Podcasts about Korea

Korea 24 is a daily current affairs show that covers all the biggest stories coming out of South Korea. Every weekday, Korea 24 brings you the latest news updates, as well as in-depth analysis on the most important issues with experts and special guests, providing comprehensive insight into the events on the peninsula.

Korea 24 is a daily current affairs show that covers all the biggest stories coming out of South Korea. Every weekday, Korea 24 brings you the latest news updates, as well as in-depth analysis on the most important issues with experts and special guests, providing comprehensive insight into the events on the peninsula.

Bret Weinstein speaks with philosopher of biology Nathan Cofnas on the subject of race realism and the implications of genetic differences among populations.Find Nathan Cofnas on X at https://x.com/nathancofnas and on Substack at https://ncofnas.com. *****Join DarkHorse on Locals! Get access to our Discord server, exclusive live streams, live chats for all streams, and early access to many podcasts: https://darkhorse.locals.comCheck out the DHP store! Epic tabby, digital book burning, saddle up the dire wolves, and more: https://www.darkhorsestore.orgMusic: "Marble Machine" by WintergatanThis track can be downloaded for free at www.wintergatan.netFree License to use this track in your video can be downloaded at www.wintergatan.net*****Mentioned in this episode:DEI Fraud and Cover-Up at Cambridge https://ncofnas.com/p/dei-fraud-and-cover-up-at-cambridge*****Tiemstamps:00:00:00 The Rules of Engagement00:03:46 Race Realist?00:08:47 Lineage vs. Race00:13:58 The Euphemism Treadmill00:16:38 Is Morality Real?00:24:51 Westermarck and the Kibbutz00:35:34 The Incest Taboo Test00:41:53 Where Moral Beliefs Come From00:52:34 Wokeism as an Existential Threat00:57:54 Darwinism Applied to Humans01:02:21 Does Winter Make You Smarter?01:07:53 Inuits and Ethiopians01:10:46 Why Aren't We All Von Neumann?01:18:10 What About West African Sprinters?01:24:37 The Black Cats in the Dark01:28:21 The Longest Childhood on Earth01:35:07 Everything Is an IQ Test01:40:59 What Slavery Broke01:45:21 Ethiopia, Korea, Japan, China01:49:32 The Second Mode of Inheritance01:54:18 What We Actually Disagree About01:57:47 The Arms Race Has No CeilingSupport the show

Korea 24 is a daily current affairs show that covers all the biggest stories coming out of South Korea. Every weekday, Korea 24 brings you the latest news updates, as well as in-depth analysis on the most important issues with experts and special guests, providing comprehensive insight into the events on the peninsula.

Lawfare Foreign Policy Editor Daniel Byman sits down with Andrew Yeo, senior fellow and the Korea Foundation Chair at the Brookings Institution's Center for Asia Policy Studies and professor of politics at The Catholic University of America in Washington, D.C., to talk about Trump's decision to engage in talks with North Korea's leader while curtailing military exercises with South Korea. They discuss what might be expected from negotiations with North Korea, how Seoul perceives Trump's policy changes, and how curtailing military exercises will affect South Korean and U.S. capabilities.To receive ad-free podcasts, become a Lawfare Material Supporter at www.patreon.com/lawfare. You can also support Lawfare by making a one-time donation at https://givebutter.com/lawfare-institute.Support this show http://supporter.acast.com/lawfare. Hosted on Acast. See acast.com/privacy for more information.

This week we are joined by Chris Lowder, co-founder of Lowder-Tascarella Hospitality, two-time Tales of the Cocktail nominee for Best Global Bar Mentor, and one of Drinks International's 100 Most Influential Figures in the Global Bar Industry. After leading World's 50 Best-recognized bars across China, Chris co-founded LTH, now a team of 18 spanning New York, Miami, and Bangkok, with his latest work on display at Miami Beach's Shelborne by Proper. So tune in as we discuss why sustainable operations and financial literacy beat chasing accolades, his years bartending in Korea and China, and the hidden challenges of hotel F&B development. Get your free samples:  https:perfectpuree.com/bal ____________________________________ Join us every Monday as acclaimed bartender, Erick Castro, interviews some of the bar industry's top talents from around the world, including bartenders, distillers & authors. If you love cocktails & spirits then this award-winning podcast is just for you. SUPPORT US ON PATREON: Get early access to episodes, exclusive bonus episodes, special content and more: https://www.patreon.com/BartenderAtLarge WATCH OUR VIDEOS ON YOUTUBE: https://www.youtube.com/bartenderatlarge FOLLOW US ON INSTAGRAM: Erick Castro: www.instagram.com/HungryBartender Bartender at Large: www.instagram.com/BartenderAtLarge FOLLOW US ON TIKTOK: Erick Castro: https://www.tiktok.com/@hungrybartender Bartender at Large: https://www.tiktok.com/@bartenderatlarge FOLLOW US ON TWITTER: Erick Castro: www.twitter.com/HungryBartender Bartender at Large: www.twitter.com/BartendAtLarge  

Korea 24 is a daily current affairs show that covers all the biggest stories coming out of South Korea. Every weekday, Korea 24 brings you the latest news updates, as well as in-depth analysis on the most important issues with experts and special guests, providing comprehensive insight into the events on the peninsula.

This week we are joined by Chris Lowder, co-founder of Lowder-Tascarella Hospitality, two-time Tales of the Cocktail nominee for Best Global Bar Mentor, and one of Drinks International's 100 Most Influential Figures in the Global Bar Industry. After leading World's 50 Best-recognized bars across China, Chris co-founded LTH, now a team of 18 spanning New York, Miami, and Bangkok, with his latest work on display at Miami Beach's Shelborne by Proper. So tune in as we discuss why sustainable operations and financial literacy beat chasing accolades, his years bartending in Korea and China, and the hidden challenges of hotel F&B development. Get your free samples:  https:perfectpuree.com/bal ____________________________________ Join us every Monday as acclaimed bartender, Erick Castro, interviews some of the bar industry's top talents from around the world, including bartenders, distillers & authors. If you love cocktails & spirits then this award-winning podcast is just for you. SUPPORT US ON PATREON: Get early access to episodes, exclusive bonus episodes, special content and more: https://www.patreon.com/BartenderAtLarge WATCH OUR VIDEOS ON YOUTUBE: https://www.youtube.com/bartenderatlarge FOLLOW US ON INSTAGRAM: Erick Castro: www.instagram.com/HungryBartender Bartender at Large: www.instagram.com/BartenderAtLarge FOLLOW US ON TIKTOK: Erick Castro: https://www.tiktok.com/@hungrybartender Bartender at Large: https://www.tiktok.com/@bartenderatlarge FOLLOW US ON TWITTER: Erick Castro: www.twitter.com/HungryBartender Bartender at Large: www.twitter.com/BartendAtLarge  

This week we are joined by Chris Lowder, co-founder of Lowder-Tascarella Hospitality, two-time Tales of the Cocktail nominee for Best Global Bar Mentor, and one of Drinks International's 100 Most Influential Figures in the Global Bar Industry. After leading World's 50 Best-recognized bars across China, Chris co-founded LTH, now a team of 18 spanning New York, Miami, and Bangkok, with his latest work on display at Miami Beach's Shelborne by Proper. So tune in as we discuss why sustainable operations and financial literacy beat chasing accolades, his years bartending in Korea and China, and the hidden challenges of hotel F&B development. Get your free samples:  https:perfectpuree.com/bal ____________________________________ Join us every Monday as acclaimed bartender, Erick Castro, interviews some of the bar industry's top talents from around the world, including bartenders, distillers & authors. If you love cocktails & spirits then this award-winning podcast is just for you. SUPPORT US ON PATREON: Get early access to episodes, exclusive bonus episodes, special content and more: https://www.patreon.com/BartenderAtLarge WATCH OUR VIDEOS ON YOUTUBE: https://www.youtube.com/bartenderatlarge FOLLOW US ON INSTAGRAM: Erick Castro: www.instagram.com/HungryBartender Bartender at Large: www.instagram.com/BartenderAtLarge FOLLOW US ON TIKTOK: Erick Castro: https://www.tiktok.com/@hungrybartender Bartender at Large: https://www.tiktok.com/@bartenderatlarge FOLLOW US ON TWITTER: Erick Castro: www.twitter.com/HungryBartender Bartender at Large: www.twitter.com/BartendAtLarge  

Korea 24 is a daily current affairs show that covers all the biggest stories coming out of South Korea. Every weekday, Korea 24 brings you the latest news updates, as well as in-depth analysis on the most important issues with experts and special guests, providing comprehensive insight into the events on the peninsula.

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

Don’t miss the marketplace revolution! Subscribe to our Newsletter: https://theultimatepartner.com/ebook-subscribe/ Check Out UPX: https://theultimatepartner.com/experience/ In this episode of the Ultimate Partner Podcast, Vince Menzione sits down with AWS Marketplace leaders George Maroulakos and Arif Razvi to uncover the rapidly shifting ecosystem of technology procurement and partner transformation. They dive deep into the evolution of buyer experiences, the critical necessity of executive alignment, and how agentic AI is redefining software discovery and consumption. If you want to accelerate deal velocity and ensure your business isn’t left behind, this conversation outlines exactly why integrating the AWS Marketplace into your core co-sell motion is no longer optional. https://youtu.be/Avp0sIyxRU8 Key Takeaways Embracing the AWS Marketplace should be viewed as a natural extension of your co-sell motion, not an interrupt-driven exception. Successfully leveraging the marketplace requires top-down executive sponsorship to overcome internal friction across legal, finance, and revenue operations. Partners must prepare for global expansion by ensuring local entities and currencies are wired up to meet buyers where they are. Agentic AI and natural language queries are leveling the playing field for software discovery, moving beyond traditional SEO-driven presence. SaaS pricing models are shifting toward consumption and outcome-based structures, demanding highly detailed product metadata. The speed of deal closure is drastically increased when partners are prepared to transact seamlessly through the marketplace. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags AWS Marketplace, partner transformation, buyer experiences, hyperscalers, co-sell motion, revenue operations, strategic alignment, agentic AI solutions, outcome-based pricing, metadata optimization, software discovery, private offers, global expansion, deal velocity, cloud centers of excellence. Transcript Arif Razvi and George Maroulakos Audio Episode [00:00:00] George Maroulakos: From my perspective, think about marketplace as a why not as opposed to a why. [00:00:06] Vince Menzione: You can feel it happening. The ecosystem is shifting beneath us, the way Hyperscalers are partnering, how AI is remaking the channel and what it means to win in 2026. [00:00:17] Arif Razvi: Welcome to the Ultimate Partner Podcast. I’m Vince Menzi, own your host. [00:00:22] Arif Razvi: And each week I sit down with leaders at the intersection of technology, [00:00:26] Vince Menzione: partnerships and outcomes. The voices shaping how ecosystems actually work. We talk about what’s real, what’s changing, and what it takes to lead in this era where the partner channel isn’t just part of the strategy. [00:00:39] Arif Razvi: It is the strategy because being in the room changes everything. [00:00:44] Arif Razvi: Let’s start. [00:00:48] Vince Menzione: I’m excited to, because we were talking about some of this earlier with Matt, but I thought this would be a great conversation. I’m gonna ask each of you to introduce yourselves, George and Arif, and your roles. ’cause you have two very unique roles. Uh. Contrasting roles, I would call it, within the AWS Marketplace Organization. [00:01:06] George Maroulakos: Yep. Happy to do so. So I think Vince just didn’t wanna say my last name, so that’s why I’m gonna introduce myself. Mayor. Very, very good. So Mayor Laki, George Meki, pleasure to meet. Hey, I can say that, uh, all of those that I haven’t met before, lots of familiar faces as well here. Uh, so it’s really great to see. [00:01:21] George Maroulakos: Uh, I’m part of the AWS Marketplace team and our center of excellence. That focuses on buyer experiences. So like why is a buyer guy here in this partner session? And I think, you know, I’m gonna help to try to bring some perspective around what our buyers see as the value for using marketplace and. [00:01:40] George Maroulakos: Working with all of these great partners that are here in the room and, uh, you know, the experiences that we see in helping to drive, you know, the blood to all organs. I’m using that a lot, Alison, to copyright it. It’s, it was really good. I love that, that analogy. But, um, for, but for both our partners as well as for our customers. [00:01:59] Vince Menzione: That’s awesome. [00:01:59] Arif Razvi: Hey everybody. I’m Arif Roski. I’m based here in New York City, although I’m a Celtics fan. Um, uh, go, go. I, I still supported the Knicks through the championship and I’m very happy for them. So excited to be here. Excited to be among, uh, other channel and alliance leaders. I’ve spent my entire 25 plus career in channels and alliances. [00:02:20] Arif Razvi: The last seven years, uh, on AWS marketplace where I lead, uh, a couple of functions. One is I support all of Matt Ian’s feature launches. So from a go-to market perspective, uh, I support his launches. I also focus on international expansion of marketplace. So, uh, Matt alluded to this earlier. I’ll mention some, I’ll talk about some stuff in a little bit. [00:02:39] Arif Razvi: And then, uh, we do some deep engagements with some of our most strategic partners to help them accelerate and unblock their marketplace business. By embedding a subject matter expert from my team into their organization to really help drive, uh, adoption of marketplace. [00:02:55] Vince Menzione: So important. Both of your roles. [00:02:56] Vince Menzione: By the way, this is standout roles. I, you know, I. I don’t want to get into trouble here. I talk about other hyperscalers, but I said this with Matt on stage earlier. You guys have been at the forefront of driving marketplace in such a strong way, having somebody who’s customer focused, right? And that lens is so important. [00:03:15] Vince Menzione: I don’t feel, I feel like that’s missed so many times. And then you also have. You know, quite a bit of an important role here in terms of what you’re doing in embedding resources. ’cause a lot of times people get lost in the process and it seems to be the, the common currents. So, um, really great both sides of the same equation here, right? [00:03:33] Vince Menzione: Buyer led, partner built, uh, let’s start George here. Um. Let’s talk about how buying has changed. Right. We go back to the early days of marketplace. It felt like it was a listing at first and it started to become important. Organizations like Work Span started to come to fruition and started to drive, at least in my part of the world. [00:03:53] Vince Menzione: And then we started talking a lot more about Marketplace. And of course the, the cus customer commits really started driving things in a different direction. You’ve got the customer, so talk about what your journey’s been like. [00:04:05] George Maroulakos: So I’ve been with Marketplace nine and a half years, which is pretty long time since the beginning of it, and watching the evolution of, of where things have come and where they started. [00:04:15] George Maroulakos: And I remember, uh, I was covering the, the northeast region when I very first started and working with, uh, you know, different enterprise accounts and financial services and, and, and healthcare and life sciences, and talking about this. This creation that occurred called, called AWS Marketplace, and even folks within AWS didn’t really know what that meant or how to talk about it or sell it. [00:04:38] George Maroulakos: So there was this creation of a infield based business development function to help supplement the account teams and the value and the importance of marketplace as a part of a customer’s AWS journey. And when I first started the, the, the highest, uh, or most frequently subscribed to products that existed were. [00:04:57] George Maroulakos: Uh, Amazon machine images a amis and it’s very specifically open source amis. So think of Bantu or Cintas as the predominant things that were being subscribed out of marketplace. So, um, by definition, not really revenue generating. You’re, these are not the a hundred million dollar transactions or the billion dollar club that we talk about today. [00:05:19] George Maroulakos: It was very much helping. The builders go again, go back 10 years, uh, who are just getting started with the cloud and how could they find partner solutions that they trusted or that they felt were secure and helping them to start to build out and migrate their workloads into, into AWS. Um, so I existed before the. [00:05:39] George Maroulakos: Very first private offer was transacted. Nice. Um, there’s actually a, a gentleman towards the back, uh, who helped with one of our very first private offers. I see you back there, Joe. Nice, nice. Um, so we, we worked on, uh, uh, uh, really big transaction with, uh, with, at the time AppDynamics, uh, and, and helping to get. [00:05:58] George Maroulakos: You know, a customer really up and running with their implementation that, so watching over the 10 years from, you know, the builder mentality of, of getting started with the cloud to really embracing all kinds of partner based, um, solutions that go along with AWS. It’s really been, uh, you know, a, a rocket ship you referenced earlier, moving to the top right quadrant, if we want to use the, the Gartner analogy, and I think we’re at another. [00:06:24] George Maroulakos: Inflection point because with what’s going on with Ag agentic solutions, the way that our customers are finding and discovering different partner solutions is better than ever I would think. And I think it gives much more of a equal opportunity in playing field for all partners of all shapes and sizes because you’re not driving your presence based on SEO or whether or not you have the best Google search results. [00:06:53] George Maroulakos: With AgTech and how you differentiate your solutions, you’re gonna be able to really get yourself in front of more customers more quickly. And as Matt talked about earlier, the, the, the really big penetration that we’ve seen thus far is real, is on the discovery piece, the, the research area, the pre-purchase activity, so validate what’s available. [00:07:13] George Maroulakos: So I think marketplace has really evolved over the 10 year, nine and a half years-ish that I’ve been there. And I think it’s gonna continue to change here in the coming months ahead. Yeah. [00:07:23] Vince Menzione: Any predictions? [00:07:25] George Maroulakos: Yeah, I, I think, you know, the, I, the, the importance of marketplaces is only going to continue to grow. [00:07:32] George Maroulakos: Uh, and I think we’re going to see, you know, even more innovation and expectation from our customers on being able to find the right solutions and being able to procure and deploy them as quickly as possible. [00:07:44] Vince Menzione: Nice. Arif, from your side. Partner transformation. So, you know, embedding resources with the partners. [00:07:51] Vince Menzione: Right. So you basically help kickstart a lot of the, the efforts, right? George? George is looking at it from the customer side, and then you’re kick-starting it on the partner side. [00:08:00] Arif Razvi: Yeah. Either kickstarting it, uh, on the initial stages with a strategic partner. Yeah. Or accelerating their adoption of something that’s already there. [00:08:07] Arif Razvi: Right. So what we’re seeing, what I’m seeing a lot of more recently is partners who say, I didn’t know you had capabilities for us to sell into Korea or Japan, or we just, we just launched India. Yep. Matt alluded to this easier, uh, earlier that we’re making it easier for partners to expand globally. And now partners are realizing, wait a minute, I can match my. [00:08:26] Arif Razvi: Uh, my business processes on marketplace, so revenue recognition, tax collection, locally, local invoices, and now they’re starting to set up multiple entities outside the US to meet those buyers, uh, where they are. [00:08:41] Vince Menzione: Nice. And you mentioned having these resources. How do you determine who gets resources and who? [00:08:47] Arif Razvi: Well, there’s, uh, staging and there’s, uh, there’s qualification mechanisms we use. We work with the PDMs for the partners. So any PDM, uh, any pd DM managed partner can be nominated for, uh, what, what we call a compensation. [00:08:59] Vince Menzione: So you’re overlaying that organization sense. [00:09:00] Arif Razvi: We’re overlaying the PDs. We’re not replacing, we’re only doing time bound sprints to unlock very specific activities. [00:09:05] Arif Razvi: Very [00:09:06] Vince Menzione: cool. So it’s not a poll. Yeah. Cool. You’re not, you’re not there forever. [00:09:08] Arif Razvi: No. [00:09:08] Vince Menzione: You’re there to get the job done. We [00:09:09] Arif Razvi: did that before. Uh, yeah, we’re, we’re doing short sprints now. [00:09:12] Vince Menzione: Yep. Let’s talk about how the buyers are using marketplace. You’ve done some really in, we talked about this earlier with Matt, but we’ve talked about some of the innovative things you’ve done. [00:09:21] Vince Menzione: You know, being, being able to embed into a website and in the storefronts. All these things seem to be unique to AWS. Talk to us about how the buyers are using the marketplace today. And [00:09:31] George Maroulakos: yeah, I think, you know, Matt talked about earlier the. Pre the, the preconception around why customers use marketplaces for the financial incentives, the, the financial benefits that go along with it, whether it’s direct incentives that are associated with the individual product that’s out there, or the Association of Marketplace to our private pricing agreements and the ability for. [00:09:55] George Maroulakos: Transactions that occur in marketplace to count towards those commits. Um, I’m gonna amplify a, a particular point that Matt made, uh, in his talk that, you know, we have many more customers who do not have PPAs with AWS than those that do. And the volume of transactions that take place in marketplace. [00:10:11] George Maroulakos: Dwarf, uh, in volume from those that, uh, are occur from our PPA customers. So yes, there are very large transactions that occur and there is Ben financial benefit that goes along with it. But there’s many other value points that our customers find from discovery. From ease of use, from consolidated billing, from post-purchase, um, you know, management and governance that goes along with it, and reconciliation that’s available, that’s, that exists. [00:10:36] George Maroulakos: Um, you mentioned storefronts. I’m really excited. That was one of the most exciting launches. That’s why I reminded him when he was talking about it, uh, around, you know, what that means. And it’s both a presence for, um, our partners and being able to create their own storefronts on behalf of, of customers or within their own property, but also for our buyers directly too. [00:10:54] George Maroulakos: Take that next generation of something that was previously, you know, our private marketplace and curate a catalog that is very specific and intentional for the things that they’re interested to innovate with and the partners that they’re interested in using. And so I think meeting our customers where they’re at. [00:11:10] George Maroulakos: And being, you know, marketplace anywhere and everywhere is I think really important for our customers. And then the other aspect, you know, in terms of how our customers use marketplace, there’s more than one persona at our customer that we need to be, be, be aligning with. Right. Interesting. We typically talk about procurement and the value points that procurement find in marketplace, but just as important as, as the technologists. [00:11:32] George Maroulakos: It’s the Cloud centers of Excellence. It’s the innovators who are looking for those business applications or those infrastructure solutions that exist and making sure that we have the right things from the right partners available to them. So we see value all over the place with our customers and how they inter interface with [00:11:47] Vince Menzione: more. [00:11:47] Vince Menzione: You brought up something really interesting, insightful, is the fact that all the different personas in the organization that are touching the marketplace, right? [00:11:54] George Maroulakos: And it’s at different points of the journey, right? Yeah. So while there may be early discovery and research and experimentation going on from, you know, the technologists or, or, or the, or the, the, the business application owners, um, as it progresses through the, the, the. [00:12:09] George Maroulakos: The opportunity lifecycle procurement and sourcing and legal, and the shared services then become a more important part of, of making sure that we get those opportunities closed and launched. [00:12:18] Vince Menzione: Yeah, and that was one of the things we were talking about earlier is the fact that how, how, uh, it, it, the work arduous it could be. [00:12:25] Vince Menzione: To go through that whole process at a customer side. Right? ’cause they have to. [00:12:29] George Maroulakos: It is, and and you know, I think when Cloud first got started, it was quite scary for procurement, right? It became another version of Shadow it. And we were seeing things that were getting purchased on P cards because they could quickly stand up infrastructure versus waiting for the IT team to do it. [00:12:44] George Maroulakos: And so now you introduce all of these other things that. Procurement kept near and dear to their heart, and here comes another wave of, is this truly positive disruption? Is it going to affect what’s, what I’m doing affect really my goals and objectives of supporting the company. And as you, you know, you continue to express the value of marketplace, they start to see, hey, this is actually very complimentary and helped me can get better control and governance in a way that I. [00:13:11] George Maroulakos: Perhaps didn’t have before with what was going on inside the cloud. [00:13:15] Vince Menzione: So this is a question for both of you actually, but I was thinking about these partners in the room and what did they get wrong? Like what are they doing? Like what are the things we always talk about, things that they’re doing right? [00:13:26] Vince Menzione: We’re having the happy talk about all the great success that’s been going on, but what is your advice to partners that maybe are not getting it right? Like what, what, what are the things you see that. The easy stumbling blocks that could be fixed. [00:13:38] Arif Razvi: Yeah. Well, probably the easiest is, uh, waiting to talk about marketplace at the last minute and not making it part of the full commercial journey [00:13:45] Vince Menzione: Yeah. [00:13:45] Arif Razvi: That the sellers will go through. Right. So, um, but that includes being able to have those internal conversations from the executive level, getting that executive sponsorship from marketplace upfront. That filters down through the rest of the organization. So you’ve gotta get rev ops teams, finance, legal, you know, the marketplace terms, the, the standard, uh, contract that goes into your listing has to be approved by legal. [00:14:08] Arif Razvi: Uh, and then you get down to the sales teams, making sure that you’re not penalizing sales teams for doing transactions on marketplace, which will create friction, then meeting buyers where they are, right? So local entities, local currency, um, having all that wired up. Uh, I met with a, a partner yesterday at Summit who is literally wiring up 10 new regions, 10 new entities in 10 new locations on marketplace in advance of what they know is a pipeline that’s going to be building into those regions. [00:14:37] Arif Razvi: So they’re getting ready and not waiting for the last minute for marketplace. [00:14:40] Vince Menzione: I love it. And the internal, go ahead. [00:14:43] George Maroulakos: Yeah, I, I echo definitely what Arif was saying and I think the, the other aspect of that is that our customers shouldn’t feel like it’s more painful. To use marketplace versus they would any other way. [00:14:54] George Maroulakos: And so whether that shows up in pricing, it shows up in awareness of what marketplace is and isn’t, or what it can or cannot do. Um, whether it’s introducing it naturally or it’s this, oh, by the way, on the end, like the, the more. Integrated marketplace is as a part of your co-sell motion, whether you’re doing it directly on your own or through a reseller or with AWS or all of the above. [00:15:17] George Maroulakos: Um, it’s, it should be a natural extension and a value point that you’re bringing to your respective customers, not some. Interrupt driven or, or exception based activity that goes along with selling your, your, your, your solution. [00:15:31] Vince Menzione: Why do you think customer, some customers or some partners are held back or what, what, what is, is it a mindset? [00:15:38] Vince Menzione: I mean, I talk about the principles, you know that, but like, is it mindset? What is it? Is it, [00:15:42] Arif Razvi: it’s, it’s internal. Friction. Yeah. A a a lot of what I see, especially in these, in, in embedded engagements is the amount of, you know, I have a lot of empathy for this room, right? Because you are the ones that have to go internally and navigate all of these stakeholders to be able to convince them that marketplace is the route to market. [00:16:01] Arif Razvi: It’s the preferred route to market, and it’s the way that a AWS wants to co-sell. With its partners. And that’s a hard thing to do if you don’t speak the same language that the tax legal, rev ops, finance, accounting, because everything changes. When you start doing transactions on marketplace, your revenue is booked differently, right? [00:16:20] Arif Razvi: It’s booked as a w from AWS and not from the, you know, from the, so things change and having that conversation is often challenging. Uh, we are working on some tools that will help make that conversation easier. Um, and we’ve already written blogs, and again, there are mechanisms that your partner managers have internally that they can request support and guidance. [00:16:43] Arif Razvi: Uh, and we’re happy to provide that. [00:16:45] Vince Menzione: What needs to change so that marketplace becomes a true go to market engine. [00:16:49] Arif Razvi: Top down, top down, top down where, where I’ve seen the most success. From a partner is where they’ve gotten strategic alignment at the executive level. That marketplace is the way we are going to, uh, sell globally. [00:17:02] Arif Razvi: Right? Then that starts to filter down, as I mentioned earlier, into the different teams and yes, it is a journey and you may start with the US or if you’re EMEA based. Partner, you may start with just amea, but eventually you will start to expand your, you’ll want to expand your business. Um, and marketplace is a great place to do that because we have all of those mechanisms globally to help you scale without adding incremental resources to handle the tax or the compliance or the invoicing and collection and all that stuff. [00:17:30] George Maroulakos: Yeah. I’ll add to that because I’m gonna steal a second thing you said, Allison, about centering around a customer. I, I loved a lot of Allison. Come on now. I’ll be your hype guy. I absolutely, but, but, but the but demand will drive supply. And I think to what you were talking about, Arif, when are customers, when. [00:17:46] George Maroulakos: Our customers come to you about wanting to use marketplace. Yeah. How are you ready to adapt to that? How are you ready to respond to it? And if it becomes a disjointed, not centered around a customer, bespoke based, independently based interaction with the customer, everybody loses. Versus if you’re ready to embrace what that means and how to make that as good of an experience, if not better, versus how they might have traditionally procured your solution and deployed it. [00:18:13] George Maroulakos: Um, now we have a much better together story. So I think understanding that customers more and more are going to use marketplaces, particularly AWS marketplace, and want to make use of partner solutions that embrace marketplace as a part of their way to procure and deploy. You have a much better chance of being successful with them. [00:18:33] Vince Menzione: You know, it made me think about this. Is there a seminal event? Like I think about, I think back to COVID changing buying behavior, right? I’m, we’ll use AWS, an example, three boxes show up in my house every day, right? That didn’t happen before. We used to go to the store. Is there a seminal event we’re waiting to happen? [00:18:50] Vince Menzione: I, I know that the millennial buyer, we talked about this earlier with Matt, is the new buying persona. Over 50% of buyers are millennial and they’re used to doing comfortable with phones and trust Is all there. Is there something else we’re missing or what do, what do you think’s gonna happen? [00:19:03] George Maroulakos: I really think it’s what in front of us right now. [00:19:05] George Maroulakos: Yeah. Vince, I think what. Agen solutions, what Agen SaaS offers, what the power of information and research that’s now directly available to customers versus maybe indirectly available through consultancies or deeper research. Um, the velocity of what our customers are going to be able to do and with partners that they may not have even heard of right before. [00:19:32] George Maroulakos: You know, they started their journey. I, I think this is a present day. Inflection point. Yeah. Um, going back, you know, over the past several years, the advent of supporting private offers I think was a pretty big milestone for marketplace. It allowed for our partners to be able to. Work through customized terms and conditions and commercials that were important for a particular opportunity in a customer. [00:19:54] George Maroulakos: But today, the here and now I think becomes the next inflection point for the success of marketplace. [00:19:59] Arif Razvi: That would [00:19:59] Vince Menzione: Go ahead. [00:20:00] Arif Razvi: I was just gonna add on top of that, but partners need to be ready for that. Right? And if they’re not ready to accelerate a deal and get it closed in days versus stalling it for weeks to negotiate. [00:20:11] Arif Razvi: Yes. Like they’re not gonna stand. Customers are not gonna stand for that. So partners need to be ready to move quickly. If you think about all the innovations that Matt is delivering for Marketplace and Partner Central. They’re about accelerating co-sell. They’re about accelerating deal velocity. They’re about, we know f from, from the Forrester studies and others that we presented, that we’ve made public that the deal value goes up when you’re dealing with marketplace and co-selling with AWS. [00:20:36] Arif Razvi: So how can we just accelerate that? Yeah. Partners need to be ready for that and move quickly. [00:20:40] Vince Menzione: And you use partners in sort of a, you know, plural sense, but I think about those organizations as so many multifaceted. We talked about finance, we talked about all of different functions in the organization. [00:20:51] Vince Menzione: What are you doing to help that? I mean, we talked about you’re doing a lot of readiness work, but I do feel like there’s a lot of evangelism still to be done internally with those organizations. How do you think about [00:21:02] Arif Razvi: that? Yeah, we, um, obviously events like this are fantastic mechanisms to at least get the conversation started and get your head thinking about what you need to think about. [00:21:10] Arif Razvi: But we have other activities. We have, uh, rev Ops squads, right? So revenue operations teams, and we bring them together, uh, around the world op, uh, ops squad, which is operational teams, so deal desks and others that help them, uh, understand the capabilities that marketplace can bring to accelerate deal velocity. [00:21:28] Arif Razvi: And then we have, like, obviously other events like the, the, uh, marketplace Seller Conference in September where we bring marketplace sellers together and give them, you know. Guidance. And so there are ways to get this information beyond just like getting somebody from my team, of which there are very few as you, you know, as we start to, to consolidate down. [00:21:47] Arif Razvi: But um, but they are available and there’s other mechanisms and we’re happy to share those out. [00:21:50] Vince Menzione: Nice, nice. Well coming here doing this and we’ll make a podcast episode out of this as well. Can you hear. I hear us all, uh, because I do think it’s important to get in front of, especially the Chief Finance Officer and operations and all those different departmental heads who aren’t really embedded into, like, they don’t come to these events. [00:22:07] Arif Razvi: Yeah. And they also don’t log into Partner Central. That’s right. So how do they get this information right? Yeah. So we have to one to one it with them. Yeah. But that doesn’t scale when you think about what’s gonna happen now with this next wave of GSIs and sis and, and others coming onto Marketplace who hadn’t been there. [00:22:22] Arif Razvi: Yes, they’re gonna need the same guidance. Yeah. So we have to start thinking about what we’re building is AI tooling to help with those conversations. [00:22:28] George Maroulakos: Well, and, and you know, if, when I think about it from, from our buyer’s perspective, even just yesterday we held a, a couple of round table discussions with some procurement leaders that were, we’re here for the summit and, you know, we do a lot of enablement and, and awareness. [00:22:42] George Maroulakos: With alliance leaders at our partners, and that’s certainly a, the tip of the spear of getting the conversation started, but it’s not enough. And one of the things that we hear from our customers is. Well, you may have a great partnership with particular partner A, but the individual experience that I had with that sales rep doesn’t match that. [00:23:02] George Maroulakos: And so yeah, the evangelism and the awareness, yes, and the understanding of marketplace has to go beyond just the alliance team. You guys are the amplifier to it. But the folks that not only are in the back office and all the, the, the operational teams, but on the front lines and field sales need to also understand and embrace, not understand all the features and capabilities of marketplace. [00:23:25] George Maroulakos: AWS needs to handle that, but understand how marketplace fits into the co-sell strategy in what’s going on for that particular customer is very, very important to make sure our customers get the right experience. [00:23:37] Vince Menzione: Well, I think Arif, you mentioned this earlier about compensation models. And that’s a, that’s a factor too. [00:23:42] Vince Menzione: ’cause people, people, um, they’re fearful of change. They’re fear, fearful of compensation changing. Especially, especially sellers. I, um, again, that’s a coaching area. [00:23:54] Arif Razvi: Yeah, it’s a coaching area. Um, it, it, it is, um. It’s probably one of the easier ones to solve. Um, and, and, and, you know, we have these conversations with partners about net, uh, cost neutral or uh, seller neutrality and things like that. [00:24:06] Arif Razvi: Once you show them the economics of how AWS can increase their deal value, those economics sort of, they go away. The problems go away, but they, you have to get in front of those. Uh, you know, senior leaders, the CROs and the CFOs, to have that conversation to then go, okay, I get what’s going on here. I get why I’m paying. [00:24:23] Arif Razvi: The listing fee is, is about funding all of these marketing activities that we do for our partners, right? [00:24:29] George Maroulakos: Well, and then you use the word neutrality. The other end of that neutrality problem or, or, or challenge is the pricing that goes along with the opportunities that are made available to customers. [00:24:38] George Maroulakos: And so to one of your earlier questions on points of friction or what, what stops it from taking off further? When, when partners and our customers have a disconnect on. What they’re expecting to pay when they use marketplace, or if there is a unnatural cost that goes along with procuring that solution through marketplace. [00:24:57] George Maroulakos: That also tends to bring a, a pretty bad experience, not only for that opportunity in front of them, but for respective opportunities that may, may be in play thereafter. So thinking about not, you know, having an a, an unnatural experience for our customers relative to pricing as well as compensation and, and everything else is, is also very important. [00:25:17] Vince Menzione: So we’ve got a few minutes left, and we haven’t really touched on ag agentic AI and agen AI solutions. A little bit different than the SaaS solutions per se. How, how are these solutions brought and sold differently than SaaS? [00:25:30] Arif Razvi: Uh, well, as Matt said, the SaaS apocalypse is not real. [00:25:34] Vince Menzione: Yeah. [00:25:34] Arif Razvi: Um, he doesn’t, I’m glad to hear that, nor nor do I. [00:25:37] Arif Razvi: Um, I, I think what, you know, where, where SaaS has been very user seat based, you’re moving now to a world that’s gonna be more consumption based or outcome-based pricing. And so that’s really changing the dynamic and I think partners need to think about what does an outcome-based, uh, pricing model look for My particular. [00:25:55] Arif Razvi: Um, product, uh, Zendesk is a good example of, Matt published a blog, uh, earlier this week, I think it was, where we, we referenced Zendesk pricing on closed tickets or, or resolutions to tickets, right? For, for outcome-based pricing. So I think we need to think about on the pricing side, how to reprice, how to think about pricing. [00:26:13] Arif Razvi: But on the discovery side, thinking about what your, what your listing looks like, it’s no longer about marketing copy. It’s about, it’s almost, I was talking yesterday about being an API contract. ’cause the agent needs to have all the details that a person doesn’t necessarily need to have in order to make a recommendation that your product is the best fit based on the technology that’s underlying that, where it’s gonna fit in the infrastructure. [00:26:37] Vince Menzione: So three and a half minutes left lightning round. Um, but seriously, what, what if I’m in the room or even any, any partner that’s listening today, what do I need to change in the next 30 days? [00:26:50] George Maroulakos: From my perspective, think about marketplace as a why not as opposed to a why. And if you change your mindset around marketplace can be better together in helping to accelerate and expand your opportunities with our mutual customers. [00:27:03] George Maroulakos: You’ll get a whole lot more value out of it. You’ll get away from the fud in the system or some of the traditional roadblocks that you either have been or could be encountering when you think about marketplace, uh, together with your, with your solution. So think about the why not think about the value that the, the, the total solution can bring to our mutual customers and integrate it much more naturally into your total sales motion. [00:27:26] Vince Menzione: Nice. [00:27:27] Arif Razvi: I would say maybe three things. One, um, don’t just sell globally. Operate locally. Think about how your buyers wanna buy and then meet them there, right? As a partner, even if you’re doing CPPO transactions, um, you know, meet them in the location. Number two, top down, go get that executive alignment so the problems start to disappear, or at least are easier to manage when you’ve got that executive alignment. [00:27:51] Arif Razvi: And the third was, uh, don’t wait till the last minute to introduce marketplace. Work with your sales teams to make sure it’s part of the early conversation versus no procurement leader wants to know at the end of the day, oh wait, it’s coming. I gotta deal with this marketplace thing. They don’t wanna deal with it on the buyer side. [00:28:06] Vince Menzione: No, absolutely. Alright, we’ve got time for like one question in the back. Is that Eric? Yeah, that’s [00:28:12] Guest: me. [00:28:13] Vince Menzione: Hey. [00:28:14] Guest: Hi, Eric Rosenstein, um, with Cornerstone Strategy XAWS. So George, you talked about, uh, tools around that customers can use to research for self discovery. So what guidance, uh, first of all, like what capabilities are these tools gonna bring and what guidance would you give an industry specific ISV? [00:28:36] Guest: So someone that’s focused on law enforcement or private equity. That solution may be Angen solution, or it may be something more SaaS related. What guidance would you give an ISV to think about how to best leverage those tools? Is it metadata? Is it like. The outcome that your solution’s gonna drive, how would you tell ’em to best utilize those coming tools? [00:28:58] George Maroulakos: Yeah, excellent question and, and I think this is gonna, going to continue to emerge in the days, literally in weeks, weeks ahead. But recently, I, I’d say over the last six months, the advent of agent mode I think has been a game changer and the ability for our customers to use marketplace directly to research. [00:29:18] George Maroulakos: Efficiently what’s in our catalog. Prior to that, we had a category based. Old school based, search based category, click through way, which became very buried beyond the first page for any solution that was out there. Now with natural language query and the ability to propose, what am I specifically looking for, it gives partners that were on the first page or the last page in equal opportunity to be surfaced. [00:29:48] George Maroulakos: So then for a partner being able to do many of the things you just enumerated. Better metadata, better keywords, better description and differentiation for what your solution offers allows for the Ag agent search, whether it’s natively within AWS or outside of AWS through Claude Chat, GBT Crock Pick your, your, your agent of choice. [00:30:10] George Maroulakos: To be able to identify and know that your solution’s available. [00:30:13] Arif Razvi: And from a partner side to your question, I would say, uh, rich metadata, uh, both so that agent mode can find it. Yeah. Use cases, problems, it solves, you know, uh, technical specifications, pricing where you can, right. So that agents can really understand the solution, uh, that the buyer is, um, is looking for. [00:30:33] Vince Menzione: GEO is. [00:30:37] Guest: Perspective should be like as detailed as saying, so this agent is gonna act on these various data sources. [00:30:44] Arif Razvi: Yes. [00:30:45] Guest: Bringing it all together to drive that outcome that you want the agent [00:30:48] Arif Razvi: drive, if you can include what it needs to connect to in order to deliver that outcome as part of the listing. [00:30:53] Arif Razvi: Absolutely. ’cause the agent will want to know. For sure. [00:30:57] Guest: Thanks. [00:30:58] Vince Menzione: Alright, we are at time and this was a great session. [00:31:01] Arif Razvi: Thank you Vince. [00:31:01] Vince Menzione: Great to see you, George. George and I grew up, uh, five miles away from each other and went to the same college. I love it. Great to, great to spend some [00:31:09] Arif Razvi: time with you, George. [00:31:11] Vince Menzione: Thanks for listening to the Ultimate Partner Podcast. If today’s conversation resonated, share it with a partner leader in your network. Subscribe where you listen, and head over to the ultimate partner.com. For show notes related content and the resources for this episode, and if you haven’t already, now’s the time to register for the Ultimate Partner Live Event in Reston, Virginia, October 26th through October 28th. [00:31:38] Vince Menzione: Until next time, keep showing up in the rooms that matter because being in the room changes everything.

Look Forward breaks down Trump threatening Oman over the Strait of Hormuz, having backed himself into a corner with no diplomatic exit. Trump's tabloid style "not-mistress" saga gets a new chapter as his rumored companion lands in the spotlight thanks to John Ossoff. Meanwhile, the administration quietly signals a scale-back of joint military exercises with South Korea, raising fears of history repeating itself in the region. Republicans, once skeptical of data centers, suddenly need voters to love them ahead of the midterms; we break down the political reversal and why this is a horrible move for them and a great win for Democrats.In corruption news: the Lakers may be heading to be owned by a Kushner, conveniently easing federal pressure on the team's current owner. Trump Media's latest earnings report shows the company floundering just as badly as the rest of Trump's business ventures. In a stunning court admission, the Trump administration confirmed it's been withholding federal dollars from blue states specifically because they didn't vote for him. Setting off a disastrous precedent that could have far reaching implications long after the nightmare of the Trump era is over.Plus: A sitting US Senator admits on video she's "not that informed on national security." All the chaos, corruption, and unintentional comedy.Look Forward is a weekly progressive political podcast covering U.S. politics, government policy, Democratic strategy, elections, voting rights, Supreme Court rulings, and political news. Featuring progressive commentary, political analysis, and unapologetic opinions on the fight for democracy. Hosted by Jay and Brad. A TNP Studios production. New episodes weekly on Spotify, Apple Podcasts, YouTube, and all major platforms. For more TNP Studios content, check out The Nerdpocalypse (movie & TV news), Black on Black Cinema (Black film reviews), and Dense Pixels (video game news).

The climate crisis got horribly real this summer – but is it really true that ordinary people can't do anything about it? The National Emergency Briefing is a growing movement around a stunning documentary film that makes clear the reality of the crisis and what we CAN do about it. Green economist Angela Francis joins us to explain where the fight for a liveable climate is heading; how to beat the  Reform angle that it's just too expensive so we might as well not bother; and bring surprisingly good news about tech, investment and the coming battery.  Plus, house prices are falling (in some places), ULEZ is working, and Donald Trump decides North Korea is his favourite Korea. Well, it can't all be good news. • Find a screening of the National Emergency Briefing here and if there isn't one near you, you can request one.  ESCAPE ROUTES • Hannah went to see Jimmy Eat World and Rise Against at Crystal Palace Bowl.  • Jason has been enjoying the podcast Kevin Eldon's Speakers   • Angela has been reading How To Lose A Country by Ece Temelkuran and finally watching Call My Agent on Netflix.  • Matt watched Spider-Man: Brand New Day.  Buy books through our affiliate bookshop and you'll help fund the podcast by earning us a small commission for every sale. Bookshop.org's fees help support independent bookshops too. Questions for But Your Emails? Thoughts? Comments? Email us at ogwn@podmasters.co.uk.  www.patreon.com/ohgodwhatnow Presented by Matt Green with Hannah Fearn and Jason Hazeley. Audio Production by Tom Taylor. Art direction: James Parrett. Theme tune by Tom Taylor and Simon Williams. Managing Editor: Jacob Jarvis. Group Editor: Andrew Harrison. OH GOD, WHAT NOW? is a Podmasters production. www.podmasters.co.uk  Learn more about your ad choices. Visit podcastchoices.com/adchoices

Breaking down the Team USA roster. Who is the favorite to win it all? Are there any dark horses or sleepers? All of that and more with Helen Williams and Richard Cohen. HerHoopStats.com: Unlocking better insight about the women's game.The Her Hoop Stats Newsletter: https://herhoopstats.substack.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Many thanks to SRAA contributor Liam Spencer, who shares the following recording and notes:Broadcaster: Voice of Mongolia (Mailbox Program)Date of recording: May 13, 2024Starting time: 14:43 UTCFrequency: 12.015 MHzReception location: KiwiSDR in Khabarovsk, RussiaReceiver and antenna: UnknownNotes: Here is a recording of Voice Of Mongolia Mailbox show which occurs every Monday and was hosted by Bilguun and Robert during this time.This was recorded during the 14:30 to 15:00 UTC slot on 12015, which is a challenging catch, even on WebSDRs, due to Voice of Korea interference on the same channel, along with Voice of Vietnam 12020 and CRI on 12010.The transmission was poor anyway. Voice of Mongolia broadcasts are off frequency by 20 or 30 Hz, there's a hum on broadcasts, and distorted modulation. Although the modulation has improved over the past 2 years.As for the mailbox hosts. Bilguun left in November or December 2024 and was replaced by Bumblebee. Robert announced in January 2025 that he needed to travel to New Zealand to visit family and renew his visa. Bilguun returned in April 2025 and co-hosted a mailbox show with Bumblebee and said he was on vacation in the USA. He took over the Wednesday and Thursday programs. Robert returned in May 2025 for 1 or 2 mailbag shows and hasn't been heard on Voice of Mongolia since then. Bilguun has also not been heard from recently. Bumblebee has continued to host the mailbox program since 2024. Voice of Mongolia (Mailbox Program): May 13, 2024 Liam Spencer Download

Korea 24 is a daily current affairs show that covers all the biggest stories coming out of South Korea. Every weekday, Korea 24 brings you the latest news updates, as well as in-depth analysis on the most important issues with experts and special guests, providing comprehensive insight into the events on the peninsula.

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

This year, K-pop Demon Hunters picked up a couple of Oscars, the pop group BTS made its triumphant return to sold out crowds, and K-pop took center stage at the World Cup final. It's all part of Korea's 50-year game plan to achieve global cultural dominance. This week on our Hidden Histories series, we dig into the government policy that leveraged sports, screens, and sex to transform Korea's economy and influence.Guests: Michael Kim, professor at Yonsei University in Seoul John Dimoia, professor of Korean history at Seoul National UniversityHeijin Lee, professor of women and gender studies at the University of HawaiiSupport public media with NPR+ and enjoy perks for over 25 podcasts like this one. This show's perks include 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

Trump explains why he pulled U.S.-South Korea military exercises as tensions surrounding Iran, global oil supplies, tariffs and the Strait of Hormuz continue to dominate the geopolitical conversation. We break down the potential strategy behind the administration's moves, the economic consequences of an Iran blockade and reports surrounding U.S. shipping operations.Back home, the show dives into the latest political and cultural stories, including a Fairfax County murder case involving an illegal immigrant defendant, controversy surrounding DSA figures and foreign influence claims, AOC's political messaging, Florida's latest primary upset and the growing attention surrounding Angie Nixon.Plus: reactions to immigration debates in Europe and the U.S., Tucker Carlson's Israel/Spain migration claim, a fiery Dearborn City Council speech, James Comey's latest legal developments, Lindsey Clancy's trial, viral parenting trends, Sophie Cunningham's response to boos and Sophia Hunt's TPUSA confrontation.And yes, we revisit a hilariously chaotic clip from election night 2012.SUPPORT OUR SPONSORS TO SUPPORT OUR SHOW!Upgrade your everyday. Download the Quince app for app-exclusive offers, or go to https://Quince.com/Chicksfree Get free shipping and 365-day returns. Now available in Canada and the UK.Don't change your dog's food—just add Ruff Greens. Get your FREE jumpstart trial bag (just cover shipping) with code CHICKS at https://RuffChicks.comRight now, Trees.com has up to half off select plants. And you get an additional 20% off any purchase. Go to https://Trees.com and use code CHICKS at checkout.Lose meaningful weight and keep it off with LEAN  Get started with 20% Off and Free Rush Shipping at https://TakeLean.com with promo code CHICKSSubscribe and stay tuned for new episodes every weekday!Follow us here for more daily clips, updates, and commentary:YoutubeFacebookInstagramTikTokXLocalsMore InfoWebsite

Live August 20, 2026  | Yaron Brook Show(Season 12, Episode 140)Econ War; N.Korea; Ukraine; Army; DataCenters; Interest; Buy Elections; Rogan; AI | Yaron Brook ShowIs North Korea's economy booming because communism finally works — or because it's selling dead soldiers to Putin by the truckload, and Washington is too busy gutting its own Navy and juicing the AI boom to notice the world is quietly rearranging itself around America?North Korea's economy is booming — and it has nothing to do with communism working. It's an arms factory for a dying Russian war machine, and Yaron Brook breaks down exactly how that racket works, why it collapses the day the Ukraine war ends, and why nobody in Washington is paying attention.That's just the opening. Today Yaron rips into the Treasury's dangerous game of swapping long-term debt for short-term debt to dodge high interest rates, explains why ripping the guts out of America's military readiness is a five-alarm strategic error, and calls out the billionaire-bashing "buy an election" narrative for the lie it is. Plus: the data center backlash that's really a war on prosperity, whether AI is capitalism's greatest proof of concept or its next moral panic, and a hard look at the ideas floating around in Rogan-world.If you think economics, war, and technology are separate conversations, this episode will change your mind. Reason. Individual rights. Capitalism. No exceptions.

A podcast about a podcast about one of the strangest sports scandals of the modern era. Brian Beckner welcomes back Reality Steve and Jason Stewart for a special Baller Lifestyle Podcast crossover dedicated to the ESPN 30 for 30 podcast The Betrayal of Shohei Ohtani. The guys go deep on the unbelievable story of Ippéi Mizuhara, Shohei Ohtani's former interpreter and trusted intermediary, who was ultimately accused of stealing approximately $17 million from Ohtani while accumulating enormous gambling debts. The central question: Did Shohei Ohtani really know nothing about it? Coming into the podcast, the guys had very different theories. Reality Steve was torn between the possibility that Mizuhara initially told the truth and the possibility that Ohtani genuinely had no idea what was happening. Jason Stewart entered firmly in the conspiracy/fall-guy camp. Brian was somewhere in between. After listening to the full story, however, the evidence presented in the 30 for 30 dramatically changed the conversation. The guys examine the three major possibilities surrounding the scandal: Ohtani was secretly involved in the gambling. Mizuhara gambled, lost millions, and Ohtani knowingly bailed him out. Mizuhara was secretly stealing from Ohtani, while Ohtani had absolutely no idea. The discussion dives into the evidence supporting the third scenario — including the enormous volume of communications investigators reviewed without finding evidence of gambling conversations between Ohtani and Mizuhara. But the guys aren't willing to completely let Ohtani off the hook. How can one of the richest and most famous athletes on Earth fail to notice $17 million disappearing from his own bank account? The answer may have more to do with Ohtani's unusual lifestyle, his dependence on other people, his limited English-language communication, and his extraordinary level of wealth than most people realize. The guys also explore Mizuhara's increasingly bizarre behavior, including his deception about his background, his relationship with Ohtani, his interactions with bookmaker Mathew Boyer, the enormous volume of bets placed, and the shocking story involving Ohtani's money and dental work. They also question why Boyer received a dramatically shorter sentence than Mizuhara and discuss how federal cooperation agreements can change the outcome of criminal cases. And then there's the biggest "what if?" of all: What happens if the federal government never catches Boyer? Would Mizuhara still be stealing from Ohtani today? The episode also explores one of the most revealing details from the documentary: Ohtani's description of Mizuhara not necessarily as a best friend, but as a business partner. That distinction changes the entire way the relationship can be viewed. Approximate Timestamps Note: The uploaded source is a transcript rather than an audio file, so these are suggested editorial timestamps based on the sequence and relative length of the discussion. 00:00 — Welcome to the Special Bonus Episode Brian introduces the special Baller Lifestyle Podcast episode. Reality Steve joins the show. Jason Stewart returns as the "Stu Gatz's favorite producer." Why this episode is different from the normal Baller Lifestyle format. 03:00 — The First-Ever Baller Lifestyle / Reality Steve Crossover Reality Steve explains why he wanted the episode on his own feed. The guys joke about doing a "podcast about a podcast." Why the Shohei Ohtani story was perfect for a baseball-focused crossover. 06:00 — What Did Everyone Believe Before Listening? Brian's initial impression of Ippéi Mizuhara. Reality Steve's three possible theories. Was Ohtani gambling? Did Ohtani knowingly pay Mizuhara's gambling debts? Or was Mizuhara secretly stealing from him? 10:00 — The $17 Million Question How does someone steal approximately $17 million from one of the world's richest athletes? Why Ohtani may genuinely not have noticed. Ohtani's extraordinary wealth and unusual lifestyle. The idea that he may have essentially never needed to monitor his own bank accounts. 14:00 — Jason Stewart's Fall-Guy Theory Why Jason initially believed Mizuhara was taking the fall. Why the conspiracy theory was more entertaining than the actual explanation. Comparisons to other famous sports scandals. Why the evidence eventually changed Jason's mind. 18:00 — 19,000 Bets and $325 Million in Action The staggering volume of Mizuhara's gambling. More than $300 million in total bets. Approximately $142 million in winning bets. Approximately $182.9 million in losing bets. The resulting roughly $41 million net gambling loss. How someone could gamble at that scale and continue getting credit. 23:00 — The Bookmaker Mathew Boyer Reality Steve explains the betting numbers. Why Boyer kept increasing Mizuhara's credit. The importance of Mizuhara's connection to Ohtani. How much did Boyer actually know about his client's finances? The enormous amount of money moving through the operation. 27:00 — The $500-a-Week Problem Mizuhara's claims about how much Ohtani paid him. Why the guys immediately questioned the number. What Mizuhara actually did for Ohtani on a daily basis. Driver, personal assistant, translator, errands and more. Why the story didn't seem to add up. 31:00 — The FBI Investigation What federal investigators actually examined. Thousands of pages of communications. The surprising absence of gambling conversations between Ohtani and Mizuhara. Why that evidence dramatically changed the guys' opinions. 35:00 — Was Shohei Really Completely Clueless? Jason explains why he still had doubts. Why the absence of gambling conversations initially seemed suspicious. Could Ohtani have used Mizuhara as a buffer? Why the guys ultimately lean toward Ohtani being unaware. 39:00 — Ohtani's Extraordinary Dependence on Other People Ohtani's limited English. His dependence on agents, bankers, interpreters and other representatives. Why the guys think Ohtani may have been unusually insulated from everyday life. The theory that Ohtani has simply never had to handle normal financial responsibilities. 43:00 — The Dental Work Scam The shocking $60,000 dental story. Mizuhara receives money for dental treatment. What allegedly happened to the money. How the guys view this as another example of Mizuhara's willingness to exploit Ohtani. 47:00 — What If the FBI Never Caught Boyer? How the entire scandal came to light. The federal investigation was focused on Boyer. What could have happened if investigators never uncovered the connection to Ohtani? Would Mizuhara still be stealing from Ohtani? Reality Steve's theory that Ohtani might eventually have discovered everything privately. 51:00 — The Original Story Mizuhara Told ESPN Mizuhara initially claimed Ohtani knew about his gambling. The original explanation for the money transfers. Why Mizuhara's story completely changed. How Ohtani reportedly learned about the allegations. 55:00 — The South Korea Team Meeting Ohtani's shocking position as the last person to understand what was happening. Mizuhara's explanation of what he had told the team. Ohtani asking what had been said. The surreal nature of the situation. 59:00 — Could Ohtani Have Been Suspended? Reality Steve poses the hypothetical: What if the original story had been true? What if Ohtani knowingly paid off Mizuhara's illegal gambling debt? What would Major League Baseball have done? The difference between gambling himself and helping a friend pay a debt. Pete Rose and the Astros enter the discussion. 1:05:00 — MLB, Gambling & the Changing Sports Landscape How professional sports' relationship with gambling has changed. Pete Rose's precedent. The Astros' cheating scandal. Modern sports betting controversies. Why punishment for gambling-related behavior has become increasingly complicated. 1:10:00 — Was Mizuhara Really Ohtani's Best Friend? The guys challenge the public perception of their relationship. Ohtani reportedly referring to Mizuhara more as a business partner than a close friend. How Mizuhara may have cultivated the image that they were essentially brothers. Ohtani's language barrier and how it affected that perception. 1:15:00 — Mizuhara's Sentencing vs. Boyer's Mizuhara receives 57 months. Boyer receives only five months. Why the sentencing disparity surprises the guys. Federal cooperation agreements. The guys compare Boyer's situation to other famous criminal cases. 1:20:00 — What Does Boyer Know? Boyer's claims about his gambling operation. His alleged connections to major sports figures. Why the guys wonder who else might have been betting with him. The unanswered question of how much information Boyer actually possesses. 1:24:00 — Mizuhara's Future in Japan Mizuhara's Japanese citizenship. Why he may ultimately be deported. The implications of returning to Japan after stealing from Ohtani. The guys speculate about what his life could look like after prison. 1:28:00 — Ohtani's Incredible Ability to Compartmentalize The scandal breaking right around Opening Day. Ohtani's ability to perform despite the controversy. His historic 2024 season. Winning the World Series while the scandal surrounded him. How remarkable his focus was. 1:33:00 — Brian's Ohtani Hot Take The guys discuss Ohtani's current performance. Brian insists on consistency. The conversation turns to international baseball and MLB's overseas strategy. 1:37:00 — Should MLB Start the Season Overseas? The guys revisit MLB's decision to open the season in Korea. Why Brian thinks regular-season games should begin in the United States. Growing baseball internationally versus inconveniencing American viewers. Why exhibition games might accomplish the same goal. 1:42:00 — NFL Games Overseas The NFL's increasingly aggressive international strategy. Games in Australia and other international locations. The guys joke about how far the NFL will go to test the limits of its audience. Streaming and unusual kickoff times. 1:46:00 — Reality Steve's NFL Survivor Pool Reality Steve discusses his 2026 NFL Survivor Pool. Entry fee and contest structure. The Reality Steve Survivor Contest on Splash Sports. Why Survivor pools can be so much fun — and so frustrating. The guys discuss huge survivor pools and the problem of people entering hundreds of times. 1:51:00 — The Diamond Bar Connection Brian reveals his personal connection to the Ippéi Mizuhara story. Diamond Bar High School. The legendary basketball game against Los Alamitos. Brian's 24-point performance and six three-pointers. A young Keith Van Horn on the opposing team. The guys connect the story to the six degrees of separation from Shohei Ohtani. 1:57:00 — Keith Van Horn, Rick Majerus & Krispy Kreme Keith Van Horn's college career. Rick Majerus and the Utah Utes. Jason's memories of Majerus. The legendary Krispy Kreme story. A brief detour into Majerus lore. 2:01:00 — Final Thoughts on The Betrayal of Shohei Ohtani The guys recap what they learned. Why the 30 for 30 podcast changed their opinions. Why Mizuhara increasingly appears to have acted alone. Why Ohtani's financial naivete remains the most unbelievable part of the story. The unanswered questions that remain. 2:04:00 — Wrap-Up Brian thanks Reality Steve and Jason Stewart. Discussion of doing another crossover episode. Final plugs and sign-off. TBLS. Key Takeaways The Three Theories The episode revolves around three possible explanations for the scandal: Ohtani was secretly gambling himself. Ohtani knew Mizuhara was gambling and knowingly paid his debts. Mizuhara secretly stole from Ohtani without his knowledge. After reviewing the evidence presented in the ESPN podcast, the panel largely moves toward the third explanation. The Scale of the Gambling Mizuhara's gambling activity was enormous: Roughly 19,000 bets Approximately $325 million in total wagers About $142 million in winning bets About $182.9 million in losing bets Roughly $41 million net losses The scale of the operation becomes one of the most astonishing aspects of the entire story. ️ The Communication Evidence One of the most important points discussed is the federal investigation's review of thousands of pages of communications between Ohtani and Mizuhara. The guys focus heavily on the fact that the investigation reportedly did not uncover conversations between the two directly discussing gambling. For Jason in particular, this becomes a major reason to reconsider the original fall-guy theory. How Could Ohtani Not Know? The panel's explanation is that Ohtani's extraordinary wealth and insulated lifestyle may have made it possible for him to remain completely unaware of the missing money. The guys discuss how Ohtani delegated virtually every aspect of his day-to-day life to other people — making Mizuhara's position of trust extraordinarily powerful. Mizuhara's Deception The episode portrays Mizuhara as someone who repeatedly manipulated people around him, fabricated elements of his background and cultivated a much closer public relationship with Ohtani than Ohtani himself may have considered accurate. The guys particularly focus on the contrast between the public perception of them as best friends and Ohtani's description of Mizuhara as more of a business partner. ️ The Sentencing Question Another major unanswered question is why Mizuhara received a dramatically longer sentence than bookmaker Mathew Boyer. The guys discuss how cooperation with federal investigators can produce dramatically different sentencing outcomes — and speculate about what information Boyer may have provided. The Biggest "What If?" The biggest hypothetical of the episode: What if the federal government never investigated Boyer? The guys debate whether Mizuhara would have continued stealing from Ohtani until he eventually ran out of money, got caught by Ohtani himself, or somehow managed to keep the scheme going. Most Shocking Moments Approximately $17 million allegedly stolen from Shohei Ohtani. Nearly 19,000 gambling bets. Approximately $325 million wagered. Mizuhara's reported losses exceeding $40 million. The absence of gambling discussions in thousands of pages of communications between Ohtani and Mizuhara. Ohtani allegedly being the last person to learn what Mizuhara had told the Dodgers. The bizarre $60,000 dental-work story. Mizuhara's original claim that Ohtani knowingly paid his gambling debts. The dramatic reversal of that story. Mizuhara receiving 57 months compared with Boyer's five months. The possibility that Ohtani genuinely never noticed millions disappearing from his accounts. Ohtani's ability to produce an historic season while the scandal exploded around him. Featured Topics Shohei Ohtani Ippéi Mizuhara The Betrayal of Shohei Ohtani ESPN 30 for 30 Los Angeles Dodgers MLB gambling Sports betting Mathew Boyer Baseball scandals Federal investigation Ohtani interpreter scandal Reality Steve Jason Stewart The Baller Lifestyle Podcast NFL Survivor Pools International sports About the Episode Brian Beckner hosts this special edition of The Baller Lifestyle Podcast with Reality Steve and Jason Stewart. Rather than simply recap the ESPN documentary, the guys debate the evidence, challenge each other's theories and ask the questions they felt the original podcast left unanswered. It's part sports documentary breakdown, part true-crime discussion, part gambling conversation and part completely unhinged Baller Lifestyle detour. And yes, Brian also finds a way to connect the entire story back to Diamond Bar basketball and Keith Van Horn. Follow / Subscribe If you enjoy sports, comedy, gambling stories, baseball, controversial sports scandals and completely unnecessary tangents, subscribe to The Baller Lifestyle Podcast and leave a review on Apple Podcasts. Keywords Shohei Ohtani, Shohei Ohtani scandal, Ippéi Mizuhara, Ippei Mizuhara, The Betrayal of Shohei Ohtani, 30 for 30, ESPN 30 for 30, Los Angeles Dodgers, MLB, baseball gambling, sports betting, Mathew Boyer, Ohtani interpreter, Ohtani gambling scandal, Ohtani $17 million, Reality Steve, Jason Stewart, Brian Beckner, Baller Lifestyle Podcast, Dodgers podcast, baseball podcast, sports comedy podcast, MLB scandal, sports documentary, gambling scandal, federal investigation, Shohei Ohtani documentary Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hello! Today, we talk about Trump's kinda weird and ultimately wishy-washy war games with South Korea, what it means about his relationship to the north and a whole lot of history with Van Jackson, an Associate Professor at Victoria University of Wellington, where he specializes in peace studies and the class politics of geopolitics. He is also host of The Un-Diplomatic Podcast, and author of The Un-Diplomatic Newsletter. I really enjoyed this pod because of something Van asked in his substack: What good are US Troops in Korea? He answers that question for us, drawing on his former life as what's known as a Korea Hand in the Obama Pentagon and how he made a turn to the left and anti-imperialist politics. Enjoy! This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit goodbye.substack.com/subscribe

Episode 362 hosts Dr Eunice Park (Facial Plastic & Reconstructive Surgeon and founder of AIREM, USA) & Sun Lee (CEO of Vera, USA)  In this episode we do a deep dive into "K-Beauty". We first unpack the concept of "Gwallee" - the Korean philosophy and priniciple of self-nuturing and learn about the cultural pressures behind Korea's beauty standards. We then try to get to the bottom of what K-Beauty actually is, distinguishing simple skincare trends and contrast these with "clinical K-Beauty" - built around strategic business principles, sequenced in-clinic protocols and continual device innovation. 00:00 Introduction 01:53 Meet Our Special Guests Dr Eunice Park and Sun Lee 08:59 Gwaellee Origins And Innovation 12:29 Genetics Vs Skincare Frequency 17:17 Beauty Pressure And Technology 22:55 What K Beauty Really Means 29:14 Protocols And Provider Education 32:38 Building AIREM And Scaling 40:14 Building Brand Demand 43:48 Research Over Trends 46:16 Defining Regenerative Aesthetics 48:27 Future Technology And Robots 51:01 AI Skin Analysis 53:23 Access Beyond Big Cities 56:07 Private Equity Pros And Cons 59:55 Biggest Wrong Bets 01:04:38 IA Competition 01:09:59 Outro IA COMPETITION:  In this episode we annouce our latest huge giveaway - an all-inclusive trip to South Korea to take part in the 'Vera Fellowship in association with AIREM Academy'. This is a once in a lifetime prize for one lucky winner for a five day immersion into Korean clinical and business education. CLICK HERE TO APPLY - then use the links below to download and sign up to our app (part of the application!) DOWNLOAD OUR APP IA COMMUNITY: DOWNLOAD FOR APPLE DEVICES DOWNLOAD FOR ANDROID DEVICES THEN ACTIVATE A FREE 30 DAY SUBSCRIPTION (after you've downloaded the app and signed up for free): FOR HEALTHCARE PROFESSIONALS FOR BUSINESS OWNERS/NON-CLINICAL PROFESSIONALS

Subscribe to my podcast: https://rss.com/podcasts/tomlaipply/FOLLOW ME:Rumble: https://rumble.com/c/tomlaipplyX: https://x.com/tom_laipplyWebsite: https://tomlaipply.com/Instagram: http://instagram.com/tom_laipply/Facebook: https://facebook.com/pastortomlaipplyDiscover other shows on the Talk Red Podcast Network, and get your daily fix of news, sports, and entertainment at https://TalkRed.com.DISCLAIMER: Some elements of this podcast may include AI-generated content, such as cover thumbnail images, show descriptions and some background audio. Hosted on Acast. See acast.com/privacy for more information.

Korea 24 is a daily current affairs show that covers all the biggest stories coming out of South Korea. Every weekday, Korea 24 brings you the latest news updates, as well as in-depth analysis on the most important issues with experts and special guests, providing comprehensive insight into the events on the peninsula.

You hear him chat with Vineeta every so often - Todd Walker, Todd About Town - and his dad Lee Walker, a veteran who served in the Korean War for the U.S. Military - he shares his father's story, some of the things Todd took away from traveling to Korea and the honorary flag raising this week!

This week's stop on our world tour - Korea. A country divided into two very different power structures since the end of WWII. Today South Korea is the maker of some of the world's favorite exports – from Samsung TV's to BTS. But 75 years ago it was a much different story. How the textile industry and a partnership with Bangladesh lifted the country out of poverty and paved the way for rapid economic growth.Then later in this episode – how entrepreneurial endeavors look different on the other side of the 38th parallel. We'll follow a woman who started her own small business selling goods in North Korea. How are the Donju, members of the affluent entrepreneurial class, faring and can capitalism exist in a highly restrictive socialist society?Featured Episodes:Richard Nixon, Kimchi and the first clothing factory in Bangladesh (2013)North Korea's Capitalists (2017)Featured Terms: VolatilityInflation expectationsCapital controlsAusteritySupport:NPR+ (sponsor-free listening & bonus episodes) And please click “follow” in your podcast app so you don't miss an episode.Read: Our book: Planet Money: A Guide to the Economic Forces That Shape Your Life (Audiobook here) Our weekly longform Planet Money newsletterOur weekly Indicator link round-up newsletterFollow: InstagramTikTokYouTubeFacebookThis episode of Planet Money Summer School is hosted by Robert Smith. It was produced by Schuyler Swenson and Sophia Paliza-Carre and edited by Planet Money Executive Producer Alex Goldmark. It was fact-checked by Charlotte Isidore and engineered by Annlie Huang.Support public media with NPR+ and enjoy perks for over 25 podcasts like this one. Planet Money'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

After a historic rally and a sharp correction, South Korea's equity market may be approaching a turning point. Our Chief Korea Equity Strategist, Joon Seok, explains that the next cycle will need stronger foundations and more sectors joining in.Read more insights from Morgan Stanley.----- Transcript -----Welcome to Thoughts on the Market. I'm Joon Seok, Morgan Stanley's Chief Korea Equity Strategist.Today: Why Korea's equity market may be moving from a sharp reset toward a broader and more sustainable recovery.It's Tuesday, August 18th, at 2pm in Seoul.South Korea's stock market has delivered the kind of ride that makes even long-term investors check their phones more often than they would like. The KOSPI surged 101 percent in the first half of 2026, then fell more than 38 percent from its peak by July 30th. But the market now appears to be moving toward a more durable recovery.The first reason is valuation. Take the KOSPI's forward price-to-earnings ratio, which compares share prices with expected profits over the next year. It fell below five times, its lowest level since 2004. Our capitulation index also dropped to minus 2.53. This index combines market momentum with the breadth of the sell-off, so it helps show whether fear has become widespread. Readings below minus two have often marked troughing territory outside the major crises.The second reason is that forced selling appears to be easing. Now, we have seen leverage as a double-edged sword as leverage helped fuel the rally, but it also made the decline sharper as investors were forced to cut positions. Assets in leveraged single-stock ETFs have fallen about 70 percent from their June peak, and margin lending has also come down. Now, hedge funds have completed roughly three quarters of a typical risk-reduction cycle. Put simply, the most intense selling may already be behind us.Still, a healthier recovery needs more than a rebound by the tech sector. Tech remains central because AI infrastructure continues to drive demand for advanced memory. Morgan Stanley Research expects global spending by large tech platforms to reach 805 billion U.S. dollars in [20]26 and 1.2 trillion dollars in [20]27. That creates a lot of opportunity – but it also keeps markets sensitive to any change in capital spending, chip pricing or competition.The broader Korean economy offers support. Real GDP growth has exceeded 3 percent for two consecutive quarters, up sharply from 1.1 percent in 2025. Full-year growth is now likely to land in the mid-3 percent range; and generally, Korea's growth is around 2 percent. Importantly, the improvement is spreading beyond exports. Consumption is recovering, tourism has surpassed pre-pandemic levels, and the government is targeting 23 million foreign tourists this year.There are trade-offs. Inflation reached 3.2 percent in June, and the Bank of Korea raised its policy rate to 2.75 percent. A measured hiking cycle could take rates to 3.5 percent by the first quarter of 2027. Higher rates may help financial-sector earnings, but they also raise financing costs for households and businesses.The source of market liquidity is changing as well. Domestic retail investors drove much of the first-half rally, but tighter leverage rules mean foreign investors are likely to determine the next leg higher. Corporate-governance reforms and better capital management could also encourage broader international participation.We continue to see a path toward a KOSPI target of 9,000 by June 2027, with a bull case of 10,500 and a bear case of 5,500. The next phase should be steadier and more balanced. Industrials, financials, healthcare, communications, and consumer staples should also contribute alongside technology.Korea still has room to run. But the stronger signal may be quality – meaning earnings resilience, disciplined capital management and broader participation. The stock market's initial rally was fueled by speed and concentrated leadership. The next phase will require wider and more durable support.Thanks for listening. If you enjoy the show, please leave us a review wherever you listen and share Thoughts on the Market with a friend or colleague today.

Why is North Korea so weird? Is it Stalinism? Maoism? Japan-style emperor worship? Ancient Korean hermit kingdom energy? No, argues Jonathan Cheng, Beijing bureau chief for The Wall Street Journal and author of The Korean Messiah: The Failed Dynasty That Shaped the Hermit Kingdom. You've all been sleeping on the influence of 19th-century American revival Protestantism. To discuss why Kim Il-sung kind of thought he was Jesus — or at least wanted to convince everyone else he was — we have the honor of Jonathan joining ChinaTalk today, with Alex Boyd. Our conversation covers: Billy Graham meets Kim Il-sung — how America's pastor visited Pyongyang in 1992 and bonded with the dictator over their shared Presbyterian childhoods, while touring a birthplace resembling the nativity story. From “Jerusalem of the East” to totalitarian theocracy — how 22-year-old missionary Samuel Moffett brought eschatological founder energy to the wickedest city in Korea, and transformed Pyongyang into the most Protestant city in Asia, complete with jazz clubs and a Chevrolet dealership. The Manchurian cauldron — how the lawless frontier of 1920s Manchuria blended Christianity, Korean nationalism, and Marxism to forge Kim Il-sung's messiah complex — and why the 33-year-old decided to let peasants believe he could walk on water. Jim Jones, superfan — how the Jonestown cult leader became obsessed with Kim Il-sung, adopted Korean children, and traded tips with North Korean diplomats on running a closed religious society. Christianity as political technology — how Kim weaponized Protestant doctrine and ritual to build a state religion that has outlasted the Soviet Union by three decades. Plus, why North Korea is best understood not as a nation-state but as a religious society, and what Hong Xiuquan and Xi Jinping have to do with any of this. Learn more about your ad choices. Visit megaphone.fm/adchoices

Crypto News: The SEC proposed new rules, “Regulation Crypto Assets,” that would create a clear and fit-for-purpose framework for certain investment contracts involving crypto assets. Citi plans to launch bitcoin custody for institutional clients. Jeonbuk Bank is the first regional bank in Korea to deploy Ripple XRP Payments. Tiffany Smith interview https://youtu.be/UUAgkEZpun8

Donald Trump's political problems are getting harder to ignore — and his options overseas may be shrinking just as fast. A brand-new Reuters/Ipsos poll has Trump's approval at just 33 percent, the lowest of his presidency, with 64 percent disapproving and a remarkable 80 percent of Americans now expecting the Iran war to drag on. Meanwhile, Trump's promised quick resolution with Iran looks increasingly elusive: the negotiating period has expired without a deal, the Strait of Hormuz remains a dangerous sticking point, and Trump is escalating his rhetoric even as his practical choices for ending the conflict appear increasingly limited. We'll look at a president boxed in abroad, battered in the polls at home, and running out of room to maneuver on both fronts. Former CNN anchor Jim Acosta may be pulling a trick from Johnny Carson's Carnac the Magnificent when it comes to predictions. Acosta recently guessed that President Trump would check out of the White House early, before his term is up. Acosta cited trump's issues with cognitive decline, a disasterous job on affordability and the possibility that the Democrats could sweep the midterm elections as reasons for Trump's growing frustration. Acosta also said a major event could force Trump out, like if he is caught overtly trying to steal the midterm elections. Military analysts think a big catastrophe forcing Trump out might not be far off if he continues with his Iran plans. We will put it to our Pulitzer Prize winning author and investigative journalist David Cay Johnston. Find David Cay Johnston: https://substack.com/@davidcayjohnston https://www.dcreport.org/ Christian nationalists are gaining more of a foothold in America with Trump‘s Religious Liberty task force and the promotion of laws that would force public schools to display the 10 Commandments in classrooms. Congressman Jared Huffman will drop by to talk about his new book “No Prophets: The Fight to Save Democracy from Christian Nationalism. The book is being released today. Find it here: https://www.amazon.com/No-Prophets-Democracy-Christian-Nationalism/dp/1324130482 For more on Congressman, Jared Huffman, please check out his sub stack: https://repjaredhuffman.substack.com/ The Mark Thompson Show 8/18/26 Today's Guests Links Prof. David Cay Johnston at RIT, Pulitzer Prize winning Author & Investigative Journalist https://bsky.app/profile/davidcayjohnston.bsky.social Congressman Jared Huffman https://www.commonwealthclub.org/events/2026-08-18/congressman-jared-huffman-fight-save-democracy-christian-nationalism https://huffman.house.gov/ https://wwnorton.com/books/9781324130482 The Rundown- 0:00 Welcome 10:08 Iran and Korea 27:18 David Cay Johnston 1:04:07 Comments 1:05:58 Trump to step down 1:12:05 Congressman Jared Huffman 1:37:51 Comments Patreon subscribers are the backbone of the show! If you'd like to help, here's our Patreon Link: https://www.patreon.com/themarkthompsonshow Maybe you're more into PayPal. https://www.paypal.com/donate/? The Mark Thompson Show has an official new Facebook page. Please join! Here's the link: https://m.facebook.com/TheMarkThompsonShow/ Show sponsors: coachellavalleycoffee.com - use code MarkT at check out to save 10%

Korea 24 is a daily current affairs show that covers all the biggest stories coming out of South Korea. Every weekday, Korea 24 brings you the latest news updates, as well as in-depth analysis on the most important issues with experts and special guests, providing comprehensive insight into the events on the peninsula.

Plus: BHP annual profit rises on record copper prices. And a tech selloff that began on Wall Street Monday, gathers steam. Luke Vargas hosts. Sign up for WSJ's free What's News newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The More Sibyl Podcast Presents: 한국 비자 | The Nigerian Passport Tax — The Stress of Getting a Korean Visa: Episode 6 (2026)If you've been with me for a while, you know how much I love South Korea. I spent a full year there with my mum and my kids, and that memory is one I'll always cherish. But loving a place doesn't mean staying quiet about the parts of it that aren't lovely at all.In this episode, I sit down with a guest I'm calling Sis. Her real name stays anonymous, because she's not alone in this, and a lot of people going through the same process right now are still afraid that speaking up could cost them their own application. Sis recently applied for a visa to visit Korea, where she now has family. She'd already been through other embassies before this one, so by every reasonable expectation, she assumed the Korean embassy in Lagos would be just as straightforward.It was not.Nine or ten visits. To an embassy that, on any given day, sees a handful of people, nothing close to the queues at the UK, US, Canadian, or Australian embassies. A checklist that changed depending on who was behind the desk, and sometimes even when it was the same person behind the desk. Staff who denied ever giving her instructions they'd given her days before. A two-hour drop-off window that assumes nobody applying has a job to get to. And in the end, her passport didn't land in her hands until the day of her flight, even though she'd booked that flight two months out specifically to give the process room to breathe.I bring my own family into this one too. My mother went through her own version of this: flown back to Nigeria for a TB test the embassy insisted on, even after we offered to have it done and verified locally. Seven trips to the embassy. Her visa finally came through the day before she flew out. Comparing notes with Sis made one thing painfully clear to both of us: this isn't a bad day at one counter. It's a pattern.This episode isn't really about Korea, the country I love. It's about a process that doesn't match the country everyone's currently falling for from a distance, and about the gap between the people running that system and what's actually happening to applicants on the ground. We talk about what we'd each fix if we could, and what we wish someone had told us before we started.This was a hard one for me to sit with, and an even harder one to put out. But if it reaches even one person going through this right now, or one person who can actually do something about it, it's worth it. If you've had a similar experience, at this embassy or any other, I want to hear it. Drop your story in the comments. Let's stop letting this stay in the shadows.

Korea 24 is a daily current affairs show that covers all the biggest stories coming out of South Korea. Every weekday, Korea 24 brings you the latest news updates, as well as in-depth analysis on the most important issues with experts and special guests, providing comprehensive insight into the events on the peninsula.

Fresh off a jam-packed Fresh Start Sunday at First Baptist Arlington, Luke Stehr, Katy, and Dr. Dennis Wiles catch up on promotion Sunday chaos, a record class of visiting students from their sister church in Korea, and a full slate of fall kickoffs, including Alpha launching this September and a very special 25th anniversary celebration for their senior pastor.Then the conversation turns to the heart of the episode: prayer. Dr. Wiles unpacks Jesus's high priestly prayer in John 17, why He stopped to pray specifically for His disciples rather than just sending them out on their own, and what that means for how believers today pray for one another. The group talks about praying with real specificity instead of just "remembering" someone, the Baptist conviction of the priesthood of the believer, and why praying out loud with someone in the moment — hand on the shoulder, eyes open — can be one of the most disarming, powerful forms of evangelism there is. Includes a great story about praying with a Muslim Uber driver on the way home from vacation.If you only take one thing from this episode: pray for each other, specifically and often.

Donald Trump has ordered a substantial reduction in joint US-South Korea military exercises, saying they are "too expensive" and risk damaging his “very good relationship” with North Korean leader Kim Jong Un. In a post on Truth Social, he also noted that South Korea had recently declined to join the US in the "denuclearisation" of Iran.Also: Russia and Ukraine trade deadly attacks, with a well-known book market in Kyiv among the targets; Brazil's presidential campaign gets under way; a 60-day US-Iran agreement expires without resolving key disputes; four paintings by Renaissance master Antonello da Messina are stolen from his hometown of Messina in Sicily; Cameroon makes history by winning the Women's Africa Cup of Nations for the first time; and the alarming lengths that some social media influencers are going to for a photo close up with a shark.The Global News Podcast brings you the breaking news you need to hear, as it happens. Listen for the latest headlines and current affairs from around the world. Politics, economics, climate, business, technology, health – we cover it all with expert analysis and insight.Get the news that matters, delivered twice a day on weekdays and daily at weekends, plus special bonus episodes reacting to urgent breaking stories. Follow or subscribe now and never miss a moment. Get in touch: globalpodcast@bbc.co.ukPhoto: U.S. President Donald Trump meets with North Korean leader Kim Jong Un at the demilitarized zone separating the two Koreas, in Panmunjom, South Korea, June 30, 2019. Credit: Reuters

Londinium Chronicles — Multi-Part, Part Two: Gaius (John Batchelor) and Germanicus (Michael Vlahos) turn to the ragged ends of wars and the long-term consequences of current strategic decisions. Germanicus argues that by handcrafting the war in Ukraine and blockading Russia, the United States has inadvertently fostered a united Eurasia, a powerful condominium of Moscow, Beijing, Tehran, and Pyongyang that threatens to eclipse Western power. He compares modern strategic failures to historical precedents such as the 1919 Treaty of Versailles and the 1953 Koreanarmistice, both of which created conditions for future catastrophes. A major theme is the collapse of American industrial capacity. Germanicus asserts that the United States has lost the skilled labor and shipyards necessary to maintain naval dominance, describing the current state as atrophied and sclerotic. He suggests that the United States must now rely on allies such as Japan and Korea for naval deterrence against China because American shipbuilding cannot revive itself for at least fifteen to thirty years. He concludes that the nation is in deep trouble but lacks the pragmatic builders required to fix it, suggesting a period of drift that may last until 2055. (2)

In this episode of The PDB Afternoon Bulletin: • New intelligence reveals the alarming scope of Iran's plan to widen the war across the Middle East—including coordinated proxy attacks, threats against Gulf energy facilities, sabotage operations, and possible incursions into neighboring countries. • President Trump scales back major US-South Korean military exercises, giving Kim Jong Un a concession North Korea has demanded for years without securing anything in return. To listen to the show ad-free, become a premium member of The President's Daily Brief by visiting https://PDBPremium.com. Please remember to subscribe if you enjoyed this episode of The President's Daily Brief. YouTube: youtube.com/@presidentsdailybrief Thanks to today's sponsor, Spring Sleep: eXciteOSA is an FDA-cleared, prescription-only device for primary snoring and mild obstructive sleep apnea. Go to springsleep.com/PDB and unlock 20% off with the code PDB. QUO: Make this the season where no opportunity slips away. Try QUO for free PLUS get 20% off your first 6 months when you go to https://Quo.com/PDB Pendulum: Ditch the afternoon crashes and naturally curb your cravings with 48% off the doctor-recommended Pendulum Metabolic Daily at https://pendulumlife.com/PDB Learn more about your ad choices. Visit megaphone.fm/adchoices

   We got our first peak at the 2026 Chiefs on Saturday and as you might expect, it was a mixed bag.  Lots of sloppy play, one  player with a disappointing defensive performance and a couple guys on offense that turned heads.  It's a start and was nice to see football again.  One question... why are the Chiefs hyping Patrick Mahomes so much publicly?  The whole broadcast felt like an "oversell."    You're going to need a bowl of popcorn to watch this one.... Steven A Smith of ESPN is given a thumbs down award from the National Association of Black Journalists and he has now vowed to report things he says he's known for years about this organization.  I can't wait.    Royals left Noah Cameron is on a heater and his latest is a near no-hitter against the Angels.  Jac Caglianone is on his way to becomgin Royals player of the year. Legendary pitcher Tommy John dies at age 83 and it's a crying shame he's not in the Hall of Fame.  I'll explain why.     In the news, Donald Trump does a 180 on North and South Korea.  Ty Masterson gets a big union endorsement.  My interaction with an off duty KCK police officer is just fantastic and scientists have discovered the dolphins know how to fish with bait.

Jonathan Feuer earned his Ph.D in Asian Languages and Cultures from UCLA in 2023. His current book project is entitled Buddhist Militarism, Violence, and Religious Freedom: The South Korean Buddhist Military Chaplaincy and his research interests include Buddhist modernity, South Korean history, and religion and violence. On this episode, we discuss Buddhist military chaplaincy in detail and preview what readers can expect with his forthcoming book! You can find his work at https://jonathancfeuer.com/. Visit Sacred Writes: https://www.sacred-writes.org/  

In this episode, Darren Tyler sits down with Pastor Son and Rob McCoy to discuss the jailing of Pastor Son, the growing pressure facing churches in Korea, and why American Christians should pay attention. They also discuss Charlie Kirk, religious freedom, and a sobering warning for the American Church: if it can happen there, it can happen here.

President Trump is sending a new, stark message to the world about where his loyalties lie. He ordered the Pentagon to scale back joint military exercises with South Korea because of his "very good relationship with" North Korea's Kim Jong Un. This move undercuts a key American ally with tens of thousands of US troops stationed there.    Learn more about your ad choices. Visit podcastchoices.com/adchoices

The Army told Paul Benfield he was colorblind. That left him two job options: mortician, or truck driver. So he enlisted as a human resources clerk instead. On purpose. Because the human resources clerk is the person who files the transfer paperwork. Retired US Army Major Paul Benfield served 21 years, from a broke peacetime Army in 1997 to the Pentagon in 2018. He enlisted, went to war, came back, commissioned as an officer, and did it all again. He was in a hangar in Hungary on September 11th, 2001, five days after his first daughter was born. He took an RPG to his truck in Fallujah. He raided the wrong house in Baghdad. And he is one of the few people who can tell you honestly what changed across the entire Global War on Terror, because he saw it from the bottom and from the top. Michael Schwartz has known him since high school. They were next door neighbors. Michael tried to talk him out of enlisting, and says so on the record. This is not a special operations highlight reel. It is a career, start to finish, told by a friend. Chapters: 0:00 Cold open 2:28 Why this interview, 25 years after 9/11 5:33 Meet retired US Army Major Paul Benfield 6:36 The peacetime Army: saying "bang bang" because there was no money for blanks 11:03 He went to Airborne School before he ever joined the Army 13:33 Colorblind: your choices are mortician or truck driver 16:11 The loophole: enlist as the clerk who files the paperwork 16:56 Korea, the 82nd, and the plan backfiring 18:20 The phone call from JSOC, February 2001 19:58 What JSOC was actually doing before 9/11 26:51 September 11th: a hangar in Hungary and a colonel who thought it was a drill 31:16 Tracking casualties, and six hours total in Afghanistan 35:41 "Done by Christmas," and the whack-a-mole that followed 38:02 Afghan small arms: AKs, PKMs, and a British Enfield 42:47 Becoming an officer, and curing his own colorblindness 48:15 What "human resources" means at a tier one unit 52:23 M16 to M4: the rail system that changed everything 55:22 Ranger, Green Beret, Delta: who is who 1:00:29 Why nobody can tell you how Delta picks 1:04:30 Hollywood and the broken veteran myth 1:09:22 Ranger School, and the vendetta that backfired 1:15:50 Sadr City during the surge 1:20:52 The RPG that hit his truck and did not go off 1:29:45 "We shot him on purpose. He was just the wrong guy." 1:38:19 Two definitions of corruption 1:41:15 Kabul falling 1:44:07 1997 to 2018: what actually changed 1:45:38 Twenty one years, four kids, and a marriage that survived it 1:49:33 "I told him it was the stupidest idea. I was wrong." 1:51:29 Is the war on terror even over? If you served, if you have a kid thinking about enlisting, or if you have only ever seen this war through a movie, this one is worth your time. New episodes premiere every Sunday at 5 PM PT. More at gunownersradio.com #GunOwnersRadio #Veterans #WarOnTerror #ArmyVeteran #MilitaryHistory

Today on the show: Karen Travers from ABC News with the latest on Iran and Korea. Jon Decker live in Alaska on the campaign trail. Erick Erickson joins us live. Zach Schonfeld from The Hill covering the SCOTUS emergency docket. Ilena Peng from Bloomberg updates the beef market. Plus, the stunning loss of Hayden Panettiere. 9am-noon on 95.5 WSB.

Today on the show: Karen Travers from ABC News with the latest on Iran and Korea. Jon Decker live in Alaska on the campaign trail. Erick Erickson joins us live. Zach Schonfeld from The Hill covering the SCOTUS emergency docket. Ilena Peng from Bloomberg updates the beef market. Plus, the stunning loss of Hayden Panettiere. 9am-noon on 95.5 WSB.

Today on the show: Karen Travers from ABC News with the latest on Iran and Korea. Jon Decker live in Alaska on the campaign trail. Erick Erickson joins us live. Zach Schonfeld from The Hill covering the SCOTUS emergency docket. Ilena Peng from Bloomberg updates the beef market. Plus, the stunning loss of Hayden Panettiere. 9am-noon on 95.5 WSB.

https://expatmoney.com/antiwarPhone bank for Defend the Guard: https://defendtheguard.us/phonebankSign up for our newsletter: https://www.antiwar.com/newsletter

Episode 5574: Freedom Under Fire In Korea; Saving Connecticut