Podcasts about 10x

A $4 million roofing contractor is busy and short on cash. He thinks he needs more leads. But once we examine the whole business, the real problems are somewhere else.A roofing contractor calls me and says:Dave, we're doing $4 million a year. We're busy, but there's never enough cash. I think we need more leads.Maybe he does.But what if leads aren't the problem? What if spending more money on marketing actually makes things worse?In this episode, I walk through a fictional $4 million roofing company using the same whole-business approach behind my new Roofing Business Health Check.We examine all three legs of a healthy roofing business:• Sell Work• Do Work• Keep ScoreThe owner thinks he needs more leads, but we find missed calls, estimates nobody followed up on and old opportunities sitting untouched in the CRM.Then we look at production. Sales promises aren't making it into the job package, change orders aren't always collected and completed jobs aren't being properly job-costed.Finally, we examine the numbers. Gross profit is slipping, overhead is growing and the owner is running a $4 million company without a clear financial scorecard.More leads won't fix those problems. They could make them worse.In this episode, you'll learn:Why revenue doesn't tell you whether your business is healthyHow poor follow-up wastes leads you already paid forWhy more sales can make a weak production system worseHow a seven-point gross profit drop can cost a $4 million company $280,000Why leadership, ownership and accountability matterHow to use the Two-Week Vacation testThe three priorities I would give this contractorWhy you need to diagnose the real problem before deciding on the solutionROOFING BUSINESS HEALTH CHECKThe Roofing Business Health Check is a whole-business examination with me, Dave Sullivan.We'll look at:Financial HealthSales and MarketingOperationsLeadershipSystemsWe'll spend up to two hours looking at the business. I'll identify the three things I believe should be addressed first.Within two business days, the contractor will receive a personalized Roofing Business Health Record and written 90-Day Action Plan.Financial statements are strongly recommended, but they are not required.The cost is $495.This isn't a big program, a long-term commitment or a promise to 10X the business. It's about finding the real problems, getting the basics right and building a strong foundation to grow on.Go to TheRooferCoach.com, click on Roofing Business Health Check and book a quick 15-minute call with me.There is no obligation. We'll discuss what is happening in the business, answer any questions and decide whether the Health Check makes sense.LINKSRoofing Business Health Check:https://theroofercoach.com/health-check/ProLine CRM:https://theroofercoach.com/prolineSMA Support:https://theroofercoach.com/smasupportLinked Episodes#489: Is Your Roofing Business Healthy?#490: Why Your Roofing Company Has Plenty of Work But No Money#491: Sell Work with John DeLaurier of ProLine#492: Do Work with Chris Diroll of SMA Support

Are you struggling with inflammation and feeling exhausted? If you're ready to 10X your energy, productivity, and impact with the body that fully supports your dreams, this episode is for you!Join Anita and Jarrod as they uncover the one daily habit most have that's fueling inflammation and low energy, along with the top 2 ways to reverse it naturally so your body finally backs you 100%. Grab our cookbooks for the delicious time-saving recipes that will simplify your life, optimize your health, and save you countless hours every week. Shift your body from being your biggest hindrance to your #1 asset, inside Accelerator. This is the 8 week experience built for the high-performing, faith-driven entrepreneur ready to trade the world's way of "managing" health for the body optimization system that's backed by the Bible and the reliable research. This is not for everybody, but it is perfect for some of you. Seats are limited! Apply for the next cohort here.Want our favorite dried fruits and frozen berries shipped directly to your door? Find them here.

You started a business because you wanted freedom. But now every client, invoice, follow-up, and decision depends on you—and you're working more hours than you ever did at a job.So did you actually build a business… or did you just create a harder job?In this episode of Mama's House To Penthouse, Prinston Hicks and Country Cowboy break down the mindset shift required to go from self-employed worker to true business owner.They discuss:• Working ON your business vs. working IN it• Why entrepreneurs become bottlenecks• The player vs. coach mentality• Delegation and hiring• Organizational charts and leadership• Building repeatable systems and SOPs• Key-man risk and key-man thinking• Why working harder isn't the same as scaling• Creative ways to build a team before you can afford large salaries• Moving from a worker mindset to an owner mindsetThey also discuss YouTube monetization, AI-generated content, content distribution, and why creating great content means nothing if nobody sees it.If your business can't function without you, this episode will challenge you to rethink what you're actually building.Follow Mama's House To Penthouse for weekly conversations about entrepreneurship, business systems, AI, leadership, marketing, mindset, and building a life of freedom.⏱️ CHAPTERS00:00 Intro01:35 YouTube Monetization Is Getting Harder03:29 Platforms Are Cracking Down On Engagement Bait & AI Slop04:43 Creating Content Isn't Enough Anymore05:03 Why Businesses Need Content Distribution06:31 Consistency & Getting Your Content Seen07:28 Penthouse Plays Recap07:58 Mama's House Mail08:00 Did You Build a Business or Just a Harder Job?12:15 Why Entrepreneurs Become Their Own Bottleneck15:00 Stop Thinking Like an Employee18:30 Building an Organizational Chart22:00 The Player vs. Coach Mentality25:00 Stop Doing Everything Yourself29:30 Building a Team That Can Execute Without You34:00 The CEO Isn't the Top of the Food Chain38:00 Key-Man Risk & Key-Man Thinking42:00 Systems Turn Work Into a Business45:00 Why Working 18 Hours Doesn't Mean You're Building48:00 Delegation, SOPs & Getting the Right People52:00 Why You Need to Step Back to Scale55:00 Stop Glorifying How Hard You Work58:00 How to 10X a $100K Business1:02:00 Creative Ways to Hire Before You Can Afford Full-Time Staff1:05:00 Stop Making Business Harder Than It Has To Be1:08:00 From Worker Mindset to Owner Mindset1:11:01 Outro

Ginger King, founder of Grace Kingdom Beauty and The Beauty Shark, a cosmetic chemistry and beauty brand development business helping doctors, investors, influencers, and beauty entrepreneurs create their own beauty brands from concept to launch.Through formulation, manufacturing, brand development, and 10X business coaching, Ginger guides clients through the fundamentals of building a beauty business they can truly own and grow.Now, Ginger's 30 years of experience in the beauty industry demonstrates the power of expertise, autonomy, and knowing how to choose the right clients and opportunities.And while helping entrepreneurs avoid costly mistakes and bring their ideas to life, she continues to show that the fundamentals of building a strong business are the same across every industry.Here's where to find more:https://www.linkedin.com/in/gingerking/ https://www.gracekingdombeauty.com/ https://www.thebeautyshark.com/ https://www.instagram.com/thebeautysharkginger/________________________________________________Welcome to The Unforget Yourself Show where we use the power of woo and the proof of science to help you identify your blind spots, and get over your own bullshit so that you can do the fucking thing you ACTUALLY want to do!We're Mark and Katie, the founders of Unforget Yourself and the creators of the Unforget Yourself System and on this podcast, we're here to share REAL conversations about what goes on inside the heart and minds of those brave and crazy enough to start their own business. From the accidental entrepreneur to the laser-focused CEO, we find out how they got to where they are today, not by hearing the go-to story of their success, but talking about how we all have our own BS to deal with and it's through facing ourselves that we find a way to do the fucking thing.Along the way, we hope to show you that YOU are the most important asset in your business (and your life - duh!). Being a business owner is tough! With vulnerability and humor, we get to the real story behind their success and show you that you're not alone._____________________Find all our links to all the things like the socials, how to work with us and how to apply to be on the podcast here:https://linktr.ee/unforgetyourself

Same product. Same customer. A $900 difference. Two businesses can sell almost identical products to almost identical customers, yet see completely different business revenue growth. One generates $100 from that customer. The other generates $1,000. That's not a 10% difference. That's a 10X difference in customer value.I've spent the last 17 years working with online businesses through my payment processing company, DirectPayNet. Most founders assume that revenue difference comes down to traffic, conversion rates, or ad spend. I get to see patterns across entire industries, and the businesses generating the highest customer value are almost always running a smarter problem solving business, not just a better-optimized one.In this episode, I break down the 5 patterns I consistently see among businesses built for real business scaling, including a payment mistake most merchants never catch, one that quietly kills upsells even when the offer is working perfectly.

AI is changing the security landscape, but are we automating the wrong things?On this episode of Crying Out Cloud, Eden Koby Naftali & Amitai Cohen sit down with Daniel Miessler to unpack why most companies are completely unprepared for the AI revolution. Daniel drops some heavy truths about what happens when we automate the "boring" parts of security, and why attackers are currently winning the AI arms race.What's Inside:- The "Gym Robot" analogy for operational toil and analyst training- Why attackers are moving 10X faster with AI than defenders- The TELOS framework and defining your security goals in text

Ryan Belanger — Founder & CEO, Claro Advisors Most firms are adding AI to existing workflows. Ryan Belanger chose a different path, acquiring a fintech company and rebuilding Claro Advisors around an AI-native platform. He explains why he believes the future belongs to firms that rethink how they operate, not just the tools they use. In Summary Most firms view AI as another technology investment. Ryan Belanger sees it as a business strategy. Louis sits down with the Founder & CEO of Claro Advisors to discuss why his $1.5 billion RIA acquired a fintech company, built an AI-native operating platform, and believes the firms that gain the biggest advantage won't simply adopt new technology—they'll rethink how their businesses are built. The conversation also explores the broader philosophy behind that decision. Ryan shares why he's consistently chosen unconventional paths—from recruiting younger advisors and embracing a partnership model built around ownership to investing in proprietary technology instead of relying on third-party solutions. For advisors, the bigger question isn't simply how AI will change their workflow. It's how it may change what it takes to build a durable, differentiated advisory firm. The Storyline Every generation of wealth management has been shaped by a different competitive advantage. For some, independence paved the way to build unique branding and a bespoke client experience. Inorganic growth and M&A gave many firms access to scale and growth. Today, many believe the next advantage will come from artificial intelligence. But simply adopting AI may not be enough. Ryan Belanger has spent his career challenging conventional thinking. He left Morgan Stanley in 2012, well before independence became mainstream. He built Claro Advisors by investing in younger advisors instead of competing for established producers. He embraced a partnership model centered on advisor ownership rather than restrictive employment structures. And when AI began reshaping the industry, he made another unconventional decision: instead of licensing another technology platform, Claro acquired a fintech company and built its own AI-native operating system. Louis explores the reasoning behind each decision and the philosophy that connects them. Ryan explains why he believes proprietary technology will become a defining competitive advantage, how Claro's AI platform, Claire, is changing advisor workflows, and why the biggest opportunity isn't replacing advisors; it's giving them more time to do the work clients value most. The conversation also tackles practical questions facing every advisory firm: how to integrate AI responsibly, where human judgment continues to matter most, and why the firms best positioned for the future may be the ones willing to redesign their businesses instead of simply adding another layer of technology. Topics Covered AI-native advisory firms Acquiring a fintech versus licensing technology Building proprietary advisor technology Advisor productivity and workflow automation Recruiting and developing younger advisors 1099 partnership model and advisor autonomy Enterprise building and long-term differentiation AI governance and advisor trust The future of wealth management technology > Download a transcript of this episode… Listen and Learn Highlights for Advisors Why did Ryan launch independently long before it became common? (7:30) Ryan explains why leaving Morgan Stanley in 2012 wasn't simply about independence—it was about creating a better business model while betting on himself. Why recruit emerging advisors instead of established producers? (15:00) Ryan shares why investing in younger advisors has become one of Claro's greatest competitive advantages and succession strategies. Why would an RIA buy a technology company? (23:45) Rather than licensing another platform, Ryan explains why Claro acquired NDVR to build proprietary technology that could fundamentally change advisor workflows. How does Claire actually help advisors day-to-day? (33:00) From meeting preparation and client follow-up to portfolio management and workflow automation, Ryan walks through how AI is saving advisors meaningful time. Will AI replace advisors—or make them better? (36:30) Ryan discusses where AI belongs, where human advice remains essential, and why he believes technology should enhance – not replace – the advisor relationship. What does the advisory firm of the future look like? (38:20) Ryan shares his long-term view of how AI, proprietary technology, and advisor expectations will reshape wealth management over the next decade. Key Takeaways Ryan believes firms that build AI into the foundation of their businesses will create greater long-term differentiation than those simply adding new software. Claro's acquisition of a fintech company reflects a strategy of owning core technology rather than relying exclusively on third-party vendors. AI is most valuable when it eliminates administrative work, allowing advisors to spend more time serving clients. Recruiting younger advisors and investing in long-term talent has become a defining part of Claro's growth strategy. Advisor autonomy, equity participation, and technology can create stronger retention than restrictive employment models. Human relationships remain central to wealth management, even as AI becomes increasingly capable. The firms that adapt fastest may be those willing to rethink their operating model—not just their technology stack. https://youtu.be/7XvSXi0PzXI Quotable Moments “I wanted to build something that was integrated instead of just layering another tool on top.” “We're trying to make really good advisors become super advisors.” “Clients still want advice from a person—but they're going to expect that person to know how to use AI.” “The firms that win won't necessarily be the ones using the most technology. They'll be the ones building differently.” FAQs Why did Claro Advisors acquire a fintech company? Ryan believed owning proprietary technology would create greater long-term differentiation than licensing another collection of third-party tools. What is Claire by Claro? Claire is Claro Advisors' AI-powered chief of staff, designed to automate advisor workflows, prepare meetings, organize client information, and streamline operational tasks. How is Claro using AI differently than many RIAs? Rather than layering AI onto multiple disconnected applications, Claro built an integrated operating platform where AI has access to the advisor's workflow, planning, portfolio, and client information. Will AI replace financial advisors? Ryan believes AI will automate much of the administrative work advisors perform today, but that clients—particularly those with more complex needs—will continue to value human advice and relationships. How does Claro recruit advisors? The firm emphasizes advisor ownership, partnership, equity participation, technology, and operational support instead of relying primarily on acquisition-based recruiting models. What does Ryan believe will differentiate advisory firms in the future? He believes proprietary technology, integrated AI, and the ability to improve advisor productivity will become increasingly important competitive advantages. Ryan believed owning proprietary technology would create greater long-term differentiation than licensing another collection of third-party tools. Claire is Claro Advisors' AI-powered chief of staff, designed to automate advisor workflows, prepare meetings, organize client information, and streamline operational tasks. Rather than layering AI onto multiple disconnected applications, Claro built an integrated operating platform where AI has access to the advisor's workflow, planning, portfolio, and client information. Ryan believes AI will automate much of the administrative work advisors perform today, but that clients—particularly those with more complex needs—will continue to value human advice and relationships. The firm emphasizes advisor ownership, partnership, equity participation, technology, and operational support instead of relying primarily on acquisition-based recruiting models. He believes proprietary technology, integrated AI, and the ability to improve advisor productivity will become increasingly important competitive advantages. Related Resources Why AI Matters Now: Filling the Estate Planning Gap with Wealth.com Emotional Intelligence: The “Untouchable” Differentiator in an AI World Diamond Consultants Annual Advisor Transition Report Ryan BelangerChief Executive Officer & Founder Ryan founded Claro Advisors in 2012 after seven years at Morgan Stanley. He named the company after a Latin phrase “to make clear in the mind.” All Claro advisors strive to give their clients clarity and transparency, core tenants of the firm. Claro is continuously recognized within industry for its growth and thought leadership. In 2004, Ryan received a BA in Economics from The College of the Holy Cross and in 2009, he earned the Certified Financial Planner™ distinction. He is most proud of his philanthropic activity. Along with his wife Rachel, they started a foundation that raises money for genetic research in the name of their late daughter, Bella. Their focus is on extreme rare disease. Ryan resides in Boston’s Back Bay with his wife Rachel and their three children. He enjoys exercising, golfing, reading and spending time with his family. He has been featured in numerous magazines and industry publications and is regularly on television sharing his market thoughts. 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… Why AI Matters Now: How a $1.5B RIA is Building the Firm of the Future A conversation with Louis Diamond and Ryan Belanger, Founder & CEO of Claro Advisors.      Louis Diamond: Welcome to the latest episode of our podcast series for financial advisors. Today’s episode is Why AI Matters Now: How a $1.5B RIA is Building the Firm of the Future. It’s a conversation with Ryan Belanger, the Founder and CEO of Claro Advisors. I’m Louis 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. Louis Diamond: Artificial intelligence has quickly become one of the biggest topics in wealth management in the world. Almost every firm is experimenting with new tools, looking for ways to automate tasks, improve efficiency, or help advisors serve clients more effectively. But what if AI isn’t just another technology to plug into your business? What if it becomes the foundation for how your business is built? That’s exactly why I wanted to have Ryan Belanger on the show. Ryan is the Founder and CEO of Claro Advisors, a billion and a half dollar RIA that’s taken a very different path than most firms in the industry. Rather than simply adding AI to an existing tech stack, Claro acquired a FinTech company and is building its own AI native operating system designed specifically for advisors. What’s interesting is that this isn’t really a conversation about software, it’s about strategy. Ryan has consistently gone against the grain from leaving Morgan Stanley to launch an independent firm in 2012 before it became commonplace, to recruiting younger advisors when others chased established producers, to betting that proprietary technology will become one of the biggest competitive advantages an advisory firm can have. If AI is going to reshape wealth management, and I think it will, the firms that benefit most may not be the ones using the most tools. They may be the ones rethinking how the entire business operates. Ryan shares what that looks like in practice, what he’s seeing from advisors today, and why he believes the next generation of advisory firms will look fundamentally different from the firms we’ve known over the last two decades. There’s a lot to cover, so let’s get to it. Ryan, thanks for joining us today. Ryan Belanger: Yeah, nice to see you. Louis Diamond: You too, good to see you again. For those who aren’t familiar with you and your firm Claro, why don’t you walk us through your background and how you found your way into the industry to set the table. Ryan Belanger: Yeah, sounds good. So background was after college, I got a job at Morgan Stanley. I’d done an internship while in college and that gentleman, Morgan Dewey, said you should look at the Morgan Stanley. So I applied, got a job immediately, and just a couple weeks after graduating, I began as a financial advisor in a training program at Morgan Stanley and spent a good amount of time there and was able to develop skills necessary that really I had all along just growing up, a lot of entrepreneurial spirit I think is important in this business, how to relate to people, some competitiveness. I just happened to luck out and get into a profession that rewarded some of those skill sets. Louis Diamond: I’d say it was the right choice for you. So I think you started at Morgan Stanley in 2004. You were 21, 22 years old, just cutting your teeth, but the financial crisis happens a handful of years later. So what was it like being a relative newbie and seeing client accounts falling, the world crumbling every day? What did living through that crash teach you that’s shaped how you’ve built your business or serve clients now? Ryan Belanger: I did learn a tremendous amount at Morgan Stanley and I do still tell people if they’re looking to start at a big shop with big training programs and resources and really try to figure out what you like and then you can go off and get more specialized. But I do feel like it was a great place to get trained. They would post how many cold calls we were making every day. So on the board every morning you’d walk in and say, “Okay, where did you fall?” And I’m a competitive person, and I just want to make sure I was first every single day. So it was those type of things that really propelled me to keep interested in this business but also see the benefits. It’s really hard to get clients and that’s what people underestimate the most is to build the level of trust with someone that they’ll allow you to manage their retirement nest egg is it takes time. And I was 22, I looked really young, I had no experience, but I was fortunate to have two great mentors at Morgan Stanley, a gentleman named Todd Wetzel. He was brilliant at developing relationships, really caring for people. And then the gentleman that I had done an internship with went to Morgan Stanley as well, and he allowed me to work on some small accounts and really cut my teeth with some customers. And I was very fortunate to have done that, but you’d asked about the crash, and I think what I learned from that when people were literally weeping when their account values were down by 50%, 60% was that money is really emotional, and you have to understand how much it means to people, it’s not just the number on your screen. So having some empathy towards someone who’s really in a period of distress is now a critical skill that those of us have been around for this long understand. And there’s a whole generation, Louis, of advisors that have never experienced a real bear market, and I do fear for them at some point because when you go through that, it really changes the perspective that you have. But for me, it happened, I was four or five years into the business at that point, so I’m thankful that it happened just for my own personal development and I’ll never forget it. Louis Diamond: Yeah. Things have a way of happening for a reason and then the best advisors, best humans, they learn from them, and they’re better off for it. You’re very much right. I like that perspective about how the empathy around the emotions of money was something that you still carry and wear as a badge of honor today. So you left Morgan Stanley in 2012. I think you were 30 years old I read. One, that’s very young to consider leaving a firm like that nonetheless to go independent when in 2012, it wasn’t like everyone was going independent. There weren’t as many infrastructure providers or tech vendors or as much capital available as there is today. It definitely wasn’t a path that was as well-worn as it was. So two-part question, what pushed you to leave the firm presumably without a huge book of business? And second part, how’d you think about risk and reward at that age? Ryan Belanger: Yeah, what drove me was ultimately I felt like I was not seeing the value from the firm I was at, Morgan Stanley at the time. They were just taking an exorbitant amount of the revenue I felt. And I would see product managers strolling through and going to steak dinners, and I’m thinking, geez, I’m here every night on weekends. I’m busting my butt, and I should be creating more value to myself. And so that was one kind of thing. And I think there was a right level of naivete just to think that I could pull this off. I did believe that I had a small number of clients. I was hopeful that they would come because I had to hit a minimum for the custodian platform to start the RIA, which I was able to do. But I felt that they would come with me and that I had developed enough trust with them that I could be their advisor for a long time. And so for me, it felt like the technology wasn’t great. I was just told the mother-in-law is an expression. She says to my kids sometimes, “You get what you get and you don’t get upset.” Have you heard that expression? Louis Diamond: I have. My daughter reads a book where that line is repeated frequently. Ryan Belanger: Yeah, okay. So that’s how I felt then. I was like, “This is what you have and deal with it.” And to me, it just felt like there had to be a better way, but I didn’t have any capital backing, so I bootstrapped it. I Craigslisted an office from an estate planning attorney. I cold called Fidelity at the time they were our only custodian. I called to get some compliance help and I just thought that there’d be other people that would want to join. I named the firm, it’s a Latin phrase, it’s Claro Advisors, and it means to make clear in the mind. And I felt like not only was I trying to do that for clients, but I was trying to push advisors to challenge the norms here. There are other solutions out there. So I purposefully did put my name on it, I knew that there’d be other people that might feel the same way. I’ve always been a team sport guy. I like being around other people and collaborating. And I did have a good friend and credit to him. He said, “If you put this together, I’ll come with you.” And so just a couple weeks after I did, we talked and I said, “It’s up and running.” He came and Dana was our first, he’s still with us. And then a couple of months later, another guy I used to work with called and said, “Hey, I’m at this bank, and it looks like what you’ve done is interesting.” And I said, “We like it if you’d like to give it a try.” And so he came, his name’s Mike. He’s still with us. And so teams started to get put together. But I met someone in 2014, so I was two years in at that point and I was doing legitimately everything, not only as an advisor, but just all the stuff that you have to do to run the business. And it was becoming too much, especially the compliance. And I think nowadays starting an RIA, the threshold is so much higher. That’s why you see better than anyone else. You just see a lot more tuck-ins. But Jen Street was someone that I met and she really allowed me to catapult the business and scale it, so she took over all the operations and compliance and that really freed me up to be an advisor. And I really was just an advisor moonlighting as someone running. I would recruit a little bit or just be introductions, very soft. All that has changed based on what we’ve done in the last couple of years. Louis Diamond: Amazing. So thinking about risk spectrum, obviously now if you look back and say, “Hey, I had 30 million or whatever it was, I didn’t have anything to lose.” Right? But when you’re in it and you had income, you had recurring revenue, you had a paycheck versus the dynamic of, “I’m going to incur a bunch of expenses. I’m not positive who’s going to come with me. I’m not going to have a paycheck for a period of time.” Did the fact that your business was relatively small and you were just getting up and running, do you think it made it easier for you to reconcile that risk, or in some ways it was harder because your dispersion, if someone didn’t come, was that much higher? Ryan Belanger: I think it was easier for me, I knew I could always go to another firm. They would take me and whatever clients I had. I did it at a time when I had little personal risk, no kids, no mortgage. I didn’t have a wife at that point. So for me, it felt like the right time to take a risk. And I had been entrepreneurial in my life. I mean, I had a business in high school and my parents and grandparents were entrepreneurial. So that was in me, even if I didn’t really recognize it, was that I was okay with a good level of risk. And I do say this now to anyone that I’m hoping to partner with is that if you want to bet on yourself, I’ll go all in on you too. But you’ve got to be able to take that jump. I won’t let you fail, but you’ve got to be the one. I think that inertia is what a lot of advisors are like, “Geez, I don’t know, I got to give something up.” And that’s why the data’s important and you have all the data. The clients overwhelmingly go with the advisor. These days it’s just much harder to try to establish a new relationship with a trusted advisor than it is to just DocuSign some forms and move your account somewhere. So to me, it’s just trying to support people, and really push them to the edge and say, “No, this is possible. You should definitely explore this.” And I get it’s totally different, and you might be at a different life stage, but you know the numbers. I mean, tens of thousands of advisors are moving every year and not all of them have a small book like I did when I did it. Louis Diamond: Right, exactly. On one hand, making this entrepreneurial move as early in your career as you did, it was a benefit, right? Because you didn’t have as much to lose, like you said, the stage of life you’re in allowed you to absorb more risk. On the other end of the spectrum, if someone who has a massive business with immense value, they’re well situated financially, maybe their kids are through college, et cetera. And then most people are somewhere in the middle. So it’s interesting hearing that dynamic in real time. Let’s talk about Claro today. So you launched the business, like you said, you had to work hard to meet a minimum custodial threshold. So started from a very small base in 2012, but where is it today as far as assets, team size? Just give us some stats or perspective on what you’ve built in the last decade and a half or so. Ryan Belanger: Yeah, sure. So we enjoyed a tremendous amount of organic growth, Louis. We are not capital-backed. We don’t buy books of businesses, so I would recruit or partner with advisors that were coming from all the various places that you could think of that were finding us to be a very friendly place to work where you had a high level of autonomy, freedom, control, just great economics. We stayed out of people’s ways. We were just good people trying to help other good people, and it was just that friendly environment that allowed us to grow. And of course, we can’t discount market. I think markets had a tremendous growth for everybody in the business. And so the business as it stands right now, we’re about 1.5 billion in assets, 15 to 20 advisors. We got a 40-person team based primarily at a Boston headquarter, but we have advisors all over. And I think as we’ll get to, we’ve just gone through a really exciting new chapter for us where the next 15 years are going to look a lot different than the previous 15 years. Louis Diamond: Very cool. That’s amazing, and I’m in the recruiting businesses and doing recruiting yourself, it’s not easy to tell your story, get in front of the right people, the right like-minded people too, who are willing to take the leap to you, especially if you don’t have the capital backing and you can’t pay big deals or write big checks like others could, so that’s a massive testament to you and your vision. I know the average age of an advisor at Claro is around 40, yet the average advisor in the industry is 59, 60, 61, depending upon what data source you look at. What do you think you figured out about attracting, training, and really cultivating younger advisors that the rest of the industry either gets wrong or ignores? What’s been your hack in that regard? Ryan Belanger: I’ll just take a chance on people that others might not. And typically what that really means is someone with nothing, I’ll make them a deal and I’ll say, “Look, I believe in you. I think you’d be a great advisor. Let’s work on an arrangement where you feel like you can do this and I’ll support you.” And so our specialty was growing advisors from 20 million or 30 million into hundreds of million of client assets. And some of it was just being willing to look where others wouldn’t possibly want to spend their time. But when I was 22, someone took a chance on me, and so I owe it to the next generation to do that as well because there’s some great talent out there that really just isn’t getting the attention they deserve because they don’t have big books of business yet. But one of my core values is long-term thinking, and so that’s the way I frame my decisions is it doesn’t have to be a win today, but it can be a championship tomorrow or down three or five years from now. And so that’s how I’ve positioned it. I think that’s why we tend to get younger advisors. And then what happens when you get a lot of younger advisors, you have some older advisors say, “Hey, look, that’s an attractive bench of talent. I needed a succession plan. You guys seem to have a bunch of guys and gals that know how to do really great work and serve clients.” But I think that’s probably one of the things that I just was willing to take some chances on people at an earlier stage. Louis Diamond: Yep. I love it. I mean, once again, you said in the beginning, you developed an empathy for the emotional side of money and what people were going through that you carry through to this day. So not losing touch with the fact that you started. I mean, everyone starts in this business at some time, but I feel like once you’re successful or you’re through the first few years, you forget what it was like to be a newbie. So keeping that perspective and appreciation for the mentors you had, et cetera, is great. And honestly, from a business building standpoint, to me in this environment, unless you take on private equity capital, or you have capital from a BD or from a wirehouse behind you for recruiting, it’s really hard to win advisors with large books of business. So going in the blue part of the ocean instead of the red ocean, if anyone’s read that book, is very smart, looking under rocks that others don’t or really buying into or leaning into folks that you see something in that you know you can cultivate is a brilliant way. And it’s honestly more scalable, cheaper, you build a better business as well doing it the way that you do, but still, it’s hard. And my guess is the ROI is shorter. I’m sure you’ve made some hires that don’t pan out. So you have to have the tolerance and the demeanor to really invest in people. So long-winded way to say I love what you’re doing. How much of your recruitment of advisors and the retention of that talent as they become successful would you tie to how you compensate them, or equity if that’s available versus the culture of the firm and the mentorship that you and your team provide? Ryan Belanger: Yeah, I mean I’ll speak to what we’re offering now just because that’s more relevant, and so we are positioning ourselves now as the best home for advisors in the country and we really believe that’s the case, but our problem is we’re just a secret. We’ve just come to the market after our deal and all the technology that I know we’ll talk about. So we’re now marketing this message to advisors that want to partner with us. Economics will help them grow. We have a really interesting growth program. We’ll give them equity and Claro. I firmly believe that we should tie each other, just get in the same boat, so to speak. So our success is their success, but allowing them to operate in a 1099 model, which I know is not a popular strategy. I know everyone wants to buy books and own the assets and own the clients, but I feel there’s a tremendous amount of advisors that do not that probably should not be monetizing their businesses so quickly. And so I’m trying to foster a home for those like-minded advisors that want the autonomy to own their clients, maybe even still have a brand, but partner with a firm that’s got really credible technology, just unbelievable back office support and a firm of the future so that they can grow at 10X to what they could have on their own and then they could monetize. That’s what we’ve tried to put together here with our partnership model. Louis Diamond: Love it. Yeah, I mean it is definitely going against the grain a little bit, leaning into growing a 1099 model versus more of an acquisition model where everyone coming over as W-2s. So do you think about those trade-offs when it comes time to raising capital down the line or if you want to sell the business or even just an advisor wants to leave, that would stink if that happened. How do you think about those trade-offs? The ability to let advisors keep control and ownership. And honestly, in my view, probably win many people that you wouldn’t otherwise versus the stickiness, and the enterprise building abilities of owning the books of business. Ryan Belanger: Yeah, it’s a paradox because I understand why you want to own the client, but that’s a different business model. And frankly, I think it attracts different type of people. I had to really look myself in the mirror a couple years ago. We had enjoyed a tremendous amount of success, high growth and all organic, growing at 30% more per year on a CAGR basis. Nothing could stop us. But what happened was when private equity entered the space, everyone wanted to buy Claro. And to me, it didn’t feel like I did a lot of due diligence. I talked to a lot of firms. I didn’t see any differentiation in the market, Louis. To me from a technology perspective, everyone was doing the same thing. They’re using six to 12 different tools. We all know who they are. And now there’s a bunch of AI tools they’re layering on. And to me, it just didn’t feel like that was going to be any… There was no differentiation in the market. But admittedly, I had a couple of friends who I’d brought in at very low levels of AUMB that wanted to leave. And they said, “Look, I want to go to a firm that has more resources.” And so I had to just make a business decision and say, “Where do I want to take this?” And so it was only after some real adversity because you get emotionally attached to these people that you’ve developed friendships with and they still are friends, no doubt, but they can leave and they’re not captive. So we have to plan for that at Claro now, and I think we’ve got two ways that we’ve done that where it really ties the advisors to us, but in a way where they want to be here because we have something that’s really different. Louis Diamond: I like it. I’m sure we’ll get into that. But before we do, we’ll get into what you’re doing on the technology side, which is very cool and unique. How do you balance being an advisor and being a CEO? And what percentage of your time is advisor versus CEO and has that fluctuated or changed over time? Ryan Belanger: Drastically changed in the last year, two years or so. So the first 10, 12 years, I was really an advisor first and foremost. That’s inverse at this point, I’m strictly running the business. I have a great team here that deals with our clients, and I’ll still attend the client meetings and such, but I’m really laser-focused on running the business, trying to develop new partnerships with advisors, running an engineering team, sales and marketing. So the change for me has definitely occurred, and I’ll miss not keeping up with planning as much. I’m a CFP, but I just recognized that for me, I had to make a clear change and commit all my time to running the business, and so that’s the decision that I’ve made. Louis Diamond: It is a hard balance. I mean, there’s some people that try to do both, run a business, be an advisor, be a rainmaker, and something breaks. You’re not able to give all yourself to one thing. Then there’s others that would much prefer to be an advisor over a business owner. Others who say, “I’m over being an advisor. I want to be a business owner.” So I think the cool thing about doing what you’ve done is you get to choose, right? Some of it might be circumstances, but you really got to decide which elements of the business you personally want to invest your time in. And you really push your chips in the middle of the table. So let’s get into what you did in November of 2025. I read that you acquired a tech company of all things called NDVR. I’ve done this podcast for a while, speak to a ton of people. I can’t really think of anyone, any advisor or RIA that’s actually bought a tech company. So what made you puck the trend, buy a tech company and not just license all the FinTech that’s available today? Ryan Belanger: Yeah, that was the decision I had to make was do I really want to be different, or do I want to just say that I’m different? And so I was fortunate enough to get introduced to a gentleman named Michael Simon about 18 months ago, two years ago. And him and I immediately could see that we were both trying to solve the same problem, and we had perfectly mirrored image skills of one another so I had this deep wealth experience and he had a deep tech experience. And sometimes it’s just about timing in life, about catching someone at the right time. And I think we each caught each other at a really good time where we could see that coming together, we could create something really magical. And this AI wave was cresting. And I could see when I was talking to all the national PE firms or RIA firms about what people wanted to do, no one had quite figured out how AI was going to come into the technology mix, and it appears as though it’s just going to be another add-on tool to everything else. And for me, I wanted to try to build something that was integrated an all- in-one platform for an advisor so they didn’t have to use a ton of different tools. And I thought if you could do that, couldn’t you have AI that’s really much more rich and purposeful to help the clients? And so I felt like here’s an opportunity to elevate financial advice throughout the country, really give the clients all the value. And so what we’ve built allows advisors who are really good advisors to become super advisors because they’ve got this technology cape that no one else has that is allowing them to save a bunch of time and do all these really cool things for their clients. But it just felt like right time, right place. I’d been through a little bit of adversity and I felt like taking another swing just like I did 15 years ago going for it. I’ve really never been averse to risk, and so this felt like it was too good to pass up and so we went for it. Louis Diamond: Interesting. So that makes sense on the build or acquire versus rent dynamic, wanting to own the IP that makes you actually different. What does NDVR actually do? Ryan Belanger: Yeah, so everything’s all integrated. So we’ve kept the Claro Advisors name. We feel like clients really want to know that they’re still getting a person to deliver the advice. And so having the advisor’s name in our brand is important, but we have a Claro Intelligent Hub, and that’s where it’s an AI native operating system for the advisors. They spend their entire day in there, Louis. So they’re not toggling between 10 different Chrome tasks to perform all their business. And so what that allows them to do is not only it’s CRM, calendar, contacts, emails, messages, but we also have all the portfolio information. So trading history and we can do tax loss harvesting and factor-based investing. So we’ve got institutional grade portfolio management, and that’s really what Endeavor had created through their R&D was the hyper-personalized portfolios where you have a customer’s financial plan directly tied to their account. So there’s never any de-linking between the two. It’s really sophisticated technology that we can provide to our clients. So that’s all integrated as well. And so we’ve since continued to build the build upon that layer of integrated proprietary technology. Louis Diamond: It’s very interesting. And we have to imagine part of you maybe now or in the future is, okay, we’ve built this amazing technology mousetrap for our advisors, but do we become a FinTech? Is there any thought of eventually licensing what Endeavor is doing for your business and your clients to other RIAs? How do you think about that dynamic of just building something unique and different for Claro that advisors can latch onto versus making what you and your partners have developed into something that someone else can take and license themselves? Ryan Belanger: Yeah, it’s a fair question. We get it a good amount. While there might be a possibility that we license this to some other businesses, our main goal right now is to keep it captive to RIAs that want to partner with Claro. And so we feel like this gives them a true level of differentiation in the market, and so that’s the approach that we’re taking right now. Being a FinTech company, there’s a lot of different skills. The setup and tear down of getting someone to use the platform and I think all that time and resources we want on sales and marketing to try to attract new advisors and continue to develop just jaw-dropping technology for the existing advisors. Louis Diamond: Very cool. Let’s talk a little bit about your partnership model. So it does sound unique in that you have people that are 1099, but you don’t usually also hear partner. So how does it work? Ryan Belanger: Yeah, so we’re offering advisors to come and use Claro as a back office so you can have your own brand if you want or you can just be a Claro advisor. We have both here and you’ll be a 1099 advisor so you’ll still own the business that you’ve owned. So if you were at a wirehouse or something, you would actually now be creating some enterprise value for yourself. But if you’re an existing REA, you’d be coming to us because you’re tired of doing tech vendor due diligence all the time or you’re tired of the compliance, the AI regulations. That’s just coming. So that’s going to be a huge challenge for REAs, so we’re seeing a lot of interest from REAs saying, “Look, you’re not asking me to give up really anything except the stuff that I hate to do anyway, so this sounds great.” So they partner with us. In return, they get all access to our technology And we’ll provide all the back office support, office space, dedicated resources, planning, everything you could want to have to operate a business. We do have a growth program that’s really interesting. And then we’ve got this equity in Claro. As you’re a partner with Claro, you should get equity so we give stock options to our advisors who are here and every year thereafter. And naturally, that’s a way to stay connected with the advisor. So hopefully they never want to leave, and I do believe that once you experience our technology, you never want to go back to trying to do it the way you were doing it before. Louis Diamond: It’s like instead of building the most enclosed box that you keep people in with sticks and with locks and keys like a lot of firms do, it’s we’re going to keep advisors here, but not by force, but because they have the stock options, because you’re delivering value, because they have this amazing technology. To me, that’s the dynamic that so many firms across the industry get wrong is that they try to keep advisors where they are by restrictive covenants and by fear, and by retribution rather than if we just do good work for people, we add value, we make ourselves indispensable to the advisor. To me, it creates a healthier dynamic. I think firms would actually retain more even if it’s a gentler approach. And I love what you’re doing there. I think it’s the exact right way to think about we have advisors that are 1099, so yeah, they could leave us, but we’re doing things that make it that they don’t want to leave us. And that’s your charge as the owner to create the infrastructure and the structure where people could go out on their own, but there isn’t an advantage to do so. Ryan Belanger: Yeah, I think the culture is a big thing for us. And if you have people here that don’t want to be here, that’s a problem. And I think that’s what you see in a lot of the wirehouses. Frankly, they scare people and they don’t. It’s like, oh my God, if I leave. And for us, it’s like personally, life is too short. I want to work with people that want to work with me. I’ve got other things going on in my life and these things are just work things. And so I want to enjoy being in the office every day with people that want to be here. And if you think you’ve found a different place, you should go explore that. It’s really a soft approach. I know it’s not the most popular approach, but that’s just the style that I have. Louis Diamond: Yeah. I mean, it sounds like the trend in your career and in launching Claro was we’re going to do things that aren’t popular, but that work for us, like hiring younger advisors that may not have a book or have a small book, buying a tech company instead of licensing it, being 1099 when you’re recruiting instead of owning books of business. There’s a series of decisions you’ve made as the business owner that they’ve worked out, they’ve paid off, but they’re definitely against the grain. And I very much respect that. Ryan Belanger: I really have never been afraid to be a little different, and so I think typically you find other people that might be interested, but it’s a big pool out there. There’s 300,000 advisors so there’s something for everyone, which is awesome. Louis Diamond: Totally agree. Let’s get back to the AI platform that you’ve built, or that you’re building. Maybe give a real tangible example. If I’m a Claro advisor, how has my life changed now that I’m using this platform versus before? So the old model was I log in, like you said, to 10 different Chrome tabs. I’m meeting with clients, doing planning, et cetera. What is the day in the life? How does it look different from what an advisor’s actually doing today versus before this platform was rolled out? Ryan Belanger: Yeah. All right. I’ll just give you a couple examples. So a client will send you a request and say, “Louis, I need $25,000.” And so a typical advisor would either write a note down, go drop it off at the CSA’s desk, or maybe forward that email to the CSA and then that person would have to input it into their CRM, and they go perform the task. And then the advisor would want to know where things are in that process so that there’s a lot of back and forth. With our system, Claire, our intelligent chief of staff, AI chief of staff, you just forward that task to tasks@claroadvisors.com. It recognizes the email address that the client is emailing from, it knows the account number. It talks to our portfolio engineer. It knows which account to raise the cash from because it knows the tax jurisdiction, and otherwise, and it performs the task. And the last push of a button is that CSA just moving money from the custodian. So all along the way, the advisor can check on the task and see where it is in the process. It’s beautifully integrated in the intelligent hub, but you could see how that would save a tremendous amount of time and it’s a better customer experience. The mistakes get limited. So it really allows the advisor to get things done at a much higher level. So we’re raising productivity quite a bit. First of all, she’ll establish your meetings, Claire will. So she’ll schedule them for you. She’ll prep them for you. So we have a button, say prep the meeting because we have all the notes, emails. If you’re texting portfolio data, because she has all that information in about 30 to 45 seconds, she’s going to present to the advisor a really nice meeting summary that, “Hey, here’s the things that we should talk about.” She’s going to surface things that the advisor’s forgotten about because she doesn’t forget things. And so she’s prepped the meeting for you, so you’ve saved a couple hours there. She joins the meeting, she takes all of your notes, stores them in the system. She’ll give you a follow-up email. She knows your writing style, so she’ll know that you like to call this client this, and you send these emails typically at this time. And so she’ll deliver a nice follow-up email instantly for the advisor. They click that button, that’s done. So there’s just a lot of things that where she’s efficiency-wise where on 20, 30 hours a week that we’re saving advisors just on the productivity tools alone, so that’s where we’re seeing advisors seeing a ton of value in this. Louis Diamond: It’s very cool. Ryan Belanger: And then there’s a whole portfolio management capabilities, sweeping idle cash and tax loss harvesting and rebalancing that gets done while advisors are having a cup of coffee. They don’t have to think about these things. It just gets done for them. Louis Diamond: It’s so cool because it’s like I think I can conceptualize or think of building in Claude any one of those functionalities for the most part, but the way that the flow of things works and the journey of it is unique. I think every advisor would be interested in that type of promise of saving that much time. So how do you think now in the future, how do you think about the human advisor interaction, and what the human and the advisor will do versus what can be offloaded to AI? Ryan Belanger: Yeah, certainly a lot of the non-client-facing activity can be unloaded and that’s where advisors spend, according to recent studies, almost 60% of their time non-client-facing. So we’re trying to take all that off of their plates for them. We strongly believe clients still want the message to come from a person that has a level of experience and understands them. But at the same point, I think there’s a growing curiosity about, geez, what could it do for me? And so shouldn’t my advisor know how to use it? And so I think you’re seeing a lot of advisors put their head in the sand and say, “I don’t know. I’m just going to hope people don’t really want to use this and adopt it.” They’re a little bit shortsighted there. Our bet is that clients are going to want an advisor that knows how to use tech, has really sophisticated tech, but it isn’t just another tool layered on top that now my data is in that tool. The reason our system is so beautiful and integrated is because it captures everything in a structured and secure way. So all of the compliance is in there. We whitewash all the PII that’s sensitive information, so we’re not layering another tool on, because it’s integrated, we have an AI governance committee that really takes it seriously. How are we using this information? And so we’ve got an approach and we’ve put guardrails around what it can do and what it can’t do. Might there be a generation, Louis, that wants an AI advisor? I don’t know, that could happen. A twin, a digital twin where you say, “Look, I want to talk to Louis.” It’s 10 o’clock at night. He might be in a different time zone than me. He’s got little kids, but I do have this question. And so we’re iterating ideas on how we can surface that for an advisor to be advisable 24/7 without actually having to be available 24/7. Louis Diamond: Seven. It’s amazing to think about. I mean, obviously you’re deeply in this. You have a front row seat into the power of AI, how it’s transforming your business, doing due diligence on acquiring this technology five years from now, 10 years from now, what does the industry look like as a result of AI? What’s your big bet? Ryan Belanger: A lot of the big firms are going to try to figure out how to layer in tech. It’s going to be very difficult to do that. It’s built on extremely old legacy technology. They’ll be slow. They’ll figure out how to do some things. What we’re already seeing from advisors is the wow factor. Wow, I didn’t know this was even possible, and so I think just given our size and where we are, we have an advantage that we can build things from the ground up very quickly. I mean, what used to take an engineer a couple of months or years can be done in a couple of days or weeks, so things have really sped up in terms of the development. It’s much easier to build it than buy it. And so I think you’ll see a lot of firms trying to do what we’ve done, really build proprietary technology. And I think there’ll be a few winners that are able to do that, but being tech forward and aligned with someone who’s thinking about it, I think is what a lot of advisors are going to want to be. That’s the type of firm people would want to partner with, I think. Louis Diamond: What about the dynamic of, like you said, the digital twin thing is equal parts cool as it is terrifying, how do you see, we’ll say the threat of AI impacting the profession of being a financial advisor? Do you look at it as the entire pie is going to grow because everyone’s more efficient? Or do you look at it as it’s going to take out a lot of the advisor capacity we have because it’s no longer necessary? Where do you fall on that spectrum? Ryan Belanger: So robo-advisors came and went, you remember those. I mean, not that they went, but they never took off the way that it was projected. They’re still great businesses, but the human advisor won that battle. Clients do want an advisor, particularly at the higher end, and so I think at the lower end of the market, you’re going to see some AI solutions where people are perfectly comfortable just talking to someone in AI, and they’ll figure out if there’s a hallucinization or not. But I think there’s definitely going to be a market for it, and so I think it just depends on where the clients are and what level of complexity they have. On the higher end, I do feel like the advisors will continue to have a huge advantage there. But we’re building tools to give optionality to advisors. There might be some advisors who say, “Look, I’ll charge half the fee that I used to charge so you can get my digital twin. And that’s a win-win situation for everybody.” Louis Diamond: Yep, that’s fair. So do you look at your competitive ecosystem now? Not for recruiting advisors, let’s say for winning clients. Do you look at Farther and Savvy and different AI or FinTechs as your competition or do you still look at it as the wirehouses and other traditional RIAs? Ryan Belanger: I mean, Farther and Savvy have done a great job of going after this market. I think we’re not as well known yet as they are. We’ve certainly built out what we think is tremendous technology second to none. There’s a huge market of the IBD space that is just these guys and gals are stuck on these old platforms and things are okay, but they’re not super compelled to switch until maybe they see something like this, and so we have a massive pipeline of advisors and I’ve been recruiting for a long time. I’ve never had a pipeline like this. So I know it feels different to me. People really are interested in this. It’s enough for them to want to see tech demos and come visit us and really understand, okay, this is a firm that is challenging what’s possible and that’s someone that maybe I want to be aligned with, and so I think that there’s a lot of places where we can get the talent. And so for us, it’s just trying to find the right people that we want to partner with for the long term. Louis Diamond: Very cool, I got two more questions for you. It’s pretty remarkable that to get from where you started to now, the recruiting you’ve done, buying a FinTech, integrating it, that you still don’t have private equity investor outside capital. So you think it’s on the roadmap, whether it’s a certain size or you’re looking for personal liquidity where the business will just need it because it’s expensive to operate a FinTech platform and to scale up and to keep growing the firm. Do you think there’s a world in which you take on external capital to fuel your growth? Ryan Belanger: Most certainly. I mean, things have developed for us very quickly here, and outside capital and venture particular is a space that we’re actively in discussions with firms that believe in our vision, understand the value that we can create, and there’s just no doubt that you have to have some wind at your back to get to the market, and so while we’re not a household name right now, I’m confident in two years we will be, and our plan is to grow to hundreds and thousands of advisors across the country. Louis Diamond: Wow, big vision, but I love it. Last question for you. If you were 30 years old again, which I think everyone would kill for that opportunity, leaving Morgan Stanley today instead of in 2012, what do you think you would do differently knowing what you know now? Ryan Belanger: At that point, interest rates were near zero, Louis. Valuations you remember were two to three times revenue. It felt expensive then. Obviously things have changed quite a bit. So I would’ve begged, borrowed, and stole all the money I could from friends and family and said, “I need to buy as many businesses as I could at two times, three times revenue and pay, I don’t know, 3% loan.” Just in hindsight, that’s what everyone should have done. That’s not the path that we chose, but I think there’s a huge opportunity in front of us to elevate financial advice across the country, make really good advisors even better by putting that super cape on them. And so we’re very excited about the future, what we’ve got in store, and what we’re going to deliver to the market. And it seems like just yesterday that I walked out of Morgan Stanley with very little assets and tried to start this RIA, but I’m very thankful for all the people that have been supporting me throughout this journey. Louis Diamond: Amazing. And that’s a great spot to end, but let me ask the inverse of that question. Let’s say you leave in 2026, so leave today, you’re 30 years old, but you have the benefit of hindsight. You know what you know now. What would you do differently around the transition or building the firm other than of course be amazing if you can buy businesses for a fraction of what they cost today? Ryan Belanger: I would want to make sure that I’ve got an integrated solution. I don’t want to be picking a bunch of different vendor tools. I know that’s going to become way too time-consuming for me. So I would really try to figure out how you can get something that’s integrated that can scale, but I wouldn’t change anything about the people. I think you got to be able to connect with people that are like-minded and you still take the risk. What I can’t believe, Louis, is that people that sit at the wirehouses take a home team discount and they’re so fearful of leaving Morgan Stanley or Merrill Lynch or UBS, but why are they taking that? The market says you should be paid double what you paid. And it’s not just like that’s 20, 30 years of data here that show that. And so I just would keep pushing people to bet on yourself. Your clients will come with you. Yes, that firm that you love will be the first ones to try to steal your clients. They’re going to call them, and that’s one way, loyalty. Another thing I don’t understand, but that’s the way the business is structured. I think there’s a huge opportunity to just educate advisors about what’s out there and I would take the risk. Louis Diamond: Love it. Ryan, this has been very fun. What you’ve accomplished, like I said earlier, gone against the grain at every turn. Leaving on the younger side without a huge business, buying and integrating a technology company, recruiting younger advisors without books of business. Every single thing you’ve done has been a different playbook. So I’m pumped to watch how we make Claro a household name and how this approach is going to pay off in spade. So I appreciate hearing this different perspective, and I know our listeners did as well, so much appreciated today. Ryan Belanger: Well, thanks for having me on. I know it’s a long time coming. Thanks for your patience. I wanted to make sure we had something really exciting to talk about when we finally did this, and hopefully I can come back in a couple years and catch up. And congratulations on everything you guys have built. You guys are just a premier name out there, and it’s been fun to watch your success as well. Louis Diamond: Thank you, Ryan, I 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. Why AI Matters Now: How a $1.5B RIA is Building the Firm of the Future A conversation with Louis Diamond and Ryan Belanger, Founder & CEO of Claro Advisors.      Louis Diamond: Welcome to the latest episode of our podcast series for financial advisors. Today’s episode is Why AI Matters Now: How a $1.5B RIA is Building the Firm of the Future. It’s a conversation with Ryan Belanger, the Founder and CEO of Claro Advisors. I’m Louis 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. Louis Diamond: Artificial intelligence has quickly become one of the biggest topics in wealth management in the world. Almost every firm is experimenting with new tools, looking for ways to automate tasks, improve efficiency, or help advisors serve clients more effectively. But what if AI isn’t just another technology to plug into your business? What if it becomes the foundation for how your business is built? That’s exactly why I wanted to have Ryan Belanger on the show. Ryan is the Founder and CEO of Claro Advisors, a billion and a half dollar RIA that’s taken a very different path than most firms in the industry. Rather than simply adding AI to an existing tech stack, Claro acquired a FinTech company and is building its own AI native operating system designed specifically for advisors. What’s interesting is that this isn’t really a conversation about software, it’s about strategy. Ryan has consistently gone against the grain from leaving Morgan Stanley to launch an independent firm in 2012 before it became commonplace, to recruiting younger advisors when others chased established producers, to betting that proprietary technology will b

Send us Fan Mail“Enjoy your self-discovery process now, not later, to welcome self-esteem and self-determination.” -Matteo TuriExclusive Insights from This Week's EpisodesSimilar profits can produce radically different valuations. Valuation Expert Matteo Turi reveals why systems, intellectual property, leadership depth, and transferability create the gap between a 2X and 10X business.Episode Highlights[00:07:00] Why revenue growth does not automatically create valuation[00:10:00] The three pillars inside the High Valuation Triangle[00:14:00] How founders can turn personal expertise into transferable intellectual property[00:22:00] Why control obsession destroys value during the scale-up phase[00:27:00] The emotional execution mistakes that quietly derail strong companies[00:31:00] Why a founder's absence can become one of the company's most valuable assets[00:41:00] How identical financial statements can hide a valuation gap from 2X to 10XFull show notes, transcript, and resources for this episode:https://podcast.deepwealth.com/565The Deep Wealth PodcastMost entrepreneurs do not fail.They just carry too much for too long.The business grows. Pressure grows faster. Profits get harder to predict. Decisions cost more energy. Over time, focus slips and health takes the hit.The Deep Wealth Podcast and Deep Wealth Mastery are built from real experience. We're the only system based on a 9-figure exit. This system exists because guessing gets expensive.

Return on Mission Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing. Startups generate a return on investment for their investors by measuring how much their business generated funds returned compared to investment. For the impact space, one can calculate the return on mission. Here is how to measure the return on mission for your impact startup. Calculate the overhead-to-program expense ratio. See how much of the program cost goes to overhead. Overhead is anything that does not directly drive the cost to produce the product or service. This should be less than 10% in most cases. One can calculate return on mission by dividing a financial investment by the amount of producing the product or service. For example, if an expense is $10K, and the cost of producing a product or service is $1K, then the cost of the expense is 10X. The objective is to determine how much a business expense compares to the cost of providing a service or product. This casts expenses in terms of producing the product or service, which frames the expense in mission terms. Consider calculating the return on mission for your impact startup. Thank you for joining us for the Startup Funding Espresso where we help startups and investors connect for funding. Let's go startup something today. _______________________________________________________ For more episodes from Investor Connect, please visit the site at: http://investorconnect.org Check out our other podcasts here: https://investorconnect.org/ For Investors check out: https://tencapital.group/investor-landing/ For Startups check out: https://tencapital.group/company-landing/ For eGuides check out: https://tencapital.group/education/ For upcoming Events, check out https://tencapital.group/events/ For Feedback please contact info@tencapital.group Please follow, share, and leave a review. Music courtesy of Bensound.

Watch the full episode on YouTube:We first covered Baseten last year when DeepSeek mania was at peak hype. Now they have raised a monster $13B round and become one of the new cohort of AI Infra decacorns that are (with Nvidia, Intel, and the semis complex) chief beneficiaries of the Inference Inflection. We return to Baseten at the peak of the 2026 edition of Open Weights debate. Ali has published a viral breakdown of Kimi K3:And since you last saw him, Philip has spoken at AI Engineer and written the definitive book on Inference Engineering spotted all over SF:Three years ago, inference engineering barely existed as a category.Today, it is one of the most critical disciplines in AI. Inference engineering inherently tackles a different question than standard model training: “How do you turn those weights from training into a product that is fast, reliable, and affordable at scale?” Focusing on these creates an entirely new optimization problem.In one recent GLM-5.2 experiment, quantizing more of the model actually preserved its benchmark quality while increasing throughput by 20%, because the errors introduced in different layers could cancel each other out.Inference is no longer just the final step after training. It is becoming its own engineering discipline, with its own research problems, infrastructure, and increasingly specialized roles.In this episode, Baseten's Philip Kiely and Ali Taha join swyx and Vibhu to explain what actually happens after a new open model is released and what it takes to turn “we generated a token” into a fast, reliable, production-ready API.We go deep on cache-aware routing, disaggregated prefill and decode, quantization, speculative decoding, KV-cache movement, model parallelism, GPU kernels, and the race to make frontier models up to 10× faster. Philip and Ali explain why inference optimizations can still produce gains of 20%, 100%, or even 200%; how quantization errors can cancel one another out; why identical weights can behave differently across clusters; and how Baseten grafted a Kimi vision encoder onto GLM-5.2 without changing the underlying language model.The conversation then expands beyond LLMs into NVIDIA Dynamo, mega kernels, Rubin, AI-specific chips, local inference, video generation, diffusion versus autoregressive models, and the enormous compute barrier to generating coherent long-form video. Finally, we explore the convergence of training and inference, continual learning through persistent KV cache, and the emerging loop where models help optimize the infrastructure that runs them.We discuss:* What happens when a 200,000-token request enters an inference system* Cache-aware routing and reusing previously computed KV cache* Why prefill and decode are increasingly handled by different GPUs* When dedicated deployments become cheaper and more reliable than shared APIs* How speculative decoding uses a smaller model to accelerate a larger one* Tool calling, structured outputs, and what LLMs actually do* What it takes to support a new open model on day zero* Grafting Kimi's vision encoder onto GLM-5.2* Retrofitting inefficient model layers with components from other architectures* Why models sometimes collapse into repeating the same token* How hardware, kernels, and race conditions create nondeterministic failures* Preserving model fidelity while making inference faster* How quantization errors can cancel each other out* Why inference optimizations still deliver gains of 20%, 100%, and 200%* How optimized serving can make a model up to 10× faster* NVIDIA Dynamo, KV-aware routing, and distributed model serving* Speculative decoding the speculative decoder* Why local AI is about making models less dumb while data-center AI is about making them less slow* Tensor, expert, and pipeline parallelism across GPUs* Hardware-aware model design, auto-tuning, and the case against mega kernels* Rubin and why inference is becoming a systems problem* Whether modern GPUs are evolving into programmable AI ASICs* Why enormous models like Kimi K3 require GB300-class hardware* Why open-source video generation still trails Veo, Kling, and other closed models* The quadratic attention bottleneck behind long-form AI video* Autoregressive video, real-time generation, and compounding quality drift* Why future video systems may combine autoregressive and diffusion architectures* Training for inference and inference for training* Continuous post-training, deployment, evaluation, and improvement loops* How GLM-5.2 helped optimize the kernels serving GLM-5.2 itself* Why faster networking could unlock dramatically faster decoding* Continual learning, KV-cache compaction, and persistent model memoryShow Notes* How to build a day-0 API for Kimi K3* 22580: From GPT2 to Kimi3, ExplainedPhilip Kiely* LinkedIn: https://www.linkedin.com/in/philipkiely* X: https://x.com/philipkiely* Inference Engineering: https://www.baseten.co/inference-engineering/Ali Taha* LinkedIn: https://www.linkedin.com/in/aliestaha/* X: https://x.com/waterloointernTimestamps00:00:00 Introduction and the 200K-Token Prompt00:03:18 Dedicated Deployments, Speculative Decoding, and Tool Calling00:11:26 Launching Production-Ready Open Models00:19:06 Model Retrofits, Failure Modes, and Nondeterminism00:28:22 Quantization and Canceling Errors00:32:15 The Race to 10× Faster Inference00:40:48 Dynamo, Speculation, and Local vs. Data-Center AI00:50:18 Model Parallelism, Auto-Tuning, and Mega Kernels01:00:55 Rubin, GPUs vs. ASICs, and Custom AI Chips01:10:03 Giant Models and the Limits of GPU Memory01:12:42 AI Video, Quadratic Attention, and Autoregressive Generation01:21:47 Audio, Images, and Diffusion Models01:27:32 Training, Self-Optimizing Models, and Continual Learning01:40:06 Closing ThoughtsTranscriptIntroduction: Baseten, Waterloo Intern, and Inference EngineeringSwyx [00:00:00]: Okay, we're here in the studio with Philip, old friend from Inference Engineering, the book, as well as Baseten and everything that you've done, you and I have done before, as well as Ali. Welcome.Ali [00:00:15]: Pleasure to meet you.Swyx [00:00:15]: Waterloo intern.Ali [00:00:16]: Waterloo intern, always.Swyx [00:00:17]: When did you get “Waterloo intern” as a handle?Ali [00:00:19]: As a handle? Oh.Ali [00:00:20]: I think the rebranding happened mid-March. When I saw it was open, I was like, “I have to take it. Up for grabs.”Philip [00:00:26]: The problem is that Ali is really good at his job and is not gonna be an intern much longer.Philip [00:00:30]: So we have to figure out who's gonna get the handle.Ali [00:00:33]: Well, I'll pass the torch over to the next intern.Swyx [00:00:34]: Oh, okay. It can be, like, you just pass it to another Waterloo grad.Ali [00:00:37]: To another Waterloo intern. No, bruh.Philip [00:00:39]: Yeah.Ali [00:00:39]: Intern.Swyx [00:00:40]: Intern, yeah.Ali [00:00:40]: And no.Philip [00:00:41]: You gotta get an intern from Waterloo.Ali [00:00:42]: Yeah, I've gotta get an intern from Waterloo.Swyx [00:00:44]: Right.Ali [00:00:44]: But they have to follow the path.Swyx [00:00:45]: Oh, it could, but it could come from Baseten, so it's like whoever Baseten gets from Waterloo.Ali [00:00:48]: Right.Swyx [00:00:49]: Has the title of Waterloo.Ali [00:00:50]: It stays in the ecosystem.Philip [00:00:51]: Exactly.Ali [00:00:52]: Halfway through the internship, you either get it or you're out.Philip [00:00:55]: You should also do, like, a big graduation ceremony where you change the handle.Ali [00:00:59]: Just say it.Philip [00:00:59]: For everybody.Swyx [00:01:00]: You guys are good at ceremonies, clearly. We had a nice launch of the book, very successful. But before we get into all that, I wanna start off with a fun question for you. Okay, you're an expert inference engineer. What happens when I send a long query, say two hundred thousand tokens into Baseten's inference? What's the process of query through GPU model routing, balancing, all that? What is all the stuff that we don't think about?Long Context Requests, KV Cache, and Cache-Aware RoutingPhilip [00:01:26]: With a long query specifically, the first thing that I'm gonna ask is, “Have you sent me this query before, or at least part of it?” and I really hope you have, because it's gonna be a lot easier for me and a lot cheaper for you. So the first thing that we're gonna look at is some cache-aware routing, where we're going to see, we probably have a number of instances, a number of replicas up serving whatever model you're hitting. We want to send this one to something with, number one, available prefill workers, and number two, ideally some cached input already there so that we can skip prefill on at least part of these two hundred thousand tokens. If you're doing two hundred thousand tokens, it's probably coding or a multi-turn agent or something where you would expect to have that cached. If you don't, we're gonna have to send it to a prefill worker. We've at least on certain models disaggregated prefill and decode, so you're going to have one set of GPUs that's solely going to process the input, create the KV cache, and get you your first token, and then that's going to be passed over to a separate set of GPUs, which is going to run decode. We're going to iteratively make those tokens. We're probably going to have some speculator model in front of that. I'm going to assume that you're doing coding, and because of that, our speculator model, which assumes you're doing coding, is gonna have a high draft token acceptance rate. If I'm wrong and you're asking me to summarize every Harry Potter book, it's gonna be slower. And then we stream that output to you and account for it, charge you, a couple of pennies and say, “Hey, would you like to send another one?”Swyx [00:03:04]: Except Baseten doesn't charge by pennies.Philip [00:03:07]: Well, yeah, we charge. I'm assuming that we're talking about the public model APIs. If you are setting up a dedicated deployment, then yeah, it's not pennies.Public APIs vs. Dedicated DeploymentsSwyx [00:03:18]: Yeah, one of the key differentiators when I was talking with Baseten initially was that people who want very high volume just need to rent by the box, ‘cause then it's up to you to figure out how to saturate the box.Ali [00:03:31]: And more often than not, it's, like, way cheaper if you're pushing, like, millions of tokens per hour, if you just pay per hour instead of pay per token.Philip [00:03:37]: Yeah, they do. I think that we've increasingly seen a lot of demand for the pay per token APIs, just because everyone wants to try open models, and then once they find a use case that's really sticky, then they move over to dedicated.Swyx [00:03:51]: Is there a best practice on when it's time to swap over?Philip [00:03:54]: Couple reasons. Yeah, reliability, that's a big one, right?Ali [00:03:57]: Like, if they have a very specific use case, they want you to train something specifically for them, like they want their own spec dec, for instance, for their own traffic.Swyx [00:04:04]: Spec dec is speculative decoding.Speculative Decoding and Custom SpeculatorsAli [00:04:05]: Speculative decoding, yeah.Swyx [00:04:07]: You have to explain.Ali [00:04:07]: Sorry. Like, speculative decoding is like, if you have a huge model, right? And so the model is going to be generating one token at a time every single turn, every single forward pass. So we attach, like, this little, like, parasite, like this layer that goes on top of the model, and this model just has to predict. It does three very fast autoregressive forward passes, and it will predict, like, three certain tokens, and then you do one forward stage over the entire original model in order to see if those predictions were correct or not, and then you accept them or you reject them. Now, this draft model is traffic specific, so if you, like, Philip said, if you're summarizing Harry Potter books, I can train exclusively that draft model on Harry Potter books, and I can guarantee you that I'm gonna accept the three tokens every single time. And so with that case, I increase your decode speed. I wouldn't be able to provide this to you if you're a shared endpointSwyx [00:04:53]: YeahAli [00:04:53]: ‘cause I have no idea if you're doing Harry Potter, if you're doing coding, if you're doing English. We don't know. Also, there was a thing in the book that mentioned that if they really cared about a specific threshold, chapter four, I think. Do you remember that?Philip [00:05:06]: Yeah. The things that you can do is you can set a specific, like, batch sizing, a specific, like, parallelism strategy if you're trying to optimize for, like, throughput versus latency. You can. Maybe a NVFP4 quant doesn't pass your benchmarks and you wanna run a model at higher precision, you could do that. There's just a bunch of reasons why you might wanna have your own endpoint and the biggest one, of course, just being, like, you don't have to deal with someone else doing a hundred million tokens of benchmarking traffic at the endpoint when you happen to be trying to serve your users.Swyx [00:05:40]: Yeah. I think one thing that is. That is a classic journey. Like, it's people is asking the, what happens when you type Google into the browser. Tool calling, is that just, you're generating JSON or is there more complication beyond that?Tool Calling, JSON, and Structured OutputsAli [00:05:58]: Certain customers that we have, they have their own post-trained models, and so they demand a tool calling that's not just, like parse a file or go find the weather. It's something that's very specific and you have to do post-training on this. And if the post-training on the model is not good or if the quantization after the post-training to get the inference to be fast, the model will struggle reading the JSON file and reading the tool calling. But it doesn't require its own like sandbox. It's not like it's going to use that tool calling to like escape a sandbox or like it doesn't have to be contained. It can just be a normal dedicated deployment. The challenge with tool calling more and more seems to be that the companies want certain tool calling which is a very sensitive thing to train. And because you're dealing with all of the JSON outputs, if it doesn't like close the end of the request in a very certain manner, you end up with a model that did the tool calling and like the thinking and so as a result of that, it didn't see the result and just hallucinated the result as it decoded. That seems to be the most challenging thing with tool calling, not really the sandboxes model.Philip [00:06:56]: Yeah, that's a challenge on the training side and then on the inference side, there's work that you can do to scope the possible output. So we published this at this point close to two years ago, the solution to this problem which is you make a state machine and you use that to constrain the output to a specific format. So this is the structured output problem. If you remember backSwyx [00:07:27]: Yeah, the specific grammar is,Philip [00:07:29]: Yeah, exactlySwyx [00:07:30]: GML had this thing.Philip [00:07:31]: Yeah. So it's like the old-school “make sure this is only JSON”, return only JSON orSwyx [00:07:38]: YeahPhilip [00:07:38]: Grandma's gonna die type of prompts.Swyx [00:07:39]: Is it BNF grammar? At some point OpenAI had released a thing that was like, yeah, if you want to constrain your output, write BNF grammar, back as NOR.Philip [00:07:47]: In our inference system, it's just a specified output format. And you get the guarantee that your output's gonna be structured along that format. And so applying that to tool calls can like help cut down on. You can still call the wrong tool or call no tool. It doesn't solve the certainty problem but it at least solves the output structuring problemSwyx [00:08:10]: YeahPhilip [00:08:10]: Within tool calls.Swyx [00:08:12]: And MCP is just another form of tool, right.Philip [00:08:14]: Yeah, exactly.Swyx [00:08:15]: As far as there's no special thing there.Philip [00:08:16]: The thing I'm always like explaining to people is the LLM is not capable of doing anything. It's only capable of making suggestions of what to do and then if those suggestions are formatted in a certain way and applied to a system that knows what to do with them, then an action occurs.Swyx [00:08:32]: Yeah. Part of the fun stuff is, this is solved outside of tool calling too. Like in an agent loop if the output is not correct or you're right, like reasoning, tool calling was done in the reasoning trace, just be like, “Oh, I don't know what to do. Let me just try again.” And it might get there after a few tries. And on your point of training, sometimes this is harder in smaller models, so you don't have the same exact quality outputAli [00:08:56]: Right.Swyx [00:08:57]: When you just swap from a big model, right?Ali [00:08:59]: Yeah. I will say that, before, I think we need to go back to inference engineering proper.Ali [00:09:04]: But, I had expected that something would replace JSON because it's hard to stream JSON ‘cause JSON must be complete and you must have open and close brackets and everything. So it's hard to parse something or validate something while it's being streamed. So people invented all sorts of things that are like, I forget the name of some of these alternatives, but it's something like TOML, something like YAML. But JSON seems to be dominant still.Philip [00:09:30]: The JSON outputs aren't that long, right? Like you could have a long-- ‘cause tool calls also contain the arguments in them and perhaps for a certain tool you might pass like a very long argument. But my impression of the median tool call is that it's a relatively small number of tokens, right? So I would expect that speculators are generally fairly good at something as formatted as JSON. And so you would have like a pretty fast decode step there and that the streaming wouldn't be as valuable, but maybe I'm wrong about that.Ali [00:10:02]: I think you're also bounded by the software or that the model is gonna integrate with if the software is built with JSON for the tool calls or if the company that you'- if your customer says that this is how our software works and our tools are interfaced with JSON, you can ask them to like, change their software and say like, “Yeah, this is gonna be better for the model.” but like with the right training shouldn't be that much of a difference. Also more profitable if it outputs more tokens probably.Swyx [00:10:25]: Depends on your business model.Swyx [00:10:27]: It really depends. But I will say that, as a writer with like experience a lot with generated output, I do try to move from text to JSON text which is very long JSON, right? Like there's paragraphs in every field because I'm trying to structure it, right?Philip [00:10:44]: Right.Swyx [00:10:44]: I want you to first make factual statements, then make opinions then make bullet point summaries, have dates, have entity references have your sources for references, all these things. Anyway, so these are things that like I think people who really experiment with structural output have to really care about. But, let's, let's recurse up the stack a little bit. Before we started recording, you mentioned something really cool, which is that there's a lot of engineering that-- inference engineering that goes on when a new model provider releases a new model, right? So let's call it GLM-5.2, Kimi K3. I had previously assumed, especially if it's like, well, GLM 5 to 5.1 to GLM-5.2, like that you've supported them before. Is it that much work?What It Takes to Support a New Open ModelAli [00:11:26]: It's a lot of work.Swyx [00:11:28]: Yeah. Okay. So like, a lot of people, all you guys, right whenever a new model launch like, people rush to say like, “Oh, Hugging Face supports this, Fireworks supports this, Spacetime supports this,” and I'm like, “Yeah, of course we support it.” But what goes into that? What goes intoPhilip [00:11:40]: I think it's more than just support it too, right? It benefits the consumer a lot. Like I think it was with Kimi K2.5 or GLM-5.2 the latest, there was an inference war, right? X provider is at 90 tokens a second. The next day we're at 150. The nextSwyx [00:11:55]: I kinda kicked that off with the GLM-5.2.Swyx [00:11:58]: I wrote a Twitter article about. It got like half a million views,Ali [00:12:02]: Based on being numberSwyx [00:12:03]: YeahAli [00:12:04]: Or it's for something else.Swyx [00:12:05]: Yeah. Which,Ali [00:12:06]: Oh my GodSwyx [00:12:07]: Which then got everyone really excited about, hey, how can we, bend tracks a little bit further and,Philip [00:12:14]: There's a difference between support the model, as in I can make a token out of this model, and support a model, as in I have a production-ready API from this model.Philip [00:12:26]: Getting to the point of I can make a token out of this model is not that hard because generally the, open source inference engines, vLLM, SGLang of the world oftentimes even receive weights ahead of time, maintainers do, or the people making the model merge PRs to ensure support. So you generally can, just get it working on the standard open source stack without too much pain in most cases. The challenge is, every inference company is gonna have own proprietary stack. Some open source components, some in-house stuff. And for any arbitrary model, there's going to be some new stuff. Sometimes you get lucky, like K, two five to two six was, like, pretty similar.Quantization, Speculators, and Production ReadinessAli [00:13:16]: Yeah. It was pure continued post-trainingPhilip [00:13:18]: YeahAli [00:13:18]: If I remember correctly.Philip [00:13:19]: Even in those cases, there's still stuff you have to do. You have to redo the quantization work. You're taking the model from. Generally, these models are not released in NVFP4, and we want them to be in NVFP4 for maximum Blackwell compatibility. So we have to perform that quantization, and, calibrate the quantization to make sure that we're not causing any regression in the model's intelligence. And then we also have to train the speculator, as we've talked about. Generally, we have. We have ZDR, zero data retention on our model APIs, so we don't know exactly the traffic that people are sending us, but we know what's popular. We know that coding use cases are popular. We know that agents, agentic use cases are popular. So we can get public data sets that are representative of that traffic and train general speculators. Now, with speculators today, you need to train the speculator using the base model itself because you're getting hidden states out of the model from running inference on these specific prompts, and that is the training data you use to create the speculator. So there's that process which you need the real model weights for. And then there's of course just the process of, standing up all the infrastructure behind it, loading all this stuff, testing it. And then when there's a new model with a newer architecture, I think that, like, the DeepSeek models tend to be the most challenging as they have, like, the most novel architectural stuff going on, model after model. But every new model has something. Kimi K2 had. Oh, sorry, GLM-5.2 hadAli [00:14:53]: Sparse attention.Philip [00:14:54]: Yeah,Ali [00:14:54]: YeahPhilip [00:14:54]: the DSA.Ali [00:14:55]: Right. Which is brought from DeepSeek.Philip [00:14:57]: Yeah. AndAli [00:14:59]: So you can copy-paste then?Philip [00:15:01]: It kindAli [00:15:01]: I don't know how this works.Philip [00:15:02]: So, like we had to, like, build support for that into our runtime. And you're right, like it is really interesting the way that all of these open source labs borrow from each other. For example, like GLM-5.2 doesn't have vision. So something that, Haley, a guy on our team, if we could take a look at this, he, like, grafted the Kimi vision encoder onto GLM-5.2.Retrofitting Vision into GLM-5.2Ali [00:15:27]: We'll be training the projector.Philip [00:15:28]: Exactly. So if you think about, like, the encoder, there's the encoder, which is the part that looks at the image and turns it into latent information, and then there's the projector which likeAli [00:15:38]: You can say latent space. It's okay.Philip [00:15:41]: And then there's the projector that maps it onto, the model itself, and then there's the model weights. You don't wanna mess with the model weights because you run a chance of making the model dumber at something else for the purpose of giving it vision. So instead, Haley started with just a projector, which is only a handful of millions of parameters.Ali [00:16:02]: That would be, yeah.Philip [00:16:02]: Yeah.Ali [00:16:03]: Can you show the training one?Ali [00:16:04]: Like the way it groksPhilip [00:16:05]: YeahAli [00:16:06]: Very interesting.Philip [00:16:06]: And maybeAli [00:16:07]: That right therePhilip [00:16:07]: Maybe Ali, you should take it from here. You've got a betterAli [00:16:10]: Ooh, double the sandPhilip [00:16:11]: Understanding of this than I do.Ali [00:16:11]: Yeah. You can see, like, he. The way he trained this is really cool. At the beginning, he was training it using just like, “Here's a picture of a mountain. Can you describe what's in this mountain?” And that caused it just like the first, learning walls. Like here you can see this all we're trying to teach it is to translate the encoded. Like it's already taken the encoder from Kimi K. It's taken the image. It'Philip [00:16:31]: Yeah. FrozenAli [00:16:31]: FrozenPhilip [00:16:32]: With adapter.Ali [00:16:32]: Exactly.Philip [00:16:33]: Yeah.Ali [00:16:33]: So the brain is frozen and the eyes are frozen. It's just we're tryingPhilip [00:16:37]: AlignAli [00:16:38]: Interconnect between the eye and the brain, right? So the projector. And so you take the tokens and then he's like, “Oh, can you describe what's in this image?” And he's like, “Oh, it's a mountain,” or it's a person or it's a human, whatever the case is. But that didn't cause complete understanding. So he changed it such that every image was associated with a data set of questions. Like, does this image have a white male? Does this image have birds in the top corner? Does this image have a scientist in it? All of that stuff. And it would have to answer questions correctly. And using not just training on describing an image, but being able to answer question, another question, answer over time. Like you can see the grokking, which is like genuinely insane, that retrofitting vision into a large LLM can learn to that extent. And even for images that it doesn't perform well on, for instance, if you ask it a picture of like Stephen Hawking, “Who is this?” Maybe it doesn't get it, but it will say something like, “This is Albert Einstein.” Like it still understandsPhilip [00:17:25]: Close enoughAli [00:17:26]: That this is a scientist who is a man who has, some significant achievements, all that stuff. So that's like really cool.Philip [00:17:32]: Yeah. So, we've covered Hao Tian before, who the author of the LLaVA paper that did this, a while ago. And I think that's very foundational work for anyone who hasn't done vision work before.Ali [00:17:41]: Same with the CLIP and MetaCLIP, where you go from just captioning to building out questionsPhilip [00:17:47]: RightAli [00:17:47]: Off the image and how much better you can get performance.Philip [00:17:50]: Right. Right. Right. Yeah. But what's, what's so exciting about this is if you look at a model like this. Now, this is a little bit more of a research project. It's not. It got to 56% on MMLU Pro, I think. So not quite frontier. But if you're running this model, you haven't suffered any loss on your GLM-5.2 quality. If you don't have an image, it'll just behave exactly the way it used to. And ultimatelyAli [00:18:14]: Which in the inference code you literally do not include the other part, right?Philip [00:18:18]: Yeah. You would just skip the encoder if you don't have an image input.Ali [00:18:22]: Okay.Philip [00:18:22]: Just confirming.Philip [00:18:23]: YeahAli [00:18:23]: Does it affect a lot on the overall inference side? Like you're not adding much, you're adding a very small vision encoder. These are typically likePhilip [00:18:30]: They're super fineAli [00:18:31]: Less than a billion parameters, right?Philip [00:18:32]: Yeah. It's, - There's a little bit less standardization among vision encodersSwyx [00:18:37]: YeahPhilip [00:18:37]: So the support matrix can be a little bit, sparser. But overall, yeah, it's a pretty, it's a pretty minor component of the overall system. And ultimately what you get out of the system is all of a sudden you have Kimi Vision, GLM weights, and DeepSeek attention all in one model.Open Source Model Grafting and Franken-MergesPhilip [00:18:56]: And that's, I think, a lot of the power and beauty of open source, is that you can take all of these different components and combine them together into a system that's better than anyoneSwyx [00:19:05]: YeahPhilip [00:19:05]: Can be individually.Swyx [00:19:06]: People used to say that you would also do Franken-merges where you would take likePhilip [00:19:10]: YeahSwyx [00:19:10]: Layers from each model.Swyx [00:19:11]: Does anyone do that anymore?Ali [00:19:13]: Well, to your point previously when you were mentioning like, the work that goes into supporting a model when it first comes out, like GLM-5.2 or MiniMax M3 or whatever the case is. Sometimes you do have to like, you do have to switch out some things. Like, for instance, the MiniMax M3 head uses full attention, and with full attention you end up with this like insane bottleneck in spec dec ‘cause you're doing auto-regressive token generation for three tokens, and you're doing this like N squared over all of the tokens that are in your sequence. Your KV cache is like very large because it's not sparse, it's not top K. So we find it better to like, okay, we're gonna replace this, we're gonna replace this layer with a layer from another model that's using like GQA, for instance. And then just with the right training, you can get it to have the same acceptance rate. So it is very possible to retrofit layers from other models and very much needed. If a layer is like inefficient, the training just becomes the challenge, like how do you ensure that you train it properly? Which again to your earlier point is like the mesh between training and inference. As in like you need very good training in order to do fast inference. That's like, I feel like more and more becoming true.Swyx [00:20:21]: Yeah. Anything else on the support side when you say like get it to fully production ready?Loop Detection, Race Conditions, and Non-DeterminismPhilip [00:20:26]: Yeah. I think that there's also a question of just, we can test a model to a pretty extensive degree, but we're trying to get it out quickly and then you see a bunch of other people test it and you get interesting results. There was an issue with, GLM briefly where we had some like mode collapses where it would just output the same token over and over again for certain prompts on certain temperatures. Like once you expose an endpoint to the real world, there's going to be, so many more varieties of things given to it that you're able to, discover and patch things. So it's not just a, day zero process, it's then like for the first week, for the first month, if a model remains popular, like how do you both fix bugs and then continue to push the envelope on performance?Ali [00:21:21]: What do you mean you don't want your model outputting S?Swyx [00:21:24]: Is there loop detection on that stuff, by the way? It still happens like quite a lot, which is surprising.Ali [00:21:30]: We have like we, in our endpoint, like if a model was to output the same token like four plus times, we just cut the generation. We say like, “Oh, sorry, this-- Like try again,” or like we will reprocess the request. ‘Cause we know then, like if it, like if, yeah, it's four times the same token, it's probably collapsed.Swyx [00:21:45]: Yeah. Is there a way to opt out in case I really want that?Ali [00:21:48]: You want that?Ali [00:21:50]: I think there's a way that we have to handle it. I'm not exactly certain, but I feel like in certain models, like when they output something like you can imagine, like a table for instance, and so they want, they wanna draw like 12 dashes and 12 dashes. Yeah, I think there's a way for that to happen. I think we only do it on certain tokens. Like we exclude certain special characters.Swyx [00:22:07]: Yeah.Ali [00:22:07]: So we only do it on like certain like S is the most common almost. GLM-5.2Swyx [00:22:11]: OhAli [00:22:11]: And I think it was DSV 4 as well. Like you'd just have like looping issues where like you literallySwyx [00:22:17]: ItAli [00:22:17]: Just have like S.Swyx [00:22:18]: Yeah. Is there a special, something special about S? No, just randomlyAli [00:22:21]: It just seems to be the one token involved.Swyx [00:22:23]: Yeah. And it'Philip [00:22:24]: Is thereSwyx [00:22:24]: And it's only temperature 0Ali [00:22:27]: NoSwyx [00:22:27]: Even at other temperaturesAli [00:22:27]: Even at like 0.9 or whatever, it will still, it will still collapse.Swyx [00:22:30]: That's weird, right?Ali [00:22:30]: It's, it is an inference problem to be honest, like a software problem. Like oftentimes, the image you run will-- like NVIDIA will release an image for instance, and if we will upstream the changes from their latest TensorRT-LLM image into our stack, we'll find that it fixes it. Or oftentimes this will only happen in an inference engine that you're using like SGLang. But if you were to switch to vLLM, that isn't the case. So it seems to be like an extremely like deterministic software issue and not really a model issue. It's not like a weights problem. Like I'- we'll say like, “Oh, it's a problem with the quant. We did PTQ wrong,” right? But that isn't, that doesn't make sense because the same weights used with a different inference engine does not repeat the problem. And sometimes it's, the kernels that are being used in the backend have like these very subtle sometimes race conditions, where if you were to use this model hosted on one cluster, you will never get this problem.Swyx [00:23:19]: Oh my God.Ali [00:23:19]: But if you host it on a different cluster, you will. And the reason is the KV cache transfer from a node to node in that one cluster is using a slower interconnect than the node to node in another cluster. So that exposes the race, whereas in another cluster it doesn't. So then you end up just like, okay, this model is not gonna be hosted on this cluster. We're gonna host it on, another cluster because that cluster exposed that problem. But then it ends up with like, okay, is it the software? Is it the model weights or is it the hardware?Swyx [00:23:42]: There is a thing about this with temperature 0 still not being deterministic, right?Ali [00:23:46]: Right.Swyx [00:23:46]: Mostly because of hardware. Even at temperature 0 same model, you won't always get the same output.Swyx [00:23:52]: Even-- But I'm surprised by the race condition one because, I thought PyTorch was a graph that like guarantees that you at least, execute things in the right order.Ali [00:24:02]: Well, yeah, true. Like I'm not, I'm not saying that there is. Like well, you have things like PTL optimizations where like you can start a kernel before the end of the previous kernel, and that's like ‘cause you want to do that because there'sSwyx [00:24:12]: It's like pipeliningAli [00:24:12]: Expense. Exactly.Swyx [00:24:13]: Yeah.Ali [00:24:13]: But it'- But you don't do it cleanly. Like you overlap a little bit of the execution. No, it is very possible that the kernel itself, like that one block that is supposed to be running in this instance of time, that kernel itself has a race condition. For instance, like a missing barrier. Like often if you're designing a kernel and you want it to make it to be very fast, if you don't test it extensively, you'll, you'll have certain threads access data points from registers before they've been written to by other threadsSwyx [00:24:36]: YeahAli [00:24:36]: For example, because like your barrier is wrong or your synchronization was wrong. But yeah, like the testing itself is very difficult in those like, andSwyx [00:24:42]: And there's no like borrow checkerAli [00:24:45]: What does that mean?Swyx [00:24:46]: Like Rust. Like the. If you're trying to have like memory safety It sounds like a comparable problem.Ali [00:24:52]: Well, yes, but you're working in CUDA, right, NVIDIA GPUs. Like- You just need a higher level language like modular Maybe that's what modular is supposed to do. I don't know.Quantization Quality and Vendor FidelityVibhu [00:25:00]: How do you see keeping quality of the model? So you talked about all these steps of, okay, you gotta do quantization, train your own speculative decoderAli [00:25:07]: RightVibhu [00:25:07]: Run on different hardware. Looking at other model providers, okay, you kicked off a inference speed race on the consumer end. What goes into keeping quality the same across them, right? Sure, you can run benchmarksAli [00:25:22]: YeahVibhu [00:25:22]: But, like, how do you determine how much quantization are there standards? What goes intoPhilip [00:25:27]: There's a few things on quality. Most inference optimizations are lossless. KV caching, for example. You are just recomputing or preventing recomputing the same values. Speculation, of course, if a draft token is wrong, it gets rejected. The main lossy optimization is quantization. And that really comes down to, number one, data format, number two, which parts of the model you choose to quantize, which layers, and number three, like doing a lot of calibration on the quantized weights, to ensure that you're preserving all the outliers. There's other tricks that you can do, though. A big one is long context, ‘cause one thing you asked at, right at the beginning is, “Oh, what's gonna happen if I send a 200,000 token request in?” So with a long input sequence, you need to, store a lot more information. You need to process a lot more tokens. And so even if a model has a context of a certain length, you might, as an inference provider, choose to build an API with a shorter context length, and of course a full length one as well. Because if someone doesn't need the full million token context, for example, you can get them better performance. I don't know if that's exactly like quality of the model. The way that I think about quality is to what degree are we faithfully serving the original model? If you think of a golden implementation of a model that performs exactly the way the model is designed to perform, I think of quality as how close are we getting to that, 100% fidelity of the model.Philip [00:27:13]: You can also, of course, think about quality from the training side and how do you push yourself past 100%. But when I think about purely inference optimizations, it's getting faster while staying as close to that 100% fidelity mark as possible. And certainly our standard internally is that, like you should not be able to tell the difference between our API and a, official API. I think Kimi in particular does a good job of vendor benchmarking hereAli [00:27:41]: YesPhilip [00:27:41]: Where they haveAli [00:27:42]: They released an actual vendor benchmark.Philip [00:27:43]: Exactly, yeah.Ali [00:27:44]: ‘Cause they accused, some people, Amazon? There was some provider that was not doing very well on Kimi's benchmark.Philip [00:27:50]: Yeah.Philip [00:27:51]: So, with Reflect we probablyVibhu [00:27:52]: This was a long time ago, right?Philip [00:27:54]: No.Ali [00:27:54]: Yeah, like threeVibhu [00:27:55]: They alsoAli [00:27:55]: Four, five months agoVibhu [00:27:57]: This also happened with, I don't remember which model, but they pulled out quite a few, and then they started a whole chart about this. It might have beenPhilip [00:28:03]: Kimi Vendor Verifier.Ali [00:28:04]: Yeah.Philip [00:28:05]: Yeah.Ali [00:28:05]: Yeah, ‘cause you, ‘cause you'd be pissed, right? Like if you'Philip [00:28:07]: Yeah.Ali [00:28:07]: If like if I'm a consumer and I'm using like Amazon's endpoint for instance, and I've used Kimi and I'm like, “Oh my God, like this is bad,” I'm not gonna say, “Oh, Amazon quantized the model in a bad way.” I'm gonna say, “Oh, Kimi sucks.” Right?Philip [00:28:17]: Yeah.Ali [00:28:17]: So it seems like that makes sense.Philip [00:28:19]: Yeah, they care. They care.Vibhu [00:28:21]: Justifiably.Ali [00:28:21]: Yeah, justifiably.Vibhu [00:28:22]: This is probably a stupid question, but just checking, has anything improved from main quantization?Philip [00:28:28]: Yeah.Vibhu [00:28:28]: Like, is quantization always strictly worse?Ali [00:28:30]: Well technicallyVibhu [00:28:32]: NoAli [00:28:32]: It's a lossy. QuantizationPhilip [00:28:33]: YeahAli [00:28:33]: Is a lossy, it's a lossy implementation.Philip [00:28:36]: Speed improvesVibhu [00:28:36]: Speed improves.Ali [00:28:37]: It the number, likeVibhu [00:28:38]: No, I' always look for inverse scaling laws.Philip [00:28:40]: Yeah.Ali [00:28:40]: Yeah.Vibhu [00:28:40]: This is something I learned from Noam Brown, where like things that normally act in one direction sometimes do.Philip [00:28:45]: Well, technically when you run a benchmark, because these models are deterministic, sometimes your,Ali [00:28:52]: YeahPhilip [00:28:52]: NVFP4 quant is like, two basis points higher than yourAli [00:28:56]: No, it's noise. It's noise.Philip [00:28:57]: Yeah, exactly. I'm like, yeah, it's, it's within. That's why I always say within margin of error.Philip [00:29:01]: And I stopped saying that because everyone assumes that what is, well, within some margin of error, we're barely inside of that to the worst, so we're saying. But yeah, sometimes it's just like, gives you a higher output score. But like Ali said, that's noise. To my knowledge, you're not necessarily making the results better. You're just trying to, again, like keep your fidelity as close to 100% to the original model.Layer Selection, KL Divergence, and Better QuantizationAli [00:29:27]: There is, to your point, research that we did on MP. I don't know if you are able to pullPhilip [00:29:31]: YeahAli [00:29:32]: A tweet we did. One of our research interns, Joshua, I think it's a tweet on how we have 20% better quantized GLM-5.2 than NVIDIA. Essentially what we found throughout like this month research is, okay, quantization is a lossy. It's. You're compressing the data from, occupying 16 bits to occupying, four bits, for instance. And so you're losing some information, and you're trying to minimize that. And so when I say that I'm gonna quantize the model, my job becomes how do I find the layers that I can quantize, and how to find the layers to not. For instance, with image models, I don't quantize modulation layers, and I don't quantize out projections because those two are. Like out projection is what you see as the user. Modulation is what the model sees or understands. Right, exactly. And so to his paper, do you have the. It doesn't have the. Yeah. It's a long paper. I don't know if I can findVibhu [00:30:25]: If there's a part to search or it's probably in the thread.Ali [00:30:28]: It's probably in the thread.Vibhu [00:30:29]: Yeah.Ali [00:30:29]: But the long and the short is it is very possible that quantizing more of the model makes the results. Like if I have a model that I quantize layers one, five, and 10, and another model where I only quantize layers one and It is possible that the model in which I quantized more information is going to perform better because the quantization errors have canceled out. And so what Joshua showed in his mathematical proof where he had like a verifier in, is that you can predict which layers are going to have quantization errors that will cancel out with each other, and you choose to quantize those layers. And so the result of doing this mathematical quantization is you end up with a model that's 20% more quantized than another provider, so you get 20% more throughput of it because there's more layers than running an NVFP4, and your quality is better than that other quant because the layers that you chose to quantize have their errors cancel out, like one layer skewed to the right one layer skewed to the left, one layer skewed to the right. Your final logits distribution is more similar to the original distribution of the model, so you have better fidelity. And so the way we proved this was with KL divergence. So instead of just scoring on the benchmarks, we scored the KL divergence between the logit distribution of the quantized model and the logit distribution of the original full precision model, and we showed that with this technique we get. If your probability distribution on the logits which token it wants to select is more of the same as the original model, you're probably gonna end up staying true to the original model. So yeah, so it seems like previously before this, it seemed like the industry was, well, the more you quantize, the worse it's gonna be, ‘cause the more loss you introduce. That's not exactly, not necessarily true. So yeah, doesn't improve it, but can cancel out.Philip [00:31:57]: I think it might be this, but reminds me a good bit about pruning where you can prune off certain layers.Philip [00:32:03]: But very interesting. Didn't know this was a whole paper you guys put out.Ali [00:32:06]: It's. Fun fact, it was originally 72 pages, this paper, and then we decidedPhilip [00:32:11]: WowAli [00:32:11]: We can't tell. We couldn't release it. So it's now 45.Swyx [00:32:15]: Still 39 pages, so very substantive. We talked about evals and all these things and, like what's possible in terms of speedup? Like it's like probably like the numberInference Speedups and BenchmarkingSwyx [00:32:25]: Thing that people do wanna care about, and it's something that you wrote about in your post. Like official API is 70 tokens per second, and you push it up to 90. Is that like a normal thing?Philip [00:32:36]: So what's cool about working in inference, the reason that I think inference is going to be a useful place to do engineering for a long time, is that if you look at highly optimized domains like, say, finance, if you're in finance, you measure how much better you got in basis points. It's like, “Oh, I got five basis points better, like twentieth of 1% better,” that's huge news because everything is so optimized. When we publish optimizations, it's 20%, it's 100% it's 200%. So there's still probably like a lot further to go, honestly. Like you'll, you'll know that inference is pretty much solved when researchers start publishing about how they got 1% faster at something.Swyx [00:33:19]: Which by the way, because I am from the finance background, in the ‘70s, that was the margin at the time. When you did quantitative finance research, you would findAli [00:33:27]: And like 20%, tens of percent.Swyx [00:33:29]: That's. Yes.Philip [00:33:29]: Yeah.Swyx [00:33:30]: And now it'Philip [00:33:31]: Tiny fractionsSwyx [00:33:32]: For those people interested, look up Andrew Lo's paper. He had a really interesting illustration of quant, stat arb, distribution, narrowing down from like those kinds of 20% differences in the ‘70s, down to nothing today, which is very cool.Philip [00:33:48]: Exactly, and we're at the beginning of the same type of thing. Now benchmarking is hard. I think anyone will tell you that, and benchmarking provider speeds is hard because there's so many variables that go into it. What hardware are you using? How much load do you have on the system? What's the exact nature of the prompts and input and output sequence lengths? All that stuff. But overall, when you start stacking these improvements, you're looking at multiples. You can look at it. The most common form, of course, is TPS, tokens per second, which is bad naming by us in the industry, ‘cause there's two tokens per second. There's tokens per second, the throughput number, and the latency number.Ali [00:34:31]: TTMT, yeah.Philip [00:34:32]: Like total tokens per second out of the, out of the GPU as a throughput number. Most people only care about tokens per second as the latency number, which we should call ITL, intertoken latency, but we don't.Philip [00:34:44]: Anyway, so you can imagine a standard API without many optimizations for a 1 trillion parameter model operating somewhere in the 30 to 50 tokens per second range for reasonable traffic profile. And we generally see the goal of, pushing to 10X that. But, not necessarily day zero, but by stacking enough optimizations, if you have, say like four optimizations, each of which doubles performance. Or sorry, three optimizations, each of which doubles performance, then you stack that up, that's an 8X gain. That's the order of magnitude that we're working with in this space. We're trying to make things substantially faster, not just go from like 70 to 90.Swyx [00:35:38]: Are you saying you've. You have done that?Philip [00:35:40]: So let's say you have as a reasonable baseline, 30 or 40 tokens per second. You can achieve 10X that. So like on GLM-5.2, if you run it unquantized, perhaps on H100s even, and you're just using an off-the-shelf inference engine with no particular optimizations, no speculator, nothing extra around like KV routing, no disaggregation, you're, you're probably, yeah, looking at that like 30 to 40. You think that's like a reasonable baseline?Swyx [00:36:12]: Right. Right.Philip [00:36:12]: To get to something like 10X, there's a lot of trade-offs that you're making. If we're running at more like a 300, 400 tokens per second range, you are using the best hardware possible. You have a optimized speculator. You have done all of your quantization work. You are Seeing a pretty high cache hit rate. You are running with a reasonably small batch size and a parallelism configuration that is tuned for latency versus throughput, but it is possible. So the spreads that you see if you, like, go on artificial analysis or you go on OpenRouter and you look at, the worst provider to the best provider, oftentimes can hit that range. 10X is of course very aggressive. It's oftentimes maybe more of a four to six times improvement. But that's the performance that makes us really excited, is when we can get these huge gains, not just go from 70 to 90 tokens.Stacking Optimizations: NVFP4, Speculation, and DisaggregationAli [00:37:19]: It's also, like, hardware dependent. Like, ifPhilip [00:37:20]: YeahAli [00:37:20]: If you have a thing where you're serving it on just, like, a node of H100s and then you throw, like, you shard the model across, like, four nodes of B200s. Like, you can definitely increase the speed with just throwing more hardware at it. Like, normalizing for the same exact hardware and the same number of GPUs.Philip [00:37:35]: Yeah. Then you're looking at, like, a two to 4X improvementAli [00:37:38]: Right. RightPhilip [00:37:38]: Depending on the inference optimizations. So yeah, it's. Some of it's, what's the call, and some of it's who's the driver.Vibhu [00:37:46]: If you break down the two to 4X, say the example is run GLM-5.2Ali [00:37:51]: YeahVibhu [00:37:51]: On B200sAli [00:37:53]: YeahVibhu [00:37:53]: Single node, right? What's, like, the cost trade-off for effort to get, like, the last bit of juice out versus what should people just think of, right?Ali [00:38:01]: Spectre quantization. Yeah.Vibhu [00:38:03]: Spectre quantization.Ali [00:38:04]: That's, that's, that's like 95%. LikeVibhu [00:38:06]: And how far does that get you? And how easy is that for the average person to do? So say right I wanna throw the weights of GLM-5.2 on a node of B200s, how easy is it to find speculative decoder- decoder model or already quantized model? How much work goes into it?Philip [00:38:23]: If you're doing it up front, it's quite a lot of work. If you're doing it today, there's going to be people who have published things that you can just, you can just grab some NVFP4 weights. You can grab a speculator. Yeah, if we're thinking about, like, what are the 2Xs we're stacking, going from, BF16 to NVFP4 is, it's not quite a 2X, right? It's like. I think it's about, like, 30 to 40%, from 16 to 8, and then another 30 to 40% multiplied from, 8 to 4. So that doesn't quite get you a 2X, but, like, roughly a 2X. Speculator, roughly a 2X. Disagg on top of that if you're able to get enough hardware and put enough traffic through it, another roughly a 2X. And then you add in some, double-digit percent increase from having just a better runtime with, the latest kernels and stuff behind it. And that's how it stacks up.Ali [00:39:21]: YeahPhilip [00:39:21]: So building each of those, like, building the, quantized weights is, for someone who really knows what they're doing, hours to days of work. Building the speculator, again, like, hours to days of work. And the, disagg setup, hours to days. Well okay, but like once you haveAli [00:39:39]: Once set up. Once set up. YeahPhilip [00:39:40]: Yeah, getting disagg working for the first time, I'm saying, of course, is very difficult.Philip [00:39:44]: The marginal implementationAli [00:39:48]: Like, if you're just grabbing, like if you are a person, like just a normal consumer who has access to, like, a node of B200s and you're wondering, “How can I just host it myself?” You don't need to quantize the model yourself. There's always gonna be, like, an open source quantized checkpoint. NVIDIA's gonna push one out if no one else does. You. Usually, the providers will have their own spec dec that they've trained as well. You don't need to train your own spec dec. You can just use that as well.Philip [00:40:09]: Yeah. Like, GLM-5.2 has its own MTP.Ali [00:40:13]: Right. Right.Vibhu [00:40:14]: What's multi token prediction?Philip [00:40:15]: Yes.Ali [00:40:16]: I'm justVibhu [00:40:16]: Can you explain that?Ali [00:40:16]: I'm just an expert.Ali [00:40:18]: I can do it for you in case I get it wrong?Vibhu [00:40:20]: No.Vibhu [00:40:21]: Yeah, you should correct if we're wrong, but their multi-token prediction can be used for self-speculative decoding.Ali [00:40:27]: I'm not sure. I'm not gonna correct that.Vibhu [00:40:28]: Okay. I'm semi-confident in thatAli [00:40:30]: Okay. YeahVibhu [00:40:30]: But someone can check. But it's useful to paint the story of, okay, not just the average person, but say a company wants to switch from serverless inference I wanna throw this up on. I wanna rent some GPUs, throw it up. These are the steps you take to do significantly faster than just put it behind vLLM.Ali [00:40:48]: Right.Vibhu [00:40:49]: I was waiting for a mention of Dynamo.Vibhu [00:40:51]: I feel like, that's supposed to be the baseline that you measure against.Dynamo, KV Routing, and Disaggregation ToolkitsPhilip [00:40:55]: I would think of Dynamo as less of a box system and more of a toolkit for building with. So when we talk about doing aware routing, when we talk about doing KV offloading, when we talk about doing, PD disaggregation, Dynamo fundamentally is. By the way, Dynamo is an open source library from NVIDIA.Ali [00:41:17]: We've done a pod with KylePhilip [00:41:18]: OkayAli [00:41:19]: Kyle Cranin.Philip [00:41:19]: Cool. So then your listeners know then that it supports all the different inference frameworks. And it is multi hardware, which is interesting.Ali [00:41:28]: But it's just a router, it's not like an optimizer layer.Philip [00:41:30]: Yeah. All it does, like, what Dynamo is good at, it is a library for moving information around your cluster, around your hardware. So if you have, KV cache on one place and you need it to be somewhere else, Dynamo coordinates NIXL for you to move that around.Philip [00:41:49]: That doesn't mean that, like, out of the box, you just say, “Pip install Dynamo,” and then you get, like, a massive performance speed up. It's more of a developer toolkit.Ali [00:42:01]: Yeah. I would have said it would. It comes with a set of defaults that you can then swap out.Philip [00:42:06]: It does. If the industry at large, I think, was, like, rolling out all of these deployments, standard, then I think it would be, like, a credible baseline. But, we've got to, we've got to benchmark against, like, what we're seeing in the wild.Speculative Decoding Methods: Medusa, EAGLE, n-Gram, and Spec-SpecVibhu [00:42:23]: I did wanna talk a little bit more about PD disagg, because that is probably, like, number three after quantized and speculative decoding. In your book though, I was just gonna pull out the book.Philip [00:42:31]: Yeah.Vibhu [00:42:32]: Like section 522 on Medusa, 523 on EAGLEPhilip [00:42:35]: YeahVibhu [00:42:36]: 524 on gram.Philip [00:42:37]: It's 55, would be disaggregationAli [00:42:42]: Yeah. Well, no, I just wanted to dwell a little bitPhilip [00:42:44]: YeahAli [00:42:44]: The other. Like, so what do you choose to include? What do you choose to not to include? Because there was all these other techniques.Philip [00:42:51]: Yeah.Ali [00:42:51]: Are these still relevant? Because I think they came out, like, a year and a half ago maybe.Vibhu [00:42:55]: Medusa is quite old.Philip [00:42:56]: Yeah, Medusa's old.Ali [00:42:58]: It was old.Vibhu [00:42:58]: But is it in the book as a good, here'sPhilip [00:43:01]: BaselineVibhu [00:43:01]: Baseline vanilla understand it?Philip [00:43:02]: Like you should know this.Vibhu [00:43:03]: Like I read the paper, I'm like, “ it makes so much sense.”Philip [00:43:05]: Yeah.Philip [00:43:05]: So with the book, I had a couple goals. One was to give people just a working vocabulary for the space as a whole, and the other was to give them some intuition about how each of these techniques works. As I mentioned in my AI Engineer talk, which is the first public addendum to this, the speculation space has moved much faster than everything else. So yeah, even at the time that I wrote the book Medusa, I very much included as a way for people to understand how the space evolved rather than what the most modern technique is. And now of course, there's DFlash, dSpark. There's, there's newer techniques even than EAGLE, although EAGLE is still very commonly used.Ali [00:43:51]: SpecSpecta.Philip [00:43:52]: Yes. Speculative decoding.Vibhu [00:43:54]: What canAli [00:43:56]: Oh, it's a paper by Tri Dao and it's like, it's doing speculative decodingVibhu [00:44:00]: HuhAli [00:44:01]: For the speculative decoder.Philip [00:44:02]: Oh, in spec- oh my God.Ali [00:44:02]: It's literally just an another. It's like, yeah, that's the most simple way to explain it, and it seems like he got trivial speed ups there. But it seems that the complexity with training, it's almost like in our mind at least, it's almost as complex as training GANs. Like it's like a very delicate balance and oftentimes you, it's just but yeah, it's literally speculative decoding on speculative decoding.Vibhu [00:44:21]: Speculative.Ali [00:44:22]: Yeah. We saw this paper.Vibhu [00:44:24]: It's interesting, right?Ali [00:44:24]: Yeah.Vibhu [00:44:24]: I wouldn't even expect it to be very particular to train, I wouldAli [00:44:29]: Right.Vibhu [00:44:29]: The naive part of me is like, okay, train speculative decoder.Ali [00:44:32]: But like, and it makes sense, like the whole idea of speculative decoding is you. It's like, it's like almost like the iPhone auto predict version but for a normal model, right? Like you're just, you're just, generating three tokens and you're like, okay, I'll do prefill on them. And so you save those three turns for your original model. Now your speculative decoder is doing three turns of auto regression, so why not just have an even smaller model?Ali [00:44:53]: The other question there is what are the size of speculators? So say forPhilip [00:44:58]: Right. It's like a billion parameters.Ali [00:45:01]: Like for MiniMax, it's. Yeah. It's like one layer. It's like one 60th of the original model usually.Philip [00:45:06]: Yeah. I think we should do a paper when we get back to the office.Philip [00:45:10]: SpeculativeAli [00:45:11]: SpeculativePhilip [00:45:11]: Decoding.Ali [00:45:13]: No, it's, it does seem like how, when do you stop? But then it also seems like if you're able to train spec-spec decode for instance, right? Like if you're able to have a small model that is accurately predicts what the intermediate speculator is gonna predict, that is able to predict what the original target model's gonna predict, then why not just use that smallest model directly, right?Vibhu [00:45:34]: Yeah. This isAli [00:45:35]: Like it seems likeVibhu [00:45:35]: Adjacent to the routing problem.Ali [00:45:36]: Right.Vibhu [00:45:36]: Yeah.Ali [00:45:36]: Right.Philip [00:45:37]: The thing with speculators is one of the practical constraints on using them is that you do have to run a small model on the same hardware that you're running the big model on. There is a orchestration and resource competition problem inherent in that, and that is one of the constraints on speculation in general, is that draft tokens cost resources to create and cost software complexity to manage. And so if you have like infinitely recursive speculators, you add in quite a bit of that complexity on the actual implementation within the inference engine as well, not just in the training process.Vibhu [00:46:17]: I was gonna say, I would wonder if you could do similar, like distillation and pruning of, it's the same thing, it's just a model. Can we not just distill a lot of the weights, quantize the speculator, out of my domain? The question that also comes up is, this is all for big server workloads, right? How much of this applies to, say I have this MacBook, I wanna run Gemma really efficiently. Similar problems, not the same?Local AI vs. Data Center InferencePhilip [00:46:45]: Pretty different. I talked to Selo, about this on his podcast a couple weeks ago. The difference between inference engineering for the data center and for production workloads versus inference engineering for local AI, is that we start with fundamentally like different constraints and different goals. With local AI, it's how do I fit this model onto my hardware and then make it less dumb? And with data center influence, it's how do I load this model and then make it less slow? And we care about less dumb, and they care about less slow. But the local AI inference engineering ecosystem, I think has a lot for us to learn from in the data center space. They are experts in various forms of quantization, including dynamic quantization that we just don't touch, in the pruning, in the distillation, in the, layer removal. There'Ali [00:47:42]: Layer removal matters less.Philip [00:47:43]: Yeah. There'Ali [00:47:44]: No one loves pruning really.Philip [00:47:45]: Yeah. Well, but the, but they doVibhu [00:47:46]: Which is surprising, right? But that's, that's a whole different thingPhilip [00:47:48]: Just to fit something on the laptop.Ali [00:47:50]: Right.Philip [00:47:50]: So yeah, it's a, it's an interesting, it's an interesting space. Not necessarily that like their techniques make sense for us to do in the data center, because we have different resources and different goals, but more that the process as well as the openness of that field is something to, admire.Ali [00:48:12]: Yeah. Like to your point, like, certain optimizations that would. Like for instance, Turbo Quantum Sharper, like it made such huge hype on that and we did like a whole deep dive on Twitter and like said, what is it? How does it work? Why is it good or not? And it took off and it was implemented on local devices because your memory bandwidth is so slow on like a MacBook, for instance. But try putting the same thing on like an NVIDIA GPU on a B200 Turbo quant would not be. Like, it would not be used. Like, NVIDIA - Like, NVIDIA made it clear that this is not a good optimization, and we've seen it firsthand where the overhead of doing dequantization, quantization of, in the kernel itself with turbo quant kernel, each end is much slower than the time that you save from doing the bandwidth. ‘Cause on the B200s, you have like 3.5 terabytes per second. You don't need decrease the storage that much. You don't need to do, FP4 KV cache. You don't need to use a requant. There's, there's, there's better optimizations to be made. But on Edge devices, it's extremely important, it's extremely useful. So, seems to be, like, different optimizations there, but then they're all uniquely combined with like all you wanna quantize the model, you wanna do speculative decoding, like certain common prefixes with bothPhilip [00:49:18]: Principles.Ali [00:49:19]: Yeah, exactly. Exactly. Exactly.Philip [00:49:20]: They also do a lot of work on, model parallelism, especially over, heterogeneous topology, where you have, some sparks and they are wired together with, Ethernet, DGX sparks.Ali [00:49:35]: Yeah, this is the Exo Labs guys.Philip [00:49:36]: Yeah. You have, a nu

Every healthy roofing business is built on a strong foundation. In this episode, Dave shares why the smartest contractors inspect their business just as carefully as they inspect every roof.EPISODE DESCRIPTIONIs your roofing business healthy?Most roofing contractors know exactly what's happening on every roof they're working on—but very few know the true health of the business they're building.The biggest business problems usually don't happen overnight. Just like high blood pressure, they often develop quietly over time until they become expensive and difficult to fix.In this solo episode, Dave Sullivan introduces a different way of looking at your business. Instead of chasing the latest marketing tactic or trying to "10X" your company, Dave explains why building a healthy business starts with understanding your financials, your systems, and your foundation.In this episode you'll learn:Why most contractors treat symptoms instead of the real problemThe "Three-Legged Stool" framework: Sell Work • Do Work • Keep ScoreWhy revenue alone doesn't tell you if your business is healthyThe silent warning signs every roofing contractor should recognizeWhy diagnosis should always come before adviceWhether you're doing $500,000 or $10 million in annual sales, this episode will help you step back, evaluate your business differently, and focus on what really matters.Resources MentionedThe Roofing Business Health Check™A personalized one-on-one evaluation of your roofing business designed to help you identify strengths, uncover hidden profit leaks, and build a healthier, more profitable company.Learn more or schedule a consultation:https://theroofercoach.comSponsorsSMA SupportNeed help answering phones, following up with leads, scheduling appointments, or handling administrative work?SMA Support provides trained virtual team members who specialize in the roofing industry, helping contractors free up their time and focus on growing their business.Learn more at:https://theroofercoach.com/smaProLineIf you're serious about knowing the health of your business, you need accurate information.ProLine helps roofing contractors manage sales, production, job costing, and financial reporting—all in one place—so you can make better business decisions with confidence.Learn more at:https://theroofercoach.com/prolineUse promo code:DAVE50Connect with Dave

Competing in a Future World of Infinite Intelligence Navigation: Intro From Knowledge Workers to Judgment Workers The AI-Native Company: Org, Hiring, Culture The Human Element: Are We Underestimating It? Scenarios Our Take Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show:   Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Nuno Gonçalves Pedro Introduction Welcome to episode 79 of Tech DECIPHERED. Today, we take a leap into the big unknown. This is a thesis episode, not your classic analysis, in-depth sharing episode. The big idea for this episode is that we may be approaching the cognitive age, and how would one, or how would a company compete in a world of infinite intelligence? The big idea, again, is that intelligence, which has been mostly scarce and expensive for all of human history, might become abundant and cheap. If that happens, what happens to work, what happens to companies, what happens to society? This episode will be really framing a lot of these discussions. From knowledge workers to judgment workers, addressing the AI native company and how does that change, going into the human element and whether or not we’re underestimating it, and finally, ending up going into scenarios, feasible scenarios of a future where, well, intelligence is abundant. Intelligence is quasi-infinite or infinite itself.Bertrand Schmitt Yes. Big questions for this episode 79. From Knowledge Workers to Judgment Workers We can start with from knowledge workers to judgment workers. Let’s go back first to how came the knowledge worker. It’s a 20th-century invention from Peter Drucker in 1959. The idea here is that that category might be splitting. The production of knowledge itself is on its way to being commoditized by AI. However, our perspective is that judgment around production of knowledge is not disappearing and is staying for a bit control managed by humans. What’s your take on this, Nuno? Do you agree with this split?Nuno Gonçalves Pedro I think it’s a little bit more profound than that. It’s not just judgment. Definitely, human judgment will be needed. We’ve seen agents perform all sorts of funny things in the wrong way when left alone to their own devices. Even some very well-known AI researchers coming forward and saying, “Hey, I tried to use this myself, and actually I messed up some of my systems,” or “I messed some of my code. I messed up some of my flows for a period of time.” I think just having human-in-the-loop from a judgment standpoint will be needed for a significant amount of time. That is something you can’t just delegate into machines, into algorithms, et cetera. The second part is, ultimately, there needs to be contextualization, and that contextualization, I think, comes from two forms. One from actual data, where the machine, I think, at some point will catch up, or the machines will catch up. The algorithms, at some point, on the data analysis will get better and better and have probably the closest to the truth that you can get, minus all the biases that are in the data, just to be clear, because data has a ton of biases. We’ve looked at this in the past and discussed it at prior episodes. But maybe on that, I think the machine has a chance to catch up, or the machines have a chance to catch up, so there’s less of distinctiveness from the human standpoint. But then, on just the attributes, the ability when you’re judging some situation, you’re in the middle of the situation. You’re judging the person and how it’s acting, in some ways, a lot of the things that end up happening, end up happening because there’s human interaction. There’s someone on the other side. I see how they’re delivering the message, how they’re implicating. We’ll talk about it later in the context of the organization and what changes in companies. I don’t think it’s just judgment. I think there’s a little bit more than that. One of the reasons I went to the dark side of management early on in my career from being an engineer was Peter Drucker and this notion of the knowledge worker, which he later on reemphasized with the publishing of his book, which for me was seminal and defined a lot of my career in life, the post-capitalist society, which is this notion that information rich and information poor is going to be the key distinctiveness that will happen in the world. The two big camps, information rich, information poor, which links back to this invention of the term knowledge worker, that knowledge is going to be key in some ways. I think that’s what we’ve seen for the last decades. Again, I think judgment is not going anywhere, but I think it’s beyond judgment. There’s elements of humanity and involvement that won’t go away anytime soon, where human-in-the-loop are particularly critical. We’ll discuss later some scenarios, but for me, that’s my stick in the ground. I think human-in-the-loop is going to be critical for many decades to come.Bertrand Schmitt While we are talking about all of this, and we share some possible scenarios, there is always that question. This is moving so fast right now. If you think about AI 10 years ago, AI 5 years ago, AI with the launch of ChatGPT 3, and then AI the past 2 years, now we have agents that are running at scale. Things are moving very fast. I can tell you, me in 6 months, the change has been pretty dramatic in terms of what I can use AI for. There is always that question that whatever we are thinking about cannot just be connected to what we were able to do 6 months ago or even today, we have to think and project ourselves at least in the next 6–12 months. Of course, we can go beyond that, and we will do that with some future scenarios, but it’s a very fast-moving, and it’s not clear yet where are the limits.Nuno Gonçalves Pedro I think that’s a very fair point. Let me try to analyze things that I don’t think will change anytime soon for the next few years. Agreed with you that many things will change, and we’ll have a lot better tools, platforms out there. That will be difficult to predict what exactly won’t change. I think there’s elements of humanity, and some of them do relate to judgment, like having good or bad taste, having a view on it, on whether something looks good or bad. Obviously, all of this sometimes is subjective, but some of it may not be as subjective as people think it is. The elements of contextualization. I think a little bit going back to what we did at Chamaeleon ourselves, where we built this platform, Mantis, and the objective of building Mantis was not really to replace us, was that it was a core augmentation layer in some ways that we would use investment or investor judgment as humans in the loop to systematize pattern recognition and a variety of other things, but that Mantis would really elevate all that judgment, not just in terms of timing, us being more productive, but also in terms of the quality of the decisions we’re making. Think of it as a little bit like having our human judgment in the context of operating Chamaeleon at a higher altitude, where we are more aware of the things that are happening and how they actually happen. The ability to really get to the data pieces and then make decisions on top of that that generate the needed alpha in our case for investors. What I mean by this is I think there’s always going to be core elements of humanity that I do think are going to be difficult for the machines to replace. For example, the taste piece people are like, “I can figure out what’s the taste in the market.” Yeah, but that’s mainstream. That doesn’t identify what’s the next big thing, which normally doesn’t start from mainstream. It starts from something else. It could start from opinion leaders and influencers. It could start by someone having a different way of addressing a problem and having a solution that hasn’t been thought through. For example, elements of creativity, I think, in human judgment and in human operations is something that I feel the machine will still have difficulty to replace.Bertrand Schmitt Let’s not forget how today current algorithms are working by feeding them enormous quantity of data, actually as much data as we can find. Finding more data is becoming a limitation these days. What it means is that it’s very hard for AI to think beyond its training data. There is some level of logic that’s being added, but at the same time, take the launch of the iPhone. What was the opinion before launch? Is that no, it doesn’t make sense. Not enough battery life, no keyboard, no this, no that. If you just base your analysis on what’s written out there, what’s being sold out there, you would just say, “It’s going to fail.” AI might really follow that more generic advice and perspective because that’s what in the training data and that’s what they’re in volume. It’s, of course, raising a lot of questions of, how do you improve the quality of the training data? How do you separate the weed from the chaff? There are a lot of questions there, and obviously, it will get better over time. But it’s still a critical part of how it’s working today. It won’t be that easy to change. I really like your point regarding Mantis, and I will say in general, platforms that you build with AI or leveraging AI capacity. Because when we say knowledge production is going to disappear, but we’ll keep judgment, it will be a different type of judgment because the quantity and quality of knowledge we will have in front of us to build our judgment will be very different. If suddenly we have for free the work of 10 interns or 5 junior analysts or whatever, and you can run that on nearly anything you do in life or at work, it’s completely dramatic. Your judgment was not used to be exercised so often because often you were missing quality data to have a judgment. Before it was a lot of finger in the wind and trying to smell something, but you didn’t have enough to make a serious analysis. Except if you are working as a strategy consultant, as you used to do, Nuno. That part is actually quite interesting. That the judgment itself will be exercised much more often and hopefully on the base of much more in-depth analysis for a lot of things. We will work very differently.Nuno Gonçalves Pedro We will go in-depth, faster and more fact-based, more data-based along the way. The question some of you might have right now is, is there some judgment that’s going to go away? Is there some judgment? We seem to be defining that there’s this organization, we’ll talk about it later, that goes from doers more into deciders. I think there’s some nuances to that, so I’ll just hit pause on that. In terms of judgment, obviously, there’s judgment that has been hidden over the years under the pretense of being wisdom, but it’s actually not wisdom. It’s just repetitive tasking, and it’s rules-based for the most. There’s a lot of judgment done, in particular in the white-collar space, that you could say it’s just reps. People have been doing it all along like that, and so therefore to say, “I’ve done it before like this, so I’ll do it the same way.” There’s actually no best in class, no analysis, no nothing. It’s just, “I’ve done it like that before.” I think that type of judgment will disappear because, again, algorithms will be as good, if not much better at that. They’ll be better at figuring out, actually, this would be the better way to do this. That’s how you play it forward. Then the question is, if there are fundamental, wise people in the organization, people that can really take that more complex elements of judgment, how do you go from the world we have today, which is a world of apprenticeship, where people come out of college, they go and work, and they learn their way, and therefore, hopefully over time, some of them, not all of them, we know that, but some of them will develop that wisdom to be great decision makers 15, 20 years down the road? How do we do that in a world that now is saying, “I don’t need people out of college because I can do it myself, and I can do individual contributor, and I can have agents doing the work that would require some manifestation of management in the middle.” Basically, “I don’t need this stuff. I don’t need you.” It’s a little bit the story we’re in. How do you create then this apprenticeship? How do we create then wisdom? My two cents on that is that wisdom, because of what we were just discussing and what, for example, myself and Bertrand was just saying, because of more often interactions with more data-stressed information and insights, what will happen is people will get better through their own reps in whatever form they’re doing, in day-to-day life, in internships, et cetera. In some ways, that will create the accelerated growth. It’s a little bit the interactions with agents and the interactions with our beloved AI algorithms that will create that growth over time and maybe not as much with other people. That still leaves the question around social interactions, but that’s probably the way this gets sorted. Apprenticeship gets sorted through the machine and the human having more interactions in effect.Bertrand Schmitt I agree with you because when we talk about apprenticeship, in some ways a lot of time was wasted on stuff that were not that important. But in a way, that was the price you had to pay in order to be there when people make the big decision to try to get some wisdom from that one hour of interactions that’s really useful and make a difference out of your full week. But the rest of your full week was just basic stuff that you had to do like a machine in a way. Why not let a machine do that? That, for me, is a big question. You could argue there is a transition period where it could be hard. For instance, if you can work hand in hand with AI smartly while you are doing your 4, 5 years of universities, you could graduate with a very different knowledge, perspective, judgment, skill set than anyone who graduated 5 years ago. I think that part will require a question around, “How do you change education?” You see what I mean? If you keep education the same way, expecting that the output is someone that should go now into 5 years of apprenticeship, that’s not going to work because companies will be, “No apprenticeship anymore.” On the contrary, you have to come much more knowledgeable and ready to use the tools. The tools are so efficient that the bar pretty high. You need to come already very well-grounded. If the education is not doing their job, that will be trouble. That part for me, I think is often forgotten. In some ways, the new-found importance of universities as a place to, and not just universities, the trade to really deliver people who are ready for the workforce. If on the business side, the expectation can change, of course, you have to change the education on the other side. My worry probably right now is that it doesn’t look like universities are in touch with what businesses are looking for, businesses are working on. Of course, that’s very worrisome because the cost of university has increased very significantly. It’s not clear quality of education has improved at all. If anything, it could be the opposite. It’s pretty scary. Of course, it’s going to raise a lot of questions. How much is education worth in that type of situation? Maybe another point because we talk a lot about apprenticeship, how this stuff was useful, but at the same time, if we go back in time, not long ago in the ’50s, if you wanted to be a developer, for instance, ’50s, ’60s, the job was very different. There was barely any programmation language out there. You had to use punch cards. Your time truly spent doing the coding was very limited. Once you had your stuff working, then, the debugging was a total nightmare. My point is that no one is looking back to that time saying, “You know what? It was great. It was a great way to learn and to do an apprenticeship for 5 years. To do that crappy job of punching cards for the boss.” There was little value in this. Guess what? Everyone is happy it’s not being done anymore by anyone. I think we also have to see what AI is bringing in a similar way is that everyone’s job is going to become quite different. There are a lot of big parts of the job who are not going to look back with fondness. Just looking back as, “Wow, that was very machine-like type of job. I’m glad I’m done with it.” People will want to jump directly to the next step. You don’t need to go to the punch card phase to be able to be a good developer for the past 40 years. I guess it will be the same with AI.Nuno Gonçalves Pedro I think so. The difficulty we have as humans is to also visualize dramatically different scenarios and landscapes, professionally. It’s difficult for us to anticipate what are the jobs of the future. Jobs have changed a lot in the last few decades, not even the last century. What people do, the migration initially from the agricultural society to then the industrial society to then the services society, and in some ways, the shift within the services industry, and now we’re seeing another shift, so we can’t really anticipate what those jobs look like. Back to your point on education, because I think that’s a very important point. If you’re right now an undergraduate student or a postgraduate student, for that matter, and you’re not figuring out your own mechanisms of learning outside of your syllabus, outside of what your professors are telling you, et cetera, you’re going to face very difficult times. If you’re not right now using all these AI tools proficiently, all these cycles of vibe coding, co-working, et cetera, with agents in the mix, you’re going to have a really tough time. If you’re not at this point in time as proficient as someone like myself or Bertrand, and given that we’re nerds, we’re relatively proficient with a lot of these tools that are out there. On top of it, some of us have our own platforms in-house. If you’re not as proficient as we are with those tools, you’re going to have a very difficult time because then people like us won’t need you. I think that’s the sad truth. It’s like at some point, if you’re not needed, you’re not needed. Then again, you may find something else that’s more interesting for you to do. Start your own company, go join a new exciting job doing whatever it is that you need to do next, et cetera. But again, I think the bar is very high. If you’re in college right now, again, undergrad, postgraduate, this is the time of transition. This is the worst time. It’s not the best time, it’s the worst time. Because education and all these institutions haven’t adapted to it yet. You need to adapt. You need to adapt. You need to adapt. If you don’t, you’re going to pay for it, not just in the loans you need to repay, but also in terms of actually having difficulty finding your career path in those first few critical years.Bertrand Schmitt You need to be especially proactive when you’re facing this type of period where businesses are adapting as fast as they can because they all know it’s going to be survival of the fittest very quickly. Universities typically are working on a very different pace, and it’s pretty guaranteed they are not going to have adapted as fast as businesses. In time of big dramatic change, it will be trouble. It will be trouble. Yes, you will have not fun. Not saying it was part of the deal when you sign up for that loan and decided to go for university. But that’s life. There has been issues before. It’s not the first time. You have to do something about it. You talk about your perspective about, “Hey, why do we need you if you are not already fluent and very efficient with these tools and stuff?” The truth, in some ways, it’s even worse than that. Each time we spend with someone who is not efficient with all of this is less time we spend with the tools that are already providing magic for us.Nuno Gonçalves Pedro Exactly.Bertrand Schmitt It’s a very big choice of, “Hey, do I spend more time training this person?” Do I just… there is an opportunity cost. Or, do I spend more time staying at light speed? Why do I slow down to do something else in the hope that maybe I will get to return versus the light speed I’m already on? It’s a lot of tension. Again, it’s certainly new. But if we want to look back, I think you talk about the switch from agriculture and society, industrial society, and now the service industry. The reality is that, yes, we have made dramatic changes in the past before. 140 years ago, we were 90% agricultural society in Europe, in the US, 90% of us. Today, it’s what? 2%. So my point is that that’s a normal evolution. There is no progress without change. Sometimes the rate of change is soft, and sometimes you have a step function. Now it’s a step function, and it’s also a pretty fast step function. Before, it could take decades to get new stuff being put in place, to have electricity come up, this or that. Now we see that the rate of investment in AI is insane, way beyond anything we have seen before. Two, in a way, a lot of the architecture behind the scene was already there to support an even faster transition. What’s new might be the pace of the transition, how unnatural it might look. But at the same time, if you put yourself in the shoes of someone who lived 150 years ago, I mean, this was also a dramatic change for them. From horses to cars to planes to rockets, pretty big change, maybe even bigger change.Nuno Gonçalves Pedro Maybe the silver lining, just to bookend this section, is one, there will be new roles. There are a lot of things we can’t anticipate. There will be new roles, there will be new jobs being created, and new things that we can’t really quite grasp yet. The second part is that the rules are changing, and they’re changing, I would say, in general, for the better. If you are a decision-maker or an organization, and you still have your job, you’re probably making more important decisions with more data, with more tooling around you, with less red tape, hopefully over time. I know that will not hold true for all the big corporations out there that are listening to us, but it is starting to happen. Things are making an impact on how decision-making is made. There’s less and less red tape along the way in certain organizations. There are more and more fact-based discussions happening as we move along. The silver lining is better jobs, more jobs, different jobs in the future, hopefully as well. Secondly, the second part of the silver line is that the jobs that exist today, hopefully, will be more interesting, certainly on the knowledge space and on this judgment space that we’re now introducing as part of this episode. The AI-Native Company: Org, Hiring, Culture Switching gears, maybe to how does that shift? How does the company of the future look like? How does an AI native company look like? I feel there are a lot of discussions on, “Oh, you only need one person to run everything.” Let’s not go to that level. We’ve had a couple of episodes where we focused on AI as your co-founder and a couple of other elements that you guys can go back to. Let’s focus on a more evolutionary view of what’s happening to organizations, and maybe start with the org structure. In general, we should see more flat organizations where mid-level managers have to justify their pay in some ways because middle management are routers. They are normally routing tasks. It’s sometimes aggregating it, synthesizing it, and pulling it back up. Guess what? AI and agents in general are very good at that. The synthesis piece, et cetera, is not as well needed. One could say there are several elements of middle management that are valuable, like the coaching of people, the creation of apprentices, and the accountability that comes with some of middle management. But lo and behold, most of middle management is seen as a little bit of a thin line that doesn’t need to necessarily exist. I feel we’re moving into a world of smaller teams, more senior teams, where there’s more judgment at the top, where you’ll have people that both do a mix of what we used to call management in its new form, but also a lot of individual contribution. If you’re not used to that, if you’re not used anymore to be an individual in the future, again, and if you’re a very senior in an organization, maybe this is the right time to either reinvent yourself, find some other job that doesn’t require as much of that, which we’ll have plenty of those jobs for the next few decades, or maybe retire. I’ve actually, shockingly enough, seen people who have said, “You know what? This thing is changing too fast, too dramatically. My industry is changing quite aggressively right now. I’m about to retire in a couple of years. I’m just going to retire now.” I’ve literally met two people who have done that. Again, there’s nothing wrong about it. I think we’re, again, going through a step function and a huge shift, but figuring out where you fit in this new model of organizations, more senior at the top, smaller teams, more of a mix of individual contribution with management than ever was done before.Bertrand Schmitt I agree with you. In some ways, I’m not surprised that some people might say, “You know what? It’s now time to retire.” I feel a bit sad, maybe because it means you don’t like to keep reinventing yourself and changing your habits and thinking about new stuff. You were a creature of habits, I would say, if that’s your conclusion. But everyone is entitled to their own opinion, obviously, and a way of life. I guess that’s what happened, again, at regular times in the past in terms of big change. What I can see is that the rise of, you can call it the full-stack individual, someone who will have multiple roles inside the team. Before, you had to really separate the role. Especially in the US, there is such a clear separation between every role you can have in a company. Let’s take a tech company. You will have people doing design, people doing different types of designs, people doing front-end development, back-end development, and operations. You see step-by-step hyper-specialization. I have seen that, and it’s true that the level of complexity you had to deal with at some point requires some level of hyper-specialization because it will take you 6, 12 months in order to be really, really strong on a specific topic, a specific language. God forbid, trying to go deep into something that you had no real experience into. But I feel with AI, it’s a big change, actually. It’s the opportunity to go beyond that. It’s the opportunity to do more, to touch more. You can combine designing and shipping code, product managing and shipping code, being an analyst and deploying. Of course, we have to think how it works because putting a marketer shipping code to production, maybe that will get you into trouble. But I think that there must be some change. We see it changing dramatically, how fast we can get into something, something different from what we are used to. I think it would be crazy not to take that opportunity to dramatically change the scope of many positions and put an end to that hyper-specialization. I think for me, in some ways, hyper-specialization was bad. There is only so much you want to be a specialist in because a lot of things, a lot of opportunities are actually coming from the mixing of many different ideas, many different perspectives, and you lose if you go to hyper-specialization.Nuno Gonçalves Pedro I don’t think the age that is coming is the age of the generalist. I think it’s going to be the age of the multispecialist. We’re going to go into an age of multispecialization, which is a little bit, we’ve mentioned it as well in the past, what Amazon defines as an athlete or T-shaped or pie-shaped people, people that have on top an amazing ability to do general management, strategy, managing teams, et cetera, then have spikes. Spikes into business development, corporate development, product management, whatever it is. With AI and with agents, the development of those spikes, as we’ve been discussing in this episode, will actually be easier. It’s almost like a given. If you want to go deeper and deeper into a certain area, you can go much faster. I think that level of multispecialization is going to be really cool to observe. I’m not sure we’ve had an age of multispecialization over the years. Maybe people would point out, well, the Da Vinci example, people that are great across very different areas. Maybe that’s an example of multispecialization. But honestly, from my perspective, this is going to be an exciting time because of that, because you’ll have people who, instead of being just focused on this area of sales, and I only do that, they can actually and should actually do a lot of other things. So the work, as we were talking before, can be more interesting. More demanding as well, because the judgments you need to make are more complex. The context you need to actually gain needs to be gained much faster. At a level of magnitude, you haven’t been able to do it before. Talk about information overload. But actually, ultimately, the roles can be a lot more interesting, a lot more exciting, because I can jump around. If I’m an investor, in this case, we have two investors on this conversation. But if I’m an investor, one of the things that we start looking at is actually not just looking at a startup as, is this startup doing something in AI or not? Is it AI-enabled or not? Is it an AI platform or not? But actually, more fundamentally, is this an AI native startup? Meaning, organizationally, culturally, is this the company that’s already in the AI age? How is the team working? How are they defining things? It’s not just that they only have two or three people. It’s like, what are those two or three people doing? How are they doing it? What cadence are they doing it on? What tools are they using? How are they making decisions? I feel we’re still actually relatively early on that track. It’s very interesting because we’ve had all these companies raising mega rounds. First round out, we invested in one of them, but there have been many frontier labs out there raising a ton of money. But a lot of them don’t have a fundamentally different way of doing business. Of organizing themselves, of how they do the day-to-day. Although they’re working on cutting-edge stuff, with very notable exceptions, they’re actually not using it themselves. They’re not actually shifting how they do stuff themselves.Bertrand Schmitt For me, that’s very interesting because in the past, I used to be quite conservative on how you manage and run a company in the sense that if you’re already in tech, if you are already on the cutting edge of what technology can deliver, and this and that, don’t waste time trying to invent a new org structure. Just focus on delivering something great, amazing, and be great at technologies. That’s already your huge differentiator. At the time, there was no real reason to innovate on the team organization. I have seen so many teams that tried to innovate, and it was just catastrophic because there was not much to innovate on, because we had decades of optimization that we could leverage. There was no reason to invent. But here it’s very different. There is a dramatic shift in how you can organize differently a company. I don’t think there are any blueprints yet on what’s the best way to do it because it’s too new. But at the same time, I would feel very bad to invest or support a company that first is not focused on AI or AI-enabled, but at the same time is not trying to innovate on the team itself. Because if you don’t do that, you’re going to get killed by someone who is going to innovate better than you on not just the product, but on the org as well.Nuno Gonçalves Pedro Indeed. The shifts are pretty substantial. If you look, for example, just at hiring, what do you hire for? Certainly, there’s this element of the multispecialized orchestrator, which normally will be someone with quite a lot of wisdom and expertise. It doesn’t necessarily mean someone who’s old, but someone who has the ability to work with all the AI tooling and platforms out there and be an orchestrator of agents. Why do they make judgments, make decisions, move stuff forward really, really, really quickly? Again, those jobs are going to be the best jobs. The second part, I think that is very interesting, around hiring, is you’re going to skew towards the elements that are potentially either very aligned with the use of AI tooling and platform, AI expertise, or being AI native, or someone who’s used to using AI. That’s one side of the fence. On the other side, you’re going to actually be optimizing to hire people that have the characteristics that will be difficult for AI to replace immediately, like taste and the notion of fundamental accountability and notion of implications, the notion of how you affect change in organizations, how you affect change in individuals, the elements of coaching, and beyond coaching. You’ll be optimizing for those kinds of hires as well. Then, last but not least, for me, I feel that there is a momentum already happening. I think it will happen even more, which is the tendency to under-hire rather than over-hire. The moment of the good old days of blitz scaling, “Oh, let me go and hire 300 people to scale my go-to-market and just land grab market.” Now, that’s not how it’s going to work. People are going to try and first get the efficiencies in-house with top talent and see if there’s, at the end, the need to hire more people or not, rather than the other way around. I think the issue here is a little bit of what we alluded to before in this episode. There is a tax on individuals. If you hire more people, you’ll have to manage people, you’ll have to work with them, et cetera. If I don’t need to, I might as well work with the agents that the tools and platforms that I use give me access to. Because that’s a world that’s much more efficient, right?Bertrand Schmitt I’m in total agreement with you on this. It’s definitely raising way more questions than before because, again, on one side, you have the product, the technology used to build products that are completely different. At the same time, all of this is also enabling new ways to design organizations and to scale differently, especially in a world where, as we have seen in 3, 6, and 12 months, stuff that you thought were impossible are suddenly becoming possible. So you’re, “Hey, I’m going to scale and burn a shitload of money for 6 months before I know if there is any return.” Versus, “You know what? Maybe I just wait 6 months. The AI has improved enough so that we don’t need this new team. We don’t need these people to do stuff.” Because actually, if you just wait 6 months, we will have stuff coming for free from either new AI models or new AI tools or this or that. If you remember, we used to say that in mobile, things were going three times as fast as on the web in terms of pace of innovation and speed of development and stuff. I mean, with AI, it’s 5X mobile.Nuno Gonçalves Pedro Maybe even more. Yes, well.Bertrand Schmitt Maybe even more, maybe 10X. Every assumption around blitz scaling or scaling in general was based on past assumptions. It’s not based on how is the industry evolving today. Might make more sense for you to really grow your agents and spend more money on more tokens. I remember, of course, Jensen is selling his business interest, but he was saying, “Hey, for each one of my 450K engineers, he better spend 250K in tokens a year.” I’m not saying it’s the right way to say it, but I think there is some truth in it, and that would be something to think about. Have we maxed out the token usage per employee? I’m not talking in a stupid way because token maxing and wasting money has no value and is as stupid as it gets. But if you are truly getting a return on these tokens, can you use more? Can you generate more? Can you create more loops so that one engineer manages not just 10 agents, but 50 agents, but 200 agents? I think that’s the big question. We’re trying to add more people. More people means more management, more issues, more this, more that. That would be a fair question. Another piece of the puzzle is how do you build in a way your… I don’t know if it’s a digital twin, but more like the digital version of your companies represented by agents. How do you make sure that everything you do as a business is truly captured, is truly leveraged so that your agents are getting better and better? Not just because the model gets better, but because you are putting more data into it, because it has more opportunity to learn, and as a result, gets better at your specific business.Nuno Gonçalves Pedro The next big thing is culture. How does culture change? I think the biggest shift that I see is, why would you do meetings all the time?Bertrand Schmitt Yes.Nuno Gonçalves Pedro At least at Chamaeleon, we have a very small team, just by the way. We have a very small team at Chamaeleon. We’ve reduced by way more than 50% the time we spend on meetings between each other across the board, one-on-ones, partner meetings, et cetera. I think we’re really pushing to be more and more asynchronous. There’s stuff you can process via message. I was just asking one of my colleagues, “Can you just send me that prompt for that so I can just do that on CoWork?” Or “Can I just go on Mantis and do this? Can you tell me the cycle?” Or vice versa. Basically, it’s a little bit like you’re just going to do it. I don’t need to meet. I don’t need to meet all the time. There are some things where we still need to meet and interact, and we need to brainstorm at times, and we need to go to a different level of abstraction on the top end. Then on the lower end, there might be things that are a little bit more specific and governance-related and operational-related that we need to agree on that are more sticky. But otherwise, the culture is going to be biased towards build. “Go and do it,” rather than, “Let’s do a meeting.”Bertrand Schmitt Yes.Nuno Gonçalves Pedro Async is the thing. I’m more and more like we have a couple of interns this summer. “Can we async this?” They’re like, “What does that mean?” “Can we make this interaction asynchronous?” Because synchronous interactions for me are very expensive. Can you send me something that I can process, and then I can send it back to you? We don’t waste time on you giving me context and whatever. Then I’m not ready quite yet because I need to process it. Maybe I’m in between two meetings that I’m actually thinking about other things in my mind.” Again, I feel that shifts how stuff is done. One, build rather than meeting. Two, asynchronous versus synchronous. In some way, millennials had it right when they shifted a lot to messaging and stuff like that. Let’s do more asynchronous rather than synchronous, those two elements from just an operating model of the company are significant. Maybe this is a good time for me just to put one parenthesis because there’s this thing that’s bugging me as we’re talking here. Everyone who is listening to us at this point in time might be saying, “Cool, but I work for this large organization. We’re just now…” Everything we’re saying here is contextualized by time. We’re giving you extreme situations. We’re looking into the future. Some companies that we’re talking about might be doing this already as we speak. Some of them might be in the process of doing this and might in the next couple of months be doing it like we are describing it here. Some of them might take years to get there. Then again, some of the companies that might take years might actually be destroyed in between or meanwhile, and be disrupted. Some of them might not because they’re in very legacy businesses, and it’s fine, and it’s okay. Again, don’t take everything that Bertrand and I are saying today as this is gospel, and it’s going to happen tomorrow, and why the hell are we not doing it? We think that aspirationally, this is where you should be moving to as an organization, whatever size you’re at. Speed will matter, as we discussed before, but not everyone, obviously, is going to move as fast as we’re describing it here.Bertrand Schmitt Yes. Me, for instance, take inspiration often with what some of the AI labs, frontier AI labs, are doing, the way they are working, especially in OpenAI and Anthropic. They are clearly at the top of the spear in terms of what is it that you can do because they have access to models we don’t have access to, because they have unlimited tokens they can use for tasks. They hire people who are, of course, 100% on AI. They are the best example of what is achievable if you have the top minds, if you have the latest models, if you have unlimited tokens. From there, you can take that for our needs and for our situation, and others in industries that are not as advanced. Definitely, you have some time. But as you say, things are moving fast, things are changing. Wall Street is going to expect better returns because when we discuss all of this, the conclusion is that you should be able to do more with less. That’s as real as it gets at some point. By the way, that’s what you see. You see better performance, a better business performance right now. So even if you might not get disrupted, you’d better start there. For some, it might take more time, and they might still be fine.Nuno Gonçalves Pedro Maybe to bookend this section, clearly what we’re saying is organizations are going to change. Their MOs are going to change, the structures are going to change. There are elements of what we discussed before in terms of judgment that are fundamental to this. The ability that in some ways, one would say a lot of the technique of getting solutions out there, even in brainstorming or problem-solving, is going to get democratized. The algorithms are able to do that. On the other hand, having points of view and having wisdom is not necessarily democratized, necessarily by the machines. It can be facilitated, it can be more productive in achieving that level of wisdom, but wisdom still will matter at the end of the day. We’re not saying that’s out of the question. Actually, that’s going to be the asset. People who have fundamental wisdom that can come to the table and frame things. We see this even today in prompt engineering, on just creating prompts. The better your prompt is, the better the outcome is going to be, the result that you get from the algorithms. That’s not going to change, in my opinion, anytime soon. That UI interaction piece is not going to change anytime soon. Again, if you’re an organization thinking through organizational structure, culture, if you’re thinking through hiring, these are some of the elements that we think will give you an opportunity, but I would actually go one step further. On the positive side, I would say, they give you arbitrage. If you’re able to move faster than your competitors and really adapt your org faster, you’ll reap the benefits faster as well. That’s what many still say and relate to as the word innovation. That’s how innovation gets accelerated. I think there’s a huge opportunity right now for arbitrage. If you move fast, experiment, experiment on new org structures, experiment with talent, you’ll know that some of them will work well, some of them will fail miserably, so you can’t experiment on literally everything. On the other side, I think the doomsday scenario is if you don’t, if you’re on the other side and your competitor is outpacing you on trying these different organizational models, structure, hiring models, and operating models, they’ll potentially just disrupt you. They’ll do stuff that you thought you had the moat on, and lo and behold, you don’t anymore. Sometimes it comes just from org, just from injection of people with a different MRO, different operating model.Bertrand Schmitt The Human Element: Are We Underestimating It? Maybe we can move to our next section about the human elements. Are we underestimating it or are we overestimating it? The three things that are a big part of the human elements, emotion, creativity, and synthesis. Is it just soft skills, replaceable part? On the contrary, is it the durable part now that we have automated intelligence?Nuno Gonçalves Pedro I’ll start with emotion first because I think it’s probably the easiest of all the ones you’ve mentioned. Emotion is key. Many of you listening to us will know this. The way you deliver a certain message, the emotion that you have when you deliver it, just in and of itself, this could be a sentence, it’s something verbal, et cetera. Makes a difference between the person or the people on the other side actually adopting it or actually just resisting it. Emotion is critical. It’s what runs the world. Everyone talks about a bunch of things, but emotion is a currency that is still naturally human. It will be, I feel, difficult for these AI tools and platforms to recreate it fully until there’s some literally very high-definition manifestation of them as avatars or some physical manifestation of them as robots and all that stuff. It will take a while for that emotion to be manifested. Emotion, I think, is still something that we as humans have as a moat, and it’s critical. As you mentioned before, I was a strategy management consultant at McKinsey, and getting people to action is actually 80% about the delivery, communication, the emotion that you surround the project itself, more than sometimes the truth. It’s great to have the truth and to have something that is similar to the truth in terms of analysis, but in some ways, that’s not what really moves change. Change is moved by, I would argue, a significant amount of emotion and alignment on emotions.Bertrand Schmitt You could argue that’s something that most politicians have perfectly understood. If you look at most campaigns these days, everything on emotions, maybe the tagline might be one word. It’s interesting when you see from that perspective that actually it’s very little on facts, very little on all of this, but more about emotion. You could argue it’s the same for businesses in the future? That’s a fair question. I think creativity is another one that’s quite important. At the same time, it’s not so easy because I must say I’m quite amazed when I’m looking for creativity from AI, either to generate the image, to generate video, to generate audio, or to generate text. AI can be pretty creative. I still think you need to control its creativity; you need to understand what’s good, what’s bad, what’s quality, but at the same time, I can see even in creative tasks, AI can be a very strong partner. I’m talking about any creative task, like invent a name for a product, let’s brainstorm the mission for the company. AI can actually be doing a pretty impressive job. That’s the type of job where you will hire experts, where you will use some of the best people in your team to help you for days. We say, “You can do quite a lot.” It’s an interesting one because I think there is some unique human creativity, and at the same time, AI can be pretty strong at creative task as well.Nuno Gonçalves Pedro I agree. In particular, if it represents benchmarking, if it represents repetition, if it represents seeing the world and then coming up with something that presents itself as creative, to be honest, it can actually outpace humans. If it’s like genuine light bulb moments of creativity, angles that haven’t been tried before, certainly not in the same way, I think humans still have the advantage. To your point, I agree. This is not a humans-win situation. On the previous one, on emotion, still, part of it is because, also on emotion, there are exchanges. You and I might be looking at each other, and from the facial expressions and the reactions, where you judge that for AI to get there, it’s going to take a long time. There’s going to be a lot of very complex algorithmic stuff put into that for AI to be able to create synthetic emotional behaviors, but creativity, I agree with you. There are a lot more nuances to it today, where AI does have significant advantages at the end of the day. Synthesis depends. Synthesis, I feel, if we’re talking about holding a bunch of messy assumptions, contextualized inputs with different layers of data adjacent to them and then trying to create and form one coherent, fully accountable point of view that you stake something on, like a decision, a company, a business unit, whatever, I think humans have the advantage. Part of it is the complexity of what we have today with generative, pre-trained transformers, today with GPTs, where the hallucination comes through, where it’s really more statistical analysis. Over time, maybe synthesis will be a forte for AI. Right now, I think we still have that ability to really be the ultimate decision-makers and judge-makers and have that wisdom put at the table to make those decisions. Honestly, models are very good on balancing both sides, so ended up, as we say in Portuguese, neither fish nor meat. It’s to balance both sides’ answers. That’s not helpful in most cases. When you’re in a difficult position where, for example, the future of a company, company is almost dying, what do you do? I’m not sure your AI algorithms that are going to give you a great solution. Because it will give you a median or average solution, which likely will lead you to a median or average outcome, which in this case would be failure. Again, on synthesis, there are some areas of advantage for human beings. If you are looking for clearly synthesized perspectives on certain elements that are maybe less edge-focused, they’re more than the normal part of the normal distribution, then probably AI agents are brilliant at that. All the tools we have today are pretty good at that, and I think they’ll just get better over time. That’s how I see synthesis.Bertrand Schmitt I think a lot of improvements will come with a better fine-tuning of agents to what’s special about your company. Because if you just take a general agent, there is only so much. It can understand your industry, your company, and your way of working. I think that part of making sure your agents are finely trained, finely tuned on your own business, so that they can give you a really well-calibrated feedback, will have a lot of importance.Nuno Gonçalves Pedro I think that’s absolutely spot on. Maybe to end it, what is definitely different about humanity? Definitely, emotion, as we discussed, some pieces of synthesis. Creativity, maybe the light bulb creativity, not the more repeatable creativity, the one that you can put and encapsulate into processes in some ways. There are elements of us being physical, which robots can’t still recreate. That’s definitely an advantage. The embodied, we’re embodied. That’s obviously a huge advantage. With that also comes advantages because we have to interpret each other, and we have to see the complexities in physicality that land to it. Is human and the human element categorical difference? If we’re having a more philosophical discussion around this, I think it is. I think it will be for at least the foreseeable future and maybe decades to come, even in whatever scenarios we’ll discuss, which is our next section, scenarios.Bertrand Schmitt I would say projecting beyond 10 years is always pretty hard on this because, again, some of the improvements we are talking about we can imagine based on how it has evolved, but at the same time, there will be disruptions in AI. Stuff that we take for granted in terms of weakness, especially, might not be there in a few years from now. Either because it has been solved through brute force or because the field will have made significant change and improvements and discoveries, making some of our points moot. If we talk about embodiment, obviously, robots are coming. How fast, how cheap? That will be a big question. Right now, they’re not very smart. They’re usually very specialized. The more we move to a more general form factor, humanoid form factor, the more I think it will change. Also, another piece of the puzzle is that we have the assumption of agents having trouble to convince humans and stuff. At some point, we keep assuming that humans in the loop. If we’re talking about agents convincing another agent, not having embodiment might be even more efficient. That will be another perspective. Going forward, we will have not just agents we control who are doing a job and scanning the job, but agents truly interacting with other agents. You have agents controlled by one person, one team in your company, working either together or maybe not confrontationally, but trying to think and having different perspectives with another agent, controlled by other teams. I don’t think we have seen much of that now. We have seen mostly agents that are controlled by one team doing one job in one direction. Not multiple teams agents working together, or against or in parallel with another team agent. I think we will see some interesting things coming out of that.Nuno Gonçalves Pedro Scenarios Switching to scenarios, we love our two-by-twos. We haven’t done one in a while. This time it’s a two by two. We have four scenarios. I think on one axis, we would have potentially the capabilities of AI. One side would be more incremental. The other side would be the extreme full AGI. I’ll define it in a bit so that we can at least have a little bit of a definitional view on what the AGI is. Then the other axis would be how gains are distributed, concentrated versus broad. Obviously, if they’re very concentrated, it’s more unequal. It only goes to a few companies, a few people, a few individuals. If it’s broad, it’s much more dispersed through society, et cetera. AGI, just to try to define it, the formal definition of it is that it’s a hypothetical AI that matches or exceeds human capabilities across virtually all cognitive and practical tasks. In some ways, AGI can learn, reason, and adapt to novel situations across any domain. Then there are several mutations on this, but there’s one notion, or rather, there are three notions that normally are across a lot of these definitions. One is generalization, ability to seamlessly transfer knowledge from one domain to another without needing retraining, which is a very impressive skill that we humans still seemingly have. Autonomy in agency, the capacity to operate independently, set goals, plan and execute complex tasks. I think AI is their issue with agents to a lot of that extent. Then, last but not least, human parity, performing economically valuable work at or above the level of a typical human knowledge worker. If you listen to one of our last episodes, you’ll realize that Bertrand and I have slightly different views on AGI, and if it’s already here or not. I think, definitionally, maybe we have slightly different views on what the definition actually is. For me, maybe AGI is a little bit more what some would call superintelligence and generalized superintelligence. Strict to census, Bertrand is more connecting to AGI as in its prime definition. It behaves as well or better than a human thing. Maybe that’s what’s leading us to differences on whether AGI has arrived or not.Bertrand Schmitt Personally, I will have a different scale where I will put AGI, as you just said, in some ways, relatively similar in performance to your average human being. On top of it, it’s able to touch different domains that most humans are not able to do. Usually, there is some level of specializations where in AI, it can be more generic. I will put ASI, Artificial Superintelligence, as clearly the step beyond. Something that, on any dimension you pick, it’s able to beat a human expert. From my perspective, I think we already discussed that, but we are at AGI already. We have AI that can do way better, not just way better, but at least as well as humans on many topics, sometimes better. Yes, there are some topics that are not for AI yet. Embodiment, for instance, to flock with your humanoid robot in 2026. For me, we are partially there or fully there in AGI. If we take the stricter definition, ASI, we are definitely not there, but my guess is that it’s moving quite fast. We might be there in a few years from now. I don’t think we are talking about multi-decades. It’s 5 years, maybe 10. Of course, there are questions because people will say, for instance, “Hey, how do you become truly super-intelligent when all your training is based on human data?” That’s not an easy one because how do you train on that? To be way better, not just a bit better, but way better. Maybe I’m going on a tangent, but some are looking at AI learning from AI, AI being taught from AI, AI fighting with AI, AI challenging AI. The same way we saw this AlphaGo moment where AI was not trained anymore, like in chess with human moves, but has been trained to play against itself. That’s when it reached superintelligence in Go. It reached superintelligence by playing against itself and basically letting go of that human baggage, if you want, and going to the next level. What I found interesting in that, actually, first, that’s what happened, but two, there was some analysis that the average level of Go players and the top players went up after AlphaGo because AlphaGo, in a way, opened doors that humans didn’t believe were open in front of them, or they didn’t see them. They didn’t see these doors, so they didn’t bother to open them. AI opened new doors, but interestingly enough, humans improved after that, thanks to AI. You see what I mean? It was an interesting, okay, that self-learning from AI was the way to go beyond the current level of human knowledge and human expertise, but at the same time, humans were able to follow up. It was not like suddenly humans are totally useless crap. They improved. Did they still beat AI? Maybe not, but it was definitely also helpful.Nuno Gonçalves Pedro Back to our scenarios. We’re going to take the definitional extreme just for argument’s sake for scenarios. We’re going to talk about maybe what you were saying, ASI rather than full AGI, but like ASI. Again, artificial superintelligence as the extreme on the one hand. Let me talk about maybe the first scenario that would come to mind. Maybe we can call it the plateau scenario. All of this was great, but it was all smoke and mirrors. They were great at some cognition stuff. They’re a great tool. At some point, they’re going to hit a wall. Hallucinations are never going to be a thing of the past. We can’t fully trust them on really hardcore stuff. We’ll gain productivity enhancements. We’ll keep gaining those productivity enhancements, but at some point in time, we really won’t reach ASI. We really will be stuck with what we have. It’s a little bit like we get the next big thing, the next big spreadsheet, the next big internet, but it’s not going to change the whole world beyond just productivity, enhancements, and amazing tools that we have available to us that makes us much better. In that scenario, the winners will continue being fast adopters, probably small and medium businesses, because there won’t be a push for maximum speed either, so they’ll catch up at some point. Then AI native companies will be better companies than other companies, but not necessarily overall disruptors across the board. It’s not necessarily a new species of companies. It’s just companies that are a little bit better at doing stuff, which we also saw during the internet phenomenon and that first big push forward and then bubble, where we had some companies that were fundamentally different on how they operated. It took us another couple of decades for companies to be more and more digitally native along the way. Basically interesting, but it’s boring. It’s like, cool, we got tools, we got promised the world. What are the implications? All these companies that are worth trillions and trillions of dollars are not worth trillions and trillions of dollars. Because at some point we’ll face competition, commoditization. It will just be tools and platforms. They will not unlock that next stage. Therefore, this will have been a bubble, and likely it would be a hard landing to that bubble. That’s the implication.Bertrand Schmitt I would just say that, yes, I agree with you, but I would just say overall, even if it stopped today in terms of quality improvement, speed or stuff, or it barely improves, I still think we will have 10 years of madness just to leverage everything that we have today.Nuno Gonçalves Pedro Understood, Bertrand. This is a scenario. I understand, but maybe we’re going to hit a wall, and we’re going to hit that wall next year, or we’re going to hit that wall in 2 years or whatever.Bertrand Schmitt Possibly. I’m just saying we still have 10 years of goodness from that big push in AI we experienced the past few years.Nuno Gonçalves Pedro Absolutely. Agreed, but it’s boring.Bertrand Schmitt It’s boring. It’s a plateau.Nuno Gonçalves Pedro It’s a plateau. The second one is more of something that we have AI, but humans in the loop are going to be critical along the way. The judgment work that we described earlier in the episode is going to be critical to everything that happens. It’s, I would call it the augmentation scenario. The AI will be a great augmentation tool for humans, but humans will never really quite stop being in the loop. Some of the gains that AI has are broadly distributed in society and in the startup, big corporation and small medium business world. Everyone will have access to them. We humans, are still very important. We have all these augmentation things, and AI is mostly benign. There will be a couple of issues, but honestly, at the end of the day, we’re just better. We’re better, faster, more data-driven, more factually current. We’re doing stuff faster, but humans

In this episode of Tank Talks, host Matt Cohen sits down with Julia Maltby, a Principal at Fengate Asset Management who has lived the full venture capital lifecycle. Julia started as the first employee at Plum Alley Investments, ran partnerships at WeWork during its hyper-growth phase, spent over five years rising from Associate to Principal at Flybridge Capital, and even ran her own seed fund, Deco Ventures, before moving to the LP side at Fengate in 2025. Today, she invests in early-stage VC funds and direct opportunities across North America.Julia offers a brutally honest perspective on what she wishes every emerging manager knew about fundraising. She explains why LPs value process over outputs, why founder references from failed companies are more valuable than those from winners, and how GPs can stop leaving first meetings as a “polite maybe” and start qualifying LPs like a sales funnel. She also pulls back the curtain on LP-to-LP communication (which she says is 10X stronger than GP gossip), shares tactical advice on building data rooms that actually get read, and reveals how Fengate pre-approves co-investments to move at startup speed.Whether you're a GP in the middle of a raise, an LP sorting through an endless stack of emerging manager pitches, or just curious what venture looks like from every seat at the table, this episode is for you.From Liberal Arts to Venture Capital (02:07)* Julia's unconventional path from studying architecture and social inequality to becoming one of venture's most respected emerging investors.* How a cold LinkedIn message landed her first job in venture capital.* Why having no finance background became an advantage instead of a limitation.Learning Venture from the Ground Up (04:13)* Building crowdfunding platforms and SPVs before venture became mainstream.* Why early-stage investing means wearing product, operations, and fundraising hats.* Lessons learned from saying “yes” before knowing exactly how to do the work.Inside WeWork's Hypergrowth Machine (05:11)* Joining WeWork during its explosive expansion and learning from one of startup history's fastest growth stories.* What Adam Neumann got exceptionally right about building mission-driven teams.* The leadership lessons worth keeping and the scaling mistakes worth avoiding.The Flybridge Investing Framework (10:39)* Why durable venture investing starts with disciplined systems rather than intuition alone.* The importance of pricing integrity and staying focused on core business strengths.* How evaluating customer urgency shaped Julia's investment philosophy.Becoming a Solo GP (14:14)* Launching Deco Ventures with Flybridge's support.* The challenges of making investment decisions without partners.* Why every solo GP needs trusted people whose job is to challenge—not validate—their thinking.What Makes a Venture Manager Truly Different? (18:00)* Why there isn't just one formula for becoming a successful GP.* Understanding your competitive advantage instead of copying other managers.* How LPs think about portfolio fit beyond fund performance.The Data Room Mistakes GPs Keep Making (23:18)* Why withholding information often slows fundraising rather than helping it.* Julia's advice: send everything instead of drip-feeding documents.* How GPs should reference-check LPs before sharing sensitive materials.Looking Beyond Markups and Valuations (27:16)* Why portfolio KPIs matter more than inflated funding rounds.* How disciplined reserve strategies separate thoughtful investors from reactive ones.* Using follow-on decisions as a measure of investment discipline.The Power of Great References (31:45)* Why founders from failed companies often provide the strongest references.* How LP references reveal governance, transparency, and communication quality.* Why perfect references can actually make LPs more skeptical.Stop Leaving Meetings as a “Maybe” (34:35)* The questions every GP should ask before ending a fundraising meeting.* Understanding the difference between genuine interest and structural misalignment.* How qualifying LPs like a sales pipeline saves months of wasted fundraising.Building Better Co-Investment Relationships (37:06)* How proactive communication makes co-investments move faster.* Why LPs build internal pipelines long before deals officially launch.* The importance of giving institutional investors time to prepare.The Future of Early-Stage Venture (39:27)* Why Julia remains optimistic despite today's challenging fundraising environment.* The growing divide between mega-funds and smaller venture firms.* Why smaller funds continue delivering meaningful returns that often go unnoticed.Using AI to Build Better LP Portfolios (41:17)* How Fengate uses AI to understand portfolio exposure across hundreds of startups.* Moving beyond broad fund branding into detailed market analysis.* Why better portfolio intelligence leads to better future investment decisions.About Julia MaltbyJulia Maltby is a Principal at Fengate Asset Management, where she invests in early-stage VC funds and direct opportunities across North America. She has lived the full VC lifecycle: she was the first employee at Plum Alley Investments, ran partnerships at WeWork, spent 5+ years rising from Associate to Principal at Flybridge Capital, and founded her own seed fund, Deco Ventures. She holds an MBA from Harvard Business School and writes about her LP experiences on her Substack, Julia's Field Notes.Connect with Julia Maltby on LinkedIn: linkedin.com/in/juliamaltbyLearn more about Fengate Asset Management: https://fengate.com/Read Julia's Field Notes:Connect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

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THE RUNDOWN | WEEK OF 7/26Daniel and James are back with The Rundown. This week they cover everything from women taking over the app economy to TikTok building an Amazon Prime rival, Jersey Mike's $8 billion IPO, and Larry Page saying he'd go bankrupt before losing the AI race. They kick off with a compelling case for why vibe coding and built-in influencer distribution mean women are about to dominate the next wave of app builders, then dig into Meta's new Sellers app (not a Shopify killer) and TikTok's inevitable evolution from ads to commerce to payments to logistics.The back half covers why open weight AI models are an openness fight worth having, how Chinese labs are distilling Western AI models the same way TikTok copied Snap, and why a sandwich chain IPO being 10X oversubscribed tells you everything about where real value is being created. They close with Primary Posts — Sean Frank's "can't win if you don't try" ethos (plus a tease of ADSN's new podcast "30 Minutes"), and Ashwin's moat framework that should be required reading: brand, distribution, speed, and cleverness beat product every time.Thank you to our sponsors:AdQuick – Making OOH advertising as easy to plan, buy, and measure as digital. adquick.comThrad.ai — Building the advertising infrastructure for AI. thrad.aibeehiiv — The all-in-one platform for newsletters, websites, and every tool you need to grow and earn. beehiiv.comThe Farm — Fractional commercial legal with an in-house approach to outside counsel. thefarmllp.comSTAY CONNECTEDJames on Twitter & LinkedIn – /jamesborowDaniel on LinkedIn, Instagram, TikTok – /danieldrugerSubscribe & leave a ⭐⭐⭐⭐⭐ review on Spotify & Apple Podcasts.

I'm a fan of mind games, because I feel most all of what we experience is just that, a mind game. But I've always helped myself by considering different angles to stories. And today I bring you one I've benefited myself with. You've probably heard of the concept of “10X,” as in 10 Xing your goal. But I want to be clear, today I look at this as a practice or exercise and am not presenting it as what you or I should do. This then is a conversation I'm revisiting with renowned organizational psychologist and author, Dr Benjamin Hardy. I perceive Ben as having great insight into human transformation and a knack for creating clear, contrasting, and innovative messages around concepts we've long grappled with. Along with celebrated business coach, Dan Sullivan they wrote a book titled, 10x is easier than 2x: How World-Class Entrepreneurs Achieve More By Doing Less. I've altered some of my views on this concept since this talk, but I feel this is a wonderful exercise for anyone to do in order to divest themselves of a lot of clutter we let get in our way and get to what is important for a destination or achievement we desire. You could almost compare it to asking what you would take from your home if you only had 30 minutes till you must evacuate for a flood or fire. This concept can get us super focused and realize what is truly necessary. Sign up for your $1/month trial period at shopify.com/kevin Go to shipstation.com and use code KEVIN to start your free trial. Learn more about your ad choices. Visit megaphone.fm/adchoices

This week on Autonomy Signals presented by KPMG, Grayson Brulte and Rob Grant discuss Tesla expanding robotaxi to Tampa and Orlando, Zoox recalling its entire 105 vehicle fleet after driving into an active fire scene in Las Vegas, and China embedding autonomous truck manufacturing in Kazakhstan.Tesla continues their robotaxi expansion launching service in Tampa and Orlando, establishing their third Florida market alongside Miami. All three Florida service areas are next to the airport, signaling Tesla's ambition to secure airport service, while Tampa marks the first market where Tesla launched commercially ahead of Waymo.With Tesla moving from ground validation vehicles to driverless operations in roughly three weeks compared to Waymo's 30 week timeline in Charlotte, Tesla's deployment velocity is running approximately 10X faster than its main competitor.A company that has struggled to scale, Zoox voluntarily recalled its entire US fleet of 105 autonomous vehicles after an unoccupied robotaxi drove into a smoke-obscured fire scene in Las Vegas, requiring a remote teleoperator to reverse the vehicle after its sensor suite was blinded.The recall arrives as NHTSA escalates enforcement around autonomous vehicles interfering with first responders and as Zoox's FMVSS exemption petition for 2,500 driverless vehicles remains pending. With nine vehicles operating in Miami and no Amazon branding anywhere on the vehicles, rider lounges, or website, questions are mounting about Zoox's commercial direction and Amazon's willingness to stand behind its bet.Closing out the show, Grayson and Rob discuss China and Kazakhstan formalizing an agreement to establish domestic assembly lines for autonomous SITRAK heavy trucks, turning Kazakhstan into a regional production and deployment hub for driverless freight across Eurasian trade corridors and accelerating China's Autonomous Belt and Road Initiative.Episode Chapters0:00 KPMG Sponsor Introduction01:34 Signal 1: Tesla Expands Robotaxi to Tampa and Orlando27:15 Signal 2: Zoox Recalls Its Entire 105 Vehicle Fleet57:15 Signal 3: China Embeds Autonomous Truck Manufacturing in KazakhstanFollow The Road to Autonomy Indices--------About The Road to AutonomyThe Road to Autonomy is the leading applied intelligence platform covering the convergence of automation, autonomy, and the Autonomy Economy.™.Through our podcasts, newsletter, and proprietary applied intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Sign up for This Week in The Autonomy Economy newsletterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Eran Galperin is a Brazilian jiu-jitsu black belt who had already had one VC-backed marketplace failure when he started Gymdesk. Originally called Martial Arts on Rails, it launched in 2016 as a naive version of what he thought a gym needed. He was training five or six times a week and every gym owner he knew hated the software they used. For four years he couldn't acquire customers, so he took a job as CTO of an e-commerce company and built the product on nights and weekends. Growth finally came through organic SEO, which still drives over half of new leads. He hit $3M in ARR by the end of 2023 with 16 employees, no salespeople, and over 40% of free trials converting without a demo. In May 2024 he sold a majority stake to Five Elms Capital for $32.5M in cash for his share. What they paid the premium for wasn't size — it was churn under 1% a month, three straight years of more than doubling, profit margins over 50%, and a payments business compounding underneath. He stayed on eighteen months and now lives in Tokyo, building a custom house and an AI vision product for real estate. Key Takeaways Churn Ceiling — Churn is the cap on growth; under 1% monthly is what buyers pay premiums for. Slow Bake — Four years of nights and weekends let the product mature in ways funded companies never can. Payments Compound — Profit share from payment providers grows as volume grows, and buyers pay extra for those rails. Buyers Differ Wildly — One expert said 5X was his cap; Five Elms paid 10X because they buy outcome, not value. Get Representation — A $60K legal bill and a good M&A broker closed the information asymmetry with private equity. Quote from Eran Galperin, Founder of Gymdesk "What we did have was very low churn, and that's one of the factors that helped us get the premium when we sold the company. Everybody building SaaS eventually realizes that churn is the cap your company has on growth. Eventually churn, which is a relative number, grows to the point where it meets the absolute numbers of your growth. "Because we had very low churn, less than one percent month over month, that definitely helped us start the conversation from a very good position. The other element was that growth was very consistent year over year. I think we more than doubled three years straight. "That in combination with the low churn and high profit margins was the last big item. We had a lean team, and we were over fifty percent profit margins when we sold. This was a firm that had multiple other portfolio companies similar to us, so they had a pretty good idea what a successful outcome would look like for them, and we filled all those criteria." Links Eran Galperin on LinkedIn Gymdesk on LinkedIn Gymdesk website Tinyseed.com website Five Elms Capital Podcast Sponsor – Full Scale This podcast is sponsored by Full Scale, one of the fastest-growing software development companies in any region. Full Scale vets, employs, and supports over 300 professional developers, designers, and testers in the Philippines who can augment and extend your core dev team. Learn more at fullscale.io. The Practical Founders Podcast Tune into the Practical Founders Podcast for weekly in-depth interviews with founders who have built valuable software companies without big funding. Subscribe to the Practical Founders Podcast using your favorite podcast app or view on our YouTube channel. Get the weekly Practical Founders newsletter and podcast updates at practicalfounders.com. Practical Founders CEO Peer Groups Be part of a committed and confidential group of practical founders creating valuable software companies without big VC funding.  A Practical Founders Peer Group is a committed and confidential group of founders/CEOs who want to help you succeed on your terms. Each Practical Founders Peer Group is personally curated and moderated by Greg Head.

"If you use low-cost capital, you don't need tax credits to make the economics work. That's what excites me the most about how to scale this thing up. [...] How do I help every other community bank get in the game of building their green lending muscle and thinking about resilience? And if we can do that, then we don't need all of the tax credits and all the incentives that everybody has thought we needed in this industry." — Chris Castro, founding director and chief sustainability officer, Climate First BankFederal support for climate and clean energy programs is being dismantled, and the question of who will finance resilience has turned urgent for communities throughout the Ten Across geography. The money that flowed from Washington is thinning; the need is not.Few people have approached that question from as many sides as Chris Castro. He built Orlando's sustainability and resilience office under Mayor Buddy Dyer, then moved to Washington as a presidential appointee, helping stand up the Department of Energy arm created to move historic infrastructure and climate funding to states, cities, and school districts — until the change in administration halted the work.Now Castro is pursuing the same goal from inside the private sector. As founding director and chief sustainability officer of Climate First Bank, he is testing whether a community bank can finance resilience at a scale federal programs no longer will.In this episode, Ten Across founder Duke Reiter and Chris Castro trace that arc from city hall to federal government to mission-driven banking, and land on a question 10X is now taking up with its own networks: what resilience officers need to know about finance.

Everybody thinks 10X takes 10 times the work. It doesn't. 2X is the grind. You just do more reps, more hours, more volume until it works. But you can't out-hour your way to 10X. There aren't enough hours in the week. 10X isn't a math problem you solve with effort. It's a different game entirely, and almost nobody plays it right.In this episode I break down why 10X is actually easier than 2X, and why the reps and leaders who get it stop adding and start cutting.What we cover:    •    Why 2X is linear and 10X is exponential (and why that changes everything)    •    The 80/20 rule most people quote but nobody actually applies    •    Why 10X is a subtraction game disguised as a growth game    •    The 3 things every 10X move comes down to: identity, time, and leadership    •    How to spot the 20% of activities driving your results, and kill the rest    •    The team application: stop spreading your time evenly across reps    •    The individual application: what to delegate and what to guard with your lifeIf you run a sales team or you're trying to break your own ceiling, this one will change how you spend every hour of your week.Drop a comment with the one thing you're cutting this week.New episodes of the Rainmaker Podcast every week. Subscribe so you don't miss the next one.Connect with me:Instagram: @nicknsales (https://www.instagram.com/nicknsales/)Youtube: @nicknsales (https://www.youtube.com/@nicknsales/)#SalesLeadership #Rainmaker #DoHardShit

(Original Run Date: 7/02/2026) Dr. Tom Curran discusses the journey that led him to moments of conviction and commitment to walk the Camino de Santiago. Tom addresses the pressures of raising a family and testifies to discerning God's 10X vision for his life, pursuing a gracious disruption and overcoming obstacles.Referenced Book/ Podcast10x Is Easier than 2x: How World-Class Entrepreneurs Achieve More by Doing Less by Dan Sullivan and Benjamin Hardy

There are only 2 numbers that define the speed of your wealth. Dive into the episode to get the goods.  You'll discover why not all dollars are created equal. And the secret to financial freedom 10X faster than anyone else. Inside you'll find: The two numbers that define the speed of your wealth and why your income isn't one of them Why some dollars are worth nearly 200X more than others--and which ones are more important How to collapse your financial freedom timeline from 30 years to as little as 3 Why your portfolio, not your business revenue, is the real measure of your wealth And the danger of celebrating cash flow (what you're missing)

Terry Briscoe is five years into his voiceover career and starting to see real momentum. His question for Marc: what do you do next? How do you take that leap from where you are to the next level? In this Summer Series episode, Marc gets honest about his own leveling-up process - including the year he booked zero agent jobs, the hot seat session that left him beat red in front of 121 people, and the mindset shift that led to four national bookings in the first quarter of 2026. Inside this episode: Why leveling up never stops - and why the moment you feel like you've arrived is actually the moment you start going backwards The right kind of coaching for where you are right now - and why you need someone who will tell you when you suck Why leveling up your network is just as important as leveling up your skills - and what that actually looks like in practice The rooms voice actors avoid because they don't feel ready - and why those are exactly the rooms they need to be in Marc's hot seat session with Hugh Klitzke - 121 people, beat red, sweating, and convinced he was going to make a fool of himself in front of the whole industry Andy Roth's four-word response that changed how Marc thinks about auditioning Zero agent bookings in all of 2025, four nationals in Q1 2026 - what changed Why finding your why is the foundation of every level-up decision you'll ever make Whether you're five years in like Terry or fifteen years in like Marc, this episode is a reminder that the next gear is always there. You just have to be willing to feel uncomfortable to find it.

Welcome back to another episode of the unSeminary podcast. Today we're joined by Alvin Sanders, President and CEO of World Impact, an organization committed to equipping leaders to build healthy churches in every community experiencing poverty. After decades in pastoral ministry and leadership development, Alvin has become passionate about helping ministry leaders avoid one of the greatest threats to long-term effectiveness: burnout. Are you serving faithfully but feeling increasingly exhausted? Wondering how to sustain ministry for the long haul without sacrificing your health, family, or relationship with God? In this conversation, Alvin shares the personal lessons that transformed his own leadership after experiencing burnout. Tune in as he offers a practical framework for building a ministry that lasts. When work becomes worship. // Ministry itself can quietly become an idol. He didn’t intentionally choose work over God, but somewhere along the way, serving Christ replaced walking with Christ. Spiritual disciplines gave way to ministry responsibilities. Prayer, Sabbath, retreat, fasting, and personal communion with God slowly disappeared beneath endless demands. The result was a ministry that looked productive on the outside while becoming spiritually unsustainable on the inside. Return to the basics of spiritual formation. // Rather than searching for complicated solutions, Alvin returned to the foundational practices Christians have embraced for centuries. Prayer, engaging Scripture, fasting, Sabbath, retreat, generosity, commitment to the Church body, and intentional spiritual rhythms are not optional extras for ministry leaders. They are essential practices that sustain healthy leadership. Just as championship football teams return to blocking and tackling when they lose, pastors must continually return to the spiritual basics that anchor their lives. Pay attention to the warning signs. // In hindsight, Alvin sees numerous warning lights he ignored. His daughter once looked at him during dinner and innocently asked, “Daddy, why are you here?” because she had grown so accustomed to his absence. He gained significant weight, neglected his physical health, ignored emotional exhaustion, and consistently placed ministry ahead of family. None of these issues appeared overnight, but together they revealed a leader drifting toward collapse. Burnout is often less about one dramatic event and more about a thousand small compromises over time. Healthy leaders steward their whole person. // Pastors often underestimate the importance of caring for their physical and emotional health. Exercise is no longer something he squeezes into his schedule. Now, it is part of his workday because caring for his body directly affects his ministry. Likewise, emotional health is not separate from spiritual maturity. Leaders who fail to process stress, trauma, criticism, and disappointment eventually carry those burdens into every aspect of ministry. Healthy ministry begins with healthy people. A three-legged stool for sustainable ministry. // At World Impact, staff health is built around three interconnected priorities: spiritual formation, engaging stressors, and leadership development. Spiritual formation keeps leaders rooted in Christ. Engaging stressors helps them process trauma, maintain healthy boundaries, and avoid adopting a “savior mentality.” Skill development—including emotional intelligence, leadership growth, and contextual ministry effectiveness—equips leaders to serve wisely. Remove any one of these three “legs,” and long-term ministry becomes unstable. If you’re approaching burnout, don’t ignore it. // Alvin closes with practical encouragement for leaders who recognize themselves in his story. First, honestly acknowledge where you are instead of pretending everything is fine. Second, intentionally rest by embracing Sabbath, vacations, and healthy rhythms. Finally, if the burden runs deeper, don’t hesitate to seek help from a trusted counselor. Burnout isn’t a badge of honor—it is a warning that something needs attention. To learn more about Alvin or World Impact, visit worldimpact.org or connect with Alvin on LinkedIn. You can find his expanded book Redemptive Poverty Work on Amazon and his recent webinar on redemptive poverty work here. Thank You for Tuning In! There are a lot of podcasts you could be tuning into today, but you chose unSeminary, and I'm grateful for that. If you enjoyed today's show, please share it by using the social media buttons you see at the left hand side of this page. Also, kindly consider taking the 60-seconds it takes to leave an honest review and rating for the podcast on iTunes, they're extremely helpful when it comes to the ranking of the show and you can bet that I read every single one of them personally! Thank You to This Episode’s Sponsor: TouchPoint As your church reaches more people, one of the biggest challenges is making sure no one slips through the cracks along the way.TouchPoint Church Management Software is an all-in-one ecosystem built for churches that want to elevate discipleship by providing clear data, strong engagement tools, and dependable workflows that scale as you grow. TouchPoint is trusted by some of the fastest-growing and largest churches in the country because it helps teams stay aligned, understand who they're reaching, and make confident ministry decisions week after week. If you've been wondering whether your current system can carry your next season of growth, it may be time to explore what TouchPoint can do for you. You can evaluate TouchPoint during a free, no-pressure one-hour demo at TouchPointSoftware.com/demo. Episode Transcript Rich Birch — Hey, friends, welcome to the unSeminary podcast. So glad that you have decided to tune in. Listen, I know you’ve got a million things going on this week. In fact, if you’ve got a lot of things going on, you really need to set aside the next half an hour or so to lean in on this conversation because I really think this is going to be helpful for you. I know it’s the kind of thing that you and your team is wrestling with plus we have a return guest on the podcast which what that indicates to you is this a person I want you to hear from. Honored to have Reverend Dr. Alvin Sanders with us he is the president and CEO of World Impact which empowers leaders to build healthy churches in communities of diversity and poverty. When urban leaders so when urban leaders are empowered that to own and lead their own ministry, individuals, families, and neighborhoods ultimately flourish. Super excited to have him with us. Enjoyed his episode from maybe a couple years ago and honored to have you back today. Alvin, welcome to the show. So glad you’re here.Alvin Sanders — Glad to be here, Rich, and thanks for having me back.Rich Birch — Oh, it’s great to connect a little bit. Why don’t you bring us up to speed? Tell us a little bit about yourself, about World Impact. Give us some context if people don’t remember.Alvin Sanders — Yeah, so President, CEO World Impact – our vision is a healthy church in every community experiencing poverty. We’re working to solve the problem that 95% of the world’s pastors have no formal ministry training whatsoever.Alvin Sanders — So we we try to bring that training to them and make it affordable and accessible. I don’t know if you’re paying attention to what’s going on in the Christian educational world right now in terms of universities, but it’s ugly out there. Rich Birch — Yes.Alvin Sanders — So we are really trying to make sure that people are equipped and they can be able to do ministry in the in the context that they’re called to do it.Rich Birch — Yeah, that’s cool. Yeah. And I really, friends, I would commend you to to, if you don’t know World Impact, you should get to know them. You know, all them and the team do such good good work here. Well, we I want us I want you to take us back to 2007, so almost 20 years ago, which is insane to say that that’s 20 years ago, because it doesn’t feel like 20 years ago. It feels like just yesterday.Rich Birch — What was going on in your ministry and life? Yeah, kind of bring us into that part of the story. Let’s start there.Alvin Sanders — Yeah. So in 2007, I was wrapping up a 10-year project of planting and starting a church in inner city Cincinnati. And the denomination that I was a part of, the Evangelical Free Church of America, had offered me a position to leave the pastorate and to come work for them and run what’s called our “all people initiative”.Alvin Sanders — So me and my wife prayed, thought about it and said, yep, you know what? It’s time to make a good transition. And we made that transition. Yet for the first six months after that transition, we were, it was really a weird dynamic in the fact that when we would run into other people, people would say, hey, you guys look so much better than…, I mean, they didn’t say it this directly… Rich Birch — Right. Alvin Sanders — …but essentially what they were saying is you look so much better now that you’re out of the pastorate.Rich Birch — Right. What happened? You look great.Alvin Sanders — Yeah. What happened? You look good. Did you, did you lose weight? Did you, you know, glasses, whatever, you know?Rich Birch — Oh, no. You’re like, thank you, I think.Alvin Sanders — Yeah, and Rich, this wasn’t like a small amount of people. It was like every time we ran into people. So after about six months of this, we were like, okay, so what’s going on? And then we when we looked back in hindsight, felt like the last year or so of being in the pastorate, we had burned out.Alvin Sanders — And up until that point, I really didn’t know what burnout was. You kind of sort of hear about it, but you don’t really think about it. Why? Because you’re too busy, right? So you don’t sit back and think about ordering the chaos of ministry or anything of that nature.Alvin Sanders — And so we vowed that going forward, never again would this happen to us. So essentially I divide my ministry life in the two halves. Rich Birch — Right.Alvin Sanders — The first half is pre-2007. It’s kind of like how people look at the pandemic, right? Rich Birch — Yes.Alvin Sanders — So it’s like pre-2007 and then post 2007. And so we began to implement a series of things that helped us to be able to do ministry in a sustainable way and not experience burnout because you do not have to burn out doing ministry. That’s what I’ve learned since 2007.Rich Birch — Yeah, when you so when you think about that time, yeah, I love that. Like, hey, friends, thanks for telling me I look great. Or you look terrible a year ago. Okay, thanks.Rich Birch — When you look back on at that moment, you’re like, hey, I think we were burnt out. What were some of those those patterns, habits, evidence, signs that you look at and were like, oh, that is what was going on there? You know, what what was it that, you know, got you, yeah, that you kind of saw it yourself in that moment as you looked back?Alvin Sanders — In hindsight, what I looked at is we were basically on autopilot. When I first started that ministry project, I felt a call. I felt like I was walking with the Lord. I felt the Holy Spirit’s presence. You know it was like hindsight, it’s like the book of Acts stuff. You know it was kind of like God was right there. I was energized. I was excited. I truly wanted to make a difference in the lives and the hearts of the people who were there. I wanted to make a difference in the community. Alvin Sanders — But that last year or so, I look back at it. It was just 60, 70 hours a week, flip the switch, doing what I need to do. Rich Birch — Right.Alvin Sanders — Very jaded, cynical all the time, low energy, you know, vacation didn’t matter because I had to go back to work. You know, these are, you know, it was sort of like a call of duty, so to speak. It was this is my duty to do it.Rich Birch — Right, right, right. Interesting.Alvin Sanders — Right.Rich Birch — So now you made a few decisions to, you know, in the words you use was, we do a series of things. We changed the way that we work. What were those things that you changed? What were some of those things when you look back, now that you’re on the other side of that, what were some of those decisions you made to say, hey, we’re going to live in a different way? And what was what was that? You know, what is that? What were some of those?Alvin Sanders — Well, one of the big things that I realized, and it’s going to sound elementary when I say it, but I think as pastors and people in ministry, we forget about the basic things.Alvin Sanders — You know, I’m a big football fan, Ohio State Buckeyes to be exact, right? And so I remember, I can’t remember which one of those coaches, but I remember one of those coaches, I was listening to an interview one day and they said, hey, you know, when we lose, our first practice back is we go back to the basics.Alvin Sanders — Just basic stuff. That’s all we do. Blocking, tackling, you know, simple things. And one of the things we did was we went back to the basics of what it means to be a Christian. You know, over the years, they call that spiritual formation.Rich Birch — Yep.Alvin Sanders — So the basic things of are you praying? Are you fasting? Rich Birch — That’s good. Alvin Sanders — You know, you know, just if you type in spiritual formation in Google, you’re going to get a gazillion things that pop up. Right?Rich Birch — Right. Yes.Alvin Sanders — Well, we should be doing many of those things. Rich Birch — Right.Alvin Sanders — You know, retreating. You should not be accessible all the time. Sabbath. You know, it’s Hebrews 11 tells us that, you know, we’re among a great cloud of witnesses and that great cloud of witnesses are our people who, any and everybody who has ever been Christian ever in in in the entire history of the world. And Rich, hate to break it to you, but it’s not rocket science of how to be a good Christian.Rich Birch — Right.Alvin Sanders — We just ignore it. Rich Birch — Yes. Right. True. Alvin Sanders — Because there have been people before us who have worked out, hey, these are the rhythms. These are the things that you do…Rich Birch — Yes.Alvin Sanders — …you know, to be Christian. Rich Birch — Yes. Alvin Sanders — And what had suddenly happened is I made work my worship.Rich Birch — Oh, that’s painful.Alvin Sanders — The job became what I worship. Now, I didn’t intentionally set out to do that, but I had so much passion and I had so much zeal to want to make a difference in the world that suddenly the rhythms of what made you a Christian took a backseat to the work that I was doing.Rich Birch — Yeah, that’s good. I want to come back to that. I want to put a bookmark in you made work your worship. I think that’s a powerful idea. I want to come back to that. But just before we leave this idea, you know, I love that the football analogy. I had and a nephew that was a receiver, college ball receiver. And I remember talking to him, you know, once as well. Rich Birch — And I was like, like, tell me, what you know, kind of what is your practice look like? And how are you, how do you keep on top of that? And he said, he said, well, you know, I’ve got these three formations, these three patterns that we run. And he’s like, I just run those formations all day long. Alvin Sanders — Yes. Rich Birch — It’s like, you just, you know, it’s like the same, you know, four steps this way, three steps that way, you know, turn, you know, it’s like, I’m just do that for four years. We’re just going to run that, which stuck with me similarly that, you know, I think oftentimes, you we can complicate this and we can say, hey, that you know we can complicate staying connected to praying, fasting, Sabbath, reading scripture, these kinds of things. We can drop them. Rich Birch — Were there any other warning signs looking back now that you missed? Again, in hindsight, you look back and say, oh, there were some some things on the dashboard that were blinking and I didn’t notice that you were leading up to this breaking point.Rich Birch — Were there other things in your life besides these dropping off that you know, that you, again, in in hindsight noticed and maybe are looking for now?Alvin Sanders — There was zero what they call work/life balance. Rich Birch — Right.Alvin Sanders — Zero. Rich Birch — Right. Alvin Sanders — At that time, I was working on my PhD. I was pastoring a church full time. Rich Birch — Right. Alvin Sanders — And I was running the church’s Christian Community Development Corporation that was attached to it.Rich Birch — Yep.Alvin Sanders — And oh, by the way, I was also teaching classes at the local Bible college.Rich Birch — Right. Yes. Alvin Sanders — And had a wife and I had two kids.Rich Birch —Yep.Alvin Sanders — And so I remember during that period, a huge red flag that in hindsight, I’m ashamed to say, I did not pay attention to. One Friday night I was sitting down and I was eating dinner with my family and my youngest, Gabby, she looked up in her little toddler face and she said, Daddy, why are you here?Alvin Sanders — And I said, what do you mean? I’m I’m sitting here. I’m I’m eating dinner. You know I’m here. You know she’s and she and she was so confused that I was home.Rich Birch — Oh my goodness.Alvin Sanders — So that’s so that was a neglect of family. Right. Rich Birch — Yes. Alvin Sanders — That was a huge red flag. Rich Birch — Yep.Alvin Sanders — Gained a ton of weight because I was not taking care of myself physically. I think that’s something we underestimate in ministry. Rich Birch — I agree. Alvin Sanders — You know, to the point now that I even I count when I go work out, that’s part of my work day.Rich Birch — Right. Interesting.Alvin Sanders — Because I said I can’t be a successful minister if if I am not in shape. So I see that as a you know, I don’t have this sort of dichotomy of the physical and the spiritual, all that. No, I it’s all one. You know, it’s going to hurt me spiritually if I’m out of shape, if I’m not eating right, I’m not properly fit and all those types of things.Alvin Sanders — So I wasn’t taking care of myself physically. I clearly was not taking care of myself emotionally.Rich Birch — Yeah.Alvin Sanders — We are emotional beings. We are emotional. I have come to believe, and this is maybe a controversial point, but I don’t know. It’s my belief. I have come to believe, Rich, that we are emotional beings that rationalize our lives with facts.Rich Birch — Yeah, that’s true.Alvin Sanders — But there’s a lot of research out there that says we can think we’re rational all we want, but what we really do, we have never made a rational decision that does not agree with our emotions.Rich Birch — Right.Alvin Sanders — So we must take care of ourselves.Rich Birch — Yeah.Alvin Sanders — I was not taking care of myself emotionally.Rich Birch — Right.Alvin Sanders — Therefore, out of shape physically, neglecting family, which by the way, God says that’s the first ministry, you’re supposed to lead well. So if you’re not leading your family well, it really doesn’t matter what else you’re doing, you know. And just an emotional, just not emotionally regulated is the easiest way that I can say it. Didn’t know I wasn’t emotionally regulated, but I wasn’t.Rich Birch — Yeah. Yeah, that’s good. You know, the Carey Nieuwhof, friend of mine, ah you know, he says around this whole that topic, he says, you know, if you’re an alcoholic and you stumble into work one day drunk, you know, you get fired, or you or there’s a consequence, right? If you are a gambler and you’re addicted to gambling and you know you lose your house, that’s gonna show up and you know you’re gonna lose your house, there’s consequence to that. And you know one of those might be you lose your job. Rich Birch — But this area of like overworking, if we overwork even in good things, you’re rewarded for it. Like, you’re like, way to go. You’re a good soldier.Alvin Sanders — Yeah. Yeah.Rich Birch — You know, there’s, it’s almost like a badge of honor in ministry to be exhausted for Jesus. When you said, man, I made work my worship, that stung because I was like, ooh, I think I’ve done that before.Alvin Sanders — Yeah. I mean…Rich Birch — So take us inside, you know, ah other leaders’, you know, heads. How does that mindset quietly quietly set us up for this crash? How do we, you know, kind of unpack that even more for us?Alvin Sanders — Yeah, I mean, I say all the time I’m a recovering work workaholic.Rich Birch — Right.Alvin Sanders — You know, that’s the way I put it.Alvin Sanders — And then a big thing that really set me up for this that I didn’t realize is that I come from a family of workaholics. You know, my father, who is my hero, other people have heroes outside of the household. My father is my hero. He’s 87 now, that’s my guy.Rich Birch — It’s so good. Yeah.Alvin Sanders — He, I grew up in a household, my father grew up in Jim Crow, Alabama. And he overcame so much. Rich Birch — Right. Incredible.Alvin Sanders — He went to when he joined the military, military was was sort of like a saving grace. He was in the military for 30 years. Rich Birch — Wow. Alvin Sanders — And he went to night school and he got two degrees in night school while working during the day.Rich Birch — Wow.Alvin Sanders — You know, my mom was an old farm was a was a farm girl, you know. Protestant work ethic. You know, you want she would say, son, you want to kill time, work it to death. You know, idle hands is the devil’s workshop. You know I grew up on all these idioms.Rich Birch — Yes, yes, yes.Alvin Sanders — And, and so that was in my DNA. And just by the fact that we live in United States of America, where busyness is a badge of honor, I believe you said, and we all are under the Protestant work ethic, you know, you know, who, what, what, what bum works only 40 hours.Alvin Sanders — I mean, you want to get ahead.Rich Birch — It’s true.Alvin Sanders — The minimum is 50.Rich Birch — Yeah. Right. Right.Alvin Sanders — You know, when I was pastoring, how many people in my congregation, you know, in terms of the white collar crew, you know, I had ah I had all spectrums in the church that I was in, I pastored. It was no big deal for them to just be gone all the time.Rich Birch — Right.Alvin Sanders — If you get offered a promotion, who would tell you don’t accept it for the sake of your family?Rich Birch — Right.Alvin Sanders — Of course I’m going to accept it. It’s more money… Rich Birch — Right. Alvin Sanders — …because more money puts us in a better position…or doesn’t it? Rich Birch — Right. Alvin Sanders — It might, or might not. So counterintuitive thinking is something that we have to do. I mean, I’m in a place now where um God has blessed me that I have a lot of opportunities and I limit my opportunities. Because I say if if I’m gone all the time again, guess what I’m on the road to? Rich Birch — Right.Alvin Sanders — You know, and we may have a little bit more money, but then, you know, I’m in the I’m in the empty nesthood stage of season of life and I’d be missing valuable time with my wife. So it’s a reordering of the priorities, you know? Rich Birch — Yeah, that’s good.Alvin Sanders — Yeah.Rich Birch — Yeah, it’s so good. And this is one of those areas where I see hope in you know next generation. You know i see hope in Gen Z leaders. There there is, like I think, a healthy pushback on on some of this area, which is I think is positive. I know sometimes, you know, I’m Gen X, classic Gen X, born 1974, the lowest birth rate year of the 20th century.Alvin Sanders — Yes. Yeah.Rich Birch — And, you know, and I think sometimes, you know, my peers and particularly the folks older than me, that generation, they they really did, they handed down this idea. And you know, there’s good change happening, I think, on this front. Rich Birch — But so how do you think about this now? So, you know, as you’re thinking about the future, or maybe you’re coaching your team, people you’re interacting with, you know, how do you kind of articulate this, maybe with a framework or thinking around this, so that we don’t just pass on more burnt out organizations?Rich Birch — How how are you helping, you know, lead an organization? Because I think there’s the reason why asked that question is I think it can be easy for us folks that are in charge. I run my own organization. I like often joke, like my boss is a bit of a taskmaster because it’s me, you know. But you know, when, but when we’re the people in charge, we get to set the pace. We get to lead in a way. How are you leading different now? How are you helping your people understand this different now? What are, how are you, what’s that look like for you?Alvin Sanders — Yes. So we, we have baked in in the cake at World Impact. Um, three things, three areas. We talked, we touched on it before spiritual formation, you know, that’s a big deal here at World Impact.Alvin Sanders — You know, we, we really want to make sure that people understand your work is not your worship. You know, part of our sustainable strategy is, you know, we care more about who you are than what you do. And we mean that. Alvin Sanders — And it’s funny because when you first say that to some people, they go, oh well, you don’t want me to work. It’s like, no we didn’t say that. What we said is we understand you are a human being. And we care about you and we’re going to invest deeply in you as you invest deeply into our our our vision. And so um making sure that work’s not to worship, make sure that people understand our identity is in Christ. Alvin Sanders — Understanding also the the spiritual disciplines. We incorporate eight particular spiritual disciplines. I probably should know them the my head, but I don’t. But let me see, I mean, I can roll off the top of my head.Alvin Sanders — Let’s see, you got church membership. You know, we, we, and It’s not cool, you know, to be a church member and people love to kill the church and criticize the church and this that the other. But I leave that to the accuser of the brethren to use the KJV version and use that to the devil.Rich Birch — Nice.Alvin Sanders — Jesus came to establish the church. Period. End of discussion. So you ought to be part of one. So, you know, we encourage our folks to be part of churches, to fast, to pray.Alvin Sanders — We have three times a year we’ve shut everything down and we have prayer days. You know, we we encourage people to be generous with their money. We encourage people to empower others. You know, so there’s we we we encourage we have a personal retreat days that we offer people for for, it’s a work day, but it’s a different kind of structured work day. We say we’ll give you one day a month where you go off and you examine yourself. You do a spiritual examen off basically, you know, off the old St. Ignatius type of spiritual examen. And then you also look at your professional development. You go off and we do that and we’ll pay you for that.Rich Birch — Right. That’s great.Alvin Sanders — You know, so there are a number of things that we do in terms of developing a rhythm. We call, you know, having a rhythm. Rich Birch — That’s good.Alvin Sanders — You know, so a lot of spiritual formation. And then the other s is we look at the, you could think of these S’s as three S’s or a three-legged stool that serves the foundation and they’re all intertwined.Alvin Sanders — So besides spiritual formation, we also encourage people to engage stressors, right? We there’s there’s a way that you work in the world. There’s I got this from my friends at Praxis that there’s three ways that you can engage the world. You can engage the world um exploitatively, ethically or redemptively. Rich Birch — That’s good.Alvin Sanders — So we want people to we want our staff to engage the world, and engage their jobs in a redemptive manner, which is, you know, sacrificing our lives in the pattern of Jesus Christ. Right. That’s we follow the pattern of Jesus Christ, that that we are doing this in order to make the world a better place.Alvin Sanders — We also have a posture of that we’re not the saviors. Many people who are in ministry, we get in it for the right reasons, but then we think that we’re the king and not a subject of the kingdom. Right. And so we think that whatever ministry we’re in, whether it’s urban, suburban, whatever it may be, that we’re the saviors. It’s like, no no, no, no, that’s not the right posture. The right posture is that to be utilized by God, you know.Alvin Sanders — And then also anybody who’s been in ministry any any time in their life, any, you know, they are going to experience a lot of what’s called secondary trauma.Alvin Sanders — You know, like mine was very intense being in a very violent neighborhood and ministering there. But even in suburban context, there’s a lot of things. Now, Rich, don’t know if you ever experienced this, but, you know, people, the sheep bite. Rich Birch — True.Alvin Sanders — They talk about the pastor. Rich Birch — Yeah.Alvin Sanders — They do a lot of things. You know, you’re misinterpreted all the time. There’s a lot of hurtful things that happen to you… Rich Birch — Yeah, it’s true. Alvin Sanders — …from an emotional perspective. And if you if you try to blow them off or roll them under, you know, let them it’s water under the bridge. No, no, no. Those things need to be engaged. Alvin Sanders — So that’s keeping the right posture, making sure we keep the right pace in ministry. So we have one school of spiritual formation, one one one leg stool of engaging stressors. And then the third leg stool is is skill set development. Alvin Sanders — And we have to understand that developing the right skills will not save us from burnout. That’s a false dichotomy. People think, oh, well, if I just develop X, Y, and z well, then, you know, I won’t burn out. No. um There are three primary skill sets that I think people need to understand when it comes to any type of ministry or wherever they’re doing it. A big one is emotional intelligence.Alvin Sanders — It’s there’s a guy by name of Daniel Goldman who really popularized the concept. He wrote this book years and years ago called “Primal Leadership”. And that book makes the case that therere there are things that you can develop to emotionally regulate yourself.Alvin Sanders — There’s a book they they don’t call it emotional intelligence, but it’s another classic called “Leadership and Self-Deception”. It’s a classic book which teaches you how you can engage the workplace and and by emotionally regulating yourself, which I think is directly correlated to spiritual maturity.Rich Birch — True.Alvin Sanders — There’s also the skill set of leadership development. I believe that everybody is a leader, right? We don’t all have the same responsibilities as leaders, but there are things that we can do to develop ourselves to become better leaders in the situations that we find ourselves in. Alvin Sanders — And then there’s also a a skill set that we can develop where we engage our ministry context in a dignified manner. So that’s sort of like our three-legged stool at World Impact, you know, spiritual formation, engaging stressors and skill set. And you can’t, just like a stool, if you remove one of the legs…Rich Birch — Right.Alvin Sanders — …the stool will not work. So all three have to be intertwined and working together to give you that strong foundation. And if you do those things, I have found that, you know, you won’t touch burnout. Because… Rich Birch — Right. It’s so good. Alvin Sanders — Yeah. So I’m very, very focused on that. And we have interwoven all of those things within the DNA of World Impact.Rich Birch — Yeah, I love that.Alvin Sanders — Yeah, I love…Rich Birch — There’s there’s so much to unpack there, friends. And there’s, you know, in six minutes there, you gave us what a great picture of how we should be thinking and leading in our environments. I love, particularly, you know, you talked you touched on the emotional quotient, the eq issue.Rich Birch — And, um you know, I’ve had a a friend of mine say, you know, it’s it’s impossible to be spiritually mature if you’re emotionally immature. And I think…Alvin Sanders — One hundred percent.Rich Birch — I think that’s true. And there are, we all know these people who they they walk around like they’re spiritually mature, but they’re they’re, you know, emotionally immature and it comes off. It’s like they’re bleeding. It’s like the internet’s made for these people.Rich Birch — Like they are, those people get good followings, which is terrible. And you know, they get people around them who are just like, yes, sir. Yes, sir. It’s usually sirs. Unfortunately, it’s a lot of guys… Alvin Sanders — Yes. Rich Birch — …you know, we’ve got to fight that. And I love that you’re, you’re attempting to do that, you know, at, you know, in the organization you lead at World Impact, I think is, is incredible. And one of the things, you know, calling this out, I’m not directly engaged in urban ministry, but the thing I, I think is, like, I think every ministry has its stressors to it. Alvin Sanders — Yes.Rich Birch — I think every ministry has trauma related to it. And I think what you’ve outlined here, we can apply really in any context. Alvin Sanders — Yes.Rich Birch — But I do want to underline for our listeners, the fact that this has come out of a context where, again, I don’t want to stigmatize or create stereotypes around urban ministry, but there is a lot of trauma. There is a lot of stress in the ministry.Alvin Sanders — Yes. Rich Birch — You know When you’re dealing in communities that are experiencing homelessness and poverty and you know, you know at the conflict, at the you know the epicenter of racial conflict, and you know all ah all of that creates the context that makes it next to impossible to serve in. And the fact that you’re leading there, I just want to commend you you know on that. Alvin Sanders — Yes. Rich Birch — And so this is actually a part of, well, talk to me about the expanded second edition of “Redemptive Poverty Work” and how does that tie into what we’re talking about today? Because I want to move people even towards that call to action out of today’s conversation.Alvin Sanders — Yeah. So the the things that I’ve talked about that I just talked about that we instilled at World Impact, it definitely works in in any context, no doubt about it. What I tell my suburban colleagues is that whatever you experience in the suburbs, 10x it. And that’s in the urban context.Rich Birch — That makes sense. Okay, that’s good.Alvin Sanders — Just 10x it. It’s not that you don’t experience these things.Rich Birch — Right.Alvin Sanders — It’s that it’s much more intense…Rich Birch — Yeah.Alvin Sanders — …within the urban context.Rich Birch — Yeah.Alvin Sanders — So the expanded edition of “Redemptive Poverty Work” is is my attempt to really have our vision at World Impact come to fruition. Because at World Impact, if our vision is a healthy church in every community experiencing poverty, we cannot not talk about burnout.Alvin Sanders — Because the burnout rate within the urban context, because of the 10X that happens down there in in this in the urban community—but increasingly in suburban communities, to be quite honest with you. The same dynamics are happening in certain suburban communities—is that you have to be able to deal with it. And the big way that you deal with it is that you are intentionally engaging those things that can cause you to burn out.Rich Birch — That’s good. Alvin Sanders — Cause you cannot have a healthy church unless you have spiritually formed pastors who are engaging stressors and who are developing the skillset to make the community a better place.Alvin Sanders — So, It was basically by doing things in the field and spending a lot of time with our partners that I said, you know what, these folk need an expansion. Because when I wrote “Redemptive Poverty Work” a couple of years ago that we talked about, it was a little booklet and it was more academic.Rich Birch — Right.Alvin Sanders — But what I realized is, hey, these folk need to know how to implement this in a practical sense. So that’s what the expanded version is about. It’s not only teaching you the principle, it’s like, here here’s how you can apply this to your life. It’s sort of like a field handbook, so to speak.Rich Birch — Yeah, that’s fantastic. I think it’d be great for people to pick up, you know, a copy of this. And, you know, I think it could be really helpful for them as they’re thinking about, you know, ministry in any context. Even to help if there are folks that are listening in, you know, that serve in a suburban context and they’re trying to gain, you know, a bit more understanding of there are urban, but you know, brothers and sisters as they’re, you know, leading there. I think this could be a helpful resource.Rich Birch — For folks that picked up the first edition, you know, should they pick pick up a second? I know you’re not just trying to sell books, but like, help me understand, you know, obviously you’ve added a lot to it. For folks that are familiar with your work, what would your, you know, kind of what’s your recommendation that, you know, for them?Alvin Sanders — Yes, because the the first Redemptive Poverty Work booklet is really only one chapter in the expanded edition. It’s, as I said earlier, it’s really about how to apply and how not to burn out.Rich Birch — That’s great.Alvin Sanders — The whole thing is it assumes that there’s a point in your walk in doing your work of the ministry that you’re going you’re going to hit a wallRich Birch — Yep.Alvin Sanders — I don’t know anyone in ministry, let alone urban ministry, who hasn’t at some point hit a wall.Rich Birch — Right.Alvin Sanders — Right. To go back to sports analogy. Right. Talk about how if you’re a rookie and you’re coming into the and NBA, you only play like 40 or some college games. NBA has 82 games. No matter how skilled you are, no matter how much you play, about game 50, you’re going to hit a wall because you’ve never really played at that intense level for that long.Rich Birch — Yep.Alvin Sanders — Same thing with ministry.Alvin Sanders — At some point, you’re going to hit a wall. So within this urban context, you know when you hit that wall or hopefully before you hit the wall, read this book and begin to implement it in your life. It’s a failure for you just to buy the book and read it. Rich Birch — Yes.Alvin Sanders — A lot of books you just write, you buy it and you read it and you go, oh, I got the knowledge. I’m good. No, this book is like, read it and apply it. And I can tell you, it’ll work if you do so.Rich Birch — That’s so good. Where do we want to send people to pick up copies of the book? Where online? you know how how can they How can they get their hands on it?Alvin Sanders — Yeah, just go to Amazon. you can get it off of Amazon. Redemptive Poverty Work Expanded Edition. Rich Birch — That’s great.Alvin Sanders — You type that in or you can type in Alvin Sanders and I’m sure it’ll pop up. Rich Birch — That’s great. Alvin Sanders — Yeah, we’d we’d love for you to get it. You know, got paperback and hardback versions that are there. Rich Birch — Nice.Alvin Sanders — And then you can also go on YouTube. We just had it. We launched it last week. We had a webinar… Rich Birch — Great. Alvin Sanders — …around the topic of redemptive poverty work. It was about an hour long. So if you’re really interested in this topic, you can check that webinar out as well.Rich Birch — Nice. Yeah, we’ll link to that in the show notes to make it even easier for folks to to find that. We’d love for for people to track with you, Alvin. You’re doing such a great, great work. So let’s take us back to 2007.Rich Birch — Let’s say there’s a leader who’s listening into, well, I know that there’s a leader who’s listening in today. There’s at least one… Alvin Sanders — Yeah. Rich Birch — …in the couple thousand people that are listening in who are heading towards where you were in 2007. Not the new part, not the new job part, but the like, oh, I’m burnt out.Alvin Sanders — Yes.Rich Birch — This is not good. Maybe today they’ve been listening in and they realize, oh, wait a second. Maybe they had a similar thing with their kid. Hey, dad. Hey, mom. Why are you here? What’s one thing you’d want them to do this week? What’s one action step that they could take coming out of today’s conversation? Alvin Sanders — Well, I would say, I’m going to cheat. I’m going to give you more than one. But first is to admit where you’re at.Rich Birch — That’s good.Alvin Sanders — Because there are so I hate to say it. There are some faith traditions where it’s downright almost sinful to admit that you might be burned out. What? You don’t you don’t pray enough. You don’t do this. You don’t do that. Say, you know what? I’m tired. I’m still committed, but I’m tired. I’m jaded. I’m whatever. So the first thing is to embrace that. Alvin Sanders — The second thing is, you know, get some rest. You might it might just be an Elijah situation. You know, Elijah wanted to die and God said, no, man, just just go lay down.Rich Birch — Yeah, have a nap.Alvin Sanders — You know, you just had a really, really intense situation. Go lay down. Take some time. Go take a nap. Get some refreshment. Get some food. Look at your schedule and say, am I actually taking at least one day a week off? You know, are what’s your rhythm of rest, right? Rich Birch — Yeah, that’s good. Alvin Sanders — Are you resting as hard as you’re working?Rich Birch — That’s good.Alvin Sanders — Are you taking vacation? You know, or is vacation for the weak, right? Are you taking your paid holidays off? Do, actually really start to take the commandment of Sabbath seriously… Rich Birch — That’s good. Alvin Sanders — …and actually start to rest?Alvin Sanders — And then there are some of us, and, you know, and there’s nothing wrong with this either. We may be too far gone, and we may need to take some time to go see a counselor, and to unpack things. Rich Birch — That’s good. Alvin Sanders — And to unpack some emotional wounds and things of that nature. So I would say, you know, first, acknowledge where you’re at. Second, actually start to rest. And then third, if these things aren’t really working, don’t be prideful. Go see a mental health counselor.Rich Birch — Yeah, that’s good. Alvin, you’ve been a gift again to our audience. Thank you so much for for being here today, for helping us think through these issues and take some steps. If people want to track with you or with World Impact, where do we want to send them online?Alvin Sanders — Well, if you want to track with me, you know, as a good Gen Xer like you, I’m on LinkedIn. So you just place to find me.Rich Birch — Love it. Love it.Alvin Sanders — It’s LinkedIn. Find me on LinkedIn. I have an Instagram account. I ain’t been on that Instagram account in forever. So, you know, that’s just not me. But you can find me on LinkedIn.Rich Birch — Love it.Alvin Sanders — If you want to personally interact, talk, whatever. Great thing to do on LinkedIn. And then the other thing is go to worldimpact.org and you can learn a lot more about World Impact and what we do.Alvin Sanders — We’d love to serve you.Rich Birch — That’s great. Thanks so much for being here today. I really appreciate you taking some time to be with us.Alvin Sanders — All right. Thanks for having me. 

In this episode, Linda McKissack and Dana Gentry reflect on the halfway point of the year, sharing why now is the perfect time to revisit goals, eliminate distractions, and focus on the opportunities that create the biggest long-term impact. They discuss lessons from the viral "Yap Challenge" and why authentic, simple content often outperforms polished perfection, before diving into the importance of thinking 10X instead of settling for comfort, building passive income through the right partnerships, and challenging yourself to pursue goals that feel just beyond reach

Dr. Tom Curran discusses the journey that led him to moments of conviction and commitment to walk the Camino de Santiago. Tom addresses the pressures of raising a family and testifies to discerning God's 10X vision for his life, pursuing a gracious disruption and overcoming obstacles.Referenced Book/ Podcast10x Is Easier than 2x: How World-Class Entrepreneurs Achieve More by Doing Less by Dan Sullivan and Benjamin Hardy

In this episode of the Cannabis Accounting Podcast, Andrew Hunzicker, founder of DOPE CFO, recaps the biggest cannabis industry news from June 2026.Listen to learn about:✅ Trulieve becoming the first cannabis company to uplist onto the New York Stock Exchange, with Curaleaf right behind them✅ Why Q1 2026 capital raises are already up almost 10X year-over-year, with the biggest months still ahead✅ The June 29 DEA hearing on recreational rescheduling, and why 93% of the original public comments were in favor✅ The FDA's breakthrough therapy designation for a cannabis-derived drug for chronic back pain✅ SAFER Banking back on the table, plus merchant services and Visa entering the space✅ State-level momentum from Virginia, Pennsylvania, Connecticut, and the tribes leading on adult-use compactsArticles mentioned in this episode:https://norml.org/news/2026/05/28/federal-judge-rejects-lawsuit-from-prohibitionist-groups-seeking-to-halt-dispensing-of-hemp-derived-products-to-medicare-beneficiaries/ https://ganjapreneur.com/minnesota-gov-signs-law-streamlining-medical-and-adult-use-cannabis-supply-chains/ https://ganjapreneur.com/republican-attorneys-general-sue-to-block-trumps-cannabis-rescheduling-order/ https://themarijuanaherald.com/2026/06/oregon-marijuana-2026/ https://themarijuanaherald.com/2026/06/one-third-of-adults-say-marijuana-improves-their-sleep-finds-american-academy-of-sleep-medicine-survey/ https://www.marijuanamoment.net/hemp-companies-sue-dea-challenging-agencys-claim-that-synthetic-cannabis-compound-hhc-is-federally-banned/ https://www.marijuanamoment.net/legalizing-marijuana-in-pennsylvania-will-be-a-lot-easier-now-that-trump-federally-rescheduled-it-senator-says/ https://themarijuanaherald.com/2026/06/idaho-legislative-council-approves-ballot-language-for-amendment-giving-legislature-sole-power-to-legalize-marijuana/ https://www.forbes.com/sites/ajherrington/2026/06/05/trulieve-makes-history-with-first-nyse-cannabis-listing/ https://jessicam420.substack.com/p/federal-cannabis-policy-convergence https://www.marijuanamoment.net/gop-lawmakers-file-amendments-to-prevent-federal-recriminalization-of-hemp-thc-products-this-year/ https://www.marijuanamoment.net/virginia-governor-touts-productive-negotiations-on-bill-to-legalize-marijuana-sales-this-month/ https://ganjapreneur.com/fda-grants-breakthrough-therapy-designation-to-cannabis-derived-drug-for-chronic-back-pain/ https://www.linkedin.com/posts/ryan-handelman-85bb00187_raising-capital-in-the-lmm-post-1-your-share-7469731763548737536--bl3/ https://www.marijuanamoment.net/virginia-lawmakers-and-governor-have-a-deal-on-bill-to-legalize-marijuana-sales-this-month/ https://www.marijuanamoment.net/dea-begins-on-site-inspections-at-marijuana-businesses-that-applied-for-federal-protections-under-trumps-rescheduling-move/ https://apnews.com/article/supreme-court-guns-drugs-marijuana-texas-a60ce6df9e735c6bc7def285ca396784? https://www.dea.gov/marijuana-rescheduling-regulatory-actions? https://www.youtube.com/watch?v=matFWe-Uzu0 https://ganjapreneur.com/connecticut-enters-adult-use-cannabis-compact-with-mashantucket-pequot-tribe/? https://www.marijuanamoment.net/bipartisan-senators-file-marijuana-banking-bill-as-trumps-rescheduling-move-advances/? https://mjbizdaily.com/news/germany-could-be-auroras-backdoor-into-us-cannabis-market/616603/?

¿Tienes talento pero pocos resultados? El problema puede no ser tu capacidad, sino tu visibilidad, tu disciplina y la escala a la que estás actuando. En este episodio César Tánchez junto a Verónica Escobar de Tánchez conversamos de los principios del 10X de Grant Cardone de cómo pasar de sobrevivir a prosperar con un plan concreto para los próximos 12 meses. Espero te lo disfrutes. Para más recursos visita www.CesarTanchez.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

The Cardone Zone – Episode 313: Be Dangerous, Not Careful  What separates those who achieve extraordinary success from those who spend their lives playing it safe? In this unforgettable episode of The Cardone Zone, Grant Cardone is joined by Tyrese Gibson and Drew Brees for a powerful conversation on vision, preparation, relationships, and the courage required to pursue greatness. From Tyrese's rise in South Central Los Angeles to becoming a global entertainer and entrepreneur, to Drew Brees' Hall of Fame career and success in business, both guests reveal the mindset shifts that allowed them to break through limitations and perform at their highest levels. Tyrese shares his powerful philosophy of being "dangerous, not careful," while Drew breaks down the disciplined routines and preparation that made him one of the greatest quarterbacks in NFL history. Together, they deliver a blueprint for anyone looking to think bigger, act bolder, and create a life beyond the limits others place on them. Follow us on all social platforms @GrantCardone for daily content on wealth, business, entrepreneurship, sales, and the 10X mindset. Visit GrantCardone.com for upcoming events, exclusive training, and the latest products designed to help you scale your business, increase your income, and expand your future.  

Real estate SEO can bring your hottest motivated seller leads without more ad spend. In this episode, I sit down with Greg Berney, a Joe Homebuyer franchisee who built nearly a $2M real estate business while still protecting his family time and building a strong team culture. Greg breaks down how SEO, Google Business Profile updates, geo-tagged photos, and a disciplined review process helped him create a 10X+ return from organic marketing. We also get into the systems, mindset, and community support that helped him go from wearing every hat to building a business with more freedom and leverage. --------------------- Quotes: - “SEO is the tree you wish you planted years ago—but once it takes root, it compounds into the hottest leads in your market.” - “Freedom doesn't happen after you hit the next revenue goal. It happens when you build your business with intention from the start.” --------------------- Chapters: 0:00 Intro: $2M Revenue, Strong Family Life & Franchisee of the Year 2:51 The Unexpected Path Into Real Estate Investing 6:05 The Community Advantage That Accelerated Growth 9:01 Why Most Entrepreneurs Never Achieve Real Freedom 11:31 The Lead Generation System Behind Greg's Growth 15:16 The SEO Strategy Generating 10X Returns 17:17 How Google Reviews Turn Into More Deals 28:44 Scaling From $200K to $2M Without Burning Out 33:40 The Mindset Shift That Changes Everything --------------------- ➨Our Evergreen Marketing Podcast: https://plnk.to/Carrot ➨Our CEO, Trevor Mauch's Entrepreneur Freedom Formula Podcast: https://link.chtbl.com/EFF ➨ Facebook Group for Evergreen Marketing: https://www.facebook.com/groups/officialcarrotcommunity ➨Subscribe to our YT channel: https://www.youtube.com/@GetCarrot ➨Instagram: https://www.instagram.com/getcarrot/ ➨Take a demo of Carrot.com: https://carrot.ly/GQ8I --------------------- About Us: At Carrot, our vision is to inspire & empower real estate professionals to gain true freedom and make a greater impact with their businesses. We do that by providing industry-leading websites, marketing tools & training that help you generate more motivated seller leads than any other platform. ➨Our CEO, Trevor Mauch's Entrepreneur Freedom Formula Podcast: https://link.chtbl.com/EFF ➨ Facebook Group for Evergreen Marketing: https://www.facebook.com/groups/officialcarrotcommunity ➨Subscribe to our YT channel: https://www.youtube.com/@GetCarrot ➨Instagram: https://www.instagram.com/getcarrot/ ➨Take a demo of Carrot.com: https://carrot.ly/GQ8I Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The noise around productivity has gotten really loud lately, and I think a lot of it has to do with AI. There are apps, tools, and technology promising to 10X your output, and every corner of the internet is telling you how to squeeze more into your day. But what if that constant push to produce more, faster, is actually making you worse at the very thing it's claiming to help you with?In this episode, I'm breaking down why two of the most important types of productivity are being completely ignored right now and what it's costing you.What You'll Learn:Why the push to always be producing is working against you in ways you might not see yetThe type of productivity that happens when you step away from your to-do list completelyHow well-meaning advice is quietly converting your best thinking time into more output modeWhy what happens when you sleep matters more to your productivity than any app or tool ever couldTwo small things you can do this week to start getting it back________________________________

Same presentation. Same offer. Same you. $91,000 in sales. Two weeks later with a different audience? $4,000. In this solo episode of The Expert Edge, I break down the insight that separates six-figure coaches from seven-figure coaches: the price you charge is based less on what you do and more on who you serve. This isn't about changing your offer or tweaking your funnel. It's about understanding that a psychologist charging per hour makes $250-$350. The same person doing emotional release work with an upleveled avatar charges $50K per package. Same work. Different avatar. 100X different price. Most coaches obsess over their product. "How do I improve my course?" "What sales funnel works better?" They're focused on the wrong thing. The real leverage is identifying your upleveled avatar and understanding what problems they actually have. What you'll learn: → Price is determined by who you serve, not what you offer - Two people doing identical work charging 10X different prices based on avatar → Higher level avatars have higher level problems - Beginners ask "What's a lead magnet?" High-level clients ask "How do I own market leadership?" (premium problems = premium prices) → Beginners require volume, premium clients don't - One $50K client is easier to work with than ten $5K clients and takes action faster → Different avatars have different desires - Beginners want their first $10K. Upleveled avatars want market leadership and multi-six to seven-figure businesses → Why resourcefulness is the real differentiator - Beginners are less resourceful because they haven't developed the internal mechanisms to push through hard things Real insights from the episode: The $91K vs $4K story - same presentation, same offer, different audience, wildly different results Why beginners are "stuck" - they're still dealing with foundational problems (foundational problems don't pay premium prices) The woman in my Platinum group who left a "high-level" program because everyone was asking basic questions like "What's a lead magnet?" Why you shouldn't focus only on the lowest common denominator (beginners) The emotional release work example: $250-$350/hour as a psychologist vs $50K packages with upleveled avatar Higher level problems: team problems, profitability problems, scaling problems, system problems, market leadership The consistency difference: Beginners making $3K one month and $20K the next (chaos). Premium clients with stable recurring revenue Why premium clients are easier to work with - they're resourceful, know what they want, and take action How to identify what problems your upleveled avatar actually has Building specific products for different avatar levels (not everyone joins high-level programs) If you're running an established expert business doing $300K+ (or aiming there), Platinum is our highest-level mastermind. It's for people ready to build a highly profitable, 2-3 million dollar business with a small team, full lifestyle, and zero complexity. We focus on market leadership, scaling profitably, and building systems that work. Apply at colinboyd.co/platinum Short application. If it's a fit, we'll hop on a call. Join our next Speak to Convert Masterclass. In this live workshop, you'll discover how to build and launch a high converting presentation that gets you clients every time you present. https://colinboyd.co/speak Discover how to authentically connect with your audience & fill your programs with a Conversion Story - Version 2.0 (AI Edition) is now available. https://www.conversionstoryformula.com Hit the "Follow" button so you don't miss an episode! Love this podcast? Write a review and give it a 5-star rating!  For all the show notes and links: https://www.expertedgepodcast.com/blog/episode326 Connect with Colin on Instagram: https://www.instagram.com/colinboyd/  

In this special episode, hosts David Millili and Steve Carran sit down with Will Gilbert, Co-Founder of Bodhi, live from HITEC, to explore how hospitality technology is rapidly transforming hotel operations, guest experience, and revenue performance.Will shares how Bodhi has achieved 10X growth since last October, and why the company is positioning itself not just as a product vendor—but as a true hotel operating platform designed to unify fragmented systems across the hospitality ecosystem.The conversation dives deep into how Bodhi is helping hotels deliver measurable ROI through its powerful ROI calculator, which links operational improvements directly to revenue impact, guest satisfaction, energy savings, and operational efficiency.Rather than bolting on features or claiming AI hype, Will explains Bodhi's philosophy of building a purpose-built, fully integrated platform that improves over time and delivers tangible business outcomes. From housekeeping optimization and valet integration to work order management and predictive insights, Bodhi is redefining what “platform” really means in hospitality tech.The discussion also covers: Why most hotel “platforms” are actually disconnected products  How guest experience issues directly impact revenue and ratings  The importance of data security, including SOC 2 Type 2 compliance  What's next for the future of hotel operating systems Watch the FULL EPISODE on YouTube: https://youtu.be/BF9nGmpAU-kLinks:Will on LinkedIn: https://www.linkedin.com/in/will-gilbert-0348586/Bodhi: https://www.gobodhi.com/For full show notes head to: https://themodernhotelier.com/episode/290Follow on LinkedIn: https://www.linkedin.com/company/the-..Join the conversation on today's episode on The Modern Hotelier LinkedIn pageConnect with Steve and David:Steve: https://www.linkedin.com/in/%F0%9F%8E...David: https://www.linkedin.com/in/david-mil.

Is your SaaS company stuck in the valuation doghouse while a handful of names trade at a massive premium? In episode #378, Ben Murray breaks down Meritech's June 2026 public software comps report and the widening valuation gap across SaaS. The median revenue multiple has fallen 64% from its pre-ZIRP peak, and most public software now trades below 5X. If you are a SaaS founder or CFO, the multiple attached to your business depends on a short list of traits the market now rewards. This episode shows you which ones, and why the rules quietly changed. Why only 9 of roughly 100 public software companies trade above a 10X revenue multiple, while 77 sit below 5X How the Rule of 40 shifted under the surface, with revenue growth now 3.3x more correlated with the multiple than free cash flow margin Why two companies with the same Rule of 40 score can trade at 7.3x versus 3.7x, depending entirely on how they got there What the top 9 share in common: free cash flow margins above 20% and ARR growth above 20% at the same time How AI exposure now sorts the market, and why a weak AI ARR story lands horizontal SaaS in the doghouse Tune in to see exactly what separates the premium names from the rest before you benchmark your own SaaS valuation. Resources Mentioned Meritech June 2026 Public Software Comps (Pulse Report): https://meritech.substack.com/p/meritech-software-pulse-12-june-2026 Ben's academy: https://www.thesaasacademy.com/

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.

Do you want to know God's vision for your life but feel like you keep shrinking your dreams down to what you can figure out on your own? In this teaching, Travis Lee Peters breaks down what vision, mission, and purpose actually mean for the believer, and how to hear what God wants to do in your life clearly, then walk it out through daily obedience. You'll learn why most of us settle for a vision that's "too small" (just get out of debt, just get comfortable), how to stop dreaming without God involved, and why His vision for you is always bigger than anything you could engineer in the natural. We cover the spiritual version of "10X is easier than 2X," how to get quiet and let God drop the vision in your heart, and how that vision turns into a mission you live out one obedient step at a time. If you've ever felt stuck, scattered, or unsure of your purpose, this one is for you. If this blessed you, don't just watch and leave. Get plugged in today so you can begin to experience God's Promise for Increase on new and exciting levels:

Zoey Greco is a spiritual teacher, intuitive guide, numerology expert, "delusional in the best way" queen, and the host of the highly-rated podcast The Higher Self Hotline. For over a decade, she has supported thousands of clients across the globe through periods of deep personal redirection and expansion. She is the creator of the *Already Are* philosophy — which teaches that true transformation isn't about becoming someone new, but rather remembering the person you have always been.In this episode, we cover:- The *Already Are* philosophy and why the caterpillar already is the butterfly on a cellular level- Why we are drawn to numbers — frequency, resonance, and how humans make sense of the world- The four numerology numbers Zoey uses for any reading: Life Path, Birthday Number, Attitude Number, and Personal Year- Amy's full live numerology read — Life Path 8, Birthday 8, Personal Year 8, Attitude 7 — and what concentrated eight energy actually means- The eight as the number of power, abundance, leadership, money, and the karmic loop of infinity- Why every number has a light and a shadow — and how to use the shadow for your own shadow work- Angel numbers, license plates, and how the universe uses the external world as a mirror to speak to you- Why "delusion" is your number one resource for manifestation and timeline jumping- The 10X list exercise Zoey runs in her container for clients calling in a partner- Why being it before you see it begins with releasing what other people think of you — "what anybody thinks about me is none of my fucking business"- The four intuitive gifts explained: claircognizance, clairvoyance, clairaudience, clairsentience — and why knowing your type changes everythingConnect with Zoey:Website (and the free intuitive gifts quiz): https://zoegreco.comYoutube: https://www.youtube.com/@TheZoeyGrecoInstagram: https://www.instagram.com/zoeygrecoFree weekly live open coaching with Zoey and her friend Brittany — starting in JunePlease remember to rate, review, and follow the show – and share with a friend!Subscribe to the newsletter:https://mailchi.mp/amyedwards/sign-up-to-amys-newsletterCheck out our new Comedy Wellness Podcast: Anything But Mid, cohosted with Whitney Stropp:https://podcasts.apple.com/us/podcast/anything-but-mid/id1849386215https://www.youtube.com/@AnythingButMidFind Amy's affiliates and discount codes: https://amyedwards.info/affiliatepageAll links: ⁠⁠⁠amyedwards.info⁠⁠⁠ - https://amyedwards.info/Instagram: ⁠⁠⁠@realamyedward - ⁠⁠⁠ https://www.instagram.com/realamyedwards/Fight For Her: https://www.fightfortheforgotten.org/fight-for-herTikTok:⁠⁠⁠ @themagicbabe⁠⁠⁠ -  https://www.tiktok.com/@themagicbabe?lang=enYouTube:@TheAmyEdwardsShow -  https://www.youtube.com/c/theamyedwardsshowPodcast: ⁠⁠⁠The Amy Edwards Show Podcast⁠⁠⁠ - https://podcasts.apple.com/us/podcast/the-amy-edwards-show/id1543432633Free Course:⁠⁠⁠ The Ageless Mindset⁠⁠⁠ - https://best-you-life.teachable.com/p/the-ageless-mindset-the-ultimate-guide-to-look-younger-feel-happierFull Course: ⁠⁠⁠The Youthfulness Hack⁠⁠⁠ - https://best-you-life.teachable.com/p/the-youthfulness-hack Amy's hair by ⁠https://www.thecollectiveatx.com⁠Podcast editing by https://podcastmagician.com/Get my FREE course "The Ageless Mindset: The Ultimate Guide to Look Younger and Feel Happier!" HERE: ⁠https://best-you-life.teachable.com/p/the-ageless-mindset-the-ultimate-guide-to-look-younger-feel-happier⁠Get the full course “The Youthfulness Hack: The Secret System to Reverse Aging Fast and Create a New, Radiant You!” Out now! ⁠https://best-you-life.teachable.com/p/the-youthfulness-hack⁠

Passion or Profit?  What does success look like for the next generation in a world being reshaped by AI, automation, and constant disruption? In this thought-provoking episode of The Cardone Zone, Grant Cardone is joined by Swedish podcaster Theodor Ekstrand for an honest conversation about the realities facing young people today. Together, they explore the rapidly changing landscape of work, the impact of artificial intelligence on careers, and the opportunities emerging from one of the biggest technological shifts in history. The discussion also tackles a timeless question that every ambitious young person eventually faces: Should you follow your passion, or follow the money? Whether you're just starting your career, building a business, or trying to navigate an uncertain future, this episode offers practical insights on how to stay relevant, valuable, and prepared for what's next. Follow us on all social platforms @GrantCardone for daily content on wealth, business, sales, investing, and the 10X mindset. Visit GrantCardone.com for information on upcoming events, exclusive training, and the latest strategies on wealth creation, business growth, and real estate investing. TRT: 53:00:02  

“Do the right thing, not the easy thing.”  on the Daily Grind ☕️, your weekly goal-driven podcast. This episode features Kelly Johnson @kellyfastruns and special guest Gene Gerovich @nyc_hvac_heroes. Gene is a sales leader, operator, and entrepreneur with over 15 years of experience scaling businesses with a relentless focus on process, discipline, and people.He's helped grow multiple companies 3X to 10X in under five years, and today he leads B&M Mechanical in New York City—an HVAC and refrigeration company serving the hospitality industry and major brands like Macy's, McDonald's, Burger King, Dunkin', and more.S9 Episode 23: 6/16/2026Featuring Kelly Johnson with Special Guest Gene GerovichFollow Our Podcast:Instagram: @dailygrindpod https://www.instagram.com/dailygrindpod/  X: @dailygrindpod https://x.com/dailygrindpod Facebook: https://www.facebook.com/dailygrindpodTikTok: https://www.tiktok.com/@dailygrindpodPodcast Website: https://direct.me/dailygrindpod   Follow Our Special Guest:Website: https://bmmechanical.nyc/Instagram: @nyc_hvac_heroes

On this episode of Public Keys from the New York Stock Exchange, host Jennifer Sanasie is joined by GSR's Joshua Riezman on SpaceX's $1.29 billion Bitcoin bet and the outlook for the CLARITY Act; Citi's Artem Korenyuk on the bank's new platform for tokenizing private company shares; and Coinbase's Lincoln Murr on AI agent trading and the future of autonomous finance. Plus, Ponzi Trader's highest-conviction trade of the week on 10X, presented by Kraken Pro. - 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/⁠. - Register now for CoinDesk's Policy and Regulation event on September 24, 2026: ⁠https://policy-regulation.coindesk.com/⁠. - Timecodes: 00:00 Welcome to Public Keys 01:00 U.S.-Iran Deal Reopens Strait of Hormuz 01:28 SpaceX IPOs With $1.29B in Bitcoin 02:01 GSR's Joshua Riezman Joins Public Keys 03:23 Bitcoin's Corporate Treasury Era 04:47 AI vs. Crypto for Capital Flows 07:06 CLARITY Act: Now or Never 10:20 10X: Ponzi Trader's Top Trade is XPL 12:04 Citi's Artem Korenyuk Joins Public Keys 13:29 Digital Depository Receipts Explained 16:13 Citi's Broader Tokenization Strategy 18:12 Investor Rights and Private Share Ownership 20:20 BTC and ETH ETF Flows 21:18 Coinbase Launches for Agents, Lincoln Murr Joins 23:03 AI Agent Safety and Coinbase Advisor 26:02 The Agentic Financial Future and x402 28:19 Fear & Greed Index Climbs to 20 - This episode was hosted by Jennifer Sanasie.

Read my new book, "The Price of Becoming." www.LearningLeader.com/Becoming This is brought to you by Insight Global. If you need to hire one person, hire a team of people, or transform your business through Talent or Technical Services, Insight Global's team of 30,000 people around the world has the hustle and grit to deliver. My Guest: Scott Harrison is the founder and CEO of charity: water, a non-profit that has raised over a billion dollars and funded tens of thousands of water projects to bring safe drinking water to millions. He previously spent a decade as a New York City nightclub promoter before a dramatic career shift led him into humanitarian work. Key Learnings Scott started a charity: water with $20 from a birthday party. Then $15,000... Twenty years later: over a billion dollars raised, 21 million people served. He says it should be 10 to 100 times more. The cure for water already exists. We're looking for water on Mars while 700 million people drink dirty water on Earth. We solved this hundreds of years ago. We just haven't implemented it. 25% of the money sitting in American donor-advised funds would give every human on Earth clean water. That's parked philanthropic capital. Already tax-benefited. Just waiting. The goal is always 10X what you're doing. If we raised a million last year, we want ten this year. If we raise $100 million, we should raise a billion. The opportunity is always orders of magnitude larger than the moment. Show, don't bullet. Scott shows 210 photos in a 45-minute keynote. No PowerPoint. Single images. A story unfolds frame by frame. Be early to the technology. First charity on Instagram. First to hit a million Twitter followers. First to use VR. The question is always the same: how does this new thing further the mission? The 100% model: solve for the cynic.  Public donations go to one bank account that funds only water projects. Overhead is raised separately from entrepreneurs and business leaders. Then track every donation to a specific village. Don't be mid. Scott's 11-year-old daughter says nobody wants to be mid. Excellence is a core value. There's a lot of mid out there. Design everything. The fact cover sheet. The PowerPoint. The website. The package. "We're always dating." If the message comes in an ugly package, you're at a disadvantage before you start. Treat the donor like a Michelin three-star guest. If a restaurant can think that carefully about a meal, you can think that carefully about a donor who can save a million lives. The Goldman Sachs partner who changed Scott's paradigm. Before making an eight-figure ask, Scott asked a partner: "How does it feel when people ask for a lot more than you expected?" The expected answer was irritated, offended, put off. The actual answer: "I feel flattered that they think I would be that generous." People are generous. The well is there. You just have to drill deep enough. Scott has spent 20 years asking for too little. That might be his next obsession. People give to people, not causes. A dynamic leader who transfers their enthusiasm gets the donation. The cause doesn't. Most of the donations Scott and his wife give are to people, not topics they were already passionate about. Talk 10% of the time. When Scott meets a donor for the first time, he wants to know their whole life story. Their marriage. Their kids. What they wanted to be when they grew up. Be genuinely curious or don't bother. Hire for integrity, humility, curiosity, and energy... 16,000 applicants for 36 roles last year. Energy matters most. Someone who can get you fired up about pickleball, Patagonia, or a new running shoe is exactly who you want on the executive team. The dinner test for hiring: Can you imagine having this person at your home for two hours at dinner? And wanting to keep them for another hour? Get the whole life story. Scott wants the arc from the beginning to the present in an interview. If someone can't tell their own story coherently, they probably don't know themselves yet. The 11-year-old with the piggy bank. He told his parents he was going to fund a whole village. They told him to set a realistic goal. He went knocking on doors. He came back with $10,000. Scott's experience lab in Nashville. A 60-minute immersive tour. A 100-degree room with a treadmill where you carry a 40-pound water vessel. Microscopes that show you parasites. A VR film that ends in celebration. The "give shop," not the gift shop. 53% of visitors donate. 10,000 visitors. $3.9 million raised in year one. Scott's champagne moment: a single billionaire who picks water. The water sector doesn't have one. Republicans and Democrats agree on it. Atheists and people of faith agree on it. Everyone has to drink. Reflection Questions What is the 10X version of your current goal? Where are you asking for too little because the smaller ask felt safer? Who in your work or life is the Michelin three-star guest, the customer, donor, or partner who deserves your most thoughtful experience design?  When was the last time you went 10% talking, 90% genuinely curious about someone else's story?  More Learning:  #290: Scott Harrison – Redemption, Compassion, & The Transformative Power Within Us #680: Scott Galloway - Don't Follow Your Passion, Follow Your Talent #682: Will Guidara - Adversity is a Terrible Thing to WasteAudio Chapters 00:00 The Price of Becoming - Pre-Order Now! 01:18 Welcome Back, Scott Harrison 02:56 From a $20 Bill to Over $1 Billion Raised 04:59 Why the Goal Should Always Be 10X (or 100X) 07:54 Storytelling: How to Get People to Care About a Problem They Don't Feel 10:30 Being Early to Instagram, Twitter, and VR 16:10 Radical Transparency: The Bank Account That Built Trust 19:51 The Beauty of a Healthy Obsession 21:22 Drilling Deep for the Artesian Wells of Generosity 25:04 What It Feels Like in the Room When Generosity Breaks Through 27:01 "Nobody Wants to Be Mid." 30:56 Design Everything: We're Always Dating 32:13 Treat Your Donor Like a Michelin Three-Star Guest 35:39 Selling With Integrity: Talk 10%, Listen 90% 39:15 16,000 Applicants for 36 Jobs: What Scott Looks For 43:12 The Power of Vulnerability in Hiring 45:39 Inside the Nashville Experience Lab 50:34 The Champagne Question: A Billion-Dollar Vision 52:10 The 11-Year-Old Who Raised $10,000 Door-to-Door 54:25 EOPC  

Are you falling behind—or getting 10X more productive—with AI? Kevin Surace, the father of the virtual assistant and AI futurist. Here are my 5 biggest takeaways: ✅ If you're not AI-first, someone who is will replace you ✅ AI isn't a tool — it's a workflow multiplier ✅ Productivity is now 10X minimum, not 10% better ✅ Your voice still matters — edit AI, don't fear it ✅ The winners? People who learn faster than AI evolves What's ONE way you're using AI today to boost productivity? Kevin is the father of the Virtual Assistant and a Silicon Valley innovator, serial entrepreneur, CEO, and futurist. He was INC Magazines' Entrepreneur of the Year, a CNBC top Innovator of the Decade, World Economic Forum Tech Pioneer, Chair of Silicon Valley Forum, Planet Forward Innovator of the Year nominee, featured for 5 years on TechTV's Silicon Spin, and inducted into RIT's Innovation Hall of Fame. He has 94 worldwide patents and led pioneering work on the first cellular data smartphone (AirCommunicator), the first human-like AI virtual assistant (Portico), soundproof drywall, high R-value windows, AI-driven building management, Generative AI for QA automation, supply-chain auctions, and the window/energy retrofits of the Empire State Building and NY Stock Exchange. Connect with Jon Dwoskin: Twitter: @jdwoskin Facebook: https://www.facebook.com/jonathan.dwoskin Instagram: https://www.instagram.com/thejondwoskinexperience/ Website: https://jondwoskin.com/LinkedIn: https://www.linkedin.com/in/jondwoskin/ Email: jon@jondwoskin.com Get Jon's Book: The Think Big Movement: Grow your business big. Very Big! Connect with Kevin Surace:Website: https://www.kevinsurace.com/ X: https://twitter.com/kevinsurace Instagram: https://www.instagram.com/kevinsurace/ LinkedIn: https://www.linkedin.com/in/ksurace/ Facebook: https://www.facebook.com/kevin.surace/ *E - explicit language may be used in this podcast.

The Cardone Zone Episode 311  "That will never work" Couldn't think of a better title for this episode than to quote Marc Randoph's book title: "That will never work", because that is the essence of this episode. The entrepreneur's journey through adversity. What does it take to build technology that changes the way the world lives, works, and communicates? In this fascinating episode of The Cardone Zone, Grant Cardone welcomes two pioneering innovators: Marc Randolph, co-founder of Netflix, and Adam Cheyer, co-founder of Siri. Together, they share the principles, methodologies, and decision-making frameworks that helped transform groundbreaking ideas into globally recognized technologies. From disrupting the entertainment industry to revolutionizing how humans interact with technology, these visionaries reveal what it takes to innovate, adapt, and scale in rapidly evolving markets. Whether you're building a startup, growing an established business, or looking to sharpen your competitive edge, this episode delivers valuable insights from two leaders who helped redefine entire industries. Follow us on all social platforms @GrantCardone for daily content on wealth, business, entrepreneurship, investing, and the 10X mindset. Visit GrantCardone.com for information on upcoming events, exclusive products, training programs, and the latest strategies on wealth, business, and real estate. TRT: 53:00:02  

On this episode of CoinDesk's Public Keys from the New York Stock Exchange, host Jennifer Sanasie is joined by Bloomberg Intelligence Senior Research Analyst James Seyffart to break down the SpaceX IPO's pull on crypto capital, four consecutive weeks of Bitcoin ETF outflows topping $1.7 billion, and the Zcash counterfeiting bug. VanEck Director of Digital Assets Product Kyle DaCruz unpacks VBNB, the first US spot BNB ETF, the rise of "revenue chains," and what staking rewards will mean for the product. 100X Capital CIO Joy Pathak — also known as the Wizard of SoHo — shares his top conviction trade in the 10X segment. Plus, Benchmark-StoneX Managing Director Mark Palmer breaks down why the market overreacted to Strategy's first publicized Bitcoin sale, his $570 price target on the company, and his Buy rating with a $32 target on Strive. - 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/.-Register now for CoinDesk's Policy and Regulation event on September 24, 2026: https://policy-regulation.coindesk.com/. Timecodes: 00:00 Welcome to Public Keys 00:38 SpaceX IPO, BTC Drops 01:50 BTC ETF Outflows: Overreaction or Trend? 03:23 Zcash Counterfeiting Bug and the Privacy Narrative 06:34 VanEck's Kyle DaCruz on the First US Spot BNB ETF 07:18 Ghost Chains vs Revenue Chains: BNB by the Numbers 08:56 BNB Staking and How VanEck Picks Its Next ETF 11:17 BNB Chain's Decentralization 14:55 ETF Flows Deep-Dive: Bitcoin, Hyperliquid, XRP, Solana 18:02 Bitcoin ETFs vs Gold's $300B in Assets 19:14 The Yin-Yang of Crypto: "We're So Back" vs "It's So Over" 21:39 Joy Pathak's ‘10X' Trade: NEAR 24:04 Benchmark-StoneX' Mark Palmer on Strategy's First Publicized BTC Sale 25:32 Why S&P's October Critique Drove the Sale 27:47 Path to a $570 Price Target on Strategy 29:40 $32 Buy Rating on Strive and a $95K BTC Assumption 32:18 Crypto Fear & Greed Index at 8 - This episode was hosted by Jennifer Sanasie.

What does it take to become a household name, stay at the top of your game, and negotiate your way through decades of success? In this star-powered episode of The Cardone Zone, Grant Cardone is joined by John Travolta and Kevin Hart for a candid conversation about the journeys that transformed them from ambitious dreamers into global icons. From navigating setbacks and rejection to building lasting careers in one of the world's most competitive industries, both guests reveal the principles, habits, and negotiating strategies that helped them create opportunities where others saw obstacles. In Episode 310, you'll discover: The mindset required to achieve long-term success How confidence and preparation shape every negotiation The art of creating value before asking for anything in return Lessons learned from decades of navigating high-stakes deals Why persistence remains one of the most underrated skills in business and life Whether you're negotiating a contract, growing a business, or pursuing a bigger vision for your future, this episode delivers insights from two masters of their craft who have consistently found ways to create leverage, build relationships, and win. Follow us on all social platforms @GrantCardone for daily content on wealth, business, sales, entrepreneurship, and the 10X mindset. Visit GrantCardone.com for information on upcoming events, exclusive training programs, and products designed to expand your business skills, increase your knowledge, and accelerate your success.

On this episode of CoinDesk's Public Keys at the New York Stock Exchange, Jennifer Sanasie is joined by CoinDesk Indices President Dave LaValle to unpack a $2.97 billion outflow streak from Bitcoin ETFs and what it really means for institutional adoption.Bloomberg Intelligence Senior ETF Analyst Eric Balchunas joins the show to explain why the recent outflows may be more noise than signal, share his bullish outlook on the fast-rising HYPE ETFs, and discuss how firms like Morgan Stanley, Goldman Sachs, and BlackRock are expanding access to Bitcoin through new investment products. In this week's 10X segment, LaValle breaks down the fundamentals of margin trading, explaining what separates professional traders from retail investors when it comes to managing leverage, risk, and conviction. Plus, Stellar Development Foundation CEO and Executive Director Denelle Dixon discusses DTCC's decision to select Stellar as the first public blockchain connected to its upcoming tokenized securities settlement platform, and what it means for the future of tokenization and institutional blockchain adoption. - This episode of Public Keys is brought to you by Kraken. For more: ⁠https://pro.kraken.com/⁠ - Timecodes: 00:00 Welcome to Public Keys 00:54 Jamie Dimon vs Brian Armstrong on Stablecoin Yields 03:21 Bitcoin ETFs Shed $2.97B in Outflows 05:50 BTC ETFs Post Worst Week Since January 06:50 Grayscale Amends HYPE ETF Filing 08:36 Bloomberg Intelligence's Eric Balchunas Joins Public Keys 09:39 Why BTC ETF Outflows Are Just 'Noise' 13:00 Wall Street's New BTC Products: Goldman, Morgan Stanley, iShares 15:33 HYPE Is the 'Hansel from Zoolander' of Crypto ETFs 17:57 Will SpaceX ETFs Pull Capital from Crypto? 20:42 10X: What Separates Pro Traders from Retail 22:25 Knowing Your 'Out': The Biggest Mistake in Margin Trading 25:06 Stellar Development Foundation's Denelle Dixon on the DTCC Tokenization Deal 26:14 Stellar Hits $3B in Tokenized Assets in Five Months 28:46 Can Blockchains Handle DTCC-Level Volume? 30:21 Digital Twins and the Issuer-Led Tokenization Question 31:50 Will One Blockchain Win the RWA Race? - This episode was hosted by Jennifer Sanasie.

The Cardone Zone – Episode 309: Origins  Before the growth or partnerships. Before success at scale, there were the origins. In this episode of The Cardone Zone, Grant Cardone and Brandon Dawson take us on a journey into their past and the early stages of their careers, the mindset that fueled their rise, and the joint venture that brought two powerhouse operators together. It's the foundation it takes to build something meaningful, scalable,  Follow us on all social platforms @GrantCardone for more content on wealth, entrepreneurship, scaling businesses, and the 10X mindset. Visit GrantCardone.com for information on upcoming events, business training, and the latest tools designed to help you expand your success. TRT:53:00:02

Welcome to the 10X Growth Conference at Marlins Stadium in Miami, the legendary event that ignited ambition, transformed lives, and brought together entrepreneurs, sales professionals, business owners, and dreamers from around the world under one roof for 3 days, on Super Bowl weekend. In this electrifying episode, Grant delivers some of his most powerful and timeless principles on sales, marketing, money, and success. From the unforgettable concept that "money follows attention" to his ultimate definition of sales as a contact sport, "if I hit you, you might buy,"- Grant breaks down the mindset and strategies required to dominate in business and stay relevant in a constantly changing marketplace. Packed with massive energy, real-world insights, and unforgettable moments from one of the biggest business conferences in the world, this episode is guaranteed to become one of your go-to sources for motivation, clarity, and execution. Follow us on all social media platforms @GrantCardone for more content on wealth, business, sales, marketing, and the 10X mindset. Visit GrantCardone.com for upcoming events, exclusive products, and training designed to accelerate your success. TRT: 53:00:02