Podcasts about Efficiency

Everyone wants innovation. But what exactly are we asking for?In this Post-Pod, Marc and Vassilis unpack their conversation with Fiona Stevenson, co-author of Built for Breakthrough, and start with one of the simplest problems: organizations frequently use the word “innovation” without agreeing on what it actually means.Breakthrough? Disruption? A game changer? Incremental growth? A new tactic?That distinction matters because, as Fiona argued, an idea doesn't become innovation until it is implemented and creates value.From there, the conversation turns to the organizational conditions that innovation requires. Marc and V discuss why teams rush to solutions before properly understanding the problem, why genuine innovation creates fear, and how the pursuit of certainty can push organizations toward safer incremental improvements.They also revisit Fiona's Six I's framework:Identify → Insights → Inspiration → Ideation → Iteration → ImplementationRather than treating those stages as a checklist, Vassilis argues they should be viewed as multipliers. Skip one and you risk weakening the entire system.The conversation closes with two bigger questions.First, if innovation requires focused thinking, why do we expect it to happen between back-to-back meetings?And second, if AI is giving us unprecedented productivity gains, will we use that extra capacity to explore new possibilities — or simply fill it with more of the same work?A Post-Pod about innovation, uncertainty, AI, marketing and why being busy isn't the same thing as building the future.Chapters00:00 Welcome to the Post-Pod00:21 Why “Innovation” Means Different Things to Everyone01:43 What Kind of Innovation Are We Actually Asking For?02:24 Defining Innovation Before Starting the Work02:53 An Idea Isn't Innovation Until It Creates Value04:04 Are We Solving Before Understanding the Problem?04:47 The Optimization Trap06:42 Why Fear Kills Innovation08:39 Why Incremental Innovation Feels Safer10:04 Fear Creates Overanalysis10:21 “How Might We?” vs. “We Should”11:10 Fiona's Six I's of Innovation11:40 Innovation as a Multiplicative System12:53 Is Intuition the Seventh I?13:20 How Marketing and Innovation Overlap14:28 Should Marketing Be an Innovation Function?17:39 Innovation Doesn't Happen Between Meetings18:32 Are We Using AI for Efficiency or Growth?19:48 Why AI Should Create Possibility20:08 AI Increases the Need for Discernment20:55 Final Thoughts

"Practices don't necessarily have a staffing problem." Connect With Our SponsorsSolventum - https://go.solventum.com/clarityGreyFinch - https://greyfinch.com/jillallen/A-Dec - https://www.a-dec.com/orthodonticsSmileSuite - https://getsmilesuite.com/ Summary On this episode of the Hey Docs! Podcast, Jill sits down with Amanda Ketcham as she shares her journey of launching My Mountain Mover, a healthcare outsourcing company that connects remote talent with healthcare practices. Amanda explains the importance of viewing staffing as a capacity issue rather than a staffing problem, the benefits of delegation and outsourcing, and how to build a strong culture with remote teams. Jill and Amanda also address common fears associated with outsourcing, the role of AI in the future of practices, and the importance of choosing reputable outsourcing partners. The conversation concludes with insights on navigating the hiring process and the significance of relational skills in the age of AI. Connect With Our Guest My Mountain Mover - https://mymountainmover.com/ Takeaways Amanda is the founder of My Mountain Mover, which connects remote talent with healthcare practices.The pandemic opened doors for remote staffing solutions in healthcare.Practices often face capacity problems rather than staffing issues.Delegation is crucial for business owners to focus on their strengths.Outsourcing can lead to higher ROI for practices.Building a culture with remote teams is essential for success.Choosing a reputable outsourcing company is vital for compliance and quality.AI will play a significant role in handling administrative tasks in the future.Relational skills will be essential in the age of AI.Chapters 00:00 Introduction05:19 Why Capacity Matters10:55 Overcoming Outsourcing Fears13:55 Delegate and Elevate Framework20:43 Building Remote Culture24:36 AI and Remote Staffing Future27:57 Risks HIPAA and Turnover32:13 Where to Learn More Episode Credits:  Hosted by Jill AllenProduced by Jordann KillionAudio Engineering by Garrett LuceroAre you ready to start a practice of your own? Do you need a fresh set of eyes or some advice in your existing practice?Reach out to me- www.practiceresults.com.    If you like what we are doing here on Hey Docs! and want to hear more of this awesome content, give us a 5-star Rating on your preferred listening platform and subscribe to our show so you never miss an episode.    New episodes drop every Thursday!   

In this episode, host Josh interviews Jeff Campbell, a digital marketing expert and co-founder of AI Commerce. They discuss effective strategies for e-commerce sellers on platforms like Amazon and Walmart, focusing on increasing repeat purchases, leveraging first-party data, and tracking key metrics such as ROAS, CPC, and customer lifetime value. Jeff emphasizes the importance of consistent data analysis, understanding break-even points, and balancing efficiency with growth. He shares practical examples and actionable tips for optimizing advertising spend and expanding sales channels, providing valuable insights for both new and experienced e-commerce entrepreneurs.Chapters:Introduction to Jeff Campbell and AI Commerce (00:00:00)Josh introduces Jeff Campbell, his background, and AI Commerce's focus on e-commerce marketplaces.Strategies to Increase Repeat Purchases (00:00:32)Jeff discusses the importance of first-party data, email collection, and retargeting to drive repeat sales.Key Metrics and KPIs for E-commerce Sellers (00:02:49)Discussion on tracking impressions, clicks, CPC, CPO, customer acquisition cost, LTV, and the relationship between paid and organic sales.Tools and Frequency for Data Tracking (00:04:26)Jeff recommends using Excel or dashboards, and emphasizes daily tracking with monthly/quarterly insights.Competitive Strategies and Market Trends (00:05:26)Explains aggressive spending strategies, increased competition, and the need to monitor KPIs closely.Balancing Volume and Efficiency Metrics (00:06:17)Jeff highlights the importance of balancing sales volume and efficiency (Roas), and avoiding analysis paralysis.Identifying the Two Most Important Metrics (00:07:17)Focus on breakeven Roas and sales volume as the primary metrics for most sellers.Importance of Long-Term Data Trends (00:07:58)Josh and Jeff discuss not overreacting to short-term data, but focusing on monthly and quarterly trends.Case Study: Food Brand and Revenue Formula (00:09:05)Jeff shares a food brand example, explaining the revenue formula: traffic x AOV x conversion rate.Optimizing Traffic Sources and Channel Diversification (00:10:23)Describes switching to Walmart for cheaper CPCs and the impact on overall revenue.Using Data for Dayparting and Efficiency (00:11:31)Jeff explains using conversion and CPC data by hour/day to optimize ad spend and efficiency.Three Actionable Takeaways for E-commerce Sellers (00:12:32)Josh summarizes: know your numbers, review metrics monthly/quarterly, and expand channels only after mastering the basics.Closing and Contact Information (00:15:23)Jeff shares how listeners can contact him and learn more about AI Commerce.Links and Mentions:Tools and Websites  "AI Commerce": "00:00:31"  "Excel": "00:04:26"  "Google Sheets": "00:04:26"  Key Concepts and Metrics  "First Party Data": "00:01:14"  "Cost Per Order (CPO)": "00:03:22"  "Return on Ad Spend (ROAS)": "00:04:26"  "Average Order Value (AOV)": "00:10:23"  "Conversion Rate": "00:10:23"Transcript:Josh 00:00:00  Now. I'm super excited to introduce you all to Jeff Campbell. Jeff is a digital marketing veteran and e-commerce entrepreneur and professor at Wake Forest University. His strategic vision and operational approach have led multiple successful business acquisitions and exits. Jeff currently leads AI Commerce, a global digital agency focused on marketplaces and e-commerce, which he co-founded in 2020. So with that introduction, welcome to the show, Jeff.Jeff 00:00:31  Thanks for having me, Josh. I'm excited.Josh 00:00:32  What is there anything that you've done with your agency or with previous experience to really start turning a business that the average order for? I guess the average number of orders per customer, instead of it being one, you start increasing it to 2 to 3 because that's where you start to get this flywheel of, all right, I can spend I could even spend more to acquire this customer that it's not even break even. I'm losing money on the front end, but I make it up in the long in the long run. So are there any strategies or advice you would share to our listeners in order to help get those second, third, fourth sales and repeat purchases?Jeff 00:01:14  Yeah, I can't stress enough how important first party data is, specifically email addresses.Jeff 00:01:19  So if you can get them to your website to somehow register a product or an, you know, there's some rules with Amazon, you know how how you can get information, what you can stick in the box, etc. but, figure that out because I think that first party data is going to be so crucial for brand owners in the future for targeting as as privacy laws will hopefully come out soon. And, and, and the data sharing of of the platforms are probably going to stay heavily with the platform. So, here and now, rant over about one p data. You know, Amazon and most of these platforms have some sort of retargeting, right. So if you know that you're you're hawking air filters and they go out every three months and you need a replacement. Use your email database if you have it, use some of the the first party data of your platforms. Be it Amazon or Walmart. A lot of that does take some DSP work, which comes with a higher price tag. But retargeting is one strong, strong way to to remind people about the previous purchase and time to to repurchase.Jeff 00:02:24  I think that's probably the big one. Again, the email relationship and again, having first party data to be able to do that and then from there create and lookalike models. Right. So understanding the behaviors of those folks and starting to target people, to make maybe their first purchase because they look a lot, a lot like or they act and behave like, past purchasers of yours too. Works really well for that initial. And then you can retarget them as well.Josh 00:02:49  Yeah, it makes great sense. I love that now, Jeff. I would love to dive in further into these profitability or just overall metrics that people should be tracking that become those mile markers that lead to scale. And let's dive deep into what each of those, you know, metrics mean and how, you know, let's say take an an Amazon e-commerce seller today, how they should be tracking them now, and maybe some of the tools they should be using to make sure that they're setting up the correct foundation.Jeff 00:03:20  Sure, sure.Jeff 00:03:22  so, you know, I, I'd really look at making sure you are tracking the standard, you know, impressions, clicks, click through rate, you know, cost, CPC, etc.. we always want to look at that variability as CPC. That's really important. some, some of the other ones, the new to brand metrics where you can get them, the cost per orders and kind of go into that customer acquisition cost and LTV. so CPO cost per order is, is important. and then as we as we know, especially with Amazon and some of the other marketplaces are starting to do this. The reliance and relationship between paid and organic. The more you pay to get some keyword wins, the more you're going to see yourself organic. So you have to compile some of those sales from both paid and organic channels against the cost of the advertising, because there is a relationship there. So looking at that total Roas or that total ACOs or tacos, as the hungry people like to say is important as well.Jeff 00:04...

The inventor of DTG printing joins the show. Matt Rhome filed the original patent in 1996, sold the tech to Brother, and now runs Quality & Efficiency at Sticker Mule. He tells the full story — the garage prototype, the $1.5M machine that almost never shipped, why he sold his own patent, and where DTG is headed next.

If you've ever tried to day trade stocks, you've probably run into the requirement to keep at least $25,000 in your brokerage account just to trade actively. For more than two decades, that threshold, and the Pattern Day Trader rule behind it, governed how investors could access margin for day trading. But markets have changed dramatically since 2001, and earlier this year, FINRA replaced the Pattern Day Trader rule with a modern, risk-based framework under FINRA Rule 4210—the Intraday Margin Standard. On this episode, Racquel Russell, Director of Capital Markets Policy and Head of the Office of Financial and Operational Risk Policy, and James Barry, Senior Director, Credit Regulation, tell the story behind that change: why the old rules existed, why they stopped working, and what the new framework means for investors and member firms. Resources mentioned in this episode: FINRA Rule 4210 Interpretations of Rule 4210 Investor Insights: Know What Triggers a Margin Call Investor Insights: Frequent Intraday Trading: Understanding the Basics Investor Insights: Understanding the New Intraday Margin Requirements Reg. Notice 26-10: FINRA Adopts New Intraday Margin Standards Reg. Notice 24-13: FINRA Requests Comment on the Effectiveness and Efficiency of its Requirements Relating to Day Trading FINRA Forward FINRA Forward: A Year of Progress Blog Post: FINRA Forward's Rule Modernization—An Update Blog Post: Vendors, Intelligence Sharing and FINRA's Mission Blog Post: FINRA Forward Initiatives to Support Members, Markets and the Investors They Serve Blog Post: A Progress Update on Rule Modernization Find us: LinkedIn / X / YouTube / Facebook / Instagram / E-mailSubscribe to our show on Apple Podcasts, Google Play and by RSS.

When margins get tight, reducing headcount can look like the fastest route to a healthier balance sheet. But in professional services, there's a catch: your people aren't just a cost—they're a big part of what clients are paying for. Cut too deeply, and the short-term savings can quickly turn into overloaded teams, weaker client service, unwanted attrition, and eventually, the cost of hiring people back.In this episode, Michael Gold and James Leigh explore another path: getting more value from the people you already have. They unpack what it means to “activate” human potential, why AI adoption needs more than a stack of new tools, and how organizations can rethink work around people's actual strengths instead of forcing everyone into the same job-shaped box. The result is a different way of thinking about efficiency—one that asks how you can grow capability before assuming you need to shrink the team.Resources from this episode:Join the Digital Project Manager CommunitySubscribe to the newsletter to get our latest articles and podcastsConnect with Michael and James on LinkedInCheck out Aria, Gold Strategic Delivery, Vanberra, and Kintara

Dell Technologies (DELL) has learned from Nvidia (NVDA) in its data center buildout, says Don Gentile. He explains how Dell uses its knowledge, old and new, in creating a full AI stack. Efficiency and profitability will come down to how Dell maintains margins, says Don. On the general AI trade, he talks about difficulties in companies maintaining backlogs without diminishing quality. ======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

--{ "Elite's Working Complicity Toward Efficiency"}-- Biggest Exercise in Mind Control and Mental Warfare on the Whole Population of the Planet - Psy-Ops and Revolution - Fauci, Bill and Melinda Gates Foundation - Black Lives Matter - Communism Hides Behind Covers - The Cold War, Billionaires - Covid-19 Wartime Scenario, Event 201, Rockefeller's Lockstep - Control of Media - Gangs Run the World; Big, Organized Crime - Bioethics - The Scientific Takeover of Society - Carroll Quigley, CFR, RIIA (Chatham House), Rockefeller, Milner Groups - Endless Wars in the Middle East; PNAC - Business Plans - Tax-Free Foundations and Privately-Owned Think-Tanks Run the World - Book, Foundations: Their Power and Influence - Norman Dodd, Reece Commission - Free Trade - The Basics, Food and Water - Videos on WWII Farms (Britain), Blossoming of Government Departments - Conditions During Industrial Revolution - Winston Churchill, a United Europe - Heads of BLM Admit They are Trained Marxists; Antifa - Genocide in Rwanda - Real History is Inside You - Soviet System, Troops to Other Countries, Settle in the Area, Breed into that Culture - Normans, Primogenitor - Learning and the Leisure Class - Socrates, Subversion of Youth, Training them to be Revolutionaries - Inner and Outer Parties - Elitism - Movie, The Third Man; How Psychopaths View People - Eugenics, Euthanasia

In 1888, Willard Bundy created the first employee "time clock" — essentially a souped-up grandfather clock that could, for the first time, produce an accurate record of when workers arrived and left. Though Bundy's aim was simple, his invention soon sparked a change in American life far broader than he intended. Roman Mars and reporter Ellie Lightfoot trace how the time clock not only ignited a revolution in workplace efficiency and surveillance, but transformed our relationship to time itself. A History of the United States in 100 Objects is a production of 99% Invisible and BBC Studios. Subscribe to SiriusXM Podcasts+ to listen to new episodes of 99% Invisible ad-free and a whole week early. Start a free trial now on Apple Podcasts or by visiting siriusxm.com/podcastsplus. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Krassi Fotev is a maritime technology entrepreneur leading innovation in vessel efficiency and decarbonization. He is driving the adoption of passive hardware and soon data-driven solutions to reduce emissions and transform global shipping operations. Top 3 Value Bombs 1. Small improvements in shipping efficiency can significantly reduce global carbon emissions. 2. Nature inspires powerful engineering solutions that improve efficiency without extra energy. 3. Maritime decarbonization will rely on better hardware, smarter operations, and greater vessel efficiency. Check out Krassi's website to learn more about the technology - 13 Mari Sponsors HighLevel - The ultimate all-in-one platform for entrepreneurs, marketers, coaches, and agencies. Learn more at HighLevelFire.com. Strawberry - Discover what's possible with a professional coach by your side. Start today and get 50 percent off your first coaching session at Strawberry.me/fire.

AI is transforming the technology industry, but it's also transforming how we think about energy. As AI models continue to grow, data centres continue to pop up, and agentic AI becomes part of everyday life, our tech infrastructure is consuming more energy than ever before. So how do we power the next generation of AI? In this penultimate episode of our HPE Labs miniseries celebrating 60 years of innovation, Technology Now is joined by Cullen Bash, Deputy Director of HPE Labs, to discuss:Why energy is becoming a strategic concern for CIOs and CEOsHow the increasing demand for compute in data centres is reshaping how we think about energyWhy innovation in electricity generation is as vital as making infrastructure more efficientHow AI could help solve the very problems it is creating

Show notes:Is AI killing your marketing? I'm not anti-AI, in fact, I use it every single day in my business! But I am starting to see more and more wedding businesses using AI in a way that is making their marketing sound exactly the same. In today's episode, I'm talking about how to use AI without losing your personality, creativity and unique voice. I'll be sharing some honesty checks to help you work out whether AI might be hurting your marketing, the difference between generative and agentic AI, and exactly how I'm using AI behind the scenes in my own business. Because AI should be your partner, not your replacement!Download my AI worksheetThe AI-Driven Leader: Harnessing AI to Make Faster, Smarter DecisionsTime stamps:00:03 - The Problem with Wedding Marketing01:23 - The Impact of AI on Marketing12:51 - Understanding AI in Business: Generative vs. Agentic22:43 - Using AI as a Thinking Partner26:52 - Using AI to Enhance Creativity and Efficiency

Are you leaving money on the table because your sales process is too clunky, too slow, or too complicated? If you're a 6 or 7-figure business owner, streamlining your sales process might be the quickest way to boost your revenue, improve client satisfaction, and save precious time. In this episode, I'll show you exactly how to simplify and optimize your sales operations for both brick-and-mortar and virtual businesses. What if you could: •    Close deals faster without overwhelming your team? •    Increase revenue per lead without adding complexity? •    Deliver a seamless customer experience that builds trust and loyalty? If any of this sounds like a "Hell Yes!" for your business, this episode is for you!   Mentioned in this episode Subscribe to Email List Leave a Podcast Review Work/Connect with me: Offer Optimization Scorecard Book a Call   Tune in to start taking your business and life to the next level today and don't forget to subscribe or follow the podcast to make sure you don't miss any future episodes. Visit https://jessicamillercoaching.com/ to learn more. You can also follow me on Instagram (@jessicadioguardimiller) and Facebook.

What if I told you the difference between a stressful orthodontic practice and an efficient one could come down to eliminating just a handful of recurring problems? The episode is about orthodontic efficiency with Dr. Ron Roncone, exploring decades of strategies to optimize orthodontic practices, reduce appointments, and improve patient care while discussing broader business and treatment philosophies in orthodontics.

Mary Mallon became known as Typhoid Mary in the early 20th century. She has often been vilified in in modern references, though her story is actually quite complex. Research: "Mary Mallon." Encyclopedia of World Biography Online, vol. 21, Gale, 2001. Gale In Context: Opposing Viewpoints, link.gale.com/apps/doc/K1631007781/GPS?u=mlin_n_melpub&sid=bookmark-GPS&xid=23f34010. Accessed 3 Aug. 2026. Brooks, J. “The sad and tragic life of Typhoid Mary.” CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne vol. 154,6 (1996): 915-6. https://pmc.ncbi.nlm.nih.gov/articles/PMC1487781/ Brooks, Janet. “The Sad and Tragic Life of Typhoid Mary.” Canadian Medical Association Journal. Vol. 154, No. 6. 3/15/1996. Cutter, Laura. “Typhoid Mary/Mary Mallon: An Asymptomatic Carrier of Salmonella typhi.” National Museum of Health and Medicine. 6/18/2020. https://medicalmuseum.health.mil/micrograph/index.cfm/posts/2020/typhoid_mary_mary_mallon_salmonella “Research Starter: Typhoid Mary.” https://www.ebsco.com/research-starters/life-sciences/typhoid-mary Faherty, Anna. “The cook who became a pariah.” Wellcome Collection. 6/29/2017. https://wellcomecollection.org/stories/WsT4Ex8AAHruGfW_ Leavitt, Judith Walzer. “‘Typhoid Mary’ Strikes Back: Bacteriological Theory and Practice in Early Twentieth-Century Public Health.” Isis , Dec., 1992, Vol. 83, No. 4 (Dec., 1992). Via JSTOR. https://www.jstor.org/stable/234261 Leavitt, Judith Walzer. "Typhoid Mary: Captive to the Public Health." Beacon Press. 1997. Mallon, Mary. Letter to Dr. William H. Park. June 1909. Via PBS NOVA. https://www.pbs.org/wgbh/nova/typhoid/letter.html Marineli, Filio et al. “Mary Mallon (1869-1938) and the history of typhoid fever.” Annals of gastroenterology vol. 26,2 (2013): 132-134. https://pmc.ncbi.nlm.nih.gov/articles/PMC3959940/ Mason, W.P. “Typhoid Mary.” Science. Vol. 30, No. 760. 7/23/1909. Via JSTOR. https://www.jstor.org/stable/1635174 Murtagh, Joseh. “David DeKok presents gripping, heartbreaking view into Ithaca’s 1903 typhoid outbreak.” Ithica.com. 4/11/2012. https://www.ithaca.com/visit_ithaca/david-dekok-presents-gripping-heartbreaking-view-into-ithaca-s-1903-typhoid-outbreak/article_943a556c-6b6d-11e0-92b9-001cc4c002e0.html New York Times. “Hospital Epidemic from Typhoid Mary.” 3/28/1915. New York Times. “HOSPITAL EPIDEMIC FROM TYPHOID MARY; Germ Carrier, Cooking Under False Name, Spread Disease in Sloane Institution. CAUGHT HIDING IN QUEENS Blamed for Twenty-five Cases of Fever Among Doctors and Nurses -- Now In Quarantine.” 2/28/1915. https://www.nytimes.com/1915/03/28/archives/hospital-epidemic-from-typhoid-mary-germ-carrier-cooking-under.html?eafs_enabled=false Ogan, ML. “Immunization in a Typhoid Outbreak in the Sloane Hospital for Women.” New York Medical Journal. 3/27/1915. 609-610. Othman, Amani and William W. Darrow. “The Wall, the Ban, and the Objectification of Women.” The International Journal of Social Quality, Winter 2019, Vol. 9, No. 2 (Winter 2019). https://www.jstor.org/stable/10.2307/26948452 Poczai P and Karvalics LZ (2022) The little-known history of cleanliness and the forgotten pioneers of handwashing. Front. Public Health 10:979464. doi: 10.3389/fpubh.2022.979464 Prabhu, Maya. “The tragedy of Typhoid Mary.” Gavi. 6/18/2021. https://www.gavi.org/vaccineswork/tragedy-typhoid-mary Pusey, Allen. “Precedents.” ABA Journal. Vol. 104, No. 3. March 2018. Via JSTOR. https://www.jstor.org/stable/10.2307/26516280 Sawyer, Wilbur. “The Efficiency of Various Anti-Typhoid Vaccines.” Journal of the American Medical Association. Vol. LXV, No. 17. 10/13/2015. Soper, G A. “The Curious Career of Typhoid Mary.” Bulletin of the New York Academy of Medicine vol. 15,10 (1939): 698-712. https://pmc.ncbi.nlm.nih.gov/articles/PMC1911442/ Soper, George A. “The Curious Career of Typhoid Mary.” Read May 10, 1939 before the Section of Historical and Cultural Medicine. Bulletin of the New York Academy of Medicine. Vol. 15, No. 10. October 1939. https://pmc.ncbi.nlm.nih.gov/articles/PMC1911442/ Soper, George A. “Typhoid Mary.” The Military Surgeon. Vol. XLV. No. 1. July 1919. Soper, George. A. “The Work of a Chronic Typhoid Germ Distributor.” Journal of the American Medical Association. Vol. XLVIII. No. 24. 6/15/1907. Teicher, Amir. “Typhoid Mary Was Not a Super-Spreader (and Super-Spreaders Are Not "Typhoid Marys").” American journal of public health vol. 113,12 (2023): 1249-1253. doi:10.2105/AJPH.2023.307434 See omnystudio.com/listener for privacy information.

Nick and Tyler sit down with Architect David Hornstein to talk about what has been lost as residential construction has become more specialized, more layered, and more expensive. Drawing from decades as a carpenter, builder, architect, and product designer, David makes the case that highly custom work can still be efficient if the people doing it understand the entire process. They get into the master builder mindset, the value of having experienced people on site, why communication gets diluted through layers of management, and how relentless attention to small efficiencies can dramatically change the cost of custom construction. David also shares how that same mindset eventually led him to create Dura Gutter. David Hornstein https://www.light-house-design.com/ https://www.duragutter.com/ Join the Modern Craftsman Community

Tom Murray of Dairy Farmers of America talks about their partnership with Samsara, overcoming challenges; safety & efficiency; & driving success every day.     IN THIS EPISODE WE DISCUSS: [01.50] An introduction to Tom, and the Dairy Farmers of America. "We're a dairy co-operative. We're owned by 9,000 dairy farmers across 5,000 farms… and they govern us! The farmers run the business." [03.52] Tom's roots on his family's creamery, and how it informs his current role at DFA. "It's given me a really great appreciation for the supply chain, from farm to the consumer, and all the complexities that brings. I've lived it." [04.49] The DFA members, and what it really means to them to be part of a co-operative. "We're creating something that represents their values." [06.19] The DFA supply chain, and its unique position in terms of speed, flexibility and supply and demand. [07.46] The challenges involved in moving fresh produce, and how DFA adapt. [10.04] Why technology and innovation are critical to DFA's ongoing success. "We're very focused on cost, safety, compliance and visibility, and technology has really improved all of that. We have control, and we know where our opportunities are." [11.48] What safety and efficiency mean to DFA, and why they're foundational elements for success. "You can be efficient, but you better be effective too." [14.28] How DFA determined that Samsara were the right partner. "Sometimes it's better to be lucky than to be smart." [15.55] Which Samsara solutions DFA use, and how they work to solve their unique challenges and complexities. [17.20] How one small success metric drove big results for DFA. [20.02] Tom's role on Samsara's Customer Advisory Board, the value for DFA, and the importance of collaboration and open feedback loops. [22.09] DFA's culture of excellence, how it helps drive success, and how Samsara feeds into that internal culture as an external partner. [25.02] The future of technology and innovation at DFA, and why unification will be key.   RESOURCES AND LINKS MENTIONED: Head over to Samsara's website now to find out more and discover how they could help you too. You can also connect with Samsara and keep up to date with the latest over on LinkedIn, Instagram, YouTube, Facebook and X (Twitter), or you can connect with Tom on LinkedIn. You can also follow Dairy Farmers of America on LinkedIn, Instagram or Facebook. If you enjoyed this episode and want to hear more from Samsara, check out: 557: Maintenance Reimagined, with Samsara 555: How To Build A Technology Partnership That Drives Results, with Samsara Customer XPO 552: Expand Your Visibility and Achieve Asset Tracking at Scale, with Samsara 529: Empower The People Who Power The World, with Samsara 524: Increase the Safety, Efficiency and Sustainability of Your operations, with Samsara Check out our other podcasts HERE.

Welcome to episode #1050 of Thinking With Mitch Joel (formerly Six Pixels of Separation). Fredric Marshall has spent decades helping people navigate change. An entrepreneur, investor in AI startups, CEO of Quantum Learning, and advisor on leadership and transformation, he has watched multiple waves of technological disruption unfold from the inside. His latest book, Thrive - The Antidote to Future Shock, revisits Alvin Toffler's famous concept of "future shock" and argues that today's anxiety isn't a personal failing... it's the predictable result of living through an era where change, information, and technological progress are arriving faster than most people can absorb. In this episode, Fred explores why the conversation about AI has become so emotionally charged, why younger generations feel a profound sense of uncertainty about their future, and why the real challenge isn't learning new tools... it's learning to direct our attention with intention. He explains why thriving requires more than technical skills, discussing the importance of agency, relationships, health, systems, and managing the growing tension between our digital and physical lives. The conversation also examines evidence-based optimism, the cultural backlash against AI, leadership in an era of disruption, and whether the pursuit of endless growth has reached its limits. Fred makes the case that while artificial intelligence will radically reshape work, the future belongs to people who can simplify complexity, think clearly, and consciously choose where to invest their attention. It's a thoughtful discussion about resilience, technology, and building a life that doesn't just survive change... but flourishes because of it. Enjoy the conversation... Running time: 59:29. 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 Fredric Marshall. Thrive - The Antidote to Future Shock. Quantum Learnin. Follow Fred on Instagram. Follow Fred on LinkedIn. Chapters: (00:00) - Introduction to Future Shock. (05:57) - The Impact of Technology on Work and Life. (08:48) - The Concept of Thriving vs. Surviving. (12:04) - The Role of AI and Economic Disruption. (15:14) - Navigating the Digital Layer and Social Media. (18:09) - The Future of Work and Community. (20:56) - Addressing Youth Concerns and Uncertainty. (24:03) - The Balance of Digital and Analog Life. (26:51) - The Quest for Enough in a Technological World. (29:52) - The Madness of Constant Growth. (30:41) - Evidence-Based Optimism and Humanity's Response. (31:34) - Historical Progress and Modern Stressors. (33:10) - Dialing in Life: Eight Key Areas for Thriving. (36:09) - Understanding Agency in a Complex World. (39:51) - The Role of Environment in Personal Growth. (40:48) - Leadership in a Changing Landscape. (43:22) - Future Readiness and Cultural Shifts. (46:52) - Youth Perspectives on Agency and Change. (50:42) - Balancing Humanity and Efficiency in the Future. (57:41) - The Importance of Attention and Time Management.

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

This episode of The Full Arch Podcast, we continue the conversation with Dr. Derek Williams and Dr. Taylor Anderson following Dr. Aaron Miller's mentorship visit to Taylor's practice. This time, the discussion gets into the practical changes that helped Taylor improve his All-on-X surgical efficiency. Together, they break down how standardization, assembly-line thinking, team communication, and intentional planning can reduce unnecessary movement and create a more predictable surgical workflow. The conversation also explores the habits clinicians should build early, where efficiency can be improved without compromising clinical outcomes, and how mentorship can reveal opportunities that are difficult to see on your own. Key Highlights

S6:E71 AI has raised the floor. But has it raised the ceiling? Gee Ranasinha doesn't think so. AI has democratized content production, giving small businesses access to capabilities once reserved for larger organizations. But Gee argues that we're simultaneously creating what he calls a greater "preponderance of mediocrity" meaning more acceptable content, more quickly, from more companies, increasingly saying similar things. Queue up this episode of Small Business Stories for a thoughtful (and occasionally provocative) conversation about what marketing actually is, why businesses confuse marketing with promotion, and why understanding human behavior matters more than simply mastering the latest tools. Gee brings behavioral science into the discussion because people don't make buying decisions through purely rational analysis. Context, emotion, unconscious biases and mental shortcuts all affect how messages are interpreted. And that's where Dr. LL sees an important connection to misinterpretation risk. Businesses communicate what they intend to say. Customers respond to what they actually understand. Those aren't necessarily the same thing.

Since our Faculty Factory podcast's launch in 2019, we have had the honor of interviewing some of the world's most impressive people, with a constant recurring theme being that these folks know how to safeguard their time. In this Faculty Factory best-of compilation episode, we're diving into some quick-hitting time management tips packed into a quick 15-minute broadcast with some of the most powerful mic-drop segments in this show's 7+ year history. Three-time management experts in academic health are featured this week, with clips from the following episodes: Episode 63: Time Management and Efficiency with David M. Yousem, MD, MBA Episode 68: Using Time Productively with Donna L. Vogel, MD, PhD Episode 119: Habits and Hacks with Shameema Sikder, MD Themes covered throughout include goal setting, prioritizing, list-making, saying no, and how to face the items on your list that you dread most (or simply eliminate them if that's an option). "Time is ultimately our most precious commodity, so if you are saying yes to certain projects, it eventually means you will be saying no to something else," said Dr. Sikder. Drs. Yousem and Vogel discuss the importance of prioritization, particularly in the context of Stephen Covey's (author of The 7 Habits of Highly Effective People) Time Management Matrix, which is mentioned in-depth in both Dr. Yousem's and Dr. Vogel's clips.

3hl - 8-21-26 - Hour 2 - Discussing Cam Ward and the offense's efficient day vs the Super Bowl Champs, Jayson Swain of the Vol Network talks Tennessee Football, and Charles Pulliam breaks down the weekend slate of High School Football. For More coverage of 3HL follow us here:https://x.com/brentdoughertyhttps://x.com/TheRonSlayhttps://x.com/DawnDavenportTNhttps://x.com/jmbonanno13https://x.com/3HL1045https://x.com/1045TheZonePodcasts: https://podcasts.apple.com/us/podcast/3hl/id1103395659 Facebook: https://www.facebook.com/1045thezone/I Instagram: https://www.instagram.com/3hl/ https://www.instagram.com/brentdougherty3hl/ https://www.instagram.com/theeronslay/ https://www.instagram.com/dawndavenporttn/ https://www.instagram.com/1045TheZone/ Tik Tok: https://www.tiktok.com/@1045thezonehttps://www.tiktok.com/@3hl1045 #1045TheZone #TennesseeTitans #NFLFootball #Titans #NFLFootball #TennesseeVols #NashvilleSCSee omnystudio.com/listener for privacy information.

S6:E71 AI has raised the floor. But has it raised the ceiling? Gee Ranasinha doesn't think so. AI has democratized content production, giving small businesses access to capabilities once reserved for larger organizations. But Gee argues that we're simultaneously creating what he calls a greater "preponderance of mediocrity" meaning more acceptable content, more quickly, from more companies, increasingly saying similar things. Queue up this episode of Small Business Stories for a thoughtful (and occasionally provocative) conversation about what marketing actually is, why businesses confuse marketing with promotion, and why understanding human behavior matters more than simply mastering the latest tools. Gee brings behavioral science into the discussion because people don't make buying decisions through purely rational analysis. Context, emotion, unconscious biases and mental shortcuts all affect how messages are interpreted. And that's where Dr. LL sees an important connection to misinterpretation risk. Businesses communicate what they intend to say. Customers respond to what they actually understand. Those aren't necessarily the same thing.

The California Energy Commission (CEC) has just passed the Replacement Tire Efficiency Program (RTEP). The Nation's first efficiency standards for replacement tires sold for passenger vehicles and light-duty trucks. Lots of people are freaking out about this because everyone is afraid that their tires will no longer be available to them. But there is a long list of excluded tires from this program. https://www.energy.ca.gov/proceeding/replacement-tire-efficiency-program-proceeding Solve Function Wolfbox: https://solvefunction.com/shop/ols/products/wolfbox-cord-coverDiscount Code: WolfSnail GROUP BUY: Its time to buy your Wolfbox for their G900Tripro. We have sent out the codes and have the information on the Discord. We have till the end of August to use the discount code. This deal ends September 1st. SnailTrail4x4 Discord: https://discord.gg/yFyFFkQbuyCome hang out with us on the SnailTrail4x4 Discord — it’s the easiest way to connect with Tyler and Jimmy directly, chat with fellow offroad enthusiasts, and get first access to Group Buys and Treasure Hunt token drops. MORRFlate Giveaway at 900 Reviews on Apple Podcast. But our next giveaway is when we reach 800 reviews; we are giving away an OnX Elite Membership. We will also give away an OnX Elite membership when we get to 850. However, when we reach 900 Reviews, we are teaming up with MORRFlate for a $1000 MF Product Giveaway. Go over to Apple Podcasts to leave your review now and become eligible to win. Congratulations to A13XMONT, who won a set of tires from Yokohama Tire! Call us and leave us a VOICEMAIL!!! We want to hear from you even more!!! You can call and say whatever you like! Ask a question, leave feedback, correct some information about welding, say how much you hate your Jeep, and wish you had a Toyota! We will air them all, live, on the podcast! +01-916-345-4744. If you have any negative feedback, you can call our negative feedback hotline, 408-800-5169. 4Wheel Underground has all the suspension parts you need to take your off-road rig from leaf springs to a performance suspension system. We just ordered our kits for Kermit and Samantha and are looking forward to getting them. The ordering process was quite simple, and after answering the questionnaire, we ensured we got the correct and best-fitting kits for our vehicles. If you want to level up your suspension game, check out 4Wheel Underground. SnailTrail4x4 Podcast is brought to you by all of our peeps over at irate4x4! Make sure to stop by and see all of the great perks you get for supporting SnailTrail4x4! Discount Codes, Monthly Give-Always, Gift Boxes, the SnailTrail4x4 Community, and the ST4x4 Treasure Hunt! Thank you to all of those who support us! We couldn’t do it without you guys (and gals!)! SnailSquad Monthly Giveaway August giveaway is with our good friends over at GEARWRENCH Tools. GEARWRENCH has a new line of tools with Hi-Viz. These have one side of the wrench painted and easy to read, where the other side is still etched into the wrench, but the etching is filled with orange paint. We are giving away one set of Standard and Metric Wrenches, and a set of Standard and Metric sockets to a lucky winner. If you want a chance to win, make sure you sign up for the giveaway tier on either Irate4x4 or the SnailTrail4x4 Discord. Congratulations to Josh Taylor for winning this month’s giveaway with Wolfbox. They are giving away one G900Tripro to a lucky winner. This is the same three-camera rearview mirror setup that they received during TrailHeroX. If you want a chance to win this system from Wolfbox make sure you sign up for the giveaway tier on either Irate4x4 or the SnailTrail4x4 Discord. Massive Congratulations to Liz Sandstorm for winning the Iceco Freezers. We are excited to work with and share their exciting new releases. One lucky winner has a chance to win an ALP20 Fridge. Big thanks to Iceco for sponsoring this month’s giveaway. If you want a chance to win, sign up for the Giveaway Tier on Irate4x4 Listener Discount Codes: SnailTrail4x4 –SnailTrail15 for 15% off SnailTrail4x4 MerchMORRFlate – snailtraill4x4 to get 10% off MORRFlate Multi Tire Inflation Deflation™ Kits4WheelUnderground – snailtrail 10% offIceco – SnailTrail 12% offRUSOH Fire Extinguishers – RusohCrawlers discount code gets you 12% offDevos Outdoor – snailtrail12 gets you 12% off sitewideIronman 4×4 – snailtrail20 to get 20% off all Ironman 4×4 branded equipment!Sidetracked Offroad – snailtrail4x4 (lowercase) to get 15% off lights and recovery gearSpartan Rope – snailtrail4x4 to get 10% off sitewideShock Surplus – SNAILTRAIL4x4 to get $25 off any order!Mob Armor – SNAILTRAIL4X4 for 15% offSummerShine Supply – ST4x4 for 10% offBackpacker’s Pantry – Affiliate LinkLaminx Protective Films – Use the Link to get 20% off all products (Affiliate Link) Show Music: Midroll Music – ComaStudio Outroll Music – Meizong Kumbang

“When will be the end of thus exhausting the earth, and how far will avarice finally penetrate?” the Roman natural historian Pliny the Elder asked in 77 AD. Two thousand years later, the answer seems clear. At least according to the contemporary Scottish natural historian James Crawford. In his new book, The Vanishing Earth, the Edinburgh-based Crawford provides dispatches from what he calls the “frontiers of extraction.” From the Atacama Desert in northern Chile to the melting coastline of Greenland, Crawford describes the apocalyptic consequences of our avarice toward the earth. The ultimate frontier of extraction, Crawford warns, is ourselves. Thus he reports on big tech's designs to harvest our thoughts. Consumer headsets are already reading our brainwaves, Apple holds patents for brain-reading earbuds, and another self-styled “leader in real-world neuroscience” once promised “neuro insights for marketing purposes.” The Vanishing Earth's apocalyptic warnings appear both timeless and immediate. Watching Roman miners destroy Spanish mountains, Pliny the Elder wondered when we would finally exhaust the earth. Today, we can quantify the answer. Sometime around 2020, the weight of everything humanity has built came to exceed the weight of all life on Earth. It was just three percent in 1900. We have extracted more in the past fifty years than in all prior human history combined. We now build a city the size of Paris every five days. The ideology of endless growth is only 300 years old, Crawford warns. Pliny the Elder's earth is history. Crawford confesses that he is a product of extraction himself. On his family's windowsill sits a lemonade bottle of the first North Sea crude, siphoned by his father the day it came ashore. “I am Scottish, after all,” he notes wryly. Will our avarice transform extraction into extinction? Not necessarily, Crawford explains. His last dispatch is from Butte, Montana, and offers a ray of hope. There he finds people trying to repair the seemingly apocalyptic consequences of copper mining on the earth. So apocalypse now doesn't mean apocalypse tomorrow. We still have the power to unvanish the earth, the agency to undo the avarice of our ancestors. Five Takeaways •       Pliny's Two Questions. Watching Roman gold miners collapse entire Spanish mountains — ruina montium, “the fall of mountains” — the great naturalist Pliny the Elder asked, two thousand years ago: “When will be the end of thus exhausting the earth, and how far will avarice finally penetrate?” Those two questions run like an entwined seam through Crawford's book, and he believes we're now getting the answers. The ideology of endless economic growth, he argues, is only about 300 years old — born in Enlightenment Scotland as freedom from famine and the caprices of nature, and entirely sensible at the time. The problem is that the planet has changed and the ideology hasn't. The “vanishing” of the title isn't only resources: it's the sixth extinction, the biodiversity collapse — and, potentially, us.•       A Lemonade Bottle of First Oil. Crawford is a product of extraction, and knows it. His great-grandfather fled Shetland fishing at sixteen, wrote “engineer” on his immigration form, and ended up on Ford's very first assembly line building the Model T. His father apprenticed in a coal mine, worked the Zambian Copperbelt, then spent thirty years in oil — which is why Crawford was born on the Shetland Islands, beside one of the world's largest construction sites. The day the first North Sea oil came ashore, his father — who ran the pipelines and knew where to go — filled a lemonade bottle from an outlet valve; it still sits on the family windowsill. “I am a product of extraction. But then we all are”: microplastics in our bodies, forever chemicals passing from mother to unborn child. As a species, we have merged with what we mine.•       Thought Extraction. The journey's final landscape is the mind. Consumer neurotech — EEG headsets promising calm and focus — is already reading brainwaves, and the direction of travel is unmistakable: Emotiv scrubbed “neuro insights for marketing purposes” from its website between fact-check and publication (Crawford found the old language on the Wayback Machine); Apple holds patents for EEG-reading earbuds, with Meta and Snap filing alongside. Rafael Yuste's Neurorights Foundation surveyed the roughly thirty companies selling neurotech to consumers and found that every one of them claimed unlimited access to customers' brain data — with no legal protection in place. The attention economy harvested our behavior; the next extraction, Crawford warns, is the thought itself.•       Heavier Than Life Itself. The statistics that stopped Crawford in his tracks: around 2020, the weight of everything humans have built — buildings, roads, plastics — came to exceed the weight of the Earth's entire biomass, up from three percent in 1900. We've extracted more in fifty years than in all prior history; we build a Paris every five days; in ten years China poured more cement than America managed in the whole twentieth century. Hence the global sand shortage (desert sand is useless — “like building with marbles”) that sent him to Greenland, where melting glaciers make the coastline grow while the world's shrink. And hence the hard questions about the energy transition: in 2025 humanity burned more wood, more coal, and more oil than ever before — not a transition but an accumulation, exactly as the Jevons paradox predicted when Victorian Britain worried about coal. Efficiency doesn't reduce use; it multiplies it.•       The Forever Repair. The book ends not in despair but in Butte, Montana — once the center of world copper production, its open-cast pit now a lake of sulfuric acid in the heart of the city. After thirty years of argument, a settlement around 2020 gave the city something unprecedented: BP holds a blank check to monitor Butte's soils and water in perpetuity, and the people, rather than abandon their home, stayed to watch over it and tell its story. “It's not you fix and you forget. Everything is a repair.” That, for Crawford, is the ideological shift the whole journey points toward — from extraction to repair. He is hopeful for one precise reason: we got here through a way of seeing the world, and minds can change. In the tradition of Silent Spring — which banned DDT and birthed the EPA — a book can still move the world. “I am Scottish, after all” — but the glass, on inspection, is half full. About the Guest James Crawford is a Scottish writer, broadcaster, and historian based in Edinburgh. The author of Fallen Glory: The Lives and Deaths of History's Greatest Buildings and The Edge of the Plain: How Borders Make and Break Our World, he presents book programmes for the BBC and spent over a decade with Historic Environment Scotland. The Vanishing Earth: Dispatches from the Frontiers of Extraction (Bloomsbury, 2026) is out now in the US, with the UK edition imminent from Scribe. The New York Review of Books places him with Iain Sinclair, Rebecca Solnit, and Robert Macfarlane: “Riveting.” References: •&nb...

>Join Jocko Underground Full Episodes< Breaking down raw, firsthand combat lessons from U.S. Army soldiers fighting their way through World War II—lessons written in the field by men who had just experienced the consequences of good decisions, bad decisions, aggression, hesitation, discipline, and leadership.Support this podcast at — https://redcircle.com/jocko-podcast/exclusive-content

One of the biggest mistakes in AI? Thinking that your company's AI use is noteworthy. Or, even a competitive advantage. It's not. We break it down in Volume 3 of our 'Start Here Series.' AI as an Operating System: LLMs Are the Internet Now -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:AI As An Operating System ExplainedLarge Language Models Replace Traditional AppsAI Integration in Knowledge Work PlatformsChoosing the Right AI Operating SystemMicrosoft Copilot vs. Google Gemini vs. Claude vs. ChatGPTAgentic Browsers Powering Autonomous WorkflowsModel Context Protocol (MCP) for AI AgentsOrchestration Layer and Agent CollaborationChatGPT Apps Merging AI and InternetEnterprise Data Integration with AI ToolsContext Switching Reduction Through AI AgentsStrategic AI Adoption and Platform RedundancyTimestamps:00:00 "AI: A New Operating System"03:58 "AI Transforming Work Interfaces"06:41 "Collaborating in AI-Native Workspaces"12:25 Anthropic's Innovations in AI Tools13:46 "OpenAI's Strategy and Market Focus"18:02 "Cognitive Evolution Through AI"20:57 "Agentic Browsers: Key 2025 Advancement"25:12 Improving Content Through Data Insights26:42 "Anthropic's MCP: The AI Connector"32:19 "AI Tools for Productivity Integration"34:20 "AI: Unlocking Context and Efficiency"36:32 AI Governance and System Portability39:35 "AI Operating System Insights"Keywords: AI operating system, large language models, LLMs, AI as infrastructure, enterprise AI, AI adoption, agentic workflows, AI agents, orchestration layer, Copilot, Microsoft 365 Copilot, Google Gemini, Gemini business, Gemini enterprise, Anthropic Claude, Claude cowork, MCP, model context protocol, OpenAI, ChatGPT, ChatGPT apps, ChatGPT business, ChatGPT enterprise, AI native, dynamic data integration, productivity with AI, collaboration tools, agentic browsers, autonomous AI agents, context window, memory and personalization, expert-driven loops, app hop tax, context switching, AI integration in business, AI tools for teams, AI platform selection, data governance, modular AI workflows, permissions and audit logs, backup and redundancy in AI, competitive advantage with AI, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Profit or cash. Efficiency or flexibility. Speed, or doing it right.Leaders feel like they have to make trade-offs like these all the time. But the choice often isn't real.Some contradictions are unavoidable. Others only look that way until you understand the real problem. The most effective organizations realize they don't have to pick a side if they can resolve the underlying tension.Olivier Larue has spent three decades helping companies do this, which he's documented in his three-book series The Toyota Economic System: a philosophy about people and society, the technical design that turns it into results, and the management practices that keep it alive through continuous learning.The real breakthrough is never the tools, optimizing one part of the system, or making unnecessary tradeoffs. It's how every part connects, and understanding the real problem to solve, so people remain the focus and results last.You'll Learn:How to tell a contradiction that's real from one that only looks real, so you stop choosing between things you can't afford to loseWhy an economic lens, not an operational one, changes where you look for the answerWhy the number and order of Toyota's seven wastes aren't arbitrary, and why the first one drives how you understand the restWhy declaring people "empowered" means nothing without the conditions that let them act on itHow respect for people extends to avoiding layoffs even in downturns, and why that matters in a world of AI and supply chain disruptionsABOUT MY GUEST:Olivier Larue was trained by Toyota Motor Corporation and has spent thirty years helping Fortune 500 companies design and implement industrial manufacturing solutions, including more than a decade as a consultant with TSSC, the Toyota Production System Support Center, and hands-on work in the biopharmaceutical industry. He is the founder and president of Ydatum, and the author of the three-book series The Toyota Economic System.IMPORTANT LINKS:Full episode show notes: ChainOfLearning.com/82 Connect with Olivier Larue: linkedin.com/in/olivierlarue Purchase a copy of The Toyota Economic System (three-book series): https://www.amazon.com/Toyota-Economic-System-Environmental-StewardshipLearn more about Ydatum: ydatum.com Follow me on LinkedIn: linkedin.com/in/kbjanderson Subscribe to my newsletter: kbjanderson.com/newsletter Check out my website for resources and working together: KBJAnderson.com Join us on the Japan Leadership Experience: KBJAnderson.com/japantrip TIMESTAMPS FOR THIS EPISODE:02:43 How a NUMMI experience shaped Olivier years later05:14 Why call it the Toyota Economic System?09:42 Profit vs. cash: the trade-off companies get wrong12:32 Why Just-in-Time doesn't mean zero inventory16:30 What happens when company values and work conflict20:20 Why leaders have to “earn” how they manage21:42 Why Toyota's seven wastes are in a specific order23:38 The hidden cost of human waiting27:20 Why leadership skills alone aren't enough30:05 Why organizations keep repeating the same problems31:26 What Andon actually means for empowerment35:17 The leadership response that makes problems safe to surface39:16 Why Toyota can avoid layoffs during downturns44:30 The unanswered question about AI and economic growth48:30 How enthusiasm turns pull into push50:44 Why isolated improvements don't transform systems54:35 Refusing the either/or trade-off Learn more and apply for the November 2026 cohort of my Japan Leadership Experience: https://kbjanderson.com/japantrip/

The California Energy Commission has approved the nation's first replacement tire efficiency standards, aiming to reduce fuel consumption and emissions. Supporters say the rules will save drivers money at the pump, while critics argue they could eliminate many tire options, raise costs, and limit consumer choice in coming years. Please Like, Comment and Follow 'Broeske & Musson' on all platforms: --- The ‘Broeske & Musson Podcast’ is available on the KMJNOW app, Apple Podcasts, Spotify or wherever else you listen to podcasts. --- ‘Broeske & Musson' Weekdays 9-11 AM Pacific on News/Talk 580 AM & 105.9 FM KMJ | Facebook | Podcast| X | - Everything KMJ KMJNOW App | Podcasts | Facebook | X | InstagramSee omnystudio.com/listener for privacy information.

Robert Nordlander spent over 20 years as a special agent with IRS Criminal Investigation — investigating complex criminal tax and money laundering violations, working undercover operations, executing search and arrest warrants, and building the cases that sent tax evaders and money launderers to federal prison — and in this episode of Locked In with Ian Bick, he finally tells the complete truth about what that career really looked like from the inside. He shares what cases the IRS Criminal Division actually goes after and prosecutes, what it actually takes to put someone in prison for taxes, the different types of money laundering he investigated, why small business owners evade taxes more than anyone else, how cases came to him and what the investigation process actually looked like, some of the most significant cases of his career, and what the new world of influencers and social media income is producing in terms of tax crime that most people never see coming. _____________________________________________ #irs #taxes #truecrimestories #accountant #cops  _____________________________________________ Thank you to CASH APP for sponsoring this episode: Download Cash App Today: https://capl.onelink.me/vFut/ksjh06pb  #CashAppPod Cash App is a financial services platform, not a bank. Banking services provided by Cash App's bank partner(s). Prepaid debit cards issued by Sutton Bank, Member FDIC. Cash App Visa® Debit Flex Cards issued by Sutton Bank, Member FDIC, and The Bancorp Bank, N.A., pursuant to a license from Visa U.S.A. Inc. See terms and conditions for the Sutton prepaid card, Sutton debit flex card, and Bancorp debit flex card. Discounts and promotions provided by Cash App, a Block, Inc. brand. Visit cash.app/legal/podcast for full disclosures. _____________________________________________ Connect with Robert Nordlander: Website: https://www.nordlandercpa.com/ Buy his books: https://www.amazon.com/stores/Robert-Nordlander/author/B0BMZT4CNK?ref=ap_rdr&shoppingPortalEnabled=true&ccs_id=a4257f70-b089-4db0-8020-c9bd7e35d743 Hosted, Executive Produced & Edited By Ian Bick: https://www.instagram.com/ian_bick/?hl=en  https://ianbick.com/ _____________________________________________ Timestamps: 00:00 Meet the Ex-IRS Agent 00:21 Growing Up and Early Career 02:00 From Chips to IRS Agent 03:48 The CPA Advantage 05:40 IRS CI Origins and Its Role 06:34 Stationed in Alabama 07:19 Dad's Blessing and Career Shift 09:03 First Case: Identity Theft 10:50 The IRS 'Funny Box' Explained 12:05 Tax Protesters and False Refunds 13:37 Sentencing for Tax Protesters 14:59 Statute of Limitations for Tax Crimes 15:38 Hiding Income: The Small Business Owner 16:36 How Agents Find Cases 18:40 Data Mining for Evasion 20:47 The Value of IRS CI to Prosecutors 21:09 Drug Dealers and Tax Returns 22:12 Civil vs. Criminal: Making the Call 24:30 Choosing Cases Worth Prosecuting 25:40 Cash App Sponsorship 27:40 Dollar Amounts Drive Cases 29:34 Most Common Businesses for Fraud 30:42 Contractor Cash Schemes 31:32 Investigating Contractor Fraud 32:51 Why Celebrities Don't File 34:14 The Tax Gap and Who's Responsible 35:30 Are Business Owners Honest? 36:50 Influencer Tax Issues 38:12 The Fiji Hotel Example 39:22 Influencer Contracts and Tax 40:20 Ignorance and Willfulness 41:30 Influencer Cases and Richard Hatch 43:08 Tax Preparer Liability 44:42 Return Preparers: No License Needed 45:29 Preparer Mistakes vs. Crimes 46:53 Abuse of Earned Income Tax Credit 48:55 What Happens to the Clients? 50:00 When to Tell a Subject They're Investigated 51:42 Undercover Work and Surveillance 55:27 Common Lies from Suspects 57:37 Finding the Second Set of Books 58:26 Pissed-Off Partners as Informants 58:49 State vs. Federal Cases 01:00:35 The Length of Federal Investigations 01:01:36 Finding Bank Accounts 01:03:40 Using Flight Rosters as Leads 01:05:27 Structuring: The $10,000 Myth 01:07:19 A Surprising Case: Murder and Taxes 01:12:28 Expectations of Repayment 01:13:12 Most Egregious Money Hiding 01:15:40 PayPal, Venmo, and Cash App 01:17:52 Money Laundering Evolution 01:20:51 Cryptocurrency and the IRS 01:24:17 Unreported 1099 Income 01:26:46 Tips and Minor Tax Evasion 01:27:43 Is the System Fair? 01:29:00 The Tax Boycott Myth 01:31:00 Tax Protesters Are Filing 01:34:19 Jury Trials and Complex Cases 01:37:52 Testifying and Simplifying for Juries 01:42:10 Winning at Trial: The Odds 01:44:00 Robert's Role as a Consultant 01:45:58 Retiring from the IRS 01:47:53 Life on the Defense Side 01:52:56 IRS Layoffs and Efficiency 01:56:58 The Most Important Lesson 01:58:52 Truth Has Many Friends 02:00:18 Final Thoughts and Resources _____________________________________________ To advertise on the show, contact sales@advertisecast.com or visit https://advertising.libsyn.com/LockedInWithIanBicka

Discover how insurance—often seen as dull—actually powers our world and is evolving at lightning speed thanks to artificial intelligence. What happens when an industry rooted in caution becomes a hotbed of technological innovation? On this episode of Thrive LouD, Lou Diamond welcomes Nick Lamparelli—the self-proclaimed "Insurance Nerd"—to explore the unexpected excitement, challenges, and opportunities in insurance as it enters the AI era.You'll learn how agentic AI is transforming underwriting, claims, and customer experience, what makes insurance a foundational yet underappreciated societal force, and how culture and regulation are just as crucial as tech when it comes to industry change. Nick Lamparelli also reveals why insurance professionals and non-professionals alike should care about these shifts, and what the future might hold for an industry that touches every facet of our lives.Plus: Make sure to check out the new podcast series hosted by Nick - The Agentic Insurer: The New Frontier of AI.00:00 Intro and Welcome00:41 Who is Nick Lamparelli?01:44 Nick's Unexpected Path into Insurance03:07 Building and Celebrating the Insurance Community05:07 Why Insurance is a Great Human Invention07:17 Insurance and Tech: Past and Present09:22 AI's “Hockey Stick” Moment in Insurance11:17 Agentic AI Explained13:18 Agentic AI's Impact on Industry Pros and Consumers15:10 What Happens to Underwriting—and Underwriters—Now?17:11 Speed and Efficiency for Policyholders18:54 The Claims Process: Fraud, AI, and What's Changing21:58 How AI is Used for Detecting—And Committing—Fraud23:07 “The Agentic Insurer” Program and What Listeners Gain27:27 Culture Change, Regulation, and Lessons for All Industries30:25 Where to Find Nick & Final Thoughts32:10 Fun Street: Nick's Favorites and Rapid-fire Q&A36:07 Closing

As US video ad spend reaches $82 billion, media buyers are demanding granular audience targeting capabilities alongside bottom-funnel performance metrics. IAB Vice President Chris Bruderle breaks down the shifting dynamics between CTV and social video, the rise of multimodal contextual AI, and why live sports platforms must prove direct ROI to capture long-tail budgets. Key Highlights

What You'll Learn in This Episode:In this episode of the Lean Solutions Podcast, Shayne Daughenbaugh and Andy Olrich welcome James Martin to explore why batching often feels more efficient, but can actually hurt the flow of an entire organization. James shares real-world examples of reducing batch sizes and moving toward flow, including a transformation that reduced processing time from 14 hours to as little as 1.5 hours while significantly increasing output.The conversation explores how batching creates waiting, excess inventory, bottlenecks, and localized efficiency, and why teams need to focus on the flow of the entire system rather than individual productivity. James also explains how smaller batch sizes, experimentation, visual management, and employee empowerment can help organizations identify problems more quickly and foster a culture of continuous improvement.Whether you're trying to reduce waste, improve manufacturing flow, or build a stronger continuous improvement culture, this episode offers practical examples of how reducing batching can help work move faster and more effectively through an organization.Key Takeaways:Focus on flow—not localized efficiencySmaller batches can expose and eliminate wasteMake bottlenecks and problems visibleEmpower teams to experiment and improveLinks:https://www.linkedin.com/in/james-martin-4a4629254/https://srasolutions.com.au/https://www.findleansolutions.com/lean-summit/https://www.findleansolutions.com/

Today I'm very excited to have Listener John from Maryland joining us today to share his trip with his family to Disney's Beach Club Resort! We talk about how he found the show recently, his "Disney Story", how they "landed" at Beach Club for this trip, fun times at Epcot's Festival of the Arts, exciting adventures in both Epcot and the Magic Kingdom, great meals at places like Trattoria al Forno, Spice Road Table, Skipper Canteen, and more! We hope you can continue the conversation with us this week in the Be Our Guest Podcast Clubhouse at www.beourguestpodcast.com/clubhouse! Thank you so much for your support of our podcast! Become a Patron of the show at www.Patreon.com/BeOurGuestPodcast.  Also, please follow the show on Twitter @BeOurGuestMike and on Facebook at www.facebook.com/beourguestpodcast.   Thanks to our friends at The Magic For Less Travel for sponsoring today's podcast!

Meta Platforms (META) "is turning into one of our favorite names," says CFRA's Angelo Zino, who says the core business and growth prospects both appear healthy. The cheapest valuation among the Mag 7 and new products are other key factors he sees buoying the social media giant. Austin Lyons says Meta can use its own AI models to improve ad efficiency while keeping costs contained. ======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

As part of a monthlong series about doing less, host Greta Johnsen talks to a theater director and an author about attention, achievement, effort, and ease. First, Greta speaks with Michaela Goldhaber, a Bay-Area playwright, director, and dramaturg who is also involved in disability justice work through her theater company Wry Crips Disabled Women's Theater Group. Then, she talks to Cathy Haynes, the author of The Fullness of Time: Marking the Day by Birdsong, Blooms, Shadows, and Stars.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode of The Full Arch Podcast, Dr. Aaron Miller, Dr. Derek Williams, and Dr. Taylor Anderson discuss the learning curve of full-arch dentistry and how focused mentorship can help clinicians become more efficient and confident in surgery. Dr. Taylor Anderson shares his journey into implant dentistry and the challenges he faced as he developed his All-on-X experience. The conversation explores how tracking surgery times, setting intentional goals, and involving the entire surgical team can help identify inefficiencies and create a more predictable workflow. Key Highlights

On The Coaching 101 Podcast, Daniel Chamberlain and Kenny Simpson return after a busy summer, share a John Wooden quote on composure under pressure, and promote Simpson's Fieldhouse session with his offensive coordinator. They spotlight sponsors Winning Edge Performance Analytics (real-time in-game data and automated film tagging with a Coaching 101 discount), Ace Sports video board/scoreboard fundraising, and Blended Threads apparel. The main discussion focuses on fall camp priorities: stripping away what doesn't fit, emphasizing fundamentals over endless install, establishing depth across positions and special teams, defining team identity based on personnel, introducing light game-plan variations, and avoiding overworking players (“don't kill your cats”). They explain why priorities prevent being average at everything, keep practice organized and shorter, and help teams improve throughout the season, including using football drills—not conditioning—to build game readiness.00:00 Competitive Edge Pitch01:07 Fundraising Video Boards02:57 Podcast Intro Catch Up05:03 Quote on Composure08:57 Fieldhouse Session Invite11:26 Sponsor Roundup13:29 Fall Camp Priorities15:00 Peel Back and Build Depth18:04 Identity and Base Mastery20:22 Avoid Camp Burnout21:14 Coaching First Graders23:58 Basics Before Scheme24:48 Prioritize Core Plays26:53 Depth And Special Teams28:36 Shorter Organized Practices33:42 Evolving Camp Plan37:47 Game Speed Without Gassers39:26 Build Around Strengths43:06 Plan It On Paper44:26 Sponsors And Sign OffDaniel Chamberlain:@CoachChamboOKChamberlainFootballConsulting@gmail.comchamberlainfootballconsulting.comKenny Simpson:@FBCoachSimpsonfbcoachsimpson@gmail.comFBCoachSimpson.com

AI is changing hiring from both sides of the desk—companies are creating stronger job descriptions while candidates are creating stronger resumes, leaving recruiters with a growing blind spot between what looks good on paper and what actually predicts performance. In this FDE+ conversation, host Kortney Harmon is joined by Dr. Maya Huber, CEO and co-founder of Tatio, to explore why traditional, keyword-based hiring processes are becoming less reliable in an AI-driven market.Maya makes the case for shifting from promise to proof by measuring actual skills, competencies, and job-specific performance. She explores how staffing and recruiting teams can define what “good” looks like, standardize qualification across recruiters, and gather more meaningful evidence through structured questions, work samples, and virtual job simulations. The goal isn't simply greater speed or efficiency—it's greater certainty, transparency, and trust for clients and candidates alike.Explore how performance-based hiring can help recruiters make smarter talent decisions, strengthen client relationships, and become the performance authority their market trusts.________________Follow Maya Huber on LinkedIn: LinkedIn | MayaFollow Crelate on LinkedIn: CrelateWant to learn more about Crelate? Book a demo hereSubscribe to our newsletter: The Full Desk Experience

If you think you don't have enough clients to need systems in your business, this episode might completely change your mind. Whether you're balancing a business with a full-time job or running a part-time side-hustle, you don't have less need for systems—you have less time to operate without them.In this episode, I'm chatting with my client Jess Hoffman, a Florida-based pet photographer who runs her photography business alongside a full-time corporate job. Jess and I have worked together on her Dubsado systems and client communication, and we're digging into what changed when she stopped trying to piece everything together and built a client experience that could actually support the way she works.We talk about replacing required discovery calls with a book-first process, using video to educate potential clients before they book, and automating client communication without losing personality. Jess also shares how making one simple change to her booking process led clients to prepay for her highest collection—before she even photographed their session.Find It Quickly02:00 - Why Pet Photography02:55 - Charging From Day One05:52 - Systems Mindset Begins09:58 - Simple Sales Explained10:53 - Dubsado Workflow Struggles11:50 - Audit to Blueprint Jump13:00 - Video Instead of Calls19:49 - Handling Corporate Inquiries21:06 - Upsells and Pay Upfront Wins25:06 - Gallery Education and Mockups29:05 - Selling Wall Art With Samples35:00 - Why Systems Matter Part TimeMentioned in this EpisodeThe Experience EditConnect with the GuestWebsite: hoffhousephotography.comInstagram: @hoffhousephotography

The Bowhunter Chronicles Podcast - Episode 414: Kevin Vistisen - Deer Hunter Podcast In this episode, we sit down with Kevin from Deer Hunter Podcast to dive deep into the realities of modern deer hunting, public land strategy, and what it takes to consistently find success during the rut. Kevin breaks down how he balances a full-time plumbing career, family life, and a growing hunting business while staying focused on the narrow window that matters most: Halloween through Thanksgiving.We talk trail cameras, mature buck behavior, hunting pressured ground, scent control, and the mindset required to stay effective when the woods get crowded. Kevin also shares the story behind Deer Hunter Synthetics, his approach to developing better products for serious hunters, and why he believes public-land deer hunting is still the ultimate challenge.If you're looking for practical deer hunting tactics, rut hunting tips, and an honest conversation about chasing mature bucks on public land, this one is packed with insight.https://huntworthgear.com/  https://www.yellowstone.ai/  - CHRONICLES for 15% off  https://www.paintedarrow.com - BHC15 for 15% off https://www.spartanforge.ai (https://www.spartanforge.ai/)  - save 25% with code bowhunter   https://www.latitudeoutdoors.com (https://www.latitudeoutdoors.com/) s https://www.zingerfletches.com (https://www.zingerfletches.com/) https://www.bigshottargets.com (https://www.bigshottargets.com/)   https://genesis3dprinting.com (https://genesis3dprinting.com/) https://vitalizeseed.com (https://vitalizeseed.com/) http://bit.ly/BHCPatreon FREE DATA FOR A YEAR WITH YELLOWSTONE.AI Purchase Yellowstone trail cameras from anywhere at MSRP pricing. https://yellowstone.ai/products/y2-camera?Title=Default+Title Create your account under the Yellowstone Command Center app and add a credit card, (it won't be charged) Before activating your data plan, you must fill out the referral form so the Yellowstone team can apply your free data credit. https://yellowstone.ai/friend USE CODE: BHC for the discount Download the Yellowstone Command Center app and activate your data plans before September 1, 2026. Enjoy FREE Annual Unlimited data for up to 4 cameras — a $129 value per camera! *You can purchase an unlimited amount of cameras, but free annual unlimited data can apply to a maximum of 4 cameras. *Yellowstone requires a credit card to be added to your Command Center app account before activating your free data plan(s). You won't be charged for an entire year. After a year, the card on file will be charged based on whether you have active plans at that time. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, Madelyn O'Farrell and Santosh Sankar unpack the data center boom and the idea of compute as the next utility powering an “industrial renaissance.” They explore how AI models are commoditizing, shifting value to the application layer, and draw historical parallels to industrialists like Rockefeller and Carnegie in terms of capital intensity, vertical integration, and long-lived infrastructure. The discussion dives into the biggest bottleneck (access to energy and grid capacity) along with underwhelming GPU utilization, the need for better observability and efficiency, and trends like prefab “constructuring” in data center construction. They also highlight labor and skills constraints in specialty construction, tools like Record Lens to digitize field operations, and the potential for a Foxconn-style contract manufacturer for electrical equipment. The episode closes on what excites them about founders in this space: deep problem understanding, real industrial pain points, and the ambition to build in the physical economy rather than chasing AI hype. Highlights from their conversation include: Setting up Compute as a New Utility and AI Data Center Boom (0:38) Why Compute Becomes a Utility and Implications for Trillion Dollar Tech (3:50) Drawing Parallels Between AI Infrastructure and the Industrial Revolution (6:56) Capital Intensity, Supply Chains, and Long Lived Industrial Assets (7:50) Financing Data Centers Like Power Plants and Identifying Key Bottlenecks (11:44) Energy Queue, Grid Constraints, and Alternative Generation Opportunities (12:25) Efficiency, Grid Utilization, and Rising Importance of Operational Arbitrage (15:13) Utilization, ROI vs. Dark Capacity, and Lessons from the Dot Com Era (21:23) Constructuring Trend and Prefab Manufacturing for Data Centers (26:16) Record Lens and AI Native Project Management for Grid Scale Construction (29:24) Idea of a Foxconn Model for Electrical Equipment Manufacturing (32:47) Standardization, Certification, and Cyber Risk in Grid Infrastructure (36:22) Founder Traits, Industrial Ambition, and Solving Top Three Customer Problems (38:00) Gold Rush Dynamics, Real Pain Points, and Building in the Physical Economy (41:31) FInal Thoughts and Takeaways (42:42) Dynamo Ventures is a venture firm backing founders upgrading the physical economy. As intelligence moves into critical infrastructure and technology collides with physics, industry is entering a new era of transformation - the industrial renaissance. Born from the dirt and grit of supply chains and shaped by operations, not spreadsheets, Dynamo focuses on the complex realities of building in the real world. We invest in companies transforming infrastructure, manufacturing, logistics, transportation, and the systems that power global commerce. Dynamo works closely with founders who combine ambition with a bias to action, bringing a builder mindset to venture capital through deep operational insight, systematic pressure-testing and hands-on partnership. Our purpose is simple: to back the relentless shaping the industrial renaissance. Learn more at www.dynamo.vc Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Importance of focusing on key metrics like PPR points per touch and big time run rate How these metrics predict a player's NFL success and fantasy value Examples of top players meeting these thresholds Limitations of relying on single metrics Practical approach to player evaluation using minimal data Chapters   00:00 Introduction and episode overview 00:27 Why keep it simple with metrics 01:24 Key metrics for evaluating running backs 02:52 The significance of PPR points per touch 03:48 Big time run rate and explosiveness 05:45 Top players meeting thresholds in college 07:05 Analyzing the list of top college running backs 09:01 Thresholds for potential NFL studs 13:19 Lower-tier players and red flags 19:30 Players below thresholds and concerns 23:16 Players who barely meet thresholds 30:57 Efficiency and future potential 34:23 Summary: Using metrics to predict success     Subscribe to The Devy Devotional on Apple Podcasts, Spotify, or wherever you listen. Leave a 5-star review and help more dynasty managers find the show.Follow us on X: @DevyDevotional Hosts: John Arrington (@DynastyCoachA), Aaron Wilcox (@AaronWilcox86), Andy Starr (@AstarrFF) Watch live episodes on the Gridiron Ratings YouTube channel. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

See what the team at The Successful Bookkeeper has on right now → Nancy Benet, CPA, fractional CFO, and founder of Fix It Accounting, joins Michael Palmer for a conversation that is equal parts hard-won life lessons and practical bookkeeping wisdom. If you work with small business owners — or you are one — this episode will give you a sharper eye for the financial warning signs that show up long before a crisis does. Chapters [00:00] Nancy's Origin Story Begins [02:28] Five Failed Businesses, $650K in Debt [06:08] Robbing Business Cash Flow [08:40] Opening Up and Taking Responsibility [12:28] Hiring Before You're Ready [15:30] Hidden Costs of a New Hire [18:00] Building the Firm From Scratch [22:00] Advisory Work and a $600K Win [24:30] Finding Clients Today [27:00] Using AI With Clients From Rock Bottom to Running a Firm Nancy's path to accounting was anything but straight. After leaving a CPA firm, failing at five businesses with her husband, and waking up at 45 with four children, no income, and $650,000 in debt, she had no obvious next step. What she did have was her CPA license and a willingness to start over. She landed a lease on a building the day before her intended business deal fell apart, and instead of walking away, she decided to fill that space herself. "I think that was probably the moment in my life when I stopped being a victim and I started taking responsibility," she says. That shift — from blame to ownership — gave her the confidence to hustle her way to a real client base. The Hidden Cost of Robbing Business Cash One of the most common mistakes Nancy sees — and lived herself — is pulling cash out of the business too early to fund a lifestyle the business hasn't earned yet. Big houses, expensive vehicles, family payroll that isn't justified by revenue: the money leaks out gradually and owners are often blindsided. Her advice is direct: keep three to six months of business expenses in the bank at all times, and live below your means in the early years. "That is what leads to problems later on," she says. "You just don't realize." Fix the Systems Before You Hire When clients come to Nancy wanting to add staff, her first question is whether they've made their existing operation as efficient as possible. Hiring looks like growth, but without the cash or the systems to support it, it just accelerates the leak. She walks clients through the full cost picture — not just salary, but holiday pay, absenteeism, the time spent fixing errors, and a training curve that can stretch a full year even for experienced hires. "A lot of newer businesses are not efficient at all," she says. "How can we address your systems first before you go into hiring, especially if you can't afford it?" When Clients Won't Listen — and What to Do Bookkeepers and accountants often see red flags that clients aren't ready to act on. Nancy is candid about the limits of what advisors can control: you can raise the concern, frame it carefully, and ask if they're open to a conversation, but you can't force a decision. What you can do is stay consistent and keep the door open. The clients who are willing to be guided, she finds, are also the ones most likely to grow — and to ask for the deeper advisory work that makes the relationship genuinely valuable. Building Advisory Services and Using AI Nancy started offering CFO-style work after a quick conversation with a client helped him decide to buy two pieces of equipment instead of paying off a loan — a choice that increased his net income by $600,000 the following year. That moment convinced her the advisory layer was worth building intentionally. On the technology front, her firm now uses a private AI instance to compare monthly financials year-over-year, then distills the output down to three bullet points for clients. "The client can't comprehend" a long analysis, she notes, so keeping it tight opens the door to a deeper conversation rather than closing it. Links mentioned Fix It Accounting — Nancy Benet's CPA and fractional CFO firm CareerSource — free hiring and skills-testing resource for business owners (search your state's local CareerSource office) Pure Bookkeeping — episode sponsor The Successful Bookkeeper — show resources and guest information About the guest Nancy Benet is a CPA, fractional CFO, and the founder of Fix It Accounting, based in Florida. She built her firm from the ground up after overcoming significant personal and financial setbacks, starting with IRS problem resolution and growing into a full-service accounting, bookkeeping, and business advisory practice. Nancy works with small business owners to help them understand their numbers, make smarter financial decisions, and build businesses that actually support the life they want. About the hostMichael PalmerMichael Palmer is the host of The Successful Bookkeeper podcast and co-founder of Pure Bookkeeping and The Successful Bookkeeper. He started this work because of his father — a brilliant electrical contractor who worked twice as hard as he should have had to, because nobody on the financial side was in his corner. That gap is what The Successful Bookkeeper exists to close. His view: bookkeepers are the most undervalued force in small business — and every bookkeeper who builds a real business changes two families: theirs, and their clients'.

Digital surgery has morphed from buzzword into ubiquitous high-impact technology. In this episode, the BTK MIS team steps into the rapidly evolving ecosystem of cameras, sensors, robotics, and software that are turning surgical operations into structured, analyzable datasets. From AI segmentation to real-time telepresence, don't miss this opportunity to catch up on what's new and what's next, what's exciting and what's terrifying, just on the horizon of your surgical career. Hosts: ·      James Jung, MD, PhD, Assistant Professor of Surgery, Duke University·      Jacob Greenberg, MD, EdM, MIS Division Chief and Vice Chair for Education, Duke University·      Zachary Weitzner, MD, Minimally Invasive and Bariatric Surgery Fellow, Duke University, @ZachWeitznerMD·      Joey Lew, MD, MFA, Surgical resident PGY-3, Duke University, @lew__actuallyLearning Goals: By the end of this episode, listeners will be able to:·      Define “digital surgery” and describe its core components, including video capture, instrumentation, robotics, data analytics, and system connectivity. ·      Discuss current and emerging applications of digital surgery in surgical education, including video-based self-assessment, procedural segmentation, and structured feedback models. ·      Describe how digital tools can be used to improve OR efficiency and resource utilization, including instrumentation usage, operative time analysis, and workflow optimization. ·      Summarize current telepresence and teleproctoring technologies and their clinical use cases, including augmented reality–based collaboration across institutions and geographic boundaries. ·      Identify key ethical, legal, and operational challenges in digital and tele-surgery, including credentialing, cross-state licensure, liability, and trust in remote expertise. ·      Discuss how digital surgery may reshape surgical training and competency-based assessment, including implications for autonomy, deliberate practice, and feedback frequency. ·      Recognize future directions of digital surgery integration, including predictive analytics, simulation using patient-specific imaging, and AI-assisted intraoperative decision support.References:  Balvardi S, Semsar-Kazerooni K, Kaneva P, et al. Validity of video-based general and procedure-specific self-assessment tools for surgical trainees in laparoscopic cholecystectomy. Surg Endosc. 2023;37(3):2281-2289. doi:10.1007/s00464-022-09466-6 [https://pubmed.ncbi.nlm.nih.gov/36307525/] Hospital Utilizes Intraoperative Idle Time Metrics to Improve Surgical Safety and Efficiency. Theator | The Surgical Intelligence Company. Accessed March 20, 2026. https://theator.io/customer-story/hospital-utilizes-intraoperative-idle-time-metrics-to-improve-surgical-safety-and-efficiency/ Campbell KK, Abreu AA, Zeh HJ, et al. Using OR Black Box Technology to Determine Quality Improvement Outcomes for In-situ Timeout and Debrief Simulation. Ann Surg. 2026;283(1):122. doi:10.1097/SLA.0000000000006438 [https://pubmed.ncbi.nlm.nih.gov/38317208/] Al Abbas AI, Meier J, Daniel W, et al. Impact of team performance on the surgical safety checklist on patient outcomes: an operating room black box analysis. Surg Endosc. 2024;38(10):5613-5622. doi:10.1007/s00464-024-11064-7 [https://pubmed.ncbi.nlm.nih.gov/39069926/] Proximie's telepresence platform helps surgeons across the globe grow and exchange experience in real-time. World Health Expo Insights. Accessed March 20, 2026. https://www.worldhealthexpo.com/insights/telemedicine/proximie-s-telepresence-platform-helps-surgeons-across-the-globe-grow-and-exchange-experience-in-real-time Hassan AE, Desai SK, Georgiadis AL, Tekle WG. Augmented reality enhanced tele-proctoring platform to intraoperatively support a neuro-endovascular surgery fellow. Interv Neuroradiol. 2022;28(3):277-282. doi:10.1177/15910199211035304 [https://pubmed.ncbi.nlm.nih.gov/34538166/] SAGES Digital Surgery Working Group; Ali JT, Yang G, Green CA, Reed BL, Madani A, Ponsky TA, Hazey J, Rothenberg SS, Schlachta CM, Oleynikov D, Szoka N. Defining digital surgery: a SAGES white paper. Surg Endosc. 2024 Feb;38(2):475-487. doi: 10.1007/s00464-023-10551-7. Epub 2024 Jan 5. PMID: 38180541. [https://pubmed.ncbi.nlm.nih.gov/38180541/] Please visit https://behindtheknife.org to access other high-yield surgical education podcasts, videos and more.  If you liked this episode, check out our recent episodes here: https://behindtheknife.org/listenBehind the Knife Premium: https://behindtheknife.org/premiumOral Board Review: https://behindtheknife.org/oral-boardOral Board Simulator: https://behindtheknife.org/oral-board/simulatorGeneral Surgery Oral Board Review Course: https://behindtheknife.org/premium/general-surgery-oral-board-reviewTrauma Surgery Video Atlas: https://behindtheknife.org/premium/trauma-surgery-video-atlasDominate Surgery: A High-Yield Guide to Your Surgery Clerkship: https://behindtheknife.org/premium/dominate-surgery-a-high-yield-guide-to-your-surgery-clerkshipDominate Surgery for APPs: A High-Yield Guide to Your Surgery Rotation: https://behindtheknife.org/premium/dominate-surgery-for-apps-a-high-yield-guide-to-your-surgery-rotationVascular Surgery Oral Board Review Course: https://behindtheknife.org/premium/vascular-surgery-oral-board-reviewColorectal Surgery Oral Board Review Course: https://behindtheknife.org/premium/colorectal-surgery-oral-board-reviewSurgical Oncology Oral Board Review Course: https://behindtheknife.org/premium/surgical-oncology-oral-board-reviewCardiothoracic Oral Board Review Course: https://behindtheknife.org/premium/cardiothoracic-surgery-oral-board-reviewOBGYN Oral Board Review Coures: https://behindtheknife.org/course/obgyn-oral-board-reviewEPA Playbook: https://behindtheknife.org/course/epa-playbookSurgical Instrument Flashcards: https://behindtheknife.org/course/surgical-instrument-flashcardsABSITE Review: https://behindtheknife.org/course/absite-2026-exam-reviewDownload our App:Apple App Store: https://apps.apple.com/us/app/behind-the-knife/id1672420049Android/Google Play: https://play.google.com/store/apps/details?id=com.btk.app&hl=en_US

Playing the Long Game: Lessons from a HUD Specialist and a Soft Serve Stand I sat down with Gemma Geldmacher, Managing Director of Mortgage Banking at Berkadia, to talk about her successful career and her blog — Playing the Long Game: Hard Lessons Through Soft Serve. It's the story of watching her parents immigrate from Australia in 1984 with three kids, buy a local community ice cream shop, and the lessons that came with it. The business lessons inside that shop can help all of us in business and life. Here are a handful of the lessons Gemma and I talked about: * Efficiency is an act of love. * The universe may have plans you didn't expect. * Consistency isn't a tactic. It's a legacy. * The partner you choose changes everything. * Showing up isn't optional. * The rigid path is a trap. * Someone saw it before you did. * Legacy is built in the ordinary moments. * Reflection is a competitive advantage. * AI won't replace you. Accepting the output will. --- Gemma is a managing director at Berkadia where she has been a loan originator for 10 years. Prior to that she had over eight years experience underwriting and screening commercial real estate loans. Connect with Jon Dwoskin: Twitter: @jdwoskin Facebook: https://www.facebook.com/jonathan.dwoskin Instagram: https://www.instagram.com/thejondwoskinexperience/ Website: https://jondwoskin.com/LinkedIn: https://www.linkedin.com/in/jondwoskin/ Email: jon@jondwoskin.com Get Jon's Book: The Think Big Movement: Grow your business big. Very Big! Connect with Gemma Geldmacher: Website: www.berkadia.com LinkedIn: https://www.linkedin.com/in/gemmageldmacher Article: https://www.linkedin.com/pulse/playing-long-game-hard-lessons-through-soft-serve-gemma-geldmacher-q05he/?  Bio: https://www.berkadia.com/people-and-locations/people/gemma-geldmacher  *E - explicit language may be used in this podcast.

If AI can do what I do, does my life still matter? That's the million-dollar question we're diving into today, and trust me, it's not just some economic crisis; it's a full-on existential crisis of calling. The episode unpacks how our identities and worth don't hang on job titles or paychecks, but on being created in God's image and invited into relationship with Jesus. We're tackling this topic with a thoughtful Christian lens—because, let's face it, panicking over technology is so last year. Instead, we explore how AI might change our work, but it can never touch the divine call on our lives. So, whether you're feeling lost in a sea of tech or just trying to figure out what it means to be human in a digital world, we've got your back.Takeaways:If AI can do my job, it's not just about economics; it's a deeper crisis of calling that we can't ignore.Human dignity isn't tied to our productivity; it comes from being made in God's image, and that's what truly matters.Christian vocation exists beyond employment; our worth is rooted in our identity as children of God, not our job titles.AI may shift the tasks we perform, but it can never cancel the divine call on our lives or diminish our worth.Efficiency isn't the highest Christian good; some work requires our presence and love, which AI simply can't replicate.The church must be a community where we recognize each other's gifts and purpose, especially in times of job transition.Links referenced in this episode:discovertherefinery.orgCompanies mentioned in this episode:Missional Church PlantingHearth Commonsdiscovertherefinery.orgThe International Labor OrganizationAIAlright, let's talk AI and what it means for us regular folks. This isn't just another tech talk; it's a wrestling match with our very essence as humans. If machines can handle our tasks, what does that mean for our purpose? Dr. Skinner leads us down a path that reminds us that our worth isn't tied to our productivity or our job title. Instead, it's rooted in our calling from God. Forget the panic about technology; let's approach AI with a thoughtful Christian lens. We explore how technology can help minimize the grunt work but shouldn't strip away our responsibilities or the human touch that's so vital. This episode challenges us to think deeply about the implications of AI on our identity and our Christian vocation, reminding us that while our jobs might change, our divine calling does not. Grab your headphones and join the conversation!This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacyOP3 - https://op3.dev/privacy

Amy shares a story that she's never told before of an unexplained time where she ended up in a wheelchair and has no memory of it. We talked to the show member who left the group chat and found out the real reason why. We find out why their feelings were hurt and Bobby helps them work through it. It turns into a great moment of understanding. We also get some behind the scenes as Scuba Steve ranks each member of the show on who replies to the fastest. Number 1 was a shock to us all.See omnystudio.com/listener for privacy information.