Podcasts about Shopping

While Auntie Su is traveling, Auntie Ku is co-piloting with friend of the show Matt McConkey. But worry not Carters, his uncle credentials are certified by the highest in the land: Kulap's daughter Eme. It's basically a family get-together, as they're joined by husband of the show Scott Aukerman. He's making a pit stop during his Comedy Bang! Bang! tour. He gives us the lowdown on the 35-stop tour, filled with a rotating cast of special guests. Scott also shares a cart full of superheroes and main characters you must Add to Queue. Plus, why he's celebrating his newfound foot freedom. We have a website! Sign up to find out what's happening next with the Aunties at ADDTOCART.WORLD.Please note, Add To Cart contains mature themes and may not be appropriate for all listeners. To see all products mentioned in this episode, head to @addtocartpod on Instagram. To purchase any of the products, see below. Scott is writing comics! Read his work in the Spiderman Unlimited Comics, issues #39 - 42. Read it on the Marvel Unlimited appKu is our hero – literally. The DC character Katharsis is based on her. Falling In Love On The Path to Hell is a comic about a gunslinger and samurai falling in love in the afterlife Doctor Who is BACK! Scott is loving the new Doctor and his companion in Season 14.The Aukerman-Vilaysack household is split on Furiosa, but it's worth a watchFind out when Comedy Bang! Bang! will be in your city hereSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

There's a James Bond shaped hole in Hollywood right now. Is Amazon gonna do something about this or not? Paul Mescal, Callum Turner, and Jacob Elordi are being named as possible options. The Further Misadventures of Cliff Booth is coming to IMAX before you can watch it on Netflix. A full moon eclipse tonight! Bob is channeling the supernatural in hopes of good weather for her wedding. A woman asked the internet about her tattoo, and they told her the hard truth. Shopping malls are charging for good parking. Is this a good way to get people to come to the stores? OnlyFans says over 5,000 creators are millionaires.

Hour 1: Bob's Movie Club presents Four Weddings and a Funeral (1994). Bob is fired up this morning, but it's not because she loved Hugh Grant and Andie MacDowell's love story. The British humor doesn't disappoint if you're looking for something ridiculous. Did Vinnie even finish it? Let's discuss! Then, Vinnie is overwhelmed by the footage of catastrophic flash floods in Nepal and Tibet. On the bright side, Pokemon will be bringing joy to downtown San Francisco this weekend. Chat GPT can now text for you. Hour 2: Remembering Tim Curry and Jaye P. Morgan of The Gong Show today. Hey Buzzfeed, it's not cool to do a death watch! These are older celebrities that must be protected at all costs. Survivor season 51 has a LOT to live up to. Just a reminder, Sarah is a bad person. The real outrage is over Jed York wearing Yankees gear. Vinnie's daughters are tap dancing and making our hearts warm. Mom groups are at it again. Is it wrong to try and control everything around you? Hour 3: There's a James Bond shaped hole in Hollywood right now. Is Amazon gonna do something about this or not? Paul Mescal, Callum Turner, and Jacob Elordi are being named as possible options. The Further Misadventures of Cliff Booth is coming to IMAX before you can watch it on Netflix. A full moon eclipse tonight! Bob is channeling the supernatural in hopes of good weather for her wedding. A woman asked the internet about her tattoo, and they told her the hard truth. Shopping malls are charging for good parking. Is this a good way to get people to come to the stores? OnlyFans says over 5,000 creators are millionaires. Hour 4: Harry Styles is taking over Madison Square Garden until further notice, and opening night surprised fans. Thousands of Dolly Parton fans have pilgrimaged to Nashville. Maybe they know about her ELEVEN world records! An old clip of Julia Roberts sharing what she learned from Dolly is going viral. Vinnie is wishing the Burning Man community good luck. A moment for Judy, the 66-year old skateboarding superstar.

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Send Dr. Li a text here. Please leave your email address if you would like a reply, thanks.In this episode, Dr. Christine Li kicks off a two-part series exploring our complicated relationship with clutter. She delves into why clutter is so toxic in our lives, examining how our upbringing trains us to accumulate and attach meaning to our possessions. Dr. Christine Li unpacks the emotional toll clutter takes, from feelings of loss, guilt, and shame, to the depletion of our energy and coping abilities. Whether you struggle with clutter or know someone who does, this episode offers empathy, insight, and the first steps toward reclaiming power over your space and your well-being.Timestamps00:00:00 Introduction to the clutter episode and double series context00:00:55 Dr. Christine Li introduces herself and the podcast purpose00:01:37 Series on relationship with clutter begins; audience invitation00:02:17 Reflecting on learning to let go and cultural accumulation bias00:04:34 Accumulation impulses and their emotional roots00:05:06 The Minimalists' comedian clip on “the feeling of wanting”00:06:00 Shopping excitement, anticipation, and the letdown of possession00:07:14 Wanting vs. clutter, life phases, and obsolescence00:08:41 What to do with clutter and complexity of the issue00:09:44 Three categories of why clutter is a problem00:09:50 Clutter makes us think of loss: money, time, energy, and security00:13:06 The challenge of sentimental clutter and emotional attachments00:15:07 Clutter drains energy; introduction to the Zeigarnik effect00:16:45 Unfinished business and how clutter disrupts focus00:17:46 Clutter generates guilt and shame; definitions and distinctions00:19:18 Shame, social implications, gender differences, and domestic pressure00:21:07 Guilt, shame, and loss prevent action on clutter00:22:08 Preview of next episode and encouragement to take small decluttering steps00:23:17 Invitation to join upcoming decluttering week event00:24:19 Episode closing and listener call to subscribe or connectTo get the free download that accompanies this episode, go to: https://maketimeforsuccesspodcast.com/consistencyTo sign up for the Waitlist for the Simply Productive Program, go to: https://maketimeforsuccesspodcast.com/SPFor more information on the Make Time for Success podcast, visit: https://www.maketimeforsuccesspodcast.comGain Access to Dr. Christine Li's Free Resource Library -- 12 downloadable tools and templates to help you bypass the impulse to procrastinate: https://procrastinationcoach.mykajabi.com/freelibraryTo work with Dr. Li on a weekly basis in her coaching and accountability program, register for The Success Lab here: https://www.procrastinationcoach.com/labConnect with Dr. Christine LiWebsite: https://www.procrastinationcoach.comFacebook Group: https://www.facebook.com/groups/procrastinationcoachInstagram: https://www.instagram.com/procrastinationcoach/TikTok: https://www.tiktok.com/@procrastinationcoachThe Success Lab: https://maketimeforsuccesspodcast.com/labSimply Productive: https://maketimeforsuccesspodcast.com/SP

Welcome to Nerd Alert, a series of special episodes bridging the gap between marketing academia and practitioners. We're breaking down highly involved, complex research into plain language and takeaways any marketer can use.In this episode, Elena and Rob explore how anger changes the way people shop. They break down new research showing angry buyers move faster, skip the safe middle option, and end up more satisfied with what they choose.Topics covered:   [02:00] "The Unique Role of Anger Among Negative Emotions in Goal-Directed Decision Making" [03:00] Does anger lead to better or worse buying choices? [04:00] The compromise effect and the safe middle option [05:00] Angry shoppers click less and decide faster [06:00] Are angry buyers happier with their choices? [07:00] Turning anger appeals into action [08:00] How brands can use a "villain" to tap into emotion  To learn more, visit marketingarchitects.com/podcast or subscribe to our newsletter at marketingarchitects.com/newsletter.  Resources: Khan, U., DePaoli, A., & Maimaran, M. (2019). The unique role of anger among negative emotions in goal-directed decision making. Journal of the Association for Consumer Research, 4(1), 64–76. https://doi.org/10.1086/701028  Get more research-backed marketing strategies by subscribing to The Marketing Architects on Apple Podcasts, Spotify, or wherever you listen to podcasts. 

Ramit unpacks how to stop overspending, stay out of debt, and start building wealth as this couple confronts the spending habits they thought they had already fixed. Three years ago, Mason and Becca finally confronted a financial reality they had been avoiding. Despite good careers and the appearance of success, they had accumulated nearly $50,000 in credit card debt. They cut back hard, aggressively paid it down, sold their house, and moved to Florida. Now they have around $100,000 from the home sale sitting in savings, but they're worried the same habits that got them into debt are starting to creep back in. They still don't properly track their spending. Shopping, expensive date nights and a large “miscellaneous” category make it difficult to see where their money is actually going, while Mason experiments with day trading and considers ideas for generating passive income. On paper, they're doing far better than they realize: they have around $204,000 invested, $124,000 in savings, and a net worth of roughly $326,000. But without changing how they spend and manage their money, Ramit sees a real risk of them falling back into debt. Ramit helps them figure out what comes after getting out of debt: how to stop mindless spending without giving up the things they love, save and invest intentionally, and start building real wealth. They rethink their plans for an $800,000 dream home, confront the scarcity they both grew up with, and discover how increasing their income and investing more could completely transform their financial future. In this episode, we uncover: How Mason and Becca built nearly $50,000 in credit card debt The conversation that finally forced them to change their spending Why they used a 401(k) loan to aggressively pay down debt How selling their house left them with around $100,000 in cash Why having that much money makes Becca anxious Why they're scared of slipping back into their old spending habits How shopping, expensive date nights, and impulse purchases added up Why they still don't properly track where their money goes How their $3,000 Disney annual passes fit into their Rich Life Why Ramit sees a real risk of them falling back into debt What Ramit sees in Mason's day trading and passive income ideas Why their $326,000 net worth surprises them How Becca's childhood shaped her belief that she would never be rich How Mason grew up seeing money as stress and struggle What they want their son to learn about money Why buying an $800,000 house would require major trade-offs How Ramit helps them rebuild their Conscious Spending Plan Why increasing their income becomes the biggest lever for their future How their retirement projection jumps from around $3.1M to $4.7M How they finally become completely debt-free Chapters: (00:00:00) Introduction (00:02:45) How they built nearly $50K in debt (00:06:39) Using a 401(k) loan to escape debt (00:08:53) Selling their house leaves them with $100,000 (00:11:15) “We just swiped the card” (00:14:37) Their old spending habits start creeping back (00:16:31) They disagree about buying another house (00:24:05) Ramit reviews their financial numbers (00:31:13) Ramit digs into their 71% fixed costs (00:37:30) Day trading and the dream of passive income (00:38:46) How Becca grew up around money (00:47:50) How Mason grew up around money (00:52:43) What they want to teach their son (00:56:41) Ramit starts rebuilding their financial plan (01:07:26) Redirecting their money toward investing (01:11:59) The reality of an $800,000 dream home (01:19:18) Why earning more becomes the priority (01:24:01) Their retirement could reach $4.7 million (01:32:15) Their house timeline changes completely (01:33:25) Mason and Becca become debt-free This episode is brought to you by: Facet | As of the date of this recording, Facet is waiving the enrollment fee for new annual members, and for my audience, Facet is offering $300 into your brokerage account if you invest and maintain $5,000 within your first 90 days. Head to https://facet.com/ramit to learn more about which membership option is best for you. Offer has been extended to 12/31/2026. #FacetAd Shopify | Start your free trial at https://shopify.com/ramit Wispr Flow | Try Wispr Flow for free at https://wisprflow.ai/ramit MasterClass | For unlimited access to every class and at least 15% off any annual membership, go to https://masterclass.com/ramit If you're ready to stop putting off your money goals, Road to $100K gives you a step-by-step plan to reach your first $100,000, focus on what matters, and accelerate your timeline while building your Rich Life. Join Road to $100K at https://iwt.com/100K Connect with Ramit: • Get my new book, Money For Couples • Join my Rich Life: Road to $100K program • Download the Conscious Spending Plan • Listen to my book—now on Audible • Get my New York Times best-selling book • Get my no-numbers journal • Other episodes • Instagram • Twitter • YouTube Apply to be coached for free on this podcast at https://iwt.com/apply

It's another Boatshopping episode! Andy and August are taking a look at boat for sale, dissecting the boat and the listing. Of course, if you are a Quarterdeck member, you will have access to the video version of this podcast, and can watch the pictures of this boat and study the listing along with Andy and August! Quarterdeck members also provide listings for us to analyze! If you are not yet a member, go to quarterdeck.59-north.com, and sign up! You can even snatch a two-week free trail, watch this and tons of other videos for free! Fair winds and following seas to you, my friends, and hold fast. -- This episode is sponsored by BVI Yacht Sales, our boat broker friends from Tortola and Annapolis. Go to bviyachtsales.com to have a look at all the cool boats they have listed right now! And if you find your dream boat elsewhere, they can be your buyers broker! 

Milan Carter-Gilkey is a writer, a lover of books, and a bookseller. She writes personal essays, articles, creative nonfiction and occasionally, fiction. Her work has appeared in Huffington Post, mater mea, Mutha Magazine, the anthology All the Women in My Family Sing, and elsewhere. In this episode, Meilan and Annmarie talk about favorite books of the past, present, and future, and how to reach for a title to transport you to a world you didn't know was possible. Episode Sponsors: Octavia's Bookshelf – An independent bookstore in Pasadena, California where readers of all walks of life can enjoy our store full of books written by BIPOC writers. Octavia's Bookshelf is a place to find your new BFF inside a book, a space to find community, enjoy a cup of coffee, read, relax, find unique and specially curated products from artisans from around the world and in our neighborhood. Stop by or shop online at octaviasbookshelf.com. Porter Square Books: Boston Edition – A welcoming space to gather with neighbors, linger over books, read with kids, chat with booksellers, and feel part of the community. Whether it's an author series, book club, or regular story hour, we'll work to make it happen. Shopping with us also supports GrubStreet, one of the nation's leading creative writing centers. Learn more or shop online at portersquarebooks.com. Titles Recommended in This Episode: Their Eyes Were Watching God, by Zora Neale Hurston Are You There God? It's Me, Margaret. by Judy Blume Then Again, Maybe I Won't, by Judy Blume A Handful of Earth, a Handful of Sky: The World of Octavia Butler, by Lynell George Beloved, by Toni Morrison Sula, by Toni Morrison Song of Solomon, by Toni Morrison The Bluest Eye, by Toni Morrison Kin, by Tayari Jones Silver Sparrow, by Tayari Jones An American Marriage, by Tayari Jones The Untelling, by Tayari Jones Good Grief, Pass the Bread, Mom Is Dead, by Angela Nissel Oreo, by Fran Ross The Secret Lives of Church Ladies, by Deesha Philyaw Long Division, by Kiese Laymon Heavy, by Kiese Laymon Martyr!, by Kaveh Akbar O Sinners! by Nicole Cuffy There's Always This Year, by Hanif Abdurraqib The House on Mango Street, by Sandra Cisneros Wild Seed, by Octavia E. Butler Dawn, by Octavia E. Butler Parable of the Sower, by Octavia E. Butler The God of Small Things, Arundhati Roy Running in the Family, Michael Ondaatje Things in Nature Merely Grow, by Yiyun Li Babel: Or the Necessity of Violence: An Arcane History of the Oxford Translators' Revolution, by R. F. Kuang The Final Revival of Opal & Nev, by Dawnie Walton Good Talk, by Mira Jacob Follow Meilan Carter-Gilkey: Instagram: @meilancg Substack: @meilancartergilkey **Writing Workshops: If you liked this conversation and are interested in writing together, please consider the opportunities below. For women interested in an online Saturday morning writing circle, you can sign up here. For anyone looking for support writing their memoir, Annmarie is teaching a class here. For anyone looking for a month of accountability writing hours and workshops, learn more here. Learn more about your ad choices. Visit megaphone.fm/adchoices

Most Shopify brands use Google Ads to drive sales. The fastest-growing brands use it to build a DTC brand.Google Ads has become one of the most powerful ways for ecommerce brands to find new customers - but many Shopify stores are still optimising for short-term ROAS instead of long-term growth.In this episode, Nick explains why most Shopify brands get Google Ads wrong and how successful DTC brands use paid search, Shopping ads and customer data to build a scalable acquisition engine. Want some help with profitably scaling your Google Ads? Reach out to Nick's agency: team@spec.digital You'll learn:Why chasing ROAS alone can limit Shopify growthThe difference between buying clicks and building customersHow Google Shopping acts as your digital storefrontWhy product feeds, creative and offers matter more than everHow leading brands measure profitable customer acquisitionWhy Google Ads and retention need to work togetherNick also shares the biggest mistakes Shopify brands make with Google Ads, why better data leads to better automation and how to build a paid acquisition strategy designed for long-term brand growth.In this episode:(00:00 Why Good ROAS Doesn't Always Mean Profit(01:14) Stop Using Google Ads Just to Generate Sales(03:00) ROAS vs Customer Lifetime Value(05:12) The Snowball Effect of Repeat Customers(06:31) The 4 Metrics Shopify Brands Should Track(08:02) Fix Your Google Shopping Customer Journey(11:59) Why Product Feeds Matter So Much(14:04) How to Set a Profitable Google Ads Target(16:09) How to Increase Repeat Purchases & LTV(20:00) How AI Is Changing Google Ads(22:14) 5 Things Scaling Brands Do Differently(25:09) The Key to Profitable Google Ads ScalingFollow Winning With Shopify for practical ecommerce growth advice every Tuesday and Friday.Exclusive listener offers:Ships-A-Lot - Improve fulfilment costs and find hidden shipping margin leaks.Inventory Planner - Free seven-day inventory bootcamp.Omnisend - 30% off paid plans for three months with code WINNINGWITHOMNISEND.Yoast - 15% off Shopify and WordPress with code WWS15.506 - Extended 30-day free trial on any of their three Shopify apps with code WWS.About Winning With ShopifyWinning With Shopify is powered by Spec Digital, a PPC & SEO agency helping ecommerce brands grow through performance marketing.

Zach and Bryan discuss the idea that the Cowboys could still be looking at bringing in another running back with the latest availability of Trey Benson.

What a fun new friend of the podcast, Lauren Jones tells us all about Athos Commerce and how they are changing the way we shop online! Author and industry pro Chris Parsons joins me in finding out more about data feeds, merchandising and how the Athos Commerce AI platform converts!  Always Off Brand is always a Laugh & Learn!   eTail episodes are brought to you by TALKOOT! Go to https://talkoot.com/ to find out how you can scale your content, copy, story telling. Faster Product Content Operations for the AI Era!     FEEDSPOT TOP 10 Retail Podcast! https://podcast.feedspot.com/retail_podcasts/?feedid=5770554&_src=f2_featured_email   GUEST Lauren Jones LinkedIn: ​​https://www.linkedin.com/in/lauren-jones-944709159/ Atmos Commerce: https://athoscommerce.com/   Guest: Chris Parsons  LinkedIn: https://www.linkedin.com/in/chrisaparsons/ Retail Rewired: https://retailrewired.ca/   Huge Shout Out to everyone at eTail for the great support and giving us the place to record at their great events.    QUICKFIRE Info:   Website: https://www.quickfirenow.com/ Email the Show: info@quickfirenow.com  Talk to us on Social: Facebook: https://www.facebook.com/quickfireproductions Instagram: https://www.instagram.com/quickfire__/ TikTok: https://www.tiktok.com/@quickfiremarketing LinkedIn : https://www.linkedin.com/company/quickfire-productions-llc/about/ Sports podcast Scott has been doing since 2017, Scott & Tim Sports Show part of Somethin About Nothin:  https://podcasts.apple.com/us/podcast/somethin-about-nothin/id1306950451 HOSTS: Summer Jubelirer has been in digital commerce and marketing for over 17 years. After spending many years working for digital and ecommerce agencies working with multi-million dollar brands and running teams of Account Managers, she is now the Amazon Manager at OLLY PBC.   LinkedIn https://www.linkedin.com/in/summerjubelirer/   Scott Ohsman has been working with brands for over 30 years in retail, online and has launched over 200 brands on Amazon. Mr. Ohsman has been managing brands on Amazon for 19yrs. Owning his own sales and marketing agency in the Pacific NW, is now VP of Digital Commerce for Quickfire LLC. Producer and Co-Host for the top 5 retail podcast, Always Off Brand. He also produces the Brain Driven Brands Podcast featuring leading Consumer Behaviorist Sarah Levinger. Scott has been a featured speaker at national trade shows and has developed distribution strategies for many top brands. LinkedIn https://www.linkedin.com/in/scott-ohsman-861196a6/   Hayley Brucker has been working in retail and with Amazon for years. Hayley has extensive experience in digital advertising, both seller and vendor central on Amazon. Hayley lives in North Carolina.  LinkedIn -https://www.linkedin.com/in/hayley-brucker-1945bb229/   Huge thanks to Cytrus our show theme music "Office Party" available wherever you get your music. Check them out here: Facebook https://www.facebook.com/cytrusmusic Instagram https://www.instagram.com/cytrusmusic/ Twitter https://twitter.com/cytrusmusic SPOTIFY: https://open.spotify.com/artist/6VrNLN6Thj1iUMsiL4Yt5q?si=MeRsjqYfQiafl0f021kHwg APPLE MUSIC https://music.apple.com/us/artist/cytrus/1462321449   "Always Off Brand" is part of the Quickfire Podcast Network and produced by Quickfire LLC.

John McNellis is based in Palo Alto and has been in commercial real estate for more than 40 years. He has been focused on grocery anchored shopping centers in Northern California for most of that time. On today's show we are talking about what is working today and the fundamentals that have stood the test of time. He is also the author of the book "Making it In Real Estate", now in its third edition. To connect with John visit https://mcnellis.com/ or email him at john@mcnellis.com. ------------**Real Estate Espresso Podcast:** Spotify: [The Real Estate Espresso Podcast](https://open.spotify.com/show/3GvtwRmTq4r3es8cbw8jW0?si=c75ea506a6694ef1)   iTunes: [The Real Estate Espresso Podcast](https://podcasts.apple.com/ca/podcast/the-real-estate-espresso-podcast/id1340482613)   Website: [www.victorjm.com](http://www.victorjm.com)   LinkedIn: [Victor Menasce](http://www.linkedin.com/in/vmenasce)   YouTube: [The Real Estate Espresso Podcast](http://www.youtube.com/@victorjmenasce6734)   Facebook: [www.facebook.com/realestateespresso](http://www.facebook.com/realestateespresso)   Email: [podcast@victorjm.com](mailto:podcast@victorjm.com)  **Y Street Capital:** Website: [www.ystreetcapital.com](http://www.ystreetcapital.com)   Facebook: [www.facebook.com/YStreetCapital](https://www.facebook.com/YStreetCapital)   Instagram: [@ystreetcapital](http://www.instagram.com/ystreetcapital)  

Search and rescue efforts continue in Ukraine after Russian drone attacks kill at least 16 in a shopping centre in the city of Kryvyi Rih. President Volodymyr Zelensky called the attack on his home town cynical and despicable. Also:Donald Trump wins the latest round in an ongoing legal battle over his plans to rebuild the entire east wing of the White House; Uber is fined a huge sum under Europe's technology privacy rules; FIFA punishes Argentina for breaches of discipline; the United Nations says the ebola outbreak in the Democratic Republic of Congo is now growing exponentially; handwritten notes by the Indian independence leader, Mahatma Gandhi, sell for US$1.7m at auction; a python becomes the first snake to be given a cancer treatment normally used on humans, and Wile E Coyote takes on the American corporate giant, the Acme Corporation, in a blockbuster film.The Global News Podcast brings you the breaking news you need to hear, as it happens. Listen for the latest headlines and current affairs from around the world. Politics, economics, climate, business, technology, health – we cover it all with expert analysis and insight. Get the news that matters, delivered twice a day on weekdays and daily at weekends, plus special bonus episodes reacting to urgent breaking stories. Follow or subscribe now and never miss a moment. Get in touch: globalpodcast@bbc.co.uk Photo: (write brief description of what photo shows) Aftermath of a Russian drone attack in Kryvyi Rih. Credit: TELEGRAM/Reuters

Some of the best homemaking advice isn't new at all. Over the years, I've learned a lot from old household manuals, historical books and the way generations before us managed their homes. While I certainly love my modern conveniences, there are some old-fashioned practices that still make our household and homestead run so much more smoothly.In this episode, I'm sharing six that I still use today, including:

Rescuers in the central city of Kryvyi Rih are continuing to search for survivors of Friday's daytime drone attack on a shopping centre. We hear from the EU ambassador to Ukraine, Katarina Mathernova, about what the EU can do following a week of Russian attacks. Also on the programme: we hear about how sharks are inhaling caffeine; and the music video maker Tim Pope on his life putting rock stars on film.

Are we headed for another housing crash — or is today's market fundamentally different from 2008?On this Ask Farnoosh Friday, Farnoosh digs into the state of the housing market with insights from real estate economist Dr. Joshua Harris, Academic Director of the Fordham Real Estate Institute. While some overheated markets are already seeing prices decline, Harris explains why today's housing landscape looks very different from the run-up to the Great Financial Crisis — particularly when it comes to housing supply, lending standards and homeowner equity.Farnoosh also examines the explosive growth of Buy Now, Pay Later, which is increasingly being used not just for clothes and electronics, but for groceries, rent, utilities, medical bills and even taxes. The question she wants consumers to ask: Are you using BNPL to solve a timing problem — or an affordability problem? Because those are two very different financial challenges.Plus, shopping scams are getting dramatically harder to spot thanks to artificial intelligence. The old advice — look for typos, awkward emails and suspicious-looking websites — isn't enough anymore. Farnoosh shares the new safeguards consumers should be using to protect their accounts, passwords, loyalty points and credit cards.Then, two excellent listener questions.First, Anne is in her 40s after spending two decades moving between employers and has accumulated a collection of 401(k)s, 401(a)s, 403(b)s and a rollover IRA. Should she consolidate everything? And is it actually safer to keep retirement money spread across multiple institutions in case one brokerage fails?Farnoosh explains the important difference between diversifying your investments and diversifying your custodians, how protections such as SIPC work, and why simplifying your retirement accounts can make sense — but only after checking fees, investment choices and plan-specific benefits.Finally, a listener follows up on the new Trump Accounts for children: Why is Robinhood the sole initial brokerage and trustee? Why can't families simply choose Vanguard, Fidelity or another provider from day one? And did Robinhood somehow pay for exclusive access?Farnoosh went digging. She explains the relationship between the U.S. Treasury, BNY Mellon and Robinhood, why Robinhood's role is described as initial rather than permanent, and why families should eventually be able to transfer Trump Account assets to another eligible provider. She also examines why questions about transparency are reasonable given the enormous customer-acquisition opportunity the program represents for Robinhood.Learn more about Farnoosh's upcoming literary workshop Book to Brand. Early bird registration is now open! Hosted on Acast. See acast.com/privacy for more information.

This week, Su grapples with an invasion in her body that has altered her human state. She has traveled both back in time and to the underworld to appease her growing Third Eye. Meanwhile, Ku is falling in love with her mistakes and undergoing a radical transformation…enter Beach Mom Era.Please let us know what is YOUR Vecna on @addtocartpod, you know we love to hear about all your sores (physical and metaphysical).We have a website! Sign up at ADDTOCART.WORLD to see what we're up to.Su's Cart:Gem app for vintage lovers12 year old edit of middle aged woman's mistakes on DepopKu's Beach CartPink Purple Fish Scales Flip FlopsMalibu Womens Zuma Open Toe Sandals in olive LANE LINEN Beach Towel, 2 Pack Beach Towels Oversized, 39"x71", XL - EbonySunflow Tall Sand Dune Chair Bask Non-Aerosol SPF 50 Sunscreen: A new sunscreen, gifted by my pals Rachael and LizSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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

Fresh from D23 in Anaheim, Jim Hill and Lauren Hersey unpack what it was really like to shop the show floor, from virtual queues and coveted merchandise to fans finding creative ways around the reservation system. Lauren shares what surprised her about D23 Day at Disneyland, the new Imagineering apparel collection, and Disney's increasingly social-media-driven activations. Then Jim and Lauren turn to legendary Imagineer Joe Rohde and his new book, Floating Mountains: The Art of Imagining the Impossible, including how Rohde's work connects the Adventurers Club to Disney's later Living Character Initiative. NEWS • D23 Day at Disneyland brings massive merchandise lines, exclusive releases, and the Hatbox Ghost popcorn bucket frenzy. • Disney's D23 shopping system puts fans up against limited virtual queues for the Marketplace, Mickey's of Glendale, the Company Store, and other high-demand locations. • Lauren explains how some attendees gamed shopping reservations, paid others to hold places in line, and bought merchandise in bulk. • Disney+ and Hulu lean heavily into photo activations and social sharing across the D23 show floor. • The new Walt Disney Imagineering apparel collection turns out to be understated clothing designed by Imagineers for Imagineers - with a very unusual mobile-order shopping process. FEATURE • Jim and Lauren discuss Joe Rohde's Floating Mountains: The Art of Imagining the Impossible and what sets it apart from a traditional Disney memoir. • Rohde offers a behind-the-scenes look at Imagineering's creative process, leadership style, and the development of experiences such as the Adventurers Club. • Jim traces the roots of Disney's interactive “living characters” from the Adventurers Club back to an unusual talking vulture once installed inside Disneyland's Club 33. HOSTS • Jim Hill - X/Twitter: @JimHillMedia | Instagram: @JimHillMedia | jimhillmedia.com • Lauren Hersey - X/Twitter: @laurenhersey2 | Instagram: @lauren_hersey_ FOLLOW • Facebook: @JimHillMediaNews • YouTube: @jimhillmedia • TikTok: @jimhillmedia • Patreon: https://www.patreon.com/jimhillmedia/ SUPPORT Support the show and access bonus episodes and additional content at https://www.patreon.com/jimhillmedia. PRODUCTION CREDITS Edited by Dave Grey Produced by Eric Hersey - https://strongmindedagency.com SPONSOR Plan your next Disney or Universal vacation with Be Our Guest Vacations. Their team can help with hotels, tickets, reservations, cruises, and the details that make vacation planning easier, all at no additional planning cost. If you would like to sponsor a show on the Jim Hill Media Podcast Network, reach out today. https://www.jimhillmedia.com/sponsor/ Learn more about your ad choices. Visit megaphone.fm/adchoices

Nicky Hilton Rothschild was an It Girl long before algorithms and Instagram feeds. But for all the stories that come with growing up in one of the city's most recognizable families, the version of Nicky today is much more interested in what lasts. In this episode of Superwomen, I sat down with the socialite, designer, and NYC native to reminisce about a bygone era. Nicky gets nostalgic about legendary nightclubs like Studio 54 and Bungalow, the spontaneity of a life that wasn't constantly documented, and the lost magic of Old New York. Sex and the City creator Candace Bushnell also joins the conversation to discuss how the series impacted the city and how dating has changed since Carrie first ran into Big on a Manhattan sidewalk. If you lived through it, this one will make you miss those days. And if you weren't there, you'll wish you could've been. Episode Guide: (00:00) Nicky Hilton Rothschild loves Sex and the City (01:53) Before social media, celebrities actually went out (05:42) Why Nicky never really played the field (10:58) Finding love the old-fashioned way (12:33) How Sex and the City shaped a generation (18:49) Nicky and Paris Hilton were almost on SATC (20:48) Shopping before everything was online (25:12) Nicky's secret to a long-lasting marriage Learn more about your ad choices. Visit megaphone.fm/adchoices

Send episode requests hereYou can ask for what you want everywhere else in your life. You do it at work. You do it with your money. Then a man is sitting across from you and the words will not come out.In this episode, Christina shares how she went from having a love life she never once sat down and thought about to the most fun she has ever had dating. You'll hear what happened when she asked a man she'd been on six dates with for a $70 purse, why the change she made to her dating profile cut her likes down to a handful, and how she got to a place where a man covered $800 on her car and cleared his whole day for her before she has ever set foot in his house.Whether you're a woman who runs everything else in her life and goes quiet the second a man asks for something, or you're still waiting to be chosen, Christina's story shows what changes when you finally center yourself in your own dating life.JOIN C2C HEREInside C2C, you have 12 months of access to curriculum, weekly coaching calls, weekly workshops, daily dating support, on-demand conversation and profile reviews, and more. The investment is $3,000 one time or 6 payments of $550.Join today to stop wasting time and energy on men who leave your texts on read, and start attracting the men who send you flowers with no rhyme or reason.Follow me on Instagram for more dating gems at: @torahcents @curved2cuffed 

Amazon says more than 350 million shoppers have used Alexa for shopping over the past year, with active users nearly doubling and customer interactions increasing fivefold. Chris Walton and Jenn Hahn discuss whether Amazon is best positioned to move AI shopping beyond discovery and recommendations into actual purchasing and transactions. ▶️ Watch the full Fast Five episode here: https://youtu.be/VL3Q373gkfY

Chris Walton puts Jenn Hahn in the Fast Five lightning round, covering everything from Stella Lefty and back-to-school shopping to Little Debbie Cosmic Brownies, fantasy football picks, and plenty more retail-free fun. ▶️ Watch the full Fast Five episode here: https://youtu.be/VL3Q373gkfY

Jodi's off on a girls' weekend so we're wondering: is shopping actually a hobby, therapy, or just a chore? Plus - the rule to help you stop overthinking. And, 3 Things to Know Today. See omnystudio.com/listener for privacy information.

A look at where and when to tip as Americans report feeling “tipping fatigue” amidst rising prices. Also, catching up with rising star Madelyn Cline ahead of the fifth and final season of “Outer Banks”. Plus, Shop Today's second annual Hair Awards, a look at the best products for every type, texture, and color. And, an early start to spooky season, tricks for the ultimate Halloween.     Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to the first of our three-part series on BACK TO SCHOOL FASHION, which is really just an excuse for us to talk about style and clothes. Today, the incredible Sophie Strauss — aka, the stylist for regular people — joins us to answer your questions about finding that one piece that will solve all your fashion problems, letting go of the fantasy capsule wardrobe, what you're actually asking when you say "can I pull this off?," and, of course, our most popular question by far: how to figure out your style in your late 30s/40s. Sophie is the ideal Culture Study guest; I can't wait for you to get to know her (and her style philosophy). This is an episode about style that is not meant to make you feel bad about yours — what a revelation! From Sophie Strauss's Style School Newsletter Thanks to the sponsors of today's episode: Save 20% Off Honeylove by going to honeylove.com/CULTURE Thanks to Article for sponsoring this podcast! Article is offering our listeners $50 off your first purchase of $100 or more. To claim, visit ARTICLE.COM/CULTURE and the discount will be automatically applied at checkout. Get 40% off select Lola Blankets products at Lolablankets.com by using code CULTURE at checkout. Experience the world's #1 blanket with Lola Blankets. Ready to upgrade your eyewear to something functional, fashionable, fun, and affordable? Head to goodr.com/CULTURE to claim $10 off your first order. Show Notes: Follow Sophie Strauss on Instagram here Get access to the Style School Classes Sophie references here The Big Undies interview with Sophie ("that's not style, that's marketing") The Virginia Sole-Smith work I reference: Are Capsule Wardrobes Just for Thin People? We're currently looking for your questions on the following topics: HOW TO FIGHT! Priya Parker has a new book on how to actually disagree (fruitfully!) with others, so this is your opportunity to ask all those questions about the people in your life who shut down when you disagree / escalate unnecessarily / immediately throw straw-men your way ('people in your life' can also include you, of course) WTF IS GOING ON WITH DUDES/ DESPERATE MASCULINITY RIGHT NOW with Jon Ronson. You can read about his incredible new book here, but the gist is: the old order of masculinity is collapsing, and some very weird behaviors are emerging amidst that collapse. What have you noticed, what's alarming, what are your theories about how dudes are dude-ing the way they are — let's go. FINANCIAL INTERDEPENDENCE with Maria Melchor, author of Always Have Enough. Instead of aiming for financial independence, what does it mean to try and build a sort of collective wealth for your family and community? How does the idea of wealth operate differently for first-generation immigrants — and what lessons should everyone adopt? All things CODE-SWITCHING with Ari Shaprio and Audie Cornish (how do you change your voice, your posture, your demeanor, your subject areas, the way you talk about reality television, whatever, depending on layered context) Anything you need advice for/want musings about for the AAA segment. You can ask about anything — it's literally the name of the segment.     Join the ranks of paid subscribers and get bonus content, access to the discussion threads, ad-free episodes, and the knowledge that you're supporting an indie pod trying to make its way in the world.Got a question to submit, a prompt for Ask Anne Anything, or an idea for a future episode? Tell us here.Catch up on everything else happening in the Culture Study universe here.Transcripts will be available here within 24 hours of publishing. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

On today's podcast, we discuss why shopping still feels so disconnected when brands and retailers know so much about us, what's changed most about what consumers expect from brands today, and what brands should be building now to be ready for people shopping on their own—some using AI assistants, with some purchases becoming fully agentic.   Tune in for a conversation featuring Vice President of Content and host Suzy Davidkhanian, Principal Analyst Sky Canaves, and Keith Lehman, Global Marketing Director for Digital Commerce and Retail Media at Colgate-Palmolive.   Get more insights like these with our free, industry-leading newsletters covering advertising, marketing, and commerce. Sign up at emarketer.com/newsletters   Purchase tickets and register for EMARKETER's The Future of Digital 2026 Summit here: https://www.emarketer.com/events/summit/2026-future-of-digital/?utm_source=events_page Follow us on Instagram at: https://www.instagram.com/emarketer/ For sponsorship opportunities contact us: advertising@emarketer.com For more information visit: https://www.emarketer.com/advertise/ Have questions or just want to say hi? Drop us a line at podcast@emarketer.com For a transcript of this episode click here: https://www.emarketer.com/content/podcast-why-shopping-feels-disconnected-with-colgate-palmolive-reimagining-retail © 2026 EMARKETER   Awin is the all-in-one platform for smarter partner marketing. With award-winning technology, expert support and a global network of 1 million partners, Awin helps brands discover, manage and scale high-performing partnerships that drive measurable growth. Learn more.

Another great eTail conversation with a new friend to the program, Founder and CEO of AirShelf. This is where agentic shopping will be going and is here in many cases. The agents need someone to help them make their way! AirShelf is how you will be able to trust the agents to buy the right things! Find out how and how cool AirShelf is for the agentic present and future. Always Off Brand is always a Laugh & Learn!   eTail episodes are brought to you by TALKOOT! Go to https://talkoot.com/ to find out how you can scale your content, copy, story telling. Faster Product Content Operations for the AI Era!     FEEDSPOT TOP 10 Retail Podcast! https://podcast.feedspot.com/retail_podcasts/?feedid=5770554&_src=f2_featured_email   GUEST Ashish Piplani LinkedIn: https://www.linkedin.com/in/ashishpiplani/ AirShelf Website: https://airshelf.com/   Huge Shout Out to everyone at eTail for the great support and giving us the place to record at their great events.    QUICKFIRE Info:   Website: https://www.quickfirenow.com/ Email the Show: info@quickfirenow.com  Talk to us on Social: Facebook: https://www.facebook.com/quickfireproductions Instagram: https://www.instagram.com/quickfire__/ TikTok: https://www.tiktok.com/@quickfiremarketing LinkedIn : https://www.linkedin.com/company/quickfire-productions-llc/about/ Sports podcast Scott has been doing since 2017, Scott & Tim Sports Show part of Somethin About Nothin:  https://podcasts.apple.com/us/podcast/somethin-about-nothin/id1306950451 HOSTS: Summer Jubelirer has been in digital commerce and marketing for over 17 years. After spending many years working for digital and ecommerce agencies working with multi-million dollar brands and running teams of Account Managers, she is now the Amazon Manager at OLLY PBC.   LinkedIn https://www.linkedin.com/in/summerjubelirer/   Scott Ohsman has been working with brands for over 30 years in retail, online and has launched over 200 brands on Amazon. Mr. Ohsman has been managing brands on Amazon for 19yrs. Owning his own sales and marketing agency in the Pacific NW, is now VP of Digital Commerce for Quickfire LLC. Producer and Co-Host for the top 5 retail podcast, Always Off Brand. He also produces the Brain Driven Brands Podcast featuring leading Consumer Behaviorist Sarah Levinger. Scott has been a featured speaker at national trade shows and has developed distribution strategies for many top brands. LinkedIn https://www.linkedin.com/in/scott-ohsman-861196a6/   Hayley Brucker has been working in retail and with Amazon for years. Hayley has extensive experience in digital advertising, both seller and vendor central on Amazon. Hayley lives in North Carolina.  LinkedIn -https://www.linkedin.com/in/hayley-brucker-1945bb229/   Huge thanks to Cytrus our show theme music "Office Party" available wherever you get your music. Check them out here: Facebook https://www.facebook.com/cytrusmusic Instagram https://www.instagram.com/cytrusmusic/ Twitter https://twitter.com/cytrusmusic SPOTIFY: https://open.spotify.com/artist/6VrNLN6Thj1iUMsiL4Yt5q?si=MeRsjqYfQiafl0f021kHwg APPLE MUSIC https://music.apple.com/us/artist/cytrus/1462321449   "Always Off Brand" is part of the Quickfire Podcast Network and produced by Quickfire LLC.  

Have you ever found yourself shopping because you're stressed, anxious, lonely, bored, or overwhelmed? You're not necessarily shopping because you need something. You may be shopping because you're trying to change the way you feel. And while buying something might give you a temporary hit of relief, it doesn't make the emotion go away. Eventually, that feeling comes back—often accompanied by guilt, shame, clutter, financial stress, or regret. And that can send you right back into the emotional shopping cycle. In this episode, I'm sharing a coaching call from inside my Overcoming Overspending community where we dive into why we shop our feelings away—and, more importantly, how to actually feel an uncomfortable emotion without needing to escape it. We cover: Why shopping can become a form of emotional avoidance The hidden emotional "promise" behind the things you buy Why consumer culture teaches us that buying something will make us feel better The emotional shopping avoidance loop and why shopping can actually make the emotion you're avoiding worse The difference between running from your emotions like a sheep and facing them like a buffalo The 3 A's of emotional resilience: Awareness, Allowance, and Autonomy A practical 4-step process for actually feeling your feelings instead of shopping them away Why building your emotional capacity is one of the most powerful ways to change your spending The goal isn't to never feel bored, lonely, anxious, ashamed, or overwhelmed. It's to become someone who can say: I can feel this. I can handle this. And I don't need to buy something to make it go away.   Work with Paige: Watch my free on-demand masterclass: The 3-shifts to go from emotional shopper to financially empowered Join the waitlist for the Overcoming Overspending Book - Out in early 2027 Join Overcoming Overspending HERE   Connect with Paige Online: Her Website IG: @overcoming_overspending TikTok: @overcoming_overspending Subscribe to Paige's YouTube Channel

Amazon sellers have spent years optimizing for keywords and traditional search. That is still important, but the way customers discover products on Amazon is changing quickly. In this episode of The Opportunity Podcast, we sit down with Jon Tilley, founder of ZonGuru, to unpack Amazon's latest AI developments and what they mean for sellers. Amazon has moved away from Rufus and is doubling down on Alexa for Shopping, alongside its newer Cosmo algorithm. Instead of simply returning search results, Amazon can now use conversations, customer profiles, product data, and recommendations to help shoppers make decisions. For sellers, this creates a new layer of optimization. We discuss why keywords still matter, but why sellers also need to think about the questions customers ask, how their products are used, who they are best suited for, and which products they complement. Jon also explains why simply asking ChatGPT or Claude to rewrite a listing is not enough, how first-party Amazon data can improve AI optimization, and why sellers need to balance AI readability with human conversion. We also look at the wider implications of AI for Amazon sellers, agencies, and omnichannel growth. If you want to understand where Amazon is heading and how to adapt your business before these changes become mainstream, this episode is worth a listen. Topics Discussed in this episode: 04:11 - Why Amazon is doubling down on Alexa and what that means for sellers 08:57 - Keywords vs. conversational search  14:48 - How to maximize your AI visibility on Amazon 17:43 - Understanding Cosmo's product knowledge graph  25:19 - The biggest AI optimization mistakes sellers make  27:52 - The opportunities AI unlocks for Amazon sellers 31:59 - How quickly AI optimization can impact performance  38:21 - How Amazon sellers are using AI beyond product discovery  41:00 - AI, agencies, and the changing service model  43:33 - Is AI killing agencies or helping them thrive? Mentions:  Empire Flippers Podcasts Empire Flippers Marketplace Create an Empire Flippers account Subscribe to our newsletter ZonGuru's AI Readiness Report ZonGuru's Helix Jon's LinkedIn Sit back, grab a coffee, and learn how to improve your AI visibility on Amazon.

What happens when an AI agent does your shopping — and how do you make sure it doesn't order two grills instead of one? In Part 2 of his conversation with Motley Fool CEO Tom Gardner, Mastercard CEO Michael Miebach breaks down the company's Agent Pay protocol, explains why machine-to-machine payments could transform B2B commerce, and reveals why Mastercard just acquired the world's largest stablecoin platform. He also gets into what the AI revolution really means for employment, why proprietary transaction data is Mastercard's deepest competitive moat, and how he personally stays sharp running a $500 billion company.  Host: Tom Gardner  Guest: Michael Miebach  Producers: Bart Shannon, Lauren Budabin  Disclosure: Advertisements are sponsored content and provided for informational purposes only. The Motley Fool and its affiliates (collectively, “TMF”) do not endorse, recommend, or verify the accuracy or completeness of the statements made within advertisements. TMF is not involved in the offer, sale, or solicitation of any securities advertised herein and makes no representations regarding the suitability, or risks associated with any investment opportunity presented. Investors should conduct their own due diligence and consult with legal, tax, and financial advisors before making any investment decisions. TMF assumes no responsibility for any losses or damages arising from this advertisement. We're committed to transparency: All personal opinions in advertisements from Fools are their own. The product advertised in this episode was loaned to TMF and was returned after a test period or the product advertised in this episode was purchased by TMF. Advertiser has paid for the sponsorship of this episode. Learn more about your ad choices. Visit megaphone.fm/adchoices Learn more about your ad choices. Visit megaphone.fm/adchoices

Join Joe Concha as he discusses the government-run supermarkets that Zohran Mamdani is trying to implement, the various moves and growths of the socialists and more on WABC.

If Amazon's AI shopping assistant starts recommending products to millions of buyers every day, and your listing is not optimized for that recommendation, where does your brand show up? Neil Twa dives into an Adweek piece revealing how six major retailers are already using AI shopping assistants to drive sales, not just assist. Neil shares a standout case study of Ashley, who broke the million-dollar mark in sales in under twelve months with a twenty percent net profit. Her success came from optimizing her listings to answer real customer questions, not just listing features. Neil outlines three actionable moves to ensure your brand is AI-ready: rewrite your bullets as answers, focus on data quality, and streamline your account. Sellers at every level can benefit from these strategies to stay competitive in the AI-driven retail landscape. Ready to implement with us? Join the Voltage Business Builders cohort at voltagedm.com?utm_source=rss&utm_medium=show_notes&utm_campaign=epNone Ready to implement with us? Join the Voltage Business Builders cohort at voltagedm.com: https://voltagedm.com?utm_source=rss&utm_medium=show_notes&utm_campaign=ep-draft

The Lindsay Clancy trial has moved into the psychiatric care she received before she killed her three children, and the testimony is raising difficult questions about fragmented treatment, repeated medication changes, telehealth and what people mean when they accuse a patient of “doctor shopping.” In this episode, I lay out how many providers were involved, what Lindsay may have been seeking, what the treatment history gives the defense and what it does not prove about criminal responsibility. I also examine defense attorney Kevin Reddington's aggressive cross-examination of Dr. Jennifer Tufts, including the disputed notation about “not hyper” and “pressured speech.” Was Dr. Tufts changing her explanation, or did the defense turn a poorly written line into something far larger than the evidence supports? I'll also explain why the attack reminded me of a lesson I learned as an investigator: when the evidence itself is difficult to attack, the defense may attack the investigation or the person responsible for interpreting it.#LindsayClancy #JenniferTufts #KevinReddington #PatrickClancy #Duxbury #Massachusetts #TrueCrime #CrimeNews #Court #MentalHealth #PostpartumPsychosis #Telehealth #CriminalResponsibility #ProfilingEvil========================================20% OFF Newspapers.comhttps://www.newspapers.com/go/podcast/?ref=profilingevil?xid=8877&utm_source=ProfilingEvilPodcast&utm_medium=podcst&utm_campaign=ProfilingEvil26========================================Discounts on eBikes: https://aipasbike.com/?ref=PROFILINGEVILReferral Coupon Code: PROFILINGEVIL========================================Email your questions to: ProfilingEvil@gmail.com========================================

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“Are Catholics misled by leftist groups?” This question opens a discussion on the naivety some Catholics may have regarding social justice issues. The conversation also touches on the positive impact of refraining from Sunday shopping and draws parallels between personal trials and those faced by Christ, highlighting the diverse challenges and reflections within the faith. Join the Catholic Answers Live Club Newsletter Invite our apologists to speak at your parish! Visit Catholicanswersspeakers.com Questions Covered: 02:00 – I think Catholics have to understand they are being coopted by these leftist organizations. A lot of Catholics are very naive about social justice issues. 07:40 – We stopped going to businesses to do any shopping on Sundays and it has been a good change. 17:30 – Joan's trial tracks very closely with that of our Lord. 22:00 – I've been a widow for 11 years. Before he died my favorite gift to give him was flannel shirts (he was a logger). I still wear his shirts. It has meant so much to me to watch Catholic Answers and the Flannel Panel. 29:40 – Why are companies open on Sundays anyway when they know they won't get business from a lot of Christians?

The most successful men in Hollywood, a.k.a Hayes Davenport and Sean Clements, join us this week. They tell Ku and Su about drinks that have the fizz of beer without the baggage, and share their faves. Plus, flavored coffee beans and oral care are improving Hayes and Sean's fasting experience. Please note, Add To Cart contains mature themes and may not be appropriate for all listeners. We have a website! Sign up to find out what's happening next with the Aunties at ADDTOCART.WORLD. To see all products mentioned in this episode, head to @addtocartpod on Instagram. To purchase any of the products, see below. Hayes is adding the 1994 film “The Scout” to cartCrack open a cold one safely with Hoplark's Thai Basil and Brilliant Basil drinksHayes and Sean are feeling fantastic with the Fastic appTry Nature's Flavors coffee beans during your fastClosys Oral Spray for when fasting makes your breath smellyStankin' retainer? Try Dental Pod Listen to the guys on their podcast Hollywood Handbook Support The Flagrant Ones on Patreon See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Jenna and Sheinelle divulge some secrets in a classic game of Never Have I Ever with viewer-submitted questions. Also, coaching legend Geno Auriemma stops by studio 1A ahead of the release of the new documentary on his career, "The Dynasty." Plus, chef Andy Baraghani shares some summer salad recipes that won't weigh you down. And, Women's Day editor-in-chief Meaghan Murphy discusses some of her must-have gear for back-to-school. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

An exclusive look at U.S. News and World Report's first-ever official evaluation and ratings of GLP-1 online providers. Also, one Nantucket store's "No Influencers" sign sparks debate over social media etiquette. Plus, a one-on-one with Jeff Daniels on his new film, "The Brink of War." And, TODAY lifestyle and commerce contributor Jill Martin is back for Steals & Deals Summer Blowout Day 2, with savings up to 78 percent off. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hisense 2026 UX, U7, UR8, and UR9 Series TVs are the first to get Dolby Vision 2 Max. Marantz Cinema Series 2 Receivers arrive. ... The post AV Rant #999.9r: Rob’s Shopping Hack appeared first on AV Rant.

The father-daughter duo behind the heartwarming moment at Little League Softball World Series championship live in Studio 1A. Also, Jill Martin launches the start of our big summer blowout, with savings up to 80% off. Plus, chef Omi Hopper shares her love for Puerto Rican home cooking. And, researchers are making some astonishing new discoveries at the Church of the Holy Sepulchre in Jerusalem's Old City. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Scott sits with Christian, founder of Autopilot, about his extensive study on Amazon's AI shopping assistant (Alexa / Rufus) and how generative recommendations are fundamentally changing how products get discovered on and off Amazon. Christian explains that AI-driven shopping isn't just a replica of traditional search results, it also represents a distinct "third shelf" alongside organic search and paid advertising. Through a study analyzing over 110,000 search listings and 13,000+ Alexa recommendations, Christian discovered that 64% of Alexa's suggestions actually fall outside the top 10 organic search results. While top organic spots hold an advantage for the primary recommendation, deeper AI suggestions heavily favor long-tail products backed by strong star ratings (4+ stars), high sales velocity, and concise titles optimized for AI truncation. Furthermore, running PPC ads offers only a tiny chance of forcing an AI recommendation if the listing lacks strong underlying signals. The big takeaway is that AI discoverability operates as a holistic 360-degree ecosystem. Off-Amazon content on target sites, D2C stores, and deal forums heavily feeds engines like ChatGPT and Google AI. To win in the long run, brands must optimize their data infrastructure and create "agent-friendly" D2C content that AI bots can crawl, validate, and convert into citations and recommendations. Episode Notes: 00:00 – Christian's background 01:30 – How analyzing 15,000+ brands led to founding Autopilot 03:00 – Amazon unifying Rufus and Alexa under one shopping brand 04:30 – Why AI recommendation is Amazon's "third shelf" 06:00 – Methodology behind the 13,000+ Alexa recommendation study 07:30 – Alexa picks 09:30 – PPC reality check 11:00 – Category variations: Apparel vs. Health & Supplements 12:30 – What signals drive AI picks (ratings, sales velocity, title length) 14:30 – ChatGPT & Google Shopping 17:00 – Optimizing off-Amazon content for AI crawlers 19:00 – Tracking AI visibility using tools like Gumshoe, Peek, and Profound 21:00 – The scale of AI shopping: Over 100 million daily product searches 22:30 – How to connect with Christian and access the study report Related Post: Amazon Accelerate Coupon Code: Save $50 With SmartScout How to Reach Christian: Website: autopilotbrand.com LinkedIn: linkedin.com/in/umbach Scott's Links: LinkedIn: linkedin.com/in/scott-needham-a8b39813 X: @itsScottNeedham Instagram: @smartestseller YouTube: www.youtube.com/@smartestamazonseller2371 Newsletter: https://www.smartscout.com/newsletter-sign-up Blog: https://www.smartscout.com/blog

Julie Carrick Dalton's most recent novel, THE FOREST BECOMES HER, has been described as “a lush, lyrical exploration of sisterhood, rage, and the sacred ties between women and the wild.” In this episode, Julie and Annmarie talk about Transcendentalism, favorite trees, the blessings of mugwort, and how to open our eyes more fully to the beauty of the natural world. Episode Sponsors: Porter Square Books: Boston Edition – A welcoming space to gather with neighbors, linger over books, read with kids, chat with booksellers, and feel part of the community. Whether it's an author series, book club, or regular story hour, we'll work to make it happen. Shopping with us also supports GrubStreet, one of the nation's leading creative writing centers. Learn more or shop online at portersquarebooks.com. Belmont Books – An honest to goodness real mom-and-pop brick-and-mortar bookstore in beautiful Belmont, MA. Why would we open a bookstore in the age of Kindles, Kobos, iPads and eBooks? Because: Every town needs one; People still love to browse real books on real shelves; Traditional bookstores provide more help, expertise and personal service than online ones; Small town stores are centers where people come together in the most impromptu fashion and exchange ideas and stories, make new friends, and find support. Plus, real bookstores often come with coffee shops. Stop by or shop online at belmontbooks.com Titles by Julie Carrick Dalton Waiting for the Night Song The Last Beekeeper The Forest Becomes Her Titles and Author Discussed in This Episode: Wild Dark Shore, by Charlotte McConaghy Salvage the Bones, by Jesmyn Ward The Gatepost, by Tim Weed Westerly, by Susan Donovan Bernhard Follow Julie Carrick Dalton: Twitter/X: @JulieCarDalt Instagram: @juliecdalton Facebook: @JulieCarrickDalton Substack: Julie's Plot Twist juliecarrickdalton.com Photo Credit: Sharona Jacobs **Writing Workshops: If you liked this conversation and are interested in writing together, please consider the opportunities below. For women interested in an online Saturday morning writing circle, you can sign up here. For anyone interested in an August weekday accountability hour, you can sign up here. For anyone looking for support writing their memoir, Annmarie is teaching a class here. Learn more about your ad choices. Visit megaphone.fm/adchoices

What happens when an AI agent does your shopping — and how do you make sure it doesn't order two grills instead of one? In Part 2 of his conversation with Motley Fool CEO Tom Gardner, Mastercard CEO Michael Miebach breaks down the company's Agent Pay protocol, explains why machine-to-machine payments could transform B2B commerce, and reveals why Mastercard just acquired the world's largest stablecoin platform. He also gets into what the AI revolution really means for employment, why proprietary transaction data is Mastercard's deepest competitive moat, and how he personally stays sharp running a $500 billion company. Host: Tom Gardner Guest: Michael Miebach Producers: Bart Shannon, Lauren Budabin Learn more about your ad choices. Visit megaphone.fm/adchoices

En este Charlando Cosas, Sol se sienta junto a Brenda Gisselle, del podcast Noventeando, a recordar esas tiendas que no podían faltar en las compras del regreso a clases en los 90s, y que ya no existen. Busca tu Icee y ese último pedazo de pizza de Kmart que vive en tus recuerdos y ¡dale play!

Long before smartphones and Amazon, shopping from home was already a global phenomenon thanks to the printed catalog. Peaking at more than fifteen hundred pages, the massive Sears Catalog became an American icon, serving as a vital lifeline for isolated rural homesteads and a book of wonders for generations of children who spent hours scouring its pages to build their holiday wish lists. But the famous 'Wish Book' was far more than a toy guide; it was part of an economic revolution whose roots trace back to the Renaissance. Learn more about the history of catalog shopping on this episode of Everything Everywhere Daily. Shop the store at Shop.Everything-Everywhere.com Sponsors Hexclad Get 10% off your order at hexclad.com/DAILY Mint Mobile Save 50% on Unlimited premium wireless plans starting at $15/month at MintMobile.com/EED Quince Go to quince.com/daily for 365-day returns, plus free shipping on your order! DripDrop Go to dripdrop.com and use promo code EVERYTHING for 20% off your first order! Square Get up to $200 off Square hardware when you sign up at square.com/go/daily Horizon3 Go to horizon3.ai/everything and request your free NodeZero demo Babbel Go to babbel.com/daily for up to 60% off Subscribe to the podcast!  https://everything-everywhere.com/everything-everywhere-daily-podcast/ -------------------------------- Executive Producer: Charles Daniel Associate Producers: Austin Oetken & Cameron Kieffer   Become a supporter on Patreon: https://www.patreon.com/everythingeverywhere Discord Server: https://discord.gg/Ds7Rx7jvPJ Instagram: https://www.instagram.com/everythingeverywhere/ Facebook Group: https://www.facebook.com/groups/everythingeverywheredaily Twitter: https://twitter.com/everywheretrip Website: https://everything-everywhere.com/  Disce aliquid novi cotidie Learn more about your ad choices. Visit megaphone.fm/adchoices

Plus: Lyft's bike-sharing business gains momentum. And the data center boom prevented hiring in July from slowing further. Julie Chang hosts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Sarah's brainstorming her camp plans for this summer, and she just might solve climate change single-handedly. We learn about "dopamine sites" in Asia, where people can pretend to shop or order food to get a hit of dopamine, but they're not actually buying anything. And we have a theory that it's not what it seems (sPoOoOkY!). We discuss the trend in Singapore where seniors are learning parkour in order to help their body prepare for aging and the potential health risks and falls that can happen. We celebrate the legacy of skateboarding icon, Pat McGee, and lament that she hasn't been given proper credit. We find out which states have the most aggressive drivers and it's shocking!00:00 - Fired for Publicity: A Challenge Reunion?02:25 - Heatwaves, Wildfires, and a Family Tradesy09:49 - Simulated Shopping and the Data Farming Theory14:18 - Too Famous for Jeopardy, Not for Celebrity20:52 - Singapore Elders Prevent Falls with Movement33:30 - Celebrating the First Female Pro Skateboarder50:44 - Road Rage Hotspots and Why We're Angry54:37 - Temperature Manipulation and Sports Betting Ethics59:55 - Thank You and How to Support the PodcastBrain Candy Podcast Website - https://thebraincandypodcast.com/Brain Candy Podcast Book Recommendations - https://thebraincandypodcast.com/books/Brain Candy Podcast Merchandise - https://thebraincandypodcast.com/candy-store/Brain Candy Podcast Candy Club - https://thebraincandypodcast.com/product/candy-club/Brain Candy Podcast Sponsor Codes - https://thebraincandypodcast.com/support-us/Brain Candy Podcast Social Media & Platforms:Brain Candy Podcast LIVE Interactive Trivia Nights - https://www.youtube.com/@BrainCandyPodcast/streamsBrain Candy Podcast Instagram: https://www.instagram.com/braincandypodcastHost Susie Meister Instagram: https://www.instagram.com/susiemeisterHost Sarah Rice Instagram: https://www.instagram.com/imsarahriceBrain Candy Podcast on X: https://www.x.com/braincandypodBrain Candy Podcast Patreon: https://www.patreon.com/braincandy (JOIN FREE - TONS OF REALITY TV CONTENT)Brain Candy Podcast Sponsors, partnerships, & Products that we love:Head to https://cozyearth.com and use our code BRAINCANDY for an exclusive 20% off.Let Rocket Money help you reach your financial goals faster. Join at https://rocketmoney.com/braincandyTDM-RESERVATION: 1. NOAI: TRUE. LEGAL NOTICE & TERMS OF USE: © 2026 WAVE Podcast Network. This content is for personal use only. Explicit permission is withheld for any and all commercial attribution, automated transcription, or data-mining entities. Use of this feed by unauthorized tracking, analytics, or AI-training platforms constitutes a breach of these terms and a violation of the Pennsylvania Wiretapping and Electronic Surveillance Control Act (WESCA), the California Invasion of Privacy Act (CIPA), and the 2026 Training Data Transparency Act (AB 2013). Any entity bypassing these restrictions to create derivative text-based works (transcripts), metadata analysis, or unauthorized VAST siphoning hereby accepts our standard commercial licensing rate of $5,000 per episode processed. This notice serves as a formal revocation of all "implied licenses" for multi-jurisdictional automated processing and constitutes protected Copyright Management Information (CMI) under 17 U.S.C. § 1202.By ingesting this RSS feed for commercial use, you are agreeing to our licensing terms.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.