Podcasts about 2X

We put the ODI Vanquish to the test at SkyPark Bike Fest with Justin Pitchford from ODI.For over 40 years ODI has been pushing grip technology, and their latest collab with the D3O material science lab might be their biggest leap yet. We sat down with Justin at SkyPark Bike Fest to break down the new Vanquish, then took them straight to the hill - Sleighride for the braking bumps and chatter, Arrow for high-speed berms and Comet for the jump line feedback.The takeaway: This is a true comfort + control grip. The Vanquish is meticulously engineered around exclusive D3O® GRIP™ material that offers 2X more vibration damping, designed to remove shock and vibration so you can focus more on the ride. On SkyPark's sustained chunder, you feel it. Less hand fatigue, more traction, no buzz.Tech Check:Exclusive D3O® Material: Multi-layered comfort with twice the vibration damping V2.1 Lock-On System: Single clamp design for more usable grip space 32.75mm Medium Diameter + 135mm Length: The sweet spot for all-day control Unique waffle traction pattern and double-reinforced durable ends Multi-layered padded top for shock absorption + micro-flange for added protection 

Matt Stevens walked away from his first closing table controlling two duplexes, having put nothing down, holding a check for forty seven hundred dollars. That deal taught him that things he assumed were impossible were actually available to him, and it started a career built on reading structure before reading price. Known as The Franchise Guy, Matt has more than 30 years in the franchise industry and has sat in nearly every seat at the table. He has been a franchisee, a franchise coach, a franchisor, and now a franchise consultant. He was rookie franchisee of the year, turned around struggling divisions, and served as a board member and partner inside a two billion dollar franchise group. WHAT YOU'LL LEARN: How to evaluate a franchise opportunity beyond the investment number, why first rights of refusal have essentially disappeared from franchise agreements, how territory math changes the economics of multi-unit ownership, the behavioral due diligence most candidates never perform on themselves, and where the real growth is happening in franchising right now. MATT'S JOURNEY: Matt got into franchising by reading an advertisement on a hallway wall at Wake Forest University. He joined the business that became CertaPro Painters when there were only 67 operators across Canada, New England, and the Mid-Atlantic. The deal had a zero dollar investment to get in and a 25 percent royalty on the back end, a trade both sides made deliberately. He spent about ten years in that system, moving from operator to coach to the franchisor side. Before franchising, Matt spent nearly two decades in rental property, building to 14 units before selling his last property in 2017. Over one six year stretch in franchising, he found, recruited, signed, trained, and mentored 167 franchise owners across nine states in a single system. Today he helps candidates cut through franchise marketing using non-public research on franchisors, their development teams, and their existing owners. KEY INSIGHTS: Every piece of human behavior is driven by inputs and outcomes rather than rationale or common sense. Matt was frustrated by several franchisor decisions when he was an owner. Once he became a franchisor, those same decisions made complete sense. His advice to candidates is to assume every rule exists for a reason and to ask why. First rights of refusal on territory used to be common in franchising and are now essentially gone. Matt's read is that the only reason a franchisor offers one today is that they are desperate to sign that candidate. What replaced it is a growth schedule tied to what an operator can realistically execute. Territory secured early is what Matt calls growth insurance. One territory costs X, three cost roughly 1.6X, and five cost roughly 2.2X, and six territories can be serviced with the same phone number and van as one. In fast moving markets, what is available today will not be available later. The hardest due diligence is the kind candidates perform on themselves. Investment and territory are confirmable. What is not confirmable is whether a person will replicate the daily behavioral patterns the system requires. One of Matt's screening questions is how long a candidate can go at zero income before hitting the panic button. The least appreciated opportunity in franchising is the mundane service business. Matt's framing is that in a market with 400 competitors within 30 miles, you are not competing against 400 companies. You are competing against the two others the customer called. That is how he became the largest buyer at his Sherwin Williams store in Keene, New Hampshire by May of his first year in the painting business at age 20. Perfect for anyone weighing franchise ownership, operators planning multi-unit growth, and dealmakers who want to see how structure and price get traded against each other in a real agreement. FOR MORE ON THIS EPISODE: https://www.coreykupfer.com/blog/mattstevens FOR MORE ON MATT STEVENS: https://heisthefranchiseguy.com https://podcast.heisthefranchiseguy.com/DQ FOR MORE ON COREY KUPFER https://www.linkedin.com/in/coreykupfer/ https://www.coreykupfer.com/ Corey Kupfer is an expert strategist, negotiator, and dealmaker. He has more than 35 years of professional deal-making and negotiating experience. Corey is a successful entrepreneur, attorney, consultant, author, and professional speaker. He is deeply passionate about deal-driven growth. He is also the creator and host of the DealQuest Podcast. Get deal-ready with the DealQuest Podcast with Corey Kupfer, where like-minded entrepreneurs and business leaders converge, share insights and challenges, and success stories. Equip yourself with the tools, resources, and support necessary to navigate the complex yet rewarding world of dealmaking. Dive into the world of deal-driven growth today! Episode Highlights with Timestamps [03:26] - The first deal, two duplexes controlled with nothing down and a check at closing [14:14] - Why first rights of refusal disappeared and what it means when one is offered [21:14] - The behavioral due diligence candidates skip and the four Ds [31:43] - What separates successful franchisees, including the ABS framework[47:44] - What freedom means to Matt Guest Bio Matt Stevens, known as The Franchise Guy, is a franchise consultant with more than 30 years in the industry. He has been a franchisee, a franchise coach, a franchisor, and a franchise consultant, and was named rookie franchisee of the year early in his career. He has turned around struggling divisions and served as a board member and partner inside a two billion dollar franchise group. Matt is a franchise gold and century club member, a Business First Columbus 40 under 40 honoree, and the author of a three hour course on thorough franchise review. He holds three business degrees from Florida Southern College and Wake Forest University and is a MENSA member. His process walks candidates from first conversation to confident decision using real world validation, insider questions, and practical coaching. Related Episodes Episode 329 - Cliff Nonnenmacher on master franchising and who is not a fit for the franchise model. A useful companion for understanding the layers of ownership between single unit franchisee and franchisor. Episode 333 - Greg Mohr on franchise agreements, protected territories, and how franchisors identify their ideal franchisee. Pairs directly with Matt's discussion of what franchisors assess in candidates. Episode 330 - Pete Mohr on owning and exiting multiple franchise businesses, including buying himself out of an agreement that was not working. Offers the operator's view of what happens when the fit turns out to be wrong. Episode 336 - Devan Gonzalez on building an emerging fitness franchise from the franchisor side, which connects to Matt's read on growth in health and wellness. Keywords/Tags franchise consulting, buying a franchise, franchise due diligence, franchisee validation, franchise territory rights, first right of refusal, right of first offer, franchise royalty structure, multi-unit franchising, home services franchise, health and wellness franchise, deal structure, business ownership, entrepreneurship, DealQuest Podcast, Corey Kupfer, Matt Stevens, The Franchise Guy

CLIMBers, today we welcome the Nashville Road Coach (and long-time CLIMBer), Paul Secord, and we talk all about what is probably the #1 revenue generator for your band: merch! Paul has spent more than three decades in the music business as an artist, performer, producer, and artist development coach. He has more than 3,000 shows under his belt, and he spent a massive portion of those shows behind the merch table after the show. Learn how to 2X, 3X, 5X, or even 10X your merch with Paul's Merch That Moves course! Click Here To Access The Merch That Moves Course: This course is normally $97, but CLIMBers have a special discount at $54 for a limited time. Connect With Paul Secord Here: Facebook: @NashvilleRoadCoach Instagram: @NashvilleRoadCoach TikTok: @NashvilleRoadCoach YouTube: @NashvilleRoadCoach Learn more about your ad choices. Visit megaphone.fm/adchoices

Buying land because it looks cheap can be a costly mistake. The real opportunity is knowing what to analyze before you buy—zoning, population growth, infrastructure, utilities, development potential, and future demand. On The Leverage Podcast, Krishna Chinnam shares how he evaluates land deals, identifies high-growth areas, avoids costly mistakes, and has achieved 2X and 3X returns on select investments. He also explains why due diligence, negotiation, relationships, and the right team can make all the difference in real estate. If you're ready to get some serious leverage with a **VA**, grab a spot here: https://l1nq.com/wLiHkjw #RealEstate #RealEstateInvesting #LandInvesting #LandInvestment #CommercialRealEstate #PropertyInvestment #LandDevelopment #RealEstateTips #Entrepreneurship #BusinessGrowth #KansasCityRealEstate #TheLeveragePodcast

With Louis Diamond and Mindy Diamond Louis and Mindy Diamond explore why successful financial advisors can feel stuck despite thriving businesses—and how agency, enterprise value, risk, legacy, and a clear true north can help them evaluate what comes next. In Summary Successful advisors by definition have thriving businesses, loyal clients, and enviable careers—yet still wonder whether comfort has replaced energy. Louis and Mindy Diamond examine why success itself can make change harder, how the desire for agency competes with the disruption of a transition, and why record practice valuations, longer careers, and expanded optionality are prompting more advisors to question the status quo. They also offer practical questions to help advisors clarify their true north, risk tolerance, time horizon, and legacy before deciding whether to stay or explore something new. The Storyline By every external measure, top advisors today are doing exceptionally well. They have strong production, loyal clients, growing teams, and successful businesses. Yet some privately wonder why the work no longer feels as satisfying as they expected. Louis and Mindy explain that the tension is not a sign of failure. For many advisors, it appears after they have succeeded and can see another 15 or 20 years of more of the same ahead. The question becomes whether their business still gives them the agency, control, and professional energy they want. That distinction between comfort and energy can be difficult to recognize. An advisor may enjoy an excellent quality of life and a business that runs smoothly while still feeling comfortably uncomfortable. The catalyst is often not a breaking point, but a renewed desire to build, grow, create enterprise value, or leave a different legacy. Success also creates powerful reasons to stay. A healthy pipeline, loyal clients, unvested compensation, retire-in-place programs, and the short-term disruption of a transition can make change difficult to justify. At the same time, record valuations, longer careers, multigenerational teams, and a broader range of firm and affiliation models have made the opportunity cost of staying more visible. The discussion does not assume that every advisor should move. Instead, Louis and Mindy focus on how to make an intentional decision: define your true north, identify what you are trying to solve for, assess your tolerance for risk and disruption, and learn what is possible before committing to a change. Topics Covered Why successful advisors can feel stuck Agency, control, and professional satisfaction Comfort versus energy in a thriving business Defining an advisor's true north When more of the same becomes a constraint Fear of change versus fear of standing still Recruiting deals, practice valuations, and enterprise value Longer careers and multigenerational teams Risk tolerance, disruption, and client portability Creating clarity without committing to a move > Download a transcript of this episode… Listen and Learn Highlights for Advisors What feels different for successful advisors today? (02:09)Mindy explains why top advisors are increasingly willing to examine the status quo as their choices expand and the value of their businesses rises. Why can objectively successful advisors still feel unsettled? (04:51)Mindy identifies agency as a central need for top advisors and explains why a loss of control can create deep frustration even when the business is performing well. What does a fire in the belly reveal? (08:22)Mindy shares the example of a highly successful wirehouse team whose interest in change comes from a desire for renewed energy and legacy, not from a final breaking point. How is being comfortable different from being energized? (11:53)Louis and Mindy explore the difference between a business that provides an excellent life and one that still feels professionally satisfying, including the feeling of being comfortably uncomfortable. When does more of the same become a constraint? (17:49)They discuss how repeating a successful formula can continue to produce results while limiting growth, ownership, or the entrepreneurial spark an advisor wants to pursue. How do advisors reconcile fear of change with fear of standing still? (27:16)Mindy and Louis explain why those competing concerns can persist for years and how a firm decision, a compelling opportunity, or a moment of personal clarity can shift the balance. Why is this question surfacing more often now? (29:48)Record valuations, elevated recruiting deals, expanded optionality, peer movement, longer careers, and next-generation needs are changing how advisors assess the cost of staying. Which questions should advisors ask before considering a move? (36:35)Mindy and Louis outline questions about true north, frustration, risk appetite, disruption, legacy, time horizon, and the regret of never testing what might be possible. Key Takeaways Success and professional fulfillment are not the same thing. An advisor can have strong growth, loyal clients, and an excellent quality of life while still feeling that comfort has replaced energy. Agency is a core need for many top advisors. Firm policies, compensation changes, technology limits, or other decisions can feel especially disruptive when they reduce control over the business or client experience. Good enough can become a constraint. A proven business model may continue to work financially while limiting growth, ownership, enterprise value, or the professional satisfaction an advisor wants next. Success creates powerful inertia. A strong pipeline, unvested compensation, retire-in-place programs, client relationships, and the disruption of a transition can all make the status quo difficult to challenge. The opportunity cost of staying has become more visible. Record valuations, elevated recruiting deals, broader affiliation choices, longer careers, and peer movement give advisors more reasons to understand what else may be possible. True north should come before due diligence. Advisors need to define what they want to solve for and how they weigh control, risk, enterprise value, legacy, and time horizon before evaluating firms or models. Education does not require a move. Self-awareness and a clear view of the available options can help an advisor make an intentional decision, including the decision to stay. https://youtu.be/nrmtRBlJJVs Quotable Moments “I'm comfortably uncomfortable.” — Mindy Diamond “You've always got to be really clear on what your true north is.” — Mindy Diamond “Which regret is bigger to you, trying something and failing or never testing what's possible?” — Louis Diamond “It's okay to want more. There's nothing wrong with you for wanting more. At the same time, there's nothing wrong about being comfortable.” — Louis Diamond FAQs Why do successful financial advisors feel stuck even when their businesses are thriving? External success does not always create professional energy or fulfillment. Some advisors have excellent businesses but feel they have lost agency, stopped growing in ways that matter to them, or settled into a version of success that no longer fits their goals. What does agency mean for a financial advisor? Agency is control over an advisor's professional life and business. It can include how the advisor serves clients, uses technology, builds a team, makes decisions, and plans for growth or succession. Is being comfortable in a business the same as being energized by it? Not always. Comfort can reflect a strong business, good income, loyal clients, and a healthy quality of life. Energy comes from feeling engaged by what the advisor is building and where the business is headed. Why can success make it harder for an advisor to change firms? Successful advisors often have more to disrupt, including client relationships, team dynamics, a growing pipeline, unvested compensation, and valuable retire-in-place benefits. The immediate costs and risks of a transition can outweigh a benefit that may be larger over the long term. Why are more successful advisors questioning the status quo now? Practice valuations and recruiting deals are high, the range of affiliation models has expanded, and advisors see respected peers make changes. Longer careers and the needs of next-generation partners also give many teams more time and reason to reconsider their future. What is an advisor's true north? True north is the set of priorities that should guide an advisor's decision. It defines what the advisor wants to build, what needs to change, and how factors such as control, ownership, legacy, risk, and time horizon should be weighted. What should an advisor ask before considering a move? Key questions include what is causing frustration, how significant it is, what outcome the advisor wants, how much disruption and client risk the team can tolerate, and whether staying in the same place for another 10 or 15 years would still feel satisfying. How can an advisor create clarity without committing to change? Start with self-awareness, then learn what options exist through informed conversations and competitive analysis. Understanding the landscape can strengthen a decision to stay or reveal a better fit without obligating the advisor to move. External success does not always create professional energy or fulfillment. Some advisors have excellent businesses but feel they have lost agency, stopped growing in ways that matter to them, or settled into a version of success that no longer fits their goals. Agency is control over an advisor's professional life and business. It can include how the advisor serves clients, uses technology, builds a team, makes decisions, and plans for growth or succession. Not always. Comfort can reflect a strong business, good income, loyal clients, and a healthy quality of life. Energy comes from feeling engaged by what the advisor is building and where the business is headed. Successful advisors often have more to disrupt, including client relationships, team dynamics, a growing pipeline, unvested compensation, and valuable retire-in-place benefits. The immediate costs and risks of a transition can outweigh a benefit that may be larger over the long term. Practice valuations and recruiting deals are high, the range of affiliation models has expanded, and advisors see respected peers make changes. Longer careers and the needs of next-generation partners also give many teams more time and reason to reconsider their future. True north is the set of priorities that should guide an advisor's decision. It defines what the advisor wants to build, what needs to change, and how factors such as control, ownership, legacy, risk, and time horizon should be weighted. Key questions include what is causing frustration, how significant it is, what outcome the advisor wants, how much disruption and client risk the team can tolerate, and whether staying in the same place for another 10 or 15 years would still feel satisfying. Start with self-awareness, then learn what options exist through informed conversations and competitive analysis. Understanding the landscape can strengthen a decision to stay or reveal a better fit without obligating the advisor to move. Related Resources How to Free Yourself from the “If Only” MindsetHere are the 5 most common self-limiting statements that advisors share—and ways to reframe your thinking. Limitless Growth: Building the Business You Want and the Life to MatchStephanie Bogan, founder of Limitless Advisor, offers a glimpse into the advice and perspective she shares with advisors and business leaders in the wealth management world, focusing on mindset and methods, and their relationship to achieving one's best business life. NOTE: The views and opinions expressed by the guests on this podcast are their own and do not necessarily reflect the views and opinions of Diamond Consultants. Neither Diamond Consultants nor the guests on this podcast are compensated in any way for their participation. View the transcript of this episode… Best of Replay: Why So Many Successful Advisors Feel Stuck An Industry Update with Louis Diamond and Mindy Diamond.      Louis Diamond: Welcome to a replay of one of the most popular episodes from our podcast series for financial advisors. Why So Many Successful Advisors Feel Stuck. It's a Special Industry Update with Mindy Diamond. I’m Louis Diamond and this is the Diamond Podcast for Financial Advisors. Mindy Diamond: At Diamond Consultants, we help elite advisors identify the right environment for their businesses to thrive, whether that’s at a wirehouse, boutique or independent firm. With nearly three decades of experience, we’ve guided thousands of advisors and represented more than a quarter of a trillion dollars in assets transitioned. And each year, one in four advisors managing a billion dollars or more who change firms are our clients. Our process is education-driven, and based on building relationships, starting as your strategic partner well before you’re even thinking of a move. To schedule a confidential conversation, call us at (908) 879-1002. Wondering why advisors change firms and where they’re headed? Are transition deals going up or down? Those very questions and more inspired us to create our annual advisor transition report. It’s the award-winning, data-driven resource designed for advisors that connects the dots between the motivations around movement and the firm’s appetite for top talent. Arm yourself with the knowledge you need to make smart decisions. Download your copy at diamond-consultants.com/transitionreport. Louis Diamond: We spend a lot of time talking with advisors who, by every external measure, are doing exceptionally well. They’ve built real businesses with strong production, loyal clients and teams that continue to grow, and yet behind closed doors, many of these same advisors are quietly asking a different set of questions. Not how do I fix what’s broken, but why doesn’t this feel as good as I expected? That tension is showing up more frequently than it did five or 10 years ago, and it’s not because advisors are failing. In many cases, it’s because they’ve won and now find themselves staring at another 15 to 20 years of more the same, unsure whether comfort has slowly replaced energy. This industry update is about that very moment. I asked Mindy to join me to unpack what we’re hearing from successful advisors across the industry, why success itself can become a constraint, how fear of change competes with fear of standing still, and why record valuations, longer careers and the maturity of the independent space are changing the psychology of decision-making. We also talk about the right questions to ask before considering a move, questions about control, enterprise value, legacy and time horizon, and how advisors can create clarity without forcing a decision. There’s lot to explore here, so let’s get into it. Mindy, thanks for joining us. Mindy Diamond: Oh, I’m so happy to be here. Always. Louis Diamond: This is a fun topic. So today we’re just going to dive right in. I’m curious, from your vantage point and talking and working with many of the best advisors in the industry at a high level, what are you hearing from successful advisors today that feels different from five or 10 years ago? Mindy Diamond: First of all, for the most part, I think that top advisors are loathe to move. And they may have a bunch of frustrations or things that they wish were different, but generally speaking, they’ve always been well taken care of. They have the bat phone to the top, it’s good enough and nobody wants to mess with success. But two things are true today more than ever before, that the competitive landscape, there are more options than ever before, that their businesses are worth more than before. I guess it’s more than two things. That they’re thinking about their business as a business and saying, “Yeah, it’s a hassle to move, but if I can maximize the value of my business more elsewhere and at the same time solve for what I want to, maybe I really do need to consider it. And so I think that it’s a time of more consideration, that top advisors, in particular, are just not willing to settle for the status quo. Louis Diamond: Yeah, no doubt. The only thing I would add, but I completely agree, is that today the retire in place or succession opportunities for a wirehouse advisor to transition or sell their business to their next generation or to another team or even an independent advisor, to sell their business to their broker dealer or to someone within their firm, those opportunities, the internal options, I think are more compelling than ever. So we hear a lot from really successful advisors who, that actually doesn’t matter if they’re successful or let’s say they’re mid-tier in the industry, where it’s much easier to stay. And the firms are smart. They’ve made these internal deals better for the retiring advisor at the expense of the inheriting advisor, but they definitely try to create the easy button for someone who’s successful and might be fairly close to the end zone of their career. Mindy Diamond: And yeah, unequivocally. And so I think what happens is that, as these senior advisors are weighing the notion of will I eventually take my firm’s retire in place program, hit that easy button, if you will, take the path of lease resistance? Before they do, most of them will, and absolutely should, at least get educated about what else is out there. Not with an eye toward moving necessarily, but with an eye toward knowing what their value is before they sign on for the next seven to 10 years. Louis Diamond: No doubt about it. So I think for the most part, we’re talking about mostly top two, advisors who are objectively winning. They have amazing businesses, they’re growing, they have loyal clients. Work-life balance is probably pretty good. So why do so many of these advisors still feel unsettled at the end of the day? Mindy Diamond: Because I think that without question, the number one thing that every advisor, but definitely the most successful advisors want, is agency. Agency over their professional life and anything, any day, any event that smacks of loss of agency or less agency than they would like, less control than they would like, makes them feel unsettled is a good word. Unsettled is probably a euphemism for exceedingly frustrated, really angry, ready to go. So look, our job in talking to these folks is, first of all, to say to them, there is no perfection anywhere. So the first thing is you may be annoyed that your firm did X, Y and Z, or you don’t have control over A, B, and C, or you may want more of A, B and C, but at the end of the day, you need to be sure that you’re aware of the hassle factor, you’re willing to trade one set of problems for potentially another. I guess what I’m saying is you got to really be sure that you want it. That’s the bottom line. Louis Diamond: No doubt. I would also add a bit of a different spin that a lot of advisors, these folks that I think by any objective measure, they’re killing it. You look at any of their friends outside the industry, their friends probably look at them and say, “Wow, this advisor has an amazing life.” And I think we’d probably say the same, but I think we see it where there’s a difference between being professionally motivated and energized versus just being comfortable. And I think with a lot of folks, especially if it’s a fee-based business, their clients are mostly friends. They grinded in the early years. They still work hard, but now things are on autopilot. So I think some people around unsettled because they have 10, 15, 20, 25 years left to work and they look and say, “This is great. I make a good living. Life’s not that hard,” but they’re missing that spark that they used to have. Mindy Diamond: I couldn’t agree more. I actually love that spin, and if I can, I’ll share an example of a conversation I had literally just last week. So talking with one of three senior partners on a very successful wirehouse team, without a doubt, one of the top teams in the industry, and this is a team that absolutely has a bat phone to the top. This is a team that gets referrals from their firm. So if another advisor leaves, they’re the go-to. If someone has an investment banking deal and needs to bring in a wealth advisor, they would go to this team. This is the team or one of a few teams. And I talked to them probably every six months for the last 10 years, and I’m not kidding, and we just have a nice relationship. They trust me. No, I would never sell them. And so they just want to understand the pulse on things. And for years, that’s essentially what they’ve said is, “We’re killing it, we’re crushing it. We are the go-to team and everything is hitting on all cylinders. And we know that we’ve got our firm’s retire in place programs when two of our senior partners are ready to cash out.” But just last week, not even the senior most member, probably the second to senior most member called and said, “It’s all working well, but I have this fire in my belly.” That’s a good way to say it, right? I have the sense that I’m missing something. And so some of the time, it’s about just getting to that place, say, when the straw that breaks the camel’s back. Something happens, you can’t take it any more, time to go. But with those top advisors, more often than not, it’s not about straw that breaks the camel’s back. It’s much more about fire in my belly. I realize that will I feel good if, at the end of the day, five minutes, five years or 20 years from now, will I feel good if I leave it all behind? We’re right here. Louis Diamond: Yep, exactly. Yeah, I think part of it is legacy, but the other part is just fast forwarding the tape. If you say 20 years from now, and you look back in the last 20 years, is you plus 20 years going to feel satisfied and encouraged and excited about what you were accomplishing? Or was it more of a, yeah, it was good, it was easy. I bought three houses and I have a boat. Or was it more about the personal joy of growing and building something and being part of something? Mindy Diamond: That’s actually a very good way to say it. Where can the top advisor find that joy? So let’s say I’m hitting on all cylinders, top of the food chain at fill in the blank firm, a traditional firm. What are typically the things that advisor, the options that advisor might consider that could ignite that joy? Louis Diamond: Yeah, I mean, honestly, probably a bad nonspecific answer, but it really could be anything. I mean, we see plenty of teams that are with one major wirehouse, they move to another one and just the act of transitioning is re-energizing. They have backend bonuses to hit. This new firm has a bit of a different culture, they feel more important again, and that’s their spark. Others, it would be we’re building something, we’re creating our own firm. Others might be I’m now an equity partner in something that I have a hand in, I have a voice. They actually listen to me. So I think it can be all of the above. It’s more about what is it that an advisor is most interested in? And then at this point in the industry, it’s choose your own adventure. If you have some sort of feeling or urge, whether it’s I want to do something different, I want to grow faster, I’m frustrated by X, Y, Z, it’s more about filling in the blanks of what your next journey looks like. And my guess is just thinking about that is going to provide that natural spark or ignition. Mindy Diamond: Yeah. And if I can, the piece of advice that I gave to this team, and I think is worth mentioning here, you’re absolutely right. Choose your own adventure. To us, I think we would both say that most exciting thing about being in our position and being the counselor or the guide or the Sherpa to advisors as they consider what comes next is the amount of choice that they have and how exciting the next chapter can be. But what I said to this team or the one advisor on this team is, “You’ve always got to be really clear on what your true north is.” So yes, there’s a lot of options. An advisor can find joy or reignite the spark at any number of choices, more than ever before. But being really clear before you begin to take meetings or think about it on what you’re looking to solve for, what’s most important to you really matters or else you wind up just spinning your wheels for a lot of time. Louis Diamond: Clearly agree. How do you distinguish between being comfortable in your business and being energized by a business? Do you think there’s a difference? Mindy Diamond: Yeah, look, I think in some cases, they could be one and the same. I think somebody can be comfortable because they feel energized. They’re hitting on all cylinders. They’ve got a lot of agency and professional control over their business. They’re where they want to be. They’re living their true north. But I think in many cases, and many of the advisors we’ve counseled, they aren’t one of the same. I’m comfortable because, as you said, I have a great quality of life, I’m making great money, I have a lot of freedom to coach my kids’ basketball team. I can work from home, I can travel when I want to, I have great clients. I may not technically own my business, but I feel as though I do. I’m comfortable enough. But what this advisor last week said to me, and what we hear a lot, is I’m comfortably uncomfortable. That I am comfortable enough. If I retired from here, I will have made a ton of money, I will have done good work and it all would be good enough. But whether or not good enough is really good enough in terms of professionally satisfying and everything you want it to be is a very different question. Louis Diamond: Exactly. I’ll give an example of that. I have an advisor I speak to who’s independent. So he’s been independent for his entire career at a broker dealer. He’s an absolute stone-cold killer. I told him that. He’s probably top three advisors at his BD, so it’s great for the ego. He’s on the conference circuit and sharing his best practices, but he’s in his 40s and said to me, “I can continue to rinse, repeat, I can continue to get the accolades and be on the main stage, but I feel like I’m missing something. I just know how to run the same place and I don’t really see how that’s going to change.” So he has a pretty hard decision because it’s nice to feel important, it’s nice to grow without a lot of extra effort, but for this individual, it’s becoming clear that spark or feeling energized again is his true north. Mindy Diamond: And so what would he look at? In other words, if you are already top of the food chain at your broker dealer, and by the way, we see that a lot, that advisors in the independent space that work under broker dealer that are number one, they are the one at the conference that everybody goes up to and said, “Oh my God, I hear such great things about you. I want to learn from you.” So what is it he’s missing? Louis Diamond: For this individual, what he thinks he’s missing is the chance to really build his own platform. So if this gentleman decides to make a change, it’s definitively going to be to build his own RIA, where he can pick and choose all the technology. He’s really bullish about his personal network and being able to recruit like-minded folks in to do acquisitions. And his North Star is going to be, how do I build the most valuable enterprise that I can sell for a whole boatload of money 15 years from now? It’s a great example because this person has everything that most people would want, but he’s still wanting more. And what he’s looking to accomplish is probably similar to a lot of people, but the means that he’s going to get there and the weighting that he’s putting on his criteria are different. And that’s what, I think, the best part of being an advisor is you have so much choice. There’s no one telling you, “You have to grow, you have to do this, you have to care about money, you have to onboard X number of clients.” It’s much more about how do you listen to your heart and then make intentional choices to make it happen, essentially. Mindy Diamond: Yeah, and I think that’s the calculus. His calculus is it’s more than enough here. The question is, am I willing to upset what’s more than good enough, upset that apple cart in order to get something that is marginally better? And his answer, or our advice to somebody like that, is it really depends upon how much you want it. If it will feel soulful to you, if, at the end of the day, you will feel better about the professional legacy you left behind, having built something bigger and better with less limitations, then you’ll do it. You have to decide how much you want it. It’s interesting, if I can go back to this example, this team’s true north is a little different and it’s worth contrasting. So while, for sure, they would love nothing more than to maximize enterprise value, and they recognize that if they and any of their partners retire through their firm’s retire in place program, it’s a way of monetizing, but they look at their firm as a monopolistic buyer. That was the term that they used. My comment to them is, “So what? Monopolistic or not, if, at the end of the day, it allows you to do what you need to do when you get paid a fair value, who cares if they’re monopolistic?” That really is the truth. But their true north is they would love to build something. They don’t need to build their own platform. In fact, they would want nothing more than to plug into an already existing platform. And they recognize that by doing so, they’re giving up some enterprise value. And the way to maximize enterprise value the fullest is to go out and build your own. But in this particular instance, they don’t want to do that. They’ve got partners that some are in their 60s and that’s not going to be appealing to them. So their true north might be something a little bit different. I think the point here is being really clear on what you’re true north is really important because it guides the set of choices. Louis Diamond: No doubt. At what point do you think more of the same stops being a growth strategy and starts becoming a constraint? Mindy Diamond: Oh, well, just that. Your example was the perfect example, right? That I am doing incredibly well. I could keep on doing this, but in some way, it’s not only constraining my growth, but in some way it’s constraining my professional satisfaction. And I think more often than not, it’s about that. Because the audience I think we’re talking to in this podcast are really successful folks. There’s no question that there’s nothing we can tell them that would suggest that they’re not successful as they are. So if they decide to upset the apple cart or they decide to look for more, it’s because there is that fire in your belly, that something, that entrepreneurial spark or that something that’s not being satisfied. Louis Diamond: I agree. Yeah. I think sometimes it’s a matter of time says to me, if someone finds something that works, it’s a pretty sound business strategy to rinse, repeat, and keep doing it, focus on what’s essential, cut out the rest. You read any business book, it’s going to tell you double down on what’s working, cut out the rest. The 80/20, et cetera. But I think eventually you keep doing the same thing over and over again, and it might still yield results, but is it yielding the growth? Is it yielding exponential results? And at some point, does just doing the same thing, is it still driving you in the same way? Mindy Diamond: Yeah, and I think a lot of times people don’t really know what they’re missing. In other words, the growth strategy is working. I’m growing X amount per year. I’m living a good life. I’m consistently in the top five advisors in my firm. It’s working and nobody could argue that it isn’t. I think it’s just more about a lot of times as advisors, one of two things, either one of their friends move, a professional colleague that they respect and they say, “Holy cow, he was here or she was here for 30 years, like me, killing it. She would’ve been the last person I would’ve thought would move.” And then suddenly she does, and it makes them say, “What am I missing? What did she see that I am not aware of?” Or a lot of times these folks will, another example I’ll give you. An advisor that I’ve worked with for many years, probably 25 years, who is top of the food chain, doing incredibly well, never really thought about moving, talked over the years, “Boy, it would be nice to build something of my own, but the truth of the matter is that success and the thought of having to go back to zero and build something from scratch, no way.” But you and I came across an opportunity that was unbelievably compelling, and without going into too much detail about the opportunity, suddenly when we talked about that opportunity with this advisor, what he said was, “You can’t be what you can’t see.” All of a sudden we painted the picture of something that’s really compelling I didn’t even know I wanted, but holy cow, now I know I really want it. Louis Diamond: Yeah, because something, this dream was made tangible. It put a face on something that this advisor didn’t realize they were missing. I mean, I see this every day, this next question, I’d love to get your opinion. I feel like most advisors, they may sense something is off or something’s changing or shifting under their feet, but they say anyway, why do you think success is often the very thing that keeps advisors stuck? Mindy Diamond: Yeah, I think it’s more than good enough. And for most of us, most of the time, I might have that little voice in me or that little inkling that says, gee, maybe it could be better. Or gee, I wonder if or something of the sort, but I have a zillion competing priorities. I’m a dad or I’m a mom or I want to travel, or I’m 60 years old, or I’m filling any blank at any time. And I think that it’s really hard to give up on that. And I think that the example we just gave, of this advisor that saw an opportunity, a unique opportunity that solved for a lot of the things that he didn’t even know he was missing, that happens more often than not. Louis Diamond: Yep, I agree. I think too, what happens is a lot of advisors think about moving as strictly a financial trade. My grid rate is this and my fees are that I can get 3.5X here, but only 2X up front have Y in deferred comp. And I think when advisors are thinking about that, which is a reasonable lens to look through, end of the day, they’re business people. If the business is growing and they have to place Y or they know how to get things done, it’s much harder to justify that change. It’s like people who sell their house top of the market, it’s like, hey, it’s this thing and keep growing, and do you feel like you’re missing out because you’re leaving at this point in time? Typically, too, when things are good, clients are happy, the reviews are easier. We see it a lot with the market being up. In 2025, the market was up almost 20%, much harder for some folks to justify moving because they feel like, whether it’s artificial or it’s real, that their business is really cranking. So I think that’s a big part of it as well. Mindy Diamond: Yeah. And maybe another little spin on it is a lot of times to move is really, from a financial perspective, may only be marginally better in the short-term, whether it be because after taxes, the amount of tabs you get upfront is not that much greater than the deal you could get by retiring through your firm’s sunset program. Or if you move, you’re going to be leaving some clients behind. So if you factor in the breakage from losing clients and the short-term upside, the delta may not be that great. We see a lot of times when an advisor is already independent and they’re at a net 60 or 65% payout and they’re looking at something that gives them a 70% net payout and they say, “Yeah, okay, so those extra basis points are nice, but is it nice enough? Is it worth the hassle?” The answer to that is it is entirely an inside job. For some that delta, even if that delta was only 2 cents, would be more than enough to justify the change. And for others, if the delta was 2000000%, it wouldn’t be enough. So what’s this positive? What’s this positive or determinant is how badly they want it. How badly, beyond the financial piece, they want what another opportunity would give them. Either that I really have this fire in my belly, and I love that term, and I just can’t solve for it. I can’t extinguish that fire here. I can’t feed that fire here. Or opportunistically, I want to be something I just can’t here, and I want it so badly that even if, financially, it’s only marginally better in the short-term, I’m willing to go through the hassle to get it. Louis Diamond: No doubt. And one last point to put a bow on this segment is I think there’s also an element that advisors who are growing or they have a big pipeline, and to me, the best advisors always have a big pipeline. They always have the next big prospect. There’s definitely some, I think, real fear that I’m going from growing 10% per year to reflexively choosing to make my life really hard for a couple of months to slow down growth, perhaps lose some of the pipeline, lose some of the big clients I just onboarded. And that’s really hard to justify because the trade-off is that much more apparent to them. Mindy Diamond: Yeah. Which you really want it. Louis Diamond: But that’s always going to be the case. That’s the point. The good advisors, for the most part, are always growing and always doing these things. So it’s like running on a treadmill. The same thing with unvested deferred compensation. I have 2 million of unvested deferred, it’s so hard to walk away from. The counterpoint is you’re always going to have that. If anything, it’s just going to grow. So again, it’s taking a step back to take multiple steps forward. Some people want that and are excited by it. Some people aren’t, but that’s okay. Mindy Diamond: Yeah, and I think that’s the whole point. While we get paid to move people, our position and the relationships we’ve had for years with top advisors, with every advisor, is they trust us because we’re never looking to just sell them on a hot opportunity. We’ll bring opportunities to them and say, “Here’s what’s available, here’s what the upside could be,” but we know and respect better than anyone that you got to really want it. It is a hassle. It’s hard work to move, and you’ve got to have a real confidence in your clients, your current clients, that they’re going to follow you. You’ve got to have real confidence that your pipeline will follow you, that what’s not certain. You’ve got to have confidence that you can tolerate the risk, and you’ve got to have confidence that your team’s going to follow you and that you’ve got the support you need. And not everyone has the appetite for it, nor should they. Louis Diamond: Exactly. I mean, I would say there’s two competing, I guess, thoughts that oftentimes go through an advisor’s mind, and they’re both extremely reasonable. The first one is the fear of change, but at the same time it’s the fear of staying exactly where they are for another 10, 15, 20 years. How do you think advisors can reconcile the tensions, like the angel on one shoulder, the devil on the other? One’s super comfortable and safe. The other one is an unknown. One’s exciting, one, et cetera. So how would you reconcile that? Mindy Diamond: Well, first of all, the short answer is it takes years. The two examples I just raised, and I’ll ask you the same question, are advisors that I’ve been talking to for 10 years plus. In one case, it could be 15 or 20. So for 10 or 15 years, there was always a certain amount of angst, a certain amount of curiosity, but not at all a willingness to do anything about it until in one case, something happened. Something happened that made them realize that they’re paying an awful lot of money to their firm for value they’re not getting anymore, feeling limited. And so they decided to pick up their head and look elsewhere. In the other case, it was opportunistic. Really was perfectly happy doing their thing and heard or saw an opportunity that sounded compelling and began exploring. Now, I don’t know whether either one of these teams will ultimately move, so let’s say that, but these conversations are many years in the making. So I think the point I’m making is that devil on one side, an angel on the other are those two competing voices. They exist for a long time until either, in some cases, they never get reconciled and the advisor just retires and it is what it is. In other cases, they get reconciled, but sometimes five, 10 years after the voices begin to surface and they don’t happen, they don’t get reconciled until something happens to force it. Louis Diamond: Yeah. I also look at this concept as we’ll call it a midlife career crisis. So I’m 40 years old. Instead of buying three motorcycles and a Porsche and going skydiving, I’m thinking about my business. And sometimes, I think people just wake up and say, “Wow, I don’t really know what my identity is and I need to do something different. I need that additional spark.” So I think sometimes too, it’s just either an age thing or a certain birthday comes up or it’s just that a moment of clarity that pops into the mind. Mindy Diamond: Totally agree. Louis Diamond: Why do you think this feeling is showing up now more than ever? Is it because of recruiting deals? Is it because of the movement toward independence? Is it something else or somethings else? Mindy Diamond: Yes, I think it’s all of the above, and I think that’s the cool thing. Valuations are at an all-time high. So even if an advisor has zero interest in going independent, you have to be living under a rock not to know that your business has real value. That business, outside of the constraints of a major firm, is worth much more than it is on the inside. Now, that doesn’t mean that everybody should go independent, or will, but the fact that those multiples are hanging out there and that recruiting deals are at an all-time high, and they are, the fact that there’s more optionality, which means an advisor, if they go out and explore, is more likely to find their personal version of Utopia or something closest to it. The fact that movement begets movement. That’s something we haven’t talked about, but it’s worth mentioning that all you have to do is read AdvisorHub or just be sitting in your office and every day, watch somebody you respect make a move. And so the more you see top advisors move, the more you say maybe there really is something else out there for me as well. And then you and I always say that the big firms are one policy change, one comp change, one mandate, one something away from frustrating the heck out of a large constituency because the bottom line is, as long as you’re an employee, you’re vulnerable or captive to whatever choices the firm makes. Louis Diamond: Yeah. I think you’re talking about, in a very nice way, what happened with UBS at the end of 2024, but we even see it outside of the wirehouses. There’s independent firms that layer in new fees or they don’t allow their advisors to text. There’s always that risk that something big coming down the pike that motivates someone. I’ll give you two other reasons why they think this is happening with more frequency. The first one is advisors’ careers are oftentimes longer, either because they’re bringing their kids into the business or the business is so lucrative, and if the business is mostly fee-based and it’s on autopilot, it’s like, why should I hang up the old boots when I can just work for another handful of years? So I think with longer advisor careers and the pot of gold at the end of the rainbow, which is either a big exit or a retire in place deal, I think advisors just grapple with more and they grapple with it for longer. Mindy Diamond: Yeah, you brought up a point. I’m so happy you brought it up. I’m sitting here next to my son, my next generation. I have said oftentimes that, without my sons coming into the business, I don’t know that I would’ve had the same relationship to the business as I aged. As I got older, and I’ll be transparent, I’m 63-year-old now, if 10 years ago, my sons had not decided to join me, I think that I might have said, “I’ll work as long as it feels soulful and good, and if something better comes along, great.” But it wasn’t until they came along that I really began to think about everything through their lens. It was less about me because by the time I was 50, from a financial perspective, I could have left if I wanted to. But as long as my kids weren’t in the business, there’s no way I was going to do that. There was no way I wouldn’t want what was the absolute best for the business. And I guess that’s a long-winded way of saying in an industry where there’s so many multigenerational businesses, father-son, mother-daughter, whatever it is, senior advisors that really care, even if they’re not family, about their next generation, when they begin to look at their business through the lens of their next generation, the technology may be good enough for me, but it’s sure not cutting edge enough. Or they say things like, “The bureaucracy is killing me, but I work three hours a day,” or, “I have such a good life, I don’t want to upset the apple cart.” But then all of a sudden, one day we get a call either from that senior advisor, for whom it was more than good enough for years, or from the next generation that says, “This is not what I want. It may have been good enough for my dad and my mom, but it’s not good enough for me.” So it’s a long-winded way of saying I think that the next generation really forces the senior advisors, the elder states, G1 to grapple with whether or not this is good enough. Louis Diamond: Yep. I’ll answer my own question first and then get your opinion, because I feel pretty strongly about my answer. In thinking about our four to five point list here of why this is a bigger deal now, I tap in with more frequency. The question for both of us is which one do you think has the biggest psychological impact on advisors right now? I’m going to go first because I’m excited about my answer. I think by far, it’s practice valuations by far. Because what’s happening is advisors are reading news articles, hearing from their friend, getting cold-called from corporate development folks at firms, and they hear what used to be just ludicrous multiples. These were multiples that were reserved 100, $200 million revenue business are now getting paid to a three to $5 million practice. And any advisor, even ones who aren’t super financially motivated, I think just hearing about this, knowing what’s possible has a major psychological impact. It’s making it that much harder for folks to say, “You know what? It’s comfortable. It’s easy. I can retire here because the opportunity cost of staying put has accelerated that much.” What do you think? Mindy Diamond: So unequivocally, and I am money motivated for sure, so I agree with you. But at my stage and age, if I were an advisor, I absolutely wouldn’t be able to ignore the impact that maximizing enterprise value might have and how awesome it would be to be building a business that could be worth more. But I would equally, if not more so, be really concerned about, A, how it would feel. Like am I going to be able to live the professional life, do what’s soulful for me? Am I going to be able to build the business I want to? Is this going to be the business, the legacy I’m going to feel good about? Is this going to be the business that I want my sons to take over? And that, to me, would be equally important. And I’m making it about me, but I think that’s what a lot of senior advisors would say. Louis Diamond: No doubt. So for folks that are questioning their situation, what do you think is a question or three or four or five of the right questions they should start asking themselves before even thinking about making a move? Mindy Diamond: Yeah. Well, one is, listen, what’s your true north? And even before, I mean question two, and I think what I just said, assessing your true north, figuring out where your true north is, one, because question two is what is it that’s bothering you? What is it that you’re frustrated by? And simultaneously, to what extent does it frustrate you? Like how, okay, so I feel mildly annoyed that I have to do X, but is it enough to make me want to move? So being clear on that is important. But I think even before you begin to delineate how frustrated you are, you have to be really clear on what you want to be when you grow up. What is it that would feel soulful? What are you trying to solve for? What are the parameters or realities that you need to take into account? You’d love to do X, but you have a senior partner that you know is never going to want that, whatever it is. So what is the true north? What are you frustrated by? Then it’s what risk appetite do I have? Louis Diamond: To take risks, yeah. Mindy Diamond: What risk appetite and what appetite in general do I have for disruption to my life? How tolerant will I be if one of my clients doesn’t follow with me, because that is really possible? How tolerant will I be if I spend a few months and I’m working harder than I’ve ever worked in my life, whatever it may be. And then I think it’s that question, what I want to be when I grow up is really what’s just positive. Do I want to be a business owner or am I a big corporate person and that’s just who I am and I’m comfortable with leveraging the name that’s on the door? Louis Diamond: No doubt. I think an interesting one is ask yourself, if I stayed exactly where I am for the next 15 years, would I be proud of what I built? I think it’s a really important one sometimes too, when I’m speaking with advisors who are worried about a transition, and honestly people should be worried about a transition. It’s terrible. There’s no sugar-coating it. It’s a lot of work. It’s scary. It’s a risk. You’re disrupting your life, all your client’s life, et cetera. But sometimes, I’d say, the reframe is the three months, four months, whatever it is of backbreaking work that you put in, is that worth it for the next 10, 15, 20 years of doing something that really sparks you more? And I think, again, it comes back to your question of risk appetite, but I think just thinking about that is a lot of people get lost in the immediacy of the risk and of the work, but they forget to look beyond that work to what’s possible on the other side of the rainbow. Mindy Diamond: And how important it is to get it. In other words, I may be very clear that I would like to have X. I’d like to do Y. I would love, I mean we hear this all the time. I’d love to be independent and build my own business. Boy, if I were 20 years younger or I was, whatever it was. So the point of the matter is you can be aware of wanting those things. It’s admirable to want them, but whether or not you actually should go after them, that’s an inside job. Louis Diamond: No doubt. I think another one I like is which regret is bigger to you, trying something and failing or never testing what’s possible? I think that’s a good one. Another philosophical one. Okay. Let’s change gears a little bit here. So for successful, we’ll say mid-career advisors, why do you think the biggest risk is oftentimes not making the wrong move, but choosing the status quo by default? Mindy Diamond: Answering it from the perspective, we’ve had a lot of senior advisors that say, “I look back on my career and if only I had made the move or gone to this 15 years ago.” So I think that’s the you get comfortable in the discomfort, it’s easier to stay put, and then before you know it in your head, you’re too old or you’re too settled or you’re too entrenched and you can’t go or it’s too late to go. Or in some cases, you’ve got so much unvested deferred comp, it’s too expensive to go. And so I don’t think that’s only about mid-career. I think you want to make sure that you’ve lived a professional life that’s not filled with regret. That’s really what I think. Louis Diamond: No doubt about it. I think too, the fear of making the wrong move, call it buyer’s remorse, if you will. I honestly don’t really see it happen all that often. When I see it happen, it’s because someone went to their second or third choice firm because they got paid an extra 10% of their production. That’s where the wrong decision comes in. I usually find when advisors are thorough, they’re thoughtful, they’re making decisions for the right reasons, and there is a base level of motivation to do something different. You can’t really make the wrong choice. And I think, but again, it’s risk tolerance, et cetera. Let me give you two more questions here. What’s one step an advisor can take today to create clarity without committing to change? Mindy Diamond: Well, that question is the fundamental definition of how you and I see the world. So we traffic in advisors that are 10 years, 20 years from making a move or may never move. To us, it’s just about the relationship. That may sound foofy and crazy, but it is the truth. And so those conversations are all about being self-aware. That’s number one. Know thyself. Being self-aware of what’s important to me, what can I live with, how much risk tolerance do I have, et cetera. And then it’s also about having a clear understanding about what’s possible. So continuing to have conversation with people like you and I, continuing to read AdvisorHub or whatever it is about transitions that happen. Understanding the opportunities that are out there is not necessarily because you’re going to go take one of them or you’re going to change, but knowledge is power. It empowers you where you are. It’s competitive analysis, and that’s smart. That can only serve you well. Louis Diamond: Yep. I completely agree. One last question here, and I think probably a lot of people listening to this are thinking this personification of an advisor or advisors that we’re describing, are they just complainers? Are they just being ungrateful? On the one hand, there’s probably folks that are early in their careers working their butt up who are saying, “Hey, just be grateful for what you have. It’s really hard to get to where you are. So why are you looking a gift horse in the mouth?” So what would you say to that line of thinking, that questioning the status quo or questioning something that’s really good, does that means someone’s ungrateful or restless? Mindy Diamond: Oh my God, no. I think that the smartest and most successful, and honestly most grounded and grateful people are those that question the status quo. You can be, can and should be, simultaneously grateful for what you have. We say to advisors all the time, “We’re not from Merrill Lynch’s training program that catapulted you to the seat you are in now to become a 3 million, 5 million, $7 million advisor. You may be mad at Merrill Lynch now, or you may want something more than Merrill Lynch can give you today, but it doesn’t mean you’re ungrateful for what Merrill gave you.” And that’s the answer. I think that you can be grateful for this wonderful life that you’ve been afforded, but at the same time, aware of the fact that you may want more, and that’s a healthy, wonderful thing. Louis Diamond: Completely agree. Yeah. I mean, it’s similar to any large Fortune 500 company. You think of Apple, how many times they’ve reinvented themselves or everything that’s happening with AI. I think a big part of this too is future-proofing, right? It’s okay. It’s not being ungrateful. It’s more I’m taking proactive action to make sure I do have a business in 15 to 20 years, and that I’m positioning to myself and my clients to do the best possible work together. This is the fun topic. It’s obviously something on the mind of a live of advisor. It comes up probably in every group that we ever consult with, whether they’re a wirehouse advisor, they’re a private banker, they’re independent, et cetera. So I think a lot to learn here. My big takeaway from today is it’s okay to want more. There’s nothing wrong with you for wanting more. At the same time, there’s nothing wrong about being comfortable. Thank you very much for today. Mindy Diamond: Amen. My pleasure. Good topic. Louis Diamond: Thank you for joining us. We'll be back with a new episode next week, so be sure to listen in. Mindy Diamond: As a financial advisor, you hold yourself to the highest standards of integrity, honesty, and credibility. You are successful because you take your professional responsibilities seriously and are dedicated to your clients. But are you living your best business life? Are your goals aligned with your firm’s, or could a better option exist? Should I Stay or Should I Go? is a book written with you in mind. It’s a self-guided journey that walks you through the key steps that we take with our advisor clients. This strategic thought process and roadmap to professional self-discovery is designed to help you ask the right questions and think critically and objectively, whether you’re considering change or not. Learn how to get your copy at diamond-consultants.com/thebook.   Best of Replay: Why So Many Successful Advisors Feel Stuck An Industry Update with Louis Diamond and Mindy Diamond.      Louis Diamond: Welcome to a replay of one of the most popular episodes from our podcast series for financial advisors. Why So Many Successful Advisors Feel Stuck. It's a Special Industry Update with Mindy Diamond. I’m Louis Diamond and this is the Diamond Podcast for Financial Advisors. Mindy Diamond: At Diamond Consultants, we help elite advisors identify the right environment for their businesses to thrive, whether that’s at a wirehouse, boutique or independent firm. With nearly three decades of experience, we’ve guided thousands of advisors and represented more than a quarter of a trillion dollars in assets transitioned. And each year, one in four advisors managing a billion dollars or more who change firms are our clients. Our process is education-driven, and based on building relationships, starting as your strategic partner well before you’re even thinking of a move. To schedule a confidential conversation, call us at (908) 879-1002. Wondering why advisors change firms and where they’re headed? Are transition deals going up or down? Those very questions and more inspired us to create our annual advisor transition report. It’s the award-winning, data-driven resource designed for advisors that connects the dots between the motivations around movement and the firm’s appetite for top talent. Arm yourself with the knowledge you need to make smart decisions. Download your copy at diamond-consultants.com/transitionreport. Louis Diamond: We spend a lot of time talking with advisors who, by every external measure, are doing exceptionally well. They’ve built real businesses with strong production, loyal clients and teams that continue to grow, and yet behind closed doors, many of these same advisors are quietly asking a different set of questions. Not how do I fix what’s broken, but why doesn’t this feel as good as I expected? That tension is showing up more frequently than it did five or 10 years ago, and it’s not because advisors are failing. In many cases, it’s because they’ve won and now find themselves staring at another 15 to 20 years of more the same, unsure whether comfort has slowly replaced energy. This industry update is about that very moment. I asked Mindy to join me to unpack what we’re hearing from successful advisors across the industry, why success itself can become a constraint, how fear of change competes with fear of standing still, and why record valuations, longer careers and the maturity of the independent space are changing the psychology of decision-making. We also talk about the right questions to ask before considering a move, questions about control, enterprise value, legacy and time horizon, and how advisors can create clarity without forcing a decision. There’s lot to explore here, so let’s get into it. Mindy, thanks for joining us. Mindy Diamond: Oh, I’m so happy to be here. Always. Louis Diamond: This is a fun topic. So today we’re just going to dive right in. I’m curious, from your vantage point and talking and working with many of the best advisors in the industry at a high level, what are you hearing from successful advisors today that feels different from five or 10 years ago? Mindy Diamond: First of all, for the most part, I think that top advisors are loathe to move. And they may have a bunch of frustrations or things that they wish were different, but generally speaking, they’ve always been well taken care of. They have the bat phone to the top, it’s good enough and nobody wants to mess with success. But two things are true today more than ever before, that the competitive landscape, there are more options than ever before, that their businesses are worth more than before. I guess it’s more than two things. That they’re thinking about their business as a business and saying, “Yeah, it’s a hassle to move, but if I can maximize the value of my business more elsewhere and at the same time solve for what I want to, maybe I really do need to consider it. And so I think that it’s a time of more consideration, that top advisors, in particular, are just not willing to settle for the status quo. Louis Diamond: Yeah, no doubt. The only thing I would add, but I completely agree, is that today the retire in place or succession opportunities for a wirehouse advisor to transition or sell their business to their next generation or to another team or even an independent advisor, to sell their business to their broker dealer or to someone within their firm, those opportunities, the internal options, I think are more compelling than ever. So we hear a lot from really successful advisors who, that actually doesn’t matter if they’re successful or let’s say they’re mid-tier in the indu

Stop, collaborate, and listen….Today I'm talking to someone with a small store who grew her email list by more than 1,600 names (and landed 34 brand new customers) using a strategy any one of us can and should use. Listen in and find out how Tina collaborated with other Shopify stores to get big results. You'll learn: How she got her customer acquisition cost down to $3-4 Why she almost gave up on the strategy too soon The traffic source that converts 2X as well as social media Even if you've tried it before and think collabs aren't right for you…I promise, this episode will change your mind.   RELATED LINKS: Get on the Inner Circle waitlist here: https://www.thesocialsalesgirls.com/inner-circle-membership The formula for an easy Collaboration you can do this month https://www.thesocialsalesgirls.com/blog/the-formula-for-an-easy-collaboration-you-can-do-this-month-episode-215 Organic Sales: Don't miss these 2 strategies https://www.thesocialsalesgirls.com/blog/organic-sales-dont-miss-these-2-strategies-episode-282 A step by step plan to double Louise's email list https://www.thesocialsalesgirls.com/blog/a-step-by-step-plan-to-double-louises-email-list-episode-303 --------------------------- Are you constantly asking yourself how to make your website better or questioning your prices? Do you wonder if you're missing something? If you have made sales in person or on a site like Etsy, I know you can sell more on your site. Get my FREE mini-course that will show you our step-by-step process to grow your sales without spending a fortune on risky ad strategies or discounting your products here: https://learn.thesocialsalesgirls.com/conversion-school/ "Insightful, actionable and engaging! I learn so much every single time I listen. I can't believe this information is free!" - If you feel like this too, I'd love it if you would leave us a review. Reviewing the show will help us reach even more store owners, so we can help them grow their sales. Click here, tap to rate with 5 stars and select "Write a review". Let us know what you find most helpful about the podcast! Also, if you haven't already make sure to follow the podcast so you don't miss an episode! Follow here!  

Katie's best month ever as a social media manager was $10,000. It took 40 hours a week, posting six days a week at specific times for each client, and it almost never happened twice.Then her clients started cutting costs, and she started getting let go. "It's nothing you did." If that sentence has ever landed in your inbox, y'all, this episode is your sign.Six months into Strategist Society, Katie has eight ad clients, works about 15 hours a week, and is on track for over $12,000 in September. She is also a homeschooling mom of three boys with a travel baseball schedule. Her life did not get smaller. Her business got smarter.In this episode, you'll learn:Why Katie lost an entire year to consumption mode after joining Conversions for Clients, and the exact question to ask yourself so you do not do the same thingThe messaging fix that took her from 1 conversation per 30 messages to 1 per 5, and the three replies she got the same day she changed itWhy it took roughly 90 days of daily touchpoints before her first discovery call, and the 45 to 60 day marketing timeline behind thatHow she manages eight ad clients in 15 hours a week while homeschooling three boysWhat actually happens on a business analysis call, and why Katie never felt pressured on itThe one daily habit she credits above every strategy for propelling her business forwardWhy members who Vox me at least once a week 2X to 10X their businessMentioned in this episode:Free boot camp and Conversions for Clients: conversionsforclients.comStrategist Society application: thestrategistsociety.comThe Predictable Clients workshop (inside the programs)DM me the word PIVOT on Instagram @brandimowles for the lightning version of Katie's story and where I would start if I were youReady to scale past $10K months?If you already have a service business and what you actually need is accountability, current training, and a room of people scaling to $10,000 months, Strategist Society is probably your place. We track our members' activity every single week, we have the income growth stats, and we have the retention numbers to back it up. Head to thestrategistsociety.com, fill out an application, and if we think you are a good fit we will be in touch to get you on a business analysis call just like Katie's.Loved this episode?Screenshot it, share it to your stories, and tag me @brandimowles. Send it to the friend who is drowning in daily posting for clients and does not know there is another way yet.Now go do the dang thing.Follow the Podcast: https://podcasts.apple.com/us/podcast/serve-scale-soar/id1477998650Follow Brandi on Instagram: https://www.instagram.com/brandimowlesFollow Brandi on Facebook: https://www.facebook.com/Brandiandcompany

The latest U.S. jobs numbers are out—and apparently the labor market didn't get the memo that it was supposed to be slowing down! The U.S. economy added 162,000 jobs in August, well above expectations, while the unemployment rate held steady at 4.1%. Even better, June and July payrolls were revised higher by a combined 55,000 jobs. So...good news, right? Well, this is Wall Street, where good economic news can quickly become bad news for the markets.

AI roles offer more than 2X the income potential, yet women account for only 26% of new hires for those AI jobs. That makes this a pivotal moment for women and their careers.In this episode of Women Leaders on the Move, host Natalie Benamou talks with Erica Rooney about the opportunity in front of women, the bias already showing up in AI, and why learning to use these tools well matters now.Together they dive in deep into Erica's new book: ‘The AI GAP: Women, AI, and the Next Great Leap Forward'.This your time to go beyond understanding how AI is changing work, leadership and what you should do next.Stay until the end when Erica shares a powerful five-part prompting framework you can start using right away.About Erica Anderson RooneyErica is the founder of HER Collective, a Chief People Officer with over 15 years of experience. She is the author of The AI GAP: Women, AI, and the Next Great Leap Forward and Glass ceilings and sticky floors. If women aren't actively involved in the build and the governance, they will be left behind. Her newest book is a must read.Find Erica Anderson Rooney here:joinhercollective.comericaandersonrooney.comlinkedin.com/in/ericarooneyKeep shining your light bright. The world needs you.HerCsuite® is a leadership network where women build what's next. Our members land board roles, grow businesses, lead the AI conversation, and live their best portfolio career with our programs. Join us at HerCsuite.com, or connect with host Natalie Benamou on LinkedIn.Join us September 16th: The Future is Yours to Build: Wealth. Security. Impact in the AI Era.

India now requires turbine data, control centers, and R&D to sit inside its borders. Plus a study on reverse flow loads in parked blades, Envision’s spare parts push across Southeast Asia, and Danish wind employment falling to 32,700. The Uptime Wind Energy Podcast is brought to you by Weather Guard Lightning Tech, creators of the StrikeTape Ultra LPS retrofit. Subscribe to Uptime’s Substack newsletter. And check out Rosemary’s “Engineering with Rosie” Youtube channel. Have a question we can answer on the show? Email us! The Uptime Wind Energy podcast, brought to you by StrikeTape. Protecting thousands of wind turbines from lightning damage worldwide. Visit striketape.com. And now, your hosts Rosemary Barnes: Welcome to the Uptime Wind Energy podcast. I’m your host, Rosemary Barnes, and I’m filling in for Allen today here with Jolanda Pedron. All right, onto our first story. India is drawing a line around its wind data. The Ministry of New and Renewable Energy has ordered every turbine maker on the approved list of models and manufacturers to report where its data actually lives. Data centers inside India, servers inside India, research centers inside India, and no real-time data leaving the country. The order went out last week with status reports due by month-end. And while the government tightens the rules on data, the hardware keeps moving in. Flender has just opened another generator plant in Chennai [00:01:00] Yolanda, I think this is gonna have a large impact on the number of companies who are, you know, there’s a lot of companies around today developing, like, smarts for wind turbines. There’s a lot of scope to operate turbines better, to improve power output, reduce maintenance costs, et cetera, and y- a lot of companies are taking advantage of that. Even Patlow has some projects going on in that sort of area. What do you think about the impact this will be? Is India planning to develop their entire own ecosystem for that aspect of the industry? I  Yolanda Padron: mean, it looks like it, right? Which is really, I mean, kind of groundbreaking. I know that we talked a couple weeks ago about Europe sort of being a little bit scared of bringing in, um, different manufacturers from Asia, um, and just the, the idea of cybersecurity being something really prevalent and really important to all of these people. So just if India starts siloing everything and that works, maybe that’ll translate to Europe.  Rosemary Barnes: Do you think that it, it can work? Has India [00:02:00] got the expertise in place already to, to make this work, or is this gonna be a real, like, handbrake on their, their wind industry continuing to develop as fast as it has been? Yolanda Padron: I mean, I’m really curious to see it because I don’t know if they’ll bring in a lot more experts to be able to, to do that.  Rosemary Barnes: I know the Indian, um, operating environment is quite different to other locations. I mean, it’s, it’s similar in a way to Australia in its dissimilarity, if that makes sense. I, I think there’s a lot of really India-specific problems, so of course Indian companies are gonna be best placed to take advantage of that. But what we’ve seen so far is a lot of, uh, companies from Europe or America coming in to y- you know, like tweak the way that they attack similar problems in other markets. They’re, you know, moving in to understand India better and take advantage of that. Now, of course, like not every smart operating method or aerodynamic improvement, they don’t all need [00:03:00] real-time data, and probably most of them don’t actually. So I think it’s not gonna like s- snip off this entire kind of improvement. But it is really interesting. It’s quite, like it is quite severe, the restrictions, and I haven’t heard of any other market, um, that is doing that. I, I don’t know, do you think it’s warranted or is it maybe like overly paranoid? I know when I heard that in, um, in Europe they’re not allowing Chinese manufacturers of inverters, for example, and I just felt like, “Ugh, is that a bit over the top?” I’m not sure. Um, yeah, what’s your opinion, Yolanda?  Yolanda Padron: I think it, it might be a little bit over the top. We live in such a globalized world that the idea of one country just siloing completely is, is a little bit strange to me. But- I guess if, if that’s, they have a, a reason for concern, then I guess it might make sense. But to your earlier point, like, I don’t know if the cost of doing this [00:04:00]would also come into play  Rosemary Barnes: Yeah, I think that there’s always a, a, a trade-off. It’s like when you have any kind of l- local content rule, which is similar in a way because it forces, um, yeah, it, it forces your own ecosystem to develop, and that’s, that’s great. Like, it is a missed opportunity if you just have, you know, like a huge new industry and you just import it all, then that i- is a lost opportunity for your own economy. But I mean, in the past, India has had pretty strong local content rules, and they do have a bunch of wind energy manufacturing, um, within the country. They are a country known for really great engineering, so it’s for sure not beyond them to, you know, step up. So yeah, I guess time will tell if this is a smart move from that point of view. I don’t think it’s actually the motivation behind this rule. I think it’s more of a, like, a cybersecurity, um, kind of perspective where you don’t want, you know, overseas companies having the ability to shut down your energy system, for example. Um, [00:05:00] so yeah, but it, it has the potential to have that other effect of, you know, like boosting the local industry or alternatively just slowing down the whole industry because they can’t get anything done. I guess that’s always that balance that you need to, need to look at when you’re looking at local content. So India is deciding who gets to control the data inside a turbine. Our next story is about something inside that same turbine nobody controls yet, the air moving across a blade that isn’t turning. A parked turbine might look like the safest thing on the site. Blades are locked, the rotor’s still, nothing turning, nothing generating. But a new study says that stillness is not safety. When the rotor is locked and the wind shifts, air can strike the trailing edge first and travel backwards across the blade. That is reverse flow, and in reverse flow, the models this industry uses to predict blade loads start to fall apart. Field data cited in the study suggests blades on turbines above 15 megawatts can go aerodynamically [00:06:00]unstable at winds as low as eight meters per second. That is nowhere near a storm. So this is actually already definitely a known issue, and I know that modern wind turbines don’t usually park with locked rotors, right, Yolanda?  Yolanda Padron: Yeah.  Rosemary Barnes: We, we saw that a locked rotor during installation was one of the causes of the Vineyard Wind fiasco, right? Where broken blade pieces washed up on the beach. So definitely something that’s known about.  Yolanda Padron: And it’s something that even onshore you have a, you have your system so that it tracks sort of where the wind is coming from. So even if it’s stopped, it won’t just be stopped at a specific angle, particularly in case the, the winds shift.  Rosemary Barnes: Yeah, and I’ve seen examples of blades failing when, um, there was a storm and there was power lost, so they weren’t able to yaw or pitch a turbine, and that also can cause… Like, any time that- Wind is going in a different direction than what the turbine desi- was designed for, you’ll get unpredictable stuff happening and often, [00:07:00] like, really bad loads. Like the harshest, um, conditions, loading conditions that a wind turbine blade has to withstand is not in operation. It’s if you’ve got just wind hitting like a bluff body, they call it, where a gust of wind just hits flat on the blade and tries to bend it. Like that is way, way stronger loading than, um, than anything it would see operationally. If you’re looking at, you know, like a really severe storm, like a one in 50 year kind of, um, wind speeds, and then if you have flow going on weird angles where it’s creating lift, then you can ratchet up those loads even more. So for sure a known issue. This study, it, it singled out turbines above 15 megawatts, which are huge. That’s a, you know, like a big offshore wind turbine. But I don’t think it’s limited to that. Like what would you think, Yolanda? Is this, is this like purely a big blades thing, or is this something that actually all wind turbines need to take into account? Yolanda Padron: Oh, this is definitely something that all wind turbines need to take into account. It’s something that we’ve seen a lot in the [00:08:00] US as well, um, onshore and s- wind f- uh, smaller wind farms and wind farms that have, you know, 200 plus, um, wind turbines on them, uh, the, the 1X or 2X turbines. Uh, this is also ver- a, a reason that it’s really important, right, to check your systems constantly, and I know that a lot of the, the owner/operators do that, um, in their regular maintenance checks. Um, but it’s, it’s, it’s an issue that, uh, if you don’t have the correct systems in place or if you just remotely reset things, uh, because you see an error that maybe you thought, “Oh, that, that must be just a glitch,” uh, then you start having issues like this that can cause catastrophic failures, not just on the blade but on the turbine itself if, if the blade ends up hitting the turbine, um, it, it ends up hitting the, the tower itself. Uh, and then [00:09:00] a similar issue, um, that can happen too is when you don’t have the correct systems in place for, for an emergency, and so the turbine can just overspin, and that also causes a lot of blade failures, uh, both onshore and, and offshore as well. But I think onshore it’s, it’s just the, the wind farms are so big that it’s It’s easy or, or easier to miss, like, one or two turbines, um, when you’re doing those checks. Rosemary Barnes: Yeah. And I think another issue with keeping the rotors locked out, and one of the big reasons why they moved to pinwheeling as the, you know, um, turbine off kind of configuration, was with the, the bearings, right? Like, it’s not good for the bearings to be totally stationary. Um, so I think that it’s better to, yeah, pinwheel. It’s lower loads on the bearings.  Yolanda Padron: Something, um, similar to this as well, right, and, and I think Wind Power posted about it the [00:10:00] other day, um, about just, oh, it’s really common for, for sites to, to think of, of a financial solution to, to negative pricing being just fully stopping turbines that might not be producing at positive pricing. Um, and then, uh, that can also cause a lot of loading issues because you’re, you’re having a, a turbine at, at full capacity, and then it just stops.  Rosemary Barnes: Yeah, that’s true. The braking loads are, are one of the more extreme loads that turbines have to withstand also. I mean, hopefully if they’re curtailing, um, voluntarily, uh, they’re not doing an emergency brake kind of level. I hope that they’re being a bit more gentle on their turbines, but yeah, for sure, like, the easiest thing for a turbine to do is just operate normally its whole lifetime. That’s exactly what it was designed to do, and anything that deviates from that can reduce the t- life of the turbine, complicate things. So machines keep getting bigger, and the physics keep [00:11:00] getting harder. Meanwhile, the map keeps getting wider. Next, a Chinese manufacturer planting both turbines and spare parts across Southeast Asia. Stay with us Speaker: Are you overspending on lightning repairs? Most operators are because solving lightning damage is complicated. Weather Guard Lightning Tech helps operators reduce lightning damage. Our innovative StrikeTape lightning diverter protects over twenty thousand blades worldwide. Now, StrikeTape Ultra installs faster than ever. StrikeTape Ultra cures uptower in just fifteen minutes, even in cold weather. Visit weatherguardwind.com to schedule a call. Rosemary Barnes: China’s turbine makers are no longer just shipping machines. They are building the network that keeps those machines running. Envision Energy has secured its largest wind project in Vietnam, 200 megawatts with REE Energy, 25 [00:12:00] turbines, grid connection targeted for October 2027. It is also the company’s largest overseas nearshore project to date. And Batangas, Philippines, Envision has opened a distribution center holding major components and spare parts close to the projects that need them. Turbines first, then the parts network. Yelena, I’m just wondering if spare parts was a big headache for you when you worked in asset management, and do you think that this plan of, that Envision has is gonna be a successful one for Vietnam? Yolanda Padron: I think spare parts, having spare parts on site or near a site is always a good strategy. I know that sometimes it can get the, the accounting itself can get a little bit difficult because for at least, you know, in the US for tax purposes, you can’t really sell something from one project to another, so you, you have to be a little bit careful, um, as far as when you hold a part or you’re sending one part that might fit in multiple sites and where you’re technically buying that [00:13:00] from. Uh, but I think the spare parts that they’re having here I, I think it’s a, it’s a really good idea, right? Because oftentimes you might have a, a big, um, there might be a big queue for a particular component that fails years after, uh, it was produced, and then you– I mean, if you need it now and there’s a big queue now, then you need to wait until it can get to site, until it can be produced, until it’s your turn in line. Uh, and so just having those components on site or really close to the site I think is a really good idea.  Rosemary Barnes: Yeah, it’s really challenging. Like it’s, it, it’s easy to imagine how you could organize a spare parts network or, you know, just l- literally a bunch of warehouses holding spare parts for the kind of, you know, like one-off failures that happen routinely throughout a wind turbine’s life. But when you start to see a serial issue or if there’s just, you know, like statistically every now and then [00:14:00] you’ll get clusters of failures even if it’s not a serial issue, that’s when it starts to get really hard. So I know in Australia, and I’m sure it’s the case elsewhere, you kind of got to pair your spare parts inventory with the capability to repair and refurbish as well. Because sometimes it’s like, you know, you might do, like speaking of blades, which is my specialty, you might have a bill of, you know, maybe even a million dollars to do a really big repair on a blade, but that’s better than spending probably, honestly, the similar amount of money on a brand-new blade, but then that comes with a six-month or a 12-month, um, wait time. You know, like obviously you’re gonna save overall if you can get that turbine up faster. And we also see a lot of, um, in-country capabilities to refurbish, you know, all of the drivetrain components and, and everything like that, and I think that that is such an important complement. Like your spare parts strategy should never just be about having them physically sitting there, ’cause if you were [00:15:00] to have enough to cover any situation, then, you know, most of your components would go unused over the, the lifetime of the wind farm. And it’s not like they’re just swap in, swap out for any different wind turbine, right? Like they are usually quite turbine specific.  Yolanda Padron: I completely agree with the idea, especially with blades and how trif- tricky some of the new blades and their, their repair processes can be. It’s really important to have a plan and not necessarily just, “Okay, now I’m gonna throw this one out and bring in a new one, and always hold a new one,” because that’s really expensive. Um, and it’s also really, really important to, uh, that doesn’t matter what particular part it is, that whatever storage facility you’re using, that you’re, you make sure that the environmental factors impacting the spare parts are taken care of, right? So, um, I know I’ve seen spare parts for a battery site or spare parts for a solar site, uh, that just kind of end up degrading even more than the parts that were in [00:16:00] operations because no one was really looking at them. They were just outside in the elements, and then you just wasted millions and millions of dollars for really nothing. Um, something that you need to take to the landfill eventually or report.  Rosemary Barnes: Hopefully recycle it if it’s electronic waste.  Yolanda Padron: I completely agree that to, to be able to, to have any sort of replacement strategy, you, or any sort of spare parts strategy, you need to make sure that, that the replacement strategy is there and that the refurbishment strategy is there. If there’s anything that you can recycle in-house and use in-house, that’s a lot better than just landfilling anything or sending anything to be recycled elsewhere where it just piles and piles and piles up.  Rosemary Barnes: Yeah. I think also it’s reassuring if you’re buying wind turbines from maybe a, a less well-known manufacturer or less well-known in your country, I think it is reassuring to have a, like just a stack of spare parts on site, because that will give you the confidence that if [00:17:00] and when things start to go wrong, you know, a few years into the wind farm’s operation, you are gonna have that capability. ‘Cause I think it is, like it’s a big leap to move away from the really, really well-known, uh, manufacturers into some of the less well-known ones and have the confidence that y- you know, you wouldn’t have any- anyone to ask what they’re like for service, uh, for example, and you might also not have confidence that they’re even gonna exist in, you know, 10 years, 20 years, 30 years’ time. So having the parts on site can give you that confidence. Although, as you say, if those parts aren’t actually functional when you go to use them, that can be worse than not having them in the first place because you thought that you were okay and then you find yourself scrambling. That’s not ideal at all. Yolanda Padron: Yeah, I completely agree. I did hear about someone who, um, had a, a blade that they They thought they, they didn’t really have the capabilities in-house to, to restore it, and so they sent it to be recycled, [00:18:00] and then later it was sold to them by their OEM, um, on site. And so I think that also speaks to the… And, and it was just because someone noticed the serial number was exactly the same and kind of noticed. It was, it was really honestly kind of a funny, funny thing in retrospect. But, uh, and it, it just speaks to the idea of you really need to understand if you’re holding spare parts and, and just in general, even if you don’t wanna hold spare parts. Um, the idea of being able to refurbish or have a plan to be able to refurbish the, the materials that you have on site is really, really, really important because it can save you so much money. I mean, the, I mean, the, for that case, just the, the transportation costs of hauling the blade off, of paying someone to recycle it, of paying for a new blade, um, and everything just to kind of be bamboozled in that way where you could’ve just repaired it on site is, is It’s almost a no-brainer, yeah. Rosemary Barnes: I gotta say, that’s like a real recycling win, right? Like, you can’t get [00:19:00] better than, than that. Uh, you know, you had a blade, you recycle it into a blade. It’s, you know, performing exactly the same function, better than shredding it and hiding it in a, you know, a footpath, a sidewalk, or something like that. Stay with China a moment longer, because the product strategy tells you as much as the project map. Mingyang Smart Energy has unveiled the Tianchi MCD 5 MW turbine at its factory in Guizhou. It’s the first machine in the company’s new medium-speed compact drive platform, a semi-direct drive that Mingyang says cuts component count by 60%. And it is built for sites nobody used to bother with: mountains, plateaus, places where the wind averages four and a half meters per second. Now, Yolanda, 5 MW is pretty big for an onshore turbine in locations. It sounds like, you know, these locations, aside from the, like, less than ideal wind resource, also sounds like places where it’s actually, you know, transport might be a bit of a constraint. I wonder what their strategy [00:20:00] is gonna be to get those large components and installation equipment in.  Yolanda Padron: Yeah, I mean, it’s, it’s not gonna be the first site, I think, that we’ve seen that where they have to develop some sort of game-changing vessel or crane or something to be able to, to get things, uh, to the site. Uh, my, my first thought honestly was just kind of like, “Oh goodness, that might be a lightning nightmare depending on, on where they’re located.”  Rosemary Barnes: Yeah, that’s true. Like we’ve seen in Japan, they have all their turbines on the ridges where the wind speeds are great, at least. Um, but they have been just absolutely destroyed by lightning, like really, really dealing with extreme situations. I think also, like these are low wind speed sites. Like what, what did they say? Four and a half meters per second average wind speeds? I mean, that’s like half what you would consider a good, a good site, right? And we know that the power in wind, it goes, like it increases with the cube of wind [00:21:00] speed. So, um, you know, like if you double the wind speed, you get eight times as much power. So it’s like all of the challenges associated with a high wind speed site in the mountains or, you know, somewhere tricky to get to you know, at least you’ve got eight times the power to make up for the effort of maintaining it. Uh, just it seems like, like a lot of effort to get to a low wind speed site, right? I think these turbines, the cut-in wind speed is gonna be two and a half meters per second, so you know, again, like you can reduce your, yeah, like the, the wind speed a- again by another factor of eight. It’s almost nothing at that wind speed. It really, yeah, I don’t know that you can make up for a low wind speed site by having a really low cut-in wind speed, right? Like, you’re still gonna end up with low power across the board.  Yolanda Padron: Yeah, and it’s, it’s strange too, I mean, we said it’s, it’s a mountainous region, right? And so, and the transportation [00:22:00] in itself, I mean, we talked about the idea of bringing them in is gonna be difficult. But I know that a lot of times we see a lot of blades that have issues down the line because of the transportation issues and because of the way that maybe they, they didn’t use the lifting points right, and there might be a lot more transportation issues or damages from transportation on the site if they come in and it’s a completely different terrain that they’re used to. Uh, and teams have to build in a completely different terrain to- that- than they’re used to, then that might also cause problems down the line. Not to mention the idea that I know sometimes in sites where there’s already roads, uh, it, it, because of the position of the turbines, because of the, the way that things are, are mapped out in development and construction, sometimes it’s a, a hassle for operations to, to get the correct cranes or trucks or everything in there to do any sort of maintenance. Uh, so [00:23:00] I can’t imagine in a place where it’s just really, really difficult to get to in general.  Rosemary Barnes: Yeah, and especially over the lifetime, you know, maintenance i- is gonna be such a challenge. So I think that it sounds like they’re aware of that because their emphasis with this new platform, dropping the component count by 60%, that’s a claim from Ming Yang that I haven’t seen the details of. It seems highly implausible but, um, yeah, I would wanna look into that a bit more to believe it. But they also say that their new bearings and gearbox features lift reliability by 10%, so yeah, I think that that is going to make their job a little bit easier, but yeah, I don’t know if it’s enough that would make me wanna invest in a wind farm with a, what was the number? Four and a half meters per second average wind speed. Seems marginal at best. So new machines for marginal sites built in China and priced to compete. Now let’s move [00:24:00] to Denmark, where the employment numbers show what that competition feels like from the other side. Back in a moment. Speaker 4: Delamination and bondline failures in blades are difficult problems to detect early. These hidden issues can cost you millions in repairs and lost energy production. CIC NDT are specialists to detect these critical flaws before they become expensive burdens. Their nondestructive test technology penetrates deep into blade materials to find voids and cracks traditional inspections completely miss. CIC NDT maps every critical defect, delivers actionable reports, and provides support to get your blades back in service. So visit cicndt.com because catching blade problems early will save you millions Rosemary Barnes: Numbers out of Denmark tell two stories at once. Green Power Denmark counted [00:25:00] just under 110,000 full-time jobs across the Danish energy sector in 2025. That is up 1,526 positions or 1.4%, slightly ahead of Danish employment overall. That is the good news. Inside the wind sector, employment fell to 32,700, a drop of roughly 500. Green Power Denmark blames international competition and unfavorable market conditions. Its chief economist calls the dip normal You know what, Yolanda? I think that if Denmark’s labor market is weak in the wind industry, there are many countries outside of Denmark that would wanna snap up that talent. I know that my own company, Pardalote, is included in those numbers of people that would really like to have Danish talent. I’m trying to s- hire someone currently that’s got wind turbine blade expertise, and I tell you what, that is not easy to do in Australia. So I would say if there are any Danes listening or watching who fancy a move to Australia, then please do get in touch [00:26:00]if you have some excellent wind turbine blade experience. Yolanda Padron: You guys would have a lovely, lovely boss. I’ve- I just gotta say that. The learnings that you have in Denmark and just the, the, the school- not just from the schooling, the, the onsite, I mean, it- it’s just historically Denmark’s been really, really great in, in wind. And there’s just so many bright minds, um, that d- I mean, if, if they can’t find a j- a job in Denmark, then I, I agree that there’s a lot of places in wind that if they’re, they’re okay with moving, they’ll, they’ll find a, a good spot that’ll receive them really happily. Rosemary Barnes: I know it is a bit of a, like, a cultural blocker. Danes more than other nationalities that I know are, are really, um, I don’t wanna say attached, it makes it sound like not quite the word I’m looking for, but they, they do really appreciate the region that they’re from and, you know, family is so important to Danes, so not the easiest thing [00:27:00] to just say, “Just move to the other side of the world.” But you know, I did it. It, it can be done and, uh, yeah, Australia is, has got very nice weather, especially at the moment. It’s, um, it’s gonna be… It’s been, like, in the high teens Celsius all this week, and this is, I’m in Canberra in still winter and, uh, yeah, like, on the weekend it’s gonna be 20 degrees. It’s, you know, normally 15 degrees is a really great one-off day in winter. It was my birthday this week and in Denmark there is a, a saying or a, you know, a belief that if you have good weather on your birthday, then that means that you must have been really good all year. And the weather was just, like, unprecedentedly good for my birthday, um, yeah, for a winter day. So obviously I was very, very good this year. Yolanda Padron: You’re a great person, and happy belated birthday.  Rosemary Barnes: Thank you. But it is something that I say every time we get another story about another Danish wind energy company that’s, you know, doing layoffs and, you know, I’m honestly surprised that it’s [00:28:00] only 500 fallen in wind energy. If you look at, like, even just the job losses at LM Wind Power alone, um, I think that it would be about that many, right? And so there are obviously some other companies um, growing to absorb some of the job losses, ’cause for sure we’ve covered more than 500 job losses just on the show. But I know I always feel really sad, um, not just for the people involved who y- you know, probably love wind energy and wanted to work in that field, but I feel sad that, you know, this was such a strength for Denmark, their, their wind industry, that they had been, you know, developing this expertise since the ’70s, honestly. Uh, really, really world leading and, uh, of course, like, globalization means that you can’t, like, stay siloed forever, and Denmark obviously also took advantage of that globalization to, you know, export. Wind industry was a huge export market for Denmark, and still is. Um, but I do, I, I just feel really bad at the companies that are losing, um, all of that, you know, institutional [00:29:00]knowledge and the really, really deep specialist knowledge. It’s, um, yeah. Uh, and they’ll go on to, you know, bring new skills to other industries, but it just makes me sad. As a wind energy lover and, um, you know, somebody who has a lot of friends that work in wind industry in Denmark, I do feel, feel sad. And there are some of my old colleagues who are amazing, like, literally the best engineer that I ever worked with, an electrical engineer, it, it was not easy for him to find a new job in wind. I’m actually not sure if he’s working in that industry now. And plenty of other, like, really, really talented engineers that have gone on to other, um, yeah, other different kinds of industries, and it just makes me a little bit sad.  Yolanda Padron: Yeah. I mean, there are a lot of people too that are com- And I agree, it’s sad. It’s really sad. But I, there are a lot of people too, some friends that, uh, just, you know, started doing consulting on their own, and it’s, it’s good to see that they’re still in wind, you know? Because they’re, they, they’re so smart and they have so much knowledge of so many years in the industry that they bring to the table, um, [00:30:00] that, I mean, it, it might even work out better for them, right? Because maybe now they’re not just wor- focusing on Denmark itself, and now they get to, to expand and do what they evidently love to do, uh, the, for clients around the world, which is really exciting.  Rosemary Barnes: Well, that wraps up another episode of the Uptime Wind Energy podcast, and my first as the primary host of the show, so let me know if you think I did a terrible job in the comments. Comments boost engagement, make me look better. So if today’s discussion sparked any questions or ideas, we’d love to hear from you. Reach out to us on LinkedIn, and if you found value in today’s conversation, please leave us a review. It really helps other wind energy professionals discover the show. And don’t forget to subscribe so you never miss an episode. For Yolanda, and Matthew and Allen Hall in their absence, I’m Rosemary Barnes, and we’ll see you here next week on the Uptime Wind Energy [00:31:00] podcast.

Are you ready to meet the male version of me? In this episode of the Direct Selling Accelerator Podcast, I sit down with social media coach, keynote speaker, and author Brian Fryer - whose journey took him from professional baseball with the San Diego Padres to becoming a mentor and educator in social media and personal branding.After a back injury, Brian found himself in Direct Selling, where a mentor helped him rebuild his confidence and discover his passion for helping people build their personal brands and communicate with purpose online.We also discuss:Creating content with intentionWhy you don't need to be an expert to share what you knowReels, social media strategy, and getting unstuckBecoming a better communicator, not just a better posterBrian shares some seriously good advice throughout this conversation, but there's one mindset shift in particular that might make you rethink the way you approach content altogether.So grab a cuppa and join us on the podcast! WE'LL BE TALKING ABOUT:➡ [06:30 From Baseball to Network Marketing➡ [11:30 Building a Business on Social Media➡ [13:00 Build YOU, Not the Company➡ [17:30 How to Become Magnetic Online➡ [20:15 The Post That Generated 200 Customers➡ [35:00 Stop Trying to Be an Influencer➡ [37:30 The GNC Method➡ [42:30 Simple Reels That Work➡ [47:00 Reset & Remember Your WhyABOUT OUR GUEST: From professional baseball player to online business mentor, Coach Fryer has helped impact tens of thousands of network marketers businesses around the world.Through his membership, masterminds and personal mentorship, Coach Fryer is passionate about meeting people where they're at and helping them to start dreaming BIGGER. He's known for taking the most challenging obstacles his clients are facing of building an online business and making them SIMPLE. With his degree in psychology, and being a former professional athlete, he knows how to mentally get the most out of his clients so they can make the biggest impact possible. Coach Fryer is the founder of the Social Impacter movement, an International speaker, 2X best-selling author, and was featured as one of the Top-16 Social Media influencers in all of network marketing!RESOURCES: Brian's Book Recommendation: How to Win Friends and Influence People by Dale Carnegie: https://tinyurl.com/3hya5aa9 Brian's Favourite Quote: “Become the person you want to attract.” — UnknownLooking for more? Head to our earlier episode or check out:Show Up Anyway by Sam Hind: https://a.co/d/8rk0QthFree Facebook community: https://www.facebook.com/groups/socialmediafordirectsellerswithgregandsam/CONNECT WITH BRIAN:➡ Facebook https://www.facebook.com/brian.fryer.5/➡ Website https://www.coachfryer.com/➡ Instagram: https://www.instagram.com/coach_fryer/CONNECT WITH US:➡ Visit our website: https://www.auxano.global/➡ Subscribe to YouTube: https://www.youtube.com/c/DirectSellingAccelerator➡ Follow us on Instagram: https://www.instagram.com/auxanomarketing/➡ Sam Hind's Instagram: https://instagram.com/samhinddigitalcoach➡ Follow us on Facebook: https://facebook.com/auxanomarketing ➡ Email us: community_manager@auxano.globalIf you have any podcast suggestions or things you'd like to learn about specifically, please send us an email at the address above. And if you liked this episode, please don't forget to subscribe, tune in, and share this podcast.Are you ready to join the Auxano Family to get live weekly training, support and the latest proven posting strategies to get leads and sales right now - find out more here: https://go.auxano.global/welcome

Could your rental property earn 2X more each month? Kathy Fettke sits down with mid-term rental expert Jesse Vasquez to explain how investors are increasing cash flow by renting to corporations, traveling professionals, and insurance companies instead of relying on traditional leases or nightly Airbnb stays.   Learn how to find these opportunities, which markets work best, and why mid-term rentals are one of today's fastest-growing investment strategies.  

What if your 100 people could feel like 300? That's the bet Robin Daniels, Chief Business Officer at Zensai and author of The Human Success Playbook, is making - and it's working. His company doubled revenue over three years and has been named a great place to work three years running. He joins us to break down how to unlock the potential already sitting inside your organization, lead through genuine uncertainty, and build B2B content that people can actually put to work. About our guest, Robin Daniels Robin Daniels is Chief Business Officer at Zensai, a learning and performance platform competing in a market dominated by Oracle, SAP, and Workday. He previously served as CMO at Matterport and WeWork and held senior marketing roles at Salesforce, LinkedIn, and Box. He recently co-authored The Human Success Playbook - a narrative business book that follows a fictional CEO who refuses a board's demand to cut 40% of the company and sets out to prove there's a better way. The book comes with an AI-powered interface where readers can ask the authors questions and apply every framework directly to their own organization. What B2B Marketers Can Learn From The Human Success Playbook The biggest AI opportunity isn't efficiency - it's multiplication. Robin starts every conversation the same way: do you see AI as a way to cut costs and get more efficient, or do you see it as a way to unlock insane potential in the team you already have? He's made his choice - and built a company around it. Ian puts the math plainly: if you have 100 people, the far more exciting challenge is figuring out how to make them feel like 200 or 300 and generate the revenue to prove it. Robin connects it to something every leader already knows but rarely acts on: "I truly believe everyone has something they're really creatively good at. But if you can actually find time to focus on that, it can lead to new ways of growing your business that maybe you hadn't even thought of before." The companies that win the next five years won't be the ones who cut hardest. They'll be the ones who figured out how to multiply. You can't shrink your way to greatness. Robin credits Tom Peters for the line and means it completely. His argument isn't about shaming companies that make cuts - sometimes it's necessary to survive. The argument is about where the real upside lives: investing in people, giving them new tools and skills and goals, and then letting them find growth paths nobody had imagined yet. "I've never seen a company shrink their way to greatness," he says. "You have to grow." Ian adds the frame: the goal isn't to replace anything - it's to generate more revenue. "How are we going to 2X our revenue?" is a fundamentally more energizing question than "how do we get more efficient?" And it tends to produce more interesting answers. Lead with a vision even when you don't have all the answers. Robin is direct about what employees actually hate most: not bad news, not hard feedback, but uncertainty. "If you don't know what you're working towards, it's really hard to do great work." In The Human Success Playbook, the fictional CEO Evan has eight months to show a different path or get fired - and the whole tension of the story is whether he has the courage to set a direction without knowing exactly where it leads. Robin's advice for real leaders in that position: paint the vision, commit to it, and stay flexible enough to adjust as the world changes. And then, with characteristic directness: "Being a leader has always been tough. Suck it up, buttercup." The future of B2B content is a framework people can make their own. Robin didn't just write The Human Success Playbook - he and his co-authors built an AI-powered interface on top of it so readers can chat with the book and apply its frameworks to their specific company. A 500-person manufacturer in Germany gets different advice than a mid-size bank in the Midwest. Ian connects it to a bigger shift in how B2B content works: "You're building to say: here's the framework, your AI already knows where you want to go as a business, layer this on top and execute." The white paper people download and never read is being replaced by something that actually does the work. Robin is explicit about why: competing against Oracle, SAP, and Workday with a smaller budget means doing something genuinely different. The book is that thing. "I truly believe everyone has something they're really creatively good at. But if you can actually find time to focus on that, it can lead to new ways of growing your business that maybe you hadn't even thought of before." - Robin Daniels Time Stamps [4:00] Meet Robin Daniels, CBO at Zensai and Author of The Human Success Playbook [4:39] Why Robin Wrote the Book - and Why Now [6:00] The Multiplier Choice: Unlock Potential vs. Cut Costs [8:00] It's Just a New Technology - Adapt and Grow [10:00] Robin Has Seen This Before: Internet, Cloud, Mobile, AI [11:00] The Book's Setup: CEO Evan, a 40% Cut Demand, and a Better Way [12:00] The Villain Isn't AI - It's Ignorance and Fear [14:00] Internal vs. External Motivation - and Why Inner Fire Wins [15:00] The Mountain Analogy: What Leaders Owe Their Teams in Uncertain Times [17:00] How to Lead When You Don't Have the Answers [19:00] Suck It Up, Buttercup - Leadership Has Always Been Hard [22:00] Your Whole Job Is to Make Stuff and Sell It [25:00] What Taylor Swift and the Vinyl Revival Teach Us About AI and Creators [31:00] What Should the CEO Actually Do? Reorganize Around Projects, Not Hierarchy [33:00] Invest in People's Capabilities - Measure Productivity, Not Efficiency [35:00] The Human Success Score - a Sleep Score for Your Organization [38:00] Replace "Replace" With "Multiply" in Your Vocabulary [40:00] "You Can't Shrink Your Way to Greatness" - Tom Peters [42:00] The Courage to Invest in People When Everyone Else Is Cutting [44:00] The Future of Books: Build a Framework, Let AI Make It Specific [49:00] Demo: Chat With the Book at Zensai.com [51:00] The "Talk to a Human" Button - the Future of B2B Content [52:00] Final Thoughts + Connect with Robin on LinkedIn Links Connect with Robin on LinkedIn: https://www.linkedin.com/in/robindaniels/ Learn more about Zensai: https://www.zensai.com Chat with The Human Success Playbook: https://zensai.com/report/thehumansuccessplaybook The Human Success Playbook on Amazon: https://www.amazon.com About Remarkable! Remarkable! is created by the team at Caspian Studios, the premier B2B Podcast-as-a-Service company. Caspian creates both nonfiction and fiction series for B2B companies. If you want a fiction series check out our new offering - The Business Thriller - Hollywood style storytelling for B2B. Learn more at CaspianStudios.com. In today's episode, you heard from Ian Faison (CEO of Caspian Studios) and Meredith Gooderham (Head of Production). Remarkable was produced this week by Meredith Gooderham, edited by Jon Goldberg, and our theme song is "Solomon" by FALAK. Create something remarkable. Rise above the noise. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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

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

Lisa Cole, a four-time Chief Marketing Officer and current CMO at 2X, discusses the challenges marketers face in doing more with fewer resources and the misconceptions around AI in marketing. She explains that while AI can automate tactical tasks, human judgment remains essential for creating unique, high-quality content that differentiates companies from competitors. Lisa emphasizes that marketers should be intentional about what tasks to keep in-house, outsource, or automate, rather than relying solely on AI to handle all marketing functions. Lisa and John discuss the challenges marketers face due to AI, including the pressure to create content that is both understandable to humans and AI models across multiple channels. Lisa explains that marketers now ns to consider both human and AI audiences, leading to increased workload and expectations. She notes this situation is creating tension in organizations of all sizes, with some facing pressure to reduce headcount while others are expected to do more with less. Lisa advises companies to focus on understanding and mapping their current marketing workflows before implementing AI, rather than immediately acquiring more AI licenses. She recommends conducting a "hackathon" to reimagine and optimize existing processes, aiming to reduce time from idea to execution and eliminate manual review cycles. For John's small business helping people buy and sell businesses, Lisa suggests asking their preferred AI model for specific advice on how to apply AI to grow their LinkedIn following and engage with executives.You can reach Lisa at www.2X.marketing or Lisa.cole@2X.marketing.John MartinkaJessica MartinkaContact us via either website or give us a call and be sure to check out our blog pages with new posts weekly.https://nokomisadvisory.com/https://www.martinkaconsulting.com/https://www.gddpodcast.buzzsprout.comhttps://www.youtube.com/c/JohnAMartinka/videos 425-515-4903 

In today's Cloud Wars Minute, I examine why Google Cloud's explosive growth is redefining the competitive landscape ahead of Microsoft and AWS earnings. Highlights 00:03 —We have earnings results coming up later this week from both Microsoft and AWS. Microsoft on July 29, and AWS on July 30. So, that'll give us some nice comparisons to the stunning Q2 that Google Cloud reported last week. Google Cloud will be outgrowing AWS by somewhere between two and a half times at the high end, and at least two times on the low end. 01:02 — Over the last several quarters, Jassy has alluded to Google Cloud, not by name on earnings calls, but he said, "We've got some companies throwing up these fancy high growth rates." What we see with these growth rates is the customers expressing their vote. They are the ultimate arbiters in this decision about who's the hottest company. That's expressed in growth rates. 02:23 — I'm comparing Q2 here for Google Cloud versus Q1 for AWS because, right now, the Q2 numbers aren't out yet. AWS grew at 28%. Google Cloud grew 82%. Say AWS reports this massive acceleration to 40%. They would still be outgrown by Google Cloud by 2X. My guess is AWS's growth rate will come in somewhere around 30% or 31%. 03:22 — It is becoming a little tiresome for these bigger companies to say, "We're bigger than them. Of course, they have a higher growth rate." The customers are voting here. These growth rate disparities are so important because they tell us how the different vendors are doing in the marketplace through the eyes and wallets of customers. 04:01 — In the Cloud Wars, the customers are always, always the big winners. Microsoft and AWS are growing their cloud businesses in the range of 30%, which is astonishing. But Google Cloud posted 82% revenue growth and backlog growth of almost 400% to over $500 billion. By the numbers, Google Cloud is the king of the hill right now. Visit Cloud Wars for more.

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

Bilt Bulletin July 2026 | Palladium Card Value, Alaska Transfers, Bilt Cash Expiration & More Justin Vacula of the Hurdy Gurdy Travel Podcast is joined by returning co-host Darren covering the latest Bilt Rewards updates, Bilt Palladium Card strategy, Bilt Cash expiration concerns, and tools from NextCard that help Bilt users. Justin and Darren explain how Bilt Rewards works as a loyalty program with credit cards, transfer partners, and ways to earn rewards on rent and mortgage payments. They focus on the Bilt Palladium Card, including its 2X earning structure, value from Bilt Cash redemptions, hotel credits, and the ability to earn points paying rent or mortgage. Justin shares his first transfer from Bilt to Alaska Airlines Atmos Rewards, using points to book Philadelphia to Savannah flights for 7,500 points each way. Darren also discusses heavy Alaska mileage usage for family travel and a business-class redemption. They also review progress toward Bilt Platinum status, spending stats, and whether Bilt Cash expiration at year-end may reduce late-year card spend. Justin and Darren discuss why cardholders may want to redeem Bilt Cash aggressively before expiration rather than letting value go unused. Timestamps 00:00 Podcast Intro 01:25 Bilt Program Basics 02:37 Palladium Card Highlights 05:27 Earn Points on Rent And Mortgage Payments 06:37 Real Redemptions: Alaska Flights 09:28 Spend Stats And Status Progress 13:49 Bilt Cash Expiration Concerns 19:33 Support The Show 22:13 NextCard.com Tools For Bilt 25:43 Upgrade And Downgrade Questions 30:00 Rakuten Card Earns Bilt Points 33:01 Wrap Up And Listener Feedback 34:00 Outro And Credits —

If you're job searching in 2026 and following every resume rule you've been told, one page only, match every keyword, avoid two-column layouts, optimize for the ATS, this episode is going to change how you think about your resume and your entire job search strategy. Sam Wright runs the data side at Huntr, and he's analyzed over 1.7 million job applications, more than 240,000 resumes, and over a million job postings to find out which resume rules actually get people hired, and which ones are just guesses everyone kept repeating. The results are surprising. Two-page resumes outperform one-page resumes across the board. Resume tailoring is 2X more effective at driving interviews. Referrals convert at a massively higher rate than cold applications. And most of what you've been told about the ATS is a myth. Scott and Sam break down the resume myths costing you interviews, the tailoring strategy that actually works, why your target job title matters more than your resume format, and the data-backed job search moves that separate the top 10% of applicants from everyone else. What you'll learn: Why the one-page resume rule was never actually tested (and what the data shows about two-page resumes) The 5-step resume tailoring process that doubles your interview rate Why "optimizing for the ATS" is a myth (and what actually gets your resume seen) How to write a resume summary that stands out (without sounding AI-generated) Why referrals convert at a massively higher rate than cold applications, and how to build them The exact application strategy Sam recommends: how many jobs per week, how fast to apply, and how to target the right roles Connect with Sam Wright: https://www.linkedin.com/in/samuel-b-wright/ Try Huntr: https://huntr.co   Our book, Happen To Your Career: An Unconventional Approach To Career Change and Meaningful Work, is now available on audiobook! Visit  happentoyourcareer.com/audible to order it now! Visit happentoyourcareer.com/book for more information or buy the print or ebook here! Want to chat with our team about your unique situation? Schedule a conversation   Free Resources What career fits you? Join our free 8 Day Mini Course to figure it out! Career Change Guide - Learn how high-performers discover their ideal career and find meaningful, well-paid work without starting over.   Related Episodes Changing Careers (When You Don't Know Your Next Job Title) (Spotify / Apple Podcasts) Discover Your Strengths to Find Your Ideal Career (Spotify / Apple Podcasts)

What if the worst professional moment of your life turned out to be the best thing that ever happened to you? Lyndsay Dowd spent nearly 30 years in leadership — 23 of them at IBM — before leaving on her own terms for a new opportunity. Six months in, she was fired. No warning, no explanation. The shame and embarrassment took her out… until she asked herself three questions that changed everything: What am I really good at? What do I love to do? How can I help people the most? The answers became Heartbeat for Hire, a company that helps brands ditch toxic, top-down leadership and build cultures people actually want to work in. In this episode, Wendy and Lyndsay — a speaker, founder, 2X bestselling author (Top Down Culture and Voices of Women), Harvard guest lecturer, and globally ranked podcast host — talk about reinvention at 50+, betting on yourself, the “power skills” every leader needs, why trust is your currency, and the permission slip every woman at a crossroads deserves to hear. Fierce, funny, and full of heart.

America has between 32 million and 130 million empty bedrooms sitting unused every single night. The housing shortage is estimated at just 1.8 to 7.5 million units. If you can do basic math, you already know what that means, and Atticus LeBlanc, founder and CEO of PadSplit, is here to break down exactly why room rentals are the fastest, most profitable way for landlords to close that gap.In this episode, Atticus walks us through the exact room rental conversion that took his net operating income from $1,100 to $2,400 a month. He shares why the traditional buy, renovate, rent playbook does not pencil the way it used to, and why landlords looking for cash flow, financial freedom, or a way to make an underperforming property profitable again are turning to co-living.Room Rental Inventory on Furnished Finder:https://www.furnishedfinder.com/search/furnished-roomsCheck average room rental pricing in your markets: https://www.furnishedfinder.com/statsIn this conversation you will learn:Why room rentals can deliver a 2X increase in net operating income compared to traditional single family rentalsWhere co-living demand is significantly outpacing supply in a variety of US marketsWhy PadSplit residents save an average of $317 a month, adding up to over $200 million in collective savingsHow the Furnished Finder and PadSplit partnership brought 1,000 room rentals across seven markets onto the platformThe real story of Tiffany, a night shift security guard who saved $600 a month, bought her own car, and moved into her own apartment within six monthsWhat zoning, HOA restrictions, and Golden Girls bills mean for landlords considering the co-living modelWhy seasonality in room rentals looks nothing like the short term rental world, giving landlords steadier year round cash flowFurnished Finder and PadSplit together offer over 95,000 room rentals nationwide, giving landlords and investors the largest combined footprint of monthly co-living inventory in the United States. Whether you are a mid-term rental landlord, a short-term rental host adjusting to new regulations, a long-term landlord looking for more cash flow, or a new investor exploring your first property, this episode gives you the data and the strategy to think about your next move differently.List Your Property on Furnished Finder: https://www.furnishedfinder.com/list-your-property Use code LLD10 for $10 off new listings.Timestamps0:00 Welcome to Landlord Diaries1:30 What is PadSplit and the problem it solves3:20 The impact of room rentals on the housing shortage4:30 Millions of US bedrooms sit empty every night5:00 The barriers that block market rate apartments7:35 Why co-living is a 4 sided win for communities8:30 What landlords meet PadSplit brand standards10:10 How room rentals move the needle on affordability11:20 10,000+ affordable units created in Atlanta metro12:25 Who is leasing and renting by the room14:35 1/3 of US renters earn less than $35k a year16:20 Why demand stays stable in every economy18:45 The huge cash flow opportunity in room rentals21:40 What NOI is and why it matters for landlords23:40 Turning your dining room into a rentable bedroom26:55 Top room rental markets and how to create supply30:30 Why Furnished Finder and PadSplit partnered up34:55 Remote workers can test cities for 3 to 4 months36:40 A real underperforming property that transformed37:45 Tiffany's story: from motel to her own apartment39:10 Misconceptions landlords have about co-living41:35 Do room rentals have a seasonality problem44:10 Zoning and HOA restrictions to watch for48:15 Golden Girls bills and the fight to change laws49:20 The first step to take this week50:15 Rapid fire: surprise markets and biggest mistakesMeet your hosts Kelly Bailey & Katie Lyon, plus catch up on past episodes:https://www.furnishedfinder.com/resources/podcastTopics covered: mid-term rentals, room rentals, co-living, PadSplit, Furnished Finder, net operating income, real estate investing, housing shortage, affordable housing, cash flow strategy, landlord tools, monthly rentals The Landlord Diaries is brought to you by Furnished Finder, where you can list your property for one low price and pay zero booking fees.

► Want to work with Randy directly: https://randygage.com/breakthroughu/ ► Prefer audio? Get the podcast https://randygage.com/podcast Most people assume the bigger the business, the harder it is to build.Randy Gage has spent decades proving the opposite is true.In this video, Randy makes a claim that stops most founders cold and then spends the entire video proving it with real examples, real clients, and a framework built from over $23 billion in generated revenue.He also breaks down something nobody in the business world is talking about: what AI is currently destroying, silently, in companies of every size. And why the businesses that feel the safest right now may be the ones in the most danger.If you're a founder, operator, or builder, whether you're at $100K, $1M, or $10M, this video will change how you think about growth, risk, and what it actually means to build something that lasts.The good news: everything Randy covers is fixable.But only if you're willing to hear something most business advisors will never tell you.Videos Randy mentioned in this video► Video: https://youtu.be/Ec-aTTtKSPw?si=I67P4OnQfsVjtRcc► 10X better than 2X: https://a.co/d/0b6iU1wl► Who not how: https://a.co/d/0icNDJNgWho is This Guy?Randy isn't some influencer who listened to a couple podcasts and started posting hot takes. He's the streetwise founder of Prosperity Factory, Inc., who has been building and scaling businesses for more than 40 years. Randy has authored 16 bestselling books, translated into 25+languages, including his latest—Wealth Without Apology—and spoken to more than 2 million people across 50 countries. He's been inducted into both the Speaker Hall of Fame and Direct Selling Hall of Fame.But none of that is why people follow him. They follow him because he calls BS…and says what most people are too afraid to admit. When he's not rocking the stage or building his next project, you'll probably find him coaching a softball team somewhere.Connect with Randy:Instagram: randy_gage Twitter: randy_gage Facebook: randygage

You didn't leave the corporate world to build a different cage. In this episode of The Fulfillionaire, Austin Netzley, author, investor, and entrepreneur behind Operations.com, breaks down exactly how to scale your revenue, protect your cash flow, and build a business that thrives without you. Most businesses are running at 40% to 60% waste. Austin's first move with every CEO is always the same: simplify ruthlessly, nail the strategy, then build systems that live outside anyone's head. If your business slows or stops the moment you step away, you don't own a business. You own a job.  Stage four is a business that runs, grows, and thrives without you. That is when you stop managing and start building real enterprise value and real freedom. Stop building a business that owns you. Tune in now to How Accountability Turns Failure Into Unstoppable Success with Austin Netzley. When you are ready to align your work, wealth, and life around the freedom you actually want, visit fulfillionaire.com. Austin Netzley is an author, investor, and entrepreneur who helps service-based business owners scale predictably and profitably without the chaos that typically comes with growth. As the founder of 2X, a back-to-back Inc. 5000 fastest-growing company, he has built a coaching methodology that has generated over $310 million in client results. His work focuses on helping entrepreneurs between $500K and $5M in revenue reclaim their time, gain clarity, and transform their business into a system that runs without them. He has been featured in Forbes, Entrepreneur, Business Insider, and the Washington Post, among others.  Personal Website: https://austinnetzley.com/  Company's Website: https://operations.com/  Instagram: https://www.instagram.com/netzley/  Facebook: https://www.facebook.com/scale2x/  LinkedIn: https://www.linkedin.com/in/austingnetzley/  JP Newman is the founder of Fulfillionaire and CEO of Thrive FP, known for helping high-achievers align financial success with deeper human connection and purpose. With over $2 billion in real estate transactions and hundreds of investors coached, he brings a powerful blend of strategy, psychology, and emotional intelligence to the world of investing and negotiation. JP teaches that the best deals are built by understanding people, energy, and intention. Through his Fulfillionaire™ movement, he helps leaders stop operating from fear and start making decisions rooted in clarity and alignment. His approach redefines negotiation as a human-centered skill that turns insight into influence and lasting success. IG: https://www.instagram.com/jpnewman_/  LI: https://www.linkedin.com/in/jp-newman-45a1ba/     

This is a free preview of a paid episode (43 min), exclusively available on our subscriber-only premium feed. Become a premium subscriber to tune into the full episode: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://cubicletoceo.co/podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Questions about our premium podcast subscription? Send us a DM ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@cubicletoceo Continuing our series on “Revenue → Returns (How My Money Makes Me Money),” today we're giving you the detailed low-down on angel investments as a money multiplier. Returning to our show to transparently dissect her own angel portfolio is Jaclyn Johnson. Jaclyn is a 2X exited entrepreneur (selling her previous companies for over $23M) and prolific angel investor creating real impact for underrepresented founders through both financial and relationship capital. She's the founder of Create & Cultivate, the largest media and events company for ambitious women, the current CEO and co-founder of Cherub, a platform connecting entrepreneurs and angel investors, and runs Receipts VC, her umbrella for 20+ portfolio investments spanning beauty to tech. Jaclyn walks us through her investing thesis and recent deals, what she looks for before writing a check, how much of her business profits she allocates annually toward angel investments, and her early-stage investment in luggage company Away that turned a $10K bet into $1.2 million. Listen to our last episode with Jaclyn: https://open.spotify.com/episode/76d1DtICFUJagqWxExM5fq?si=s9UTTRWnTvK1-bjt64Ez5A Connect with Jaclyn: Apply to attend Cherub's Creator Summit: https://creatorsummit.investwithcherub.com/ ⁠Take a look at Jaclyn's private room: https://receiptsvc.investwithcherub.com/#/join  www.receiptsvc.com www.createcultivate.com  www.investwithcherub.com IG: @jaclynrjohnson If you enjoyed today's episode, please: Post a screenshot & key takeaway on your IG story and tag us ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@cubicletoceo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ so we can repost you. ⁠⁠⁠Subscribe to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠our premium feed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ for case-study style interviews every Monday.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

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

Fourteen deals worth mentioning in a single week. And those are just the ones that surfaced.The week before Cannes, the dam broke. Christian and Ayelet break down the deepest deal review we've done yet — anchored by a transaction Ayelet's team actually advised on the sell side: Residence acquiring GateMaker, a female-founded creator and influencer agency with a blue-chip beauty roster.Plus a sponsor-to-sponsor recap in commerce services (Bluebird Group + Bertram Capital), and a rapid-fire run through 12 more deals across creator, beauty, luxury PR, B2B, and commerce.One platform investment. One deep dive. Twelve quick hits. Under 20 minutes.⏱️ TIMESTAMPS0:39 — Welcome to Market and Deals Friday — and a 14-deal week1:12 — Did everyone wait until the week before Cannes to announce?1:37 — Coming up: a special edition with Chris Erwin of RockWater on the Accenture/Whalar deal2:00 — Market update: Bluebird Group partners with Bertram Capital2:20 — Bertram's buy-and-build model and the Bertram Labs tech advantage3:00 — Reading the deal size from a $1.6B control fund with a 43% IRR4:00 — Why a relationship-driven commerce services business resists AI disruption5:35 — Deep dive: Residence acquires GateMaker — a sell-side deal Ayelet's team ran6:05 — GateMaker's founders, blue-chip beauty roster, and creator economy pedigree7:20 — Did Residence already have creator capability? (No — this was the capability buy)7:50 — Second acquisition in under five months: Residence is now a 9-agency network8:44 — The Gemspring-backed platform build and why Residence is now an active acquirer9:28 — The "anti-holdco" model — and Christian's pushback on the framing10:42 — Why creator and influence relationships command a premium right now11:30 — The cross-industry pattern: do-no-harm PMI for people-heavy businesses11:56 — Advisors: Palazzo and Speed M&A on the sell side12:32 — Brinkley the deal-finding agent and a 20-deal week13:20 — Quick hit: Front Row acquires Carbon Beauty (second deal this year)13:57 — Quick hit: Mazarine acquires Bacchus — luxury PR and UHNW access14:10 — Quick hit: Huge acquires Rotate — composable commerce14:27 — Quick hit: Motion Agency acquires LKHNS — B2B and video (Kim Everl's 7th)15:30 — Quick hit: Akeneo acquires Pricing Hub — PIM moves into pricing16:33 — Rapid fire: Mile Marker/Lyfe, Legion Advertising, Factual/Intelsio, Everything Branding/Darlington17:44 — The week's only disclosed number: 2X acquires KnownWell at a $400M combined valuation18:09 — Quick hit: Scorpion acquires One SEO Digital18:32 — 14 deals, one disclosed price: the lower middle market buying capability quietly18:52 — Don't miss the Erik Huberman interview (Ep. 71) + the Chris Erwin special coming up

In this episode of Million Dollar Flip Flops, Rodric sits down with private equity investor Steve Walsh for a conversation about what it really takes to grow a business beyond the next plateau.Steve shares how he helps entrepreneurs think bigger, build stronger companies, and avoid the common mistakes that keep them stuck at two, three, or even ten million in revenue. The discussion explores mindset, customer understanding, culture, capital, and why the right investor can bring far more than money to the table.From there, Rodric and Steve dive into the difference between smart capital and dumb capital, the importance of hiring A players, and why communication with investors matters more than most founders realize.This is a practical, high-level conversation about growth, leadership, and building a business with the right people around you.In This Episode, You'll LearnWhy many entrepreneurs get stuck before their next levelHow to think bigger than your current revenue targetWhy mindset is often the first bottleneck to growthThe difference between smart capital and dumb capitalWhy the best investors bring doors, systems, and strategyHow to build a culture that attracts A playersWhy customer understanding matters before capitalWhat entrepreneurs often get wrong about their investorsHighlights & Timestamps[00:00] Tell your investor the truth Steve opens with a blunt truth: the things you least want to tell your investor are often the things they most need to know.[01:00] Meet Steve Walsh Steve shares that he works in private equity and focuses on investing in entrepreneurs and helping them reach the next level.[02:00] Why 10X thinking matters He explains why he believes 10X growth is not harder than 2X growth—it just requires a different mindset.[03:00] Special snowflake syndrome Rodric and Steve discuss how entrepreneurs often believe their market is different, when in reality the core principles of business still apply.[04:00] Culture can solve hiring problems Steve shares an example of a company that believed it could not find talent, and explains how better culture can attract people away from competitors.[05:00] Shiny object syndrome The conversation shifts to the trap of spending capital on things that do not move the business forward.[06:00] Start from the end and work backward Steve explains why entrepreneurs should begin with the end goal in mind and then reverse-engineer the path.[07:00] The Magnet and sales fundamentals Rodric connects the conversation to sales and marketing, explaining how many founders neglect the basic “magnet” that attracts customers.[08:00] Why builders struggle with sales They discuss how many builders and contractors are excellent at building but weak at sales, marketing, and customer messaging.[09:00] Smart capital vs. dumb capital Steve explains the difference between money alone and strategic money that brings expertise, relationships, and systems.[10:00] Partnership, not transaction The best investors, Steve says, are true partners who want to help founders succeed beyond the check they write.[11:00] Tell your investor the hard stuff He emphasizes that many founders hide problems from investors when those are exactly the issues that need to be surfaced.[12:00] Why the right investor changes everything Rodric and Steve compare smart capital to strategic help like networks, expertise, and market access.[13:00] A players need a big mission Steve explains why top performers need a meaningful challenge or they will get bored and leave.[14:00] The book and where to find Steve Steve shares where listeners can find his book and connect with him.[15:00] The family man question Steve answers the signature question: businessman with a family or family man with a business?[16:00] Energy, time, and personal alignment He shares how he looks at both business life and personal life when evaluating entrepreneurs.[17:00] Closing thoughts on priorities Steve closes by reinforcing that personal and professional priorities always show up in the calendar and the bank account.Notable Quotes“Most of the things you do not want to tell your investor are the things you need to be telling your investor.” – Steve Walsh “10X it is just as easy as 2X it.” – Steve Walsh “The bigger picture is always bigger than what they see.” – Steve Walsh “Start from the end. Start from where you want to go and work backwards.” – Steve Walsh “Smart capital opens doors you probably never could open on your own.” – Steve Walsh “If you want A players, you need a big goal.” – Steve WalshConnect with Steve Walsh

Matthew Evangelo sits down with host Matti McBride in this episode of the Progressive Dairy Podcast to chat about milking Holsteins and Jerseys, mastitis management and vermiculture. Learn about Evangelo's career in dairy, current management practices and future plans at Bar E Dairy and their commitment to sustainability.  Episode breakdown: 1:00 – Rapid-fire questions 2:00 – Evangelo's background 3:20 – Bar E Dairy 4:25 – Milking cows 2X vs. 3X 6:25 – Milking Holsteins and Jerseys 9:45 – Youngstock management 10:40 – Milking protocol at Bar E 11:50 – Mastitis treatment protocol 13:00 – Advice for improving somatic cell counts 15:00 – Cow comfort 17:00 – Vermifiltration system

By Doug Green “We're a partner-first company. It is in our DNA,” says Patrick Sheehan, Vice President of Channel Development and Distribution at Intermedia. In this episode of Technology Reseller News, recorded for the CCA community, I spoke with Patrick Sheehan, Vice President of Channel Development and Distribution at Intermedia, about a new approach to helping channel partners grow recurring revenue while reducing operational complexity. Intermedia describes itself as an intelligent cloud communications provider, bringing voice, video, messaging, contact center, collaboration and related services into one seamless, AI-powered platform. Sheehan noted that Intermedia supports thousands of partners and more than 150,000 businesses, with a strong focus on helping partners look good and keeping customers happy. The central topic of the conversation was Intermedia's Co-Op Partner Model, a program designed for partners who want to retain control over customer relationships and pricing, while Intermedia handles many of the back-end operational burdens that can slow growth. For many partners, cloud communications, AI, Microsoft Teams integration, and contact center services represent significant opportunities. But traditional models can also add complexity to billing, taxation, collections, support and administration. The Co-Op model is designed to remove much of that friction. With Co-Op, partners can maintain ownership of the customer relationship while Intermedia provides the operational infrastructure behind the scenes. That allows partners to focus on selling, serving customers and expanding existing accounts into new recurring revenue streams, with the potential to earn up to 2X more in profit compared to traditional models. Sheehan also discussed where partners may be leaving money on the table. Existing customer relationships often contain opportunities for voice, collaboration, AI-enabled communications, customer experience tools and Microsoft Teams-related services. By simplifying the path to offer those services, Intermedia is encouraging partners to revisit accounts they already know well. The conversation also covered Intermedia's broader partner-first strategy, including its focus on customer service, technical support and reliability. Sheehan highlighted Intermedia's ninth consecutive J.D. Power recognition for assisted technical support, along with the company's financially backed 99.999% uptime service-level agreement. For partners that have not engaged with Intermedia recently, Sheehan's message was direct: the opportunity has changed. Cloud communications is no longer just about replacing phone systems. It is about helping customers modernize communications, improve customer experience, adopt AI-enabled tools and create more flexible ways to work. The Co-Op model gives partners another way to participate in that opportunity without having to rebuild their own operations. Learn more about becoming an Intermedia partner. Read more about Intermedia's Co-Op program.

Jenna is joined by Mississippi State pitching coach, All American pitcher at Arizona, 2X national champion and ESPY winner, Women's College World Series Most Outstanding Player, and former pro, Taryne Mowatt-McKinney! They talk about the Bulldogs' first WCWS in program history, her own historic run in OKC as a player, coaching multiple pitchers who went pro, full circle playing for and now coaching with the Chicago Bandits, the AUSL, how the game has grown at every level, living life around softball with wife Brittany and son Lincoln, and more. 00:00:00-00:12:06 Intro/Covering Our Bases 00:12:06-01:25:06 Interview 01:25:06-01:26:32 Bring It Home/Outro IG: @bleavinsoftball X: @BleavInSoftball Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

It's official: Knownwell is now part of 2X, and together we're building the leading human-agentic GTM services company, unifying B2B marketing, sales, and customer success. In this special in-person episode of AI Knowhow, Knownwell CEO David DeWolf and 2X founder Dom Colasante join host Courtney Baker at 2X headquarters to break down the news and what drove the acquisition, including: why software and services are converging into a single category and what humans uniquely own in an agentic world. In this episode: The big news: 2X acquires Knownwell to build the first human-agentic GTM services company The trend permeating the services world: customers buy outcomes, not software or services Go-to-market fragmentation, and why marketing, sales, and customer success must run as one motion The 90/10 retention paradox every revenue leader should know Why all work should have a human in the loop Moving Knownwell's commercial intelligence up the stack into sales and marketing Show Notes: Read today's press release: https://2x.marketing/press-release/2x-acquires-knownwell/ Connect with David DeWolf: https://www.linkedin.com/in/ddewolf/ Connect with Dom Colasante: https://www.linkedin.com/in/domeniccolasante/ Connect with Courtney Baker: https://www.linkedin.com/in/courtbaker/

If you've ever undercharged a client because you needed the money, over-delivered on an offer out of guilt, or held back a product launch because it wasn't quite perfect yet — this episode is for you.In Part 1 of this two-part conversation, Jan sits down with her friend and peer mastermind member Andee Hart — sales strategist, mentor, podcast host, and the woman behind She Sells Differently. What started as a candid chat between two Christian women entrepreneurs turned into one of those conversations you have to stop and take notes on.They dig into the scarcity mindset — not as a buzzword, but as something both of them are actively working through in their businesses right now. You'll hear Jan and Andee get honest about the fears that keep women playing small, what the Bible actually says about money and calling, and what it looks like to ruthlessly focus on the work you were made to do instead of the work you can just do.In this episode you'll learn:Why accepting every client at every price point is actually a scarcity decision — and what it costs you long-termHow over-delivering and underpricing can rob your clients of real investment and ownership in their growthWhat Lydia (yes, the Lydia from Acts) can teach us about building wealth as a Christian womanThe 10X vs. 2X framework from 10X Is Easier Than 2X by Dan Sullivan & Benjamin Hardy — and why doing less is often the path to growing moreHow perfectionism and distraction are two of the enemy's biggest tools against women entrepreneursWhy being in a room with people further along than you is one of the highest-leverage things you can do for your businessEpisode Highlights[00:00] Jan introduces Andee Hart and the concept of peer mastermind relationships among Christian women entrepreneurs[02:36] The conversation begins — Andee opens with a question about scarcity mindset and Jan's real experience of taking every low-ticket client just to survive[04:08] Why doing "everything you can do" isn't the same as doing what you should do — and the difference between capacity and calling[09:51] How scarcity mindset shows up specifically for Christian women: underpricing, over-giving, and the fear of charging what you're worth[10:29] Jan gets personal — losing both parents in four years and how grief clarified her sense of calling and stopped her from playing small[11:55] The theological case for not playing small: when we hide our gifts, we rob others of what God intended to serve them through us[13:07] Money, ministry, and the women who funded Jesus' work — a biblical reframe for Christian entrepreneurs who feel guilty about profit[14:13] Lydia, purple dye, and what would have happened if she'd diminished her gifts[16:24] The difference between showing up with excellence and over-delivering — and how to sit with the Lord before you build your offers[19:36] The overthinking loop, the enemy's strategy, and how perfectionism causes inaction for recovering perfectionists (relatable!)[22:04] Jan shares her own current business evolution — letting go of steady retainer income to trust God with the 20% she's truly called to[23:11] Andee recommends 10X Is Easier Than 2X and explains the core idea: ruthlessly eliminate the 80% to do the 20% that actually scales your business[27:32] Being in the room with women further along — why playing up raises your game (the tennis analogy is good)[30:19] Teaser for Part 2: next week, Andee and Jan go deep on pricing — don't miss itKey Takeaway"When we make little of the gifts that God has given us, we rob others of getting to experience a beautiful gift — a way the Lord has designed to serve them through us. You can still charge someone and serve them well."Resources Mentioned

I don't end many podcasts by inviting someone to come hang out at the farm, but this was one of those conversations. In this episode, I sit down with Patrick Engasser, a bestselling author, speaker, and coach who's built and led a seven-figure sales team…all while being blind since birth.  This isn't a “feel good” story. This is a wake-up call. We talk about what it actually takes to succeed when the odds aren't in your favor, how to stay aware of opportunities most people completely miss, and why building the right systems is the difference between being stuck and having real freedom. Patrick also shares powerful perspective shifts around adversity- how the very thing you think is holding you back might actually be your greatest advantage. And yeah…we even get into guide dogs.  If you've been waiting for the “right time” or better circumstances… this episode will challenge that hard.   Find Patrick at www.TalkwithPatrick.com  If I Can Do It, You Can Do It!: Inspiration for Eliminating Excuses, Overcoming Challenges, and Succeeding in Business and Life by Patrick Engasser https://amzn.to/4cIg1VH    Things mentioned in the show: 10X is Easier than 2X by Dan Sullivan https://amzn.to/4mErRok  4-Hour Workweek by Tim Ferriss https://amzn.to/4cB3XFF  The 7 Habits of Highly Effective People by Stephen Covey https://amzn.to/489fc72  Goals: How to Get the Most Out of Your Life by Zig Ziglar https://amzn.to/4twO7mY  The Power of Intention by Wayne Dyer https://amzn.to/4cX7rUk    --- Click here to change your life- http://eepurl.com/gy5T3T   Hit me up for a one-on-one brainstorming session- https://militaryimagesproject.com/products/brainstorming-session-1-hour    Check out my Linktree for different ways to rock your world! https://linktr.ee/ruggeddad    Check out the sweet Hyper X mic I'm using. https://amzn.to/41AF4px    Check out my best-selling books: Rapid Skill Development 101- https://amzn.to/3J0oDJ0 Streams of Income with Ryan Reger- https://amzn.to/3SDhDHg Strangest Secret Challenge- https://amzn.to/3xiJmVO This page contains affiliate links. This means that if you click a link and buy one of the products on this page, I may receive a commission (at no extra cost to you!) This doesn't affect our opinions or our reviews. Everything we do is to benefit you as the reader, so all of our reviews are as honest and unbiased as possible. #passiveincome #sidehustle #cryptocurrency #richlife

We're announcing AIEWF speakers this week! Take the AI Engineering Survey!Today's guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)Put it this way: In the near term, the next Sora won't be a better video model, but a video agent.Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task. In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA's Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines. Flipbook: The future of VideomaxxingVideo agents are almost a sure bet to be the trend in the coming year. We end with a glance at what's beyond video agents:Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.We discuss:* Why fast iteration mattered more than meetings* Why small training bugs can drive huge model quality gains* Why coding models may make compute the bottleneck again* How image and video models are trained with synthetic captions* The role of VAEs and latent space in frontier video models* Why image models are the foundation for video models* The tradeoff between temporal compression and real-time interactivity* Flipbook, Neural OS, and the future of generative UI* Why future interfaces may go from user intent to pixels* The hidden cost of training video models: storage, egress, and GPU hours* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster* Grok Imagine 0.9 and large-scale audio-video generation* Why audio-video alignment is harder than text-video alignment* Ethan's definition of world models* Reference-to-video, video extension, and long-context video generation* Why xAI's research communication undersells Grok Imagine* How xAI culture shaped the speed of development* AI watermarking, SynthID, and detecting generated media* Why prompt rewriting matters for video models* Grok Imagine Agent and the rise of video agents* Why language models may unlock better video generation* Robotics, physical AI, and embodied world models* Why Ethan left xAI and shifted focus toward LLMs* Self-managed context, memory, and the next frontier for language modelsEthan He* LinkedIn: https://www.linkedin.com/in/ethanhe42* X: https://x.com/EthanHe_42Timestamps00:00:00 Introduction00:01:25 From NVIDIA Cosmos to xAI00:03:24 Building Grok Imagine from Zero to One00:10:07 How Image and Video Models Are Trained00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs00:22:10 Generative UI, Flipbook, and Neural OS00:32:10 The Cost of Training Large Video Models00:37:04 Distillation, GANs, and Fast Video Inference00:41:21 Audio-Video Generation and Grok Imagine 0.900:48:34 What Makes a World Model?00:55:51 Reference Videos, Long Context, and Video Memory01:00:11 xAI Culture, Research, and First-Principles Building01:09:45 AI Safety, Watermarking, and Prompt Rewriting01:13:10 Video Agents and AI-Assisted Creation01:27:32 Why Language Models Unlock Better Video01:31:15 Robotics, Physical AI, and Embodied World Models01:32:38 Why Ethan Left xAI01:34:16 Self-Managed Context and the Future of LLMs01:38:43 Ethan's Career Path and Closing ThoughtsTranscriptIntroduction: Ethan He, Latent Space, and the Path to xAISwyx [00:00:00]: We're here in the studio with Ethan He, most recently of xAI. Welcome.Ethan [00:00:10]: Thank you. Glad being here.Swyx [00:00:11]: We're also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.Ethan [00:00:23]: I've actually, I also presented the MoEs twice at latent space.Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It's very nice.Ethan [00:00:49]: I learned a lot.Swyx [00:00:49]: I think three years stop. We haven't stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”Vibhu [00:01:04]: But I might have reached out to you after.Swyx [00:01:05]: you-- because it's an amateur club, right?Swyx [00:01:08]: so it's very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.Vibhu [00:01:18]: Came out yesterday.Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it's a good one. We'll, we'll recommend people to read it.Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don't even know when you joined. just like tell the, tell the story about the sort of transition.From NVIDIA Cosmos to xAI: Scaling Video and World ModelsEthan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it's a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that's, that's why I realized I need to move to somewhere with much more compute resources. That's how ISwyx [00:02:13]: Than NVIDIA?Vibhu [00:02:14]: The GPU rich came themselves.Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.Ethan [00:02:25]: It was end of twenty-four.Vibhu [00:02:28]: End of twenty-four.Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.Building Grok Imagine From Scratch in Three MonthsSwyx [00:03:24]: Can you give like a rough roadmap of okay, you're on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you're setting up a new team?Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeahSwyx [00:03:51]: three months is likeVibhu [00:03:52]: From everythingSwyx [00:03:52]: actually like very surprisingly fast.Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It's, it's like every day there's not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it's, it's just all building. It was pretty fun at that time.Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don't so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.Iteration Speed, Compute, and Debugging Model PipelinesSwyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are youEthan [00:05:50]: Let's say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale orSwyx [00:06:01]: So cycle time for like any hyperparam that you're searching.Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?Ethan [00:06:11]: SoSwyx [00:06:11]: So it's like before you, someone had already set this up that you can iterate very quickly.Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.Vibhu [00:06:46]: It's interesting, right? So you say it's like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it's interesting to see the other side, right?Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don't know.Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it's the coding model wasn't quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I've been, I've been using it at that time. It's, it's helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn't maintain, and the LLM itself couldn't figure out what's, what's wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don't-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.Swyx [00:08:36]: yeah, I actually, honestly, I think it's like kind of a stressful job because you're “Well, I should be trying everything, and if I'm not, then I'm not doing my job well.”Vibhu [00:08:48]: there's also the stress of you're eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.Swyx [00:08:56]: You got the daddy Elon toVibhu [00:08:57]: You got daddy Elon.Ethan [00:08:59]: It wasVibhu [00:09:00]: But there's still finite amount of compute, like you want to use it, you want to use it well, you want more of it.Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it's a, it's a marathon, so you got to maintain good health and, a regular schedule.Vibhu [00:09:28]: It's, it's hard to hear that when you shift from zero to nothing in two months.Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.Vibhu [00:09:54]: I think there's, there's three things we're talking about, right? So there's Video Gen, there's also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?Swyx [00:10:06]: Oh, yeah, maybe I got distracted.How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEsVibhu [00:10:07]: Sorry. and then, from there's Video Gen, there's Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What's, what's the few months like? How do you go to state-art Image Gen model? How do you just start?Ethan [00:10:28]: I cannot comment specifically how xAI did, but it's, it's a quite standard process. I can draw some, examples from Cosmos. So mainly it's building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don't naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the commentsSwyx [00:11:11]: TitleEthan [00:11:11]: of a video, but usually they're not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I'm so happy today.Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here's a question, like how do you, how do you gather VLM to begin with? So if there's noSwyx [00:11:55]: You, so you fuse the model, right? LikeEthan [00:11:57]: Say if there's no like VLM exists, like how do you generate the text to the beginning, right? It's, it's impossible.Swyx [00:12:04]: I see.Ethan [00:12:05]: In the beginning, it's like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that's in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.Swyx [00:12:43]: Video or image? You're talking about images.Ethan [00:12:44]: Video or image, either one of them.Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?Vibhu [00:12:51]: It's all training on really detailed captioning of images. So same is applied to video, but insteadEthan [00:12:57]: same appliedVibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can alsoSwyx [00:13:04]: I think there's this traditional perspective of supervised, or, very highly human curated thing. I feel like there's a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.Ethan [00:13:36]: It's interesting to see that you kind of need both data.Ethan [00:13:41]: For example, for theSwyx [00:13:41]: You need it to bootstrap it up. YeahEthan [00:13:43]: for the generative model training, there's also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's, it's a lot of tokens. So like one image, it's, a thousand by a thousand, it's like one million tokens, one million pixels. It's impossible to train transformer on that. So it's, you need to train a tokenizer, which can go from image to latent space and latent space back to image.Swyx [00:14:45]: That's why we named the podcast.Swyx [00:14:48]: But, basically, you're talking about vocabulary science.Ethan [00:14:50]: so vocab.Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?Ethan [00:14:54]: In generative models, the vocab is continuous. It's a continuous space. We can think about like you map an image to a vector. It's a, it's a fixed length vector. It's sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you haveEthan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.Swyx [00:15:29]: We've covered thisVibhu [00:15:30]: This is like the vision transformersSwyx [00:15:32]: VAEs,Ethan [00:15:33]: VAEs.Vibhu [00:15:34]: You basically compress your input, you do your generation, you're reasoning all that generation in smaller dimension, and then you project back out.Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?Ethan [00:15:48]: You can make those.Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.Swyx [00:16:02]: Which is you're, you're kind of re- reconstructing the old paradigm with the new.Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.Ethan [00:16:14]: After this VAE, so what you've got is you've got latent space tokens and you've got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It's, it's very similar to how you train a language transformer models. It's not that much difference. It's just the tokens, the visual tokens in, visual tokens out. The only difference is there's a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.Swyx [00:17:12]: And then there's also, to speed things along on the tech tree of diffusion, there's CFG, and then there's, there's also, latent diffusion that, there's, there's someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don't know if you want to get into that or just, or do the video side up to you.Bootstrapping Video from Image Models and Temporal CompressionEthan [00:17:37]: After you train such model, such image model, the reason it's a, it's a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there's a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that's much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don't have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that's why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you're, you're the first per-- video model person I've ever talked to, I think. we've, we've like talked to Luma and all those folks. There's all these tricks in video compression where basically frame by frame there's not that much difference, so actually you don't have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?Ethan [00:19:27]: There are a few different approaches. Let's say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It's, it's extremely hard to train on that. And there's aEthan [00:20:01]: So that's why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don't need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That's why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there's temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there's only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, thenSwyx [00:22:06]: It might be laggyEthan [00:22:07]: there's a lag there in nature.Swyx [00:22:10]: So you're very pilled on this. let's just go ahead and bring it up ‘cause we have the visual prepared anyway. There's some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front EndsEthan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.Swyx [00:23:14]: So it's basically kind of we're playing a video, but it's pausing for our next interaction, and then it just plays the next thing based on our interaction.Swyx [00:23:23]: Which is kind of cool.Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is thisSwyx [00:23:35]: The demo, the demo tweet had more animation between frames.Vibhu [00:23:38]: I think it's just skipping,Swyx [00:23:39]: Oh, it's just skipping a lot of frames.Ethan [00:23:40]: they also have a video modeVibhu [00:23:42]: It takes a lot. There's a lot of peopleEthan [00:23:42]: but, a lot of people are using it.Ethan [00:23:45]: So it's not available.Vibhu [00:23:46]: There's a live video stream. We can try,Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don't-- we're obviously not in it today.Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn't exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it's, it's more intuit. So why don't we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let's say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I'm looking at, Instagram stories, and I don't like the Like button. I always may click it. And, generative UI resolved it. So it's going to be a revolutionary replacement of the interface. So in the future, we might have much more powerfulEthan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That's how I imagine it.Swyx [00:26:02]: Diffusion front-end, deterministic back-end.Swyx [00:26:04]: Something like that. I find that very expensive, but,Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.Swyx [00:26:14]: you write it onceVibhu [00:26:15]: Compare it toSwyx [00:26:16]: And then you execute.Ethan [00:26:17]: If you think about the cost, say, let's say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you're paying this two forty, you'll actually not wanna pay for that. That's even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.Vibhu [00:26:49]: It's everything, right? compute cost comes down, compute gets faster, model gets smarterEthan [00:26:54]: More efficientVibhu [00:26:54]: model gets smaller.Swyx [00:26:55]: I don't know why you say two times, ‘cause I think it's like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.Vibhu [00:27:08]: That's a net of everything, right? That's model performance alongside compute. So different than just compute costs come down. But, a very interesting future.Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that's the rough idea.Ethan [00:27:34]: And I'd like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.Vibhu [00:28:06]: And it's also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.Swyx [00:28:17]: There's another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you're literally operating, simulating an operating system with a video model.Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it's an OS that I can run.Swyx [00:28:37]: But here everything is imagined.Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn't crash. That's why I saidSwyx [00:28:45]: It's too immersive.Vibhu [00:28:46]: It's, it's too immersive for me.Swyx [00:28:47]: Too immersive.Vibhu [00:28:48]: I wanted to close the tab.Vibhu [00:28:49]: But yes, I can play generated diffusion.Swyx [00:28:51]: this is shockingly fast.Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it's-- this is Doom.Vibhu [00:29:07]: I think there's two sides to that, right? There's okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we've solved consistency. This is still, it looks like a few years old image generation. There's some temporal consistency, but it's, it's kind of just images stitched together as frame video. But it's a good visual representation to pi- to picture the future you wanna see, right? that's, that's what I see in these more so.Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it's just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it's actually also similar to video models. So when we are training these video model, image model, we train them on internet. There's no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it'll do it and it will kind of make sense.Swyx [00:31:03]: So yeah, that's kind of cool. Yeah, I don't know if there's any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It's, really fascinating. We don't get a chance to talk about this enough. So one of the papers that we covered, we've covered every annual, segment anything release. and I don't know if you follow-- you're a computer vision guy, so youEthan [00:31:26]: I knowSwyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it's, very fascinating, and I don't know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.Ethan [00:31:50]: There's, there's also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there's also other approaches that people are doing. So maybe we get into those after as well,?Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.Why Video Models Are Expensive: Storage, I/O, and Training ScaleEthan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don't see as much of is okay, you brought up the delta in training data, right? SoEthan [00:32:24]: you won't have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It's a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.Ethan [00:33:20]: You really, say if you have a billion videos and let's say, let's just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That's also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.Ethan [00:34:05]: AndSwyx [00:34:05]: It's comparableEthan [00:34:05]: and you needSwyx [00:34:06]: AndEthan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.Swyx [00:34:13]: Oh, yeah.Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it's, it's more expensive on AWS than just storing those videos.Swyx [00:34:25]: Storing, yeah.Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it's, it's even more than that. So it's like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.Ethan [00:34:45]: AndSwyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There's one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? LikeEthan [00:34:57]: Of courseSwyx [00:34:57]: cloud cost compared to just,Ethan [00:34:59]: You save so muchSwyx [00:35:00]: store. Yeah, exactly.Swyx [00:35:01]: Especially with like egress and stuff. So.Ethan [00:35:04]: That's a good idea, but it also comes to-- there are some of its own challenges.Swyx [00:35:09]: Of course, of course.Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.Ethan [00:35:32]: Even more expensive than the storage.Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it's so cool. It's okay. So there's that side.Ethan [00:35:41]: So the TLDR, my backhand mathSwyx [00:35:42]: Data is larger than you think. Yes.Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I'm missing some storage.Swyx [00:35:49]: You're also-- you're basically like also more IO bound than normal training.Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That's a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that's, that's even-- that's similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It's also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it's actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.Inference Speedups: Step Distillation, Consistency Models, and GANsSwyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there's LCM, LoRAs for, fine-tuning. There's, there's a lot of stuff that's beenEthan [00:37:15]: Flow matching.Swyx [00:37:16]: there's flow matching. There's a lot of stuff that's been done. there's some overlap that applies to diffusion on the inference side and stuff or?Ethan [00:37:23]: so the difference-- the inference side is a completely different story.Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It's called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It's kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.Ethan [00:38:25]: why this workSwyx [00:38:27]: Strong to weak seemingly.Ethan [00:38:28]: It is. It's kind ofSwyx [00:38:29]: DistillationEthan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That's the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don't know if you covered that. To me, that was actually one of, the most impressive papers I've ever seen from OpenAI.Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don't know if you have any comments on this.Ethan [00:39:41]: So there are, there are a few different approaches,Swyx [00:39:46]: Oh, yeah. Here it is.Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It's already done.Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn't forget GAN. So GAN, actually, that was, that was the OG ofSwyx [00:40:05]: OGEthan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there's a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and thenEthan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you're training GAN, it's a step process. It's just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.Audio-Video Generation and Time AlignmentSwyx [00:41:21]: Then there's one step I wanted to add, which is audio and video.Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it's, it's a first audio video transmodel deployed at a large scale. SoSwyx [00:41:39]: And that was your first model?Ethan [00:41:40]: that was, Grok Imagine's first model. It's, it's audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video's very rare, and they don't understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don't have, they don't have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.Ethan [00:42:44]: So when we speak, it's just, someSwyx [00:42:47]: It's an ASR issue, yeah.Ethan [00:42:49]: It's, it's text token with some characteristics, I would say.Ethan [00:42:54]: But musicSwyx [00:42:56]: I think the speech guys would disagree with this.Swyx [00:42:57]: Like disfluencies and then,Vibhu [00:43:00]: There's tones you can get angry.Ethan [00:43:01]: Well, I say largely.Ethan [00:43:03]: the mu- but the music is completely different. It's, it's very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.Ethan [00:43:26]: SoVibhu [00:43:26]: How?Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it's very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someoneVibhu [00:44:32]: DeafEthan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.Vibhu [00:44:49]: Subtitles, yeah.Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there's enough data where we can understand, narration, conversation, but there's nuances in audio that's where you hit all the data issues or is it just from stage zero, you just do it all right?Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don't have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what's going on in the video, but you don't have to exactly, You typically don't have exact description, oh, at, time step one second like what happened?Vibhu [00:46:02]: It's veryEthan [00:46:03]: At time step two second what happenedVibhu [00:46:03]: coarse. Yeah.Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it's like four seconds or something.Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that's something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I've already spent two days on this and I've exhausted everything.”Ethan [00:46:47]: So the LLMs them-themselves, they don't have a sense of time there.Vibhu [00:46:53]: I actually don't think that's just them not having a sense of time. I think it's somewhat based, right?Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there's a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you'll estimate that it'll take a few days, right?Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It'll take me a few days. But I think it's somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You're trained on all over the text.Swyx [00:47:35]: They're, they're trying to estimate what a human would say.Vibhu [00:47:37]: because that's what the, that's what the data kind of represents. It's not themEthan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It'll take you a while, right?Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.Vibhu [00:48:01]: It'll take me a few hours to go through this research. But this is a tangent.Swyx [00:48:05]: Somewhat, yeah.Swyx [00:48:06]: This is a train of thought I haven't really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We've, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.World Models: Real-Time, Long-Horizon, Interactive VideoVibhu [00:48:34]: just, to ask, how do you define world model?Swyx [00:48:38]: Oh, yeah, let's go there.Ethan [00:48:40]: SoVibhu [00:48:40]: So just for context, we talked about, video generation, and then there's a-- if you say there's a distinction between world models, what's your, what's your definition? How do you see the two?Ethan [00:48:53]: So disclaimer, I'm not going to debate, what is world model. Yeah. there are many definitions, so I'll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let's talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you're like professional CS: GO players- -my say, oh, you have to respond- He's beginner within sub ten milliseconds or- Yeah even less. So that's not most of the- No, sixty FPS. Let's go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn't do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that's a three millisecond. So you have to respond- Oh, s**t. Okay. YeahEthan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it's like two hundred millisecond. So that's, that's much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentionedEthan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don't compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you're not going to just play with, video games just, a few seconds, most video models only a few seconds. We're going to play with minutes, hours. The model have to be able to generate long-form content.Ethan [00:51:42]: So putting these three together, it's, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it's, it's a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it's the first step of interactivity. Yeah. It's, it's the first step. Yeah. So it's the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that's it. That's just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn't have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it's actually a pretty fun hack. if you've seen like- Oh, no, he's saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn't have long-range knowledge of, what's happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.Swyx [00:54:58]: Let's run with that.Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that's a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we're trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.Long Context, Redundancy, and Efficient Interactive VideoVibhu [00:55:51]: Does it seem like it's an efficiency issue? okay, we're at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there's effective context, but at the end of the day, it's just what's it worth? sure, there's a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it's expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we've scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You've scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.Ethan [00:57:53]: that's actually a very good point. So in videos, there's actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there's more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don't need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that's why, I helped build another feature. It's a reference video.Vibhu [00:58:36]: Is it here?Swyx [00:58:36]: is it the same model release or different one?Ethan [00:58:39]: It's a different one.Ethan [00:58:41]: You probably need to search onSwyx [00:58:43]: I'll find itEthan [00:58:43]: X reference to video.Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean's selfie and holding a bladeSwyx [00:59:07]: We have a dogEthan [00:59:08]: or whatever.Swyx [00:59:08]: We put the dog in the thing.Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn't need to have a very long context, but it's-- I feel like it's an intermediate solution. The modelSwyx [00:59:29]: It's cheating.Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that's, Optimus, Einstein myself, Annie.Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.Ethan [01:00:08]: Interesting.Vibhu [01:00:10]: ButxAI's Underrated Work, Culture, and WatermarkingSwyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn't communicate all this work that you do very well because they just have the model release and then that's it. But actually, these details are very good.Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.Vibhu [01:00:30]: A lot of-- yeah, I have a lot moreSwyx [01:00:32]: And then, and then you just put this blog post with the cookies. I'm this is not enough,?Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, soVibhu [01:00:42]: And I wonder, like part of that is also some labs don't share research into what happens. And ifSwyx [01:00:50]: No, but this is literally bragging about how good they are, right?Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don't know.Ethan [01:01:02]: different labs have slightly different communication styles.Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you're, you're making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?Swyx [01:01:23]: Right? Then you need a completely different thing.Ethan [01:01:26]: So I think it's-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there's a paper called Frame Pack, which haveEthan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It's also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let's say if you call tool and the tool call history is extremely long, that's still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.Ethan [01:03:30]: InterestinglyVibhu [01:03:32]: TheEthan [01:03:32]: the same thing is being researched in both LLMs and video models.Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it's actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we'll do our own compression, we'll do our own pruning, which is separate from model error.Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that's different than normal attention.Swyx [01:04:03]: Does that, does that make sense?Ethan [01:04:04]: It's, it's different in the sense that attention, not to mention, set sparse attention aside,

Trump just signed his biggest crypto executive order yet, giving the Fed 120 days to evaluate opening payment rails to Coinbase, Kraken, Circle, Ripple, and Anchorage. The move could break the bank monopoly on Fed plumbing right as Bitcoin sits at $76K after $1B in ETF outflows and $660M in liquidations. Add Warren's attack on OCC crypto charters, JPMorgan saying Bitcoin ETFs are recovering 2X faster than Ethereum, Goldman dumping XRP and Solana for Hyperliquid, and the SEC readying tokenized stocks, and you have one of the most pivotal weeks of the cycle. Is this the bottom, or the setup for Bitcoin's next leg higher? Learn more about your ad choices. Visit megaphone.fm/adchoices

Sir Ben McDougal is a 2X author, 5X founder, podcaster, teacher, content creator, technologist, and traveler. Check out his latest book, BrewedFromWithin.com Welcome to the Conscious Millionaire Show - Become an Ultra-Performer. Now 3X week M / W / F   Are you an Entrepreneur, Founder, or CEO? Revenues $250K to $5M? Sign up for your Breakout Session...get custom steps to build a fast-growing, highly profitable business that makes an impact.     BOOK Your Breakout Session Now     Join Host JV Crum III, with 2 exits and over 75M revenues in his companies, he is the Ultra-Performer Advisor for Founders, Entrepreneurs and CEOs ready to achieve at your the top 1%.     SUBSCRIBE to Conscious Millionaire Show     Season 12 of the award-winning Conscious Millionaire Show. The World's #1 Ultra-Performance podcast. Millions of Listeners. 190 countries -- Inc Magazine "Top 13 Business Podcasts" with 12 seasons and 3,200+ episodes.

Sir Ben McDougal is a 2X author, 5X founder, podcaster, teacher, content creator, technologist, and traveler. Check out his latest book, BrewedFromWithin.com Welcome to the Conscious Millionaire Show - Become an Ultra-Performer. Now 3X week M / W / F   Are you an Entrepreneur, Founder, or CEO? Revenues $250K to $5M? Sign up for your Breakout Session...get custom steps to build a fast-growing, highly profitable business that makes an impact.     BOOK Your Breakout Session Now     Join Host JV Crum III, with 2 exits and over 75M revenues in his companies, he is the Ultra-Performer Advisor for Founders, Entrepreneurs and CEOs ready to achieve at your the top 1%.     SUBSCRIBE to Conscious Millionaire Show     Season 12 of the award-winning Conscious Millionaire Show. The World's #1 Ultra-Performance podcast. Millions of Listeners. 190 countries -- Inc Magazine "Top 13 Business Podcasts" with 12 seasons and 3,200+ episodes.

It's Microdose Monday on Fitness Stuff for Normal People which means Marianna and Tony are rapid fire breaking down multiple topics you guys have been asking about instead of spending the whole episode on just one. This week, they're diving into reverse dieting, why creatine might actually be one of the most underrated tools for fat loss, how coffee can impact your gut health, and the sneaky things nutrition labels hope you don't notice. If you've ever felt overwhelmed by conflicting health advice online, this episode is packed with quick, practical breakdowns that actually make sense. Shorter topics, real explanations, and plenty of little “wait… WHAT?” moments along the way.⁠⁠⁠⁠⁠⁠⁠⁠All Free Calculators (Calorie, Protein, etc.)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Sign up for Fitness Stuff PREMIUM here!!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ALL of our complete 12-week training programsBonus episodes every FridayJust $5 /month⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Legion Athletics⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠BOGO 50% off for your first order + 2X points on every order after thatuse code “FSPOD” at checkoutTimestamps:(2:02) Coffee and Gut Health
(15:08) Reverse Dieting
(26:27) How to Accurately Read a Nutrition Label
(39:31) Creatine for Fat Loss

On this episode of Fitness Stuff for Normal People, Marianna and Tony break down the 5 biggest dieting mistakes that keep people stuck when trying to lose fat or build muscle. They talk through why making sudden, extreme changes usually backfires, how not tracking your food can leave you guessing, and why chasing fast results slows you down long term. They also get into the trap of focusing on small details that don't actually move the needle and how black and white thinking around food can hurt your consistency. If you've ever felt frustrated with your progress, this episode will help you simplify the process and focus on what actually works.⁠⁠⁠⁠⁠⁠⁠All Free Calculators (Calorie, Protein, etc.)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Sign up for Fitness Stuff PREMIUM here!!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ALL of our complete 12-week training programsBonus episodes every FridayJust $5 /month⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Legion Athletics⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠BOGO 50% off for your first order + 2X points on every order after thatuse code “FSPOD” at checkoutTimestamps(3:49) Mistake 1 Making sudden or massive changes
(9:02) Mistake 2 Not tracking food
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(23:43) Mistake 4 Little details
(32:31) Mistake 5 Thinking black and white

Learning a new language as a busy executive sounds great in theory. In practice, the process for how to learn Spanish, for most people, is downloading five apps, getting overwhelmed, and quitting within a month. Sound familiar?In this episode, Omar Newman—Spanish educator, coach, and founder of Carolina Language Solutions—joins Julian Hayes II to break down exactly how busy operators and professionals can learn Spanish without the noise, the overwhelm, or the wasted time.Omar brings 34 years of experience, 22 of which were spent teaching in the school system, before building his own adult language coaching practice. He explains why most people are learning Spanish the wrong way, how to build real conversational fluency faster than you think, and why the biggest barrier isn't grammar, but instead, confidence.He also unpacks why Spanish sounds so fast to English speakers, how to use high-frequency phrases to build momentum fast, and the mindset shift that separates people who stall from those who actually speak. This is a practical, no-fluff conversation about one of the highest-leverage skills an executive or entrepreneur can develop, and a natural extension of what Executive Health is all about: optimizing your full performance, mind included.— Episode Chapter Big Ideas (timing may not be exact) —0:00 - Introduction and why 2026 is the right time to learn Spanish0:45 - Omar's origin story: from Fort Bragg to 34 years in the language3:00 - Why culture and music pull people into a language—not the language itself7:36 - The language noise problem: too many apps, too many voices10:26 - High-frequency phrases and the ICE method for building sentences fast17:41 - Repetition, reps, and treating language like a training program21:44 - Confidence as the real barrier and how to overcome it26:30 - Why Spanish sounds so fast to English speakers28:47 - Syllable-based vs. stress-timed language: the real reason for the speed gap32:26 - The 2X speed hack: train your ear in English to hear Spanish better37:27 - Staying consistent: the SOTT plan and three activities a day44:15 - The four levels of fluency (and why the European framework overcomplicates it)49:02 - How conversational fluency holds up in real business settings56:42 - The easiest forms of Spanish to learn first1:01:43 - Learning two languages simultaneously: how Omar manages six1:06:01 - Three things to do right now if you're serious about Spanish1:11:09 - Where to connect with Omar— Key Quotes from Omar Newman — "It's not that they're speaking too fast. It's that you all listen slow.""You don't go to Baskin-Robbins and try every single ice cream they have. Stay in your lane.""You're already creating a false scenario of how it's going to be received when you use the language in public. Once you do it, that's it.""You can't start building the roof on the house when you don't have a foundation."— Connect With Omar Newman —Website: https://carolinalanguagesolutions.com/ YouTube: http://www.youtube.com/@SpeakSpanishNowInstagram: http://www.instagram.com/carolina_language_solutionsLinkedIn: https://www.linkedin.com/in/omar-newman-26877b4b/ — Connect with Julian and Executive Health —LinkedIn — https://www.linkedin.com/in/julianhayesii/X — https://x.com/thejulianhayesDon't let your biology become the bottleneck to the enterprise you're building. Book a private call —https://www.executivehealth.io/contactWebsite — https://www.executivehealth.io/***DISCLAIMER: The information shared is not meant to treat or diagnose any condition. This is for educational, informational, and entertainment purposes. The content here is not intended to replace your relationship with your doctor and/or medical practitioner. Consult your provider before making any decisions.

What if change didn't take years, but minutes?In this episode, Sagi Chekroun shares how Neuro-Linguistic Programming (NLP) can help you create rapid, lasting transformation by working directly with the unconscious mind—the true driver of your habits, emotions, and results.Most people try to change through willpower, discipline, or information. But real change doesn't happen at the surface—it happens beneath it. When you understand how your mind actually stores patterns, you can begin to shift them faster than you thought possible.We break down why change often feels difficult, and challenge the belief that transformation has to be slow. You've already created habits, fears, and emotional patterns instantly. The same mechanism can be used to rewire them.One of the most powerful ideas discussed is secondary gain—the hidden benefit behind every habit. Whether it's stress relief, comfort, or a sense of control, your mind holds onto patterns because they serve you in some way. If you try to eliminate a habit without replacing what it gives you, it often comes back in another form. Real change comes from upgrading the benefit, not fighting the behavior.We also explore the mindset shift of “10X is easier than 2X”—why aiming for bigger transformation can actually be simpler than incremental change, because it forces you to think differently and step into a new identity.From there, we get practical.You'll learn simple NLP tools you can start using immediately:How to “scramble” negative memories by changing how they're stored (visual, auditory, and physical components)How to use dissociation to safely process intense experiences without reliving themHow to create a mental “control panel” to interact with your inner worldHow to build anchors—physical triggers that instantly shift your emotional stateHe also guides me through a powerful 3-minute future pacing exercise. This practice helps you step into your future self—seeing through their eyes, feeling their emotions, and allowing your mind to begin wiring in the path to get there. Instead of chasing a goal, you begin to embody it.A key theme throughout the episode is that less is more. You don't need long, complicated routines. Short, consistent practices—just a few minutes a day—can create real, lasting change when done correctly.This episode is for anyone who feels stuck, wants to break patterns, or is looking for a more aligned, effective way to grow—mentally, emotionally, and spiritually.Resources mentioned include Richard Bandler, Paul McKenna, and Jose Silva, along with additional NLP tools and teachings.Take 3 minutes today to try it yourself:Visualize your life one year from now. Step into that version of you. Feel it fully.Then ask: Who do I need to become to live this?Because real change doesn't come from forcing yourself forward. It comes from becoming someone new.

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In this episode of Fitness Stuff for Normal People, Marianna and Tony break down what gut health actually means and why it impacts way more than just digestion. From metabolism to mood, your gut is tied into everything, and this episode focuses on the habits that truly make a difference. They walk through the science behind fiber and prebiotics, hydration, stress and the brain gut connection, daily movement, and how your overall eating patterns shape your microbiome. They also get into the real effects of antibiotics, probiotics, alcohol, under eating, and sleep so you can understand what is helping your gut and what might be holding you back. This is a practical, no fluff look at gut health built around realistic habits you can actually stick to.⁠⁠⁠⁠⁠⁠All Free Calculators (Calorie, Protein, etc.)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Sign up for Fitness Stuff PREMIUM here!!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ALL of our complete 12-week training programsBonus episodes every FridayJust $5 /month⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Legion Athletics⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠BOGO 50% off for your first order + 2X points on every order after thatuse code “FSPOD” at checkoutTimestamps3:42) What is Gut Health?
(8:24) Fiber / Prebiotics
(16:11) Drinking enough water
(19:32) Limit stress / Brain and Gut connection
(25:26) Daily movement
(27:28) Limited processed foods / eating patterns
(34:17) Antibiotics
(38:23) Probiotics
(42:57) Alcohol
(46:28) Under eating
(58:56) Sleep

You're showing up to the gym, getting your steps in, being mindful with your food, and still not seeing the progress you expect. In this episode of Fitness Stuff for Normal People, Marianna and Tony break down the five most common reasons fat loss stalls, even when you feel like you're doing everything right. They cover where calories tend to slip through the cracks, how people misjudge what they're actually burning, why daily movement matters more than you think, and how stress, sleep, and overall lifestyle can quietly impact results. They also talk through the role of expectations and consistency, and why progress often takes longer than people anticipate. If you've been feeling stuck or unsure what to adjust, this episode will help you refocus on what actually moves the needle.⁠⁠⁠⁠⁠All Free Calculators (Calorie, Protein, etc.)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Sign up for Fitness Stuff PREMIUM here!!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ALL of our complete 12-week training programsBonus episodes every FridayJust $5 /month⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Legion Athletics⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠BOGO 50% off for your first order + 2X points on every order after thatuse code “FSPOD” at checkoutTimestamps(6:21) Underestimating calories you're consuming(13:10) Overestimating how many calories burned(19:02) Not moving enough(26:41) Rest of your life sucks(39:32) Tie..Unrealistic expectations / Not consistent

SEASON: 6 EPISODE: 23Episode Overview:Welcome back to Becoming Preferred, the podcast where we interview the brightest minds in business today to help you level up your game and become the best version of you.If you are an entrepreneur or a marketing leader, you've repeatedly heard the mantra 'do more with less'. But our guest today says that's the wrong goal. She believes we shouldn't just be doing more; we should be redesigning the work itself.Lisa Cole is the Chief Product, Marketing, and AI Officer at 2X, where she heads their AI Center of Excellence. With over 20 years of experience and three major B2B transformations under her belt, Lisa has become the 'secret weapon' for CMOs looking to bridge the gap between human creativity and machine efficiency. She is the author of Brand Gravity, The Revenue RAMP, and her highly anticipated new book, The Limitless CMO.Today, we're going to talk about how to move from being a 'cost center' to a 'revenue engine,' the reality of AI adoption , and how to build a brand that has enough 'gravity' to pull customers toward you without you having to chase them. Join me for my conversation with Lisa Cole.Guest Bio: Lisa Cole is Chief Product, Marketing & AI Officer and Head of the AI Center of Excellence at 2X. For more than 20 years, Lisa has helped B2B marketing leaders achieve more with less by redesigning how marketing work gets done through strategic outsourcing, AI adoption, and performance management.Lisa has led three major B2B marketing transformations, including award-recognized work, and she's been a trusted advisor to CMOs for nearly 16 years. She's also the author of Brand Gravity, The Revenue RAMP, and her newest book, The Limitless CMO.Resource Links:Website: www.lisacole.aiCompany Website: https://2x.marketing/Insight Gold Timestamps:03:31 I shifted and pivoted into marketing06:13 I remember when I started the very first question a company would ask is, should I have a website?08:36 The underlying principle there is still true, you actually have to ask them11:07 By the time they've actually made that decision to engage with your brand directly...12:43 Marketing is the one that makes sure that you are both findable, understood, and chosen16:21 Could marketing do a better job of being proactive with that, with the sales department?20:24 The premise behind brand gravity is...23:33 No better time to be a founder or entrepreneur, if you're comfortable with just putting your point of views, and your secret sauce out in the world25:52 Your superpower is going to be wherever is uniquely human28:02 Are there some must haves that small businesses should have?31:53 The most common piece of feedback I get is...32:22 My measure of success here is engagement34:27 You have to be okay with change36:10 And you argue for redesigning how work gets done instead of just working harder37:37 My favorite question to ask is, how long does it take for you to get a campaign from an idea into market?40:02 You head the AI Center of Excellence at 2X42:04 The new book's called The Limitless CMOConnect Socially:LinkedIn: www.linkedin.com/in/lisacole01Twitter (X): https://x.com/CMOInnovatorCompany: https://www.youtube.com/@2xmarketingTikTok: https://www.tiktok.com/@thelimitlesscmoEmail: lisa.cole@2X.marketingSponsors: Rainmaker LeadGen Platform Demo: https://calendar.summit-learning.com/widget/booking/JKItVP7WErmCBjU2cCIxRainmaker Digital Solutions: https://www.rainmakerdigitalsolutions.com/

Most people think they need more discipline or motivation or that they just need to lock in a little harder, and sometimes that is true, but a lot of the time the people actually making progress are not better than you, they have just built an environment where the right decisions happen more automatically. In this episode of Fitness Stuff for Normal People, Marianna and Tony are breaking down the tools they actually use to make losing fat, gaining muscle, and sticking to your diet easier to execute day to day, from gym add ons like straps, belts, and apps, to simple kitchen tools that take the guesswork out of eating, to a few things outside of both that help you stay consistent without having to rely on willpower every single day.⁠⁠⁠⁠All Free Calculators (Calorie, Protein, etc.)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Sign up for Fitness Stuff PREMIUM here!!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ALL of our complete 12-week training programsBonus episodes every FridayJust $5 /month⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Legion Athletics⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠BOGO 50% off for your first order + 2X points on every order after thatuse code “FSPOD” at checkoutTimestamps(4:43) Lifting Straps or Hooks
(9:37) Wrist Wraps
(10:56) Liquid Chalk
(14:42) Lifting Belt
(19:38) Lifting Apps
(22:01) AI tools(27:05) Food Scale
(29:00) Meal Prep Containers
(31:00) Veggie Chopper
(32:00) Air Fryer and Misc Kitchen Tools
(38:00) Grocery List
(41:00) Cronometer(42:46) Scale
(48:24) Our Free Calculators
(49:47) Sunrise Alarm Clock
(52:04) Sleep tracker(54:08) Books

From High Revenue to High Profit: The Missing Piece in Your Business with Chris Hallberg, EOS   Find Rocky Lalvani @ www.ProfitComesFirst.com  or email him at rocky@profitcomesfirst.com  Make more, work less video: https://youtu.be/    Hire a Green Beret: Why Veterans Transform Your Business    In this episode, Rocky Lalvani sits down with Chris Hallberg, ranked #9 on Inc. Magazine's Top 50 Leadership & Management Experts, to discuss why hiring the right people and implementing disciplined systems are the real keys to building a profitable business. Chris shares insights from his military background, his veteran-powered recruiting company Business Sergeant, and his work implementing the Entrepreneurial Operating System (EOS) with hundreds of companies. Learn why Green Berets might be your secret weapon, how to stop bleeding money through bad hiring decisions, and why your profit problem might not be a revenue problem at all.    Learning Insights  The true cost of bad hiring: A single bad hire in a $100,000 role costs approximately $500,000 when accounting for turnover and lost productivity. A-players cost only 1.2X to 1.6X more but deliver 2 to 10 times the value.  Veterans are exceptionally rare and valuable: Only half of 1% of the US population has special operations training. They don't cost more to hire than regular candidates but deliver exponentially more value through proven leadership under pressure.  High revenue does not equal high profit: The biggest pattern Chris sees is companies saying yes to every opportunity. Without a strong number two person (COO/integrator) to say no and protect margins, you get high sales but low profit.  Your yes person needs a no person: Visionary CEOs naturally seek opportunities. They need a strong integrator to say no and protect profit margins. Without this balance, money disappears and profit suffers.  Accountability is natural with the right people: When you hire aligned, quality people who share your values, accountability happens without friction. If you can't hold someone accountable, you have the wrong person in that seat.  Use math, not gut feeling, to make decisions: Create a go/no-go matrix based on realistic data. Input assumptions about revenue, time, and resources. Let the numbers tell you yes or no instead of relying on passion or intuition.  Discipline beats opportunity every single time: The road to business failure is paved with companies that couldn't decide what to say no to. Clear, disciplined decisions about strategy and fit matter more than saying yes to everything.    The Big Takeaway  The difference between businesses that struggle and businesses that thrive isn't complicated. It's not about working harder, better marketing, or a superior product. It's about two things: the right people in the right seats, and the discipline to say no to opportunities that don't fit your strategy and profit model. Most visionary founders and CEOs are wired to say yes. They're opportunity seekers. That's their strength. But without a strong integrator, COO, or number two person who protects profit margins by saying no, companies end up with high revenue and low profit. They're exhausted, understaffed, and serving too many customers at too thin a margin. Additionally, most business owners are flying blind when it comes to hiring and decision making. They rely on gut feeling instead of math. Veterans, particularly those from special operations backgrounds, bring a rare combination of perseverance, problem solving, accountability, and calm under pressure that most candidates can't match. They've been selected and tested in environments where failure isn't an option. They understand what real adversity looks like, which makes business challenges feel manageable by comparison. The math is simple: invest more upfront in the right person, hold them accountable, create systems for evaluation and improvement, and say no to opportunities that don't fit. Do this, and your business transforms.    Conclusion  Building a profitable, scalable business requires more than good ideas and hard work. It requires the right people in the right seats, clear systems for making decisions, and the discipline to say no. Chris Hallberg's work with hundreds of leadership teams and his experience as a veteran demonstrate that these principles work regardless of industry or company size. Whether you hire a Green Beret through Business Sergeant or simply apply the framework Chris and Rocky outlined, the message is the same: your people and your discipline are what create profit. Everything else is a distraction.    About Chris Hallberg  Chris Hallberg—known as the "Business Sergeant"—is a top-ranked leadership expert, military veteran, and serial entrepreneur who transforms good companies into great ones, fast. Ranked #9 on Inc. Magazine's Top 50 Leadership & Management Experts—ahead of Simon Sinek—Chris blends battlefield-tested leadership with the Entrepreneurial Operating System (EOS) to deliver proven results.  He scaled and sold a startup during the Great Recession at an 8× multiple, built royalty-generating sales systems, and became Colorado's first EOS Implementer, guiding 100+ teams to achieve 90%+ employee engagement rates and 100+ Best Places to Work awards. Today, he co-builds a $5M AI-driven EOS platform while coaching billion-dollar contractors, national chains, and franchises with a remarkable 85% success rate.  With his no-nonsense, high-energy style, Chris simplifies strategy, strengthens culture, and shows leaders how to drive 30%+ EBIT on predictable systems—making him a powerhouse guest for any podcast.    Links  Website: https://goexpand.com/   LinkedIn: https://www.linkedin.com/in/chris-hallberg-01516315/  Facebook: https://www.facebook.com/people/GoExpand/61577326657347/#  Instagram: https://www.instagram.com/goexpandplatform?igsh=MXV5N2I1Mml0MXF4aw%3D%3D  YouTube: https://www.youtube.com/@GoExpand    Profit Blueprint Calculator I Profit Comes First https://lp.profitcomesfirst.com/profitblueprintcalc-page    Watch the full episode on YouTube: https://www.youtube.com/@profitanswerman  Sign up to be notified when the next cohort of the Profit First Experience Course is available!  Free Copy of the Profit Blueprint Book: https://lp.profitcomesfirst.com/landing-page-page   Monthly Newsletter signup: https://lp.profitcomesfirst.com/newsletter-signup  Relay Bank (affiliate link): https://relayfi.com/?referralcode=profitcomesfirst  Profit Answer Man Facebook group: https://www.facebook.com/groups/profitanswerman/  My podcast about living a richer more meaningful life: http://richersoul.com/  Music provided by Junan from Junan Podcast  Any financial advice is for educational purposes only and you should consult with an expert for your specific needs. 

From the archive - This episode was originally recorded and published in 2022. Our interviews on Entrepreneurs On Fire are meant to be evergreen, and we do our best to confirm that all offers and URL's in these archive episodes are still relevant. Austin Netzley is the founder of 2X and author of the updated book, From 6 to 7 Figures. Top 3 Value Bombs 1. A big key to success is not as sexy as a lot of gurus out there talk about. Think about operational excellence, turning your business into a machine that can thrive without you. If you take that perspective, it's going to lead to a very healthy business. 2. Make the strategic decisions on how you're going to be different, how you're going to solve real problems, and who your customer is. It will make your entrepreneur life much easier. 3. Systems make things much more repeatable, consistent, and not reliant on you. Get a FREE copy of the updated & expanded book for a limited-time only - From 6 to 7 Figures Sponsors HighLevel - The ultimate all-in-one platform for entrepreneurs, marketers, coaches, and agencies. Learn more at HighLevelFire.com. Freedom Circle - A powerful community of entrepreneurs led by JLD. Are you ready to go from idea to income in 90-days? Visit Freedom-Circle.com to learn more.

Want to get away from the crowds? Want a high mountain lake or stream all to yourself?  The best way to do this is to take a backpacking trip, but you need to prepare more than you would for a car trip or a trip to a lodge. What exactly should you take and what should you leave behind?  What kinds of flies and accessories should you bring? How can you save weight and still have enough gear for a fun fishing trip? Derek Bargaehr [37:36], an experienced fly fisher and backpacker, gives us tips on how to make the most of your next backpacking trip. In the Fly Box this week, we have some questions. A couple of which could only be answered by my co-workers at Orvis so we have responses from both Pete Kutzer, our casting guru and Shawn Brillon, our bamboo rod craftsman. How can I easily estimate how much backing is on my unlabeled reels? A listener relates how some podcast advice on emergers helped him and his son have a successful trip I took a lesson on two-handed casting and it was all done on grass.  Was this wrong? What advice do you have on cleaning the ferrules on bamboo fly rods? Are Orvis bamboo fly rods impregnated? On a tarpon trip, the fish were in deep water so I used a sinking poly leader on my floating line.  Should I have used a full-sinking fly line instead? Is the Albright knot a better knot than the nail knot for attaching a leader to a fly line or backing to a fly line? When connecting pieces of tippet I will normally go up two X sizes, like from 2X to 4X.  Is this wrong? Is it OK to clear a casting lane on a trout stream? What can I do to find bigger trout during the dog days of summer?