Podcasts about revenue streams

In this episode of PowerTips Unscripted, host Victoria Downing interviews Robert Gurinowitsch who shares insights on incorporating state-funded projects into a remodeler's revenue stream. Robert explains how these opportunities can be lucrative but require navigating government regulations and compliance.Robert Gurinowitsch is the regional program manager for Best Bath. Best Bath has been providing over 25 years in Strategic Solutions for Home modifications and Home access. They work with Home and Community Based Service programs, Managed Care Organizations, Workers Comp, and Bank Trusts.Victoria and Robert talk more about:Challenges and benefits of state-funded workHow to start doing state-funded workExamples of how state work has helped companies And more… The post Adding State Funded Work to Your Revenue Stream with Robert Gurinowitsch – [Best of PowerTips Unscripted] appeared first on PowerTips Unscripted.

Dating photography might not be the first niche that comes to mind when photographers think about growth — but in this interview, Emily Cummings explains why it's become one of her most consistent, high-value revenue streams. We unpack how Emily identified a specific emotional problem clients were already motivated to solve, why a dedicated dating photography offer pre-qualifies serious enquiries, and how experience design — from consults to styling to same-day image selection — makes premium pricing far easier to hold. This conversation also explores the role Emily's studio plays in credibility and efficiency, how Google Ads and strategic partnerships attract clients from well beyond her local area, and why simplifying pricing and systems across genres has helped her scale without burnout. If you're curious about building steadier income, attracting more decisive clients, or creating offers that work year-round — this episode will give you plenty to think about. The post 652: Emily Cummings – How Dating Photography Became a High-Value, Year-Round Revenue Stream appeared first on Photography Business Xposed - Photography Podcast - how to build and market your portrait and wedding photography business.

Get the full recap from the New Orleans national conference! Giuseppe Grammatico shares the major theme of 2026: unmatched franchisee support. From AI callers to shrinking footprints and new funding models, this is everything you need to know before investing. Choose the right path at https://ggthefranchiseguide.com.00:00 Introduction to Franchise Freedom Podcast00:30 Host Introduction and Podcast Overview01:53 Conference Highlights and Key Takeaways02:45 The New Era of Franchise Support02:54 Main Theme: Support in Franchising03:58 AI and Automation in Franchising04:44 Shrinking the Footprint, Growing the Profit08:01 Technological Innovations in Franchising08:52 Revenue Streams and Financial Strategies08:52 Diversified Revenue Streams & E-commerce09:45 Franchisee Support and Financial Planning10:39 The "Right-Fit" Funding and Selection Process11:01 Conclusion and Listener EngagementConnect with Franchise Freedom on:Website: https://ggthefranchiseguide.com/podcast/ LinkedIn: https://www.linkedin.com/in/giuseppe-grammatico/ Facebook: https://www.facebook.com/GGTheFranchiseGuide X: https://x.com/ggfranchguide Instagram: https://www.instagram.com/ggthefranchiseguide/ YouTube: https://www.youtube.com/@ggthefranchiseguide Apple: https://podcasts.apple.com/us/podcast/franchise-freedom/id1499864638 Spotify: https://open.spotify.com/show/13LTN5UzA57w2dTB4iV0fm TikTok: https://www.tiktok.com/@ggthefranchiseguide The Franchise Freedom: Discover Your New Path to Freedom Through Franchise Ownership, Book by Giuseppe Grammatico https://ggthefranchiseguide.com/book or purchase directly on ...

Send us a textInvest in pre-IPO stocks with AG Dillon & Co. Contact aaron.dillon@agdillon.com to learn more. Financial advisors only. www.agdillon.com00:00 - Intro00:07 - ClickHouse $400M Series D $15B Valuation Langfuse M&A01:29 - Runpod $120M Run Rate 500K Developers 31 Regions02:59 - Humans& $480M Seed $4.48B Valuation 3 Months Old04:03 - Baseten $300M Raise $5B Valuation Nvidia $150M04:49 - Noveon Magnetics $215M Raise 2,000+ Tons Per Year Target06:17 - Zipline $600M+ New Funding $7.6B Valuation 2M+ Deliveries07:34 - Hatch Yelp Acquisition $300M Deal 12x ARR08:26 - Brex Capital One Acquisition $5.15B Deal $13B Deposits09:30 - DayOne Data Centers IPO Target Up to $20B10:14 - World Labs Funding Talks at $5B Potential $500M Balance Sheet Add11:08 - TikTok US New Entity 50% New Investors 200M US Users12:42 - Inferact $150M Seed $800M Valuation vLLM Commercialization13:16 - RadixArk $400M Valuation SGLang Commercial Push14:07 - Blue Origin TeraWave 6 Tbps 5,408 Satellites Q4 2027 Deployment15:15 - OpenAI Shopify Checkouts Jan 26 Launch 4% ChatGPT Fee16:24 - OpenAI API Revenue +$1B ARR in One Month $20B+ Total ARR16:51 - OpenAI Ads Testing Coming Weeks Go Plan Included17:31 - OpenAI ServiceNow 3-Year Enterprise Integration

On this episode of Roots of Success, Chris Psencik welcomes Lex Mason from Weathermatic for a candid conversation on the systems, strategies, and tech elevating the landscape industry. Discover the secret to hitting the coveted 20% revenue mark for irrigation repairs, streamline your inspection processes, and learn direct-from-the-trenches tips to boost productivity and retain top talent—even in a labor crunch. Plus, hear how team development and focused innovation are shaping the road to 2026, and get a sneak peek at SmartCon—a premier industry event. For business owners ready to maximize resources and drive results, this episode delivers both motivation and a practical roadmap. THE BIG IDEA:  Focus drives profitability KEY MOMENTS: [03:37] "Streamlining Irrigation Operations Efficiency" [07:49] "Evaluating Operations Beyond Controllers" [09:41] "Irrigation Revenue and Maintenance Insights" [15:59] "Measure to Manage Effectively" [18:20] Streamlining Repairs with Kit Pricing [20:49] "Improving Performance Through Simplicity" [25:14] "Be a Champion of Initiatives" [29:28] "Driving Engagement Through Alignment" [33:45] "Evaluate, Question, Align, Maximize" [34:18] "Consultative Sales and Training" QUESTIONS WE ANSWER What strategies can be used to grow pre-approval limits for irrigation repairs, and why does it matter for field efficiency? How does upgrading operational processes in an irrigation division impact labor productivity and business profitability? What role does technology, especially automation and API integrations, play in streamlining the workflow from field inspection to billing? Why is tracking the penetration rate of irrigation repairs in relation to maintenance contract value considered a "golden metric"? What are the main benefits of implementing monthly irrigation inspections compared to seasonal startup and shutdown practices? In what ways can standardizing the inspection process and utilizing field management dashboards lead to better accountability and team performance? How has repair kit pricing improved efficiency for irrigation departments, and what parallels exist with the automotive industry? What are the risks for landscape businesses that delay adopting technology to optimize their operations and preserve profit margins? Why is champion development within a team critical for implementing new initiatives and advancing career growth in a landscape business? How does participating in industry events, peer networks, and cause-driven initiatives add value to both business performance and community impact?

What would it actually take to add an extra $5,000 a month to your therapy practice without burning out or stacking more one-to-one sessions? In this episode, I dive headfirst into one of my favorite topics: additional revenue streams and how therapists can strategically scale beyond the traditional one-to-one model. I walk you through why relying on a single income stream is risky, how unexpected life events can impact your business, and why building flexibility into your revenue can transform not just your income, but your health, energy, and quality of life. I break down the two most common paths therapists take: building a profitable group practice and stepping into the creator economy with offers like courses, coaching, memberships, speaking, or retreats. We talk real numbers, realistic expectations, and what it actually looks like to design offers that work in today's fast-changing market. I also share how I think about pricing, program structure, audience growth, and why 2026 is shaping up to be a powerful year for connection-based business models. If you've been thinking about scaling but feel overwhelmed, stuck, or unsure where to start, this episode will help you zoom out, think bigger, and create a strategy that fits your lifestyle and long-term vision. Topics Covered in this Episode: 3:42 - Why adding revenue streams is about freedom, not just money 7:15 - The real profit math behind hiring your next associate 11:08 - The mistake most therapists make when scaling a group practice 14:30 - How the creator economy is shifting and what still works 18:05 - The hidden numbers behind selling a $997 course 21:47 - Why in-person components may become your biggest advantage 24:12 - Choosing between high-ticket depth or high-volume scale If you're serious about adding consistent income without sacrificing your sanity or your mission, this is your invitation to take action. The Scale Up Mastermind is officially open for registration. This is a program that has sold out each time in the past 5 cohorts and focuses on helping therapists develop additional revenue streams for their practice. Apply for the Scale Up Mastermind - We start Feb 13th.   Resources Mentioned: Apply for the Scale Up Mastermind Find out more about Alma here: helloalma.com/danielle Take 50% off your first 4 months of Simple Practice + a 7 day free trial using the link: simplepractice.com/danielle

On this episode of Chit Chat Stocks, Aria Radnia stops by to discuss his thesis on Uber (Ticker: UBER). We discuss:(00:00) Introduction(02:24) Understanding Uber's Revenue Streams(03:51) Geographic Diversification and Growth Opportunities(08:15) The Autonomous Vehicle Threat and Opportunity(30:17) Profitability of Uber Eats(31:03) Uber's Expansion into Grocery and Retail(33:36) Advertising Revenue: A Key to Profitability(37:43) Exploring Uber's Moonshot Projects(42:51) Uber's Investment Portfolio and Market Penetration(49:18) Valuation Insights and Future GrowthAria's Twitter: https://x.com/QualityInvest5Aria's YouTube: https://www.youtube.com/@QualityInvest5*****************************************************Sign up for our stock research service, Emerging Moats: emergingmoats.com *********************************************************************Chit Chat Stocks is presented by Interactive Brokers. Get professional pricing, global access, and premier technology with the best brokerage for investors today: https://www.interactivebrokers.com/ Interactive Brokers is a member of SIPC. *********************************************************************Fiscal.ai is building the future of financial data.With custom charts, AI-generated research reports, and endless analytical tools, you can get up to speed on any stock around the globe. All for a reasonable price. Use our LINK and get 15% off any premium plan: ⁠https://fiscal.ai/chitchat *********************************************************************Disclosure: Chit Chat Stocks hosts and guests are not financial advisors, and nothing they say on this show is formal advice or a recommendation.

In this episode of Wheel Talk, Ryan and Becca talk with artist, author, and educator Megan Auman about the business lessons behind Try It and See. Megan shares how her revenue streams and pricing strategies have evolved since publishing the book, along with insights on production volume and communicating value to customers. They discuss what it means to ask for the sale outside of wholesale, how artists can know when they're charging the right price, and how tools like ChatGPT and social media fit into today's creative businesses.

Payroll is one of the most overlooked revenue opportunities for payment professionals. In this episode, James Shepherd talks with Evan Perdikouris, President of Nexroll, about how payroll fits naturally alongside payments and how agents can generate new residual income without becoming payroll experts. They break down the economics, the best merchant targets, and simple referral strategies that deepen relationships and increase stickiness. Plus, Patti Murphy joins James for the latest Today in Payments segment, covering key industry updates you need to know. If you're looking to expand beyond processing and add a high-value service your merchants already need, this episode will show you how to get started.

Today we discuss Alternative revenue streams for physicians! So what are some things doctors can do outside of the clinic to make some revenue? We discuss some things in this episode!   Alfred Atanda Jr., MD, is the director of the Sports Medicine Program, and a pediatric orthopedic surgeon and sports medicine specialist. He serves as assistant professor of orthopedic surgery and pediatrics at Sidney Kimmel Medical College of Thomas Jefferson University. Dr. Atanda is a graduate of the University of Pennsylvania School of Medicine, completed an internship and orthopedic surgery residency at the University of Chicago Medical Center, and fellowships in pediatric orthopedic surgery at Nemours Children's and in sports medicine at the Rothman Institute at Thomas Jefferson University. He performs arthroscopic surgery of the knee, elbow, ankle and shoulder, as well as general orthopedic and trauma surgical procedures. His research interests are in upper extremity overuse injury prevention and general orthopedic trauma. Recently, he has developed an interest in technology and digital health innovation and routinely uses telemedicine in his sports medicine practice. He is working with several stakeholders in the organization to re-imagine the process by which pediatric orthopedic patients are triaged, navigated, evaluated and treated during the continuum of their health care experience. Provides care in Wilmington, Del., and Abington, Pa.   We answer questions you may have on the things you will encounter when it comes to billing, like: What is an IME? Expert Witness + more

In this episode of the Insurance Town Podcast, I sit down with Clint Houke for a wide ranging, high trust conversation to kick off the new year the right way. This is one of those talks that feels like two industry vets standing at the whiteboard, talking honestly about what is working, what is changing, and where the real opportunities are.Clint and I dig into the trends he is seeing across the insurance landscape, especially around MGAs and why they continue to play such a critical role in today's market. We talk about speed, specialization, and why MGAs are not just filling gaps, but actively shaping the future of distribution.From there,  how agencies, MGAs, and carriers can think about AI as a tool to enhance relationships, not replace them. The conversation then turns to Clint's newest chapter and his work with FAIR. We talk about what FAIR is building, why it matters, and how it is creating real opportunity in the insurance space. This is not just another product. It is a business model that opens up meaningful new revenue streams while staying aligned with transparency and customer experience.What stood out most in this conversation is Clint's perspective on timing. The industry is changing fast, but the fundamentals still matter. Relationships. Trust. Doing right by the client. FAIR feels like one of those companies that understands both sides of that equation.If you are an agency owner, producer, MGA leader, or anyone looking to add smart, sustainable growth to your business, this episode is worth your time.Pull up a chair, grab a cup of coffee, and join us in Insurance Town for a conversation about trends, technology, and finding opportunity in a changing market.Welcome back to town.Sponsors:Smart Choice- the Fastest growing agency Network hands downMAV- Ditch the Call center and Hire Mav the AI solution to engaging your clients Canopy Connect - The One Click Solution to getting dec pages you need to quote your clients. 

Happy New Year! You may have noticed that in 2025 we had moved toward YouTube as our primary podcasting platform. As we'll explain in the next State of Latent Space post, we'll be doubling down on Substack again and improving the experience for the over 100,000 of you who look out for our emails and website updates!We first mentioned Artificial Analysis in 2024, when it was still a side project in a Sydney basement. They then were one of the few Nat Friedman and Daniel Gross' AIGrant companies to raise a full seed round from them and have now become the independent gold standard for AI benchmarking—trusted by developers, enterprises, and every major lab to navigate the exploding landscape of models, providers, and capabilities.We have chatted with both Clementine Fourrier of HuggingFace's OpenLLM Leaderboard and (the freshly valued at $1.7B) Anastasios Angelopoulos of LMArena on their approaches to LLM evals and trendspotting, but Artificial Analysis have staked out an enduring and important place in the toolkit of the modern AI Engineer by doing the best job of independently running the most comprehensive set of evals across the widest range of open and closed models, and charting their progress for broad industry analyst use.George Cameron and Micah-Hill Smith have spent two years building Artificial Analysis into the platform that answers the questions no one else will: Which model is actually best for your use case? What are the real speed-cost trade-offs? And how open is “open” really?We discuss:* The origin story: built as a side project in 2023 while Micah was building a legal AI assistant, launched publicly in January 2024, and went viral after Swyx's retweet* Why they run evals themselves: labs prompt models differently, cherry-pick chain-of-thought examples (Google Gemini 1.0 Ultra used 32-shot prompts to beat GPT-4 on MMLU), and self-report inflated numbers* The mystery shopper policy: they register accounts not on their own domain and run intelligence + performance benchmarks incognito to prevent labs from serving different models on private endpoints* How they make money: enterprise benchmarking insights subscription (standardized reports on model deployment, serverless vs. managed vs. leasing chips) and private custom benchmarking for AI companies (no one pays to be on the public leaderboard)* The Intelligence Index (V3): synthesizes 10 eval datasets (MMLU, GPQA, agentic benchmarks, long-context reasoning) into a single score, with 95% confidence intervals via repeated runs* Omissions Index (hallucination rate): scores models from -100 to +100 (penalizing incorrect answers, rewarding ”I don't know”), and Claude models lead with the lowest hallucination rates despite not always being the smartest* GDP Val AA: their version of OpenAI's GDP-bench (44 white-collar tasks with spreadsheets, PDFs, PowerPoints), run through their Stirrup agent harness (up to 100 turns, code execution, web search, file system), graded by Gemini 3 Pro as an LLM judge (tested extensively, no self-preference bias)* The Openness Index: scores models 0-18 on transparency of pre-training data, post-training data, methodology, training code, and licensing (AI2 OLMo 2 leads, followed by Nous Hermes and NVIDIA Nemotron)* The smiling curve of AI costs: GPT-4-level intelligence is 100-1000x cheaper than at launch (thanks to smaller models like Amazon Nova), but frontier reasoning models in agentic workflows cost more than ever (sparsity, long context, multi-turn agents)* Why sparsity might go way lower than 5%: GPT-4.5 is ~5% active, Gemini models might be ~3%, and Omissions Index accuracy correlates with total parameters (not active), suggesting massive sparse models are the future* Token efficiency vs. turn efficiency: GPT-5 costs more per token but solves Tau-bench in fewer turns (cheaper overall), and models are getting better at using more tokens only when needed (5.1 Codex has tighter token distributions)* V4 of the Intelligence Index coming soon: adding GDP Val AA, Critical Point, hallucination rate, and dropping some saturated benchmarks (human-eval-style coding is now trivial for small models)Links to Artificial Analysis* Website: https://artificialanalysis.ai* George Cameron on X: https://x.com/georgecameron* Micah-Hill Smith on X: https://x.com/micahhsmithFull Episode on YouTubeTimestamps* 00:00 Introduction: Full Circle Moment and Artificial Analysis Origins* 01:19 Business Model: Independence and Revenue Streams* 04:33 Origin Story: From Legal AI to Benchmarking Need* 16:22 AI Grant and Moving to San Francisco* 19:21 Intelligence Index Evolution: From V1 to V3* 11:47 Benchmarking Challenges: Variance, Contamination, and Methodology* 13:52 Mystery Shopper Policy and Maintaining Independence* 28:01 New Benchmarks: Omissions Index for Hallucination Detection* 33:36 Critical Point: Hard Physics Problems and Research-Level Reasoning* 23:01 GDP Val AA: Agentic Benchmark for Real Work Tasks* 50:19 Stirrup Agent Harness: Open Source Agentic Framework* 52:43 Openness Index: Measuring Model Transparency Beyond Licenses* 58:25 The Smiling Curve: Cost Falling While Spend Rising* 1:02:32 Hardware Efficiency: Blackwell Gains and Sparsity Limits* 1:06:23 Reasoning Models and Token Efficiency: The Spectrum Emerges* 1:11:00 Multimodal Benchmarking: Image, Video, and Speech Arenas* 1:15:05 Looking Ahead: Intelligence Index V4 and Future Directions* 1:16:50 Closing: The Insatiable Demand for IntelligenceTranscriptMicah [00:00:06]: This is kind of a full circle moment for us in a way, because the first time artificial analysis got mentioned on a podcast was you and Alessio on Latent Space. Amazing.swyx [00:00:17]: Which was January 2024. I don't even remember doing that, but yeah, it was very influential to me. Yeah, I'm looking at AI News for Jan 17, or Jan 16, 2024. I said, this gem of a models and host comparison site was just launched. And then I put in a few screenshots, and I said, it's an independent third party. It clearly outlines the quality versus throughput trade-off, and it breaks out by model and hosting provider. I did give you s**t for missing fireworks, and how do you have a model benchmarking thing without fireworks? But you had together, you had perplexity, and I think we just started chatting there. Welcome, George and Micah, to Latent Space. I've been following your progress. Congrats on... It's been an amazing year. You guys have really come together to be the presumptive new gardener of AI, right? Which is something that...George [00:01:09]: Yeah, but you can't pay us for better results.swyx [00:01:12]: Yes, exactly.George [00:01:13]: Very important.Micah [00:01:14]: Start off with a spicy take.swyx [00:01:18]: Okay, how do I pay you?Micah [00:01:20]: Let's get right into that.swyx [00:01:21]: How do you make money?Micah [00:01:24]: Well, very happy to talk about that. So it's been a big journey the last couple of years. Artificial analysis is going to be two years old in January 2026. Which is pretty soon now. We first run the website for free, obviously, and give away a ton of data to help developers and companies navigate AI and make decisions about models, providers, technologies across the AI stack for building stuff. We're very committed to doing that and tend to keep doing that. We have, along the way, built a business that is working out pretty sustainably. We've got just over 20 people now and two main customer groups. So we want to be... We want to be who enterprise look to for data and insights on AI, so we want to help them with their decisions about models and technologies for building stuff. And then on the other side, we do private benchmarking for companies throughout the AI stack who build AI stuff. So no one pays to be on the website. We've been very clear about that from the very start because there's no use doing what we do unless it's independent AI benchmarking. Yeah. But turns out a bunch of our stuff can be pretty useful to companies building AI stuff.swyx [00:02:38]: And is it like, I am a Fortune 500, I need advisors on objective analysis, and I call you guys and you pull up a custom report for me, you come into my office and give me a workshop? What kind of engagement is that?George [00:02:53]: So we have a benchmarking and insight subscription, which looks like standardized reports that cover key topics or key challenges enterprises face when looking to understand AI and choose between all the technologies. And so, for instance, one of the report is a model deployment report, how to think about choosing between serverless inference, managed deployment solutions, or leasing chips. And running inference yourself is an example kind of decision that big enterprises face, and it's hard to reason through, like this AI stuff is really new to everybody. And so we try and help with our reports and insight subscription. Companies navigate that. We also do custom private benchmarking. And so that's very different from the public benchmarking that we publicize, and there's no commercial model around that. For private benchmarking, we'll at times create benchmarks, run benchmarks to specs that enterprises want. And we'll also do that sometimes for AI companies who have built things, and we help them understand what they've built with private benchmarking. Yeah. So that's a piece mainly that we've developed through trying to support everybody publicly with our public benchmarks. Yeah.swyx [00:04:09]: Let's talk about TechStack behind that. But okay, I'm going to rewind all the way to when you guys started this project. You were all the way in Sydney? Yeah. Well, Sydney, Australia for me.Micah [00:04:19]: George was an SF, but he's Australian, but he moved here already. Yeah.swyx [00:04:22]: And I remember I had the Zoom call with you. What was the impetus for starting artificial analysis in the first place? You know, you started with public benchmarks. And so let's start there. We'll go to the private benchmark. Yeah.George [00:04:33]: Why don't we even go back a little bit to like why we, you know, thought that it was needed? Yeah.Micah [00:04:40]: The story kind of begins like in 2022, 2023, like both George and I have been into AI stuff for quite a while. In 2023 specifically, I was trying to build a legal AI research assistant. So it actually worked pretty well for its era, I would say. Yeah. Yeah. So I was finding that the more you go into building something using LLMs, the more each bit of what you're doing ends up being a benchmarking problem. So had like this multistage algorithm thing, trying to figure out what the minimum viable model for each bit was, trying to optimize every bit of it as you build that out, right? Like you're trying to think about accuracy, a bunch of other metrics and performance and cost. And mostly just no one was doing anything to independently evaluate all the models. And certainly not to look at the trade-offs for speed and cost. So we basically set out just to build a thing that developers could look at to see the trade-offs between all of those things measured independently across all the models and providers. Honestly, it was probably meant to be a side project when we first started doing it.swyx [00:05:49]: Like we didn't like get together and say like, Hey, like we're going to stop working on all this stuff. I'm like, this is going to be our main thing. When I first called you, I think you hadn't decided on starting a company yet.Micah [00:05:58]: That's actually true. I don't even think we'd pause like, like George had an acquittance job. I didn't quit working on my legal AI thing. Like it was genuinely a side project.George [00:06:05]: We built it because we needed it as people building in the space and thought, Oh, other people might find it useful too. So we'll buy domain and link it to the Vercel deployment that we had and tweet about it. And, but very quickly it started getting attention. Thank you, Swyx for, I think doing an initial retweet and spotlighting it there. This project that we released. And then very quickly though, it was useful to others, but very quickly it became more useful as the number of models released accelerated. We had Mixtrel 8x7B and it was a key. That's a fun one. Yeah. Like a open source model that really changed the landscape and opened up people's eyes to other serverless inference providers and thinking about speed, thinking about cost. And so that was a key. And so it became more useful quite quickly. Yeah.swyx [00:07:02]: What I love talking to people like you who sit across the ecosystem is, well, I have theories about what people want, but you have data and that's obviously more relevant. But I want to stay on the origin story a little bit more. When you started out, I would say, I think the status quo at the time was every paper would come out and they would report their numbers versus competitor numbers. And that's basically it. And I remember I did the legwork. I think everyone has some knowledge. I think there's some version of Excel sheet or a Google sheet where you just like copy and paste the numbers from every paper and just post it up there. And then sometimes they don't line up because they're independently run. And so your numbers are going to look better than... Your reproductions of other people's numbers are going to look worse because you don't hold their models correctly or whatever the excuse is. I think then Stanford Helm, Percy Liang's project would also have some of these numbers. And I don't know if there's any other source that you can cite. The way that if I were to start artificial analysis at the same time you guys started, I would have used the Luther AI's eval framework harness. Yup.Micah [00:08:06]: Yup. That was some cool stuff. At the end of the day, running these evals, it's like if it's a simple Q&A eval, all you're doing is asking a list of questions and checking if the answers are right, which shouldn't be that crazy. But it turns out there are an enormous number of things that you've got control for. And I mean, back when we started the website. Yeah. Yeah. Like one of the reasons why we realized that we had to run the evals ourselves and couldn't just take rules from the labs was just that they would all prompt the models differently. And when you're competing over a few points, then you can pretty easily get- You can put the answer into the model. Yeah. That in the extreme. And like you get crazy cases like back when I'm Googled a Gemini 1.0 Ultra and needed a number that would say it was better than GPT-4 and like constructed, I think never published like chain of thought examples. 32 of them in every topic in MLU to run it, to get the score, like there are so many things that you- They never shipped Ultra, right? That's the one that never made it up. Not widely. Yeah. Yeah. Yeah. I mean, I'm sure it existed, but yeah. So we were pretty sure that we needed to run them ourselves and just run them in the same way across all the models. Yeah. And we were, we also did certain from the start that you couldn't look at those in isolation. You needed to look at them alongside the cost and performance stuff. Yeah.swyx [00:09:24]: Okay. A couple of technical questions. I mean, so obviously I also thought about this and I didn't do it because of cost. Yep. Did you not worry about costs? Were you funded already? Clearly not, but you know. No. Well, we definitely weren't at the start.Micah [00:09:36]: So like, I mean, we're paying for it personally at the start. There's a lot of money. Well, the numbers weren't nearly as bad a couple of years ago. So we certainly incurred some costs, but we were probably in the order of like hundreds of dollars of spend across all the benchmarking that we were doing. Yeah. So nothing. Yeah. It was like kind of fine. Yeah. Yeah. These days that's gone up an enormous amount for a bunch of reasons that we can talk about. But yeah, it wasn't that bad because you can also remember that like the number of models we were dealing with was hardly any and the complexity of the stuff that we wanted to do to evaluate them was a lot less. Like we were just asking some Q&A type questions and then one specific thing was for a lot of evals initially, we were just like sampling an answer. You know, like, what's the answer for this? Like, we didn't want to go into the answer directly without letting the models think. We weren't even doing chain of thought stuff initially. And that was the most useful way to get some results initially. Yeah.swyx [00:10:33]: And so for people who haven't done this work, literally parsing the responses is a whole thing, right? Like because sometimes the models, the models can answer any way they feel fit and sometimes they actually do have the right answer, but they just returned the wrong format and they will get a zero for that unless you work it into your parser. And that involves more work. And so, I mean, but there's an open question whether you should give it points for not following your instructions on the format.Micah [00:11:00]: It depends what you're looking at, right? Because you can, if you're trying to see whether or not it can solve a particular type of reasoning problem, and you don't want to test it on its ability to do answer formatting at the same time, then you might want to use an LLM as answer extractor approach to make sure that you get the answer out no matter how unanswered. But these days, it's mostly less of a problem. Like, if you instruct a model and give it examples of what the answers should look like, it can get the answers in your format, and then you can do, like, a simple regex.swyx [00:11:28]: Yeah, yeah. And then there's other questions around, I guess, sometimes if you have a multiple choice question, sometimes there's a bias towards the first answer, so you have to randomize the responses. All these nuances, like, once you dig into benchmarks, you're like, I don't know how anyone believes the numbers on all these things. It's so dark magic.Micah [00:11:47]: You've also got, like… You've got, like, the different degrees of variance in different benchmarks, right? Yeah. So, if you run four-question multi-choice on a modern reasoning model at the temperatures suggested by the labs for their own models, the variance that you can see on a four-question multi-choice eval is pretty enormous if you only do a single run of it and it has a small number of questions, especially. So, like, one of the things that we do is run an enormous number of all of our evals when we're developing new ones and doing upgrades to our intelligence index to bring in new things. Yeah. So, that we can dial in the right number of repeats so that we can get to the 95% confidence intervals that we're comfortable with so that when we pull that together, we can be confident in intelligence index to at least as tight as, like, a plus or minus one at a 95% confidence. Yeah.swyx [00:12:32]: And, again, that just adds a straight multiple to the cost. Oh, yeah. Yeah, yeah.George [00:12:37]: So, that's one of many reasons that cost has gone up a lot more than linearly over the last couple of years. We report a cost to run the artificial analysis. We report a cost to run the artificial analysis intelligence index on our website, and currently that's assuming one repeat in terms of how we report it because we want to reflect a bit about the weighting of the index. But our cost is actually a lot higher than what we report there because of the repeats.swyx [00:13:03]: Yeah, yeah, yeah. And probably this is true, but just checking, you don't have any special deals with the labs. They don't discount it. You just pay out of pocket or out of your sort of customer funds. Oh, there is a mix. So, the issue is that sometimes they may give you a special end point, which is… Ah, 100%.Micah [00:13:21]: Yeah, yeah, yeah. Exactly. So, we laser focus, like, on everything we do on having the best independent metrics and making sure that no one can manipulate them in any way. There are quite a lot of processes we've developed over the last couple of years to make that true for, like, the one you bring up, like, right here of the fact that if we're working with a lab, if they're giving us a private endpoint to evaluate a model, that it is totally possible. That what's sitting behind that black box is not the same as they serve on a public endpoint. We're very aware of that. We have what we call a mystery shopper policy. And so, and we're totally transparent with all the labs we work with about this, that we will register accounts not on our own domain and run both intelligence evals and performance benchmarks… Yeah, that's the job. …without them being able to identify it. And no one's ever had a problem with that. Because, like, a thing that turns out to actually be quite a good… …good factor in the industry is that they all want to believe that none of their competitors could manipulate what we're doing either.swyx [00:14:23]: That's true. I never thought about that. I've been in the database data industry prior, and there's a lot of shenanigans around benchmarking, right? So I'm just kind of going through the mental laundry list. Did I miss anything else in this category of shenanigans? Oh, potential shenanigans.Micah [00:14:36]: I mean, okay, the biggest one, like, that I'll bring up, like, is more of a conceptual one, actually, than, like, direct shenanigans. It's that the things that get measured become things that get targeted by labs that they're trying to build, right? Exactly. So that doesn't mean anything that we should really call shenanigans. Like, I'm not talking about training on test set. But if you know that you're going to be great at another particular thing, if you're a researcher, there are a whole bunch of things that you can do to try to get better at that thing that preferably are going to be helpful for a wide range of how actual users want to use the thing that you're building. But will not necessarily work. Will not necessarily do that. So, for instance, the models are exceptional now at answering competition maths problems. There is some relevance of that type of reasoning, that type of work, to, like, how we might use modern coding agents and stuff. But it's clearly not one for one. So the thing that we have to be aware of is that once an eval becomes the thing that everyone's looking at, scores can get better on it without there being a reflection of overall generalized intelligence of these models. Getting better. That has been true for the last couple of years. It'll be true for the next couple of years. There's no silver bullet to defeat that other than building new stuff to stay relevant and measure the capabilities that matter most to real users. Yeah.swyx [00:15:58]: And we'll cover some of the new stuff that you guys are building as well, which is cool. Like, you used to just run other people's evals, but now you're coming up with your own. And I think, obviously, that is a necessary path once you're at the frontier. You've exhausted all the existing evals. I think the next point in history that I have for you is AI Grant that you guys decided to join and move here. What was it like? I think you were in, like, batch two? Batch four. Batch four. Okay.Micah [00:16:26]: I mean, it was great. Nat and Daniel are obviously great. And it's a really cool group of companies that we were in AI Grant alongside. It was really great to get Nat and Daniel on board. Obviously, they've done a whole lot of great work in the space with a lot of leading companies and were extremely aligned. With the mission of what we were trying to do. Like, we're not quite typical of, like, a lot of the other AI startups that they've invested in.swyx [00:16:53]: And they were very much here for the mission of what we want to do. Did they say any advice that really affected you in some way or, like, were one of the events very impactful? That's an interesting question.Micah [00:17:03]: I mean, I remember fondly a bunch of the speakers who came and did fireside chats at AI Grant.swyx [00:17:09]: Which is also, like, a crazy list. Yeah.George [00:17:11]: Oh, totally. Yeah, yeah, yeah. There was something about, you know, speaking to Nat and Daniel about the challenges of working through a startup and just working through the questions that don't have, like, clear answers and how to work through those kind of methodically and just, like, work through the hard decisions. And they've been great mentors to us as we've built artificial analysis. Another benefit for us was that other companies in the batch and other companies in AI Grant are pushing the capabilities. Yeah. And I think that's a big part of what AI can do at this time. And so being in contact with them, making sure that artificial analysis is useful to them has been fantastic for supporting us in working out how should we build out artificial analysis to continue to being useful to those, like, you know, building on AI.swyx [00:17:59]: I think to some extent, I'm mixed opinion on that one because to some extent, your target audience is not people in AI Grants who are obviously at the frontier. Yeah. Do you disagree?Micah [00:18:09]: To some extent. To some extent. But then, so a lot of what the AI Grant companies are doing is taking capabilities coming out of the labs and trying to push the limits of what they can do across the entire stack for building great applications, which actually makes some of them pretty archetypical power users of artificial analysis. Some of the people with the strongest opinions about what we're doing well and what we're not doing well and what they want to see next from us. Yeah. Yeah. Because when you're building any kind of AI application now, chances are you're using a whole bunch of different models. You're maybe switching reasonably frequently for different models and different parts of your application to optimize what you're able to do with them at an accuracy level and to get better speed and cost characteristics. So for many of them, no, they're like not commercial customers of ours, like we don't charge for all our data on the website. Yeah. They are absolutely some of our power users.swyx [00:19:07]: So let's talk about just the evals as well. So you start out from the general like MMU and GPQA stuff. What's next? How do you sort of build up to the overall index? What was in V1 and how did you evolve it? Okay.Micah [00:19:22]: So first, just like background, like we're talking about the artificial analysis intelligence index, which is our synthesis metric that we pulled together currently from 10 different eval data sets to give what? We're pretty much the same as that. Pretty confident is the best single number to look at for how smart the models are. Obviously, it doesn't tell the whole story. That's why we published the whole website of all the charts to dive into every part of it and look at the trade-offs. But best single number. So right now, it's got a bunch of Q&A type data sets that have been very important to the industry, like a couple that you just mentioned. It's also got a couple of agentic data sets. It's got our own long context reasoning data set and some other use case focused stuff. As time goes on. The things that we're most interested in that are going to be important to the capabilities that are becoming more important for AI, what developers are caring about, are going to be first around agentic capabilities. So surprise, surprise. We're all loving our coding agents and how the model is going to perform like that and then do similar things for different types of work are really important to us. The linking to use cases to economically valuable use cases are extremely important to us. And then we've got some of the. Yeah. These things that the models still struggle with, like working really well over long contexts that are not going to go away as specific capabilities and use cases that we need to keep evaluating.swyx [00:20:46]: But I guess one thing I was driving was like the V1 versus the V2 and how bad it was over time.Micah [00:20:53]: Like how we've changed the index to where we are.swyx [00:20:55]: And I think that reflects on the change in the industry. Right. So that's a nice way to tell that story.Micah [00:21:00]: Well, V1 would be completely saturated right now. Almost every model coming out because doing things like writing the Python functions and human evil is now pretty trivial. It's easy to forget, actually, I think how much progress has been made in the last two years. Like we obviously play the game constantly of like the today's version versus last week's version and the week before and all of the small changes in the horse race between the current frontier and who has the best like smaller than 10B model like right now this week. Right. And that's very important to a lot of developers and people and especially in this particular city of San Francisco. But when you zoom out a couple of years ago, literally most of what we were doing to evaluate the models then would all be 100% solved by even pretty small models today. And that's been one of the key things, by the way, that's driven down the cost of intelligence at every tier of intelligence. We can talk about more in a bit. So V1, V2, V3, we made things harder. We covered a wider range of use cases. And we tried to get closer to things developers care about as opposed to like just the Q&A type stuff that MMLU and GPQA represented. Yeah.swyx [00:22:12]: I don't know if you have anything to add there. Or we could just go right into showing people the benchmark and like looking around and asking questions about it. Yeah.Micah [00:22:21]: Let's do it. Okay. This would be a pretty good way to chat about a few of the new things we've launched recently. Yeah.George [00:22:26]: And I think a little bit about the direction that we want to take it. And we want to push benchmarks. Currently, the intelligence index and evals focus a lot on kind of raw intelligence. But we kind of want to diversify how we think about intelligence. And we can talk about it. But kind of new evals that we've kind of built and partnered on focus on topics like hallucination. And we've got a lot of topics that I think are not covered by the current eval set that should be. And so we want to bring that forth. But before we get into that.swyx [00:23:01]: And so for listeners, just as a timestamp, right now, number one is Gemini 3 Pro High. Then followed by Cloud Opus at 70. Just 5.1 high. You don't have 5.2 yet. And Kimi K2 Thinking. Wow. Still hanging in there. So those are the top four. That will date this podcast quickly. Yeah. Yeah. I mean, I love it. I love it. No, no. 100%. Look back this time next year and go, how cute. Yep.George [00:23:25]: Totally. A quick view of that is, okay, there's a lot. I love it. I love this chart. Yeah.Micah [00:23:30]: This is such a favorite, right? Yeah. And almost every talk that George or I give at conferences and stuff, we always put this one up first to just talk about situating where we are in this moment in history. This, I think, is the visual version of what I was saying before about the zooming out and remembering how much progress there's been. If we go back to just over a year ago, before 01, before Cloud Sonnet 3.5, we didn't have reasoning models or coding agents as a thing. And the game was very, very different. If we go back even a little bit before then, we're in the era where, when you look at this chart, open AI was untouchable for well over a year. And, I mean, you would remember that time period well of there being very open questions about whether or not AI was going to be competitive, like full stop, whether or not open AI would just run away with it, whether we would have a few frontier labs and no one else would really be able to do anything other than consume their APIs. I am quite happy overall that the world that we have ended up in is one where... Multi-model. Absolutely. And strictly more competitive every quarter over the last few years. Yeah. This year has been insane. Yeah.George [00:24:42]: You can see it. This chart with everything added is hard to read currently. There's so many dots on it, but I think it reflects a little bit what we felt, like how crazy it's been.swyx [00:24:54]: Why 14 as the default? Is that a manual choice? Because you've got service now in there that are less traditional names. Yeah.George [00:25:01]: It's models that we're kind of highlighting by default in our charts, in our intelligence index. Okay.swyx [00:25:07]: You just have a manually curated list of stuff.George [00:25:10]: Yeah, that's right. But something that I actually don't think every artificial analysis user knows is that you can customize our charts and choose what models are highlighted. Yeah. And so if we take off a few names, it gets a little easier to read.swyx [00:25:25]: Yeah, yeah. A little easier to read. Totally. Yeah. But I love that you can see the all one jump. Look at that. September 2024. And the DeepSeek jump. Yeah.George [00:25:34]: Which got close to OpenAI's leadership. They were so close. I think, yeah, we remember that moment. Around this time last year, actually.Micah [00:25:44]: Yeah, yeah, yeah. I agree. Yeah, well, a couple of weeks. It was Boxing Day in New Zealand when DeepSeek v3 came out. And we'd been tracking DeepSeek and a bunch of the other global players that were less known over the second half of 2024 and had run evals on the earlier ones and stuff. I very distinctly remember Boxing Day in New Zealand, because I was with family for Christmas and stuff, running the evals and getting back result by result on DeepSeek v3. So this was the first of their v3 architecture, the 671b MOE.Micah [00:26:19]: And we were very, very impressed. That was the moment where we were sure that DeepSeek was no longer just one of many players, but had jumped up to be a thing. The world really noticed when they followed that up with the RL working on top of v3 and R1 succeeding a few weeks later. But the groundwork for that absolutely was laid with just extremely strong base model, completely open weights that we had as the best open weights model. So, yeah, that's the thing that you really see in the game. But I think that we got a lot of good feedback on Boxing Day. us on Boxing Day last year.George [00:26:48]: Boxing Day is the day after Christmas for those not familiar.George [00:26:54]: I'm from Singapore.swyx [00:26:55]: A lot of us remember Boxing Day for a different reason, for the tsunami that happened. Oh, of course. Yeah, but that was a long time ago. So yeah. So this is the rough pitch of AAQI. Is it A-A-Q-I or A-A-I-I? I-I. Okay. Good memory, though.Micah [00:27:11]: I don't know. I'm not used to it. Once upon a time, we did call it Quality Index, and we would talk about quality, performance, and price, but we changed it to intelligence.George [00:27:20]: There's been a few naming changes. We added hardware benchmarking to the site, and so benchmarks at a kind of system level. And so then we changed our throughput metric to, we now call it output speed, and thenswyx [00:27:32]: throughput makes sense at a system level, so we took that name. Take me through more charts. What should people know? Obviously, the way you look at the site is probably different than how a beginner might look at it.Micah [00:27:42]: Yeah, that's fair. There's a lot of fun stuff to dive into. Maybe so we can hit past all the, like, we have lots and lots of emails and stuff. The interesting ones to talk about today that would be great to bring up are a few of our recent things, I think, that probably not many people will be familiar with yet. So first one of those is our omniscience index. So this one is a little bit different to most of the intelligence evils that we've run. We built it specifically to look at the embedded knowledge in the models and to test hallucination by looking at when the model doesn't know the answer, so not able to get it correct, what's its probability of saying, I don't know, or giving an incorrect answer. So the metric that we use for omniscience goes from negative 100 to positive 100. Because we're simply taking off a point if you give an incorrect answer to the question. We're pretty convinced that this is an example of where it makes most sense to do that, because it's strictly more helpful to say, I don't know, instead of giving a wrong answer to factual knowledge question. And one of our goals is to shift the incentive that evils create for models and the labs creating them to get higher scores. And almost every evil across all of AI up until this point, it's been graded by simple percentage correct as the main metric, the main thing that gets hyped. And so you should take a shot at everything. There's no incentive to say, I don't know. So we did that for this one here.swyx [00:29:22]: I think there's a general field of calibration as well, like the confidence in your answer versus the rightness of the answer. Yeah, we completely agree. Yeah. Yeah.George [00:29:31]: On that. And one reason that we didn't do that is because. Or put that into this index is that we think that the, the way to do that is not to ask the models how confident they are.swyx [00:29:43]: I don't know. Maybe it might be though. You put it like a JSON field, say, say confidence and maybe it spits out something. Yeah. You know, we have done a few evils podcasts over the, over the years. And when we did one with Clementine of hugging face, who maintains the open source leaderboard, and this was one of her top requests, which is some kind of hallucination slash lack of confidence calibration thing. And so, Hey, this is one of them.Micah [00:30:05]: And I mean, like anything that we do, it's not a perfect metric or the whole story of everything that you think about as hallucination. But yeah, it's pretty useful and has some interesting results. Like one of the things that we saw in the hallucination rate is that anthropics Claude models at the, the, the very left-hand side here with the lowest hallucination rates out of the models that we've evaluated amnesty is on. That is an interesting fact. I think it probably correlates with a lot of the previously, not really measured vibes stuff that people like about some of the Claude models. Is the dataset public or what's is it, is there a held out set? There's a hell of a set for this one. So we, we have published a public test set, but we we've only published 10% of it. The reason is that for this one here specifically, it would be very, very easy to like have data contamination because it is just factual knowledge questions. We would. We'll update it at a time to also prevent that, but with yeah, kept most of it held out so that we can keep it reliable for a long time. It leads us to a bunch of really cool things, including breakdown quite granularly by topic. And so we've got some of that disclosed on the website publicly right now, and there's lots more coming in terms of our ability to break out very specific topics. Yeah.swyx [00:31:23]: I would be interested. Let's, let's dwell a little bit on this hallucination one. I noticed that Haiku hallucinates less than Sonnet hallucinates less than Opus. And yeah. Would that be the other way around in a normal capability environments? I don't know. What's, what do you make of that?George [00:31:37]: One interesting aspect is that we've found that there's not really a, not a strong correlation between intelligence and hallucination, right? That's to say that the smarter the models are in a general sense, isn't correlated with their ability to, when they don't know something, say that they don't know. It's interesting that Gemini three pro preview was a big leap over here. Gemini 2.5. Flash and, and, and 2.5 pro, but, and if I add pro quickly here.swyx [00:32:07]: I bet pro's really good. Uh, actually no, I meant, I meant, uh, the GPT pros.George [00:32:12]: Oh yeah.swyx [00:32:13]: Cause GPT pros are rumored. We don't know for a fact that it's like eight runs and then with the LM judge on top. Yeah.George [00:32:20]: So we saw a big jump in, this is accuracy. So this is just percent that they get, uh, correct and Gemini three pro knew a lot more than the other models. And so big jump in accuracy. But relatively no change between the Google Gemini models, between releases. And the hallucination rate. Exactly. And so it's likely due to just kind of different post-training recipe, between the, the Claude models. Yeah.Micah [00:32:45]: Um, there's, there's driven this. Yeah. You can, uh, you can partially blame us and how we define intelligence having until now not defined hallucination as a negative in the way that we think about intelligence.swyx [00:32:56]: And so that's what we're changing. Uh, I know many smart people who are confidently incorrect.George [00:33:02]: Uh, look, look at that. That, that, that is very humans. Very true. And there's times and a place for that. I think our view is that hallucination rate makes sense in this context where it's around knowledge, but in many cases, people want the models to hallucinate, to have a go. Often that's the case in coding or when you're trying to generate newer ideas. One eval that we added to artificial analysis is, is, is critical point and it's really hard, uh, physics problems. Okay.swyx [00:33:32]: And is it sort of like a human eval type or something different or like a frontier math type?George [00:33:37]: It's not dissimilar to frontier frontier math. So these are kind of research questions that kind of academics in the physics physics world would be able to answer, but models really struggled to answer. So the top score here is not 9%.swyx [00:33:51]: And when the people that, that created this like Minway and, and, and actually off via who was kind of behind sweep and what organization is this? Oh, is this, it's Princeton.George [00:34:01]: Kind of range of academics from, from, uh, different academic institutions, really smart people. They talked about how they turn the models up in terms of the temperature as high temperature as they can, where they're trying to explore kind of new ideas in physics as a, as a thought partner, just because they, they want the models to hallucinate. Um, yeah, sometimes it's something new. Yeah, exactly.swyx [00:34:21]: Um, so not right in every situation, but, um, I think it makes sense, you know, to test hallucination in scenarios where it makes sense. Also, the obvious question is, uh, this is one of. Many that there is there, every lab has a system card that shows some kind of hallucination number, and you've chosen to not, uh, endorse that and you've made your own. And I think that's a, that's a choice. Um, totally in some sense, the rest of artificial analysis is public benchmarks that other people can independently rerun. You provide it as a service here. You have to fight the, well, who are we to, to like do this? And your, your answer is that we have a lot of customers and, you know, but like, I guess, how do you converge the individual?Micah [00:35:08]: I mean, I think, I think for hallucinations specifically, there are a bunch of different things that you might care about reasonably, and that you'd measure quite differently, like we've called this a amnesty and solutionation rate, not trying to declare the, like, it's humanity's last hallucination. You could, uh, you could have some interesting naming conventions and all this stuff. Um, the biggest picture answer to that. It's something that I actually wanted to mention. Just as George was explaining, critical point as well is, so as we go forward, we are building evals internally. We're partnering with academia and partnering with AI companies to build great evals. We have pretty strong views on, in various ways for different parts of the AI stack, where there are things that are not being measured well, or things that developers care about that should be measured more and better. And we intend to be doing that. We're not obsessed necessarily with that. Everything we do, we have to do entirely within our own team. Critical point. As a cool example of where we were a launch partner for it, working with academia, we've got some partnerships coming up with a couple of leading companies. Those ones, obviously we have to be careful with on some of the independent stuff, but with the right disclosure, like we're completely comfortable with that. A lot of the labs have released great data sets in the past that we've used to great success independently. And so it's between all of those techniques, we're going to be releasing more stuff in the future. Cool.swyx [00:36:26]: Let's cover the last couple. And then we'll, I want to talk about your trends analysis stuff, you know? Totally.Micah [00:36:31]: So that actually, I have one like little factoid on omniscience. If you go back up to accuracy on omniscience, an interesting thing about this accuracy metric is that it tracks more closely than anything else that we measure. The total parameter count of models makes a lot of sense intuitively, right? Because this is a knowledge eval. This is the pure knowledge metric. We're not looking at the index and the hallucination rate stuff that we think is much more about how the models are trained. This is just what facts did they recall? And yeah, it tracks parameter count extremely closely. Okay.swyx [00:37:05]: What's the rumored size of GPT-3 Pro? And to be clear, not confirmed for any official source, just rumors. But rumors do fly around. Rumors. I get, I hear all sorts of numbers. I don't know what to trust.Micah [00:37:17]: So if you, if you draw the line on omniscience accuracy versus total parameters, we've got all the open ways models, you can squint and see that likely the leading frontier models right now are quite a lot bigger than the ones that we're seeing right now. And the one trillion parameters that the open weights models cap out at, and the ones that we're looking at here, there's an interesting extra data point that Elon Musk revealed recently about XAI that for three trillion parameters for GROK 3 and 4, 6 trillion for GROK 5, but that's not out yet. Take those together, have a look. You might reasonably form a view that there's a pretty good chance that Gemini 3 Pro is bigger than that, that it could be in the 5 to 10 trillion parameters. To be clear, I have absolutely no idea, but just based on this chart, like that's where you would, you would land if you have a look at it. Yeah.swyx [00:38:07]: And to some extent, I actually kind of discourage people from guessing too much because what does it really matter? Like as long as they can serve it as a sustainable cost, that's about it. Like, yeah, totally.George [00:38:17]: They've also got different incentives in play compared to like open weights models who are thinking to supporting others in self-deployment for the labs who are doing inference at scale. It's I think less about total parameters in many cases. When thinking about inference costs and more around number of active parameters. And so there's a bit of an incentive towards larger sparser models. Agreed.Micah [00:38:38]: Understood. Yeah. Great. I mean, obviously if you're a developer or company using these things, not exactly as you say, it doesn't matter. You should be looking at all the different ways that we measure intelligence. You should be looking at cost to run index number and the different ways of thinking about token efficiency and cost efficiency based on the list prices, because that's all it matters.swyx [00:38:56]: It's not as good for the content creator rumor mill where I can say. Oh, GPT-4 is this small circle. Look at GPT-5 is this big circle. And then there used to be a thing for a while. Yeah.Micah [00:39:07]: But that is like on its own, actually a very interesting one, right? That is it just purely that chances are the last couple of years haven't seen a dramatic scaling up in the total size of these models. And so there's a lot of room to go up properly in total size of the models, especially with the upcoming hardware generations. Yes.swyx [00:39:29]: So, you know. Taking off my shitposting face for a minute. Yes. Yes. At the same time, I do feel like, you know, especially coming back from Europe, people do feel like Ilya is probably right that the paradigm is doesn't have many more orders of magnitude to scale out more. And therefore we need to start exploring at least a different path. GDPVal, I think it's like only like a month or so old. I was also very positive when it first came out. I actually talked to Tejo, who was the lead researcher on that. Oh, cool. And you have your own version.George [00:39:59]: It's a fantastic. It's a fantastic data set. Yeah.swyx [00:40:01]: And maybe it will recap for people who are still out of it. It's like 44 tasks based on some kind of GDP cutoff that's like meant to represent broad white collar work that is not just coding. Yeah.Micah [00:40:12]: Each of the tasks have a whole bunch of detailed instructions, some input files for a lot of them. It's within the 44 is divided into like two hundred and twenty two to five, maybe subtasks that are the level of that we run through the agenda. And yeah, they're really interesting. I will say that it doesn't. It doesn't necessarily capture like all the stuff that people do at work. No avail is perfect is always going to be more things to look at, largely because in order to make the tasks well enough to find that you can run them, they need to only have a handful of input files and very specific instructions for that task. And so I think the easiest way to think about them are that they're like quite hard take home exam tasks that you might do in an interview process.swyx [00:40:56]: Yeah, for listeners, it is not no longer like a long prompt. It is like, well, here's a zip file with like a spreadsheet or a PowerPoint deck or a PDF and go nuts and answer this question.George [00:41:06]: OpenAI released a great data set and they released a good paper which looks at performance across the different web chat bots on the data set. It's a great paper, encourage people to read it. What we've done is taken that data set and turned it into an eval that can be run on any model. So we created a reference agentic harness that can run. Run the models on the data set, and then we developed evaluator approach to compare outputs. That's kind of AI enabled, so it uses Gemini 3 Pro Preview to compare results, which we tested pretty comprehensively to ensure that it's aligned to human preferences. One data point there is that even as an evaluator, Gemini 3 Pro, interestingly, doesn't do actually that well. So that's kind of a good example of what we've done in GDPVal AA.swyx [00:42:01]: Yeah, the thing that you have to watch out for with LLM judge is self-preference that models usually prefer their own output, and in this case, it was not. Totally.Micah [00:42:08]: I think the way that we're thinking about the places where it makes sense to use an LLM as judge approach now, like quite different to some of the early LLM as judge stuff a couple of years ago, because some of that and MTV was a great project that was a good example of some of this a while ago was about judging conversations and like a lot of style type stuff. Here, we've got the task that the grader and grading model is doing is quite different to the task of taking the test. When you're taking the test, you've got all of the agentic tools you're working with, the code interpreter and web search, the file system to go through many, many turns to try to create the documents. Then on the other side, when we're grading it, we're running it through a pipeline to extract visual and text versions of the files and be able to provide that to Gemini, and we're providing the criteria for the task and getting it to pick which one more effectively meets the criteria of the task. Yeah. So we've got the task out of two potential outcomes. It turns out that we proved that it's just very, very good at getting that right, matched with human preference a lot of the time, because I think it's got the raw intelligence, but it's combined with the correct representation of the outputs, the fact that the outputs were created with an agentic task that is quite different to the way the grading model works, and we're comparing it against criteria, not just kind of zero shot trying to ask the model to pick which one is better.swyx [00:43:26]: Got it. Why is this an ELO? And not a percentage, like GDP-VAL?George [00:43:31]: So the outputs look like documents, and there's video outputs or audio outputs from some of the tasks. It has to make a video? Yeah, for some of the tasks. Some of the tasks.swyx [00:43:43]: What task is that?George [00:43:45]: I mean, it's in the data set. Like be a YouTuber? It's a marketing video.Micah [00:43:49]: Oh, wow. What? Like model has to go find clips on the internet and try to put it together. The models are not that good at doing that one, for now, to be clear. It's pretty hard to do that with a code editor. I mean, the computer stuff doesn't work quite well enough and so on and so on, but yeah.George [00:44:02]: And so there's no kind of ground truth, necessarily, to compare against, to work out percentage correct. It's hard to come up with correct or incorrect there. And so it's on a relative basis. And so we use an ELO approach to compare outputs from each of the models between the task.swyx [00:44:23]: You know what you should do? You should pay a contractor, a human, to do the same task. And then give it an ELO and then so you have, you have human there. It's just, I think what's helpful about GDPVal, the OpenAI one, is that 50% is meant to be normal human and maybe Domain Expert is higher than that, but 50% was the bar for like, well, if you've crossed 50, you are superhuman. Yeah.Micah [00:44:47]: So we like, haven't grounded this score in that exactly. I agree that it can be helpful, but we wanted to generalize this to a very large number. It's one of the reasons that presenting it as ELO is quite helpful and allows us to add models and it'll stay relevant for quite a long time. I also think it, it can be tricky looking at these exact tasks compared to the human performance, because the way that you would go about it as a human is quite different to how the models would go about it. Yeah.swyx [00:45:15]: I also liked that you included Lama 4 Maverick in there. Is that like just one last, like...Micah [00:45:20]: Well, no, no, no, no, no, no, it is the, it is the best model released by Meta. And... So it makes it into the homepage default set, still for now.George [00:45:31]: Other inclusion that's quite interesting is we also ran it across the latest versions of the web chatbots. And so we have...swyx [00:45:39]: Oh, that's right.George [00:45:40]: Oh, sorry.swyx [00:45:41]: I, yeah, I completely missed that. Okay.George [00:45:43]: No, not at all. So that, which has a checkered pattern. So that is their harness, not yours, is what you're saying. Exactly. And what's really interesting is that if you compare, for instance, Claude 4.5 Opus using the Claude web chatbot, it performs worse than the model in our agentic harness. And so in every case, the model performs better in our agentic harness than its web chatbot counterpart, the harness that they created.swyx [00:46:13]: Oh, my backwards explanation for that would be that, well, it's meant for consumer use cases and here you're pushing it for something.Micah [00:46:19]: The constraints are different and the amount of freedom that you can give the model is different. Also, you like have a cost goal. We let the models work as long as they want, basically. Yeah. Do you copy paste manually into the chatbot? Yeah. Yeah. That's, that was how we got the chatbot reference. We're not going to be keeping those updated at like quite the same scale as hundreds of models.swyx [00:46:38]: Well, so I don't know, talk to a browser base. They'll, they'll automate it for you. You know, like I have thought about like, well, we should turn these chatbot versions into an API because they are legitimately different agents in themselves. Yes. Right. Yeah.Micah [00:46:53]: And that's grown a huge amount of the last year, right? Like the tools. The tools that are available have actually diverged in my opinion, a fair bit across the major chatbot apps and the amount of data sources that you can connect them to have gone up a lot, meaning that your experience and the way you're using the model is more different than ever.swyx [00:47:10]: What tools and what data connections come to mind when you say what's interesting, what's notable work that people have done?Micah [00:47:15]: Oh, okay. So my favorite example on this is that until very recently, I would argue that it was basically impossible to get an LLM to draft an email for me in any useful way. Because most times that you're sending an email, you're not just writing something for the sake of writing it. Chances are context required is a whole bunch of historical emails. Maybe it's notes that you've made, maybe it's meeting notes, maybe it's, um, pulling something from your, um, any of like wherever you at work store stuff. So for me, like Google drive, one drive, um, in our super base databases, if we need to do some analysis or some data or something, preferably model can be plugged into all of those things and can go do some useful work based on it. The things that like I find most impressive currently that I am somewhat surprised work really well in late 2025, uh, that I can have models use super base MCP to query read only, of course, run a whole bunch of SQL queries to do pretty significant data analysis. And. And make charts and stuff and can read my Gmail and my notion. And okay. You actually use that. That's good. That's, that's, that's good. Is that a cloud thing? To various degrees of order, but chat GPD and Claude right now, I would say that this stuff like barely works in fairness right now. Like.George [00:48:33]: Because people are actually going to try this after they hear it. If you get an email from Micah, odds are it wasn't written by a chatbot.Micah [00:48:38]: So, yeah, I think it is true that I have never actually sent anyone an email drafted by a chatbot. Yet.swyx [00:48:46]: Um, and so you can, you can feel it right. And yeah, this time, this time next year, we'll come back and see where it's going. Totally. Um, super base shout out another famous Kiwi. Uh, I don't know if you've, you've any conversations with him about anything in particular on AI building and AI infra.George [00:49:03]: We have had, uh, Twitter DMS, um, with, with him because we're quite big, uh, super base users and power users. And we probably do some things more manually than we should in. In, in super base support line because you're, you're a little bit being super friendly. One extra, um, point regarding, um, GDP Val AA is that on the basis of the overperformance of the models compared to the chatbots turns out, we realized that, oh, like our reference harness that we built actually white works quite well on like gen generalist agentic tasks. This proves it in a sense. And so the agent harness is very. Minimalist. I think it follows some of the ideas that are in Claude code and we, all that we give it is context management capabilities, a web search, web browsing, uh, tool, uh, code execution, uh, environment. Anything else?Micah [00:50:02]: I mean, we can equip it with more tools, but like by default, yeah, that's it. We, we, we give it for GDP, a tool to, uh, view an image specifically, um, because the models, you know, can just use a terminal to pull stuff in text form into context. But to pull visual stuff into context, we had to give them a custom tool, but yeah, exactly. Um, you, you can explain an expert. No.George [00:50:21]: So it's, it, we turned out that we created a good generalist agentic harness. And so we, um, released that on, on GitHub yesterday. It's called stirrup. So if people want to check it out and, and it's a great, um, you know, base for, you know, generalist, uh, building a generalist agent for more specific tasks.Micah [00:50:39]: I'd say the best way to use it is get clone and then have your favorite coding. Agent make changes to it, to do whatever you want, because it's not that many lines of code and the coding agents can work with it. Super well.swyx [00:50:51]: Well, that's nice for the community to explore and share and hack on it. I think maybe in, in, in other similar environments, the terminal bench guys have done, uh, sort of the Harbor. Uh, and so it's, it's a, it's a bundle of, well, we need our minimal harness, which for them is terminus and we also need the RL environments or Docker deployment thing to, to run independently. So I don't know if you've looked at it. I don't know if you've looked at the harbor at all, is that, is that like a, a standard that people want to adopt?George [00:51:19]: Yeah, we've looked at it from a evals perspective and we love terminal bench and, and host benchmarks of, of, of terminal mention on artificial analysis. Um, we've looked at it from a, from a coding agent perspective, but could see it being a great, um, basis for any kind of agents. I think where we're getting to is that these models have gotten smart enough. They've gotten better, better tools that they can perform better when just given a minimalist. Set of tools and, and let them run, let the model control the, the agentic workflow rather than using another framework that's a bit more built out that tries to dictate the, dictate the flow. Awesome.swyx [00:51:56]: Let's cover the openness index and then let's go into the report stuff. Uh, so that's the, that's the last of the proprietary art numbers, I guess. I don't know how you sort of classify all these. Yeah.Micah [00:52:07]: Or call it, call it, let's call it the last of like the, the three new things that we're talking about from like the last few weeks. Um, cause I mean, there's a, we do a mix of stuff that. Where we're using open source, where we open source and what we do and, um, proprietary stuff that we don't always open source, like long context reasoning data set last year, we did open source. Um, and then all of the work on performance benchmarks across the site, some of them, we looking to open source, but some of them, like we're constantly iterating on and so on and so on and so on. So there's a huge mix, I would say, just of like stuff that is open source and not across the side. So that's a LCR for people. Yeah, yeah, yeah, yeah.swyx [00:52:41]: Uh, but let's, let's, let's talk about open.Micah [00:52:42]: Let's talk about openness index. This. Here is call it like a new way to think about how open models are. We, for a long time, have tracked where the models are open weights and what the licenses on them are. And that's like pretty useful. That tells you what you're allowed to do with the weights of a model, but there is this whole other dimension to how open models are. That is pretty important that we haven't tracked until now. And that's how much is disclosed about how it was made. So transparency about data, pre-training data and post-training data. And whether you're allowed to use that data and transparency about methodology and training code. So basically, those are the components. We bring them together to score an openness index for models so that you can in one place get this full picture of how open models are.swyx [00:53:32]: I feel like I've seen a couple other people try to do this, but they're not maintained. I do think this does matter. I don't know what the numbers mean apart from is there a max number? Is this out of 20?George [00:53:44]: It's out of 18 currently, and so we've got an openness index page, but essentially these are points, you get points for being more open across these different categories and the maximum you can achieve is 18. So AI2 with their extremely open OMO3 32B think model is the leader in a sense.swyx [00:54:04]: It's hooking face.George [00:54:05]: Oh, with their smaller model. It's coming soon. I think we need to run, we need to get the intelligence benchmarks right to get it on the site.swyx [00:54:12]: You can't have it open in the next. We can not include hooking face. We love hooking face. We'll have that, we'll have that up very soon. I mean, you know, the refined web and all that stuff. It's, it's amazing. Or is it called fine web? Fine web. Fine web.Micah [00:54:23]: Yeah, yeah, no, totally. Yep. One of the reasons this is cool, right, is that if you're trying to understand the holistic picture of the models and what you can do with all the stuff the company's contributing, this gives you that picture. And so we are going to keep it up to date alongside all the models that we do intelligence index on, on the site. And it's just an extra view to understand.swyx [00:54:43]: Can you scroll down to this? The, the, the, the trade-offs chart. Yeah, yeah. That one. Yeah. This, this really matters, right? Obviously, because you can b

We sit down for our annual year-end conversation, reflecting on 2025 and mapping out intentions for 2026. The discussion moves between practical revenue planning and deeper questions about identity, authenticity, and what it means to build a creative life without losing yourself in the process.We explore the tension between chasing grandiose visions of success and learning to be present with who we actually are—people who source vintage records, make photographs, create videos, and build websites. The conversation touches on the difference between "playing the part" of a successful creator versus doing work that genuinely reflects our interests and values. We discuss building infrastructure: getting websites live, returning to photography, potentially publishing short stories, and establishing outlets for work that's been internal for too long. Both of us grapple with the pull of consumption and distraction versus the slower work of being present, disciplined, and engaged with the actual world.The episode ends on the idea of returning to being generalists rather than specialists—people with broad interests and connections across different areas of life, people who haven't traded their souls for narrow visions of achievement. -Ai If you enjoyed this episode, please consider giving us a rating and/or a review. We read and appreciate all of them. Thanks for listening, and we'll see you in the next episode. Links To Everything: Video Version of The Podcast: https://geni.us/StudioSessionsYT Matt's YouTube Channel: https://geni.us/MatthewOBrienYT Matt's 2nd Channel: https://geni.us/PhotoVideosYT Alex's YouTube Channel: https://geni.us/AlexCarterYT Matt's Instagram: https://geni.us/MatthewIG Alex's Instagram: https://geni.us/AlexIG

Reproductive success is now central to both milk and beef calf revenue in progressive dairy herds. The episode explores how methionine, lysine, and histidine drive fertility and calf health, making amino acid balancing a critical strategy for profitability. Listeners discover how small gains in conception rates can yield significant financial impact when paired with strategic breeding and nutrition. Key takeaway: Strategic amino acid nutrition is a proven driver of reproductive efficiency and dual revenue streams in dairy production. This episode is sponsored by Actus Nutrition, innovators of precision dairy nutrition solutions. Actus proudly manufactures RPMet, a leading rumen-protected methionine designed to maximize component yields and reproductive efficiency. To learn more about RPMet and other nutrition products by Actus, such as Energy Booster and custom milk replacer products, visit https://actus.com/livestock/specialty-ingredients/

Most pharmacies lose thousands monthly by treating CCM and RPM as competing services instead of complementary revenue streams. The difference between profitable programs and claim denials primarily stems from documentation boundaries, time allocation rules, and an understanding of billing codes.Learn more: https://ccmrpmhelp.com/contact CCM RPM Help City: Herriman Address: 12953 Penywain Lane Website: https://ccmrpmhelp.com/ Phone: +1 866 574 7075 Email: brad@ccmrpmhelp.com

In this conversation, Richard Lewis and Adam Hanover, founders of Redwood Services, discuss their unique approach to private equity in the home services industry. They emphasize the importance of partnerships, culture, and a long-term investment strategy that prioritizes the growth and success of local businesses. The discussion covers their backgrounds, the philosophy behind their 'build to hold' strategy, and the significance of operational excellence and leadership in achieving sustainable growth. They also address the perception of private equity and the role of technology in enhancing business operations. 00:00 Introduction to Redwood Services and Its Founders 06:01 Adam Hanover's Background and Investment Philosophy 08:57 The Build to Hold Strategy in Private Equity 11:46 Partnerships and the Importance of Culture 14:56 Revenue Streams and Operational Excellence 20:55 Identifying Ideal Partner Companies 23:47 Economies of Scale vs. Local Management 32:47 Marketing and Customer Retention Strategies 38:57 Lessons Learned and Advice for New Entrepreneurs 42:08 The Role of Technology in Home Services 44:46 The Perception of Private Equity

Microgrids offer utilities eight distinct value stacks (or streams), yet most still treat them as experimental pilots. Today on the Clean Power Hour, we reveal how microgrids are strategic network assets that deliver value every single day, not just when the grid fails. Martin Szczepanik is Director of Energy and Resources at Baringa, a global energy consultancy. He brings 11 years of utility strategy experience, including work with SolarCity, major West Coast utilities, and renewable energy developers. Baringa recently released a white paper called "From Pilots to Portfolios: Scaling the Rollout of Utility Microgrids."Key Discussion Points:• The complete microgrid value stack: resilience, distribution capacity deferral, transmission capacity deferral, ancillary services, energy arbitrage, generation capacity deferral, avoided emissions, and avoided public safety power shutoffs (PSPS)• Why resilience differs from reliability: climate-driven extreme weather events versus age and condition-driven outages• How billion-dollar natural disasters increased from once every 90 days in the 1980s to once every 19 days today, which necessitates the increased need for microgrids• Distribution and transmission capacity deferral: using microgrids to avoid $5-10 million infrastructure upgrades• Energy arbitrage opportunities: charging batteries when solar floods the grid, discharging during peak demand• FERC 2222 enabling distributed energy resources to participate in wholesale markets and earn revenue• California wildfires and PSPS (Public Safety Power Shutoffs) events: how microgrids reduce the cost of de-energizing lines• Why utilities need to assess microgrids in distribution planning instead of defaulting to substations and reconductoringConnect with Martin Szczepanik, Baringa LinkedIn: www.linkedin.com/in/martinszczepanik/Website: www.baringa.com/en/about/regions/north-america/microgrids/Whitepaper: www.baringa.com/en/insights/digitising-the-energy-system/scaling-microgrids/ Support the showConnect with Tim Clean Power Hour Clean Power Hour on YouTubeTim on TwitterTim on LinkedIn Email tim@cleanpowerhour.com Review Clean Power Hour on Apple PodcastsThe Clean Power Hour is produced by the Clean Power Consulting Group and created by Tim Montague. Contact us by email: CleanPowerHour@gmail.com Corporate sponsors who share our mission to speed the energy transition are invited to check out https://www.cleanpowerhour.com/support/The Clean Power Hour is brought to you by CPS America, maker of North America's number one 3-phase string inverter, with over 6GW shipped in the US. With a focus on commercial and utility-scale solar and energy storage, the company partners with customers to provide unparalleled performance and service. The CPS America product lineup includes 3-phase string inverters from 25kW to 275kW, exceptional data communication and controls, and energy storage solutions designed for seamless integration with CPS America systems. Learn more at www.chintpowersystems.com

In this episode of Acta Non Verba, host Marcus Aurelius Anderson sits down with Rod Yancy — entrepreneur, writer, attorney, and founder of Oath and Bootleg. Rod shares how he’s found success across multiple industries, from law and financial planning to software and now music. The conversation explores what it means to build companies that serve people, live with intention, and innovate within the modern music industry. Rod offers timeless lessons on creativity, courage, and taking action to turn vision into reality. Episode Highlights: 5:04 - The Power of Mortality in EntrepreneurshipRod discusses how contemplating mortality (memento mori) shaped his philosophy and inspired the founding of Oath, emphasizing the importance of living intentionally and making meaningful decisions. 9:39 - Overcoming the Success TrapRod and Marcus explore the “success trap” many entrepreneurs fall into—chasing money or status at the expense of fulfillment, health, and relationships, and how true success requires self-awareness and balance. 40:07 - Delegation and Leadership Lessons from Richard BransonRod shares advice from Richard Branson about the importance of delegation, empowering others, and stepping back as a leader to allow the team to thrive, even if it means feeling less “needed.” 56:00 - Empowering Artists with New Revenue StreamsRod introduces his new venture, Bootleg, which helps artists monetize live concert recordings, providing fans with unique experiences and artists with ongoing revenue, illustrating innovation and creative entrepreneurship. Rod Yancy is a serial entrepreneur, attorney, and founder of Oath, a company dedicated to helping people live intentionally by contemplating their mortality and planning their legacy. With a background in philosophy and law, Rod has built and scaled multiple businesses, including Oath Planning and Bootleg, a platform empowering artists to monetize live performances. Known for his creative approach, resilience, and commitment to meaningful work, Rod draws inspiration from both ancient philosophy and modern mentors like Richard Branson. He is passionate about fostering innovation, supporting artists, and helping others find purpose beyond financial success. Learn more about the gift of Adversity and my mission to help my fellow humans create a better world by heading to www.marcusaureliusanderson.com. There you can take action by joining my ANV inner circle to get exclusive content and information.See omnystudio.com/listener for privacy information.

In today's episode, I'm pulling back the curtain on every single revenue stream (there are over 13 of them) that powers my business as a content creator — the ones you see, the ones you don't, and the ones people rarely talk about publicly.So often, being a creator looks like posting on social media and doing the occasional brand deal. But behind the scenes? It's a layered, strategic business with multiple income streams happening at once (at least it can be). In this episode, I walk you through how it actually works — from brand partnerships and sponsored content, to platform payouts, whitelisting and boosting, coaching, courses, digital products, affiliate income, speaking engagements, podcast monetization, and more.This episode gives you the overview of all aspects of my business, but if you want the REAL numbers, deeper strategy, pricing insights, and screenshots I can't share publicly on social media, that's all living inside my new paid newsletter, Creator Confidential. Creator Confidential is where I go all in every other week: sharing real earnings, platform breakdowns, behind-the-scenes decisions, and the exact strategies I use to run my business. If this episode intrigues you, the newsletter is truly this conversation on steroids. Sign up at luciefink.com/confidential. I also share how and why I've intentionally built additional revenue streams beyond brands and platforms, not just to grow, but to protect myself long-term in an industry that's constantly changing.And as a special bonus segment, I'm bringing you a short conversation with Denise Woodard, founder of Partake Foods, about entrepreneurship, building a business from scratch, and how Amazon has helped her scale her brand — because this episode isn't just about creator income, it's about modern entrepreneurship in all its forms. You can shop Partake and a curated selection of incredible products from small businesses by looking for the Small Business Badge and visiting the Amazon Small Business Holiday Shop at amazon.com/smallbusinessgifts.If you've ever wondered how creators actually make money or how to think like a creator-entrepreneur, this episode is for you! Apply to do 1:1 social media coaching with me: https://luciefink.com/apply-coaching Apply for my 8-week course, "Launch Your Dream YouTube Series" here: https://luciefink.com/youtube-course Check out my Motherhood Superguide: http://luciefink.com/motherhoodApply to join ShopMy here: https://shopmy.us/join/luciefinkStart your own newsletter on Beehiiv: https://www.beehiiv.com?via=Lucie-FinkSponsors:Magnetic Me: If you're a parent or shopping for one, visit MagneticMe.com now, and new customers will receive 15% off!Storyworth: Right now, save $10 or more during their Holiday sale when you visit Storyworth.com/REALSTUFF Watch this episode in video form on YouTube: https://www.youtube.com/playlist?list=PLjmevEcbh5h5FEX0pazPEtN86t7eb2OgX To apply to be a guest on the show, visit luciefink.com/apply and send us your story. I also want to extend a special thank you to East Love for the show's theme song, Rolling Stone. Follow the show on Instagram: https://www.instagram.com/therealstuffpod Find Lucie here: Instagram: https://www.instagram.com/luciebfink/ TikTok: https://www.tiktok.com/@luciebfink YouTube: https://www.youtube.com/luciebfinkWebsite: https://luciefink.com/ Subscribe to my free newsletter "The Lucie List" here: https://thelucielist.beehiiv.com/subscribeSubscribe to "The Creator Confidential": http://www.luciefink.com/confidentialExecutive Producer: Cloud10Produced by Dear Media.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Today we are breaking down Amadeus, the dominant infrastructure powering global travel bookings. If you've used a travel agent or corporate booking system, you've likely interacted with Amadeus's technology—though most of what they do happens behind the scenes in airline operations, hotel inventory management, and logistics coordination. Ben Needham, portfolio manager at Ninety One Asset Management, explains how Amadeus built its market-leading position, common investor misconceptions about the business, and the company's value proposition across the travel ecosystem. We also explore AI's potential impact on the industry and how Amadeus's balance sheet strength positions it relative to competitors. Please enjoy this breakdown of Amadeus.  For the full show notes, transcript, and links to the best content to learn more, check out the episode page⁠⁠⁠⁠⁠⁠ here.⁠⁠⁠⁠⁠⁠ —- This episode is brought to you by⁠⁠⁠ ⁠Portrait Analytics⁠⁠⁠⁠ - your centralized resource for AI-powered idea generation, thesis monitoring, and personalized report building. Built by buy-side investors, for investment professionals. We work in the background, helping surface stock ideas and thesis signposts to help you monetize every insight. In short, we help you understand the story behind the stock chart, and get to "go, or no-go" 10x faster than before. Sign-up for a free trial today at⁠⁠⁠ ⁠portraitresearch.com⁠⁠⁠⁠ — Business Breakdowns is a property of Colossus, LLC. For more episodes of Business Breakdowns, visit⁠⁠⁠⁠⁠⁠ joincolossus.com/episodes⁠⁠⁠⁠⁠⁠. Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠). Show Notes (00:00:00) Introduction to Amadeus (00:02:49) Understanding Amadeus' Business Model (00:04:11) Amadeus' Market Position and Competitors (00:05:35) Historical Background of Amadeus (00:07:25) Revenue Streams and Profitability (00:10:50) Impact of AI and Technology on Amadeus (00:13:07) Revenue Models and Pricing Mechanics (00:22:35) Growth Opportunities and Market Dynamics (00:25:47) Execution and Future Prospects (00:30:01) Financial Health and Capital Allocation (00:31:57) Valuation and Market Perception (00:34:21) Risks and Challenges (00:37:13) Lessons From Amadeus

Experts say that word of mouth is one of the most powerful marketing channels—but in today's world, publishing a book uniquely establishes credibility and authority by showcasing your expertise.A book can work for you 24/7, build trust before you ever meet someone, and open doors that would otherwise remain closed. Yet most entrepreneurs delay writing one because they think they don't have the time, clarity, or expertise. Today's guest is an expert in eliminating those excuses and will show you why writing a book may be the highest-ROI move you can make for your business.Chandler Bolt is the founder & CEO of Self-Publishing School and SelfPublishing.com, one of the fastest-growing companies in the Inc. 5000. He's helped publish over 7,000 books, has become a multi-seven-figure entrepreneur, and built his entire business by helping people turn their ideas into authority-building books that generate leads, sales, and long-term assets.In our conversation, Chandler explains why a book can be the #1 leverage tool for entrepreneurs and investors, how self-publishing gives you all the upside and the freedom to release updated versions, and the biggest mistakes people make when marketing their book. In this episode, you'll learn: 1.) Why writing a book is the #1 authority-builder for entrepreneurs and investors—and how it can generate leads, sales, referrals, and high-quality deal flow on autopilot.2.) How Chandler scaled SelfPublishing.com into an eight-figure company and his framework for helping busy founders publish a high-impact book faster than they thought possible.3.) The hard money investing lessons Chandler learned, both wins and losses, that every entrepreneur should hear before partnering on deals and deploying capital.Show Notes: LifestyleInvestor.com/268Tax Strategy MasterclassIf you're interested in learning more about Tax Strategy and how YOU can apply 28 of the best, most effective strategies right away, check out our BRAND NEW Tax Strategy Masterclass: www.lifestyleinvestor.com/taxStrategy Session For a limited time, my team is hosting free, personalized consultation calls to learn more about your goals and determine which of our courses or masterminds will get you to the next level. To book your free session, visit LifestyleInvestor.com/consultationThe Lifestyle Investor InsiderJoin The Lifestyle Investor Insider, our brand new AI - curated newsletter - FREE for all podcast listeners for a limited time: www.lifestyleinvestor.com/insiderRate & ReviewIf you enjoyed today's episode of The Lifestyle Investor, hit the subscribe button on Apple Podcasts, Spotify, or wherever you listen, so future episodes are automatically downloaded directly to your device. You can also help by providing an honest rating & review.Connect with Justin DonaldFacebookYouTubeInstagramLinkedInTwitterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Help us with our Jeffrey Award Winners by voting here: https://forms.gle/GAcHKf5QZrR7GAR79The Month of Jeff continues with another elite Jeff: Jeff Dengate, better known on the internet as @dengatorade. Jeff is the director of product testing and de facto runner in chief at Runner's World, and one of the most experienced shoe and gear testers in the world. He walks me through how Runner's World actually tests shoes with hundreds of wear testers, why some products never make it to a review, and how he personally ends up in well over 100 different pairs of shoes every year.We get into what trust looks like in the age of AI, affiliate links, and endless gear noise, and why having a real human you can bump into at a race still matters. Jeff talks about the changing landscape at Runner's World, the COVID running boom, how trail and ultra fit into the broader running world, and why the world marathon majors craze is exploding. We cover super shoes, sky high prices, why comfort still rules, why you probably do not need a 300 dollar racer to start running, and yes, we revisit my infamous Runner's World Crocs 5K headline. We finish with his case for Jeff of the Year, including BQing three times in a year and rotating through more shoes than most runners log runs.Follow Jeff Dengate: https://www.instagram.com/dengateradeShow supported by Janji.com, Garagegrowngear.com, and CSinstant.coffeeChapters04:00 Introduction to Dengatorade and Running Background06:54 Role at Runner's World and Product Testing09:38 The Art of Product Reviews12:26 Navigating AI in the Running Community15:08 Revenue Streams and Content Creation at Runner's World17:50 Understanding Runner's Needs and Popular Topics20:43 Shoe Testing and Personal Experiences23:25 Finding the Right Shoe for You25:59 The Evolution of Running Gear and Nutrition28:59 Excitement in Running and Gear Unboxing31:42 Jeff's Unique Journey to the NBA37:16 The Journey of a Sports Journalist40:06 Impact of COVID-19 on Running Community43:33 The Growth of Trail and Ultra Running48:20 Trends in the Running IndustrySubscribe to Substack: http://freeoutside.substack.comSupport this content on patreon: HTTP://patreon.com/freeoutsideBuy my book "Free Outside" on Amazon: https://amzn.to/39LpoSFEmail me to buy a signed copy of my book, "Free Outside" at jeff@freeoutside.comWatch the movie about setting the record on the Colorado Trail: https://tubitv.com/movies/100019916/free-outsideWebsite: www.Freeoutside.comInstagram: thefreeoutsidefacebook: www.facebook.com/freeoutside

In this episode, Brian sits down with Garrett from Neufit to talk about the 2026 Hybrid Practice Model—how physical therapy owners can balance insurance and cash-based services for stronger, more predictable revenue. Learn how the Neubie helps clinics boost retention, attract cash-paying patients, and create new income streams that fuel long-term growth.  

Former University of Utah athletic director Chris Hill joined DJ & PK for an hour to talk about the private equity investment in the Utes, his forthcoming book, Beth Launeiere's retirement and what he wants to see from the future of the College Football Playoff.

Today, we are breaking down one of the more impressive B2B media businesses I have come across, Doximity. It's been called “the LinkedIn for doctors.”  Jim Jones, partner and analyst at William Blair Asset Management, helped explain exactly how Doximity works as a business. Jim gets into the community engine that works for and around medical professionals. And yes, there is a social network, but it's the add-ons, such as the required continued education that doctors can complete on the platform, including script signing, and all of those little tools that make a doctor's or medical professional's life much easier. The revenue engine is advertising, and Jim delves into the nuances of how that spend works, explaining why this is the business model they've chosen. Please enjoy this Breakdown of Doximity.  For the full show notes, transcript, and links to the best content to learn more, check out the episode page⁠⁠⁠⁠⁠ here.⁠⁠⁠⁠⁠ —- This episode is brought to you by⁠⁠ ⁠Portrait Analytics⁠⁠⁠ - your centralized resource for AI-powered idea generation, thesis monitoring, and personalized report building. Built by buy-side investors, for investment professionals. We work in the background, helping surface stock ideas and thesis signposts to help you monetize every insight. In short, we help you understand the story behind the stock chart, and get to "go, or no-go" 10x faster than before. Sign-up for a free trial today at⁠⁠ ⁠portraitresearch.com⁠⁠⁠ — Business Breakdowns is a property of Colossus, LLC. For more episodes of Business Breakdowns, visit⁠⁠⁠⁠⁠ joincolossus.com/episodes⁠⁠⁠⁠⁠. Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠). Show Notes (00:00:00) Welcome to Business Breakdowns (00:02:52) Overview of Doximity (00:03:46) Doximity's Business Model and Revenue Streams (00:07:25) History and Evolution of Doximity (00:08:33) Competition and Market Position (00:13:27) Advertising Trends and Digital Shift (00:20:20) Doximity's Financials and Profitability (00:22:39) AI Integration and Future Prospects (00:29:32) Valuation and Market Perception (00:32:40) Lessons From Doximity

Pharmacies already perform clinical work daily that Medicare will pay for through physician partnerships. These programs can generate thousands monthly without new hires, turning medication reviews and patient education into billable services that create financial stability outside prescription reimbursements. Learn more: https://ccmrpmhelp.com/contact CCM RPM Help City: Herriman Address: 12953 Penywain Lane Website: https://ccmrpmhelp.com/ Phone: +1 866 574 7075 Email: brad@ccmrpmhelp.com

On this episode we discuss the significance of Club 520 and Adidas historic shoe deal (19:30), the WNBA offers CBA deal but want to remove "housing" (45:38) and more!

In today's episode of The Profitable Play Podcast, I'm joined by Justine, the owner of Liv and Mimi's Play Café, who is now two years into her business journey.WATCH on YouTube (plus get a tour of Justine's retail section: https://youtu.be/BpxtDLo8SNIz)If you're inside Play Maker Society, you already know how thoughtful and generous she is with her real-world experience—and today she's sharing it all.We focus heavily on retail, because Justine brings a strong background in merchandising and has used that expertise to turn her retail section into a powerful, profitable part of her business.She walks us through how she chooses products, how she thinks about display and flow, what sells best for her audience, and the small changes that have had the biggest impact on her sales.Beyond retail, we dig into the lessons she's learned in her first two years of ownership—what she expected versus what surprised her, what she changed after year one, and how she continues to refine her operations, staffing, birthday parties, and membership strategy based on customer behavior.If you've been wanting to grow your retail revenue—or you've felt stuck trying to choose the right products or manage inventory—this conversation will give you tons of practical insight and real examples you can apply right away.Tune in to hear how Justine approaches retail with intention and how those strategies have contributed to both her revenue and her customer experience at Liv and Mimi's Play Café.Justine's WebsiteJustine's IGJustine's Tik TokOTHER RESOURCES:Play Cafe Academy & Play Makers SocietyGetting Started With Your Play Cafe [YouTube Video Playlist]What's Working In The Indoor Play Industry 2025 GuideFund Your Indoor Play Business [Free Training]Indoor Play Courses & 1:1 Consulting WaitlistMichele's InstagramMichele's WebsitePlay Cafe Academy YouTube ChannelETSY Template ShopPrepare Your Indoor Playground For a RecessionPlay Cafe Academy & Play Makers SocietyQuestions and Support: Support@michelecaruana.com Play Cafe Academy & Play Makers Society: http://bit.ly/3HES7fDQuestions and Support: Support@michelecaruana.com Simplify and Scale with 50% OFF WellnessLiving: https://discover.wellnessliving.com/playcafeacademyActive Campaign Free Trial: https://www.activecampaign.com/?_r=D6IYK3HG

The New Reality for Nonprofit Funding For decades, nonprofits have relied heavily on a mix of grants, donations, and government funding to sustain their missions. But as global philanthropy evolves, those traditional sources are becoming less predictable. According to The Nonprofit Finance Fund's 2024 State of the Nonprofit Sector Report, nearly 56% of nonprofit leaders said funding uncertainty is their biggest operational challenge. On a recent episode of the Nonprofit MBA Podcast, host Stephen Halasnik and guest Joe Reed, founder of the Exponent Group and SolveLine, discussed an urgent question: What happens when your biggest grant disappears? Their answer—nonprofits must begin to think like startups, diversifying income streams and even launching mission-aligned for-profit ventures to ensure long-term impact. "We don't have the luxury to not have this conversation anymore," Reed emphasized. "Healthy nonprofits must start thinking creatively about how they generate revenue."  

In this episode of DemystifySci, we peer into the very separate roots of banking and blockchain finance to find what's alive beneath the myth. Bitcoin guru and amateur mycologist, Brandon Quittem, joins us to trace the fall of old empires and the rise of a new code-born freedom. The conversation drifts between history and possibility, where gold becomes data and control slips through the fingers of the elite. What remains is a question: can money succeed without those who steer its value?PATREON https://www.patreon.com/c/demystifysciPARADIGM DRIFThttps://demystifysci.com/paradigm-drift-showHOMEBREW MUSIC - Check out our new album!Hard Copies (Vinyl): FREE SHIPPING https://demystifysci-shop.fourthwall.com/products/vinyl-lp-secretary-of-nature-everything-is-so-good-hereStreaming:https://secretaryofnature.bandcamp.com/album/everything-is-so-good-here00:00 Go! 00:04:26 The Genesis of Bitcoin and the Cipherpunks 00:08:54 The Fear of Government Control and Digital Currency 00:10:41 The Failure of Early Digital Currency Attempts 00:17:54 The Collapse of the Gold Standard 00:19:56 The Nature of Trust in Currency 00:20:54 Discussion of Properties of Money 00:25:31 Critique of Modern Monetary Theory (MMT) 00:29:52 The Risks of Unchecked Debt 00:33:31 Introduction of Bitcoin as a Solution 00:39:00 Understanding Bitcoin's Structure and Security 00:41:58 The Origin of Bitcoin Ideas 00:46:49 Understanding Blockchain and Mining 00:53:06 The Mechanics of Block Creation 00:58:04 The Finite Nature of Bitcoin Mining 01:01:57 Miners' Revenue Streams 01:03:05 Genesis of Bitcoin Mining 01:05:00 Competition Among Miners 01:07:08 Future Perspectives on Bitcoin #bitcoinanalysis , #cryptocurrencies , #blockchaineducation ,#bitcoinmining , #cryptoanalysis , #cryptotradingforbegineers , #digitalcurrency , #moneymanagement , #fintech , #investingtips , #economics , #monetarypolicy , #decentralization , #satoshinakamoto , #physicspodcast #philosophypodcast MERCH: Rock some DemystifySci gear : https://demystifysci-shop.fourthwall.com/AMAZON: Do your shopping through this link: https://amzn.to/3YyoT98DONATE: https://bit.ly/3wkPqaDSUBSTACK: https://substack.com/@UCqV4_7i9h1_V7hY48eZZSLw@demystifysci RSS: https://anchor.fm/s/2be66934/podcast/rssMAILING LIST: https://bit.ly/3v3kz2S SOCIAL: - Discord: https://discord.gg/MJzKT8CQub- Facebook: https://www.facebook.com/groups/DemystifySci- Instagram: https://www.instagram.com/DemystifySci/- Twitter: https://twitter.com/DemystifySciMUSIC: -Shilo Delay: https://g.co/kgs/oty671

On today's episode, Dr. Mark Costes is joined by Dr. Brandon Chapek, a general dentist turned surgical powerhouse who's built a thriving, high-production practice centered around wisdom tooth extractions and IV sedation. Brandon shares his unique path from growing up on a farm to walking away from oral surgery residency, opting instead for a fellowship and a business model that prioritizes autonomy and lifestyle balance.  With over $3.5 million in personal production, Brandon dives into how he streamlined surgery protocols, scaled with PPO patients, and created an in-house system that now runs with surgical efficiency. He also introduces his new book, Wisdom Tooth Surgery for the General Dentist, packed with step-by-step guidance for GPs who want to add this lucrative skillset to their practice. Whether you're looking to stop referring out, expand your clinical scope, or just work smarter, this is a conversation full of actionable takeaways. Be sure to check out the full episode from the Dentalpreneur Podcast! EPISODE RESOURCES brandonchapek@gmail.com https://www.truedentalsuccess.com Dental Success Network Subscribe to The Dentalpreneur Podcast

If you've been looking for a smart, scalable, low-lift revenue stream to add to your pharmacy—this session is absolutely worth your time. Independent pharmacies are perfectly positioned to support clinical trial recruitment, yet most have never been invited to participate. SiteLabs is changing that. In this session, Paige and Darren break down exactly how you can plug into a nationwide colorectal cancer clinical trial and earn $140–$200 per qualified patient enrollment, all while improving patient care in your community.   **Show Notes:** 1. **Introduction** [0:00] 2. **Overview of Site Labs and Clinical Trial Program** [6:30] 3. **Details of the Clinical Trial Program** [15:50] 4. **Q&A and Additional Information* [21:57]     ----- #### **Becoming a Badass Pharmacy Owner Podcast is a Proud to be Apart of the Pharmacy Podcast Network**

Family Office 2.0: What Modern Founders Really Need to Grow w/ Angela ThomasIntroductionShe started in the beauty industry – and became one of Europe's most respected strategic minds in business growth.Angela Thomas is the founder of Angel Success Consulting, a Dubai-based entrepreneurial family office that helps founders, doctors, and service providers scale sustainably and internationally. With nearly 30 years as a serial entrepreneur, Angela has built and sold businesses across industries – from retail and real estate to aviation, healthcare, and luxury.Today, she is known as Europe's #1 Scaling Expert and the visionary behind high-impact programs like Skillionair, Skillionize, and Dubai Docs Fast Track – a relocation success model for physicians entering the UAE market.But Angela does more than build companies. She builds legacies.She mentors with precision, structures with strategy, and scales with soul.Please welcome to the show – Angela Thomas.Links:https://calendly.com/beautybizz/skillionair-sessionhttps://www.instagram.com/angelsuccess_consulting/Tags:Accountability,Business,Business Strategy,Coaching & Mentoring,Entrepreneur,Entrepreneurial Mindset,Female Entrepreneur,Revenue Streams,Sales,Spirituality,Family Office 2.0: What Modern Founders Really Need to Grow w/ Angela Thomas,Live Video Podcast Interview,Podcast,PodmatchSupport PEG by checking out our Sponsors:Download and use Newsly for free now from www.newsly.me or from the link in the description, and use promo code “GHOST” and receive a 1-month free premium subscription.The best tool for getting podcast guests:https://podmatch.com/signup/phantomelectricghostSubscribe to our Instagram for exclusive content:https://www.instagram.com/expansive_sound_experiments/Subscribe to our YouTube https://youtube.com/@phantomelectricghost?si=rEyT56WQvDsAoRprRSShttps://anchor.fm/s/3b31908/podcast/rssSubstackhttps://substack.com/@phantomelectricghost?utm_source=edit-profile-page

What if the only way you were making money in your business was about to shift and you didn't have to say yes to one more client to do it? As a service provider I got comfortable: one offer, bookings flowing, dream clients, but I also knew there was a cap. If I didn't add something, I was stuck trading time for money forever. So I took action. And you can too. In today's episode, I'm sharing 3 revenue streams that every creative should have in their business! Plus, you'll hear a bonus revenue stream that I've included in my own business.In this Episode:How affiliate income can plug into your existing content and audience for a quick and real boost.Why digital products are still a win, how to start small, and how they scale without you being in every session.Mentorship/consulting: how you bring your expertise forward, test it gently, and build something new without abandoning your core service.Find It Quickly: 00:45 - Understanding the Need for Diversification01:43 - Creating Predictable Income in Seasonal Work03:17 - Personal Journey and Goals05:14 - Revenue Stream 1: Affiliate Income08:20 - Revenue Stream 2: Digital Products14:48 - Revenue Stream 3: Mentorship and Consulting17:04 - Bonus Revenue Stream: Paid Partnerships21:19 - Podcast RecommendationMentioned in this Episode:Episode 78: Stepping into the World of Selling Digital Products: Getting StartedEpisode 79: Navigating Sponsorships and Brand Deals: Are They Worth It? A Guide to More Successful Brand CollaborationsEpisode 133: Affiliate Marketing for Photographers: Where to Start and What to PromoteEpisode 134: The Biggest Mistakes Creatives Make with Affiliate Marketing (and Fixes!)Affiliate Boss - Get 20% Off with code PODCASTJereshia SaidIf you're enjoying the content we're creating on the podcast and want to connect with others who are called to both, make sure you come join us in the PhotoBoss® with Joy Michelle Facebook Group! Join Now >>

Welcome to The Game w/ Alex Hormozi, hosted by entrepreneur, founder, investor, author, public speaker, and content creator Alex Hormozi. On this podcast you'll hear how to get more customers, make more profit per customer, how to keep them longer, and the many failures and lessons Alex has learned and will learn on his path from $100M to $1B in net worth.Wanna scale your business? Click here.Follow Alex Hormozi's Socials:LinkedIn | Instagram | Facebook | YouTube | Twitter | Acquisition

Today we are breaking down GE Aerospace. We did cover GE several years ago, but that episode focused on Larry Culp's turnaround of the conglomerate.  Ramesh Narayanaswamy, co-founder and portfolio manager of Tourbillon Partners, joins me to explore what is now a pure-play aerospace business. We discuss the unique dynamics of the aerospace supply chain and the long-cycle nature that differentiates this industry. We also explore the complexity of aircraft engine manufacturing and how GE exemplifies the powerful model of selling services attached to equipment. Please enjoy our conversation on GE Aerospace. For the full show notes, transcript, and links to the best content to learn more, check out the episode page⁠⁠⁠⁠⁠ here.⁠⁠⁠⁠⁠ —- This episode is brought to you by⁠⁠ ⁠Portrait Analytics⁠⁠⁠ - your centralized resource for AI-powered idea generation, thesis monitoring, and personalized report building. Built by buy-side investors, for investment professionals. We work in the background, helping surface stock ideas and thesis signposts to help you monetize every insight. In short, we help you understand the story behind the stock chart, and get to "go, or no-go" 10x faster than before. Sign-up for a free trial today at⁠⁠ ⁠portraitresearch.com⁠⁠⁠ — Business Breakdowns is a property of Colossus, LLC. For more episodes of Business Breakdowns, visit⁠⁠⁠⁠⁠ joincolossus.com/episodes⁠⁠⁠⁠⁠. Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠). Show Notes (00:00:00) Welcome to Business Breakdowns (00:01:52) Overview of GE Aerospace (00:04:01) Commercial Jet Engines: Market and Segments (00:08:16) Military and Defense Applications (00:10:07) Financials and Revenue Streams (00:15:57) The Legacy and Transformation of GE (00:20:31) Jet Engine Industry and GE's Role (00:22:04) Challenges and Partnerships in Jet Engine Manufacturing (00:28:39) Revenue Models and Customer Segments (00:30:29) Understanding the OE and Aftermarket Revenue Models (00:31:50) The Profitability of Aftermarket Services (00:34:25) Revenue Models in the Aftermarket (00:36:11) Growth Strategies and Market Dynamics (00:39:38) Impact of Economic Cycles and Resilience (00:43:33) Capital Intensity and Return on Capital (00:47:12) Competitive Landscape and Technological Risks (00:55:07) Valuation Approaches and Market Perception (00:57:39) Key Takeaways and Lessons from GE

This is Matt Reustle. Today we are breaking down Robinhood. My guest is Arthur Olson, founding partner at Ravenswood Partners. We get into how Robinhood grew from a mobile-native brokerage idea that faced many challenges along the way into the third-largest broker in the USA.  Arthur educates on how the business model has evolved and diversified away from a pure pay-for-order market. We discuss how product velocity is the foundation behind everything in Robinhood's continued penetration and how new talent has made a material difference. Please enjoy this Breakdown of Robinhood For the full show notes, transcript, and links to the best content to learn more, check out the episode page⁠⁠⁠⁠ here.⁠⁠⁠⁠ —- This episode is brought to you by⁠ ⁠Portrait Analytics⁠⁠ - your centralized resource for AI-powered idea generation, thesis monitoring, and personalized report building. Built by buy-side investors, for investment professionals. We work in the background, helping surface stock ideas and thesis signposts to help you monetize every insight. In short, we help you understand the story behind the stock chart, and get to "go, or no-go" 10x faster than before. Sign-up for a free trial today at⁠ ⁠portraitresearch.com⁠⁠ — Business Breakdowns is a property of Colossus, LLC. For more episodes of Business Breakdowns, visit⁠⁠⁠⁠ joincolossus.com/episodes⁠⁠⁠⁠. Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠). Show Notes (00:00:00) Welcome to Business Breakdowns (00:01:51) Founding Story of Robinhood (00:04:29) The Rise of Mobile Trading (00:11:37) Payment for Order Flow Explained (00:15:39) Robinhood's Competitive Edge (00:22:28) Adapting to Market Changes (00:24:52) Product Evolution and Customer Retention (00:29:32) Analyzing Robinhood's Growth and Investor Behavior (00:30:16) Historical Parallels: Robinhood vs. Charles Schwab (00:31:19) Crypto and Regulatory Challenges (00:32:41) The Role of Prediction Markets and Sports Betting (00:34:41) Revenue Streams and Future Prospects (00:36:54) Banking and Financial Services Expansion (00:41:32) Cost Structure and Competitive Advantages (00:44:30) Account Growth and International Opportunities (00:50:10) Regulatory Relationships and Risks (00:55:15) Lessons from Robinhood

In this episode of the Healthy, Wealthy, and Smart Podcast, host Dr. Karen Litzy welcomes Dr. Nick Schmidt, a practicing physical therapist and founder of PT Assist and the Physical Therapy Project. Dr. Schmidt shares his journey from a pre-pharmacy student to a passionate advocate for innovative revenue streams in physical therapy. The conversation delves into the importance of cash-based services, the potential of retail sales in clinics, and the creation of a supportive community for physical therapists. Dr. Schmidt emphasizes the need for sustainable business practices and the role of physical therapists as musculoskeletal experts. Takeaways The transition from traditional to cash-based models can enhance revenue. Retail sales in clinics offer a viable revenue stream with the right products. Building a community among physical therapists fosters growth and innovation. Understanding the market and patient needs is crucial for success. Integrating new technologies requires careful consideration and planning. Consistency and passion are key to professional growth. Collaboration with other practice owners can provide valuable insights. The importance of aligning new revenue streams with clinic capabilities. The role of physical therapists as primary musculoskeletal care providers. The value of connecting with like-minded professionals in the field. Chapters [00:00] Introduction and Welcome · [02:15] Dr. Nick Schmidt's Background and Journey · [05:30] Exploring Cash-Based Services · [10:45] The Role of Retail Sales in Clinics · [15:20] Building the Physical Therapy Project Community · [20:00] Innovative Technologies and Their Implementation · [25:30] Challenges and Opportunities in Revenue Streams · [30:15] Advice for Practice Owners · [35:00] Closing Thoughts and Contact Information More About Dr. Schmidt: Nick Schmit is a practicing physical therapist and the founder of The PT Assist and The Physical Therapy Project community on Skool. Throughout his career, Nick has developed a deep passion for helping practice owners build sustainable businesses while reinforcing the role of physical therapists as the musculoskeletal experts. This passion led him to create The PT Assist, a platform that helps clinic owners successfully integrate retail sales into their practices. His newest venture, The Physical Therapy Project, is both a community and a resource hub for physical therapists exploring cash-based services and modalities such as dry needling, saunas, shockwave therapy, laser, retail offerings, wellness services, and more. The community provides guidance on what services are available, how they work, which might be the best fit for a clinic, and practical strategies to implement them smoothly and effectively. Nick grew up in central Minnesota, earned his bachelor's degree in Zoology from North Dakota State University, and went on to complete his Doctor of Physical Therapy at the University of Nebraska Medical Center in Omaha. He now lives in North Dakota with his wife and their growing family. Grounded by his faith and family, Nick is grateful for the many blessings in his life. His mission is to add as much value to the profession as possible and to connect with others who are boldly pursuing their dreams and passions. Resources from this Episode: Nick's email: nick@theptassist.com The Physical Therapy Project Website Nick on LinkedIn Jane Sponsorship Information: Book a one-on-one demo here Mention the code LITZY1MO for a free month Follow Dr. Karen Litzy on Social Media: Karen's Instagram Karen's LinkedIn Subscribe to Healthy, Wealthy & Smart: YouTube Website Apple Podcast Spotify SoundCloud Stitcher iHeart Radio

I'm chatting with Dianuh from Chasing Linen about simplifying your business for profitability. Dianuh is an incredibly successful business woman who has great insights into how cutting revenue streams can actually help your business Get started now with the Inventory Genus Course. Click here to save $250! Work with Me - https://www.ciarastockeland.com/work-with-meVisit the Bookstore - https://www.ciarastockeland.com/bookstoreSign Up for Free Weekly Tips and Trainings - https://www.ciarastockeland.com/subscribe Connect with Dianuhhttps://www.instagram.com/dianuhaerin https://www.instagram.com/chasinglinen www.chasinglinen.com

I'm chatting with Dianuh from Chasing Linen about simplifying your business for profitability. Dianuh is an incredibly successful business woman who has great insights into how cutting revenue streams can actually help your business Get started now with the Inventory Genus Course. Click here to save $250! Work with Me - https://www.ciarastockeland.com/work-with-meVisit the Bookstore - https://www.ciarastockeland.com/bookstoreSign Up for Free Weekly Tips and Trainings - https://www.ciarastockeland.com/subscribe Connect with Dianuhhttps://www.instagram.com/dianuhaerin https://www.instagram.com/chasinglinen www.chasinglinen.com

CLASS SLIDESIf you're stuck with just one way of making money, you're limiting your growth.In this class, I share 9 smart, creative business ideas you can add to your brand to build new revenue streams — from digital products and memberships to events, services, and offers that scale.⚡️JOIN MY FREE REBEL ACADEMY 80+ FREE COURSES https://www.alexhouseofsocial.com/freerebelacademy

In this episode, I talk with Shay Brown, co-founder of Bucket List Bombshells, about building a multi-seven-figure business while embracing a four-day workweek. Shay shares insights on achieving professional success without the hustle and how her business has empowered over 10,000 women globally through efficient business practices.Key highlights include:Four-Day Workweek Benefits: how this shift has enhanced personal well-being and productivityLead Generation Strategies: their successful partner workshop model for generating leads with minimal timeLeveraging Strengths: the effective division of responsibilities between Shay and Cassie, her business partnerPersonalization in Client Engagement: the need for connection in a changing digital landscapeAdapting to Trends: observations on consumer behavior changes post-pandemicThis episode is filled with practical advice for entrepreneurs seeking balance and success. Tune in for inspiration and actionable strategies to fuel your business's growth!FREEBIE! SHAY'S DREAM CLIENT PLAYBOOK: https://bucketlistbombshells.com/worklessearnmoreShay's Website: https://bucketlistbombshells.comShay's Instagram: https://instagram.com/bucketlistbombshellsShay's Programs: https://bucketlistbombshells.com/programsRegister now for your FREE ticket to Start-Scale-Succeed 2025:https://gillianperkins.com/sss-registerChapters:0:02: Growing Your Business with Less Time1:28: Freedom and Balance in Entrepreneurship2:14: Shea Brown's Journey to Success4:06: Balancing Roles in a Business Partnership7:01: Effective Client Coaching Strategies9:17: Marketing Strategies for Online Businesses17:56; Nurturing Leads After Initial Contact23:27: Expanding Audience Beyond Workshops26:49: Understanding Product Offerings and Sales29:32: Revenue Streams and Financial Insights31:07: The Origin Story of Bucket List Bombshells39:17: Adapting to Changes in the Online Space39:46: Connecting with Shay Brown OnlineWant to quit your job in the next 6-18 months with passive income from selling digital products online? Check out Startup Society.Have you already started your business, but it isn't generating consistent income? Schedule a free, 30-minute strategy session with our team to get unstuck!FREE Resources to Grow Your Online Business:Grab our free course, Small Business 101: https://gillianperkins.com/free-training-small-business-101/ Write a Profit Plan for Your Business : http://gillianperkins.com/free-profit-plan Work with Gillian Perkins:Apply for $100K Mastermind: https://gillianperkins.com/100k-mastermind Get your online biz started with Startup Society: https://startupsociety.com Learn more about Gillian: https://gillianperkins.com Instagram: @GillianZPerkins

Another Monday means it's time for another installment of our “5 Ways To Monday” series… Today, it's all about adding extra income streams. Rich is going to talk about adding product lines, selling your leads, engineering joint ventures with other companies… and more. Watch on Youtube: 6 Side Hustles for Contractors [WITHOUT Losing Focus on Your Main Thing]: https://youtu.be/y7puzZ-R9Us

**In this Episode of the Becoming a Badass Pharmacy Owner Podcast, discover the benefits of the new GLP-1 Support Kit, introduced by Dr. Mark Nelson, designed to enhance patient care and improve weight management. **   **Show Notes:** 1. **Introduction and Purpose of the Webinar** [0:00] 2. **Dr. Mark Nelson's Introduction and Background** [2:54] 3. **Details of the GLP-1 Support Kit** [3:50] 4. **Nutrition Support Plan and Optimization Kit** [5:14] 5. **Healthy Muscle Products and Non-GLP Weight Loss Plans** [9:10] 6. **Online Resources and Healthy Habits** [12:34] 7. **Business Opportunities and Revenue Streams** [17:18] 8. **Q&A and Closing Remarks** [22:56]   **Links mentioned in this episode:** [Download for more info:] (https://drive.google.com/file/d/1PK4kr3K43rmKsd9Z720YIAAPoexs10Fm/view?usp=sharing https://drive.google.com/file/d/1ns8z4bOcqEJBg5tJpVA4BZtbJogNO8oE/view?usp=sharing) Websites Mentioned: https://www.drlisafaast.com/----- ####   **Becoming a Badass Pharmacy Owner Podcast is a Proud to be Apart of the Pharmacy Podcast Network**

Hey guys! This episode is a little different because it's a few snippets of an actual main training in The Friday Society Membership. This month we focused on diversifying our revenue streams so we can maximize our time and profitability. As a small business owner, there is nothing more important than having fun in my business and ensuring I'm not on the road to burnout. Figuring out ways to make the most of my time, and make the most profit, is what we are going to talk about today.If this episode is what you need to hear then check out The Friday Society Membership. An incredibly affordable marketing membership so you can have the business of your dreams. And of course, please share with a friend who could benefit from these marketing tips!Download my app! It's a free marketing coach in your pocket. To keep up with me on instagram, follow me @alexagrowmybusinessTo learn more about The Friday Society Membership, click here To join my newsletter for free marketing advice, click hereTo view all of my free resources, click here!

Send us a textSchedule an Rx AssessmentSubscribe to Master The MarginWe're seeing an uptick in pharmacies starting their own insurance agencies which can be a great way to get paid for work you're already doing. But there's a lot of complexities and compliance elements to consider. In this episode, Bonnie Bond, CPA, MBA, and Austin Murray sit down with Seth Schlegel, Owner of Independent Community Broker Network to explore the evolving strategies for generating revenue around insurance consultation in a rapidly changing healthcare environment.We cover:Maximizing Patient Profit CentersRevolutionizing Patient Care with Low-Cost SolutionsEnsuring compliance in offering insurance consultationsMore About Our Guest:Seth Schlegel is the Founder of Independent Community Broker Network and is a healthcare strategist with deep expertise in helping providers reimagine financial and patient models for the modern era. He is passionate about creating sustainable solutions that balance revenue growth with meaningful patient outcomes, ensuring pharmacies and healthcare practices can continue serving their communities for generations to come.Learn more about Seth and ICBN: ICBN Website: https://www.myicbn.com/ICBN LinkedIn: https://www.linkedin.com/company/icbn-independent-community-broker-network/Stay connected with us on social media:FacebookTwitterLinkedInScotty Sykes – CPA, CFP LinkedInScotty Sykes – CPA, CFP TwitterBonnie Bond – CPA LinkedInBonnie Bond – CPA TwitterMore on this topic:Podcast: Relationships That ScalePodcast: Becoming a Pharmacy GladiatorPodcast: The Evolution of Pharmacy Accounting and Tax

Ever wondered how to turn your short-term rental into more than just a stay—but a shopping experience?In this episode, I sit down with Nisha Franklin, co-founder of From Where, a platform that turns your STR into a fully shoppable experience. Imagine your guests falling in love with your mattress, coffee table, or artwork—and then being able to purchase those exact items through your personalized digital storefront.Nisha's path to entrepreneurship is anything but typical. From her days as a marketing maven in Los Angeles to running a cheese education business, she and her husband David stumbled on a brilliant idea while vacationing in Hawaii. Their lightbulb moment? Creating a platform where hosts can effortlessly monetize the items guests constantly ask about, earning commissions on every single sale.We dive into the nuts and bolts of how From Where works, including how to set up your storefront, source forgotten items, and start generating new income streams. Nisha also shares insider tips on promoting your digital store, the types of products guests love most, and how even local furniture finds can be made shoppable.Beyond the tech and strategy, this conversation is a refreshing look at entrepreneurship, time management, and building a community-driven platform. Whether you're a seasoned host or just getting started, Nisha's insights will inspire you to see your short-term rental not just as a place to stay—but as a goldmine of untapped opportunity.HIGHLIGHTS AND KEY POINTS:[00:46] A short introduction about our guest Nisha Franklin and including how she first got started in the short-term rental space and what drew her into the industry[05:17] Nisha explains what From Where is — a unique platform designed for hosts to share, recommend, and earn from the products they use in their properties — and why it's becoming a valuable tool for the STR community[07:25] How hosts without receipts or with locally purchased items can still participate on FromWhere and create storefronts that reflect their property's style[10:04] Nisha talks about the items that are trending on FromWhere storefronts[10:48] Nisha walks us through the From Where sign-up process, explaining how pricing works, what commissions look like, and how hosts can leverage promotions to boost their earnings[14:08] Nisha describes how timing for sales varies depending on host engagement and promotion [16:09] How Nisha juggles multiple businesses and family life through planning and time blocking [18:00] Nisha shares her vision for From Where's growth over the next five years and how she relies on her tech-savvy team to power the backend[20:37] The lightning round Golden Nuggets:"Don't be afraid to make mistakes.”“The biggest failure is not the mistake itself. It's failing to learn from that mistake.”“When we have that one thorn in our side, that one pain point, if we ignore it and we just say, Oh, it'll go away; It might, but it might come back to bite you in the future. So take care of that pain point, solve the problem.”Lets Connect:Website: https://fromwhere.com/Facebook:

Join the Imagen Community on Facebook to continue the discussions between episodes.In this episode of Workflows, Scott Wyden Kivowitz welcomes wedding photographer Miles Witt Boyer, who shares behind-the-scenes stories, his approach to building lasting relationships with clients and vendors, and practical tips for optimizing every stage of the photography workflow.Miles Witt Boyer is an international wedding and portrait photographer, educator, and founder of The Photographic Collective. Recognized for his cinematic storytelling and deep human connection, his work has spanned nearly 40 states and countries. A former professor, marketing consultant, and mentor, he is also a husband and father of two sons.You'll learn how Miles shifted from reactive to proactive service in wedding photography, the secret sauce behind his client prep, and how AI editing tools like Imagen allow him to focus on what matters most, artistic quality and meaningful moments. Not only does he reveal workflow wins, but he also gets honest about challenges like diversifying client leads and building resilient business strategies.“If we want to give our clientele a level of service that is much higher than what's expected, and then by doing that, want to be able to charge considerably more than what is average, that has to start early.” — Miles Witt BoyerResourcesIconic AI ProfilePic-TimeB&H's BILD ExpoWhy You Should ListenGain real-world strategies for improving your wedding photography workflow from a seasoned pro.Hear how top photographers are using AI tools like Imagen to save time and focus on creativity.Get tips on building trust with clients and collaborating with vendors for unforgettable wedding days.Learn honest approaches to overcoming business challenges in today's competitive photography market.Discover why a proactive workflow pays off, for your art, your clients, and your sanity.Don't miss this episode if you want to sharpen your photography workflow, spend more time behind the camera, and serve your clients better. Subscribe to Workflows wherever you listen and visit workflowspodcast.com for exclusive offers and more resources.(00:00) - 68 (00:25) - Experiences at BILD Expo (04:59) - Special Moments and Challenges (10:05) - Team Integration and Dinner Highlights (11:18) - Upcoming Topics and Workflow Discussion (11:53) - Pre-Production Workflow Enhancements (14:52) - Client and Vendor Collaboration (23:35) - Impact of AI Editing on Workflow (24:47) - The Power of AI in Photography (25:29) - Evolution of Photography Tools (26:23) - Integrating AI with Traditional Editing (28:33) - Real-World Application: New York photowalk (31:22) - The Importance of Adapting to AI (42:29) - Diversifying Client Sources and Revenue Streams (47:38) - Final Thoughts and Advice for Photographers

Angel Studios https://Angel.com/ToddJoin the Angel Guild today and stream Testament, a powerful new series featuring the retelling of the book of Acts. Alan's Soaps https://www.AlansArtisanSoaps.comUse coupon code TODD to save an additional 10% off the bundle price.Bizable https://GoBizable.comUntie your business exposure from your personal exposure with BiZABLE.  Schedule your FREE consultation at GoBizAble.com today.  Bonefrog https://BonefrogCoffee.com/toddThe new GOLDEN AGE is here!  Use code TODD at checkout to receive 10% off your first purchase and 15% on subscriptions.Bulwark Capital https://KnowYourRiskPodcast.comBe confident in your portfolio with Bulwark! Schedule your free Know Your Risk Portfolio review. Go to KnowYourRiskPodcast.com today. Renue Healthcare https://Renue.Healthcare/ToddYour journey to a better life starts at Renue Healthcare. Visit https://Renue.Healthcare/ToddLISTEN and SUBSCRIBE at:The Todd Herman Show - Podcast - Apple PodcastsThe Todd Herman Show | Podcast on SpotifyWATCH and SUBSCRIBE at: Todd Herman - The Todd Herman Show - YouTubeEpisode links:Sydney Sweeney was heckled and called racist for her American Eagle ad. A beautiful white woman appearing in an ad is racist according to leftists. Unreal “Starlink is cancelled in Ontario because Elon Musk was attacking our Country…” says Premier Doug Ford citing ZERO evidence to this claim.2 years ago today, a historically illiterate Al Sharpton asked the following: Can you imagine if James Madison or Thomas Jefferson tried to overthrow the government?American healthcare worker explaining their Anthem health insurance planThe American Academy of Pediatrics Wants To Ban All Religious & Philosophical Vaccine Exemptions. Mandating The 72 Doses On The Childhood Vaccine Schedule Assures Pediatricians Of Their Continued 50% Revenue Stream. Making Vaxelis 6-in-1 Vaccine Mandatory Is Criminal & Unjust.Right above the produce at Publix they are now telling you they are coating fruits and vegetables with a ‘petroleum based' coating. “Petroleum, hormone disrupt. It's made from oil. It shouldn't even be consumed. It's remarkable — all these things there have this petroleum based food grade stuff on it”Did you know that the dairy-alternative entomilk is already being produced in many nations, and is made from the maggot larvae of black soldier flies?Agriculture Sec. Brooke Rollins said they will be making soda candy and Junk Foods not eligible for Food Stamps