Podcasts about Doubling

Struggling to consistently attract new clients to your clinic, even though you know the demand is there?In this episode of the Grow Your Clinic podcast, we break down the exact strategies clinics can use to double new clients in the next 90 days. We unpack how to optimise the foundations first - from improving phone call handling and training your reception team to creating a frictionless website booking experience that converts more enquiries into appointments. We also dive into the three core growth drivers for clinics: publishing content consistently, building strategic partnerships, and using paid advertising to amplify what's already working. You'll learn how to create compelling offers, track where your best clients are coming from, and build a marketing system that drives predictable growth.If you're ready to stop relying on luck and start building a clinic that attracts new clients consistently, this episode gives you the blueprint.Need to systemise your clinic? Start your free trial of Allie! https://www.allieclinics.com/ In This Episode You'll Learn:  

This week’s guest is Ben Hansen. Ron and Ben discussed how Ben helps companies improve their profit margins, dealing with “profititis,” shifting your focus from revenue to profitability, and more. An MP3 audio version of this episode is available for download here. In this episode you’ll learn: Ben’s quote (2:23) His background (3:59) How Ben helps companies double their profits (6:57) The most common “profit leaks” (9:28) An example using a manufacturing facility (20:17) About “profititis” (23:29) How you address it (26:05) Shifting the focus from revenue to profitability (27:38) Ben’s thoughts on accounting (32:05) Separating the signal from the noise (36:09) Ben’s parting thoughts (37:24) Podcast Resources Right Click to Download this Podcast as an MP3 Ben on LinkedIn Get All the Latest News from Gemba Academy Our newsletter is a great way to receive updates on new courses, blog posts, and more. Sign up here. What Do You Think? Has your business ever suffered from profit leaks or profititis? How did you know?

Alkarim Devani — developer and founder of mddl — is in good traffic this week for a conversation on how Canada's housing realities compare/contrast with that of the U.S., and how we can better prepare developers to address our continental crisis. Timeline:00:00 Alkarim Devani is in good traffic.02:47 Canada's housing crisis.04:36 Cities not sharing solutions with each other.05:14 The competitive silence in real estate development.05:51 Al's background as a developer in Calgary.06:43 The Plus 15 downtown system and its failures.07:31 Downtown office conversions and retrofitting.08:23 Starting infill development with his brother.09:14 From luxury duplexes to more attainable housing.10:16 Realizing the market sucks and social impact matters.11:33 Founding Roundsquare to bring families back.12:43 The first fourplex proposal that no one had done.13:39 Meeting resistance and finding city support.16:54 Why missing middle isn't happening at scale.25:31 The regulatory framework problem.28:27 How to make economics work for missing middle.31:15 Building at the right price point.34:12 The slow-growth lesson from setbacks.37:00 Sometimes you don't get the outcome you want.40:15 Iterative change and incremental progress.43:03 Solving for community impact.46:33 A 350-square-foot cafe.49:21 Revenue-sharing lease structures.52:24 Building a heated vestibule for winter survival.55:12 Doubling revenue per square foot through design.56:01 The General Block and Village Ice Cream.58:04 Why the right tenant anchor matters.59:35 University District and the 99-year lease model.1:01:23 Inglewood and Calgary's coffee culture.1:03:02 The commute question and river path biking.1:05:26 Wrapping up.Links:mddl.mddl U.Mddl, on Instagram.

For this week's PicklePod, we're joined by two of the most exciting young stars in pickleball: Hayden Patriquin and Gabe Tardio of the St. Louis Shock. We dive into everything from Shock team culture and MLP pressure to why Gabe ignored traditional pickleball advice and built his own game from scratch. Hayden also opens up about his on-court personality, social media backlash, and finding the balance between playing with emotion and staying under control. We also get into: - The “Shock are chokers” debate - Why Hayden might have the highest ceiling in pickleball - What makes Ben Johns so difficult to beat - Signature shots and unique techniques - The future of pickleball strategy - Gambling stories, trash talk, and complete chaos This episode goes all over the place in the best way possible. One minute we're breaking down elite strategy and the next we're talking baccarat, sleep habits, and why nobody actually knows the “right” way to play pickleball. Subscribe and let us know: Who has the highest ceiling in pickleball right now?

On this week's podcast we talk with Drew Cramer of Ghost House Farm in Michigan about how he more than doubled his greenhouse tomato yields through good management and automation. Automation makes the difference between just controlling the temperature in a greenhouse and controlling the climate. And controlling the climate is what creates a growth-promoting, disease-discouraging greenhouse environment. In this interview we get nerdy to talk about the management practices Drew used and the ways he managed the climate for increasing yields and decreasing disease over time. We talk about how he used pulse irrigation and humidity control together to reduce diseases and seconds. We also discuss the small-scale biomethane digester they're using to turn their greenhouse prunings into fuel. We also focus on how much cucumber grafting has increased their yields, what they're grafting cucumbers to, and so much more in this wide-ranging interview filled with greenhouse tips. Connect With Guest: Website: ghosthouse.farm Instagram: @ghosthouse.farm Podcast Sponsors: Huge thanks to our podcast sponsors as they make this podcast FREE to everyone with their generous support: Tilth Soil makes living soils for organic growers. The base for all our mixes is NOP-compliant compost, made from the 4,000 tons of food scraps we divert from landfills each year. And the results speak for themselves. Get excellent germination, strong transplants, and help us turn these resources back into food. Try a free bag and learn more at tilthsoil.com/gfm. Nifty Hoops builds complete gothic high tunnels that are easy to install and built to last.  Their bolt-together construction makes setup straightforward and efficient, whether it's a small backyard hoophouse, or a dozen large production-scale high tunnels- especially through their community build option, where professional builders work alongside your crew, family, or neighbors to build each structure -- usually in a single day.Visit niftyhoops.com to learn more. Farming is hard. Running it shouldn't be. Tend helps you plan your season, map your farm, and track every task from seed to sale. No spreadsheets, no guesswork, just seamless workflows. Tend is the all-in-one farm management platform that brings together planning, field mapping, fulfillment, real-time inventory, sales, labor, traceability, and accounting in one easy platform. Built for small market gardens, CSAs, and large diversified farms. Get started with a free account at Tend.com. No credit card required. If you grow for market, you know performance is everything. That's why so many farmers are turning to Burpee's Farmers Market. Dedicated to professional growers, Burpee is now offering non-GMO seeds in larger quantities – bred and selected for standout flavor, strong yields, and the kind of visual appeal your customers crave. Burpee's been doing this for 150 years, and they're still creating new varieties with growers like you in mind. You can check out the full lineup at Burpee.com/FarmersMarket. There are a lot of farm sales platforms out there, but there's only one that's cooperatively owned by farmers. That's GrownBy — your all-in-one solution to simplify farm sales. GrownBy makes online farm sales easy and affordable; setting up your shop is free, and you only pay when you sell. Join over 900 farms who have already signed up for GrownBy, at grownby.com. For more on veg and flower market farming, subscribe to Growing for Market Magazine!

What a joy it is to sit down with Mandy Heard, owner of Lavender Glow and Maevora skincare. Mandy is a mom of 5 and a year ago was mostly booked while feeling exhausted all of the time. She was making money, but the kind of money she wanted. In today's episode, Mandy explains how she started making more money in her business (without adding client hours) and the MANY exciting updates she has. You can find Mandy here: https://www.lavenderglowco.com/ and here https://www.maevoraskin.com/Learn more about working with me 1:1 to Double Your Esthetician Business. Click here!The 5 Mental Blocks Costing You 5k/ Month Download for FREE here⁠.Follow me on Instagram: @esthetician.coach

How do you convince people to spend £400 on a suit… and then wait up to eight weeks for it?That is exactly what Sam and Julian, the founders of Batch LDN, have managed to do.In this episode of Building The Brand, they share how two founders with no fashion background built one of the most interesting menswear brands in the UK by doing almost everything differently.Batch LDN is not a traditional fashion brand. They do not hold huge amounts of stock. They do not rely on fast fashion cycles. They do not lead with endless paid ads. Instead, they batch customer orders together, make every item to order in London using premium Italian fabrics, and have built a brand around quality, community, retail experience and smart casual menswear that actually fits modern life.But this conversation is not just about suits.Sam and Julian talk openly about why industry naivety became an advantage, why sustainability alone was not enough to drive sales, how a real-life robbery became one of their most successful marketing moments, why having the right co-founder changes everything, and why they chose to build through physical retail first when most fashion brands start online.They also break down how Batch LDN has attracted celebrities, sports teams and investors, why Romesh Ranganathan became involved in the brand, how they became the official menswear supplier to Burnley Football Club, and what comes next as they look to expand the product range, grow online and take Batch international.SHOP @ Batch LDN CONNECT WITH OUR BUILDING THE BRAND COMMUNITY▪️ How Batch LDN created a made-to-order casual suit brand▪️ How batching orders helps reduce waste, stock risk and cost ▪️ Why premium Italian fabrics and London manufacturing became core to the brand ▪️ Why Sam and Julian's lack of fashion experience became a superpower ▪️ How sustainability shaped the business internally but failed as the lead marketing message▪️ How a robbery at their store became a viral marketing campaign ▪️ Why the “See It. Say It. Suited.” campaign put Batch on the map ▪️ The importance of having the right co-founder in a startup ▪️ Why physical retail became Batch LDN's strongest sales channel ▪️ Why the founders hired a creator and doubled down on storytelling instead of paid ads ▪️ How celebrities including Romesh Ranganathan, Ashley Walters, Simon Pegg, Ant and Dec, Josh Denzel and others have worn the brand  Key Moments:0:00 — Intro03:33 — How Batch LDN's made-to-order fashion model works06:42 — How Sam and Julian started Batch LDN with no fashionexperience08:03 — The fashion waste problem behind the made-to-order model 12:00 — Why sustainability alone does not sell fashion15:32 — How startup experience helped Batch challenge the fashion industry17:41 — PAUSE POINT: Industry naivety can be a competitive advantage19:34 — The Batch LDN robbery story23:18 — Why the co-founder relationship matters in startup life26:36 — Why Sam chose Julian as his Batch LDN co-founder30:34 — PAUSE POINT: The right co-founder helps carry the weight32:58 — Building the Batch Members Club and fashion community35:20 — How the Covent Garden flagship store became a retail and events space36:54 — Why 80% of Batch LDN revenue comes through physical retail39:20 — Replicating the in-store fitting experience online40:58 — PAUSE POINT: Do not blindly follow the direct-to-consumer startup playbook43:35 — Why Batch LDN hired an in-house content creator46:59 — Doubling revenue without paid social advertising48:55 — Celebrities, social proof and Batch LDN suits in the wild52:17 — Why Romesh Ranganathan invested in Batch LDN53:59 — Taking Batch LDN to America and testing international growth54:30 — Becoming Burnley Football Club's official menswear supplier56:00 — Why sports teams and smart casual menswear are a major opportunity58:29 — New Batch LDN products: corduroy suits, cropped jackets and wider-leg trousers1:00:20 — The five-year vision for Batch LDN

Download your free personalized $100M scaling roadmap in under 30 seconds: https://www.acquisition.com/roadmap?el=yt-alex-486r&htrafficsource=youtubeBigger businesses demand bigger tradeoffs, but most business owners just don't want to pay them. In this episode, Alex coaches six business owners through the real bottlenecks stopping them from scaling. He breaks down why inbound and outbound sales teams should never mix, why premium businesses require premium talent, and why the fastest way to grow is often killing the thing that made you successful in the first place.In this episode00:00 The real cost of scaling from $6M to $100M07:14 Raising prices to fund staffing needs10:46 Outreaches to get more “whale” HVAC partners12:52 Building the dream team to handle high-end clients18:18 Lead generation strategies for a roofing company26:41 Doubling down on networking to generate more leads27:55 Calculating the opportunity cost of exiting a businessMore Value:Join The Live Scaling Workshop In Las Vegas: https://www.acquisition.com/o-vegasDownload your free personalized $100M scaling roadmap in under 30 seconds: https://www.acquisition.com/roadmap?el=yt-alex-486r&htrafficsource=youtubeDiscover The Easiest Business I Can Help You Start (Free Trial): https://www.skool.com/hormoziFree Books and Video Courses: https://www.acquisition.com/trainingGet the $100M Book Bundle: https://shop.acquisition.com/pages/100m-book-bundleFollow Alex Hormozi's Socials:⁠⁠LinkedIn ⁠⁠ | ⁠⁠Instagram⁠⁠ | ⁠⁠Facebook⁠⁠ | ⁠⁠YouTube ⁠⁠ | ⁠⁠Twitter⁠⁠ | ⁠⁠Acquisition ⁠DISCLOSURE Information shared here is for educational purposes only. Individuals and business owners should evaluate their own business strategies, and identify any potential risks. The information shared here is not a guarantee of success. Your results may vary. Copyright © 2026.

See what the team at The Successful Bookkeeper has on right now → Natalia Zacharin had no bookkeeping background, no clients, and no safety net when she started Zacharin Consulting in January 2019. She was 49, a single mom, and her invoicing clerk job was being phased out. In part one of this two-part conversation, Natalia lays out exactly how she went from that starting point to building a firm that now employs 16 people, targets $4 million in revenue, and landed at number 802 on the 2025 Inc. 5000 list. Chapters [00:00] Cold open: asking the right questions [00:52] Introducing Natalia Zacharin [03:00] From invoicing clerk to business owner [07:00] Where the firm stands today [10:00] First client via LinkedIn [13:00] Self-teaching bookkeeping on YouTube [16:30] Hiring a CPA manager — lessons learned [19:00] Building the pipeline: 4 clients to 12 [23:00] Doubling down and working 7 days a week [26:00] COVID, the PPP pivot, and early burnout Starting From Zero — And Meaning It Natalia's entry into bookkeeping wasn't a career pivot she planned. Her fiancé suggested it over coffee when she couldn't find another job. She enrolled in an online course, started messaging business owners on LinkedIn in January 2019, and landed her first client on January 29th of that year — a landscaping company owner in California who is still a client today. Her prior experience as an invoicing clerk in Microsoft Dynamics and NetSuite gave her attention to detail, but she had never opened QuickBooks. YouTube as a Training Program When Natalia got into her first client's books, she found the revenue hadn't been recorded — showing a negative $2 million on the P&L. Rather than walking away, she cleaned it up herself using YouTube, watching videos second by second and refreshing the balance sheet after every change to see what happened. "That was how I learned," she says. "I taught myself how to read financials, not how to do just data entry." It was slow, it was self-directed, and it worked. The Formula for Getting Clients Early On Natalia built her first pipeline through two channels: LinkedIn outreach and a local women's business group. Her LinkedIn approach was simple — start genuine conversations, never lead with a pitch, and ask questions. "No one likes it when you just come out with a full three paragraphs of what you do. No one cares." At in-person events, she set a clear intention before walking in: she was there to get a client, not just to have lunch. By August 2019 she had 4 clients and quit her day job. By November she had 12 — enough to cover her bills. Doubling Down on What Works After quitting her job, Natalia worked 12 to 14 hours a day, seven days a week, iterating constantly on what was generating new business. Her core principle: if something moves the needle, do more of it faster. She avoided prescriptive networking formats like BNI in favour of methods she could control and test. "Selling is psychological, it's not intuitive," she says. "I just didn't give up. I didn't feel like I had other options, so I just kept going." COVID, the PPP, and a Hard Lesson About Burnout In January 2020, Natalia called her mother — who had been helping her financially — to say she no longer needed the support. Three months later, COVID hit and her clients' revenues collapsed. She pivoted immediately to helping clients apply for PPP and EIDL loans, achieving near-perfect approval and forgiveness rates. But by August 2020, working alone under enormous pressure, she stopped exercising and started running out of energy. Part two of this series picks up there — with hard-won lessons on burnout, pricing, and scaling a team. Links Mentioned Zacharin Consulting: zacharinconsulting.com Pure Bookkeeping: purebookkeeping.com The Successful Bookkeeper: thesuccessfulbookkeeper.com About the Guest Natalia Zacharin is the Founder and Principal of Zacharin Consulting, a full-service accounting firm based in Maryland that offers bookkeeping, accounting, and fractional CFO services. She started the firm in 2019 with no formal bookkeeping training and grew it to a 16-person team tracking over $3 million in annual revenue. In 2025, Zacharin Consulting was named to the Inc. 5000 list of fastest-growing companies in the United States, ranking number 802. About the hostMichael PalmerMichael Palmer is the host of The Successful Bookkeeper podcast and co-founder of Pure Bookkeeping and The Successful Bookkeeper. He started this work because of his father — a brilliant electrical contractor who worked twice as hard as he should have had to, because nobody on the financial side was in his corner. That gap is what The Successful Bookkeeper exists to close. His view: bookkeepers are the most undervalued force in small business — and every bookkeeper who builds a real business changes two families: theirs, and their clients'.

How do you scale a financial advisory firm to 40+ staff and $630M AUM in just a few years without breaking your hiring, culture, or client experience? In this episode, you'll hear about the hiring and marketing strategies fueling this kind of growth and how being open about a firm's values can lead to greater retention of both clients and team members.  Joe Schmitz Jr. is the founder of Peak Retirement Planning, a hybrid advisory firm based in Columbus, Ohio, that oversees $630 million in assets under management for approximately 1,000 client households. He joins the show today to share how he built a high-performance hiring engine that attracted over 5,000 applicants in a single year and turned them into a values-aligned, growth-focused team. He also talks about how his firm uses a structured, tech-enabled hiring process to identify the right people, why recruiting straight from colleges has been a competitive advantage, and how engaging in multiple synergistic marketing tactics has led to the strong flow of prospects his firm receives.  For show notes and more visit: https://www.kitces.com/488  

Olympic paddler Luuka Jones-Yaxley's lined up an unexpected project after her success in Paris. Shortly after retiring from the high-performance canoe Slalom at the Paris Olympics, Luuka received a call and an offer to appear as Charlize Theron's stunt double in the Netflix film Apex. She says the offer felt like an 'April Fools joke' at first, but she was on a plane to the South Island to begin filming before she knew it. "I just received at text from a friend down in the South Island and he called me and said he's been doing a bunch of water safety on some films and that this film had approached him and they needs a kayak double for Charlize Theron - and it kind of went from there." LISTEN ABOVESee omnystudio.com/listener for privacy information.

In this episode, we're breaking down why having the most expensive product in your market gives you the best chance at building a real business. From the placebo effect to premium branding strategy, this is the case for raising your prices instead of cutting them. Get on the waiting list at https://capitalism.com/bootcamp Timestamps (0:00) Why premium pricing strategy wins in e-commerce (1:00) The counterintuitive thesis (2:00) The placebo effect in Parkinson's treatment and consumer (3:00) How higher prices increase customer expectations and satisfaction (4:00) Margin protects your presence as a business owner (5:00) Building better systems and customer experience with profitability (7:00) Why luxury dealerships deliver better experiences than used (8:00) The math of discounting premium vs cheap products (9:00) Using aggressive discounts on premium products to acquire (11:00) Why premium discounts feel like events, not gimmicks (12:00) Margin funds the marketing that actually grows your (13:00) Why e-commerce entrepreneurs use price instead of marketing (14:00) Combining discounts and advertising for competitive advantage (15:00) AG One case study (18:00) What premium branding actually means (19:00) Kill Switch sleep supplement (21:00) Doubling your price requires upgrading your entire brand (23:00) Premium pricing as a forcing function for better (25:00) Pricing resistance is a self-worth issue (26:00) How to market premium products (27:00) Real marketing compares your product to inaction, not (29:00) Switch Supplements example (30:00) Connecting product benefits to customer life outcomes (32:00) The premium marketing funnel (35:00) Building premium brands on Amazon and TikTok Shop (36:00) Email relationships drive conversion more than sales pages (38:00) Final wrap

Guest host Mark Rosinski, CFP®, CPA, RICP®, from Dunes Financial does a "hot topics" episode where he talks about:US Government obligation interest and what to look for on your consolidated 1099 to make sure you properly reflect the state income tax treatment on your tax return ( 9:06 )Understanding and tracking after-tax "basis" in inherited IRAs ( 20:14 )Required Minimum Distribution ("RMD") aggregation rules ( 25:03 )Potentially doubling up contributions to governmental 457 employer retirement plans, and other unique aspects of 457 plans ( 33:27 )Different approaches for investing money during the period where income from working has stopped but Social Security has not yet been started. Options include total return, bond ladders, bucket strategies, and hybrid approaches ( 46:35 )To send Andy questions to be addressed on future Q&A episodes, email andy@andypanko.comLinks in this episode:Mark's company's website - https://www.dunesfinancial.com/Mark's first time on the Retirement Planning Education podcast - #146 – Retirement planner chat, with Mark Rosinski from Dunes FinancialMark's second time on the Retirement Planning Education podcast - #165 - "Hot topics" edition...Andy and Mark Rosinski talk about different withdrawal strategies, rule of 55 distributions, allocating the stock portion of a portfolio and MORE!Andy's YouTube video - IRA after-tax "basis," the pro rata rule and Form 8606Tenon Financial monthly newsletter/blog - Retirement Planning InsightsFacebook group - Retirement Planning Education (formerly Taxes in Retirement)YouTube channel - Retirement Planning Education (formerly Retirement Planning Demystified)Retirement Planning Education website - www.RetirementPlanningEducation.com

Are you spending more on traffic than you're actually making and calling that “growth”? Business owners often chase scale, obsess over ROAS, and completely miss the bigger picture. Most of them don't have a traffic problem but a strategy problem. And if you don't fix it, you're just working for Meta, Google, or whoever owns your traffic source.Today, we revisit a conversation we had with Kasim Aslam. He breaks down a hard truth about scaling: diminishing returns are still returns, but most businesses quit too early. We explain why entrepreneurs stop at the peak of performance instead of riding the full curve, how margin obsession kills growth, and why doubling down on what's already working beats chasing every new platform or tactic. We get into what's actually happening with AI, automation, and why “scalable” might be the most dangerous word in your business right now. We also unpack what really drives sustainable business growth and why the future belongs to those willing to do what doesn't scale.In This Episode:- Why traffic sometimes costs more than profit- Doubling down on what works- Riding the bell curve of ad performance- Margin vs net profit- Incalculable business advantages- The flip-and-exit mentality- How AI is killing easy business modelsMentioned in the Episode:Apply to Work With Tier 11's Marketing Experts: https://www.tiereleven.com/apply Previous episodes on the law of inverse profitability: https://perpetualtraffic.com/?s=inverse+profitability Listen to This Episode on Your Favorite Podcast Channel:Follow and listen on Apple: https://podcasts.apple.com/us/podcast/perpetual-traffic/id1022441491 Follow and listen on Spotify:https://open.spotify.com/show/59lhtIWHw1XXsRmT5HBAuK Subscribe and watch on YouTube: https://www.youtube.com/@perpetual_traffic?sub_confirmation=1We Appreciate Your Support!Visit our website: https://perpetualtraffic.com/ Follow us on X: https://x.com/perpetualtraf Connect with Kasim Aslam:Website: https://kasimaslam.com/LinkedIn: https://www.linkedin.com/in/kasimaslam Connect with Ralph Burns: LinkedIn - https://www.linkedin.com/in/ralphburns Instagram - https://www.instagram.com/ralphhburns/ Hire Tier11 - https://www.tiereleven.com/apply-now Mentioned in this episode:https://perpetualtraffic.com/advertise-with-us/Apply for an ad spot on Perpetual Traffic for Q1 or Q2. Visit www.perpetualtraffic.com today to secure your spot!We're opening up sponsorship spots for Q1 and Q2! Apply now by visiting www.perpetualtraffic.com https://perpetualtraffic.com/advertise-with-us/

There's a persistent claim that indexed universal life insurance is doomed to fail because rising costs of insurance will eventually eat the policy alive. The story usually goes something like this: someone bought a universal life policy decades ago, paid faithfully, and one day got a notice that the policy was about to lapse unless they wrote a big check. That story has a grain of truth behind it, but the magnitude of the claim is wildly overstated. The original problem traces back to universal life policies sold in the 1980s as cheap alternatives to whole life. Those sales relied on interest rate assumptions above 8 percent that never materialized, which meant the premiums being paid were never enough to keep the policies functioning long term. The question worth asking today is different. If you set out to deliberately design an indexed universal life policy badly — to actually make it collapse — how badly would you have to screw it up? To find out, we ran the test. Starting with a properly structured policy on a 35-year-old male, $30,000 annual premium, and the minimum non-MEC death benefit of about $637,000, we then doubled, tripled, quadrupled, and kept going to see when the policy would actually fail. Doubling the death benefit didn't break it. Tripling didn't break it. Quadrupling didn't break it. Even five times the appropriate death benefit kept the policy alive through age 121. It took six times the correct death benefit — a $3.8 million death benefit on a premium meant to support $637,000 — before the policy finally collapsed in the client's early 90s. The lesson is straightforward: when an IUL fails, the product isn't the problem. The design is. And a properly designed policy carries lifetime fees averaging around 0.2 to 0.25 percent of cash value, which is a remarkable deal for managed money. _______________________________________________________ If you're holding an IUL illustration and want to know whether it's structured correctly — or if you're trying to figure out whether what you already own is built to last — schedule a call or send us a message and we'll take a look at it with you.

Hour 2 - Doubling up on the Mocks full 2618 Thu, 23 Apr 2026 18:57:52 +0000 m4VsZZoDf1kJVVIWCdMnacvIyxUXaFzy nfl,nfl draft,kansas city chiefs,society & culture Cody & Gold nfl,nfl draft,kansas city chiefs,society & culture Hour 2 - Doubling up on the Mocks Hosts Cody Tapp & Alex Gold team up for 96.5 The Fan Radio's newest mid-day show "Cody & Gold."  Two born & raised Kansas Citians, Cody & Gold have been through all the highs and lows as a KC sports fan and they know the passion Kansas City has for their sports teams."Cody & Gold" will be a show focused on smart, sports conversation with the best voices from KC and around the country. It will also feature our listeners with your calls, texts & tweets as we want you to be a part of the show, not just a listener.  Cody & Gold, weekdays 10a-2p on 610 Sports Radio.  2024 © 2021 Audacy, Inc. Society & Culture False https://player.amperwavepodcasting.com?feed-link=https%3A%2

Doubling your net worth isn't about making more money, it's about what you do with the money you already have. In this episode, I'm breaking down how to rapidly grow your net worth, the biggest mistake business owners make when it comes to money, and the toxic thoughts that are quietly eroding your wealth. We dive into the identity shift required to actually build wealth, the quick wins that can put cash back into your pocket immediately, and how to stop leaking money so you can scale your net worth faster than ever. Tune in to learn The one thing most business owners focus on — and why it's the exact wrong thing for building wealth Why revenue does NOT equal profit (and why more income doesn't automatically grow your net worth) Why investing needs to feel urgent and how waiting is quietly costing you thousands How to stop the “bleed” in your cash flow and immediately free up more money to invest The toxic thought that keeps women stuck and what to shift into instead to build wealth faster

A new podcast episode features immigration policy expert Rosemary Jenks, Policy Director and co-founder of the Immigration Accountability Project, providing a detailed analysis of the DIGNIDAD Act introduced for the third time in six years by Rep. Maria Salazar (R-Fla.). Jenks characterizes the legislation as a broad amnesty proposal, referring to it as the “SAW Act” - short for “Screw All American Workers,” while also alluding to the 1986 Special Agricultural Worker program associated with widespread fraud.Key points discussed include:Scope of AmnestyDirect pathway to citizenship for an estimated 2.5 million “Dreamers,” extending beyond current DACA recipientsRenewable temporary visas for individuals who entered before 2021, with weak documentation requirements that will invite fraud; these visas are indefinitely renewable, effectively allowing recipients to remain in the U.S. permanently.A “rolling amnesty” mechanism tied to family-based immigration, including marriage to U.S. citizens Enforcement and Legal ConcernsA two-year deportation moratorium, allowing individuals, including those currently in detention, to avoid deportation and applyRestrictions on using applicant information for enforcement, shielding employers who hired illegal alien workersConcerns about increased incentives for fraud, including marriage fraudSystem Capacity and SecuritySkepticism about USCIS being able to manage a minimum of 10 million applications, numbers that will grow substantially if fraud is widespreadStrict timelines with rapid processing within a two-year window, raising concerns that vetting standards, particularly national security screening, would be among the first elements weakened under pressure, echoing issues seen in past programsDoubts that application fees would cover the full cost, especially given applicants' limited financial resources; critics warn this could create openings for third-party financing, including from cartelsHistorical comparisons to the 1986 amnesty program, where rapid processing contributed to massive fraud and lack of vettingEconomic and Labor Market ImpactImpact on wages and job opportunities for low-, medium-, and high-wage American workersExpansion of legal immigration pathways, including:Doubling employment-based green cardsCodifying OPT and allowing STEM PhD and medical students to stay permanently in the countryPermitting those on the visa waiting list for 10 years to enter regardless of capsThe episode also explores the political outlook for the legislation, including the possibility of a discharge petition in the House, which would allow it to come to the floor despite Speaker Johnson's wishes.In his closing commentary, podcast host Mark Krikorian highlights the recent election in Hungary, which resulted in the defeat of Prime Minister Viktor Orbán, widely known for his hardline immigration stance. But his successor, Péter Magyar, is expected to maintain, and perhaps even strengthen, the current strict immigration policies.HostMark Krikorian is the Executive Director of the Center for Immigration Studies.GuestRosemary Jenks is the Policy Director and co-founder of the Immigration Accountability ProjectLinksImmigration Accountability ProjectThe 'Dignity Act'The Price of DignityThe DIGNIDAD (Dignity) Act as ‘Rage Bait' for Those Who Want More EnforcementIntro MontageVoices in the opening montage:Sen. Barack Obama at a 2005 press conference.Sen. John McCain in a 2010 election ad.President Lyndon Johnson, upon signing the 1965 Immigration Act.Booker T. Washington, reading in 1908 from his 1895 Atlanta Exposition speech.Laraine Newman as a "Conehead" on SNL in 1977.Hillary Clinton in a 2003 radio interview.Cesar Chavez in a 1974 interview.House Speaker Nancy Pelosi speaking to reporters in 2019.Prof. George Borjas in a 2016 C-SPAN appearance.Sen. Jeff Sessions in 2008 comments on the Senate floor.Candidate Trump in 2015 campaign speech.Charlton Heston in "Planet of the Apes".

The Gameweek 33 Preview, Answering your Questions, looking at the fixtures, the data and Wildcard and Free Hit Drafts!

Most people hate and fear uncertainty. It causes such stress and anxiety that we often choose certain surrender over doubt, becoming passive, dependent, addicted―and more anxious than ever. Doubling down on the certainties promised by technology and micro-management only makes things worse, leaving no opportunity for innovation, adaptation or invention. Artists live with uncertainty constantly―but instead of waiting for the future, they run towards making it, with agency and freedom. What can we learn from them, about facing into a future that grows more uncertain daily? At a time when organizations of all kinds crave innovation but complain their people lack creativity and initiative, the arts have never been so essential to our future. We may not all be artists, but we can learn to think like them. In Embracing Uncertainty: How Writers, Musicians and Artists Thrive In An Unpredictable World (Policy Press, 2025) Margaret Heffernan makes a compelling argument for the vital integration of art into all aspects of our lives and for artists to guide us with their stamina, freedom and endurance. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

Most people hate and fear uncertainty. It causes such stress and anxiety that we often choose certain surrender over doubt, becoming passive, dependent, addicted―and more anxious than ever. Doubling down on the certainties promised by technology and micro-management only makes things worse, leaving no opportunity for innovation, adaptation or invention. Artists live with uncertainty constantly―but instead of waiting for the future, they run towards making it, with agency and freedom. What can we learn from them, about facing into a future that grows more uncertain daily? At a time when organizations of all kinds crave innovation but complain their people lack creativity and initiative, the arts have never been so essential to our future. We may not all be artists, but we can learn to think like them. In Embracing Uncertainty: How Writers, Musicians and Artists Thrive In An Unpredictable World (Policy Press, 2025) Margaret Heffernan makes a compelling argument for the vital integration of art into all aspects of our lives and for artists to guide us with their stamina, freedom and endurance. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/art

Most people hate and fear uncertainty. It causes such stress and anxiety that we often choose certain surrender over doubt, becoming passive, dependent, addicted―and more anxious than ever. Doubling down on the certainties promised by technology and micro-management only makes things worse, leaving no opportunity for innovation, adaptation or invention. Artists live with uncertainty constantly―but instead of waiting for the future, they run towards making it, with agency and freedom. What can we learn from them, about facing into a future that grows more uncertain daily? At a time when organizations of all kinds crave innovation but complain their people lack creativity and initiative, the arts have never been so essential to our future. We may not all be artists, but we can learn to think like them. In Embracing Uncertainty: How Writers, Musicians and Artists Thrive In An Unpredictable World (Policy Press, 2025) Margaret Heffernan makes a compelling argument for the vital integration of art into all aspects of our lives and for artists to guide us with their stamina, freedom and endurance. Learn more about your ad choices. Visit megaphone.fm/adchoices

Most people hate and fear uncertainty. It causes such stress and anxiety that we often choose certain surrender over doubt, becoming passive, dependent, addicted―and more anxious than ever. Doubling down on the certainties promised by technology and micro-management only makes things worse, leaving no opportunity for innovation, adaptation or invention. Artists live with uncertainty constantly―but instead of waiting for the future, they run towards making it, with agency and freedom. What can we learn from them, about facing into a future that grows more uncertain daily? At a time when organizations of all kinds crave innovation but complain their people lack creativity and initiative, the arts have never been so essential to our future. We may not all be artists, but we can learn to think like them. In Embracing Uncertainty: How Writers, Musicians and Artists Thrive In An Unpredictable World (Policy Press, 2025) Margaret Heffernan makes a compelling argument for the vital integration of art into all aspects of our lives and for artists to guide us with their stamina, freedom and endurance. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/popular-culture

What if your real estate assistant never slept and handled your entire transaction for you? In this episode of the Real Estate Excellence Podcast, Tracy Hayes sits down with Karan Khanna. Karan is the product lead behind ListedKit AI and the creator of Ava an AI assistant transforming how real estate agents and transaction coordinators manage deals. In this episode he shares how he entered the real estate space from outside the industry and quickly identified inefficiencies in transaction management that could be solved with AI. The conversation dives into how Ava reads contracts in seconds builds timelines automates communication and allows agents to focus on client relationships instead of paperwork. Karan also explains the future of autonomous AI assistants that can manage deals proactively giving agents more freedom and scalability than ever before. If you want to stay ahead in real estate and leverage AI before everyone else catches up this episode is a must listen! Highlights 00:00 - 01:25 Introduction to Ava and AI in real estate Overview of AI assistant Ava Speed of contract processing Impact on agents and transaction coordinators Industry recognition and scale Why this conversation matters 01:25 - 06:05 Karan Khanna background and origin story Coming from outside real estate Product manager mindset Harmony Venture Labs influence Identifying real estate inefficiencies Shift from static software to AI 06:05 - 12:40 Understanding the core problem in transactions Challenges of independent transaction coordinators Managing multiple systems and workflows Agent struggles with admin work Broker expectations for productivity Defining the true industry bottleneck 12:40 - 20:10 Listing workflow and pre contract automation Uploading listing agreements Task creation and deadline tracking Managing offers with Ava AI generated listing descriptions Handling multiple offers efficiently 20:10 - 30:15 Contract to closing automation Extracting contract data instantly Building timelines and calendars Email drafting and communication CRM integration and contact syncing Mobile usage and real time updates 30:15 - 45:10 Scaling agents and teams with AI Template creation for repeat workflows Doubling transaction volume Team collaboration features Improved client communication Reducing manual workload 45:10 – 01:14:25 Future of AI and Agentic Ava Autonomous AI handling deal Inbox monitoring and task execution Text messaging and voice interaction Trust and approval systems Vision for fully automated transactions   Quotes: "Reading four offers at once is now a thing" – Karan Khanna "I just want to be out there selling instead of stuck doing paperwork" – Karan Khanna "We're trying to create an invisible workflow" – Karan Khanna "Ava can wake up and start working on your deals automatically" – Karan Khanna   To contact Karan Khanna, learn more about his business, and make him a part of your network, make sure to follow his Website and LinkedIn.   Connect with Karan Khanna! Website: https://www.listedkit.com LinkedIn: https://www.linkedin.com/in/karkhanna   Connect with me! Website: toprealtorjacksonville.com   Website: toprealtorstaugustine.com    SUBSCRIBE & LEAVE A 5-STAR REVIEW as we discuss real estate excellence with the best of the best.   #RealEstateExcellence #KaranKhanna #RealEstateAI #AIForAgents #TransactionCoordinator #PropTech #AIAutomation #RealEstateTools #AgentProductivity #RealEstateTech #AIAssistant #ListedKit #AvaAI #RealEstateWorkflow #CRMIntegration #AIRevolution #FutureOfWork #AutomationTools #RealEstateBusiness #SalesProductivity #DigitalTransformation #TechInRealEstate

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Listeners weigh in on Greg's college tuition lead. They Said it hears form Tom Brady who is doubling back on his strict diet saying it's been overblown.

Fresh off raising a monster $15B, Marc Andreessen has lived through multiple computing platform shifts firsthand, from Mosaic and Netscape to cofounding A16z. In this episode, Marc joins swyx and Alessio in a16z's legendary Sand Hill Road office to argue that AI is not just another hype cycle, but the payoff of an “80-year overnight success”: from neural nets and expert systems to transformers, reasoning models, coding, agents, and recursive self-improvement. He lays out why he thinks this moment is different, why AI is finally escaping the old boom-bust pattern, and why the real bottleneck may be less about models than about the messy institutions, incentives, and social systems that struggle to absorb technological change.This episode was a dream come true for us, and many thanks to Erik Torenberg for the assist in setting this up. Full episode on YouTube!We discuss:* Marc's long view on AI: from the 1980s AI boom and expert systems to AlexNet, transformers, and why he sees today's moment as the culmination of decades of compounding technical progress* Why “this time is different”: the jump from LLMs to reasoning, coding, agents, and recursive self-improvement, and why Marc thinks these breakthroughs make AI real in a way prior cycles were not* AI winters vs. “80-year overnight success”: why the field repeatedly swings between utopianism and doom, and why Marc thinks the underlying researchers were mostly right even when the timelines were wrong* Scaling laws, Moore's Law, and what to build: why he believes AI scaling laws will continue, why the outside world is messier than lab purists assume, and how startups can still create durable value on top of rapidly improving models* The dot-com crash and AI infrastructure risk: Marc's comparison between today's AI capex boom and the fiber/data-center overbuild of 2000, plus why he thinks this cycle is different because the buyers are huge cash-rich incumbents and demand is already here* Why old NVIDIA chips may be getting more valuable: the pace of software progress, chronic capacity shortages, and the idea that even current models are “sandbagged” by supply constraints* Open source, edge inference, and the chip bottleneck: why Marc thinks local models, Apple Silicon, privacy, trust, and economics all point toward a major role for edge AI* American vs. Chinese open source AI: DeepSeek as a “gift to the world,” why open models matter not just because they're free but because they teach the world how things work, and how open source strategies may shift as the market consolidates* Why Pi and OpenClaw matter so much: Marc's claim that the combination of LLM + shell + filesystem + markdown + cron loop is one of the biggest software architecture breakthroughs in decades* Agents as the new “Unix”: how agent state living in files allows portability across models and runtimes, and why self-modifying agents that can extend themselves may redefine what software even is* The future of coding and programming languages: why Marc thinks software becomes abundant, why bots may translate freely across languages, and why “programming language” itself may stop being a salient concept* Browsers, protocols, and human readability: lessons from Mosaic and the web, why text protocols and “view source” mattered, and how similar principles may shape AI-native systems* Real-world OpenClaw use: health dashboards, sleep monitoring, smart homes, rewriting firmware on robot dogs, and why the most aggressive users are discovering both the power and danger of agents first* Proof of human vs. proof of bot: why Marc thinks the internet's bot problem is now unsolvable via detection alone, and why biometric + cryptographic proof of human becomes necessaryTimestamps* 00:00 Marc on AI's “80-Year Overnight Success”* 00:01 A Quick Message From swyx* 01:44 Inside a16z With Marc Andreessen* 02:13 The Truth About a16z's AI Pivot* 03:29 Why This AI Boom Is Not Like 2016* 06:33 Marc on AI Winters, Hype Cycles, and What's Different Now* 10:09 Reasoning, Coding, Agents, and the New AI Breakthroughs* 12:13 What Founders Should Build as Models Keep Improving* 16:33 AI Capex, GPU Shortages, and the Dot-Com Crash Analogy* 24:54 Open Source AI, Edge Inference, and Why It Matters* 33:03 Why OpenClaw and PI Could Change Software Forever* 41:37 Agents, the End of Interfaces, and Software for Bots* 46:47 Do Programming Languages Even Have a Future?* 54:19 AI Agents Need Money: Payments, Crypto, and Stablecoins* 56:59 Proof of Human, Internet Bots, and the Drone Problem* 01:06:12 AI, Management, and the Return of Founder-Led Companies* 01:12:23 Why the Real Economy May Resist AI Longer Than Expected* 01:15:53 Closing ThoughtsTranscriptMarc: Something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic. Having said that, I think what's actually happened is an enormous amount of technical progress that built up over time. And like for, for example, we now know that neural network is the correct architecture.And I, I will tell you like there was a 60 year run where that was like a, you know, or even 70 years where that was controversial. And so, so the way I think about what's happening is basically, I think, I think about basically the, the, the period we're in right now is it's, I call it 80 year overnight success, right?Which is like, it's an overnight success ‘cause it's like bam, you know, chat GPT hits and then, and then oh one hits, and then, you know, open claw hits and like, you know, these are open, these are, these are like overnight, like radical, overnight transformative successes, but they're drawing on an 80 year sort of wellspring backlog, you know, of, of, of, of ideas and thinking it's not just that it's all brand new, it's that it's an unlock of all of these decades of like very serious, hardcore research.If I were 18, like this is a hundred, this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough.swyx: Before we get into today's episode, I just have a small message for listeners. Thank you. We will not be able to bring you the ai, engineering, science, and entertainment contents that you so clearly want if you didn't choose to also click in and tune into our content.We've been approached by sponsors on an almost daily basis, but fortunately enough of you actually subscribed to us to keep all this sustainable without ads, and we wanna keep it that way. But I just have one favor to ask all of you. The single, most powerful, completely free thing you can do is to click that subscribe button.It's the only thing I'll ever ask of you, and it means absolutely everything to me and my team that works so hard to bring the in space to you each and every week. If you do it, I promise you will never stop working to make the show even better. Now, let's get into it.Alessio: Hey everyone, welcome to the Lidian Space Pockets. This is CIO, founder Kernel Labs, and I'm joined by s Swix, editor of Lidian Space.swyx: Hello. And we're in a 16 Z with a, uh, mark G and welcome.Marc: Yes, yes. A and what, half of 16? Something like that. A one. Exactly,swyx: exactly. Uh, apparently this is the, the final few days in your, your current office.You're moving across the road.Marc: Uh, we're, yeah. We have a, we have some, we have some projects underway, but yeah, this is actually, oh, this is the original. We're in actually the original office. We're in the, we're in the, we're, we're in the whole thing.swyx: It's beautiful. Yeah. Great.Marc: Thank you.swyx: So I have to come out, uh, this is a, you know, I wanted to pick a spicy start in October, 2022.I just made friends with Roone and, uh, I wanted to give him something to sort of be spicy about. And I said, uh. Uh, it'll never not be funny. The A 16 Z was constantly going. The future is where the smart people choose to spend their time and then going deep into crypto and not in ai. And that was in October 22nd, 2022.And Ruen says there was an internal meeting in a 16 Z to reorient around Gen ai. Obviously you have, but was there a meeting? What, what was that?Marc: I mean, I don't, look, I've been doing AI since the late eighties.swyx: Yeah.Marc: So I, I don't know, like all that, as far as I'm concerned, this stuff is all Johnny cum lately.Yeah. You, I mean, look, we've been doing ar entire existence. I mean, we've been doing AI machine learning deep, you know, deeply. We've been doing this stuff way from the beginning. Obviously a AI is just core to computer science. I, I, I actually view them as like quite, uh, quite continuous. Um, you know, Ben and I both have computer science degrees.Um, you know, we, we both, Ben, Ben and I actually both are world enough to remember the actual AI boom in the 1980s. Yeah. There was like a, there was a big AI boom at the time. Um, and there was a, was names like expert systems. Um, and they of like lisp and lisp machines. Uh, I, I coded in lisp. I was coding a lisp in 1989.When that was the, the language of the AI future. Um, yeah. So this is something that we're like completely, you completely comfortable with. I've been doing the whole time and are very enthusiastic aboutswyx: is there a strong, like this time is different because, uh, my closest analog was 20 16 17. It was an AI boom.Mm-hmm. And it petered out very, very quickly. Um, we, it just, it just in terms of investingMarc: sort of, sort of,swyx: yeah. Investment, investment excitement.Marc: Although that's really when the, the, the Nvidia phenomenon really, it was, I would say it was in that period when it was very clear that at, at the time it, the vocabulary was more machine learning, but it, it was very clear at that time that machine learning was hitting some sort of takeoff point.Alessio: Yeah.Marc: Well, and as you guys, you guys have talked about this at length on, on your thing, but, you know, if you really track what happened, I think the real story is, it was, it was the Alex net, uh, basically breakthrough in like 2013. That was the, that was the real knee in the curve. Um, and then it was obviously the transformer breakthrough in 17.Alessio: Yeah.Marc: Um, and then everything that followed. But, but, you know, look, machine learning, you know, there were, you know, look, uh, I mean look, I've been working, you know, I've been working with, uh, one of my, you know, kind of projects working with Facebook since 2004. Um, and on the board since 2007, and of course, you know, they, they started using machine learning very early, um, and, you know, have used it basically, you know, for like 20 years for, you know, content, you know, feed optimization and advertising optimization.And obviously many, you know, financial services. You know, many, many, many companies, many different sectors have been doing this. And so it's like one of these things, it's like, it's not a, it's not a single thing. Like it's, it's like, it's like layers, right? Yeah. Um, and, and the layers arrive at different paces and, but they kind of build up.swyx: Yeah.Marc: Uh, they kind of build up over time and then, and then, yeah. And then look, in retrospect, it was 2017 was kind of the, you know, the key, the key point with the trans transformer and then. And then as you guys know, there was this really weird like four year period where it's like the, the transformer existed and then it was just like,swyx: let's go.Yeah.Marc: Well, but, but it was just, but, but between 2020, but between 2017 and 2021, I mean, that was the era of which like companies like Google had internal chat Botts, but they weren't letting anybody use them.swyx: Yeah.Marc: Right. And then, you know, and then OpenAI developed Chat GT or GPT two, and then they told everybody, this is way too dangerous to deploy.Right. Yeah. You know, we can't possibly let normal people, normal people use this thing. And then you, you guys, I'm sure remember AI Dungeon, um mm-hmm. So the o for, there was like a year where like the only way for a normal person to use GP T three was in, in AI dungeon.Alessio: Yeah.Marc: And so you, you, we would do this, you'd go in there and you'd pretend to play Dungeons and Dragons.In reality, you're just trying to talk to talk to GPT. And so there was this, you know, there was this long, you know, and I, you know, the big, big companies, you know, big companies are cautious and, you know, the big companies were cautious. It, it, by the way, it took open ai. You know, they, they, they talk about this, it took open AI time to actually adjust, you know, kind of re redirect their researchswyx: path.I, I think, uh, let say Rosewood, right? Uh, the, the dinner that founded OpenAI was right there.Marc: Right, right. But that, that dinner would've taken place in 20swyx: 18Marc: 19. The formation of OpenAI Uhhuh as late as 2018.swyx: Uh, uh, sorry. Uh, no, I'm, I'm, I'm, I'm wrong. Probably It should be 20. Yeah. They just celebrated a 10 year anniversary, so it it is 2025.Yeah, so, so 2015?Marc: Yeah. 2015. Yeah. 2015. But then, uh, um, Alec Radford did G PT one in what, probablyswyx: mm-hmm. 17, 18,Marc: yeah. 17, 18. So it, yeah. For, and then, and then they didn't really, and then GPT three was what? 2020? 2020.swyx: 2020.Marc: Because that became copilot immediately. Even open ai, which has been, you know, the leader of, of this thing in the last decade, you know, e even they had to adapt and, and, and lean into the new thing.And so. Um, yeah, I, I think it's just this process of basically sort of wave after wave layer after layer, you know, building on itself. And then you kind of get these catalytic moments where, where the whole thing pops and, and obviously that's what's happening now.swyx: Is it useful to think about will there be any ai, winter?‘cause there's always these patterns. Like, is this, in the summer is something I constantly think about because do I get, do I just like. Just get endlessly hyped and just trust that I will only be early and never wrong or right. Well, are we, will there be a winter?Marc: So there's something about, say the following.There's something about AI that has led to this repeated pattern. Um, and, and, and you guys know this,swyx: it's summer, winter, summer,Marc: winter, summer, winter, summer, winter. And it goes back 80 years. Yeah. 80 years. Uh, so the original neural network paper was 1943. Right. Which is, which is amazing. Uh, that it was, it was far back that long.And then there was you, if you guys have ever talked about this on your show, but there was this, uh, there was a big, uh, there was an a GI conference at Dartmouth University in 1950. 55. 55, yeah. And they got a NSF grant to, uh, for the, all the AI experts at the time to spend the summer together. And they figured if they had 10 weeks together, they could get a GI, uh, at the other end.And they got their, by the way, they got the grant, they got the 10 weeks and then, you know, 1955, you know. No, no. A GI. And like I said, I, I lived through the eighties version of this where there was a big, a big boom and a crash. And so, so there is this thing, and there, there is something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic.Um, and, and it's probably on both sides of like the, the, the boom bus cycle. You, you kind of see that play out. Having said that, I think what's actually happened is like just, and you know, and we now know in retrospect like an enormous amount of technical progress that built up over time. And like for, for example, we now know that neural network is the correct architecture.And I, I will tell you like there was a 60 year run where that was like a, you know, or even 70 years or that was controversial. And, and we now know that that's the case. And so we, we now, you know, everything we're building on today just sort of derives from the original idea in 1943. And so, so in retrospect, we, we now know that like, these, these guys are right.They, they, you know, they would get the timing wrong and they thought, you know, capabilities would arrive faster, or they were, it could be turned into businesses sooner or whatever, but like, they were fundamentally, the, the scientists who worked on this over the course of decades were fundamentally correct about what they were doing.And, and the, and the payoff from, from, from all their work is happening now. And so, so the way I think about what's happening is basically, I think, I think about basically the, the, the period we're in right now is it's, I call it 80 year overnight success, right? Which is like, it's an overnight success.‘cause it's like bam, you know, chat, GPT hits and then, and then oh one hits, and then, you know, open claw hits and like, you know, these are open, these are, these are like overnight, like radical, overnight transformative successes, but they're drawing on an 80 year sort of wellspring backlog, you know, of, of, of, of ideas and thinking it's not just that it's all brand new, it's that it's an unlock of all of these decades of like very serious, hardcore research.Um, and thinking, and look, there were AI researchers who spent their entire lives. They got their PhD. They, they worked for, they've researched for 40 years. They retired in a lot of cases, they passed away and they never actually saw it work.swyx: Yeah. It's all sad.Marc: It is. It is sad. It's sad. Knewswyx: Jeff Hinton was like the last guy.Marc: Yeah. Yeah. Well, there were the guys, uh, was a guy, Alan Newell. I mean, there's tons of John McCarthy. You know, John McCarthy was like one of the inventors in the field. He's one of the guys who organized the Dartmouth Conference and you know, he taught at Stanford for 40 years. Wow. And passed, you know, passed away, I don't know, whatever, 10, 10 years ago or something.Never, never actually go. Got to see it happen. But like, it is amazing in retrospect, like, these guys were incredibly smart and they worked really hard and they were correct. So anyway, so then it's like, okay, you know, say history doesn't repeat, but it rhymes. It's like, okay, does that mean that there's gonna be another, like, you know, basically boom buzz cycle.And I, I will tell you, like, let, like in a sense, like yes, everything goes through cycles and, you know, people get overly enthusiastic and overly depressed and there's, there's a time, there's a timelessness to that. Having said that, there's just no question. Um, so the form, the foremost dangerous words in investing this time are, this time is different.Do you know the 12 most dangerous words investing? No. The four most d foremost dangerous words in investing are this time is different. Yeah. Um, the 12 most dangerous words. And so like, I'll tell you what's different. Like now it's working like, like there's just no, I mean, look, there's just no question.And by the way, I, I'll just give you guys my take. Like L LLMs, like from, from basically the Chad G PT moment through to spring of 25. I think you could still, I think well intention, well, and of. Form skeptics could still say, oh, this is just pattern completion. And oh, these things don't really understand what they're doing.And you know, the hall hallucination rates are way too high. And, you know, this is gonna be great for creative writing and creating, you know, Shakespeare and so sonnets and, you know, as, as rap lyrics or whatever, like, it's gonna be great and all that stuff, but we're not gonna be able to harness this to make this relevant in, you know, coding or in medicine or in law or in, you know, you know, kind of feels that, you know, kind of really, really matter.And I think basically it was the reasoning breakthrough. It, it was oh one and then R one that basically answered that question basically said, oh no, we're gonna be able to actually turn this into something that's gonna work in the real world. And, and then obviously the coding breakthrough over the, over basically the coding breakthrough that kind of catalyzed over the holiday break was kind of the third step in that.Mm-hmm. Where you're just like, alright, if, if, you know, if Linus Tova is saying that the AI coding is no better than he is like. Like, that's, that's never happened before. That's theswyx: benchmark.Marc: Yeah. That's never happened before. And so now we know that it's, it's gonna sweep through coding and, and then, and then we, we know, you know, we know that if it's gonna work in coding, it's gonna work in everything else.Right. It's just then, because that's, that's like, that's like, that's like the hardest in many ways. That's the hardest example. And how everything else is gonna be a, a derivative of that. And then on top of that, we just got the agent breakthrough, you know, with Open Claw, which is fantastic. Which is amazing and incredibly powerful.And then we just got the, the, um, the auto research, uh, you know, the, the self-improvement. You know, we're now into the self-improvement breakthrough. And so the, so the way I think about it is we've had four fundamental breakthroughs in functionality, l OMS reasoning, uh, agents, um, and then, uh, and, and then now RSI, um, and, and they're all actually working.Um, and so I'm, I'm just, as you like, you can tell I'm jumping outta my shoes. Like, like this is, like this is it like this, this is the culmination of 80 years worth of worth of work, and this is the time it's becoming real.Alessio: Yeah.Marc: I, I'm completely convinced.Alessio: I think the anxiety that people feel is like during the transistor era, yet Mors law, and it's like, all right, we understand why these things are getting better.We understand the physics of it. Yeah. With ai, it's. It's so jagged in like the jumps where like, like you said, it's like in three months you have like this huge jump like, and people are like, well this can keep happening. Right? But then it keeps happening,Marc: it'll keep happening.Alessio: And so like how do you think about also timelines of like what's we're building?I think we always have this question with guests, which is like, you know, should you spend time building harness for a model versus like the next model just gonna do it one shot in the lead space. Right. And how does that inform, like how you think about the shape of the technology? You know, you talk about how it's a new computing platform.If you have a computing platform, then like every six months it like drastically changes in what it looks like. It's hard to build companies on top of it.Marc: Yeah. So, so a couple things. So one is like, look, the, the Moore's law was what we now call a scaling law. Like Moore's Law was a scaling law and for your younger viewers, more Moore's Law was every chip chip chips either get twice as powerful or twice as cheap every, every 18 months.And that, and that and that, you know, that it's gotten more complicated in the last few years. But like that, that was like the 50 year trajectory of, of, of the computer industry. And then, and then by the way, and that's what took the mainframe computer from a $25 million current dollar thing into, you know, the phone in your pocket being, you know, a million times more powerful than that.Like that, you know, for, for 500 bucks. And so that, that was a scaling law. And then, and then, and then key to any scaling law, including Moore's Law and the AI scaling laws is, you know, they're not really laws, right? They're, they're, they're, they're predictions, but when they work, they become self-fulfilling predictions because they, they, they, they, they set a benchmark and, and then the entire industry, right?All the smart people in the industry kind of work to make sure that, that, that actually happens. And so they, they kind of motivate the breakthroughs that are required to, to keep that going. And, and in and in chips, that was a 50 year, that was a 50 year run. Right. And it, it was amazing. And it's still happening in, in some areas of, of chips.I think the same thing is happening with the, the core scaling laws. The core scaling laws. In, in, in ai, you know, they're, they're not really laws, but like they, they are basically. There are predictions and then they're motivating catalysts for the research work that is required to be. And, and, and, and by the way, also the investment, uh, dollars, um, uh, you know, required to basically keep, you know, keep the curves going and, and look, it, it is, it's gonna be complicated and it's gonna be variable and they're, you know, there're gonna be walls that are gonna look like they're fast approaching, and then they're gonna be, you know, engineers are gonna get to work and they're gonna figure out a way to punch through the walls.And obviously that's, you know, that's been happening a lot, you know, and then look, there's gonna be times when it looks like the walls have, you know, the, the, the laws have petered out and then they're gonna, they're gonna pick up again and surge and then, and then, and then it, it appears what's happening to the eyes is there's not multiple, you know, multiple scaling laws.Um, there's multiple areas of improvement. And, and I think, you know, I don't know how many more there are already yet to be discovered, but there are probably some more that we don't know about yet. You know, they, like, for example, there's probably some scaling law around, um, world models and robotics that we don't fully understand, you know, kind of acquisition of data at scale in the real world that we don't fully understand yet.So that, that, that one will probably kick in at some point here. There's a bunch of really smart people working on that. Um, and so, yeah, I, I think the expectation is that, that, you know, the, the scaling laws generally are gonna continue. Yeah. The, the pace of improvement will continue to move really fast.Um. To your question on like what to build. So, uh, I'm a complete believer the scaling laws are gonna continue. I'm a complete believer the capabilities are gonna keep getting amazing, um, you know, leaps and bounds. Uh, the part where I kind of part ways a little bit with how, what I would describe as the AI purists, um, you know, which is, which I would characterize as like the people who are.In many ways, the smartest people in the field, but also the people who spend their entire life, like at a lab, um, and have, have, I would say, have very little experience in the outside world. Um, the, the, the nuance I would offer is the outside world of 8 billion people and institutions and governments and companies and economic systems and social systems is really complicated.Um, and, um, and doesn't, you know, it it 8 billion people making collective decisions on planet Earth is not a simple process of like, just like you see this happening now. It's like a bunch of AI CEOs have this thing, which is just like, well, there's just this, they just all have this kind of thing when they talk in public where they're just like, well, there's these, these obvious set of things that so society to do.Alessio: Mm-hmm.Marc: And then they're like, society's not doing any of those things. Right. And it's like, how can society not, you know, what, whatever their theory is, how can society not see x, y, Z? Mm-hmm. And the answer is, well, society is number one. There's no single society, it's like 8 billion people. And they like all have a voice, and they all have a vote, like at the end of the day of how they, they react to change.And then, you know, it just like, it's just human reality is just really complicated and messy. Um, and, and, and so the specific answer to your question is like, as usual, it depends. Um, you know, it, it depends. Look, pe there's no question people are gonna, like, there's no question they're gonna be companies.It's already happening. There are companies that think that they're building value on top of the models and then they're just gonna get blissed by the, by the next model. There's no question that's happening. But I think there's no question also that just the process of adaptation of any technology into the real and into the real messy world of humanity is, is just going to be messy and complicated.It's, it's not going to be simple and straightforward. It's gonna be messy and complicated. And there are gonna be a lot of companies and a lot of products, um, uh, and in, in fact entire industries that are gonna get built to, to, to basically actually help all of this technology actually reach real people.Alessio: The amount of capital going into these companies, I mean, Dario talked about it on the Door Cash podcast and Door Cash was like, why don't you just buy 10 x more GPUs? And he is like, because I'm gonna go bankrupt if the model doesn't exactly hit the, the performance level. How do you think about that?Also as a risk on, you know, you guys are investors, open AI and thinking machines and world apps. It seems like we're leveraging the scaling loss at a pretty high rate, right? Like how comfortable, I guess, do you feel with the downside scenario, like, and say like things Peter out, you think you can kind of like restructure uh, these build outs and uh, you know, capital investments.Marc: Yeah. So should start by saying, so I live through the.com crash, um, and I can tell you stories for hours about the.com crash and it was horrible. No, it was awful. It was, it was, it was apocalyptic by the way. The, a lot of the.com crash was actually at the time, it was actually a telecom crash. It was a bandwidth crash.Like the, the thing that actually crashed, that wiped out all the money with the tele, the telecom companies.swyx: GlobalMarc: crossing. Global, global, yeah.swyx: I'm from Singapore and they, they laid so much cable o over over our oceans.Marc: Actually there was a scaling law in the.com. Era. And it was literally the, the US Commerce Department put out a report in 1996 and they said internet traffic was doubling every quarter.Um, and, and actually in 1995 and 1996, internet traffic actually did double every quarter. And so that became the scaling law. And so what all these telecom entrepreneurs did was they went out and they raised money to build fiber, anticipating that the demand for bandwidth is gonna keep doubling every quarter.Doubling every quarter though is like, you know, grains of chess and the chessboard, like at some point the numbers become extremely large. Right. And, and, and it really, and really what happened was the internet. The internet by the way, continuously kept growing basically since inception. And it's, you know, it's, it's continuously grown.It's never shrunk. And it's grown really fast compared to anything else. Mm-hmm. You know, in, in, in human history. But it wasn't doubling every quarter as of 19 98, 19 99. And so there was this gap in the expectation of what they thought was a scaling law versus reality. And that's actually what caused the.com crash, which was the, it they, they way over companies like global crossing way overbuilt fiber, which is sort of the, and by the way, fiber, telecom equipment, you know, so all the, all the networking gear, you know, and then, and then by the way, the actual physical data centers, like that was the beginning of the, of the, of the data center build and then, and the data center overbuild.And so you had that, but it was, it was literally, I think it was like $2 trillion got wiped out, right? It was like Jesus, it was like a big, it was. And by the way, the other, the other subtlety in it was the internet companies themselves never really had any debt. ‘cause tech, tech companies generally don't run on debt, but the telecom companies run on debt.Physical infrastructure companies run on debt. And so the companies like Global Crossing not just raise a lot of equity, they also raise a lot of debt. So they're highly levered. And so then you just do the thing. It's just like, okay, you have a highly levered thing where you're, you're just over, you're overbuilding capacity.Demand is growing, but not as fast as you hoped. And then boom, bankrupt. Right. And, and then it, and then it's like they say about the hotel industry, which is, it's always the third owner of a hotel that makes money. It has to go bankrupt twice, right? You have to wash out all of the over optimistic exuberance before it gets to actually a stable state.And then it makes money. So by the way, all of those data centers and all of those, all the fiber that they're in use, it's all in use today. Yeah. But 25 years later. But it, it, it took, and actually the elapsed time was, it took 15 years. It took 15 years from 2000 to 2015 to actually fill, fill up all that capacity.The cautionary warning is the, the overbuild can happen. Um, and, and, and, and, you know, you, you get into this thing where basically everybody, everybody who basically has any sort of institutional capital, it's like, wow. It's just, I, I don't know how to invest in these crazy software things. For sure I can put build data centers and for sure I can buy GPUs that I can deploy, you know, compute grids and, and all these things.Um, and so, you know, if you're a pessimist, you could look at this and you could say, wow, this is like really set up to be able to basically replicate, you know, what we went through, what we went through in 2000. Obviously that would be bad. The counter argument, which is the one I I agree with, which is the counter on, on the other side is a couple things.One is the companies that are investing all the, the companies that are investing the money are like the bluest chip of companies. And so back, back, back in the, in the do, like Global Crossing was like a, it was like an entrepreneur. It was like a, a new venture, but like the money that's being deployed now at scale is Microsoft, and, you know, and Amazon and Google, Facebook and Facebook and Nvidia and, you know, these, these, these, and, and now you know, by the way, open ai philanthropic, which are now at like, you know, really serious size, um, you know, as companies with, you know, very serious revenue.These are very large scale companies with like, lots, lots of cash, lots of debt capacity that they've, they've never used. And so th this is institutional in a way that, that really wasn't at the time. And then the other is, at least for now, every dollar that's being put into anything that results in a running GPU is being turned into revenue right away.Like so, and you guys know this, like everybody's starved for capacity, everybody's starved for compute capacity and then, you know, all the associated things, memory and, and, and interconnected and everything else. Um, data center space. And so e every dollar right now that's being put into the ground is turning into revenue.And, and it, and in fact, I actually think there's an interesting thing happening, which is because everybody starve for capacity, the models that we actually have that we can use today are inferior versions of what we would have if not for the supply constraints. That's true. Um, if Right pose a hypothetical universe in which GPUs were 10 times cheaper and 10 times more plentiful mm-hmm.The models would be much better. ‘cause you would just allocate a lot more money to training and you'd just build better models and they would be better. Um, and so we're, we're actually getting the sandbag version of the technology.swyx: Yeah. No. Everything we use is quantized because the, the labs have to keep the, the full versions,Marc: right?swyx: LikeMarc: we're not even getting the good stuff.swyx: Yeah.Marc: But, but getting the good stuff, it's, it's just, even if technical progress stops. Once there's like a much bigger build of like GPU manufacturing capacity and memory, you know, all, all the things that have to happen in the course of the next five or 10 years.Once it happens, even the current technology is gonna get, gonna get much better. And then as you know, like there's just like a million ways to use this stuff. Like there's just like a million use cases for this. Mm-hmm. Like, it, it, you know, this isn't just sending packets across a, a thing, whatever, and hoping that people find something to do with it.This is just like, oh, we apply intelligence into every domain of human activity. And then it works like incredibly well. Yeah. Um. Here's what I know, here's what I know. Um, in the next three or four year, it's like somewhere between three or four years out, basically everything is selling out. So like the, the entire supply chain is, is, is, is sold out or, or, or selling out.And so there, there's no, like, we're just gonna have like chronic supply shortage for, you know, for years to come. Um, there's going to be a response from the market that's gonna result in an enormous, you know, it's happening now. An enormous flood of investment in a new fab capacity and ev you know, every, everything else to be able to do that, at some point the supply chain constraints will unlock, you know, at least to some degree that will be another accelerant to industry growth when that happens.‘cause the products will get better and everything will get cheaper. Um, and so, so I know that's gonna happen. I know that, you know, the deployments, you know, the, the actual use cases are like really compelling. And then, like I said, you know, with reasoning and agents and so forth, like, I know they're just gonna get like much, much better from here.And so I, I, I know the capabilities are like really real and serious. I also know that the technical progress is not going to stop. It. It, it is excel. It is, is accelerating. Like the, the breakthroughs are are tremendous. I mean, even just month over month, the breakthroughs are really dramatic. And so, you know, I think if you were a cynic and there, there are cynics, you can look at 2000, you can find echoes.But I can't even imagine betting it that this is gonna like somehow disappoint and, you know, at least for years to come, I think it would be essentially suicidal to make that bet. Yeah. Um, it was that Michael Burry, uh, uh, that'sswyx: anMarc: interesting guy, huh? We'll pick on a guy. We'll pick, let's pick on one guy.We'll pick. Well ‘cause he did, he he came out with, it was, it was the, heswyx: doesn't mind.Marc: It was the Nvidia short. Right. He came with the Nvidia short. And then if you guys probably talked about this, which is the, the analysis now that like the current models are getting better faster at such a rate that if you are running an Nvidia, if you're running an Nvidia inference chip today, that's three years old, you're making more money on it today than you did three years ago because the pace of improvement of the software is, is faster than the, the, the depreciation cycle, the chip.And then my understanding is Google is running. I don't if they've, I don't know exactly what, uh, these are rumors that I've heard or maybe it's public, but, um, I think Google's running very old TPUs, very profitably. Ference. Yeah. And very profit and very profitably. Yeah. Um, and so, so it actually turns out, as far as I can tell, it's actually the opposite of the Beery thesis is actually.He was actually 180 degrees wrong. It's actually the, the, the, the old Nvidia chips are getting more valuable, which is something that's like literally never happened before. Like it's never been the case that you have an older model chip that becomes more valuable, not less valuable. And that, and again, that's an expression of the just ferocious pace of software progress.Ferocious pace of capability payoff. Yeah. Uh, that you're getting on the other side of this. And so I just, the idea of betting against that, like.swyx: Yeah. Yeah. Well, one ofMarc: my, it seems like an invitation to get your face ripped up.swyx: One of my early hits was like modeling the lifespan of the H 100 and h two hundreds and, and going like, you know, usually they advise like four to seven years and it was, you know, maybe you sort of realistically haircut cut it down to two to three.Yeah. But actually it's going up and not down. Yeah. And, and uh, that's, I mean that's, I think that's the dream. Uh, we are finding utilization and I think utilization solves all problems. Like, you can, you can find use, use cases for even like the poor, like even memory, we're having a shortage. Right. And, and even like the, the shittier versions of, of memory that we do have, we are finding use cases for it.So like That's great.Marc: Yeah.Alessio: How, how important is open source AI and kinda like edge inference in a world in which you have three years of supply crunch. Like, do you think in the, like, you know, if you fast forward like five years, like how do you think about inference, uh, in the data center versus at the edge?Marc: Well, so just to start, yeah. So I think, I think open source is very important for a bunch of reasons. I think edge, edge inference is very important for a bunch of reasons. I, I think just practically speaking, if we're just gonna have fundamental construc, supply crunches for the next, I mean, you, you guys know if you just project forward demand over the next three years, right?Yeah. Relative to supply, one of the, its main predictions you can do is what's gonna, what, what's gonna happen to the cost of, of inference in the core, uh, over the next three years? And like, it may rise dramatically, right? Like, so, so what is, and then is, is, you know, like the, the, the big model competition are subsidizing heavily right now.Right? Right. And so, so what's the, what will be the average person's, you know, per day, per month token cost, you know, three years from now to do all the things that they want to do. And I, I don't know, it's gonna. I mean, I have, you guys probably have friends, I have friends today who are paying a thousand dollars a day for open claw, for claw tokens to run open claw.Right? And so, okay. $30,000 a month. Right? And, and by the way, those, those friends have like a thousand more ideas of the things that they want their claw to do, right? Yeah. And so you, you could imagine there, there's like latent demand of up to, I don't know, five or $10,000 a day of, of, of tokens for a fully deployed, you know, per personal agent.Uh, and obviously consumers can't pay that, right? And so, so, but it gives you a sense of the fu of the fu of the future scope of demand, right? And so, so even, even if there's a 10 x improvement in price performance, that still, you know, goes to a hundred dollars a day, which is still way beyond what people can pay.Mm-hmm. So there's just gonna be like. Ferocious to me, by the way. The agent thing, the other interesting thing is I think the agent thing, so up until now, a lot of the constraints of GGPU constraints, I think the agent thing now also translates into CPU constraints. Mm-hmm. Right?swyx: CPU memory.Marc: Yes. CPU memory, right?And so, like the entire chip ecosystem is just gonna get wait,swyx: wait for network constraints, that that will be the killer.Marc: It's all bottleneck potentially for years. And so, so I, I think that Brad, and, and I think it's actually possible, I mean, generally inference costs are gonna keep coming down, but I think the, let's put it this way, the rate of decline, I think may level out here for a bit because of these supply constraints.And then at some point, maybe the lab stops subsidizing so much and that, that, that again, will be, be an issue. And so there's just gonna be so much more demand for inference than, than can be satisfied. Um, you know, kind of with the centralized model. And then, and then, you know, you guys know this, but like all the, just the dramatic, I mean just the dramatic innovations that have happened in the Apple silicon to be able to do, uh, inferences, it's quite amazing the level of effort being put.Like the open source guys are putting incredible effort into getting, you know, this recurring pattern where the big model will never run on a pc, and then six months later mm-hmm. Oh, it runs in a pc, right? It's like amazing. And there's very smart people working on that. So there's all that. And then look, there's also, you know.There's also like other, there's other motivators. There's other motivators which is just like, okay, how much trust are the big centralized model providers? You know, how much trust are they building in the market versus, you know, how much are, you know, at least for, in certain cases with some people, for certain use cases, people being like, well, I'm not willing to just like, turn everything over.So there, there, there's all the trust issues. Um, by the way, there's also just like straight up price optimization. There's many uses of AI where you don't need Einstein in the cloud. You just need like a, a a, a smart local model. There's also performance issues where you want, you know, you want, you know, you're gonna want your doorknob to have an AI model in it.Right. You know, to be able to, you know, do, um, you know, to be able to do access control. Um, obviously like everything with a chip is gonna have an AI model in it. Mm-hmm. And it, a lot of those are gonna be local. Um, and so, yeah. No, like I think, I think you're gonna have ti and then you're gonna, by the way, also wearable devices, you know, you don't wanna do a complete round trip.You want, you know, you, whatever your smart devices are, you want it to be like super low latency. Yeah.swyx: The question, do we care who makes it? Yeah. One of the biggest news this week was the collapse of AI two, the Allen Institute. Mm-hmm. One of the actual American open source model labs. Yeah. Um, and, uh, I'm not that optimistic on, on American open source.Yeah. Like you, you guys invested in MIS trial and MIS trial's doing extremely well outside of China. That's about it.Marc: Yeah. We'll see. We'll see. I look, I, number one, I do think we care. Uh, I do think we, I do think we care who makes it. Um, I would say this, the, the, the, the previous presidential administration wanted to kill it in the us Oh yeah.They wanted to drown in the bathtub. Um, and so they wanted to kill it. So at least we have a government now that actually like, actually wants it wants it to happen. And youswyx: earned to councilMarc: and Yeah. And the new and the P pcast. Yeah. So the, the, you know, this admin for whatever other political issues people have, which are many, you know, this administration has, I think a very enlightened view and in particular an enlightened view on AI and in particular on open source ai.Uh, and so they're very supportive. Um, my read is the Chi. The Chinese have a very, the various Chinese companies have a very specific reason to do open source, which is, they, they, they don't fundamentally, they don't think they can sell commercial, uh, AI outside of China right now. And or at least specifically not, not in the US for a combination of reasons.And so they, they kind of view, I think, open source AI as a bit of a loss leader against basically domestic, uh, you know, paid, paid services. And then kind of an, you know, kind of an ancillary products. You know, they're, they're very excited about it, by the way. I think it's great. I think it's great that they're doing it.Um, you know, I think Deeps seek was like a gift to the world. Um, I think. The great thing about open source, open source, the, the, the impact of open source is felt two ways. One is you, you get the software for free, but the other is you get to learn how it works, right? And so like the paper, the paper, the paper and, and the code, right?And the code. And so, like, for example, I thought this was amazing. So open comes out with L one and it's an amazing technical breakthrough, and it's just like, absolutely fantastic. But of course they don't explain how it works in detail. And then of course they hide the, they hide the reasoning traces, right?And, and then, and then, and then everybody's like, okay, this is great, but like, who's gonna be able to replicate this? Are other people gonna be able to do this? You know, is their secret sauce in there? And then our one comes out and it's just like, there's the code and there's the paper, and now the whole world knows how to do it.And then, you know, three months later, every other AI model is, is adding reasoning. And so, so you get this kind of double, like even if the Chinese models themselves are not the models that get used, the education that's taken place to the rest of the world, the information diffusion, you know, is incredibly powerful.So that happens and then, I don't know. We'll, we'll see. You know, there are a bunch of American, you know, open source, you know, ai, uh, model companies. I mean, look, there's gonna be tremendous, you know, there already is. There's, you know, there's gonna be tre there's tremendous competition, uh, among the primary model companies.You know, there's, depending on how you count, there's like four or five, you know, big co model companies now that are, you know, kind of neck and neck, uh, in different ways. Um, uh, you know, and, and, and, um, you know, and then obviously Bo Bo both X and then MetAware involved are, you know, both have huge, you know, huge attempts to, you know, kind of, to kind of leapfrog underway.And then you've got, you know, a whole fleet of startups, new companies, including a whole bunch that we're backing, that are, you know, trying to come out with different approaches. And then you've got whatever it is. I don't know how, how many, how many, like main line foundation model companies are there in China at this point?It's probably six. It'sswyx: five Tigers is what they call it. Yeah. Uh, Quinn is in questionable because there's change in leadership,Marc: right?swyx: Yeah.Marc: But that, does that include, that includes like Moonshot,swyx: yes. Can deep seek, uh, uh, ZI, um, Quinn oh one is in there.Marc: Right. And then, um, and by dance and, and then you see,swyx: ance would be like the next tier ance.They weren't as prominent. They weren't, didn't haveMarc: a leading. Yeah. But they, you at least, you know, ance is very inspiring and presumably they have more stuff coming and Tencent probably has more stuff coming and, and so forth. And so, so, so like, look, here, here would be a thing you can anticipate, which is there are not these markets, there are not going to be between the US and China right now, there's like a dozen primary foundation model companies that are like at scale, at, at some level of a critical mass.It's not gonna be a dozen in three years, right? Like, it just because these industries don't bear a dozen, it's, it's gonna be three or you know, there's gonna be three or four big winners or maybe one or two big winners. And so there's gonna be like a whole bunch of those guys that are gonna have to figure out alternate strategies.Um, and I think like open source is one of those strategies. And so I, I think you could see like a whole, i, I, I think the questions like, who's gonna do open source? I think that could change really fast. I, I think that, that, that's a very dynamic thing. I think it's very hard to predict what happens. And, and I think it's very important.swyx: NVIDIA's doing a lot.Marc: Well, I was gonna say. Well, exactly. And then you're got Nvidia and then, and then, you know, just to, again, indu, there's an old thing in business strategy, which is called, uh, commoditize Compliments. Commoditize the compliment. That's right. And so if your Jensen is just kind of obvious, of course, you wanna commoditize the software.Yeah. And he's, and to his enormous credit, he's putting enormous resources behind that. And so maybe it, maybe it's literally Nvidia and I think that would be great.Alessio: Yeah. Uh, narrative violation to European projects, uh, in the, uh, damn.swyx: I'm hosting my, uh, Europe, uh, conference soon. And I got both of them.Alessio: They got us.They got us. MarkMarc: finished. They got us, us. Well, wait a minute. Where was Peter? So where was Steinberger when he did? In AustriaAlessio: was, yeah, yeah, yeah.Marc: He was in what? He was in Vienna. Oh, he was in Vienna. And then where is he now?swyx: Uh, he's moving to sf.Marc: Okay. Okay. Alright. Okay, there we go. And then, yeah, the PI guy, right?The PI guys are European.swyx: Yeah, they're also, they're buddies inAlessio: Australia. Mario's also there. Yeah.Marc: Right. And are they, yeah, they haven't announced yet. Any sort of change changed or have theyAlessio: No, they're, they have a company there.Marc: Okay. Got, okay. Good.Alessio: Good, good,good.Alessio: Um,Marc: yeah, good.swyx: Anyways, I think pie and open cloud very important software things and, and I just wanted you to just go off on what you think.Marc: Yeah. So I think in co the, the combination of the two of them I think is one of the 10 most important softwares. Openswyx: Claw got all the attention, but Right. Talk about pie,Marc: pi pie's, kind of the Yeah. PI's, PI's kind of the architectural breakthrough for those of us who are older. There was this whole thing that was very important in the world of software basically from like 1970 to, I don't know, it still is very important, but like 19, from 1973 to like basically the creation of Linux, which is basically this, this thing used to call like the Unix mindset.Like so, so, ‘cause there were all these different, you know, theories. There are all these different operating systems and mainframes and, and then you know, all these windows and Mac and all these things. And then there was this, but kind of behind it all was this idea of kind of the Unix mindset. And the Unix mindset was this thing where basically you don't have these, like, like in the old days, like, like the operating system that like made the computer industry really work, like in the 1960s mm-hmm.Was this thing called o os 360, which was this big operating system that IBM developed that was supposed to basically run everything. And it was this like giant monolithic architecture in the sky. It was like a, you know, it was like a giant castle. Um, of software. And, and by the way, it worked really well and they were very successful with it.But like, it was this huge castle in the sky, but it was this thing, it was almost unapproachable, which is like, you had to be kind of inside IBM or very close to IBM. And you had to really understand every aspect, how the system worked. And then the, the Unix sky is originally out of at and t and then out out of Berkeley, um, you know, came out and they said, no, let's have a completely different architecture.And the way architecture's gonna work is we're gonna have, we're gonna have a, a prompt and, and a, and a shell. And then, and then we're gonna, all, all the functionality is gonna be in the form of these discreet modules, and then you're gonna be able to chain the modules together. Mm-hmm. Yeah. And so like the, the, the op, it's almost like the operating, operating system itself is gonna be a programming language.Um, and then that led led to the, the, the sort of centrality of the shell. Um, and then that led to sort of, uh, you know, basically chaining together Unix tools. And then that led to the emergence of these, these scripting languages like Pearl, where you, you could basically kind of very easily do this, and then the shells got more sophisticated and then, and then, and then look like, you know, that, that, that number one, that worked and that, that was the world I grew up in.Like I was, I was a Unix guy. You know, sort of from, call it 1988 to, you know, kind of all, all the way through my work and it worked really well. It, it's in the background, um, you know, nor normal people don't need to, didn't need to necessarily know about it, but like, if you were doing like system architecture, application development, you, you, you knew all about it.Um, and then, you know, it's been in the background ever since. And, you know, look, your Mac still has a Unix shell, you know, kind of in there, and your iPhone still has a Unix shell kind of buried in there somewhere. So they're kind of in there. And then, you know, the Windows shell is kind of a, you know, sort of a weird derivative of that.But, um, you know, but look, the inter, the internet runs on Unix, um, and that smartphones, actually, both iOS and Android are Unix derivatives. And so, you know, kind of Unix did end up winning. But, but anyway, and then we just started taking that for granted. And then, and then so, so basically the, the way I think about what happened with Pie and then with Open Claw is basically what those guys figured out is, I always say the, the great breakthroughs are obvious in retrospect, right?Which is the best kind, the best kind. They weren't obvious at the time or somebody else would've done them already. Um, and so there is a, like a real conceptual leap, but then you look at it sort of the backwards looking and you're just like, oh, of course. Mm-hmm. Like the, the, to me those are always the best breakthroughs.Well, actually language models themselves are like that. It's just like, oh, next token completion. Oh, of course.swyx: Yeah. What other objective mattered?Marc: Yeah, exactly. But, but like it, right. But she's even saying it wasn't obvious until somebody actually did it. Right. And so the conceptual breakthrough is real and deep and powerful and, and very important.And so the way I think about pie and olaw is it's basically marrying the, the language model mindset to the un to the Unix, basically shell prompt mindset. And so it's, it's basically this idea that what, what, so what is an agent, right? And as, as, and as you know, like many smart people who have been trying to figure out what an agent is for, for, for decades, and they've had many architectures to build agents and the whole thing.And it turns out what is an agent. So it turns out what we now know is an agent is the following. It's, so it's a language model. And then above that, it's a ba, it's a bash shell. Um, so it's a, it's a Unix shell, and then it's, and then the agent has access, uh, has access to, to the shell. And, you know, hopeful, hopefully in a sandbox, maybe in, maybe in a sandbox.So it's, it's the model. Um, it's the shell. Um, and then it's a fi, it's a file system. Um, and then the state is stored in files. And then, you know, there's the markdown format for the, you know, for, for the files themselves. And then, and then there's basically what in Unix is called Aron job. There's a loop and then there's a heartbeat for the, there's heartbeat and, and the thing basically Wake Wakes up.Wakes up. So it's basically LLM plus shell, plus file system, plus markdown, plus kron. And it turns out that's an agent. And, and, and every part of that, other than the model is something that we already completely know and understand. And in fact, it turns out that like the latent power of the Unix shell is like extraordinary because basically like all, like, there's just like an, there's just enormous latent power in the shell.There's enormous numbers of Unix commands, there's enormous number of command line interfaces into all kinds of things already in the, you know, your entire, I mean your entire, just to start with, your computer runs on a shell. If you're running a Mac or a, or, or a phone, your computer, your computer's running on a shell, uh, already.And so like the full power of your computer is available at the command line level. Um, and then it turns out it's really easy to expose other functions as a command line interface. And so like this whole idea where we need like MCP and these like product mm-hmm. Fancy protocols, whatever, it's like, no, we don't, we just need like a command, command line thing.So that's the architecture. And then it turns out what is your agent? Your agent has a bunch of files starting a file system. And then there's the thing that just like completely blew my mind when I write my head around it as a result of this, which is like, okay. This means your agent is now actually independent of the model that it's running on.Because you can actually swap out a different LLM underneath your agent and your, your agent will change personality somewhat. ‘cause the model is different, but all of the state stored in the files will be retained.swyx: Yeah. Different instruction set, but you just compiledit.Marc: Right, exactly. And it's all right.It's like right. Swapping out a ship and recompiling, but it's, it's still, it's still your agent with all of its memories. Um, and with all of its capabilities. And then by the way, you can also swap out the shell, uh, so you can move it to a different execution environment that is also, is also a b shell, by the way, you can also switch out the file system, right.Uh, and you can, and you can, and you can swap out the, the, the heartbeat for the, the crown framework, the, the loop that the agent framework itself. And so your agent basically is ba basically at the end of the day, it's just. It's just, its files. Um, and then, and then there's of course it a openswyx: call.Marc: Yeah, it's, it's basically, it's, it's just the files.Um, and then by the way, as a consequence of that, the agent and then the agent itself, it turns out a couple important things. So one is it, it's, it, it can migrate itself, right? And so you're, you can instruct your agent, migrate yourself to a different, uh, runtime environment, migrate yourself to a different file system, migrate yourself to a different, you know, swap out the language model.Your agent will do all that stuff for you. And then there's the final thing, which is just amazing, which is the agent is the agent actually has full introspection. It actually, it actually knows about its own files and it could rewrite its own files. Right. Which by the way, is basically no widely deployed software system in history where the, the, the thing that you're using actually has full introspective knowledge of how it itself works and is able to modify itself.Like that, that, I mean, there have been toy systems that have had that, but there, there's never been a widely deployed system that has that capability and then that leads you to the capability. That just like completely blew my mind when I wrap my head around it, which is you can tell the agent to add new functions and features to itself and it can do that.Extend yourself. Yeah. Right? Extend, extend yourself. Like extend yourself. Give yourself a new capability. Right? And so, and so literally it's just like you run into somebody at a party and they're like, oh, I have my open claw, do whatever, connect to my eat, sleep bed, and it gives me better advice and sleep.And you go home at night and you tell your claw, or if they're at the party, by the way, you tell your claw, oh, add this capability to yourself. And your claw will say, oh, okay, no problem. And it'll go out on the internet and it'll figure out whatever it needs and then it'll go out to claw code or whatever.It'll write whatever it needs. And then the next thing you know, it has this new capability. And so you don't even have to, like, you can have it upgrade itself without even having to, without having to do anything other than tell it that you want it to do that. And so anyway, so the, the combination of all this is just, I mean, this is just like a massive, incredible, I mean, it's just incredible.Like if I, if I were, if I were 18, like this is a hundred, this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough. Yeah. And again, pe people are gonna look at it and they already get this response. People are gonna look at it and they're gonna say, oh, well, where's the breakthrough?‘cause these, the, all of these components were already known before. Mm-hmm. But, but this is the key, the key to the breakthrough was by using all these components that were known before, you get all of the underlying capability of that's buried in there. And so all, and so for example, computer use all of a sudden just kind of falls, trivi, trivial.Of course it's gonna be able to use your computer. It has full access to the shell. Right. And then, and then you just, you, you give it access to a browser, and then you've got the computer and the browser and, and often away it goes. And, and then you've got all the abilities of the browser also. Um, yeah.And so, and so the capability unlock here is profound. My friends who are, you know, deepest into this, are having their claw do like a, like, literally like a thousand things in their lives. They have new ideas every day. They're just like constantly throwing new challenges at the thing. And by the way, it's early and, you know, these are, you know, these are prototypes and there are, you know, as you guys know, there's security issues.Yeah. And, and so, you know, there's a bunch of stuff to be ironed out, but the, the unlock of capability is just incredible.swyx: Yeah.Marc: And I, I have absolutely no doubt that everybody in the world is gonna, is gonna have at least, you know, an agent like this, if not an entire family of agents. And w

What does a strong marriage look like for an entrepreneur? In today's episode, I'm sharing my own experience of what marriage has looked like with the demands of owning a business, raising a family, and so much more. Plus, what it has meant for my husband and I to truly choose each other.The Shoot It Straight Podcast is brought to you by Sabrina Gebhardt, photographer and educator. Join us each week as we discuss what it's like to be a female creative entrepreneur while balancing entrepreneurship and motherhood. If you're trying to find balance in this exciting place you're in, yet willing to talk about the hard stuff too, Shoot It Straight Podcast is here to share practical and tangible takeaways to help you shoot it straight. Review the Show Notes:Sharing our lowest point (2:40)Doubling down and choosing each other (4:51)Hidden pressures of marriage (6:36)What we did to make things better (8:50)Mentioned In This Episode: The First Class Lounge: sabrinagebhardt.com/membershipConnect with Sabrina:Website: sabrinagebhardt.comInstagram: instagram.com/xo.sabrinagebhardtTikTok: tiktok.com/@xo.sabrinagebhardt Hosted on Acast. See acast.com/privacy for more information.

What does it actually take to raise your prices and have it work? Not just the courage to do it, but the client experience, the artistry, and the intentionality that make higher pricing undeniable. In this episode, Kim sits down with Morgan Williams, a Raleigh-based family photographer, educator, and mama to four boys, to talk about the real work behind building a photography business that is so distinctively yours that price shopping stops altogether. Why raising your prices without elevating your process first will not work, and what has to change before the numbers can follow How a deep pre-session client experience, from personalized wardrobe guidance to thoughtful questionnaires, is what actually produces exceptional images The shift Morgan made in 2023 to photograph almost exclusively in-home, and how that decision, alongside more than doubling her prices, changed everything about her business and her life What it means to tell a family's story when you actually know their story, and how Morgan translates the details clients share into photographs that represent something real Practical tools Morgan uses to stay present as a mom and business owner, including the Eisenhower matrix for prioritizing tasks and a phone-locking app called Brick If you have been telling yourself you just need more clients before you can make a change, this episode is worth your full attention. Morgan's story is a clear and honest look at what becomes possible when you stop going through the motions and build a business around the work only you can create. Willow Canvas: ⁠⁠⁠⁠⁠⁠⁠⁠http://willowcanvasbackdrops.com/ Connect with Morgan: https://morganwilliamsphoto.com/education/ Connect with TMA: Website | Membership | Courses:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.themotherhoodanthology.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Free Community:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.facebook.com/groups/themotherhoodanthology ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Our Instagram:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠instagram.com/themotherhoodanthology⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Connect with Kim: Site:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://kimbox.com ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ IG⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/kimbox 

5. Russia's Economic Bonus from Iran Conflict. Guest: Michael Bernstam. Michael Bernstam explains how skyrocketing oil prices have rescued Russia's economy, doubling weekly revenues. While Europefaces severe diesel shortages and high costs, Moscow benefits from increased prices and reduced discounts to Asian buyers.,, (5)1880 SERFDOM

Bill and Chris Murphy increased their firm's value by half a million dollars in six months. They weren't desperate to sell, just ready to work reasonable hours with clients they liked. So they did something about it. They fired almost all their individual tax clients and more than doubled their fees.The result? They gained revenue and clients happily paid for the valuable work they provided. Their firm had the capacity to provide even better service to their remaining clients. . And when they sold their firm with us back in 2022, they closed for $500,000 more than their original listing price.This is a husband-and-wife team who merged their separate practices, worked themselves into the ground, and then made the hard decisions that transformed everything. They cut unprofitable clients, raised prices dramatically, restructured staff workload through weekly meetings, and learned to trust the process even when it felt uncomfortable.Bill and Chris didn't just sell their firm. They sold a better firm, a more profitable firm, a firm that didn't depend on them working unreasonable hours. And they did it by firing clients who don't fit, charging what they're worth, and trusting that the revenue will come back.This episode is for firm owners who know there is a better way to build a balanced practice, but haven't known how to get there. This is a success case study on the power focus! Key Timestamps:02:21 - Bill's biggest roadblock: staying in the weeds and not developing staff04:18 - The mental, spiritual, and physical toll of holding onto all the work07:12 - The big move: firing almost all individual tax clients08:35 - How the first APA lesson paid for the entire workshop09:57 - The biggest obstacle to letting clients go: themselves, not the clients12:49 - How pruning clients created capacity for everything else13:21 - Doubling fees and the apology that worked14:37 - "I'm sorry for undercharging you before."16:02 - You have to put the work in, APA isn't passive17:14 - Monday morning meetings: agenda, time limit, workload reallocation19:16 - Driving the bus vs. riding in the back seat24:24 - Book recommendation: Walk Away Wealthy by Mark Tepper

In this episode of the Grow A Small Business Podcast, host Troy Trewin interviews Alesha Henley, founder of A Dose of Insight, shares how she launched her marketing business at age 50 after a simple happy hour conversation turned into her very first client with 30 years of marketing experience, she successfully doubled her revenue within four years and built a team of four after leaving her secure 9-5 job. Alesha explains how consistency in marketing and truly understanding your ideal client became the backbone of her business success. One standout moment she shares is how clients "entrust me with their babies," describing the deep trust business owners place in her to grow their brands. Her journey highlights the power of believing in yourself, taking opportunities when they appear, and staying dedicated even when the path feels uncertain.

Join our Email List: https://eternaldurdles.kit.com/b3d4a4dc9bSupport us on Patreon: https://www.patreon.com/EternalDurdlesTCGPLAYER AFFILIATE LINK:https://partner.tcgplayer.com/OexAAnWhat if every Magic format idea… went too far?In this episode, we spiral into absolute chaos trying to invent new Dan Dan-style formats, including:❄️ Stasis Stasis — a nightmare of islands, Gush, and decking

What if every Magic format idea… went too far?In this episode, we spiral into absolute chaos trying to invent new Dan Dan-style formats, including:❄️ Stasis Stasis — a nightmare of islands, Gush, and decking

You've no doubt seen Tom McComas flash across the big screen, usually falling off a building or wheelying a motorcycle through rush-hour traffic. That's because he's a bonafide Hollywood stuntman. McComas credits his career choice to a best-selling self-help book, “What Color is Your Parachute?” Summing it up, he says, “Figure out what you like to do, and find out a way to get paid doing it.” McComas discovered his first love, motorcycling, growing up in Chicago. Bitten by the competition bug, he road raced for six years before discovering he could get paid to crash bikes. “I thought, ‘I'm not afraid to fall down. I like to fight. Let's go!'” His big break? Doubling actor David Hasselhoff on the popular 1990s television series “Baywatch.” McComas has since chalked up hundreds of appearances in film and on TV. “Getting into stunts was so difficult,” he says in reflection. “There's a lot of rejection. I'm really grateful to be in the position I'm in, because I don't go to work, I go to  play . Some days are better than others, for sure, but it's amazing you can make a career out of doing what I do.” Connect with Us:Website: www.driventoridepodcast.comInstagram: www.Instagram.com/driventoridepodcastFacebook: www.facebook.com/driventorideEmail:hello@driventoridepodcast.com

-To capitalize on Claude's recent spike in popularity, Anthropic is offering a limited-time promotion that doubles usage limits for anyone using its AI chatbot during off-peak hours. The promotion started last Friday and runs until March 27, users on Free, Pro, Max, and Team plans will get double the usage limits in a five-hour window when using Claude outside weekday hours between 8 AM and 2 PM ET. -A month after Seedance 2.0's launch in China sparked cease-and-desist letters from Disney and Paramount Skydance over its use of copyrighted materials, its developer ByteDance has reportedly hit pause on the release of the AI video tool in other regions. -Humans have taken some jobs back from AI. Embark Studios' CEO Patrick Söderlund recently told GamesIndustry.biz that the studio "re-recorded" some of the AI-generated voice lines in Arc Raiders with human voices. Learn more about your ad choices. Visit podcastchoices.com/adchoices

When markets shift, most agents wait. They wait for clarity. They wait for confidence. They wait for proof that a new approach is “working.” Productive agents don't wait. Right now a quiet separation is happening inside real estate. Some agents are adjusting their businesses with intention — while others are hoping the market goes back to what it was. In this episode, we break down 11 specific behaviors the agents pulling ahead are implementing right now, including: • Focusing on controllable activities • Doubling down on listings • Simplifying lead generation • Strengthening follow-up systems • Tracking pipelines with precision • Designing businesses for predictable income Markets reward preparation, not panic. Agents who move early gain clarity, momentum, and market share — while others fall behind quietly. If you want to understand what the most productive agents are doing differently right now, this episode will give you the blueprint. Free Resources for Agents Start here → https://HarrisRealEstateDaily.com/ Free coaching → https://PremierCoaching.com Mastermind program → https://HarrisMastermind.com Learn more → https://WhyLibertas.com/Harris

Max Jungestål, CEO of Legora, joins Jacob Effron and Logan Bartlett to discuss the company's $550M Series D and share a candid account of what building an AI-native company at speed actually looks like from the inside. Max argues that the AI application layer requires a fundamentally different operating model than traditional SaaS, one built on low ego, constant reinvention, and a willingness to watch nine months of work get washed away by a model update. He walks through how step-function improvements in the underlying models, particularly Opus 4.5 and 4.6, have repeatedly forced Legora to rebuild core product features from scratch, and why he sees that as a feature, not a bug. On the legal industry, Max offers a ground-level view of how AI is actually diffusing through law firms, less through top-down mandates and more through competitive pressure between firms and, increasingly, from enterprise clients demanding efficiency from their outside counsel. He pushes back on the viability of AI-native law firms, dismisses outcome-based pricing as harder than it looks, and makes the case for why foundation model competition creates tailwinds rather than threats for a company with Legora's depth. The episode closes with a detailed look at the US expansion strategy, including the deliberate cultural decisions, like flying all New York hires to Stockholm for onboarding, that Max believes are the real source of Legora's compounding advantage.   [0:00] Intro [1:16] Legora's Series D Story [3:24] Why You Need Low Ego to Build in AI [5:58] From 60% to 100% Accuracy in One Summer [7:04] Law Firm Economics Shift [14:09] Pricing Seats Vs Outcomes [18:31] Why Foundation Models Entering Legal Helps Legora [30:10] Convincing a 75-Year-Old Partner to Go All In [33:02] Hiring Legal Engineers [34:32] Running an AI-Native Company [35:57] The Opus 4.5 Christmas Breakthrough [40:02] Building With Customers [44:01] All In On US Expansion [51:22] Stockholm Startup DNA   With your co-hosts:  @jacobeffron  - Partner at Redpoint, Former PM Flatiron Health  @patrickachase  - Partner at Redpoint, Former ML Engineer LinkedIn  @ericabrescia  - Former COO Github, Founder Bitnami (acq'd by VMWare)  @jordan_segall  - Partner at Redpoint

Imagine being slammed with client work… deadlines piling up… and still looking at your income like, “Wait—why doesn't this match how hard I'm working?” That was Ingrid Lange—fully booked, wildly underpaid, and what she calls a “busy fool.” In this episode, Ingrid breaks down how she shifted from underpriced project work, to hourly for protection, and then back to premium project pricing based on value—plus how she doubled her rate with existing clients and got zero pushback. Quick heads up: we had a few choppy connection moments early on, but the insights are gold. Let's dive in.About Ingrid:Ingrid Lange is a technical fashion designer who helps turn designs into physical, sellable products. She works across the full product development journey—from tech packs and global factory and material sourcing to prototype fittings and sustainable decisions. Ingrid has lived and worked in Bangladesh on assignment for major international brands, fully immersed in large-scale factory environments and overseeing product development from the inside out. She  has collaborated with major European brands including Zara, Next, Celio, and more. When she's not bringing collections to life, Ingrid is exploring the world and has just visited her 65th country.Connect with Ingrid:Email her at ingrid.pupi@gmail.com Connect on LinkedIn   Download my Freelance Price List just for fashion (it's free!): sewheidi.com/price

Today, we’re bringing you the best from the KUOW Newsroom… First, two Haitian immigrants in Spokane tell their stories, and how they’re trying to move forward despite the controversy around their temporarily protected status. Next, a new exhibition at the Burke Museum showcases the Coast Salish peoples and the art of weaving. And finally, the City of Tacoma is doubling the number of public trash cans in the city this year. We can only make Seattle Now because listeners support us. Tap here to make a gift and keep Seattle Now in your feed. Got questions about local news or story ideas to share? We want to hear from you! Email us at seattlenow@kuow.org, leave us a voicemail at (206) 616-6746 or leave us feedback online.See omnystudio.com/listener for privacy information.

Ep. 984 - Doubling up one more time this week. Craig Grialou and Dani Sureck continue to look back at their time in Indianapolis and the 2026 Scouting Combine. It was a busy week which produced a number of great interviews, two of which are featured on today's show. From ESPN, Dan Graziano and Ben Solak joined Craig and Dani to discuss whether the Cardinals might lean edge rush or offensive line in the first round; they give their thoughts on last year's draft class which had a handful of players flash and regardless of what happens at quarterback, both agree there is talent on that side of the ball. All this on what is the final Friday of the year.See omnystudio.com/listener for privacy information.

On the podcast: testing prices from $5 all the way to $120 per year, why rising CACs forced a pricing rethink, and how raising the price allows them to discount more aggressively.This conversation is shorter than usual and will be featured in RevenueCat's State of Subscription Apps report. Each episode in this series will explore one crucial topic and share actionable insights from top subscription app operators.Top Takeaways:

In this episode of Next in Media, I sit down with Philip Inghelbrecht, Co-Founder and CEO of Tatari, to unpack why one of the most innovative companies in TV advertising has built its entire thesis on a contrarian idea: that programmatic CTV is the wrong tool for most of the television market. Philip walks through how Tatari operates as a full infrastructure holding company, combining a demand-side platform, a supply-side solution called Upstream, and a privacy and identity layer called Vault. From day one, Tatari has argued that unlike display advertising, connected TV is dominated by a small number of premium publishers, and that automating around them rather than through open exchanges is the smarter path forward. Philip breaks down the $30 billion US CTV market, explaining how roughly half flows through programmatic channels and how up to half of that programmatic slice is fraud or low-quality inventory. The premium inventory that actually drives results, including sports, tentpole events, and top-tier streaming placements, lives almost entirely outside programmatic pipes and has historically required massive budgets and manual negotiation to access. That is exactly the gap Upstream was designed to close. By building custom, direct integrations with the five biggest TV publishers, including Disney, Warner Bros., NBCUniversal, and Paramount, Tatari has automated that direct buying process end to end, giving a much broader range of brands access to premium TV inventory without sacrificing pricing control, brand safety, or transparency.   Key Highlights

In this episode, I break down what it actually takes to go from $200,000 to $500,000 in annual revenue without creating chaos in the process. I walk you through how I helped a client stop chasing tactics and start identifying the real bottlenecks holding her back, from refining her $2,500 offer positioning to removing the sales call constraint and validating cold traffic through a webinar. If you have a big revenue goal and feel tempted to add more offers, more platforms, or more complexity, this episode will help you think differently about scale and start sequencing growth with clarity and strategy.

You can't deal with a gap or a weak spot in your performance by ignoring it. Doubling down on your arrogance, as many leaders do, doesn't help anyone…least of all you.If you don't face your blind spots, they'll dent your confidence, eventually becoming a source of fear and paranoia!This Moment will help you commit to taking that first step towards greater confidence, whatever that is for you.If you want to take a deeper dive, and get your hands on some practical scripts for that first step, have a listen to Ep.276: Building Leadership Confidence ————————Get the insider edge with the No Bullsh!t Leadership Hub on Skool - completely FREE!Join like-minded leaders who love the podcast and dive into exclusive resources, real conversations, and weekly challenges designed to sharpen your leadership game.Don't miss out, join now!————————You can connect with me at:Website: https://www.yourceomentor.comFacebook: https://www.facebook.com/yourceomentorInstagram: https://www.instagram.com/yourceomentorLinkedin: https://www.linkedin.com/in/martin-moore-075b001/Youtube: https://www.youtube.com/@YourCEOMentor————————Our purpose here at Your CEO Mentor is to improve the quality of leaders, globally.

Daniel Jeremiah dropped real Detroit Lions intel on a two-hour NFL pre-combine conference call with roughly 150 media members. Three Lions-centric questions made the queue. The answers steered straight to offensive tackle and contingency planning. This Detroit Lions Podcast zeroes in on what matters for pick 17 and March. OT at 17: Monroe Freeling and Blake Miller Asked about offensive tackles at No. 17, Jeremiah immediately named Monroe Freeling of Georgia and Blake Miller of Clemson as fits he believes the Detroit Lions could consider. It is early in the process, and these are his opinions, but those were the first two prospects he tied to Detroit's draft slot. Both are squarely in the conversation before the NFL combine. Why Freeling resonates: learning curve and toughness Jeremiah outlined why Freeling stands out. Quick learner. Still improving. Limited experience but trending up. He added an off-field note with on-field value: Freeling's mother is a yoga instructor, which he views as a positive for injury prevention. He also relayed a durability moment. Freeling was expected to miss a game with a high ankle sprain. He said he felt healthy enough to go, entered on an emergency basis, then played the entire game and played well. That combination of growth, recovery habits, and resilience landed with the room. Free-agent tackle buzz and the contingency map Unprompted, Jeremiah said the Lions are sniffing around the free agent offensive tackle class. He did not elaborate. On the podcast, we walked through the practical outcomes of that note. It can be veteran insurance if a rookie tackle is the pick at 17. It can cover the possibility that Giovanni Manu is not ready to be the next man up. It can protect the depth spot that Dan Skipper filled. The class lacks sizzle, but there are playable options. Jermaine Illuminore has had decent starting stretches with the Lions and Raiders. He is not Taylor Decker, but he can start if needed. Braxton Jones is coming off a rough season. Jack Conklin brings a long injury history in Cleveland. Former Michigan State Spartan. Chicago area roots. Tough profile, but questions remain. Many in this market are primarily right tackles. This draft also gives Detroit room to stack swings. Beyond Freeling and Miller, there are many tackles in range throughout the weekend. Names mentioned as possibilities included Spencer Branch Manu, Caleb Holmes, Caleb Tiernan, and Dimitris Brown of Texas A and M as a Day 3 type the Lions could like. Doubling up is not out of the question if the board cooperates. For the Detroit Lions, the path at tackle runs through No. 17 and the veteran aisle. The next two weeks before the NFL combine will sharpen it. #detroitlions #lions #detroitlionspodcast #nfl #monroefreeling #blakemiller #freeagentoffensivetackles #taylordecker #giovannimanu #danieljeremiah #highanklesprain #jermaineilluminore #braxtonjones #jacksonconklin Learn more about your ad choices. Visit megaphone.fm/adchoices

Welcome to the ThrivetimeShow.com Cleaning Business Podcast Series. During this 100 episode business coach podcast series Clay Clark teaches how you can achieve success in automotive repair, carpet cleaning, dog training, grooming, home building, home cleaning, home remodeling, manufacturing, medical, online sales, podcasting, photography, signage, skin care, and other industries. #CleaningBusinessPodcast   Where You Find Thousands of Clay Clark Client Success Stories?  https://www.thrivetimeshow.com/testimonials/    Breaking Down the 1,462% Growth of Stephanie Pipkin with Clay Clark: An EOFire Classic from 2022 - https://www.eofire.com/podcast/clayclark8/    Who is Clay Clark?  Clay Clark is the co-founder of five kids, the host of the 6X iTunes chart-topping ThrivetimeShow.com Podcast, the 2007 Oklahoma SBA Entrepreneur of the Year, the 2002 Tulsa Metro Chamber of Commerce Young Entrepreneur of the Year, an Amazon best-selling author, a singer / song-writer and the founder of several multi-million dollar businesses.  https://www.forbes.com/councils/forbescoachescouncil/people/clayclark/    Where Can You Learn More About Clay Clark? https://www.thrivetimeshow.com/need-business-coach/#coaching-about-founders    Where Can You Read Clay Clark's 40+ Books? https://www.amazon.com/stores/Clay-Clark/author/B004M6F5T4?ref=sr_ntt_srch_lnk_1&qid=1767189818&sr=8-1&shoppingPortalEnabled=true    Where Can You Discover Clay Clark's Songs & Original Music?  https://open.spotify.com/album/2ZdE8VDS6PYQgdilQ1vWTP?si=Am65WUlIQba4OLbinBYo1g   

Tommy and Chris are joined this week by Josh Francis from the Friendly Fire Podcast Comedians Chris and Tommy Pope are making all kinds of Stuff on the paytch. Each week they talk about anything & everything under the sun. Tommy also chefs up some delicious meals. It's a blast, folks. Check out our second channel @LookatDish where Tommy Pope and Chris O'Connor cook elaborate meals with your favorite comedians Head to https://www.squarespace.com/STUFFISLAND to save 10% off your first purchase of a website or domain using code STUFFISLAND. #ad Get 10% off your first month of BlueChew Gold with code STUFFISLAND. That's promo code STUFFISLAND. Visit https://www.BlueChew.com for more details and important safety information #comedy SUB TO PATREON: patreon.com/stuffisland Control Body Odor ANYWHERE with @shop.mando and get $5 off off your Starter Pack (that's over 40% off) with promo code [STUFFISLAND] at https://www.Mandopodcast.com/[STUFFISLAND]! #mandopod Click the link http://kalshi.com/r/stuff or download the Kalshi App and use code STUFF to sign up and trade today! #ads Download Cash App Today: [https://capl.onelink.me/vFut/knz4su0l #CashAppPod. Cash App is a financial services platform, not a bank. Banking services provided by Cash App's bank partner(s). Prepaid debit cards issued by Sutton Bank, Member FDIC. See terms and conditions at https://cash.app/legal/us/en-us/card-agreement. Cash App Green, overdraft coverage, borrow, cash back offers and promotions provided by Cash App, a Block, Inc. brand. Visit http://cash.app/legal/podcast for full disclosures Follow Chris on IG: https://www.instagram.com/achrisoconnor Follow Tommy on IG: https://www.instagram.com/tommyjpope #comedy #comedypodcast Learn more about your ad choices. Visit megaphone.fm/adchoices

If you miss a workout, should you double up the next day? Short answer: No. In this episode, Kim explains why doing two sessions in one day often leads to holding back — and why that kills muscle growth. You'll learn: Why strength training should be treated like a sprint, not a marathon How subconsciously conserving energy limits your results What “training to failure” actually means Why finishing the full minute isn't the goal How women misunderstand load and intensity Why muscle only grows when you force it to do more than it currently can If you're working hard but not seeing muscle growth, this episode will shift your entire mindset. Follow Kim: