Podcasts about limits

God's mercy is not a license to live in disobedience

In IP, getting the language wrong isn't a quality issue but a legal one. James Lacey, CEO of Protect at RWS, joins our latest Cultural Intelligence Podcast episode to talk about something that rarely makes it into conversations about IP strategy: context. Not just the legal context of a jurisdiction, but the full lifecycle context of a portfolio; where every decision, every filing, every ownership update is connected to what came before and what comes next. A Chief IP Counsel James met in Taiwan put it simply: IP is too often seen as a cost centre managing legal rights. The shift is towards treating it as a value centre managing strategic assets. That reframe changes everything, including how you think about where AI fits in. The human in the loop isn't a safety net. It's the whole architecture.

Gaius and Germanicus: Gaius references a modern memoir detailing Tehran under bombardment and randomly cruel regime retaliation. He highlights a billboard comparing Israeli-US forces to the defeated Roman Emperor Valeriankneeling before Persian King Shapur I, which stiffens Iranian defiance. Germanicus agrees, analyzing Rome and Persia's superpower deadlock and criticizing narcissistic, uneducated American elites who rely on "shock and awe." He observes that precision bombing fails to break morale. Instead, it unexpectedly binds civilians closer to the state as the regime visibly protects them, normalizing conflict and hardening resistance. (2)

In this episode, Jason explains why data alone cannot tell you everything you need to know about a construction project. Metrics matter, but leaders still need visibility, experience, and direct observation to understand what is really happening. Jason discusses the limits of KPIs and lagging indicators, the danger of confusing correlation with causation, and the importance of field walks in understanding team morale, preparation, training, planning, and project behaviors. He explains why the most valuable information is often difficult to capture in a dashboard and must instead be seen and interpreted by experienced people. What you'll learn in this episode: Why project data and KPIs rarely tell the complete story on their own. How confusing correlation with causation can lead to poor conclusions from data. Why field walks help leaders understand behaviors, preparation, morale, and project conditions. How visual systems and experienced observation help teams identify information that metrics cannot capture. Are you managing your project only through reports and KPIs, or are you also getting close enough to the work to see what the data cannot show you?   If you like the Elevate Construction podcast, please subscribe for free and you'll never miss an episode.  And if you really like the Elevate Construction podcast, I'd appreciate you telling a friend (Maybe even two

What happens when success on paper is no longer enough, and you realize you've been playing it safe? On this episode of I Am Refocused Radio, Shemaiah Reed sits down with Dianna Fioravanti, an executive leader, keynote speaker, leadership mentor, TEDx speaker, and author of Flip the Switch: From Playing It Safe… to Crushing It! With more than 25 years of executive experience, Dianna built a successful career in insurance before taking a major leap into the logistics industry and becoming the first female President of Kuehne+Nagel Canada. Today, she leads thousands of logistics professionals while championing a leadership philosophy centered on authenticity, courage, resilience, empathy, and creating opportunities for others. Under her leadership, female executive representation increased from 15% to 50%, while the organization earned recognition for workplace culture, trusted leadership, and women-led workplaces. Dianna shares the personal moment that forced her to question whether she was dreaming big enough, why leaders sometimes become trapped by their own success, and what it means to truly “flip the switch” from cautious thinking to courageous action. We explore finding your authentic leadership style, navigating fear and uncertainty, building high-performing teams through trust, creating inclusive cultures, and making decisions before you feel completely ready. This conversation is about more than climbing the corporate ladder. It's about becoming the leader you were meant to be, owning your voice, challenging the limits you've accepted, and creating a legacy defined by both results and the people you impact along the way.https://diannafioravanti.com/homeBecome a supporter of this podcast: https://www.spreaker.com/podcast/i-am-refocused-radio--2671113/support.Subscribe now at YouTube.com/@RefocusedNetworkThank you for your time. 

Welcome to episode #1051 of Thinking With Mitch Joel (formerly Six Pixels of Separation). Dr. Sebastian Wernicke has built his career at the intersection of data science, artificial intelligence, economics, and decision-making. He leads the Data Science and AI team at Oxera, one of Europe's leading economics consultancies, and has become widely recognized for his ability to make complex ideas about data both accessible and deeply human. Many people first encountered Sebastian through his memorable TED Talk, which cleverly used data to satirize TED Talks themselves, but his work has always been driven by a much bigger question: how can organizations use data to challenge assumptions instead of reinforcing them? His new book, Data Inspired - Building An Organizational Culture Of Inquiry For Lasting Transformation, argues that businesses have become too focused on being "data-driven" and not focused enough on becoming "data-inspired." In this episode, Sebastian explains why more data doesn't automatically lead to better decisions, why confirmation bias often causes us to use data to strengthen existing beliefs, and why the greatest value of data lies not in optimization but in transformation. We also discuss the promises and disappointments of big data, the rise of generative AI, synthetic data, AI agents, and why the hardest challenge facing organizations isn't technological... it's cultural. Along the way, Sebastian makes a compelling case that leadership is ultimately about designing environments where people are encouraged to ask better questions, embrace uncertainty, and use evidence to change their minds rather than defend them. It is a thoughtful conversation about data, intelligence, curiosity, and why the organizations that thrive in the AI era will be the ones that build cultures of inquiry instead of cultures of certainty. Enjoy the conversation... Running time: 57:16. Hello from beautiful Montreal. Listen and subscribe over at Apple Podcasts. Listen and subscribe over at Spotify. Please visit and leave comments on the blog - Thinking With Mitch Joel. Feel free to connect to me directly on LinkedIn. Check out ThinkersOne. Here is my conversation with Dr. Sebastian Wernicke. Data Inspired - Building An Organizational Culture Of Inquiry For Lasting Transformation. Sebastian's viral TED Talk. Oxera. Follow Sebastian on LinkedIn. Chapters: (00:00) - Introduction to Sebastian Wernicke and Data Inspired. (02:52) - The Meta Nature of TED Talks and Storytelling. (06:12) - Confirmation Bias and Data Interpretation. (09:03) - The Limits of Data-Driven Decision Making. (12:10) - The Concept of Data Inspired. (14:58) - The Role of AI in Data Utilization. (18:07) - The Promise and Pitfalls of Big Data. (21:12) - Cultural Elements in Data Transformation. (24:02) - Trust in AI and Machine Learning. (26:52) - Human Nature and the Quest for Perfection. (29:30) - The Balance of Autonomy and Control in AI. (30:30) - The Rise of Synthetic Data. (32:00) - Skepticism Towards Synthetic Data. (34:54) - Exploring Personas and Assumptions. (37:40) - The Role of Data in Decision Making. (39:27) - The Complexity of Uncertainty in Business. (41:52) - Rethinking Intelligence and Creativity. (46:54) - Building Beautiful Questions with Data. (51:30) - Navigating the Data Dilemma. (54:46) - Cultural Attitudes Towards Privacy and Surveillance.

In today's gospel, a continuation of last week's, Jesus rebukes Peter for "setting your mind not on divine things, but human things." The world Jesus lived in tried to suppress his message of God's love and forgiveness as a way to keep the Jewish people under control. We see oppressive governments doing much the same today as the Romans were doing in Jesus time. But if the message of God's love is shared, there are no limits to how far it can go.

Texas frees immigration officer Christian Castro, who is wanted for a shooting in Minnesota, raising critical legal and practical questions about federalism.An attempt by the Trump administration to lower beef prices has many Texas ranchers upset.Also, a look at what appears to be a groundbreaking new treatment in Texas for people with Parkinsons using ultrasound […]

I am Rolf Claessen and my co-host Ken Suzan and I are welcoming you to episode 178 of our podcast IP Fridays! Today's interview guest is Caitlin Byczko, who is partner with Marnes & Thornburg in their IP team. Ken is discussing dupe culture with her. Here is the profile of Caitlin Byczko https://btlaw.com/en/people/caitlin-byczko But before we launch into this very interesting interview, I have some news for you: On August 10th, 2026, Navitas Semiconductor filed suit against Renesas Electronics in the Eastern District of Texas, accusing Renesas of infringing four US patents on gallium nitride semiconductor technology through its SuperGaN product lines. The filing follows a countersuit Renesas brought on July 22nd, 2026, accusing Navitas and two of its employees of misappropriating trade secrets. It shows how patent disputes and trade secret claims between competitors are increasingly being fought on multiple fronts at once, and often as tit for tat. It also emerged on August 12th, 2026, that an EPO Board of Appeal had dismissed an appeal by Atlas Global Technologies and ruled that its WiFi patent, EP 3 353 901, case T 1230/25, could not be maintained in any form at all, even though the original opponents, TP-Link and Vantiva, had already withdrawn their oppositions. That knocked out the basis for several parallel infringement suits at the Unified Patent Court, which were then withdrawn. For suppliers and implementers, the takeaway is that fighting a patent held by a non-practising entity can still be worth it, even once the original opponent has thrown in the towel. On August 10th, 2026, the Unified Patent Court in The Hague fully revoked Maxell’s patent EP 2 061 230, covering technology for handing off content to a second device, and at the same time dismissed Maxell’s infringement claim against several Samsung entities, in cases UPC_CFI_251/2025 and UPC_CFI_769/2025. The judges found the patent to be nothing more than an obvious combination of routine adaptations, with no additional technical effect. And now – let's hear the interview with Ken and Caitlin! A dupe used to be a quiet, slow thing. You’d stand in the cereal aisle, notice the generic box next to the name brand, buy it, tell a friend. Word spread over months. That world is gone. On this episode of IP Fridays, Ken Suzan sat down with Caitlin Byczko, partner at Barnes & Thornburg LLP in Indianapolis, to talk about what’s replaced it: a TikTok-driven economy where a single video can sell out a dupe product within hours, sometimes before the original brand’s own team even knows it exists. Byczko litigates and prosecutes trademarks across retail, fashion, luxury goods, technology, and pharmaceuticals, and she’s watched dupe culture evolve from a marketing footnote into one of the more active battlegrounds in trademark law. Here’s what she told us, and why it matters even if your brand has never heard the word “dupe” used about it. Counterfeit and Dupe Are Not the Same Thing, Legally Byczko opened with what she called the most important distinction in this entire conversation: the difference between a counterfeit and a dupe. A counterfeit uses someone else’s actual trademark. Think of a fake Chanel bag stamped with the interlocking C’s, or a fake Louis Vuitton logo. That’s straightforward infringement, and above certain thresholds, a federal crime. A dupe is different. It mimics the look, feel, or performance of a product without using the name or the logo at all. Elf Cosmetics, Zara, Costco’s Kirkland brand, and Quince have all built parts of their business on exactly this model. No one is pretending to be Chanel. They’re offering something that looks and performs similarly, at a fraction of the price, under their own name. Media and social media use “dupe” and “counterfeit” interchangeably. Legally, that’s sloppy, and it matters, because the two categories trigger completely different legal analyses. If There’s No Logo, What Are Brands Actually Suing Over? This is where trade dress comes in. Trade dress protects the overall look and feel of a product: packaging, color combinations, shape, label design. Byczko pointed out that most of us interact with trade-dress-protected products every day without realizing it. The test is likelihood of confusion. Courts look at how similar the products actually look, how sophisticated the shoppers are, and whether there’s real evidence that people were confused. Byczko flagged one case as a genuine roadmap for this area: Van Leeuwen v. Rebel Creamery, an ice cream trade dress dispute that came out of the Eastern District of New York. In her view, the strength of that case came down to how precisely the brand defined its trade dress for the packaging. That precision, she said, did a lot of the work toward the outcome. She’s also watching Lululemon v. Costco, which she expects to be significant partly because it doesn’t rely on trademark and trade dress alone. Byczko noted that brands are increasingly stacking causes of action together: trademark, patent, false advertising, all pointing at the same product. And she’s tracking Sol de Janeiro v. Macau Beauty, a case she finds notable because it pulls in influencer content and testimonials as evidence, not just packaging and trade dress claims. Macau Beauty, she noted, has already been sued multiple times across different jurisdictions. A note for readers outside the US: trade dress as a distinct doctrine doesn’t exist as such in Germany. The closest tools here are the three-dimensional trademark and, more practically, the wettbewerbsrechtlicher Nachahmungsschutz under Section 4 No. 3 of the German Act Against Unfair Competition (UWG). That provision protects product shape, packaging, and get-up against imitation when the original has wettbewerbliche Eigenart, competitive distinctiveness, and the copy creates avoidable confusion about origin, unfairly exploits the original’s reputation, or was built on dishonestly obtained know-how. It’s a narrower, more fact-specific tool than US trade dress, but the underlying logic Byczko describes, define your product’s distinctive features early and precisely, applies just as much on this side of the Atlantic. Why the Evidence Problem Changed Everything Ken asked what’s actually driving the current wave of disputes, and Byczko’s answer was simple: evidence. Ten or twenty years ago, if you sent a cease-and-desist letter or went to trial, you had almost nothing concrete to show about how consumers actually perceived two products. Now you have TikTok comment sections, influencer testimonials, and entire genres of “dupe content” documenting exactly what shoppers think, in their own words, in real time. In the Sol de Janeiro case, Byczko noted that part of the complaint isn’t just about packaging and trade dress. It’s about what influencers said, what claims they made, and what that content reveals about actual consumer confusion or the absence of it. That’s evidence litigators simply didn’t have access to a decade ago, and it cuts both ways: it can prove confusion, or it can just as easily prove there wasn’t any. Why Dupes Took Off: Economics, Status, and a Generational Shift Byczko was careful to frame this part as her personal read, not a sociologist’s conclusion, but it’s a read shaped by watching these disputes up close. Part of it is straightforward economics. Gen Z is shopping under real affordability pressure, and dupes let them participate in trend cycles without the price tag. Byczko cited a projected $12.6 trillion in Gen Z spending power by 2030, a generation too significant for brands to write off. The other part is cultural. A generation ago, owning a visible logo was the status symbol. Now, for a lot of younger shoppers, being the savvy one, the person who finds the dupe first and tells their followers about it, carries its own status. It’s less “I have the real thing” and more “I outsmarted the markup.” Byczko also pointed to growing public skepticism toward paying five or ten times more for a product purely because of the name on the packaging, particularly in beauty and fashion. The PR Trap: When Enforcement Backfires One of the sharpest points in the conversation was about what happens after a brand decides to enforce. Suing over a dupe can read very differently in public than it does in a courtroom. Byczko put it directly: going after a dupe can easily look, to the public, like a big corporation coming down on a small competitor, or worse, on its own customers, the same people who made the original brand aspirational in the first place. She’s seen this dynamic play out repeatedly in high-profile cases. Her advice: treat enforcement as a communication strategy, not just a legal one. Sometimes the smarter move isn’t a lawsuit at all. It’s a quieter cease-and-desist letter, a takedown request, or doubling down on marketing that explains what actually makes the original worth the price. Charlotte Tilbury has leaned hard into this approach, building campaigns around the idea that the original simply can’t be remade. Olaplex ran a similar play with its “OlaDupe” campaign. Legal and marketing, Byczko said, have to work together on this, not in sequence. What Brands Should Actually Do Byczko laid out three practical layers, all before litigation ever enters the picture. First: register your trademarks, and where a product design is genuinely distinctive, pursue trade dress or design patent protection early, before a dupe exists and before you know whether the product will even take off. That timing problem is real. Brands rarely know in advance which product will become the one worth copying. Her advice was to look at long-standing anchor products, the ones that have quietly carried a brand identity for years, and ask whether they’re actually protected. Second: monitor. A large share of dupe disputes start on social media, not in a courtroom. That means someone needs to be watching hashtags and influencer content, not just from direct competitors, but from adjacent or even unrelated brands that could end up duping a product without anyone noticing until it’s already trending. Third, and the one Byczko clearly considers most underused: consumer education and brand storytelling. “This is the original” is a weak pitch on its own in a market flooded with cheap alternatives. What works better is explaining, specifically, what makes a product different: its formulation, its sourcing, its performance, its longevity. Give people a real reason to pay more, not just a claim to authenticity. Where This Goes Next Byczko doesn’t think dupe culture is a passing trend. Her expectation is closer to “there will eventually be a dupe of everything,” and she’s watching an interesting generational pattern where teenage shoppers are teaching their mothers about dupes, who are in turn teaching their own mothers. On the legal side, she expects more clarity as cases like Van Leeuwen work their way through the system, giving brands a clearer formula for how to define and defend trade dress. On the brand side, she expects less reliance on litigation as the primary weapon and more investment in what’s genuinely hard to copy: real innovation, ingredient transparency, and storytelling that a dupe simply can’t replicate. One data point she raised stuck with us: search interest in the word “craftsmanship” is at its highest point in twenty years. After years of leaning into dupe culture, there are signs some consumers are swinging back toward wanting the original, the real ingredient, the real technique, the thing that can’t be copied to the same quality. For brands sitting on distinctive packaging, a signature shape, or a product identity they’ve never formally registered, that’s less a trend forecast than a to-do list. Here is the full transcript: Ken Suzan: Thank you, Ralf. Our guest today on the IP Friday’s podcast is Caitlin Byczko. Caitlin is a partner with Barnes and Thornburg LLP and is based in Indianapolis, Indiana. Caitlin crafts and defends global brand strategies, protecting intellectual property assets with creative solutions and highly tactical advocacy. She excels in trademark prosecution and litigation before the Trademark Trial and Appeal Board and federal district courts, safeguarding trademarks and digital properties for businesses of all sizes and at every stage of the business life cycle. From startups to Fortune 500 companies, Caitlin manages clients’ intellectual property needs across diverse industries. Her experience spans retail, fashion, luxury goods, sports, technology, agriculture, venture capital and pharmaceuticals. Beyond trademark law, Caitlin brings valuable insights from her law school experience with the National Collegiate Athletic Association, NCAA, and her previous work serving in a technology company’s in-house legal department. Her tenacious nature and clever problem-solving skills shine through in complex matters, earning praise from clients and colleagues alike. Caitlin is co-author of “Dupe Culture Meets the Courtroom,” published in Global Cosmetic Industry on March 16, 2026. Welcome, Caitlin, to the IP Friday’s podcast. Caitlin Byczko: Hi, Ken. Thank you so much. I’m very honored to be here. Ken Suzan: Yeah, so Caitlin, today we’re talking about dupe culture, a topic that is rapidly becoming front for many brands around the world. What’s the actual difference between a dupe and a counterfeit? Caitlin Byczko: That is one of my favorite questions. This is the most important distinction to draw when we are talking legally about dupes because the difference, because media and social media often use the words interchangeably and legally they’re very different. A counterfeit is a product that uses someone else’s actual trademark. We often think of a fake Chanel bag with interlocking C’s or a fake Louis Vuitton. It’s relatively straightforward trademark infringement and generally above certain thresholds is a federal crime. A dupe, by contrast, is a product that mimics the look, feel, or performance product without actually using the name or logo. We often think of it in the beauty products, in the fashion space, some brands like Elf Cosmetics, which was in the article you just mentioned, Zara, Costco’s Kirkland brand, Quince, who are all very well known in the dupe space. Ken Suzan: What has led to the rise of dupe culture? I’m reading about it virtually every day. Caitlin Byczko: I feel very strongly about this and I’m always talking about it in my legal and non-legal worlds. It’s a very interesting societal change that I think we’ve seen over the past year. I am a lawyer, I am not a marketer, I am not a sociologist, but in my opinion, social media and influencer culture specifically has really created the kind of rise in dupes that we see today. I don’t think we can talk about modern dupe culture without talking about TikTok specifically. Dupe content is its own genre, essentially on TikTok and on Instagram. There’s a whole vocabulary that people are dupe influencers, where their whole product, everything that they’re doing and selling, all of the content they’re making is dupe related. What’s really changed is the speed, I think, around when other products or when a dupe product comes out, how it can be marketed and how people can find out about it. The speed of commerce itself has increased wildly as a result, in part because of social media. A product used to take months to build a reputation as a good alternative. When we think about things, generic cereal is one thing that I have been talking about with my parents with respect to dupe culture. It was one of the things that there used to be, you would go to the store and there would be the cereal, the name brand cereal, and then there would be the generic version of the cereal, which was usually less expensive. That in a way was a dupe. It took a long time. Your friends knew about the dupe cereal and then you knew about the dupe cereal. Then it all got around. Now a single video can send a dupe product sold out within days, within hours sometimes. Oftentimes, a brand’s own team doesn’t even know about the dupe until it’s already been wildly out. Part of it becomes this legal issue when there are claims coming around the dupe. In the Sol de Janeiro case against Macau Beauty, part of the complaint isn’t just about the trade dress and the packaging, which I think we’ll talk about. It’s about the influencer content and the testimonials and what people are saying about the dupes. There’s so much evidence now and there’s so much content and there’s so much out there regarding dupes on social media, on TikTok, and in other places. Ken Suzan: Yeah, and it’s an ever-evolving story. Every day there’s new social media content, more evidence for a potential gain, right? Caitlin Byczko: Absolutely. Ken Suzan: So if dupes aren’t using a particular brand name, how are companies suing over them at all? Caitlin Byczko: So this is really where trade dress comes in, and trade dress, as most of us know, has been around for a long time. There are a lot of very well-known things that you probably see or use every day that you don’t know are protected by trade dress, but they are. And the trade dress protects the overall kind of look and feel of a product. So if you think about things like packaging, color combinations, shape, label design, when that becomes distinctive enough, right? When consumers see that and kind of immediately understand it has the secondary meaning related to the brand owner, then it can become a protectable trademark. And so the test for trademark infringement is likelihood of confusion. And courts will look at the factors of how similar the products actually look, how sophisticated the shoppers are, whether there’s evidence that people were actually confused. I think one of the big cases in the trade dress space that came out since you and I discussed originally, Ken, is the Van Leeuwen versus Rebel Creamery ice cream case. And so for any of those interested, it’s a very interesting opinion. It just came out of the Eastern District of New York. I think that really helps people, brands specifically, kind of provide a roadmap with respect to how to define a trade dress. I think they did an excellent job there defining what the trade dress was for the packaging. And I think that that had a lot to do with the success. Ken Suzan: Yes. Ken Suzan: Why do you think younger consumers gravitate towards dupes so much more than past generations did? Caitlin Byczko: I think there are a few things that are kind of top of each other. The obvious one is economics. I think younger consumers, especially Gen Z, they’re shopping in an environment where there’s affordability pressure. And I think that dupes let them participate in certain trend cycles without the price tag. Gen Z is a significant demographic behind the growth of dupes. And they have a predicted spending power we saw recently, $12.6 trillion by 2030. Ken Suzan: Wow. That’s incredible. Caitlin Byczko: I think it is really also coupled with more of what I would say is a cultural shift. Again, I am a lawyer and this is just my opinion. But what feels aspirational is really changing, I think. And a generation ago, we saw in the fashion world, there was a really big, people really liked logos. Having a logo, owning a logo was the point. And now for a lot of younger shoppers or even more savvy shoppers, actually being a savvy shopper is the status symbol itself. So for a lot of creators, finding the dupe before anyone else or being the one who tells your followers about the dupe really has its own, and it’s less “I have this real thing” and more, “oh, I outsmarted the markup.” And I think it’s that kind of value. I think younger consumers are more publicly skeptical of the idea that something is worth five or ten times more just because of the name on the packaging. And I think that that becomes the case particularly in beauty and then clothing as well. And so I think it’s coupled with the question of craftsmanship and all of these different things. Like we can’t view anything in a vacuum, which is why I could talk for 500 years about this topic. Ken Suzan: Yeah, definitely. Now brands obviously want to protect themselves. That’s an important thing. But going after a dupe can backfire publicly, particularly on the internet. Can you comment on this possibility and what should brands do? Caitlin Byczko: Sure. I think the biggest thing is what you just said. So I think there’s the legal component. And when we’re assessing this for one of our brand clients, I think we cannot review one without the other. So I think you have to say, do we have this claim? Do we have a protectable trade dress or a look and feel claim? And what is the potential backlash in the public? I think it is also a joint PR concern, because what we see is suing over a dupe can very easily read to the public as, oh, this big brand or big corporation is coming down on a small competitor or on its own customers that made the original brand aspirational. I think that we see this in a lot of the big cases that are out right now. So brands, I think, can start to think about enforcement as a communication strategy first, because sometimes the smarter play isn’t necessarily filing a lawsuit. It’s kind of a quieter cease and desist, a takedown request, or separately, it might be really doubling down on the craftsmanship or on a marketing side of what makes the product worth the price. I think Charlotte Tilbury is a great example of this, who’s really leaned into this kind of “can’t be duped” or “you can’t remake the original” with respect to some of their really core products. And so I think people really need to, or brands really need to, assess: one, how can we define what it is that’s being duped? And again, that’s where I think that Van Leeuwen case, I think, will be really interesting over the next few years, with respect to kind of providing this roadmap to help people say, here is a way that we can try to really define what the trade dress is and then be able to protect it and be able to enforce it. Ken Suzan: Caitlin, are there cases right now, I know we just talked about the Van Leeuwen case, but are there cases right now that you think will actually set the tone for how this area of law develops? Caitlin Byczko: Absolutely. And I think some of the cases probably are even in the works, they’re not even filed yet, which is probably very exciting to us as trademark nerds. I do think the Lululemon versus Costco case will be a big one. It’s not just trademarks and trade dress, as many of them are not. I think a lot of times we see in these cases brands are very smartly using kind of all of the different types of causes of action that they bring: trademark, patent, false advertising, a lot of different things. And so I think that that one is definitely certainly one to watch kind of in the fashion space. And then the Sol de Janeiro versus Macau Beauty. Macau Beauty has been sued multiple times, I think, in various jurisdictions. And so I think part of that one is very interesting to me because I think it’s this, it loops not only trademark protection, but also it brings in false advertising, it brings in influencers, it brings in all of these different things. And so I think, like we had talked about previously, nothing is really viewed in a vacuum. And I think for all of these cases, one really important thing, maybe that we didn’t necessarily have access to 10 years ago, or certainly 20 years ago, is this like just ripe amount of evidence of potential confusion or potential non-confusion. When we go on social media and look at all of these things, and then read the comments and all of this different data that’s out there, it’s fascinating, because if you’re in trial, or if you’re going to trial, you’re sending a cease and desist letter, like there is evidence of what the consumers think right here in front of you, right? And the weight of that evidence obviously depends on what it is. But it’s fascinating the way that you can very quickly identify, you know, is there confusion? Is there not confusion, in a way that you likely could never have even thought to consider 10 to 20 years ago? Ken Suzan: Now beyond litigation, what should brands actually be doing to protect themselves in a dupe-driven market? Caitlin Byczko: I think one of the best things that we can do, right, is starting to register the trademarks. I think that’s an obvious one. And really start to consider where the product is genuinely distinctive. And so if it is genuinely distinctive, pursuing a trade dress or a design patent early, before a dupe exists, before anything happens. And I know that can be difficult, because oftentimes brands don’t know for sure what’s going to take off and what’s not. It can also be a surprise. But I think it’s really pushing brands that when you are innovating and when you are doing something that is truly unique and truly distinctive, or when you’re looking back on your brand assets and saying, this thing has been an anchor brand asset for 10 years, you know, have we sought trade dress protection? Is there a way that we can do that? The second layer really is monitoring, in my mind, because a lot of dupe disputes do start on social media. And I think it is important to have people within a company, if you have a product that you’re really keeping an eye on, or that you’re concerned about being duped or causing confusion, having someone who is keeping eyes on hashtags and influencer content and all of these various things. You know, we’re not watching just your direct competitors, we’re watching other completely different brands, or kind of made-up brands even, who could be duping the product. And then I think the third thing that I see as very important is this kind of consumer education and brand storytelling, which is when we kind of get outside of the purely legal side of it. And, you know, legal and marketing and brand and social kind of all need to work together, right? I think if the only pitch to consumers is “this is the original,” it kind of becomes a weak argument in a market where there are cheap alternatives everywhere. But I think the brands that really explain what actually makes their product different, in its formulation, its sourcing, its performance, its longevity, it really gives people a real reason to say, I want to pay more for this brand because of XYZ, you know, the technology or whatever that is. Charlotte Tilbury is one that I had mentioned. I know Olaplex kind of had a big campaign around “OlaDupe” is what they called it. So I think really unique and interesting marketing also assists with that. Ken Suzan: Caitlin, where do you see dupe culture heading? Is this a trend that plateaus or does it fundamentally change how brands operate? Caitlin Byczko: I think dupe culture itself is here to stay. I mean, I think we are only getting into a world where there is truly going to be a dupe of everything. And it’s not good or bad necessarily. I think it is just where we are in life. And I think, you know, things serve different purposes. And it all depends a lot on how the younger consumers shop. And it’s also changing how older consumers shop. You know, I’ve read a lot about teenage girls teaching their moms about dupes, who are then teaching their grandmothers about dupes, right? So on the legal side, I think we will get clarity eventually. I think right around, you know, all like all of these things, which seem so complex, and we’ll never know the answer. You know, five [years] from now, we will probably have certainly more clarity, because a lot of these cases will move forward. You know, the Van Leeuwen one, which I’ve now talked about multiple times, but I just obviously think it’s very fascinating. I think that that’s one where you have a roadmap, right? And it may be contested, or, you know, everything is very fact-specific in the trademark world. But I think it will open the door to allow people one more aggressive brand enforcement. But it will give people a roadmap proactively to kind of say, if we follow this formula for our trade dress, or, you know, defining our trade dress, then, you know, we have something we can potentially protect. And then I think on the brand side, we’ll likely see less reliance on litigation as the primary weapon and more investment in things that are actually, you know, difficult to dupe: innovation, ingredient transparency, marketing, genuinely interesting brand storytelling is something that we’ve seen. I read this past week that searches for craftsmanship, just like generally the word craftsmanship, and kind of products with craftsmanship, is at an all-time high, than it’s been in like the past 20 years. And so it’s interesting, right, that we have gone from this kind of luxury item or high craftsmanship to this dupe culture that we are in now. But there is some potential shift where people are saying, you know, now, I’ve seen all of this and I have all of these options, but now actually what I do care about is the original, right, the innovation or the ingredient transparency or all of those things that can’t necessarily be copied to the same quality. So it’s why it will just provide us with endless topic of discussion, because I think it will only just keep changing forever. Ken Suzan: That’s right. Caitlin, I want to thank you for spending time with us on the IP Friday’s podcast. This has been very insightful, and I’m sure we’ll be talking about this issue in the months and years to come. Caitlin Byczko: Thank you so much, Ken. I really appreciate it. Ken Suzan: Thank you.

Can our economy keep growing forever on a finite planet?In this third installment of Finding Crazy Town, we explore the idea of exponential economic growth, why economists and politicians have become so committed to it, and what happens when our economic models fail to account for the environmental systems that make the economy possible.Along the way, Jason, Rob, and Asher revisit the problems they see in conventional economics, Jason recounts the experience that sent him down the rabbit hole of Limits to Growth and ecological economics, and the trio asks whether growth and progress really mean the same thing.From Robert Wadlow to Ronald Reagan, Donella Meadows to Julian Simon, the IPCC to the three musketeers, we follow the clues—and discover that sometimes limits aren't the enemy of creativity. They're what make creativity possible. Originally recorded on 7/21/26 with archival episodes.Related EpisodesEpisode 2, “Punching Ronnie in the Mouth”Episode 8, “Mosquito-Flavored Popcorn, or What Climate Scientists Are Getting Wrong”CreditsProduction and editing by Alex Leff. Editorial assistance and transcripts by Taylor Antal.Theme music is “Way Huge” and “Don't Give Up” by Midnight Shipwrecks, used with permission.Thanks to all the Crazy Townies, our listeners who are trying to understand humanity's overshoot predicament and do something about it.

Arun Parameswaran: Using Scrumban and WIP Limits to Help Agile Teams Find Focus Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.   "The WIP limit helped the developers not context switch." - Arun Parameswaran   Arun brought a practical challenge to the Wednesday coaching conversation: a team doing both operations and development work needed more visibility and focus than their Scrum board was giving them. The trigger was context switching. Work stayed "in progress" even when blocked, people picked up new topics before finishing old ones, and stakeholders kept asking what was actually happening. Arun helped the team experiment with Scrumban, keeping useful Scrum events while adding a Kanban-style board and explicit WIP limits. The breakthrough was not just visualizing the work. It was helping the team own the limits. They created an "operator of the week," rotating the responsibility for watching WIP and calling out when the team was taking on too much. That small practice made focus a team responsibility instead of a Scrum Master lecture.   Self-reflection Question: Who owns your team's WIP limits in practice, not just on the board?   [The Scrum Master Toolbox Podcast Recommends]

Beatrice begins her destruction of Dante's explanation for moon spots by taking apart his notion of dense and rare spots on the lunar surface . . . with a dizzying display of mixed metaphors.Her argument lofts to the highest visible heaven, the eighth sphere; then falls back into scholastic reasoning; and offering both experimental and poetic rationales for why he's wrong.Join me, Mark Scarbrough, as we explore the first part of Beatrice's explanation for moon spots, predicated on the bass line of the incarnation.Here are the segments for this episode of WALKING WITH DANTE:[01:20] My English translation of PARADISO, Canto II, Lines 64 - 84. If you'd like to read along or continue the conversation about this passage, find its entry at my website, markscarbrough.com.[03:27] Paraphrasing this difficult passage in Beatrice's argument about moon spots.[04:46] Lofting the argument to the eighth heaven, the highest part of visible creation (from Dante's perspective).[08:15] Offering diversity as complexity beyond a simple continuum for an answer.[12:31] Moving into experimental (or experiential) proofs for her argument (and implicitly for an understanding of the incarnation of Jesus).[15:13] Inspiring a dizzying vertigo through mixed metaphors to state the negative of her argument.[18:41] Rereading the passage: PARADISO, Canto II, lines 64 - 84.

In this episode, Axel sits down with Zach Hoereth — real estate investor, operator of Midwest Storage, and one of the more entertaining (and polarizing) voices on real estate Instagram — for a genuinely unfiltered conversation about what actually works in this business.He brings a uniquely blunt, been-there perspective on direct-to-seller acquisitions, the limits of AI and technology in a relationship-driven business, and why chasing unit count and AUM is one of the most overrated status games in real estate investing.This episode is essential listening for any investor who wants a no-BS look at what it actually takes to source deals, build a lean operation, and avoid the traps that sideline so many people who get into this business.Join us as we dive into:Zach's path from a college leasing hustle to buying his first $20–30K house in Indianapolis in 2018, and how that snowballed into today's business.How Zach's operation is structured: direct-to-seller mail feeding wholesaling and flipping, which funds acquisitions of small multifamily, single-family rentals, and self-storage — all without outside equity to date.Why "just buying rentals" isn't a wealth strategy — cash flow keeps you in the game, but equity and capital events are what actually move the needle.Why AI and chatbots can't fix a bad reputation or replace the human-to-human trust that wins deals, retains tenants, and keeps LPs engaged.The "70 dudes who partnered on a fourplex" problem — why unit count and AUM get wildly overrated as status symbols, and why doing a few deals solo teaches more than riding shotgun on a syndication.The three things you actually need to start doing direct-to-seller deals: a CRM, a targeted list (tools like PropStream and Reonomy), and a mail houseHow to make aggressive offers respectfully, and why "running toward the confrontation" beats avoiding it.The three Ds of distress — death, divorce, and drama — and how to quickly identify which sellers are actually motivated versus wasting your time.Why a seller's real pain point is often bigger (and weirder) than investors assume, and why being present when they decide to sell matters more than trying to convince them.Connect with Zach Hoereth:Follow him on InstagramAre you looking to invest in real estate, but don't want to deal with the hassle of finding great deals, signing on debt, and managing tenants? Aligned Real Estate Partners provides investment opportunities to passive investors looking for the returns, stability, and tax benefits multifamily real estate offers, but without the work - join our investor club to be notified of future investment opportunities.Connect with Axel:Follow him on InstagramConnect with him on LinkedinSubscribe to our YouTube channelLearn more about Aligned Real Estate Partners

(August 25, 2026) Supreme Court allows Trump to pursue mail voting limits, for now. The decline of public pools. Big food winning the war for America’s diet. Baby boomers are smoking more weed… their neighbors are fuming.See omnystudio.com/listener for privacy information.

Richard McGirr talks about how most fund-of-funds hit a ceiling after 99 investors, but what if scaling beyond that could become your greatest advantage? Richard reveals the counterintuitive secrets to explosive growth in real estate and debt funds that most never see coming. If you're tired of hitting strategic walls and want to unlock the true potential of your capital business, this episode is your blueprint to breaking through limitations and building a powerhouse. For more information, visit https://superhuman.com/. Podcast production done by⁠ ⁠Outlier Audio⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

Host Myrna Young delves into the world of intuition, clairvoyance, and near-death experiences with guest Kim Bantle, a self-proclaimed "closeted clairvoyant." Kim shares her journey of keeping her abilities hidden while thriving in a corporate career, and how a near-death experience transformed her understanding of purpose and consciousness. They discuss the various types of clairvoyance abilities, the importance of intuition, and how to become more aware of synchronicities in our lives. Join them in exploring what it means to trust your inner knowing and the unseen connections shaping our existence.Dive deep into the themes of intuition, synchronicity, and spiritual guidance in this enlightening episode. Kim stresses the importance of trusting one's gut and recognizing life's synchronicities as a profound form of connection with the universe. With powerful anecdotes and scientific insights, Kim challenges societal norms and invites everyone to embrace their innate spiritual sensitivities for a more fulfilled and aware life.Key Takeaways:Intuition is a powerful tool that can guide individuals through life's complexities, aiding them in making decisions aligned with their highest potential.Near-death experiences often provide profound insights into consciousness and reveal a purpose-filled existence, enhancing one's understanding and appreciation of life.Synchronicities and spiritual signs should be viewed as important, non-random events that invite individuals to pay attention to the deeper meanings in their lives.Many people possess latent spiritual sensitivities, such as clairvoyance and mediumship, often passed down generationally, that remain untapped due to societal constraints.Encouraging conversations on spirituality, especially among youth, can foster a healthier understanding of intuition and potentially mitigate issues like depression and violence.Timestamp Summary3:40 A Spiritual Journey from Reluctance to Fulfillment6:02 Discovering Clairvoyant Abilities Through Personal Loss and Connection10:53 Embracing Intuition and Sensitivity in a Skeptical World18:27 The Transformative Power of Near Death Experiences24:19 The Rise of Light Workers and Spiritual Conversations26:17 A Child's Uncanny Prediction of a Natural Disaster28:58 Communicating With Spirits and Understanding Their Signs36:25 Intuition, Dreams, and Spiritual Connections39:26 The Power and Limits of Prayer in Healing and Loss42:25 Exploring Synchronicities and Dreams as Invitations to Awareness46:21 Unveiling Hidden Psychic Abilities and Family Secrets49:03 Exploring Intuition, Consciousness, and Unseen ConnectionsResources:Visit Kim Bandle's official website for more information: KimSBantle.comPurchase Kim Bandle's memoir: Confessions of a Closeted Clairvoyant on AmazonLink to Transcript https://www.buzzsprout.com/1761155/19672612-intuition-series-the-transformative-power-of-clairvoyance/transcriptSee this video on The Transform Your Mind YouTube Channel https://www.youtube.com/@MyhelpsUs/videosTo see a transcripts of this audio as well as links to all the advertisers on the show page https://myhelps.us/Follow Transform Your Mind on Instagram https://www.instagram.com/myrnamyoung/Follow Transform Your mind on Facebookhttps://www.facebook.com/profile.php?id=100063738390977Please leave a rating and review on iTunes https://podcasts.apple.com/us/podcast/transform-your-mind/id1144973094Feedspot Top 100 Mental Health Podcast For sponsored Brand interviews and sponsorship inquires please visit Partner With The Transform Your Mind Podcast | Myrna Young Life Coach

What happens when words are no longer enough to explain a life? Composer and jazz artist Ray Seol joins Dave Carter for a deeply personal conversation about immigration, language, music, memory, and the unlikely journey that led him to search for meaning through sound. At the heart of the conversation is Professor Seol's extraordinary relationship […]

Have you ever wondered how it's possible to explore the depths of faith, find the kingdom, and yet overlook the role of the Holy Spirit? This episode of Seek Go Create challenges common assumptions about religion, correction, and the true center of the Christian life. The discussion explores how visible structures, traditions, and politics can distract from the transforming presence of the Spirit—and asks what it really means to live as a Spirit-filled people. If you've ever felt like something central is missing in your spiritual journey, this episode will invite you to look again.“Correction is not the center. Jesus Christ is the center.” - Tim WindersAccess all show and episode resources HEREEpisode Resources:NT90 Hub – This is the central website for the 90-day New Testament reading plan, with downloadable, printable plans, background information, and links to all episodes and resources.Episode Highlights:00:00 Correction Is Not Center 00:45 NT90 Series Setup 02:33 Kingdom Beyond Structures 04:21 Limits of Correction 07:10 Spirit Holds Story 09:52 Threads of the Spirit 11:48 Familiar Spirit Overlooked 13:56 Unseen Reality vs Substitutes 16:45 Spirit Filled People 18:25 Spirit for Daily Life 19:50 Look Again Next Steps 20:56 Download Plan Farewell

The parable gives us a final harvest. The Gospel gives us the time before the harvest. Becoming remains possible precisely because we haven't reached the harvest. Until the final harvest, the story of the human being is not yet finished.BE ON THE LOOKOUT FOR CHROMA'S NEW LIGHTS!!!Elevate How You Navigate with Len & a free call https://elevatehowyounavigate.comMAYU Water, use "autism" for 10% off at https://mayuwater.comDaylight Computer Company, use "autism" for $50 off at https://buy.daylightcomputer.com/autismDaylight Kids (!!!) https://kids.daylightcomputer.com/autism ‪Chroma Light Devices, use "autism" for 10% discount at https://getchroma.co/?ref=autism00:00 The Weeds & the Battle Before the Harvest; Two Kingdoms, Two Seeds02:19 What the Kingdom of Darkness Offers; Reward Without the Destination03:24 The Beatitudes vs. the Third Temptation; Two Visions of Human Flourishing04:20 Attention Becomes Allegiance; What Are You Feeding?05:13 From Seed to Culture; How What We Become Shapes Other People07:24 What Is Actually Growing in You?; Jung, Values, Habits & Repetition09:18 Kierkegaard & Becoming; You Are Building the Person You Will Become10:30 The Harvest & Final Judgment; What Has Been Growing All Along?12:15 Before the Harvest; Repentance, Forgiveness & the Possibility of Transformation16:45 Judgment Now vs. Judgment Later; The Christian Hope of Becoming Different18:19 What Does It Profit to Gain the World?; What You Get vs. What You Become20:15 Two Kingdoms, Two Visions of the Good Life; Who Taught You What to Want?21:34 The Seeds We Pass to Others; What Grows Within Us Can Outlive Us23:21 Humility, Responsibility & What You Can Actually Cultivate24:36 Why Does Evil Grow?; The Limits of Human Vision & Raskolnikov25:35 The Sower vs. the Weeds; What Will What You've Received Grow Into?27:02 Until the Final Harvest, the Human Story Is Not Finished27:21 Closing & Podcast Information28:04 Sponsors; MAYU Water, Daylight & MoreX: https://x.com/rps47586YT: https://www.youtube.com/@FromTheSpectrumemail: info.fromthespectrum@gmail.com

Today's episode of ID The Future comes from our sister podcast Mind Matters News, a production of Discovery Institute's Walter Bradley Center for Natural and Artificial Intelligence. On this episode of Mind Matters News, guest host Pat Flynn welcomes Dr. Winston Ewert to the show to discuss Ewert's chapter in the volume Minding the Brain.  In his contribution, titled “The Human Mind's Sophisticated Algorithm and Its Implications,” Dr. Ewert argues that the human mind's problem-solving cognition can be modeled as a sophisticated algorithm. Ewert explains that any cognitive task can be expressed as a version of the halting problem from computer science, where an algorithm either halts or continues infinitely. He suggests that humans are able to solve a large and sophisticated subset of these problems, but are limited in the same way that no algorithm can solve all halting problems. Ewert contends that this algorithmic view of human cognition has implications that challenge reductive materialist and neo-Darwinian views, despite resistance from some thinkers who believe human abilities transcend any algorithm. Source

Weekend Edition for August 22-23, 2026 Show Notes: Support the work at 1517 1517 on YouTube 1517 Podcast Network on Apple Podcasts 1517 Events Schedule 1517 Academy - Free Theological Education Germany / Switzerland - Study Tour What's New from 1517: The Turk at the Door by Adam Francisco Luther and the Lion: A Narnian Catechism by Samuel Schuldheisz By Water and the Word by Brian Thomas Being Family by Dr. Scott Keith More from the hosts: Dan van Voorhis Follow 1517:  Instagram X/Twitter Facebook SHOW TRANSCRIPTS are available: https://www.1517.org/podcasts/the-christian-history-almanac CONTACT: CHA@1517.org Facebook Twitter Audio production by Christopher Gillespie (outerrimterritories.com)

Two athletes who worked with bodybuilding coach Bleu Taylor have passed away, raising difficult questions about coaching responsibility, athlete health and knowing when a situation is beyond a coach's role. Dr. Ashley speaks publicly about the loss of her husband and her concerns about his coaching, and Scott, Skip Hill and Andrew Berry break down her message and discuss what responsible coaching should look like. Then we get into your listener questions on mini cuts, first steroid cycles, how quickly compounds build up, Tren Ace vs. Tren E, glycogen loading for competitors with physical jobs, SHBG, Bro Splits vs. Push Pull Legs while dieting, physique critiques and more. Hear Dr. Ashley's full message here: https://www.instagram.com/p/DcJ5BMHSE4o/ 0:00 Dr. Ashley Speaks Out About Bleu Taylor 2:30 Two Former Bleu Taylor Clients Have Passed Away 3:45 Coach-Athlete Relationships & Responsibility 5:40 Knowing the Limits of a Bodybuilding Coach 12:40 What Makes a Responsible Coach? 13:15 Coaching Athletes With Questionable Health 22:45 Staying Healthy in Bodybuilding 24:30 How Common Are Mini Cuts? 34:30 Choosing Compounds for a First Steroid Cycle 38:00 How Quickly Do Steroids Build Up? 41:30 Tren Ace vs. Tren E — Is There a Difference? 43:40 Contest Prep Glycogen Loading With a Physical Job 53:30 Why SHBG Matters 55:20 What Is a Bro Split? 58:00 Bro Split vs. Push Pull Legs While Dieting 1:08:00 Physique Critique - Plus Scott gets SO Confused

Carlos opens this one by admitting taxes are his blind spot, so he brought in someone who has built a career on them. Rachel Phillips founded Fully Accountable, an accounting and CFO firm that served e-commerce brands exclusively. After BELAY acquired it, she stayed on and now runs the entire financial solutions division as Senior VP. Her core point: tax hacks are not something you find in a shoebox of receipts in April. They work because they are a plan you put in place inside your business strategy. This is part one of two, covering the first three hacks, the ones at the top of the stack that make everything downstream work. In part one: Why "just be an S Corp" is bad advice. An S Corp is a tax election, not a business structure, and most people passing the advice around cannot define it. Rachel walks the real options and explains why the C Corp still earns its place when you need to raise money or take on debt. How you get paid changes with your structure. Guaranteed payments versus a W-2 salary, and how the wrong entity can quietly put you out of compliance. When to actually build a tax plan. The profit and revenue marks Rachel uses, why inventory-heavy sellers should start earlier, and who belongs in the room. Your CPA and your CFO, not your bookkeeper, and not a business lawyer who does not do tax. Retirement plans as a retention tool. The SEP IRA most owners have never heard of, how matching turns into money you never paid tax on, and the question every employee asks: what happens to my balance if I leave in five years. The Augusta rule. Rent your own home to your own business up to 14 days a year, tax free to you and deductible to the business. Carlos asks the question everyone asks at the bar: can nine businesses each run it against the same house? Rachel shuts that down and explains the one narrow case where it works. Setting fair market value on your home without overthinking it, and why your mortgage payment has nothing to do with the number. The best months of the year to do this work, plus the retirement funding deadline that is not December 31. Part two lands next week and goes straight at the e-commerce specific hacks: Section 179 bonus depreciation, prepaid expenses, and Rachel's checklist of old faithfuls that everyone forgets. Our guest: Rachel Phillips is an entrepreneur, a lawyer by training, founder of Fully Accountable, and Senior VP of Financial Solutions at BELAY. She is most active on LinkedIn. Connect with BELAY: text WIZARDS to 55123 and they will send resources and connect you with their team. BELAY is a sponsor of the Wizards of Ecom community, and as our listeners know, we say no to far more partnerships than we say yes to. This is a conversation between two business owners, not tax advice. Limits and rules change year to year. Take anything here to your own CPA before you act on it.

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

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The future of climbing difficulty isn't only about the size of holds or the distance between them. In fact, that's rarely what changes things. It's more about the climbers and their skills. Check out the Written in Stone Podcast. ASSESS AND IMPROVE YOUR TECHNIQUE➡️ https://link.powercompanyclimbing.com/ytmove OVER 150 BONUS EPISODES ➡️ https://patreon.com/powercompanypodcast MONTHLY CLIMBING THOUGHTS IN YOUR INBOX ➡️ https://link.powercompanyclimbing.com/ytcurrent BETTER UNDERSTANDING GRADES PLAYLIST ➡️ https://link.powercompanyclimbing.com/grades   DISCLAIMER: Climbing and training for climbing is inherently dangerous. Anything you do is at your own risk and Power Company Climbing and our coaches takes no responsibility.

Book a free call with Robert Sikes to break through your keto or low-carb plateau: https://www.ketobodybuilding.com/callDoes muscle matter more than costly biohacks for longevity and health? In episode 910 of the Savage Perspective Podcast, host Robert Sikes sits down with sports medicine doctor Dr. Jordan Metzl to expose the truth about peptides, BPC-157, GLP-1 drugs, supplements, and quick fixes. They explain why movement, strength training, real food, sleep, purpose, and community matter more for health span than wellness trends. Learn how muscle helps metabolism, blood sugar, brain health, mobility, and healthy aging. He also explains why motivation fades, how to make habits last, what changes after age 50, and why fast-twitch muscle matters. This episode gives clear steps to stay strong and active for decades.Follow Jordan on IG: https://www.instagram.com/drjordanmetzlGet Keto Brick: https://www.ketobrick.com/Subscribe to the podcast: https://open.spotify.com/show/42cjJssghqD01bdWBxRYEg?si=1XYKmPXmR4eKw2O9gGCEuQChapters0:00 - Why Most Longevity Biohacks Miss What Actually Works2:38 - Why Expensive Biohacks Cannot Replace the Basics5:02 - Are GLP-1 Drugs Becoming the New Health Shortcut?5:43 - Why Medical Trust Collapsed After the Pandemic7:07 - What Does Healthy Longevity Really Mean?8:25 - Why Exercise Should Be Prescribed Like Medicine9:51 - What Is the Science of Motivation? Dr. Jordan Metzl's Push10:44 - How One Woman Ran 15 Marathons After Starting at 6912:44 - Why Some People Stay Motivated for Life14:04 - What Does "Lowering the Cost to Act" Mean?15:45 - Do Consequences Make Fitness Habits Stick?16:58 - Can You Start Powerlifting at 59 and Set Records at 70?17:29 - What Is Muscle Span and Why Does It Matter?18:50 - Is More Muscle Always Better for Longevity?20:48 - Why Everyone Should Think Like a Bodybuilder23:53 - Does Metabolism Really Slow Down With Age?25:21 - How Strength Training Changes Metabolism After 6527:06 - Why Consistency Beats the Perfect Fitness Plan28:18 - Why Your Fitness Goal Must Be Bigger Than You29:03 - What Blue Zones Teach Us About Purpose and Longevity30:48 - How Family and Purpose Change the Way We Age32:18 - Why Social Wellness Matters as Much as Physical Health33:39 - Is Constant Stimulation Damaging Our Health?34:40 - How to Find Calm in an Overstimulated World35:46 - What Are the Most Common Sports Injuries?37:30 - Why You Never Appreciate Mobility Until It Is Gone39:14 - Do Injuries Come From Overtraining or Inactivity?41:10 - How to Preserve Fast-Twitch Muscle After Your Mid-30s42:12 - Does BPC-157 Work for Injury Recovery?44:25 - Functional Medicine, AI and the Limits of Health Advice46:22 - Why Experts Are Building Private AI Knowledge Bases47:19 - Why the Wellness Industry Is Harder to Trust Than Ever48:21 - Why More Health Information Has Not Made Us Healthier49:25 - How Dr. Jordan Metzl Structures His Day for Long-Term Fitness50:24 - Inside Dr. Metzl's 1,000-Person Fitness Community51:53 - Where to Find Dr. Jordan Metzl

Your teenager barely leaves their room. They're gaming for hours, avoiding school, pushing every button, and every conversation ends in an argument. You've tried consequences. You've tried backing off. You've tried talking, therapy, and taking devices away. Yet nothing seems to change.If this sounds familiar, you're not alone.In this episode of The Child Psych Podcast, psychologist Tania Johnson sits down with family therapist Dr. Scott Sells, author of Parenting Your Out-of-Control Teenager, to discuss what parents can do when they feel like they've lost both their authority and their connection with their teen.Rather than focusing on quick fixes, Dr. Sells explains why so many families become trapped in cycles of conflict and power struggles. Together, they explore how anxiety, gaming, depression, trauma, entitlement, and family dynamics often become intertwined, making it difficult for parents to know where to begin. Most importantly, Dr. Sells offers practical strategies that help parents step out of constant battles and begin leading their families with both confidence and compassion.This conversation is filled with hope for parents who feel exhausted, discouraged, or afraid that it's too late to reconnect with their teenager.In this episode, you'll learn:The first thing parents should stop doing when their teen seems completely checked out.Why power struggles around screens often make the problem worse.How to stay calm when your teen knows exactly how to push your buttons.The difference between treating the behavior and understanding what's driving it.Practical screen boundaries that reduce conflict instead of escalating it.How to rebuild motivation, responsibility, and hope in a teenager who seems to have given up.Why healthy parenting isn't about choosing between love or limits—it's about learning how to provide both at the same time.Whether you're parenting a teen who is struggling with gaming, anxiety, school refusal, depression, or constant conflict, this episode offers practical tools and a fresh perspective to help you move from surviving each day to rebuilding your relationship—and your confidence as a parent.About Dr. Scott SellsDr. Scott Sells is an internationally recognized psychologist, family therapist, speaker, and the creator of the evidence-based Parenting with Love and Limits® model. For more than 30 years, he has helped families navigate some of the most challenging adolescent behaviours, including defiance, aggression, school refusal, anxiety, depression, substance use, and technology-related conflict. His practical, compassionate approach has helped thousands of parents rebuild both authority and connection with their teens.To learn more about Dr. Sells, click here or to purchase Parenting Your Out-of-Control Teenager: 8 Strategies to Reestablish Authority and Reclaim Love, click here. Hosted on Acast. See acast.com/privacy for more information.

A few thoughts that combine all of the things I spend time thinking about while I get together the Bleau interviews. The future of climbing difficulty isn't only about the size of holds or the distance between them. In fact, that's rarely what changes things. It's more about the climbers and their skills. ASSESS AND IMPROVE YOUR TECHNIQUE➡️ https://link.powercompanyclimbing.com/ytmove OVER 150 BONUS POWER COMPANY PODCAST EPISODES ➡️ https://patreon.com/powercompanypodcast MONTHLY CLIMBING THOUGHTS IN YOUR INBOX ➡️ https://link.powercompanyclimbing.com/ytcurrent BETTER UNDERSTANDING GRADES PLAYLIST ➡️ https://link.powercompanyclimbing.com/grades   DISCLAIMER: Climbing and training for climbing is inherently dangerous. Anything you do is at your own risk and Power Company Climbing and our coaches takes no responsibility.

In this Unfiltered episode of Fixing Healthcare, Drs. Robert Pearl and Jonathan Fisher join cohost Jeremy Corr for a wide-ranging conversation about burnout, kindness, leadership, medical errors and what American healthcare can learn from other nations. The episode begins with Fisher's recent trip to Australia, where he joined physicians and other healthcare professionals for a program focused on individual well-being, organizational well-being and burnout. Held near Uluru, on sacred Aboriginal land, the gathering included conversations with tribal elders about the connection between land, culture, traditional remedies and healing. Fisher explains that some Australian hospitals have incorporated Aboriginal traditions alongside Western medicine, not as a replacement for modern care, but as a way to build trust and honor patients' cultural identity. That approach, he says, has helped Indigenous patients feel more respected by the healthcare system. Pearl connects the discussion to a larger economic reality. Although the United States spends far more on healthcare than other nations, many countries are facing the same underlying pressure: medical costs rising faster than wages, GDP or general inflation. For clinicians, the result is familiar across borders: more work, more bureaucracy, more isolation and growing frustration. From there, the conversation shifts to kindness in leadership. Fisher draws an important distinction between being kind and being nice. Niceness, he argues, is often about wanting people to like us or avoiding discomfort. Kindness is different. True kindness may require difficult conversations, honest feedback and decisions that create short-term discomfort but protect patients, teams and organizations over time. That distinction leads into one of the episode's central leadership questions: How should medical leaders respond when a physician is struggling, underperforming or causing avoidable harm? Pearl emphasizes that leaders must keep the patient at the center. Fisher adds that fairness matters deeply, especially in how feedback is delivered. Leaders should be prompt, clear, transparent about process and respectful of the physician's dignity. Finally, the discussion shifts to generative AI and diagnostic errors. Pearl raises the possibility that AI tools could help identify missed diagnoses in emergency departments before patients are discharged or admitted. Fisher agrees that AI can help prevent harm, but warns that emergency physicians are already overwhelmed. A tool that produces too many alerts, even if well intended, could worsen cognitive overload and be ignored. Jeremy closes with a patient-centered question about what both physicians have seen in other countries that they wish existed in the United States. Fisher points to the sense, in some countries with socialized medicine, that everyone deserves a basic level of care. Pearl describes a hospital visit in Sweden, where strong social supports and clinician collaboration produced better outcomes by addressing many social determinants before they reached the medical system. For more unfiltered conversation, listen to the full episode and explore these related resources: ‘Just One Heart' (Jonathan Fisher's newest book) ‘ChatGPT, MD' (Robert Pearl's newest book) Monthly Musings on American Healthcare (Robert Pearl's newsletter) * * * Fixing Healthcare is a co-production of Dr. Robert Pearl and Jeremy Corr. Subscribe to the show via Apple Podcasts or wherever you find podcasts. Join the conversation or suggest a guest by following the show on X and LinkedIn. The post FHC #225: Kindness, burnout and the limits of medical leadership appeared first on Fixing Healthcare.

Richard McGirr challenges the common narrative around fund of funds, revealing why this approach is arguably the best way to enter commercial real estate, especially in a market starved for equity. He breaks down how capital, operational efficiency, and reputation are your secret weapons to access deals that others can only dream of. But there's a catch, and understanding its pitfalls could be the key to unlocking exponential growth. This episode isn't just a critique; it's a blueprint for savvy entrepreneurs ready to challenge the limits of their business models. Whether you're an aspiring real estate investor, a fund manager, or simply curious about how scale really works, Richard's insights could reshape your strateg Book your free demo today at bill.com/bestever and get a $100 Amazon gift card. Visit https://malabarhillcapital.com/ for more info. Podcast production done by⁠ ⁠Outlier Audio Learn more about your ad choices. Visit megaphone.fm/adchoices

Andrew O'Hara flies solo this week and covers a packed smart home update with new Apple Home and iOS 27 details, notable product launches from Govee, Eufy, Abode, Sonos, Lutron, SwitchBot, PetLibro, Matic, Dashboard, and Mophie. The episode is especially useful if you care about how Apple Intelligence is being tied to iCloud tiers, or you want to see which new devices are actually doing something meaningfully different.In this episode, Andrew breaks down the newest Apple Home camera summary limits, explains why Govee's updated light strip matters for Matter users, and highlights Eufy's standout solar-powered wall light camera. He also dives into a new AI assistant in SwitchBot, a troubling PetLibro server outage, and Matic's unusually smart vacuum controls that let you point and say what to clean.Key topicsAndrew is flying solo this week after traveling, and he sets up a news-heavy episode focused on smart home updates and reviews.iOS 27 Developer Beta 5 adds a new Apple Home camera summary explainer in Home settings, showing that Apple Intelligence features are being tied to iCloud plan tiers.Apple Home now appears to limit summarized video cameras based on the iCloud tier, separate from HomeKit Secure Video camera limits.Govee's new Strip Light 2 moves from Bluetooth to Wi-Fi and adds Matter over Wi-Fi, making it much more relevant for Apple Home users.The strip also gets updated LumiBlend tech, improved white light reproduction, cut-to-length support, protective coating, and multiple sizes from about 10 feet to 65.6 feet.Eufy's Solar Wall Light Cam S4 is the standout product of the week for Andrew, combining a 4K camera, wall sconce lighting, solar charging, a 10,000 mAh battery, and millimeter-wave motion sensing.Andrew emphasizes why Eufy's radar-based motion detection is a big upgrade over basic PIR or pixel-difference motion sensing.Abode adds two outdoor-friendly sensors: an outdoor contact sensor with weather resistance and a garage tilt sensor for doors, sheds, and hatches.Sonos and Lutron expand their integration so Sonos voice control can operate Lutron Caseta lights, shades, and plugs, while a Pico remote can control Sonos playback.SwitchBot's beta AI assistant, KATA, can control devices across rooms, check battery status, guide setup, and even create automations through natural language.PetLibro had server issues that disrupted some pet feeders, fountains, and litter boxes, raising Andrew's recurring concern about cloud dependence in smart home gear.Matic unveils Matic Cues, a major AI-driven update for its vacuum that lets users speak naturally, point to messes, and command the robot to clean specific spots.Andrew also reviews a new Dashboard app for Apple Home and RTSP cameras that focuses on a clean dashboard-first experience.The episode closes with the Mophie 4-in-1 charging stand, which adds a hidden retractable USB-C cable and manages to stay compact without active cooling.Send me your smart home questions and recommendations with the hashtag #SmartHomeInsider. Tweet and follow your host at:@andrew_osu on Twitter@andrewohara941 on ThreadsEmail me hereSponsored by:Hims: Visit https://hims.com/homeinsider to get a personalized plan that gets you!Copilot Money:  Limited-time: Get two months free when you sign up at copilot.money/smarthome and use coupon code SMARTHOME!Smart Home Insider YouTube ChannelSubscribe to the Smart Home Insider YouTube Channel and watch our episodes every week! Click here to subscribe.Links from the showiOS 27 Beta 5 Apple Intelligence limitsGovee Strip Light 2 on AmazonEufy Solar Wall Cam Light S4Abode Gate & Tilt SensorSonos & Lutron IntegrationsPetlibro Reddit ThreadPetlibro OutageMatic RobotDashboard App for Apple Home & RTSPMophie 4-in-1 Wireless Charging StandThose interested in sponsoring the show can reach out to us at: andrew@appleinsider.com

What if your teen's extreme behavior isn't proof that you're failing as a mom—but a sign that you simply need different tools? When your teen is disrespectful, defiant, anxious, aggressive, using substances, or struggling emotionally, it's easy to wonder, Where did I go wrong? In this episode of Power Your Parenting: Moms of Teens, I talk with Dr. Scott Sells, family therapist and author of the newly revised Parenting Your Out-of-Control Teenager: Eight Strategies to Reestablish Authority and Reclaim Love. Dr. Sells reminds parents that extreme behavior often isn't a “bad parenting” problem—it's a tools problem. Drawing from decades of working with families as well as his own experience parenting twin teenage sons, he explains why parents need a balance of love and limits—or connection with correction. Dr. Sells explains why gentle parenting alone may fall short with teens who have extreme behavioral or emotional challenges. Teens need connection, but they also need clear guardrails. As he puts it, “rules without relationships lead to rebellion.” You'll hear practical strategies for rebuilding connection, including catching your teen doing something right, creating intentional special outings, and staying committed to the relationship even when your teen initially pushes you away. We also explore the complicated question many moms face: Is this anxiety, trauma, neurodivergence, a mental health issue—or simply oppositional behavior? Dr. Sells discusses looking at both skill and will, reducing over-accommodation of anxiety, and examining how smartphones, instant gratification, nutrition, and family patterns may contribute to emotional struggles. Most importantly, this conversation offers hope. You don't have to parent perfectly, and trying harder with the same strategies isn't always the answer. Sometimes you need a new tool—one that helps you lower the drama, restore your authority, and rebuild the loving connection underneath all the conflict. Scott Sells, PhD, is the founder and developer of the evidence-based treatment models Parenting with Love and Limits and Family Systems Trauma, used by therapists across the United States and Europe. He earned his PhD and MSW from Florida State University and has served as a professor of social work at Savannah State University and the University of Nevada, Las Vegas. He has delivered keynotes for major mental health organizations and authored more than 20 professional publications. Dr. Sells is the author of three books, including the newly revised Parenting Your Out-of-Control Teenager: Eight Strategies to Reestablish Authority and Reclaim Love, Treating the Traumatized Child, and Treating the Tough Adolescent. 3 Key Takeaways 1. It may not be a parenting problem—it may be a tools problem. When the strategies you've always used stop working, trying harder isn't necessarily the solution. You may need a different, more specialized tool. 2. Teens need both love AND limits. Connection without boundaries can leave teens without needed guardrails, while rules without a relationship can fuel rebellion. The goal is balanced parenting: connection with correction. 3. Look beneath the behavior. Disrespect, defiance, anxiety, or withdrawal may be the surface problem. Getting curious about what is driving the behavior—and then choosing the right tool—can help parents move from constant firefighting toward real change. Learn more at https://familytrauma.com/dr-scott-sells/ Follow at: https://www.instagram.com/familytraumainstitute/ Learn more about your ad choices. Visit megaphone.fm/adchoices

Most of the limits holding your business back aren't market conditions. You built them, which means you can move them.In this conversation, Matt Bonelli and Garrett Frey, dig into why agents stall out at a ceiling they invented, and what it takes to find the real edge. You'll get the case for testing yourself outside your profession, the traction control analogy that reframes burnout as sliding off the track, why the guardrails you build into your business are what let you go faster, and the habit change that can obliterate a limit six months from now. For those of you interested in an experience that will not only allow you to break through limiting beliefs, but have a whole lot fun while also making massive improvements to your business, check out LIMITLESS'26 at https://www.lifeattentenths.com/limitless

I suspect we all have accepted limits; they keep us feeling safe, comfortable, secure, etc., and yet we also most likely know that limits restrict us, bind us, and keep us smaller than perhaps we are created to be.How do we break out of our limits? Listen in for a lively discussion of what may be possible for all of us.Thank you for being here; you matter.I am offering sessions on Tuesday mornings. If you want an elder to hold space for you and reflect on your amazingness, sign up on my website. I am always happy to hear from you.You can reach me at terces@tercesengelhart.com, and I will reply. Additionally, if you would like to order my book directly from me, I am happy to send you a signed copy. Please email me, and I'll send it to you. ($15 plus shipping)If you know of anyone who might benefit from listening in, share a link to an episode with them; in other words, be an invitation to join us. Get full access to Terces's Substack at engelhart.substack.com/subscribe

What does it mean to be human? Psalm 8 answers this question by portraying human beings as both glorious and limited. God has “crowned us with glory and honor” as persons made in his image. He has also made us “a little lower than the angels,” reminding us that we are finite and limited. In this sermon we explore the wisdom of Psalm 8 to discover how embracing both our glory and our limits is key to human flourishing.

All about the distant future. What does cosmology tell us about the fate of the universe, life and intelligence in some ultimate sense? Frank Tipler's The Omega Point Theory is the primary focus, but throughout we consider the centrality of The Turing Principle for predictions about the future of the cosmos, Roger Penrose's "Conformal Cyclic Cosmology" and some of Freeman Dyson's ideas. Timestamps and Chapters: 0:00 – Introduction: Longevity, Immortality, the Singularity & the Omega Point 5:50 – Recap: The Four Strands, Knowledge & the Multiverse 8:20 – Frank Tipler's Omega Point Theory Introduced 9:00 – Anthropic Principles (Weak, Strong & Final) 10:30 – The Turing Principle as the Key to the Omega Point 1 3:40 – Modern Cosmology Challenges: Expanding Universe, Dark Energy & Heat Death 20:20 – Cosmological Models That Allow Unlimited Computation (Big Crunch Conditions) 22:00 – Tipler's Rejoinders & the Case for Eventual Collapse 24:00 – Computers Near the Omega Point & Mutual Implication with the Turing Principle 30:00 – Defending the Strong Turing Principle & the Comprehensibility of Reality 35:00 – Infinite Thoughts, Virtual Reality & Colonisation Deadlines 37:00 – Roger Penrose's Conformal Cyclic Cosmology (CCC) 43:00 – Freeman Dyson's Ideas & Infinite Computation in an Expanding Universe 46:50 – Properties of the Omega Point: Omniscience, Omnipotence & Omnipresence 1:15:00 – The Omega Point as “God”, Resurrection & Virtual-Reality Heaven 1:18:50 – Critique of the Technological Singularity (Kurzweil) & Mind Uploading 1:22:00 – The Limits of Far-Future Prediction (Ancient Builder Analogy) 1:25:00 – Conclusions & Takeaways: Take the Turing Principle Seriously

Subscribe for uplifting messages: http://www.youtube.com/c/PlantationSDAChurchTV Theme: The Power of the Right Relationships Call to Action: Comment below and let us know what you've learned about building relationships today. Speaker: Pastor Taurus Montgomery Title: Check Your Circle Series: No Man Is An Island Highlights: This message challenges men to evaluate the people closest to them and intentionally build relationships with those who sharpen their character, strengthen their faith, and push them toward God’s purpose for their lives. Key text: https://www.bible.com/bible/114/PRO.27.17.NKJV Bulletin/Notes: http://bible.com/events/49649687 Date: August 15, 2026 Tags: #psdatv #relationships #character #faith #purpose #build #evaluate#CheckYourCircle #IronSharpensIron #RightRelationships For more life lessons and inspirational content, please visit us at http://www.plantationsda.tv. Church Copyright License (CCLI): 1659090 CCLI Streaming Plus License: 21338439Support the show: https://adventistgiving.org/#/org/ANTBMV/envelope/startSee omnystudio.com/listener for privacy information.

Steve Hayes is joined by Jonah Goldberg, Megan McArdle, and Kevin Williamson to discuss the results of this month's Democratic primaries in Wisconsin and Michigan and the left's shift away from the radical social justice positions of 2020. The Agenda: —Francesca Hong's loss —Abdul El-Sayed's slim victory —The limits of the far left —The woke reckoning —AOC: “Woke 1 was crazy” —NWYT: A People magazine “exclusive” Show notes: —WSJ: “Midwesterners Show the Limits of the Far-Left” —NYT: “A Stunning Loss in Wisconsin Shows the Limits of the Left” —Megan's Washington Post column on Wisconsin primary (Gift link) —Jonah's Wednesday G-File —What Steve, Kevin, and Jonah have gotten wrong The Dispatch Podcast is a production of ⁠The Dispatch⁠, a digital media company covering politics, policy, and culture from a nonpartisan perspective. To access all of The Dispatch's offerings—including audio versions of all our articles and newsletters—⁠click here⁠. If you'd like to remove all ads from your podcast experience, consider becoming a premium Dispatch member ⁠by clicking here⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

Taylor Frankie Paul is sharing a petition, made by fans, to get her season of the Bachelorette released, but it is lacking in signatures.Unwell, Alex Cooper's drink line, is too watermelon based to make it, and Kim Kardashian and Lewis Hamilton aren't fooling anyone. Ric Flair tweets with zero understanding of how the app works, while Katseye dresses for interviews with an apparent wish to be really cold in the studio.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Do friction, struggle, and silence make you fully human?   AI can generate an answer, but it cannot suffer with you, worship, love a neighbor, or become wise. Host Curtis Chang and Andy Crouch, the partner for theology and culture at Praxis, explore how AI may weaken our minds, distort relationships, and tempt us to outsource the very experiences that form wisdom, wonder, and sacrificial love. From chatbot "companionship" and AI-assisted grief to medicine, spiritual counsel, and the loss of silence, Curtis and Andy ask whether convenience is quietly creating a kind of cognitive metabolic syndrome and what we can do about it.   00:01:22 - Introduction to AI's Impact on Our Minds. 00:04:35 - Lessons from Social Media's Evolution 00:08:26 - Cognitive Metabolic Syndrome 00:18:45 - What Is the Purpose of the Human Mind? 00:25:23 - Wonder vs. Power 00:26:49 - Love Requires Suffering and Friction 00:34:38 - The Limits of AI in Healthcare 00:40:53 - The Danger of Outsourcing Life's Hardships 00:51:23 - The Loss of Silence 00:58:11 - The AI Forced Conversation on Being Human 01:01:03 - Practical Ways to Protect Our Humanity   To give to Good Faith: https://goodfaith.org/donate   Mentioned in This Episode: Andrew Briggs and Roger Wagner's The Penultimate Curiosity: How Science Swims in the Slipstream of Ultimate Questions Thomas Fuchs' In Defence of the Human Being  Victor Hugo's Les Misérables Tristan Harris on How AI Worsens Ills Caused by Social Media (The Economist) Explore the work of The Future of Life Institute Emma Pearson's I'd Rather Risk Cancer Than See AI Move This Fast (The Atlantic)   Scriptures: Mark 12:30–31 (ESV) Matthew 22:37–39 (ESV) Deuteronomy 6:5 (ESV) Luke 10:27 (ESV)   More From Andy Crouch: Check out Andy's website Check out Andy's work at Praxis Read Andy's book: The Life We're Looking For Read Andy's book The Tech-Wise Family: Everyday Steps for Putting Technology in Its Proper Place    Follow Us: Good Faith on Instagram Good Faith on X (formerly Twitter) Good Faith on Facebook   The Good Faith Podcast is a production of a 501(c)(3) nonpartisan organization that does not engage in any political campaign activity to support or oppose any candidate for public office. Any views and opinions expressed by any guests on this program are solely those of the individuals and do not necessarily reflect the views or positions of Good Faith.

Growing faster doesn't have to mean becoming a settlement mill. The firms that scale successfully know exactly which numbers prove they're delivering better outcomes—not simply processing more cases. Thaddeus Wendt is the Founding Partner and CEO of Feller & Wendt, a multi-state personal injury firm with more than 120 years of combined experience and over $100 million recovered for clients. As the firm expands across Utah, Idaho, and Arizona, Thaddeus has built an operating model focused on speed, efficiency, and consistently achieving policy limit settlements without sacrificing client care. In this episode, Thaddeus explains the two KPIs his firm uses to measure quality, why reducing time on desk improves both client outcomes and firm cash flow, and how a radically different medical treatment strategy can accelerate case resolution while increasing settlement values.  You'll learn: What policy limit settlement rates reveal about the health of a personal injury law firm. Why reducing time on desk improves both client outcomes and contingency fee cash flow. When fast-track pain management outperforms the traditional treatment timeline. How in-house marketing teams and agency partners can work together to scale a PI firm. The operational metrics that help growing firms avoid becoming settlement mills. If you're ready to build a beast of your own, you can't rely on cookie-cutter campaigns. You need a team that knows the PI landscape inside and out. Head over to Rankings.io. Like what you hear? Hit Subscribe! We do this every week. If you want to keep learning from the best voices in PI, join us at PIMCON 2026. Buy your tickets now! Subscribe to our newsletter and get the freshest news every Monday: newsletter.rankings.io Get Social! Personal Injury Mastermind w/ Chris Dreyer powered by Rankings.io is on Instagram | YouTube | TikTok

The Expert Authority Coach Podcast Presents: How To Use ChatGPT and AI Without Losing Your Humanity  Christine Blosdale interviews David Dean, an author and enterprise AI strategist, about how AI has long shaped behavior behind the scenes and is now commoditized through language-based tools.  Dean argues AI “industrializes” existing organizational behavior, so dysfunction scales unless leaders use AI for organizational self-realization, governance, and authentic leadership.  They discuss prompting, AI as a symbiotic partner lacking lived experience, ethical risks like voice scams, and the need for regulation, transparency, and intent. Dean warns that communication data can become an e-discovery “behavioral record” for companies.  They also explore practical future uses like localized AI for home inventory and workplace status updates. LINKS MENTIONED: The Expert Authority Quiz - http://ExpertAuthorityQuiz.com Get David Dean's Book An Inbox Between Us - https://amzn.to/4qc9gSx David Dean's Website - https://inboxbetweenus.com The Expert Authority Coach Website - http://www.ExpertAuthorityCoach.com -------------------------- 00:00 Expert Authority Quiz 00:53 Podcast Welcome 01:42 Meet AI Strategist 03:25 AI Before ChatGPT 06:09 AI Mirrors Culture 09:00 Prompts and Partnership 12:07 Humans Still Matter 14:19 Energy and Data Centers 19:55 Future of AI 21:51 Behavioral Record Risks 24:51 Limits of AI Controls 25:42 Intent and Responsibility 27:49 Bad Actors and Deepfakes 29:26 Transparency and Governance 31:43 IP Theft and Vibe Coding 34:11 AI as a Mirror 37:12 Building an AI Relationship 39:14 Local AI and IoT Ideas 41:39 Push Button AI Products 43:17 Voice First AI Workflow 44:57 Storytelling to Find Signals 47:15 Wrap Up and Where to Find David 48:38 Final Thanks and Call to Action #chatgpt #ai #artificialintelligence #technology 

Flock cameras can help police find stolen cars and solve crimes. But they can also create a record of where vehicles have been — raising questions about privacy and police surveillance.MPR News guest host Catharine Richert talks with a data privacy expert about what kind of information Flock cameras collect, who can access that information and what limits the Constitution puts on tracking our movements.

Grace discusses the Healey admin signing away any limits on abortion in Massachusetts. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Charlie Goldsmith, known to many as The Healer from the TLC series, joins Andrea for a conversation that goes far beyond his extraordinary results. While Charlie first became widely known for the remarkable ways he could alleviate pain and other conditions, his deeper mission is now focused on a larger problem: how to identify genuine healing ability, study it responsibly, and make real help easier to find for the people who need it most. As the conversation moves through Charlie's unexpected discovery of his ability, the resistance surrounding energy healing, the challenges of research, and the platform he is now building, the episode opens into something much larger than one healer's story. It is about evidence, access, human potential, and what it might take for healing ability to be taken seriously without losing what makes it extraordinary.  Find out how Charlie's free app is designed to identify genuine healing ability, connect people in need with those healers, and generate data that could shape the future of energy healing research. This mission needs people like you now. ✨ Learn more about Charlie Goldsmith: https://charliegoldsmith.com/Recorded: May 25th, 2026Episode Highlights: 00:00 - Limits Of Understanding00:52 -  Meet Charlie Goldsmith02:50 - Energy Awakening At 1806:11 - First Healing Moment09:45 - Mission To Prove It14:29 - Identity And Judgment17:40 - How The TV Show Happened22:06 - Why Media Stayed Closed24:39 - History Of Healing Backlash28:31 - Placebo Nocebo Debate31:58 - Placebo as Medicine33:31 - Challenging the Skeptics36:17 - What He Can Heal40:18 - Limits and Success Rates41:31 - The App Study Protocol47:33 - Why AI Makes It Safe51:24 - How to Use The Ennie App54:06 - Affordable Access Mission58:15 - Join the Collective Effort01:01:39 - Final Thanks and Follow Us!Channel Your Divine Self, a twelve-week live course where you build a direct, grounded relationship with your own inner guidance, alongside the Emissaries of Light. The waitlist is open now, and you'll be the first to know when registration begins. Join the waitlist: https://mainstream-reiki.kit.com/cydswaitlistAndrea's Links: https://beacons.ai/andrea_kennedyAndrea's Reiki Business Success Course:https://www.mainstreamreiki.com/reiki-business-success-courseVisit our websiteVisit our Amazon Shop  Sponsored by The Mainstream Reiki Community https://members.mainstreamreiki.com/HealthyLine offers revolutionary PEMF  and far-infrared mats. Get 10% off and free shipping in the continental US with code "Mainstream10FS". What Resonates? is produced by Twisted Spur MediaAndrea may earn money through Amazon for qualifying purchases.Disclaimer: The views and opinions expressed in this program do not  reflect those of the podcast or anyone affiliated with its production. This program is presented for entertainment purposes only. The utilization of the information provided is at the listener's own discretion.

Get my new book: https://bronsonequity.com/fireyourselfDownload my new special report - How to Use Inflation to Your Advantage - www.bronsonequity.com/inflationIn this episode of The Mailbox Money Show, host Bronson Hill and co-host Nate Hambrick sit down with Tarl Yarber to unpack why an investor who openly says he hates real estate has still built a multi-million-dollar empire through it.They dig into the realities of creating and running large-scale events (including the nearly $2 million Limitless Expo), the real reasons Tarl keeps putting himself through the chaos of hosting, how to network effectively (and what not to do) at high-level conferences, and the systems and mindset that allowed him to scale past 700 properties while minimizing his own time in the day-to-day grind.About the Guest:Tarl Yarber is a real estate investor, private lender, and co-creator of the Limitless Expo. Despite completing nearly 700 value-add properties, he openly admits he has never liked the work of real estate—he simply built systems, processes, and teams so he doesn't have to do it himself. A straight-talking operator focused on financial freedom and education, Tarl also helps raise millions for veteran charities through his events.TIMESTAMPS0:40 - Welcome to the Mailbox Money Show1:20 - How Events Transformed Nate's Career & Network2:36 - Origin Story of Limitless Expo3:43 - Why Host a Nearly $2M Event Despite the Pain5:21 - Creating Events You Actually Want to Attend7:22 - Best Way to Leverage High-Level Events9:05 - What NOT to Do When Approaching High-Value People13:56 - Why Limitless Exists: Education Over Selling16:14 - Raising Millions for Charity Through Limitless17:39 - “I Hate Real Estate” – Building an Empire Anyway18:46 - Building Systems So You Don't Have to Do the Work20:36 - Limits of True Passivity in Value-Add Real Estate22:42 - Private Lending & Investing in Operators24:32 - 1031 Strategies for More Passive Assets26:51 - Critical Tips for Successful Out-of-State Investing30:15 - Nate's Takeaway: Success Despite Hating the Work30:52 - Bronson's Takeaway: Systems, “Who Not How,” and Buying Back Time31:43 - Episode Wrap-Up & ClosingCONNECT WITH THE GUESTWebsite: https://www.tarlyarber.com/Instagram: @tarlyarberLinkedIn: https://www.linkedin.com/in/tarl-yarber-7584a847/#MailboxMoney#RealEstateInvesting#LimitlessExpo#PassiveIncome#EventNetworking

What if sleep isn't just something your body needs but a gift from God that can actually teach you something about Him? In this fascinating conversation with Brett McCracken, author of The Wisdom of Sleep, we explore why God designed us to spend nearly a third of our lives asleep, how sleep gives us a beautiful picture of grace and the gospel, and why our resistance to rest may reveal more than we realize. We also talk about dreams, Sabbath, hustle culture, and what to do when good sleep feels impossible. I walked away seeing something I do every single night in a completely new way, and I think you will, too. Remember, I'd love to connect more on Instagram, where you'll find me at @donnaajones. And don’t forget to subscribe so you don’t miss a single episode! Xo, Donna Key Takeaways: 0:02:27 - Sleep as a Gift and Theology, Not Just a Health Hack 0:07:59 - Sleep, Hustle Culture, and Spiritual Warfare 0:11:02 - Nightly Sleep as a Living Picture of the Gospel 0:15:31 - Dreams as God’s Classroom in the Night 0:23:33 - Sabbath, Limits, and Resisting the Idol of Productivity What We Talk About Why Brett wrote The Wisdom of Sleep What a "theology of sleep" actually means Why Scripture describes sleep as a gift from God The physical, mental, and emotional benefits of sleep How sleep gives us a miniature picture of the gospel Why waking up can be viewed as a picture of resurrection What science tells us about why we dream How God uses dreams throughout Scripture How to discern whether a dream might have spiritual significance Why Scripture must always be our standard for discernment The connection between sleep and Sabbath How hustle culture can keep us from receiving God's gift of rest Why accepting our limitations is an act of trust Finding hope when good sleep feels elusive Sleep as a Nightly Picture of the Gospel Brett offers a beautiful way to rethink something we normally take for granted: We lie down. Sleep requires us to stop striving and surrender control. We receive. While we sleep, restoration happens without our effort. We don't achieve the benefits of sleep—we receive them. We rise. Each morning, we wake to new life, giving us a small picture of resurrection. We remember. Sleep reminds us that we're limited, God isn't, and His grace sustains us even when we're doing absolutely nothing. That changes the way I think about going to bed! Donna’s Resources: Order a copy of my latest book - Healthy Conflict, Peaceful Life: A Biblical Guide to Communicating Thoughts, Feelings, and Opinions with Grace, Truth, and Zero Regret. It is available anywhere books are sold– here is the link on Amazon. If you need a helpful resource for someone exploring faith and Christianity or simply want to strengthen your own knowledge, you’ll want a copy of my book, Seek: A Woman’s Guide to Meeting God. It’s a must for seekers, new believers, and those who want to deepen their faith. Connect with Brett: Brett’s Website: https://www.brettmccracken.com/ Brett’s Book: The Wisdom of Sleep: https://www.amazon.com/dp/0310175712 Let’s Connect: Instagram: @donnaajones Website: www.donnajones.org Donna’s speaking schedule: https://donnajones.org/events/ Discover more Christian podcasts at lifeaudio.com and inquire about advertising opportunities at lifeaudio.com/contact-us.

Remember the old hotel ice machines? The kind where you'd slide your bucket under the chute and hear the avalanche of cubes crashing into it? There was something so fun and strangely gratifying about that thing.Or think about breaking an icicle off the edge of a roof. Such a gratifying feeling from such a small act.My guest has written an ode to the outsized pleasures that arise from these kinds of everyday interactions with the physical world. His name is Ian Bogost, and his new book is The Small Stuff. Today on the show, Ian argues that a fulfilling life isn't built solely by chasing the big components of happiness but also by embracing the small moments of gratification that are woven throughout ordinary life. We discuss how those moments can be found in tactile experiences like shifting a manual transmission, turning a well-made knob, or flipping through the pages of a real menu. We explore how these small opportunities for engagement — for contact, connection, and control— have been disappearing as the world becomes more digitized and automated and everything becomes a touchscreen. And Ian shares how opportunities for "sensory enchantment" can still be found in our increasingly dematerialized world.Resources Related to the PodcastIan's previous appearance on the AoM Podcast: Episode #247 — The Pleasure of Limits, the Uses of Boredom, and the Antidote to Excessive IronyIan's Atlantic article "The End of Manual Transmission"Andy Rooney compilationConnect With Ian BogostThe Small Stuff websiteIan's websiteIan's faculty page0:00 Introduction0:40 Ian Bogost and 'The Small Stuff'2:25 What Kickstarted This Book6:38 Happiness, Satisfaction, and Gratification14:58 Gratification Is Everywhere — But Disappearing18:28 Dematerialization: How the World Is Losing Sensory Richness26:41 How Work Has Become Less Gratifying33:09 How to Reclaim Your Sensory Life38:35 Keep More Junk Around44:38 Talk About Small Pleasures48:17 Living Life Sideways (Orthogonality)50:03 Wrap-UpSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this second episode of You Could Make This Beautiful, hosts Maggie Smith and Faith Salie talk to Peter Sagal, host of the NPR news quiz show Wait Wait... Don't Tell Me! about a prophetic reading at his first wedding, the poem that has haunted him throughout his life, and about the poetry he's found in the kindness and grace of his children. They also hear from poet and Harvard professor Stephanie Burt about how she found her way to poetry by way of science fiction, and how she's used the work of Taylor Swift to introduce her students to poets of centuries past like Alexander Pope. Show Notes (in chronological order): Maggie Smith on Wait Wait... Don't Tell Me! Cartoonist Tom Tomorrow "Aedh Wishes for the Cloths of Heaven" by W. B. Yeats "Ithaka" by C. P. Cavafy "The Illusion" by Tony Kushner "Most Like an Arch This Marriage" by John Ciardi Adrienne Rich's poems are "like dreams" quote Poet William Corbett "The Postmoderns: The New American Poetry Revised" by Donald M. Allen "The Crystal Lithium" by James Schuyler "The Elephant Man" on Broadway "Les Liaisons Dangereuses" with Alan Rickman "Angels in America, Part One: Millennium Approaches" by Tony Kushner "Arcadia" by Tom Stoppard "The Collar" by George Herbert "Poetry's Cross-dressing Kingmaker," The New York Times "Advice from the Lights" by Stephanie Burt, featuring "My 1979" Star Trek's "Is There in Truth No Beauty?" "We, in Some Strange Power's Employ, Move on a Rigorous Line" by Samuel R. Delany "Epistle to Dr. Arbuthnot" by Alexander Pope "Look What You Made Me Do" by Taylor Swift "Tim McGraw" by Taylor Swift "The Nancy Mace" by Stephanie Burt "Taylor's Version: The Poetic and Musical Genius of Taylor Swift" by Stephanie Burt Poetry Prescriptions Maggie's prescription: "Go to the Limits of Your Longing" from Rainer Maria Rilke's "Book of Hours" Faith's prescription: "Wild Geese" by Mary Oliver If you want a hand-picked, heart-plucked "poetry prescription" to help you with something you're going through, send a brief voice memo to PoetryPrescriptions@gmail.com. All three episodes of You Could Make This Beautiful are out now and can be heard HERE. This show is an experiment, so if you love what you've heard and want more episodes, let us know! Leave us a glowing review in your podcast app, share the series with friends, and write to us: PoetryPrescriptions@gmail.com.