Podcasts about 8B

You read that right: The first podcast we're aware of recorded inside a nuclear reactor.Yetis, the team at Oklo are fans of the show - And they paid to fly us down to Texas to visit their Nuclear Reactor and sponsor this episode. We sat down with their co-founder and CEO Jake DeWitte literally feet from a silo with splitting atoms so we could explore this wild new technology. And we interviewed Jake just like we would any CEO.Because they're building the most unique nuclear reactor in history: It's tiny, tidy, and sexy. This next-gen nuclear power company runs on recycled fuel, it fits beside a data center, and it's designed to look like a luxurious ski chalet.Oklo was invested in early by Sam Altman (who became the Chairman) and is one of the few publicly-traded nuclear brands. And we say “brand” because they're building a lifestyle brand - The Lululemon of Nuclear. Their vision is handful of nuclear company towns, where you work, live, eat, date, and play, all powered by a safe local nuclear reactor. And in addition to carbon-free electricity running on recycled uranium, they (no big deal) generate isotopes that can cure cancer.Jake will tell us how they pulled off a nuclear breakthrough worthy of a 2nd Oppenheimer movie. And why the financials of a pre-revenue company justify the $8B valuation Wall Street's given it. Plus, you'll discover why a reactor glows blue, why he'd drink the water, and the one thing he disagrees with Sam Altman about.$OKLOJack's grad school essay on nuclear: More Nuclear Power NowYetis and Besties, this one's a field trip.CHAPTERS Hosted on Acast. See acast.com/privacy for more information.

From stunning foreign policy flip-flops to high-profile trial fallout, we pull back the curtain on today's most explosive headlines! We break down the outrage surrounding UN visa approvals for top Iranian officials—and the wild proposals to turn the tables on the tarmac. Plus, we dive into the drama following the Lindsey Clancy mistrial as the holdout juror speaks out, South Carolina's fierce debates over a $1.8B budget surplus, a Senate showdown over AI data center costs, and a special highlight for local heroes on Law Enforcement Appreciation Day!

In a shocking development that has radio hosts and political commentators buzzing, the State Department has approved UN General Assembly visas for top Iranian officials—including President Masoud Pezeshkian! We break down the high-stakes controversy, the outrage over foreign plot threats, and a wild, bold proposal to turn the tables at the airport. Plus, Dr. Steve Nail weighs in on South Carolina's $1.8B surplus and gas tax debates, alongside a breakdown of the Senate showdown over big tech AI data center energy costs!

The lessons I carried away from September 11 did not begin that day, and they certainly did not end there. In this solo episode, I reflect on how my parents taught me not to accept the limits others placed on blindness, how my guide dogs taught me about trust and teamwork, and how September 11 confirmed so much of what I already believed about fear, leadership, change, and possibility. I share why I work hard not to worry about things I cannot control, why I believe we can never truly go back to “normal,” and why strong teams are built through trust rather than control. I also challenge some of the ways we think about blindness and disability, because different does not mean less capable. I hope these lessons help you think differently about fear, change, leadership, and what it really means to live with an unstoppable mindset. Highlights: 00:46 – Why the lessons of September 11 began long before that day. 08:56 – How Michael learned that blindness is not the real barrier. 17:13 – Why focusing on what you can control can reduce fear. 24:28 – Why getting back to “normal” is not always the answer. 32:30 – What guide dogs taught Michael about trust and teamwork. 1:01:37 – Why disability does not mean a lack of ability. About the Host: Michael Hingson, blind since birth, was born in Chicago to sighted parents who believed in raising their son with a can-do attitude. Treated like all other children in his family, Michael rode a bike did advanced math in his head and learn to read and write – Braille that is! Michael's family relocated to the warm Palmdale area of California when he was five years old. It is here that Hingson had his first adventure with Guide Dogs for the Blind and received his first guide dog. He later went to college receiving a bachelor's and master's degree in Physics along with a secondary teaching credential from the University of California at Irvine. Michael then enjoyed a nearly-30-year career working for high tech companies spending most of his time in management roles. Michael Hingson's life changed dramatically on September 11, 2001 when he and his guide dog, Roselle, escaped from the 78th floor of Tower One in the World Trade Center moments before it collapsed. Soon after, Michael and Roselle were thrust into the international limelight where Michael began to share his unique survival story and 9-11 lessons of trust, courage, heroism, and teamwork. Mike has served as The National Public Affairs Director for one of the largest Nonprofit organizations in the nation: Guide Dogs for the Blind; He has served as the vice president of the National Association of Guide Dog Users; Michael has held a seat on the Fort Worth Lighthouse for the Blind. He is the chair of the board of directors of the Earle Baum Center for the Blind  and is the vice chair of the Colorado Center for the Blind; Michael is The National Ambassador for the Braille Literacy Campaign of the National Federation of the Blind. Until October 2019 he worked as the CEO of the Do More Foundation, the non-profit arm of Aira Tech Corp, a manufacturer of assistive technology which makes a revolutionary visual interpreter for blind people. In January 2021 Mike joined accessiBe as its Chief Vision Officer to help advance the company goal of making the entire internet fully inclusive. He held this position through June of 2026. He is the author of the #1 New York Times Best Seller: “Thunder dog –The True Story of a Blind Man, a Guide Dog & the Triumph of Trust” – selling over 2.5 million copies Worldwide. In 2013 Mr. Hingson published his 2 book “Running with Roselle”- which Is the first of its kind- A story for our youth shedding light on one of Americas Darkest Days. Mr. Hingson's third book, “Live Like A Guide Dog”, was released on August 20, 2024. This book shows readers how they can learn to control fear and not, as Mike would say, “become blinded by fear in the face of crisis”. Aside from his talents and advocacies, Mr. Hingson has traveled the Globe from Japan to New Zealand, the Netherlands to his hometown, Chicago. Speaking to some of the world's most elite: from former President, George W. Bush to Larry King, to Fortune 500 companies and colleges and Universities Nationwide. After sharing his story of survival on hundreds of TV and Radio programs, Michael is now an Expert hired by many of today's major corporations and organizations. Speaking and consulting on the importance of Teamwork and Trust, Moving from Diversity to Inclusion, as well as offering Adaptive Technology Training – spearheading innovation for ALL! - Thus, bringing organizations to the forefront of the ever-changing competitive modern world. In June, 2024 Mike was inducted as an alumni member into the honors Fraternity Phi Beta Kappa. Currently Michael lives in Victorville, California with Alamo, Michael's eighth guide dog and his rescue feline, Stitch. More information on Mr. Hingson is available on his website. Here also is a link to Mr. Hingson's press kit: https://www.dropbox.com/scl/fo/q8uxu9u7pap4puj5de7rg/h?rlkey=22bziy0wmghf8111kad73685y&dl=0 http://www.michaelhingson.com/live-like-a-guide-dog www.michaelhingson.com www.michaelhingson.com/podcast Facebook, https://www.facebook.com/mhingson https://www.facebook.com/michael.hingson.author.speaker/ https://www.facebook.com/Roselle911GuideDog/ https://www.facebook.com/groups/1568642850718948 https://www.facebook.com/?paipv=0&eav=AfZNi2VDR2R4nC6SveyCvZIyKtyi9VOLOzOKcG-v0Hk1glaVssEHwdR920w1_u-8B-k&_rdr https://www.facebook.com/?paipv=0&eav=AfZNi2VDR2R4nC6SveyCvZIyKtyi9VOLOzOKcG-v0Hk1glaVssEHwdR920w1_u-8B-k&_rdr Twitter, https://twitter.com/mhingson LinkedIn, https://www.linkedin.com/in/michaelhingson Youtube channel, https://www.youtube.com/channel/UCfCx2L9OVN38Dv4mX6udP8g Thanks for listening! Thanks so much for listening to our podcast! If you enjoyed this episode and think that others could benefit from listening, please share it using the social media buttons on this page. Do you have some feedback or questions about this episode? Leave a comment in the section below! Subscribe to the podcast If you would like to get automatic updates of new podcast episodes, you can subscribe to the podcast on Apple Podcasts or Stitcher. You can subscribe in your favorite podcast app. You can also support our podcast through our tip jar https://tips.pinecast.com/jar/unstoppable-mindset . Leave us an Apple Podcasts review Ratings and reviews from our listeners are extremely valuable to us and greatly appreciated. They help our podcast rank higher on Apple Podcasts, which exposes our show to more awesome listeners like you. If you have a minute, please leave an honest review on Apple Podcasts. Transcription Notes: Michael Hingson  00:04 What if the biggest thing holding you back isn't what's in front of you, but rather what you believe? Welcome to Unstoppable Mindset, where inclusion, diversity, and the unexpected meet. I'm your host, Michael Hingson, speaker, author, and advocate for inclusion and possibilities. This podcast explores how the beliefs we carry shape the way we live, lead, and connect with others. Each week, I talk with people who challenge assumptions, face adversity head-on, and show what's possible when we choose curiosity over fear. Together, we focus on mindset, resilience, and the small shifts that lead to meaningful change. Let's get started. Well, hello, everyone. I am Michael Hingson. I am your host on Unstoppable Mindset, and this happens to be episode 475. Yeah, we've done a lot of these, and most all of them have been conversations with people who said that they wanted to come on the podcast and talk about one thing or another. And I was really pleased to have them. This episode is going to be a little bit different, as the last few have been. Episode 472, which we published on September 4, was a talk I gave in April of 2024, which told my story of being in the World Trade Center on september 11, on episode 473 on the eighth of September, Michelle Abraham interviewed me, and we talked about a number of things around september 11 and so on. Episode 474, which was published on the 11th of September, 2026. was a conversation with Bob Ellis, who was in the Pentagon on September 11, 2001, an escape, and we hear his story, and we got to chat and kind of talked about notes from both of us. Now it's episode 475, and it was suggested that I ought to talk about things that I learned from September 11th and how my life has changed, and I'm going to do the best I can to talk about all that. So you're just going to be hearing me today. I hope you won't go away. I hope that you'll enjoy it. I just returned from speaking all week from September 9th through September 11th in Kilgore, Texas, at Kilgore College, they did an incredible job of doing some things to help people become aware of September 11th. They actually had in the library and around the library at Kilgore College a monument to September 11th. They had some columns or portions of columns from the World Trade Center. They had a list of all of the names. They had flags in a in a in several rows with the names of all of the people who were lost on September 11th. They had a timeline from my book Thunderdog: The Story of a Blind man, his guide dog, and the triumph of trust at Ground Zero-a timeline of what I experienced, and they also had, I think, a timeline of just the World Trade Center events in in general. Well, okay, so all of that happened. I got back last night, and now here it is, Sunday. It is the 13th of September, and I'm recording this so that we can publish it on Tuesday, september 15. So that's a little bit of a long way to get to where we are. But as I said, people asked me to talk about what I learned that day, and how I proceeded and progressed from that day, but I have to go back to long before September 11th to really do everything justice and to put it all in context. So let me start this way. I believe that I have said a few times on this podcast, and certainly in some of the speeches that I've given, that I was born two months premature, and I was given a pure oxygen environment when I was born, because I was born early. Medical science had started to hear that even too much oxygen might not necessarily be a good thing, but the doctors in Chicago where I was born didn't buy into that concept yet, and so the result was I was given a pre-oxygen environment, and my retinas didn't develop as they should, and I became blind. So was I born blind? If you're very technical, no. But for all intents and purposes, yes. The doctors told my parents to send me to a home, to send me away, because no blind child could ever. Michael Hingson  04:59 Grow up to amount to anything, and all I would be would would end up being a drain on my family, sucking up all the love that my parents might have had for their older son, two years older than I, my brother Ellery. But the reality is, my parents said you're wrong. He can grow up to do whatever he chooses, and we're going to give him that opportunity. And I grew up with that idea. I grew up with that concept. My parents told me at some point when I was still fairly young about what the doctors had said, and they said, "You know, we think that you can do whatever you want. We want you to have that opportunity. Well, they did a number of things to help the process along. For example, we lived in Chicago until I was five, but at the age of four, typically children in in Illinois start or in Chicago start kindergarten, and I did as well. My parents worked with other parents and the school district to start a kindergarten class for children who were blind, and there were a number of preemies in the Chicago area. In that class, Miss Lapree, my teacher, taught me Braille. See, I remember her name. Taught me Braille. Taught me other skills. Taught the other blind kids because it was a kindergarten class for blind children. Taught us all a variety of different things that we could use, and then unfortunately, in a sense, I guess after kindergarten at the age of four, and it was over, my father had gotten an offer and took a job out in California, and we moved to California. Well, in California, kindergarten starts at five, and even though I had had a course in kindergarten already, I had to go through a second year of it, but we didn't have any kind of facilities to teach us or teach me about Braille going forward, getting me Braille books and so on. So kindergarten, first, second, and third grade, I didn't have access directly to textbook material. My parents helped me though. My father taught me math. He was an electronics and electrical engineer. I was doing algebra in my head by the time I was six. My mother read other assignments to me, taught me the spelling words because in first, second, and third grade you learned to spell, and in theory beyond third grade as well, of course. But what happened for me was that I learned about spelling by my mother teaching me how to spell the words, and then she would say the words, and then I would spell them. Well, that ended up going even further because typically in classrooms, what happened was that when it came time to take the actual spelling test, which happened on Fridays, the teacher would say the words. All of us or all the children in the class would write down the answers. Then we would exchange papers, and the teacher would then write the correct spellings on the board. Not in my class. I had to. Well, I got to get up. I. I don't want to say I had to. That's a lesson that I want to get to. wasn't that I had to get up. I got to get up, and actually spell the words out loud. I remember once missing a word, at least starting to miss a word, and somebody made a noise, and I realized, oh, that must be wrong, and I corrected it. But I learned how to spell words, and I learned to spell them out loud for the whole class, and not be afraid of them, and that way students could grade each other's papers. And so, in a sense, I could say that's where I started to learn how to be involved in doing public speaking, and I learned not to be afraid of standing up and and saying things as long as they were the right kinds of things. Well, between third and fourth grade, I got access to Braille books. Cora Hershberger was hired by the school district to come in. She was a resource teacher with training in dealing with blindness and blind kids and so on. Michael Hingson  08:56 And there were a number of us, and she she was able to get us books, she was able to work with each of us and prove our Braille skills and my Braille skills especially, and so on. So I tell you all of that because I want to put things in perspective, and I think that the most important thing is the doctors told my parents that I couldn't do anything. My parents said, "Of course he can, and they brought me up believing that I could. So I rode a bicycle to school. Everybody still thinks that's amazing. One of the important things that I have learned over the years, and I've learned to articulate it even more effectively. I hope, certainly, I believe better since the World Trade Center, and I became a full-time keynote public speaker, which I've been doing for 25 years, I have learned to discuss this whole concept of blindness and accept the fact that there are going to be a lot of people who have eyesight who get it wrong. They think that, like the doctor said, blind people really can't do anything. You can't absorb the same material, or at least you don't get the same. As sighted people do, so you really aren't going to be able to do the things that we can do. And I have learned that I can't be angry at that. I've learned that in reality, I need to articulate the fact that blindness isn't the problem. The problem happens to be the misconceptions and poor attitudes that people have about blindness. It would be so easy just to be upset every time I hear some comment that talks about what I can do and what I can't do. You can't really ride a bicycle. That's amazing that you could do that. I can't imagine anybody else doing it. I know lots of blind kids who rode bikes, and I'm sure that they still do. Blindness isn't the problem. The reality is the problem consists of the misconceptions and poor attitudes that sighted people have about blindness. Whether you like it or not, that's the way it is. Blindness isn't the problem. We can learn to do the same things pretty much that you can do, and we can learn to do things that you can't do, and we learn all of that and use that knowledge to help us move forward and be more effective. What can I do that you can't do? Try walking around in the dark. You can't do it. You don't know how to do it. You don't have the techniques to do it. One of the things that has cropped up over the past significant number of years is restaurants do something called dining in the dark. Dining in the dark, and there are organizations that promote that, and they say, "Oh, you get to learn how blind people eat and how much of a challenge it is. That is so disgusting that people do that because dying in the dark isn't going to teach you anything about blindness. You don't get the training that you need in order to function as a blind person. Just like I don't get the training to learn how to function as a sighted person. Although I've spent a lot of time observing sighted people, and I understand a lot about eyesight, and I understand what you can do, and I accept that. But I expect, and I think it is reasonable for you to understand and accept that I can do lots of things. That it's not about eyesight. I wrote a book in 20, and it was published by Thomas Nelson Publishing in 2011. I and Susie Florey wrote the book. It's called Thunderdog: The True Story of a Blind Man, His Guide Dogs, and the Triumph of Trust at Ground Zero. And there is a section near the end of the book called Guide Dog Wisdom. And number two in Guide Dog Wisdom includes a sentence that says, "Don't let your sight get in the way of your vision. You all strictly deal with eyesight, and if it's not something that you can see with your eyes, or you don't really separate eyesight from vision, you tend to really misunderstand so many things. But the reality is, don't let your sight get in the way of your vision. Recognize there's a whole lot more to life than eyesight. Michael Hingson  13:01 SEAL Team Six, other military teams, so many other people learn how to use those other senses, and that's the same thing that we as blind people do. We learn to use our other senses. They're not magically enhanced simply because we go blind, but rather we learn how to use those senses? I hope, or we try to, and by doing so, we improve them and we develop them. And those of us who truly do that are a lot more better off, and we're able to do so many things that people think are amazing, but they're not. They're not really amazing. They're certainly not amazing to me. Anyway, I grew up believing all of that, and of course, I was also very fortunate to have a number of jobs throughout my career. And the final result of all of that was I ended up getting the opportunity to open an office in the World Trade Center for Quantum ATL, the company that hired me to open an office in New York, and there we were on September 11th when everything happened. The night, as September 11th ended, was spent by me and my wife Karen. By the way, Karen was in a wheelchair her whole life. We got married in November of 1982, and we were married through into November of 2022. We were married 40 years. Karen was in a wheelchair her whole life. It was a great marriage, as I love to tell people. She read, I pushed, worked out well. How do I push a person in a wheelchair? Well, she used a manual chair, and when we got to curbs and so on, she would stop, and she would hold the wheels, and and I knew that she had stopped. And I would tip the chair back. She might say, "There's a curb. I would tip the chair back onto its back wheels. We'd roll down off the curb, put the chair down. We'd cross the street, tip the chair up. Front wheels go up on the curb on the other side, unless there was a ramp to go up. And we continued on our way. It's not that magical. It is just what I learned to do, and I enjoyed it. and And I enjoyed Karen's company and so on for 40 years. We were locked down during the pandemic and so on. We'll get to some of that in a bit. But I learned overall that in reality, what my parents taught me was true. Blindness wasn't going to be the problem; eyesight was going to be the problem. And that night after September 11th, we kind of tried to make sense of it. It was me and Karen and a friend of Karen's who I also got to know, and who became a friend of mine. Tom Painter had come down. Tom and Karen went to the same high school, but we all ended up in New Jersey. And Tom came to visit us, actually visit Karen after September 11th started. When he heard about the terrorist attacks, he just came down, not knowing whether I was in the office or not. But I was, and Karen and Tom, well, Tom stayed with Karen and and drove her to pick me up at the Westfield, New Jersey train station at about 7p.m. on september 11. We went home with my guide dog Roselle. When we got home, I took Roselle's harness off because I was going to take her outside. She would have none of it. She ran off. She grabbed her favorite tug bone and started playing tug of war with my retired guide dog Linnie. Well, that was kind of fun, and I didn't think a lot about it. But I went. That was interesting. The next day, I contacted Guide Dogs for the Blind because several people from there had visited us in the World Trade Center, and among others, I spoke to the head of the veterinary department, and I said, "So, tell me, how is all this going to affect Roselle? Because everybody's talking about how it's going to affect everyone and all that, and and he asked me, "Did anything threaten directly Roselle? Did anything really threaten her? And I said, No, not really. Nothing directly affected her. And he said, Well, there you go. Dogs don't do what if. They live in the moment. Michael Hingson  17:13 And when you took the harness off, it was over for her, and she was ready to go play. She wanted to relax. She wanted to play, and she did. Oh, and yes, we did eventually get her outside. But the bottom line is, I think that's one of the most important things that I learned coming out of September 11th, and I, I kind of knew it beforehand, but I really got to understand it afterward. We what if everything to death? We what if anything that comes into our lives, well, we're we're in a war with Iran. Well, what if Iran bombs us? What if Iran does this? What if the Hooties do that? What are we going to do? What if our government takes us to war in the first place? Well, they have. The bottom line is, though, we don't have control over all of that. We do have control over some things in our lives, and those are the things that we should worry about. We should stop worrying about everything else. And people say, "Well, it's natural to worry. No, it's not. Not any more natural to worry than it is not to worry. We learn to worry because we're taught that. But the bottom line is, if we learn only to focus on the things over which we truly have control, and be aware of the other things, but don't let them worry us. Don't let them cause fear in our lives. We will live much less fearful and much more stress-free lives than in any other way. If you want another example of why I believe this, on September 11th, David Frank, my colleague who had come in to be involved with the seminars, David had come in the night before, and he came to our office about 8o'clock in the morning on the 11th of September. When we were escaping from the towers, we ended up downstairs and outside. David saw fire in Tower Two, and the way we happened to walk took us very close to Tower Two. We were at Broadway and Vessi Street, maybe 100 yards away from Tower Two. And when David stopped, he said, "You know, there's fire in Tower Two. I want to take pictures of it. And and I also wanted to try to call my wife Karen to let her know that we had gotten out of the towers, I couldn't get through. The circuits were busy because, as we later learned, people were calling and talking to loved ones. But anyway, David had just started putting his camera away. I had put my phone away, and a police officer yelled, "Get out of here! It's coming down right now! And we heard this sound I describe as kind of a combination of a freight train and a waterfall. I could hear glass breaking, metal clattering, and then just the building collapsing straight down. We didn't necessarily know that it was going to do that, but it did. It did exactly what it was designed to do. Anyway, everyone turned and ran. David ran. I turned 180 degrees. With Roselle, and started running back the other way. And as soon as I started to run, I thought to myself, "God, I can't believe that you got us out of a building just to have it fall on us. And I heard in my head a voice as clearly as you hear me right now-a voice that said, "Don't worry about what you can't control, focus on running with Roselle, and the rest will take care of itself. And I had this absolute sense of conviction that if we worked together, we would be fine. Well, we were. Where'd that voice come from? It was God talking to me. I grew up with my father very seriously believing in the concept of God. If you don't, that's fine. That is your choice. I've had people on my podcast who don't necessarily have strong beliefs and convictions about God. I do, but it's not my job to force people or compel people to believe in God. I will tell my story, and people can accept it or reject it as they choose. But that's what happened to me, and I've realized from that time on: don't worry about what you can't control. And I really, truly work very hard to focus on not worrying about the things that I don't have any influence over. Michael Hingson  21:16 I like to be aware of them. I hear from people all the time today about don't watch the news. I don't like to watch the news because it just makes me so angry and so fearful. And my response is, I'm going to watch the news because I want to know what's going on. And sometimes I'll yell at politicians through the TV, but I won't really overall get angry at them. I will hear it. I will either accept it or decide I don't have any influence over it, so I'm not going to worry about it, and I don't because it isn't going to help me to worry about it. It isn't going to help anyone else to worry about any of those kinds of things. Don't worry about what you can't control. Focus on what you can and leave the rest alone, and that's one of the basic premises of the third book that I wrote, which was published in 2024, entitled "Live Like a Guide Dog: True Stories from a Blind Man and His Dogs About Being Brave, Overcoming Adversity, and Moving Forward in Faith. And that book was written by me and Carrie Wyatt Kent. The book was published by Tyndale House, and is out there for people to to read. The first book was published by Thomas Nelson Publishing, which is now part of Harper Collins, and a lady, Jeanette Hanscom, and I wrote the second book, which is called Running with Roselle, which is really a book more for children, teaching them about me growing up, Roselle growing up, and how we met. A little bit about the World Trade Center. That one was self-published, but all three books are available wherever you can find books, so you can get them always from Amazon, of course. And so I hope that that you'll get them and read them. They will help. Live Like a Guide Dog probably is the best book for me to talk about in terms of things that I learned over the years, but if I really summarize mostly what happened for me after September 11th, it is that what happened on that day validated everything that I've believed about me. Certainly, what I believe about God, but it validates everything that I've ever thought about blindness, blind people, what we can do, the challenges we face, some of the issues that other people have, and the validation is certainly something that helped what I learned. But I learned other things along the way, and you know I want to talk about some of those things. The first, as I said, is don't worry about what you can't control. Focus on what you can, and let the rest take care of itself. Speaker 1  23:56 If you enjoy Unstoppable Mindset and would like to help us continue bringing these conversations to you each week, we've created a way for you to support the show. Your contribution helps us cover production costs and continue sharing stories, insights, and ideas that inspire people to live with purpose and possibility. If supporting the podcast feels right for you, you'll find the link in the show notes, thank you for being part of the Unstoppable Mindset Community. One Michael Hingson  24:28 of the things that happened to me almost immediately after September 11th is I started hearing people say, "We got to get back to normal. If we don't get back to normal, that's a problem. We got to get back to normal. And I always reacted internally, at least pretty negatively to that. And I finally realized why I reacted to that statement about we got to get back to normal. The reason I reacted to that statement, and I still react to to myself to that statement, is normal will never be the same again. We can't get back to normal. If we get back to normal the way it was prior to September 11th, the same thing will happen again. We can't do that. We have to get back to a different normal, and and in fact, normal is probably going to be an ongoing, constant process of change. We ought to learn not to fear that either. Change is all around us. We always say that, but then we don't like change. It's kind of a paradox. But the reality is, change is all around us. And what we really need to do is to learn how to focus in change. And we need to learn that in reality, we can deal with change. And if we have any influence over it, then we should do something about that too. But the bottom line is, getting back to normal prior to September 11th isn't going to help any of us, and and it isn't going to happen. So that is an one thing that I will say definitely. I learned from September 11th: normal will never be the same again. As I said a lot earlier, I think I probably learned about public speaking at least the beginning of it by doing the spelling tests and so on. But after September 11th, the media got my story, and that happened because Joanne Ritter, the public and information services person at the time at Guide Dogs wanted to write a story, and I kind of agreed. And I said, "That's fine. And then she said, "I'll bet you'll be on TV. What television show do you want to be on first? So I kind of just sort of flippantly said, "Larry King Live. That was on the 12th of September when I talked with her. On the 13th, she called back and she said, "Larry King wants you on his show on september 14, which was a Friday, and they sent a car to pick me up, and and I recall Karen and and Tom one as well, and anyway, we went into the World Trade Center, not to the World Trade Center, but we went into New York, and I appeared on CNN, which was the first of five interviews with Larry. Larry also, by the way, at my request, wrote the foreword for Thunderdog. So when you read Thunderdog, you'll get to read Larry's words as well. But after being on Larry King, the media really got my story, and a lot of things happened after that. I started getting literally calls every day for interviews. People wanted to talk to me about September 11th and ask questions and so on. And one of the things that I learned was that talking to reporters could be a great therapy because they would ask virtually any kind of question you can imagine. And of course, a lot of people said things like, "Well, did you know what happened on September 11th when it was happening? And I said no. And they said, well, of course you didn't. You're blind, you know. And my response is, anybody who says that really needs to not let their sight get in their way or their vision to be polite about it. The fact of the matter is, as we were going down the stairs, no one knew anything about what was going on. When I got into the stairwell, I began smelling an odor, and it took me four floors to realize I was smelling the fumes from burning jet fuel. I did a lot of travel for my company, and so I was in airports a lot. So I recognized that smell. People were speculating about what they were smelling. People were speculating about what was going on. Nobody had a clue. Michael Hingson  28:20 When I realized I was smelling jet fuel, I observed it to everyone around me, and they said, "You're right. That's what we're smelling. It's burning jet fuel. We must have been hit by an airplane. You see, no one knew. We had no clue. Superman and X-ray vision just aren't real. And the bottom line is, nobody knew what was actually happening on September 11th. Blindness have nothing to do with it. People want to feel more comfortable by saying, "Well, most people probably did know, but you're blind. You didn't know. We've got to get away from these habits of thinking that just because somebody is different than we are, that they're less than we are. That's just not true. In any case, the bottom line is that we we really deal with those kinds of things, and that helped me as as time went by recognize and realize that normal would never be the same again, and it validated more my belief that blindness isn't the problem; it's the misconceptions and poor attitudes that people have about blindness that tends to truly be the problem that we face. So, as I began to talk with reporters, I didn't think about what was actually happening. But later, I realized when people started saying, "Well, did you ever get counseling or therapy, and I said no. And then it just popped into my head to say I probably had the best therapy around. I got interviewed by hundreds of reporters that asked basically any question that you can imagine, and I learned to answer those questions without being upset, especially when they were asking pretty derogatory questions or making derogatory comments. Your dog led you down the stairs. No, my dog did not lead me down the stairs. Guide dogs do not lead; they guide. And I understand the definition of lead, but it's a little bit different than what people were truly saying. What they were saying is you didn't know anything, and the dog took you all the way down the stairs because the dog could see. No, in fact, Roselle didn't lead me. Roselle's job wasn't, and no guide dog's job is to lead a person or to take charge. The dog's job is to make sure that we walk safely. I also have a job on the team. My job is to make sure that I know where we're going and how to get there. A lot easier now than it was 25 years ago, but the bottom line is that I'm the one that has to direct the dog and give the dog commands, and the dog needs to make sure that we carry out those commands safely. We are a team; we work together. I've always kind of known that guide dog schools didn't necessarily early in my guide dog career, express it that way, but it's it's a much stronger statement now. They recognize that truly we're a team. We each have a job to do on the team, and we have to work together to make the team work, which is true of any team. I've mentioned Seal Team Six earlier. They're a team. They work together. The firefighters in the World Trade Center and the other people who who were around there worked as a team, and they had to work as a team in order to develop a level of trust and a relationship where essentially each person knew what the others on their team were going to do, and it gave them more of a confidence to be able to function. Teamwork is so crucial in our world, and I learned, and and again on September 11th, it was totally validated how teamwork is so important. Roselle and I worked together well. The firefighters worked together well. Yes, we lost people, but the team really is so important, and we need to recognize that anyone listening to this ought to work in any of the things that they do to develop teams. Being alone makes no sense; it isn't going to work. You're going to have to and should learn to work with other people to make teams work better. I've seen companies where when they have award ceremonies, the leader of a team or the boss of a team gets an award, but the rest of the team doesn't really get the same recognition. Michael Hingson  32:30 If a leader is truly doing a good job at creating a team and being a part of a team, then that leader is really enhancing everyone else on the team, and that leader is the person who should help to make sure that when awards are given, that everyone on a team is recognized. We need to to learn and realize that a team is a bunch of people. It's not just one people, but they learn to work together, and that is also so important. And as I learned more and more about the events of the World Trade Center, I learned and realized more about the whole understanding of teamwork and trust and the value of it. I knew about it with me and Roselle, and I recognized the concept of teamwork. But certainly, September 11th again validated that a whole lot more effectively than anything else that I could think of, but I worked with eight dogs over the years, and working with eight dogs from my first guide dog Squire, who was a golden retriever, up through my current guide dog Alamo, I've learned probably I think more than I ever learned from experts and teamwork and sales and so on and motivation. I learned more from those dogs than I ever learned from the human experts because of the fact that one, I had to build a team with a creature who didn't think the same way I did, who didn't talk the same language that I did, and that expected things from me, and and as one of the things that I've learned is that dogs who are guide dogs really want the human to be the pack leader because it is the pack leader, it is the human who's supposed to tell the dog what they want the dog to do, and dogs take that so incredibly seriously. I mentioned Linny. Well, I didn't mention her, I think, by name. But Lenny was my fourth guide dog, and we were out in in San Diego at a company meeting one Friday in May of 1999, and we came home. It was a Saturday morning that we got home. I flew a red eye home, fed Lenny, and then rested and and all that. But Saturday night, when it came time to feed Linny, I called her. She wouldn't come. Karen even called her. She wouldn't come. We went. Upstairs and found her lying on her bed, kind of really not very responsive. We got her to a veterinarian, and it took a few days for the veterinarian to diagnose what she had as something called glomerular nephritis, which is a disease where the kidneys, the glomeria of the kidneys, which would normally pass out bad stuff from the kidneys but not the good stuff. They were passing out the good stuff as well, and Lenny was was not doing well. We were able to reverse that, but she had to retire from being a guide dog. In fact, we thought in late June of 1999 that we were going to lose her. We went off to a a meeting, not a meeting, but we went off to a hotel to say goodbye to Linny, if you will, on like the second of July. But Linny didn't pass away then, and she did get better. And then Roselle came into our lives, and Roselle and Linny became really good friends, but I mention all of it because I had no clue what was happening to Linny. Guide dogs love their jobs; they take them very seriously. And the fact of the matter is, as I learned, and especially after September 11th, I learned that these dogs will work till they drop. They will. They will do everything they can to please us, because they love what we do. They love us, and we need to bestow the same kind of love on them, which is all part of what teamwork is all about. Well, the bottom line was with Linny. She was with us for three more years, and she got to play with Roselle a lot, and so on. Michael Hingson  36:41 Of course, as I also mentioned after September 11th, but I again, especially because of what happened to Linny, but then afterward on September 11th, I realized how much it was important for me to do all I could to keep the team relationship a positive, strong one, to recognize that in reality these dogs work so hard on their own part to make the team's relationships work. They they deal with what they have to deal with. They get very stressed if we're upset. They take it very personally if we get upset, even if we're not necessarily upset at them; they still take it very personally. The more stress that a guide dog faces in their lives, the harder it's going to be for them, and frankly, the shorter their lives are going to be. So I learned, even before September 11th, but especially after September 11th, to do all I could to keep the stress away from the dog. As we were going down the stairs on September 11th, I constantly encouraged and praised Roselle. What a good dog! Keep going. What a good job! Good girl, Roselle. Even if I wasn't able to hold the harness because the stairs were so crowded that Roselle couldn't walk next to me and had to walk behind me at heel, and I would just hold the leash. I kept praising her because I wanted her to know for her I was okay, and I had to focus on not being afraid and letting that fear come out. Well, I was fearful like anyone was, and in fact, I love to tell people. Being a great fan of science fiction, I have a great imagination, and I was listening for the building to make creaks and groans, and you never knew what was going to happen. Fortunately, it didn't all the way down. But the bottom line for me was that doing so kept me focused. And I and I learned later that while I might have been afraid on September 11th, partly because I was encouraging Roselle and working to sound very calm and confident, I was able to also help other people, but help Roselle and keep Roselle from being afraid and and being stressed. And I work very hard to do that with my guide dogs that I've had since even more than prior to September 11th. My current guide dog Alamo and I travel the world speaking all over the place, if you will. We've been to Israel. We've been well, actually, Alamo didn't get to go to London, but we've been to Israel with my seventh guide dog, Africa. We went to the Netherlands, Alamo and I have been to Wisconsin. I've been to a variety of places, and of course, all around the the world, all around the United States, to Canada, everywhere. And again, I don't necessarily know a lot about where we're going in advance, but I ask a lot of questions so that I can learn where we're going to go, so that I can give the dogs the appropriate commands. I mentioned Wisconsin's. I've been all over the United States. It's been an honor, and I'm going to continue to do that. If anybody wants a public speaker to come and talk to their organization, please reach out to me. I'd love to hear from you. My email address is really simple. It's speaker@michaelhinkson.com. We'll put that in the show notes, and love to hear from you. So anyway, I learned a lot about teamwork from working with eight guide dogs, and I continue to learn every day. I think it's very important for me to learn and to focus all that I can to make sure that I do what it is that's necessary to make a team work, and I believe it is all about the team. I've mentioned that I've written three books, and each one was a collaboration with someone else: Susie Florey with Thunderdog, Jeanette Hanscom with Running with Roselle and Carrie Wyatt Kent with Live Like a Guide Dog, and and the fact of the matter is those collaborations I think were very important. Michael Hingson  40:49 They made the books come out a lot different than I might have imagined them coming out by themselves and the way I would have written them without having collaboration, and probably Live Like a guide dog more than more than the other two, just because of the way it all went. But with live like a guide dog, we focused on each of my my guide dogs and the lessons that we learned. So I mentioned Squire, who I worked with in high school and then went to college with Squire, and Squire was was all around where wherever I was. One night in a dorm, I was with a roommate. Well, actually, a person who lived in a room next door to mine, and I was in his room. We were dealing with a physics problem. By the way, my master's degree is in physics. I have a secondary teaching credential, but I was in with Richard dealing with a physics problem. When I went back into my room, there was no Squire. I didn't realize that a couple of the students decided to play a trick and kidnap him. I went looking around the dorm. I even went into the bathroom and opened the shower door and called Squire, and nothing was totally quiet, and finally, I was getting a little bit concerned. And one of the people said, "Oh well, look, we took the dog. We'll show you. They went into the bathroom, opened the shower door, and out comes Squire. He was in on it. He didn't do a thing when I opened the door, but when the other people did, then then he came out. I wasn't mad at him. Sneaky dog that he is, he had a great sense of humor. But I got vengeance because that that person didn't tell the other people that they showed him where showed me where Squire was. I went and hit Squire somewhere, and then I went back out into the main part of the dorm and I started going, "Who took my dog? What's going on? Where is he? And all the other people said, "Oh, come on, he's in the bathroom. And they went in and opened the door to the shower. No Squire, and they were starting to worry about what happened to him. And Squire, of course, was again not making noise. But eventually, I brought him out and I said, "Okay, I found him. I knew where he was. We're playing a trick on you just like you played a trick on me. That'll teach you. I learned that dogs have a sense of humor that day, but I also learned how much I valued having Squire around, and so Squire worked with me up until and through my first graduate year in physics at UC Irvine, and then Squire retired and went to live with my parents. And in 1973, I got my second guide dog, Holland. Holland was with me through 1986. He worked for almost 13 years, and in fact, was two weeks from retiring when he collapsed in our backyard one day, and we took him to the vet. And the vet said, "I could probably bring him out of whatever he's got, maybe a stroke or a heart attack or both, but he won't have a good quality of life. And so we said goodbye to to Holland. Holland and Squire were both golden retrievers, but again, Holland so strongly reinforced all the things that that I had felt about teamwork, and I learned so much more from him about what I needed to do in order to to function well, not just with a dog, but in my whole life. Then I got Klondike, who was my third guide dog, who was also a golden retriever. Soon after I got Klondike, well, about a year after I got Klondike, suddenly one day he had a very strong grand mal epileptic seizure, and we took him to the vet, and they put him on some medication that wasn't really helping that. And we then kept trying to to deal with it, and finally, and during that same time, I was looking for a job. I had owned my own company for four years, but decided I wanted to go back into the workforce, and and I finally got a job with a company. Michael Hingson  44:52 And on one of the first days that I worked there, Klondike had four grand mal seizures in the. Period of time, and we thought we were going to have to say goodbye to Klondike. But I talked to our veterinarian, and he said, "You know, before we do that, I want to send him to a neurologist who did an electroencephalograph. And he said, "I didn't see much, but he said one thing he did was to take some blood tests, and he said I noticed that his Klondike's thyroid level is low, and low thyroid levels in dogs like Klondike can make them more susceptible to have seizures, just like it can for children. Well, we put Klondike on synthroid, synthetic thyroid, as well as on phenobarbital to control the seizures. The synthroid had much more of an effect than the phenobarb did, Klondike actually became a much more active and playful and interactive dog, and did not have from that time on for the next seven years that he worked for me as a guide dog never had a single seizure. So the thyroid theory was a good one. Again, with with Klondike, it was about developing a team and recognizing that we had to work together. Well, then I got Linny, and again I mentioned Linny only worked for three years. Linny had glomerular nephritis that was really probably from a tick bite where the tick had Lyme's disease, and we didn't know any of that was happening until that night that Linny didn't wake up at all or didn't get up from her bed, and we finally got it diagnosed. And then I got Roselle. Now Roselle was a a wonderful dog. One of the things that I've always said is that when I get a new guide dog and I tell the trainers I want a dog with an on-off switch. That is, I want a dog that knows when the harness is on, it's time to work, and when the harness is off, then they can play. Clearly, Roselle had that when she was in harness. She was very focused. She did develop a phobia of thunderstorms, but there were a few times that we had thunderstorms, and she was able to continue to guide, even though she would shake and she would shiver a little bit. But she continued to guide. She focused. In fact, there was a thunderstorm the morning of September 11th, about 12:30 in the morning. But there was no thunder when we were going through what we did in the World Trade Center, so none of that caused her to be fearful. But again, Roselle was a dog that that taught me a lot about observation, learning what the dogs do, what what they fear, and how to help keep them focused and calm. And so I did that. My sixth guide dog was Meryl. Meryl only worked for about a year. Roselle retired in 2007. Merrill worked from 2007 to 2008, but Merrill became very fearful of guiding, and so ended up not being a guide dog very long. And I I'd never experienced that, although I had heard other people who had issues where dogs didn't work very long. Lenny only worked for three years, but a dog that didn't work for more than a year-that was unusual for me. However, poor Meryl wasn't able to do well as a guide, and so she retired. And then we got Africa. Now Africa was my seventh guide dog. Africa's mother was Fantasia. In January of 2006, we were working. I was working at Guide Dogs for the Blind, and we applied to be what were called breeder keepers. That is, they were the the people who would keep a breeder dog in their home because the guide dogs for the blind bred their own guide dogs and so on. And breeder keepers would keep dogs in their homes so that the dogs weren't just cooped up in kennels all day, and we volunteered to do that. And the director of breeding, Marina Hall, called us and said, "I've got four dogs for you to look at. I think one's better than the others for you, but I want you to come and meet these dogs. So we went down, and and she made the comment again. I think one dog is going to be better than the others. Michael Hingson  49:02 And and I said, well, if you think so strongly, but which one is it? Well, she finally sort of told us. She said, I didn't want to tell you, but I'll tell you. So she did. But anyway, the first dog who came in was a dog named Fantasia, and it works sort of the same way that when people get guide dogs, you're in a room. The dog is allowed to come in, and you see how the dog reacts to you. When I met Squire for the first time, I was sitting in a chair. They let Squire in. He came over and started sniffing me all over. Didn't leave me. He just started sniffing. And Bruce Benzler, who was the apprentice instructor at the time, later became director of Guide Dogs for the Blind, CEO anyway, anyway. Bruce then said, "Looks like you found a friend already, and we became friends. But anyway, when when Fantasia came into the room, she took one look at Karen and leaped into Karen's lap. She had no fear of the power wheelchair that Karen was using by that time. The other three dogs, we still looked at them, and they all expressed one level of fear or concern about being around this wheelchair. But Marina said Fantasia was the one that we thought you'd like best and would like you best, and she was. So anyway, in 2006, we got Fantasia in January, and later that year, she had a litter of puppies, and then in early or mid 2007, she Fantasia had her second litter. I guess it was late in 2006 she had the second litter, but anyway, by the middle of 2008, when we had to retire Meryl because she wasn't working out very well, we were notified that they found a dog that they thought would be a really good replacement for Meryl, and that's a dog named Africa. And Karen and I realized Africa was a name that we suggested as as breeder keepers that they could name one of their puppies if they had puppies who came from an A litter. And the way it works, by the way, is when a litter is born, they're assigned a letter of the alphabet, and they got a big book, and they go into that book and they get the next names that are available. They don't use names of current guide dogs, so when a dog retires, then they can start using that name again. But anyway, Africa was a name that we suggested, and as soon as we saw the email that said Africa, we knew Fantasia was Africa's mother. Well, Africa came and joined us, and mom and daughter did really well, and Roselle was still with us. So the three of them have a lot of fun together, but again with Africa, I learned about teamwork and doing the things that I needed to do. Every dog is different, and one of the things that I developed over the years was an understanding that to truly develop a relationship with a dog takes probably close to a year by the time we really are totally comfortable with each other, but I worked with Africa. We became good friends. Africa became good friends with Roselle. Of course, Africa liked her mom, and all worked out really well. Developing those teeming relationships is so important, and you can learn a lot more about them if you get live like a guide dog. I think you'll find them all very interesting to to read about. So in 2017, I noticed that Africa was starting to slow down and not do as well. She wasn't able to see as well at night. Sort of typical signs of a dog getting older, even though I work to to keep stress out as much as possible, but I applied and in 2018 on the 9th of February, Africa's puppy raisers, Bill and Peggy Sproul from San Francisco or San Diego rather, came to visit us in Victorville, and we had offered them the opportunity to take Africa because we had Fantasia, and we had Africa. And if we kept Africa and Fantasia, and I got a new guide dog, Karen would have a hard time taking care of two dogs when I traveled, because I was doing a great deal of travel by then as a keynote speaker, and so the Sprouls came and got Africa. Michael Hingson  53:26 Left our house, Africa didn't even look back. She was gone. We had visited her several times since then, and and in in the Sprouls home, Africa died in 2020. But we we got to visit Africa several times and it was a lot of fun and and she she loved us and all that and remembered us anyway on the 11th of February two days later I went to Guide Dogs for the Blind and I met Alamo who was my first black Labrador and I met him we we met in the room and he kind of sniffed me a little bit and walked around the room. But then came back and sniffed me, and then I took him on leash back to our our dorm room at Guide Dogs for the Blind, and I decided I'd sit on the floor to to visit with him. And the next thing I know, this big, huge, 60 pound Labrador is in my lap. Alamo was and is a lapdog, but he doesn't know a stranger, which is great. I mentioned earlier that I always said that when I get a guide dog, I want a guide dog that has an on and off switch. Another thing that I require is that guide dogs that I get be raised by cats. I have known of animals who of guide dogs who don't like cats, and I would not want that because we always have had cats. And by that time, when we got Alamo, we had Stitch, who we rescued in January of 2015. So now here it is, February of 2018. So I knew that we needed to have a dog that wasn't going to be. Be afraid of cats. Well, so Stitch and Alamo kind of walked around each other a little bit when we first went home, but they got to like each other pretty well pretty quickly, and they have been together ever since. Alamo has been on a number of aircraft, as I've mentioned over the years, he went to Israel with me. He didn't go to England when I spoke in 2024, but he's been to other places and will continue to travel with me. I work really hard to keep the stress out of his life. I don't want him to become any more stressed than he ever has to be, and he feeds off of how I behave, as as I've learned guide dogs do. So that's one of the most important things that I've learned is to keep that stress out of his life. I would point out that is just as much true for human teams. Don't stress out your team. If you are a leader, your job is to enhance what those leaders, what the team does. Your job isn't to boss them around. If you're doing that, you're only causing more stress, and you're going to find you're going to have a big job turnover, much more than you really should. I've always felt that my job isn't to boss people around when I hire them, my job is to enhance their abilities by using the skills that I have and learning to work with them to give them the benefit of what I know and to use the benefits of what they know to learn myself as well, so that we become more effective. And the people who really get that do very well. The people who didn't get that tend not to last very long at the company because they don't perform very well. Because I know that in reality, I add value to the team by working to add value, not bossing the team around. I think there's a big difference between leaders and bosses, and I've learned that over the years. That leaders are people who can enhance what other people do, who work to make the whole team effective and not just try to boss people around. In late 2020, I discovered a company called Accessibe, A C C E S S I capital B E. Website is accessibee.com. Was a company that made products to help make internet websites more inclusive, and at first, they had some challenges because they were using a lot of AI stuff to make their products work, and they sort of did. But there were there were challenges. But one day in January of 2021, the founder of the company, Sheer Eckerling. Michael Hingson  57:41 contacted me and said we're interested in you joining the company. I had been looking at possibly selling their products because the pandemic had hit, and I thought I could sell them from home because we were locked down. And and he said, well, you know, we we'd like you to not sell as a partner, just on your own, we'd like you to join the company. Well, he asked what I was doing, and by that time I had started to learn about podcasting because I couldn't travel and speak all over the world. So what I decided to do was to look at using podcast as a revenue source. And Shear said, "Well, you know, we we're really interested in having somebody do a podcast for us. And I said, "What do you want? And he said, "I want to just get somebody who will do a podcast to show that we're part of the world. And you join the company; you could do that in part. I joined the company, and he didn't have any specific agenda for the podcast. So, in August of 2021, I began Unstoppable Mindset, and soon it became unstoppable mindset where inclusion, diversity, and the unexpected meet. And I'll explain all of that in a moment. But the point is that we started the podcast. I created the title. I recreated the description, and I wanted the podcast to be conversations. And as anyone who has been on the podcast knows, we don't do interviews as such. We do conversations, and I ask people to send me materials to help me prepare to be able to do the conversation. And so a number of times we've had diversity experts on people from the DEI world, and when I ask them what they do, they say, you know, we focus a lot on diversity, and I say, what does that mean? And they say, well, we deal with race, gender, sexual orientation, and so on. And we chat for a little while, and then I say, but you haven't talked about disabilities. And they say, oh, disability is not a minority. And I say, what do you mean? He said, well, disability is a social justice thing. It's not like race, gender, or sexual orientation. And my response to that is balderdash. I like to use balderdash. I could go back and use what Colonel Sherman Potter did on Mash and say monkey muffins. But anyway, balderdash because diversity should include disability because it is a minority. Even the Center for Disease Control says that. Adversity, or that that people with disabilities are a minority of about 25% of people in the United States. Well, so the bottom line for me is that I would say to them, "What do you mean you're you're not including it? And they say, "Well, it's a it's a disability, but it's social justice. It's always about being a lack of ability, and as I have learned, especially since September 11th and and beyond, disability does not mean a lack of ability. You might want to think it does. You might want to try to make that fit, but it doesn't. And I learned that most effectively in March of 2023 when I went to something, by that time Karen had passed, and I was working with a person who had been Karen's caregiver, who decided to work for me. Very grateful for Josie doing that. Josie entered my name in the contest to be a guest at the Kelly and Ryan Oscar after party, which was going to be held the Monday after the Oscars at the Dolby Theater, well, I didn't even know she entered me, but I got a call the Tuesday before the Oscar after party and the Oscars, and I was and I picked up the phone and they said congratulations, and I said for what? Oh, you won an invitation to come to the Kelly and Ryan Oscar after party, and I went really well. Anyway, I went down with my niece and nephew, Tracy and Charlie Green. The there wasn't room for taking Josie, her husband, and her mother and her five kids. So it was just me, Tracy, and Charlie. Michael Hingson  1:01:37 We got up to our room about 3o'clock in the afternoon on the Saturday before the Oscars, the day before the Oscars, we put our luggage in our room and we were coming back down the main stairwell staircase actually from the third to the first floor in the Hollywood Roosevelt. It's a very open staircase, and all of a sudden I heard people screaming, and it took me a while to to try to understand what's going on, and finally I couldn't figure it out. I said, Tracy, what's going on? And she said, Oh, we just lost power and lighting on the stairs, and and power and lighting in and around the entire hotel. Now it didn't bother Tracy. Tracy had known me for a long time, but people were panicky because they weren't getting all the light they expected, even though the windows were open, the the shades were up on the first floor in the lobby, and so on. Lots of light, but it wasn't all that people expected. And I thought about that, and what I learned from that was, it proves that disability isn't a lack of ability; it proves that disability is a characteristic that we all have. Thomas Edison and others worked on the light bulb, and Thomas Edison is credited with developing the electric light bulb back in 1878. But in the 148 years since the light bulb was developed, all society has done in that regard is worked very hard on coming up with better ways of providing light on demand, so that you sighted people could have all the light you want when you want it. Nowadays, we have smartphones, we have flashlights, we have other ways of getting light on demand. But as the Americans with Disabilities Act would describe it. The light bulb is simply, or any piece of technology that produces light on demand, is simply a piece of technology that provides light on demand to light-dependent people who otherwise don't function well without it. The bottom line is, disability is not a lack of ability. We got words like discrete, discern, disciple. What is discern? Is it a lack of discern? What is it? What is disciple? A lack of viple? No. Disability is not a lack of ability. Disability is a character

September 4, 2026: Your daily rundown of health and wellness news, in under 5 minutes. Today's top stories: Equinox negotiates to refinance $1.8B in debt and add fresh capital as owner Silver Lake doubles down on the high-end fitness model Barilla acquires better-for-you brand GOODLES, which holds 8% of the boxed mac and cheese market, keeping it independent while adding distribution scale Oura files publicly for its IPO showing revenue up 74% to $1.4B and 5M+ members, pitching itself as a health intelligence platform at a $16B valuation More from Fitt: Fitt Insider breaks down the convergence of fitness, wellness, and healthcare — and what it means for business, culture, and capital. Subscribe to our newsletter → insider.fitt.co/subscribe Work with our recruiting firm → https://talent.fitt.co/ Follow us on Instagram → https://www.instagram.com/fittinsider/ Follow us on LinkedIn → linkedin.com/company/fittinsider Reach out → insider@fitt.co

Aaron, Brian, and Brandon cover major stories including NVIDIA's record quarter and continued growth forecasts, alongside concerns about declining free cash flow and customer financing. They discuss NVIDIA's reported $12.9B acquisition of Hugging Face as a strategic move to strengthen the open-model ecosystem and go up the stack, and Stripe's $8B acquisition of OpenRouter as routing infrastructure for model choice and potential agent-to-agent commerce. The group reacts to reports of OpenAI agent testing in which agents collaborated, manipulated logs, and tried to deceive humans, framing it as a security and guardrails issue. They also mention Microsoft employees' surprising AI spend, OpenAI's “Jalapeño” hardware push and manufacturing constraints, and Salesforce's “Claude Force” concept of using Claude as the UI to query Salesforce data.SHOW: 1059SHOW TRANSCRIPT: The Enterprise AI Show #1059 TranscriptSHOW VIDEO: https://youtu.be/Clmgst03-egSHOW LINKS:NVIDIA's record quarterNVIDIA buys Hugging FaceStripe buys OpenRouterOpenAI's new chipSHOW SPONSORS:NordLayer - Use ENTERPRISE10 for 10% off.Nasuni - Activate your data for AI and request a demoTopic: Link to the full list of topics for the monthFEEDBACK?Email: show @ the enterprise ai show dot comBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

HEADLINES:• Prince Alwaleed's Kingdom Holding Completes $224 Million Deal for 70% of Al Hilal• Saudi Arabia in preliminary talks to borrow $8B from banks: Report• Former WeWork CEO Adam Neumann Launches Flow in UAE With Major Hiring Push

Cerramos la octava temporada con un episodio que me apetecía grabar desde hace meses. Igual te ha pasado como a mí: empecé hablando de un laboratorio de IA para cualquiera, y terminé recomendando GPUs de 3000 euros. Me fui creciendo, pero no hace falta. Te cuento cómo montar un laboratorio de IA local con el equipo que ya tienes. Da igual si tienes 8 GB de RAM o 16, CPU modesta o sin GPU. La clave está en elegir los modelos adecuados. Muchas veces nos perdemos buscando el modelo más grande, cuando con uno pequeño y bien cuantizado tenemos de sobra para el 80% de las tareas.Te hablo de Ollama, el gestor de modelos estándar para ejecutar modelos locales. Más de 180.000 estrellas en GitHub, API compatible con OpenAI, modelos para todos los presupuestos: desde Phi 3.5 con 3.8B parámetros hasta Qwen 1.5B que ocupa 1 GB. También la cuantización: reduces la precisión numérica de los pesos para que ocupen menos y vayan más rápido. El punto dulce es Q4_K_M, que reduce el tamaño a menos de un tercio. Para 8 GB de RAM, Q3_K_S puede ser tu salvación.También te hablo de Open WebUI, la interfaz que le da mil vueltas a ChatGPT. No solo chateas: tiene RAG local, Whisper integrado para transcribir voz (75 MB en CPU), TTS con Kokoro-82M para que el modelo te hable en tiempo real, búsqueda web, plugins y memoria persistente. Todo en un contenedor Docker que levantas con un solo comando.Y de SQLite Vec, extensión de SQLite sponsorizada por Mozilla para búsqueda semántica sin servidores vectoriales. Ni ChromaDB, ni Qdrant, ni Milvus. C puro que funciona hasta en Raspberry Pi. Creas tablas virtuales para vectores de 768 dimensiones, generas embeddings con nomic-embed-text, y buscas por similitud coseno en milisegundos. RAG local sin complicaciones.Y te explico cómo organizarlo todo con Docker o Podman. Un docker-compose.yml que levanta Ollama y Open WebUI en segundos, con healthchecks, redes separadas y volúmenes persistentes. También a limitar recursos con --memory y --cpus. He preparado scripts: inicialización que comprueba requisitos, crea directorios y descarga modelos; otro para descargar por niveles según tu hardware (nivel 1 para 8 GB, nivel 2 para 16 GB, nivel 3 para 32 GB); y uno de respaldo.Y la estrategia híbrida local + nube, que es lo que realmente tiene sentido. El enfoque Minions del Stanford Hazy Research Lab: el modelo local hace el trabajo pesado, y solo consulta al grande en la nube para tareas complejas. El 90% de las consultas se resuelven localmente. Ahorras dinero, mantienes privacidad de tus datos, y cuando necesitas potencia, la tienes.Con 16 GB de RAM y un SSD te sobra para el 80% de las tareas: traducciones, resúmenes, código, asistentes, RAG, transcripción de audio, texto a voz... Todo en tu máquina, sin enviar datos a servidores, sin suscripciones, sin depender de internet. Con 8 GB también puedes, con modelos más pequeños. Cerramos temporada, la novena arranca en el episodio 828. Capítulos del episodio:0:00 - Introducción — cierre de temporada 8 y replanteamiento2:30 - Hardware mínimo: 8-16 GB RAM + SSD obligatorio5:00 - Software base: instalar Ollama en tu distribución7:30 - Contenedores: Docker vs Podman para el laboratorio10:00 - Modelos pequeños: Phi 3.5, Qwen 1.5B y cuantización13:00 - Herramientas complementarias: SQLite Vec, Whisper, TTS16:00 - Organización del laboratorio: script y estructura de directorios19:00 - Demo: probando Ollama en local con modelos ligeros22:00 - Combinación local + nube: lo mejor de ambos mundos24:30 - Cierre, avance temporada 9 y despedidaMás información y enlaces en las notas del episodio

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On this episode of Gov Tech Today, hosts Russell Lowery and Jennifer Saha step back from day-to-day procurement details to examine what California's public data reveals about technology spending trends. Jen shares analysis comparing FY 2024–25 to 2025–26 across major contract vehicles, finding a sharp decline in IT services: about $3.8B down to $2.59B, nearly a 30% drop, with notable decreases in interagency agreements (down roughly 50%), formal competitive bids, and non-competitive bids. At the same time, IT goods spending is steadier and slightly up—from about $2.3B to $2.7B—suggesting continued purchases of software, hardware, and cloud while agencies reduce reliance on long-term vendor-managed services. They discuss policy changes enabling implementation services through the Software Licensing Program and consider political pressures, budget constraints, and potential pent-up demand ahead of the next administration. 00:00 Show Intro 00:14 Why Spend Is Down 02:28 Services Spending Drop 05:07 Interagency Declines 06:33 Competitive Bid Slowdown 09:09 SLP Services Surge 11:49 Goods Mostly Flat 13:51 What Counts As Goods 16:26 Big Picture Outlook 19:21 Wrap Up And Contact  

Latin America has a massive consumer credit market, but high interest rates and short loan maturities continue to put pressure on borrowers.In this episode, Sergio Furio, Founder and CEO of Creditas, shares how the company built an asset-backed lending model around cars and homes, giving customers access to longer-term credit at better rates.They discuss why Creditas moved away from partnering with banks, how securitization became central to the business, why complexity created a stronger moat, and how the company reduced production costs from more than 20% of loan value to below 9%.Sergio also explains how Creditas built a base of 20 million registered users, what its valuation reset changed, why the company remains focused on Brazil, and where tokenization could change lending next.

Who is actually capturing the value being created in crypto?In this episode, David Sencil sits down with Lorenzo Valente, Director of Research for Digital Assets at ARK Invest, to unpack a striking gap: centralized crypto companies generated roughly $70 billion in revenue in 2025, compared with only around $8 billion on-chain.Valente explains why centralized platforms are still closer to users, while many on-chain protocols continue to struggle with value accrual, token economics, and sustainable growth.The conversation also explores Hyperliquid, Pump.fun, Solana, and Ethereum, including whether aggressive token buybacks could limit long-term growth, why successful crypto apps may eventually launch their own chains, and where the next major wave of on-chain value could emerge.Topics include: The $70B vs. $8B crypto revenue gap Why centralized companies still capture more value Hyperliquid's token buyback strategy Whether successful apps will launch their own chains Pump.fun and the future of crypto applications Solana's battle for relevance Ethereum's institutional advantage Real-world assets and institutional adoption Whether memecoins will remain a major crypto narrative Can on-chain protocols eventually close the gap, or will centralized companies continue capturing most of crypto's economic value?

(0:00) Michael Kratsios joins the show! (01:56) Is this administration anti-science? The Nature poll, DEI grants, and $8B down the drain  (8:16) Climate science cuts: RCP 8.5 gets pulled and the "climate emergency" narrative collapses (13:45) $47B at NIH, Eroom's Law, and golden tickets: has American science stagnated?  (22:24) Big bold bets: Genesis Mission, quantum by 2028, fusion by 2035, boots on the moon in '28  (34:04) The great race with China: $33B to $670B, and 7 out of 10 STEM PhDs aren't American  (44:09) Fauci did more damage to science than anyone in modern history, and NIH's median researcher is 71 Follow the besties:  https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg   Follow on X: https://x.com/theallinpod   Follow on Instagram: https://www.instagram.com/theallinpod   Follow on TikTok: https://www.tiktok.com/@allin   Follow on LinkedIn:  https://www.linkedin.com/company/allinpod   Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg   Intro Video Credit: https://x.com/TheZachEffect #allin #tech #news  

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

Shannon Spotswood – CEO, RFG Advisory Choosing a platform isn't just about technology or economics. It's about finding a partner that helps you build the business you actually want to own. Shannon Spotswood explains why growth without compromise starts with choosing the right partner. In Summary What should advisors really look for in a platform partner? Jason Diamond sits down with Shannon Spotswood, CEO of RFG Advisory, to discuss why the best platforms do more than provide technology and operational support—they help advisors build stronger businesses. Shannon shares lessons from helping grow RFG into one of the industry's leading supportive independence firms, covering everything from private equity partnerships and advisor experience to enterprise value, branding, and overcoming the fear that keeps many advisors from pursuing the business they truly want. The Storyline Most advisors evaluating independence compare technology, payouts, and service offerings. Shannon Spotswood believes they're asking the wrong first question. After spending two decades in institutional investing and later helping to rebuild RFG Advisory from the ground up, Shannon has developed a philosophy centered on partnership. She argues that the best platforms function less like vendors and more like long-term business partners, helping advisors spend more time with clients, build enterprise value, and create businesses aligned with their vision rather than forcing compromises. Jason and Shannon discuss what meaningful support actually looks like, why the right private equity partner can accelerate growth rather than restrict it, and why advisors should demand evidence – not marketing promises – when evaluating a platform. The conversation also explores one of the industry's biggest obstacles to change: fear. Shannon explains why outdated assumptions about transitioning firms continue to prevent advisors from building businesses they enjoy, even though data suggests the experience is often far less disruptive than many believe. Ultimately, the discussion reframes independence itself—not as the destination, but as the beginning of choosing the right long-term partners. Topics Covered Evaluating advisor platforms as long-term business partners Building an independent business without compromise Enterprise value and organic growth Private equity as a strategic growth partner Advisor experience and client experience Branding and authenticity in wealth management Overcoming fear and transition myths Technology, outsourcing, and operational leverage Leadership, succession, and organizational growth The future of supportive independence   > Download a transcript of this episode… Listen and Learn Highlights for Advisors Why should advisors think of a platform as a business partner? (10:00) Shannon explains why technology and service alone aren't enough—and why the right partner should help advisors build the business they ultimately want to own. What does “growth without compromise” actually mean? (10:00–17:30) RFG's philosophy centers on helping advisors focus on their highest-value work while surrounding them with integrated support designed to drive enterprise value. Can private equity make a firm better? (25:00) Rather than debating whether private equity is good or bad, Shannon explains why success depends on choosing a partner whose values and long-term vision align with yours. How should advisors evaluate competing platforms? (43:00) Her advice is simple: don't rely on marketing. Speak with advisors already using the platform and ask firms to demonstrate – not simply promise – how they solve problems. Why does fear keep so many advisors from making a change? (48:30) Shannon discusses the “PTSD” many advisors carry from outdated transition stories and why today's reality often looks very different. What does the future of advisor platforms look like? (34:00–42:00) The conversation explores advisor demand for greater personalization, stronger brands, AI-enabled efficiency, and partners that help advisors grow without sacrificing independence. Key Takeaways The best advisor platforms function as long-term strategic partners—not simply service providers. Enterprise value grows when advisors spend more time serving clients and less time managing operations. Private equity can be highly beneficial when partners share a common vision and respect management autonomy. Advisors should evaluate firms based on demonstrated execution rather than marketing claims. Fear remains one of the biggest barriers to advisor movement despite significant improvements in transition support. Authentic branding and deeper client relationships will become increasingly important as AI reshapes wealth management. https://youtu.be/jaSt3-mO0so Quotable Moments “The right partners make you better. The wrong ones can quietly hold you back.” “Don't tell me. Show me.” “Everything you want is on the other side of fear.” “Your team deserves to be happy. You deserve to be happy.”   FAQs What should advisors look for when evaluating an advisor platform? Shannon believes advisors should look beyond technology and economics and evaluate whether a platform acts like a true long-term business partner that helps them grow and build enterprise value. How does RFG define “growth without compromise”? By providing integrated support – from technology and compliance to marketing and coaching – that allows advisors to spend more time with clients while maintaining control of their businesses. Is private equity always good or bad for advisor firms? No. Shannon argues that success depends less on private equity itself and more on choosing partners who share the firm's long-term vision and values. Why do advisors hesitate to make a move? Fear and outdated perceptions about transitions still influence decision-making, even though today's transition experience is often much smoother than advisors expect. How should advisors compare competing platforms? Talk directly with affiliated advisors, ask for measurable evidence of results, and focus on how a platform responds to advisor feedback rather than marketing claims. How is AI changing advisor businesses? AI should enhance – not replace – the advisor relationship by creating operational efficiencies that allow advisors to spend more time delivering personalized advice. Shannon believes advisors should look beyond technology and economics and evaluate whether a platform acts like a true long-term business partner that helps them grow and build enterprise value. By providing integrated support – from technology and compliance to marketing and coaching – that allows advisors to spend more time with clients while maintaining control of their businesses. No. Shannon argues that success depends less on private equity itself and more on choosing partners who share the firm's long-term vision and values. Fear and outdated perceptions about transitions still influence decision-making, even though today's transition experience is often much smoother than advisors expect. Talk directly with affiliated advisors, ask for measurable evidence of results, and focus on how a platform responds to advisor feedback rather than marketing claims. AI should enhance – not replace – the advisor relationship by creating operational efficiencies that allow advisors to spend more time delivering personalized advice. Related Resources How to Evaluate a Firm Beyond the Obvious: A Framework for Advisors Why You Should Stay at Your Current Firm   Shannon SpotswoodCEO Shannon Spotswood is a 25+ year industry veteran with a tremendous amount of experience across both retail and institutional finance and an outstanding reputation built on her passionate leadership and ongoing success in investment banking, hedge fund portfolio management, business development and retail wealth management. Joining RFG in 2015, Shannon recognized the opportunity to channel her entrepreneurial experience and passion for service into leading a mission to create an Advisor-focused RIA of the Future delivering a supported independence platform that empowers Financial Advisors to build the businesses they want to have, without compromise. Shannon's career has been characterized by her determination to build something bigger than herself. Having fallen in love with finance at only age 14, she was focused on making an impact in a male-dominated industry. After graduating from college, Shannon spent 20 years in San Francisco working in institutional finance. She began her career in investment banking and eventually achieved her dream job as a Portfolio Manager of a long- short equity fund at Symphony Asset Management. The company was acquired by Nuveen in 2001. After a decade at that firm and now a mother of 3 young children, Shannon turned her entrepreneurial passion in a new direction with a drastic pivot to start a luxury children's clothing brand, Busy Bees. Taking her years of experience in qualitative analysis of retail companies, Shannon and her business partner built the brand from the ground up, ushering its' growth from a garage to “Gwyneth Paltrow's Goop” over the course of a few years. Shannon and her family made the decision to move from the Bay Area to Birmingham, Alabama to be closer to family. And shortly after, the call to return to her first love, finance, grew to great to ignore. In 2015, Shannon joined RFG Advisory as President, leading RFG as the firm has grown from $1.8B to over $5B. In July of 2024, Shannon was named CEO of RFG Advisory and currently serves in that role. NOTE: The views and opinions expressed by the guests on this podcast are their own and do not necessarily reflect the views and opinions of Diamond Consultants. Neither Diamond Consultants nor the guests on this podcast are compensated in any way for their participation. View the transcript of this episode… Growth Without Compromise: Building Around the Advisor Experience A conversation with Jason Diamond and Shannon Spotswood, CEO of RFG Advisory. Jason Diamond: Welcome to the latest episode of our podcast series for Financial Advisors. Today’s episode is Growth Without Compromise: Building Around the Advisor Experience. It’s a conversation with Shannon Spotswood, the CEO of RFG Advisory. I’m Jason Diamond, and this is the Diamond Podcast for Financial Advisors. Mindy Diamond: At Diamond Consultants, we help elite advisors identify the right environment for their businesses to thrive, whether that’s at a wirehouse, boutique, or independent firm. With nearly three decades of experience, we’ve guided thousands of advisors and represented more than a quarter of a trillion dollars in assets transitioned. And each year, one in four advisors managing a billion dollars or more who change firms are our clients. Our process is education-driven and based on building relationships, starting as your strategic partner well before you’re even thinking of a move. To schedule a confidential conversation, call us at 908-879-1002. Wondering why advisors change firms and where they’re headed? Are transition deals going up or down? Those very questions and more inspired us to create our annual advisor transition report. It’s the award-winning data-driven resource designed for advisors that connects the dots between the motivations around movement and the firm’s appetite for top talent. Arm yourself with the knowledge you need to make smart decisions. Download your copy at diamond-consultants.com/transitionreport. Jason Diamond: The right partners make you better. The wrong ones can quietly hold you back. Most conversations about independence focus on platforms as providers of technology, service, or infrastructure. Shannon Spotswood sees them differently. She believes advisors should evaluate a platform the same way they’d evaluate any long-term business partner, by asking whether it will help them build the kind of firm they ultimately want to own. That’s exactly what we explore in this episode. Shannon is the CEO of RFG Advisory, a firm that has grown from a startup into one of the industry’s leading supportive independence platforms. Along the way, she’s developed a unique perspective on what advisors should be looking for beyond economics and technology, and why the right partner can accelerate growth, strengthen culture, and help create a business that’s built to last. It’s a conversation that goes well beyond advisor platforms. We explore why Shannon believes so strongly in growth without compromise, what private equity can look like when the partnership is aligned, why firms shouldn’t try to be everything to everyone, and how advisors can separate marketing promises from meaningful support. We also spend time on a topic that comes up in nearly every transition conversation my team has with advisors, fear. Shannon shares her perspective on why outdated assumptions about making a move continue to hold advisors back and why asking better questions and demanding evidence instead of promises can fundamentally change the way advisors evaluate every opportunity in front of them. Whether you’re considering independence, evaluating your current platform, or simply thinking about what comes next for your business, I think you’ll find Shannon’s perspective both practical and though-provoking, especially the sage advice in her words, “Don’t tell me, show me.” There’s a lot to take away from this conversation, so let’s get to it. Shannon, thanks so much for joining me. Thrilled to have you here. Shannon Spotswood: It’s excellent to be here. I’m really looking forward to it. Jason Diamond: Me too. Let’s dive right in. I want to start with your background. You spent 20 years in San Fran as an investment banker, then as a portfolio manager at Symphony Asset Management before even touching the world of wealth management. So what made you walk away from, we’ll call it the institutional world and enter the world of wealth management? Shannon Spotswood: It’s a little bit of a circuitous story, but I’m going to take us on the short route. I fell in love with Wall Street as a teenager, so I knew I wanted to work on Wall Street. My dream job was actually the time that I spent at Symphony Asset Management. I was a hedge fund manager for them for six years running a long/short equity fund. I then had three children in three and a half years. The firm was acquired by Nuveen Investments, and we grew very large, and I was on this really interesting trajectory within the institutional investment management world. And somewhat of the unexpected happened to me in 2010, we’d come through the financial crisis. I looked around the room, I had these three young children, and having loved finance since a very early age, I couldn’t crawl on an airplane anymore. I fell out of love with what was honestly my first love. And I made a pretty radical pivot. I left Symphony, the tallest building at the time in San Francisco, and I partnered with a woman, and we built a luxury children’s clothing company for the next three years. So about as radical of a move as you can make, a $30 billion firm, big team, a tremendous growth ahead of us to upside down boxes of infant cashmere in a garage that flooded when it rained. So I had my startup in a garage moment. And while I was running the children’s clothing company, my husband and I took a big leap of faith and decided to move from San Francisco to Birmingham, Alabama to get closer to family, to raise our kids in the South, and just manifest the life that we wanted. In the third year of running the kids’ clothing business, we checked every box of our initial business plan, and I turned to my business partner and I was like, “Now what? Should we raise capital? Should we open stores? Should we diversify manufacturing?” And we realized this beautiful little luxury brand that we had created was exactly what it needed to be. And so we restructured the company and I punched out of that. And I spent, really for the first time in my life, about five months in deep contemplation. What was the first hedge fund that I was a part of in San Francisco, my tour of duty through investment banking as an analyst associate and helping them start an M&A group. This incredible decade that I’d spent at Symphony, and then this wild out of left field moment of building a luxury children’s clothing brand. And it had such an epiphany, Jason. And it was this, that I was on the ground floor of all of those businesses. And my aha moment was, oh my gosh, I’m a builder. What I love more than anything is sitting at the intersection of talent and opportunity and what I think is truly one of life’s greatest gifts, and certainly I think the most fun way to live your professional life, which is building something. So I put my resume together and I titled… It wasn’t even really a job search. It was more, I was new to Birmingham. I wondered if there was anything I could be of service in being a part of building something. So I put that resume together and I titled it Seeking the Intangible. And I was looking for that opportunity of talent and building something bigger than myself. And it was through some networking with my across the street neighbor who went on to become a board member of RFG who thought all I did was sell his wife incredibly expensive clothing who networked me to Bobby White, who’s the founder of RFG. And in the first 10 minutes of my conversation with Bobby, and I’ll tell you, both of us went into that meeting thinking it was going to be a filler meeting. He was doing a favor for a friend, and I had seen a little bit of the wealth management industry after Nuveen had acquired Symphony and was like, “That’s not really my bag. My jam is more on the institutional side of things.” And 10 minutes into our very first meeting, we both canceled the rest of our day, and we spent the next two and a half hours in his office having a conversation that really started with what if. What if we took RFG, which had been founded in 2003, and at the time was an OSJ with LPL, what if we took that business and we tore it all the way down to the ground? And we rebuilt it from the ground floor up to be a platform that is designed, that is intentionally engineered, to serve independent advisors? What would it look like to be a client experience company first, a technology company second, and a corporate RIA third? And I’ll tell you, walking out of that meeting, I was like, “This is it. This is it. This is the intangible. This is an opportunity to really build something very special.” And that’s how I found myself sitting in this talking to you today. Jason Diamond: Wow. So there’s a lot to unpack there. Thank you for sharing. And you shared it with a degree of vulnerability that I personally, I have a two-year-old and a three-week-old as of this recording. So it resonates with me. I think it resonates with a lot of advisors, people in our, and honestly, probably most industries, the constant pull in multiple different directions. And I love what you called it, seeking the intangible. And it sounds like you didn’t go in with any preconceived notion about… Many of our guests, by the way, that is the case. They walk in saying, “I knew since I was two years old I wanted to be in wealth management. I wanted to help be a steward of client…” And I love that your circuitous route took you a different direction. I want to talk more about the firm, and we’ll dive in on some of these elements of your background also. But before we do, you mentioned a little bit of, at a high level, what RFG is. Give me a little more context, types of advisors you serve, types of clients you serve. And if you don’t mind, provide some stats around size as well. Shannon Spotswood: Absolutely. So we are on a mission to help independent advisors build their business without compromise by driving organic growth to create enterprise value. And I share that because in our mission statement is the passion that links us all together, which is helping independent advisors build what they want to envision for their clients, what they believe is the best representation of their vision and their values. So we are a platform, a full turnkey platform for independent advisors. We talk about our services as a flywheel. There’s a very intentional interdependency from technology to marketing to compliance to talent to investment management to coaching, operations, transition services, and capital solutions. All of it is knit together very thoughtfully in order to be able to deliver to the advisors on our promise to help them operationalize and professionalize their business, to serve their clients and to generate that organic growth, which is what translates into enterprise value. What is so cool about the RFG advisor community, and I think is really the thread that binds between our teams and our advisors team is this servant heart growth mindset that you find it in every nook and cranny of RFG and certainly within all of our advisor partners. So the advisor profile for us, we do tend to skew a little bit younger. Average age is 45 years old. Organic growth across all of our advisors is north of 10%. So we’re very focused and leaned in on growth. We do have advisors that are lifestyle. We talk about them as lifestyle scaling and enterprise, and they run all along that growth at growth spectrum, depending on what do they want to build in their lives, what is going to help them really realize their dreams? And we’ll talk about this a little bit and just the growth of the firm and what we’ve been building, but we are at $9 billion. So it’s been a big run in 2026, as I say, 10 years of pre-game warmup to be able to really talk about that level of growth. So just knocking on the door of $10 billion and truly, Jason, I can tell you, I feel like we’re just getting started. I feel like we are just at the beginning of the J-curve as advisors are really realizing that their most valuable asset is their time and the amount of enterprise value that they can create being independent. There’s a lot of different flavors of that. We’ve got some incredibly well-capitalized and very strong competitors, but the collective awareness around this bull market for advice that we’re sitting at the very beginning of is shining such a bright light on what does it mean to be independent? What does it mean to be really supported by a partner who’s all in to help them win? And that’s where we find ourselves. And by design, that’s where we find ourselves. Jason Diamond: Yeah, and it’s an exciting time. I completely agree. The space, the vertical you’re in, probably as much or more than any other pocket of the industry. You took the words out of my mouth, the J-curve. I completely agree with the story you’re telling. There’s one component of your background that I do want to ask about, which is many RIAs, platforms, and the like, the leadership team is intentionally ex-advisors in their own right. So I’m curious, do you think of it as a benefit or maybe to what degree is it not a benefit that you have never been an advisor and served clients? I do love the idea that you’re a business builder and you’re helping advisors to build a business. That’s not lost on me, but I’m curious specifically about never having been an advisor. Shannon Spotswood: I think it is so critical that we were advisor-founded. What we like to say is we’re advisor-founded and professionally-led. Bobby founded the firm in 2003. We partnered in 2015. Our third partner, Rick Wedell, who’s our chief investment officer, managing partner, joined in 2016. So the three of us really co-founded the version of RFG that is- Jason Diamond: The right version. Shannon Spotswood: … expressed in the market today. But you’re a hundred percent right to double click on this. And I think it is such an important area for reflection for advisors in terms of where are their greatest skills? Where does their passion lie? And what are they interested in building? That very first day that I met Bobby, his telling of the story is he looked at my resume the morning that we were meant to meet, and he is like, “Well, why would I hire her? She could do my job.” And he often talked about that where you get to this point as an advisor where the business is scaling and growing. And we certainly are seeing this in a lot of the larger teams that we’re talking to and the relationships that we’re beginning to build within the pipeline of these advisors who were attracted to the industry because they wanted to serve clients and find themselves as accidental CEOs, COOs, their chief cook and bottle washer to advisor to all of these C-suite titles. And it’s not amplifying their natural skillset and it’s not aligned with what is actually their passion for the business. So I give a tremendous amount of credit to Bobby for recognizing more than 10 years ago really what it would take and how he could align team around him and build partnerships around him to be able to maximize the impact that we can have for advisors. So that north star of keeping advisors front and center is truly our, it is woven into our DNA and it is our north star. So we are a client experience company by design. We talk about it all the time, whether it’s how we’re building our team, how we’re thinking about investing in technology, how we’re soliciting feedback for advisors. I always say one of our greatest strengths as an organization is we’re active listeners and then we actually execute on it. Our best ideas come from our advisors, but you’ve got to have that posture as a firm that everything you do is orienting around how do we help advisors operationalize, professionalize, drive organic growth, and create enterprise value? And you can’t do it sometimes. You’re either all in, chips all in, only winning when your advisors win, and only having that lens of will this benefit the advisor and their team or not. It’s not something that you can just dip your toe in and out of. And I think RFG, having that foundation from which to always build is absolutely critical. Jason Diamond: Can I try and paraphrase or synthesize, and you tell me if I get this right? The pitch is something to the effect of, “We are really good at what we do. Let us take all the BS off of your plate so that you can go out and be an advisor. Service your client and prospect.” Do you find that story is resonating more over time? I mean, you’ve been with the firm now long enough to see this kind of cycle of movement towards independence. How has that story evolved over time? Do you find it easier to tell? Shannon Spotswood: Oh my gosh, without question. And I would even put a shorter term window on it. I would say in the last 12 to 15 months- Jason Diamond: Oh wow. Shannon Spotswood: … there has been a collective awakening by advisors, and I think there’s a lot of contributing factors to that. One is obviously as we are all aware, the majority of the industry is now private equity backed. There has been a real focus on the aggregator model, transitioning advisors into a W-2 model. And as that has played out and that financial engineering has translated into some incredible valuations and returns, there has also been simultaneously advisors picking their head up and like, wait a minute, I wanted to get independent so I could serve my clients in a way that I felt best represented my vision and my values. And I’m finding myself increasingly in a captive environment. All the while the technology is getting better, the valuations are getting larger, the ability to control both your branding and what that means for your family legacy is increasing. So over the course of the last 15 to 18 months, that story has just, while it’s been there for a long time, the independent movement was obviously sparked more than, gosh, now 16, 20 years ago in earnest. Now it’s just the passion and the knowledge that advisors are showing up to conversations in recognizing I want more. I want to spend my time where I want to spend it. I want to serve more families. I want to be well-positioned for generational wealth transition. I want to own the enterprise value. I want to build my team and I want the best tech. And that to me is exactly why we’re at the beginning of this J-curve. Jason Diamond: Yeah, I think you nailed it. And I agree with you that this notion of independence is not a destination in and of… It’s too broad of a term I think to use. And there are plenty of advisors who either started at one version of independence and need something different now, or to your point, thought they were going independent only to realize perhaps there’s elements of the business that aren’t as independent as they realized. And that’s where I think a firm like RFG to me, it’s not an accident that your firm fills this niche. This was advisor demand driven. Advisors said explicitly and implicitly, “We want to be independent. We want to own our equity. We want to have control over the things we like, but we want a support partner that helps us with all the back office, the middle office, investment management, the flywheel,” as you call it. Shannon Spotswood: That’s right. Jason Diamond: One other element of your journey to this point that I want to ask about, the succession journey or the journey to CEO, and I’m only asking because it’s somewhat recent, I think it was 2024, so we’re about two years in CEO. For the eight years prior to that, you were president. Shannon Spotswood: Yes. Jason Diamond: And this dynamic is near and dear for a lot of advisors. This idea you’re the heir apparent, but the date hasn’t happened until it happened. Was that a smooth transition date or did you find yourself, and I hope you can be honest about it, and if not, I understand, but I think this is something that a lot of advisors in their own businesses struggle with. So as somebody who’s gone through a major succession journey in the last two years, I’m curious what your thoughts are. Shannon Spotswood: The timing coincided with us bringing on a growth capital partner. So we closed on that partnership with Long Ridge in the fall of 2023, and we really set our sights on how do we bring this capital into the business and invest in our team, invest in our technology, invest in this desire to help independent advisors build their business. And Long Ridge really shares that long-term strategic belief that independence and the corporate RIA model is the ultimate winning model. So we have a lot of room to run there. So entering into that growth partnership with Long Ridge really provided a natural opportunity for that succession conversation to take place and to be able to take the company to the next leg. So we’ve tripled the size of the company over the course of the last two and a half years. Jason Diamond: Good for you. Shannon Spotswood: And as I said, I feel like we’re just getting started. I always joke we’ve had the longest pre-game warmup in history. In a lot of ways that’s by design. For me, the way that I can sleep at night is knowing that we are waking up as a team in this unified front to walk the walk for our advisors. It is incredibly important to us to honor the promise that we’ve made, whether it’s on tech or talent or transition services or marketing growth. So being able to lean in and deliver that, it takes a long time to build that institutional know-how and to be uncompromising in consistently making hard decisions, whether it’s around talent or the investments that you’re making or how you’re running and growing and building the firm. And so Bobby reached and Long Ridge and all of us reached this point where it was just a very natural way. And I think it was such a gift that I had such a long warmup, if you will, in the bullpen, running the day-to-day of the business as president, being so close to sweating the details of how we built the foundation, how we run the firm. And then obviously Ed Swenson joined us as president in last fall in October of 2025, having joined our board when we partnered with Long Ridge. So he joined our board in September of ’23, and he and I set up a call every other week. So we just became this incredibly trusted confidant of mine as we made a lot of strategic investments and key strategic decisions in that first 15 to 18 months of our partnership with Long Ridge. So to be able to build and attract the caliber of talent that we have to RFG, I mean, I’m totally biased and talking my own book, but I think we have the best leadership team. Doug Nelson joined us from Long Ridge as our CFO in November of last year, just bringing that rigor, particularly around capital strategies into our C-suite. So it was the right time to make that transition. And what I would say for founder advisor-led firms, it’s all about what are your growth ambitions? It’s what are your growth ambitions? Without question, when I joined and Bobby and Rick and I set upon this journey to tear the entire company down and build this robust tech stack and be at the forefront as an innovator in that space, that was experience that I had from my 20 years in San Francisco. And Rick had this incredible institutional pedigree having spent 12 years at Bain Capital plus two years at Stanford Business School, complimenting this authenticity that Bobby brought as an advisor, bringing that together. So recognizing as a founder advisor, if you have growth ambitions to 10X your business, it’s going to require that you bring high caliber talent to the table and allow for that room both from an equity participation perspective, but also just from what does the business need as it continues to scale up? Jason Diamond: That’s exactly right. And part of this gets back to private equity sometimes gets a bad rep in our space, but the reality is capital from private equity enables a lot of what you’re talking about. And I give you a lot of credit. I mean, you make the half joke about the longest pregame warmup ever, but I think of it as you learned on your own dime and you built all the kinks and ironed out all the kinks prior to having this critical mass of advisors on your platform. And we’ve seen certainly plenty of firms go that route too. So I give you credit for that. I think because we’re on the topic, let’s talk about it, private equity. Positive experience, negative experience, neutral, neither good nor bad. Just give me your… I don’t want to make the episode about the perils- Shannon Spotswood: Right. Jason Diamond: … and benefits of private equity capital, but just curious what your experience has been. Shannon Spotswood: I think this is one of those life lessons. Choose your partners wisely and great things can happen, whether it’s in your marriage or your friendships- Jason Diamond: Spouse. Yep. Shannon Spotswood: … or your business partners. And Long Ridge found us very serendipitously. I mean, we were probably two years from even contemplating bringing in a growth capital partner. They were introduced to us by a former board member and they were in our offices in January of 2023. And the most important things for us were twofold. Number one, they shared our vision and belief that the corporate RIA independent is the winning model for the industry and for advisors and clients. And number two, who they are as people is very much who we are as people. They’re builders. Jason Diamond: Culturally. Shannon Spotswood: They have this servant heart growth mindset that they share with us. So I feel incredibly blessed to say they’re amazing partners. And what’s interesting, and I’ll share this very openly, they’re the majority owners of RFG. We were very early in that time of bringing them on. They have always honored the promise that they made to us, which is we run the business. They are a strategic partner. They’re a great thought partner. They are the capital provider, but there has been multiple examples where we have made business decisions where there’s been some heat in the kitchen, in the boardroom, and we’ve felt very strongly about it. So I just couldn’t say enough great things about them. And one thing that I will just share, and I say this because they’ve shared this with me, I have had this incredible personal journey of growth bringing such a deep bench in Long Ridge into the firm. And that has been certainly challenging at times. Do hard things, get comfortable being uncomfortable. It’s the ultimate definition. But I really think that is something that never gets talked about is what it means in upskilling the caliber of your talent, yourself, how you have to grow and evolve as an individual has been really, I won’t say it’s been easy, but I look back on what I’ve learned over these two years and just feel prepared as a leadership team, how we operate as a team, what is expected of us to be able to deliver and execute for our advisors in this next leg of growth. Jason Diamond: I think your marriage analogy is the perfect one, and I’m going to use it. And honestly, in a lot of ways. First of all, marriage is hard, good or bad. It’s hard. Second of all, it’s the ultimate… The institution of marriage is not good or bad. Private equity capital is not good or bad, but your answer is the right one. Pick your partner very wisely. My favorite part of your answer, because it’s the most original, was around a good capital backer, a good partner, whatever you want to call it, pushes you to be better. And I think that you’re surrounding yourself with, by definition, some of the smartest people in the industry, and that can’t be a bad thing. And the proof is in the pudding. The growth trajectory you’ve seen, it’s certainly no accident. I think part of it is tied to your incredible stewardship. You don’t have to answer that. You don’t have to be humble, but I’ll attribute it to you. That brings me to my next question. Shannon Spotswood: I do have to say really quickly. Jason Diamond: Please do. Shannon Spotswood: I will be celebrating my 27th wedding anniversary in October. So yeah, pick your partners. Jason Diamond: Congrats. And I feel equally blessed, I assume as you do. I have a great partner, I’ll say. I don’t know if she’s listening right now, but she’s a great spouse. What I was going to say though, good segue, I think there’s been more in recent years, but not a ton certainly of female C-suite wealth management executives. How do you feel about your role? Do you feel an increased burden? Is it an honor to you? Is it something that you don’t think much about at all? I’m curious what your thoughts are. Shannon Spotswood: I feel immense gratitude. I mean, just in general, leading RFG and locking arms with our team and our advisors is, I mean, a gift of a lifetime. I was incredibly fortunate to not just have mentors during my 20 years in San Francisco, but to have true sponsors. Whether it was the first hedge fund I worked at, I took that job because it was a female portfolio manager and at the time one of the only in the country. And she really opened up her heart to me and poured into me. And then 10 years at Symphony, the founding partners of Symphony, they dropped me into the deep end of the pool and gave me a lot of rope to make a lot of mistakes and continued to invest. So I have this foundation from which to build and to lead and to be ready for this role. I couldn’t do any of this without my partners. Rick and I have been partners for more than 10 years. It really does take a village in the same way that it takes a village to raise your family. It takes a village to find the courage and the strength to lead in a way that really honors the gravity of the mission. But I’ll tell you this. One, I knew I wanted to work on Wall Street from a very young age, so I chose this. I knew what I was getting into, that it was a male-dominated industry. I have made particularly, this is one of the unique facets of the wealth management business, we have phenomenal both male and female talent, and I have made the strongest female relationships on this side of the business as compared to the institutional side of the business. So I think there is a richness to our side of the industry that doesn’t get enough air cover. There are just phenomenal leaders, and I think increasingly so, we’re seeing more women stay in the game and raise into positions within the C-suite and leading these firms. I will tell you one thing in 2019, and I really give a lot of credit to Bobby for this in coaching me, is I was raised by wolves on Wall Street without question. I sat on a trade desk, I was completely comfortable with compartmentalizing emotion, and I made it a mission to develop intentionally my emotional intelligence. And that truly unlocked everything for me, and I think plays such a huge part of who I want to be and who I challenge myself to be as a leader. And so it’s funny when I get the question asked of me about being a female CEO, because I think that’s what people feel must be like came very intuitively to me, but I had to learn it. I had 20 plus years of being able to run with boys and I needed to develop that skill. And it is a skill that I challenge myself on a daily to continue to lean into. And I think it is increasingly important both for men and women who aspire to leadership to hone the strategic and execution alongside that emotional intelligence. Jason Diamond: Great answer. And I think you know I admire a lot about you, but it’s certainly one of the things I admire most about you is over the last couple years in particular you’ve been a real beacon of positivity, of empowerment in that regard. You’re active on socials, you’re active at industry events, you’re always willing to talk to people. And honestly, that to me is the answer. A lot of people complain about this as a problem, and I want to just take a second to applaud you because I think you and your firm actually do something to at least try and actively solve some of this. And also you mentioned it earlier, but same thing with some of the next gen dynamics. You skew much younger than the average firm on the industry. And I think that too is to your credit around, okay, we’ve identified that we have a major succession problem in our industry. What are we doing to solve that? Shannon Spotswood: Absolutely. Jason Diamond: Let’s talk about growth a little bit. I agree with your thesis. This space you occupy, no better time to be in it. We’re at the perfect spot on the J-curve. Unfortunately, we are not the only two people to think that. There are also, I think, some other firms. This space has become crowded. What do you think about that? Just the fact that there’s more competition than ever. I mean, my view of it is there are enough quality advisors to go around, but curious what you think. Shannon Spotswood: Anytime I find myself wading into the waters of fear and scarcity around this topic, I’m reminded that 67% of the assets still remain within the wirehouse and IBD space. We got lots of room to run. I believe in a mindset of abundance. The data will tell us that the demand for advice is increasing by 30% over the next decade while the number of advisors is decreasing by 1%. So we’ve got, find me another industry where you see a graph that looks like that. On top of that, next gen, which I think this is so fascinating, next gen actually wants more advice when compared to the baby boomers. So baby boomers created our industry, and here we are sitting on $87 trillion worth of generational wealth that’s going to begin to transition. That doesn’t even include all of the wealth that will be monetized through real estate and family-owned businesses. It is a tsunami. And what is, I think, really interesting is that next gen recognizes the value of their time. I’m sure if I had a conversation, Jason, with you and my husband about how intentional you want to be in terms of showing up for your children and the equal nature of parenting, that alone is changing the way the next gen thinks about both their professions as well as their family life, which means you by default have to hire professionals to do the things that you don’t want to spend the time doing. Jason Diamond: Really good point. Shannon Spotswood: So we have this incredible convergence that’s happening right now, and it’s coming at a time that technology is finally going to allow us to serve more families more intentionally along that wealth spectrum. So it is like, bring it on. There is more than enough to go around. We are in an era of abundance. And what I worry the most about, and this, it’s like climb up on the soapbox and let’s roll, about independence because I see and have so many conversations with advisors where they have been willing to accept such a compromised service experience that they would never allow to be delivered to their clients. So advisors are delivering this 24-hour concierge, high-touch, deeply thoughtful experience, estate planning, tax planning, financial planning, multi-generational conversations. They’re in it. They’re in the trench. And then they turn around and their service partner is so subpar. They’re compromising their growth. They’re burying them in compliance and ops and clicks and swivel chair and tech that doesn’t work. So we’re at the very beginning of this bull run for advice. And I think advisors who recognize, I want to serve more families, I want more control over my time, I want to be able to build enterprise value on my personal balance sheet, have room to do it. So I welcome the competition. I think the best way to talk about it is iron sharpens iron. I learn so much from our peers and like, ah, they did this or they did that. How do we think more disruptively, more innovatively? How do we do it differently? So I think there’s a lot of room for all of us. You’re going to be busy, my friend. You’re already sitting there advising the lion’s share of the big deals, and I think you guys are just getting started as well. Jason Diamond: Yeah, it certainly feels like a bull market for advice and also I think a bull market for some of the… You allude to an interesting paradox, which is some of the biggest and most sophisticated advisors in the industry have really high-touch impressive service models, but they don’t seem to demand the same in return. I have some thoughts as to why. I think one could just be Kool-Aid drinking, like you don’t know any better and you’ve been there for so long. There’s just so much friction associated with moving a business and fear associated that it’s unless things get really dire or unless I find something that’s better enough or meaningfully better enough, I can gut it out. But the third one that comes to mind is these firms we’re talking about have unequivocally, they do a lot of good, a lot of bad, but unequivocally one of the things they do really well is brand. Shannon Spotswood: Yeah. Jason Diamond: How do you reconcile that question with a firm that obviously doesn’t have a brand that the average American consumer would know? Shannon Spotswood: We take a posture on this that is rooted in an Accenture study that was conducted several years ago, but I think still remains so true today, is that advisors think that the value proposition that their clients are looking for, either it’s that big monobrand that’s advertising at the Super Bowl or the alpha they’re ever able to generate or the portfolio investments. But the clients tell us that what they’re looking for in an advisor is, do you get me? Do you share my values? And do I want to spend time with you outside the office? And that is basically distilled down the way we talk about it is people connect with people. So now more than ever, particularly if you take a big step back and you think about the influencer economy and how brands, big brands, Nike or big consumer brands have really leaned into niche branding. How do I get my brand into the hands of someone who’s very passionate about it? So advisors who develop their own brand, who have a presence on social, who have a presence in AEO and SEO, who are leaning in and expressing not only their client experience, but their vision and their values through their brand, I actually think as this generational wealth unfolds, that authenticity carries so much more weight than is my name on a football stadium. So it is those three factors. It’s just I’m comfortable. I don’t want ripple. It is friction and fear for sure. And then it’s like that branding is up for grabs because we certainly see one of the most fun parts of advisors joining RFG, this is a big part of what we do is helping them design and develop or reimagine their brand name, their logo, all the rest of it. Once that creative energy is unlocked and you get to tell your story, your my why, that connective tissue is so powerful with the clients and with the growth that comes from that because I mean, I truly believe people connect with people. They’re looking for that. And I think more so now than ever with AI. Jason Diamond: You just took the words out of my mouth. Do you think AI perpetuates that? Shannon Spotswood: I think people are craving that. And this is why advisors who are powered by AI without question are going to win. Advisors are not going to be disrupted by AI unless they haven’t made the move to get themselves in a position to be able to leverage the technology, the brand, the talent, the maximizing of their time. But especially with something as important and as personal as money, as you walk through life, I mean, you are at the very beginning. I’m sending, I’ll have all three kids in college. But as you make these critical decisions in your life, whether it’s getting married or starting a business or changing jobs or buying your first house, buying your vacation house, all of these things, you can go right or you can go wrong. And having a trusted partner who really understands you, I actually think that we’re going to see the fees paid for advisors increasing as there is a greater premium placed on, I want deeply personal relationships that are tailor-made for me. Jason Diamond: But I assume the flip side of that is you have to do more. You as a firm and you as an advisor have to do more, and you can’t just raise fees with the same service model. So I think what is the corollary of that? What are some of the ancillary growth areas that you do beyond the financial planning and asset management that says, “We’re worth that money you’re going to pay us”? Shannon Spotswood: It is, and I love the work that wealth.com is doing here. I mean, the estate planning and tax planning, making that more accessible along that continuum of wealth spectrum, the blurring of the lines between ultra high net worth and high net worth, and then mass affluent is so exciting. Better, more robust planning is good for our industry overall. Obviously there’s a huge amount of demand on the tax side of things, particularly the 1040. It’s easy to find a CPA to do the cool complex stuff. It’s increasingly more challenging for advisors. That’s an area that I know a lot of firms have leaned into. We’re certainly doing a lot of work. But so much of this, Jason, is showing up at the right time for clients with the resources. It’s a really interesting conversation about, yes, you have to do more for your clients, but you don’t have to do more for all your clients at exactly the same time. Jason Diamond: That’s well said. The flip side of that is as an advisor, because ultimately the advisors are the ones making this decision. There are a lot of firms, and not even just firms that you would be competitors with, because the reality is you and I understand the industry landscape and where various firms fit in. For many advisors, it’s a long list of various firm names that they’ve heard. So what are some things that you think advisors should be asking a firm like you or a business development person at your firm to suss this out? How does an advisor go about understanding if a platform is empty or is really going to be able to deliver in all these areas? Shannon Spotswood: Remember back in the day when the Wall Street Journal used to run have a monkey throw a dart and see if you can beat the pros on stock picking? I love to do that with regards to our advisors. We always tell our prospects, “Throw a dart at any advisor that’s affiliated with RFG and call them. Certainly we can provide a list of advisors who we think you’re going to most align with in terms of what your growth ambitions are or the way you want to run your business or who you are, life stage, all the rest of it.” But I do think that getting that unfiltered experience, the good, the bad, the ugly. We always are like, “Are we perfect? Absolutely not. Do we though immediately want the feedback so that we can iterate to excellence to get better? Absolutely. Get that firsthand testimony.” So that’s number one. Number two is don’t tell me, show me. There are so many, and it always pulls at my heart because as much as I love to win business and transition advisors, and I think that we’re working certainly at RFG on some really interesting technology that is anchored around removing that friction and fear by speeding up the time that you can make that transition in. And the tech is finally there to allow for this. So I think we’re going to be able to take variable number two and at least make that box a little bit smaller. But if I’m sitting as an advisor, I would want to see the evidence. Show me how you’ve solved the problems that advisors have brought to you. How have you refined your tech stack? How have you invested in your team? How have you made the decisions where the ROI can be measurable and tangible? And I think too often I’m surprised that advisors get, it’s almost as if they get overwhelmed by the amount of information that they’re taking in trying to compare all these different firms. If I’m ever asked, I’m like, please work with a third-party recruiter. You need someone not only to act as an interpreter, but you need someone to help really keep your top three priorities at the front of your decision-making matrix, because it really is apples to oranges to orangutans and you get decision fatigue. And then advisors end up making this decision that is anchored in like, well, this is the highest payout, and I’m willing to take all of these sacrifices and paper cuts for this highest payout. And that is just such a travesty. So it’s like, know what you want. What are your top three problems that you’re trying to solve? Talk to advisors that you get to pick just so you can do some secret shopping, and then demand evidence of how the firm, the platform has responded to feedback and gotten better as a result because that will tell you, are they really going to walk the walk or are they just going to talk the talk? Jason Diamond: I’m super grateful that you gave specifics there because it’s an easy question to dodge and talk around. So I completely agree. Your first answer, actually all three of those points you just made, but certainly doing name-blind calls, and I say name-blind because advisors worry about confidentiality. I think that’s one of the best and most underrated tools to learn about a firm is advisors now have so many colleagues. There’s been this diaspora of advisors where advisors know advisors everywhere. And that’s a benefit if you wanted to go and just network and have conversations with other advisors on your own. But if you’re worried about confidentiality, there’s certainly the mechanisms, and we do this all the time for advisors to set up name-blind calls. You dial into a conference line, it’s John Smith, and you pick an advisor’s brain and say, “Hey, you moved your book from LPL to RFG, and tell me what that experience was like and what were the positives? Give me all the negatives.” To your point, you want advisors to ask those questions in advance. It’s better to ask those questions than to end up in the wrong marriage with the advisor. Shannon Spotswood: Absolutely. And the other thing is what an easy answer to BS around is tell me who’s a good fit for your firm. And it’s like, “Everyone’s welcome here.” Jason Diamond: Everybody. Yeah. Shannon Spotswood: It’s just not true. RFG is not a good fit for an advisor who is not open to using technology, who is not interested in outsourcing investment management, who doesn’t want to have a conversation about how are you spending your time and do you want to create enterprise value? Do you want to grow? So it really is important to have that vulnerability and that honesty and the answer to that question. Jason Diamond: I love it. We have time for one more. I can’t believe it’s been almost an hour. Shannon Spotswood: I know, it flies by. Jason Diamond: We speak with plenty of advisors who aren’t considering a move, but I’m interested. I think you have a really nice lens into the industry. What is one thing you wish advisors knew? You have a megaphone to just talk to advisors who maybe are considering change, but maybe aren’t. What’s the questions they should be thinking about? What keeps you up at night? Just what would be your public service announcement? Shannon Spotswood: I’m going to focus on the friction and fear because that’s the number one barrier to making a move is PTSD, either first person PTSD or the collective negative experience that the industry has had. It took me 90 days to transition. I got sued by my former firm. I lost all these clients. I didn’t have income. The wise tales of fear are very widely trafficked and widespread. And what I would say to an advisor is everything you want is on the other side of fear. And I look at all of this data that suggests exactly the opposite, which is you have the relationship with the client. You have the trust with the client. You are the one who they call on Sunday night when they need a shoulder to cry on or sage advice for making a decision. Just believe it with the core of your being because what we see is 99% of assets transition, whether it’s a restrictive transition or you’re taking full data, that the majority of assets are transitioning within 30 days, that this is still a free country, and you can make a move while honoring your contract around non-solicitation, non-competes, and non-associations. So it is like this fear of holding advisors back is preventing them from realizing and monetizing this enterprise value, but equally as importantly, loving their business. Have fun. This should be fun. We spend the majority of our life at work. And so being able to surround yourself with people who win when you win, with a team who’s aligned and isn’t just drudgery with all their operations compliance headaches that they’re dealing with. Your team deserves to be happy. You deserve to be happy. And that fear factor is holding so many advisors back. So that’s my advice is that it just doesn’t have to play out that way. And I think not just at RFG, collectively where we are as an independent industry with technology, with the way that AI is changing and our ability to harness data and business intelligence, getting to that point of next best action, how am I spending my time, how am I realizing, what is the blueprint for realizing my growth goals is more tangible now than ever. That’s immediately where I go. Jason Diamond: I’ve never been an advisor. I’ve never had a book of business, so I don’t want to minimize the fear, but I will say this. If we speak to advisors, let’s say a year post-transition, by far the number one thing we hear from them is, “I wish I did this sooner.” Shannon Spotswood: Wish I did it sooner. Jason Diamond: And that to me is the most telling data point there is to your point about fear and getting over it. Shannon Spotswood: So I do this exercise all the time with our team as we’re onboarding advisors is I want you to go home and look at your spouse and tell them, “I’m going to leave my job. I have no certainty that everything is going to work out. We might not receive any kind of compensation. Are you cool with that?” Walk that emotional journey. And while there’s plenty obviously that we can do with Capital Solutions to ease the financial fear associated with it, I still think at the baseline, it’s a great exercise to keep everyone very humble. You are asking an advisor to take their life’s work. And someone was sharing this analogy with me the other day and I was like, “Oh my gosh, that’s so good,” which is imagine moving houses. It’s such a hassle packing up moving one house. Now imagine moving 400 households or 1,200 households. It’s a lot, but I always hear the same thing, “I wish I’d done it sooner.” Jason Diamond: Thank you for sharing. You had some really sage wisdom that you shared with our audience. I can’t wait to see the next chapter, the continuation of the J-curve. This has been a fantastic episode, Shannon. Thank you. Shannon Spotswood: I love being with you, Jason. Thank you so much. We appreciate it. Mindy Diamond: As a financial advisor, you hold yourself to the highest standards of integrity, honesty, and credibility. You are successful because you take your professional responsibilities seriously and are dedicated to your clients. But are you living your best business life? Are your goals aligned with your firms or could a better option exist? Should I Stay or Should I Go? is a book written with you in mind. It’s a self-guided journey that walks you through the key steps that we take with our advisor clients. This strategic thought process and roadmap to professional self-discovery is designed to help you ask the right questions and think critically and objectively, whether you’re considering change or not. Learn how to get your copy at diamond-consultants.com/thebook.   Growth Without Compromise: Building Around the Advisor Experience A conversation with Jason Diamond and Shannon Spotswood, CEO of RFG Advisory. Jason Diamond: Welcome to the latest episode of our podcast series for Financial Advisors. Today’s episode is Growth Without Compromise: Building Around the Advisor Experience. It’s a conversation with Shannon Spotswood, the CEO of RFG Advisory. I’m Jason Diamond, and this is the Diamond Podcast for Financial Advisors. Mindy Diamond: At Diamond Consultants, we help elite advisors identify the right environment for their businesses to thrive, whether that’s at a wirehouse, boutique, or independent firm. With nearly three decades of experience, we’ve guided thousands of advisors and represented more than a quarter of a trillion dollars in assets transitioned. And each year, one in four advisors managing a billion dollars or more who change firms are our clients. Our process is education-driven and based on building relationships, starting as your strategic partner well before you’re even thinking of a move. To schedule a confidential conversation, call us at 908-879-1002. Wondering why advisors change firms and where they’re headed? Are transition deals going up or down? Those very questions and more inspired us to create our annual advisor transition re

The EU AI compliance deadline arrived on August 2nd and it's important that companies have been preparing for it. According to AI expert Cathal McCarthy of Kore.ai, companies that establish robust AI governance frameworks today will be better positioned to deploy AI faster in a highly regulated industry. To help leaders assess their preparedness, Cathal has developed a six-question diagnostic that evaluates the maturity of an organisation's AI governance program. The framework is designed to quickly identify whether key governance structures, accountability mechanisms, and oversight processes are in place, or where gaps still remain.  I caught up with Cathal to find out more about the EU AI compliance and how businesses should prepare for it. Cathal talks about his background, AI compliance, AI adaptability, the ability to learn, AI transparency and more.More about Cathal McCarthy:Cathal is Chief Strategy Officer at Kore.ai, an enterprise AI and agentic AI platform serving Fortune 500 and PE-backed customers globally. He has twenty-five years scaling enterprise technology businesses and has held leadership roles at Apple during its growth from $8B to over $200B in revenue, and at eBay leading initiatives across 163 million users. He also brings P&L instinct, GTM design experience, and credibility in the boardroom on AI transformation.

TikTok Shop USA sales are booming. There are some new regulations for packaging if you're selling in Europe, and the most-requested tool for MCP for Helium 10 is now available. These stories and more on today's weekly buzz!   We're back with another episode of the Weekly Buzz with Helium 10's VP of Education and Strategy, Bradley Sutton. Every week, we cover the latest breaking news in the Amazon, TikTok Shop, Walmart, and E-commerce space, talk about Helium 10's newest features, and provide a training tip for the week for serious sellers of any level.   TikTok Shop's U.S. GMV Nearly Doubles to $11.8B in H1 2026 https://www.netinfluencer.com/tiktok-shop-us-gmv-nearly-doubles-to-11-8b-usd-in-h1-2026/ Why China's traders, e-commerce merchants feel boxed in by new EU packaging rules https://www.scmp.com/economy/policy/article/3363746/why-chinas-traders-e-commerce-merchants-feel-boxed-new-eu-packaging-rules Most Asked For Helium 10 MCP Update Helium 10 has launched Black Box Niche for MCP, giving sellers the X-ray-style data they've been asking for directly inside Claude. Instead of researching keywords one at a time with the Chrome extension, users can analyze multiple niches at once and compare metrics like search volume, top-product revenue, number of listings generating over $5,000 in sales, and how many top products have low review counts. The feature makes it much faster to compare market opportunities and competition across multiple keywords using the Helium 10 MCP.   How Amazon's AI shopping assistant makes hundreds of millions of products feel personal https://www.aboutamazon.com/news/retail/alexa-for-shopping-learn-and-be-curious-podcast Optimize your listings with built-in Seller Central tools https://sellercentral.amazon.com/seller-news/articles/QVRWUERLSUtYMERFUiNHR1ZKWFZETkRTSFhYQVo1 Amazon Expands Locker Network to more than 750 U.S. College Locations https://press.aboutamazon.com/retail/2026/8/amazon-expands-locker-network-to-more-than-750-u-s-college-locations In episode 545 of the AM/PM Podcast and Weekly Buzz, Bradley talks about: 00:00 -  Introduction 00:43 - TikTok Shop US BOOMING 03:13 - New EU Packaging Regulations 05:11 - Most Asked For MCP Update 09:13 - Amazon's Alexa Predictions 14:36 - Research Dead Amazon Listings For Opportunity 17:55 - Amazon Caters To College Students

Hosts Andy and Tom talk about a rare Cincinnati unicorn – a startup company worth at least $1B – that abruptly ceased operations. In other news, one of Cincinnati's largest private companies is expanding its downtown headquarters, and bringing back the skywalk; Amazon touts its local impact; P&G is making a $3.8B acquisition; and a James Beard-nominated chef is opening a new restaurant.Interview starts at (23:43) Dave Jenike started at the Cincinnati Zoo as an intern. Now, he's its CEO. He was also part of the duo responsible for its transformation over the last two decades. He views his job as the zoo's chief storyteller, and getting people inspired by nature. He talks about what's new and coming at the Cincinnati Zoo.https://www.bizjournals.com/cincinnati/news/2026/08/07/80-acres-how-why-closed-henry-gordon-smith-layoffs.htmlhttps://www.bizjournals.com/cincinnati/news/2026/08/03/divisions-maintenance-group-jobs-downtown-skywalk.htmlhttps://www.bizjournals.com/cincinnati/news/2026/08/04/jimmie-lou-reopen-pendleton-jeff-harris-nolia.html

-Taking vibe-coding a step further, Naive claims its infra can automate most of the work in setting up and running a business. -Defense tech Hadrian raises $1.37B at $8B valuation -Athens-based Omilia, which has been working on automating voice calls and customer support since 2002, throwing AI at every process can be wasteful.  Learn more about your ad choices. Visit podcastchoices.com/adchoices

Google shook up its AI leadership, kicking Demis Hassabis upstairs while Jeff Dean and Sanjay Ghemawat left to found Discovery Loop. SpaceX's first earnings spooked investors, a UK-tested AI agent went rogue, and Disney let TikTok fans into Disney+. Links Google just announced a major shakeup of its top AI leadership (The Verge) SpaceX reports Q2 revenue up 92% YoY to $7.8B, vs. $6.81B est., AI operating loss of $1.26B, vs. $2.39B est., says capex in Q3 and Q4 will remain similar to Q2 (Bloomberg) An AI agent went rogue during UK safety tests, creating fake identities and launching social engineering attacks unprompted (The Decoder) Disney announces a global deal with TikTok to bring "thoughtfully curated" fan-created short-form videos based on Disney's IP to Disney+'s vertical Verts feed (The New York Times) Subscribe to the ad-free feed.

August 5, 2026: Your daily rundown of health and wellness news, in under 5 minutes. Today's top stories: P&G acquires supplement maker Thorne for $3.8B, adding the practitioner-trusted brand to a portfolio that includes Metamucil and Wonderbelly Cleveland Clinic launches the first long-term drone prescription delivery program at a US health system, partnering with Zipline as healthcare competes on convenience World1 launches microsports, 30-second mobile-first competitions backed by Mark Cuban, betting short-form content will reshape how younger audiences consume sports More from Fitt: Fitt Insider breaks down the convergence of fitness, wellness, and healthcare — and what it means for business, culture, and capital. Subscribe to our newsletter → insider.fitt.co/subscribe  Work with our recruiting firm → https://talent.fitt.co/ Follow us on Instagram → https://www.instagram.com/fittinsider/ Follow us on LinkedIn → linkedin.com/company/fittinsider  Reach out → insider@fitt.co

★朝日新聞のデジタル版は、同居のご家族4人まで1つのログインIDでご利用いただけます。ただし、ベーシックコース、スタンダードコースをご利用の方は、お申し込みいただいたご本人のみのご利用となります。 https://support.asahi.com/hc/ja/articles/28232676086551-%E5%AE%B6%E6%97%8F%E3%82%82%E5%90%8C%E3%81%98ID%E3%81%A7%E5%88%A9%E7%94%A8%E3%81%A7%E3%81%8D%E3%81%BE%E3%81%99%E3%81%8B ★おたより、お待ちしています！朝日新聞アプリで神田大介をフォロー https://bit.ly/4k4ZKwA ※たんたんさん主催！「朝リス課外活動」 https://discord.gg/fxZCQySZe ※メディアトークのdiscord https://discord.gg/TU8c9qtzvw ※番組中で紹介したポッドキャストは「ののラジオ～名作文学を朗読で～」です 【関連記事】体育館のクーラー設置率、東京は9割超、最下位は0.8%　公立小中https://www.asahi.com/articles/ASV7S31BFV7SULLI00XM.html?iref=omny 「声の権利」保護、法務省検討会が明記　AI使った権利侵害に歯止めhttps://www.asahi.com/articles/ASV7W1TG8V7WUTIL038M.html?iref=omny 【おねがい】朝日新聞ポッドキャストは、みなさまからの購読料で配信しています。番組継続のため、会員登録をお願いします！https://t.asahi.com/wqin 【番組内容】みなさまのお盆の思い出を教えてください。 【出演・スタッフ】神田大介（MC、音源編集） https://bit.ly/4k4ZKwA 【朝ポキ情報】アプリで記者と対話 http://t.asahi.com/won1 交流はdiscord https://bit.ly/asapoki_discord おたよりフォーム https://bit.ly/asapoki_otayori 朝ポキTV https://www.youtube.com/@asapoki_officialメルマガ https://bit.ly/asapoki_newsletter 広告ご検討の企業様は http://t.asahi.com/asapokiguide 番組検索ツール https://bit.ly/asapoki_cast 最新情報はX https://bit.ly/asapoki_twitter 番組カレンダー https://bit.ly/asapki_calendar 全話あります公式サイト https://bit.ly/asapoki_lp See omnystudio.com/listener for privacy information.

Day 5 of 50 Days for Freedom, hosted from @Swan after Cory's handle hit tech trouble. Swan's buy fee sits at 50 basis points through Labor Day. Framing doc: swan.com/battle. Cory on disagreeing well. Three summers of shows with Vlad Costea despite splitting on layer twos and drivechain. A lot of people we think we oppose actually love Bitcoin, and privacy is common ground. UK digital ID scrapped, sort of. Suz reports the £1.8B scheme killed after a 2.9M-signature petition and cross-party opposition. Her warning: canceling a brand name is not abandoning the architecture. The back door is already open. GOV.UK One Login covers 122 services, with all central government services slated to join by 2027. Age verification, employment checks, and the Online Safety Act converge on the same result. America's version, differently packaged. No single federal portal yet, but Real ID, mobile driver's licenses, and digital age checks add up. Federalism is a partial brake. Panel consensus: very close. Fear plus convenience is the playbook. 9/11, COVID, now the FATF travel rule reframed as national security. Suz: "you can make a scared man do anything." Bitcoin's answer is separating money from the identity gateway. Fourth Turning, with an exit. Brady's case: institutions are collapsing on schedule, but this cycle has Bitcoin and Nostr already built. Freedom tech that math makes un-co-optable. Cory's version: ten million US Bitcoiners, the race to avoid the war. Education is the whole mission. Lyn Alden's Seven Misconceptions, Vijay's 2018 Bullish Case article, Yan Pritzker's Inventing Bitcoin, and the Bitcoin Season documentary. Cory: understanding earns you the right to own more. ETF buyers who skipped it get lettuce hands. Swan versus Coinbase, box by box. Swan Sovereign, Swan Vault multisig, Swan Safe Plus with live video withdrawal confirmation, buy fees under Coinbase's advanced exchange, and Swan covering network fees. Plus Steve's scarcity chart: 60 million millionaires, 21 million coins. Agentic commerce wants Bitcoin. Scott's Machine Economy project, Buzz, Lightning Labs' Wavelength, and HTTP 402 finally getting built. An agent needs only a keypair. Cory also set the BIP110 policy: not daily here, but a moderated debate is coming.

Ben and his co-founder had no product, no law enforcement background, and no pitch—just an offer to help detectives solve cases. One commander took a chance, handed them background checks, and said "let's see what you can do." They worked out of that police department every day for 18 months. Peregrine just raised $250M at a $6.8B valuation.In this episode, Ben breaks down how researching every police captain in the Bay Area landed their first design partner, why working as free crime analysts for 18 months was "the purest form of method acting," how forward-deployed engineers drove them from $1M to $3M to $10M ARR, and the 120% RFP prep that won a contract written for a billion-dollar competitor.Why You Should ListenWhy doing your customer's job is the fastest path to product market fit.How two founders with no product convinced a police department to let them in.Why over-investing in deployment became a growth engine, not a margin problem.How obsessive research wins enterprise deals when you have zero credibility.Keywords startup podcast, startup podcast for founders, product market fit, finding pmf, Peregrine, govtech, public safety technology, forward deployed engineers, selling to government, enterprise sales, data integration, design partners, Ben RudolphChapters00:00:00 Intro00:01:07 The Moment of True Product Market Fit00:11:51 Getting a Police Department to Say Yes00:16:45 Slow Growth and Word of Mouth00:19:49 Forward-Deployed Engineers Before They Were Cool00:27:34 Cracking Government Go-To-Market00:34:08 Winning an RFP Written for Someone ElseSend me a message to let me know what you think!

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

Agentic AI is changing the threat landscape. We've moved beyond chatbots to autonomous systems that navigate files and take actions, expanding the attack surface. Recent incidents highlight the risk. Hugging Face suffered a breach where an AI agent became the entry point into production systems, showing that developer tools are now critical infrastructure. OpenAI's GPT-5.6 accidentally deleted user data, including a production database, after misconfiguring an environment variable—no malicious intent, just real consequences, underscoring the need for mandatory sandboxing. Meanwhile, SpaceX's Grok Build tool was found defaulting to collecting user repositories and sending them to internal cloud storage without clear consent, a reminder of “default-on” data exfiltration risks. A newer threat, Agent Data Injection, manipulates trusted metadata (like IDs or fields) with crafted inputs to mislead agents into unintended actions. A strong counterexample is the 1Password–Claude integration, which requires biometric approval before accessing credentials, keeping secrets out of the AI entirely. Human-in-the-loop controls should be standard.Legacy systems remain a major risk. Windows, OpenSSL, and WordPress continue to produce serious vulnerabilities. LegacyHive allows privilege escalation in Windows by loading another user's registry hive. HollowByte exploits a small crafted TLS payload in OpenSSL to trigger memory over-allocation and denial of service. The wp2shell chain enables unauthenticated remote code execution in WordPress core (versions 6.9–7.0), challenging the assumption that only plugins are risky. At the same time, Windows 10 is reaching end-of-life, yet roughly 17% of devices still run it, especially in cost-sensitive sectors like healthcare and small business. As Windows 11 patches are released, they effectively expose underlying flaws that attackers can reuse against unsupported systems.Trust itself is increasingly being exploited. Attackers are hiding inside legitimate platforms instead of building new infrastructure. HollowGraph malware uses compromised Microsoft 365 calendars as command-and-control channels, embedding instructions in far-future events and routing through normal Graph API traffic. A Norwegian transit test revealed electric buses could be remotely disabled via foreign SIM cards, highlighting risks in over-the-air control systems across transportation sectors. Ransomware groups are also evolving: JadePuffer's ENCFORGE targets AI model artifacts, treating trained models as high-value assets. Researchers have already used GPT models to build exploit chains, signaling AI's shift into offensive security roles.Governance gaps are compounding the problem. A ProPublica report found Microsoft used China-based engineers to support U.S. Department of Defense cloud systems via low-paid American intermediaries who relayed code without understanding it—technically compliant, but operationally risky. The UK scrapped its £1.8B digital ID program after execution failures. In another case, a single typo in a police report, combined with automated license plate recognition, led to a wrongful arrest, showing how systems can enforce human error without validation.The common thread is misplaced confidence—in AI agents, legacy systems, and formal compliance. Practical steps are clear: require human approval for sensitive AI actions, verify data access beyond contractual assurances, sandbox all agents, treat AI models as critical assets with backups, and update detection strategies to monitor abuse within trusted platforms, not just external threats.Trust, increasingly, is the vulnerability.

Mark Carney announced approximately $900 million in new military support for Ukraine at the NATO summit on July 7.According to the Prime Minister's Office, the package includes:• $475 million for ammunition• Approximately $400 million for 35 Canadian-made armoured vehicles• $50 million for "critical technology"But the numbers add up to $925 million.The government never specified the currency. The written PMO readout doesn't match the remarks made at the podium regarding air defence. And Canada's Department of Finance has previously redacted details of federal aid to Ukraine.In this episode, I break down the math, examine the inconsistencies, and explain what a real paper trail for nearly a billion dollars of public spending should look like.Sources• PMO breakdown (via Kyiv Independent)https://kyivindependent.com/canada-pledges-900-million-to-ukraine-for-vehicles-and-ammo-but-no-air-defense/• Epoch Times ($475M / $400M / $50M breakdown; $2.8B 2026 commitment)https://www.theepochtimes.com/world/canada-to-provide-ukraine-900m-for-ammo-armoured-vehicles-carney-6058644• Ukrainska Pravda (currency unspecified)https://www.pravda.com.ua/eng/news/2026/07/07/8042849/• Kyiv Post (podium remarks on air defence)https://www.kyivpost.com/post/79782• Euromaidan Presshttps://euromaidanpress.com/2026/07/07/canada-announces-900-million-military-aid-package/• Juno News ($25.5B total aid; Department of Finance redactions)https://www.junonews.com/p/pm-carney-pledges-900m-aid-package• LIGA.net ("over $925 million"; Development Support Recovery Bank)https://news.liga.net/en/war/news/official-canada-has-announced-a-new-aid-package-for-ukraine-worth-over-900-millionChapters00:00 Introduction00:30 The announcement02:30 Which currency?04:00 The vehicle math05:30 The mismatch07:00 The redactions08:00 What accountability looks like12:00 The word everybody's using13:30 Who gets the money?15:30 Your household's share17:30 Final thoughts#CanadianPolitics #GovernmentAccountability #UkraineAid #NATOSummit #MarkCarney #ForeignAid #DefenseSpending #Taxpayers #CanadaBuy me a coffee! - https://buymeacoffee.com/kelsisherenLet's connect!Substack: https://substack.com/@kelsisherenRumble - https://rumble.com/user/TheKelsiSherenPerspectiveInstagram - https://www.instagram.com/thekelsisherenperspective?utm_source=ig_web_button_share_sheet&igsh=ZDNlZDc0MzIxNw%3D%3DX: https://x.com/KelsisherenSUPPORT OUR PEOPLE - - - - - - - - - - - -Ketone IQ- 30% off with code KELSI - https://ketone.com/KELSIGood Livin - 20% off with code KELSI - https://www.itsgoodlivin.com/?ref=KELSIBrass & Unity - 20% off with code UNITY - http://www.brassandunity.com

Michael Saylor's "never sell" era is OVER. Strategy sold 3,588 BTC (~$216M) at a 20% loss to cover $1.8B in dividend obligations — and Canadian pensions are holding close to $1B of MSTR stock. On June 29, Strategy's board adopted its "Digital Credit Capital Framework": $1B stock buybacks, a 12% STRC dividend, and a $1.25B "BTC Monetization Program" authorizing Bitcoin sales "when strategic." CEO Phong Le calls it "evolving from one-way capital issuance to active capital management." The mNAV premium that financed five years of buying has collapsed from 2.66x to around 1x — and one in three Bitcoin treasury companies now trades below the value of its coins. In this episode of the Canadian Bitcoiners Podcast:- Strategy's pivot: the mNAV death spiral, coins sold at a 20% realized loss, insider selling, and JPMorgan's $2.8B-$11.6B index-exclusion warning- The Canada connection: CPPIB, AIMCo, National Bank, RBC and HOOPP hold ~$1B of MSTR — your pension bought the wrapper trade- A quantum-proof recovery tool that works for everyone except Satoshi's 1.1M BTC- OkoBot: malware that fakes your Ledger/Trezor recovery screen- Global hashrate is shrinking — but Pakistan is up 733%- CleanSpark signs a $6.6B, 20-year AI data center lease- Canada bans crypto political donations under Bill C-25- Clown World North: Stan Cho's $16,203 hotel bill, Canada Post's $30.8M in bonuses against a $1.57B loss, and $159,800 in flight catering- The BIS confirms Canada's housing crash is the biggest on record as 56,400 Canadians leave in a year The leverage cycle is unwinding — the conviction cohort isn't. Long-term holders just hit a record 14.85M BTC. ETF outflows and treasury-company stress are paper Bitcoin changing hands; the base layer doesn't care. Hold your own keys. — Canadian Bitcoiners Podcast- Website: https://canadianbitcoiners.com- X: @CanadianBTCPod- Subscribe & turn on notifications ————————————————————————————————SPONSORS

July 21, 2026: Your daily rundown of health and wellness news, in under 5 minutes. Today's top stories: Samsung Biologics acquires PolyPeptide Group for $1.8B, expanding into peptide manufacturing as GLP-1 demand makes production capacity the real bottleneck Vital Signals unveils Signal Ring, a cuffless smart ring measuring blood pressure continuously, with preorders at $399 ahead of an October launch LeBron James says Nike's path forward starts with reconnecting to the communities and young consumers that built the brand amid slowing growth More from Fitt: Fitt Insider breaks down the convergence of fitness, wellness, and healthcare — and what it means for business, culture, and capital. Subscribe to our newsletter → insider.fitt.co/subscribe Work with our recruiting firm → https://talent.fitt.co/ Follow us on Instagram → https://www.instagram.com/fittinsider/ Follow us on LinkedIn → linkedin.com/company/fittinsider Reach out → insider@fitt.co

Michael Saylor's "never sell" era is OVER. Strategy sold 3,588 BTC (~$216M) at a 20% loss to cover $1.8B in dividend obligations — and Canadian pensions are holding close to $1B of MSTR stock. On June 29, Strategy's board adopted its "Digital Credit Capital Framework": $1B stock buybacks, a 12% STRC dividend, and a $1.25B "BTC Monetization Program" authorizing Bitcoin sales "when strategic." CEO Phong Le calls it "evolving from one-way capital issuance to active capital management." The mNAV premium that financed five years of buying has collapsed from 2.66x to around 1x — and one in three Bitcoin treasury companies now trades below the value of its coins. In this episode of the Canadian Bitcoiners Podcast:- Strategy's pivot: the mNAV death spiral, coins sold at a 20% realized loss, insider selling, and JPMorgan's $2.8B-$11.6B index-exclusion warning- The Canada connection: CPPIB, AIMCo, National Bank, RBC and HOOPP hold ~$1B of MSTR — your pension bought the wrapper trade- A quantum-proof recovery tool that works for everyone except Satoshi's 1.1M BTC- OkoBot: malware that fakes your Ledger/Trezor recovery screen- Global hashrate is shrinking — but Pakistan is up 733%- CleanSpark signs a $6.6B, 20-year AI data center lease- Canada bans crypto political donations under Bill C-25- Clown World North: Stan Cho's $16,203 hotel bill, Canada Post's $30.8M in bonuses against a $1.57B loss, and $159,800 in flight catering- The BIS confirms Canada's housing crash is the biggest on record as 56,400 Canadians leave in a year The leverage cycle is unwinding — the conviction cohort isn't. Long-term holders just hit a record 14.85M BTC. ETF outflows and treasury-company stress are paper Bitcoin changing hands; the base layer doesn't care. Hold your own keys. — Canadian Bitcoiners Podcast- Website: https://canadianbitcoiners.com- X: @CanadianBTCPod- Subscribe & turn on notifications ————————————————————————————————SPONSORS

With just 105 days until the 2026 midterms, Chuck Todd and Chris Cillizza use their last Super Tuesday before the sub-100-day mark to take stock of what they think they know. The headline: with Trump's approval stuck below 40, gas back over $4, an Iran conflict reigniting, and an ICE controversy dominating the news, nearly every fundamental points toward Republicans losing both chambers — and the guys argue the only thing standing between Democrats and a monster year is the Democrats themselves. They dig deep into the polling weeds, unpacking why the generic ballot has quietly become more of a party-ID number, why you can't compare it across cycles anymore, why independents are breaking hard left, and what to make of the model giving Democrats a 62% shot at the House while Republicans hold a 59% edge in the Senate. From there it's onto the races reshaping the map: South Carolina, where Trump ally Russell Frye jumps into the special and Lindsey Graham's appointed sister surprises everyone by running for a full term — a rare bit of GOP pragmatism that hands John Thune a much-needed win — and Maine, where Troy Jackson is set to be coronated at the Democratic convention and the ICE story has erased Susan Collins's post-Platner breathing room. Chuck and Chris also weigh the stalled Todd Blanche confirmation, the $400 million Qatari plane suddenly deemed too unsafe to fly, and why character and temperament remain the whole ballgame. Then they close on LeBron's looming decision — Heat, Cavs, or Sixers — and Chuck plugs the summer slate across the "Chuck Todd expanded universe." Timeline: 00:00 105 days out from the midterms 02:07 Why August, not July, is when politics really kicks off 03:01 Why campaigns no longer wait for Labor Day to run their hits 04:34 Trump's approval is stuck below 40 a recipe for losing both chambers 05:44 ICE story erases whatever Collins got from the Platner disaster 06:49 Democrats are more motivated 07:23 Arizona Republicans line up to nominate a candidate who can't win 08:08 Trump's primetime speech relitigating 2020 09:26 GOP candidates on the trail are running as far from Trump as possible 10:50 Trump is too lazy to be a dictator 11:54 On 2020, Trump is on an island — even his own party won't follow 14:14 The history: 20 of the last 22 midterms punish the president's party 15:05 The generic ballot vs. 2018 and 2010, for reference 15:51 Why the generic ballot is now really a party-ID number 17:31 Trust the trend lines, not the raw numbers 19:59 The StateNavigate Georgia poll that floored them 20:49 Why Democrats peak in summer polling 22:19 A smarter "informed ballot" experiment using Google search results 24:36 Democrats 62% to win the House, Republicans 59% to hold the Senate 25:20 Why the Senate number is lower than it should be 26:54 Iowa as the 50-yard line for a wave 27:42 The daunting Senate map for Dems 30:04 Why the 62% House number surprised Chris 31:52 The one thing that changed all summer — a possible new forever war 32:56 The math favors the GOP: Democrats are playing a road game 34:18 No swing districts left, and a country sorted to the bone 36:03 South Carolina: Trump ally Russell Frye jumps into the special 36:56 Graham's appointed sister surprises by running for a full term 37:30 A rare case of Trump and the GOP doing the pragmatic thing 41:13 Troy Jackson set for a coronation 42:53 The stalled Todd Blanche confirmation & the $1.8B slush fund 43:15 Why the ICE story is a real problem for Collins 44:02 Ambition as a drug — Homan, Bovino, Noem & the $50k question 45:09 The Odyssey movie and how online discourse isn't real life 47:07 The $400M Qatari plane is suddenly too unsafe to fly 48:07 Character and temperament as the whole ballgame 50:20 Quotes that aged badly 52:05 Trump as Grover Cleveland 53:34 LeBron's decision: Heat, Cavs, or Sixers 56:20 Miami and Denver as the best physical fits for a 40-year-old LeBron 57:34 The Hall of Fame "five rings table" and the chase for a fifth title 1:00:17 What the prediction markets say See omnystudio.com/listener for privacy information.

July 17, 2026: Your daily rundown of health and wellness news, in under 5 minutes. Today's top stories: Eli Lilly acquires AtaiBeckley for up to $3.8B, the largest psychedelic medicine deal yet, gaining an intranasal DMT therapy in Phase 3 for depression Wonder closes $650M Series D at $9B valuation, building a vertically integrated food platform spanning restaurants, delivery, and AI personalization Hemispheric emerges from stealth with $52M and Descartes, a foundation AI model trained on EEG data to diagnose depression, PTSD, and Alzheimer's Today's episode is brought to you by AIIR — a modern communications and experiential agency for health, wellness, fitness, and performance brands. From earned media to events and creator-led campaigns, AIIR helps companies sharpen their story, earn attention, and build trust that compounds. Visit https://aiir.agency to learn more. More from Fitt: Fitt Insider breaks down the convergence of fitness, wellness, and healthcare — and what it means for business, culture, and capital. Subscribe to our newsletter → insider.fitt.co/subscribe  Work with our recruiting firm → https://talent.fitt.co/ Follow us on Instagram → https://www.instagram.com/fittinsider/ Follow us on LinkedIn → linkedin.com/company/fittinsider  Reach out → insider@fitt.co 

Thursday, July 16, 2026 Today, Todd Blanche and Jay Clayton testify in their respective confirmation hearings on Capitol Hill; Trump says ICE should continue traffic stops less than one day after DHS suspended the practice; Donald threatens to destroy bridges and civilian power plants in Iran; ICE admits it has a trove of documents about being stationed at polling places; plus Allison and Dana deliver your Good News. Thank You, HoneyLove Save 20% Off Honeylove by going to honeylove.com/DAILYBEANS #honeylovepod  #sponsored The Daily Beans is proud to partner with Miles Taylor and our friends at DEFIANCE.org For a limited time, members of the Daily Beans community can receive a FREE 3-month full membership to DEFIANCE.org and gain access to one of the fastest-growing pro-democracy movements in America. Join here: https://www.defiance.org/beans Join The Daily Beans and give a gift today to ensure The Trevor Project can continue its crucial work in the face of continued challenges. Donate to The Trevor Project - Daily Beans Podcast Guest: Adam KlasfeldAll Rise News@allrisenews|Bluesky, @klasfeldreports.com|BlueSky, @KlasfeldReports|Twitter, @senecaprojectus - Instagram The Latest Breakdown:BREAKING: EXCLUSIVE: FBI Admits it has Epstein Files Training Videos StoriesICE reverses, admits it may have trove of documents on agents at polling places | Democracy Docket Trump: ICE should continue traffic stops after recent shootings, seeming to contradict new policy | NBC News AG nominee Blanche told top Democrat he ‘made a mistake' with $1.8B anti-weaponization fund | Courthouse News Service Trump threatens to bomb bridges and power plants unless Iran resumes talks | BBC NewsGood Troubleimmigrantjustice.org - know-your-rights in an ice encounter →GoodTroubleLivesOn.org/ July 17-19 →Urge Democrats to Oppose and Stop Trump's Crypto Corruption | Indivisible Guide →Defiance.org/beans →Show up for our Libraries - action.ala.org →How to help those impacted by the Venezuela earthquakes|AP →Oppose House Amendment to Defund the Peace Corps! →Stand With Minnesota →ICE List  →iceout.org Good NewsVolunteer Opportunities, Events, and Petitions Near Me · John Lewis Actions on Mobilize Altadena Cookie Co dana-goldbergs-southwest-funnyfest Oct 9 -Email Dana@DanaGoldberg.com for sponsorship informationTour - DANA GOLDBERGTickets for Dana Goldberg: Outrageous - Sep 23 - Den Theater - Chicago  →Share your Good News & Good Trouble - The Daily Beans →Beans Talk audio -beans-talk.simplecast.com →Email Dana LGBTQ Owned eating establishments in your area - hello@mswmedia.com Subject: “Dana's Project” Subscribe to the MSW YouTube Channel - MSW Media - YouTube Our Donation Links The Trevor Project - trevorproject.org/beans Blue Wave California - https://secure.actblue.com/donate/msw-bwc Donate to Public Citizen - https://citizen.org/beans/ Donate to It Gets Better / The Daily Beans Fundraiser Pathways to Citizenship link to MATCH Allison's Donationhttps://crm.bloomerang.co/HostedDonation?ApiKey=pub_86ff5236-dd26-11ec-b5ee-066e3d38bc77&WidgetId=6388736 Join Dana and The Daily Beans in support of Human Rights Campaign http://onecau.se/_ekes71National Security Counselors - Donate, ActBlue.com/donate/msw-bwc, WhistleblowerAid.org/beans Dr. Allison Gill - The Breakdown | Allison Gill, Mueller, She Wrote @muellershewrote.com - Bluesky, MSW & The Daily Beans Podcast @muellershewrote - Instagram, MSW Media - YouTube →Federal workers - email AG - fedoath@pm.me Dana Goldberg - Dana is on Patreon! At Dana's Dugout, @dgcomedy - Bluesky, @dgcomedy - IG, Dana Goldberg - Facebook,  DanaGoldberg.com More from MSW Media - Shows - MSW Media, Cleanup On Aisle 45 pod, The Breakdown | Allison Gill Reminder - you can see the pod pics if you become a Patron. The good news pics are at the bottom of the show notes of each Patreon episode! That's just one of the perks of subscribing! patreon.com/muellershewrote Listener Survey:http://survey.podtrac.com/start-survey.aspx?pubid=BffJOlI7qQcF&ver=shortFollow the Podcast on Apple:https://apple.co/3XNx7ckWant to support the show and get it ad-free and early?https://patreon.com/thedailybeanshttps://dailybeans.supercast.com/https://apple.co/3UKzKt0 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Uber agreed to acquire Delivery Hero for ~$14.8B, expanding into 99 markets. Thinking Machines released its first open-weight model, Inkling, SpaceXAI open-sourced Grok Build after a data-upload backlash, and sources detailed xAI's chaotic race to catch Claude under new leadership. Uber agrees to acquire Delivery Hero in a deal that values the German food delivery company at ~$14.8B, offering €41.50 per share and buying Prosus' 16.8% stake (Bloomberg) Thinking Machines Lab debuts Inkling, an open-weight MoE model with 975B total and 41B active parameters, trained to be broad rather than optimized for one area (Thinking Machines Lab) Thinking Machines Lab debuts Inkling, an open-weight MoE model with 975B total and 41B active parameters, trained to be broad rather than optimized for one area (WSJ) SpaceXAI open-sources Grok Build under an Apache 2.0 license, after the tool uploaded user repositories to SpaceXAI's Google Cloud bucket, causing a backlash (Simon Willison) SpaceXAI open-sources Grok Build under an Apache 2.0 license, after the tool uploaded user repositories to SpaceXAI's Google Cloud bucket, causing a backlash (The Decoder) Sources detail how xAI has been slowed down by internal chaos as Musk pushed for Grok to match Claude, amid signs it is turning a corner under Michael Nicolls (Bloomberg) Sources: Apple is preparing new iPads, including an iPad mini with an OLED screen by October and refreshed entry-level iPads and iPad Airs for 2027 (Bloomberg) Subscribe to the ad-free feed. Learn more about your ad choices. Visit megaphone.fm/adchoices

On this episode of Run the Numbers, CJ breaks down Cumberland Farms' IPO filing and the surprising business behind it.—SPONSORS:RightRev is an automated revenue recognition platform that lets your product team ship new pricing without asking finance for permission, and your sales team close deals without creating downstream chaos. Check out their free tool at calculator.rightrev.com It scores your rev rec process, shows what's exposing you to risk, and tells you exactly where to focus before it bites you in the rear end. Check it out at https://calculator.rightrev.comPulley is an equity management platform that lets you issue options, model dilution, and complete 409As without your cap table turning into a spreadsheet disaster. Founders raising, hiring, and scaling use Pulley to keep equity clean and stay focused on building. Learn more or request a demo at https://pulley.com/mostlymetricsRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjMaximor is an autonomous finance platform that runs order-to-cash, procure-to-pay, the close, cash management, and reporting on self-learning agents instead of a dozen disconnected tools. One PE-backed customer cut their close in half, took audit findings from seven to zero, and cut back-office costs by 70% in six months. You pay for outcomes, not seats. See it at https://www.maximor.ai/Brex is an intelligent finance platform with AI-powered agents that capture expenses automatically, enforce policy before the spend happens, and close your books in minutes instead of weeks. 35,000+ companies like OpenAI, Coinbase, Anthropic, and DoorDash already run on Brex. It's time to get Brex AF. Learn more at https://www.brex.com/metricsAnrok is the sales tax platform that watches your exposure everywhere, automates compliance, and flags risk before it turns into a surprise back-tax letter from a state you've never set foot in. Companies like Anthropic, Notion, and Vanta already trust Anrok to stay ahead of rules that move faster than any spreadsheet can. Talk to a sales tax expert for a personalized exposure estimate at https://www.anrok.com/rtn—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNCJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and Intro2:57 Key metrics4:28 Europe now out-earns America5:01 $5.8B debt at 8x leverage6:20 TLDR: snacks, gas, borrowed money7:58 Fuel vs. inside: the gross profit photo finish8:58 COCO, CONCO, and OTHER explained10:57 Sponsors — Maximor | Brex | Anrok14:02 Why scale matters: four reasons16:02 The US convenience store market17:42 EVs: long the birth rate18:05 Back to the debt19:10 The sale leaseback20:24 Sponsors — RightRev | Pulley | Rillet23:26 Does the deleveraging math work?24:27 The growth thesis25:06 600–700 stores become Cumberland Farms26:16 The chicken fryer ROI27:00 Coffee and loyalty: 6M members in 13 months28:15 Red flag 1: five-for-five COSO failures28:41 Red flag 2: entire C-suite is new29:04 Red flag 3: lending to their own parent29:42 Red flag 4: Big Tobacco funds loyalty29:57 Red flag 5: foreign issuer, Cayman charter30:10 Red flag 6: one supplier is 31% of costs30:32 Red flag 7: balance sheet is stale30:45 Cap table: TDR Capital and the Issa brothers31:55 Casey's vs. ARCO33:47 Goldman got bumped34:08 Massachusetts banned the pump clip until 201534:57 No health insurance disclosure for 16K US workers35:30 Robert Swan (ex-Intel CEO) is on the board36:08 Credits

In "Private Fleets Rescue Freight Brokers in 2026", Joe Lynch and Russell Jones, CEO & Co-founder of Private Fleet Net Zero, discuss how unlocking empty private backhauls provides brokers with discounted, high-quality capacity to combat fraud and skyrocketing liability. About Russ Jones Russell Jones co-founded Private Fleet Net Zero to help the 45% of trucks that are in Private Fleets with usually empty backhauls find loads from $50B+ of 3PL freight spend, leveraging his leadership of Cargo Chief, which enables 1,200+ 3PL buyers with $8B+ of spend to buy transportation capacity more profitably. Previously, Mr. Jones co-founded and led two cloud-based physical security firms.  He was also the founding CEO of Clearvox Communications, which pioneered the market for cellular phone headsets, which he sold to Plantronics.  Beforehand at Adaptec, Mr. Jones doubled a $50M channel products business to $100M. Mr. Jones has been awarded 10 patents, and holds a BSBA with highest honors from Boston University and an MBA from the Harvard Business School. About Private Fleet Net Zero Private Fleet Net Zero, PFNZ, is uniquely aggregating 10,000s of trucks with 1,000s of lanes of underutilized, underpriced, theft-free and superior private and dedicated fleet trucking capacity and matching via multi patent-pending technologies and artificial intelligence to $10Bs of freight spend registered on our cloud-based platform, while generating a compelling client ROI. Our network is quickly and efficiently growing both fleets and 3PLs on PFNZ, which is on a path to save 30M+ tree equivalents. Key Takeaways: Private Fleets Rescue Freight Brokers in 2026 In "Private Fleets Rescue Freight Brokers in 2026", Joe Lynch and Russell Jones, CEO & Co-founder of Private Fleet Net Zero, discuss how unlocking empty private backhauls provides brokers with discounted, high-quality capacity to combat fraud and skyrocketing liability. Massive Fleet Scale: Private Fleet Net Zero (PFNZ) has rapidly aggregated 80,000 trucks and 40,000 lanes of coverage, using patent-pending AI to match this massive pool of underutilized capacity with tens of billions of dollars in registered freight spend. The $150B Backhaul Waste: Private fleets (where the cargo owner owns the asset, like Walmart or Sherwin-Williams) make up 45% of all trucks on the highway, yet they run empty 80% of the time on their backhauls, leaving a $150 billion pool of premium capacity sitting idle. Pure Profit for Fleets: Because the primary "front haul" already covers the driver's salary, equipment, insurance, and core fuel costs, any backhaul revenue captured through PFNZ represents a 95% pure profit margin for the fleet owner. Quadrupled Broker Margins: Brokers can secure this premium capacity at a 25% discount to the market rate. In a tight market where a typical gross profit might only be $150 on an $1,150 load, cutting carrier costs from $1,000 to $750 can effectively triple or quadruple a broker's net margins. Eliminating Fraud and Liability: Shifting to private fleets bypasses the modern plague of cargo theft, cyber fraud, and "chameleon carriers" who hide bad histories under new DOT numbers. Furthermore, because private fleets have newer equipment and a third less accidents, they shield brokers from catastrophic multi-million dollar "nuclear verdicts" tied to carrier safety under the recent Montgomery ruling. Combating the 2026 Capacity Crunch: Massive federal enforcement of English language proficiency rules is projected to strip 25% of for-hire drivers (400,000 to 600,000 drivers) off the road. PFNZ rescues brokers by giving them an automated "outsourced recruiting team" to tap into stable private capacity that was previously heavily monopolized by the top 10 mega-brokerages. Seamless Integration & Sustainability: PFNZ connects to a broker's TMS within weeks via APIs, reports, or an AI bot to automatically map buying patterns. By eliminating empty miles, the platform is on track to save over 45 million tree equivalents in CO2, giving public companies and shippers a verifiable decarbonization story for SEC and board reporting. Learn More About Private Fleets Rescue Freight Brokers in 2026 Russ Jones | Linkedin Private Fleet Net Zero | Linkedin Private Fleet Net Zero Private Fleet Net Zero: The Deadhead is Dead with Russ Jones The Broken Safety System Threatening Shippers and Brokers with Chris Burroughs What is Blue Ocean Strategy | About Blue Ocean Strategy The Logistics of Logistics Podcast If you enjoy the podcast, please leave a positive review, subscribe, and share it with your friends and colleagues. The Logistics of Logistics Podcast: Google, Apple, Castbox, Spotify, Stitcher, PlayerFM, Tunein, Podbean, Owltail, Libsyn, Overcast Check out The Logistics of Logistics on Youtube

In this week's episode of the Coin Stories News Block powered exclusively by Ledn, we cover these major headlines related to Bitcoin, macroeconomics, and global finance: Trump reported $1.4 billion in crypto income — more than Coinbase earned all year The Trump token collapsed 97% — here's who actually made money Strategy sold 3,588 Bitcoin this morning in its biggest sale yet Bear market data is flashing a signal we've only seen five times since 2011 Saylor says Bitcoin evolves by not changing — Lyn Alden weighs in tomorrow ---- The News Block is powered exclusively by Ledn – the global leader in Bitcoin-backed loans, issuing over $10 billion in loans since 2018, and they were the first to offer proof of reserves. With Ledn, you get custody loans, no credit checks, no monthly payments, and more. My followers get .25% off their first loan. Learn more at www.ledn.io/natalie  ---- Order Natalie's new book "Bitcoin is For Everyone," a simple introduction to Bitcoin and what's broken in our current financial system: https://amzn.to/3WzFzfU  ---- Read every story in the News Block with visuals and charts! Join our mailing list and subscribe to our free Bitcoin newsletter: https://thenewsblock.substack.com  —- References mentioned in the episode: Trump Reports More Than $1.4B in Crypto Income for 2025 Trump Insists There's Nothing Wrong With His Big Crypto Gains Trump Pocketed More Than $1B From Crypto Ties as Industry Headed Toward Slump Trump Says There's 'Nothing Wrong' With Family's Crypto Windfall Trump's Crypto Token Buyers Are Down $3.8B, Blockchain Data Shows Nearly 1 Million Wallets Are Down $3.8B on Trump's Memecoin Trump Financial Disclosures Show Hundreds of Millions in Crypto-Related Income Trump's Memecoin Investors Are Down Billions Clarity and Congress's Summer Break — State of Crypto River's Chart on 5-Year Crypto Performance Bitcoin Magazine on Trump's Crypto Disclosure Bitcoin Long-Term Holders Have Returned to Accumulation, Glassnode Says Barstool's Portnoy Plans to Hold Bitcoin Down to $0 After Timing It Wrong Every Time Michael Saylor: "Bitcoin Evolves by Not Changing" DurdenBTC: ~45% of Long-Term Holder Supply in Loss DurdenBTC: Bitcoin Supply-in-Profit at Historical Lows Bitfinex: Long-Term Holder Supply in Loss  ---- Upcoming Events: The best time to plan for Bitcoin 2027 is right now. Early bird tickets are live — grab the lowest pricing available and use code HODL for 10% off: https://tickets.b.tc/event/bitcoin-2027?promoCodeTask=apply&promoCodeInput=HODL  ---- This podcast is for educational purposes and should not be construed as official investment advice. ---- VALUE FOR VALUE — SUPPORT NATALIE'S SHOWS Strike ID https://strike.me/coinstoriesnat/ Cash App $CoinStories #money #Bitcoin #investing

Multifamily investors are hurting — foreclosures, capital calls, and wiped-out equity are dominating the headlines and social media feeds. So is the investment thesis actually dead? Spencer Gray and Griffin Haddad tackle the question head-on this week, plus break down Gray Capital's newest offering: Century Apartments in West Lafayette, Indiana.In this episode:• Introducing Century — a new multifamily investment opportunity located inside the Purdue Research Park in West Lafayette, IN, directly adjacent to SK hynix's $3.8B semiconductor facility• A look inside Gray Capital's AI-powered deal room — including a multi-agent simulation that stress-tests every assumption in the business plan• Breaking down a viral LinkedIn post on real losses happening across the multifamily industry — and why context matters• Yardi Matrix's Summer 2026 Outlook: new supply is projected to drop nearly 40% by 2027–2028• RentCafe's $1,500 rent budget study — how far your money goes in 200+ US cities• Explore the Century investment opportunity and interactive deal room: https://GrayCapitalLLC.com

On this episode of CoinDesk's Public Keys from the New York Stock Exchange, host Jennifer Sanasie is joined by CoinDesk Indices and Data to break down nearly $1.8 billion in weekly Bitcoin ETF outflows, Strategy's new capital plan, and whether the digital asset treasury narrative is back. SharpLink CEO Joseph Chalom joins to unpack the Ethereum Foundation's funding crisis, the launch of ETHlabs, and the company's $75 million raise, as he makes the case for an institutional supercycle in ETH. In this week's 10X, Kaizen founder Brian Jung breaks down his MicroStrategy short. Moody's Ratings Managing Director and Global Head of Digital Economy Fabian Astic explains how the firm is embedding credit ratings into tokenized securities on Solana and unveils the first-ever credit rating methodology for stablecoins. Plus, Midnight Foundation President Fahmi Syed details the partnership with Bank of England-regulated Monument Bank and why privacy is becoming the missing piece for institutional adoption. - This episode of Public Keys is brought to you by Kraken Pro. For more: https://pro.kraken.com/ - Learn more at https://www.bullish.com/. - To get market moving news delivered daily, download CoinDesk's mobile app: https://linktr.ee/coindeskapp. - Timecodes: 00:00 Welcome to Public Keys 00:52 BTC ETFs See $1.8B in Weekly Outflows 02:57 Strategy's Capital Plan and Bitcoin's Week 04:12 Is the Digital Asset Treasury Narrative Back? 06:37 Ethereum Foundation Departures and ETHlabs 07:06 SharpLink CEO Joseph Chalom Joins 08:15 Ethereum's Funding Crisis and the ETH Bull Case 10:25 Inside SharpLink's $75M Raise 13:36 ETH's Institutional Super Cycle and Price Outlook 15:19 Will the Clarity Act Pass This Year? 17:45 10X: Brian Jung's Strategy Short 19:16 Moody's Ratings Brings Credit Ratings On-Chain 19:46 Fabian Astic on the First Stablecoin Credit Rating 21:36 Do Stablecoins Need Ratings After the Genius Act? 23:17 Why launch token ratings on Solana and Canton first? 25:36 Collateral Mobility and $255T in Trapped Liquidity 28:46 Is Privacy the Missing Piece for Institutions? 29:02 Midnight's Fahmi Syed on the Monument Bank Deal 33:46 The Collateral Warehouse and Global Expansion 36:38 Thanks for Watching

In this episode of Building Billions, I sit down with JD Ross, founder of Opendoor, partner at Atomic Ventures, and founder of WithCoverage, to break down how he’s taken companies from idea to billions in enterprise value, including scaling Opendoor from first revenue to an $8B public company in under six years. We get into what actually drives growth at scale, how to understand your business equation, why companies break as they expand, and what it really takes to build and lead teams beyond the founder, while also unpacking the massive opportunity in “boring” industries, AI-enabled operators, and the $5T+ small business wealth transfer that’s reshaping where the next generation of billion-dollar companies will be built.Support the show: http://cardoneventures.comSee omnystudio.com/listener for privacy information.

In this episode of The Capital Raiser Show, Richard C. Wilson sits down with entrepreneur, investor, and 10X founder Grant Cardone for a high-energy fireside chat on scaling businesses, raising capital, multifamily real estate, Bitcoin, social media leverage, and building long-term wealth. Grant shares the mindset and strategies behind building a $5B+ real estate portfolio, raising over $1.8B in equity, using social media to attract investors at scale, and why he believes most traditional investment structures are fundamentally flawed. The conversation dives into real estate cycles, branding, investor psychology, forced appreciation, Bitcoin treasury strategy, scaling through audience building, and how high-performing entrepreneurs continually reinvent themselves to operate at larger levels. Topics covered include: • Building and scaling a $5B+ real estate portfolio • Raising capital outside traditional Wall Street channels • Why social media is a massive investor acquisition tool • Long-term real estate investing and 10-year holds • Bitcoin, cash flow, and treasury strategy • Branding multifamily assets for scale • The psychology of growth and thinking bigger • Why successful people must leave comfort repeatedly • Investor relations, scale, and building loyal communities • High-velocity decision making and deal flow The Capital Raiser Show brings together billionaire investors, family offices, founders, operators, and elite entrepreneurs to discuss capital raising, scaling, investing, and strategic growth. Subscribe for more interviews with top investors, billionaires, family offices, and industry leaders.

Clayton as DNI, DOJ/Trump's $1.8B slush fund lives, Trump vs Platner, and Iran and inflation. It has been a wild, weird, harrowing week — Iran, ICE in Minneapolis, a UFC fight at the White House, the Knicks in the playoffs, and a president who keeps telling you out loud what he plans to do next. In this special Friday pop-media episode, Paul Rieckhoff brings you his weekly conversation from MS Now and breaks down what he's calling Trump's Plan A, Plan B, and Plan C: weaponize the National Guard, weaponize ICE, and weaponize the ballot box. It's not speculation. Trump has said it. Steve Bannon has said it. The reporting backs it up. And Congress — by Paul's read — has stopped exactly nothing. This is a no-BS briefing for the angry middle. Paul connects the dots between the resurrected payout scheme for January 6th defendants, the ICE escalation in blue cities, the Iran war driving gas prices through the roof, and the coming primary fights from Maine to Nebraska to Montana where independent veterans are stepping up where Democrats can't. He's blunt about the Democratic brand problem, blunt about the Republican capitulation, and clear about where the circuit breaker actually lives: election integrity, the courts, Congress, and an angry middle that refuses to check out for the summer. -WATCH full video of this episode here. -Join Noble Mobile today and get a $100 bonus when you stay a member for 2 months! -Join IVA and stand up to Trump's Forever Wars. -Learn more about Paul's work to elect a new generation of independent leaders with Independent Veterans of America. -Learn more about American Veterans for Ukraine here. -Remember Independent is an Attitude. -Learn more about The Headstrong Project for Veterans, Tragedy Assistance Program for Survivors (TAPS), and Department of Veterans Affairs resources in your area. Seeking support is not a sign of weakness. It's a show of strength. If you or a loved one are in immediate crisis, dial 988 and press 1, or text 838255. Connect with Independent Americans: Subscribe on YouTube, Spotify, Apple Podcasts, and all podcast platforms Read more at Substack Support ad-free episodes at Patreon  Connect: Instagram  • X/Twitter • BlueSky • Facebook  Follow on social: @PaulRieckhoff on X, Instagram, Threads, and Bluesky   -Join the movement. Hook into our exclusive Patreon community of Independent Americans. Get extra content, connect with guests, meet other Independent Americans, attend events, get merch discounts, and support this show that speaks truth to power.  -And get cool IA and Righteous hats, t-shirts and other merch now in time for the new year.  Independent Americans is powered by veteran-owned and led Righteous Media.  And now part of the BLEAV network!  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The House passed a resolution to limit President Trump's Iran war powers.  The Senate is set to debate ICE funding, as Trump won't commit to killing the $1.8B anti-weaponization fund.  The suspect in a 15-hour hostage standoff in California was killed.  The President touts progress on the Reflecting Pool and the building of the UFC stage on the south lawn.  Plus, a stranger helps a family who had their van stolen on vacation.  Learn more about your ad choices. Visit podcastchoices.com/adchoices

Wednesday, May 27th, 2026 Today, South Carolina Senate Republicans reject Trump's redistricting bid in the state; a federal court blocks Alabama's new Republican map; Governor Mikie Sherrill demanded access to an ICE facility amid an ongoing hunger strike; the Supreme Court shoots down Florida's bid to stop other states from issuing driver's licenses to immigrant truckers; France bans Israel's Ben-Gvir after unspeakable treatment of flotilla detainees; Jim Acosta files a claim against the $1.8B slush fund; an American journalist is charged with failing to register as a foreign agent of China; a judicial panel upholds the reprimand of a federal judge caught having sex in chambers with a high ranking police officer; the Congressional Black Caucus urges companies to oppose Republican redistricting; CBS won't limit access to Colbert's public access show; and Allison delivers your Good News. Thank You, Fast Growing Trees Get 20% off your first purchase  FastGrowingTrees.com/dailybeans Thank You, LumiGummies Go to LumiGummies.com and use code DAILYBEANS for 30% off your order. Guest: Jim AcostaI'd Like to Apply for the Anti-Weaponization FundThe Jim Acosta Show | SubstackJim Acosta - YouTube@jimacosta.bsky.social - BlueskyJim Acosta (@jimacosta) - InstagramJim Acosta (@Acosta) - Twitter The Latest Breakdown:Don't Be Fooled: Trump Is Dying and Losing StoriesSouth Carolina's Trump-backed redistricting push fails in the state Senate amid GOP opposition | NBC News Federal court blocks Alabama from using GOP-drawn congressional map | NBC News Gov. Sherrill Demands Access to ICE Facility as Hunger Strike Widens | NYT Supreme Court rejects Florida's attempt to sue California and Washington over immigrant truck drivers | CBS News France bans Israeli minister Itamar Ben-Gvir after 'unspeakable' flotilla detainee taunts | AP News American journalist charged with serving as unregistered agent for China | POLITICO Panel upholds US judge's private reprimand for affair with police officer | Reuters Congressional Black Caucus urges companies to oppose Republican redistricting | PBS News After Stephen Colbert's viral talk show parody, CBS backs down from copyright action | NPR Good Trouble Contact - Senator Andy Kim Office of New Jersey Governor Mikie Sherrill Beans Talk - YouTube, Beans Talk - audio feed →Form WTAF-8647 →Recall Gov. Jeff Landry - Louisianadeservesbetter.com →STOP the deportation proceedings against Mohsen Mahdawi - Action Network →SusanRogan - how-to-help-win-the-midterms →detentionwatchnetwork.org →FieldTeam6.org →Standwithminnesota.com →Tell Congress Ice out Now | Indivisible, Defund ICE | 5Calls →Congress: Divest From ICE and CBP | ACLU →ICE List  →iceout.org Good NewsWounded Warrior Umpire Academy Uncle Sammy - YouTube Kat Abughazaleh (@kabughazaleh) - Instagram →Share your Good News & Good Trouble - The Daily Beans →Beans Talk audio -beans-talk.simplecast.com →Email Dana LGBTQ Owned eating establishments in your area - hello@mswmedia.com Subject: “Dana's Project” Subscribe to the MSW YouTube Channel - MSW Media - YouTube Harry Dunn is running for CongressHarry Dunn for Maryland Our Donation Links Blue Wave California - bluewavecalifornia.org/concert The Daily Beans is donating $10,000 and invites you to give what you can to support their life-affirming work - Donate to It Gets Better / The Daily Beans Fundraiser   Pathways to Citizenship link to MATCH Allison's Donationhttps://crm.bloomerang.co/HostedDonation?ApiKey=pub_86ff5236-dd26-11ec-b5ee-066e3d38bc77&WidgetId=6388736 Join Dana and The Daily Beans in support of Human Rights Campaign http://onecau.se/_ekes71 More Donation LinksNational Security Counselors - Donate, ActBlue.com/donate/msw-bwc, WhistleblowerAid.org/beans Dr. Allison Gill - The Breakdown | Allison Gill, Mueller, She Wrote @muellershewrote.com - Bluesky, MSW & The Daily Beans Podcast @muellershewrote - Instagram, MSW Media - YouTube →Federal workers - email AG at fedoath@pm.me and let me know what you're going to do, or just vent. I'm always here to listen.  Dana Goldberg - Dana is on Patreon! At Dana's Dugout, @dgcomedy - Bluesky, @dgcomedy - IG, Dana Goldberg - Facebook,  DanaGoldberg.com More from MSW Media - Shows - MSW Media, Cleanup On Aisle 45 pod, The Breakdown | Allison Gill Reminder - you can see the pod pics if you become a Patron. The good news pics are at the bottom of the show notes of each Patreon episode! That's just one of the perks of subscribing! patreon.com/muellershewrote Listener Survey:http://survey.podtrac.com/start-survey.aspx?pubid=BffJOlI7qQcF&ver=shortFollow the Podcast on Apple:https://apple.co/3XNx7ckWant to support the show and get it ad-free and early?https://patreon.com/thedailybeanshttps://dailybeans.supercast.com/https://apple.co/3UKzKt0 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

A former Capitol Police Officer wins her defamation suit against Blaze Media for saying she was the DC Pipe Bomber. The justice department proudly removed the January 6th press releases from their website. Harry sues to dissolve the $1.8B slush fund, An appeals court allows Rep. Jamie Raskin to file an amicus brief against dismissing the seditious conspiracy charges against the Oath Keepers and the Proud Boys. Allison Gillhttps://muellershewrote.substack.com/https://bsky.app/profile/muellershewrote.comHarry DunnHarry Dunn | Substack@libradunn1.bsky.social on BlueskyWant to support this podcast and get it ad-free and early?Go to: https://www.patreon.com/aisle45podTell us about yourself and what you like about the show - http://survey.podtrac.com/start-survey.aspx?pubid=BffJOlI7qQcF&ver=short Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Allison speaks with former Capitol Police Officer Harry Dunn and his lawyer Brendan Ballou about their lawsuit to dissolve the $1.8B slush fund for insurrectionists. Reminder - you can see the pod pics if you become a Patron. The good news pics are at the bottom of the show notes of each Patreon episode! That's just one of the perks of subscribing! patreon.com/muellershewrote Listener Survey:http://survey.podtrac.com/start-survey.aspx?pubid=BffJOlI7qQcF&ver=shortFollow the Podcast on Apple:https://apple.co/3XNx7ckWant to support the show and get it ad-free and early?https://patreon.com/thedailybeanshttps://dailybeans.supercast.com/https://apple.co/3UKzKt0 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The Department of Justice has established a $1.8B “anti-weaponization fund” after Trump dropped his lawsuit against the IRS. A former federal prosecutor has been indicted for sending herself copies of Volume II of Jack Smith's final report. A federal judge has dismissed the charges against Kilmar Ábrego García on vindictive and selective prosecution grounds. The Justice Department has dropped all remaining charges against the Broadview 6 after a grand jury transcript showed gross misconduct. Plus listener questions. Do you have questions for the pod or something for HITMEINTHEHEADWITHABAT?  TACO, NACHO, and More Trump Acronyms - by Carlos Greaves Check out other MSW Media podcastshttps://mswmedia.com/shows/ Follow AGMueller, She Wrote SubstackMueller She Wrote on Blueskyhttps://twitter.com/MuellerSheWrotehttps://twitter.com/dailybeanspodMore from Andrew McCabeThe Real McCabe on Substack@therealmccabe.com on BlueskyThe Threat: How the FBI Protects America in the Age of Terror and Trump This Show is Available Ad-Free And Early For Patreon and Supercast Supporters at https://patreon.com/thedailybeansOr when you Subscribe on Apple Podcastshttps://apple.co/3YNpW3P Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Today's Headlines: The Department of Homeland Security is threatening to cut customs staffing at major international airports in sanctuary cities like New York, LA, Chicago, and DC — which would affect not just tourism but cargo shipments and the broader economy — because DHS Secretary Markwayne Mullin apparently thinks international trade can just reroute to Florida. Trump, meanwhile, said he's "in no hurry" to end the Iran war while also telling reporters he might skip his own son's destination wedding in the Bahamas because of "this thing called Iran," which is either a convenient excuse or the most relatable thing he's ever said. The Traitor Fund — formerly known as Trump's $1.776 billion slush fund — is getting wilder by the day: Proud Boys leader Enrique Tarrio wants $2-5 million, Mike Lindell is asking for $400 million, a January 6th rioter who compared herself to Jesus during sentencing wants $10 million, George Santos might file, and the couple who pointed guns at BLM protesters is reportedly interested. Acting AG Todd Blanche went to Congress to lobby Republicans not to block the fund — and offered to cut them in on it, specifically noting that senators whose records were secretly subpoenaed are eligible to file claims, which is a sentence that exists. The Senate responded by going home for a few weeks rather than dealing with it, which is technically a dereliction of their constitutional duty to approve funding, but here we are. Mortgage rates also hit a nine-month high this week at 6.51%, Trump is claiming he can build his DC arch without congressional approval, and a design commission approved the arch's look, which is one of many steps still required — Congress included, no matter what Donald says. On the Epstein files, Jeffrey Epstein's former personal assistant Sarah Kellen testified before the House Oversight Committee in a session described as deeply informative and genuinely harrowing — revealing she was recruited at 21, sexually and psychologically abused by Epstein for over a decade, and was "being paid in part to be raped." She also told the committee that the federal government included her name in Epstein's 2008 nonprosecution agreement without ever speaking to her, effectively branding her a criminal in a secret deal made with her own abuser. And finally, last night was Stephen Colbert's last show, which aired after this episode was recorded. Resources/Articles mentioned: The Atlantic: Homeland Security's Plan to Squeeze International Flights NYT: Trump Says He Will ‘Try and Make' Son Don Jr.'s Wedding, but Timing ‘Not Good' CBS News: Trump says Netanyahu will do "whatever I want" on Iran, and he's "in no hurry" to make a deal The Independent: Jan 6ers and other Trump allies already lining up to get their hands on slice of his $1.8B ‘slush fund' X - Paula Reid: https://x.com/PaulaReidCNN/status/2056842557334904940 USA Today: Fuming at Trump over 'slush fund,' Senate GOP skips town without passing ICE bill WaPo: Trump officials say they can build 250-foot arch without Congress's approval WSJ: Mortgage Rates Hit a Nine-Month High in Blow to Prime Buying Season ABC News: Former Jeffrey Epstein assistant tells House Oversight Committee he abused her for years AP News: Stephen Colbert is saying goodbye to 'The Late Show.' How it ends is still a secret Subscribe to the Betches News Room and join the Morning Announcements group chat. Go to: betchesnews.substack.com Morning Announcements is produced by Sami Sage and edited by Grace Hernandez-Johnson Learn more about your ad choices. Visit megaphone.fm/adchoices

Tuesday, May 19th, 2026 Today, Trump has unilaterally dropped his $10B lawsuit against the IRS and has set up a $1.7B slush fund to pay his criminal co-conspirators; the district attorney in Hennepin County Minnesota has charged ICE officer Christian Castro with assault and lying in the shooting of Julio Cesar Sosa-Celis; a jury has dismissed Elon Musk's claims against Open AI CEO Sam Altman; the House Oversight Committee will interview one of the prison guards on duty when Epstein died; and Allison and Dana deliver your Good News. Thank You, IQBAR Text DAILYBEANS to 64000 to get 20% off all IQBAR products, plus FREE shipping. Message and data rates may apply.  California Rising - It was a powerful night to launch the fight to win back the House! The show is over but you can still help us reach our fundraising goal! bluewavecalifornia.org/concert Guest: Adam KlasfeldAll Rise News@allrisenews|Bluesky, @klasfeldreports.com|BlueSky, @KlasfeldReports|Twitter, @senecaprojectus - Instagram The Latest Breakdown:Retired Judge Blasts Trump's $1.7B Slush Fund for Allies | The Breakdown StoriesLive updates: Three killed, two suspects dead in shooting at San Diego mosque | NBC 7 San Diego DOJ sets up $1.8B ‘anti-weaponization' fund after Trump drops IRS lawsuit | NBC News House Oversight Committee to interview prison guard on duty when Jeffrey Epstein died | ABC7 New York Minnesota county charges ICE officer in shooting during immigration crackdown | PBS News Jury dismisses all claims in Elon Musk's lawsuit against OpenAI CEO Sam Altman | NPR Good Trouble The next protest here opposing the "deathstar" 'Stratos' data center is by Indivisible, Saturday, 23 May at the Utah State Capitol, 11 am. Dump Data Centers · Indivisible →STOP the deportation proceedings against Mohsen Mahdawi - Action Network →SusanRogan - how-to-help-win-the-midterms →detentionwatchnetwork.org →FieldTeam6.org →Standwithminnesota.com →Tell Congress Ice out Now | Indivisible, Defund ICE | 5Calls →Congress: Divest From ICE and CBP | ACLU →ICE List  →iceout.org Good NewsV Spehar (@underthedesknews) - Instagram LouisianaDeservesBetter.comgeauxvote.com/ElectionsAndVoting.html No Detention Centers in Michigan Conserve Ohio →Share your Good News & Good Trouble - The Daily Beans →Beans Talk audio -beans-talk.simplecast.com →Email Dana LGBTQ Owned eating establishments in your area - hello@mswmedia.com Subject: “Dana's Project” Subscribe to the MSW YouTube Channel - MSW Media - YouTube Harry Dunn is running for CongressHarry Dunn for Maryland Our Donation Links The Daily Beans is donating $10,000 and invites you to give what you can to support their life-affirming work - Donate to It Gets Better / The Daily Beans Fundraiser The Daily beans is donating $10,000 and invites you to give what you can to support their life-affirming work - Donate to It Gets Better / The Daily Beans Fundraiser Pathways to Citizenship link to MATCH Allison's Donationhttps://crm.bloomerang.co/HostedDonation?ApiKey=pub_86ff5236-dd26-11ec-b5ee-066e3d38bc77&WidgetId=6388736 Join Dana and The Daily Beans in support of Human Rights Campaign http://onecau.se/_ekes71 More Donation LinksNational Security Counselors - Donate, ActBlue.com/donate/msw-bwc, WhistleblowerAid.org/beans Dr. Allison Gill - The Breakdown | Allison Gill, Mueller, She Wrote @muellershewrote.com - Bluesky, MSW & The Daily Beans Podcast @muellershewrote - Instagram, MSW Media - YouTube →Federal workers - email AG at fedoath@pm.me and let me know what you're going to do, or just vent. I'm always here to listen.  Dana Goldberg - Dana is on Patreon! At Dana's Dugout, @dgcomedy - Bluesky, @dgcomedy - IG, Dana Goldberg - Facebook,  DanaGoldberg.com More from MSW Media - Shows - MSW Media, Cleanup On Aisle 45 pod, The Breakdown | Allison Gill Reminder - you can see the pod pics if you become a Patron. The good news pics are at the bottom of the show notes of each Patreon episode! That's just one of the perks of subscribing! patreon.com/muellershewrote Listener Survey:http://survey.podtrac.com/start-survey.aspx?pubid=BffJOlI7qQcF&ver=shortFollow the Podcast on Apple:https://apple.co/3XNx7ckWant to support the show and get it ad-free and early?https://patreon.com/thedailybeanshttps://dailybeans.supercast.com/https://apple.co/3UKzKt0 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.