Podcasts about licensing

In 2012, a New York school superintendent didn't show up as expected at a conference. His brother, a former FBI agent, suspected the worst and he was right. The police had leads but it took just one tip to bring it all together. This case is *solved*A huge THANK YOU to this month's sponsors:Start your detective work today at Newspapers.com! Go to Newspapers.com/truecrime and use promo code “CRIMELINES” for 20% off a subscription — and let the past tell its story.Turn those what ifs into reality with Shopify! Sign up for your one-dollar-per-month trial today at shopify.com/crimelines. Events:AdvocacyCon September in Albuquerque: https://www.advocacycon.com/ November in Costa Rica: https://trovatrip.com/trip/central-america/costa-rica/costa-rica-with-josh-hallmark-nov-2026 Support the show!Get the exclusive show Beyond the Files plus Crimelines episodes ad free onSupercast: https://crimelines.supercast.com/Patreon: https://www.patreon.com/crimelinesApple Subscriptions: https://podcasts.apple.com/us/podcast/crimelines-true-crime/id1112004494 For one time support:https://www.basementfortproductions.com/supportLinks to all my socials and more:https://linktr.ee/crimelinesSources:2026 Crimelines Podcast Source ListTranscript: https://app.podscribe.ai/series/3790If an exact transcript is needed, please request at crimelinespodcast@gmail.com Licensing and credits:Theme music by Scott Buckley https://www.scottbuckley.com.au/Cover Art by Lars Hacking from Rusty HingesCrimelines is a registered trademark of Crimelines LLC.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Eric Goodman and Troy Renck begin the week by recapping the Spurs Game 7 win over the Thunder and look ahead to the NBA Finals matchup between San Antonio and New York. Are the Spurs beginning the start of a dynasty with all of their great, young players? Has the Victor Wembanyama era arrived in full-force? Is it better for the NBA if the next big star – Wemby – wins a championship or if a big market like New York wins? Will Nikola Jokic finish with just one NBA Title? Plus, Shedeur Sanders shattered Tom Brady's NFLPA licensing record with a massive merchandise-driven payout. Does his career have a chance to take off this year in Cleveland? Check out a Monday episode of Hot Takes! Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Jubilee Media founder and CEO Jason Y Lee joins Next in Media to break down how the digital-first studio builds scalable, format-driven IP that captures Gen Z's massive attention span without relying on a single face. Discover the monetization strategies behind their unscripted content, why creators are turning down Hollywood, and how authentic human conversation is outperforming AI in the modern creator economy. Key Takeaways: The Creator Economy Flip: Top digital creators no longer view Hollywood as the ultimate graduation point, reversing the media power dynamic as traditional studios now seek out digital-first strategies to survive. The Attention Span Myth: Massive engagement metrics on 90-minute videos prove that younger audiences aren't suffering from short attention spans; they are simply starving for unscripted, long-form authenticity. Format Over Face: Designing repeatable, host-agnostic IP rather than relying on a single charismatic personality eliminates key-person risk and unlocks true operational scalability for digital studios. Contextual Brand Storytelling: The next frontier of monetization rejects one-off, disruptive advertisements in favor of naturally embedding brands into existing, high-performing video franchises. The Anti-Echo Chamber Demand: Algorithms have hyper-fragmented public discourse, creating a massive, untapped market of viewers who actively seek out raw, multi-perspective content to escape their own echo chambers. The TV Screen Takeover: Digital-first production must now default to cinema-grade standards like 4K, as YouTube's massive growth on connected televisions blends the boundary between streaming networks and independent creators. The Human Premium in an AI Era: As artificial intelligence commoditizes automated content creation, media companies that double down on raw, real-life human connection will hold the ultimate competitive advantage. IP Upcycling and Windowing: Legacy distribution strategies like FAST channels and AVOD licensing represent the most lucrative secondary revenue streams for creators sitting on deep libraries of episodic content. Resources & Next Steps: Subscribe to Next in Media on Apple Podcasts and Spotify Key Episode Timestamps: 00:00 Jubilee's Mission and Content Philosophy 1:09 Introduction and Background 2:07 Jubilee's Format Strategy and Studio Approach 3:44 Building a Scalable Business Model 4:57 Format Development and Longevity 6:16 YouTube's Evolution and Connected TV 7:54 Multi-Platform Strategy 8:54 Brand Partnerships and Controversial Content 10:01 Successful Brand Integration Examples 11:23 Brand Partnership Philosophy 12:19 YouTube's Creator Economy Evolution 13:44 Creator Content Boosting vs Investment 15:19 Hollywood and Streaming Industry Relations 16:32 Content Licensing and Distribution 17:41 Short-Form Fiction and Experimentation 18:25 Microdrama and Asian Market Trends 19:05 AI Integration and Human-Centered Content 20:09 Generational Media Habits and Public Discourse 21:34 Gen Z's Media Consciousness 22:21 Future Political Engagement and Partnerships

SummaryIn this engaging interview, Farid Ismail shares his multifaceted artistic journey, explores the impact of AI on filmmaking and art, and discusses the future of creativity in the age of technology. Discover insights on how AI supports and challenges creators, the importance of maintaining human authenticity, and the evolving landscape of cinema and digital art.To subscribe to the Substack here - https://brandrunner.substack.com/ KeywordsAI in filmmaking, digital art, artistic journey, creativity, future of cinema, AI ethics, artistic authenticityKey topicsAI's impact on filmmaking and artMaintaining authenticity in digital creationFuture prospects of cinema with AIGuest nameFarid Ismail Sound bites"AI can create with smaller budgets.""AI films can evoke real emotions.""Over-reliance on AI can be dangerous."Chapters00:00 Introduction to Artistic Versatility01:58 The Impact of AI on the Film Industry04:03 AI as a Tool for Creativity06:31 The Emotional Capacity of AI in Film09:15 Licensing and Ethical Concerns with AI12:03 The Role of AI in Creative Processes17:14 The Future of Human Creativity in an AI World18:34 Embracing AI in Filmmaking21:13 The Impact of Technology on Creativity22:21 Personal Projects and Artistic Expression23:35 Collaboration and Professionalism in FilmmakingResourcesThe Coming Wave by Mustafa Suleyman - https://the-coming-wave.com/ChatGPT by OpenAI - https://chat.openai.com/Claude by Anthropic - https://www.anthropic.com/Farid Ismail's linksInstagram - https://www.instagram.com/faridfilms/https://www.linkedin.com/in/faridismail/

Send us Fan MailDiscover the essentials of building a solid foundation in entrepreneurship, from licensing in regulated industries to structuring your business for growth. Vicki Greco, founder of Silent G Consulting, shares her expertise on legal strategies, personal grounding, and the future of business in Las Vegas.The importance of community and global reach for small podcasts and entrepreneursVicki's unique 5G philosophy: God, Gratitude, Grace, Grounding, and GrowthKey insights into regulated industries: licensing, legal hurdles, and misconceptionsBusiness structure tips: LLC, S-Corp, and the importance of proper tax planningBuilding legacy through early business formation for youthSpecialization in niche markets like social media marketing, wellness, and crypto licensingTransition from law practice to business consulting after personal setbacksFuture projects including SEO for crypto licenses and business conversionsLas Vegas as a hub for new entrepreneurs: opportunities and hidden gemsEnd your entrepreneurial journey with confidence—leverage legal frameworks, personal growth, and strategic planning. Vicki's insights are vital for anyone looking to thrive in Vegas and beyond.Silent 'G" Consulting InstagramSilent "G" Consulting websiteStay Connected

Send us Fan MailCould you be risking a $400,000 deal by not knowing the market? In this episode, Gary Pickren breaks down why local expertise isn't optional — it's a legal and ethical obligation — and how agents operating outside their competency maybe quietly costing clients money they'll never get back.From dock permits on Lake Keowee to FEMA flood zones on the coast, Gary walks through real case studies that show exactly what's at stake when agents chase commissions into markets they don't understand.

Alan provides a new Thursday Thought episode. In today's Thursday Thought Alan shares the 3 elements that must be in your Licensing Pitch Package. You must have a clear, concise, and compelling marketing message. Also you need a curated list of companies to contact - and names, titles, and emails of specific contacts. Be sure to subscribe to the podcast on Apple Podcasts or wherever you get your podcasts, so you won't miss a single episode. Website: www.alanbeckley.com

VIDEO GUEST - JOEL ARD - ATTORNEY AT ARD LAW GROUP // The class action lawsuit against the Department of Licensing continues to get worse for the state as the victims and stories of fraud continue to pour in // VIDEO GUEST - KEVIN VAN HOLLEBEKE // There’s a new ringer John’s race to grow a Giant Pumpkin! // Man who drove Tesla Cybertruck into Grapevine Lake says he's done it before. This time he got stuck — and went to jail. 

VIDEO GUEST - JOEL ARD - ATTORNEY AT ARD LAW GROUP // The class action lawsuit against the Department of Licensing continues to get worse for the state as the victims and stories of fraud continue to pour in // VIDEO GUEST - KEVIN VAN HOLLEBEKE // There’s a new ringer John’s race to grow a Giant Pumpkin! // Man who drove Tesla Cybertruck into Grapevine Lake says he's done it before. This time he got stuck — and went to jail. 

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!On the product side, everyone is getting Computer - Perplexity, Manus, Cursor, and so on. Meanwhile on the research side, agentic evals like TerminalBench and GDPVal are also assuming computer (Harbor). On both ends, the consolidating LLM OS stack has become a standard toolkit, and Daytona is one of a small set of AI Infra companies that are booming because of it.“The end of localhost” has been Ivan Burazin's obsession for more than a decade.Something that is all too familiar…Long before agents became the default way people talked about software development, Ivan was already chasing the idea that development should not depend on a fragile local machine. CodeAnywhere, one of the first browser-based IDEs, was an early attempt at that future: move the development environment into the cloud, make setup reproducible, and free developers from the endless “works on my machine” tax.The thesis was directionally right, but the market wasn't ready yet.However, agents changed that. They do not care about a laptop, desk setup, or favorite editor. They need a computer they can access through an API: something stateful enough to keep working, fast enough to spin up instantly, flexible enough to resize, isolated enough to be safe, and composable enough to run the messy real-world workflows that real software engineering actually requires.Daytona isn't just selling “sandboxes” in the narrow code-execution sense. It is the latest version of Ivan's original localhost thesis.In this episode, Daytona's CEO joins swyx to explain why AI agents need more than code execution boxes: they need composable computers, stateful sandboxes, instant startup, dynamic resources, and infrastructure that can survive workloads going from zero to 100,000 CPUs.We go deep on the new agent compute market: Daytona's hard pivot from human dev environments to AI sandboxes, the New Year's Eve MVP that customers begged for, why Daytona runs on bare metal with its own scheduler, how one customer runs almost 850,000 sandboxes a day, and why RL/eval workloads went from 0% to roughly 50% of usage in just months. Ivan also explains why agents need Windows and macOS machines, why CLI may matter more than MCP, why Kubernetes is painful for this workload, and why the future AI cloud may look more like Stripe than AWS.We discuss:* How Daytona grew out of CodeAnywhere, Shift, and the “end of localhost” thesis* Why Daytona pivoted from human dev environments to AI sandboxes* Why agents need composable computers instead of disposable code execution boxes* The New Year's Eve MVP that customers chased API keys for* Why Daytona chose bare metal, stateful snapshots, and its own scheduler* How Daytona spins up one sandbox in ~60ms and 50,000 sandboxes in ~75 seconds* Why Daytona's biggest customer runs ~850,000 sandboxes a day* How RL/eval workloads create zero-to-100,000 CPU spikes* Why RL workloads went from 0% to roughly 50% of Daytona usage* Why customers compare Daytona against EKS/GKS and say they're “never going back”* Why every AI agent may need a computer, including Windows and macOS environments* The Apple licensing constraints that make macOS sandboxes hard* Why CLI gives agents more power than MCP* How open source helps agents integrate Daytona* Why agent-generated PRs may break today's CI/CD assumptions* Why AI SaaS companies reselling tokens may face a cold shower* Why the AI cloud may look more like Stripe than AWSIvan Burazin* LinkedIn: https://www.linkedin.com/in/ivanburazin* X: https://x.com/ivanburazinDaytona* Website: https://www.daytona.io* X: https://x.com/daytonaioTimestamps* 00:00:00 Hook* 00:01:12 Introduction* 00:03:15 CodeAnywhere, Shift, and the end of localhost* 00:05:58 What Daytona is: composable computers for AI agents* 00:08:07 The pivot from dev environments to AI sandboxes* 00:10:17 The New Year's Eve MVP and customers begging for API keys* 00:12:56 Bare metal, stateful sandboxes, and Daytona's scheduler* 00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs* 00:21:53 Spiky RL/eval workloads and the new agent infra problem* 00:28:12 RL workloads, Kubernetes pain, and dynamic resizing* 00:33:31 Why every AI agent needs a computer* 00:38:48 macOS sandboxes and Apple's licensing problem* 00:44:28 Why CLI may matter more than MCP* 00:48:11 Open source, GitHub stars, and agent integration* 00:53:11 Git, CI/CD, and agent collaboration bottlenecks* 00:58:15 Founder life and building a 25-person infra company* 01:02:44 AI SaaS, token resale, and API-first business models* 01:06:10 GPU sandboxes, data centers, and compute growth* 01:09:48 Why the AI cloud may look more like Stripe than AWS* 01:11:26 Closing thoughtsTranscriptIntroduction: Daytona, CodeAnywhere, and the End of LocalhostSwyx [00:00:02]: Okay, we're in the studio with Ivan Burazin, CEO of Daytona. Welcome.Ivan [00:00:07]: Thanks for having me, man.Swyx [00:00:08]: Ivan, you and I go back.Ivan [00:00:10]: Way back.Swyx [00:00:11]: How I don't even know how, you found, did you reach out or, for Shift.Ivan [00:00:17]: I reached out to you. The reason was you - we were just - we were thinking about I was one of the co-founders of CodeAnywhere, the first browser-based IDE, and so we were thinking a long time of, localhost should die. And you had this article.Swyx [00:00:29]: End of localhost.Ivan [00:00:30]: Then I reached out to you because of that, and then we talked, and I was actually at a different job and learning about I was the head of, developer experience, and you were quite well-versed in that, and I actually reached out to you, among other people, how do we go about that? What are the key things and whatnot at this point in time? And you were nice enough to take the call, and I remember I was late on your call with you.Swyx [00:00:51]: I don't remember.Ivan [00:00:52]: I remember because I was with my then I'm thinking of a girlfriend or wife at that point in time, I'm not sure. It's the same person, so that's great, and I was late ‘cause we were, in, Italy on, vacation, and then I was late for something. I felt so bad, and you were so nice to be, good about.Swyx [00:01:10]: The reason I'm nice is because I'm also late to other people, so it's like, who's, who's without sin here, yeah, so I have to, for those who don't know, InfoBip Shift, there's this whole thing that, you did in the past, and, and that was basically one of the inspirations for me starting AI Engineer, which is like, I have to thank you for giving me that push to be like, “Oh, you can, you can build and sell conferences?”Ivan [00:01:34]: I remember you asked you asked me at the beginning to give me advisory shares, and I was so focused on what we were doing, I said no, and I should've took the advisory shares. So I'm sorry, dude. But anyway.Swyx [00:01:43]: We're not, we're not venture backed.Ivan [00:01:44]: No, it doesn't matter.Swyx [00:01:45]: It's Yeah, anyway, so I think what's impressive about you is that CodeAnywhere is the thing that you've been trying to build, and, you kind of put it on hold and then came back after InfoBip. Just give us the story, do you - the story and the origin story, going into Daytona.From CodeAnywhere and Shift to DaytonaIvan [00:02:05]: Sure. Like, really way back, me and my co-founder have been together. I say this, I've said this multiple times, it's like we were married and divorced and married. Some people actually ask me is my co-founder my partner. they thought it literally. It's not literally, but we have done multiple companies together, and to your point, we had this shift where we went from the CodeAnywhere to the conference called Shift, and then back to, Daytona. We originally started stacking servers, doing like virtualization in the early 2000s and, routers and doing basically all these things, at a foundational level, and that was a services company which we sold to focus on what my co-founder actually invented, which was the very first browser-based IDE, right, I say the first. Before us was actually Heroku. They did it for a very short time until they became Heroku. But outside of them, we were the only one, and it was called.Swyx [00:02:55]: There was Cloud9.Ivan [00:02:57]: Cloud9 came out slightly after us. There was Replit, which came out when we stopped doing it, Replit came out, and they have been successful since then, which is great. There was Nitrous.io. There was quite a few that existed at the time, but it was like too early. But the interesting part is that we, at that point in time, because there was no VS Code, there was no Kubernetes, and Docker had just started when we Or I'm not sure if it was even public at that point in time. And so we had to build everything to the whole stack ourselves and that was the key learning that we brought into and that we've been using in Daytona today. So it was super early. There's about 3 million people used CodeAnywhere. It was slightly, it was angel-backed more than venture-backed. We ended up paying everyone back because it didn't have that sort of scale. But, three years ago, we started something similar with Daytona, which is not what we are today, but it was automating dev environments for human engineers, the basically the underlying stack of CodeAnywhere. And then we did a hard pivot last January to sandboxes. And so here we are.Swyx [00:04:01]: Historic pivot, yeah, and, it's one of those things where, I had independently invested in CodeAnywhere, but also in E2B, and then both of you pivoted into the same thing, and I'm like, “F**k.”Ivan [00:04:12]: You invested, you invested in Daytona. You invested in Daytona. But you were the first If we had not got your check, we wouldn't have done it.Swyx [00:04:18]: No way.Ivan [00:04:19]: No, it was like, “We have to get him on board first,” and you were that kicker that we, that got us off the ground.Swyx [00:04:23]: No, because you were putting me on your pitch deck, man. I was like, “Man, this is like a good trip if I don't invest.”Ivan [00:04:29]: That's because it was your quote. It's like we.Swyx [00:04:30]: Yeah. It's the end of localhost.Ivan [00:04:31]: Did a bunch of research about end of localhost and who was interested in that,.Swyx [00:04:34]: No, that's like, I put, I wrote that blog post, and every single company in that field reached out to me, and then every VC who was receiving those pitches then also had to call me and, talk it, talk through it with me.Ivan [00:04:47]: It's finally happening though.Swyx [00:04:48]: It was really super interesting.Ivan [00:04:48]: It's finally happening.Swyx [00:04:49]: It's finally happening.Ivan [00:04:49]: Yeah, it's finally.Swyx [00:04:49]: It's finally happening, with maybe sort of non-human users. Yeah, so what is Daytona today? Let's get like a quick description. I'm wearing the shirt.What Daytona Is Today: Composable Computers for AI AgentsIvan [00:04:58]: You're wearing the shirt. Yes,.Swyx [00:04:59]: It says, I think your branding is very good. Like, it's very consistent. It runs AI code. Like, it cannot be simpler.Ivan [00:05:05]: Exactly, but we're gonna probably have to change that.Swyx [00:05:07]: Oh, s**t.Ivan [00:05:07]: It's also a subset of what we do. Unfortunately, we really love this, Run AI Code is super simple. People interpret it different ways. I think we've given out 5,000, 6,000 of these shirts. People wear them with pride because it doesn't really market about us.Swyx [00:05:21]: Yeah, Daytona's on the back.Ivan [00:05:22]: It markets the back. It markets to the person itself, so I think we did a really good job on that one. But it is also a subset of what we do, because people, when they think about Run AI Code, they just think about these small, let's call it isolates, code execution boxes that, you send some code, you get an output. Whereas what Daytona is today is essentially composable computers for AI agents. It is, the market calls them sandboxes which can be misleading.Swyx [00:05:44]: All these things. All these things on.Ivan [00:05:45]: Yeah, exactly, ‘cause it can be misleading ‘cause people usually think about sandboxes as a demo or a test environment versus a production-grade environment. But what Daytona does, if you think of the laptop that you have in front of you or the computer that's over there, or, my wife is an architect, so she has like a Windows with a 3D graphics card inside to do 3D rendering. Like, as humans, we have different computers or different compositions of computers. And our belief is strongly that agents today and going forward will need all these different compositions of computers to do different types of tasks. And so we offer that basically through an API.Swyx [00:06:19]: Yeah, to give people - I'm trying to sort of front-load all the aha moments or the wow moments so that people can, stay engaged and click like and subscribe. the market is exploding, right? Like, you have been reporting 74% month-on-month growth, and it also, it's just been growing for a while. Like, it's been going like this. And every single - It's not just you guys. It's every single.Ivan [00:06:41]: Everyone, yeah.Swyx [00:06:42]: Sort of, compute provider. I don't know if you agree with me saying compute provider or not.Ivan [00:06:48]: It's fine.Swyx [00:06:48]: Yeah. So like organically PLG-driven growth, but also enterprise is doing super well, I think I wanna rewind to January of last year when you did the pivot. Like, so you obviously called this market early, and you were positioned for it, and you are now one of the market leaders. But what was the insight that made you do the pivot?The Pivot: From Human Dev Environments to Agent SandboxesIvan [00:07:06]: The insight that made us do this pivot is the quarter before that, so end of 2024, when we had - Basically, we did a demo with - I don't I think we discussed this as well, Devin was not public. You actually gave me access to Devin at that time. So Devin.Swyx [00:07:25]: I did?Ivan [00:07:26]: Yeah, you gave me access.Swyx [00:07:26]: I don't think I was supposed.Ivan [00:07:27]: Yeah, exactly.Swyx [00:07:28]: Yeah, I.Ivan [00:07:28]: So it doesn't matter. You.Swyx [00:07:29]: Yeah. I gave like three friends access.Ivan [00:07:31]: Yeah, or it was a call and you showed it to me. It doesn't matter. but OpenDevin was available, which is now called OpenHands. And so we're like, “Oh, this seems to be a thing. This is not public. Let's take our for human automation of dev environments and take, OpenDevin and launch that as a SaaS.” And we did that. Not very many people signed up and used it, but a lot of people reached out that were building agents, and they were like, “Hey, my agent needs a compute sandbox runtime,” whatever you wanna call it. I forgot what it was called at that point. And then we were like, “Oh, amazing. This is a new market. Here is our infrastructure. Here's our product, and go.” And what we found really fast, soon, was that people did not like what we had built. It didn't work. And I remember talking to people at the beginning when we're doing this, the sandbox we're building for agents. People were like, “Oh, why is it different? It's the same thing. We have like EC2, we have VMs, we have all these things.” But we saw that everyone we gave it to, it was like 20, 30 people, they all said, “No.” Like, “This is not what we need. This sort of breaks.” And basically, me and my co-founder not knowing a lot about - ‘cause we're infra people. We're not AI people. So I basically took it upon myself to like watch every single podcast that exists, including all of, all of these and all that, and sort of get up to date, read all the blogs, like get, understand what's going on.Swyx [00:08:45]: Do you wanna shout out who else was useful, just in case people are also looking.Ivan [00:08:49]: Generally we -, I looked at There's a few of podcast, different segments and different types. So there's you guys, No Priors, Bill Gurley's was great while.Swyx [00:09:04]: VG2, yeah.Ivan [00:09:05]: Yeah, while it was around. So there's a few. 20VC is interesting from a different dynamic, and some are different dynamic. But there was, also Red Points.Swyx [00:09:14]: We're not really about the compute market.Ivan [00:09:15]: It was also already - Sorry?Swyx [00:09:16]: You're, you want - You're looking at the agent infra market.Ivan [00:09:19]: I was looking at the agent market and the AI market in general and sort of understanding who are the players, what the perception, and how that goes. And like obviously you complement this with like going to conferences, going to events, going to meetups, reading white papers, like doing all the things that you have to do to understand what's happening. And so when we figured, when we sort of had an idea of what we had to build, literally over the New Year's Eve, literally on New Year's Eve, I half vibe coded the first MVP, first minimal viable product of what Daytona is today. And I went to sleep at like 3:00 AM or something like that. I was doing - I just put my like baby daughter and wife to sleep and, Happy New Year's, and go back to just, doing this. And I sent it to my co-founder, my CTO, and he saw it in the morning. He's like, “This is absolute garbage.” “Do not show this to anybody at all, but the idea is good.” And so he took two weeks, and he rebuilt it.Swyx [00:10:09]: Did it like look like that? Listen, I - It was rough idea.Ivan [00:10:12]: Oh, not even, not even close. Like it was it was way worse. But it was like a very - It was a simplistic view of what it should be. Like, it worked, but it was not ideal. And so he went, we went down the whole, which is his job as CTO, to go, and he came back with this version. We then called all the people that had said like, “This is garbage,” a quarter ago. And we set up these calls, and we gave it to - We just demoed it to everyone. And all the calls went long, every single one. They were 15-minute calls, and they all went to like 25, 30 minutes or whatnot. And everyone said, “We need, we want access.” There was no login, just an API key, ‘cause it was just a beta or an alpha. And they said, “Oh, we want access.” And we're like, “Sure, yeah. Okay, thank you very much.” But after like the next day, if we'd not send it, every single one, like every call that we did, everyone came back, “Where is my API key?” Like everyone wanted it. We're like, “S**t.” Like this is it. Like I've never felt So one, the understanding to your point was like most people thought it was the same infrastructure for humans and agents. We understood a quarter ago it's not. We just didn't know what was the right primitive. And then when we came, and we can talk about what that is, and we gave it to these people, I've never seen, I've never experienced - I've done multiple companies in my life. I've never experienced this, that people literally call you if you do not give them access. Like they want access right now. And so it's like, okay, they don't want this. the thing that they want doesn't seem to exist, or they have not found it, and they really want what we want. And then when we understood that we're onto something, and then when you think about the size of the market, like the market for human engineers and enterprise is a very large market, so think GitLab or whatnot. But the market for every single agent that will exist ever in the future is just like, what is that market? How big is that? And we're like, “We are all in on this.” And so that is where we made sort of the cut between the old product and the new one.Bare Metal, Stateful Sandboxes, and the Lambda + EC2 ModelSwyx [00:12:02]: Yeah. But it wasn't composable at the time?Ivan [00:12:05]: It was very - It was basically just a Linux box that you could change, that you could define number of CPUs, disk, and RAM. Like that is what you could do, but you couldn't have multiple operating systems, you couldn't resize it on the fly, you couldn't add a GPU, you couldn't do like all the things. It was just the, just the first sort of variation of that, yeah.Swyx [00:12:22]: Was it bare metal from the start?Ivan [00:12:24]: It was bare metal from the start. And so the interesting thing that we thought about right away, so our.Swyx [00:12:29]: Which, give people the background, what is the normal path?Ivan [00:12:32]: Yeah, so, basically most providers run this on top of VMs. And also.Swyx [00:12:37]: Firecracker.Ivan [00:12:38]: Yeah, they run on Firecracker and VM. And so we also fire - We can get - We have multiple isolation layers and we can do that. But the common way to do it is that they, one, that the state of the machine, or the hard disk is not part of the sandbox itself. And the other thing is they're not meant to last forever. So most of them are preemptible, like they can There's a time that they can live. And so our thought was when we were going into this is, agents will be like humans in the sense of you don't want your laptop to be shut down until you're done with work. Like, and you want to close the lid and open the lid, it's the same state. So you - Agents would want that, like the pause and come back. They want those two things. But also agents really want speed, right? Can they get it? So when we thought about it's like we need something insanely fast, how to make it fast, how to make it long-running, and stateful. And so those two things, it's like combining a Lambda and an EC2, right? Those two things together. And so we didn't have an idea how others did it, ‘cause we didn't know too that there was a market around this. It was more like, okay, this is what we need, what they need. And we looked at Kubernetes, it wasn't wasn't good enough for that. We looked at Nomad, it didn't enable that. And so our history in rewriting our own scheduler at CodeAnywhere is basically what my CTO came up with. Like, he's like, “Oh, the learnings from there,” and he brought it. And the funny thing is, our third co-founder, when he saw it, he's like, “Dude, what is this? This is like 2008.” Like, we went back in time, and he's like, “Exactly.” And so the reason why Daytona is like super fast, and you see this on benchmarks, is we essentially, we run on bare metal. We have our own scheduler, we use the underlying, disk, CPU, and RAM of the underlying machine, which means your IOPS are insanely fast because there's no, there's no network between an EBS or something like that. But also the snapshot, the point in time, the templates, are also preloaded on the bare metal machines. So when you fire off a sandbox from a template or a snapshot, you're essentially directed to the bare metal machine where that snapshot is based on that NVMe drive, and then it literally just turns on that machine, and it's local. There's no network latency, anything on there. And so that is sort of the specificities that we, when we're thinking from first principles, what a computer would look like for an agent, that is what we came up with, and that's what we created.Benchmarks, 60ms Startup, and 50,000 SandboxesSwyx [00:15:02]: Yeah. I should maybe, I don't know if you endorse this, but there's someone that does compute SDK, you guys do very well on there, with like the TTI, right? I. is this a, is this a is this a relevant benchmark for you guys? I don't know.Ivan [00:15:16]: I don't know, and it changes every day. So today RKL is.Swyx [00:15:18]: I don't know what RKL is. Never heard of it.Ivan [00:15:20]: Yeah. RK, yeah, so it is there.Swyx [00:15:22]: You are, at least a third of the next tier of performance, and then, there's a lot of other better-known names that are very slow to start.Ivan [00:15:31]: Yeah. We've been the number one by far for a long time, and now there's different, there's different definitions also of sandboxes, different isolation patterns, different other things. So RKL runs it literally on the S3, the data, so it's very different, and they spin up a sandbox, spin up a container for that, so it's a different type of thing. So the definition of a sandbox is something that we can all, we all need to get along with. But yeah, we're insanely fast on getting these things, up and running. And so you can see even there that it's a zero point 0.10 to 0.11, so.Swyx [00:16:03]: Close enough. Yeah. what else do you need, right?Ivan [00:16:05]: Yeah. So the benchmarks itself, so, in this, in I don't think the benchmarks equate to market ownership or revenue or anything like that. and I've seen this with multiple benchmarks, not just in sandboxes, but in general benchmarks around.Swyx [00:16:20]: It's table stakes. It's just like.Ivan [00:16:21]: Exactly. But it doesn't hurt.Swyx [00:16:22]: Just roughly check.Ivan [00:16:22]: Like you definitely have to be up there and you have to be competing so that people know that, oh, this is definitely one of the top. Because this is only one dimension of what customers look for. There's other things like how many can you spin up consecutively? There's a feature set, there's support, there's like all different things that people look at, but you definitely have to be there, on the benchmarks.Swyx [00:16:40]: How many people do people spin up consecutively?Ivan [00:16:43]: So we have.Swyx [00:16:43]: Or concurrently, is the Concurrency, right?Ivan [00:16:45]: There's three metrics that we look at. And so one is like time to spin up one, and so our time to spin up one is 60 milliseconds with network latency. So request, spin up, reply, 60, the whole thing, 60 milliseconds. That is one. But if you wanna spin up 50,000 at once, we are now at about 75 seconds. So it takes about 75 seconds to spin up concurrently 50,000. Some others, there's public data around this, like take 2,000 seconds, which is 30 minutes. Like there's different variations of that. And then there is the so it is speed of one, speed of like multiple, and then how many can you consistently have up and running. And so we basically have right now no limit to how much we can add because we basically own our own metal. But the biggest customer of ours does like about 850,000 every single day is sort of where they're, where they're just shy of a million every single day that they're running, we do have a request for half a million concurrent, which is literally half a million CPUs somewhere running. So that's an interesting.Swyx [00:17:44]: They pay by like vCPU seconds.Ivan [00:17:47]: By seconds, yeah.Swyx [00:17:47]: Or whatever. Yeah. Okay, and so and then, and the other thing is, the sleeping and the resuming, ‘cause it's all the stateful resumption of all these things, how, what kind of workload are people putting through this, right? Like how is it Do we measure by gigabytes in memory, gigabytes in storage? I don't In like network attached storage. I, what are the costly ones of, out of all these features?Workload Economics: CPU, RAM, Network, and StorageIvan [00:18:15]: The most expensive thing are CPU.Swyx [00:18:18]: Okay. Yeah, of course.Ivan [00:18:18]: The second one, yeah Then it's RAM, then it's disk. We actually don't charge.Swyx [00:18:22]: Which is snapshotting, right?Ivan [00:18:23]: No, it's actually the, snapshotting's part of it, but basically the size of your hard disk, of your machine. So do you have 10 gigabytes, do you have 20, do you have 50, do you have whatever? And then the transference of that. Right now, currently we don't charge for, network at all at Polychron.Swyx [00:18:37]: Oh, you gotta, yeah, you gotta fix.Ivan [00:18:38]: Yeah. It is very much a it's a larger and larger part of our bill, so we're working around, that part there. Obviously, that is the least, expensive, so the hard disk is the least expensive, so it's basically CPU, RAM, for us network, ‘cause we don't charge the customer, and then hard disk, is how it's split up. But there's also different types of workloads, so we basically split it up into two types of workloads in Daytona. One is what we call background agents or long-running agents. and the other is, basically RLs and evals, which I put sort of together. And so they have very different patterns of usage, and if you look at the usage of a background And I'll just name names of companies, not specifically.Background Agents vs. RL/Evals: Two Usage ShapesSwyx [00:19:21]: Yeah, open, all hands.Ivan [00:19:23]: Yeah. So like a background agent's a Cognition, a Lovable, a like all these things are Harvey. These are all long-running, background agents. And so if you look at their usage patterns, their usage patterns are similar to human, which is like follow the sun. Basically, the usage patterns of that is like noon is probably the highest, and the midnight is the lowest, and then weekends are lower. weekday is higher.Swyx [00:19:42]: Yeah, that's a fun question. How global is it? Is it very US-centric or?Ivan [00:19:46]: The US is a large part, but we have currently, we have Asia, Europe, and the US regions.Swyx [00:19:52]: So it's quite global.Ivan [00:19:53]: Yeah, it's quite global. We have it all over. It's interesting that our I talked to you a bit about this. Our number one city by user.Swyx [00:20:01]: Hmm.Ivan [00:20:02]: Is Singapore.Swyx [00:20:04]: Oh, wow. Amazing.Ivan [00:20:05]: Which is an interesting one, right? Not by revenue, just by just like by individual head count.Swyx [00:20:09]: Really?Ivan [00:20:09]: Just like an interesting thing.Swyx [00:20:10]: Singapore is, Singapore is weirdly high in the adoption charts of AI for the population. It's like an, seven, eight million population. And it's like keeps showing up.Ivan [00:20:20]: No, it's quite interesting. We were quite shocked, and I was like, “Oh, this is interesting.” And also one that's up there.Swyx [00:20:24]: There's a reason I'm doing AI using Singapore. it's because I'm from there.Ivan [00:20:27]: We're there. We're gonna, we're gonna be there as well. and it's interesting that Japan is in the top or like Tokyo's in the top, which is in all the tech cycles it has never been. It has never been, so it's quite interesting that they're.Swyx [00:20:39]: I think the Japanese just love AI. Yeah. It's that, and then it's Brazil. That's it.Ivan [00:20:44]: Brazil has always been in.Swyx [00:20:45]: I think.Ivan [00:20:46]: Even when I look, if you look at like GitHub's data and ask historically with CodeAnywhere, it was always like US, Western Europe, and then you'd have like India, Brazil, China, like that would be there. But like Singapore was not in, specifically Japan was never in sort of that top, that top.Swyx [00:21:01]: Yeah. Weird pockets.Ivan [00:21:01]: Weird. Yeah, so it's very global.Swyx [00:21:02]: Okay, so actually that, but that's helps you to distribute your load through, all time?Ivan [00:21:08]: The interesting thing is like we have those kind of loads, but if you look at the researcher loads, they're quite different. So what they are is like if you give them concurrency of 10,000 or 50,000 or 100,000 CPUs at ARMb, when they fire off a run, it's just 100%. And then it just runs, and then it stops. So it's very, the usage pattern is squares basically, right? And it's also not follow the sun, because people will fire it off at midnight before they go to sleep but then wake up and so it's very unpredictable, so you don't know where that is. So the shapes of the usage are quite different than we have had before. And also what's interesting is when it's sort of a follow the sun, even if you have a high growth company, you can sort of predict your usage patterns and have enough capacity for that, because it's sort of, it grows in a, in a way you can project. When you have companies doing sort of like evals and RL, they're super spiky. So they're gonna come in, it's like, “We're gonna use nothing, then can we have 100,000?” Right? And then go back down. And then 100,000, go back down. So it's very different, right? And.Swyx [00:22:09]: Do you want to lock them into commits so.Ivan [00:22:11]: Yeah, we do.Swyx [00:22:12]: Yeah, okay.Ivan [00:22:12]: We so we have to lock them into some sort of commits to have that capacity, because we have to have, basically we have to have the capacity for peak. Right? And so right now, Daytona's mean utilization is 15%, 1-5.Swyx [00:22:25]: Oh my God.Ivan [00:22:26]: So it's very low.Swyx [00:22:27]: Because it's very spiky.Ivan [00:22:27]: It's very spiky, but we get up to 90%. so we have these things. And so what we're, what we're looking at right now as a company is similar to Cloudflare where you can like geo move things around, but that works really well for basically the background agent where it's follow the sun. But this, it's not. Like it's a very different shape. Obviously with scale you figure these things out, but that's an interesting new problem that we have, as a compute provider in the agent space. And when we were doing the conference recently, and so we talked to like Nikita from Neon and.Swyx [00:22:57]: I should bring it up.Ivan [00:22:58]: Parag from Parallel and whatnot, everyone has the same problem. Whereas the usage is super spiky, and this is something that has not happened before, that you have these types of like it was always, it the amplitudes were not this high, right? So it's quite interesting use case and problem solve.Compute Conference and Spiky Agent InfrastructureSwyx [00:23:12]: Yeah, I don't know if we're gonna bring this up again, but let's just talk about the conference, you had like 1,000 something people at the Warriors game, at the Sorry, where is it? What's.Ivan [00:23:22]: Chase Center.Swyx [00:23:23]: Chase Center.Ivan [00:23:23]: Chase Center.Swyx [00:23:24]: I went. It was, it was very impressive. Obviously, you can, how to throw a conference, what did you learn? you put, you pulled together all these impressive names.Ivan [00:23:33]: What I.Swyx [00:23:34]: What were you looking for?Ivan [00:23:35]: My thesis behind the Compute Conference was let's bring together people that are building infrastructure for AI agents. Because when I think of what we're building, it is the agent is the primary user, what are the ergonomics and usage patterns of agents, and so we can do that. And what I found, this was a theory, it wasn't proven, is that we all have these problems, as I touched onto. And I was, as I was talking on stage, it was like we all have the same underlying infra problems, which is this spiky workloads, unpredictable workloads that we've never had before, in human, compute or human infrastructure. And it's, again, it's the same when I was talking to Parag or when I was talking.Swyx [00:24:20]: Lynn. Nikita.Ivan [00:24:21]: Lynn, Nikita. Lynn especially, I was talking to her the other day as well. Like the It is a very interesting type of problem to solve because I can touch on Cloudflare because there's a lot of like talk about that recently as to how they solve that, which is they have a bunch of geos, and basically, as users work in different places, and depending on your tier, they can move you around the geos. And so that how, that's how they get the higher utilization. But you can sort of predict these, and it's If it's something in You'll rarely get a spike that is 10 orders of magnitude. Like you'll get a like let's say one of your customers has some like an exponential curve. What is that to I'm using Cloudflare as an example. 10%, 20%, whatever it is. I don't, I don't have this data, I'm just assessing. It's surely not 10x, right? It's surely not something there. And so how do you go out and solve this problem? And we're all solving this in different ways. So we have.Swyx [00:25:11]: She also has the same thing.Ivan [00:25:12]: Yeah, I know specifically that like Neon had that issue as well. Like how are we solving these spiky loads and things like that ‘cause we talked about it. And so the interesting thing for me to actually internalize was, yes, everyone that's building for agents first is going through this, and we're all solving similar problems, which is quite.Swyx [00:25:28]: Let me let me double-click on this. Okay. So for example, Neon, I happen to know that they're very sort of S3 oriented, right? so they're just like fully bet on S3. And you get to benefit from S3's distribution and infrastructure. So I would imagine that Neon doesn't have to care, whereas Lynn maybe has to care a bit more because obviously she's doing GPU inference. And, for listeners, we did an episode with her, one and a half years ago. And you have to care. But like, right?Ivan [00:25:54]: Parag cares for sure, and Nikita.Swyx [00:25:58]: And Parag is C of, Parallel.Ivan [00:25:59]: Parallel, yeah.Swyx [00:26:00]: Former CTO of Twitter.Ivan [00:26:01]: Twitter, yeah.Swyx [00:26:02]: They are the search.Ivan [00:26:03]: Yeah, they're search, yeah.Swyx [00:26:03]: I You and I know but the listeners don't know.Ivan [00:26:08]: Yeah, we can put it down in the screen, and so ‘cause we, when we were talking.Swyx [00:26:11]: I'll put it up on the, on the screen.Ivan [00:26:12]: Yeah, right.Swyx [00:26:12]: People can look it up if they need.Ivan [00:26:14]: Look it up. And, yes, but they still have CPU and RAM, allocation that you have to have up and running. And so CPU and RAM, you have to allocate that and have that ready. And so there's basically two ways to do it. One is you either over-provision and you can handle the bursts, or two, you basically have, I don't know if this is a term, just-in-time compute, which is like as your load becomes, as your usage comes in, you can fire off requests for VMs or bare metals at other cloud providers and then get them up and running.Swyx [00:26:43]: This is if you go above 100%, right?Ivan [00:26:45]: Yeah, this is.Swyx [00:26:46]: Like your overflow.Ivan [00:26:46]: If your overflow, like spillage or whatever you do.Swyx [00:26:48]: You probably lose money on it, but it doesn't matter, right?Ivan [00:26:50]: It, not Well, you might, you might not That is a more cost-effective way to do it but it's a slower way to do it. Because basically what you have to do is you have to like queue your requests, spin up these just-in-time compute, get it all ready, provision it, and then get your workload there. And so if the time isn't important that much, that's fine, and you can do that. But if your customer, and especially for, let's say, the RL training runs, the reason why a lot of people come to us is because GPUs are more expensive than CPUs, right? So you want your GPU running at, what, 100% the entire time. And so when you're running runs on CPUs, when the when the CPU cycle is like down and spinning up the next one, you want that to be instantaneous so that your GPU doesn't go down, right? And if you then have to like go out and provision machines, you're essentially telling the GPU that it has to wait, and that's incurring our cost. So there's things that you have to try to solve for there.RL Workloads, Declarative Images, and Kubernetes ReplacementSwyx [00:27:43]: Yeah, let's talk about the different workload, right? You said that, what was it? A few months ago, you had zero RL workload and now it's 50%.Ivan [00:27:52]: It will be this one, 50%, yeah.Swyx [00:27:54]: Let's talk about how different it is, right? Like I imagine, for example, a lot less dynamic code generation of like arbitrary code. Like here, it's probably all the same code. You're just doing parallel runs or something, I don't know.Ivan [00:28:05]: Yeah. So you'll have multiple Depends on the like for each run, you'll have a snapshot. And they, for the most part, they actually do use our declarative image builder, which is like, “Oh, we, the agent wants these dependencies, these env vars.”Swyx [00:28:17]: These ones, yeah.Ivan [00:28:18]: Yeah, the declarative image builder, it.Swyx [00:28:20]: Which is a very modal like thing that they.Ivan [00:28:22]: Yeah. And so we build it on the fly and then we propagate that snapshot, and you can spin up as many sandboxes as you want against that snapshot. And then if you have to do changes, the model can, or like it could be also be automated. It's like, “Oh, now for the next run, we need to install these things or remove these things or whatever to get, a task done,” and then it goes off and runs that. So yes, that is something that it seems that they prefer. The number one reason I found, or should I say, let's take a step back. What we are competing against in that environment is essentially managed Kubernetes. So EKS, GKE, whatever. That is what the vast majority run on. And anyone that has tried Daytona versus GKE, EKS is like, “I'm never going back.” That has always been. There's a few reasons. One is the ergonomics. So if you have, if you're using Kubernetes to spin that up, you have to essentially manage the interface interactions with that. Daytona, although as a compute provider, it's more akin to a Twilio and Stripe from a consumption perspective than it is an AWS. Like you have an API, an SDK, it's quite like easy and seamless to get these things up and running, that's one. The other is the speed to which we spin up, which we mentioned earlier, which is much faster, and the scale to which we can go to. We haven't got into features, but an interesting feature is that it's very hard to OOM, or out of memory, our sandboxes, because we can dynamically on the fly.Swyx [00:29:48]: Resize.Ivan [00:29:49]: Resize, which is like impossible on almost any other thing. There are some technologies that enable you to do that, but it's like a very hard thing. And so we actually saw this when, the Terminal Revenge team is, brought us actually. So thank you, Alex and the team, that brought us into this whole space.Swyx [00:30:05]: It's just very rare that, a framework would just say, “Guys, just use Daytona.”Ivan [00:30:11]: Yeah, I think it says it somewhere. Yeah.Swyx [00:30:13]: Yeah. I was like, “What is this?”Ivan [00:30:15]: There's all, there's multiple there, but they also mention a few other places. and so Daytona specifically-We have, the, just jumping on themes here We, I don't know where it says Data Center.Swyx [00:30:27]: I, there.Ivan [00:30:27]: Doesn't matter.Swyx [00:30:28]: There's a very strong recommendation, which is, very unusual. Which is, it's.Ivan [00:30:33]: We do not pay them for this, just.Swyx [00:30:34]: I know, yeah. They just like you.Ivan [00:30:35]: Yeah, they like us. yeah, and also a thing, so, Data Center has multiple isolation sets underneath. The customer doesn't have to know what they are. But basically we have Docker, which is a container, that's hardened with Sysbox. So it's Docker's, isolation that is a security equivalent to a VM, but it's still a container. And that is the default, and they, especially in these training workloads, really like that as an interface to be able to use just a basic Docker container, and we enable Docker and Docker. Which for these RL runs, if you need to do a Docker compose or Kubernetes, you can spin up a K3S inside of these things, which unlocks a huge amount of workloads that you can do that you cannot do on other providers. So just on that part is much more interesting. And so we went that, through that. We showed them that we could do that, and they enjoyed that quite a bit. They being the general venture people.Swyx [00:31:28]: Those people, yeah.Ivan [00:31:29]: And Harbor people.Swyx [00:31:29]: Harbor people, do are they, are they a company yet?Ivan [00:31:33]: As far, I do not know.Customer Pull, Slack Connect, and the Computer Use BetSwyx [00:31:35]: Okay. All right. Yeah. It's like super obvious that like, there's a lot of excitement and success around these things, okay, so yeah, tell us more, right? Like, this is an exploding workload, Harbor adopted you, which helped speed things along. But what are you learning as this new workload comes online?Ivan [00:31:53]: There's a couple things that we learned, which we chat about in the beginning. We, and this has led our story, as we mentioned, we like talked to a lot of customers along the way, and we add more features and more tool sets as we talk to customers. And it's interesting that And I think it's that the ecosystem is so small and/or the models get smarter, where when we see one user come with a request, we know it goes on a roadmap if like three to five customers come with the same request in that week. It's like very bizarre. It happens so many times, which is.Swyx [00:32:27]: Because they're all friends.Ivan [00:32:28]: Sorry?Swyx [00:32:28]: They all, they're all friends. They're all in the same group chat.Ivan [00:32:30]: Yeah, probably, yeah. ‘Cause and they're like, “Oh, can you do this?” And I'm like, “Okay, this is interesting. We'll put it on a feature request.” And then the next one's like, “Oh, can you do this?” “Okay.” It's all the same, right? It's always the same. And so what we try to do, and I personally try to do, I try to be on as many call, quote-unquote “sales calls” I can. I'm in every Slack channel. We literally have about 1,000 Slack Connect channels, something like that. It's an interesting, there's so many interesting things you find out when you have all the Slack channels. You can also see where people, transfer between companies. You see leave Slack channel, enter Slack channel. It's an interesting thing. Also, just I digress, I feel that Slack Connect is literally LinkedIn what it should be. You have a list.Swyx [00:33:08]: LinkedIn charges you to, use your own connections, but Slack doesn't, right? Slack is like, do it for free. It's more lock-in. It's great.Ivan [00:33:15]: Yeah. It's amazing. Yeah. It's one of the reasons.Swyx [00:33:17]: You're gonna pay Slack for life.Ivan [00:33:18]: Exactly. You're there for life. So that's interesting. And so one of the things, the newer things we were talking about earlier is we made a big bet and put a lot of investment on computer use. that is not seen publicly the light of day. We haven't GA'd that yet, but we have.Swyx [00:33:32]: Is there a thing I can pull up?Ivan [00:33:33]: There is computer use there. It's right up a bit.Swyx [00:33:36]: Oh, yeah. Okay.Ivan [00:33:38]: What we have, what we talked about and what we've seen publicly is there's this theme now about, the human emulator where And Elon from XAI has talked about this publicly, and if you think about the models today, they're actually quite sophisticated and they can do a lot of work, but they still don't have access to all the tools. Like, I'm a strong believer that the most efficient way for an agent to work is essentially headless or through, terminal or whatnot. But if we, if we look at knowledge work in general, there's about 100 million knowledge workers in the US, about a billion in the world, and knowledge workers, and the salaries of them aggregate to 10 trillion in the US 50 trillion worldwide.Swyx [00:34:24]: Wow.Ivan [00:34:25]: Something like that. And if we look at, the five most important sectors of that, so like healthcare and government and financial services and whatnot, that's about 56% of that. So let's say it's about half of that. So in the US it's about 25 trillion, and most of them, most of that work is actually still locked into legacy apps inside of Windows, which is not going anywhere for a very long time. Like, people just won't invest in that. How much of it? our assumption is the following: if, in the RPA market, which is similar market, well, not the same 25% of, these white collar, workers', work is automated. If an agent is more sophisticated, can go through more runs, figure stuff out, let's say it's, 40%, right? And so if you take 40% of that, you get to essentially, $10 trillion a year.Swyx [00:35:17]: That's a TAM.Ivan [00:35:18]: That is a that is a TAM. So that's the TAM of the models, right? That's not our, essentially ours. But you get to that size, and to be able to do that, you essentially have to give agents these computers with the legacy. So computer use, either Mac or Windows or Linux. Linux we also obviously have and others have. But Windows specifically is something very new, and the only option right now is an EC2 with, Windows or on Azure. Both of them take anywhere from three to five minutes to spin up. We've created an actual sandbox, so it's a second instead of milliseconds, but you have, point in time snapshots, you have, forking, you have all the things that you have from a sandbox, but essentially enables you to hopefully unlock all this value. And so that's been our big push and bet, but we've sort of, kept our ear to the ground. What is sort of the next things in the market?RPA Returns: Why Agents Still Need ComputersSwyx [00:36:06]: Yeah, knowledge work, and building, and sort of RPA, the next wave of RPA. I got very excited about RPA kind of during COVID times. The UI path was IPO-ing. And it was, a very hot Isn't it, Eastern European?Ivan [00:36:20]: It is, Romanian.Swyx [00:36:21]: Romanian?Yeah, it might be the only Romanian, big unicorn okay, yeah. This I don't I don't, I don't have like a I think there's, I think there's a stage being set for the resurgence of RPA, ‘cause everyone understands that, yeah, no one wants to deal with these shitty apps and no one's gonna rewrite them. Like, you just have to do, a remote operation and programmatic operation of them.Ivan [00:36:45]: If you wanna unlock it, my own setup was basically the following. So I was doing a board deck recently, last month, whatever, and I'm like, “Okay, let's just, let's just do automated.” So, all our data's in, ClickHouse and PostHog and QuickBooks, where everyone else's is, and I'm basically, connected that all to, my Cloud code, like go off and go Cloud code whatever. Go off and, here's the integrations, go do that. It pulled out the first report, which was great. It connected to Brex and all these things, pulled it, which was great, and then I say, “Okay, now pull out this, and this,” and I kept getting, really well McKinsey-style design reports, but the data said partial data. all the missing data, partial data. Like, it can't access all the things, and I got so frustrated, and so I got, I got, my Mac Mini virtual sandbox with OpenClaw. I gave it its own account in our company, and then I went to all these services and created a read-only account, so literally like an intern in your company. And so I would say, “Now go and do this report,” and it would get the same, or like, “I can't via the MCP or the API or whatever. I can't get all the information.” I'm like, “Go log in.” And it will log into the website, then go in, export the data. It'll export the data and do the thing end to end. So even for things that have today APIs, not all of it is exposed, and I to get value, I get immense value right now, but it has to be a computer usage, unfortunately, and so I spend a bunch of tokens just on that, but I get the job done. And so if even a startup like ours, and using all the hottest tools, still needs a computer agent what hope does, Goldman have to have a headless, right?Swyx [00:38:22]: Yeah, what a - Why isn't Microsoft doing this?Ivan [00:38:27]: I'm pretty sure, Satya had a post yesterday.Swyx [00:38:29]: Oh, okay. I see.Ivan [00:38:29]: Which was like, “Every agent needs a computer.”Swyx [00:38:31]: I see, I see.Ivan [00:38:32]: So they have launched something recently.Swyx [00:38:34]: Yeah, they have Microsoft Power Automate, I'm sure, I'm sure, they're gonna have their version.macOS Sandboxes, Apple Constraints, and the Windows OpportunityIvan [00:38:39]: Version of that, yeah.Swyx [00:38:39]: You're gonna try to do yours, and it - I always know there's always demand for Mac, but I know it's, tricky to host, macOS sandboxes.Ivan [00:38:49]: We will have macOS sandboxes fairly soon. The problem with macOS, OS sandboxes is, I'm deep in this, I don't know how much interesting is.Swyx [00:38:55]: No, it's.Ivan [00:38:56]: MacOS has this problem.Swyx [00:38:57]: It's a licensing thing, right?Ivan [00:38:58]: Licensing thing. So one, you're allowed to run only two parallel VMs per machine, so that's one. Two, you can only license to a different user every 24 hours. So if you come in and theoretically, if I wanna charge you per second and I charge you one second, I have to have it idle for the rest of the day. I can't have anyone else doing that. So the pricing will be different in the sense that I will have to - we would have to charge for 24 hours, and that's not even, that's not even the most difficult thing. But the, thing above that is, from a security perspective, they enable you to do memory snapshot, pause, resume, but only on the same physical drive, physical machine. And so what you can do in, Windows world or Linux world is that I can move in the background, your snapshot from one to the other and manage load, right? Here, if you wanna do that, you essentially have to have your.Swyx [00:39:49]: Yeah, snapshots. Yeah.Ivan [00:39:50]: Your.Swyx [00:39:51]: It's like.Ivan [00:39:51]: Physical machine.Swyx [00:39:52]: You can't break it up.Ivan [00:39:53]: You can't, you can't move things around that, and all of that is, that part is, from a security standpoint, if it is written. Like, I understand the security aspect of that, but it disables you from doing these agentic, like really scalable agentic workloads.Swyx [00:40:08]: You need to do a vibe-coded, clean room implementation on macOS that you can then - That's like Clean OS or something. I don't know.Ivan [00:40:17]: So. We have.Swyx [00:40:18]: ‘cause like Linux was originally like a clean room rewrite of Unix.Ivan [00:40:21]: Okay. Yeah.Swyx [00:40:21]: Or something like that, right? Like same thing to macOS. Someone needs to do it.Ivan [00:40:25]: Someone will do that, and someone will have some long-running agents for a few days to figure this stuff out. But yeah. So definitely we - we're really close to offering something ‘cause people do want it, but the pricing will be different, and the feature set will be sort of stringent.Swyx [00:40:38]: Yeah, nobody's gonna use this. like, the labs, the labs will because they want to automate macOS.Ivan [00:40:42]: They have to do RL. They have to do RL again. But even if you The - So the point is with the RL part, if you, if you do RL on macOS, then the next iteration of the model comes out, it will be able to use these tools significantly. Then you actually need to run those, that somewhere. So you're gonna have to have that, later on. And from, if anyone at Apple is listening, I very much feel that they are shooting themselves in the foot of the scale of the revenue of compute or licensing they could get if they would just enable a concurrency model similar to what you can get on a Windows and a, and Linux.Swyx [00:41:17]: Yeah. Yeah. And I'm sure they've heard this before. They just don't care. Yeah, it's And maybe they will change their mind with the new CEO.Ivan [00:41:24]: Yeah. We'll see.Swyx [00:41:25]: We'll see.Ivan [00:41:25]: High hopes.Swyx [00:41:26]: High hopes.Ivan [00:41:26]: High hopes.Swyx [00:41:27]: Okay. But I, it's very clear the market opportunity is huge in Windows, and you can go for a long time on just Windows, but your customers are gonna want both. and I think, it is interesting to me that, this is the sort of God application of agents, right? Like, I don't It was - How big was OpenClaw for you guys? Like, was it, was there, a significant bump.OpenClaw, Agent Labs, and the B2B2C Sandbox MarketIvan [00:41:54]: Not for us because we.Swyx [00:41:54]: Because you already.Ivan [00:41:55]: We're kind of positioned differently. Whereas although it's completely PLG and we have individual developers that use it, most of the users that use Daytona are sort of a B2B2C. Sort of it's either B2B or B2B2C. So, in the researcher world, it's B2B, so you're selling to, labs and neo labs and things like that. But on the long-running agents, it's mostly, from a scale revenue perspective, it's mostly B2B2C, where you have a app layer agent that uses you at a big scale.Swyx [00:42:26]: Like a Manus. Yeah.Ivan [00:42:28]: Like a Manus Lovable type of thing.Swyx [00:42:31]: Yeah. I think that's the question of, well how, um-Uh, yeah, B2B to C is basically to me what I've been calling an agent lab, which is kind of like you're not in a model lab, but you're making a very good wrapper that is a platform that other people can sign up so they don't have to code those things. Yeah, it sound, it sounds like a much better market than the direct OpenClaw market.Ivan [00:42:56]: I've like - We I've done multiple things. So the CodeAnywhere's part of our career path R in the calendar, was very much an end user developer product. And so that is great. It You can get a lot of developer love, and I feel that we do as a company have a bunch of developer love. But it's a different type, where it's people building these things. Again, it's more akin to a Twilio because you don't really run - As a person, you wouldn't run Twilio. I don't know how many people remember. It was like ask your developer billboard and whatnot. And people really love Twilio, but they only used it inside of like, “Oh, I'm building this app or service for thing.” And so we're very much directly to that. And you also know that I used to work for a competitor for Twilio, so it's kind of ingrained, in my DNA.Swyx [00:43:35]: People don't know InfoBip is that big.Ivan [00:43:38]: Yeah, it's.Swyx [00:43:39]: Because.Ivan [00:43:40]: It's a billion euro.Swyx [00:43:40]: They're all American. They're like, “Whatever's in Europe doesn't matter to me.” But like it's the, it's the same size or bigger? Same size?Ivan [00:43:46]: It's about half the size.Swyx [00:43:47]: Half the size?Ivan [00:43:48]: Yeah, about half the size.Swyx [00:43:48]: It's like, yeah.Ivan [00:43:48]: Still huge. Multiple billions a year. Yes.Swyx [00:43:51]: That's crazy.Ivan [00:43:51]: Exactly, and so that - These are like really interesting and large revenue-generating, very sticky businesses. Whereas when you're selling to the - When your focus is the end developer, it is a very hard sell because they're very price sensitive, very price conscious, very around that. And there's very It's very hard to scale. Your cap is the number of people that are willing to spin up - First of all, wanna spin that up, and then spin up multiple of these. Whereas if you're in the enterprise one, like we know everyone's talking about like how many tokens they're spending, I'm spending. Like a lot of companies today are like, “If this is our company, spend as much as you can.” Like basically that is where we're going. And so if you think about that paradigm, where you're selling to companies that say, “Spend as much as you can to generate, productivity,” versus, “Oh, I'm a single person. I have this much budget, and I'm doing this thing because it's fun or it's helping me out or whatever.” Like it is a different, it's a different go-to-market, I think, strategy.MCP, CLIs, and Sandboxes as the Agent RuntimeSwyx [00:44:50]: Yeah, there's a lot of discussion. I'm just kind of going through like the mental list of things that are in your favor, which is, for example, MCP versus CLI. Like obviously you want CLI. It's been very good for you. I feel like it's maybe a drop in the bucket or maybe it's huge. I'm just checking whether it's like these are big trends.Ivan [00:45:10]: Those things you - work well in our favor, to your point just because every.Swyx [00:45:13]: They're kind of drop in the bucket, right?Ivan [00:45:15]: I think it's like sort of all the things come together. And so there's so many things that impact that. To your point, like OpenClaw wasn't huge for us, but like having the agent SDK, from Anthropic, so or Cloud Claude Code was very interesting. The reason why it was interesting is that a lot of, let's call them app I don't know what to call them, app layer agent companies, essentially they are like, “Oh, I can create this new app, this new agent. All I need, I just use Claude Code, and I throw it into a sandbox, and then I have my interface to the human to that.” And so that enabled so many more companies to actually offer this, and then they would pull on sandbox. So that was, that was interesting. And to your point, like MCP, versus the CLI, the MCP is an interface against an API, whereas the CLI is like you can actually go do things. Like this is it. The difference between integrations and actually running scripts or data or analysis against a thing. So being able to use a CLI very well enables the agent to do more things, and it's because that people will invoke a sandbox, they'll run it in the CLI, and but it'll do anal-analysis on that data and then give you an actual result versus just, pulling data from an API source.Swyx [00:46:29]: Yeah, it's a layer of indirection basically, it's the same thing as agentic search versus RAG, which where you're.Ivan [00:46:34]: Exactly, yeah.Swyx [00:46:34]: Just like you just win whenever people put more agents into their workflow. And so like it doesn't really matter, but I'm just kinda teasing out like what else have people heard about that like it's sort of, “Oh yeah, this is another sandbox use case. Oh yeah, that's another one.” Am I, am I missing any big ones?Ivan [00:46:51]: The thing, the thing that people, which is the computer use stuff, which I think is probably the most interesting one, is, and to your point, we've talked to so many people over the last year. It's like, “Oh, like why do you need a sandbox? Why do you need this? Why this?” And to your point, it's like, “Oh, I need sandbox for this. I need sandbox for that. I need sandbox-” It's like, “Oh, I need it for every single thing.” And so basically what I, what I - and it sounds like a broken record, it's like you use a laptop every single day, right? And you are n of one. It's just you. But now imagine how And by the way, the laptop, the computer PC market, the PC market is about equal to the cloud market in total. So it's about 150, 180 billion a year. Something like that. It's about roughly the three cloud hyperscalers is about equal to like Apple, HP, Lenovo, whatever, It's a little bit less, but it's sort of like that. And now imagine And that's just like, so how big is the addressable market? What, how many people are there in the world now? What's the last data?Swyx [00:47:45]: Let's call it eight billion.Ivan [00:47:46]: Eight billion. And so let's say you can have two computer, like you have one personal and one business, whatever. Like so it's double that, right? and so that's 16 billion, right? How many agents are gonna be running in two years, in 10 years, in 100 years? Like And for every single task, they will need one of these. And so how big is that? That market is essentially quote unquote “infinite”. You will get to the point, and Dylan Patel was at the conference talking about, from SemiAnalysis, that talks usually about GPUs, was also talking about how CPUs will now be a bottleneck because it will be the constraint. You won't be able to grow, or we won't be able to have enough of these because there won't be enough CPUs to basically do.Swyx [00:48:23]: Yeah. Well, I actually had a really good podcast with Doug Oliphant, who, which was his president at SemiAnalysis, where they've basically been like, yeah, it's been a GPU shortage first, but then it's cascaded down to memory and now to CPUs.Ivan [00:48:35]: CPU, yeah.Swyx [00:48:35]: It-What's next? So networking. So, networking actually has been in shortage for a while if you're looking at, just GPU networking. But, yeah, it's really crazy the amount of computer use that's going on, yeah, cool. I, other questions are, just the one very big part is the open sourceness which you didn't have to do, your competitors don't do, like it's not, a lot of people are worried about keeping their projects open source because some competitor can just slot fork it. I don't know if there's any reflections on just being an open source company.Open Source, Trust, and Enterprise ProcurementIvan [00:49:15]: Yeah. There's a bunch. So we the original product that we did was open source.Swyx [00:49:19]: Yeah. CodeAnywhere.Ivan [00:49:20]: So doing that was actually very good for us. There's basically a saying of, What's the saying? Like, companies that are, that are doing really well, measure themselves against, free cashflow, that are kinda okay, it's EBITDA, then, it's, it goes all the way down.Swyx [00:49:36]: The worst is like GitHub stars.Ivan [00:49:37]: GitHub stars. GitHub stars are the worst, yeah. So you go all the way down to GitHub stars. And so our original one was GitHub stars. That's what we talked about, we're at the point we're talking about revenue, so we're we've gone up the stack on that. And so we started.Swyx [00:49:47]: No, profit.Ivan [00:49:48]: Yeah. We haven't, we're, we'll get there. We'll get there. But basically at that point we did stars and GitHub and it was useful, and the original variation that we did, it we split the core into its own repo and it was Apache 2.0, so very, permissive. And then we basically would bundl

Elizabeth New (Hovde) of the Washington Policy Center argues Washington's compact-by-compact approach to occupational licensing leaves experienced, credentialed workers waiting too long to work. She makes the case for universal licensure recognition — already adopted by 28 states — as a broader fix. https://www.clarkcountytoday.com/opinion/opinion-fix-licensing-expand-access-lower-costs-free-workers/ #OccupationalLicensing #WorkerRights #WashingtonState #PolicyBrief #WashingtonPolicyCenter #LicensureReform #ClarkCounty #Opinion

Content disclaimer: This episode contains strong language.In 2025, a man called 911 to report finding his mother dead. But with a changing story and questions about his mental state, the victim's daughter is left alone to pick up the pieces. This case is pendingSpecial thanks to Leda's daughter Jessica and Dr. Paige Wilcoxson for their participation in this episode.A huge THANK YOU to this month's sponsors:Start your detective work today at Newspapers.com! Go to Newspapers.com/truecrime and use promo code “CRIMELINES” for 20% off a subscription — and let the past tell its story.Turn those what ifs into reality with Shopify! Sign up for your one-dollar-per-month trial today at shopify.com/crimelines. Events:AdvocacyCon September in Albuquerque: https://www.advocacycon.com/ November in Costa Rica: https://trovatrip.com/trip/central-america/costa-rica/costa-rica-with-josh-hallmark-nov-2026 Support the show!Get the exclusive show Beyond the Files plus Crimelines episodes ad free onSupercast: https://crimelines.supercast.com/Patreon: https://www.patreon.com/crimelinesApple Subscriptions: https://podcasts.apple.com/us/podcast/crimelines-true-crime/id1112004494 For one time support:https://www.basementfortproductions.com/supportLinks to all my socials and more:https://linktr.ee/crimelinesSources:2026 Crimelines Podcast Source ListTranscript: https://app.podscribe.ai/series/3790If an exact transcript is needed, please request at crimelinespodcast@gmail.com Licensing and credits:Theme music by Scott Buckley https://www.scottbuckley.com.au/Cover Art by Lars Hacking from Rusty HingesCrimelines is a registered trademark of Crimelines LLC.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Gen Alpha has completely fragmented away from traditional TV, leaving advertisers scrambling to connect with kids and parents across YouTube, FAST channels, and gaming platforms.  This week, Mike sits down with Emma Witkowski, VP of Media Solutions at WildBrain, to unpack the massive market disconnect in children's media, the power of nostalgia in family co-viewing, and how upcoming privacy regulations like COPPA 2.0 are rewriting the rules of digital targeting. Key Highlights:

Discover the story of Mark McLean's impressive journey from early influences to leading a multi-billion-dollar company. Learn how culture, relationships, humility, and strategic growth propel organizations through transitions and challenges.   Main topics: Building a career in insurance and financial services, rooted in mentorship and family lessons Navigating growth: from founding to billion-dollar company, merger, and acquisitions The importance of company culture, relationships, and core values in long-term success Personal resilience through life's challenges including health, family, and entrepreneurial pivots   Timestamps: 00:00 - Defining what takes Mark McLean out in public   00:29 - Interests beyond work: sports, golf, and family   01:54 - The role of sports and family fun in shaping his values   02:40 - Favorite golf locations and social activities   03:10 - Humble introduction and the significance of titles   04:13 - Career overview: from Senior Managing Principal to Senior VP   06:46 - The importance of faith, family, and friendships in reputation   07:49 - The influence of Jeff Warner and mentorship connections   08:42 - Early motivations: lessons from family and mentors   09:30 - Growing up in Florence, South Carolina, and early career influences   11:12 - College journey, changing majors, and love for insurance   12:42 - Industry insights: relationship building, trust, and reputation   15:36 - Industry trends, industry change, and mentorship in risk management   17:45 - Industry challenges, trust, and business growth   20:04 - Licensing, early sales experiences, and building business foundations   22:02 - The role of creativity and relationship management in success   23:11 - Industry evolution and personal insights into trend anticipation   24:46 - Handling life's challenges, family, and career pivots   25:36 - The importance of good culture and team alignment   27:24 - Transition from employment to entrepreneurship: the leap into owning and growing businesses   32:51 - Building Turnkey Benefits, sale, and subsequent ventures   36:16 - Growth from 25 to 170 employees, industry innovation   40:54 - Navigating partnership changes, relationships, and success   44:11 - Transition into digital organizations, culture, and values   50:46 - Building a unified message, controlling quality and trust   54:45 - Personal experiences: family, loss, and professional resilience   56:55 - Adapting to industry and market changes post-Obamacare   58:26 - Balancing growth, personal life, and company focus   62:22 - About joining One Digital, the corporate culture, and long-term vision   66:00 - The role of private equity, strategic capital, and maintaining independence   70:01 - The secret sauce: company culture as a competitive advantage   73:51 - Leadership values: integrity, humility, and people first   77:08 - The importance of intentional culture, talented leadership, and long-term vision   78:45 - Family, blended families, and parenting insights about nurturing strong values   79:03 - Final thoughts — being discerning, strategic, and building legacy   Resources: Connect with Mark McLean OneDigital

In this episode of the Loan Officer Podcast, host Dustin Owen welcomes back Bob Niemi, Director of Government Affairs at Weiner Brodsky & Kider, for an in-depth discussion on the latest developments impacting the mortgage industry. Together, they dive into critical updates to the Nationwide Multistate Licensing System (NMLS) - the first significant changes to the system since its inception in 2008. Bob provides a comprehensive overview of the new disclosure requirements now affecting both licensed originators and company control persons, emphasizing the importance of compliance ahead of the upcoming August 31st soft deadline. The conversation also highlights key advocacy topics addressed at the recent National Advocacy Conference in Washington, D.C. Dustin and Bob break down the industry's ongoing efforts to ban trigger leads, a controversial practice that impacts both consumers and mortgage professionals, and discuss the implications of the proposed "Road to Housing" bill, which aims to improve access to homeownership. Throughout the episode, they stress the importance of industry engagement and encourage all mortgage professionals to join the Mortgage Action Alliance, a grassroots organization that makes it easy for individuals to participate in advocacy efforts and have their voices heard on Capitol Hill. Whether you're a seasoned loan officer or new to the industry, this episode offers valuable insights into regulatory changes, legislative priorities, and practical steps you can take to stay informed and involved in shaping the future of mortgage lending.   Disclosure Questions Update:  https://mortgage.nationwidelicensingsystem.org/knowledge/Products/nmls/stateresourcecenter/SitePages/Disclosure-Questions-Update.aspx  https://mortgage.nationwidelicensingsystem.org/knowledge/Products/nmls/stateresourcecenter/SitePages/Disclosure-Questions-Update.aspx TLOP's Originator Coaching:

Christine Capone – President of MKG (Experiential Marketing)This Christine Capone is a high-profile executive in the experiential marketing and branding industry. She serves as the President of MKG, a top-tier creative agency known for producing massive live events and brand activations. Career Background: Her expertise is rooted heavily in relationship building and partner management. Before moving agency-side, she led Sponsorships and Licensing for the lifestyle brand Vineyard Vines, where she managed their "Official Style" partnership with the Kentucky Derby and orchestrated deals with the America's Cup and the New York Giants. Early Career: She also worked on the partnership marketing team at the United States Tennis Association (USTA), managing top-tier sponsors for the US Open (including Heineken, Polo Ralph Lauren, and Mercedes-Benz). She holds a degree from Lehigh University.

Dr. Philip Chan joins the show to discuss the recent, major shifting of gears regarding Kratom in Rhode Island. Following years of a strict ban, the state has officially transitioned from prohibition to strict regulation under the Rhode Island Kratom Act. Dr. Chan breaks down what this means for public health, safety, and local business enforcement. Key Takeaways & Discussion Points The Shift from Ban to Regulation: After being classified as a Schedule I controlled substance in RI, Kratom is now legal to sell and manufacture under strict regulatory guardrails to target the deadly opioid crisis and harm reduction. Age Restrictions & Retail Safeguards: Under the new law, you must be 21 or older to purchase Kratom products. Furthermore, products cannot be left on open shelves; they must be kept securely behind the sales counter. Public Health Concerns: Dr. Chan and health experts emphasize that while some use Kratom for pain management or opioid withdrawal, it is not FDA-approved. Potential risks discussed include dependency, addiction, liver damage, and seizures. Targeting Adulteration & Synthetics: A major pillar of the new enforcement is product safety. The law strictly bans highly potent synthetic forms of the drug and requires precise labeling, packaging standards, and heavy lab-testing to ensure products aren't laced with dangerous non-kratom contaminants. Licensing & Taxation: Oversight is a joint effort. RIDOH is actively managing the $1,000 retail/distribution licensing and retailer training, while the RI Division of Taxation enforces a new 15% wholesale excise tax. Resources Mentioned For businesses looking to apply for retail or distribution permits, visit the Rhode Island Department of Health Kratom Licensing Portal. To learn more about the tax structures and rules, review the RI Division of Taxation Kratom Advisory. See omnystudio.com/listener for privacy information.

Barry Ferns from Barry's economics joins Alexei Sayle and Talal Karkouti for a chat about his journey from homelessness to owning London's best comedy club, Angel Comedy, and teaches us things he learned along the way.Massive thank you to Barry and his team for hosting us, filming and even editing this podcast! Talal's been travelling a lot for work lately and this wouldn't have been possible without Barry's superb team. They filmed us with three cameras so if there was ever a time to join the Patreon and watch the full video episode, it's now! Click here to join now! Check out Barry's Economics on YouTube, learn the truth about money and stop getting your financial advice from rich w@nkers who don't give a hoot about you!Pre-order Alexei's book here.Come see The Alexei Sayle Podcast LIVE with Diane Morgan at The Roundhouse, Camden on 2nd August! Get tickets here.Get tickets to see Alexei in conversation at the Rik Mayall Festival in Droitwich Spa, 5th June here!Be a comrade and support the show! Become a Patron and get access to the video version of the podcast, live episodes and more - patreon.com/AlexeiSaylePodcastSend your fan art, thoughts and questions to alexeisaylepodcast@gmail.comPlease consider leaving us a review on Apple Podcasts or wherever you get your podcasts.Subscribe to Alexei's YouTube channel here and join him for his Bike Rides and more.The Alexei Sayle Podcast is produced and edited by Talal KarkoutiMusic by Tarboosh RecordsPhotograph from the Andy Hollingworth Archive  

In this episode the hosts analyze a $10M revenue hazmat remediation business in California and uncover how licensing, unions, and regulatory complexity can make a profitable company nearly impossible to transfer to a new owner.Business Listing – https://www.bizbuysell.com/business-opportunity/high-demand-environmental-abatement-and-structural-demolition/2391095/Welcome to Acquisitions Anonymous – the #1 podcast for small business M&A. Every week, we break down businesses for sale and talk about buying, operating, and growing them.Looking to build a professional website in minutes? Try Wix: https://wix.pxf.io/c/6898629/3115214/25616?trafcat=templateHubSpot is the backbone for how businesses scale without chaos. Try them out here: https://go.try-hubspot.com/OeG9VrSubscribe for more episodes: https://www.youtube.com/@AcquisitionsAnonymousPodcast?sub_confirmation=1Subscribe to our Newsletter: https://www.acquanon.com/newsletter

Texas is taking a bold stand against excessive regulations. In this episode of The Right Idea, Brian Phillips and Derek Cohen sit down with Jerome Greener, Director of the Texas Regulatory Efficiency Office (TREO), to discuss the state's aggressive new push to cut red tape and boost economic freedom.The team breaks down TREO's first major findings — nearly $120 million in potential savings from just the initial review of 11 agencies — and how they're using AI to make government work better for Texans. From licensing reforms for electricians, plumbers, and HVAC techs to modernizing outdated rules, this is one of the most ambitious regulatory reform efforts in the country.Plus: The growing focus on government fraud and waste, why Texas needs this office despite its pro-business reputation, and how everyday Texans can help shape the next round of cuts.01:08 – Hot Take: Government Fraud & Waste in 202604:16 – Guest Introduction: Jerome Greener, Director of Texas Regulatory Efficiency Office (TREO)06:22 – $123 Million in Potential Savings Explained07:53 – Why Texas Still Needs Major Regulatory Reform08:49 – How TREO Differs from the Sunset Process09:59 – The Massive Texas Administrative Code: 20 Million Words & 274K Restrictions10:23 – What Makes a Regulation Too Burdensome?11:30 – TREO's Review Process & Multi-Layer Accuracy Checks13:04 – How the Public Can Submit Regulations for Review14:17 – How Texas Compares to Virginia & Florida15:55 – Meet “Sam” — Texas' Powerful AI Regulatory Chatbot17:59 – A One-Stop Tool for Licensing & Rules20:00 – Transparency, Agency Follow-Through & Future Savings21:39 – Long-Term Vision: Billions in Savings & Millions of Words Cut23:22 – Creating Government to Cut Government? Addressing Conservative Concerns25:02 – Working with the Texas Legislature on Statutory Changes26:47 – Real Examples of Regulatory Modernization29:26 – Future Plans: Taking on Local Government Regulations?30:40 – Balancing Safety, Freedom & Prosperity31:55 – How Texans Can Engage with TREO

In this episode the hosts analyze a $10M revenue hazmat remediation business in California and uncover how licensing, unions, and regulatory complexity can make a profitable company nearly impossible to transfer to a new owner.Business Listing – https://www.bizbuysell.com/business-opportunity/high-demand-environmental-abatement-and-structural-demolition/2391095/Welcome to Acquisitions Anonymous – the #1 podcast for small business M&A. Every week, we break down businesses for sale and talk about buying, operating, and growing them.Looking to build a professional website in minutes? Try Wix: https://wix.pxf.io/c/6898629/3115214/25616?trafcat=templateHubSpot is the backbone for how businesses scale without chaos. Try them out here: https://go.try-hubspot.com/OeG9VrSubscribe for more episodes: https://www.youtube.com/@AcquisitionsAnonymousPodcast?sub_confirmation=1Subscribe to our Newsletter: https://www.acquanon.com/newsletter

Listen and subscribe to Money Making Conversations on iHeartRadio, Apple Podcasts, Spotify, www.moneymakingconversations.com/subscribe/ or wherever you listen to podcasts. New Money Making Conversations episodes drop daily. I want to alert you, so you don’t miss out on expert analysis and insider perspectives from my guests who provide tips that can help you uplift the community, improve your financial planning, motivation, or advice on how to be a successful entrepreneur. Keep winning! Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed April Showers. Founder of Afro Unicorn, joins Money Making Conversations Masterclass to share how she built the first Black woman-owned, fully licensed character brand in major retail. With over $20 million in sales, Afro Unicorn celebrates diversity and empowers women and children of color.

In this episode, Josh interviews Pat Yates, M&A advisor at Quiet Light and owner of Happy Feet Slippers. Pat shares insights from his Shark Tank experience, discusses the realities of TV deals, and explains the complexities of licensing with major brands like Disney and the NFL. The conversation covers the importance of intellectual property protection, strategies for evaluating and managing licensing agreements, and actionable advice on preparing an e-commerce business for a successful exit. Listeners gain practical tips on building value, protecting their brand, and planning ahead for future business transitions.Chapters:Introduction and Guest Background (00:00:00)Josh introduces Pat Yates, his background, and the episode's focus on licensing and business exits.Shark Tank Experience (00:02:06)Pat discusses his Shark Tank appearance, the process, and what it was like pitching on the show.Reality of Shark Tank Deals (00:03:36)Pat explains how deals on Shark Tank often differ from what is shown, and his ongoing relationship with Robert.Behind the Scenes of Shark Tank (00:04:45)Pat shares details about the filming process, post-show counseling, and the impact of the experience.Licensing Audits and Financials (00:05:44)Discussion about licensing agreements, financial audits by licensors like Disney, and the importance of accurate documentation.License Renewal Challenges (00:07:01)Pat explains how license renewals work, what licensors look for, and the challenges with companies like Disney.Transitioning and Subcontracting Licenses (00:08:57)Pat describes how some licenses are transitioned to subcontracted arrangements and the benefits of this approach.Direct vs. Subcontracted Licensing (00:09:18)Explanation of the differences between holding a direct license and working through a subcontracted licensee.Branding and Labeling in Subcontracted Licensing (00:10:27)Clarification on branding, labeling, and legal requirements when selling products under a subcontracted license.Actionable Takeaways for Business Owners (00:11:42)Josh summarizes three actionable tips: IP protection, evaluating licensing, and preparing your business for exit.Final Advice on Business Growth and Exit Preparation (00:15:11)Pat offers final advice on analyzing business performance, seeking help, and preparing early for a successful exit.Episode Wrap-Up (00:16:13)Josh thanks Pat and encourages listeners to reach out for further advice on exiting their business.Links and Mentions:Consulting and Strategy"Ecomm Breakthrough Consulting": "00:00:00""Email for Strategy Audit": "00:01:08"Shark Tank and Related Experiences"Shark Tank": "00:02:04""Robert Herjavec": "00:02:15"Licensing and Partnerships"DreamWorks, NCAA, NFL, Disney": "00:02:31""Licensing and IP Protection": "00:12:04""Consider Licensing": "00:13:12"Intellectual Property"IP Protection": "00:12:04"Transcript:Josh 00:00:00  Today, I'm speaking with Pat Yates, an M&A advisor at Quiet Light and owner of Happy Feet Slippers. And today we're going to be talking a lot about licensing and preparing your business to exit. This episode is brought to you by Ecom Breakthrough Consulting, where I help seven figure companies grow to eight figures and beyond. Listen, Pat, I started my E-comm business back in 2015, and it took me seven years to grow it to an eight figure brand. There were a lot of times that I struggled with the challenge of knowing whether my business could actually succeed financially, or if my brand could actually become a real well-known brand, or even myself as a leader. Whether I had the abilities and capabilities to lead a team and actually manage a group of people? Sure. For our listeners that have had similar experiences or hit similar plateaus, go to Ecom Breakthrough Comm and that's ecom with two M's. And you can learn a little bit more about how I can help you. And to our listeners, this month I'm giving away one $10,000 comprehensive business strategy audit session at no cost.Josh 00:01:08  All you need to do is email me at Josh at Ecom breakthrough.com. And in your subject line just say strategy audit and then tell me why I should choose your business as the business to do the strategy audit for this month. And don't worry if you don't win this month because you'll be entered to win for future months to come. But I'm super excited to introduce you all to Pat Yates. Pat, as a seasoned entrepreneur with a focus on eCommerce, in 2014, he struck a deal with Robert Herjavec on the Emmy Award winning show Shark Tank. Pat grew a single slipper kiosk business into a multi-million dollar, e-commerce focused business. During that time, Pat has done licensing deals with Dreamworks, the NCAA, the NFL and Disney, and in 2015, he struck up a relationship with Mark, the founder of Quiet Light Brokerage, and continued, eventually leading him to becoming an M&A advisor. So welcome to the show, Pat.Pat 00:02:04  Thanks. I appreciate you having me.Josh 00:02:06  Pat. I watched your Shark Tank episode and loved, you know, everything you kind of went through in that episode.Josh 00:02:15  You ended up doing a deal with Robert who who first kind of went out pretty early on, at least in the episode. And then he comes back in and kind of swoops up the deal. And at the last moment, how was that experience being on Shark Tank and going through that?Pat 00:02:31  Yeah, it's something I've talked a lot about it over the past few years because, as one of the people that likes on the speaking circuit with me likes to call me the one of the OGs in Shark Tank because I'm on season five. They have so many seasons now, I'm like, I can't be old at everything. I hate that, but, I mean, it's it's a difficult process in the very beginning. You have to submit several videos and a lot of written documentation, a lot of due diligence. And, you know, I was turned down in season one or season two or something like that. And then they called me back as season five was coming because they were ramping so much, and I was one of the people that came down to the very end and had to fly out there and do my pitch in front of the producers to even see if they could keep me.Pat 00:03:09  So, I did that. And then it aired in 2014 and it was awesome. I mean, the show was going, I mean, my, my time was going poorly in there for like 80% of it. The, you know, you're in there like an hour and 15 minutes. Most people don't realize that. And it's cut to eight. So for most of the time it wasn't going very well. But the end was pretty good. Yeah.Josh 00:03:27  Yeah, that's that's amazing. How was it, you know, doing a deal with Robert and what kind of his involvement been since you did that deal with him?Pat 00:03:36  Well, the deals that you do on Shark Tank and are are definitely theory and practice things. You know, one of you come up with a deal and then it closes or it doesn't. I mean, a lot of people that I talk to and I'm involved in a pretty deep Shark Tank group. You know, most of those deals don't close as you see them. And really, truly most deals don't close, period.Pat 00:03:55  you know, our deal. We did not do the financial terms we saw on the show. We just did a relationship and we didn't do any kind of money transfer, just a small equity portion to be able to help. So the relationships been mor...

This episode talks real strategy behind non-resident licensing: when expansion makes sense and when it becomes a costly distraction. From holding on to relocating clients, to the temptation of “collecting states,” our team discusses local expertise, licensing costs, operational complexity, and what it really takes to scale beyond your home state in the most effective way.Learn more at IntegraPartnerNetwork.com.

Struggling to turn your healing skills into income? This episode breaks down exactly how to set up, market, and grow a craniosacral (or any healing) practice—without sleazy tactics. If you're a spiritual entrepreneur, don't skip this.To learn more about Sacred Profits, Join Here: https://learn.kellesparta.com/sacredprofitsYou can find Ann Kelly at https://bigmouthspeech.com/00:00 Welcome to Spirit Sherpa00:22 Meet Ann Kelly01:51 Craniosacral Business Pivot02:41 Choosing Payment Processors03:50 PayPal Tips and Fees08:37 Get Paid Before Sessions09:42 Marketing to Existing Clients10:39 Education Email Sequence12:35 SEO and AI Search Changes16:38 Ethical Pain Point Marketing18:52 What Craniosacral Helps With20:21 Words to Avoid in Ads20:45 Audience Mindset Split21:23 Target Growth Communities22:21 Pain Points Messaging22:50 Safe Places To Market23:48 Local Spiritual Networking24:36 Salem Scene Politics26:00 Adjacent Referral Partners27:38 Coaches Versus Therapists30:04 Private Pay Location Fit30:33 Profit First Finances31:38 Bank Accounts Setup33:11 LLC Insurance And EIN35:07 Separate Entities Risk38:43 Wrap Up And Contact InfoKeywords:spiritual entrepreneurspiritual businessspiritual coachingsacred profitsspiritual business strategyhealing businesshealer marketingget clients spiritual businessmake money spiritual businessspiritual business growthholistic businessspiritual practitionerconscious businessspiritual entrepreneur tipsclient acquisition spiritual businesspricing spiritual servicesspiritual business modelonline business for healersrecurring revenue spiritual businessmembership business spiritualspiritual business coachingbuild a spiritual businessprofitable spiritual businessJoin the community on YouTube: https://www.youtube.com/@KelleSpartaIf you would like to learn more please book a Discovery Call here: https://kellesparta.com/discovery-call/Licensing and Credits:“Spirit Sherpa” is the sole property of Kelle Sparta Enterprises and is distributed under a Creative Commons: BY-NC-ND 4.0 license. For more information about this licensing, please go to www.creativecommons.org. Any requests for deviations to this licensing should be sent to kelle@kellesparta.com. To sign up for, or get more information on the programs, offerings, and services referenced in this episode, please go to www.kellesparta.com

Snigdha Kumar, CEO and co founder at Bricco, joins The Tech Trek to talk about a part of fintech most people never see, state by state licensing.For any financial company trying to launch in the United States, licensing can be slow, expensive, and operationally painful. Snigdha explains why that barrier limits experimentation, how Bricco is trying to automate the process, and why better compliance infrastructure could help more useful financial products reach the market.Practical takeaways• Financial innovation is not only a product problem. Licensing, compliance, reporting, audits, and exams can shape what gets built before a product ever reaches customers.• Lowering the cost of licensing does not remove regulation. It makes the process more efficient while keeping important protections in place.• The biggest barrier for fintech founders is often not knowing what path is available. Education and clearer process design can keep teams from avoiding licensing or choosing expensive workarounds.• Better financial products still need better distribution and awareness. Easy access is not the same as helping people find the right product for their actual financial life.• Responsible financial behavior may need better product design, better incentives, and a stronger cultural signal, not just more advice.Timestamped highlights00:43, Snigdha explains how Bricco is automating state by state regulatory compliance for financial licensing.02:15, How her career has focused on reducing barriers to financial services across Asia, Africa, and the United States.05:05, The reverse culture shock of finding major access gaps inside the US financial system.06:08, Why licensing costs can run into the millions and shrink the number of fintech experiments.09:58, Why reducing the barrier matters, but eliminating it completely would create real risk.12:21, The difference between making financial products easy and making sure people are using the right product.16:05, Why spending has a social identity, but saving and responsible investing often do not.21:10, How Bricco uses education and content to help founders treat licensing as a strength instead of a blocker.One Line That Stuck“Think about licensing as a strength, think about it as a way to own your destiny.”Practical TakeawaysFor fintech founders and operators, the message is simple. Do not treat licensing as a late stage legal detail. It can affect product timelines, market access, capital needs, and the type of company you are able to build.For technical and product leaders, this is a reminder that infrastructure is not always code. Sometimes the biggest product constraint is the operating system around the business.Subscribe or follow The Tech Trek for more conversations with founders, builders, and operators working through the real decisions behind modern technical companies.

The Cook Government in Western Australia has announced a major overhaul of the state's driver licensing system, strengthening requirements for new drivers. Behind the changes is a rise in road crashes involving young motorists. Yasuo Imanari has this report from Perth. - 西オーストラリア州のクック政権は、同州の運転免許制度を大幅に見直し、新人ドライバーへの要件を強化すると発表しました。その背景は増加する、若者ドライバーの交通事故があります。リポーターはパースの今城康雄さんです。

Just shocking figures coming from Rural Pathways, and their co-owner and founder, Staci Gilpin. She joined Vineeta on The WCCO Morning News for an eye opening chat.

Professionalism and the "Unknown Self": Event Leadership with Jane MonroeIn a recent episode of The Thoughtful Entrepreneur Podcast, host Josh Elledge sat down with Jane Monroe, the owner of Embrace The Grape, to explore the intersection of high-stakes event execution and intentional leadership. Jane, a seasoned beverage caterer, keynote speaker, and emcee, shares her journey of building a 17-year reputation rooted in unwavering professionalism and legal compliance. This conversation provides a strategic roadmap for entrepreneurs who struggle with the "DIY" mentality of clients, offering a deep dive into how strict adherence to standards and deep self-awareness can become a business's greatest competitive advantage.The Architecture of Authority: Compliance, "Reading the Room," and DiscoveryThe beverage catering industry is often fraught with liability and logistical hurdles, yet Jane Monroe has scaled Embrace The Grape by leaning into the complexities rather than avoiding them. By prioritizing licensing and insurance as non-negotiable assets, Jane provides a "full-service" peace of mind that allows hosts to actually participate in their own events. This level of professionalism is built on a foundation of client education, where the team explains the "why" behind legal protocols, effectively filtering for high-quality clients who value reliability over risk. For event professionals, the lesson is clear: your reputation is forged in the moments you refuse to cut corners, and long-term credibility is always more profitable than short-term convenience.Beyond the logistics of catering, Jane's background as a mobile DJ taught her the essential leadership skill of "reading the room." This intuitive ability to observe, adapt, and engage is what separates a service provider from a true experience creator. Whether managing a wedding crowd or leading a corporate team, the ability to pivot based on energy and feedback is vital. This adaptability is further refined through Jane's framework of the "Four Identities," which challenges leaders to investigate their known, blind, and hidden selves. By actively seeking feedback and pushing into the "unknown self"—the parts of our potential only revealed through extreme challenges—leaders can build a resilient internal operating system that inspires autonomy and growth in others.True discovery often happens outside the boardroom, as evidenced by Jane's participation in a 340-mile kayak race. These moments of discomfort are where the "unknown self" emerges, providing a visceral understanding of one's limits and capabilities. In the workplace, this translates to a culture where self-leadership is the prerequisite for leading others. When a founder or manager models integrity and empathy, they set a standard that empowers their staff to handle unexpected situations gracefully. By investing in soft skills like improv and deep self-reflection, leaders can ensure that when the "stakes are high," they aren't just following a script, but are instead providing the steady, calm authority that clients and teams desperately need.About Jane MonroeJane Monroe is the owner of Embrace The Grape and an accomplished keynote speaker and emcee known as "Keynote Jane." With nearly two decades of experience in the event industry, Jane has mastered the art of beverage catering, combining strict legal professionalism with high-energy crowd engagement. She is a dedicated advocate for self-awareness and leadership, helping others discover their untapped potential through her "Four Identities" framework and her personal commitment to extreme physical and mental challenges.About Embrace The GrapeEmbrace The Grape is a premier beverage catering company based in the Kansas City area, specializing in high-end weddings, corporate functions, and outdoor festivals. Unlike standard bar services, Embrace The Grape provides a comprehensive approach that includes licensing, insurance, and highly trained staff to ensure guest safety and event flow. The company is built on the philosophy of "beverage catering done right," allowing clients to focus on their guests while professionals handle the intricacies of alcohol service.Links Mentioned in This EpisodeEmbrace The Grape Official Website: embracecatering.comJane Monroe on LinkedIn: linkedin.com/in/janemonroe/Key Episode HighlightsProfessionalism as a Filter: Why following the law and maintaining high insurance standards attracts better clients and builds a lasting brand.The "Four Identities" Framework: A deep dive into self-awareness and how discovering the "unknown self" transforms leadership.Reading the Room: Lessons from Jane's DJ background on how to adapt and engage with different audiences in real-time.The Value of Discomfort: How extreme challenges, like long-distance kayaking, reveal leadership traits that remain hidden in comfort.Educating the Client: Transforming the sales process into an educational journey that builds trust and sets clear expectations.ConclusionThe conversation with Jane Monroe emphasizes that true success in the event industry and in leadership is a byproduct of self-mastery and professionalism. By educating clients on the value of compliance and pushing oneself to discover the "unknown self" through challenge, entrepreneurs can build businesses that are both legally sound and culturally impactful.More from The Thoughtful Entrepreneur

PSA President Ryan Hoge joins the crew! This week, we're going deep with the President of PSA to discuss their new fraud protection program, authenticity guarantees, and the future of the grading giant. Is PSA's massive submission volume a sign of market health or a warning? The crew debates. Also in this episode... Fanatics x FIFA: What the massive new licensing deal means for the future of soccer cards and international collecting. Gear Up: A look at BCW's new graded storage tech and the cold, hard realities of the hobby business. The Chaos: Plenty of market arguments and Sports Cards Nonsense energy. If you care about the market, the business, or just a good hobby debate, this one is for you. Learn more about your ad choices. Visit megaphone.fm/adchoices

What Every Minpaku Investor Must Understand Before Buying in Japan. The autumn session will take place on 17-18th October, 2026 - subscribe & watch this space for the official announcement, early-bird discounted tickets registration, and list of speakers for the next event!

How much does the average documentary filmmaker's biggest licensing mistake cost?A 30-second Jackson 5 clip can run a documentary $50,000 to $70,000 in licensing fees. Veteran ARC Producer Teddy Cannon has spent a decade in the messy middle between production and legal, and he is here to walk Christian through how to keep your film from becoming the next case study.In Episode 277, host Christian Taylor sits down with Teddy to break down the role most documentary filmmakers overlook until it costs them tens of thousands of dollars: the ARC Producer, the modern hybrid of the Archival Producer and the Clearance Producer.The conversation centers on three frameworks that every documentary filmmaker needs before rolling camera. First, the $70,000 Jackson 5 case study, a real licensing scenario Teddy is working on right now. Second, the Public Location is not Public Domain rule, which catches filmmakers who assume that filming a statue, mural, or artwork in a public space makes it free to use. Third, the Berry Picking method for finding rare archival footage in places the standard stock libraries do not reach. Teddy also gives a first look at ArcWorks, the digital management system he is building to replace the spreadsheet workflows the industry has been stuck with for decades.In this episode, you'll learn:Why a 30-second clip of a famous artist can cost $50,000 to $70,000 to licenseThe difference between an Archival Producer and a Clearance Producer (and why you need both)Why filming a statue in a public park can still require legal clearanceHow the Fair Use doctrine actually works for documentary filmmakersThe Duck Rule for understanding fair use in 7 secondsWhen fair use protects you and when an attorney is required for E&O insuranceThe Berry Picking method for finding rare footage in small, non-digital museumsHow a senior ARC Producer can save thousands through industry relationshipsWhat it costs to hire an ARC Producer ($2,500 to $3,500 per week)A first look at ArcWorks, Teddy's new digital management systemChapters:0:00 The $70,000 Mistake: Why Licensing Matters1:03 What is ARC Producing? (Archival + Clearance)1:51 How Teddy Became an ARC Producer2:29 What are Clearance and Third-Party Assets?3:21 Why Third-Party Assets Aren't Just Free to Use4:07 Public Location is not Public Domain6:45 Case Study: The Jackson 5 and Music Licensing Risks9:21 What is the Fair Use Doctrine?10:39 Fair Use Example: News Footage11:08 Documentary First Brought to You By Virgil Films Entertainment12:13 The Cost and Duties of an ARC Producer13:06 How Big of an Impact can an ARC Producer Make?14:49 Berry Picking: Finding the Right Footage16:34 The Importance of Unique Archival Material19:47 ArcWorks: A New System for Archival Management22:11 How to Reach Teddy Cannon22:48 Docu Deja Vu: Yacht Rock and Kiss the Future24:14 Documentary First Signing OffFREQUENTLY ASKED QUESTIONSWhat is an ARC Producer in documentary filmmaking?An ARC Producer is the modern hybrid role that combines what used to be two separate jobs: the Archival Producer, who finds and sources third-party footage, photos, and audio, and the Clearance Producer, who secures the legal rights to use those assets. In today's production pipeline, the two roles have melded into one. A senior ARC Producer is hired in pre-production, not at the end, and saves filmmakers thousands of dollars by spotting licensing problems before footage gets locked in the edit.How much does it cost to license music from a famous artist for a documentary?Licensing music from a major artist like the Jackson 5 can cost $50,000 to $70,000 for a single 30-second clip. That figure includes both the synchronization license, which is the right to use the song with picture, and the master use license, which is the right to use the specific recording. Music is among the most expensive third-party assets because it requires clearance from both the publisher and the record label, and major artists' estates are often hyper-protective of their brands.Can you film a statue or work of art in a public place and use it in your documentary?No, not without clearance. Even when a statue, mural, or painting is displayed in a public location, the work itself is owned by the artist or estate and is protected by copyright. Documentary filmmakers who include works of art in their footage, whether intentionally framed or accidentally captured behind an interview subject, must clear those works before delivery. Bringing a clearance professional into the pre-production meeting can prevent the costly post-production scramble of identifying artwork after the fact.What is the Fair Use doctrine for documentary filmmakers?Fair Use is a legal doctrine that allows documentary filmmakers to use copyrighted material without licensing it, provided the use serves a clear documentary purpose. The general rule is that the visual on screen must directly relate to what the talking head or voiceover is discussing. If you are talking about a duck and there is a duck on screen, that use typically falls under fair use. Documentary filmmakers should work with both an ARC Producer who understands fair use boundaries and a fair use attorney whose written letter is required for Errors and Omissions insurance.How much does it cost to hire an ARC Producer for a documentary?An ARC Producer's weekly rate ranges from approximately $2,500 to $3,500. Senior, veteran ARC Producers typically command $3,500 per week, while junior producers are available at lower rates. Veteran ARC Producers are often worth the higher rate because their long-standing relationships with stock houses, archives, and rights holders can save the production thousands of dollars through negotiated rates. Most documentary productions hire ARC Producers in pre-production rather than at the end of post-production to maximize cost savings.DocuView DEJA VU PICKSTeddy Cannon recommends two films:Yacht Rock: A Dockumentary (HBO, 2024). A genre-defining archival documentary where the ARC Producer received a third billing credit, a recognition Teddy says reflects the rising value of the archival role in modern documentary.Kiss the Future (Paramount+, 2024). The U2 documentary about the siege of Sarajevo, built on rare archival footage that, in Teddy's words, literally makes the piece.SPONSORED BY VIRGIL FILMS ENTERTAINMENThttps://www.virgilfilms.comABOUT THE GUESTTeddy Cannon is a veteran media producer, ARC Producer, and tech entrepreneur with over a decade of experience in archival sourcing and rights clearance for documentary and clip-based television. Teddy entered the industry as a segment producer on shows like REAL TV and World's Scariest, then transitioned into clearance work where he has spent the last ten years standing at the link in the production line between filmmakers, vendors, and legal teams.Teddy runs Crux Entertainment, the company where filmmakers hire him for his ARC Producer work. He is also the founder of 3P Sync, the tech company developing ArcWorks, a digital management system designed to replace the spreadsheet-based workflows that have dominated archival and clearance work for decades. ArcWorks will handle third-party asset intake, EDL reconciliation, fair use rating, and document signing in a single centralized platform.This is Teddy's second appearance on Documentary First. His first conversation with Christian was Episode 244, which covered his early work on 3P Sync.Connect with Teddy:Email: teddycannon@gmail.comCrux Entertainment: https://www.cruxentertainmentinc.comLinkedIn: https://www.linkedin.com/in/teddy-cannon-52447314/ABOUT CRUX ENTERTAINMENTCrux Entertainment is Teddy Cannon's ARC production company, the entity filmmakers contract for archival sourcing and rights clearance work on documentary projects. Through Crux, Teddy and his team handle the third-party asset workflow that connects filmmakers, vendors, archives, rights holders, and legal teams. Filmmakers seeking ARC Producer services for an upcoming documentary engage Crux Entertainment directly.Website: https://www.cruxentertainmentinc.comABOUT TEDDY'S COMPANY

When Brandi Celenza was shot in April of 2018, the police were told one story. That story was the same in 2018, 2019, and 2020. It only changed shortly before the case went to trial and the question came down to what it usually comes down to: would the jury be convinced?This case is *solved*A huge THANK YOU to this month's sponsors:Start your detective work today at Newspapers.com! Go to Newspapers.com/truecrime and use promo code “CRIMELINES” for 20% off a subscription — and let the past tell its story.Refresh your spring wardrobe with Quince. Go to quince.com/crimelines for free shipping and 365-day returns.Events:AdvocacyCon September in Albuquerque: https://www.advocacycon.com/ November in Costa Rica: https://trovatrip.com/trip/central-america/costa-rica/costa-rica-with-josh-hallmark-nov-2026 Support the show!Get the exclusive show Beyond the Files plus Crimelines episodes ad free onSupercast: https://crimelines.supercast.com/Patreon: https://www.patreon.com/crimelinesApple Subscriptions: https://podcasts.apple.com/us/podcast/crimelines-true-crime/id1112004494 For one time support:https://www.basementfortproductions.com/supportLinks to all my socials and more:https://linktr.ee/crimelinesSources:2026 Crimelines Podcast Source ListTranscript: https://app.podscribe.ai/series/3790If an exact transcript is needed, please request at crimelinespodcast@gmail.com Licensing and credits:Theme music by Scott Buckley https://www.scottbuckley.com.au/Cover Art by Lars Hacking from Rusty HingesCrimelines is a registered trademark of Crimelines LLC.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Listen and subscribe to Money Making Conversations on iHeartRadio, Apple Podcasts, Spotify, www.moneymakingconversations.com/subscribe/ or wherever you listen to podcasts. New Money Making Conversations episodes drop daily. I want to alert you, so you don’t miss out on expert analysis and insider perspectives from my guests who provide tips that can help you uplift the community, improve your financial planning, motivation, or advice on how to be a successful entrepreneur. Keep winning! Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed April Showers. Founder of Afro Unicorn, joins Money Making Conversations Masterclass to share how she built the first Black woman-owned, fully licensed character brand in major retail. With over $20 million in sales, Afro Unicorn celebrates diversity and empowers women and children of color.

“I don't see things like anybody else,” says Jonathan Berk, a professor of finance at Stanford Graduate School of Business. “And so I can see things people don't see.” On this episode, Berk explores recent research that pushes against conventional wisdom, from questioning the utility of the debt-to-GDP ratio to asking whether regulation is actually in the best interests of the consumer. “If you disagree with me… You have to write down a convincing theoretical model and analyze [it].”Berk admits his unique lens doesn't always make life easy. But on the other hand, “it confers an enormous advantage” — and he believes that organizations which are able to harness the power of unconventional thinking can gain a competitive edge.“It's allowed me to solve problems that other people couldn't solve,” he says. Has seeing the world differently helped you resolve a conundrum? Tell us more at ifthenpod@stanford.edu.Related Content:Jonathan Berk faculty profile What If We're Looking at the National Debt All Wrong? Chapters:00:00:00 The Fosbury Flop, innovation, & unconventional thinking00:03:18 Introduction00:04:24 Questioning conventional wisdom00:04:57 Rethinking the debt-to-GDP ratio00:08:21 A finance perspective on national debt00:10:36 Why theory matters before alarm00:12:38 Regulation, charlatans, & consumer interests00:16:22 Licensing, certification, & competition00:19:51 The cost of pushing back00:21:16 Building organizations that welcome dissent00:24:59 ConclusionIf/Then, from Stanford GSB, features conversations with faculty that explore how their research deepens our understanding of business and leadership.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Why You're Resisting Your Work (And What to Do About It)Kelle Sparta opens Spirit Sherpa by sharing her own struggle with resistance and low energy while trying to record the episode, explaining that resistance can come from discomfort, dread, feeling incompetent, or an “inner child” needing rest, and that sometimes what looks like resistance is actually necessary emotional processing. She emphasizes honoring emotions and the body, using movement to shift mindset, taking breaks, and avoiding constant pushing because burnout affects client engagement.To learn more about Sacred Profits, Join Here: https://learn.kellesparta.com/sacredprofits00:00 Welcome to Spirit Sherpa00:51 What Resistance Means01:37 Processing vs Avoidance03:06 Negotiate for Momentum05:50 Chunk Tasks Small07:09 Body Doubling and Timers07:35 Honor Your Energy08:52 Do the Dreaded First09:09 Celebrate and Rest10:12 Business Apprenticeship Invite11:46 Final SendoffKeywords:spiritual entrepreneurspiritual coachingspiritual businessspiritualityspiritual growthpersonal growthmindsetbusiness mindsetentrepreneurshipenergy managementburnout recoveryemotional processingself awarenessinner workshadow workresistanceovercoming resistanceproductivity mindsetconscious businessshamanismJoin the community on YouTube: https://www.youtube.com/@KelleSpartaIf you would like to learn more please book a Discovery Call here: https://kellesparta.com/discovery-call/Licensing and Credits:“Spirit Sherpa” is the sole property of Kelle Sparta Enterprises and is distributed under a Creative Commons: BY-NC-ND 4.0 license. For more information about this licensing, please go to www.creativecommons.org. Any requests for deviations to this licensing should be sent to kelle@kellesparta.com. To sign up for, or get more information on the programs, offerings, and services referenced in this episode, please go to www.kellesparta.com

Subscribe to DTC Newsletter - https://dtcnews.link/signupHelen Smith built Roo & You from hand-sewn mask lanyards in 2020 into a 100,000+ customer brand, and didn't touch paid ads for the first 3.5 years. In this episode she walks through the Facebook community that became her growth engine, how she landed Warner Bros and Harry Potter as a licensing partner without a media buyer, and what scaling through a tariff war actually looks like behind the scenes.For DTC founders scaling from $1M to $10M who want to lower CAC and build a real retention moat.What we cover:The mask lanyard side hustle that funded her first container of play couchesHow a private Facebook group became Roo & You's primary growth engineThe one-strike kindness rule that keeps the community aliveCold-DMing Warner Bros on LinkedIn (and getting a yes)Why licensing is a marketing channel, not a revenue playAdding tariffs as a line item instead of a stealth price hikeLaunching an affiliate program in November for existing customersWho this is for: Founders leaning too hard on paid, or operators who want to build a community moat before they scale spend.What to steal:Show up in other people's communities for months before launching your ownSet strict community rules on day one, not after things go sidewaysMake tariffs a visible line item to keep customer trust intactHand affiliate codes to existing customers before paying creators who've never used the productTimestamps:00:00 Building a brand through community02:00 Using data to make better decisions04:00 Handling tariffs and margin pressure06:00 Launching through Facebook groups08:00 Early demand and product expansion10:00 Finding manufacturers and testing products12:00 Pricing, value, and product longevity14:00 Organic growth without paid ads16:00 Transitioning into paid advertising18:00 Leveraging community for content and growth20:00 Licensing deals and brand partnerships24:00 Structuring better partnership agreements27:00 Challenges with licensing approvals29:00 Why partnerships are for marketing not growth30:00 Founder confidence and building in public37:00 Expanding into the US market40:00 Choosing the right marketing agency42:00 Turning customers into advocates47:00 Advice for founders building a brandSubscribe to DTC Newsletter - https://dtcnews.link/signupAdvertise on DTC - https://dtcnews.link/advertiseWork with Pilothouse - https://dtcnews.link/pilothouseFollow us on Instagram & Twitter - @dtcnewsletterWatch this interview on YouTube - https://dtcnews.link/video

What does crypto look like in one of the world's most regulated markets?At TEAMZ Summit 2026 in Tokyo, Matthew Owens sits down with Kondo Tomohiko, CEO of SBI VC Trade the only licensed stablecoin exchange in Japan to explore stablecoins, regulation, and adoption. From USDC lending to Japan's upcoming JPY-backed stablecoin, this episode reveals how strict regulation is shaping long-term opportunity.

For decades, the nightclub district in LoDo has been notorious for violence and other unsavory activities going down around 2 a.m. when the clubs let out. Many policy proposals have come and gone without changing much, and now Denver's newly renamed Department of Licensing and Consumer Protection has a proposal that they say will reduce problems and help businesses. Denver Post reporter Elliott Wenzler has been covering the story, and she's on with producers Paul Karolyi and Olivia Jewell Love to dig in. Plus, listeners are divided on the proposed REI boycott, and Denver is the seventh nicest smelling city in the country, according to who? Paul referenced our 2021 episode about nightclub owner and self-professed mafia member Valentes Corleons and this BusinessDen story about a nightclub owner who had to cancel his plans. Elliott talked about Denverite's 2020 smell map.  For even more news from around the city, subscribe to our morning newsletter at denver.citycast.fm. Follow us on Instagram: @citycastdenver Chat with other listeners on reddit: r/CityCastDenver Support City Cast Denver by becoming a member: membership.citycast.fm What do you think is the nicest smelling part of the city? We want to hear from you! Text or leave us a voicemail with your name and neighborhood, and you might hear it on the show: 720-500-5418 Learn more about the sponsors of this April 29th episode: The Delores Project Looking to advertise on City Cast Denver? Check out our options for podcast and newsletter ads at citycast.fm/advertise

Georg Harer was a financial lawyer for 10 years before joining Bybit. Now Co-CEO of Bybit EU, he sits with David Sencil at Paris Blockchain Week 2026 to walk through the post-MiCA European crypto landscape — including the regulatory gap MiCA hasn't closed (crypto loans), the three-license stack platforms need to operate, and his pitch to regulators for a consolidated license that would save firms millions.We cover:- Crypto loans regulatory blind spot- The MiCA + e-money + MiFID three-license operator math- Why Bybit EU started with retail before institutional- Tokenized stocks at 2am at the club- Euro stablecoin liquidity vs USDC dominanceChapters:00:42 - Bybit's Growth and History01:11 - George's Background and Transition to Bybit02:21 - Bitcoin Loans and Regulation in Europe03:11 - Crypto Loan Regulation Discussion04:03 - George's Introduction to Bitcoin05:35 - Bybit's Focus on Europe07:28 - Trust and Regulation in European Crypto Market09:00 - Bybit's Customer Base and Licensing10:30 - Institutional Focus at Paris Blockchain Week11:15 - Average Bybit User in Europe12:28 - Bybit's Licensing and Regulatory Challenges14:11 - Future of European Financial Regulation17:31 - Euro-Denominated Stablecoins19:24 - Concerns About Dollar-Denominated Stablecoins21:35 - Reporting Requirements for Stablecoins22:22 - Excitement About Tokenized Stocks23:47 - Bybit's Future Products and Success MetricsFollow Bitcoin.com for more crypto interviews, insights, and market updates.

Stop Running a “Practice” — Build a Real Healing Business FrameworkKelle Sparta welcomes Anabel Tonkovic of Nuna Therapy, a quantum reiki master and astrologer who offers one-on-one sessions, group events, and farm-based wellness retreats, and wants clarity on her next business steps. Kelle notes that Anabel has many offerings but needs an overarching brand construct and a clear client progression with defined outcomes, positioning events as lead magnets that feed a structured pathway. They discuss targeting a specific audience, aligning offers to how Annabelle works best long-term, and recognizing many Reiki clients are early in a “discovery” phase.To learn more about Sacred Profits, Join Here: https://learn.kellesparta.com/sacredprofitsYou can find Anabel Tonkovic at https://www.nunatherapy.com/00:00 Welcome and Guest Intro00:38 Annabelle's Offerings Overview01:13 What Help Is Needed01:50 Services Mix and Capacity03:38 Pricing and Client Sources05:26 From Practice to Business06:01 Define Core Healing Focus07:26 Target Market and Trauma Angle08:26 SEO and AI Search Strategy09:21 Build a Branded Client Pathway11:14 Outcomes and Program Structure13:19 Long Term Client Journey14:34 Reiki as Entry Point15:24 Redirect to Real Healing15:45 Stage Three Explained16:39 From Magic Pill to Self17:40 Podcast as Progression18:34 Calls to Action That Convert20:44 Build a Solid Container22:20 Optimize Podcast and YouTube26:20 Overarching Brand Vision27:34 Wrap Up and Next Steps28:05 Sacred Profits Pitch30:23 Find Anabel and ClosingKeywords:spiritual entrepreneurspiritual coachingspiritual businesshealing businessreiki businessenergy healingspiritual growthpersonal growthbusiness coachingentrepreneurshipreiki practitionerastrology businessoffer creationbusiness strategyhealer marketingJoin the community on YouTube: https://www.youtube.com/@KelleSpartaIf you would like to learn more please book a Discovery Call here: https://kellesparta.com/discovery-call/Licensing and Credits:“Spirit Sherpa” is the sole property of Kelle Sparta Enterprises and is distributed under a Creative Commons: BY-NC-ND 4.0 license. For more information about this licensing, please go to www.creativecommons.org. Any requests for deviations to this licensing should be sent to kelle@kellesparta.com. To sign up for, or get more information on the programs, offerings, and services referenced in this episode, please go to www.kellesparta.com

From building Applied Intuition from YC-era autonomy tooling into a $15B physical AI company, Qasar Younis and Peter Ludwig have spent the last decade living through the full arc of autonomy: from simulation and data infrastructure for robotaxi companies, to operating systems for safety-critical machines, to deploying AI onto cars, trucks, mining equipment, construction vehicles, agriculture, defense systems, and driverless L4 trucks running in Japan today. They join us to explain why “physical AI” is not just LLMs on wheels, why the real bottleneck is no longer model intelligence but deployment onto constrained hardware, and why the future of autonomy may look less like one-off demos and more like Android for every moving machine.We discuss:* Applied Intuition's mission: building physical AI for a safer, more prosperous world, powering cars, trucks, construction and mining equipment, agriculture, defense, and other moving machines* Why physical AI is different from screen-based AI: learned systems can make mistakes in chat or coding, but safety-critical machines like driverless trucks, autonomous vehicles, and robots need much higher reliability* The evolution from autonomy tooling to a broad physical AI platform: starting with simulation and data infrastructure for robotaxi companies, then expanding into 30+ products across simulation, operating systems, autonomy, and AI models* Why tooling companies came back into fashion: Qasar on why developer tooling looked unfashionable in 2016, why Applied Intuition still bet on it, and how the AI boom made workflows and tools central again* The three core buckets of Applied Intuition's technology: simulation and RL infrastructure, true operating systems for vehicles and machines, and fundamental AI models for autonomy and world understanding* Why vehicles need a real AI operating system: real-time control, sensor streaming, latency, memory management, fail-safes, reliable updates, and why “bricking a car” is much worse than bricking an iPad* Physical machines as “phones before Android and iOS”: Peter explains why today's vehicle and machine software stack is fragmented across many operating systems, and why Applied Intuition wants to consolidate the platform layer* Coding agents inside Applied Intuition: Cursor, Claude Code, internal adoption leaderboards, and how AI tools are changing engineering workflows even in embedded systems and safety-critical software* Verification and validation for physical AI: why evals get harder as models improve, how end-to-end autonomy changes simulation requirements, and why neural simulation has to be fast and cheap enough to make RL practical* From deterministic tests to statistical safety: why autonomy validation is shifting from binary pass/fail requirements toward “how many nines” of reliability and mean time between failures* Cruise, Waymo, and public trust: Qasar and Peter discuss why autonomy failures are not just technical issues, how companies interact with regulators, and why Waymo is setting a high bar for the industry* Simulation vs. reality: why no simulator perfectly represents the real world, how sim-to-real validation works, and why real-world testing will never disappear* World models for physical AI: hydroplaning, construction equipment, visual cues, cause-and-effect learning, and where world models help versus where they are not enough* Onboard vs. offboard AI: why data-center models can be huge and slow, but onboard vehicle models need millisecond-level latency, low power, small size, and distillation-like efficiency* Why physical AI is not constrained by model intelligence alone: the hard part is deploying models onto real hardware, under safety, latency, power, cost, and reliability constraints* Legacy autonomy vs. intelligent autonomy: RTK GPS in mining and agriculture, why hand-coded path-following worked for decades, and why modern systems need perception and dynamic intelligence* Planning for physical systems: how “plan mode” applies to robotaxis, mining, defense, and multi-step physical tasks where actions change the state of the world* Why robotics demos are not production: the brittle last 1%, humanoid reliability, DARPA Grand Challenge-style prize policy, and the advanced engineering gap between research and deployment* Applied Intuition's hard-earned lessons: after nearly a decade, Peter says they can look at a robotics demo and predict the next 20 problems the company will hit* Qasar's advice to founders: constrain the commercial problem, avoid copying mature-company strategies too early, and remember that compounding technology only matters if you survive long enough to see it compound* Why 2014 YC advice may not apply in 2026: capital markets, AI company dynamics, and the difference between building in stealth with a deep network versus building as a new founder today* What Applied is hiring for: operating systems, autonomy, dev tooling, model performance, evals, safety-critical systems, hardware/software boundaries, and engineers with deep curiosity about how things workApplied Intuition:* YouTube: https://www.youtube.com/@AppliedIntuitionInc* X: https://x.com/AppliedInt* LinkedIn: https://www.linkedin.com/company/applied-intuition-incQasar Younis:* X: https://x.com/qasar* LinkedIn: https://www.linkedin.com/in/qasar/Peter Ludwig:* LinkedIn: https://www.linkedin.com/in/peterwludwig/Timestamps00:00:00 Introduction: Applied Intuition, Physical AI, and 10 Years of Building00:01:37 Physical AI vs. Screen AI: Why Safety-Critical Changes Everything00:02:51 The Origin Story: Tooling, YC, and the Scale AI Comparison00:05:41 The Three Buckets: Simulation, Operating Systems, and Autonomy Models00:11:10 Hardware, Sensors, and the LiDAR Question00:14:26 The Operating System Layer: Why Vehicles Are Like Pre-Android Phones00:19:13 Customers, Licensing, and the Better-Together Stack00:21:19 AI Coding Adoption: Cursor, Claude Code, and the Bimodal Engineer00:26:41 Verifiable Rewards, Evals, and Neural Simulation00:31:04 Statistical Validation, Regulators, and the Cruise Lesson00:40:25 World Models, Hydroplaning, and Cause-Effect Learning00:43:34 Onboard vs. Offboard: Latency, Embedded ML, and Distillation00:50:57 Plan Mode for Physical Systems and Next-Token Prediction Universally00:53:04 Productionization: The 20 Problems Every Robotics Demo Will Hit00:58:00 Founder Advice: Constraints, Compounding Tech, and Mature-Company Mimicry01:05:41 Hiring Philosophy: Hardware/Software Boundary and Engineering Mindset01:08:50 General Motors Institute, Education, and the Curiosity MindsetTranscriptIntroduction: Applied Intuition, Physical AI, and 10 Years of BuildingAlessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: And today we're very honored to have the founders of Applied Intuition, Qasar and Peter. Welcome.Qasar [00:00:17]: You guys really know how to turn it on to podcast mode. That was, you guys are real pros at this.Qasar [00:00:23]: They were just joking around right before this, and then they flipped it pretty quick.Alessio [00:00:29]: Oh, yeah, it's good to have you guys. Maybe you just wanna introduce yourself so people know the voice on the mic and they'll know what they're hearing.Peter [00:00:33]: Oh, sure. Yeah, I'm Peter Ludwig. I'm the co-founder and CTO of Applied Intuition.Qasar [00:00:38]: And my name is Qasar Younis. I am the CEO and co-founder with Peter.Alessio [00:00:42]: Nice. Can you guys give the high-level overview of what Applied Intuition is? And I was reading through some of the Congress files, when you went out there, Peter, and eighteen of the top twenty global non-Chinese automakers, you two guys, you have customers in agriculture, defense, construction. I think most people have heard of Applied Intuition tied to YC when it was first started, and then you were kinda in stealth for a long time, so maybe just give people the high-level overview of what it is today, and then we'll dive into the different pieces.Peter [00:01:10]: Yeah. So at Applied Intuition, our mission is to build physical AI for a safer, more prosperous world. And so we work on physical AI for all different types of moving systems, everything from cars to trucks to construction and mining equipment, to defense technologies. And we're a true technology company, so we build and sell the technology, and we sell it to the companies that make the machines. We sell it to the government, really anyone that wants to buy a technology to make machines smart.Physical AI vs. Screen AI: Why Safety-Critical Changes EverythingQasar [00:01:38]: Yeah. And I think in the broader AI landscape, a lot of the focus, rightfully so in the last, three years has been on large language models, and so everything fits in a screen. Like, whether it's code complete products or things like that. And what's different about us is we're deploying intelligence onto a lot of things that don't have screens. they're physical machines. There are sometimes screens within the cabin or for example of a car or a truck or something like that, but most of the value we provide is putting intelligence that is in safety critical environments. So that those two words are really important because learn systems can make mistakes if you're asking for, like, some, so something like, “Tell me about these podcast hostsQasar [00:02:28]: that I'm about to go meet.” But you can't do that obviously when you run, like, as an example, we run driverless trucks in Japan right now, as we speak. We can't have errors. Those are L4 trucks. Yeah.Alessio [00:02:40]: Yeah. Was that always the mission? I remember initially, I think people put you and Scale AI very similarly for some things about being kinda like on the data infrastructure side of things. What was the evolution of the company?The Origin Story: Tooling, YC, and the Scale AI ComparisonPeter [00:02:51]: Well, from the very beginning, we always wanted to, really be a technology company that helped generally push forward the industrial sector. And so we started off working in autonomy. Our very first customers were robotaxi companies. And we started off doing a lot of work in simulation and data infrastructure. And then over the years, we've expanded our portfolios. Now we have, over thirty products, and it's a pretty broad technology play within the landscape of physical AI.Qasar [00:03:19]: Yeah, I think the Scale reason is because we're all YC Universe companies. But it was a very different company. Scale, was, is more of a services company, data labeling company fundamentally. We started and still are, do a lot of tooling. So like, you think developer tooling is now in vogue again, thanks to the AI boom. But honestly, ten years ago, it was out of vogue. It w Like, doing a tooling company in 2016, 2017 was not, like, the thing to do because, I don't know if you remember, the VCs generally, their views was that toolings are They're just workflows, and workflows ultimately are not really interesting. And we've gone and come, full circle with that. But when we started the company, our kind of it's kinda like in the periphery of what the company wants to be. It was like, from our earliest days, like, we wanna deploy software on physical machines, like on cars and on trucks and things like that. And obviously, we didn't know that the transformer boom was gonna happen. We didn't know that autonomy systems would become end-to-end. Those things we didn't know. And why that's important when autonomy systems become end-to-end, it is just now those models can be generalized to, multiple form factors. And so back nine, ten years ago, tooling was a great way, and still is a great way to, build the technology and sell technology to our end customers, a lot of them who wanna build this stuff themselves. And so we just offer like a spectrum of solutions from you can just use like one part of a development suite of tools all the way to buying the full thing. The way to think about the company, or at least the way we think about the company is, as Peter said, a technology provider. It's kinda like, what NVIDIA does or what an AMD, but we just don't do chips.Qasar [00:05:06]: We don't do silicon. But we're a technology provider fundamentally. And I think even, we used to joke when we started the company, like, we're not the guys to build, like, Instagram. Like that was just towards That's not our That's just not us in a most fundamental way. IAlessio [00:05:20]: You have thoughts.Qasar [00:05:21]: Yes.Qasar [00:05:22]: Well, it's, it's I mean, I think it's just like what And I mean, we worked on Maps and stuff, Google Maps. Consumer products are extremely difficult for a lot of different reasons. It just, I think doesn't scratch the itch. I think we're like Michigan guys who are kind of more of that traditional engineering kind of a realm, or lineage. we used to jokeThe Three Buckets: Simulation, Operating Systems, and Autonomy ModelsPeter [00:05:41]: I gotta say, though, what was clear ten years ago was that there was so much more that was possible with software and AI in vehiclesPeter [00:05:47]: and that was generally the space that we started in ten years ago.Peter [00:05:51]: And the precise path that we've taken over the years, I think we've been strategic, and we've adjusted to make sure that we're actually building stuff that's valuable to the market. And like, the technology has changed so much. Like our own technology stack has completely changed, I would say, roughly every two years. And so now we've probably done, let's say, four complete evolutions of our own technology stack. And I sort of see that cadence roughly keeping up.Peter [00:06:13]: And so the way even we think about engineering is almost on this two-year horizon, we're preparing ourselves that, hey, like, we wanna invest the appropriate amount, but then also be very dynamic as the research gets published and as our research team figures out new advancements and adapting to that.Qasar [00:06:27]: Yeah. One thing that has been consistent is the type of people we've, we've recruited. It's engineers who are fall into the sometimes very traditional, like, GoogleQasar [00:06:38]: -gen suite, but way different from, other companies. We are hiring folks who really know the intersection of hardware and software, who know really low-level systems. Obviously, traditional ML researchers and folks who've, actually, put ML systems into production. That's been pretty consistent. I think that, like, you look at the mix of our engineering, eighty-three percent of the company is engineering, so it's, like, a giant list.Qasar [00:07:05]: A lot of engineers.Alessio [00:07:06]: Which, by the way, a thousand engineersQasar [00:07:07]: Yeah. A thousand engineers.Alessio [00:07:08]: that's on your website, so I imagine it's up to date.Qasar [00:07:11]: It is, it is up to date, yes. Yes.Alessio [00:07:12]: okay. And then forty-plus founders.Qasar [00:07:15]: Yeah. We would tend to also, This was more luck than strategy. But we've recruited a lot of ex-founders. It's been a great place for founders, YC and non, ‘cause obviously I know a lot of the YC folks. It's kind of like we recruit a lot of Google people.Qasar [00:07:33]: For them to exercise both their technical and non-technical skills because, we're, we're, we're on the applied side. We have a research team that we do fundamental research, we publish, and we've, we've had great traction there. But fundamentally, the business wants to take this intelligence and deploy it into production and there's, like, a certain type of person that's more interested in that.Alessio [00:07:54]: Yeah. You mentioned the tech stack, Peter, so I just wanted to give you some rein to just go into it. I'm interested in where Wayve Nutrition, starts and ends in some sense, what won't you do? What, do you do that's common among all the verticals that you cover?Peter [00:08:10]: There's a few buckets of work that we do, and we've been at this for almost ten years now, so the technology's pretty broad. But we got startedQasar [00:08:17]: Yeah, with a thousand engineers, like, you could work on lots of things.Peter [00:08:19]: There's lots of stuff, yeah, espe-especially with AI tools to help.Peter [00:08:22]: So we got our start in simulation and simulation tooling and infrastructure. And so generally, if you're trying to build a very complex software system that involves moving machines, you need to test that, and the best way to test it is it's a combination of virtual developments, a simulation, and then also obviously real world testing.Peter [00:08:39]: And then there's a very careful process of that correlation between the simulation results and the real world results and ensuring that the simulator is in fact accurate to that. Simulation's a very deep topic.Peter [00:08:49]: We have a whole suite of products in that, and we could talk for many hours about that specifically. But that is one part of what we do as a company. Reinforcement learning as a subpart of that is also super critical. I think a lot of the a lot of the best advancements happening in a lot of these AI systems right now in some way relate to reinforcement learning, and with now we have lots of compute, and you can do tons of interesting things for reinforcement learning. The second bucket of work that we do is on operating systems technology. true operating systems. Like, think about, schedulers and memory management and middleware and message passing and highly reliable networking and data links. Like, the reality is, if you want to deploy AI onto vehicles, you need a really good operating system. And when we were getting deeper into that space, there wasn't really anything that we were happy with.Peter [00:09:39]: Like, things existed, absolutely, and we were using what was available in the market, and as an engineering organization, we roughly realized these things aren't great. We think we can do this better, and so let's, let's build something. And that was then the that was the moment of inspiration that started our operating systems business, which is now a very real business for us. And in order to write and run great AI, you need a great operating system, and so that-that's what got us into that. And then the third bucket that we work on, it's, it's true fundamental AI technology. Models, we do a lot of work in, as mentioned, the foundational research, but then the also the world models and the actual autonomy models that are running on these physical machines, and that's across cars, trucks, mining, construction, agriculture, and defense, and so that's both land, air, and sea.Qasar [00:10:31]: And also, a smaller subsector of that third bucket is the interaction of humans with those machines.Qasar [00:10:38]: So that's a multimodal, experience. Historically, if you're moving a dirt mover or any of these machines, there are, like, buttons you press, whether they're actual physical tactile buttons or something like a touch screen. That's just That fundamentally is changing to where you're just talking to the machine and the machine and you're teaming with the machine.Alessio [00:10:58]: Voice?Qasar [00:10:59]: Yeah, voice, absolutely, yeah.Alessio [00:11:00]: Oh.Qasar [00:11:00]: And also the machine just being aware of who is in the cabin, what their state is. you can think from a safety systems perspective, the most simple version of this is, like, the driver is tired, right? They're, they're if you get those alerts when you're driving your car and saysHardware, Sensors, and the LiDAR QuestionQasar [00:11:15]: -maybe take a coffee break, that take that times, a couple of order of magnitudes up. But this concept of teaming man and machine is important. When you think about running agents or just running, different instances of, Claude and doing work for you in the background, you can take that analogy out, almost copy and paste and put it into, like, a farm, where you have a farmer who's running a number of machines. So where they interact with the machine is where there's maybe a critical decision or a disengagement or something like that, but generally speaking, the agent on the physical machine is running and making decisions on the behalf of the farmer until there's something maybe critical. And that's also what we work on. So that's not pure autonomy. It's a little bit of a mix, but it falls under, autonomy. In the automotive sense, that's typically defined in SAE levels as an L2++ systemQasar [00:12:05]: -with a human in the loop. But just take that idea, to other verticals.Alessio [00:12:09]: Yeah. You've not mentioned hardware at all, like sensors or obviously we you mentioned you don't do chips. I think even in AV there's, like, a big, cameras versus lidars. Like, what are, like, in your space maybe some of those design decisions that you made, and are they driven by the OEM's ability to put things on the machinery? And like, how much influence do you guys have on co-designing those?Peter [00:12:32]: Yeah. So we don't make sensors. Like, we're, we're not a manufacturer. Obviously, we use a lot of sensors in our autonomy products. in terms of what actually goes on the vehicles, we have a preferred set of sensors that we, let's say fully support, and then our customers, they can sort of choose from those. And obviously if there's a very strong opinion on supporting something else, we'll add that to the platform as well. And the lidar question is at this point sort of the age-old,Peter [00:12:59]: topic in autonomy, and the state of the industry right now is lidar is hands down a useful sensor, specifically for data collection and the R&D phase of autonomy development. if you see, for example, a Tesla R&D vehicle, it actually has lidar on itPeter [00:13:17]: to this day, right? In the Bay Area we see these. you'll see, like, Model Ys or Cybercab that have lidars on them just driving around. So it's, it's useful because it gives you per pixel depth information. So if you can pair a lidar with a camerand you can say that, well, this camera's looking this direction, this lidar's looking this direction, and now for each pixel of the camera I can see how far away is that pixel. you can actually then use that as a part of your model training, and then the that depth information then becomes a learned, a learned state of the camera data. And then when you're doing the production system, you can now remove the lidarPeter [00:13:52]: and now you can actually get depth with just the camera. And so that difference between, like, a highly sensored R&D vehicle and then the down-costed production vehicle, we use that across our whole portfolio of products. And of course the end goal is you want super low cost and super reliable.Peter [00:14:08]: And then in certain use cases you have some more, bespoke things. Like in defense as an example, you do things at night oftentimes, and so you care about sensors like infrared, more so than And you don't, you don't wanna be putting energy out, so you don't wanna use lidar or radar.Peter [00:14:23]: but you still need to be able to see at nighttime. So yeah, we work the whole gamut.The Operating System Layer: Why Vehicles Are Like Pre-Android PhonesAlessio [00:14:27]: Cool. So that's kinda like on the hardware level. Then on the OS level, how does that look like? What is, like, unique? my drive- I drive a Tesla. Whenever I drive some other car that has a screen, it always sucks.Alessio [00:14:38]: It's on, like, cheap Android tablet. It's like, it's laggy and all of that. What does the OS of, like, the autonomy future look like?Peter [00:14:46]: When most people, it's really what you just described. When you think about operating system in a vehicle, you're thinking about the HMI, right? The human machine interface, and absolutely that's a an important part of it, but that's actually only one thin layer on top. So when we talk about operating systems for, like, AI in vehicles, there's many layers that go deep into the CPU critical realm and embedded systems, and you're talking about the real time control ofPeter [00:15:13]: let's say the electric motors or the engine and the actuators, and you have different redundancies for different, let's say, the steering actuation in the vehicle. And all of these things, need very core support in the in the operating system. And then of course for autonomy you have real time sensor data that's streaming in, and the latencies there are really important, right? If you try to Imagine you try to run Microsoft WindowsPeter [00:15:35]: like streaming your sensor data in or controlling the vehicle. Like, the latencies are gonna be absurd. Like, you can never do that. And so what's special about what we do is we really have this system level thinking, right? So we're looking at, we care about every performance characteristics of the entire system, and then we also, because we're doing a lot of the software or all of that software, we can fine-tune and control all of those things. So we can very carefully tune in the latencies for every aspect of the system. We can carefully tune in the memory management. We can have the right, fail-safes and fallbacks, for different things. ‘Cause you have to account for what if, what if there is a critical failure? What if there's a cosmic ray that flipsPeter [00:16:14]: a bit in the middle of the processor that causes some, malfunction? And you have to have a fail-safe to all of that, and so the core operating system is a part of that. And then the one last thing, which is a lot less exciting but is, actually a very big topic, is reliability of updates.Peter [00:16:30]: so the I have a Tesla and you get updates fairly frequently, right?Peter [00:16:36]: Once a month. Most companies that are making vehiclesPeter [00:16:40]: are basically never doing updates, and they're And even if they are doing updates, they're usually only updating maybe one module. Maybe they're updating the HMI module. But they're not able to update, let's say, the CPU critical parts of the system.Peter [00:16:51]: You have to go into the dealer for that. And so with our operating system now we can actually enable highly reliable updates of any system in the vehicle, and that's way easier said than done. Like, there's lots of technical, technically deep stuff, in the tech stack to do that in a way that you're not going to accidentally brick a vehicle.Peter [00:17:08]: And right? If, imagine yourAlessio [00:17:10]: That would be bad.Alessio [00:17:11]: Bad.Peter [00:17:11]: Bricking a car is a very expensivePeter [00:17:13]: and honestly, like across the industry maybe one of the most just pure impactful things that we've done is we've just, we're, we're now enabling the industry to actually do software updates.Alessio [00:17:22]: Just to clarify as well, who is the customer for this? Like, I assume a lot of hardware manufacturers have their own firmware, and I'm sure some of them would just have you write it for them because you're experts. And others would have their own. Like, who pays for this? Who invites you into the house? Is it, is it the end user, or is it, is it the manufacturer?Peter [00:17:41]: Yeah. So let me make an analogy firstly on the on the fragmentation of software. So physical machines today are more akin to the state of the phone market before Android and iOS existed, right? So I worked on Android at Google by the way many years ago, and part of the reason that Larry at Google decided to get into Android was they wanted to run Google products on a bunch of phones, and they bought all of these phones from the industry, and it turned out they had like 50 different operating systems on these phones. And it was virtually impossiblePeter [00:18:17]: for Google to make their app run on all 50 devices equally well. And so the solution was, well, actually what if, what if they created-A really great operating system and made it attractive to all of these phone makers, and that was sort of the genesis for what Android was and why Android existed. It was a way for Google to get their products onto really wide diversity of devices. The state of the physical, industry right now, it's a little bit like that. Like, there's yes, these companies have firmware, but they have so many different operating systems, it's so fragmented, and to actually get a modern AI application to run on these vehicles, you actually, you first have to consolidate the operating system, and so that's, that's why we've done that. And then, your specific question was who are our customers? It's, it's, generally it's the companies that are making these machines.Peter [00:19:06]: And we're, we're, we're selling our technology to them to really simplify the architecture and then enable these AI applications to run on them.Customers, Licensing, and the Better-Together StackSwyx [00:19:13]: How much is reusable across? Like, do you have, like, one OS that is just configured for everything, or is there some more customization that is needed?Peter [00:19:22]: Yeah, highly reusable. So the fundamental technology is quite universal, right? So things that we do have to think about though are, like, chipset support. And so if you're, if you're coding, let's say, an LLM and you have start with an assumption that, “Hey, oh, I'm gonna, I'm gonna use CUDA, and I'm gonna run this, on an NVIDIA chip,” then you don't really have to think about the hardware in that sense. Like, you're just, “Okay, I'm just I'm in the CUDA/NVIDIA ecosystem, and I'm, I'm going to use that.” But the hardware, especially in safety critical systems, it's a lot more diverse. There's not one or one or two players. There's a bunch of different chipsets that we have to support. And so our operating system doesn't just run on, like, the equivalent of X86. It has to, it has to run on a number of different architectures from chips from a bunch of different companies. But again, we've been working on this for a long time now, so we have, we have support for all of those chipsets. And then when you want to then run the AI applications, we can then do that reliably across now a variety of providers.Qasar [00:20:19]: And I think that is, like, heavily inspired by Android, right? Android has a huge suite of testing and it's a reliable operating system that runs on thousands of devices. And we think we can, we can do the same in all these physical moving machines, with the difference that we're really in a safety critical realm. Android isn't.Alessio [00:20:40]: So on Android, I don't need to use Gmail, I can use Superhuman. Like, what about your machinery? Like, can people bring somebody else's automation to it, or is it kinda like all-in-one?Qasar [00:20:50]: You have to use us. No. Yeah. we're If, Yeah. Yeah, it's totally open. Yeah.Peter [00:20:56]: Yeah. our philosophy is that we are a technology company, and so we license our technology to customers to use how they want. And so if a customer wants to If they wanna license our autonomy tech and our operating system, then great, we'll license those. If they just wanna license the operating system and then use different autonomy tech, that's fine also, and we have great documentation andSwyx [00:21:17]: Or if they wanna use developer tooling.Peter [00:21:18]: Yeah, exactly.AI Coding Adoption: Cursor, Claude Code, and the Bimodal EngineerSwyx [00:21:19]: It's, like, a better together if, obviously, if you, if they work together. Is it all C++ I assume is with different compile targets?Peter [00:21:27]: We use a lot of C++.Peter [00:21:28]: Rust is sort of a hot, the new hot kid on the blockPeter [00:21:32]: for a bunch of things as well. But yeah, the lower level you get, especially when you get to real-time constraints, you hit C++ at some point, and at some point maybe you work your way into assembly when needed.Swyx [00:21:44]: Oh, damn.Alessio [00:21:46]: I'm curious about the coding agent adoption, just, like, since you're mentioning more esoteric languages. Like, what's the adoption internally? What have you learned?Peter [00:21:55]: Yeah. We use everything. So Cursor was, I think the hottest tool in the company for a good while. Now Claude Code, I think has taken the reign on that. We have a internal leader, leaderboard that we use just to sort of encourage adoptionPeter [00:22:09]: with-within the company. And yeah, it's, they're phenomenally useful. it's, Honestly, we take inspiration from some of those tools also in how we're adapting some of that mindset of thinking to the physical realm. Like if it's so easy to build an app for this or that thing that lives just on a screen, we can We're taking now a lot of the same ideas and applying that to, “Okay, well, if you wanted a physical machine to do something, how easy can we make that, using our own tooling and platform as well?”Alessio [00:22:40]: Are you changing any of, like, the OS architecture, kinda like the way you expose services to, like, be more AI friendly or?Peter [00:22:48]: Yeah, absolutely. The in the early days of our tools infrastructure work, it was a lot about, You had engineers that were experts in certain topics, but the things that you're dealing with, they're oftentimes more mathematical or more abstract, where actually GUI tools are very useful for certain things. Like as an example, we have a product we call Sensor Studio, which is, it helps you design the sensor suite for your autonomous vehicle, whether, again, it could be a car, it could be a drone, could be a mining equipment, could be a robot. And you place sensors in different places. You There's different, There's a library. You can understand what are the trade-offs that you're making in the design of that system, and that was, like, a very, a very GUI intensive, thing ‘cause it's a little more like a CAD tool in that senseSwyx [00:23:37]: YepPeter [00:23:37]: if you've seen CAD tools. Nowadays, though, right, we expose all of the underlying APIs for that and now using, AI agents, you can actually configure a sensor suite with just text and likely reach a better result than you could've through the GUI in the past, and we're taking that thinking now through the whole product portfolio.Swyx [00:23:57]: Another thing I was thinking about is just in terms of, like, AI, adoption, does it change your hiring at least a little bit, or how do you, how do you sort of manage engineers, differently?Peter [00:24:08]: Yeah. absolutely, it does. we, I think like every company in the Valley right now, are evolving our hiring practicesPeter [00:24:16]: because the skills required to be effective are changing so fast, right? you used to really select for just rote implementation ability and now it is more the AI engineer skill set, right? Where it's like, yeah, how to implement, but actually-Just banging out code is no longer the core job, right? It's, it's actually knowing what questions to ask, knowing how to tie, how to tie together these different AI tools. And so the interviews that we give now I think are way harder than they've ever been.Peter [00:24:46]: But we also allow, right, selective use of AI tools to solve the problems. And I think in that you start to see more of a bimodal distribution of engineers, right? You start to see like wow, there's, there's this subset of people that they really get it. Like they're, they're all in and they've, they've clearly invested the hours needed to learn these tools and how to be effective.Peter [00:25:09]: And then there's sort of the group of people that haven't done that, and that the productivity gap is just enormous. And so we're, we're trying to obviously select for the people that are really into this.Qasar [00:25:20]: I first wrote the my AI engineer piece three years ago, and when I first wrote about it, I was like, “Actually, not everyone should be an AI engineer,” ‘cause I think there's a there's an extremist stance where well, every software is an engineer is an AI engineer. And my actual example of people who should not be adopting AI was embedded systems and operating systems, and database people. Are they adopting AI?Peter [00:25:41]: I think it's the classic bitter lesson, topic, which is the Six months ago I would've said the same thing, but it's, it's becoming super useful for every domain.Qasar [00:25:53]: I'm sure.Peter [00:25:54]: Right? Like,Peter [00:25:56]: there was, I think six months ago, or maybe a year ago, if you tried to use, let's say the latest Claude model for writing shaders, GPU shaders, the results were probably underwhelming. And if you use the latest model now to do that kind of task, you're a little bit blown away, like, “Wow, that actually worked. That's amazing.” And we see the same thing in the embedded realm. No question though, especially when you get into safety critical systems, the human validation isPeter [00:26:25]: is 100% key. Like I You're not gonna trust your life to a an AI written software that's, that's not been very carefully, checked by humans. And so I think now the really the challenge is about that appropriate level of human validation for these safety critical systems.Verifiable Rewards, Evals, and Neural SimulationAlessio [00:26:41]: How do you think about, yeah, touching on the simulation side, I think verifiable reward and reinforcement learning is, like, the hottest thing. What have you done internally to build around that? And like, what gives you What makes you sleep at night? Like, if somebody's like, just web coding something or likeAlessio [00:26:57]: wants to try something new, you have like a good enough system. Because I think the opposite is also true, is like if it's super easy to write anythingAlessio [00:27:04]: then it puts a lot of work on like the verifiableAlessio [00:27:07]: side of it. Like, what does that look like for people?Peter [00:27:10]: Yeah. So verifiability, a broader bucket of like evaluations, right? Like how do you evaluate the results that you're, you're getting? I think this is probably the hardest problem right now, because the As the models get better, it can be harder and harder to find the faults on the system.Peter [00:27:29]: And so like the problem of doing proper eval to find those faults, like that problem also keeps getting harder as the models get better. But it's no less important than it's ever been, right? You still there are still going to be edge cases that are not met and whatnot. And so it's, it's a big area of investment for us. On the reinforcement learning topic, the key thing is there's all these new requirements that come to be in the latest generation of these technologies. So for example, end-to-end is the big thing right now in autonomy and physical AI, which is you can now train these models that can effectively take sensor data in and then put control signals out, and get really good results out of that. But the way that you train and improve those models is really different from the previous generations. And so to do reinforcement learning on an end-to-end model, you now need to actually simulate all the sensor data, right? So then this becomes a we call our, work in this neural simulation, but it'sPeter [00:28:26]: think of it like a hybrid of Gaussian, splatting and diffusion methods, and where you really care about performance. Like performance is everything. If you can't do enough simulation fast enough and cheap enough, you actually can't get results that are worthwhile, in the end. It also gets to a lot of our work in embedded systems, which is like performance critical work, and that performance optimization, performance criticality, it carries over to a lot of the model training work. because, like, the only way to make it affordable is it has to be really fast.Qasar [00:28:58]: I think it's worth a few minutes talking about our own, evolving thoughts on verification and validation withinQasar [00:29:05]: kind of, traditional simulators, which are, you can think of like vehicle dynamics or something like that, which you're just taking textbooks and taking those formulasQasar [00:29:13]: and putting them into software, to like now this neural sim/world model universe. I think that's an interesting topic.Peter [00:29:20]: Yeah. So in more traditional development, right, you oftentimes would have, more black-and-white answers to questions.Peter [00:29:28]: And so the in Europe as an example, there's, a regulatory, system, it's called Euro NCAP. It's the European New Car Assessment Program, and as part of that, the vehicles have to pass a bunch of tests, and those tests actually, include, safety systems. So automatic emergency braking for a child that runs in front of a carPeter [00:29:51]: or let's say an occluded child that runs out and you hit it. And so you have You end up with sort of these binary answers of like, well, did the car under test pass this specific test? And there's a very well-known set of test casesPeter [00:30:05]: that the vehicle has to pass. And that was how the industry worked, let's say, until 10-ish years ago. But what's changed now is with these models, everything is statistics, right? Like you no longer have a black-and-white answer, but it's like, well, how many orders of magnitude or how many nines of reliability can I get in the system, and how can I, how can I prove that to be true? And the big unlock honestly for physical AI as an industry is that these models are just becoming much more reliable. Right? Things like things actually work a lot better. It's like the number of nines you can get out of these systems are now good enough that it actually becomes cost effective to really deploy these things. And so the big shift in, so verification and validation has been from a little bit more of a Again the past it was strictly requirements, and are you meeting or not? And now it's more of a statistical, verification and validation case where it's all about how many nines of reliability and meantime between failures, that sort of thing.Statistical Validation, Regulators, and the Cruise LessonSwyx [00:31:04]: And is the target audience regulators or even the customers are yeah, if you I imagine the customers are bought in, and it's mostly regulators that need to be satisfied.Peter [00:31:15]: We do work with the US government, we do work of course with the European governments and the government of Japan, and the government is not like an AI lab by any means.Peter [00:31:25]: So Swyx [00:31:26]: They just care about the outcome.Peter [00:31:27]: They care about the outcome.Peter [00:31:28]: And so we do education, in that regard, and like so sort of teaching about, “Hey, this is how we think validation should be done, and this is an approach that we think is reasonable,” and how to think about like when is a driverless system actually safe enough to go on the roads and that sort of thing. But I wouldn't say that the government is asking for it. It's like we're more teaching the government in that, in that sense. It's honestly, it's more so for our own, our own comfort, right? Like, we want to build very safe systems, and then of course our customers care deeply about that as well. But in that context we're also typically educating our customers.Qasar [00:32:01]: Yeah. Our first, our first core value is on round safety. So I think we can't underline enough that, us also verifying and validating that the systems that we're deploying are safe to us is probably as important as, like, some regulator or a customer saying,Swyx [00:32:19]: Of course. Okay. Yeah.Swyx [00:32:20]: You have to satisfy yourselves.Peter [00:32:22]: As I say, as a whole across the world, regulation oftentimes it's like a almost lowest common denominator. But like, you really have to substantially exceed what the regulators are expecting to make good products.Swyx [00:32:33]: Yeah. One thing I often talk about, I think and I try to make this relatable to the audience also, is Cruise, where they had an accident that basically ended the company. I wonder if people overreact to single incidents, because incidents are going to happen regardless, right? ‘Cause it's a statistical thing, but as long I don't know if regulators understand that, you cannot extrapolate from a single incident, but we do because that's all we have to go on. And your sample sizes are necessarily gonna be lower than, I don't knowSwyx [00:33:00]: consumer driving.Qasar [00:33:01]: Yeah. I think the Cruise example wasn't a technology failure. there was The real, compounding issue there was just how did the company talk to the regulators and what was their kind of behavior, and I think that became more of the issue. If you look,Peter [00:33:19]: It isn't It definitely was a technology failure, but it was made much worse by theSwyx [00:33:23]: Put the car back on the woman.Qasar [00:33:25]: Yeah. And let me put it another way. There is a version where Cruise still exists.Swyx [00:33:29]: right. Right.Qasar [00:33:30]: Right. It'sSwyx [00:33:30]: It was like the last strawQasar [00:33:31]: ItSwyx [00:33:31]: in like a long chain ofSwyx [00:33:33]: like issues.Qasar [00:33:33]: So do you feel like ATG had that horrific accident or someone actually dying, because, that was a homeless person crossing the street? So yeah, I think we can't understate enough that ultimately, like, statistical validation of something, that's one part of it, but it's not the only part of it. Like, consumer and let's say, mainstream adoption of these technologies is also gonna be part of that conversation. I think companies like Waymo are doing a lot of service positively to the industry in the sense of they're, they're setting a high benchmark and they're showing, kind of in a very responsible way how to, how to deal with these. There have been Waymo incidences as well. They've just not been as significant as the Cruise one that you mentioned. But yeah, so I think you'll just continue to see that. I think probably the long term question is really gonna be, again, around Like it is very clear humans are way worse drivers statistically.Qasar [00:34:29]: Like, there's no, there's no debate. And so at what point But we're emotional animals.Swyx [00:34:34]: Yeah. So my thing is, like, we have to get to a point as a society where we accept horrific accidents that would never happen by a human because statistically we understand that it is safer overall. In the same way that planes, they're safer, than I think they're the safest mode of transport that we have.Qasar [00:34:50]: Yeah. it's more dangerous to drive to the airport than it is to get on a flight.Qasar [00:34:53]: So if you're everQasar [00:34:54]: if you're ever getting nervous about getting on a plane, just think “I just gotta get to the airport.”Swyx [00:34:58]: Yes, we're flying.Qasar [00:34:59]: If I get to the airportQasar [00:35:00]: I'll be good.Swyx [00:35:00]: But then it's, planes also concentrate the tail risk if planesQasar [00:35:03]: Yeah. AndPeter [00:35:04]: And I was, I don't think we honestly have to worry about there ever being, accidents from these systems that are like much worse than what humans would cause, ‘cause humans do terrible things.Peter [00:35:14]: Like, people fall asleep at the wheel all the time.Swyx [00:35:16]: I have.Swyx [00:35:17]: Like, I'll call, I've been a drowsy driver.Peter [00:35:19]: Kinda drunk drivers, and that'sPeter [00:35:20]: that's the extreme end of the example. But these AI systems, you have redundancies, you have fallbacks. Like, there's many things have to go wrong for there to actually be a something catastrophic because there's, there's so many, fallbacks that these systems have.Alessio [00:35:36]: your simulation is like so vast because there's so many use cases. What are, like, maybe things that worked in a simulation and then you put it out and it's like, “F**k, this isAlessio [00:35:45]: this just did not work at all?”Peter [00:35:47]: Yes.Alessio [00:35:47]: IsPeter [00:35:47]: That's maybe a bit of a misconception, about simulation there. So let me go a little bit, more technical on this. So at first go, no simulation is going to represent the real world. There's always a process of this, sim to real matchingPeter [00:36:02]: where you actually, you need the real world feedback to basically feed into the parameters that are being used in the simulator, and you have to do that, it's like this validation flow, a number of times until you can get some confidence that, like I think the simulator is now accurately representingPeter [00:36:19]: what's gonna happen in the real world. Now, if you have a situation where you've done that full validation and you thought that it was accurate and then there's something different, those are much trickier cases, and that's, that absolutely can happen, but really I think the validation process is a really important part. You can never skip the simulation validation process, like where you're actually ensuring that, hey, the actual, my sim to real gap here is small enough that I can trust these simulation results. And there's, there's so many fun things that you can do when you get into it. Like, I'll, I'll give one fun example that came up recently is like in these humanoid robotics, systemsOverheating actuators is a real problem, right? So obviously phenomenal demos. IPeter [00:37:01]: The most amazingAlessio [00:37:02]: For 10 minutes.Peter [00:37:03]: The most amazing I can get. I love, I love watching robots do acrobatics like everybody but the these systems actually overheat, right? If, like, And one of the ways you can use simulation though is you can actually have that, the temperature of those actuators be one of the parameters that's representedPeter [00:37:18]: in the simulation. And if you're doing reinforcement learning over a certain task, then the robot can actually adjust its motions in the simulation to account for the fact that, oh, it knows that as it's moving, it's actually beginning to overheat this motor. But if you didn't have that parameter of, let's say, the heat of that motor represented in the simulation initially, then your RL policy might It will disregard that. And now you run that on the robot and the robot will overheat and fail.Alessio [00:37:43]: I guess the question is, like, how do you have all of these parameters taken care of while also understanding the deployment environment? Like, temperature is like a great example, right? WellAlessio [00:37:53]: why did you make my robot worse when it runs in like a freezer?Alessio [00:37:57]: So it actually shouldn't worry about that. it's like, yeah, how do you design these simulations?Peter [00:38:02]: This is honestly the This is what makes simulation so hard, right? it's because you Simulation is fundamentally about you're trying to optimize the development of a system, right? Like, how can I build this system faster and better and cheaper and what are all the levers that I have to actually accomplish that? And because simulation's just a software program, you can, you can change it a lot more easily than you can hardware systems. And then what's particularly awesome about the let's say, world models and using that as a part of simulation is now the simulation doesn't just scale with, let's say, adding new math equations inPeter [00:38:36]: but we can actually scale the simulation environment now with additional real world data and that also unlocks a whole new field of robotics.Qasar [00:38:46]: There is a meniscus line where you cross where still doing real world testing is better. there's, in this, sim-to-real gap, you can reproduce reality at exceedingly expensive costs and this So nothing is free. So really you have to you're finding that line where you're getting great performance, you're getting great feedback, whether it's on the training side or on the eval side, but it's way cheaper than doing it in the real world. At some point it, that doesn't make sense. And so even, from our earliest days in autonomy, our view was you're still gonna do real world testing. You There's, there's not, there's not this, magical land where you're not gonna do that. And maybe even like a more nuanced version of this in like traditional software development is, most of your testing for software in a vehicle, 95% of that can be like traditional CI/CD kind of, flows that you would have in traditional web development. But once you have Now you, let's say you have a truck. Well, you can do like 4% of those in like a rig which has all the components, the electrical and electronics of a truck, but doesn't have, it doesn't have the tires and it doesn't have the And then you have the 1%, which is actually the vehicle. There's something There's a similar analogy in terms of using simulation for intelligent systems. You can do a lot in a simulator, but in using world models, but ultimately it's, it's physical AI. So you're gonna deploy it on physical machines andQasar [00:40:17]: the freezer example comes to, comes to light.Alessio [00:40:20]: The world model thing has been to me the hardest thing toAlessio [00:40:22]: wrap my head around. Like we have Faith Eliyon on the podcast.World Models, Hydroplaning, and Cause-Effect LearningQasar [00:40:25]: We've been doing a small series with like another Intuition company, General Intuition as well.Qasar [00:40:31]: yeah, and I mean, lots of, lots of coverage on NeRFs and yes.Alessio [00:40:34]: Yeah. It feels like we talk with about, the heliocentric system, right? It's like in a world model, if you just feed visual data, the model might learn that the sun spins around the Earth. It makes sense, right? And it's like, well, not really. And I think what are like some of these other things that like hydroplaning is one thing I think about, is like can a world model understand hydroplaning and like what amount of water like causes it to happen? And it's like, yeah, to me it's like I don't understand how you guys do it. I guess it's like the real thing is like when you're doing both cars and the highway in Japan versus the excavator in a mine in,Qasar [00:41:13]: ArizonaAlessio [00:41:13]: wherever you're Arizona, wherever you're deploying them.Alessio [00:41:15]: How much of it are you relying on the world models to like generate the simulations for you and then try and close the gap after versus like giving the world models as a tool to your engineers to like curate the simulations if that makes sense?Peter [00:41:28]: Yeah, totally. So yeah, I can say at a pure engineering level, I think if you're hoping to do real world deploys and you're purely relying on a world model approach, you probably won't get to something that works, before you go bankrupt. So there is just a very practical mindset of like, world models are amazing and they're extremely useful for a lot of use cases, but there are a lot of other things that you need to do to actually get something started and something deployed and working. most fundamentally, world models are all about It's understanding the world, but also understanding what's going to happen. It's like the cause-effect relationship.Peter [00:42:01]: Right? And so like it, right, if you have a take some sort of construction tool, and that construction tool is gonna be doing some work on the Earth in some way, it's gonna be moving earth, the world model needs to understand that cause-effect relationship. Like, okay, when I, when I take this material from here and put it over there and now I have things that are over here and not over there anymore and that cause-effect, relationship. data obviously is a is a big problem. The hydroplaningPeter [00:42:26]: one is actually a really great example because it's actually quite non-obvious sometimes. Right? It's like, well, it's, it's raining and well this road, has, let's say the appropriate curvature to it so the water is running off the road and cars are driving faster here and then you approach a road that's very flat and water is now puddling on that road and all of a sudden cars are driving slower because when they were driving faster they were starting to lose control. And there are a lot of visual nuance, very nuanced visual cues in the scene and so I do think in the world model concept there's a good chance that the model actually would learn that you should just drive slower when these visual cues exist, and that's obviously the beautiful-The beauty of, these kinds of models where they just, they learn these non-obvious things.Swyx [00:43:14]: It doesn't need to know about hydroplaning to know that it needs to drive slower.Peter [00:43:17]: Yes.Swyx [00:43:17]: I guess it's Yeah. I wanna ask questions about, also deploying models. I presume, like, you use a lot of these world models for training data and simulation, but what about deploying it onto the systems in production? Presumably you have you have, like, GPUs on deviceOnboard vs. Offboard: Latency, Embedded ML, and DistillationSwyx [00:43:36]: but they're I keep saying on device. What's the what's the right term for that?Peter [00:43:40]: On machine.Swyx [00:43:41]: On machine.Peter [00:43:41]: Or embedded, yeah.Swyx [00:43:42]: Yeah. What is the embedded world like? because for people who are not used to that world, this is very alien.Peter [00:43:49]: Yeah. So it's actually We call it onboard and off board.Peter [00:43:52]: So like, onboard software and off board software.Peter [00:43:54]: And the great thing about off board software is you don't have to care about time, and you can run really large models, right? So you can, you can say, “Well, this model, I don't care if it takes one second for it to give me a result or 10 seconds for it to give me a result, because we have time.” And the models can be really big, and they can run, in a data center or on a on a huge GPU and you can obviously have distribute to compute, et cetera. But onboard you don't have any of those benefits. You're like, “Well, I need I have this many milliseconds where I need an answer from this model.” And so a lot more of the energy then is about, think of it more like distillation and it's like truly efficiency and like, literally every fraction of a millisecond counts. And you can't have a situation where the model takes too long because then the vehicle can't actually function.Peter [00:44:42]: And so you can, you can still use a lot of the same techniques, and the models themselves you can think of as like a derivative of larger models that you can run offline, and then you're, you're trying to just get a model that is still performs really well but it's, it's a it's smaller, small enough version that you can then run on this embedded system where you care about latency and power.Qasar [00:45:03]: Yeah. And I think like, the broader point I think which, maybe is not obvious but it's worth saying is in physical AI world, we're not really constrained right now by, like, the intelligence of the models. It's actually what Peter's talking about, it's actually deploying them inSwyx [00:45:19]: The hardware they give you.Qasar [00:45:21]: Yeah. On the hardware you give you.Qasar [00:45:22]: And so And there's just a reality is of safety critical systems. So those end up being the your limiting factorsQasar [00:45:29]: rather than, let's say, a limiting factor for, a foundation model companyQasar [00:45:34]: is gonna be just capital maybe or researchers.Qasar [00:45:38]: So we're, we're in that way dealing with, for us as people who kind of come in that realm with like a very interesting Those constraints force creativity.Swyx [00:45:47]: And I imagine, nobody was deploying or giving you the hardware for transformers back in 2018, whatever, but now they are. What's the evolution like? just peel back the curtains a little bit.Peter [00:45:59]: Yeah. Transformers first off, I think the paper was originally published in 2017.Swyx [00:46:02]: 2017.Swyx [00:46:02]: So there's no time.Peter [00:46:04]: And ISwyx [00:46:05]: But I'm just saying I guess I'm saying, like, embedded ML systems usually, like, a lot less parameters, a lot less compute, and now, like, orders of magnitude more.Peter [00:46:14]: Yeah. absolutely. what I was gonna say though was I think in the in the original paper in 2017, maybe it's in the last paragraph, somewhere in the paper they talk about, like, “Oh, by the way, this technique might be useful for, like, images and videos as well.”Peter [00:46:30]: These last subjects.Peter [00:46:31]: And it took a few years for that impact to really hit. But like, now, we're seeing transformers are everywhere.Swyx [00:46:39]: Yeah. Vision transformers.Peter [00:46:40]: And then then the compute just keeps getting better and better. But you do have this fundamental trade-off, right? It's like you have power, you have cost, and performance and like, getting the right, getting the right mix of those things in an embedded package that can also be, like, shaken and baked in all thePeter [00:47:00]: conditions that these things have to have to operate in. But yeah, I think that they're only going to keep getting better and so we also try to plan our strategy understanding that, we know the rate of improvements of these systems.Swyx [00:47:11]: Yeah. So like, Google just released the Gemma 2B modelSwyx [00:47:15]: that effective 2B model. Is that useful to you guys or is that too big?Peter [00:47:18]: You can run that model on an embedded system, definitely.Peter [00:47:21]: the So yes, it's, it's useful in that regard. The bigger question is, like, what do you use it for in an embedded system? Like, you actually need to customize it quite a bit to make it useful for something. But yeah, you could run a two billion parameter model, definitely.Swyx [00:47:35]: It also interesting, like, what percent is a custom ML model that only does that thing versus a generalist LLMSwyx [00:47:41]: which probably is not that useful actually for your context.Peter [00:47:46]: Like, you, like, you can imagine different use cases, right?Peter [00:47:48]: So theSwyx [00:47:49]: The voice stuff, yes.Peter [00:47:49]: Yeah, the voice test. Totally, yes.Peter [00:47:51]: So for the actual, autonomy elements, that's 100% in-house. We do every bit of that, the data simulation, the model, everything. But when you get into the more generic use cases like voice or voice assistant kind of thing, that's where these more generalist models like Gemma actually can be quite, can be quite useful.Swyx [00:48:09]: Yeah. And then there's also obviously a trade-off between, like, what percent must you do on machine, versus just call home.Peter [00:48:16]: Yeah. It's all about latency.Swyx [00:48:17]: Latency.Peter [00:48:17]: It's all about latency. Yeah.Swyx [00:48:18]: Yeah. Well, like, I think actually in a lot of contexts, especially in the US, you can just have a connection to the web.Qasar [00:48:26]: Yeah. I think though most of our universe is everything has to be fairly, embedded and local because just the nature of Even in the US there's a lot of likeSwyx [00:48:39]: PatchinessQasar [00:48:40]: don't haveQasar [00:48:41]: have coverage, right? And if you look at, like, the old world of autonomy within mining, which is, like, long before transformers and kind of, neural networks, in the like CNN and kind of a universe, they were really just hand-coded, systems. They were just like, this machine is gonna run to that place with thisPeter [00:49:03]: That was our GPS, like very accurate GPS.Qasar [00:49:05]: Yeah. And so that worked, and that worked for 20 years, so why would we actually need to use transformers or kind of more modern end-to-end systems? Mainly because you can only really run a path and run backwards. That provided a lot of value, but m-Not as much as you get when the machine is actually intelligent. It's, it's seeing, it's perceiving, it's acting in a dynamic world.Alessio [00:49:28]: I looked up RTK, real-time kinematic, one to two-centimeter accuracy.Qasar [00:49:32]: Yeah. Fantastic. But the and fantastic in faraway lands where there's not gonna be cell phone coverage.Peter [00:49:39]: Yeah, so it's widely used on the legacy mining and agricultural autonomy systems today. So like, for example, a combine that can be precise within one or two centimeters as it's driving down the field, they use RTK.Qasar [00:49:53]: Yes.Peter [00:49:53]: But it's, it's expensive.Qasar [00:49:54]: Yeah. And it's, it's, it's autonomy, but it's not intelligent in the way that I think all of usQasar [00:49:58]: if in twenty-six we'd be talking about intelligence.Alessio [00:50:00]: In one of your blog posts, you mentioned research on large scale transformers that are similar to those doing modern generative AI. What are, like, the big differences other than, “You're absolutely right. I should steer the car, so you probably wanna remove that?”Peter [00:50:14]: We have a diversified bet strategy internally, and the reason we've done that is because we operate in now a bunch of industries, a bunch of geographies, and each of the approaches has, obviously a different risk to them.Peter [00:50:27]: And so like, we're not going to put all of our eggs in a single basket for a single approach because that approach may no

Somewhere right now, a kid is kicking a ball in the street while a stadium across the world is holding its breath for a final-second win. We love sports because they create instant shared meaning, but the part most fans never see is the structure that makes those moments travel, repeat, and endure. For World IP Day 2026, we're celebrating “IP and sports” with a playful challenge that lands on a serious point: intellectual property is what helps sport scale.We break down the real sports business engine behind broadcasting rights, sponsorships, merchandising, and the rising value of sports data. Then we put the ideas to the test with “Who Wants To Own The Stadium,” a quick game that connects familiar examples to the core IP tools: patents, trademarks, copyright, licensing, and industrial design. Nike Flyknit shows how a patented invention can become a platform across product lines. The Nike swoosh shows how a trademark becomes trust, culture, and belonging. Madden NFL shows how copyright and licensing can turn a league into interactive entertainment. Air Jordan 1 shows how product design can become a collectible icon and a long-term asset.By the end, we tie everything together into a practical takeaway for founders, creators, lawyers, and curious fans: sports value is built on more than performance, and good IP strategy helps innovation travel, brands grow, and creators get rewarded. If you enjoy plain talk about intellectual property and sports law, subscribe, share the episode with your network, and leave us a review so more listeners can find Intangibilia.Send us Fan MailCheck out "Protection for the Inventive Mind" – available now on Amazon in print and Kindle formats.The views and opinions expressed (by the host and guest(s)) in this podcast are strictly their own and do not necessarily reflect the official policy or position of the entities with which they may be affiliated. This podcast should in no way be construed as promoting or criticizing any particular government policy, institutional position, private interest or commercial entity. Any content provided is for informational and educational purposes only.

April talks about licensing services as an entrepreneur, certification programs, packaging programs, and much more! April Beach is the founder of The SweetLife® Company, and multiple other brands, an award-winning podcast host, lifelong lifestyle entrepreneur, nonprofit founder, and business advisor to leaders.

Game Pass just got cheaper, China is rewriting the mobile market, and Ubisoft ships one of the most polished mobile games in years.Topics Covered:● Xbox drops Game Pass Ultimate by 23% and pulls Call of Duty from day one, but none of it actually fixes what's broken● Chinese, Hong Kong, and Singapore publishers now own 30% of Western mobile revenue, up from 16% in 2019● Pokémon Go surged 52% in downloads during its 30th anniversary, and why it's bigger than just one marketing event● Why the US should stop competing on mobile content and start owning platforms like Roblox, Steam, and Unreal instead● Division Resurgence is a genuinely great game with no audience in the West and one last hope, China● Disney quietly pulls 30 licensed games from Steam with no explanationCHAPTERS:01:56 Phil's China Trip Takeaways04:19 Joachim Shoutout and Sponsor06:52 Xbox Game Pass Price Cut News10:55 Call of Duty Math Debate15:52 Mobile Market Q1 Snapshot19:53 Top Games and Pokemon Spike22:49 China Dominance and New Hubs36:43 EU: Stop Killing Games41:21 Does Preservation Matter43:55 Division Resurgence Numbers45:18 Why Shooters Need China49:42 PC Port and Controls51:02 Mobile vs Console Culture53:29 What Makes a Platform54:16 Mods Create Mega Hits59:21 Japan Builds Game IP01:01:53 Licensing vs Hybrid Bets01:03:09 Disney Steam Purge01:04:01 Hasbro Hybrid Critique01:06:33 Final Wrap and Thanks

This episode is presented by Create A Video – A case of an entrepreneur crossing the state licensing board and the need for the UNC system to help train the state's growing workforce. | Donna King fills in for Pete.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-pete-kaliner-show--6946691/support.Subscribe to the podcast All the links to Pete's Prep are free!Get exclusive content here!Media Bias Check: GroundNews promo code!Advertising and Booking inquiries: Pete@ThePeteKalinerShow.com  

Kelle Sparta hosts a sacred business calibration with Heather Carroll, a somatic breath, movement, and tapping practitioner and creator of the Emerald Soul method, who is facing a revenue drop and a looming pivot. Heather shares a long-held but hard-to-discuss calling to channel divine feminine energies connected to figures like Mary Magdalene, Persephone, Isis, and Celtic lineage guides. Kelle advises making channeling the primary brand—positioned as a Divine Feminine Channel—with optimized YouTube and a podcast, and all other offers as offshoots. This sacred business calibration reveals how to pivot fast, attract aligned clients, and rebuild income as a spiritual entrepreneur—without abandoning your gifts.00:00 Welcome and Setup00:21 Meet Heather and Emerald Soul01:25 Why a Pivot Now03:17 The Hidden Calling06:13 Lead With Divine Feminine07:48 YouTube and Podcast Strategy08:53 Brand Umbrella and Offers12:21 Audience and Spiritual Depth14:24 Slow Down and Trust15:22 Traffic and Conversion Basics18:02 Offer Ladder and Scheduling21:41 Wrap Up and Where to Find HerKeywords:spiritual entrepreneurspiritual coachingdivine femininebusiness pivotspiritual business growthspiritual business coachenergy healing businesschanneling divine femininespiritual awakening businessbuild a spiritual businessspiritual entrepreneur strategyhealing business marketingonline spiritual businesssoul purpose businessintuitive business growthspiritual leadershipdivine feminine embodimentspiritual business tipsget clients spiritual businessspiritual business incomeTo learn more about Sacred Profits, Join Here: https://learn.kellesparta.com/sacredprofitsJoin the community on YouTube: https://www.youtube.com/@KelleSpartaIf you would like to learn more please book a Discovery Call here: https://kellesparta.com/discovery-call/Licensing and Credits:“Spirit Sherpa” is the sole property of Kelle Sparta Enterprises and is distributed under a Creative Commons: BY-NC-ND 4.0 license. For more information about this licensing, please go to www.creativecommons.org. Any requests for deviations to this licensing should be sent to kelle@kellesparta.com. To sign up for, or get more information on the programs, offerings, and services referenced in this episode, please go to www.kellesparta.com

From the time he murdered his parents as a teenager until his death under an assumed name, William Les Arnold's life intersected with several other people and it left many of them wondering who was he, really?This case is *solved*A huge THANK YOU to this month's sponsors:Refresh your spring wardrobe with Quince. Go to quince.com/crimelines for free shipping and 365-day returns.Start your detective work today at Newspapers.com! Go to Newspapers.com/truecrime and use promo code “CRIMELINES” for 20% off a subscription — and let the past tell its story.Events:AdvocacyCon September in Albuquerque: https://www.advocacycon.com/ November in Costa Rica: https://trovatrip.com/trip/central-america/costa-rica/costa-rica-with-josh-hallmark-nov-2026 Support the show!Get the exclusive show Beyond the Files plus Crimelines episodes ad free onSupercast: https://crimelines.supercast.com/Patreon: https://www.patreon.com/crimelinesApple Subscriptions: https://podcasts.apple.com/us/podcast/crimelines-true-crime/id1112004494 For one time support:https://www.basementfortproductions.com/supportLinks to all my socials and more:https://linktr.ee/crimelinesSources:2026 Crimelines Podcast Source ListTranscript: https://app.podscribe.ai/series/3790If an exact transcript is needed, please request at crimelinespodcast@gmail.com Licensing and credits:Theme music by Scott Buckley https://www.scottbuckley.com.au/Cover Art by Lars Hacking from Rusty HingesCrimelines is a registered trademark of Crimelines LLC.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In 1969, Joan Robinson Hill died of an infection of unknown origin. Her father was sure it was murder and, due to pressure, charges were eventually filed. But what happened next muddied the water more than it already was in this case of unknowns. This case is *unsolved/disputed*A huge THANK YOU to today's sponsors:Refresh your spring wardrobe with Quince. Go to quince.com/crimelines for free shipping and 365-day returns.Start your detective work today at Newspapers.com! Go to Newspapers.com/truecrime and use promo code “CRIMELINES” for 20% off a subscription — and let the past tell its story.Eventshttps://trovatrip.com/trip/central-america/costa-rica/costa-rica-with-josh-hallmark-nov-2026 Support the show!Get the exclusive show Beyond the Files plus Crimelines episodes ad free onSupercast: https://crimelines.supercast.com/Patreon: https://www.patreon.com/crimelinesApple Subscriptions: https://podcasts.apple.com/us/podcast/crimelines-true-crime/id1112004494 For one time support:https://www.basementfortproductions.com/supportLinks to all my socials and more:https://linktr.ee/crimelinesSources:2026 Crimelines Podcast Source ListTranscript: https://app.podscribe.ai/series/3790If an exact transcript is needed, please request at crimelinespodcast@gmail.com Licensing and credits:Theme music by Scott Buckley https://www.scottbuckley.com.au/Cover Art by Lars Hacking from Rusty HingesCrimelines is a registered trademark of Crimelines LLC.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

When Joan Robinson Hill died in 1969, her father was sure foul play was involved even after multiple autopsies ruled it was natural causes. The state ended up charging her husband under a rarely used law, but he was never convicted after he, too, ended up dead. Going on 60 years later, people still ask if Joan Hill was murdered and who was behind John Hill's death?This case is *unsolved/disputed*A huge THANK YOU to today's sponsors:Refresh your spring wardrobe with Quince. Go to quince.com/crimelines for free shipping and 365-day returns.Start your detective work today at Newspapers.com! Go to Newspapers.com/truecrime and use promo code “CRIMELINES” for 20% off a subscription — and let the past tell its story.Eventshttps://trovatrip.com/trip/central-america/costa-rica/costa-rica-with-josh-hallmark-nov-2026 Support the show!Get the exclusive show Beyond the Files plus Crimelines episodes ad free onSupercast: https://crimelines.supercast.com/Patreon: https://www.patreon.com/crimelinesApple Subscriptions: https://podcasts.apple.com/us/podcast/crimelines-true-crime/id1112004494 For one time support:https://www.basementfortproductions.com/supportLinks to all my socials and more:https://linktr.ee/crimelinesSources:2026 Crimelines Podcast Source ListTranscript: https://app.podscribe.ai/series/3790If an exact transcript is needed, please request at crimelinespodcast@gmail.com Licensing and credits:Theme music by Scott Buckley https://www.scottbuckley.com.au/Cover Art by Lars Hacking from Rusty HingesCrimelines is a registered trademark of Crimelines LLC.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.