Podcasts about Expense

Are you telling yourself: ‘I'm already successful. What happens if I do something different and fail?'Today, we're talking about how the fear of failure shows up for you as a high-achieving leader -- no matter how ‘successful' you are. Because here's what I see amongst the senior leaders I work with…You're successful on paper and have followed the path that's laid out in front of you. You've ticked every box that society, your industry or organisation told you was needed to progress your career. Now you're finding yourself at a crossroads and the strategies you once relied on are no longer working. You've either hit a ceiling in your current career or industry -- or the path you were on isn't aligned anymore. So when your current path has no clear next step or you decide to do something different, of course it feels uncertain. This is why it seems safer to stay where you are. And the biggest underlying fear I see at the root of this is the fear of failure. Because there's more to lose at this level: money, status, credibility, your accumulated experience and professional identity.But your next chapter doesn't have to come at the EXPENSE of the success you've already achieved. It's about using everything you've built to intentionally create the career you want now. This is the difference between starting from the ‘outside in' versus the ‘inside out'. The first keeps you on the safe path, chasing external success. The second is the approach I recommend: Taking ownership of your career and following YOUR version of success.In this episode, I'm unpacking the 3 core beliefs beneath the fear of failure that keep high-achievers at a crossroads in their careers. Discover how to stop following someone else's version of success, so you create a career by design -- not default.You'll learn:What to do if you're afraid of throwing away EVERYTHING you've already built, so you reduce risk with your next career moveThe real truth about what other people think and the ONE shift you must make so you no longer let their opinions hold you backWhy worrying about making the ‘wrong' move keeps you trapped in endless overthinking -- and how to get clear on what you want next without waiting for certaintySo hit play NOW -- and let's dive in!Thanks for listening. If you enjoyed this episode, please subscribe and leave a 5 star rating and review. It helps more people find the podcast and benefit too!LINKS:→ Ready to find clarity, build confidence and create sustainable success on your terms? Explore Ignite Your Career.Enrolling now for the September intake. Apply for your free 30 minute consult to get started.→ Learn more about my services for individuals and organisations at staceyback.com or connect with me on LinkedIn or Instagram. 

On this episode of Next Level: Good Vibes Only, Jessica and Darren Salquist sit down with Hannah Talbot, founder of Anam Cara Healing Center and a soul care practitioner whose work is built on a simple, radical idea: the power to heal already lives within you.Hannah shares the story behind her calling, the quiet inner voice that told her there had to be another way, and the leap of faith it took to follow it, even when that meant risking nearly everything to build something new in Spokane. Along the way she and the Salquists get into the hero's journey and shadow work, what it really means to come home to yourself, and how our hardest chapters can become the very gifts we return with to help others. Hannah opens up with remarkable honesty about her own path, so listen with care, this is a tender and vulnerable conversation.They also talk about the community Hannah has built at Anam Cara, a membership-based refuge of classes, healing sessions, workshops, and gatherings, with a retreat that reached all the way to Bali, and the mindfulness movement she is growing right here in the Inland Northwest. Whether you are deep in your own healing or just beginning to listen to that quiet voice inside, this is an invitation to slow down, reconnect, and remember yourself.Learn more about Hannah's work at Anam Cara Healing Center: https://www.anamcarahealing.center/Sign up for the Heroic Philosophers Notes: click hereFollow Darren Salquist, Life Changer, Self-Mastery + Heroic Performance Coach, PTA and Personal Trainer on IG: @salquid LinkedIn: Darren SalquistFollow Jessica Salquist, Life Changer, Nationally Board Certified Reflexologist, Heroic Performance Coach and Executive Leader on IG: @reflexologyjediFB: Find us by nameFind us both on IG @nextlevelreflexologycoaching @reflexologyjedi @healthyaging_itsathingWellness + Coaching — Next Level Coaching and ReflexologyWebsite: www.nextleveltransformationalcoaching.comJoin us at CoreFitinc: https://corefitinc.com/

The news of Texas covered today includes:Our Lone Star story of the day: The old “establishment” non-conservatives of the Republican Party get the chastisement they deserve from Scott McKay at The American Spectator: John Cornyn Is Owed No Revenge at the Expense of His Own Party. An absolute MUST READ and kudos to Senator Cruz for sparking this issue.Who are these self-entitled people willing to let win to salve their own overblown egos?: James Talarico Denies Jesus Is the Only Way, Says Allah Leads to Same Truth – blasphemous, essentially says the Son of God lied. This is as serious as it can get. Talarico pushed to end “tough-on-crime approach” in Texas James Talarico is an outright DSA sympathizing communist; his own words prove it Talarico shares communist revolution desire of Trotsky with DSA socialists Meanwhile, Tump's MAGA Inc. comes with with $10 million for Paxton & Elon Musk's PAC spending big for Paxton as are others.Our Lone Star story of the day is sponsored by Allied Compliance Services providing the best service in DOT, business and personal drug and alcohol testing since 1995.What to Know About the GOP's Unusual Midterm Convention – starts tomorrow in Dallas, runs through Thursday night. Reuters lies in this piece staying Trump “falsely called Democrats “communists” this year…” Falsely? That is opinion being published as news.Congratulations Regan Hutsell! The Houston woman was crowned Miss America 2027 – only the 4th Texan to win.Listen on the radio, or station stream, at 5pm Central. Click for our radio and streaming affiliates.  www.PrattonTexas.com

For decades, government reform efforts have focused on accountability, oversight and reducing risk. A new blueprint argues that approach may have left agencies better at avoiding failure than achieving success. Joining me to talk about their new reform proposal are Loren DeJonge Schulman and Rob Shriver. Loren Schulman is the director for Government Capacity with the Federation of American Scientists. Rob Shriver is Managing Director of Civil Service Strong and Good Government Initiatives at Democracy Forward.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

A battle of winless streaks came to a head to close the Southern 500, with Christopher Bell topping Kyle Larson in the end.

How much control would you hand to an AI agent when the result is a real flight, a real hotel, and a meeting you cannot afford to miss? In this episode of Tech Talks Daily, I speak with Evan Konwiser, Chief Product and Strategy Officer at American Express Global Business Travel, about the role AI can play across search, booking, disruption support, expense management, and the wider managed travel experience. Evan begins with a problem many travelers already recognize. Buying a ticket has become far harder than choosing a departure time and airline. Travelers now face different cabins, fare types, seats, amenities, loyalty benefits, corporate policies, and payment rules. Amex GBT and Ipsos research referenced during the interview also found that four in ten Gen Z business travelers consider arranging work trips too difficult. The challenge for a travel platform is to reduce that complexity while respecting the policies of the employer and the preferences of the person taking the trip. That is where AI becomes promising, but the consequences of failure are unusually tangible. A wrong answer in a chat window is irritating. A travel tool that sends someone to a closed location or recommends a train that does not stop at the required station can damage confidence immediately. Evan describes trust as the deciding factor and argues that business travel may have an advantage over leisure travel because a managed travel provider already knows the traveler's profile, company policy, payment method, and authority to book. We discuss what Evan calls trusted transaction authority. Agentic workflows can help arrange a trip, but most travelers still want to confirm the final booking. Disruption may become one of the first situations in which people accept greater autonomy. If a flight is canceled and time is short, an agent could reserve a suitable alternative, provided the action can be reversed and the traveler can reach a human advisor whenever needed. Evan also describes how AI can identify possible disruption before it happens, prepare alternative routes, and carry the context of a digital conversation to an experienced travel counselor. This matters because automation and human service do not have to operate as separate experiences. Travelers may begin in a self-service channel, move to a person when the situation becomes complicated, and expect the context to follow them. Expense management provides another practical example. Evan believes much of the manual expense report could eventually disappear as trip data, receipts, virtual cards, risk controls, and exception handling work together behind the scenes. He describes guest travelers, contractors, recruits, and event attendees receiving controlled virtual payment cards so ordinary travel spending can be processed automatically while unusual purchases are blocked or reviewed. We also look at bringing travel assistance into tools such as Microsoft Teams. The potential benefit goes beyond convenience. An enterprise assistant may already understand a traveler's calendar and meeting commitments, allowing the booking experience to exclude flights that arrive too late. That context may help employees make better choices while increasing policy compliance, although it also raises questions about data access, responsibility, and how results should be measured. Evan argues that companies should assess AI supported travel at both the program and traveler levels. Time to book and cost matter, but so do satisfaction, policy fit, channel choice, human support, and the quality of the trip itself. He also acknowledges that early agentic chat workflows can take longer than established booking tools, a useful reminder that novelty and improvement are not the same thing. Would you allow an AI agent to rebook a canceled flight automatically if you could reverse its decision, or would you always want to approve the change first? Listen to the episode and share your thoughts with me.

David Kibler's message on Sunday, September 6th, 2026 at Catalyst Christian Church.

Wine is a mocker, strong drink a riotous brawler; and whoever errs or reels because of it is not wise...MICHAELBASHAM.COM

Let's all gather ‘round the campfire, Buckaroos and let Ol' Cactus Jim here tell you about some of today's hardy, hard-working cowboys. Yes, those manly men who live free-spirited, yippy-ti-yi-yo, cowboy lives out in the rustic ranch country of the Rocky Mountain West.Oh, wait – that was a century ago. The “cowboys” who're now humming “Home On The Range” across Montana, Idaho, and Wyoming are multimillionaire and billionaire corporate titan and celebrities like Rupert Murdoch and Bill Gates. They don't really live there nor mix with locals, no do they actually “ranch” their spectacular 300,000-acre spreads, since they don't know how.So they hire real ranching outfits to bring in some cattle, sheep, and other ranching accruements, then they fly in on private jets occasionally and strut around like John Wayne. They are in a word, pathetic.But they surely are land barons, spending up to $200 million each for their vast spreads. Indeed, these dilettantes rule the availability of ranchland and scenic wilderness, pricing out people who really want to ranch and locking out families who want to experience some of nature's most majestic rivers and mountains. Fifteen years ago, the biggest private landowners held 27 million acres; now they've grabbed 42 million acres for themselves.Well, say apologists for wealth concentration, they bought the land with their money, so it's fair and square. But hold on slick. They don't come just for the views, hunting, and exclusivity – their ranches get generous land subsidies, plus, states like Wyoming provide no-tax hideaways for their wealth.This is Jim Hightower saying… So, even though you and I are shut out of these gated land baronies, at least we can take pride in knowing that it's our tax dollars that help the rich buy them… and lock the gates.Jim Hightower's Lowdown is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit jimhightower.substack.com/subscribe

Key TakeawaysMH parks = land business, not housing business. Owner rents pads, tenants own homes; owner avoids interior repairs and big capex on structures, focusing instead on utilities, roads, and management.Demand is counter-cyclical and supply is shrinking. Parks are the “Dollar Tree of housing,” performing best in downturns; new parks are almost never approved, while 100+/year are redeveloped into other uses.Economics are driven by NOI vs. interest rates. Deals are valued almost purely on income; investors seek cap rates 1–3 points over debt, targeting roughly 10–20% cash-on-cash by raising under-market rents, filling lots, and cutting waste.Expense ratios are lean vs. apartments. A well-run park often operates at 30–40% expenses (lower if tenants pay water/sewer, higher with high taxes or vacancy), compared to ~45–50% in typical multifamily.IDEAL framework for evaluating parks: Infrastructure (city water/sewer, no master meters), Density (lots big enough for modern homes), Economics (spread over debt), Age of homes (prefer 1990s+, paid-off), Location (urban-safe or strong suburban/exurban demand).Moat + controversy come from “stickiness.” Homes are effectively immobile (costly and risky to move), so tenants tend to stay long-term; this creates stable income and investor moat, but also fuels criticism around rent increases and perceived tenant lock-in.

The Customer Service Department Is Dead: What the Future Service-Centric Organization Looks Like Customer service cannot remain the department responsible for cleaning up problems created by the rest of the company. If an organization is serious about becoming service-centric, every department must understand how its decisions shape the customer experience—and one accountable leader must ensure the entire system works. In Episode 268 of the Customer Service Revolution Podcast, Denise Thompson and John R. DiJulius III examine what the future service-centric organization will look like as AI reshapes customer behavior, frontline roles, organizational design, and the economics of service. The future will not belong to the company with the fastest chatbot or the fewest employees. It will belong to the organization that uses technology to remove friction behind the scenes while making the experience more human in front of the customer. Customer Experience Must Be Enterprise-Wide—but Someone Still Has to Own It "Customer experience is everyone's responsibility" sounds inspiring, but it can become an excuse for having no accountability. John argues that successful organizations need a clear experience champion—someone who loses sleep over the experience and has the authority, KPIs, and executive access to challenge decisions that could hurt customers or employees. In a large enterprise, that may be a chief experience officer supported by a customer experience department. In a smaller organization, it may be a shared role assigned to an HR, training, operations, or other senior leader. The title matters less than the clarity of the mandate, the time committed to it, and the metrics tied to it. The role should also extend beyond the customer. A truly service-centric organization manages the entire experience ecosystem: customer experience, employee experience, and vendor experience. AI Should Remove Friction, Not Humanity AI is changing customer service, but using it only to reduce headcount can create expensive unintended consequences. Gartner predicts that by 2027, half of the companies that cut customer service staff because of AI will rehire people to perform similar functions under different titles. Gartner has also reported that only 20% of customer service leaders had reduced agent staffing because of AI. As AI handles routine questions, human employees inherit the escalations, emotional customers, sensitive conversations, and high-stakes decisions. That work can be more meaningful—but also far more demanding. Organizations must protect employees from empathy fatigue, make access to a human easier, and train people in the skills technology cannot replace: empathy, curiosity, listening, rapport building, judgment, and the ability to defuse an upset customer. Your Customer May Send an AI Agent Instead of Visiting Your Website The customer journey may no longer begin on a company's website, app, or contact center. Gartner found that customers were approximately three times more likely to use a third-party generative AI tool than a company-provided chatbot when trying to resolve a service issue. Among customers who already used generative AI, 58% had used it to complete a task—not merely find information—and that figure reached 74% in B2B settings. That creates a new strategic threat: the company can become invisible while an outside AI platform recommends brands, compares choices, completes purchases, and resolves problems. Technology is easy for competitors to copy. Human connection, trust, community, and a distinctive brand experience are harder to duplicate. Access to a Human Could Become the Next Competitive Advantage Pega research found that 77% of consumers believed they always or often achieved better outcomes when dealing only with a human, while two-thirds preferred human-led support. Nearly half said they did not trust businesses that used AI to handle customer service interactions completely. Despite that preference, many organizations continue to make people fight through layers of self-service before reaching an employee. John's answer is blunt: a customer should be able to reach a human when the customer wants to. Companies that preserve convenient human access—and equip those employees to deliver empathy, expertise, and judgment—may be able to turn humanity itself into a powerful brand differentiator. Personalization Without Integrity Becomes Exploitation More customer data creates more opportunities to personalize an experience, but not every technically possible use of data is ethical. The public controversy over Delta Air Lines' AI-supported pricing illustrated how quickly personalization can be perceived as surveillance pricing. Using customer knowledge to anticipate a need or make someone feel cared for is service. Using a customer's identity, vulnerability, income, or presumed willingness to pay to extract the highest possible price is exploitation. Customers should be allowed to choose and pay for clearly defined service levels; businesses should not quietly decide that a particular customer can be charged more. Integrity cannot become collateral damage in the race to personalize. The Best Model Is People-Led and Technology-Powered Walmart and Starbucks offer a more promising blueprint. Walmart has introduced AI tools designed to support approximately 1.5 million U.S. associates, including real-time translation and technology that reduced shift-planning time from 90 minutes to 30 minutes. Starbucks' Green Apron Service model combines staffing, workflow improvements, and technology to give employees more time for craft and customer connection. These organizations are not presenting technology as the experience. They are positioning it as the infrastructure that helps people deliver the experience. That is where AI creates real value: transcribing workshop notes, organizing information, handling repetitive tasks, accelerating internal processes, and giving employees more time to think, connect, and solve. Stop Measuring Speed at the Expense of Loyalty Average handle time, ticket volume, and cost per contact can reward speed while quietly damaging the relationship. Fast service that makes a customer feel dismissed is not a win. Neither is a warm interaction that fails to solve the problem. The future service-centric organization must measure both efficiency and experience. John recommends tracking earned sales growth—the percentage of business generated through repeat customers and referrals rather than purchased through advertising—along with the operational and experience measures that explain why loyalty is rising or falling. The most important question is not simply, "Was the issue resolved?" It is, "How did the customer feel after doing business with us?" What Leaders Should Build Now The future service-centric organization will: Assign one accountable experience champion with clear authority, priorities, KPIs, and executive access. Make every department responsible for understanding its internal or external customer and its effect on the end experience. Use AI for repetitive, administrative, and low-risk work while preserving human judgment for sensitive, ethical, financial, and health-related decisions. Train employees continuously in AI readiness and service aptitude skills. Give frontline employees the authority to solve problems without unnecessary permission-seeking. Protect easy access to a skilled human whenever a customer wants or needs one. Measure loyalty, repeat business, referrals, complaints, certainty, and customer outcomes—not speed alone. Refuse uses of customer data that exploit vulnerability or presumed ability to pay. Build strong customer service systems first, then layer AI on top of them. Customer service may stop being a department, but service must become the operating system of the entire company. Chapters 00:59 — Why the customer service department is dead 01:51 — Enterprise-wide ownership still needs one accountable champion 05:06 — Does every company need a chief experience officer? 07:34 — Breaking silos through cross-functional CX leadership 08:26 — Are companies using AI to improve service or cut headcount? 11:14 — The new role of the human service professional 13:30 — Preventing escalation overload and empathy fatigue 15:17 — When customers send third-party AI to handle your company 19:36 — Will access to a human become a premium service? 23:08 — Why community and brand experience are returning 26:11 — AI costs, disappearing entry-level roles, and the talent pipeline 30:14 — Walmart, Starbucks, and the people-led, tech-powered model 34:03 — Personalization, surveillance, and the integrity line 38:59 — Which traditional customer service metrics now work against CX? 42:23 — A practical blueprint for leadership, employees, technology, and measurement 48:05 — The first question every CEO should ask Key Takeaways Customer experience can be enterprise-wide without becoming leaderless; one person must still own the system and its results. AI creates the most value when it removes repetitive work and gives employees more time for judgment, empathy, and connection. Automating simple interactions can leave human agents with a relentless stream of emotionally difficult cases, increasing the risk of empathy fatigue. Third-party AI platforms may become the new front door to the customer journey, making a distinctive human brand experience even more important. Easy access to a knowledgeable human can become a powerful competitive advantage. Personalization crosses the line when customer data is used to exploit vulnerability or presumed willingness to pay. Metrics such as average handle time can produce unintended behavior when they are not balanced with loyalty, customer outcomes, repeat business, and referrals. A company needs clear service systems before it layers AI onto the customer experience. Quotes "Someone has to lose sleep at night over the experience the company is providing." — John R. DiJulius III "When answers are everywhere, questions become the scarce resource." — John R. DiJulius III "When the world gets more artificial, we need to become more human." — John R. DiJulius III "The more digital we become, the more human is the competitive advantage." — John R. DiJulius III "Integrity shouldn't be something that is outdated." — John R. DiJulius III "EX equals CX. Employee experience equals customer experience." — John R. DiJulius III "Customer service may stop being a department, but service must become the operating system of the entire company." — Denise Thompson Resources Mentioned in This Episode Gartner: Half of companies that cut customer service staff because of AI will rehire by 2027 Gartner: 85% of service leaders are expanding human-agent responsibilities Gartner: Customers are three times more likely to use third-party GenAI than company chatbots Pega: Consumers demand more from AI-powered customer service PwC: 2025 Customer Experience Survey Walmart: AI-powered tools for 1.5 million associates Starbucks: Green Apron Service and improved service performance Delta Air Lines: Response regarding AI-supported pricing Links: ROX Dashboard:  https://thedijuliusgroup.com/rox-dashboard/ The DiJulius Group Methdology: https://thedijuliusgroup.com/x-commandment-methodology/ Company Service Aptitude Test:  https://thedijuliusgroup.com/c-sat-forms/individual-c-sat/ Schedule a Complimentary Call with one of our advisors:  tdg.click/claudia Ask John!  Submit your questions for John, to be aired on future episode:  tdg.click/ask Customer Experience Executive Academy: https://thedijuliusgroup.com/project/cx-executive-academy/ Experience Revolution Membership:  https://thedijuliusgroup.com/membership/ Books:  https://thedijuliusgroup.com/shop/ Contacts:  Lindsey@thedijuliusgroup.com , Claudia@thedijuliusgroup.com If you want to learn how world-class organizations build cultures customers cannot live without, explore The Experience Revolution Membership. Inside the membership you'll gain access to livestream workshops, practical frameworks, and proven strategies used by organizations around the world. Learn more at https://thedijuliusgroup.com/membership/ Learn More If your organization is working to improve customer experience but struggling to connect it to measurable business outcomes, The DiJulius Group can help. Visit: https://thedijuliusgroup.com Listen to more episodes: https://thedijuliusgroup.com/the-customer-service-revolution-podcast/ Subscribe We talk about topics like this each week; be sure to subscribe wherever you listen to podcasts so you don't miss an episode.

Jason Wieser, senior vice president of mid-market and channel sales at Calero If technology expense management isn’t on your radar as a practice area, Jason Wieser thinks that’s about to change. Wieser, senior vice president of mid-market and channel sales at Calero and a 2026 CRN Channel Chief, joins In The Channel to talk about why MSPs and VARs are leaving real recurring revenue on the table by not offering technology spend management services to their customers. The conversation covers a lot of practical ground. Wieser explains why SaaS visibility has become the entry point for most partner conversations – delivering value in hours rather than the months that traditional telecom expense management historically required. He walks through how successful partners use TEM as a pipeline creation tool, turning full visibility into a customer’s contract and renewal landscape into a 3-4 year forward roadmap. And he offers a simple three-question framework – visibility, control, or optimization – that partners can use to qualify where a customer actually needs help. Wieser also touches on the recently launched Calero ConnectIQ, an orchestration layer designed to automate the flow of intelligence across technology expense data, and on the shadow SaaS problem – Gartner estimates the average enterprise runs 145 applications, and Calero’s data suggests the real number is significantly higher. For partners curious about what getting started actually looks like, Calero’s partner program has no joining fees or revenue commitments at entry level. Read Full Transcript Robert Dutt: Hello and welcome to In The Channel from ChannelBuzz.ca, bringing news and information to the Canadian IT channel community for the last sixteen years. I’m Robert Dutt, editor of ChannelBuzz.ca and your host for the show. When we talk about practice areas for MSPs and VARs, we usually start with the big ones: cybersecurity, cloud migration, and managed infrastructure. One area that rarely makes the list, but probably should, is technology expense management, or TEM. It is an area that has historically been seen as a back-office auditing function. But in an era of massive SaaS sprawl and complex mobility footprints, it is becoming an advisory service for the new C-suite. My guest today is Jason Wieser, senior vice president of mid-market and channel sales at Calero. Jason was named a 2026 CRN Channel Chief, and he has spent the last few years building a partner program around the idea that TEM is actually a pipeline-creation engine for the channel. Let’s get right into it – my chat with Jason Wieser. Robert Dutt: Jason, thanks for taking the time. I appreciate it. Jason Wieser: Thank you for having me. I really appreciate it. Robert Dutt: You’ve been in tech sales for about twenty years. I’m curious: how did you land in technology expense management, and what made you want to stay and build a channel around it? Jason Wieser: I’ll be honest: when the TEM opportunity was first presented to me, I ran for the hills. I wasn’t willing to entertain the conversation. I’m sure my reasons were similar to those of many people when they think about TEM – that it is a legacy product set and not really on the cutting edge of technology. But from my perspective, as I heard the pitch, particularly around the SaaS expense management component, that was what got me excited. I felt there was a tremendous amount of opportunity. It was a wildly untapped market, and coming out of the COVID environment, I thought there was a good opportunity for channel partners to capitalize on the SaaS sprawl that we all experienced. That is what brought me into the TEM side of the business. When we were building out the channel, there weren’t many players in the TEM space with a channel focus. During the discussions we had as we were courting each other, one thing that came up was that Calero had no desire to be an agent. That was a big differentiator for me, and it was pivotal to my willingness to jump in and build out a channel, because none of the other TEM players could say that. It was a significant differentiator when you think about the value delivered back to the channel. When you combine those two things – the SaaS opportunity and Calero’s channel-first approach – it became a great opportunity. I’m really happy with the success we’ve had over the last four years building the channel at Calero. Robert Dutt: Most of my listeners are IT resellers and MSPs who probably haven’t thought much about TEM as a line of business they would offer. But you describe it as an untapped opportunity – words that always make my ears perk up. Can you make the case for that partner? Why should this be on their radar right now? Jason Wieser: I think the difference is in where TEM was and where it has gone. Historically, TEM stood for telecom expense management. Now, we think of it as technology expense management. I see this as a pipeline-creation tool for MSPs and resellers. Partners that lean in and work with a TEM provider that supports the channel can build a three-year pipeline roadmap. A reseller might ask, “How is that the case?” The way we go to market is that when we work with partners – whether they are resellers, referral partners, or MSPs – we make them part of the solution. They get full access to the Calero platform with their respective customer. That creates a building block. They can identify which contracts are coming up and position themselves as a trusted advisor to their customer. For example, if the customer’s Zoom licenses are coming up for renewal, the partner can see the usage rate. Or perhaps the customer’s Microsoft enterprise agreement is coming up for renewal. The partner can look at how the organization is using its E5 licenses and determine whether it really needs E5, or whether some users should be moved to E3 or F3 licenses. The partner gets to change the trajectory of the conversation and add a new source of value to the organization. At the end of the day, I see that as the biggest opportunity for a partner organization. From there, the partner can build on that process. They can look at circuits that are coming up for renewal, mobility, and other technology expenses. All of that helps them build out a pipeline over the next three, four, or five years. Robert Dutt: Is this something that a smaller reseller or MSP can realistically build, or does it require a certain level of scale to be a real opportunity? Jason Wieser: The good news is that we built this for MSPs and resellers. Historically, with technology expense management – or telecom expense management – you needed to have a large customer base. You might need a customer with a million dollars in annual telecom spend, otherwise it did not make sense. Now that we’ve moved into SaaS, particularly with a mid-market focus, you can go much further down-market. Our smallest customer has 250 employees. That gives a partner the opportunity to change the conversation and use this in a much smaller-capacity environment. On the telecom side, it used to take four, five, or six months to build out the infrastructure and gather all the data. On the SaaS side, it takes four, five, or six hours to bring information in. That is a significant differentiator. Partners can scale the opportunity, realize savings much more quickly, and begin addressing the control and optimization issues associated with technology spending. Robert Dutt: Legacy TEM is rooted in telecom, but given the speed at which you can prove value with SaaS, what is typically the entry point into the conversation with a customer? Does the conversation still begin with telecom bills that have gotten out of control? Do partners lead with SaaS sprawl? Is it mobile device management? What typically opens the door? Jason Wieser: From an MSP standpoint, what we are seeing work right now is starting with SaaS. It is the gateway because you have the opportunity to show immediate results. On the telecom and mobility sides, it is a longer process. You need letters of authorization, or LOAs. You need access to the data, and you need to bring all of that information into the system. That process can take four, five, or six months if the LOAs are not completed in a timely manner. With SaaS, you can get access to an endpoint and conduct a proof of value immediately with the partner. You can start showcasing the data sets, and the decision practically writes itself for the customer. For an MSP, I would focus on SaaS because of that speed. The ability to white-label the platform and make it look like your own – with your logo in the upper-left corner and “powered by Calero” underneath – helps cement you as a true partner to the business. Robert Dutt: The thesis seems to be about the merger of telecom, mobility, and SaaS into one management problem. But for many businesses, those are still three different budget lines, with three different people responsible for them. What makes managing them together increasingly important, and who on the customer side is feeling the pain most or leading the charge? Jason Wieser: That’s the million-dollar question, because they are very different business units. We view our platform as providing a single pane of glass to accommodate all of those expense categories. But the person making SaaS decisions is usually not the same person making mobility decisions, and neither is necessarily the person responsible for telecom. The way we frame it is to start on the SaaS side. We leverage the resources and data sets that we are able to uncover with the partner, and then we ask to go wider into the organization. SaaS provides the gateway. Once we have shown results – whether that is savings, improved security, better control, or the ability to bring in data that the customer did not previously have – we can ask who owns mobility and who owns telecom. We may also uncover an optimization opportunity while reviewing an enterprise agreement, or while a customer is considering a move from Zoom to RingCentral. The partner has proof points showing business value, and that makes the conversation much easier. The customer is more willing to provide an introduction to the people responsible for those other areas. If you approach this holistically, it is generally more of a CFO- or CTO-level discussion because it is tied to a broader business-transformation objective. But that usually has to come from the top down. If you are trying to create an all-encompassing program from the bottom up, we do not see that very frequently. It is much more common to get a foot in the door and then expand from there. Robert Dutt: What does the economics look like for a partner that builds this practice well? Are we talking about meaningful recurring revenue, or is this more of a retention and stickiness play? Jason Wieser: I would say it is both. When you look at the dollar size of the opportunity, there is definitely a compensation component that can be worthwhile, depending on the time of year and the programs available. From an MSP perspective, there is also a traditional markup that the partner can earn. Our plans and packages are designed to support those upsides for MSPs. From a stickiness standpoint, that is also a key element. We are only four years old in the channel, so we do not yet have the channel data to say precisely how sticky it is. But if you look at our customer base overall, it is a very sticky product. Our average customer has been with Calero for seven and a half years. That creates an opportunity for growth within the partner community. The partner can continue demonstrating value and having those conversations over time. Robert Dutt: You talk about partners moving from transactional selling to advisory relationships, particularly around practice-building. That is the right direction, but it is a real cultural shift for a lot of partner organizations. Where do you see partners getting stuck, and what separates the ones that make the transition from the ones that do not? Jason Wieser: We see partners getting stuck when they are not completely certain how to have the conversation. I would describe that primarily as an enablement issue, combined with a willingness to lean in. Telecom was never a particularly exciting topic. Most partners have not leaned into it over the last fifteen years unless they specifically built a telecom expense management practice. There are very few of those partners. Having the understanding required to conduct those baseline conversations takes some work at the outset. The partners we have seen succeed are the ones that have leaned in. They understand how to have those initial discussions and then tie them back to what matters to the business. There are three things we look at that drive success: Are you looking for visibility? Are you looking for control? Are you looking for optimization? It could be all three. The partners that can tie the proof point back to one or more of those outcomes, and have those conversations on the fly, are the ones that move beyond dipping their toes in the water and begin to see meaningful growth within their practice. I do not want to make it sound as if they need to build a large team. One of our largest partners is a billion-dollar organization, but it has ten practices run by one person. One person has leaned into the conversation, and that person is brought in when the opportunities arise. Robert Dutt: You launched Calero ConnectIQ just a couple of weeks ago. Can you give me the quick version of what that changes for partners and their customers? The bigger question is this: as more of the heavy lifting gets automated, does that make the practice easier to build, or does it simply raise customer expectations? Jason Wieser: The ConnectIQ launch is important because it allows us to streamline our connections to the external world. It gives us API hooks in a much quicker manner and allows us to access data in a more streamlined way. From the perspective of a partner recognizing and delivering value to its customers, that is a significant benefit. As for the AI component and what those dynamics will look like, that is still to be determined. We are only two to four weeks into the launch. We have use cases that we have developed to date, but the real-world application is still being fleshed out. We will continue working with the partner community to drive those successes across the global channel. Robert Dutt: I’m guessing that having those hooks, or making it easier to establish those hooks, is especially important as SaaS comes to the forefront. Telecom is a relatively small community in terms of the number of providers, while SaaS is not. Jason Wieser: The number of SaaS applications in enterprise organizations is absolutely staggering. Gartner has cited an average of 145 applications in an enterprise. We find the actual number to be significantly higher because of shadow SaaS. I’ll use myself as an example. I have applications that the business does not provide – applications that I pay for myself – and those applications are still accessing data. I probably should not say that on this podcast because now I am going to be audited by the business. Robert Dutt: I think you mean, hypothetically, that you could possibly have applications that are not approved by corporate. Jason Wieser: Absolutely. Good clarification. It just goes to show that you cannot control what you do not have visibility into. The number of applications touching the network is growing and continues to grow because there are so many specialized applications. A new person comes into an organization and prefers ChatGPT over Claude, or vice versa. They are going to use the tools they are familiar with. ConnectIQ is there to assist with that and provide the ability to get that data in a much more streamlined manner. Robert Dutt: I want to bring this home for my audience specifically – the Canadian IT reseller and MSP. Can you tell me a bit about where Calero is in Canada? Are you active with Canadian partners? Is there anything specific about the Canadian market, including the structure of the telecom industry or the profile of the businesses that need this, that makes this a particular moment of opportunity here? Jason Wieser: One thing we have done well as a company, particularly with our customer base, is operate as a global organization. That also presents unique challenges. If I think about Canada specifically, there are businesses for which data cannot leave Canadian borders. Our ability to put that infrastructure in place and ensure that data remains on the Canadian side of the fence is important. We also need to ensure that our partners have the ability to facilitate that for customers. This remains a focal point for us, and it is something we need to continue investing in as we build our Canadian partnerships and drive growth. Robert Dutt: Last one for me. Canadian solution providers are listening to this and thinking, “This could be an opportunity. This might be something I need to look at more seriously.” What are the best first steps? What should they do next as they think about how this fits into their business and potentially build a practice? Jason Wieser: We would love to have a baseline conversation. We are happy to learn more about their business and then reference similar businesses and partners where we have seen success. We can highlight what those partners have done and how the model has worked for them. We also have an enablement track that we run with partners to help them become comfortable with the opportunity. If a partner wants to lean in, we are more than happy to provide the resources to help build the practice. That way, the partner can begin generating sales from the technology expense management side while also building a strong pipeline over the next two, three, or four years. Robert Dutt: Brilliant. That’s something they can certainly act on. It is an interesting opportunity – one that I had not thought of in quite this way, as the conversation has shifted from a telecom-centric discussion to a broader technology expense management discussion. I appreciate you bringing some of this to light. Thank you very much. Jason Wieser: Thank you for your time. Robert Dutt: There you have it, Jason Wieser from Calero. I’d like to thank Jason for his time today. I think the big takeaway for me was the idea that SaaS is the front door. We often think of expense management as a months-long slog through old telecom invoices. But Jason’s point is that a partner can deliver visibility into a customer’s SaaS sprawl in a matter of hours and then use that insight to build a three- to five-year roadmap. That is a real shift in how to think about the opportunity. If you are looking for a way to move from transactional selling to the trusted-advisor status that we are always talking about, this seems like a practical path to get there. I’d also like to thank you for tuning in. If you are enjoying the show, please make sure to follow or subscribe to the podcast on Apple Podcasts, Spotify, YouTube, or wherever you usually get your podcasts. Ratings and reviews are always encouraged. Until next time, I’m Robert Dutt for ChannelBuzz.ca, and I’ll see you in the channel.

Asking for the bill is one of the most important things to learn in any language. But חשבון, in Hebrew, is about much more than just settling the account. Guy explains how Israelis do self-reflection, how they break even… and how they don't give a damn. Hear the All-Hebrew Episode on Patreon   New Words and Expressions: Heshbon, heshbonot (m.) – Bill, bills – חשבון, חשבונות Heshbon bank – Bank account – חשבון בנק Heshbon hotsa'ot – Expense account – חשבון הוצאות Heshbon nefesh – Self examination – חשבון נפש Lehavi / Lakachat be-heshbon – To take into consideration – להביא / לקחת בחשבון Kach / K'chi / K'choo be-heshbon she- – Take into consideration that… (Imp.) – קח / קחי / קחו בחשבון K'chi be-heshbon she-kar – Take into consideration that it's cold – קחי בחשבון שקר Kach be-heshbon she-yakar sham – Take into consideration that it's expensive there – קח בחשבון שיקר שם Lo ba be-heshbon – It's out of the question – לא בא בחשבון "Panim she-lo osot heshbon" – A face that does not give a damn – פנים שלא עושות חשבון Hu lo dofek heshbon – He doesn't give a damn – הוא לא דופק חשבון Ma, ata dofek heshbon le-mishehu? – Do you care about others? – מה, אתה דופק חשבון למישהו Hi sogeret heshbonot – She's clearing the table – היא סוגרת חשבונות Hisool heshbonot – Score settling – חיסול חשבונות Zeh al heshboncha? – Is it at your expense – זה על חשבונך Al heshbon ha-bayit – On the house – על חשבון הבית Zeh al heshbonenu – This is on our account – זה על חשבוננו Heshbon aroch – A long-standing score – חשבון ארוך Yesh li heshbon aroch ito – I have a long-standing score to settle with him – יש לי חשבון ארוך איתו Be-heshbon pashut – In simple arithmetic – בחשבון פשוט Ro'eh heshbon – Accountant – רואה חשבון Heshbona'ut / Re'iyat heshbon – Accounting – חשבונאות / ראיית חשבון Hanhalat heshbonot – Bookkeeping – הנהלת חשבונות Menahel / menahelet heshbonot – Bookkeeper – מנהל / מנהלת חשבונות Tavi'i cheshbonit – Bring an invoice (imp. f.) – תביאי חשבונית Heshbonit mas – Tax invoice – חשבונית מס Lehashben le-mishehu – To care too much about something – לחשבן למישהו Lehitchashben – To settle accounts with someone – להתחשבן Bo nitchashben ba-sof – Let's do the math at the end – בוא נתחשבן בסוף Ma, ata mitchashben iti al cafe? – Forget it, it's just a coffee, my treat! – מה, אתה מתחשבן איתי על קפה Hitchashbenut – Settling an account – התחשבנות    Playlist and Clips: Ariel Zilber – Holech Batel (lyrics) Ofra Haza – Shir Ha-frecha (lyrics) Rami Kleinstein & Ha-mo'atsa – Ha-boker At Holechet (lyrics) Tea Packs & Alma Zak – Perech Ha-shchunot (lyrics) Ep. 172 about to knock Ep. 440 about receipt and invoice HEB

The Suite Spot attended the 2026 Hotel Data Conference and had the opportunity to interview some of the best and brightest hospitality leaders in the industry to gain their insights and perspectives on prevailing data trends, AI & technology, how to optimize the guest experience and much more.  Be sure to watch the full episode if you missed any of the action from the 2026 Hotel Data Conference.  Special thanks to: Amanda Hite, Jan Freitag, Erica Lipscomb, Max Spangler, & Sam Trotter. Ryan Embree: Welcome to Suite Spot, where hoteliers check in and we check out what’s trending in hotel marketing. I’m your host, Ryan Embree. Hello, everyone. Ryan Embree here at the 2026 Hotel Data Conference here with STR President Amanda Hite. Amanda, great to see you again. Congratulations here. This is our first time at the Hotel Data Conference. Amanda Hite: Oh, wonderful. Thank you. Ryan Embree: Record attendance was just announced. Welcome to The Suite Spot. We’re excited to be here. It was a ton of excitement that we just saw. Tell us a little bit about this event, and we were talking off camera about, do you ever expect it to be what it is right now? Amanda Hite: Yes, we started it 18 years ago with a couple hundred people, maybe. The very first year we’ve always had it in Nashville. This is our home base for the STR part of our business. Most of our employees are here that are in the US. So we started it with a way to connect with customers and more importantly, like, we all have this curiosity about the data. You know, we’re constantly in analyzing, looking at trends in the industry, and we wanted to get people together to hear what are you seeing and let’s talk about it. And that’s really how this started. So it’s, I think it’s for me, my most proud part of this conference is the feeling that everyone has when they come in of being really open and curious and wanting to learn from each other. So you get some really good dynamic conversations happening in the networking breaks and in the hallway. Ryan Embree: Well, it’s such an important time right now too, right? ‘Cause people are already starting, if you can believe it. Well, actually, probably you can look in 2027. Amanda Hite: That’s why we do hotel data conference when we do it. Exactly. It’s budget season. Ryan Embree: Brilliant. Brilliant. Right? And, you know, you just got off stage, like I said. One of the fascinating pieces, like I said, we weren’t here last year, but this is our first time. You said when you first stepped on stage, there were, and you showed some of those original numbers. There was a little bit of a gap in the audience last year. Amanda Hite: Yes. Ryan Embree: But this year, a little bit different story. Amanda Hite: Yes. We had a much better forecast to reveal this year. Last year at this time was when we took the forecast down to reflect what was happening in the industry. And this year, we raised the forecast, not just for the rest of this year, but also for 2027. Ryan Embree: So great to see. And a really cool inflection point, I made a note here, revenue for the first time outpacing expenses, right? What does that mean for hoteliers? Amanda Hite: Yeah. So we finally see the pace of growth on the revenue side outpacing the expense growth. I mean, we’re in a high inflationary environment. Expense growth is something that will continue and hoteliers are having to deal with. But to see that we’re actually going to get some GOP gains, it’s, it’s super helpful. I mean, the point I made this morning though is our margins are not growing. Yeah. So we’ve got some room to grow efficiencies and productivity within the hotels to try to get margins to grow at the same rate of GOP growth. Ryan Embree: Yeah, yeah. It’s challenging right now. And one of the things we’re doing to combat, or CoStar’s doing combat that, bottom line data being added to the product. What’s that mean for the hotel industry? Ryan Embree: Yeah, so within STR Benchmark and the CoStar platform, we introduced at the end of the first quarter our profitability benchmarking. P&L is something that STR has done for 30 years. We did it on an annual basis. And we introduced our monthly benchmarking back in 2020, literally as the world shut down. So maybe not the best timing. But of course, now we’re prepared in an environment like we are today, a very complex operating environment for our hoteliers. It’s, yes, we need to grow revenues, but we must make sure that that is flowing through to the bottom line and that our operators and owners are actually making money. And that’s not been the case in many types of hotels and many markets around the country. So we’re trying to make sure that we bring that visibility of not just the top line growth that we want to see for the industry, but the flow through all the way to the bottom line. Ryan Embree: Yeah, I’d love to see that. And, you know, another thing that we’re gonna hear constantly about at, and at this conference is AI, right? So I guess the overarching question would be more of like, how are you incorporating AI into your products right now? Amanda Hite: This is when I’m so thankful that we are a part of the CoStar Group entity. If you follow our other brands, homes and apartments launched AI in their products earlier this year. So we’re continuing to build off of that. We will have AI search in the CoStar product in the same way that you see in apartments and homes. But for STR benchmarks specifically, what we’re thinking about is making sure that we’re integrating AI into the product, not just sitting on top of the product, but like we interact with the clients all the time on the analysis in the industry. So we want to bring that through AI into the product for our customers to use. So we love when they pick up the phone and call us and wanna talk about data. Right. But we also wanna make it easier for them to surface it within their portfolios in product. And so that’s the path that we’re going down to bring that intelligence in the product and analyzing and spotting the trends, knowing what to look at or sometimes not look at, right? Sometimes it’s a great point. It’s just as important to say like, “Hey, I only have a limited amount of time. Where do I not need to spend time right now?” And that can be tricky, especially when you’re looking at a larger portfolio of trying to discern where, what makes the most sense to drive profitability for my business, for me to spend time on right now. Ryan Embree: 100%. Those complexities and driving efficiency so important right now. And turning those data, that data into actual insights. That what one of the promises of AI. So reason we’re here at the Hotel Data Conference, thank you for taking the time. We’ll, we’ll let you get back. I know you’re hosting almost 900 hoteliers here. So we’ll let, let you get back to Amanda. Thanks for stopping by. Amanda Hite: Thank you, Ryan. Appreciate it. Ryan Embree: Hello, everyone. Ryan Embree here with The Suite Spot live on location Nashville at the 2026 Hotel Data Conference here with Jan Freitag, National Director at CoStar. Jan, thank you so much for taking some time and very busy. You’re hosting almost a thousand hoteliers here. Jan Freitag: Yes, 18th year. Sold out again. So heads up, next year we’ll sell out again. But thanks for being here and sort of taking the pulse on the industry. We appreciate it. Ryan Embree: 100%. Congratulations. Amanda Hite opened us this morning saying last year when she unveiled the forecast, there were audible gaps in the crowd. I feel like behind us, people have been skipping, jumping down, up and down this escalators. Share with us, we got a revised forecast. Jan Freitag: So we’re proposing that RevPar this year is up 4.4%. So that is the second upward revision we had to make, quote unquote. And the data’s just so strong. But then that means that next year, we’re gonna see growth, but it’s much slower global. So next year we’re thinking that RevPargrowth is gonna be like, you know, 2-2.1% or so. So the negative way to say this is, “Oh, our growth rate is cut in half.” The positive way to say this is like, “Oh, we have growth on growth, right? 4% this year, ne – 2% next year.” Ryan Embree: 100%. I mean, a lot of people are going into, you know, we’ve talked to hoteliers here on the Suite Spot, going into their budgets. These are very, very important numbers for them as they go into their budgets because they wanna forecast. When we met last, we were at NYU. There had been zero soccer games played in the US. Now, 104 games later, we got a crown champion. Obviously had a big impact. We’re gonna talk about that in a minute. But that strong performance, one of your big takeaways from this morning was strong performance is gonna equal some tougher comps in 2027, right? Jan Freitag: Yeah, absolutely. So we had arguably easy comps this year, right? The Q2, three, and four RevPAR performance last year was negative. So yeah, we would outperform it this year. That was not a question. But because, RevPAR in the second quarter was up 5.7%, that is a, a very stout result, obviously driven in June, partially by the World Cup remember we’re gonna talk about. You know what that means for next year is, oh wow, we’re not gonna see that performance again. And so my conversation this morning with hoteliers is all about, okay, so how do you massage your owner? How do you have this conversation with your owner, with your team to say, look, there’s still gonna be growth, but we really have to think about this. And I heard this this morning from an asset manager at next year as a year of 10 months and two months, you know? So really take June and July out of your annual number and say, okay, so what’s the growth for that? And then, yeah, June, July is just gonna be tough cost. Ryan Embree: Yeah. Probably something that a lot of markets who hosted Taylor Swift a couple years ago had to deal with. And then maybe what LA’s gonna have to deal with in 2029 after the Olympics in 28. Jan Freitag: Yeah, exactly. So we’re already talking now about the Olympics. We’re gonna talk about, obviously the World Cup in four years over in Europe and what is the performance there. So these sporting events are just the gifts that keep on giving. Ryan Embree: Yeah. Yeah. And, and travelers continue what we heard this morning. Consumers continue to prioritize travel, which is really, really great for obviously our industry. But not without its cautionary tales, you also had a watch your margins kind of take away from that. Maybe expand on that a little bit. Jan Freitag: Yeah. So we’ve had for the last year and for the last couple of years, really this interplay between room rate growth and the rate of inflation being higher than room rate growth. And we’re taking the rate of inflation sort of as a proxy for how much more things are expensive. And the costs for hotels are obviously going up. Higher labor costs, higher insurance costs, higher food costs, higher costs, inner energy, everything. So if your costs are going up in order for your margins to expand, you need to drive room rate or revenue faster than the cost increase. And that just is not happening. So my colleague Isaac Collazo spent 55 slides and an hour explaining how margins are decelerating, unfortunately. Now, the total dollar amount, we’re everything gets more expensive, but it also means we’re having more money available as profit. But the margins are coming down. And that’s really the, maybe to me, the main takeaway from HCC this year for the budget conversation for 2027 is watch your margin. Ryan Embree: Efficiency is always looking for that, especially in these tight margin areas. And then lastly, you know, your whole presentation this morning was themed around the World Cup. And, and I do wanna bring it up because, obviously there was the quote was 104 Super Bowls. Yeah. Right? And you kind of explored that case a little bit. Found out maybe that might not be the case. Jan Freitag: Yeah, exactly. So the FIFA president had said at the time, just to explain to American audiences, “Hey, we have 104 soccer games and they look like 104 Super Bowls.” That is of course not the case. And that was never meant to be the case. Super Bowl is the largest cultural sport event in America. It happens once a year, right? And to sort of translate that was, I thought always a little silly. So it turns out that the 104 Super Bowls did not come to pass, and it was more like 30 Super Bowls, maybe if that. So yeah, it was still a very healthy impact. If you look at the markets that Hosta gave Kansas City, New York, Philadelphia, Boston, very, very strong room rate growth. Interestingly, in some markets, actually, occupancy declines. We saw that specifically in Vancouver, but we saw it in Atlanta, we saw it in Boston. Why is that? Well, because corporate America, meeting travelers, meeting planners said, “You know what? I don’t need to compete with the Tartan Army in Boston for our meeting. You know, let me just stay away. Let me have that meeting in August, or let me move that meeting to Chicago,” for example. Sure. Chicago had a very, very strong June, July meeting calendar. So it’s, um, the, the room rate increase was absolutely expected and is exactly what came to pass. It just wasn’t Autumn for a Super Bowl. Ryan Embree: Yeah. I mean, that just proves we are, uh, a collective of markets. Things are gonna be obviously different in each one. Yeah. Uh, with different factors there. You know, a- and there’s also an interesting stat, fascinating stat, I wanna bring it up, about booking windows, um, that, that you brought up there. Yeah. If you wanna expand on that. Jan Freitag: So I got this totally wrong in the run up to the World Cup because I thought, look, if somebody books that FIFA ticket a year out, and the airplane ticket’s six months out, surely they would book their hotel three months out. Yeah. That did not happen. Right. And so we saw specifically the chart that I had this morning for, uh, arrival dates, June 11, 12, 13, 20 basis points of, uh, 20 points of occupancy was booked after June 8th. Wow. So that’s a booking windows of, like, three or four days. Wow. For an event that you knew what happened, I mean, six years ago. Yeah. You know? And you had a ticket from one year ago. So I just completely though that the, uh, the, the leisure traveler, the, the soccer traveler would also book their room way ahead. That did not come with us. Ryan Embree: Very interesting. I wonder if that’s a macro trend happening right now, those booking windows starting to shorten a little bit. Jan Freitag: Yeah, and maybe that’s a takeaway for our friends, you know, in LA who are hosting the Olympics. Hey, you know, be very mindful how you match that booking window. Ryan Embree: Lessons from history learned there. Yes. Um, final as we wrap up, I always li- like, like to get any, you know, you look at a lot of data. So any interesting, uh, like, data points that really stood out or surprising? Jan Freitag: I mean, the July data came out yesterday and the luxury class RevPar growth was 16%. Ryan Embree: Wow. Jan Freitag: Talk about A, amazing, but B, A, tough comps. Yeah. In July of next year. But it was an amazing, amazing performance. July was very, very strong. Um, and June as well. So we clearly saw, you know, July was helped a little bit by 4th of July, World Cup, uh, 4th of July calendar year, but also the World Cup, obviously the final and the bronze medal games. They all, they all helped. So July was strong, June was strong. So now I think things are getting a little bit more normal – Yeah. Early on end. Ryan Embree: Awesome. Well, we’ll continue to look ahead as you will, but thank you again for taking time out of your busy schedule, Jan. Jan Freitag: Thanks for being here. Thank you. Ryan Embree: Hello everyone, Ryan Embree here with The Suite Spot. We are live on location of the 2026 Hotel Data Conference. I am here with Erica Lipscomb, EVP of Commercial Strategy at PM Hotel Group. Erica, thank you so much for joining me on The Suite Spot. Erica Lipscomb: Well, thank you for having me. Very excited to be here. Ryan Embree: Yeah, first time here on the Suite Spot. Yes. But not your first time here at Hotel Data Conference. Erica Lipscomb: Not my first time at Hotel Data Conference. This is conference number eight. Ryan Embree: Okay. Yes. All right. Hotel data conference. You obviously are no stranger, you’re a pro. What do you call a hotel data conference a success kind of reflecting back? What do you come here to accomplish and to learn? Erica Lipscomb: You know, I, again, this is our start of budget season. Sure. Right? Yeah. So I actually, uh, had dinner with Amanda last night and said, “You do realize what you’ve done here, right? We cannot even start our budget calendars until there’s an HTC.” Yeah. So really what I look forward to is not coming here just to hear that the amazing news of an increase year over year, or that we’re gonna increase in the year for the year. Sure. But what are those things that I can take away that can make it tactical for our teams? Mm-hmm. So learning from industry leaders that are here. We have amazingly smart people that are here at this conference. And we’re really drafting and shaping what the industry will look like. So what are those learnings? And then how do I make sure that we trickle that down within the organization and get them to our teams? Ryan Embree: Which can change so rapidly, right? As we know – Absolutely. It’s gone from, uh, a yearly change to almost, it feels like a weekly, especially with the AI and technology conversation. Yes. You were on a panel last year here at this same conference. I’m curious, what were some of the conversations then versus now? Erica Lipscomb: Yeah. And very different. I think it’s been extreme polar opposites. Okay. I feel like last year, there was a lot of conversation about AI. Mm-hmm. But more on the what is AI. Mm. And how are we gonna use AI? Yep. And it’s already started in conversations this morning. You know, we started networking last night, and most people are now really talking about what is AI doing for us to make sure that we’re efficient, making sure that our teams are effective, um, ensuring there’s profitability back to our owners. So it’s gone from a concept – Yeah. To now actually, how are we utilizing AI to be better in the industry, but keeping the forefront our customers? Ryan Embree: It feels like we’re in the sandbox now, right? And there’s a lot of companies out there trying different things. It’s the exploration process and, you know, maybe some success, but even, uh, lessons in the failure. Uh, I, I’ve been hearing a lot about that as well. So hotel data, obviously data is the name of the game. Yes. Still one of the most important tools I feel like right now on our quest of guest personalization. And so much it can do to kind of like what you said, prepare us for the rest of 2026 and even into 2027. Right. How is PM Hotel Group kind of leveraging data for growth and, uh, experiences? Erica Lipscomb: So actually you started with, with growth and experiences. Yeah. So really starting with growth. Yeah. We really are starting with AI in our business development side of our, our, of our home. Sure. And really how are we looking for the right clients that fit PM? Yeah. How, again, when you look at, uh, BD, it’s a relationship. Mm. So who are those owners? Who are the asset managers? What do their teams look like? Is that a right fit? And how can we help them grow? So that’s really the act – acquisition of the client and the customer. And then when we get to the property level – Right. Then as an enterprise, as a support center, what we’re looking to do is how do we use data, which is the, the heart – Yeah. Of revenue optimization. Sure. How are you using that data to make sure that we’re pulling through every step of the guest journey? So from the time again, acquisition of a customer. Right. So now not an owner, but that actual guest that’s gonna be staying at our properties, what does that customer journey look like? How do we find the right customer? We have a very diversified portfolio – Oh, yeah. For each one of our assets in the portfolio, ensuring that they convert. And then once they’re there in their stay, are we pulling through on all the experiences they expect? Whether it’s an independent hotel and the experiences that come along or for the brands and the brand standards. And then once our guests leave, how do we make sure that we are still speaking to them – uh-huh. And making sure that they return? Ryan Embree: I love how you walk through the entire guest experience. I think sometimes we get caught up just thinking about one or two elements of it. Right. But it really does start. I mean, the hot topic right now is that AI visibility, right? Absolutely. And being bound, uh, because our travelers are changing the way that they’re searching for hotels and doing their research. So, uh, it’s super, super important there. We’re in Nashville, Erica, uh, no stranger for PM Hotel Group. Yes. Uh, you guys just, uh – Very excited. Assumed management, 12 properties. Yes. Uh, what do you lo – like, um, from a Nashville market standpoint? I mean, this has just been such a hot market right now in hospitality. Uh, but also, you know, a big threshold of, uh, exciting 80 plus hotels for PM Hotel Group? Erica Lipscomb: Yes. We’re very excited to have the 12 hotels that, from Pinnacle that joined our portfolio. And that’s really our sweet spot, right? Finding those type of assets that fit our growth in our platform, and that we can make sure that we’re optimizing on their revenue, as well as excellent customer experience and guest operations experience. So what I really like about Nashville, and it’s not a new growth. Right. You know, Nashville never stopped growing, right? Where the, where the rest of the world really has struggled even, you know, six years ago. Through COVID. Nashville didn’t, right? Ryan Embree: It was red hot. Erica Lipscomb: But what most people think about when you hear Nashville, they’re really just thinking it’s an entertainment city. That’s not just all Nashville is. So when we peel it back and take a look at the segmentation and what’s driving Nashville, you do still have that customer that is true corporate business. And you still have conventions and groups. I was just in a group maximization winning group seminar just not too long ago. And in that breakout session, we really talk about group continues to still grow. Oh, yeah. And when you take a look at the first half of this year, that growth is really happening not only just in convention centers, but those hotels that have group meetings. Even when you take a look at those assets, what’s interesting is the growth is not just in the hotel that has most of the group, but if you are affiliated. You’re feeling that demand. Leveraging the demand and continuing to drive occupancy and ADR. Yeah, absolutely. So that’s why we’re still excited about Nashville. It’s one of those markets that continues to do well, not just in entertainment, but on the corporate business transient side, as well as group side. Ryan Embree: It’s a perfect destination. That’s why we got almost a thousand hoteliers here at the hotel data conference. Erica Lipscomb: That’s sold out again this year. Ryan Embree: Absolutely. Well, any. I mean, I can tell just by the conversation we’re having, very passionate about your work. Any projects you’re particularly fired up about right now? Erica Lipscomb: The project I’m probably most interested in is what I was hired for is to really continue to evolve commercial strategy. So commercial strategy is not just looking at every discipline in a silo. They’re all very important to revenue optimization. But how do we now continue to go from just having the commercial conversations, but also leverage the experience in each discipline? So our customers, when they look at our hotels, And they look at that curse customer journey that we just walked through – Right. They’re not looking at sales, revenue, marketing, distribution, operations. They’re looking at their holistic experience. Yeah. And so why not make sure that we internally stop looking at how well each d- division does and, and, and our, each siloed discipline, but let’s look through the lens of a gu – of a customer. Yeah. What’s that experience look like? And then how do we all play a part of it? Yeah. Exactly. So that’s what I’m excited about. And using, continuing to use AI. Yeah. How do we make sure that we’re leveraging commercial? Yeah. And then making sure that our use of AI is making our teams much more efficient – Mm. And effective in h – in how we run our businesses. Ryan Embree: That’s what I was gonna say. It’s, it’s such an inflection point, and I’m sure very exciting for, for your job with the technology in hand. Now you’ve got the power to, uh, break down those silos, right? Absolutely. Create efficiencies there. Yes. Uh, well, as we wrap up, you know, we always. One of the things here that we love to do at the Hotel Data Conference is try to predict the future, right? Forecasting, everybody. It’s a, it’s an impossible job, but we do it every single year. Right. Uh, you know, so from a commercial strategy standpoint, I know you, you, you just mentioned the projects you’re working on, but what’s your vision for PM Hotel Group as we kind of go into the latter part of the 2020s? Erica Lipscomb: So latter part of the 2020s, I think that the company’s vision is to really leverage the portfolio and the diversity of the portfolio. We saw that growth that we had just here in Nashville. I’m sure you saw the news that Reset our first brand to enter Marriott’s or outdoor collection. Yeah. We’ve noticed that when you continue to diversify and not really just say, okay, we are just this type of company, making sure that we’re leveraging the expertise of our team. Mm-hmm. We can be many things – Yeah. To many customers. Yeah. And so lev – continue that leverage, that growth, but we do see that growth continue to be in experiences. Yeah. Right? So every brand is rolling out how they’re working with experiences. But what we do see, that lifestyle, outdoor – Oh, yeah. Experiences. We’ve had our first entree into it, and we’re gonna continue to grow. Ryan Embree: Awesome. We’re excited to watch that growth, and yeah, that experiential travel continues to be something, conversations we’re having here, prioritizing, that’s what the guests are prioritizing travelers are. Congratulations on all this. We continue to watch it with PM Hotel Group. Thanks, Erica. Erica Lipscomb: Thank you. Ryan Embree: Hello, everyone. Ryan Embree here with The Suite Spot. We are live on location at the 2026 Hotel Data Conference. I am here with Max Spangler, VP of Technology at Charlestown Hotel. Max, we know you’re on a panel tomorrow. We’ll talk about that in a second, but thanks for taking the time to join us. Max Spangler: Absolutely. thanks for hosting me, Ryan. Ryan Embree: Yeah, Gotel Data Conference. Name of the game, data. We’re gonna talk about, obviously, your role and, and where data plays into that. But first, you come to a conference like this, what’s the expectation? What do you hope to get out of it? And maybe when you’re a couple weeks down the line, looking back on the conference, that was a success. Max Spangler: Yeah, you know, for me, I spend a lot of time at conferences that are very narrow in scope, right? Sure. Whether it’s high tech or the hospitality show, or even technology conferences that are outside of hospitality. Sure. So coming to HDC is always great. It’s always refreshing. The keynote panel in the beginning always gives me, hopefully, optimism. And this morning, it was very optimistic – Yes. About the way things are going. So I’m thankful for that. But it’s great to hear from commercial peers how they’re using data, how they’re surfacing insights, what tools they’re using, and how they’re turning it actionable. I mean, I think for me, as someone who spends a lot of time staring at screens, developing tools, looking at dashboards, hearing from people that actually depend on this information – Yeah. So crucially is really refreshing. So I get to, like, cut through the noise a little bit and hear what’s working, and hopefully hear what’s not. Ryan Embree: Yeah, and that’s what leads to your panel tomorrow, connecting AI to commercial strategy. Yeah. Uh, maybe give our sweet spot listeners a little bit of sneak peek and maybe your thoughts on the subject. Max Spangler: We’ve got a great panel tomorrow. Super stoked for it. You know, so we, we had a pre-cause you tend to do with those panels. Right. And as a result of that, we decided to zoom out a little bit, which I though was important. So commercial still is the through line, as you would expect at HTC, but given the man – the, the members that are on the panel, we’ve got some people that, you know, are on the, the, the revenue management side. We’ve got some people from HFTP. Um, you’ve got me as an independent operator. It w- we felt, we felt it really important to say, “Let’s, let’s zoom out. Let’s take a pause and, like, let’s look at where the industry is holistically.” Sure. And so the questions are really driving off that. So you’ll find that, um, there’s insights about a year from now, what would we like to be doing differently, right? How are we driving ac- actionable insights? What KPIs are important? What KPIs are important? Yeah. Things like what’s the difference between automation versus th- this new agentic era? Mm. So I think it, um, I’m actually really excited for it. The panel’s great, and I think you’re gonna get some, some really interesting insights from a variety of different opinions. Ryan Embree: Yeah. And what we talked about is so much can change. Yeah. And you could talk about what could happen in a year. I mean, that could be a couple cycles with technology right now. And that’s why I, I was really looking forward this conversation, Max. Yeah. Because, you know, I get industry leaders, sometimes brand leaders, but you’re, you’re in it every single day, right? Yeah. Uh, VP of technology. Yep. Where do you think we are in the AI adoption – Yeah. Uh, uh, cycle? And then maybe zoom in a little bit on Charlestown Hotels. Max Spangler: Yeah. So if we, if we look at sort of where things are globally for the state of AI, I think obviously in the technology space, it’s an existential crisis, right? Right. I mean, I think you see that in, in jobs reports. I think you obviously see it in the way that they’re measuring AI as an accelerant. Yeah. You know, so, uh, friends of mine that work for tech companies, they’re seeing their time to release production code going from five weeks, four weeks down to one week. Wow. It’s easy for them to measure. It’s easy for them to see the outcomes for us. Yeah. I think it, it is ultimately a little bit more difficult. For Charlestown, you know, we think it’s really important to keep hospitality at the center of what we’re doing, right? And so we’re not parading around trying to be an AI company or a SaaS company. We firmly believe people and hospitality at the center of, is gonna be at the center of what we do.m. How do we use AI to power that? Whether it’s through efficiencies, you know, through maybe more sophisticated RMS, through, you know, generative guest insights. How do we make sure that we’re being discovered when people are asking what’s the best hotel in downtown Charleston, South Carolina? Those are really hard questions to answer. No one’s got it figured out. But the conversations that are happening here are super encouraging because I think there is a lot of people admitting that and coming together to try to find, um, the best path forward. Ryan Embree: And you were, this is not your first per – podcast that you’ve been on recently. I saw you, uh, on CoStar News Hotel podcast where you talked about escaping hospitality’s AI hype echo chamber. Yeah, yeah. What’s your thoughts on that? And maybe how do we avoid doing that here in, in spaces like this? Max Spangler: I mean, it’s, if you go on LinkedIn, you can feel like, you know, FOMO is like a- absolutely crushing you, right? Right. Everyone is, like, piloting something new. Right. Everyone is, is advancing seemingly at the speed of light. It’s really important to come to a conference like HTC, uh, to get a real life temperature check with what people are doing and how they’re doing it. There is a tremendous amount of hype. There’s a tremendous amount of potential, but I think for a lot of us, and especially from someone sitting in the seat of an operator, you have to be very disciplined. Yes. You know, you have to have a step-by-step sequence of how you’re actually gonna accomplish this. It’s okay to introduce a little bit of chaos. We’ve done that in the early days. I mean, if you go back, you know, to 2023, 2024, we’re experimenting with all the frontier models. But eventually, we wanted to collapse that into a unified choice, pick one model so that we can move forward and start measuring, you know, are our team members crawling? Who’s walking? Who’s running? How do we devise resources to help kind of get everyone on the same page, march in the same direction, and get better at this? Yeah. And so that, that’s, that’s been our strategy. And fortunately, like, that’s what I’m hearing here at the conference. Ryan Embree: And the motivation for implementing AI can’t come out of fear of we’re not doing enough. Yeah. Or, you know, we’re just, that FOMO feeling that you’re talking about, it has to have, what you said, discipline and direction. Yeah. And Max Spangler: Ryan, like, fear is a huge part. I mean, that’s one of the things that we’re constantly up against. There’s. I, I think the, the negative attitude and apprehension towards AI is only gonna continue to grow over time, right? Just like the excitement over it is gonna continue to grow. Yeah. Same thing’s true for the negative. I mean, you have people that absolutely have their head in the sand, which is okay. Right. Um, for, for certain reasons, you have people that obviously have negative feelings about it because of the socio – uh, economic impact. Sure. Companies potentially might be laying off job just Placement or replacement as a result of LLMs and the technologies that they introduce. There’s the environmental factors. So, like, all those things are absolutely true. We don’t think it’s, as Charlestown, our responsibility to sort of correct that. Right. But we do wanna make sure our associates, team members, and corporate, and corporate leadership team know this isn’t going anywhere. Yeah. It’s fundamental core to the business, and we’re gonna make an investment into our teams to make sure that they’re prepared for this new wave, whatever it looks like. Ryan Embree: It’s exciting times. And it’s okay to experiment fail sometimes, because that, that’ll show you some lessons too. Sure. Max Spangler: Yeah, we. Yeah, we’ve run so many pilots. We’ve had so many things fail. We’ve incinerated millions of tokens and subsequently thousands of dollars as a result of – Yeah. Um, so many pilots, but we’ve learned a lot. Yeah. Uh, and we’re in a much better spot as a result of it. You have to be willing to take risks, especially now. I do believe, like, no one’s gonna be left behind yet, but there is absolutely an advantage to being a first mover. And I think the companies that are at least experimenting and building AI fluency for their teams are gonna be much better, uh, much farther along than everybody else. Ryan Embree: 100%. And, you know, one of those spaces is, is the data, right? That’s, I mean, that’s the name of the game of this conference here. Yeah. How are some ways are you leveraging data to kind of – Yeah. Grow Charlestown hotels or even just create efficiencies? Max Spangler: Yeah. So for us, it, it, it is a challenge to think about the kind of company that we are. We focus mostly on the independent space. Mm-hmm. So we don’t have sort of the technology through line like the brands have where – Sure. You know, they can force a certain PMS, POS, CRS, like, it’s very clean and organized and scalable that way. Yeah. For us, you know, when we come into a new hotel operating environment, in most cases, technology hasn’t been a major form of investment, right? I mean, most people don’t come to Charlestown hotels with a great performing asset. They’re like, “We’re in trouble. We need your help.” Right. So then I come in, you know, from the technology perspective and it’s like, okay, this is difficult. How are we gonna extract information, put it into a centralized place, be able to sort of layer a, a, a, a BI tool or reporting package on top of it to actually surface the insights so these one-off owner operators can get the insights that, like, a company like Charlestown Hotels can deliver at scale with all the independent properties and things we’ve learned across the secondary and tertiary markets that we work in. Max Spangler: So, I mean, to put it simply for us, it is about having, like, a central data repository or warehouse. Sure. I mean, there’s plenty out there. Databricks, Snowflake. We’re a BigQuery customer. We do a lot with Google. Um, but it is, you know, if, if you think about where things are going to bring it back to AI, so much of the conversation surrounds having a good data foundation, because AI is an accelerant. If you have bad data, it’s gonna accelerate you to bad outcomes more quickly. Yeah, that’s a great point. If you have a bad business strategy, it’s gonna optimize for the wrong KPIs. So for us, it is very much about having solid fundamentals. Yeah. That’s not a reason for you to stop, right? It’s just more a reason for you to proceed cautiously. Ryan Embree: Absolutely. And, you know, you do it right. All of a sudden, you get that personalization, which, you know, hospitality’s been really the last decade – Yeah. Has been striving so much for to get that personalization within the guest experience. So as we wrap up, you know, we always like to. I know this is gonna be difficult because, like we said, things change so quickly in the tech space. Yeah. But what’s your vision for Charleston Hotels from a technology perspective? Max Spangler: Yeah, great question. I thought you were gonna ask me a hard one, like, what’s my favorite color? But, uh, no, for, for. Vision for technology, you know, for us, as long as we keep, like, hospitality at the center – Yeah. As our north star, that really does simplify things for us. It is gonna be difficult. There’s, you know, obviously a whole host of different frontier models you have to choose from. Tokenomics is gonna continue to be a big part. People talk about ROI with LLMs, but no one’s really talking about the expense – Yeah. And expenses continue to grow. Great point. Right? So we’re, we’re focused really on, you know, not only the, the ROI from some of the LLM tools, but, but obviously the tremendous cost that’s associated with running them at scale. But as long as we keep people and human beings at the center, reducing mundane work, admin tasks, friction so that our people can spend less time in front of screens and just be more hospitable, I think that really is the vision. Technology’s gonna support that. It’s gonna hopefully be more invisible to the people that come to hospitality. They didn’t come to, like, move information around – Right. Push paper or spend time in front of a computer. They spent it to, like, be empathetic, to be excited – Yeah. To surprise and delight. And so our goal, that’s our north star, and technology’s gonna be there to support it. Ryan Embree: Yeah, I mean, some industries, you’re, you’re right, are gonna be completely flipped upside down – Yeah. With this technology. But hospitality, we have that advantage of being a people first industry, so. Max Spangler: I, I think it’s, like, the, the key differentiator, and it honestly, it’s like, hospitality has an opportunity to have a really strong opinion. As so many industries are completely rolled over by this AI wave – Yeah. Hospitality can actually say, “No, you know what? People are…” And people in hospitality are at the center, and so as there is potentially more AI backlash and people are seeking more authentic experiences – Right. With people and connections – Yeah. I think it’s, it’s gonna be a great benefit to our industry. Max Spangler: Yeah, and we’ve seen from the data, experiences still seem t be – Yeah. I think that’s gonna grow. Yeah. Ryan Embree: Yeah. Agreed. Uh, Max, appreciate the time. Thank you. Uh, we’ll keep an eye on Charleston Hotels and everything you’re doing over there. Great. Congratulations. Max Spangler: Thank you. Ryan Embree: Hello, Everyone. Ryan Embree here with The Suite Spot. We’re live on location at the Hotel Data Conference 2026 here with Sam Trotter, Head of Digital Marketing for Indigo Road Hospitality Group. Sam, thanks for taking some time. Sam Trotter: Thanks for having me here. Yeah. I’m excited to be here at the Hotel Data Conference. Ryan Embree: It’s our first time here, but you said you’re, you’re a pro. You’ve been here for many years. Yeah. What does a successful hotel data conference look like for you and some of the takeaways that you look for? Sam Trotter: I really love having a good sense of what’s gonna happen next year. So you get some really great data here where you actually can take to your business planning sessions and use and say, “Hey, you know, I have this from the data conference, and they’re forecasting this growth in this market.” And you have something tangible. Yeah. So you’re about to head into budget season. Yeah. So having that in hand is really, really nice. Ryan Embree: That’s a big part of it. I mean, budget season, you gotta make those operation efficiency. We talked about the margins, how tight those are right now, especially in hospitality. One of the ways that hospitality’s changing right now is through AI search. You were on a panel here. For those that weren’t able to join us here in Nashville, maybe unpack that topic a little bit, because it’d certainly be top of mind for a lot of hoteliers right now. Sam Trotter: Well, it was really fun. It was a packed house, so a lot of interest in it. There’s a lot to talk about. I think we did a little bit of an intro to the topic, just so that everybody was sort of on the same page. But this is a new thing that we’re all having to adapt to. And we’re gonna have to focus on this. And in the panel, I said, “This feels a lot like 2006 when SEO was becoming a big thing.” And I remember I hired this French couple to do our SEO for this hotel that we were opening. It was like $10,000. In 2006. And it felt kinda like magic. Yeah. You know, like, what are they, what are they actually gonna do, right? And so it’s really tough. Who do you listen to? What actually works? And it was a great panel. And there’s no main takeaway other than we’re doing a lot of AB tests. We’re trying to figure out what works. I’m looking at the dashboards from our different properties. Who’s doing well? Who’s not? Yeah. And trying to pivot. And so we’re at this really interesting phase where it’s not really clear, right? Everybody’s telling us different things. Who do you listen to? So I honestly think it’s really exciting. Ryan Embree: The good news is it’s a challenge that a lot of people are attacking at once, right? And that’s where you’re gonna kind of find maybe lessons learned, even in those failures. So I think it is interesting because ultimately what happened with SEO is, like, there became a little bit of of a game plan that you could attack it with, right? That people are still trying to kind of balance. And then there were switches, right? That’s the other thing, is you could attack it one way and then all of a sudden, next week, algorithms change and it’s back to square one. So it was very, very interesting. But I think it’s events like this and panels that you’re on, Sam, that help kind of. Where everyone’s going through this right now. And to try to get through the weeds on it and try to figure out what is a good course of action here. And it changes so quickly. I think AI gets a spotlight, obviously, for good reason because it’s just this up and coming technology. But digital marketing also feels like it’s fast changing and evolving. And it’s been doing that for the past decade. You think about social media updates and everything like that. Yeah. I guess, how do you view digital marketing right now from a strategic standpoint and, and how hoteliers should be embracing and, you know, maybe investing in It? Sam Trotter: So that was a big question, right? Ryan Embree: Yes, sorry. Sam Trotter: When I have new marketers join, junior marketers, I always tell them there’s really no such thing as an expert anymore. Because it’s gonna change next year. Right? Ryan Embree: Great point. Sam Trotter: There are certain fundamentals that will help you no matter what, from 10 years from now, they’ll always be in play. I think what’s really interesting now with AI is I feel like there’s more emphasis on brand and category ownership. So if the AI is the most educated person in the entire world about hotels in Nashville. I mean, that’s what it is. Sure. It’s the most educated person in Nashville. What do you wanna teach it? And so if you’re teaching it, I have a pool and a fitness center, that doesn’t really help, right? ‘Cause now you’re just the same. And so what category can you own? Can you be the wellness hotel of Nashville? And if you’re the wellness hotel of Nashville, what that looks like is everything we say and we do reflects that. So I have spa packages, we have spa activations, we have a spa month. We have an amazing spa. We have spa content. We have spa creators that come in. And so when you’re doing that, all of a sudden the AI’s like, “Okay, no, this is the spa hotel of Nashville.” Because it’s, there’s evidence. Yeah. Right? And so I think there’s gonna be more emphasis on this category ownership, right? More than ever. And that boils back down to your brand. Ryan Embree: No, I love that. I think that’s a great explanation to someone who might feel overwhelmed in this right now. But those searches also could get very specific, right? I’m looking for a place that’s pet friendly, that is dedicated to wellness, where I’ve got my family coming to. So that’s where it gets a little bit tricky of the categories could turn into subcategories and then very, very niche. But it’s also the beauty of it, I think on the other side, is that your travelers are gonna be able to hopefully find the right hotel for them and what they’re looking for. Sam Trotter: So going back to your question, you asked me about social media. Because things are changing so fast, the, what we don’t really realize, and we don’t talk about, is the number one AI that we talk to is Google’s AI overview. That’s the one that when you have a long search, it defaults to, right? And it is weighing YouTube more than any other social platform – Great point. Because they’re not giving access. Right? So TikTok’s not giving them access. So now all of a sudden, YouTube is like this big player. And so we’ve got 76 locations. How do you scale YouTube? Yeah. And so, you know, we’re trying to figure this out in real time, and it’s a lot. There’s a lot of change happening. Ryan Embree: No, that’s a great point, what you said about Google, because a lot of people might be listening to this being like, “Well, I’m not, I’m not really looking, or I’m not using AI in my everyday life.” Well, Google’s really, you know, that AI overview, you are using, right? And it’s just, it’s gonna become more and more, whether we know it or not, a part of our life, you know? So that’s a different conversation. You know, Sam, Indigo Road Hospitality Group, you just mentioned tons of locations. What are some of the projects you’re most excited about that you’re working on right now? Sam Trotter: So, we have amazing locations, and there’s so many that are really interesting that we have coming up. We’re dabbling in more and more to membership clubs. Which is something that, we have one right now in Bentonville, and then by the end of the year, we’ll have two more. So we’re going from zero to three in a pretty short amount of time. It’ll be like a year and two months, we’ll go from zero to three. So it’s something that has been really fun to learn about, and there’s new platforms to learn about. So that’s really exciting. And then, I’m working on some fun data projects too, fun to me. I’m trying to set us up to have our own loyalty program. And so I’ve got some cool things in the works. So I’m really excited about that because we have a diverse portfolio. We have coffee shops and restaurants and hotels. And venues and how do you get them all to talk? Right? How do we put them all in one place, but have them separate? How can we share notes? How can we improve the guest experience? So there’s a lot of really cool things that are happening now. And one of the benefits of AI is partners are, are improving their platforms faster than ever. Oh, yeah. Which is fun if you have the right partners. And they are doing it. Ryan Embree: Yeah. I mean, and loyalty programs, you also learn more about your guests and hopefully create personalization, which is, you know, a topic that has been in hospitality. But we’re getting closer and closer, I feel like, to what we may have talked about five years ago at a conference like this. Be like, “There might be a time where we could do this, and now with the power of AI, it, it’s possible.” Yeah. Well, as we wrap up, kind of what’s your. I know we just talked about the future, but, and it’s hard to predict, but what would be kind of your vision for the future, in your role at Indigo Hospitality Group? Sam Trotter: I would say that, I guess thinking more optimistically, ultimately, it’s gonna be about the guest experience. It’s gonna be about the surprise and delight. It’s gonna be about having great employees who are happy to be where they’re at and that wanna be there. And the best marketing is a great experience, right? So what’s my role in that? You know, how can I help operations do their thing? And that’s the foundation of everything. At the end of the day, I think that’s it. Ryan Embree: There is a, there is a comfort, Sam, to being in an industry that we knew can only be disrupted so much by AI, but at the end of the day, it is gonna still come down to people serving people and creating those memorable experiences, and hopefully AI gives the opportunity to do. Sam Trotter: I have a anti-trend for you, right? Okay. So we’re here at, at the Grand Hyatt. There’s no kiosks, right? Check in. There’s still front desk people. 10 years ago at the hotel data conference, I think we would’ve though it was all kiosks. So hospitality has reigned supreme, and I think that’s the future. Ryan Embree: Yeah for, they say hospitality is the first ever industry and it’ll be here for a long time. So Sam, we appreciate it. We’re gonna watch you and, and everything you’re doing over there. We appreciate you taking some time with us. Sam Trotter: Thank you you so much. This was a lot of fun. All right. Ryan Embree: To join our loyalty program, be sure to subscribe and give us a five-star rating on iTunes. Suite Spot is produced by Travel Media Group. Our editor is Brandon Bell with cover art by Bary Gordon. I’m your host, Ryan Embree, and we hope you enjoyed your stay.

This week on TechRadio: watch out for an IRS crypto scam making the rounds, while exciting research into cancer vaccines could change the future of cancer treatment. Amazon is also raising prices on its Fire hardware, including Fire TV devices and Kindles. Plus, practical ways to get longer battery life from your devices, a look at whether today's smartphone prices are really justified, and a fun change coming to Google Gboard: bigger emoji. And, as always, we'll check out what's topping the streaming charts. It's the latest tech news, trends, tips, and consumer-tech developments—without the jargon.

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U.S. Representative Dan Newhouse says with the labor shortages and high expenses, there is a lot of misinterpretations of the ag labor force in general.

Most eCom marketers are optimizing for the wrong number, and a real CFO explains exactly how to fix that! Our good friend Abir Syed has a rare take on how to scale brands: he's run an eCommerce brand, built a performance marketing agency, and now runs a fractional CFO firm specializing in eCom. In this episode, he breaks down why MER is basically useless, what cohort profit actually tells you, and the three-pillar finance framework that gives marketers and CFOs a shared language for growth. Nate also gets uncomfortably personal about his own brand's cash flow situation. You'll walk away understanding how to set real scaling targets, why over-revving your marketing engine costs you money, how to predict LTV decay as you shift from organic to paid customers, and what questions to actually ask your finance team. 00:00 Why selling out early isn't the flex you think it is01:45 Introducing Abir Syed — the CFO who hates accounting03:30 Why 80% of your business story lives in the finances05:30 The #1 thing to get right before anything else: inventory costing07:00 Why MER is a useless metric (hot take, but hear him out)08:30 How dropping MER led to 130% growth in one year09:15 Cohort profit: Abir's favorite metric explained13:00 Over-revving the engine — the hidden way brands lose money scaling16:30 Incrementality testing vs. the scaling target table18:00 LTV decay: what happens when you go from organic to paid acquisition20:00 Paid customers get stolen by ads — a concept that breaks your brain23:00 The 3 things a CFO and CMO should never fight about24:30 Pillar 2: Investing in the marketing engine (creative, tools, talent)26:00 Pillar 3: Cash flow strategy and payback periods33:30 Expense leverage — making sure every dollar has a job37:00 Nate's regret: not taking big shots during a 2.5-year hot streak38:30 The $100K YouTube deal that looked like a disaster until Q439:30 The one thing to do this week if you're looking at your numbers

EDITORIAL: SEC over-regulation at the expense of shareholder rights | Aug. 13, 2026Check out our Streaming Channel: https://streaming.manilatimes.net/Subscribe to The Manila Times Channel - https://tmt.ph/YTSubscribeVisit our website at https://www.manilatimes.netFollow us:Facebook - https://tmt.ph/facebookInstagram - https://tmt.ph/instagramTwitter - https://tmt.ph/twitterDailyMotion - https://tmt.ph/dailymotionSubscribe to our Digital Edition - https://tmt.ph/digitalCheck out our Podcasts:Spotify - https://tmt.ph/spotifyApple Podcasts - https://tmt.ph/applepodcastsAmazon Music - https://tmt.ph/amazonmusicDeezer: https://tmt.ph/deezerStitcher: https://tmt.ph/stitcherTune In: https://tmt.ph/tunein#TheManilaTimes#VoiceOfTheTimes Hosted on Acast. See acast.com/privacy for more information.

Government often relies on industry to invest ahead of future requirements. The recent pause in CMMC implementation is raising questions about how contractors weigh risk, cost and the value of moving first. Here with one company's perspective are Angie Lienert and Jeremiah Jensen of IntelliGenesis.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Paul returns from three days at the Garrett Planning Network retreat with a lesson that has almost nothing to do with investments — and everything to do with getting your money's worth from professional advice.Garrett advisors work by the hour, a business model Paul believes eliminates the conflicts of interest built into assets-under-management relationships. For $1,000 to $8,000, he's convinced most families can get extraordinary value from five to ten hours with a thoughtful, trained hourly planner. But there's a catch: the value of those hours depends almost entirely on your willingness to tell the truth. Inspired by a Seth Godin observation — people lie in focus groups, on surveys, and to themselves — Paul explains why the most valuable planning meeting isn't the one where you look financially successful. It's the one where you're completely honest. Paul and his wife are putting this to the test with an hourly planner of their own, and he'll report back in the weeks ahead.Next, Paul shares a private conversation with his longtime friend Rick Ferri, who challenged an idea Paul has taught for decades: that small cap value, large cap value, and international are equity asset classes at all. Rick argues there's only one equity asset class — the total market — and everything else is a segment or style. Paul takes the challenge seriously, does some digging, and explains why the answer matters far more than a debate over definitions. How you think about asset classes shapes the portfolio you'll live with for the next 60 or 70 years.Finally, Paul digs into AVGE, the Avantis globally diversified all-equity ETF, and how it compares to Vanguard's total market approach (VT and VTI). He walks through the meaningful differences: 70/30 U.S./international at Avantis versus 60/40 at Vanguard, and substantially larger positions in mid cap value, small cap value, and small cap blend. He looks at what those tilts have meant historically — including Vanguard's own mid cap value fund turning $10,000 into roughly $160,000 versus $102,000 for the S&P 500 — and why he believes the extra 0.17% in expenses may be money well spent. For investors who don't want to go all-in, Paul offers simple combinations, like a third VT, a third AVGE, and a third AVUV.CHAPTERS00:00 – Introduction: three topics from the Garrett retreat01:56 – Why hourly advisors have fewer conflicts of interest05:52 – The catch: your willingness to tell the truth06:38 – Seth Godin: "People lie... and they lie to themselves"08:04 – What planners can't fix if they don't know about it13:00 – Paul's debate with Rick Ferri: what is an equity asset class?18:05 – Why the definition shapes your lifetime portfolio21:34 – AVGE vs. VT: U.S./international balance23:07 – Comparing value, blend, and growth exposure25:00 – Mid cap and small cap: what history shows30:15 – Expense ratios and what you're paying for31:35 – Simple combinations: VT + AVGE + AVUV33:15 – Stay the course: closing thoughtsLearn more about the Garrett Planning Network

Teaching Kids How To Spot Manipulation (but at the family's expense?) Are we protecting our kids from family drama, or are we missing crucial teachable moments about manipulation and boundaries?

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

Who is responsible for deciding whether a business expense is tax deductible: the client, the bookkeeper, or the tax preparer? In this episode, our favorite Bookkeeping Mensch, Paul Rosenblum, shares his approach to handling missing receipts, client gifts, business meals, and other bookkeeping gray areas. He touches on where professional responsibility begins and ends, and whether bookkeepers should rely on client information or use their own judgment when categorizing expenses. Send us Fan MailSupport the showAbout the hostPaul Rosenblum has been doing hands-on bookkeeping for over 30 years, starting with QuickBooks Desktop and adapting to the world of cloud-based QuickBooks Online. He shares practical, in-the-weeds lessons from real client files every episode.

We all know life feels more expensive, but one everyday household expense has skyrocketed by a staggering 200% over the past decade. Webpage

I can't believe what we spent during the first half of 2026!? Our expenses have definitely added up and even surprised us, which is why we think tracking them is so important. We lay out all of our mid-year expenses and discuss unexpected life events, house repairs, and intentional shifts in daily habits that affect everyone's expenses. This is a fully transparent look at our real-life expenses and how we feel about them. Get the full show notes, show references, and more information here: https://www.insideoutmoney.org/172-mid-year-2026-expense-review-broken-bones-new-roofs-and-130-airport-meals/

The UV technology has been purchased, delivered, and rolled into the facility. So now what? In the final episode of "Evidence Over Expense," Kyle Morrison and Xenex's Selena Pfannstiel join us to explore why the real return on investment begins after the sale is complete. From staff training and user adoption to reporting, accountability, workflow integration, and ongoing vendor support, this conversation breaks down the often-overlooked factors that can make or break long-term program success. If your organization is investing in UV technology to reduce transmission risk and drive lasting value, this episode will change the way you think about what happens after the rollout. Tune in to this series finale today! Once you complete the interview, jump on over to the link below to take a short quiz and download your CEC certificate for 0.5 CECs! – https://www.flexiquiz.com/SC/N/ps-xenex-ep3 A special thanks to our sponsor, Xenex, for making this series possible. #PowerSupply #Xenex #HealthcareSupplyChain #InfectionPrevention #ValueAnalysis #EvidenceOverExpense #UVTechnology #Podcast

LISTEN and SUBSCRIBE on:Apple Podcasts: https://podcasts.apple.com/us/podcast/watchdog-on-wall-street-with-chris-markowski/id570687608 Spotify: https://open.spotify.com/show/2PtgPvJvqc2gkpGIkNMR5i WATCH and SUBSCRIBE on:https://www.youtube.com/@WatchdogOnWallstreet/featured  Wall Street's biggest firms are posting record trading revenue while amateur investors chase quick profits through options and addictive trading platforms. The house always wins—and individual traders are competing against sophisticated algorithms, superior information, and firms capable of manipulating every market move. Wall Street is having its best year ever because millions of inexperienced investors still believe they can beat the professionals at their own game.

Bingo: https://bingobaker.com#6a2f5f147a86d197

In this podcast, Jandevman gives us an overview of what they do at Liquidy, how the project originated, and how they ended up building on THORChain.Swap now https://swap.thorchain.org/THORChain is a decentralized crypto exchange. THORChain is the first and biggest DEX for Bitcoin. You can use any self custody wallet to swap and there's no KYC required.Timestamps:00:00:00 Intro00:02:00 Kenton update — Keplr should be working! More integrations are coming!00:03:00 Affiliate page widget is ready for testing00:04:00 Air Canada story00:06:00 Jandevman introduction00:10:00 What was so different about Kujira that attracted you to it?00:13:00 Jandevman rebuilt everything from scratch00:16:00 Treasury discussion — what were the assets?00:17:00 What was MantaDAO?00:19:00 Website walkthrough00:21:00 Valuation breakdown — comparing Liquidy to TradFi00:23:00 Kenton explains NAV and asks about redemptions00:24:00 Financial reports and analytics breakdown00:26:00 Expense breakdown00:27:00 Revenue sources00:29:00 Governance process breakdown00:30:00 Simple majority or supermajority?00:31:00 Veto power through a multisig00:32:00 Bonk comparison00:36:00 Raising external capital?00:38:00 Growing the treasury00:39:00 How does market making work?00:43:00 Why isn't the LQDY token on the base layer?00:45:00 How many wallets support secured assets?00:47:00 Which other wallets should support secured assets?00:48:00 What other financial primitives will LQDY be involved in?00:52:00 Will LQDY get involved in lending?00:55:00 What other assets will be acquired?00:56:00 What about Auto Rujira?00:57:00 Liquidity swap router breakdown00:59:00 What's the difference between the swap router on Rujira and LQDY?01:02:00 Comparing different liquidity pools01:07:00 Will redacted functionality be available?01:09:00 bRUNE breakdown01:11:00 What is a good APY for users?01:13:00 Will STO use the LQDY API?01:15:00 Is there anything else you'd like to add?01:18:00 Transparency discussion01:19:00 Proposal discussion01:22:00 Everything is on-chain01:27:00 Thoughts on inflation01:30:00 The model is beautifully simple01:33:00 We need to do this again!01:34:00 Conclusion

Every dollar sitting in your bank account is quietly losing value, and most people never realize why. On this episode of Play Big Faster, former $10 billion portfolio manager Paul Musson, author of Capital Offense: Why Some Benefit at Your Expense, breaks down the hidden inflation tax draining entrepreneurial wealth. Paul unpacks how does inflation affect your savings, why the real inflation rate differs from official numbers, and how does the government undercount inflation through substitution and hedonic adjustments. You will hear what happens to savings during inflation, why does printing money cause inflation, whether inflation hurts the middle class, and steps toward inflation proof wealth. Ideal for founders who want to protect what they have built while still growing. Tune in for straight talk on why your cost of living keeps going up.

JAY TRUITT TALKS FEED MILL FIRES AND THE FIGHT OVER AI DATA CENTERS ON FARMLAND Trent Loos welcomes Jay Truitt from Texas for a hard-hitting look at issues shaking rural America. The two dig into a troubling rise in feed mill fires, breaking down decades of data and the real dangers farmers and…

Keith Weinhold explains why inflation has become a permanent part of the post–World War II economy and what that shift means for today's financial system.  He breaks down economist Dr. Mark Skousen's five structural reasons behind never-ending inflation and ties them to the hollowing out of the middle class and the "last generation to live normally" concept.  Keith then introduces opportunity cost as the biggest financial expense most people overlook and illustrates how leveraging low-cost, long-term debt to buy productive real assets can turn inflation into an advantage.  He closes by outlining a practical hierarchy for which debts to eliminate first and which to keep as tools for long-term wealth building. Episode Page: GetRichEducation.com/614 For access to properties or free help with a GRE Investment Coach, start here: GREmarketplace.com GRE Free Investment Coaching: GREinvestmentcoach.com Get mortgage loans for investment property: RidgeLendingGroup.com or call 855-74-RIDGE  or e-mail: info@RidgeLendingGroup.com Invest with Freedom Family Investments.  For predictable 10-12% quarterly returns, visit FreedomFamilyInvestments.com/GRE or text  FAMILY to 66866  Unlock truly passive real estate income—visit flockhomes.com/GRE today to see if your properties qualify for a 721 exchange with Flock Homes. To get in the best physical, mental, and professional shape of your life, go to DanielThomasHind.com and apply for Daniel's intensive 1-on-1 coaching for burnt-out entrepreneurs and executives. Will you please leave a review for the show? I'd be grateful. Search "how to leave an Apple Podcasts review"  For advertising inquiries, visit: GetRichEducation.com/ad Best Financial Education: GetRichEducation.com Get our wealth-building newsletter free— GREletter.com  Our YouTube Channel: www.youtube.com/c/GetRichEducation Follow us on Instagram: @getricheducation Complete episode transcript:   Keith Weinhold  0:01   Welcome to GRE. I'm your host Keith Weinhold. In less than 40 years, America has gone from 75% gasoline to permanent inflation. Then learn about the biggest financial expense you will ever have in your life. It's not taxes, housing, interest charges, inflation, children, or healthcare. Most people have never heard of it today on Get Rich Education. You know, Mid South Homebuyers, that top Memphis turnkey provider. I learned that a secret weapon behind their explosive growth is more than just you buying their properties. It's an executive coach. For nine years now. Their CEO Terry Kerr and his COO Pat Nix have worked privately with a coach who I've now learned from too, and he doesn't market himself online anywhere. After 12 years behind the scenes, that coach is now making himself available exclusively for GRE listeners. His name is Daniel Thomas Hind. If you're a hard-charging business owner or investor who wants to get in the best shape of your life, physically, mentally, and professionally, you can fill out an application for a free consult. This is private one-on-one coaching for those willing to go to uncommon lengths to achieve uncommon results. Thanks to Daniel, we've all become better leaders, better operators, and better men. It started by showing up for ourselves. Now it's your turn. Go to DanielThomashHind.com. H-I-N-D. That's DanielThomashHind.com, and sign up before spots fill.   Keith Weinhold  1:41   What if you got your mortgage loans the same place I get mine? You sure can at Ridge Lending Group NMLS 42056 They provided GRE listeners with more loans than anyone because Ridge specializes in investment property. They'll help you build a long-term plan for growing your real estate empire with leverage. Start your prequal and even chat directly with President Chaley Ridge. While it's on your mind, start at ridgelendinggroup.com. That's ridgelendinggroup.com.   Speaker 1  2:14   You're listening to the show that has created more financial freedom than nearly any show in the world, this is Get Rich Education.   Keith Weinhold  2:31   Welcome to GRE from Bavaria, Germany, to Batavia, New York, and across 188 world nations. I'm Keith Weinhold, and you're listening to Get Rich Education. In the 19 the 1988 movie Die Hard, there's a California gas station sign in the background that's visible. You can see it there. The gas price on this sign is a jaw dropper. Unleaded 77.9 cents per gallon, regular 70-4.9 cents per gallon. That now looks like it belongs in a museum next to rotary phones and blockbuster video cards. Yes, California gas for 75 cents, and the movie Die Hard. It had all these actors from yesteryear, like Bruce Willis and Reginald Vel Johnson. Yet you, depending on your age, you might remember 1988. It's not like ancient history. Now we all know that inflation is always and everywhere a monetary phenomenon, like Milton Friedman said, but is there more to this? Is there more than the Fed targeting 2% inflation, just like it says on their website? Oh, there sure is. And by the way, with a little research, it looks like California Gas averaged 95 cents in 1988, not 75 like it shows in Die Hard, but in any case, the point is still there. And today, inflation keeps running hot. Four years ago, the pandemic made CPI inflation peak at 9.1 percent. Today, the hangover effects of tariffs push it up, and the Iran war are turning up the heat even more, with the latest reading above 4% Inflation is running at more than double what the Fed wants. You can even make the case now that inflation is out of control. But here's the thing: inflation has exceeded that 2% target for 60-three consecutive months now. I mean, think about what that means. My gosh, just imagine having an important target that affects every American and missing it 60-three times in a row. That's kind of what's happening now, and they're. Going to keep missing it. So this streak of inflation above 2% started back in March of 2021 during the pandemic hangover, and it is still going strong after 63 months. Nobody knows where this is going to end. Most Americans get crushed by rising prices because their wages don't keep up, and you know collectively they sort of think we are concerned, but then they mostly keep doing the same thing while their lifestyle quietly shrinks. So consumers despise inflation. Everyday investors are lukewarm about inflation, and leverage real estate investors are smiling like they found a 20-dollar bill in last winter's coat. Leverage real estate investors are pretty ecstatic about inflation. Now the history gets super interesting.   Keith Weinhold  5:59   Okay, how did we get into this, where we just always seem to have inflation? So learn the history, and then I'll tie it back to how it affects you as an investor. Because before World War II, inflation behaved differently. The old pre-1945 pattern was that we had inflation during wars and booms. We had deflation after panics and depressions. So therefore, the result was that over long stretches, price levels often just moved sideways. We used to have recessions more often back 80 plus years ago than we do now. So therefore, you just had these price levels move sideways because a recession even prompted deflation, actually a strengthening of purchasing power. But then after World War II, inflation basically went permanently positive. I mean, yeah, permanently positive, where inflation is just always turned on with very few exceptions to that. In wartime, now we have inflation. In peacetime, now we have inflation. During the Super Bowl, now we have inflation. It is inflation, no matter what is going on. Right then, so what changed? Prominent economist and GRE podcast guest here, Dr. Mark Skousen. He has cited five major reasons that inflation became a permanent fixture from 1945 until today. And Mark Skousen was here on the show with us almost exactly two years ago because he's also the founder of a great event called Freedom Fest that Nareesh and I broadcast a show from, the five reasons that Scowson cites for never-ending inflation are first, never-ending wars. Now this doesn't only mean formally declared boots on the ground wars where tanks are rolling, never-ending wars. It means this permanent state of global military readiness that we have today, where we have overseas bases, defense contractors, right with the military-industrial complex. We have NATO commitments.   Keith Weinhold  8:17   We have anti-terror operations, naval patrols, intelligence agencies, and all this enormous machinery that's required to keep America as the world's security backstop. Well, all that costs an awful lot of money, and when government wants more money than it collects, it has a favorite trick: just create more dollars and create them out of nothing. I mean, it's like ordering another round of drinks for the table and then putting it on the unborn grandchildren's tab. The second reason for the never-ending inflation is the 1913 creation of the Federal Reserve and how that's changed over time because the Fed they were originally supposed to defend the dollar, defend the gold standard, and act as lender of last resort. Today it mostly just does the last one. It acts as the lender of last resort, and it's really not even last resort. I mean, she shit seems to patch any significant hole in the economy by creating more dollars and then pumping them into the system. When markets wobble, banks panic, or politicians overspend, or the economy catches any kind of cold, you know, the Fed often just shows up with this fire hose of liquidity. Now, sometimes that's necessary, but either way, it means more currency creation. So, the Fed it began as this sort of sober hallway monitor, but now they're often the responsible party that needs monitoring. But no. No one is going to stand up and do it because no one in power wants austerity under their watch because that is extremely unpopular. The third reason for permanent inflation is the Bretton Woods Agreement. You've probably heard of this, but let me summarize what it briefly means. Okay, Bretton Woods was the 1944 deal that basically created the post-World War II global monetary system? It made the U.S. dollar the world's reserve currency. If you remember anything from Bretton Woods, just remember that it did that. It made the U.S. dollar the world's reserve currency, and the dollar was pegged to gold at $35 per ounce.   Keith Weinhold  13:29   And finally, the fifth reason for never-ending inflation post World War II is Keynesian economics. I mean, you probably at least heard the term before. It's been thrown around here from time to time. Named after John Maynard Keynes, K E Y N E S. And before I go on, I invested in real estate for a long time before I learned all this stuff. Probably close to a decade of investing first. So I taught myself this material, Keynesian economics. That's the belief that demand is what drives economic output and employment. So, if you only remember one thing about Keynesian economics, it's that you need demand, and it stokes demand. It says demand drives everything, and what I mean by that is the spending, spending from households, corporations, and government. So, in plain English, when private demand weakens, the government should step in and spend. That's what Keynesian economics says. Well, that means deficits, borrowing, stimulus, support, programs, relief, rescue packages, emergency measures, and see what happens is that temporary measures somehow become permanent measures wearing a fake mustache. Remember, even Nixon said removal from the gold standard is temporary. Well, that was now 50. 55 years ago, in theory, the government runs deficits in bad times and then tightens up in good times. But that doesn't really happen because, in practice, government often runs deficits in bad times and good times, war times, peace times, election years, non-election years, leap years, all the time running deficits, spending more than we take in, and when deficits become normal, well, then currency creation has got to follow. That's the consequence. Well, these five forces that I told you about for never-ending inflation, the reasons that I just shared with you-they are now structurally embedded. They are not going away.   Keith Weinhold  19:03   I mean, there is even political resistance to deflation in this system. Investors benefit the most when they own one thing: real assets tied to long-term debt. You probably knew that I was going to say that because if the dollar is designed to slowly melt. You don't want to be the one holding the ice cube. You want to own the freezer. That's the control that you have. The first half of the year recently ended. It's time for our asset class rundown. From the midpoint of last year to the midpoint of this year, single-family home values are up only about one and a half percent. That's the average of Case-Shiller and FHFA. Apartment building values are down 1% in the past year. When it comes to rents per Zillow, single-family home rents are up 2.8% in the past year to an all-time record of almost 20-$300 Apartment rents are up just. 1.3% nationally. Sunbelt Apartments were the weak spot. Apartments.com said the South was down seven tenths of 1% year over year, and the mountain region down one and a half percent. With San Antonio, Denver, Austin, and Phoenix among the weaker markets, that's due to oversupply in those areas. 30-year mortgage rates down from 6.8 to 6.6% The S S&P 500 up 21 percent on AI optimism, despite a war in Iran. Though down in past months for the year, gold is still up 21 percent, silver soared 63 percent, Bitcoin down 45 percent. I mean, speculative digital assets have really gotten a cold shoulder. Oil up 4% although it went on a wild ride, and CPI inflation reheated to 4.2% That's our asset class rundown.   Speaker 2  22:59   This is our rich dad poor dad author Robert Kiyosaki. Listen to Get Rich Education with Keith Weinhold. Don't quit your daydream.   Keith Weinhold  23:17   Welcome back to Get Rich Education. I'm your host Keith Weinhold. I want you to listen to something along with me, and then I'll come back to comment. This is from the parallel truth. It's called the last generation to live normally, and it's less than two minutes in length.   Speaker 2  23:32   We have to talk about something that sounds dramatic, but it is becoming true. Your parents may have been the last generation to live a normal life-not an easy life, not a perfect life, but a life where the basic deal still made sense. You could get a stable job, you could buy a house, you could raise children, you could save some money, you could retire one day. And even if life was hard, most people still believed that if they worked honestly, their future would slowly get better. But look at what happened to your generation. You work more, but own less. You study more, but feel less secure. You have more technology than any generation in history, but less peace, less time, and less confidence about the future. Your parents were told, "Work hard, and you will build a life. But you are being told that, "Work hard, and maybe you can afford rent. And the most disturbing part is that this did not happen overnight. It happened slowly. First, housing became an investment instead of a basic need. Then, education became a debt trap. Then, healthcare became too expensive. Then, stable jobs disappeared. Then, everything became a subscription: your house, your car, your software, your entertainment, even your future. Everything slowly became something you rent but never truly own. And while ordinary people were falling behind, the economy kept looking strong on paper. The stock market went up, billionaires got richer, companies made record profits. Politicians kept saying that everything was fine, but if everything is fine, why does an entire generation feel like it is drowning? The truth is, your parents did not live through normal history. They lived through a rare window where ordinary people. People were allowed to share in the wealth of the system, but that window is now closing. The old promise was simple: work hard, buy a home, raise a family, retire with dignity. The new promise is different: work forever, rent everything, delay children, carry debt, and call it freedom. So maybe young people are not lazy. Maybe they are just the first generation honest enough to admit that the old deal is dead. Your parents were not lucky because life was easy. They were lucky because they were the last ones who got the deal before it was taken away.   Keith Weinhold  25:27   Yeah, there it is-the last generation to live normally. That's really a fresh slant on the hollowing out of the middle class. The rules have changed. Inflation is entrenched. Now you know why. Back in 2020, the pandemic accelerated that effect, and yet it's just unbelievable to me that people think working hard and saving money is enough to get you the lifestyle that you desire. Now I am not against hard work, it's the fact that people think that that's all that it takes. Before we hit the permanent inflation era, it might have made sense for you to say, save your money, pay all cash for a cheap fixer-upper property, and work hard for years to fix it up yourself. Oh, and then you could own a modest home debt-free. Today, even if you could do that, why would you? Instead, you can just prudently finance your way through life. You could have instead borrowed for two or three already renovated properties and let debt, inflation, and perhaps even tenants do the work for you. Above all, do the right thing before you do things right. That's what I like to say. Well, the way you get wealthy is by owning a lot of assets, not by grinding in the salt mines to pay off your debt. Those that are debt free are often asset poor. The biggest financial expense that you will ever have in your life. Do you know what it is? It is not taxes or interest charges. It's not even inflation or housing or healthcare or having children, most people have never heard of it. You probably have, but most people have never heard of this biggest financial expense you'll ever have, and they certainly don't know how to avoid it.   Keith Weinhold  27:34   Say that you're 35 years old and you put 100k under a mattress for 30 years until you're 60- years old. Instead, if that would have been invested at a 12% annual return, do you know how much that would have grown to? That would have grown to $2.996 million All right, basically 3 million bucks, a 30x increase. Therefore, it would be a 2.9 million dollar mistake to save money, and what this means is that the biggest expense you'll ever pay in your life is called opportunity cost. Yeah, opportunity cost is life's biggest expense. It's the return that was foregone when you chose one option over another. So opportunity cost is not what you spend; it's what your money could have become had you put it somewhere more productive. All right, now that was a pretty extreme example of 100k under a mattress. As a listener to this show, you are probably more savvy than a person that would save big lumps of money for close to zero return. Let me give you a better example of how when you pay all cash for something, you've usually just made your future self poorer. A friend of mine heard the episode last year where I talked about buying a new car for myself, a BMW X3 SUV. As it is, you probably remember that episode. Though I could have paid all cash for the car, I put the minimum down payment in there and then financed as much as I could because of a favorable 4% interest rate that I got on a car loan. Well, my friend Jesse heard that episode. This influenced him. So what he did is he bought a Subaru for his wife. Although he had planned to pay all cash and could have paid all cash for the car, Jesse got financing, and he did better than me. He got just a 1% interest rate somehow. Wow! It was actually nine tenths of 1% but let's just call it 1% What a deal! Instead of paying all cash for the car, he held on to that chunk of money. Instead of tying it up in a depreciating asset, he is financing it all. Now I don't. How much the Subaru costs, but let's just say it was 50k to keep the numbers simple. Well, look, if Jesse feels like he can get a 10% return over time by investing his money instead of sinking it into a car, how much does he profit by borrowing? Of course, he has the advantage of keeping his funds more liquid as well, but how much does he actually profit from this arrangement?   Keith Weinhold  30:24   Well, the math is so easy that you can even visualize it in an audio format here. Now it depends on the loan term, but the simple spread is a 10% investment return minus a 1% car loan cost. That is a 9% positive spread on 50k. That's roughly $4,500 per year in benefit. That's before any taxes, risk, or fees. $4,500 a year just for doing some loan paperwork. Like if you wonder whether the loan paperwork is worth it or not, that's what we're talking about here, and that's 375 bucks a month. So if you're wondering if it's even worth it taking the time to get a car loan when you could pay all cash, it probably is. All right, now that's the upside. What about the risk that's associated with taking a loan instead of paying all cash, well, the caveat here is that the 1% loan is guaranteed, but the 10% return is probably not, and that risk gap does matter. If you're financially fragile and you can't make the payment with another pot of money, well, then you risk default. That is over leverage risk. That's the worst case scenario. All right, what's the flip side? The flip side is that you could earn a return even better than 10% As we know, with real estate pays five ways on investment property. If you earn a 20% return, now you're making $9,500 a year on the spread, not $4,500, but a 10% return. That is the base case. So again, by paying all cash instead of getting the loan, your future self would be poorer by $4,500 a year. And now, my friend Jesse, that learned this from me, he's actually a CFA, a chartered financial analyst, a sophisticated money guy. But he had simply been overlooking this. And said another way, what you're doing here is that over time, your investment is paying you more than your interest is costing you, and in my life, I have been doing exactly this sort of thing all over the place for decades. An interesting thing that I hear about this, although it makes me scratch my head, I've heard a few people say this. It's just like, oh well, I don't want to have to deal with a car payment? I just rather be done with it and move on. What is there to deal with? Just set up auto pay with preserving funds for say a 10% return. You're then going to see more dollars flowing into your account than you will out of it. I mean that part can just be automated.   Keith Weinhold  33:19   My life and finances are set up this way. In fact, when I get a loan for a rental property, I have had mortgage loan officers that are looking at my finances. They tell me that I have more stuff flowing into and out of my checking account than they've ever seen anyone have. I'm I'm financing and arbitraging my way through life passively. This is thanks in part to inflation. I am not paying very much at all in that biggest financial expense that we all have in our lives-not taxes or children or housing, but opportunity cost. I am avoiding paying that. This is the world that we live in today, a lot of times debt reduction is horrible advice. Debt free that can keep people from falling over a cliff, but it stalls any wealth creation. Now the debts that usually make the most sense to pay down they're the ones with high interest, variable rates, no tax benefit, and no productive asset attached. And here is the priority order that I use for paying down debt or paying off debt. First, it is credit cards. Pay down these first almost every time. I mean, a 20% or even 30% credit card rate. This is like financial quicksand. You don't need a sophisticated investment thesis when you can get a guaranteed 20-4% quote-unquote return by eliminating this debt. The next place I would pay down are payday loans, personal. Loans and consumer finance debt. I mean, these are usually bad debts because they're at a high rate, have a short amortization, and they're usually tied to consumption instead of an income-producing asset. Pay these aggressively too, and then next in priority is paying variable rate debt that could reset higher. This isn't quite as important to address.   Keith Weinhold  35:24   We're talking about things like HELOCs, adjustable rate loans, margin debt, and some business lines of credit. Some of those can become dangerous when rates rise, even if the rate's tolerable today. The uncertainty can be a bit of a problem. Now, when it comes to should you pay down student loans, consider that. low fixed-rate student loans that might not be urgent. It sure wasn't for me. High-rate private student loans that could be different. That could get more of your attention. You also got to weigh things like tax benefits. Look out for forgiveness programs when it comes to student loans, those haven't been quite as available lately under this administration. Also, look at employer repayment benefits before you rush to pay down student loans, and then really the last one: low fixed-rate mortgage debt. Pay that last if you ever do. In fact, it is quite possible that I will always keep this debt type around that low fixed rate mortgage debt. So really, my rule of thumb here is to kill toxic debt. Be careful with unstable debt, and don't rush to pay off cheap fixed productive debt if you ever pay it off at all. You and I covered a lot of ground today, starting with 75 cent gasoline in California, all the way to the biggest expense you'll ever pay throughout your life, being something that most people have never heard of: opportunity cost. Coming up on the show here, a lot of good episodes, including a great guest and I are going to discuss a new way to invest in residential real estate that we haven't discussed before, and it will massively boost your cash flow. If you found today's show valuable, whether it was the history of why we have permanent inflation or the idea of passively financing your way to wealth, rather than only working harder. I would be grateful if you share this episode with a friend. Just tap the share button in Spotify, Apple Podcasts, or wherever you listen, and send it to someone who would benefit from hearing it. Or take a screenshot of this episode and post it on social media. It helps more people find the show, and it gives you and your friends something smart to talk about with each other. Until next week, I'm your host Keith Weinhold. Don't quit your daydream.   Speaker 1  37:53   Nothing on this show should be considered specific, personal, or professional advice. Please consult an appropriate tax, legal, real estate, financial, or business professional for individualized advice. Opinions of guests are their own. Information is not guaranteed. All investment strategies have the potential for profit or loss. The host is operating on behalf of Get Rich Education LLC exclusively.    Keith Weinhold  38:21   The preceding program was brought to you by your home for wealth building at getricheducation.com.  

This is not a growth hack. It is not going to go viral on a Twitter thread. But I genuinely believe it is one of the highest leverage things you can do for your brand right now, and most founders never do it properly because it is not exciting. Here is what a mentor told me years ago that I keep coming back to: a dollar saved is a dollar earned. And depending on your margins, that dollar saved is probably worth $1.30 or $1.40 on the bottom line. In this episode, I walk you through a full line by line expense audit covering every major cost area in a typical e-commerce business, the same process we have run at Foundr that has saved us tens of thousands of dollars a month. Here's what you'll take away: Why the average growing Shopify store is paying for 15 to 30 apps but actively using only eight of them, and how to fix that fast How to negotiate your SaaS tools, 3PL rates, merchant fees, and supplier costs in ways most founders never think to try Why agency retainers are one of the most expensive line items you can cut, and what to build in-house instead How to use AI and Claude Code to replace tools and creative spend that is quietly draining your budget every month The Meta ads Net 30 arrangement that can make a significant difference to your cash flow if you are spending at scale Why businesses waste an average of 26% of their marketing budget on campaigns that are not performing, and where to redirect it If your margins are tighter than they should be or you have not done a proper audit in the last six months, this episode will show you exactly where to look and what to do about it. If you're loving this solo series, I'd love to hear your feedback. Email me directly at nathan@foundr.com — I read every reply. Hope you enjoy it. WANT TO GROW YOUR BRAND WITH META ADS? Join the Foundr Operators Waitlist → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://foundr.com/operators⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ HOW WE CAN HELP YOU SCALE YOUR BUSINESS FASTER Learn directly from 7, 8 & 9-figure founders inside Foundr+ Start your $1 trial → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.foundr.com/startdollartrial⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ PREFER A CUSTOM ROADMAP AND 1-ON-1 COACHING? → Starting from scratch? Apply here → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://foundr.com/pages/coaching-start-application⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ → Already have a store? Apply here → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://foundr.com/pages/coaching-growth-application⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ CONNECT WITH NATHAN CHAN Instagram → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/nathanchan⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/nathanhchan/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ FOLLOW FOUNDR FOR MORE BUSINESS GROWTH STRATEGIES YouTube → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/2uyvzdt⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Website → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.foundr.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/foundr/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Facebook → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.facebook.com/foundr⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.twitter.com/foundr⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/company/foundr/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Podcast → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.foundr.com/podcast⁠

Not all UV technologies are created equal, and for supply chain teams, knowing the difference leads to better purchasing decisions. In this episode of "Evidence Over Expense," Dr. Sarah Simmons, DrPH, CIC, FAPIC, and Juan Gonzalez from Xenex help cut through the confusion and explain what healthcare organizations should really be looking for before investing in UV technology. From FDA authorization and product safety to service, support, and long-term usability, this conversation gives supply chain teams a clear path to smarter decision-making. If your team is evaluating UV technology, this episode will help you ask the right questions, avoid the wrong assumptions, and look beyond the price tag to what truly drives value. BONUS CONTENT: Be sure to download the free Supply Chain UV Checklist Tool to help your team ask the right questions and make more informed purchasing decisions. Click here to download: https://9231499.fs1.hubspotusercontent-na1.net/hubfs/9231499/Power%20Supply/Podcast/Xenex%20Bonus%20Content%20-%20Episode2.pdf Once you complete the interview, jump on over to the link below to take a short quiz and download your CEC certificate for 0.5 CECs! – https://www.flexiquiz.com/SC/N/ps-xenex-ep2 A special thanks to our sponsor, Xenex, for making this series possible. #PowerSupply #Xenex #HealthcareSupplyChain #InfectionPrevention #ValueAnalysis #EvidenceOverExpense #UVTechnology #Podcast

Welcome to the Knives Templars Podcast—the show where blade enthusiasts, makers, and collectors unite! Each episode dives deep into the art and science of knife making, the stories behind legendary blades, and the vibrant community that keeps the edge sharp in the world of cutlery. Whether you're a seasoned smith, a passionate collector, or just discovering the allure of handmade knives, this podcast is your go-to resource for inspiration, education, and connection.A huge thank you to our incredible sponsors who make this show possible:·       EvenHeat Kilns – Precision heat treating for serious makers·       TR-Maker – Innovative tools for next-level knife crafting·       Pop's Knife Supplies – Your one-stop shop for premium materials·       Brodbeck Ironworks – Grinders and gear built for makers·       NJ Steel Baron – Steel that shapes legends·       Phoenix Abrasives – Abrasives that rise to the challenge·       KH Daily Knives – Blades and tools forged with passion·       Clark Iron Forge – Blacksmithing tools that strike true·       The Knifemakers' Guild – Craftsmanship, community, and traditionYou can catch the Knives Templars Podcast on all major platforms—Spotify, Apple Podcasts, Amazon, iHeart, Castbox, and wherever you get your audio fix. Be sure to subscribe, leave a review, and share with your fellow makers. Also see us on Facebook at the Knives Templars!https://knivestemplars.comBe Blessed

Quint and Allie break down the cost of investments and what to watch out for with each investment you buy. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

When we talk about infection prevention, the conversation usually starts with clinical outcomes. But what if one of the most important decisions happens long before a patient enters the room? In Episode 1 of our brand-new podcast series "Evidence Over Expense," Dr. Sarah Simmons, DrPH, CIC, FAPIC, from Xenex and Steve Egbert from XENDELLA join us to explore how healthcare teams can take a smarter, evidence-based approach to evaluating UV technology. From manual cleaning limitations to FDA authorization and the real cost of pathogen transmission, this conversation helps supply chain teams look beyond the price tag and focus on what really matters before making the investment. Because behind every cleaner room, stronger workflow, and smarter technology decision is a supply chain choice with real impact. Once you complete the interview, jump on over to the link below to take a short quiz and download your CEC certificate for 0.5 CECs! – https://www.flexiquiz.com/SC/N/ps-xenex-ep1 A special thanks to our sponsor, Xenex, for making this series possible. #PowerSupply #Xenex #HealthcareSupplyChain #InfectionPrevention #ValueAnalysis #EvidenceOverExpense #UVTechnology #Podcast

Justin Spillers talks about how to leverage NOI, the ultimate driver of property value by focusing equally on raising rents and slashing costs. Justin breaks down the precise tactics you can use, from heavy value-add renovations and innovative revenue streams like pet rents and Wi-Fi surcharges, to negotiating bulk vendor deals and minimizing repair expenses. He shares the exact math behind ROI-driven upgrades, showing how a $15,000 renovation can generate a $36,000 annual increase in revenue, boosting your property's valuation exponentially at refinance. Justin Spillers Partner & Manager of Real Estate Alpha Based in: Minster, Ohio Where to find them: https://www.linkedin.com/in/justinspillers/ realestatealpha.io/ Book your free demo today at bill.com/bestever and get a $100 Amazon gift card. Visit https://malabarhillcapital.com/ for more info. Podcast production done by⁠ ⁠Outlier Audio Learn more about your ad choices. Visit megaphone.fm/adchoices

Yuval Refua is the Chief Product Officer at Navan, the global travel and expense platform he joined seven years ago when it was still just a travel booking service. Since then, he has built out its payments and expense products from the ground up, turning the company policy that used to live in a PDF into code that runs on the card itself. This conversation matters because T&E is one of the most universally disliked workflows in business, and Navan is rethinking it from scratch just as AI and agentic commerce start to reshape how companies spend.What We CoveredFalling in love with credit cards at American ExpressWhy Navan started as a travel-only booking serviceThe reconciliation pain that led to launching a cardCoding company policy directly onto the cardReal-time approval the moment you swipeWhy travel-first beats procurement-firstContext as the key to managing distributed spendGoing global with VAT, GST, per diems and mileageThe e-invoicing wave hitting more countriesThe GTA model for revealing complexity graduallyThe Expense Admin Companion and recommended actionsFrom single approvals to bulk to full automationThe Visa partnership and the Connect productWaymo for travelers, Formula One for financeKey TakeawaysThe expense report exists to answer a question that company policy already settled. Coding that policy onto the card removes the work instead of automating it.Starting from travel gives Navan context (where the employee is, why they are there, who they are visiting) that procurement-first tools lack, which makes per-employee limits far smarter.Going global is less about features and more about mastering country-by-country tax, e-invoicing, per diem and mileage rules.The path to full automation runs through trust. Navan moves finance teams from a single recommended action, to bulk approvals, to hands-off automation, which is also how it intends to handle agentic spend.About Yuval RefuaYuval Refua is Chief Product Officer at Navan. He started two companies of his own early in his career before moving into fintech and product management at Thomson Reuters, then American Express, where he developed a deep love for credit cards and the rails behind them. He joined Navan around seven years ago and has built out its payments and expense products from the ground up.Connect with Fintech One-on-One:Tweet me @PeterRentonConnect with me on LinkedInFind previous Fintech One-on-One episodes

Should you expense a rental property cost immediately or capitalize and depreciate it over time? It's one of the most misunderstood areas of real estate investing and getting it wrong can cost you thousands in missed deductions or IRS headaches. In this episode, Thomas Castelli and Nate Sosa break down the decision framework every real estate investor needs to understand when dealing with repairs, renovations, improvements, appliances, HVAC systems, roofs, and other property expenses. You'll learn: - When an expense can be deducted immediately - How the De Minimis Safe Harbor works - The difference between repairs and capital improvements - When the BAR Test applies (Betterment, Adaptation, Restoration) - How cost segregation impacts your deductions - Bonus depreciation vs. Section 179 and when each makes sense - Common tax myths that trip up landlords and short-term rental owners Request a consultation from Hall CPA at go.therealestatecpa.com/3KSEev6 Get the FREE Ultimate STR Tax Strategy Bundle: go.therealestatecpa.com/strbundle Register for the FREE Investing Debate: go.therealestatecpa.com/debate Submit your question for Tom & Nathan: go.therealestatecpa.com/question The Tax Smart Real Estate Investors podcast is for general information purposes only and is not intended to provide, and should not be relied on for, tax, legal, or accounting advice. Information on the podcast may not constitute the most up-to-date legal or other information. No reader, user, or listener of this podcast should act or refrain from acting on the basis of information on this podcast without first seeking legal and tax advice from counsel in the relevant jurisdiction. Only your individual attorney and tax advisor can provide assurances that the information contained herein – and your interpretation of it – is applicable or appropriate to your particular situation. Use of, and access to, this podcast or any of the links or resources contained or mentioned within the podcast show and show notes do not create a relationship between the reader, user, or listener and podcast hosts, contributors, or guests. Any mention of third-party vendors, products, or services does not constitute an endorsement or recommendation. You should conduct your own due diligence before engaging with any vendor.

Execution drift rarely shows up as one big mistake. I've found that it starts with small deviations that seem harmless in the moment but eventually turn into bigger problems. For leaders, operators, and business owners, the real cost is not frustration or disappointment. It's the money, opportunities, and performance that slowly disappear when standards are not consistently enforced. In this episode, I break down the early warning signs of execution drift and how to catch them before they become expensive problems.  Show Notes: [02:32]#1 Drift compounds into hidden financial loss. [09:16]#2 Drift slows decision cycles and kills leverage. [12:22]#3 Drift erodes trust internally and externally. [16:17] Recap Next Steps: --- Execution is not a talent.   It is a standard. If your results don't match your ability, something in your approach is out of alignment. Most people do not have a motivation problem.   They have a consistency problem. Power Presence is the system for operating with greater discipline, clarity, structure, and execution under pressure. Learn more: → http://www.PowerPresenceProtocol.com  — This show is the public record of standards. All episodes and the complete archive: → http://WorkOnYourGamePodcast.com 

0:30 - Teen takeovers in Chicago 16:37 - Iran 44:54 - Remembering Tom Dreesen: Dan’s interview with Tom from 11/7/25 01:18:05 - Professor at George Mason University Scalia Law School and senior fellow at the Heritage Foundation, Eugene Kontorovich, weighs in on the Memorandum of Understanding, saying “It strengthens Iran, there is no other way to put it.” Follow Professor Kontorovich on X @EVKontorovich 01:36:35 - University of Chicago law professor emeritus Richard Epstein discusses his legal battles over the Obama Presidential Center, saying, “If you’re 100% right in a case against the government, you have a 50% chance of winning.” Check out Richard’s newest book The Myth of Birthright Citizenship 01:53:54 - Manhattan Institute researcher Neetu Arnold discusses grade inflation and why schools may need new ways to separate exceptional students from the pack. 02:07:32 - Hussain Abdul-Hussain, research fellow at the Foundation for the Defense of Democracies, on the Iran peace deal and Trading Away Lebanon: Washington’s Bargains at Beirut’s Expense. Hussain is also the author of The Arab Case for IsraelSee omnystudio.com/listener for privacy information.

Another example of the problems with Annuities.Is it OK to pay higher fund expense ratios for higher returnsLaura Pausini concert in OrlandIoniq 9 and some EV newsStill time to go to CSI Con in New York

Michael Steele tackles the stark realities of a system that favors the wealthy while leaving the middle class and the poor to fend for themselves. With insider information flowing to the elite, decisions are made that enrich a select few at the expense of the many. Michael exposes the troubling dynamics at play, from stock market manipulation to the alarming disconnect between policy and the everyday struggles of American families. Tune in to understand how this rigged system is shaping the narrative as we head into the fall.Catch Michael Steele on The Weeknight Mondays - Fridays at 7pm EST on MSNBC: https://www.msnbc.com/weeknightFollow Michael on X: https://x.com/MichaelSteeleFollow Michael on Bluesky: https://bsky.app/profile/michaelsteele.bsky.socialFollow Michael on Instagram: https://www.instagram.com/chairman_steele/Follow Michael on Threads: https://www.threads.net/@chairman_steeleListen to The Michael Steele Podcast: https://podcasts.apple.com/us/podcast/the-michael-steele-podcast/id1412905534Watch The Michael Steele Podcast: https://www.youtube.com/playlist?list=PLJNKzTkCZE9uNqPiKYw5eU5YkS_mMsr6oIf you enjoyed this, share it with a friend!

The guys talk about the tragic passing of Kyle Busch, the Vegas talk heats up as the gang revisits the infamous forgotten, deleted podcast