Kelsi's Substack⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe to the Show:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Youtube⁠⁠⁠⁠More from Amy:Forgiveness: Reclaiming Its Power in a Culture of OutrageAmy's WebsiteFollow Amy on Instagram

Joanna Coles and Daily Beast executive editor Hugh Dougherty break down the growing turmoil inside the Trump White House, starting with Karoline Leavitt's sudden departure and what it reveals about the pressures facing the administration as the midterms approach. They discuss who could replace Leavitt, why Trump's most loyal spokespeople may have limited options outside conservative media, and the increasingly bitter fight over who actually has control inside the West Wing. They also look at the departures of other senior officials, Susie Wiles' slipping grip on the administration, and the extraordinary backdoor maneuver that put a new ICE chief in position while she was away at her daughter's wedding. As more senior figures head for the exits and subpoenas loom, Joanna and Hugh consider what the accelerating West Wing exodus says about the future of Trump's presidency. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Freddy Gray is joined by Jacob Heilbrunn, editor of The National Interest and a senior fellow at the Atlantic Council, for a tour around the houses of Trumpworld. Karoline Leavitt has resigned as White House press secretary, officially to spend more time with her young family, and Washington is busy deciding whether to believe it.Freddy and Jacob discuss who might replace her and what the exit says about the mood inside the West Wing; why Trump's approval rating has sunk to a level last seen under Nixon in 1974; whether the wars in Ukraine and Iran are edging the world towards something larger; and why Jacob thinks a presidency that broke every rule about who can reach the White House may end up clearing the path for AOC.Learn how to earn yield on gold, paid in gold, at Monetary-Metals.com/AmericanoProduced by Henry LloydBecome a Spectator subscriber today to access this podcast without adverts. Go to spectator.co.uk/adfree to find out more.For more Spectator podcasts, go to spectator.co.uk/podcasts.Contact us: podcast@spectator.co.uk Hosted on Acast. See acast.com/privacy for more information.

It's Thursday, August 13th, A.D. 2026. This is The Worldview in 5 Minutes heard on 140 radio stations and at www.TheWorldview.com.  I'm Adam McManus. (Adam@TheWorldview.com) By Jonathan Clark and Adam McManus Haitian gangs target churches Violent gangs in Haiti are increasingly targeting churches. These gangs control up to 90 percent of the capital of Port-au-Prince. They have killed and kidnapped religious leaders and shut down dozens of churches.  Catholic Bishop Joseph Gontrand Décoste told Aid to the Church in Need, “We are the last remaining retaining wall: moral, ethical, spiritual, and prophetic. … That is why they want to eliminate us.” Christians help victims of Columbia's earthquake Christians are bringing disaster relief to Colombia.  A 7.4-magnitude earthquake struck the South American country on Monday. The quake killed 250 people and injured thousands more.  Many Christian relief organizations are sending aid to the country, including Samaritan's Purse. Franklin Graham, the president, wrote on X, “We … are currently working to put together an airlift of relief supplies, including emergency shelter tarp and hygiene kits. I encourage you to pray for the people of this country.” Hebrews 13:16 says, “Do not forget to do good and to share, for with such sacrifices God is well pleased.” Send a much-needed donation to the Columbian victims of this earthquake through Samaritan's Purse. You'll find the donation link in our transcript today at www.TheWorldview.com. Mexican Bible Society translates Scriptures into 8 local languages The Bible Society of Mexico is celebrating six decades of ministry this year. The Bible has been translated into eight indigenous languages in the country. And the demand for Bibles is growing as Protestant churches expand their presence.  Efraín Amaro is the head of distribution of the Bible society. He told Christian Daily International, “In 60 years, more than 374 million biblical materials have been distributed. We have reached 13 cities across the country with service and distribution centers.” Revelation 7:9-10 says, “After these things I looked, and behold, a great multitude which no one could number, of all nations, tribes, peoples, and tongues, standing before the throne and before the Lamb, clothed with white robes, with palm branches in their hands, and crying out with a loud voice, saying, ‘Salvation belongs to our God Who sits on the throne, and to the Lamb!'” White House Press Secretary Karolien Leavitt embraces motherhood White House Press Secretary Karoline Leavitt, age 28, will be leaving her position to fully embrace motherhood. She and her 60-year-old husband, Nicholas Riccio, have two children together -- their son Niko, 2, and Viviana who is three months. In a Truth Social post, President Donald Trump explained that she “will be departing her role at the end of the month so she can spend more time with her beautiful young children and family, a decision I totally understand and respect!” In a statement on X, Leavitt added, “Serving as the White House Press Secretary over the past year and a half has been the honor and adventure of a lifetime. I am incredibly grateful to President Trump for granting me so many extraordinary opportunities, such as working in the West Wing and spending countless hours in the Oval Office, flying around the world and meeting foreign leaders, and traveling across our beautiful country and meeting Americans from all walks of life. “The truth is, since returning to the White House after the birth of my daughter, I have felt in my heart that I cannot be the best mom my two young children deserve while devoting the constant time, energy, and attention required of the White House Press Secretary. And that is why I have ultimately made the bittersweet decision to depart the White House.” Titus 2:4-5 says, “Train the young women to love their husbands and children, to be self-controlled, pure, working at home, kind, and submissive to their own husbands, that the word of God may not be reviled.” Democrat socialist barely defeated in Wisconsin Socialist Democrats suffered a setback in Wisconsin on Tuesday. Moderate David Crowley barely defeated socialist Francesca Hong in the Democrat primary election for governor. According to NBC, the vote was 39.8% vs 39.3%. Just 3,796 votes separated the two in the statewide race. Leftist candidates had been winning many Democrat primaries this year. Despite being considered a moderate, Crowley believes that killing unborn babies is “healthcare” and must be “protected at all costs.” He made this comment in his victory speech. CROWLEY: “Every one of us has something much bigger to unite around. And that is keeping [Make America Great Again] extremism from bringing the chaos we see in Washington into our neighborhoods right here in the state of Wisconsin. From the very first day that I announced this campaign, I said the real threat wasn't any of my primary opponents. The real opponent has always been Tom Tiffany. That remains true today.” Tiffany, the Republican nominee for Wisconsin governor, is currently a member of Congress. The Washington Examiner reports that new Democratic-affiliated polling shows Crowley beating Tiffany by three points this fall, though the race is within the margin of error. Crowley leads Tiffany 47% to 44%, according to the poll by Protect Our Wisconsin, a group backed by the Democratic Governors Association.  Federal gov't gave mega money to religious migrant resettlement groups The federal government funded major religious groups with hundreds of millions of dollars in migrant resettlement grants. The data comes from Brian Chau, the founder of Efforts News. Tech entrepreneur Elon Musk brought attention to this research in a post on X this week. In one case, the U.S. Conference of Catholic Bishops received over $180 million dollars in migrant resettlement grants in 2024. That was over 80 percent of the group's revenue at the time. The contracts ended last year after the Trump administration cut funding to such programs.  Women hold more jobs than men in America Women now hold more jobs than men in the U.S economy.  A report from Indeed's Hiring Lab found the number of jobs held by men declined by 142,000 between February 2025 and February 2026. Over the same period, the number of jobs held by women grew by 298,000.   The report noted, “More jobs are held by women than men in the non-farm economy, not because of a recession, but because of years of eroding male labor force participation.” Americans become skeptical of capitalism Americans are becoming less positive toward capitalism. A new Gallup survey found 54 percent of U.S. adults said they have a positive image of capitalism. That's down from 61 percent in 2010. Thirty-nine percent said they have a positive image of socialism. That's up from 36 percent in 2010. The drop in positivity toward capitalism has accelerated since 2021. Americans trust small, local businesses and churches And finally, Americans tend to trust small, local institutions, according to research from the Barna Group.  U.S. adults put some of their highest trust in local businesses, individual Christians, and small churches. They put moderate trust in Christian pastors, Christian non-profits, and Christian businesses. But they had little trust for megachurches and celebrity pastors.  Compared to 2024, Christian non-profits, charities, and small churches saw the largest growth on the trust index.  Close And that's The Worldview on this Thursday, August 13th, in the year of our Lord 2026. Subscribe for free by Spotify, Amazon Music, or by iTunes or email to our unique Christian newscast at www.TheWorldview.com.  Plus, you can get the Generations app through Google Play or The App Store. I'm Adam McManus (Adam@TheWorldview.com). Seize the day for Jesus Christ.

Today on America in the MorningDemocrats Come Together In Wisconsin The candidate who at one point in the primary dropped out of the race, later announced he was rejoining the race, and was a distant second to a Democrat Socialist in polling just days before Tuesday's primary has eked out a one-half-of-one percentage point victory to become the Democrat nominee for governor in swing-state Wisconsin.  Correspondent Donna Warder reports it didn't take long for David Crowley, the moderate Milwaukee County Executive, to mend fences with Francesca Hong, the Democrat Socialist that many believed would win her party's nomination by a wide margin.  DOJ Targets SPLC The Justice Department has filed a new indictment against Heidi Beirich (pronounced bear-ik), a former top official of the Southern Poverty Law Center.  Correspondent Rich Johnson reports the government contends that, instead of fighting racism, Beirich was funneling money to racists.   Leavitt Leaving White House A high-profile exit from the West Wing as Karoline Leavitt is stepping down from her spokesperson role.  Correspondent Jennifer King reports on the youngest-ever White House Press Secretary in U.S. history leaving her post.   Texas Helicopter Crash Two members of the US military are dead and an investigation is underway following the crash of an Army helicopter in Texas. Correspondent Clayton Neville reports.   Trump Media Lawsuit Two media groups are suing to block Trump Media from charging for early access to the president's policy posts on Truth Social.  Correspondent Mike Hempen reports.   Lakers Sold Again For the second time in about 14 months, the NBA's most valuable franchise has been sold, and for the second time, it was for a record-breaking price. Correspondent Marcela Sanchez reports.   Baby Gabriel Case The baby of a California couple who asked the surrogate carrying their child has been born in Texas.  Correspondent Clayton Neville has the latest on the legal process and a move by the Texas Attorney General to protect the baby.   Response To ICE Gloves The Department of Homeland Security is looking to purchase electric shock gloves for U.S. Immigration and Customs Enforcement officers in the field.  Correspondent Jennifer King reports on the "G.L.O.V.E.," which stands for Generated Low Output Voltage Emitter. Minnesota Murders It was a day care center that turned into a house of horrors in Minnesota.  Police on Wednesday say a man, woman and child are dead in what is being investigated as a potential murder-suicide, and another killing appears to be related to this case.  Sue Aller has the story.   Lindell Questioning Minnesota Election Mike Lindell, the “My Pillow” salesman and President Trump supporter, has refused to concede defeat in Minnesota's Republican gubernatorial primary, despite trailing by a double-digit margin the morning after polls closed. Inflation Report The government's report on inflation showed that in July the numbers were better than the previous month.  Correspondent Lisa Dwyer reports   Finally   Nick Reiner is one step closer to his day in court, facing charges in the murder of his parents.  Entertainment reporter Kevin Carr has details. Learn more about your ad choices. Visit podcastchoices.com/adchoices

What happens when a Republican entrepreneur, a Democratic political strategist, and a city manager sit down—not to win an argument, but to understand each other?In one of the most thoughtful conversations we've had on American Dream Factory, Nick Smoot and Joe Toney welcome Adama Iwu, one of America's leading political strategists and one of TIME's 2017 "Silence Breakers," collectively recognized as Person of the Year for helping expose sexual harassment in politics.The conversation begins with a question Nick has wanted to ask for months.After witnessing Zohran Mamdani walk out of the West Wing following a meeting with President Trump, Nick realized something surprising: he had never actually spoken with someone who voted for Mamdani.So he asked.Why?That simple question opens the door to a remarkably honest discussion about the future of American politics, capitalism, socialism, healthcare, AI, civic engagement, and what it means to build communities that actually work for people.Adama shares her journey from growing up as an evangelical Christian and conservative to becoming one of the country's most respected voices on public policy. She explains why policies like universal childcare, public transportation, and expanded healthcare resonate with her—not as ideological positions, but as practical ways to help people flourish.Nick offers a center-right perspective, arguing for entrepreneurship, local innovation, and community-led solutions while challenging assumptions about government and economic policy.Joe guides the conversation with curiosity, helping both sides unpack difficult questions without turning disagreement into conflict.Together they explore:Why Zohran Mamdani's message connected with so many votersSocialism versus capitalism and where Americans often talk past one anotherWhether healthcare should be treated as a public goodThe future of AI regulation, privacy, and innovationOpen-source AI versus centralized technology platformsRepresentation, the Voting Rights Act, and civic participationWhy local government may matter more than national politicsBuilding stronger communities in an increasingly polarized nationOne of the recurring themes throughout the conversation is that meaningful progress rarely comes from shouting louder. It comes from asking better questions.Whether discussing healthcare, artificial intelligence, economic opportunity, or public policy, all three guests repeatedly return to the same idea: communities improve when people choose to participate rather than retreat into political tribes.The episode concludes with a reminder that democracy isn't something that happens every four years. It's something we build every day through local involvement, civic responsibility, and a willingness to engage people we disagree with.If you're looking for a conversation that challenges assumptions without demonizing people, this episode is for you.About Adama IwuAdama Iwu is a political strategist and government affairs executive with leadership experience across state government, Fortune 500 companies, and public policy. She has served in the Schwarzenegger Administration, led California Government Affairs for Farmers Insurance, served as Vice President for Political Strategy and External Partnerships at Visa, and is currently a political strategist with Brownstein Hyatt Farber Schreck.She co-founded We Said Enough, the movement that exposed widespread sexual harassment in California politics, and was recognized as one of TIME's 2017 "Silence Breakers," the collective recipients of TIME's Person of the Year honor. She also serves on the University of San Diego Board of Trustees and is a nationally recognized speaker on leadership, ethics, and public policy.American Dream Factory explores the people and ideas shaping the future of America through conversations that prioritize curiosity over certainty and solutions over slogans.

We spend the majority of our adult lives at work. We also see so many depictions of working life on TV. So where's the fictional place we'd like to work? We'll talk about Ted Lasso, Game of Thrones, The Muppet Show and The West Wing.If you want more workplace shenanigans, check out these episodes: ‘Widow's Bay' is an island in the screamThe final course at ‘The Bear' was both sweet and savory‘The Paper' is an ‘Office' spinoff where journalism takes the lede  Connect with Pop Culture Happy Hour:Letterboxd / FacebookOur weekly newsletterSupport public media with NPR+ and enjoy perks for over 25 podcasts like this one. This show's perks include bonus episodes and sponsor-free listening. Learn more at plus.npr.org.See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.NPR Privacy Policy

Send us Fan MailStep back into the final year of the 20th century with DJ Paulie and Lou as the Back in Time Brothers countdown the iconic debut records of 1999! From the birth of global superstars to underground cult classics, this week's show dives deep into the album debuts that reshaped music history.Inside This Episode:The Class of '99 Countdown: Britt breaks down the massive debut albums and singles that defined the era, featuring Britney Spears, Christina Aguilera, Eminem, Ricky Martin, Jennifer Lopez, MF Doom, Chris Cornell, and American Football.1999 Culture & News: The guys re-live the wild energy of the year, including the Y2K hysteria, the launch of Napster, the birth of the Euro, Jesse "The Body" Ventura's shock election, and the iconic 900 landed by Tony Hawk.Blockbuster Movies & TV: Looking back at game-changing cinema like The Matrix, The Sixth Sense, Fight Club, and Office Space, alongside TV debuts like The Sopranos, The West Wing, and Spongebob Squarepants.Rock Talk – The Dark Side: Todd Snyder takes a trip through the chaos of 1999, examining the infamous Woodstock '99 riot, the rise and backlash of Nu-Metal, and the music industry's panic over digital file sharing.Retro Snacks: A hilarious trip down memory lane featuring 1999's favorite bites, including the launch of Takis, Entenmann's Little Bites, and the notorious Wow Potato Chips.Stream the full episode to party like it's 1999 and relive the music, movies, and moments that closed out the millennium!Support the showThanks for listening.  Join us each Monday at 1pm Central at www.urlradio.net and follow us on Facebook!

Hosts Amanda and Maggie are here to break down everything that went down in The West Wing Season 2, Episodes 7 through 10. From major White House plot twists to the character arcs that left us completely speechless, we dive deep into the heart of these episodes. We share our unfiltered reactions to the storylines and predict exactly what these developments mean for the future of the Bartlet administration.   But you know they can't stick strictly to the script. This block of episodes features chaotic tangents about vegetable gardens, a run on Fresca at Sam's Club, and the girls include an on-camera taste test of a traditional New York egg cream Plus, they are pull back the curtain on The West Wing behind-the-scenes history. They obsess over all things Ainsley Hayes and break down Josh Lyman's intense arc in the iconic Christmas episode "Noël." And execuse us, but does the President have a knife made by Paul Freakin' Revere? Yes, Yes he does.    Whether you are here for the deep-dive episodic analysis or just the laugh-out-loud studio banter, this wrap-up has something for every show fan.  Don't forget to like, subscribe, and leave a review. It fees their egos and the algorithm! 

Tom Duncan and Sara Shea finish their journey through Season 1 of The Good Wife, this time with episodes 22 and 23.Chapters:00:00 Introduction to the Podcast and Season Finale03:01 Episode 22: Hebristophilia Overview05:59 Character Dynamics and Impressions09:03 Thematic Elements and Plot Development12:07 Disappointment in Character Arcs14:58 Filler Episodes and Narrative Structure18:10 Character Relationships and Choices21:09 Final Thoughts on Season One29:15 Character Development and Spin-offs31:59 Character Relationships and Emotional Investment34:00 Ethics in Law and Perjury38:04 The Myth of Truth in Courtrooms41:54 Acting and Writing Quality Assessment54:04 Intangibles and Emotional Engagement57:03 Reflecting on The West Wing Rewatch01:00:23 Cinematography and Narrative Structure01:09:52 Character Development and Consistency01:12:53 Editing, Pacing, and Overall Ratings01:17:15 Future Show Options and Season Decisions01:19:08 Introduction to the Conversation01:21:45 Exploring British Comedy and Its Impact01:24:34 Diving into Crime and Investigation Shows01:27:45 The Puzzle Box Concept in Storytelling01:30:35 Anticipating Future Episodes and Spoilers01:32:52 Season Three Wrap-Up01:33:10 Looking Ahead to Dexter Episode OneKeywords:The Good Wife, podcast, season finale, character analysis, plot development, Hebristophilia, narrative structure, TV review, character dynamics, relationship drama The Good Wife, character development, legal ethics, courtroom drama, acting quality, emotional investment, spin-offs, perjury, writing quality, intangibles The West Wing, The Good Wife, TV show analysis, character development, narrative structure, cinematography, editing, pacing, season reviews, television series, British comedy, crime shows, puzzle box storytelling, Dexter, television series, Rowan Atkinson, Black Adder, character development, narrative structure, viewer engagement

The WIP Morning Team ends the show the way they always do, by taking your voicemails from the Time's Yours line about Jaylen Brown, Joe's champagne and his West Wing viewing habits.

No American or foreign leader has met with as many sitting U.S. presidents as Queen Elizabeth II. Her 70-year reign witnessed the highs and lows of the close and crucial alliance between the United States and the United Kingdom, from the Suez crisis to Brexit. Following the advice of her mentor, Winston Churchill, to “stay close to the Americans,” Queen Elizabeth played an unexpected role behind the scenes that has never been thoroughly explored. Susan Page changes that with her new book The Queen and Her Presidents: The Hidden Hand That Shaped History. Described as “The Crown” meets “The West Wing,” it chronicles the largely unknown story of Queen Elizabeth II's relationship with 13 American presidents, from Harry S. Truman to Donald J. Trump. With that, she changed the world. Veteran political reporter Susan Page goes beyond the image of a staid monarch in colorful hats to reveal a skilled strategist, who, like many powerful women, was routinely underestimated and discounted. She also shows the impact American presidents had on the monarch as she developed from a shy, anxious princess to a powerful and persuasive global leader, and analyzes both the reach and the limits of the “soft power” she wielded. These accounts of the Queen's deft diplomacy provide candid and telling assessments of her partners in the Oval Office as well. What was the reality of the relationship between Donald Trump and Queen Elizabeth? What did the Queen and Barack Obama think about each other? How did she navigate between Ronald Reagan and her own prime minister, Margaret Thatcher? And what about Richard Nixon seeking the Queen's help during Watergate—and even wanting to make her a relative? Join us for a fascinating discussion about an often underestimated figure who played a big role in the history of the United States and Britain. Learn more about your ad choices. Visit megaphone.fm/adchoices

Send us Fan MailWe argue that “friction” is not always a flaw and can be a deliberate shield against bad collective decisions. We use Buchanan and Tullock's public choice framework to show why supermajorities, veto points, and other transaction costs can protect rights while still leaving room for day to day governing. • contrasting cheap outrage with intentional institutional friction • explaining why constitutional amendment rules use supermajorities • breaking down external costs versus decision-making costs • applying the framework to routine policy versus foundational rules • linking cancel culture to collapsed coordination costs and lost buffers • spotting designed friction in bicameralism, vetoes, jury unanimity, central bank independence, corporate charters, and treaties • testing objections about minority rule, the filibuster, and who classifies “big” decisions • discussing ballot initiatives, referenda, and easy-to-amend constitutions • sharing listener letters, egg market notes, and a book recommendation Links:Shruti Rajagopalan on TAITCThe Senate is the Enemy... (claims it comes from "The West Wing," but this is a story that I heard in the 1980s. West Wing just used it. Strange that people think a TV show is a SOURCE. It was just the MEDIUM, people!)Book-o-da-week:  Benjamin Labatut, THE MANIACIf you have questions or comments, or want to suggest a future topic, email the show at taitc.email@gmail.com !You can follow Mike Munger on Twitter at @mungowitz 

Ben Lindbergh and Meg Rowley break down the Dodgers’ trade for Tarik Skubal from multiple angles: the Tigers’ return, expectations for Skubal and the Dodgers, why other teams let the trade happen, and the significance for the sport. Then they discuss a smattering of other moves (including deals for Freddy Peralta and Luis Castillo) before closing banter about a shampoo disclosure in the Baltimore booth. Audio intro: Andy Ellison, “Effectively Wild Theme” Audio outro: Dave Armstrong and Mike Murray, “Effectively Wild Theme” Link to Ben on the Skubal trade Link to Other Ben on the Skubal trade Link to Longenhagen on the Skubal trade Link to Sheehan on the Skubal trade Link to Paine on the Skubal trade Link to Dodgers discourse article Link to Friedman quote Link to FG’s pre-deadline top 100 Link to SP RoS projections Link to Baumann on the Brewers/Rays Link to FG playoff odds Link to San Andreas meme Link to payroll/winning correlation Link to more on payroll/winning correlation Link to Rosenthal column Link to Dodgers playoff improvement Link to BP on competitive balance Link to Sheehan on rentals Link to Clemens on rentals Link to Paine on deadline impact Link to Cooper on prospect trades Link to FG farm rankings Link to FG on the Peralta trade Link to MLBTR on the Castillo trade Link to FG on the Twins trade Link to FG on the Brewers/Guardians trade Link to FG on the Atlanta/KC trade Link to MLBTR on the Doval trade Link to Queens vs. Heights gamer Link to The West Wing scene Sponsor Us on Patreon Give a Gift Subscription Email Us: podcast@fangraphs.com Effectively Wild Subreddit Effectively Wild Wiki Apple Podcasts Feed Spotify Feed YouTube Playlist Facebook Group Bluesky Account Twitter Account Get Our Merch! var SERVER_DATA = Object.assign(SERVER_DATA || {}); Source

Before Animal House became one of the greatest comedies ever made, Tim Matheson was already building one of the longest and most fascinating careers in Hollywood. In this episode of Still Here Hollywood with Steve Kmetko, Tim shares unforgettable stories about John Belushi, Lucille Ball, Steven Spielberg, Henry Fonda, Ryan Reynolds, Mel Brooks, Anne Bancroft, Meghan Markle, Aaron Sorkin, Clint Eastwood and many more. He reveals what Belushi was really like before fame changed everything, how Spielberg personally tracked him down in Las Vegas, the acting lessons he learned from Henry Fonda, directing Meghan Markle on Suits, working opposite Mel Brooks, starring in Animal House, The West Wing, Fletch, Van Wilder and Johnny Quest, and why he finally decided to write his memoir. If you love classic Hollywood, behind-the-scenes stories and the golden age of film and television, this episode is packed with incredible moments. Subscribe for new celebrity interviews every Monday.   00:00 Intro, Tim Matheson arrives 00:46 Knee replacement and getting older 01:46 Leave It to Beaver and becoming a child actor 04:02 Working with Steven Spielberg 07:18 The Hollywood legend who influenced him most, Lucille Ball 11:03 Henry Fonda's unforgettable acting lesson 13:48 John Belushi and making Animal House 16:50 Belushi's death and Hollywood's cocaine culture 20:30 Directing Meghan Markle on Suits 21:47 Why Tim finally wrote his memoir 24:11 Ivan Reitman, Animal House and comedy history 27:18 Growing up in broken home and finding Hollywood 36:33 Becoming Johnny Quest 41:22 The West Wing and Aaron Sorkin 44:29 Buying National Lampoon 46:52 Clint Eastwood's advice about aging 47:52 Ryan Reynolds before superstardom 50:32 Mel Brooks and Anne Bancroft stories 54:25 Final thoughts Show CreditsHost/Producer: Steve KmetkoAll things technical: Justin ZangerleExecutive Producer: Jim LichtensteinMusic by: Brian Sanyshyn https://stillherehollywood.comhttp://patreon.com/stillherehollywoodSuggest Guests at: stillherehollywood@gmail.comAdvertise on Still Here Hollywood: jim@stillherenetwork.comPublicist: Maggie Perlich: maggie@numbertwelvemarketing.com Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This week we're deep in Season 2 of The West Wing (episodes 3-6) and there's a lot to unpack. We break down why Ainsley Hayes might be the perfect narrative foil for Sam Seaborn, and get into a real debate about who exactly John Larroquette is to each of us. Somewhere along the way, we also plan our dream beach vacation and make a case for bringing jazzercise back — don't ask, just listen. We also get into the chemistry between POTUS and FLOTUS, because Bartlet is clearly obsessed with his wife, and we have thoughts on what that roleplay probably looks like (spoiler: think Grease, but make it presidential). And in true Sorkin fashion, we talk about how this fictional administration somehow feels more functional and more democratic than a lot of real ones — then and now. If you're rewatching The West Wing or discovering it for the first time, this episode has the character analysis, the tangents, and the "wait, we need to talk about this" moments that made you fall for the show in the first place.

Robin Williams plays one of the five presidents in a story about a White House Butler making an impact on how each President handles and reacts to the civil rights movement. Williams, as President Dwight Eisenhower, is one of the first faces we see through the protagonist starting his journey at the West Wing.

ANDREW GARFIELD BRINGS A SWAGGER TO SPIDER-MAN THAT WE WEREN'T READY FOR!

We're deep in the chaos of Rosslyn and we have THOUGHTS. In this episode of Only Show Fans, your favorite TV recap comedy podcast, we're breaking down The West Wing Season 1 Episodes 21 and 22 and Season 2 Episodes 1 and 2, aka the episodes that had the entire nation asking "who got shot?" Here's what we're getting into: Aaron Sorkin literally did not know who was going to get assassinated when he wrote it. We're not kidding. The man was making it up as he went and somehow it's one of the greatest cliffhangers in television history. A cast member actually got run over filming the Rosslyn scene. Yes, really. We have feelings about this. The eternal debate: Sam Seaborn vs Josh Lyman. Pick a side. We did. Allison Janney and Bradley Whitford are simply built different. We break down why every single scene they're in becomes instantly iconic regardless of who they're talking to. Whether you're a die hard West Wing fan doing a rewatch, discovering the show for the first time, or just here because you love a good TV recap with your favorite sisters-in-law, this episode is for you. Listen to Only Show Fans on Apple, Spotify, or anywhere else you get your podcasts. And don't forget to like, subscribe, and leave a review.  It genuinely helps more than you know.

If you had nearly 300 screen credits, would you spend your free time mentoring new SAG-AFTRA members and helping fellow performers navigate a rapidly changing industry?? If your hand went up, you may be this week's guest, Lee Garlington!Lee joins Weezy and Lisa to discuss the unique, "hired gun" craft of popping into iconic shows with scene stealing roles and becoming what she calls "a director's actor." You had yet to learn her name when you saw her as Joey's father's mistress in Friends, as Bobby Cannavale's mother in Will & Grace, as Betty White's daughter on The Golden Girls and as Darlene's friend on Roseanne. You will know it now!Lee takes us behind the scenes on Psycho II and III, The West Wing, Sneakers, Six Feet Under and Seinfeld and shares harrowing axe-related stories from A Killing in a Small Town. She offers candid advice for aspiring actors, tells us about her work with the SAG-AFTRA Los Angeles Conservatory and discusses the realities of longevity in Hollywood, and her work with the Ruskin Group Theatre.Plus, an entertaining round of IMDb Roulette uncovers the horrors of sitting for a death mask fitting, why she ever so gently adjusted the placement of Robert Redford and how her performance went next level when Amy Madigan called her a nazi! This is a fascinating conversation about resilience, professionalism, and a lifelong love of the craft.And in current media, Weezy and Lisa talk about Is This Thing On and additional films that explore the standup comedy experience. Path Points of Interest:Lee GarlingtonLee Garlington on WikipediaLee Garlington on IMDBLee Garlington on InstagramSAG-AFTRA L.A. ConservatoryRuskin Group TheatreGreat Movies About Stand-UpIs This Thing On?

What's better on a hot summer day than listening to a West Wing Recap podcast and dreaming of some tasty Josh Lymanciello? Not much, that's for sure. The girls talk more about Sam and Josh-will Maggie ever come aroud to Sam? I guess you'll have to keep listening to find out. They also talk about some juicy behind the scenes gossip. And Maggie makes a case for more live performance opportunities amongst friends. And Amanda takes her up on it with her one live version of a beloved rap from the 90s.  If you're loving this podcast (and how could you not?), make sure to like, subscribe, and leave a review. It really helps feed the algorithm, and their egos!  

It's the new and improved John Fugelsang Show and Podcast! John can now be heard weekdays from 9am to Noon PT on Sirius XM Channel 127. He kicks off his new daytime show talking about the controversial death of South Carolina Senator Lindsey Graham who passed away suddenly on Saturday night. It was a day after returning from an official visit to Kyiv, Ukraine. He also mentions the much-discussed return of Senator Mitch McConnell, who finally shares a fakey photo from his hospital recovery, prompting a flurry of conspiracy theories. John reflects on the devastating heatwave in Europe, resulting in tragic excess deaths, and the ongoing absurdity of climate denial. Then, John speaks about the implications of Graham Platner's withdrawal from the Maine Senate race with Boston Globe reporter Sam Brody, who provides insights into Platner's campaign collapse. Next, John welcomes the legendary Martin Sheen, whose career spans decades and includes iconic roles in films like Apocalypse Now and The West Wing. Beyond his acting accolades, Martin shares insights from his transformative journey into spirituality and activism, revealing how his experiences in India reshaped his understanding of humanity and social justice. Together, they explore the significance of art and faith in fostering a more compassionate world, discussing the moral obligations that come with breaking the law in the name of justice. Martin's new podcast is not just another celebrity platform; it's a meditative space where literature, history, and personal reflection intertwine, inviting listeners to embark on their own pilgrimages of self-discovery. Then finally, Professor Corey Brettschneider is back, and they unpack the broader implications of the political landscape. The episode wraps up with a thought-provoking discussion on the ethical obligations of public figures after their passing, and how history remembers controversial politicians.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Cam Cooksey dusts off the cobwebs and returns to Flow after a short break, and he's in a reflective, patriotic mood. This one is less breaking news and more a warm celebration of America turning 250, the golden age of national beautification, and why building beautiful things matters even when the world tries to take them. Expect Cam's signature ridiculous humor (yes, there is a "no pants hour" disclaimer), a detour into World Cup drama and camera cables that may or may not be cheating, and a genuine bit of theology on beauty being downstream from God. He walks through Trump's West Wing renovations, the newly renamed Trump International Airport (complete with a tongue in cheek "decode exit 69B" moment), and a spicy Iran post. He closes with a thought provoking Orwell versus Huxley reflection on truth, noise, and indifference. It's a timeline cleanser, as one viewer put it. The self driving cars story? Saved for next week.

Sources tell MS NOW the embattled FBI Director was forced to cancel his plans and head to the West Wing. This comes after fresh scrutiny over Patel's use of government resources. Basil Smikle, Bobby Ghosh, Rosa Flores, Allen Orr, Emily Berge, and Shawn Vandiver all join Catherine Rampell on the 11th Hour. To listen to this show and other MS podcasts without ads, sign up for MS NOW Premium on Apple Podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Veteran political columnist Jeb Lund (@mobute) returns to the podcast to dissect Ella McCay, the baffling film that marks James L. Brooks' return to the director's chair after 15 years.Set in 2008, the film stars Emma Mackey (no relation) as an idealistic young lieutenant-governor vaulted by circumstance into the governor's seat, only to be immediately dragged down by a political scandal involving her dim, manipulative husband (Jack Lowden) who hopes to "co-govern" the state with her.Jeb and I try our absolute best to explain the perplexing mysteries of Ella McCay. Why does it refuse to say what state it is set in? Why is it set in 2008? Why are actors in their thirties playing teenagers in flashbacks? Why is Julie Kavner narrating the entire movie like Marge Simpson as Ace Rothstein in Casino? Along the way, we discuss liberal Hollywood's lingering fixation on The West Wing and Hillary Clinton even here in the second Trump administration, and we try to define our personal definitions of "Grocerycore" music, as this film could have been benefitted from a soundtrack of Obama-era radio hits.Over 30% of all Junk Filter episodes are only available to patrons of the podcast. To support this show directly and to receive access to the entire back catalogue, consider becoming a patron for only $5.00 a month (U.S.) at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠patreon.com/junkfilterTrailer #1 for Ella McCay (James L. Brooks, 2025)Trailer for the May 2026 release of Ella McCay in France “Lisa's New Favorite Movie” - Ella McCay promo featuring Marge and Lisa Simpson

Who has shone this tournament? Where will Newcastle go?We are joined by former Crystal Palace and Newcastle manager Alan Pardew to discuss his favourite player of the tournament so far and analyse Newcastle's prospects for the new season.We also sit down with Saul Isaksson-Hurst, founder of My Personal Football Coach, who provides a unique look into the individual coaching behind rising stars like Madueke, Balogun, Semenyo, and Sarr.Finally, England fan Oliver Henry joins us to share the incredible story of how he was invited to tour the West Wing of the White House during his travels.Instagram: @tSHandJTwitter: @tSHandJWebsite: Live Radio, Breaking Sports News, Opinion - talkSPORT Hosted on Acast. See acast.com/privacy for more information.

Rhaenyra Targaryen now sits the Iron Throne, and discovers that it might not be all she's dreamed of these many years. Justin and Mike break down this latest episode, "Rhaenyra Triumphant" - which more resembled the West Wing than Game of Thrones. Watch out for the rats - in the halls, the Small Council, and on your dinner plate. The Socials:YouTube: YouTube: https://youtube.com/@moviepunditrypodcast7930Twitter: @movie_punditry@mikeymo1741@RDellBurnsThreads:@mikeymo1741@rdell47Facebook: https://wwww.facebook.com/MoviePunditryEmail:moviepunditry@outlook.comRandom Rewatch Letterboxd:https://letterboxd.com/mikeymo1741/list/random-rewatch/Copyright Disclaimer Under Section 107 of the copyright act 1978, allowance is made for "fair use" for purpose such as criticism, comment, news reporting, teaching, scholarship, and research. Any quoted media remains the property of the copyright holder. The opinions contain within are those of Movie Punditry. There is no paid content on this channel. Closing Music Cinematic Battle by REDProductions via Pixabay.com

Today an MS NOW host, in 2004 he was writer/producer on the popular NBC series The West Wing tasked with introducing a new character – a Republican Senator running for president. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

„Rhaenyra Triumphant“, die dritte Folge der dritten Staffel der Serie „House of the Dragon“ hatte viel Diskussionsstoff für Hanna, Bjarne und Adam zu bieten. So kam die bislang kürzeste Folge der Staffel auf die bislang längste Folge der Staffel im Podcast. Uns erinnert sie nicht nur an „Veep“, „The West Wing“ und „The Bear“, sondern auch an Birdman und sogar an einige Horrorfilme sowie Venom - Let There Be Carnage?!? Aber hört selbst... Im Fokus steht die neue Königin aus Westeros und ihre mannigfaltigen Herausforderungen, ob durch Daemon (Matt Smith), The White Worm (Sonoya Mizuno), Alicent (Olivia Cooke) oder Gegner wie Ormund (James Norton). Oder ebenso durch einen neuen Verwandten, der Anspruch auf ihren Thron erheben könnte. Auch die Seasnake (Steve Toussaint) und neue Drachenreiter wie Ulf (Tom Bennett) und Addam of Hull (Clinton Liberty) und Hugh the Hammer (Kieran Bew) haben nach der gewonnenen Seeschlacht Ansprüche. Dazu kommen die Lords und Ladys, das einfache Volk und das lästige Problem der leeren Stadtkassen. Aber auch dafür werden „kreative“ Lösungen gefunden – ob mit Gewalt, List oder Ekel. Hanna, Bjarne und Adam haben viel zu diskutieren, bringen diesmal aber auch mehr Feedback und Zeit mit. Natürlich freuen wir uns auch weiterhin auf Eure Expertise, etwa in Sachen sprachlicher Entwicklung (etwa beim F-Wort und Fluchen) oder in Sachen Cold Opens. Gab es da schon einmal eins in HotD? Schreibt es uns gerne über die etablierten Feedback-Kanäle.Hanna Twitter/ X: https://twitter.com/HannaHuge Bluesky: https://bsky.app/profile/mediawhore.bsky.social Instagram: https://www.instagram.com/mediawhore Adam: Twitter/ X: https://twitter.com/AwesomeArndt Instagram: https://www.instagram.com/awesomearndt/ YouTube: https://www.youtube.com/@AwesomeArndtBjarneBluesky: https://bsky.app/profile/bjarnebock.bsky.socialSankt Podcast: https://open.spotify.com/show/0ztNeRqXyxw8Z5QpelTjnC Hosted on Acast. See acast.com/privacy for more information.

-President Donald Trump speaks at America 250 to celebrate July 4th and highlighted the country's tenacity. -Dr. Sebastian Gorka joins “America Right Now” to reflect on his personal journey from being the son of Hungarian freedom fighters to serving in the West Wing. -President Trump speaks in front of Mount Rushmore stressing the importance of patriotism in America. -On “Sunday Report,” Rick Santorum rips the Democrat Party for “embracing” communist ideology rather than “confronting it.” -Former CIA analyst Fred Fleitz discusses the current state of U.S.-Iran relations. -VA Secretary Doug Collins reflects on the true meaning of freedom, the legacy of our nation's heroes, and the critical work being done to support those who served. Today's podcast is sponsored by : PARAMOUNT PLUS - Don't Miss "The Agency." All episodes streaming NOW on Paramount Plus Listen to Newsmax LIVE and see our entire podcast lineup at ⁠http://Newsmax.com/Listen⁠ Make the switch to NEWSMAX today! Get your 15 day free trial of NEWSMAX+ at ⁠http://NewsmaxPlus.com⁠ Looking for NEWSMAX caps, tees, mugs & more? Check out the Newsmax merchandise shop at : ⁠http://nws.mx/shop⁠ Follow NEWSMAX on Social Media:  -Facebook: ⁠http://nws.mx/FB⁠  -X/Twitter: ⁠http://nws.mx/twitter⁠ -Instagram: ⁠http://nws.mx/IG⁠ -YouTube: ⁠https://youtube.com/NewsmaxTV⁠ -Rumble: ⁠https://rumble.com/c/NewsmaxTV⁠ -TRUTH Social: ⁠https://truthsocial.com/@NEWSMAX⁠ -GETTR: ⁠https://gettr.com/user/newsmax⁠ -Threads: ⁠http://threads.net/@NEWSMAX⁠  -Telegram: ⁠http://t.me/newsmax⁠  -BlueSky: ⁠https://bsky.app/profile/newsmax.com⁠ -Parler: ⁠http://app.parler.com/newsmax⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Jim Beaver is best known for his leading roles as Bobby Singer in Supernatural and Whitney Ellsworth in Deadwood. He has also co-starred or had recurring roles in many series including Justified, John From Cincinnati, Harper's Island, Day Break, Big Love, 3rd Rock from the Sun, Thunder Alley, Reasonable Doubts and Breaking Bad. As a guest star he has been in series such as Dallas, Psych, NYPD Blue, That 70's Show, The West Wing, Six Feet Under, CSI, Criminal Minds, The Mentalist, NCIS, Dexter, Major Crimes and Revolution to name a few. He is also a published author, a historian, and an accomplished playwright. 

Allison Janney has played many memorable roles, including CJ Cregg on The West Wing, Bonnie on the CBS sitcom Mom , and as Tonya Harding's mother in I, Tonya. These days, you can catch her in the new movie Miss You, Love You, as well as the Netflix drama The Diplomat. When Allison Janney joined us back in 2014, she was starring on two TV shows – Showtime's Masters of Sex and CBS's Mom. She talked with us about her time on those shows, her early days of acting, and much more.See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.NPR Privacy Policy

In July of this year, 2026, the new, multi-million-dollar Theodore Roosevelt Presidential Library opens to the public in Medora, North Dakota, population 121. The senior historian for the T.R. Library is a man named Michael Patrick Cullinane, a professor of history at Dickinson State University in North Dakota, 36 miles from Medora. To coincide with the opening of T.R.'s Library, Professor Cullinane has written a book titled "Theodore Roosevelt and the Tennis Cabinet." He credits Mrs. Roosevelt with building a tennis court right outside the president's West Wing office. Cullinane says: "The convenient location robbed Roosevelt of any excuse to skip his daily exercise." In the book, Cullinane introduces readers to over 30 of T.R.'s tennis partners. Learn more about your ad choices. Visit megaphone.fm/adchoices

In July of this year, 2026, the new, multi-million-dollar Theodore Roosevelt Presidential Library opens to the public in Medora (muh-"DOOR"-uh), North Dakota, population 121. The senior historian for the T.R. Library is a man named Michael Patrick Cullinane, a professor of history at Dickinson State University in North Dakota, 36 miles from Medora. To coincide with the opening of T.R.'s Library, Professor Cullinane has written a book titled "Theodore Roosevelt and the Tennis Cabinet." He credits Mrs. Roosevelt with building a tennis court right outside the president's West Wing office. Cullinane says: "The convenient location robbed Roosevelt of any excuse to skip his daily exercise." In the book, Cullinane introduces readers to over 30 of T.R.'s tennis partners. Learn more about your ad choices. Visit megaphone.fm/adchoices

Torgo returns just in time to talk about Death, The Killing Floor, Jack Reacher, Disclosure Day, The Odyssey, IMAX, NextFest, Crimson Desert, Lobo, Dungeon Crawler Carl, Risk, Ticket to Ride, The West Wing, Fox buys Roku, Netflix and theaters, SanDisk prices, ads in EA games, toys in cereal, The Rocky Horror Picture Show at Sphere, Evil Dead Wrath, Batman/Superman/Weird Al, and Muppets Take the Marvel Universe. GeekShock: Stinky, but WOW!

This week Axe and Heilemann were joined by political heavyweight and MS NOW's Jen Psaki. The Hacks step into the octagon to break down Trump's 80th birthday UFC spectacle at the White House and the controversy that followed, the administration's shadowboxing over Iran, and whether the economy is headed for a knockout victory or headed to the mat. Okay, that's enough puns. They also discuss possible contenders for the next presidential ticket, whether sports can still bring Americans together, examine how Israel has become a growing point of tension for Democrats, and much more. Photo by Kent NISHIMURA / AFP via Getty Images Learn more about your ad choices. Visit podcastchoices.com/adchoices

Howie Kurtz on the New York Knicks winning their first championship in 53 years, California Governor Gavin Newsom alleging a politically motivated DOJ investigation by President Trump, and a secret West Wing memo revealing the administration's serious consideration of suspending habeas corpus for unauthorized immigrants. Learn more about your ad choices. Visit podcastchoices.com/adchoices

The boys are back! After taking a well-deserved mental health break (and weathering what we're told were hundreds of concerned emails — each one personally answered by Adam, naturally), Daniel and Adam return with fresh energy and a new member of the extended Mix family. Daniel introduces us to Sebastian, his AI personal assistant who helped prep the show, and we quickly learn that saying "Hermes" out loud leads to instant confusion with luxury handbags. The show kicks off with Adam's mysterious butt muscle injury ("I herniated a butt muscle") and some exciting James Corden news, before things take a more serious turn: Adam's mother, over lunch following her doctor's appointment, asked him to remove the word "gay" from his podcast name. Adam handled it with grace and conviction — gay isn't a bad word to people who know it isn't one — and we couldn't be prouder.From there, the show spins into classic Mix territory. Daniel delivers a forensic breakdown of Apple's WWDC keynote videos, convinced the outdoor walking shots were studio-voiced and AI-lip-synced — an audio Uncanny Valley that had the internet buzzing. A fascinating science piece argues that adults who reread the same novels aren't stuck in the past; they're using fiction as a mirror to measure who they've become, which leads to a discussion of Adam's third rewatch of The West Wing (season 5 is a struggle, we hear you) and Daniel's revelation that his local UPN station once followed Mama's Family with "more sci-fi adventure" as the lead-in to Star Trek: The Next Generation. In the Contact segment, Brian writes in about YouNify, a tool for consolidating watch lists across streaming services, and we get a moment of silence for synthesizer pioneer Michael Iceberg. Then Adam tells us the sweetest story about a baby bird in his crepe myrtle tree — which takes a hard left turn into a possible lawnmower incident he insists was pre-existing. The News Game delivers a respectable showing (even if the World Cup final being in New Jersey remains deeply funny), and the 60-second bonus round tests Daniel's trivia mettle on everything from Pixar to moonwalkers. A delightful digression into a 1982 ABC7 consumer report on home computers — complete with cassette tape programs and the immortal advice that "if you could bake a cake, you can write a program" — reminds us all that the home computer market was supposed to fully evolve by 1985. Spoiler: it took until 2000.Adam shows off his latest UV printer project — a custom metal sign for his stepfather featuring ChatGPT-generated art (sorry, artists) — and the Birthday segment brings us Noah Wyle, Anderson Cooper, and Dana Carvey. Then comes the segment Joe Betance probably won't hear: an exasperated PSA that pairing a Bluetooth phone to the RODECaster Pro 2 takes exactly three button presses. Three! The show wraps with the kind of scheduling certainty we've all come to love — they might be here next Friday, or maybe the week after, July's spotty because Daniel has a long vacation, but they'll definitely be back at some point. We hope you enjoy!Email: Contact@MixMinusPodcast.comVoice/SMS: 707-613-3284

Today's Poll Question at Smerconish.com: When attacked, should the U.S. respond proportionally or overwhelmingly? As tensions between the United States and Iran continue to escalate, Michael examines one of the oldest and most consequential questions in foreign policy: when America is attacked, should it strike back in kind or with overwhelming force? Drawing on recent developments in the Gulf, President Trump's response strategy, a memorable scene from The West Wing, and a provocative Wall Street Journal editorial, Michael explores the competing arguments for restraint and escalation. Listen here, then vote. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

We don't usually have repeat guests on this podcast… except we're making an exception for the wonderful and wise Alan Alda. Alan Alda, of course, is an award-winning actor, writer, director, and podcast host. You probably know and love him as Hawkeye on M*A*S*H or Senator Arnie Vinick on The West Wing. He is endlessly curious on just about every topic—which makes him the perfect person to talk to about empathy, learning across differences (and disagreement), and how we might age into new hobbies and careers.  In this conversation, Alan and Kate discuss:   Tricks for staying curious as we age How to talk to someone you disagree with How Alan hopes to destigmatize Parkinson's Disease The difference between empathy and compassion and how to practice these important skills This episode originally aired March 2024.

Alan and Executive Producer Graham Chedd look ahead to season 34. Episodes include one of Alan's favorite topics – humor, and its power to connect; finding joy in unexpected places; why telling stories makes for better doctors; and how Alan's character in The West Wing, Arnie Vinick, came to be. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In Episode 241 of Payne Points of Wealth, Ryan, Chris, and Bob Payne sit down with Ashley Davis, partner at S‑3 Group and former White House insider, to unpack a remarkable career at the highest levels of government. Ashley shares how she went from a young staffer to employee #1 of the White House Office of Homeland Security in the wake of 9/11, offering a firsthand account of that pivotal day, the chaos that followed, and the creation of one of the most important agencies in modern U.S. history. From the inner workings of the West Wing to today's political climate, Ashley delivers candid insights on leadership, policy, polarization, and what it really takes to navigate Washington. This is a powerful conversation about resilience, decision-making under pressure, and the lessons that still apply far beyond politics.

My guest this week is the actor Oliver Platt. You know him from The Bear, Chicago Med, The Three Musketeers, Beethoven, The West Wing, Frost/Nixon — honestly, the list goes on forever. He's one of those rare actors who somehow exists in every lane at once: beloved by movie people, television people, theater people, and apparently menswear guys too. We talk about growing up as the son of a diplomat, moving from Hong Kong to Japan to Washington D.C., discovering acting as a survival mechanism, early days in New York with Stanley Tucci and Hank Azaria, body image, GLP-1s, Paul Smith, Japanese denim, heritage workwear, and why sometimes you need to “give yourself the fuzzies.” * Sponsored by Bezel - the trusted marketplace for buying and selling your next luxury watch Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This week, we're joined by one of the greatest, Veronica Roth, whose new book, Seek the Traitor's Son, features one of her most beloved of tropes: Sad Boys Who Punch Things. We were delighted when she suggested we do an episode on this very particular, very delicious romance concept, and of course, we invited her to join us! We talk about how it works, why we love soft boys who are full of rage, and how we stan a hero who is willing to stand back and let his partner be awesome. We also talk about The West Wing & Ladyhawke (makes sense). And of course, we talk about her books!If you'd like to continue the conversation about sad boys who punch things or about Veronica's deep backlist, please join the Fated Mates Discord, which is accessible to our Patreon subscribers. By joining the Patreon, you meet other Fated Mates listeners and get an extra monthly episode from us. Support us and learn more at fatedmates.net/patreon.Our next read along is Seven Days in June by Tia Williams. Get it at Amazon, Barnes & Noble, Kobo, Apple Books, wherever you get your books, or with your monthly subscription to Kindle Unlimited.NOTESVeronica Roth's newest release is Seek the Traitor's Son, the first in a dystopian fantasy duology with a strong romance subplot.We love the follow sad boys: Toby Ziegler, Angel, Fox Mulder, Tobias from Animorphs, and Rutger Hauer from Ladyhawke.The Sixth Faction is coming soon! SPONSORSPiper Rayne, author of The Hotshot, available in print, ebook, audiobook and with your monthly subscription to Kindle Unlimited.Rose Prendeville, author of A Faire Affair, available in print, ebook and with your monthly subscription to Kindle Unlimited.Blue Box Press, publishers of Dylan Allen's beautiful new print editions of The Daredevil, The Mastermind & The Wild Card. Available in print and ebook from Amazon and Barnes & Noble.Lumi Gummies. Go to lumigummies.com and use code FATEDMATES for 30% off your order.The RestFor even more info about this episode, and to explore everything Fated Mates has to offer, visit: https://fatedmates.net/episodes/2026/5/18/s0835-sad-boys-who-punch-things-with-veronica-roth If you wish you had six more days in a week of people talking about romance, may we suggest joining our Patreon? Aside from an additional episode every month you get access to our Discord, where other romance readers are talking about books they love (and many other things!) all the time. It's so fun! Learn more about the Patreon and go join those cool people who love romance as much as you do at patreon.com/fatedmates. Beyond your favorite podcast app, you can find us on Instagram, Threads, Blue Sky, Tumblr, and probably some other places, too, if you look hard enough. If you've never listened to our Stop Book Banning episode, there's no better time than now.

Get an exclusive 60% off an annual Incogni plan at https://incogni.com/beast #ad Michael Wolff and Joanna Coles dive straight into the chaos engulfing Trump's orbit, from the escalating Melania–Jimmy Kimmel feud that's backfiring inside the White House to the stunning realization among insiders that Melania has shifted from quiet asset to liability, dragging Epstein questions and an unraveling public image back into the spotlight. As King Charles's high-stakes visit collides with Trump's ego and obsession with optics, the contrast between royal discipline and White House dysfunction becomes impossible to ignore, especially against the backdrop of a White House Correspondents Dinner thrown into literal and political turmoil. With assassination scares reframed as political currency, media missteps fueling Trump's grievances, and a presidency increasingly defined by chaos as strategy, Wolff reveals a West Wing gripped by anxiety, miscalculation, and a growing sense that everything—from foreign policy to late-night comedy—is spiraling in ways no one can fully control. Learn more about your ad choices. Visit podcastchoices.com/adchoices