Podcasts about Scaling

Welcome back to Impact Theory with Tom Bilyeu! In this electrifying continuation of his conversation with geopolitical strategist Peter Zeihan, Tom dives into the forces shaping the next era of global power. From the much-debated Thucydides' Trap and the real state of China's future to America's political and demographic crossroads, this episode is a masterclass in understanding the seismic shifts rocking our world. Peter Zeihan makes a compelling case for why China's rise might not be the threat many fear, citing geopolitical bottlenecks, demographic crises, and internal political strife. The conversation then takes a sharp, honest look at America's own challenges—from aging demographics and political party chaos to the hard realities of reindustrialization and immigration reform. Along the way, Tom Bilyeu and Peter Zeihan examine the impact of social media on truth, the lessons (and perils) of historical mass migrations, and whether technology can rescue countries facing population decline. Get ready to have your assumptions challenged and your worldview expanded with practical insights on what's really at stake for the future of nations. Whether you're interested in economics, politics, or the fate of entire civilizations, you won't want to miss this wide-ranging, thought-provoking discussion! Website: https://zeihan.comFree Newsletter: https://zeihan.com/newsletterTwitter: https://twitter.com/PeterZeihanYouTube: https://www.youtube.com/@ZeihanOnGeopolitics What's up, everybody? It's Tom Bilyeu here: If you want my help... STARTING a business: join me here at ZERO TO FOUNDER:  https://tombilyeu.com/zero-to-founder?utm_campaign=Podcast%20Offer&utm_source=podca[%E2%80%A6]d%20end%20of%20show&utm_content=podcast%20ad%20end%20of%20show SCALING a business: see if you qualify here.:  https://tombilyeu.com/call Get my battle-tested strategies and insights delivered weekly to your inbox: sign up here.: https://tombilyeu.com/ ********************************************************************** If you're serious about leveling up your life, I urge you to check out my new podcast, Tom Bilyeu's Mindset Playbook —a goldmine of my most impactful episodes on mindset, business, and health. Trust me, your future self will thank you. ********************************************************************** FOLLOW TOM: Instagram: https://www.instagram.com/tombilyeu/ Tik Tok: https://www.tiktok.com/@tombilyeu?lang=en Twitter: https://twitter.com/tombilyeu YouTube: https://www.youtube.com/@TomBilyeu Quince: Free shipping and 365-day returns at https://quince.com/impactpod Shopify: Sign up for your one-dollar-per-month trial period at https://shopify.com/impact Ketone IQ: Visit https://ketone.com/IMPACT for 30% OFF your subscription order Incogni: Take your personal data back with Incogni! Use code IMPACT at the link below and get 60% off an annual plan: https://incogni.com/impact Blocktrust IRA: Get up to $2,500 funding bonus to kickstart your account at https://tomcryptoira.com Netsuite: Right now, get our free business guide, Demystifying AI, at https://NetSuite.com/Theory Huel: High-Protein Starter Kit 20% off for new customers at https://huel.com/impact code impact Thucydides' Trap, China collapse, U.S. global power, demographic decline, Xi Jinping purges, Chinese military, First Island Chain, U.S. Navy, Japanese alliances, one-child policy, population overcount, industrialization, Han Chinese, civilizational collapse, warlords, agricultural dependence, U.S.-China relations, globalization, reindustrialization, NAFTA, industrial policy, political chaos, American demographics, immigration reform, labor shortages, party realignment, social media impact, media regulation, assimilation, European immigration. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode from Grow 2026, Troy Woodham and I talk about a core truth of scaling: as your business evolves, your systems must evolve too. We break down how replicable, duplicatable processes — powered by tools like LMN — help contractors grow with consistency, clarity, and control. Lawntrapreneur Academy (The #1 Resource for Starting, Growing and Scaling a Successful Lawn & Landscaping Company). - https://www.lawntrepreneuracademy.com/  Granum Academy Bootcamp Tour (use BRIAN25 to save!): https://www.Granum.com/Brian GROW 2026 - February 10-12 Dallas, TX: https://hubs.li/Q03Ybxs10 LMN & Coffee - https://us06web.zoom.us/j/89495679453?pwd=m0wKa6prJWrARKClJKolBaJjl00OYn.1 Coast Pay Fuel Card: www.CoastPay.com/Brian

In this episode of the Coaches Compass, Mike goes into detail about some of the potential downfalls of scaling your coaching business and why revenue screenshots can be very misleading. If you need help scaling your business, start your 7-day free trial for The Collective.------------------------------------------------Click here to apply for coaching!For some amazing resources and to be a part of a badass community, join our FB group HEREThe personality assessment is now available online! Click here to take the assessment and find out what your personality tells us about the way you should be training and eating.Take the assessment here!To learn more about Neurotyping, visit www.neurotypetraining.comFollow Mike on IG at @coach_mike_millner

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/ ----In this episode, I'm joined by Jaime Sevilla, founder of Epoch AI; Hannah Petrovic from my team at Exponential View; and financial journalist Matt Robinson from AI Street. Together we investigate a fundamental question: do the economics of AI companies actually work? We analysed OpenAI's financials from public data to examine whether their revenues can sustain the staggering R&D costs of frontier models. The findings reveal a picture far more precarious than many assume; we also explore where the real infrastructure bottlenecks lie, why compute demand will dwarf energy constraints, and what the rise of long-running agentic workloads means for the entire industry. Read the study here: https://www.exponentialview.co/p/inside-openais-unit-economics-epoch-exponentialviewWe covered: (00:00) Do the economics of frontier AI actually work? (02:48) Piecing together OpenAI's finances from public data (05:24) GPT-5's "rapidly depreciating asset" problem (13:25) Why OpenAI is flirting with ads (17:31) If you were Sam Altman, what would you do differently? (22:54) Energy vs. GPUs; where the real infrastructure bottleneck lies (29:15) What surging compute demand actually looks like (33:12) The most surprising finding from the research (38:02) The race to avoid commoditization (43:35) Agents that outlive their models  Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azhar/ Twitter/X: https://x.com/azeem  Where to find Jamie: https://epoch.ai or https://epochai.substack.com Where to find Matt: https://www.ai-street.co  Production by supermix.io and EPIIPLUS1 Production and research: Chantal Smith and Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In Session 324, Dr. Paulie Gavoni and Steve Ward join me to discuss what resilience actually looks like from a behavior science perspective — and why many well-intentioned adult responses can unintentionally teach avoidance instead of persistence. We center our conversation around their book, S.H.I.T. Happens: Building Resilient Children in a Fragile World, which reframes resilience not as a personality trait or motivational slogan, but as a set of learnable repertoires shaped by the environments adults design We talk about: Why resilience is a behavioral repertoire, not a mindset or personality trait The hidden ways adult anxiety shapes children's learning environments How overprotection and pressure both undermine skill development Designing "successful struggle" so kids contact reinforcement for effort Everyday moments — homework, sports, emotional setbacks — as resilience practice The adult's role as guide, not rescuer or drill sergeant Teaching recovery instead of avoidance Scaling challenges to build confidence and persistence Why discomfort is information, not danger This discussion emphasizes practical decision-making: how small changes in adult behavior can create conditions where children learn to try again, persist longer, and experience the satisfaction of overcoming something difficult. Whether you're a practitioner, educator, or parent, this episode highlights how resilience is built through repeated opportunities to struggle safely — and why those opportunities matter more than we often realize. Resources mentioned: Paulie and Steve's book Assent & Trauma Informed Care: A Call for Nuance in Behavior Analysis Motivational Interviewing: Getting Educator Buy-In (course) Adaptive Intelligence: The Evolution of Emotional Intelligence Through the Proven Power of Behavior Science Paulie's other books Kind Extinction: A Procedural Variation on Traditional Extinction The Four Leadership Hats: Applying Behavioral Science to Leadership and Supervision (Session 321 with John Guercio) The ACT Matrix: A New Approach to Building Psychological Flexibility Across Settings and Population Session 313: Client Assent in Behavior Analysis: Balancing Autonomy and Clinical Progress (Ethics CE available) Sponsor shoutouts The School Behavioral Solutions for Special Educators & Behavior Analysts. The Behavior Toolbox Conference is a one-day, high-impact professional convening that brings together experienced practitioners and leaders from across education and behavior science to share what actually works in schools. It's taking place virtually through BehaviorLive on March 5th, 2026, and will be available on-demand for those who can't make it on the day of the event. Behavior University. Their mission is to provide university quality professional development for the busy Behavior Analyst. Learn about their CEU offerings, including their 8-hour Supervision Course, as well as their RBT offerings over at behavioruniversity.com/observations. Don't forget to use the coupon code, PODCAST to save at checkout! CEUs from Behavioral Observations. Learn from your favorite podcast guests while you're commuting, walking the dog, or whatever else you do while listening to podcasts. New events are being added all the time, so check them out here.  HRIC Recruting. Cut out the middleman and speak directly with Barbara Voss, who's been placing BCBAs in great jobs all across the US for 15 years.

Mastering the SaaS Journey: Building, Scaling, and Exiting with Draven McConvilleIn this episode of The Thoughtful Entrepreneur Podcast, host Josh Elledge sits down with Draven McConville, a seasoned tech entrepreneur and angel investor, to discuss the high-stakes world of SaaS development and strategic exits. Draven shares his journey of founding Klipboard, a platform that revolutionized the field service industry by digitizing paper-heavy workflows for tradespeople. This conversation serves as a comprehensive guide for founders and investors alike, offering deep dives into the nuances of raising capital, identifying underserved market niches, and navigating the complex emotional and practical realities of selling a company to a global acquirer.Strategic Growth: From Underserved Niches to Global ExitsThe foundation of a successful SaaS venture often lies in identifying a "quiet" market that technology has left behind. Draven explains that his success with Klipboard was rooted in observing the daily struggles of field service professionals—plumbers, HVAC technicians, and fire safety experts—who were still buried under manual, paper-based systems. By building a tool specifically tailored to these unique workflows rather than chasing crowded, trendy markets, he was able to achieve strong product-market fit. This strategy highlights a critical lesson for modern entrepreneurs: true innovation often happens where digital transformation is lagging most, provided the founder is willing to spend time in the trenches with their target users to simplify their daily routines.Deciding how to fund that growth is a pivot point that defines a founder's trajectory. While bootstrapping offers total control and is ideal for service-based models, Draven notes that capital-intensive SaaS products often require institutional investment to build a sufficient runway for profitability. However, he cautions that raising capital is a "marriage" that demands extreme due diligence. Founders must interview potential investors as rigorously as they are being interviewed, seeking partners who offer industry connections and strategic guidance rather than just a check. Accepting external funding essentially sets the business on a predetermined path toward an eventual exit, making alignment on vision and values the most important factor in the partnership.When the time for that exit finally arrives, the decision to sell should be driven by more than just the highest valuation. Draven reflects on his own acquisition by Carriage Commercial Systems, noting that he prioritized a strategic fit where Klipboard would become a flagship product rather than just a small line item. An exit is a deeply emotional transition, and ensuring that the acquirer values the existing team and product integrity is vital for a founder's legacy. Post-acquisition, the role of the founder often shifts toward scaling the brand within a larger global infrastructure, proving that the end of one entrepreneurial chapter is frequently the beginning of an even larger leadership challenge.About Draven McConvilleDraven McConville is a prominent tech entrepreneur, angel investor, and the founder of Klipboard. With a background in both service-based businesses and high-growth SaaS, he has spent his career driving digital transformation in traditional industries. Draven is an active member of the global startup ecosystem, dedicated to mentoring early-stage founders and investing in the next generation of software innovation.About Draven McConville (Personal Brand & Klipboard)Draven McConville's professional focus centers on streamlining operations for field service businesses through his platform, Klipboard. His work empowers tradespeople to digitize scheduling, invoicing, and job management, ultimately reducing...

Today we revisit some of the most powerful lessons from my favorite past episodes, featuring incredible guests who have shaped my perspective on success, decision-making, and personal growth. First up is Codie Sanchez, who shares the importance of quick decision-making and the risks of hesitation.  Dan Martell talks about overcoming resistance, embracing risk, and asking the right questions to break through uncertainty. Ann Mahlum reminds us why trusting our talents and convictions is critical, even when facing external doubts or judgment.   Finally, we hear from Steven Pressfield, who reflects on his journey to becoming a best-selling author. He opens up about his struggle with resistance and how writing became his true calling after years of avoiding it.   Take a step back, listen to these insights, and remember: success begins with action. If you're feeling stuck or uncertain, these lessons will inspire you to keep pushing forward, trust the process, and make quick decisions that move you closer to your goals. "Time to action is the highest indicator for success." ~ Codie Sanchez In This Episode: - Codie Sanchez on making quick decisions - Ann Mahlum: The power of belief and manifestation - Dan Martell: The law of manifestation - Steven Pressfield: Commitment and overcoming resistance - Finding your true calling - How Steven became a best-selling author Listen to the Featured Episodes: Quarter Life Crisis to 4M Followers on Social Media, Turning Down a $100M Business with Codie Sanchez:  https://youtu.be/Z3ZfGcC6EFk  How Anne Mahlum Built and Sold a $98M Business: Lessons in Scaling, Leadership, and Culture: https://youtu.be/K1hSIhe6UmE  Overwhelmed? Have No Time? Here's Where to Start with Dan Martell: https://youtu.be/1RKIwqrvXrw  Steven Pressfield's Unconventional Journey to Becoming a Bestselling Author: https://youtu.be/K3Z8bcnvNlU  Where to find me: IG: https://www.instagram.com/jen_gottlieb/    TikTok: https://www.tiktok.com/@jen_gottlieb     Facebook: https://www.facebook.com/Jenleahgottlieb    Website: https://jengottlieb.com/    My business: https://www.superconnectormedia.com/    YouTube: https://www.youtube.com/@jen_gottlieb

Pithy quotes "We do our job well if the homeowner forgets about us, because the system just works." "The bar is so low in some homes that doing a quality install can genuinely change someone's life." "The best way to learn is crawling in the crawl space behind a great technician and handing them tools." Semi-famous quote that fits our theme "Stay hungry, stay foolish." © Steve Jobs Shreyas Sudhakar joined the Building HVAC Science podcast to talk about his path from rocket propulsion engineering to building high-quality heat pump installs in California. Bill and Eric found him through his thoughtful LinkedIn posts, and Shreyas shared that a friend's relentless heat-pump evangelism finally pushed him to look deeper. Once he did, the tech clicked. He realized HVAC and rockets share the same core idea: moving energy through systems, and the math is not as far apart as it sounds. What really pulled him in was the homeowner experience. After talking with homeowners on Nextdoor and Reddit, and even calling contractors for quotes himself, he kept hearing the same frustrations: heat pumps feel expensive, contractor advice is inconsistent, trust is low, and myths like "heat pumps don't work in the cold" still show up, even in mild California climates. Shreyas' view is simple: most homeowners do not care what the equipment is called. They care about comfort, noise, bills, and safety, and the best outcome is when the system is so reliable they barely think about it. Shreyas now runs Vayu, a lean heat pump installation company operating with vetted subcontractor partners, while his Heat Pumped newsletter and podcast focus on education for homeowners, technicians, and policy folks. Vayu handles the end-to-end process, from load sizing and equipment selection to permits and rebates, while partner shops focus on the craft of installation. His definition of success is not just a happy install day, but a customer still loving the system a decade later, and technicians thriving because the model removes desk work and supports quality work at scale. Shreyas' LinkedIn: https://www.linkedin.com/in/shreyassudhakar/ His websites: https://www.vayu.pro/about & https://www.heatpumped.org/ HeatPumped Newsletter sign up: https://www.heatpumped.org/subscribe Heat Pumped Podcast: https://creators.spotify.com/pod/profile/heat-pumped/     This episode was recorded in January 2026

In this Apollo House 2026 fireside chat, StartUp Health community member Sami Inkinen, CEO & Founder of Virta Health, shares how a personal diagnosis of pre-diabetes led him to build one of the fastest-growing companies in metabolic health. Virta Health is proving that Type 2 diabetes and obesity can be reversed with nutrition, supported by technology and AI. Inkinen discusses how Virta has built one of the largest longitudinal biomarker datasets in metabolic disease reversal and is deploying AI agents, supervised by clinicians, to support patients every day. He offers blunt insight into the current standard of care, explains why he believes disease reversal must become the common-sense approach, and shares how Virta became AI-native across every function, from hiring to board strategy. This episode covers: • The future of AI in healthcare operations and patient care • Why food as therapy creates a complex, software-driven challenge • The tension between GLP-1 drugs and root-cause treatment • Scaling a B2B2C healthcare company to sustainability • Advice for founders building in the age of AI As a live recording, the audio reflects the energy of the room rather than a studio setting. Do you want to participate in live conversations with industry luminaries? When you join StartUp Health – a private community for founders, investors, buyers, and industry leaders to connect year-round – you are invited to a full calendar of interactive Fireside Chats with the most influential leaders shaping health innovation. Come with questions, learn what is working right now, and connect with industry icons. » Learn more and join today.

Jeannette is live on stage with the remarkable Juliet Barratt, co-founder of ultra-successful sports nutrition brand Grenade, which was sold for £200 million.  Juliet shares her entrepreneurial journey, starting from her early days as a teacher to her pivotal meeting with her business partner, Al, and the challenges and triumphs of building a brand that stands out in a crowded market, the importance of gut feeling in decision-making, and the dynamics of working with a partner in both business and life You'll Learn Why: Success is not solely defined by monetary gains but by the experiences and choices made along the way Trusting one's instincts and gut feelings can lead to significant breakthroughs in business.  Decisions should be made based on what feels right, rather than overanalysing every aspect. Building a strong team with complementary skill sets is essential. Having the right people around can significantly impact the business's culture and success The ability to pivot and adapt to changing circumstances is vital.  Recognising when to pull back from opportunities, such as entering a new market too early, can save a business from potential pitfalls. This episode is living proof that no matter where you're starting from — or what life throws at you — it's never too late to be brave, bold, and unlock your inner brilliant. Visit ⁠https://brave-bold-brilliant.com/⁠ for free tools, guides and resources to help you take action now

Welcome back to Impact Theory with Tom Bilyeu! In this episode, Tom sits down with geopolitical strategist Peter Zeihan for a fascinating deep dive into the tectonic shifts shaping our world order. Together, Tom Bilyeu and Peter Zeihan unpack why the global landscape is changing—and why it isn't just about any single political figure, but rather the collision of long-standing trends in trade, demographics, and national priorities. You'll hear Peter Zeihan break down how the post–World War II system, built on American-led security and global cooperation, is unraveling amidst demographic decline and shifting alliances. They explore why the current moment might be unlike anything we've seen since the Black Plague, what happens when countries are forced to become economic generalists again, and why U.S. leadership is facing a critical test. Plus, Tom Bilyeu presses Peter on what this means for America's future, global productivity, and the hard choices ahead. Whether you're a geopolitical buff or just eager to understand how economics truly drives world affairs, this episode will challenge your assumptions and give you the tools to see the present—and the future—in a whole new light. Stay tuned for this thought-provoking conversation. Follow Peter Zeihan:Website: https://zeihan.com/Twitter: https://twitter.com/PeterZeihanLinkedIn: https://www.linkedin.com/in/peter-zeihan-1a8176/ YouTube: https://www.youtube.com/@ZeihanonGeopolitics What's up, everybody? It's Tom Bilyeu here: If you want my help... STARTING a business: join me here at ZERO TO FOUNDER:  https://tombilyeu.com/zero-to-founder?utm_campaign=Podcast%20Offer&utm_source=podca[%E2%80%A6]d%20end%20of%20show&utm_content=podcast%20ad%20end%20of%20show SCALING a business: see if you qualify here.:  https://tombilyeu.com/call Get my battle-tested strategies and insights delivered weekly to your inbox: sign up here.: https://tombilyeu.com/ ********************************************************************** If you're serious about leveling up your life, I urge you to check out my new podcast, Tom Bilyeu's Mindset Playbook —a goldmine of my most impactful episodes on mindset, business, and health. Trust me, your future self will thank you. ********************************************************************** FOLLOW TOM: Instagram: https://www.instagram.com/tombilyeu/ Tik Tok: https://www.tiktok.com/@tombilyeu?lang=en Twitter: https://twitter.com/tombilyeu YouTube: https://www.youtube.com/@TomBilyeu Quince: Free shipping and 365-day returns at https://quince.com/impactpod Shopify: Sign up for your one-dollar-per-month trial period at https://shopify.com/impact Ketone IQ: Visit https://ketone.com/IMPACT for 30% OFF your subscription order Incogni: Take your personal data back with Incogni! Use code IMPACT at the link below and get 60% off an annual plan: https://incogni.com/impact Blocktrust IRA: Get up to $2,500 funding bonus to kickstart your account at https://tomcryptoira.com Netsuite: Right now, get our free business guide, Demystifying AI, at https://NetSuite.com/Theory Huel: High-Protein Starter Kit 20% off for new customers at https://huel.com/impact code impact global security, US foreign policy, trade, demographics, Cold War, NATO, globalization, manufacturing, K-shaped economy, world order, American alliances, post-World War II system, containment, economic sanctions, Iraq War, war on terror, US industrial base, economic models, Modern Monetary Theory (MMT), tariffs, Trump administration, supply chain, deglobalization, NAFTA, regional globalization, policy experts, Cuban workforce, Colombia infrastructure, technological stagnation, demographic collapse, China-US relations Learn more about your ad choices. Visit megaphone.fm/adchoices

 EP 677 Josh Smith – MKC What’s up! This week on the Rich Outdoors Podcast, I’m sitting down with Josh Smith—founder of Montana Knife Company and honestly one of the most inspiring entrepreneurs in the outdoor space right now. This is a podcast I’ve been wanting to do for a long time, and we did not disappoint. Josh went from being a lineman for the power company, making knives on the side in a 200 square foot shop in his horse pasture, to building one of the most beloved brands in the hunting industry from the ground up. No investors, no conglomerates, no selling out. Just a guy who refused to quit and built something that hunters actually care about. We talk about the origin story of MKC, why he saw a massive hole in the hunting knife market, and how he quietly infiltrated the hunting community like a Green Beret special ops team before anyone even knew he was there. We get into the Benchmade lawsuit and why his entire team cheered when they found out they were getting sued. We dive deep into building company culture at scale, hiring the right people, why listening to your customer beats watching your competitor every single time, and why most people have no idea how many hunters have never even heard of a brand they think everyone knows about. But this one goes way beyond knives. We talk about Bridger Watch, building a product in a category dominated by giants, the parallels between what Josh built and what we’re trying to build, and the advice he gave me that I’m going to be thinking about for a long time. We also talk about legacy—knives that get passed down, stories behind the blades, and why sometimes the most important tool isn’t the most impressive one, it’s the one that means something. This is one of those conversations that reminds you why you started. Whether you’re a hunter, an entrepreneur, or both—this episode is for you. Let’s get into it. Interested in the Bridger Watch? If you heard us talk about the smartwatch we’re building for hunters and want to be the first to know what we’re up to—head over to bridgerwatch.com and get on the list. Three years in the making and we’re just getting started. Go check it out. Episode Sponsors Tricer Tripods – They make gear that’s fast, light, and simple, from amazing tripods to bino mounts, panhead truck mounts, and now even bipods. Trier just dropped their new updated AD and BC tripods, and I got to test the new Tritech technology this year. The center post is now a T-post, which makes it pack down smaller and cleaner—Drew is a mad scientist and he just keeps innovating. If you want to use code TRO, it’ll save you 10% at checkout. Go support a great company. Head over to tricer.com. Stone Glacier – If you’re in the market for a new pack, I ran the Sky Archer 6400 this year and packed out a lot of animals with it including a couple of elk. What I love about Stone Glacier packs is they work great whether you’re on a 10-day backpacking trip or day hunting from the side-by-side. Minimalist, tough, and they work. You don’t need to own multiple packs—this thing does it all. Check it out at stoneglacier.com and use code TRO for a discount. Chapter Timestamps 0:00 – Intro & Sponsors 3:45 – Welcome Josh Smith: Driving Across Montana for a Podcast 6:30 – Why Josh Started Montana Knife Company 10:15 – Seeing the Gap: What Was Missing in the Hunting Knife Market 14:00 – Authenticity from Day One: Building Community Without Money 18:30 – Sending Knives Out with No Ask: How Word Spread 22:00 – From the Horse Pasture to 11 Employees: The Growth Timeline 26:15 – The Green Beret Strategy: Quietly Taking Over the Hunting Space 30:00 – Getting Sued by Benchmade (And Why the Team Cheered) 34:30 – Don’t Watch Your Competitor, Listen to Your Customer 38:15 – Scaling Fast Without Losing Culture 42:00 – Hiring Doers: What Josh Looks for in Employees 46:30 – The Pizza Rule: Why You Can’t Manage Too Many People 50:15 – How MKC Uses Transparency to Build Employee Buy-In 54:00 – Taking on Giants: Parallels Between MKC and Bridger Watch 58:30 – Most Hunters Have Never Heard of You (And Why That’s Exciting) 1:02:15 – The Legacy of a Knife: Stories Behind the Blades 1:07:00 – Building a Family Heirloom vs. Building a Gadget 1:11:30 – Josh’s Advice for Bridger Watch 1:15:00 – Don’t Quit Your Day Job Yet: How to Chase a Dream Responsibly 1:18:30 – The People You Surround Yourself With Matter Everything 1:21:00 – Final Thoughts & What Josh is Most Excited About Three Key Takeaways Listen to Your Customer, Not Your Competitor – One of Josh’s most powerful pieces of advice: don’t open your competitor’s website every day and react to what they’re doing. Your product roadmap should be driven entirely by what your customer is telling you they need—not by what the big brand is doing. By the time you react to a competitor, you’re already behind. The companies that win are the ones so locked into their customer’s needs that by the time the big guy realizes what happened, it’s too late. Most People Don’t Know You Exist—And That’s the Opportunity – MKC ran surveys recently and the percentage of hunters who had heard of them was shockingly low. Most companies would find that depressing. Josh and Brandon found it energizing. If you’ve built something great and most of your target market still doesn’t know you exist, you have an enormous runway in front of you. Stop assuming everyone knows your story. Tell it again. Tell it to the 3,000 people in that gymnasium down the road who’ve never heard it. Culture Is Built Intentionally or It Isn’t Built at All – From bringing employees to trade shows as a reward, to reading the Attaboy box out loud at company meetings, to bringing in bankers and health insurance reps to teach employees about life—Josh has built a company where people feel cared about. That doesn’t happen by accident. It requires intentional decisions every single day to treat your people the way you’d want your own kids to be treated. And when people feel that, they go the extra mile and they keep the culture alive even when you can’t be in every room.

How long would it take to revamp your entire business? For Elliott Zelinskas: three weeks. Using Claude Code, he rebuilt his 4-year website, generated 100+ SEO pages, automated YouTube creation and publishing, and replaced parts of his tech stack. All without a dev team. Is agentic AI the next evolution of entrepreneurship? LINKS Follow Elliott on X Follow Elliott on Instagram Meet Elliott and other lifestyle founders inside Dynamite Circle Hang out exclusively with 7+ figure founders in DC BLACK Bento will beat your current email bill — up to 70% off or $300 in credits Million Dollar Weekend by Noah Kagan Live Well on Less Than You Think by Fred Brock CHAPTERS (00:00:00) Intro (00:02:39) Meet Elliott & His Business (00:07:05) How Elliott Started Using Claude Code (00:11:16) Upsides and Downsides of Agentic AI (00:15:13) Simple But Powerful Use Cases for Agentic AI (00:22:07) Six Tips for Non-Tech Founders (00:33:18) Security, Risk, and How to Decide What to Build (00:46:42) Digital Nomading and Personal Finance CONNECT: Dan@tropicalmba.com Ian@tropicalmba.com Past guests on TMBA include Cal Newport, David Heinemeier Hannson, Seth Godin, Ricardo Semler, Noah Kagan, Rob Walling, Jay Clouse, Einar Vollset, Sam Dogan, Gino Wickam, James Clear, Jodie Cook, Mark Webster, Steph Smith, Taylor Pearson, Justin Tan, Matt Gartland, Ayman Al-Abdullah, Lucy Bella. PLAYLIST: Bad Hiring Advice That Can Actually Work: 9 Tactics for Lifestyle Founders 4 Ways to Start a Business From Scratch in 2026 “Scaling on Steroids” with AI Automation ft. Juan Montero

Picture this: you are 7,000 miles away from home, in a new country with no familiar faces. How do you build a sense of community? You start with food. This week on SUPERWOMEN, I'm joined by Shiza Shahid, Co-Founder of the Malala Fund and Our Place. She shares how her activism shaped her journey as an entrepreneur, and how food became a powerful tool for connection and belonging. This belief laid the groundwork for Our Place—a brand built around the idea that cookware can be more than just functional; it can bring people together. We discuss how Shiza challenged the cookware industry, the balance between profit and purpose, and the impact of community-driven growth on her brand's success. Things to listen for: (00:00) Meeting Shiza Shahid, Co-Founder of Malala Fund and Our Place (02:29) Shiza's bold career pivots (03:21) Rebuilding connection as an immigrant (10:39) Scaling without losing community (14:58) What it's like building a company with your spouse (17:48) Letting go of fear and imagined risk (25:08) Choosing sustainable growth over hype (31:16) Why non-toxic materials are non-negotiable Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of the HVAC Know It All Business Edition Podcast, co-hosts Gary McCreadie and Furman Haynes of WorkHero talk with Robyn Hass, Founder and Fractional CFO of Mainstreet MEP™ by HVAC Office Solutions and Trade Finance in Ten Podcast, and Fractional MEP CFO/CHRO, FP&A and Change Management Consultant of Robyn Hass Consulting. Robyn shares her journey of building Core Mechanical, a company that successfully scaled and was eventually sold to private equity. She discusses the importance of focus in business, financial systems, and the strategies behind scaling operations, particularly for those starting small in the HVAC industry. She offers invaluable insights on niche selection, managing overhead, and the systems and tools that were crucial for her company's success. Robyn also talks about how to support solo business owners and the challenges they face in managing their businesses effectively.    Expect to Learn: - Why focus is key when scaling your business and how choosing a niche helped Robyn's company grow. - The importance of financial systems and why investing in software like QuickBooks and field management tools early on can save you headaches later. - How to manage cash flow effectively and the overhead challenges that come with scaling. - Why it's crucial to train your technicians properly and capture all job data from day one. - Robyn's advice for solo entrepreneurs, why you don't always have to grow to be successful and how to partner with others. - The best ways to ensure financial health and how understanding your net margin is more important than EBITDA. - Robyn's new offerings for business owners, including resources, boot camps, and reporting tools to help scale more efficiently.   Timestamps: [00:00] - Introduction [02:05] - Starting Core Mechanical [04:04] - Importance of Focus in the Early Stages [05:20] - The Risk of Spreading Thin [07:29] - Challenges for Small Business Owners Doing Everything [08:29] - Setting Up Systems in the Early Days [10:08] - The Need for Proper Accounting and Software [12:50] - When to Hire and the Cash Flow Challenge [13:18] - Managing Cash Flow [14:23] - The Owner's Role in Scaling [15:51] - True Profit Margins and EBITDA [17:23] - The Difference Between EBITDA and Net Profit [19:24] - Setting Owner Salaries and Business Growth [21:20] - Thoughts on Cash Payments   Follow Robyn Hass: LinkedIn: https://www.linkedin.com/in/jrobynh/ Company LinkedIn: https://www.linkedin.com/company/mainstreet-mep Company Website: https://myhvacoffice.com/   Follow Gary McCreadie: LinkedIn: https://www.linkedin.com/in/gary-mccreadie-38217a77/ Website: https://www.hvacknowitall.com Facebook: https://www.facebook.com/people/HVAC-Know-It-All-2/61569643061429/   Follow Furman Haynes on: LinkedIn: https://www.linkedin.com/in/Furmanhaynes/ WorkHero: https://www.linkedin.com/company/workherohvac/ Instagram: https://www.instagram.com/hvacknowitall1/  

#776 What does it take to go from cutting shrubs with your dad to designing luxury outdoor spaces for celebrities and high-net-worth clients? In this episode, Brien Gearin sits down with Steve Griggs, founder of Steve Griggs Design, who shares how he built a premium landscaping business in New York over the past 40 years. Steve opens up about his humble beginnings, the pivotal moment that forced him to change his business model, and how he grew from running crews himself to becoming a high-end outdoor design authority. He breaks down the subcontractor model that helped him scale profitably, why clear communication and doing what you say matters more than flashy marketing, and how to build lasting client relationships that drive repeat business. Whether you're new to the green industry or aiming to elevate your home services brand, this episode delivers real talk, hard-earned wisdom, and plenty of laughs along the way! (Original Air Date - 6/11/25) What we discuss with Steve: + Starting out cutting shrubs + Lessons from the 2008 crash + Scaling with subcontractors + Importance of client relationships + Spotting red flag clients + Pricing for profit and margin + Communicating clearly with customers + Working with high-profile clientele + Avoiding burnout and staying relevant + Knowing your numbers and costs Thank you, Steve! Check out Steve Griggs Design at ⁠SteveGriggsDesign.com⁠⁠⁠⁠. Follow Steve on ⁠Instagram⁠. Watch the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠video podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ of this episode! To get access to our FREE Business Training course go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠MillionaireUniversity.com/training⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ To get exclusive offers mentioned in this episode and to support the show, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠millionaireuniversity.com/sponsors⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, Dr. Sidney H. Raymond, Chief Medical Officer at Ochsner Health Network, discusses leading population health across more than 600,000 lives and advancing value based care beyond contract boundaries. He shares how care model redesign, prevention focused strategies, digital health, and patient centered culture are driving measurable gains in quality, cost, and care coordination across diverse communities.

Is your AI strategy ready for scale or headed for failure? In this episode of Technovation, leaders from Norfolk Southern, McCormick & Company, and Vulcan Materials joined us at our Metis Strategy Summit to discuss what it really takes to move from AI pilots to enterprise impact. From AI factories and predictive transportation platforms to hybrid operating models and reward system redesign, this conversation dives into the operational and cultural realities of scaling AI in complex organizations. Key highlights: Why 2026 is shaping up as the “scale or fail” year for AI How to industrialize AI with data platforms and governance Embedding technologists in business functions for contextual impact The critical role of AI literacy and trust Aligning incentives to drive behavioral change

This is a lesson on working within your capacity. The first few years of my boys' lives were some of my toughest, both personally and professionally. My boys were born 11 months apart, and I learned a lot about what happens when life expands faster than your plans. Motherhood taught me a lot about how to scale in a way that won't break you. Scaling isn't just about building something bigger, it's about designing systems that honor the phase of life you're in. In this episode, I'm sharing the internal shifts and external systems that actually help with sustainable growth, even in life's busiest seasons. In this episode, you will learn about: Why most women actually burn out (it has nothing to do with strategy). What was happening in my business the same year I had two babies. What we've been taught about scaling, and the important message that's been missing. Why growth doesn't feel like success when it happens too quickly. What the Irish Twins Strategy is and how it can help you grow sustainably. What I decided to do differently with my kids despite the social pressure. The mindset that broke me and what I realized I needed to shift. What you should focus on if you really want to avoid burnout in challenging times.     Check out The Pink Skirt Project, happening July 9-10, 2026 in Kelowna, BC, Canada.   Want to get unstuck, feel more confident and surround yourself with women ready to help you climb? Join The Pink Skirt Society.   Got a minute? I would love a review! ⭐⭐⭐⭐⭐ Click here, scroll to the bottom, tap, and give me five stars. Then select "Write a Review." Make sure to highlight your favorite bits. Subscribe here. Connect with Renée: @renee_warren www.reneewarren.com

Matt Gardner scaled Hiline by moving beyond referral-only growth and building systems designed to scale. By niching down, investing ahead of demand, restructuring sales roles, and embracing automation and technology, Hiline created a predictable growth engine that does not rely on founder hustle. Scaling an accounting firm beyond referrals is one of the hardest challenges firm owners face. In this episode of Growing Your Firm, Matt Gardner shares how Hiline grew by niching down, investing ahead of growth, and building a real go-to-market engine instead of relying on referrals or founder hustle. Resources: Matt Gardner's LinkedIn - https://www.linkedin.com/in/mattgardnercpa/n

On this podcast Jerry interviews Mark Stubler on how how to find and hire the best acquisitions rep for your wholesale business. Join a live training Feb 5-6 to Scale Your Wholesaler Business:https://joescalingsummit.com/Learn More About JoeHomebuyer:https://joehomebuyerfranchising.com/jerry/With over 500,000 subscribers, this is the #1 channel on YouTube for all things wholesaling and flipping. SUBSCRIBE NOW! https://www.youtube.com/@FlippingMastery Podcast fan? Listen to your favorite Flipping Mastery TV videos on your favorite podcast platform! http://FlippingMasteryPodcast.com Jerry Norton went from digging holes for minimum wage in his mid 20's to becoming a millionaire by the age of 30. Today he's the nation's leading expert on flipping houses and has taught thousands of people how to live their dream lifestyle through real estate. **NOTE: To Download any of Jerry's FREE training, tools, or resources… Click on the link provided and enter your email. The download is automatically emailed to you. If you don't see it, check your junk/spam folder, in case your email provider put it there. If you still don't see it, contact our support at: support@flippingmastery.com or 888) 958-3028.Get Access to Unlimited Free Property Searches and Downloads: https://flippingmastery.com/propwireWholesaling & House Flipping Software: https://flippingmastery.com/flipsterpodMake $10,000 Finding Deals: https://flippingmastery.com/10kpodGet 100% funding for your deals: https://flippingmastery.com/fspodMentoring Program: https://flippingmastery.com/ftpodFREE 8 Week Training Program: https://flippingmastery.com/8wpodGet Paid $8700 To Find Vacant Lots For Jerry: https://flippingmastery.com/lfpodFREE 30 Day Quickstart Kit https://flippingmastery.com/qkpodFREE Virtual Wholesaling Kit: https://flippingmastery.com/vfpodFREE On-Market Deal Finder Tool: https://flippingmastery.com/dcpodFREE Wholesaler Contracts: https://flippingmastery.com/wcpodFREE Comp Tool: https://flippingmastery.com/compodFREE Funding Kit: https://flippingmastery.com/fkpodFREE Agent Offer Sheet & Scripts: https://flippingmastery.com/aspodFREE Cash Buyer Scripts: https://flippingmastery.com/cbspodFREE Best Selling Wholesaling Ebook: https://flippingmastery.com/ebookpodFREE Best Selling Fix and Flip Ebook: https://flippingmastery.com/ebpodFREE Rehab Checklist: https://flippingmastery.com/rehabpod LET'S CONNECT! FACEBOOK http://www.Facebook.com/flippingmastery INSTAGRAM http://www.instagram.com/flippingmastery

Send a textIn this episode of the Digital and Dirt podcast, Ian sits down with Brittany Sadlouskos, Chief Operating Officer at DAA Media + Marketing, to discuss how honesty, partnership, and cultural relevance fuel long-term agency and brand success.Podcast Breakdown00:00 – 06:06 From Kitchen Dreams to the Agency World06:07 – 11:48 Growing Up With DAA11:49 – 17:58 Trust Is the Strategy17:59 – 24:04 Adapting Without Losing Yourself24:05 – 35:49 Scaling the Agency–Client Relationship35:50 – 49:00 QSRs, Culture, and Experiential Impact49:01 – 56:51 OOH, the Funnel, and Women Leading Forward

Ever needed cash for a deal and the banks said no? Loren Wernette got fed up—and decided to become the lender himself. Find out how he scaled smarter by launching his own private lending fund that helps other investors succeed while delivering high returns to capital partners. Get Interviewed on the Show! - ================================== Are you a real estate investor with some 'tales from the trenches' you'd like to share with our audience? Want to get great exposure and be seen as a bonafide real estate pro by your friends? Would you like to inspire other people to take action with real estate investing? Then we'd love to interview you! Find out more and pick the date here: http://daveinterviewsyou.com/ #PrivateMoney #RealEstateLending #DaveDubeauPodcast

From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:

If you're going to own a fleet of vehicles, why wouldn't you finance them through your own banking system instead of the bank's? In this episode of Without the Bank, we break down one of the most misunderstood—and powerful—chapters in Nelson Nash's Becoming Your Own Banker: equipment financing. WTB Episode 256 walks through how capitalizing a properly designed life insurance system allows business owners to finance trucks, equipment, and big-ticket items while building equity in the right place—their own banking system. This episode clears up common confusion around "extra interest," explains why premium is what actually makes you money, and shows how scaling vehicle financing works—from one truck to an entire fleet. No magic. No shortcuts. Just math, discipline, and control. Key Takeaways: Why equity in equipment is limited—and banking equity isn't The real meaning of "extra interest" (hint: it's additional premium) Why you don't make money just by taking policy loans How financing one, two, three, or four vehicles simply scales the same system Why capitalizing first gives you flexibility when business gets hard How policies must be structured as a system, not a single policy Chapters: (00:00) Why fleet owners should think differently about financing (01:01) Capitalizing on the policy before buying equipment (03:07) Equity in the wrong place vs. the right place (06:05) "Extra interest" explained (and why it's misunderstood) (10:38) Financing one truck step-by-step (13:59) Scaling to multiple vehicles (17:06) Using the system beyond trucks (taxes, real estate, equipment) Want help structuring your own banking system? Buy the book, read it, and then schedule a strategy call with our team today. Read the chapter. Run the numbers. Don't overcomplicate it. Links Mentioned: Without the Bank: https://www.withoutthebank.com  Contact: maryjo@withoutthebank.com tarisa@withoutthebank.com

In this episode, Adam Torres interviews Nestor Gutierrez, CEO of Rancho Express Lube, live from David Rivera's Monetized Talks in Beverly Hills. Nestor shares how he moved from being a “trapped employee” in his own automotive business to scaling by delegating, building leadership skills, and investing in coaching, masterminds, and personal development. He also explains why collaboration and community play a major role in long-term growth. Follow Adam on Instagram at https://www.instagram.com/askadamtorres/ for up to date information on book releases and tour schedule. Apply to be a guest on our podcast: https://missionmatters.lpages.co/podcastguest/ Visit our website: https://missionmatters.com/ More FREE content from Mission Matters here: https://linktr.ee/missionmattersmedia Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode of Head, Heart, and Boots, Brandon and I sit down with Andrew Dobson, owner of ServPro Team Dobson, one of the largest ServPro operators in the country with nineteen licenses across multiple markets. Andrew shares the full origin story of how he grew up in a family cleaning business, left a creative path that once pointed toward Pixar, and slowly built a restoration operation grounded in process, precision, and relentless attention to detail. We dig into Andrew's partnership with his father, what it takes to successfully merge family businesses, and how clearly defined roles, operating agreements, and trust allowed them to scale without breaking the relationship. Andrew unpacks his obsession with design and user experience, explaining how lessons from web design, baseball, and usability testing shaped everything from warehouse layout to truck setups to customer-facing processes. The conversation goes deep into leadership development, soft skills, and why technical excellence alone is not enough to win in restoration. Andrew shares hard-earned lessons about first impressions, customer perception, sales and operations alignment, and the mistake he made trying to hire leaders without enough industry depth. We also explore his newest venture, Certified Restoration Training, and his mission to raise the standard of professionalism across the industry through technical training and soft skills education. This episode is packed with practical insight for owners trying to scale, leaders developing teams, and anyone who believes that inches add up to miles when it comes to building a great company. Hope you enjoy. Chris Why You Should Listen [00:04:17] How Andrew went from a family cleaning business and a near-career in animation to building one of the largest ServPro teams in the country [00:10:19] What it really takes to successfully partner with family, including operating agreements, role clarity, and navigating shared risk [00:17:54] How an obsession with process, usability, and design transformed warehouses, trucks, and field execution into a competitive advantage [00:26:21] A powerful lesson on first impressions, soft skills, and why customer perception matters more than effort or intent [00:48:17] Why hiring professional managers without deep industry understanding became a leadership mistake, and what Andrew learned about developing true operators and leaders Did you know... Only 30% of businesses listed for sale actually find a buyer? 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Fractional Sales Leadership They act as your outsourced VP of Sales, taking full responsibility for training, managing, and growing your sales team. No six-figure hire needed. Clients often close 20 to 50 percent more deals within six months, thanks to data-driven coaching, CRM setup, scripts, and performance reviews.More at floodlightgrp.com/sales Commercial Sales MasterCourse A self-paced, video-driven B2B sales course designed specifically for restoration teams. Perfect for building commercial revenue and getting free from TPA handcuffs. Covers mindset, prospecting, pipeline building, LinkedIn lead generation, and includes a $250 discount with code SALESBOOST.Details at floodlightgrp.com/courses Tailored Consulting & Coaching Floodlight's Propel Your Business methodology offers a full-circle roadmap: financials, sales, marketing, leadership, recruiting, productivity. All built for contractors. These aren't “life coaches.” They're former restoration owners who've lived the chaos and know how to scale out of it.Explore more at floodlightgrp.com Live Training, Tools & Strategic Partnerships Floodlight also delivers live onsite and virtual training, keynote speaking, and leadership tracks covering operations, project management, and strategic growth. Bonus: They've vetted tools like Xcelerate, Liftify, and Sureti. Floodlight clients get access to exclusive discounts on tech that actually moves the needle.See all partnerships at floodlightgrp.com/partners Why it matters for you as a listener You don't need to figure this stuff out alone. If you're serious about sales growth, operational clarity, exit readiness, or leadership development, Floodlight is already helping folks like you scale smarter. And you get it from industry insiders. People who've sat in your chair, survived the fires, and built systems that actually work.

On this episode of CIO Classified, host Yousuf Khan sits down with Ravi Thadani, Global Head of IT at Enphase Energy, a company powering over 5 million homes across 160 countries with clean, solar-driven energy. With 85 million microinverters producing 30 gigawatts of power, Ravi's team is at the epicenter of a massive, real-time data operation—and every IT decision directly impacts the customer experience.About Ravi: Ravi Thadani is a seasoned IT executive with extensive experience leading large-scale digital transformations across Fortune 500 companies. He has driven strategic initiatives across ERP, CRM, PLM, HCM, SCM, analytics, AI/ML, network architecture, cloud infrastructure, and M&A integration. With oversight of multi-$10M budgets and teams of over 300, Ravi has supported business units ranging from $2B to $70B in revenue.Known for his strategic vision and execution, Ravi is recognized for fostering cross-functional alignment, driving agile transformation, and cultivating high-performing teams. His leadership approach is grounded in strong business partnerships, stakeholder governance, innovation, and a relentless focus on outcomes.Timestamps:01:50 – Enphase Energy's Global Operations03:40 – Ravi's Role and Responsibilities06:00 – Managing Customer Data and CRM16:45 – Driving Change as a CIO19:50 – The Role of Data in AI20:55 – The Importance of Data Cleaning21:20 – Effective Data Governance Strategies23:45 – Architecting for Scalability27:30 – Challenges in Hardware and Software IntegrationGuest Highlights:"AI isn't replacing you—people using AI are. The adoption curve is about enabling people to do more, not just reducing headcount.""The biggest failure point in IT projects? Treating them like IT projects. Every transformation has to be owned by the business.""Your architecture should always assume 10x growth. Even if you're not scaling today, you need a conscious plan for when you do."Get Connected:Yousuf Kahn on LinkedInRavi Thadani on LinkedInHungry for more tech talk? Check out latest episodes at ciopod.com: Ep 65 - Accelerating Software Development at Enterprise ScaleEp 64 - How Autonomous AI is Solving the Enterprise Modernization ChallengeEp 63 - How AI is Expanding the CIO RoleLearn more about Caspian Studios: caspianstudios.comOur Sponsor: Want to accelerate software development by 500%? Meet Blitzy, the only autonomous code generation platform with infinite code context, purpose-built for large, complex enterprise-scale codebases.While other AI coding tools provide snippets of code and struggle with context, Blitzy ingests millions of lines of code and orchestrates thousands of agents that reason for hours to map every line-level dependency.With a complete contextual understanding of your codebase, Blitzy is ready to be deployed at the beginning of every sprint. Blitzy handles the heavy lifting, delivering over 80% of the work autonomously. The platform plans, builds, and validates premium-quality code at the speed of compute, turning months of engineering into a matter of days.It's the secret weapon for Fortune 500 companies globally. To hear how engineering leaders are transforming the way they deliver software, visit blitzy.com. Schedule a meeting with their consultants to enable an AI-Native SDLC in your organization today. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This episode is available in audio format on the Let's Talk Loyalty podcast and in video format on www.Loyalty.TV.In this episode we are delighted to interview Ben Stirling, an experienced commercial executive with a track record of scaling loyalty platforms, transforming sales organisation and delivering GTM strategies that drive acquisition and ARR growth. He has led commercial transformation at Expedia, Tenerity and Capillary, launched new solutions, expanded into international markets and delivered results across multiple sectors.He is currently a fractional CRO at TenX Strategy and supports PE-backed and enterprise firms in building predictable revenue systems and exit-ready growth. His impact includes scaling Tenerity's loyalty marketplace solution to acquisition in two years, providing loyalty solution to Santander, C&A, British Gas, TD Bank and Frontier, and growing commercial channels at Expedia that delivered $200M+ in new revenue.In this episode, Ben shares his proven insights on how to sell loyalty internally, from aligning feature sets to user needs, to securing C-suite backing with ROI models, and ultimately winning board-level buy-in by linking loyalty to long-term enterprise value. We'll also be learning about his favourite books and highlights and key learnings from the programmes he has worked on.Hosted by Charlie HillsShow Notes :1) Ben Stirling,2) TenX Strategy3) TenX Strategy - Budget Sign Off PDF4) Hooked- Book Recommendation5) The Road Less Stupid - Book Recommendation

Scaling the Right Way: Integrity, Tough Conversations & Eliminating Leadership Noise What separates leaders who scale sustainably from those who burn out their culture? In Part 2 of this conversation, Jeremy Jenson shares the deeper leadership evolution behind building a $12M+ organization — and why integrity became the turning point in his journey. We move beyond strategy and into self-leadership. In this episode, we explore: • Why revenue alone is a dangerous focus • Letting go of high producers who lack integrity • The power of tough, honest conversations • Why leaders must eliminate "noise" to stay focused • Using EOS (Entrepreneurial Operating System) for clarity • How to give feedback that elevates performance • Why great leaders motivate through example, not fear • Building systems that protect your culture Jeremy also shares a powerful perspective shift: Success stops being about what you achieve — and starts being about what you help others achieve. If you're leading a team, scaling a company, or navigating growth, this episode will challenge how you think about clarity, accountability, and long-term success. If you haven't watched Part 1, start there to understand the foundation of the growth story. Connect with Jeremy on LinkedIn: https://www.linkedin.com/in/jeremyjenson/

In this episode of Entrepreneur | Authorities, Joe Pardavila sits down with Charles Sims, founder of SkaFld Studio and “Hurricane CTO,” to break down why nearly 80 percent of every business is built on repeatable systems and how leaders who master that foundation scale faster with less chaos.Charles introduces the SkaFld Anywhere methodology, a framework designed to systemize the core operations behind growth across any industry. From turning a single indoor pickleball venue into a franchise ready operation, to guiding school districts through AI adoption with clarity and trust, Charles shows why strong infrastructure matters more than flashy ideas.The conversation explores why smart leaders resist standardization, how perfectionism stalls momentum, and what usually breaks first when companies try to grow without clear systems in place. Charles shares real examples from startups, local businesses, and public sector organizations, explaining how modular thinking removes decision fatigue and builds consistency without killing culture.Joe and Charles also dive into the human side of scaling, including how trust lowers risk, why empathy drives adoption of new initiatives, and how leaders separate personal identity from the systems their organizations need to thrive.The episode closes with a clear takeaway: when you systemize the repeatable 80 percent of your business, the remaining 20 percent becomes your competitive edge.

Welcome to Impact Theory with Tom Bilyeu! In this eye-opening episode, Tom Bilyeu and co-host Drew tackle some of the most pressing—and controversial—headlines rocking the world this week. From Big Tech giants like Meta facing landmark lawsuits over the mental health impact of social media on kids, to explosive rumors about cancer cures being unleashed after the US's exit from the WHO, no stone is left unturned. Tom Bilyeu breaks down what's really behind these medical breakthroughs, debunking online conspiracies and explaining the critical roles of the FDA and Big Pharma. The conversation gets real about the unintended consequences of social media on developing minds, whether government intervention is the answer, and how parents can navigate the digital minefield. The hosts also deliver in-depth commentary on global power shifts, including China's economic maneuvers and Japan's political realignment, as well as the seismic advancements in AI technology that are set to reshape creative industries—and possibly life as we know it. To cap it off, Tom Bilyeu and Drew explore SpaceX's renewed focus on building a city on the moon, pondering what it means for humanity's future among the stars. Whether you're curious about the facts behind viral threads, anxious about the impact of tech on the next generation, or just want a fresh take on global headlines, this episode has something for everyone. Let's dive in! Quince: Free shipping and 365-day returns at https://quince.com/impactpodShopify: Sign up for your one-dollar-per-month trial period at https://shopify.com/impactKetone IQ: Visit https://ketone.com/IMPACT for 30% OFF your subscription orderIncogni: Take your personal data back with Incogni! Use code IMPACT at the link below and get 60% off an annual plan: https://incogni.com/impactBlocktrust IRA: Get up to $2,500 funding bonus to kickstart your account at https://tomcryptoira.comNetsuite: Right now, get our free business guide, Demystifying AI, at https://NetSuite.com/TheoryHuel: High-Protein Starter Kit 20% off for new customers at https://huel.com/impact code impact What's up, everybody? It's Tom Bilyeu here: If you want my help... STARTING a business: join me here at ZERO TO FOUNDER:  https://tombilyeu.com/zero-to-founder?utm_campaign=Podcast%20Offer&utm_source=podca[%E2%80%A6]d%20end%20of%20show&utm_content=podcast%20ad%20end%20of%20show SCALING a business: see if you qualify here.:  https://tombilyeu.com/call Get my battle-tested strategies and insights delivered weekly to your inbox: sign up here.: https://tombilyeu.com/ ********************************************************************** If you're serious about leveling up your life, I urge you to check out my new podcast, Tom Bilyeu's Mindset Playbook —a goldmine of my most impactful episodes on mindset, business, and health. Trust me, your future self will thank you. ********************************************************************** FOLLOW TOM: Instagram: https://www.instagram.com/tombilyeu/ Tik Tok: https://www.tiktok.com/@tombilyeu?lang=en Twitter: https://twitter.com/tombilyeu YouTube: https://www.youtube.com/@TomBilyeu Big Tech lawsuit, social media addiction, mental health, Big Pharma, cancer cures, World Health Organization (WHO), US healthcare, FDA, immunotherapy, mRNA cancer vaccines, CAR-T cell therapy, KRAS inhibitors, drug approvals, government incentives, socialism, AI breakthroughs, video AI, creative industry, China-US relations, de-dollarization, US debt, China demographics, Japan politics, sushi-fication of Japan, immigration, education policy, brain development, parental control, government regulation, space exploration, Elon Musk moon base. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of the Fullerton Unfiltered Podcast, I sit down with financial advisor and wealth coach Nick Bartolo to talk private equity, long-term wealth strategy, and how business owners should think beyond just today's cash flow. We break down what private equity really is, who it's for, and how disciplined investing ties into building lasting wealth and freedom. Lawntrapreneur Academy (The #1 Resource for Starting, Growing and Scaling a Successful Lawn & Landscaping Company). - https://www.lawntrepreneuracademy.com/  Granum Academy Bootcamp Tour (use BRIAN25 to save!): https://granum.com/academy-bootcamp/  GROW 2026 - February 10-12 Dallas, TX: https://hubs.li/Q03Ybxs10 LMN & Coffee - https://us06web.zoom.us/j/89495679453?pwd=m0wKa6prJWrARKClJKolBaJjl00OYn.1 Coast Pay Fuel Card: www.CoastPay.com/Brian

What you'll learn in this episode:How to identify your top three priorities before you start hiringThe right order of leverage—and why hiring too soon can kill your growthWhy belief and consistent lead generation come before building an organizationHow to retain top talent through growth and developmentThe importance of building a “bench” of leaders who can step in when neededWhat it takes to remove yourself from your business (the right way) 

In this episode, Sanjay speaks with Greg Cantori, founder of Little Deeds Accessibility Solutions, about how his background in the nonprofit sector led him into the construction space, and ultimately to building a growing accessibility-focused business.   Greg shares how years of working in nonprofits shaped his understanding of impact, why accessibility for older adults is both a social need and a business opportunity, and how simple home modifications, like installing shower grab bars, can dramatically improve quality of life.   What you'll learn: • How nonprofit experience can translate into strong entrepreneurial skills • Why accessibility is an increasingly important (and underserved) market • How to move from service work to a scalable business model • What it takes to expand a local business into a national service • How purpose and profitability can coexist     Chapters  00:00 Introduction 4:02 Building Little Deeds 8:51 Scaling revenue 16:30 Moving internationally while running a business 27:16 Reflecting on the business 29:22 Advice for other entrepreneurs 30:44 Closing and contact   Learn more about Little Deeds Accessibility Solutions at littledeeds.com   At Hiscox, we believe in supporting entrepreneurs who bring bold ideas and strong communities to life. Explore resources and coverage options to help protect and grow your business at Hiscox.com.   #entrepreneurship #accessibility #nonprofit

Save 10% on a Las Vegas Advisor 2026 membership and book with code MTM.  https://www.lasvegasadvisor.com/shop/products/lva-membership-platinum/ Episode Description This week MGM Resorts International held their earnings call. While revenues were down overall, we focus on the Strip where luxury properties are doing well, but overall the company is seeing declines. MGM's CEO described this as a reset, but what cost MGM business in 2025 and how will that be different in the future. Plus what is coming with renovations, digital check-in and more. In other news Nevada sports books took in less action on the Super Bowl compared to previous years. Is this a bad sign of things to come? We also discuss: scaling the Strat, Absinthe, the Dancing Waters of Desert Inn, Slowjamastan, scooter giveaways & who has been hired as the new head coach of the Las Vegas Raiders. Episode Guide 0:00 Casino scooter giveaway?!? 0:40 Raiders have another new head coach 1:40 Nevada Super Bowl numbers - Huge drop off! 3:15 What is causing such a drop in Nevada Super Bowl bets? 4:27 Slowjamastan visits Vegas 6:22 Primm hotels join Wyndham Rewards 7:47 Scaling the Strat - Walking up to the top for charity 9:14 The Dancing Waters of Desert Inn 11:14 Is Absinthe overrated or worth the money? 12:22 Absinthe garden & speakeasy is overlooked by many 14:36 MGM Earnings call & takeaways - The great reset? 15:30 Overall declines for MGM in Las Vegas 16:45 Luxor and Excalibur are still struggling 18:18 MGM's push for kiosk check-ins 20:00 The average age of a Las Vegas MGM Resorts room Each week tens of thousands of people tune into our MtM Vegas news shows at http://www.YouTube.com/milestomemories. We do two news shows weekly on YouTube with this being the audio version. Never miss out on the latest happenings in and around Las Vegas! Enjoying the podcast? Please consider leaving us a positive review on your favorite podcast platform! You can also connect with us anytime at podcast@milestomemories.com.  You can subscribe on Apple Podcasts, Google Podcasts, Spotify or by searching "MtM Vegas" or "Miles to Memories" in your favorite podcast app. Don't forget to check out our travel/miles/points podcast as well!

Reach Out Via Text!Recorded live in Scottsdale, Arizona, this episode dives deep into one of the hardest parts of growing a landscape business: hiring and firing. Jeremiah sits down with Brittany Auman to unpack the emotional rollercoaster of scaling from $1.2M toward $2.4M, including what went wrong when they first hired a salesperson and how using AI helped them build a real training process. They talk candidly about removing emotion from personnel decisions, protecting company culture, and why keeping the wrong person can hurt both your business and your family.Midway through, Jake Bradley of Private Paradise Landscapes joins the conversation and shares what it looked like to hire 24 employees in his first official year in business at just 18 years old. From over-hiring intentionally to building a 24-day training schedule, Jake explains why process, culture, and expectations matter more than resumes. The episode wraps with a powerful discussion on leadership, work ethic, sports, and the foundation parents lay for long-term success.If you're wrestling with when to hire, who to hire, or how to let someone go, this one hits home.Support the show 10% off LMN Software- https://lmncompany.partnerlinks.io/growinggreenpodcast Signup for our Newsletter- https://mailchi.mp/942ae158aff5/newsletter-signup Book A Consult Call-https://stan.store/GrowingGreenPodcast Lawntrepreneur Academy-https://www.lawntrepreneuracademy.com/ The Landscaping Bookkeeper-https://thelandscapingbookkeeper.com/ Instagram- https://www.instagram.com/growinggreenlandscapes/ Email-ggreenlandscapes@gmail.com Growing Green Website- https://www.growinggreenlandscapes.com/

Want to quit your job and build a real land investing business? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Welcome to the Ecomm Breakthrough Podcast, where I document my journey scaling an e-commerce brand and share the systems, strategies, and lessons learned in real-time. This episode introduces a scalable meeting cadence designed to improve business operations, outlining various types of effective meetings including weekly 1:1s, leadership huddles, and quarterly strategic planning sessions. Each meeting type has specific objectives and agendas to foster leadership and team alignment, ensuring your business is always moving forward.

Scaling a home service business is not just about running more ads or getting more leads. It is about building a real company with the right marketing strategy, the right people, and a presence that customers trust. In this episode of Service Business Mastery, Tersh Blissett sits down with Jen McKee, founder of Kee Hart Marketing, to break down what it really takes to grow and scale in today's market. Jen shares why video is now one of the most overlooked opportunities in home services, how Google is indexing videos across platforms, and why contractors must stop treating social media like an afterthought. They also dive into what makes a business scalable long-term, including profitability, building an asset (not a job), and why the right rooms and relationships are often the fastest path to growth. If you are serious about scaling your business, this episode will give you both the mindset and the marketing strategy you need. What You Will Learn in This Episode • Why video is one of the biggest marketing advantages in home services right now • How Google is indexing videos from Instagram, Facebook, TikTok, and YouTube • The simple checklist that helps your videos show up in search • Why your social media bio must clearly say what you do and where you do it • How to scale with profits, not just revenue • Why every owner should be building a business asset, not just a job • The real value of conferences, networking, and being vulnerable in the right rooms • Why you should seek advice from contractors who have actually done the thing • How to build your presence as a leader, brand, and business in your market Timestamps 00:00 Google is indexing all your videos now 03:10 Who Jen McKee is and what Kee Hart Marketing does 04:03 The Growth Experience conference (profits, people, presence) 10:55 Why great conferences avoid sales pitches 15:53 Why contractors should learn marketing from contractors 24:05 The mindset of continuous growth and not settling 25:17 Build an asset, not a job 26:34 The real value of conferences is your network 30:36 Stop having imposter syndrome (everyone has value) 36:16 Should contractors use AI for content? 43:18 How to get your videos indexed by Google right now 48:22 The Growth Experience details and how to get tickets Follow the Host and Guest Tersh Blissett: https://www.linkedin.com/in/tershblissett/ Josh Crouch: https://www.linkedin.com/in/josh-crouch/ Jen McKee: https://www.linkedin.com/in/jen-mckee-72114824/ Kee Hart Marketing: https://keehartmarketing.com/ Kee to Growth Podcast: https://www.youtube.com/@KeetoGrowthPodcast  Connect with Us • LinkedIn - https://www.linkedin.com/company/service-business-mastery • TikTok - https://www.tiktok.com/@servicebusinessmastery • Facebook Group - https://www.facebook.com/groups/servicebusinessmasterypodcast • Instagram - https://www.instagram.com/servicebusinessmasterypodcast

In this episode of Business School for the Rehab Chiropractor, Justin breaks down one of the most misunderstood topics in the profession; independent contracting. He explains why many clinics unknowingly operate in a legal gray zone, how tax realities can expose leadership gaps, and what it actually means to build a clinic that can grow beyond the owner.In this episode, you'll hear about:What most chiropractors don't get right about budgeting and financial planning.The real tradeoffs between one big clinic and multiple locations when scaling.And why leadership maturity can make or break a clinic's success. Your Host: Justin Rabinowitz Founder of RehabChiro Coach.Justin works with chiropractors and clinic owners to build profitable, scalable practices rooted in clear business models and disciplined execution.To get your first month free with Jane.app, use my code Rehabchiro1moAnd to book your demo, go to: Jane.app/demo

Welcome our guest, Rick Elmore, Founder of SimplyNoted.com | In this episode, Rick Elmore discusses the enduring power of handwritten notes in a digital world saturated with automated messages. He explains how his company, Simply Noted, leverages technology to automate the process of sending genuine, personalized handwritten notes, helping businesses cut through the noise and build meaningful connections with their customers.Rick emphasizes that while technology has evolved, the personal touch of a handwritten note remains a powerful tool for customer retention, marketing, and building lasting relationships. He shares insights on how to integrate this strategy into existing marketing workflows and leverage it to increase customer lifetime value and generate referrals.Start your 3-Day Fast Delivery with SimplyNoted.com here >>Rick Elmore's Top Key PointsThe Lost Art of Personal Connection: In an era of digital overload, a handwritten note stands out and makes a lasting impression.High Open Rates: Handwritten mail has a 99% open rate, significantly higher than any other form of direct mail or email marketing.Automation and Scalability: Simply Noted uses robotic technology to produce real pen-written notes that are scalable and can be integrated with CRMs and other marketing automation platforms.Hyper-Personalization with AI: By leveraging AI, the messages in the handwritten notes can be hyper-personalized based on customer data, making them even more impactful.Trackable and Actionable: With features like QR code tracking and delivery notifications, the impact of handwritten notes can be measured, and follow-up actions can be triggered for a multi-touch marketing approach.Podcast Episode Timestamps[02:48] Introduction to Simply Noted and the concept of automated handwritten mail.[07:07] The marketing power of handwritten notes and their high open rates.[10:31] The importance of systems and timing in a handwritten note strategy.[16:19] How to integrate handwritten notes into your marketing stack, including platforms like GoHighLevel.[22:00] How to get in touch with Rick Elmore and get a free sample kit from Simply Noted.Podcast Episode FAQsQ: What is Simply Noted?A: Simply Noted is a service that uses custom-built robots to write personalized, handwritten notes on behalf of businesses. This allows companies to send authentic-feeling mail at scale, fostering a personal connection with customers.Q: How does this integrate with my current marketing?A: Simply Noted can be integrated with most CRMs and marketing automation platforms. You can trigger the sending of a handwritten note based on specific customer actions, such as a purchase, an anniversary, or a birthday.Q: What are the benefits of sending handwritten notes?A: The primary benefits are increased customer engagement and loyalty. Handwritten notes have a near-perfect open rate and help your brand stand out. They are a powerful tool for building relationships, which can lead to higher customer lifetime value and more referrals.Next Steps with Rick ElmoreReady to add the personal touch of handwritten notes to your marketing strategy? Visit simplynoted.com to learn more and request a free sample kit. You can also connect with Rick Elmore directly via email at rick.elmore@simplynoted.com or on LinkedIn.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In his early 30s, Guillermo Lopez walked into finance as an outsider. “Nobody was giving me a chance in finance because I was an engineer,” he tells us. Then a boss took “a risk” and moved him into a finance role—partly because he was “good with numbers,” and partly because his consulting background meant he could be put “in front of…external parties,” Lopez tells us.That entry point set the tone for how he builds a career: intentionally and with breadth. At American Express, he moved across businesses and finance roles on purpose, because “it's important to get breath, especially if you're thinking about a CFO,” he tells us. Over time, he came to describe himself as “very data driven”—the “non emotional part of the decision making,” he tells us—while also learning to make decisions with “imperfect information” in global roles, he tells us.A later inflection arrived after Visa acquired Tink. Lopez became “the grown up” Visa sent to Stockholm, commuting from London each week, he tells us. The environment was smaller, faster, and short on big-company support. It was “daunting,” he tells us, but it taught him to move quickly, focus on priorities, and take bigger career risks.That same blend—speed and discipline—shows up in his definition of finance's strategic role: being embedded in investment and capital-allocation decisions with data in hand, Lopez tells us.His proof point comes from an earlier chapter. In an international CFO role, he helped reframe how a business allocated “close to $700 million a year,” building ROI insights that pointed to “$30 million more of revenue every year,” he tells us.

In this episode of the Real Estate Pros podcast, host Michelle Kesil interviews Eric Brewer, a seasoned real estate investor specializing in wholesaling, fix and flips, and commercial multifamily development. Eric shares his journey from the automobile industry to real estate, discussing the keys to his business growth, the importance of leadership and mentorship, and the strategies he employs in wholesaling and marketing. He emphasizes the significance of adapting to market changes and the value of continuous learning and training for his team.   Professional Real Estate Investors - How we can help you: Investor Fuel Mastermind:  Learn more about the Investor Fuel Mastermind, including 100% deal financing, massive discounts from vendors and sponsors you're already using, our world class community of over 150 members, and SO much more here: http://www.investorfuel.com/apply   Investor Machine Marketing Partnership:  Are you looking for consistent, high quality lead generation? Investor Machine is America's #1 lead generation service professional investors. Investor Machine provides true 'white glove' support to help you build the perfect marketing plan, then we'll execute it for you…talking and working together on an ongoing basis to help you hit YOUR goals! Learn more here: http://www.investormachine.com   Coaching with Mike Hambright:  Interested in 1 on 1 coaching with Mike Hambright? Mike coaches entrepreneurs looking to level up, build coaching or service based businesses (Mike runs multiple 7 and 8 figure a year businesses), building a coaching program and more. Learn more here: https://investorfuel.com/coachingwithmike   Attend a Vacation/Mastermind Retreat with Mike Hambright: Interested in joining a "mini-mastermind" with Mike and his private clients on an upcoming "Retreat", either at locations like Cabo San Lucas, Napa, Park City ski trip, Yellowstone, or even at Mike's East Texas "Big H Ranch"? Learn more here: http://www.investorfuel.com/retreat   Property Insurance: Join the largest and most investor friendly property insurance provider in 2 minutes. Free to join, and insure all your flips and rentals within minutes! There is NO easier insurance provider on the planet (turn insurance on or off in 1 minute without talking to anyone!), and there's no 15-30% agent mark up through this platform!  Register here: https://myinvestorinsurance.com/   New Real Estate Investors - How we can work together: Investor Fuel Club (Coaching and Deal Partner Community): Looking to kickstart your real estate investing career? Join our one of a kind Coaching Community, Investor Fuel Club, where you'll get trained by some of the best real estate investors in America, and partner with them on deals! You don't need $ for deals…we'll partner with you and hold your hand along the way! Learn More here: http://www.investorfuel.com/club   —--------------------

Finding the right 3PL is one of the most important decisions an e-commerce brand will make. It is also one of the hardest. With over 10,000 warehouses in the United States alone, most founders rely on referrals, Google searches, and guesswork.Matt built Third Person to change that.In this episode, Matt shares how he went from leading operations at early-stage startups like Rent the Runway and Birchbox to building a 3PL marketplace powered by AI. What started as a profitable consulting firm evolved into a scalable software platform designed to intelligently match brands with qualified fulfillment partners.In This Episode, We Cover✅ The Real Problem With 3PL SelectionThere are thousands of fulfillment providers. Most brands do not know how to filter them. Matt explains why this decision has become more complex, not easier, over the past decade.✅ From Consulting to Scalable SoftwareMatt and his partner were running a profitable consulting firm helping brands source 3PLs. They shut it down to build technology that could do the job better and at scale.✅ The “Dating App” Model for FulfillmentThird Person uses AI-driven scoring to match brands with top-fit 3PLs based on real operational needs. Brands see ranked matches and choose who to connect with directly.✅ Founder-Product Fit vs Product-Market FitMatt shares the difference between knowing you are the right founder for the problem and proving the market wants your solution at scale.✅ Building Value Without Charging BrandsThe platform is free for brands. Third Person earns referral fees from 3PLs by delivering qualified, high-intent leads.

Cameron discusses the critical role of practice owners in managing their medical practices effectively. He emphasizes the importance of accountability, understanding financial metrics, and making data-driven decisions rather than relying on feelings. He also highlights the need for effective marketing strategies and the transition from being a practice owner to a CEO and investor in the practice. The conversation aims to empower practice owners to take control of their business and achieve financial success.Listen In!Thank you for listening to this episode of Medical Millionaire!Takeaways:The biggest problem in practice management is often the practice owner themselves.Accountability is crucial for financial freedom and practice success.Relying on feelings rather than data can lead to poor business decisions.Understanding financial metrics is essential for evaluating practice performance.Marketing strategies should be data-driven to effectively grow the practice.Transitioning from practice owner to CEO requires a focus on numbers and strategy.Investing in business education and consulting can enhance practice management skills.Effective communication with patients about services can drive sales.Practices should analyze their service distribution to identify growth opportunities.Becoming an investor in your practice is key to long-term success.Medical Millionaire: The Blueprint for Scaling a World-Class Medical Aesthetics PracticeWelcome to Medical Millionaire, the go-to podcast for forward-thinking Medspa owners, Medical Aesthetics leaders, Plastic Surgery & Dermatology practices, Concierge Wellness clinics, and Elective Healthcare entrepreneurs who are ready to scale with intention and operate like a true, high-performing business.If you're building, growing, optimizing, or preparing to exit your aesthetics or wellness practice, this show is your competitive advantage.Hosted by Cameron Hemphill Your Guide to Sustainable, Scalable Growth Your host, Cameron Hemphill, is one of the most trusted growth strategists in Medical Aesthetics and Elective Wellness.With over 10 years in the industry, Cameron has helped scale 1,000+ practices and more than 2,300 providers, working alongside the most recognized KOLs, national brands, EMRs, tech companies, and private equity groups, shaping the future of aesthetics. From marketing to operations, from finance to leadership, Cameron brings a real-world, data-driven perspective on what it takes to turn a practice into a powerful business engine.What This Podcast Is All About: Each episode takes you behind the scenes of the fastest-growing practices in the country, revealing the systems, strategies, and mindset required to win in today's Medical Aesthetics landscape.Expect tactical insights, step-by-step frameworks, and conversations with:Industry thought leadersTop injectors & medical directorsEMR & tech innovatorsOperations expertsMarketing strategistsPrivate equity & M&A advisorsWellness and longevity pioneersThis is where aesthetics, business, technology, and wellness converge. What You'll Learn on Medical Millionaire Every week, you'll access expert guidance to help you scale profitably and predictably, including:Marketing & Brand PositioningCRM + Lead Management SystemsPatient Acquisition & ConversionEMR Optimization & Tech Stack ArchitectureSales Psychology & Consultation MasteryFinance, KPIs, and Practice EconomicsOperational Workflows & AutomationIndustry Trends Backed by Real Benchmark DataPatient Retention & Lifetime Value ExpansionMindset, Leadership & Team DevelopmentWhether you're opening your first location or running a multi-million-dollar enterprise, you'll gain the clarity and direction to grow with confidence. A Show Designed for Every Stage of Practice Growth Medical Millionaire breaks down the journey into four essential stages, showing you exactly how to move from one to the next:Startup – Build the foundation and attract your first wave of patientsGrowth – Scale revenue, expand services, and strengthen operationsOptimize – Increase efficiency, margins, and customer experienceExit – Prepare your practice for maximum valuation and acquisitionIf You're Ready to Grow, This Is Where You Start. Tune in weekly for actionable insights, expert interviews, and the exact playbooks high-performing practices use to dominate their markets. This is the podcast for Medspa owners who want more than a job; they want a scalable, profitable, industry-leading business. Welcome to Medical Millionaire.Let's build your practice into the empire it deserves to be.

On the show today, Zach and Lisa Hansen share what it really takes to sell a preschool in California. From the mandatory 30 day notice to families and staff to the emotional weight of announcing a deal before it is fully complete, this is the real story behind a business exit.Zach and Lisa built Growing Brilliant into multiple preschool locations before transitioning ownership and launching a national online early learning platform. This episode explores both sides of that journey. The process of selling a small business and the commitment to reinventing early childhood education.In this episode, we cover:• What selling a preschool actually looks like• Managing parents and staff during a major transition• Why guided play matters in early childhood education• Scaling with integrity and protecting culture• Building, selling, and building again as foundersFor entrepreneurs considering an exit, educators leading teams, or parents who care about early learning, this conversation offers a clear look at the anxiety, the process, and the reality of selling a company while staying true to the mission.Subscribe for more conversations with founders building in the Backyard Advantage.#Entrepreneurship #BusinessExit #Preschool #EarlyChildhoodEducation #EdTech #StartupJourney #SmallBusinessSale #FounderStory #Leadership #GrowingBrilliant______________________________________________________________If this episode inspires you to be part of the movement, and you believe, like me, that entrepreneurs are the answer to our future, message me so we can join forces to support building truly great companies in our region. -Subscribe to my channel here: https://www.youtube.com/channel/UCom_​... -  Mark Haney is a serial entrepreneur that has experience growing companies worth hundreds of millions of dollars. He is currently the CEO and founder of HaneyBiz -  Instagram: http://instagram.com/themarkhaney​ Facebook: www.facebook.com/themarkhaney LinkedIn: https://www.linkedin.com/in/markehaney​ Website: http://haneybiz.com​ Audio Boom: https://audioboom.com/channels/5005273​  Twitter: http://twitter.com/themarkhaney-This video includes personal knowledge, experiences, and opinions about Angel Investing by seasoned angel investors.  This content is for informational purposes only and should not be construed as legal, tax, investment, or financial advice.  Nothing in this video constitutes a solicitation, recommendation, or endorsement.#thebackyardadvantage #themarkhaneyshow #entrepreneur #PowerOfWith #SacramentoEntrepreneur #Sacramento#SacramentoSmallBusiness #SmallBusiness #GrowthFactory #Investor#Podcast

What does it really take to build homes that are beautiful, efficient, resilient, and responsible?In this special 100th episode of Home Green Homes, Izumi Tanaka welcomes Steve Glenn, founder and CEO of Plant Prefab, for an in-depth conversation that weaves together architecture, sustainability, entrepreneurship, and climate action.Steve traces his path from an early love of architecture to founding LivingHomes and later Plant Prefab—companies created to challenge the waste, inefficiency, and environmental impact of conventional construction. He explains what truly sets Plant Prefab apart: customized architectural design, a purpose-built factory capable of both panelized and modular construction, and a mission-driven commitment as a certified B Corp and public benefit corporation.The conversation also dives into Plant Prefab's work supporting communities rebuilding after devastating Southern California wildfires, and why prefab construction can offer faster, more predictable, and often more cost-effective rebuilding solutions.Along the way, Steve addresses common misconceptions about prefab homes, shares what homeowners should prioritize when designing for climate resilience, and reflects on leadership, scaling a values-driven company, and what he hopes the future of housing can become.This episode is especially relevant for homeowners, home dwellers, architects, builders, developers, and anyone curious about how housing can be part of the climate solution.Key Takeaways / Listener HighlightsPrefab ≠ mobile homes: Plant Prefab homes are legally and structurally equivalent to site-built homes and cannot be excluded from zoning, financing, or insurance.Energy matters most: Over a home's lifetime, operational energy use has a bigger climate impact than materials—efficiency and solar should be top priorities.Time is money: Faster, parallel construction can significantly reduce carrying costs, rent, and uncertainty—especially important in rebuild scenarios.Design and sustainability go together: High-quality architecture and environmental responsibility are not mutually exclusive.Rebuilding after disaster is an opportunity: Prefab can help communities recover faster while building more resilient, future-ready homes.Mission-driven businesses face real challenges: Scaling sustainably takes persistence, patience, and long-term vision—but the impact compounds over time.Chapters00:00 Personal Impact and Vision for the Future

Welcome to Impact Theory with Tom Bilyeu. In today's episode, Tom confronts the transformative power and hidden dangers of artificial intelligence, drawing from the recent revelations in the Epstein Files. He dives deep into how AI, far from being just a revolutionary tool, is increasingly leveraged for narrative control—shaping what we see, think, and remember. Tom explores the history of information manipulation, from Soviet-era censorship to modern algorithm-driven platforms, and reveals how tech elites wield influence through data, algorithms, and gatekeeping. He shares eye-opening examples of AI's opaque decision-making and discusses the critical importance of maintaining independent thought in a world where reality is curated by a handful of powerful individuals. If you're curious about how AI impacts society, the risks of mind control through technology, and what it means for freedom and truth in the digital age, strap in—this episode breaks it all down, challenging us to stay vigilant, seek multiple perspectives, and never treat chatbots as all-knowing oracles. Quince: Free shipping and 365-day returns at https://quince.com/impactpodShopify: Sign up for your one-dollar-per-month trial period at https://shopify.com/impactKetone IQ: Visit https://ketone.com/IMPACT for 30% OFF your subscription orderIncogni: Take your personal data back with Incogni! Use code IMPACT at the link below and get 60% off an annual plan: https://incogni.com/impactBlocktrust IRA: Get up to $2,500 funding bonus to kickstart your account at https://tomcryptoira.comNetsuite: Right now, get our free business guide, Demystifying AI, at https://NetSuite.com/TheoryHuel: High-Protein Starter Kit 20% off for new customers at https://huel.com/impact code impact What's up, everybody? It's Tom Bilyeu here: If you want my help... STARTING a business: join me here at ZERO TO FOUNDER:  https://tombilyeu.com/zero-to-founder?utm_campaign=Podcast%20Offer&utm_source=podca[%E2%80%A6]d%20end%20of%20show&utm_content=podcast%20ad%20end%20of%20show SCALING a business: see if you qualify here.:  https://tombilyeu.com/call Get my battle-tested strategies and insights delivered weekly to your inbox: sign up here.: https://tombilyeu.com/ ********************************************************************** If you're serious about leveling up your life, I urge you to check out my new podcast, Tom Bilyeu's Mindset Playbook —a goldmine of my most impactful episodes on mindset, business, and health. Trust me, your future self will thank you. ********************************************************************** FOLLOW TOM: Instagram: https://www.instagram.com/tombilyeu/ Tik Tok: https://www.tiktok.com/@tombilyeu?lang=en Twitter: https://twitter.com/tombilyeu YouTube: https://www.youtube.com/@TomBilyeu AI, Epstein Files, mind control, narrative control, algorithmic gatekeeping, Google Gemini, social media, information suppression, censorship, oligarchy, Iron Law of Oligarchy, elites, K-shaped economy, data fusion, Palantir, surveillance, predictive scoring, algorithmic friction, biased training data, Overton window, informational monopoly, confirmation bias, motivated reasoning, emotional contagion experiment, Facebook experiment, generative AI, independent thought, malinformation, open-source AI, information chokepoints Learn more about your ad choices. Visit megaphone.fm/adchoices