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Fresh roti is simple to eat and hard to automate. Komal Kashiramka is building Poshan, a robotics company developing next-generation kitchen appliances, starting with an automatic roti maker designed to create soft roti that lasts for hours. Komal shares how she moved from Microsoft and Meta into entrepreneurship. Why hardware requires a different level of patience than software and what founders can learn from customer discovery, AI, parenting, and building something people will trust in their kitchens.  We also discuss:  • Why soft roti is the real product challenge  • Building a hardware company in the R&D phase • What AI changes for junior engineers • Why fundamentals still matter in software • Leaving corporate life without having everything figured out • Startup 425 and Seattle Public Library business resources • Raising kids while building a company • RotiTales and making roti easier to understand  Komal  links Poshan Website: https://poshan.us/ Poshan LinkedIn: https://linkedin.com/company/poshan-inc RotiTales Website: https://rotitales.com/ RotiTales Instagram: https://instagram.com/rotitales RotiTales LinkedIn: https://www.linkedin.com/showcase/rotitales/ RotiTales Facebook: https://www.facebook.com/rotitales RotiTales YouTube: https://www.youtube.com/@rotitales Jason links Jason's Linktree: https://linktr.ee/admin Jason's LinkedIn: https://www.linkedin.com/in/jasoncavness/ YouTube Channel: https://www.youtube.com/channel/UCGsw6kzZE40sSUZgoStVaJw?sub_confirmation=1  

Harry Massey is a Bioenergetics Pioneer, bestselling author, award-winning filmmaker, and Quantum Wellness Visionary whose life's work was forged through one of the most unlikely paths to healing imaginable. After a catastrophic mountaineering accident left him bedridden for nearly a decade, Harry discovered firsthand what happens when every health system fails you — Western, holistic, and functional alike. Determined to survive, he turned to physics. Through his research, Harry co-founded one of the world's first R&D companies in bioenergetics and spent two decades building technology that could measure, manage, and master the human energy field. Today, Harry is the Founder & CEO of Energy4Life and the visionary behind some of the most groundbreaking bioenergetic technologies in the world — including the miHealth Device, the Bioenergetic Voice Scan, and the GEM Wearable, an AI-powered device that detects and corrects emotional energy imbalances in real time.  Beyond his work as an entrepreneur and inventor, Harry is a bestselling author and the writer, director, and producer of several documentaries, including the award-winning film The Living Matrix. His latest film is set to release Summer 2026 — a timely and powerful exploration of what it means to heal at the energetic level. In this episode, Tara and Harry Massey dive into bioenergetics, infoceuticals, and wearable tech that aim to restore "energy for life" by imprinting corrective information into the body's energy field.   RESOURCES: Learn more about Harry Massey here: https://e4l.com/ Instagram: @official.energy4life Get 10% off Peluva minimalist shoe with coupon code COACHTARA here: http://peluva.com/coachtara   CHAPTERS: 00:00 Bioenergetics, infoceuticals and Harry intro 00:51 Why Tara is open but skeptical about energy tech 03:53 Sponsor: Peluva minimalist barefoot shoes 07:00 Interview begins: What is Energy for Life? 09:17 Physics over chemistry and the body's energy control system 12:21 Vitality equation: information × voltage ÷ resistance 14:19 Trauma, ACE score and emotional energy drain 18:44 Inside the app: tongue, face, voice, labs and AI coaching 21:55 FIELD model: functional, integrative, energetic, longevity, direction 24:45 Reading tongue, face and voice for organ stress 28:18 Infoceuticals as optimal blueprints for cells and tissues 33:40 Harry's Addisons story and meeting Peter Fraser 37:43 Mapping the body field via resonance and photon exchange 40:48 Imprinting information into water with lasers and devices 42:18 Homeopathy history, water memory and modern infoceuticals 45:13 Quantum view of reality: information structuring energy 47:40 Films, books, Energy for Life and where to learn more    WORK WITH TARA: Are You Looking for Help on Your Wellness Journey? Here's how Tara can help you: TRY TARA'S APP FOR FREE: http://taragarrison.com/app INDIVIDUAL ONLINE COACHING: https://www.taragarrison.com/work-with-me CHECK OUT HIGHER RETREATS: https://www.taragarrison.com/retreats   SOCIAL MEDIA:  Instagram @coachtaragarrison TikTok @coachtaragarrison Facebook @coachtaragarrison Pinterest @coachtaragarrison   INSIDE OUT HEALTH PODCAST SPECIAL OFFERS: ☑️ Upgraded Formulas Hair Test Kit Special Offer: https://bit.ly/3YdMn4Z ☑️ Upgraded Formulas - Get 15% OFF Everything with Coupon Code INSIDEOUT15: https://upgradedformulas.com/INSIDEOUT15 ☑️ Rep Provisions: Vote for the future of food with your dollar! And enjoy a 15% discount while you're at it with Coupon Code COACHTARA: https://bit.ly/3dD4ZSv   If you loved this episode, please leave a review! Here's how to do it on Apple Podcasts: Go to Inside Out Health Podcast page: https://podcasts.apple.com/us/podcast/inside-out-health-with-coach-tara-garrison/id1468368093 Scroll down to the 'Ratings & Reviews' section. Tap 'Write a Review' (you may be prompted to log in with your Apple ID). Thank you!

In this episode, we welcome Alex Boekelheide from Northville, South Dakota, a fifth-generation farmer passionate about stewardship, continuous improvement, and preparing his operation for future generations. Alex shares the story of his family farm, the responsibility that comes with carrying on a legacy, and the lessons he's learned working alongside his father while transitioning leadership responsibilities to the next generation. The conversation dives into: Growing up on a fifth-generation farm Leadership lessons learned from family and mentors Why succession planning should start earlier than most farms think The value of advisory teams and outside expertise Building a resilient operation through crop diversity Incorporating oats and cover crops into a corn-soybean rotation Soil stewardship and conservation-focused farming Drainage tile, salinity management, and improving productivity Farm marketing strategies and working with trusted advisors Technology adoption and equipment decisions The importance of transparency when preparing the next generation to farm Alex also shares how Farm4Profit episodes featuring Onshore Advisors and BOA Safra inspired him to explore opportunities that ultimately generated substantial value for his operation through R&D tax credits and fertilizer tax programs. He walks through his experience, the process, and why surrounding yourself with knowledgeable experts can help uncover opportunities many farmers overlook. Most importantly, this episode is a reminder that successful farms aren't built by knowing everything—they're built by continuously learning, asking questions, and surrounding yourself with great people. Whether you're focused on succession planning, conservation, profitability, or simply becoming a better operator, this conversation is packed with practical insights and real-world experiences from a farmer who is intentionally building for the next generation. Want Farm4Profit Merch? Custom order your favorite items today!https://farmfocused.com/farm-4profit/ Don't forget to like the podcast on all platforms and leave a review where ever you listen! Website: www.Farm4Profit.comShareable episode link: https://intro-to-farm4profit.simplecast.comEmail address: Farm4profitllc@gmail.comCall/Text: 515.207.9640Subscribe to YouTube: https://www.youtube.com/channel/UCSR8c1BrCjNDDI_Acku5XqwFollow us on TikTok: https://www.tiktok.com/@farm4profitllc Connect with us on Facebook: https://www.facebook.com/Farm4ProfitLLC/Farm4Profit Media is not a financial, legal, or tax advisor. Content is provided for informational purposes only, and we serve solely as a platform for third-party opinions. Any actions taken based on this content are at your own risk. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this conversation, host Tad Hussey (KIS Organics) and Dr. Deron Caplan (CannaCribs Horticulture Consulting) discuss the intersection of agricultural science and commercial cannabis cultivation, focusing heavily on irrigation, lighting, and research methodologies. Drought Stress vs. Drybacks: Dr. Caplan clarifies a major industry misconception by distinguishing standard irrigation "drybacks" from actual "drought stress." He explains that drybacks work primarily by pulling oxygen into the root zone (which cannabis loves), whereas true drought stress requires pushing the plant past the point of water availability. The Nuance of Cannabinoid Bumps: While Dr. Caplan's landmark PhD research found that controlled drought stress in week seven of flowering could boost THC/CBD content by 30% to 40% without losing yield, he notes that repeating this in smaller pots with faster drybacks yielded no positive results. This highlights just how incredibly nuanced and difficult it is to trigger beneficial plant stress without harming the crop. Organics vs. Mineral Salts at Scale: The two debate the logistics of commercial cultivation. While mineral salts offer strict baseline consistency and easier pathogen sterilization for medical export markets, Dr. Caplan notes that data-driven, evidence-based living soil systems have come a long way and are proving to be increasingly scalable. Debunking the Leaf-Tip Cloning Myth: Dr. Caplan shares that his early research on propagation disproved a ubiquitous legacy market myth: cutting the tips off clone leaves actually reduces rooting success, unless leaves are so large that they are actively shading adjacent clones. Advanced Canopy Management: They discuss the massive yield and quality benefits (15% to 20% increases) of under-canopy lighting (UCL). Dr. Caplan details how commercial facilities can use a PAR meter to calculate the Leaf Area Index (LAI) to mathematically standardize how much foliage to prune, rather than relying on visual guesswork. The Future of Research: Dr. Caplan highlights his new commercial R&D facility in Canada designed to test applied science practices. They close by discussing the validity of peer-reviewed data versus private white papers, concluding that regardless of a grower's background, scientific data is the ultimate tool for bridging the gap between organic and conventional cultivation. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Edward Shenderovich is the Founder and CEO of Roebling, a software platform that helps industrial companies evaluate, design, and finance manufacturing projects before breaking ground. After initially setting out to solve bottlenecks in biomanufacturing, Shenderovich and his team uncovered a broader challenge: the economics of scaling physical infrastructure are often poorly understood until it's too late. In this episode of Inevitable, Cody and Edward explore whether the US is making the same mistake with domestic manufacturing that climate tech once made with the “green premium.” If consumers were unwilling to pay more for cleaner products, will they pay more for American-made ones? The conversation examines China's long-term manufacturing strategy, the gap between scientific breakthroughs and industrial scale-up, and why engineering—not invention—is often the missing link in commercial success. Edward argues that national security, data sovereignty, and AI infrastructure may become the forces that justify renewed domestic investment in manufacturing and energy systems. They also discuss the lessons learned from the recent biomanufacturing boom and bust, why many bioindustrial companies struggled to achieve economic viability, and how AI can help bridge the gap between R&D and large-scale industrial deployment. Finally, Edward shares how Roebling is using AI-powered techno-economic analysis to help companies build factories that can actually compete on cost and performance. Episode recorded on May 28, 2026 (Published on June 9, 2026). In this episode, we cover:  (0:00) An overview of Roebling  (3:37) Why consumers rarely pay more for domestic or sustainable products  (6:12) How the US can compete with China's manufacturing strategy  (7:25) The gap between R&D innovation and industrial scale-up  (9:13) Why engineering is often the bottleneck  (11:25) AI data centers as a catalyst for industrial and energy infrastructure  (14:30) National security, data sovereignty, and domestic manufacturing  (17:31) Roebling's origins in biomanufacturing  (20:03) Why AI may finally help unlock biology at scale  (23:25) Building products that are better, not just greener  (26:11) How Roebling helps companies plan and finance factories  (31:08) Lessons from the biomanufacturing boom and bio-winter  (34:33) The opportunities of nuclear energy and industrial growth  Enjoyed this episode? Please leave us a review! Share feedback or suggest future topics and guests at info@mcj.vc.Connect with MCJ:Cody Simms on LinkedInVisit mcj.vcSubscribe to the MCJ Newsletter*Editing and post-production work for this episode was provided by The Podcast Consultant

This week we speak with Auryan Ratliff of Arizona State University about how AI—especially agentic AI—is transforming course design, assessment, and the broader student experience. They explore how moving beyond simple question-and-answer tools to more autonomous, action-oriented systems enables institutions to build more dynamic, personalized, and scalable learning environments. The conversation also tackles the challenges AI introduces—particularly around academic integrity—and how institutions can respond by rethinking assessment itself. Through examples like AI-powered conversational language practice, they highlight a shift toward more authentic, interactive, and human-centered learning experiences. Ultimately, it's a call for institutions to embrace experimentation, invest in culture and collaboration, and actively engage with AI rather than waiting for the “right” moment. Guest Name: Auryan Ratliff - Director of Technology Innovation and R&D at EdPlus at Arizona State University Guest Social: LinkedIn Guest Bio: Auryan Ratliff is the Director of Technology Innovation and R&D at EdPlus at Arizona State University, where he has worked for over a decade across AI, XR and digital learning. He manages a portfolio of emerging technology projects aimed at supporting the student experience. He founded EdPlus' AI product team and led the development of DegreeMe, an AI-powered tool that helps prospective students find the right degree through a personalized quiz. He is also the founder of SPLIT Studio, the student-powered lab focused on creating immersive experiences for ASU Online. SPLIT has also produced work for Dreamscape Learn and ASU partners, including the Hall of Teachers exhibit at the Bishop Museum, created in partnership with the Polynesian Voyaging Society. - - - -Connect With Our Host:Dustin Ramsdellhttps://www.linkedin.com/in/dustinramsdell/About The Enrollify Podcast Network:The Higher Ed Geek is a part of the Enrollify Podcast Network. If you like this podcast, chances are you'll like other Enrollify shows too!Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Adeniyi Abiodun has been in crypto since 2012, built trading and risk systems at investment banks, and led R&D on Facebook's Project Libra at Meta before co-founding Mysten Labs. So when he says every other L1 has a "skill issue" baked in at architecture time, it's worth listening.David Sencil sits down with Adeniyi at Consensus 2026 to walk through how Sui solved horizontal-scale consensus, why a famous L1 founder said it was impossible, and what comes next — native stablecoins, private payments by default, Walrus storage, and the agentic payment rails Stripe is pricing at a billion TPS.We cover:- Why every other L1 is capped by a single CPU and Moore's Law- The Project Libra story — "way too early" and what survived into Sui- 300ms finality vs Solana's 12 seconds- SuiUSD: $63M in a month and a half, free stablecoin transfers- Protocol-level private stablecoin transactions launching this year- Walrus storage outgrowing Arweave in a year- Why "AI doesn't care about your tribe"Filmed at Consensus 2026.Host: David Sencil

Dr. Katie Gold and Dave Combs from Cornell University join Fritz to unpack one of the most confusing topics facing winegrape growers today: how biological fungicides truly fit into a spray program. Katie explains how the Cornell grape pathology program has expanded rapidly, moving from strictly conventional efficacy trials into extensive work with biologicals as more of these products come out of R&D pipelines. She outlines why growers can't rely on "the next new conventional" anymore and how biologicals are becoming essential tools for sustainable disease management and resistance stewardship. Dave brings decades of field experience and shares how his initial skepticism about biologicals shifted after seeing modern products perform on highly disease‑prone varieties in one of the toughest vineyards for powdery mildew, downy mildew, black rot, and bunch rots. Together, they walk through what "compatibility" really means and why water pH, formulation, and whether an organism is alive or dead matter far more than many growers realize. Listeners learn why mixing biologicals and conventionals in the same tank often provides no added control (and may waste money), where negative interactions can show up, and why tightening intervals and understanding infection periods is critical when working with protectant biologicals.  In this episode, you will hear: Why more new products in the pipeline are biologicals rather than conventional chemistries How Cornell's high‑pressure pathology vineyard reveals the real-world limits and strengths of biologicals Why many biologicals are strictly protectants and must be applied before significant disease is present How tank mixing biologicals with conventionals can reduce cost-effectiveness without improving control Why understanding water chemistry, pH, and product formulation is now an essential spray-program strategy Follow and Review: If you enjoyed this episode, be sure to follow the podcast and leave a 5-star review on Apple Podcasts! Your support helps us reach more listeners.

When an AI model discovered a catastrophic vulnerability in Zcash's cryptography, users realized a hard truth: without mathematical proof, code is never truly safe. While everyone was distracted by the latest price pumps and ETF narratives, something massive just happened in the privacy space. A security researcher leveraging the new Opus 4.8 AI model found a “soundness bug” in Zcash's Orchard protocol. We aren't talking about a small glitch. This was a constraint bug that allowed for potential infinite minting since Orchard debuted in 2022. That means, for years, anyone could have printed counterfeit Zcash out of thin air. Now, here is the kicker. While Zcash users were sweating bullets, wondering if their funds were actually real or just digital paper, Pirate Chain users slept like babies. Zero panic. Why? Because Pirate Chain operates on the older, Sapling protocol, which was unaffected. While Zcash was busy rushing to implement new, flashy tech without proper safety checks, Pirate Chain stuck to the guns that work. Watch on: Odysee | YouTube | X | Rumble | Bitchute | Vigilante.tv This brings us to the concept of formal verification. Most crypto projects skip this because it is hard, expensive, and slows down development. But here is the reality: formal verification is essentially proving mathematically that the circuit works and it cannot be exploited. You can't just “trust the devs” or “trust the auditors.” Even the smartest minds in zero-knowledge proofs missed this bug in Orchard. Human error is inevitable. Math, however, does not make mistakes. Pirate Chain's strategy is brilliant in its simplicity. They let Zcash act as an unpaid R&D department. Zcash takes the risks, breaks things, and finds the holes. Pirate Chain watches, waits, and only implements the tech once it is mathematically secured. Zcash tries to fix these supply issues with something called “turnstiles,” which force users to unshield their coins—creating metadata trails and destroying privacy—just to verify the supply. Pirate Chain says no thanks. We don't play games with metadata. We wait for Ironwood, the next formally verified pool, before we even think about upgrading. We are seeing the gap widen between the experimenters and the executioners. The market is full of confusion, which is why we constantly highlight privacy champs like Zano, Monero, and Pirate Chain that actually deliver. While Zcash is still figuring out how to stop infinite minting, Pirate Chain is rolling out the new Unified Light Wallet and preparing for a future where the tech is actually polished. One is a beta test for your financial sovereignty. The other is the finished product. Don't get caught holding the bag when the bugs turn into bank runs. Stay ahead of the curve with the latest crypto news and analysis and remember: in this game, if you aren't private by default, you aren't private at all. The post AI Finds the Hole: Why Formal Verification Saved Pirate Chain From Zcash's Fate appeared first on The Crypto Vigilante.

Why has the replacement of Cobalt driers proven to be such a persistent technical challenge for the coatings industry? In the latest episode of the European Coatings Podcast, editor Yeray Arauco speaks with Dr. Andrew Mason, R&D and technical expert at EGE Kimya, about the complex journey of phasing out Cobalt from alkyd formulations.   

Have you ever lost the joy in your creative work — that sense of fun you had when you were starting out, before the admin and the algorithms drained it away? How do mid-career creatives get it back, and what can a four-year-old teach us about play? Austin Kleon talks about productive procrastination, silly rituals, the case for paper reference books in an AI world, and how his newsletter went from a marketing cost to the day job that keeps the lights on. In the intro, Does social media still sell books? [Self-Publishing with ALLi]; Trial by algorithm [The Bookseller]; Publishing's AI Hypocrisy Problem [The New Publishing Standard]; ALLi AI survey for authors; Brave New Bookshelf Podcast, and Pics from signing at BookVault. Today's show is sponsored by ProWritingAid, writing and editing software that goes way beyond just grammar and typo checking. With its detailed reports on how to improve your writing and integration with writing software, ProWritingAid will help you improve your book before you send it to an editor, agent or publisher. Check it out for free or get 15% off the premium edition at www.ProWritingAid.com/joanna This show is also supported by my Patrons. Join my Community at Patreon.com/thecreativepenn Austin Kleon is the New York Times and international bestselling author of nonfiction books, including Steal Like an Artist, Show Your Work!, and Keep Going, as well as an artist, professional speaker, and poet. His latest book is Don't Call It Art: 10 Ways to Create Like a Kid Again. You can listen above or on your favorite podcast app or read the notes and links below. Here are the highlights and the full transcript is below. Show Notes Why Austin wrote Don't Call It Art now, and what his kids taught him about creative joy Productive procrastination, silly rituals, and treating writing like Lego Comedy as a philosophical position, and giving yourself permission to be bad in private Sharing process in the algorithm era, and why your whole life is the process Bibliomancy, paper reference books, and what AI can't give you that a dictionary can Style, the Taco Bell distinctiveness rule, and how Austin's newsletter became his day job You can find Austin at AustinKleon.com. Transcript of the interview with Austin Kleon Jo: Austin Kleon is the New York Times and international bestselling author of nonfiction books, including Steal Like an Artist, Show Your Work!, and Keep Going, as well as an artist, professional speaker, and poet. His latest book is Don't Call It Art: 10 Ways to Create Like a Kid Again. So welcome back to the show, Austin. Austin: Thank you for having me back. It's nice to talk to you again. Jo: You were on the show in March 2020, and at the time, your book was Keep Going, which was prescient considering the pandemic and politics. So I wondered, why this book, Don't Call It Art, now? Was this something you see in the creative community or your own life that made you want to write this book? Austin: Keep Going is a book about what happens when the world goes crazy around you and you're still trying to do your creative work. This is a book about what happens when inside has bottomed out. Keep Going is a book about the world bottoming out, and you're worried that your own creative work is going to bottom out too. How do you keep pushing through and keep making stuff? This book, to me, is about what happens when you bottom out inside—when you've lost that love and feeling for the thing that you wanted to do, and you're just not connecting with it in the way that you used to or the way that you want to. How do you get back? How do you return to that sense of joy and wonder and fun that we have when we're starting out? And for me, it was being around my little kids that taught me how to tap into that. My kids were natural—they didn't have any creative hangups. I would spend all day talking to people who had creative hangups, and then I'd get back in the house, and I'd just be around these beings who didn't have any of them. It was really instructive. I felt like, if I could bottle the energy of my kids when they were about four years old and try to put it in a book, I think it could really help a lot of the people that I run into, and the people with the kinds of problems I hear from. Jo: You mentioned bottoming out. How do people know when they've hit that point? Austin: You just don't want to do it anymore. You're kind of like, “This just isn't giving me back what it used to.” When we start with our creative work, that's the thing that juices us. We come away from it feeling full up. I think you hit a certain point where you start to feel drained after it. Or maybe you don't feel drained by the thing itself that you're doing—maybe it's all the stuff around it, which is more often the case. For example, if you're a mid-career writer like me, who's been publishing books for 16 years now, I still really like writing. I still really like drawing. I still really like cutting and pasting and putting things together. It's the admin around the work—the emails, the meetings, the running-a-business part of it—that's super draining for me, and that stuff can start to bleed over into the creative work. So it's really important for me to make sure that I'm having some playtime, some R&D, some research and development time, to make sure it's not just all business. When you take the thing that you love and you turn it into the thing that you make a living from, you can really run into a lot of problems. Jo: I'm at 20 years, so I know exactly what you're saying, and a lot of listeners are the same. We love writing books, but it's all the stuff that goes around it. So for those of us who do this for money as well as passion, what are some practical ways to have more fun with our creativity? Austin: Something I learned from my kids is that you really are your most creative when you're supposed to be doing something else. So one of the things I use a lot in the studio is productive procrastination. Whatever I'm supposed to be working on, I start another little project, and that's my little naughty fun time. When I first come into the studio, I try to do something that I'm not supposed to be doing—something that I won't have much to show for. That could be making one of my blackout poems. That could be making a collage in my notebook. It could also be sitting here. I have a bass in the studio now, so I can practise my bass guitar. Sometimes I'll do that for the first 15 minutes just to get in that headspace of, “Hey, what's it like to do something just for yourself? Just because you want to do it?” The juice that you get from that little naughty “I'm going to do what I'm not supposed to be doing right now” thing, that carries into the rest of the day. It's like a nice start to things. Jo: Do you think that play could be something different to what we make our money with? For me, writing novels and stories is great fun in one way, but it's also what I then publish and make money on. So writing stories is more serious, I guess, than playing with Lego or something. Austin: Right. So the trick is, how can you make writing your stories like playing with Lego? That's kind of been my whole career. I hate staring at Microsoft Word and that blinking cursor, taunting you like, “Come on, what have you got?” A lot of my creative life has been about trying to make it more playful, trying to make it feel more like a game. That's how I came up with my blackout poems. I take an article from The New York Times and I black it out until it only has a few words left behind. It sort of looks like if the CIA did haiku, for some people listening. That was one little exercise. Then weirdly, that side thing that I thought was just play, just fun—that turned into my first book. So then it's, okay, what else can I mess around with and play with? I do a lot of collage work in the studio, and I rarely actually use that for any of the books. Sometimes I use it for my newsletter to illustrate the newsletter. But it's always about trying to figure out, how can I make writing a game? How can I make it more playful? There are different things that I do to make it feel more playful. One of them's really stupid. I really believe in silly rituals because I think silliness is really powerful. People talk about their daily rituals—Mason Currey has that great book, Daily Rituals: How Artists Work. When I was reading that book, I realised it was really the silly stuff that I really liked. There was, I think it was Balzac counting out coffee beans or something before he got to write. Or Steinbeck sharpening 12 pencils or something goofy like that. So one of the things I like to do before I write is that I have these cigarette pencils. They're pencils that look like cigarettes in the studio. I put one in my mouth before I start writing, and I pretend to be some old '40s writer on a typewriter. I like doing goofy stuff in the studio because I think when you do goofy stuff—stuff that you'd be embarrassed if anyone else saw it—it gets you in that playful state. Jo: It's interesting. In your book, you have a section that says, “Don't take things too seriously.” For many of us, we write memoir for example, and that is very close to us. It's like the deepest expression of what we want to say in the world. It feels very serious. So how can we hold things more lightly and not take things so seriously? Austin: For me, comedy is actually a philosophical position. What I mean by that is, I think a lot of people set out with a tragic model of creative work. They think, “Oh, I have this special gift,” or, “I have this thing that I really need to do, and I need to put it out into the world, and I need to make the world look more like I want it to look.” They have this idea that, “Through blood and sweat and tears, I'm going to see this thing through, and I'm going to push it into the world, and I'm going to have my way.” I think there's another way of working where it's more like, “I'm just a normal person trying to play with my environment, and take my experiences and put them into something interesting. So I'm going to play and use my wits, and we're going to see what we come up with.” Those really are two modes of life. The pandemic taught me that it was really when we were keeping our sense of humour, when we were having a laugh and keeping our egos in check around the house and just acknowledging how goofy we all were and how ridiculous the situation was, that seemed to be when we were really thriving. Versus, “Well, we're in this tough situation. We've got to make it into what we want it to be.” That felt really bad. But when we cruised along and we were just improvisational, when we went at things with a kind of lightness, that worked. There's a great Italo Calvino essay about lightness in Six Memos for the Next Millennium. Lightness is really underrated. Even when we're going about heavy work, having a sense of lightness and play with it just makes the work better. That's a philosophical position of mine. I aspire to comedy. I aspire to a comic outlook on life. I'm just a creature with a body who's going to die, and I'm fundamentally ridiculous. Life is pretty absurd. You just make the best of it. Jo: There's certainly some truth there. Staying on a similar theme, you have a chapter in the book on permission to be bad. Many of the listeners also have your book Show Your Work, and it shaped many of us into sharing our work in progress. It feels quite dangerous now, in a world where judgment is much louder than it maybe was when you wrote Show Your Work. So tell us a bit about permission to be bad versus should we keep some of this private? Austin: Permission to be bad is about the making part of things. It's the private part. It's permission to be bad when you're in private, when you're actually doing the work. Show Your Work is a book about what you do after you've done the work, or while you're doing the work. It was never about putting up a webcam and running a 24/7 feed. It was more like, hey, what are the ways that I can connect with the kind of audience I can build while I'm making the work itself? So the way I see permission to be bad is, you really have to give yourself permission when you're not sharing, when you're off screen, to really be as bad as you want to be. It doesn't necessarily mean quality-wise. I think it also means letting yourself write stuff that you would never say on social media. Letting yourself read stuff that you wouldn't admit you were reading on social media. Letting yourself listen to stuff. Letting yourself really be that unfiltered, unhinged, private person that you want to be. Then when it comes to sharing, you put some time in between that input time, that making time, and the sharing time, and then you share what you think is going to be useful or helpful or interesting to other people. Jo: I think you wrote that book before TikTok, and how fast people are moving. Do you think people need to slow down a bit in what they share, maybe? Austin: I don't know. I obviously had a lot more faith in social media back then. I use all the principles from Show Your Work in my newsletter. Newsletters are very much the new kind of great thing. They're doing a lot of the work that social media used to do, in that you're still able to have this direct connection with the people that you're trying to reach. The big problem with social media now is that it's all algorithmically tuned, where the people that are following you don't see the stuff that you're doing most of the time. What you have to do now, if you want the people who are following you to see your stuff on social media, is you have to make stuff that the algorithm likes. That's a whole different thing. As far as the Show Your Work principle—which is share your process as much as your product—that carries over to any platform. In my newsletter every Friday, I share a list of 10 things that were going on behind the scenes here. It might have been what I was watching on TV, what I listened to, a new pen I was trying out, or something like that. The Friday newsletter is almost always process stuff. When I talk about process, my definition is actually very broad. For a lot of people, it's drafting, editing, whatever. For me, the process is the whole life. The process is almost everything except the finished thing. A writer's life is 24/7. My friends who have real jobs really are like, “What do you do all day?” And I'm like, “Well, what do you mean?” They're like, “Well, I see you out on your bike ride.” I'm like, “Yes, when you see me out on a bike ride, I'm thinking through something half the time.” If I'm watching TV, I'm thinking, “Hey, would this be good in the newsletter?” I'm never off. My whole life—everything is copy, as Nora Ephron said. That's part of the job. It's very hard to turn off. So I see the whole life as process, and the question becomes, what little bits and pieces of that life and that process can you share with people while you're making the things that you hope to sell them later? Right now, I'm in a cycle where I'm selling this book, but all these people have showed up because I've shared my process every week for the past seven years since I put out a book. Jo: It's funny you say that. I was at the dentist yesterday, and— My dentist literally asked me, “So where do you get all your ideas?” This is a common question for all of us, right? And it just becomes so hard to explain that to people who don't walk around in the world just constantly getting ideas. Austin: I can't believe I'm going to tell this story. I was getting my vasectomy after my second kid, and I was talking to this doctor just before the operation. He said, “So what do you do for a living?” I said, “I'm a writer.” He said, “Oh, that must be cool. You get to use your brain.” And I said, “That's everything that you want your doctor to say.” I was going to say, “Please use your brain,” before he's about to cut into you. He said, “Oh, no, no. What I mean is, I know what I'm going to do every day for the next 10 years.” He knew exactly what his day was going to look like. He said, “You have to use your brain. You've got to figure out new stuff.” I was like, “Oh, that's really interesting.” That's the trade-off, right? He's got the job security. He knows what he's going to do. Every writer has a moment where they have to talk to a normal person about what you do. Jo: I was going to say, I'm married to one. Austin: Now, my wife, on the other hand, grew up the daughter of a writer, so she knows exactly what it's like. Nothing ever phases her. She's totally used to it. She's used to me staring off into space, completely checking out of a conversation. She's used to me using lines on her that I'm going to put in a piece later. She's used to the whole rigmarole. It's very handy. I've been very lucky in that sense. Jo: Coming back to the book, you talk about your use of bibliomancy for inspiration. Since we're talking about that, tell us about it. I think all the book people listening will be happy. Austin: I'm a person who still keeps a dictionary nearby—a paper dictionary. I keep a big old American Heritage. It's just a big, thick book. When I really don't have any ideas, I will turn at random to the dictionary, close my eyes, stick my finger down the page, open my eyes, and just see what I come up with. Sometimes just that act will give me an idea. I also do that with books. I'll go around the studio, pick up a book, flip to a random page, and just see what it says there, or read an old piece of marginalia that I've left in a book. I believe deeply in the power of bibliomancy, and I think it's a case for paper books. I'm one of those people that still really believes in reference books. I've started collecting more and more of them. I have an old, big dictionary that's always open on my desk, and I look up words. I learned from John McPhee, the writer, that you should look up words that you think you know. That was the first time I'd ever heard anyone say that. So I look up words that I think I know. Instead of reaching for a thesaurus when I need a different word, I actually just look up the definition of the word that I already have. That's another McPhee tip. The other thing that happened that I thought was really interesting is, I got a Roget's for the first time—a thesaurus. I don't think most people know what an actual thesaurus is. Most people think of a thesaurus as a synonym finder, and that's not actually what a thesaurus is at all. A thesaurus is more like an encyclopaedia, weirdly. You look up things based on big concepts, and then it gives you a bunch of words to look up later. It's a very strange thing. It's not what most people think it is. I have a couple of editions of Roget's in here. I like the really old Roget's from the 1900s because they actually have opposing ideas facing each other on the page. Do you have an old-school Roget's? Have you ever looked through one? Jo: I don't have one now, but I certainly grew up with them. I was literally just thinking, I wonder if there are ones for Americans and ones for British people, because so often we say different things and mean different things. I always hear Americans say, “Oh, that's a doozy,” or something, and it means the complete opposite thing here. Austin: Like if you say “fanny pack” over there. That means something very different than it means here, right? Chips or fries, that kind of stuff. So I wonder if there are different ones for different cultural references. Jo: I don't know. Austin: As people, with ChatGPT and all these LLMs and stuff, people are like, “Why would you ever pick up a paper reference book?” And I'm like, “I actually like the friction.” I like having to move in space and go over to my dictionary. I like flipping the pages. I like having to scan a page for the word I'm looking for, because— This marvellous thing happens when you're looking for the word, where you bump into all these other words. If you're a word nerd, you get to start thinking about the root of the word—oh, why is this word next to this word? Well, it's because they share the same root. Then you're going down all these fun rabbit holes. The thing that I'm trying to do as a writer and a creative person is, I'm trying to get to the thing that I didn't know I was looking for. The thing that people misunderstand about AI, I think personally, is that it's a great tool if you know what you're looking for. If you're like, “Find me this thing. I want exactly this. I want to see a picture of a dog wearing a king's costume,” or some crap like that, then it can spit that picture out for you. Or, “I want to know what happened on this day,” and whatever. It can do that. But that's not actually what I'm doing most of the time when I'm writing or making something. I start with an idea, but what really happens—the magic of writing and the magic of making stuff in general—is when you discover something that you didn't even know you were headed for. That's the real magic for me. Sometimes I have an idea and I want to articulate it for people, but more often than not, there's something that bothers me or something that I want to talk about, and I sit down and write, and I figure out what it is that I actually have to say and what I actually think. Every writer really knows this, and that's why the dictionary, stuff like that, those are ways of training you to get in that discovery mode. “Well, let me—oh, I bumped into this. I went looking for this one thing and then I ran into this other thing.” That's why I love the library. I don't know what system you use over there, but you look for one book in the Dewey Decimal System over here, and then, okay, here's all these other weird books next to it. Then you end up with three other books other than the one that you were looking for. That's the magic. To me, that's the magic of creative work, discovering what you didn't know you were looking for. That was particularly important for me when I was writing this book because we discovered that my wife has a condition called aphantasia. It's very rare in the population, about 2 to 3% of people. There's probably some people listening to this right now who are like, “What is this? Tell me.” Jo: Aphantasia actually more common in the creative industries. Austin: Yes. What it is, is that you don't see—when I say close your eyes and picture an apple, you don't actually see the apple in your head. You can think about an apple and the qualities of an apple, but you don't actually see it. Some people, and it's a matter of degree—some people like me, I can close my eyes, I can tell you what the apple looks like, I can tell you what colour it is, I can tell you where the shading is. Someone like my wife doesn't see the apple. She can tell you what an apple is. It's really interesting because she has a degree in architecture, which is known as a very visual field. But the thing you discover about aphantasia is, it doesn't keep people from becoming artists. In fact, it's the opposite. Someone like Ed Catmull, who co-founded Pixar, writes about it in his book, and so many of the great animators at Pixar are actually aphantasics. The reason is that they learned that they had to draw in order to see things. When you don't have a picture in your head of what you want something to look like, things appear in the drawing, and you find things that you couldn't even picture. A lot of writers actually are aphantasics. John Green discovered recently that he has aphantasia. It turns out that it's a superpower for writers, because if you don't have a picture in your head, then you don't have to translate that picture into words. A lot of writers talk about thinking in radio, like they have a constant narrator. My wife—she's probably going to kill me for talking about her this much—when she describes it to me, she's like, “Oh, it's like a radio in my head. I'm constantly hearing a voice, and it's a narrator.” I was like, “Holy shit, that would be really helpful to me.” I don't have anything like that in my head. I read Mrs Dalloway for the first time, and I gave it to her and I said, “You've got to read this book. I think this must be what it's like in your head.” And she said, “Oh my God, it is.” Part of the thing that I took away from that experience—this is a long-winded way of getting here—is that I take a lot of inspiration from people with this condition. Most of the people I know in the arts or the creative fields, they set out with this grand vision, and then they start working on the thing and it's nothing like what they had in their head, and they get really depressed: “This isn't what I had in mind.” Whereas if you set out without a picture in your head, and you just start manipulating things and you see what appears, that's more of the comic mode I was talking about earlier. What would happen if we just sat down with our materials and we started playing and we saw what appeared on the page? What if we started typing and saw what appeared, and then we played with that? That's the kind of joy. That's more like how kids operate. Kids are better at that. They're better at reacting to what's actually in front of them, instead of having these grandiose visions about what they're trying to achieve. Jo: Just coming back on the longevity of a creative career. Your books are very distinctive. You have a very distinctive visual style, your handwriting and the way the books are done. I wondered if another part of the ennui, perhaps, or the draining of the later career is that we get trapped into doing something that feels like it looks the same. Or we have a voice, and we're happy in that voice, but sometimes we want to do something completely different. For authors, we have different names. I write under two different names, and that helps. But equally— How do you define author voice, and do you ever feel like doing something completely different to your normal style? Austin: Style, in a lot of ways, is self-plagiarism. Style is the repeated things that we notice in people's work. Hitchcock talked about this in films. Wes Anderson is someone like that—Wes Anderson has a style. I'm sure that he gets really sick of it too sometimes, but you also can't help it in some ways. I thought a lot about this because people worry about style so much. A lot of the time, what we call style is what Adrian Tomine one time said: “Style is just the distance between what's in my head and what comes out of my hand.” I really like that definition. With this book, I was trying to think, “Okay, if I do another book in this series, how can I push things a little bit?” And then I was reading this article about Taco Bell. You guys have Taco Bell over there, don't you? Do you have Taco Bell? Jo: No. Austin: So Taco Bell, for people who don't know, is this American Mexican chain, and they have tacos and burritos and stuff like that. They're well known for making these really insane… it's so American, this company. They make a taco with a Doritos as a shell. Doritos are crisps, I guess. Jo: Yes, we have Doritos. Austin: Okay. I spent time in England, I just don't remember if I ate Doritos when I was in England. Anyway, I was reading this article about Taco Bell. It was really funny. They have an innovation kitchen at Taco Bell, and they have a rule about new products. The rule is called the distinctiveness rule, and the rule is: you can change the flavour or you can change the taste, or you can change the form, but you can't change both at the same time. I got really obsessed with this concept because I thought, “Well, this could be kind of interesting.” If you're someone who's had success and you're known for something, this presents an interesting thing. You could do a complete break and do something completely new, or you could try the distinctiveness rule. Okay, well, what if I play with this idea of taste versus form? What if I change the taste and keep the form? So the idea for Don't Call It Art was, what if I do another one of these books, but the taste is more like if my kids made it? It had the texture of kids' art, it had lots of scribbles in it, it was loose and messy. That was kind of the idea. The actual book ended up being more like the other books. It ended up looking like an Austin Kleon book, because I just can't help that. The thing you said about having multiple names that you write under, that's kind of what I do with the newsletter. I think of the newsletter as very different from the books. The newsletter is this twice-weekly thing where I can be a little bit more of myself. In the books, I'm this very helpful, happy version of myself. It's me, but it's me on my best day. I'm really helpful and interesting for you. The newsletter is still a highlight reel in a sense, but it's a little bit more of my weird everything-I'm-into. It's more of the unclipped version of me. The newsletter becomes a place where I can do a lot of the weird stuff that's much different from the books. I have these little projects going all the time. Sometimes I'll make a bunch of prints and put them online. Sometimes I'll make a bunch of zines on a topic I haven't covered in the book. Sometimes I'll do a mixtape. As someone who's interested in a lot of different forms and genres and just different modes of output, having something like a newsletter has been really creatively fruitful for me. It's kept me from getting too bottomed out with the books because the books do a certain thing for the reader, and as much as I'd love to do a book that was radically different, I also think I've been given a real gift with the form of my books, in that I kind of own the way that they feel and look. There aren't a lot of books that look like those books and feel like those books, and so I like playing with that form. It would be hard to get rid of it now. The pseudonym for me is kind of like the newsletter in a sense. The newsletter is a little bit more of where I get to be wild and wacky. Then the books are a little bit more of a chiselled thing. Jo: The books are perfect examples of the form, as you say, but it's interesting about the newsletter. You mentioned at the beginning that we can be drained by the admin around the work. For many people listening, a newsletter becomes admin. So how does the newsletter fit into your business? The books are traditionally published, they're very professional. How do you have your independent side, and how does all of that work together in your business? Austin: Thank you for asking that question. I run the whole show at the newsletter. The newsletter is just me, and then my wife edits it, and no one else is involved. I don't have an assistant. I don't have a team. It is just me, and that's why I love it. I control everything. I pick who gets in there. I pick everything. I love that. I grew up watching David Letterman over here, and Letterman had a nightly show, and I always thought that was killer. I thought, “Man, what a fun job. You have a show every night where you have a new guest, and you have all these wacky things going on.” It was like a variety show. I always thought that would be really fun, so the newsletter is my version of that. I started the newsletter in 2013, and it was just a Friday newsletter. It quickly became a list of 10 things I thought were worth sharing. I had a friend, Hugh MacLeod, who was like, “Hey, I have a newsletter. It's bigger than any conference you've ever gone to.” He was talking about South by Southwest here in Austin. He's like, “I have a newsletter now, and it's bigger than South by Southwest.” Jo: Oh, I remember him. Austin: He would say, “Every time I have a new print, I put it out, and there's a button, and then they buy it.” He was like, “You've got to get it. This newsletter thing is killer.” This was in 2011 or something. Jo: Yes, I still have his books. Blogging in Your Underwear or something. Austin: Totally. So Hugh's a whole different story, but I was just like, “Oh, I should really get a newsletter.” Letterman always had a top 10 list on his show. I just always thought a 10 list was really fun. And of course the books are lists of 10 too. So it just worked to have a weekly list of 10. It felt good, and it felt like an infinitely repeatable format. What I'm looking for as a creative person is an infinitely repeatable format that can go on and on and on and be new every time. So the list of 10 is something that people know the form of. It goes back to the Taco Bell thing. They know the form, but they're not sure what's going to go inside. They know it's going to be a burrito, but they don't know what's going to be in the burrito, and that's the exciting part. The newsletter, business-wise, was always a marketing cost for about the first eight years of its existence. I paid MailChimp to send it out. Then in about 2021, when I hadn't done a book for a while, my agent said, “You know, you should really think about doing a paid tier of your newsletter.” And this is to his credit, because he doesn't make anything off the newsletter. He said, “There's this thing called Substack now that makes that really easy.” So we moved to Substack in 2021 in October, and I started doing a Tuesday edition of the newsletter that was just for paid people. That grew enough that it's gone from a marketing cost to something that's almost—it's not quite as much as I make on my books, but it's close. And to be candid, my books sell pretty well. So suddenly the newsletter has become this really healthy income stream. The newsletter to me is actually the day job now. The newsletter is what really keeps the lights on. It's also the perfect mix. It's the day job, it's the thing that keeps income coming in on a regular basis, but it's also the thing I like to do the most. I'm not like a traditional writer who likes to just get lost in their book and take years and years and go away. I'm someone who loves to be doing a lot of different things. The newsletter is a perfect format for me. I'm talking myself into not quitting, actually. It's funny. It's gone from this thing that was a marketing cost to now it's a significant part of our income. That journey—such a bad word, journey—that trip has been very interesting. It's been really cool. But I'm also just lucky. I've been really lucky, and I think part of my thing is, I'm always just trying not to squander my luck. Jo: Well, the book is fantastic, and I know people are going to love it. And the newsletter, of course. So tell us— Where can people find you and your books and newsletter online? Austin: The easiest thing to do is to just go to AustinKleon.com, and that has links to everything—the books, the newsletter. I do actually keep an old-school blog still. I'm one of the few people that still maintains their blog and keeps it up to date. I'm hedging my bets because I think in the end everything will come back to a self-hosted website. I think in the end everyone's going to just go back to their little websites, or at least I hope so. Jo: Well, that was great, Austin. Thanks so much. Austin: Oh, thank you. The post Don't Call It Art: Rediscovering Creative Joy With Austin Kleon first appeared on The Creative Penn.

Tait Communications is a provider of critical communications for public safety, emergency services utilities & enterprises. Yoram became CEO in 2021 and has grown the company from 450 people to over 1300. Plans include a new state of the art manufacturing facility & a Centre of Excellence with over 10 labs. Yoram says the key to the growth is having a strong business case for every R&D project or acquisition and making sure you have the right people.  www.taitcommunications.com   

On today's show, we dive into the cost structure of audio speakers. We start with an article that asks whether 'audiophile' speaker brands are milking you for $20,000. We also read your emails and cover the week's news. News: Important update to your DIRECTV account SVS Auto EQ Room Correction for R|Evolution Subwoofers YouTube TV adds Fox One, Peacock to Primetime Channels store Other: Monoprice Alpha In-Wall Speaker There's never been a better time to grab a new Google TV launcher Are 'Audiophile' Speaker Brands Are Milking You for $20,000 The listeners keep delivering great ideas for show topics. This week Mike LaBorde sent in an article published at headphonesty.com entitled A Former FTC Economist Quit His Job to Prove 'Audiophile' Speaker Brands Are Milking You for $20,000.  The author talks about how a former FTC economist quit his job to design and build affordable high-performance speakers.  He argued that many premium audiophile brands are significantly overpriced because they use similar OEM drivers from the same factories while charging massive markups for branding, cabinets, and dealer margins. We'll break down this article into five points we felt were interesting. The full article is linked and you may want to read it for more details. Many premium audiophile speaker brands rely on the same small group of OEM driver manufacturers (like Sinar Baja/SB Acoustics, SEAS (Scandinavian Electro Acoustic Systems), Scan-Speak, etc.). The same factories and engineering talent supply drivers to both high-end and mainstream brands, even when the final speakers carry vastly different logos and price tags. "Custom" or "proprietary" drivers are often overstated. Most brands customize only the "soft parts" (cone, surround, voice coil) on top of standard off-the-shelf "hard parts" from OEM suppliers, rather than designing and building drivers entirely from scratch. Pricing of speakers — The actual cost of the drivers is a tiny fraction of the retail price. In the Wilson Audio Yvette example, the three drivers cost roughly $530–$580 total, representing only about 2% of the $25,000+ selling price. The vast majority of the cost comes from cabinetry, finish, dealer margins (40-50%), distribution, marketing, and brand prestige, with a typical 5x markup from manufacturing cost to retail. Only a few brands truly manufacture their own drivers in-house. Companies like Focal, KEF, Dynaudio, Paradigm, and Bowers & Wilkins are exceptions. Most premium brands outsource driver production due to the high cost and complexity of vertical integration. High performance doesn't require extreme prices. Former FTC economist Dennis Murphy's Philharmonic Audio proves this by offering well-engineered speakers (like the $850/pair Ceramic Mini using quality SB Acoustics drivers) with minimal overhead, direct sales, and no lavish dealer/showroom costs — challenging the idea that great sound must come with five-figure price tags. The article essentially argues that much of the ultra-premium speaker market is driven more by branding and distribution economics than by revolutionary driver technology. What is the Cost Breakdown of Thousand Dollar Speakers? After going through the previous article we wondered what the actual cost breakdown of Passive bookshelf speakers retailing at $1,000 per pair? ThinkKEF Q series, ELAC Debut Reference, or similar mid to high end consumer hi-fi brands. They balance good performance with accessible pricing.  What follows is our best estimation based on the data we uncovered. If you are in the industry and have better data, please let us know and we will update this analysis. Sources for this analysis include - Audio Science Review, AVS Forum, WhatHifi, headphonesty.com, hubhifi, and a few others.  1. Design & Development (R&D) – Upfront Investment Typical cost: $50,000–$250,000+ for a new model line. Includes acoustic modeling, driver selection/tuning, crossover design, enclosure simulation, multiple prototypes, listening tests, and anechoic chamber measurements. For this price tier, brands often use a mix of off-the-shelf and mildly customized drivers rather than fully bespoke high-end ones.   Amortization: Spread over production volume and for this exercise we used a production run of 5,000–20,000 pairs. This adds roughly $5–$25 per pair at a reasonable scale. 2. Prototyping & Tooling Prototypes: 5–15 iterations at $300–$1,200 each which include custom cabinets, driver samples, hand-assembled crossovers. Tooling: CNC molds/jigs for cabinets, baffle cutting, or vinyl wrap tooling: $8,000–$40,000 upfront. Amortized to $2–$10 per pair. 3. Bill of Materials (BOM) – The Biggest Per-Unit Cost For a typical 2-way passive bookshelf (6.5" woofer + 1" tweeter) at this price point: Drivers - $80–$180 - 6.5" coated paper woofer (~$30–$70 ea.), soft dome or aluminum tweeter (~$15–$50 ea.). Brands like SEAS, SB Acoustics, or custom OEM. Cabinet -  $60-$130, - Braced MDF (18–25mm), vinyl wrap or basic veneer, internal damping, port tube, terminals. Real wood veneer adds premium. Crossover - $30-$80 - 2nd/3rd order with air-core inductors, film capacitors, resistors. Higher quality parts (Mundorf-level) push toward the upper end. Other (grille, wiring, hardware, terminals) - $20-$50 - Magnetic grilles, internal wiring, binding posts. Total BOM per pair: $190–$440 at volume production (typically in China or Vietnam for most brands). Premium touches (better drivers, thicker bracing, nicer finishes) push BOM toward the higher end. 4. Manufacturing, Assembly & Overhead Labor & Assembly: $25–$60 per pair (cabinet gluing/bracing, driver mounting, crossover soldering, final wiring, testing). Quality Control & Testing: Burn-in, frequency sweeps, distortion checks: $10–$25. Factory Overhead/Utilities: $35 - $50. Total Manufacturing per pair: $70 - $135 5. Full Cost Structure to Retail ($1,000/pair) We will assume a large brand that sells 20,000 units and has already invested in tooling and requires minimal new tooling for each new speaker design.  Design and R&D Amortized - $5 Prototype and Tooling  - $2 Bill of Materials - $315 - We split the $190 - $440 down the middle Manufacturing -  $103 - We split the $40 - $135 down the middle Shipping, duties etc to distributor per pair on average - $50 Total to Manufacture $474. The rest of the thousand dollars covers the distribution chain, branding, and profit. And in reality, depending on the efficiency of the factory and ability to leverage design histories from years of experience, the soft costs can be about a third of $110 we came up with, bringing the total cost to about $400. Key Variables Affecting Cost Volume: Higher production = lower per-unit costs. Driver Quality: Exotic materials (beryllium tweeters, carbon fiber) can double driver costs. Cabinet Finish: Vinyl vs. real walnut veneer = big difference. Brand Positioning: Established names (KEF, ELAC) have higher R&D/marketing allocation than direct-to-consumer brands. For comparison DIY builders can replicate similar performance for $300–$600 per pair in parts using higher quality drivers and crossover components and flat-pack or self-built cabinets, eliminating most of the overhead and markups. And after building over 30 sets of speakers I can say without doubt that what you build will sound as good as speakers costing ten times the amount. Plus you can use material that works best for you as well as customizing the look to match your decor. Even my latest set built from stock off the shelf components bought from Part Express for about $200 sound simply amazing!  

Increased R&D Tax Credit shows clear impact as companies prioritise research and innovation amid global uncertainty – IRDG & KPMG Report. Specific Innovation Tax Credit urgently needed to bridge structural gaps in Ireland's R&D competitiveness framework The 2026 Ireland Innovation Index report from IRDG and KPMG shows that Irish businesses are strongly committed to research, development and innovation (RDI), with fresh evidence that the Government's R&D tax credit is directly driving new investment, even as companies contend with geopolitical uncertainty, international tax changes and competitive pressures. The 2026 Ireland Innovation Index is the annual nationwide survey by the Industry Research & Development Group (IRDG) and KPMG. This fourth annual report gathered detailed responses from a record 587 companies who are actively engaged in innovation across Ireland. The findings show a significant boost in R&D activity arising from the R&D Tax Credit, which was increased from 30% to 35% in last year's budget. 69% of businesses say they have increased R&D spend over the past three years, while 77% expect to increase investment over the next three years. In relation specifically to the recent 5% uplift in the tax credit, 58% of companies surveyed said they are directing this additional incentive into existing R&D projects, while 57% say it will support entirely new R&D activity. A further 39% say the enhanced incentive will support them hiring or retaining dedicated R&D staff. The findings also show the importance of the R&D tax credit in attracting and maintaining R&D activity and jobs in Ireland, with over half (54%) of MNCs saying that, without the credit, 10% or less of their R&D would take place in Ireland. For context, in terms of actual numbers of companies availing of the incentive, the latest available Revenue figures (2023) showed 1,804 claimants – the highest figure since the credit was introduced in 2004. In 2023, 225 large companies received over €764 million in R&D tax credits, while a further €213 million in R&D tax credits was claimed by 1,579 SMEs. Companies claiming the R&D tax credit are also significant contributors to the Exchequer through corporation tax. In 2023, total corporation tax liabilities for all claimant companies were €10.53 billion, with €8.81 billion of that amount attributable to companies claiming in excess of €1 million of R&D tax credits. The report also highlights the increasing strategic importance of advanced technology in Ireland's innovation economy. AI and disruptive technology is now a priority for 67% of respondents over the next one to three years, up sharply from 45% in 2024. This is the largest movement recorded in any innovation priority category over the four-year life of the Index. 'Disruptive technology' is innovation that significantly alters established industries and markets. The trends in this area reflect a profound shift in how Irish businesses are approaching innovation, with artificial intelligence moving rapidly from experimentation to operational deployment, productivity enhancement and product development. Necessity for Specific Innovation Tax Credit The R&D Tax Credit remains a critical pillar of Ireland's competitiveness offering and continues to underpin significant investment decisions. However, many forms of modern commercially valuable innovation sit outside the traditional fields of science and technology, within which activity must fall in order to qualify. This tends to exclude innovation such as digital transformation, design-led innovation, advanced process innovation and business-model innovation, the report says. As a result, 71% of companies surveyed said a specific new Innovation Tax Credit would enable more innovative work to take place in Ireland, while a corresponding 67% believe it would support new product and service development. Almost half (45%) of respondents said an innovation tax credit would directly support increased IP creation and ...

Jessica has had a cool path in biochemistry, with work in both academia and industry, as well as an internship at the National Institutes of Health. She is the Director of New Product Development at LCG Clinical Diagnostics, where she oversees the R&D department. In addition to her work at LCG Clinical Diagnostics, Jessica has been an enthusiastic volunteer for both the Maine Science Festival and the Bioscience Association of Maine's Bioscience Day. This conversation was recorded in May 2026. ~~~~~The Maine Science Podcast is a production of the Maine Discovery Museum. It is recorded at Discovery Studios, at the Maine Discovery Museum, in Bangor, ME. The Maine Science Podcast is hosted and executive produced by Kate Dickerson; edited and produced by Scott Loiselle. The Discover Maine theme was composed and performed by Nick Parker. To support our work: https://www.mainediscoverymuseum.org/donate. Find us online:Maine Discovery MuseumMaine Discovery Museum on social media: Facebook Instagram LinkedIn Bluesky YouTubeMaine Science Podcast on social media: Facebook Instagram YouTubeMaine Science Festival on social media: Facebook Instagram LinkedIn YouTube© 2026 Maine Discovery Museum

Hey friends, Chase here Austin Kleon is back on the show, and this conversation is exactly the kind of reminder every creative person needs. You probably know Austin from Steal Like an Artist, Show Your Work!, and Keep Going, the books that have helped millions of people rethink creativity, sharing, influence, originality, and what it actually means to make things in public. But Austin's new book, Don't Call It Art: 10 Ways to Create Like a Kid Again, goes somewhere even more fundamental. It asks a question that feels especially urgent for creators, entrepreneurs, artists, writers, photographers, parents, and anyone trying to make meaningful work in a world that wants to turn everything into content: What if the way back to your best creative work is not becoming more serious, but becoming more playful? That question matters because most of us have made creativity too heavy. We have wrapped it in identity, pressure, productivity, platforms, metrics, perfectionism, and the fear of being judged. We get stuck asking whether we are real artists, serious writers, successful creators, or legitimate professionals. We worry about the noun before we do the verb. Austin's message is simpler, deeper, and more freeing: "Don't call it art. Don't worry about being an artist. Forget the nouns. Do the verbs. Just make stuff." That idea is the center of this episode. We talk about what kids can teach us about creativity, why play is not frivolous, how to build the conditions for your best work, why attention is your most valuable resource, and why some of the most important ideas in your life might come from goofing off. This conversation is about loosening the grip. It is about getting back to the part of you that makes before it judges, explores before it explains, and follows the energy before it knows exactly where the work is going. Why This Conversation Matters Right Now We are living in a strange moment for creative people. On one hand, there has never been more opportunity. An individual with a laptop, a camera, a newsletter, a sketchbook, a phone, a point of view, or a weird little idea can reach people directly. That is extraordinary. But it also comes with a cost. The pressure to turn every interest into a brand, every hobby into content, every project into a product, and every creative impulse into a strategy has never been stronger. We are constantly being asked to define ourselves: What do you do? What is your niche? What is your platform? What are you building? How are you monetizing it? What is the plan? Those questions can be useful at the right time. But when they show up too early, they can suffocate the very thing they are trying to organize. Austin's work reminds us that creativity begins before identity. Before "artist." Before "writer." Before "photographer." Before "entrepreneur." Before "content creator." Before the nouns, there are verbs. Drawing. Writing. Walking. Noticing. Building. Playing. Collecting. Tinkering. Making. Sharing. Kids understand this instinctively. They do not sit down and ask whether what they are making fits the market. They do not wonder whether they are allowed to call themselves artists. They do not freeze because the thing in front of them might not be good enough. They simply begin. And in that beginning, there is a kind of wisdom most adults have forgotten. What We Explore in This Episode Why kids can be some of the best creativity teachers because they make before they judge, label, or perform. How to reconnect with the feeling you wanted as a kid, not necessarily the exact childhood you had. Why play is not the opposite of serious work, but a form of creative research and development. How to create the conditions for creativity through time, space, materials, and permission. Why tools should feel more like toys if you want to stay curious and experimental. How phones fracture attention and why protecting the edges of your day can change the texture of your life. Why hobbies matter and how bikes, music, golf, drawing, and other forms of play can return us to ourselves. Why "don't call it art" can be liberating for anyone who feels trapped by labels or legitimacy. How to use jealousy, disgust, and frustration as creative information instead of letting them turn into bitterness. Why people pay attention when someone truly believes in what they are doing. The Core Idea: Forget the Nouns. Do the Verbs. The fastest way to get unstuck is often to stop asking what you are and start paying attention to what you do. That sounds simple, but it is one of the biggest traps in creative work. We get obsessed with identity. Am I an artist? Am I a real writer? Am I a serious photographer? Am I a professional? Am I successful enough to call myself this thing? Am I allowed? That kind of thinking can freeze you before you even start. Kids do not have that problem. They are not trying to become "artists." They are drawing. They are building. They are making noise. They are inventing stories. They are throwing materials around and seeing what happens. Austin's point is not that craft does not matter. It is not that ambition does not matter. It is not that we should abandon discipline. It is that the living center of creativity is action. The verb comes first. Make the thing. Move the pencil. Open the notebook. Pick up the guitar. Ride the bike. Take the walk. Make the zine. Shoot the photo. Write the sentence. Start the weird little project that begins with, "Wouldn't it be funny if…" That is where the energy is. Play Is Creative R&D One of the big tensions in this conversation is the voice many of us carry around that says play is not practical. That voice says: You have responsibilities. You need to make money. You need to be serious. You need to have a plan. You need to stop messing around. Austin's response is that play is not the opposite of serious work. Play is often what makes serious work possible. He talks about play as research and development. Any healthy company needs R&D. It needs space to explore, test, wander, fail, and discover things that cannot be found through pure efficiency. The same is true for a creative life. A lot of us start in explore mode. We are curious. We are trying things. We are learning. We are following our taste. We are discovering our voice. Then, if something works, we shift into exploit mode. We repeat the thing. We build a career around it. We systematize it. We professionalize it. We optimize it. That can be useful. But if you stay there forever, you eventually run out of juice. You need space to explore again. That is what play gives you. It returns you to the part of the process where you are not just producing, but discovering. And in creative work, discovery is everything. Create the Conditions, Then Get Out of the Way One of my favorite parts of this conversation is Austin's simple equation: Play = time + space + materials. That may sound almost too simple, but it is profound. When I look back at the most creative seasons of my life, the pattern is obvious. I had uninterrupted time. I had a place to go. I had the right materials around me. I had enough structure to begin and enough freedom to be surprised. That is what we often give kids when we want them to create. We give them a table, some paper, some markers, a chunk of time, and permission to make a mess. Then we grow up and deny ourselves the same basic conditions. We say we are blocked, stuck, confused, or uninspired, but often we have not created an environment where anything could actually emerge. No time. No space. No materials. No quiet. No room to tinker. The lesson is not complicated, but it is easy to forget: Set the conditions. Allow the work to happen. Get out of the way. That is not laziness. That is not indulgence. That is how the good stuff gets a chance to show up. The Best Ideas Often Come From Goofing Off I have said this before, and I mean it: so many of the best ideas in my life have come from goofing off. Not from trying to optimize. Not from grinding. Not from forcing. Not from staring at a blank screen and demanding genius. They came when I was tinkering. Playing. Walking. Talking with friends. Making something that had no obvious point. Trying something because it felt fun, strange, or impossible to explain. Austin and I talk about this because it is one of the hardest things for ambitious people to accept. We want the path to be linear. We want effort to equal outcome. We want the best ideas to come from the most serious hours. But creativity often does not work that way. The mind needs room. The body needs movement. The soul needs a little nonsense. Goofing off is not always avoidance. Sometimes it is how the deeper intelligence gets a chance to speak. Tools Should Be Toys Austin says something in this episode that every creator should sit with: Tools should be toys. That does not mean your tools are unimportant. It means the best tools invite you into a state of play. They make you want to touch them, try them, misuse them, combine them, push them, and see what happens. A sketchbook can be a toy. A camera can be a toy. A guitar pedal can be a toy. A bicycle can be a toy. A cheap notebook, a box of crayons, a microphone, a drum machine, a kitchen table, a phone in airplane mode, a pile of index cards — all of it can become part of the creative playground. The danger is when tools become only professional instruments. When every object in your creative life carries the pressure of output, performance, monetization, or proof, it becomes harder to begin. A toy invites curiosity. And curiosity is one of the most reliable doors back into making. Attention Is the Beginning of Everything Another major theme in this episode is attention. Austin shares a simple practice: start and end the day without your phone. Not as a moral performance. Not as some extreme digital detox. Just as a way to protect the edges of the day from people and companies that do not care about you, but desperately want your attention. That hit me hard. Because attention is not just another resource. In many ways, it is the resource. What you give your attention to shapes your thoughts, your desires, your mood, your relationships, your sense of possibility, and your work. If the first thing you do every morning is hand your mind to the internet, you are letting someone else set the tone for your day. Austin's practice is simple. Coffee. Breakfast. Journal. Kids. Life. Then the phone. At night, the phone charges in the kitchen. Small boundary. Huge impact. Creativity requires attention. And attention has to be protected. Return to Who You Were Before All This There is a beautiful thread in this conversation about returning to the things that made you feel alive before life got complicated. For Austin, that includes riding a bike and playing in a band. For me, golf has become one of those things. Not because it is productive in the traditional sense, but because it gets me outside, off my phone, walking with friends, and fully present for hours. That matters. A lot of people feel lost because they are trying to think their way back into aliveness. But sometimes the way back is physical. Pick up the instrument. Ride the bike. Throw the baseball. Walk the dog. Draw badly. Make noise. Get outside. Do the thing you used to love before you thought it had to mean something. Austin brings up the question: Who were you before all this? Before the career. Before the metrics. Before the audience. Before the obligations. Before the identity got heavy. There may be clues there. Not because you need to go backward, but because some part of you may have been waiting to be invited forward again. Don't Call It Art The title of Austin's book is not a dismissal of art. It is a liberation from the weight we put on the word. For a lot of people, "art" has become intimidating. Sacred. Serious. Something that belongs to museums, geniuses, experts, critics, galleries, and people who have permission. But making is older and deeper than all of that. Kids understand this. They do not call it art. They just do things. And when we stop obsessing over whether something is art, we create more room to actually make. We get less precious. Less frozen. Less performative. Less worried about the label and more connected to the act. That is the invitation: Don't call it art. Don't worry about being an artist. Forget the nouns. Do the verbs. Just make stuff. It sounds almost too simple. That is why it works. Use What Bothers You Austin also offers a surprising creative tactic: pay attention to what you hate. Not publicly. Not performatively. Not as a way to become bitter or cynical. But privately, as information. Disgust can point toward values. Frustration can reveal desire. Jealousy can show you something you want. The things that bother you can become clues, if you are willing to ask what the opposite would look like. Instead of turning your irritation into a rant, turn it into a project. What would you rather see in the world? What is the opposite of the thing you cannot stand? What would it look like to make that? That shift is powerful because it transforms complaint into creation. It turns "I hate this" into "What if we made something different?" People Pay Attention to Belief Near the end of the conversation, Austin shares a line from Kim Gordon that I love: "People will pay to watch other people believe in themselves." That is true in art. It is true in music. It is true in entrepreneurship. It is true in leadership. It is true in life. We are drawn to people who are alive in what they are doing. Not perfect. Not polished beyond recognition. Not optimized into sameness. Alive. When someone believes in what they are making, that belief travels. This does not mean you will always feel confident. It does not mean you will never doubt yourself. It does not mean every idea will work. It means you keep returning to the work. You keep paying attention to what matters to you. You keep making the thing only you can make in the way only you can make it. That is where the signal comes from. About Austin Kleon Austin Kleon is the New York Times bestselling author of a series of illustrated books about creativity in the digital age: Steal Like An Artist, Show Your Work!, Keep Going, and Don't Call It Art. He is also the author of Newspaper Blackout, a collection of poems made by redacting the newspaper with a permanent marker. His books have sold over two million copies and have been translated into more than 30 languages. Austin's work has been featured on NPR's Morning Edition, PBS Newshour, The New York Times, and The Wall Street Journal. New York Magazine called his work "brilliant," The Atlantic called him "positively one of the most interesting people on the Internet," and The New Yorker said his poems "resurrect the newspaper when everybody else is declaring it dead." He has spoken for organizations including Pixar, Google, Netflix, SXSW, TEDx, Dropbox, Adobe, and The Economist. In previous lives, he worked as a librarian, a web designer, and an advertising copywriter. He lives in Austin, Texas, with his wife and sons. Follow Austin Kleon Website Don't Call It Art Newsletter Instagram X YouTube Timecodes 04:24 – Austin returns to the show and talks about the new book 06:17 – How Austin's kids became his best creativity teachers 07:04 – What it means to take care of a creative person 10:43 – The childhood question that reveals what makes time disappear 18:34 – Why play is creative research and development 21:43 – Finding what you were not looking for 23:06 – How a fixed vision can blind you to what is actually in front of you 28:13 – Chase reflects on creating the right conditions for creative work 31:37 – Austin's equation: play equals time plus space plus materials 32:48 – Why tools should feel more like toys 35:25 – Reconnecting with the activities that made you feel alive as a kid 38:53 – Who were you before all this? 43:08 – Protecting attention from companies that want to take it 44:17 – Starting and ending the day without your phone 47:08 – Why friendship, hobbies, and shared activities matter 57:17 – Where the title Don't Call It Art came from 58:32 – Forget the nouns, do the verbs, just make stuff 01:00:01 – Why "wouldn't it be funny if…" is a clue worth following 01:03:15 – Finding your creative family tree 01:06:36 – How to use frustration and disgust as creative information 01:08:31 – Why people pay attention when you believe in what you are doing 01:09:44 – Austin's newsletter, book tour, and where to find his work Questions to Ask Yourself If you want to turn this episode into action, take a few minutes with these questions: What did I do as a kid that made hours pass like minutes? Where am I making creativity heavier than it needs to be? What noun am I clinging to that might be keeping me from doing the verb? What conditions do I need in order to make more freely? Do I have time, space, and materials available on a regular basis? What tool in my life could become more like a toy? Where is my attention being stolen before I have a chance to choose? What hobby, activity, or form of play would help me return to myself? What bothers me enough that it might contain a creative clue? What would I make this week if I stopped worrying whether it counted as art? A Simple Practice for Making Like a Kid Again Here's something practical you can do this week. Set aside one uninterrupted hour. No phone. No audience. No outcome. No need to make something good. Choose a space. Put a few materials in front of you. Paper and markers. A camera. A guitar. A notebook. Clay. Index cards. A laptop with the internet off. Whatever feels inviting. Then begin with this prompt: Wouldn't it be funny if… Follow whatever comes next. Do not evaluate it too early. Do not ask what it is for. Do not decide whether it is art. Do not turn it into a brand, a strategy, or a pitch deck. Just make stuff. Then notice how you feel. Notice what surprised you. Notice whether something small wants to keep going. That is enough. Final Thought The longer I do this work, the more I believe that creativity is not something we need to earn. It is something we need to return to. It was there before the labels. Before the pressure. Before the metrics. Before the platforms. Before the fear of being judged. Before we learned to ask whether we were allowed. Austin's invitation in this conversation is simple, generous, and quietly radical: Stop making creativity so precious that you cannot touch it. Give yourself time. Give yourself space. Give yourself materials. Protect your attention. Find your friends. Pick up the toy. Follow the weird little idea. Let yourself begin before you know what it means. Until next time: forget the nouns, do the verbs, and just make stuff.

JONDOSPORT USA is a golf-focused sunglasses brand that specializes in performance eyewear designed to help golfers see the course more clearly. Their sunglasses use what they call "KRISP" lens technology, which is designed to enhance contrast, reduce glare, improve depth perception, and make it easier to track the golf ball and read greens. On this episode of the Wednesday Match Play Podcast, brought to you by Eden Mill St Andrews, Morné talks about where JONDO Sport is based, shares how the company got started, details their approach to R&D and where he sees the future of eyewear going, discusses prescription lenses, and explains how he first met Ernie Els. He also breaks down why 17% light transmission matters, talks about their warranty and repair process, and shares how they stay competitive in such a crowded market. This was such a cool conversation and an honor having Morné on the show. Let's tee off.

Breaking into any industry is difficult, but it is especially challenging when going against the large food manufacturers. Shekhar Patel, CEO of Mushroom Squared, discusses how he has benefitted from his time spent as Director of R&D at PepsiCo's Advanced Research Group when bringing a burger alternative, made with mushrooms, to market. He shares the importance of taste, nutrition, profitability, and customer satisfaction when developing a new product.  Learn more at mushroomsquared.com and follow on Instagram @mushroomsqrd Visit ConfidenceThroughHealth.com to find discounts to some of our favorite products.Follow me via All In Health and Wellness on Facebook or Instagram.Find my books on Amazon: No More Sugar Coating: Finding Your Happiness in a Crowded World and Confidence Through Health: Live the Healthy Lifestyle God DesignedProduction credit: Social Media Cowboys

Most people get their hormone levels tested once a year — if they think about it at all. Marina Pavlovic Rivas, Co-Founder & CEO of Eli Health, is on a mission to change that.  Ramon Vela sits down with Marina for a fascinating conversation about the technology she spent nearly seven years building: a saliva-based, at-home hormone monitoring platform that delivers cortisol, testosterone, and progesterone results to your phone in minutes, giving people real-time visibility into one of the most overlooked dimensions of their health. * The gap nobody was solving. After searching online for a way to track her own hormonal data, Marina realized the product didn't exist. So she built it, spending more than six years on R&D, regulatory approval, and venture capital before bringing it to market. * Hormones affect everything, not just fertility. From energy and mood to sleep, libido, bone health, cardiac health, and cognitive performance, Marina breaks down why hormonal data is one of the most important and most underused signals in personal wellness. * The wearable parallel that puts it all in context. Checking your hormones once a year is like measuring your heartbeat once a year: technically useful, but dangerously incomplete. Eli Health gives users the same continuous feedback loop that smartwatches brought to sleep and heart rate. * A six-second saliva test. Results in 20 minutes. Users collect saliva, wait 20 minutes, then photograph the test to receive results directly in the app. Repeat over time and you build a hormonal picture that no annual blood draw could ever provide. * Cortisol first, for good reason. Eli launched with cortisol because it sits at the top of the hormonal cascade. When cortisol is off, everything else follows. Testosterone and progesterone, including their interactions, are coming next. Join us in listening to this episode for a genuinely eye-opening conversation about hormonal health, the future of at-home diagnostics, and what it means to build a category that didn't exist before you created it.  Whether you're a health-conscious consumer, a founder, or someone who just wants to understand what their body is actually telling them, this one is for you.  Visit: https://eli.health/ Try their Instant Cortisol Test: https://eli.health/products/cortisol?view=sl-44128C61 If you enjoyed this episode, please leave The Story of a Brand Show a rating and review.  Plus, don't forget to follow us on Apple and Spotify.  Your support helps us bring you more content like this! * Today's Sponsors: Saral - The Influencer OS: https://www.getsaral.com/demo SARAL is the all-in-one influencer platform that finds brand-aligned creators, automates outreach, and manages everything in one place. Request a live demo today. Let the SARAL team know you're a The Story of a Brand Show podcast listener to get an extended free trial! Visit the link above. 

Eric Ries is an author, podcaster, and founder of The Lean Startup. He hosts The Eric Ries Show and his notable books Incorruptible: Why Good Companies Go Bad... and How Great Companies Stay Great, The Lean Startup, Farther, Faster, and Far Less Drama, The Leader's Guide, and The Startup Way. Greg and Eric discuss why startups and corporations lose their mission through shifts from founder-to investor-control, changing from long-term focus to short-term focus, and purpose-driven to profit-driven behavior. Eric argues governance is “organizational soul craft” and critiques shareholder primacy as a recent, judge-and-academic-driven ideology that creates unaccountable short-term pressure, metric surrogation, and value destruction, even for shareholders.  Eric also explains how markets reward short-term cost-cutting (e.g. reduced R&D), and why mission-driven companies can outperform. He outlines practical protections such as writing mission primacy into charters, converting to Public Benefit Corporations, and stronger structures like foundation ownership (e.g. Novo Nordisk and Patagonia). *unSILOed Podcast is produced by University FM.* Episode Quotes: Mission-driven or mission-hopeful? 12:39: So I think for companies, we're seeing this world now where we have a divergence between the mission statement and the actual mission or purpose of the organization. So the mission statement is lofty. I tell the story in the book of Silicon Valley Bank before it collapsed. Its mission statement was something like, “To advance the innovation economy,” or whatever. But its actual legal purpose was just maximize shareholder value. So this divergence caused the collapse of the bank. And so, first of all, if you have a mission statement, but your purpose says “any lawful act or activity,” you're lying. Just so you know, you are lying to your customers. You are lying to your employees. You're lying to everyone you say that mission to because, according to current legal theory, you could be replaced at a moment's notice by your investors, who will then can change the mission to whatever they want. I call that not being mission-driven. You are mission-hopeful. You're hoping nobody will do this to you in the future. Governance is organizational soul craft Governance sounds really boring, but it's really the art of organizational soul craft. It's actually really interesting. And if we can get leaders and founders to pay more attention to it, they can have a much higher probability of their organization enduring. The age of temporary organizations 27:46: I say we've entered an era of temporary managers running temporary organizations for the benefit of temporary owners because executive tenure, company lifespan, and average holding period of stocks have all collapsed in the last, especially the last twenty-five years, let alone the last forty years. So, I don't think it's possible to really have—it's very difficult to build a value-creating organization in that span, and the markets will punish you for doing so. Show Links: Recommended Resources: Governance Shareholder Primacy Overlapping Consensus Silicon Valley Bank Andy Rachleff Environmental, Social, and Governance Mark Zuckerberg Guest Profile: LinkedIn Profile Wikipedia Page The Lean Startup Social Profile on X Guest Work: The Eric Ries Show YouTube Channel Amazon Author Page Incorruptible: Why Good Companies Go Bad... and How Great Companies Stay Great The Lean Startup by Eric Ries – How Today's Entrepreneurs Use Continuous Innovation to Build Successful Businesses The Leader's Guide The Startup Way: How Modern Companies Use Entrepreneurial Management to Transform Culture and Drive Long-Term Growth Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Ireland remains an attractive location for foreign direct investment (FDI) with investor sentiment positive and overall investment here holding steady even as Europe continues a multi-year decline in inbound investment. That's according to the EY European Attractiveness Survey, which tracks cross-border investment projects resulting in new facilities and job creation across the continent. Ireland attracted 75 FDI projects in 2025, matching 2024 levels. This places it 15th overall in Europe, up two places from last year, and tenth on a per-population basis. United States investment made up more than half (53%) of inbound FDI to Ireland, consistent with historical levels and considerably higher than the 19% average held by US FDI across Europe. The regional profile was balanced, with 41% of projects in locations outside of Dublin. Ireland ranked tenth in Europe by investors in terms of FDI attractiveness for 2026, with investors pointing to a range of factors that make Ireland an attractive location for future FDI investment. These include our EU location and the access to new markets and customers this brings, competitive tax policy – most notably the R&D tax credit, talent, language and cultural ties to North America in particular. In contrast, inward investment for Europe fell to a ten-year low in 2025, with a 7% drop in projects when compared to 2024. Total projects across the continent in 2025 (5,023) were 22% lower than the 2019 pre-pandemic level (6,412). While the number of projects from US investors in Europe stabilised during 2025, it remains 38% below its 2019 peak. This is driven by industrial policy decisions by successive US administrations, as well as perceptions by investors of weaker growth prospects, regulatory complexity, higher operating costs and policy fragmentation. Software and IT services (33) was Ireland's leading FDI sector during 2025, with the number of projects doubling versus 2024, and the sector accounted for more than 40% of the year's total. Business services (14) and financial services (9) projects were the next two largest FDI sectors in Ireland. A key highlight of the research is the strength of the Irish innovation economy. Research and development projects (R&D) accounted for 25% of Irish investments, far ahead of the total European share of 7%. This confirms Ireland's position as a leading knowledge economy with a strong capacity to attract innovation-driven investment and supported by an internationally competitive R&D tax credit regime. Ireland was also rated highly as a location for AI investment, innovation and deployment. However, the research also identified risks to Ireland's future attractiveness. Ireland is perceived as having challenges in terms of infrastructure, and the cost of energy, labour and other inputs. Infrastructure constraints was the top-rated risk affecting Ireland's future attractiveness, rising from sixth in the previous year's survey. EY Ireland Partner and Head of FDI Feargal de Freine said: "In what was another challenging year for FDI in Europe, holding our own is a strong outcome for Ireland as is the continued strength of investor sentiment towards Ireland. Our performances in software and R&D in particular highlight our enduring advantage in these fields, while Ireland was also rated highly as a location for AI investment, innovation and deployment. However, the broader European trend points towards a structural shift in global FDI investment that has been underway for several years now, as countries utilise industrial policy to aggressively court investment. Events over the past 12 to 18 months have accelerated this agenda, and businesses and policymakers are seeking to navigate disruption across a range of fields simultaneously, including geopolitical risk, economic shock and technological disruption. Carol Murphy, EY Ireland Partner and Head of Markets said: "It is encouraging to see Ireland continuing to secure a disproportionately strong share of in...

In this episode of Group Function, Alan connects with fellow Michiganders Austin and Lindsay Ritter, the husband-and-wife team behind Ascentcare Dental Products. Austin shares his unique journey from an HR background to dental product innovation, inspired by casual, after-hours brainstorming sessions with a dentist neighbor. They dive deep into the engineering behind their flagship Vacu-Lux Isolation System, explaining how they designed it as a true high-volume evacuation (HVE) system to turn the oral cavity into a negative pressure environment. The discussion covers the critical role of user feedback in small-business R&D, material selection for patient comfort and optical clarity, and their ultra-lightweight Feather Flex Vacuum Line which helps prevent wrist fatigue for hygienists and dentists alike. Some links from the show: Ascentcare website Vacu-lux HVE Isolation Systems VacuVUE Evacuation Mirror Featherflex HVE Vacuum Line Join the Very Dental Facebook Group using one of these passwords: Timmerman, Paul, Bioclear, Hornbrook, Gary, McWethy, Papa Randy, Frank or Lipscomb!  The Very Dental Podcast network is and will remain free to download. If you'd like to support the shows you love at Very Dental then show a little love to the people that support us! We're proud to be supported by the folks at Net32! I'm a big fan of the Bioclear Method! I think you should give it a try and I've got a great offer to help you get on board! Use the exclusive Very Dental Podcast code VERYDENTAL8TON for 15% OFF your total Bioclear purchase, including Core Anterior and Posterior Four day courses, Black Triangle Certification, and all Bioclear products. Are you a practice owner who feels like the bottleneck in your own business? If you're tired of being the hardest-working person in your office, I've got something you need to hear. Dr. Paul Etchison, is hosting a virtual event that is a total game-changer. Paul is honestly one of the most brilliant minds in dental leadership today, and he's hosting the 3-Day Freedom Practice Workshop from February 19th through the 21st. He's going to show you exactly how to break through that two-million-dollar revenue ceiling while actually compressing your clinical week. It's about building a leadership team that takes ownership so you can finally step into the CEO role you deserve. Head over to DentalPracticeHeroes.com/freedom to grab your spot. And do me a favor—mention the Very Dental podcast when you sign up. It's 100% guaranteed, so you've got nothing to lose but the stress. Crazy Dental has everything you need from cotton rolls to equipment and everything in between and the best prices you'll find anywhere! If you head over to verydentalpodcast.com/crazy and use coupon code "VERYSHIP" you'll get free shipping on your order! Go save yourself some money and support the show all at the same time! The Wonderist Agency is basically a one stop shop for marketing your practice and your brand. From logo redesign to a full service marketing plan, the folks at Wonderist have you covered! Go check them out at verydentalpodcast.com/wonderist! Enova Illumination makes the very best in loupes and headlights, including their new ergonomic angled prism loupes! They also distribute loupe mounted cameras and even the amazing line of Zumax microscopes! If you want to help out the podcast while upping your magnification and headlight game, you need to head over to verydentalpodcast.com/enova to see their whole line of products! CAD-Ray offers the best service on a wide variety of digital scanners, printers, mills and even  their very own browser based design software, Clinux! CAD-Ray has been a huge supporter of the Very Dental Podcast Network and I can tell you that you'll get no better service on everything digital dentistry than the folks from CAD-Ray. Go check them out at verydentalpodcast.com/CADRay!

(The short answer: we get paid every two weeks. The long answer is… complicated.)I've studied research and innovation for almost 20 years.I live it.And I teach it.So I think I'm fairly qualified to answer this one.There are two broad worlds to understand:industry and academia.⸻

On this week's episode of Tying it Together, host Tim Boyum is joined by former Charlotte city council members Tariq Bokhari and Larken Egleston to talk about potentially one of the most consequential months of Charlotte politics in at least a decade. The council recently reversed course and canceled a major transportation plan. It is poised to potentially ban data centers in the coming weeks, and this all comes after the mayor announced she was stepping down at the end of June, long before her term expires. Bokhari, a Republican, and Egleston, a Democrat, work at The Southern Group and host their own podcast, R&D in the N.C.

Chronic pain, fatigue, and low energy may not just be "aging"... they could be signs of energetic imbalance in the body. What if your body already knows how to heal… it just needs the right information and energy? Join me and my guest, Harry Massey, bioenergetics researcher and founder of Energy4Life, as we explore the fascinating connection between energy, information, the brain, and chronic pain. After spending seven years bedridden with chronic fatigue and pain following multiple accidents, Harry discovered a completely different approach to healing rooted in bioenergetics.

What makes a country stand out in the global race for investment? In this episode of Develop This!, Dennis Fraise speaks with Fadi Shadeh of the Hungarian Investment Promotion Agency to explore why Hungary has become one of Central Europe's most dynamic investment destinations. From its strategic location at the heart of Europe to its deep integration into the EU single market, Hungary offers companies access to major markets, efficient logistics, and strong infrastructure. Fadi explains how the country's positioning enables fast access across Europe while supporting complex global supply chains. The conversation dives into Hungary's economic transformation, particularly its shift toward higher-value industries like electromobility, battery manufacturing, business services, and R&D. While manufacturing remains a strong base, the country is rapidly expanding its role in advanced services and innovation-driven sectors. A major focus is workforce development. With a strong university network and a dual education system that connects students directly with industry, Hungary is building a talent pipeline designed to meet modern business needs—often producing job-ready graduates aligned with employer demand. Fadi also breaks down how the Hungarian Investment Promotion Agency operates as a "one-stop shop" for investors—helping companies navigate location decisions, incentives, site visits, and long-term expansion strategies. Key Takeaways Hungary's central location provides strong access to European and global markets The economy is shifting toward electromobility, batteries, and business services Talent development is supported through a strong university system and dual education model The Investment Promotion Agency acts as a one-stop shop for investors Regional cities are becoming key drivers of future growth Investment success depends on talent, infrastructure, and long-term trust Key Topics Covered Hungary's geographic and strategic advantages Economic transformation and industry mix  

「シャドーイングをやっても英語が聞き取れない…」とお悩みの方へ。リスニング力を伸ばすには、まず「母音の種類」を知ることが先決です。日本語の5個に対し、英語の母音は20種類以上！この記事では初心者が絶対に知っておくべき長母音・短母音・R母音のルールや、喉奥を開く発音のコツを分かりやすく解説します。◆━━━━━━━━━━━━━━━━━━◆

The Space Show Presents Paul Warley, CEO of Ascent Solar, Friday, 4-17-26Quick Summary:The Space Show featured Paul Worley, CEO of Ascent Solar, discussing their flexible thin-film solar technology. Paul explained that their CIGS (Copper Indium Gallium Sulfide) panels are lightweight, rollable, and designed to fit specific areas, offering 12.5-14% efficiency and costing $35-70 per watt compared to silicon's $3-10 per watt. The panels have been tested in space and can withstand radiation better than silicon, with applications including satellites, drones, high-altitude platforms, and underwater systems. Paul noted their panels can be deployed and rolled up multiple times, with TRL9 certification from a previous space mission. The company is publicly traded on NASDAQ as ASTI and focuses primarily on DoD and commercial space markets, with plans to expand in MEO, GEO, and lunar applications in the coming years.Detailed Summary:Our program focused on technical discussions about space missions and power requirements. Paul explained different power levels for various orbital ranges, noting 450-600 watts per kilogram for MEO and GEO missions, and 150-250 watts per kilogram for lower orbits. The group discussed the format of an upcoming 60-minute space show, with participants introducing themselves and their backgrounds. Technical issues with audio echo were identified but not fully resolved before the end of the meeting.Paul explained the cost and efficiency differences between various solar panel technologies for space applications. He clarified that gallium arsenide panels cost $250-350 per watt, silicon panels cost $3-10 per watt, and their SIGS (Copper indium gallium sulfide) technology costs $35-70 per watt. Paul also described how their technology uses a different coating (XBR) for underwater applications, allowing panels to be submerged at 500 meters and recharge on the surface.Paul further discussed the development and specifications of their flexible solar panel technology, highlighting its advantages over traditional silicon wafers, including being 3-10 times lighter and providing roughly 4 times the power. The discussion covered the technology's performance in high-pressure environments, manufacturing considerations, and potential applications in space, including lunar manufacturing and satellite deployment. Paul mentioned having a SpaceX founder on his advisory board and noted their current customers include a communications satellite company, with plans for manufacturing in space, as well as potential applications in drones, HAPS, and underwater systems.Paul discussed their company's solar panel technology, explaining that if a panel is damaged by space debris, electricity can still flow around the damaged area. He confirmed they are in talks with multiple space companies, though he declined to name specific clients. When asked about operating temperatures, Paul clarified that their panels can operate between -100C to +100C in space conditions, though he couldn't specify the exact temperature without cooling systems. The discussion ended with Paul highlighting their product's key advantage of faster delivery times compared to traditional gallium arsenide panels, though he didn't complete the specific timeline comparison.Paul discussed the efficiency of their flexible solar panels, explaining they measure between 12.5% and 14% efficiency in secondary cells. Marshall inquired about the durability of rolling up the panels, to which Paul confirmed they can be rolled up hundreds or thousands of times without damage, citing their TRL9 rating achieved through underwater testing. Paul also mentioned their product is patented and not restricted by ITAR, though they primarily market to Europe and India rather than China or other restricted regions.Paul talked about his company's solar product, highlighting its durability and competitive advantage over Chinese alternatives in the consumer market. He explained that while the product was previously used in camping and military applications, current focus areas include DOD commercial applications, space, and potentially drones. Paul noted that while residential rooftop installation is financially viable, it's not part of their current strategy due to reinforcement requirements for buildings. The discussion concluded with an unasked question about cell density per square meter from John Hunt, which was not answered in the provided transcript.Deployable solar panel technology, explaining that their panels can produce power at 60% angle while most silicon panels require 30-40% angle for power generation. He described different deployment mechanisms including roll-out systems and origami structures, noting that cost and mass of the support system are significant factors. Paul also mentioned that their technology is currently too expensive for widespread residential use, with installation costs being a major barrier, and that 40% of roofs would require reinforcement.We covered ongoing R&D efforts to improve product efficiency and reduce waste, including work with perovskite coatings and a collaboration with a company that has developed 400 patents around perovskite technology. The team conducted initial space testing with different FEP thickness coatings, though the sample size was too small for definitive conclusions. Paul mentioned that additional radiation and atomic oxygen testing would be conducted in the next 2-3 months, and he expected data from the Novi satellite launch on SpaceX's rocket within 2-3 weeks, including information about deployment and space debris impact.The meeting also focused on discussing Paul's space solar panel technology company, ASTI, which is publicly traded on NASDAQ. Paul explained that while the company receives about one legitimate space-related business inquiry per week through their website, they typically require NDAs to share technical details about their solar panels, which can withstand higher temperatures and radiation than traditional silicon panels. The discussion revealed that ASTI's competitive advantage lies in the flexibility of their panels, allowing them to outperform silicon panels in certain curved or complex applications. Paul noted that while the company is well-funded and manufactured-ready, the next major power push in space is expected in the third and fourth quarters of next year.Special thanks to our sponsors:American Institute of Aeronautics and Astronautics, Helix Space in Luxembourg, Celestis Memorial Spaceflights, Astrox Corporation, Dr. Haym Benaroya of Rutgers University, The Space Settlement Progress Blog by John Jossy, The Atlantis Project, and Artless EntertainmentWe use Zoom phone numbers for program participation.For real time program participation, email Dr. Space at: drspace@thespaceshow.com for instructions and access.The Space Show is a non-profit 501C3 through its parent, One Giant Leap Foundation, Inc. To donate via Pay Pal, use:To donate with Zelle, use the email address: david@onegiantleapfoundation.org.If you prefer donating with a check, please make the check payable to One Giant Leap Foundation and mail to:One Giant Leap Foundation, 11035 Lavender Hill Drive Ste. 160-306 Las Vegas, NV 89135Upcoming Programs:Broadcast 4594: Zoom: Bob Zimmerman (Special 6 PM PDT start time) | Tuesday 26 May 2026 600PM PTGuests: Robert ZimmermanZoom: (6 PM PDT Start Time) Bob Zimmerman is back on Starship and all space matters. Don't miss it! to Listen and participate use Zoom phone lines. Email DrSpace before airtime for the number access.Broadcast 4595: Hotel Mars TBD | Wednesday 27 May 2026 930AM PTGuests: John Batchelor, Dr. David LivingstonHotel Mars TBDNo Program for Friday, May 29, 2026 | Friday 29 May 2026 930AM PTGuests: Dr. David LivingstonNo program today, Friday, May 26, 2026Broadcast 4596: Zoom: Open Lines Discussion | Sunday 31 May 2026 1200PM PTGuests: Dr. David LivingstonZoom: Open Lines Discussion. Email DrSpace prior to air time for Zoom phone number access. Get full access to The Space Show-One Giant Leap Foundation at doctorspace.substack.com/subscribe

Hosted by David Cowen | Careers and the Business of Law Adam Rouse (Walgreens), Ashley Christakis (CrowdStrike), John Koss (Mintz), and Major Baisden (Lineal) pick up where they left off at LegalWeek in New York - ten weeks later, the conversation is sharper. The question on the table: how do you evaluate legal technology when the problem isn't fully defined yet? The answer involves a framework, a maturity arc, and a lot of grace. WHY THIS MATTERS? If your legal team is still waiting for the perfect data environment before acting on AI, you're already behind. This group agrees: the chaos is the condition. The only way through it is a deliberate strategy, documented workflows, and the courage to take the first step. KEY TAKEAWAYS Comfort with the unknown is the new baseline. The velocity of AI adoption has accelerated the FOMO - but the core evaluation process hasn't changed as much as we think. The three I's - Initiate, Investigate, Implement - apply to more than technology. Use them for concepts, use cases, and people, too. Most legal departments are somewhere between ad hoc and operational on the maturity arc. Very few are close to optimized - and that's okay. Stop chasing use cases. Start documenting how you actually get work done. That's the unlock for AI value. Data nirvana doesn't exist. Progression and discipline do. Don't wait for a perfect data ecosystem before extracting value. AI is the great information governance equalizer. Nothing is obscure anymore - if it's accessible, it will get indexed. The real AI dividend isn't just productivity. It's capability - doing things you were never able to do before. Know why you're doing what you're doing - and why you're not doing what you're not doing. That clarity builds organizational confidence and stronger client relationships. PEOPLE MENTIONED David Cowen - Host Adam Rouse - Sr. Counsel, eDiscovery & Director Legal Operations, Walgreens Ashley Christakis - Former Senior Manager, Legal Data Intelligence, CrowdStrike John Koss - Head of Innovation, AI, and E-Data Consulting, Mintz Major Baisden - CEO, Lineal Services COMPANIES MENTIONED Walgreens - Large enterprise legal operations navigating AI adoption CrowdStrike - Corporate legal team investing in technical curiosity and R&D thinking Mintz - Law firm with a formalized data strategy committee and Director of Data Strategy Lineal - Legal services company using AI to record, document, and optimize workflows Legal Data Intelligence (LDI) - Community behind this series; legaldataintelligence.org

About Nate HeissNate Heiss is a true game design chameleon. His 25-plus year career spans from competitive Magic: The Gathering play to designing iconic cards like Goblin Guide at Wizards of the Coast. He then took those skills into the AAA video game world, working as a designer and creative director at studios like LucasArts and PopCap on massive mobile hits like Plants vs. Zombies Heroes. Nate and I have been geeking out about game design since we met on the Magic Pro Tour in the late 90s, and now we're finally teaming up on Gundam Assemble, an upcoming tabletop skirmish miniatures game. In this episode, we dive deep into the differences between physical and digital design, the ethics and realities of free-to-play business models, and how to capture the elusive magic of discovery in an internet age. Nate delivers profound insights that will resonate with anyone building games or trying to navigate a creative career.Justin's Ah-Ha! Moments* The Shift From Player to Designer Mindset: Nate and I discuss a classic trap many pro players fall into when they start designing: trying to "beat" the players. It's easy to bring a competitive ego into R&D and focus on squashing dominant strategies to prove how smart you are. But great design isn't about winning; it's about crafting a fun experience. Once you soften that competitive edge, you realize your true goal is to empower players to make their own discoveries.* The Economics of Free-to-Play Dictate Design: We tackle the controversial topic of free-to-play games. Nate points out that companies succeeding in this space aren't necessarily making "better" games; they are mastering live service and content costs. If a studio can "turn the crank" and produce engaging content at a fraction of the cost, they gain a massive competitive advantage. It shifts the design problem from just making an great game (which is table stakes) to efficiently delivering ongoing value over time.* Roguelikes are the Modern Gold Rush: I've always wanted to recreate the feeling of opening an early Magic pack—when nobody knew the optimal strategies and everything felt like an untamed frontier. Nate brilliantly identifies that roguelikes are where this feeling lives today. By taking a core loop and exploding it into a massive, randomized possibility space on every run, roguelikes force players to adapt and experiment, capturing that communal feeling of discovery over and over again. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit justingarydesign.substack.com/subscribe

In Episode #62 of the Hindu Parenting Podcast, we sit down with Shri Muneet Dhiman - Founder of Vidyakshetra Gurukulam in Bengaluru - to explore the timeless principles of the Gurukula system and how they can be applied by Hindu parents today. From building character and discipline to nurturing a deep connection with our heritage, this conversation offers practical wisdom for raising grounded and culturally rooted children in a modern world.In this video, you'll learn:The core philosophy of the Gurukula education system.How to integrate traditional values into everyday parenting.The role of the parent as the first "Guru."Strategies for fostering emotional and spiritual growth in children.Shri Muneet Dhiman is the founder and kulapati of Vidyskshetra gurukulam, a speaker in the World Hindu Congress 2023, and a TEDx speaker. He was in IT for 15 years as Head of R&D service in Europe, and has spent the last 15 years in Gurukula education. Vidyakshetra is a gurukulam that is reimagining education in Bharat and showing us how to take gurukul education forward.Do not miss this podcast! Share widely!Hindu Parenting is a community for Hindu parents worldwide. We carry articles, podcasts, reviews, classes for teens and various other resources to help you in your parenting journey.Subscribe to get the latest articles and podcasts in your e-mail inbox.Leave a note, DM or send email to contact@hinduparenting.org if you'd like to share your viewpoints, experiences and wisdom as Hindu parents, or if you wish to join our community!If you find our work valuable, please consider upgrading to a paid subscription.You can also follow us on X (Twitter) or Instagram. Our handle is “hinduparenting”The opinions expressed by guests on The Hindu Parenting Podcast are their personal opinions and Hindu Parenting does not assume any responsibility or liability for the accuracy, completeness, suitability or validity of anything shared on our platform by them.Copyright belongs to Hindu Parenting. Get full access to Hindu Parenting at hinduparenting.substack.com/subscribe

La France reste, en 2026, le pays qui attire le plus d'investisseurs étrangers en Europe. C'est l'un des principaux enseignements du baromètre annuel d'EY sur l'attractivité économique. Mais derrière cette performance, un indicateur beaucoup plus préoccupant émerge. Les investissements étrangers dans les centres de recherche et développement ont chuté de 47% en un an. Un signal d'alerte pour l'économie française, alors que la bataille mondiale de l'innovation s'intensifie. Sur le papier, tout va bien pour l'économie française. Selon le dernier baromètre EY consacré à l'attractivité de la France, le pays conserve sa place de leader européen pour l'accueil des investissements étrangers. Une performance qui confirme, une nouvelle fois, la capacité de la France à attirer les capitaux internationaux. Mais en regardant de plus près, un chiffre interpelle. Les investissements étrangers dans les centres de recherche et développement, les fameux centres de R&D, ont chuté de 47 % en un an. Or, ces centres de recherche sont loin d'être anodins. Ce sont eux qui conçoivent les nouvelles technologies, les futurs médicaments, les nouveaux matériaux, bref, tout ce qui structurera notre quotidien de demain. Si ces investissements ralentissent durablement, c'est donc une partie de l'avenir industriel français qui pourrait s'assombrir. Le paradoxe est d'autant plus frappant que, dans le même temps, les investisseurs étrangers continuent de considérer la France comme un pays innovant. C'est même l'un de ses principaux atouts. Le pays bénéficie d'ingénieurs qualifiés, d'universités reconnues, d'infrastructures solides et d'un écosystème favorable à l'innovation. Mais entre cette image positive et les décisions d'investissement, un décalage apparaît clairement. Sans qu'il y ait un désaveu de la France, les investissements en recherche et développement ralentissent bel et bien. Un ralentissement mondial qui finit par toucher la recherche Pour comprendre cette baisse, il faut prendre du recul. La tendance n'est pas uniquement française, elle est européenne, voire mondiale. Depuis quatre ans, les entreprises évoluent dans un environnement de plus en plus instable : entre guerre en Ukraine, tensions géopolitiques, inflation, hausse des coûts de l'énergie, ralentissement économique, elles arbitrent davantage et réduisent leurs dépenses. La situation est d'autant plus particulière que, jusqu'à récemment, les grands groupes avaient plutôt cherché à préserver leurs centres de recherche. Car sans innovation, pas de croissance. Pendant plusieurs années, les budgets de R&D ont donc été relativement sanctuarisés. Mais en 2025, la pression financière est devenue telle que ces investissements ont fini, eux aussi, par être touchés. C'est généralement le dernier poste sur lequel les entreprises cherchent à faire des économies. Le fait qu'il soit désormais concerné montre l'ampleur du ralentissement. La France souffre de sa propre réussite mais doit rester compétitive Autre élément important: la France souffre aussi, paradoxalement, de sa propre réussite. Historiquement, elle est une place forte de la recherche et développement en Europe. Depuis plusieurs années, elle figure parmi les destinations les plus attractives pour les projets de recherche internationaux. Par conséquent, lorsque la vague de ralentissement arrive, elle frappe plus fortement là où il y avait le plus de projets. Mais cela ne signifie pas qu'il faille banaliser cette baisse. Car la compétition mondiale, elle, continue de s'intensifier. Si les investissements en R&D ne se font plus en France, ou plus largement en Europe, ils se font ailleurs. Les États-Unis et la Chine investissent massivement dans les technologies d'avenir, soutenus par de grands plans industriels et des stratégies de long terme. Dans ce contexte, si la France et l'Europe ralentissent trop longtemps sur la recherche, elles prennent le risque de décrocher technologiquement. Aujourd'hui, les investisseurs ne remettent pas en cause la qualité de la recherche française. Le problème est ailleurs. Le contexte politique et économique, la visibilité réglementaire, la stabilité et la capacité à offrir un cadre lisible sur le long terme pèsent sur la décision finale d'investissement. Car un investissement en recherche est, par définition, un projet de long terme. Et c'est là tout l'enjeu pour la France: être attractive ne suffit plus. Pour rester une grande nation de l'innovation, elle doit désormais prouver qu'elle peut rester compétitive dans la durée. À lire aussiEmmanuel Moulin auditionné au Parlement pour devenir gouverneur de la Banque de France

Applied Biopharm Consulting Ltd has announced a new research collaboration with the Pharmaceutical and Molecular Biotechnology Research Centre (PMBRC) at the South East Technological University (SETU) in Waterford, Ireland, to experimentally validate aspects of its artificial intelligence (AI)-driven biomolecular research programme. The collaboration is supported through the Enterprise Ireland Innovation Voucher scheme, enabling the company to access specialised laboratory expertise within the university. With strong research activity across pharmaceutical science, biotechnology and applied life sciences and supported by the Enterprise Ireland Technology Gateway programme PMBRC has developed extensive capabilities in industry-focused research and collaboration with emerging technology companies. Through this collaboration, cell-based studies will be undertaken at SETU to generate experimental data supporting the continued development of Applied Biopharm Consulting's computational viral vector engineering platform. These studies will provide experimental validation to complement the company's computational research activities. Building on its 2024 feasibility study grant and the subsequent Intellectual Property (IP) Start Grant awarded in 2026 under Enterprise Ireland's IP Strategy initiative, Applied Biopharm Consulting continues to expand its internal research and development programme focused on next-generation viral vector engineering. The Innovation Voucher collaboration represents the next step in translating computational research into experimentally validated technologies while supporting the company's ongoing intellectual property strategy. Applied Biopharm Consulting's research programme integrates artificial intelligence, structural bioinformatics and molecular simulation techniques to analyse large datasets of protein structures and explore novel biomolecular interactions. These computational approaches are being applied to explore new strategies for viral vector design relevant to advanced biologics and gene therapy development. Dr. Anthony Newcombe, Managing Director of Applied Biopharm Consulting Ltd, commented: "Establishing a research collaboration with South East Technological University represents an important step in advancing our viral vector engineering programme from computational design toward experimental validation. The Innovation Voucher scheme enables us to access specialised academic expertise and laboratory capabilities that complement our computational research platform." Dr Niall O'Reilly, Centre Director of the PMBRC added: "We are pleased to collaborate with Applied Biopharm Consulting on this research initiative. Partnerships between academia and industry provide valuable opportunities to translate innovative ideas into experimentally validated technologies, and this project highlights how academic research capabilities can support emerging biotechnology innovation. This collaboration also fits well into our current research portfolio in areas such as gene therapy and biomedical science" The collaboration represents the next stage in Applied Biopharm Consulting's internal research and development (R&D) programme, which combines computational biologics research with experimental validation and intellectual property development. Alongside its research activities, Applied Biopharm Consulting continues to support global biopharmaceutical companies in GMP compliance, Regulatory CMC, Manufacturing Science & Technology (MSAT), Quality Assurance and technology transfer. By integrating extensive regulatory and manufacturing expertise with next-generation biologics engineering capabilities, the company is positioning itself at the intersection of advanced therapy development and biologics innovation. See more stories here. More about Irish Tech News Irish Tech News are Ireland's No. 1 Online Tech Publication and often Ireland's No.1 Tech Podcast too. You can find hundreds of fantastic previous episod...

We're about one simple thing. We help the owners of companies between $2 million and $20 million in size achieve large-scale growth of their business. We don't believe in growth for growth's sake. As far as we're concerned, a company is just a tool to support the goals of its owners and its people. We help people to grow their businesses in support of whatever those goals are. We help with marketing, sales, operations, human resources, underlying structures, management, leadership development – whatever it takes to grow. That's what we do. https://walshbusinessgrowth.com/    About Walsh Business Growth Institute. Founded in 1995 by Michael Walsh, Walsh Business Growth Institute is an international business consulting organization: We support business owners across North America and Europe to take their companies to the next level of growth. Our goal is simple – to help people get what they want, using their business as the primary tool to get them there. Whether the company wants to grow to $10 million or $100 million, what most business owners ultimately want is more profit and more freedom. We're committed to synthesizing the best practices for business growth and leadership development. We continue to invest in R&D to continually evolve best practices and strategies that help our clients meet their goals. We help you grow consistent with your goals. The biggest challenge that business owners face "The reason we look at business growth is because, at any given size, a company has strengths and limitations. If you can grow it a little bit, you can actually morph it to get what you want."

Running is already punishment with better shoes. In ROI Podcast® #509, Law Smith and Eric Readinger talk with Josh Sprague, owner/operator and founder of Orange Mud, the endurance gear company making hydration packs, running vests, biking packs, transition wraps, and outdoor gear for runners, cyclists, triathletes, mountain bikers, gravel riders, ultra runners, and other people who hear "24-hour race" and somehow do not immediately fake a hamstring injury. Josh built Orange Mud because the hydration packs he used were too tight, bounced around, caused chafing, and made running feel like a backpack had filed a workplace grievance against his torso. So he made his own. Fourteen years later, Orange Mud is still solving that same core problem: helping athletes carry fluid, gear, phones, calories, and post-race beer-garden money without turning their upper body into a friction crime scene. This one starts with practical product design, then takes the scenic route through SEC football smuggling economics, premium floppy flasks, Raiders fan brawls, secret backpack wine compartments, fake casts full of booze, organic street nuts, nicotine pouches, and the kind of R&D conversation that should probably be notarized. Then Josh drops the business stuff that actually matters. Founders confuse activity with ROI. Busy is not a metric. Busy is a haunted LinkedIn carousel with a calendar invite. Josh's cleanest example: the best account he ever landed took five years of follow-up, then turned into $5 million in the first year. Most people quit after two polite emails and a sad CRM note. Josh kept showing up. The bigger lesson is marketing infrastructure. Josh explains why companies chase platforms like Facebook ads before they understand the full pipeline. If your website is bad, your tracking is broken, your CRM is decorative, your follow-up is asleep, and your customer problem is defined with the clarity of a gas station bathroom mirror, the ad platform is not the problem. You are just paying Meta to make the fire look warm. Listen if you are a founder, ecommerce operator, product builder, endurance athlete, marketer, salesperson, agency owner, or anyone who has ever said "we need Facebook ads" before knowing who the customer is, what problem they have, and what happens after the lead comes in. Hosted by Law Smith, @LawSmithWorks, LawSmithWorks.com, and Eric Readinger, @EricReadinger. Powered by Tocobaga Consulting, Tocoba.ga. ROI Podcast® is the business show for people who want the math, the mess, and the occasional product idea that belongs in a stadium parking lot. Topics: Josh Sprague, Orange Mud, hydration packs, running gear, cycling gear, endurance racing, 24-hour races, trail running, gravel cycling, product design, customer pain, marketing ROI, sales follow-up, CRM, Facebook ads, Meta attribution, ecommerce marketing, founder story

I'll admit, I'm a bit of a fusion skeptic. But! There are exciting things happening in the fusion industry these days. So, I brought Andrew Holland, CEO of the Fusion Industry Association, to talk to me about why things are looking up for fission's avant-garde cousin. Andrew's candor, insight, and enthusiasm made him a great guest. I had questions about everything from break-even hurdles to robotics and he handled it all with aplomb. I came away from this episode convinced that regardless of how near or far away commercial fusion may be, America should invest in R&D to try and crack it: the tech is too cool and the lessons we'll learn along the way too valuable.

Most product decisions get made by analogy. Someone says, "This is how we've always done it," or "This is what the market expects," or "This is what the competition is doing." The room nods. The decision gets made. And buried somewhere in the middle of all of it is an assumption nobody checked. First-principles thinking is the discipline of identifying assumptions before the market finds them for you. By the end of this episode, you'll have the tools to strip any problem down to what's actually true and build answers that hold, even when the boardroom is watching, and the clock is running. What Is First Principles Thinking? First principles thinking is the practice of breaking a problem down to its fundamental truths, then building your solution up from what actually holds. Not from industry convention. Not from what worked last time. From what's actually true about the problem in front of you. The alternative is reasoning by analogy: doing what worked before, doing what competitors do, doing what the category expects. Analogy is faster and usually right. It fails badly when the thing that used to be true stops being true and nobody notices. Why Assumptions Go Unchecked In 2005, HP's CEO, Mark Hurd, stopped me in the hallway at Building 20 in Palo Alto and drilled me on HP's R&D funding. The metric he focused on was R&D as a percentage of revenue. He wanted HP's ratio to look more like Acer's. I pushed back. I argued we should be comparing ourselves to Apple, not Acer. Mark didn't hesitate. "We are not Apple, and we never will be." What stopped me in that moment wasn't the disagreement. It was the certainty. Nobody in the room questioned whether R&D as a percentage of revenue actually measured what we thought it measured. That metric had been in use for decades. Every competitor used it. Every analyst tracked it. It felt like bedrock. It wasn't. It was an inherited constraint that had calcified into a rule. R&D as a percentage of revenue tells you about accounting categories. It tells you nothing about what that spending produces, whether the right problems are being attacked, or whether innovation output is growing or shrinking. The assumption underneath the metric had never been tested. Nobody had ever asked whether comparing R&D ratios across companies with entirely different business models actually tells you anything meaningful. The cost of that unchecked assumption didn't show up in the next quarter. It showed up over the following decade. HP's innovation pipeline quietly drained, and the Fast Company "Most Innovative" recognition we'd earned three years running disappeared with it. One inherited metric, accepted as fact by an entire room of experienced people, making a generational decision. That's what derivative thinking actually costs. Not a bad quarter. A decade. The people in that room weren't careless. They were experienced. Experience is exactly what makes inherited assumptions feel like facts. The metric felt like a fact. It was a choice nobody remembered making. That's exactly what a first principles question would have caught. Nobody asked it. The Three Core Skills The three skills run in sequence, and each one depends on the one before it. The first, Strip the Assumptions, finds the inherited assumptions baked into how the problem was framed. From there, Test What Remains and Build Up takes what survived and builds your solution from what's actually true. Finally, When to Use First Principles tells you when the process is worth running in the first place. Skip ahead, and the later skills don't hold. Run them in order, and they compound.  Strip the Assumptions Before you can reason from first principles, you have to know what you're actually working with. Most problems arrive already carrying assumptions in how they're framed. Your first job is to find them. Steps to strip assumptions: Write the problem exactly as it was given to you. Don't improve the framing yet. Use their words. Underline every word that implies a constraint. "Must," "can't," "always," "never," "the only way to." Each one is a candidate. Ask, for each constraint: is this physically true, or is it inherited? A physical truth holds regardless of what you decide. An inherited constraint is someone's prior decision that calcified into a rule. Set the inherited constraints aside and restate what remains. This is the real problem. It's usually smaller and easier to solve than what you started with. Treat what survives as your design constraints. These are your real boundaries. Take this list into your brainstorming, and test every idea against what's on it, not against the assumptions you crossed out. This step takes 20 minutes when you do it honestly. Most teams skip it entirely, then spend months optimizing a solution to the wrong problem. Test What Remains and Build Up Not every constraint is an assumption. Some things are actually true: physics, unit economics, human behavior at scale. The goal isn't to pretend those constraints don't exist. It's to be precise about which reality you're dealing with. Steps to test what remains and build up: Take each surviving constraint and push on it. Ask: Is this true because it's physically impossible to change, or because changing it would be expensive, unfamiliar, or uncomfortable? Expensive and unfamiliar are not the same as impossible. Separate the hard limits from the soft ones. Hard limits are what's actually true: things that hold regardless of how the problem is reframed. Soft limits are negotiable. Label them clearly. Most teams never make this distinction and treat every constraint as if it were granite. State your hard limits in plain language.  Write it down. One sentence per hard limit. These are the actual boundaries your solution has to honor. Reason forward from what remains. Don't start from where the industry is and work backward to justify it. Now ask: what solution do the hard limits support?  That last step is where unexpected solutions come from. When you reason backward from convention, you arrive at a modified version of the existing answer. The shape is familiar because you started with it. When you reason forward from hard limits, you land somewhere the category didn't expect, because you weren't anchored to the shape of the existing answer. Solutions built this way often feel strange at first. People will question them. That discomfort is usually a signal you've found something real rather than something inherited. That's what reasoning from what's actually true produces, rather than reasoning from what everyone assumed. When to Use First Principles Before running the process, ask these four questions. One yes is enough. Has the environment this decision was built for changed significantly? Does every solution on the table feel like a variation of the same thing? Is the current approach inherited rather than chosen? Would a bad assumption here cost you more than an afternoon to find and fix? If all four are no, past experience is the right tool. Use it.  The 20-minute assumption-strip is cheap. The cost of skipping it isn't. The Assumption Reversal Exercise For this exercise, you will need a partner. Have them watch this video first. They need to know what an inherited assumption looks like before they can spot yours. Once you're both ready, grab the free First Principles Thinking Checklist at innovation.tools or find the link in the description. It gives you both a shared reference point before you start.  Here is how it works: Each person brings one real problem. Something current, with actual stakes. Not a thought experiment. The problem should be one you've been turning over in your mind without arriving at a satisfying answer. Work on your partner's problem, not your own. You are trying to find the assumptions baked into how they've framed it. They are doing the same for yours. The reason this works is that you can see their inherited constraints more clearly than they can. You're not inside their problem the way they are. Each person lists every assumption they can find in the other's problem. Write them down. Don't argue yet. Don't evaluate. Just surface as many as possible. Quantity matters here. The obvious assumptions are easy. Push past them. Take each assumption and reverse it. If the assumption is "this requires a significant budget," the reversal is "what becomes possible if it requires no budget?" If the assumption is "the customer won't accept a different format," the reversal is "what would we build if they would?" Don't ask whether the reversal is realistic. Ask what it opens up. Discuss what the reversals revealed. Not every reversed assumption leads somewhere useful. But one of them usually exposes a constraint that was never as fixed as it felt. That's the one worth following. The point of the reversal is simple. Some assumptions hold when you push on them, and some don't. You can't tell which is which until you try. The Long Game Every time you run this process and find something that didn't hold, you get faster at spotting them. The judgment about when to use it gets sharper. That's what improvement looks like in practice: not a dramatic flash of insight, but a practiced ability to find the assumption in the room before it finds you. The assumption that costs you most isn't the one you haven't thought of yet. It's the one you stopped questioning years ago.  Find your partner. Run the Assumption Reversal this week. That's where this starts becoming a skill. Subscribe for the next episode. It builds on this.

David Cowen and Ari Kaplan have known each other for twenty years - this is their first podcast together. It is one of the most candid conversations in the series. Ari shares the philosophy that has carried him through two decades at the highest levels of corporate legal, why the US mindset about reclaimed time is a problem worth confronting, and why the most important question of the next decade may be the one Zach Kass asked from the keynote stage. Key Topics Covered: The validation tax: Ari's framework for the time you spend verifying AI output The AI dividend, US edition: Why Americans fill saved time with more work and Europeans leave earlier Permission to stop: David's confession of guilt around saved time and how to reframe it The mentorship moat: Why a Stanford-style simulation program might out-train a senior partner What AI cannot touch: Why high-stakes corporate work still requires human judgment and network Legal as R&D center: Why legal departments are becoming the experimentation hubs of the modern enterprise The renaissance of legal: Why the energy on the CLOC floor in 2026 feels fundamentally different

In this episode, John Stackhouse visits Ross on the outskirts of Ottawa to talk with CEO David Ross about how the company grew from a small Canadian manufacturer into a global live-production infrastructure player. They discuss why the economics of live events changed so dramatically, how cheaper and more powerful screens transformed stadiums and concerts into multimedia platforms, and how Ross helps turn live data into visual storytelling through graphics, overlays, motion systems and production control. Ross Video is one of Canada's most consequential technology companies, even if most audiences have never heard of its name. They work across more than 100 countries. Their technology now sits inside countless modern live-event and broadcast experience:  On field graphics, robotic camera systems, data-rich stadium presentation, newsroom and broadcast automation and the production systems behind concerts, major sports, studios and major event coverage for clients like MLB, NFL, PGA, NHL, Premier League, Metallica, Taylor Switft, Coldplay the list goes on and on and on. The conversation also surfaces a bigger business story. Ross describes its work as brand amplification technology, helping sports teams, venues, concerts and companies use screens, graphics, motion systems and production tools to deepen audience experience and strengthen commercial value. David lays out the company's operating logic clearly: expand into adjacencies, acquire expertise when needed, keep founders and technical talent engaged, and never fall behind in technology. That approach shows up in Ross's reinvestment model too: roughly one-third of the company is in R&D. This episode is about sports broadcast innovation, stadium technology, robotic cameras, concert production, real-time graphics, data storytelling, and the broader live-entertainment economy. Ross sits inside a much larger market shift: a world where live sports, concerts, venue systems, and production technology are becoming more immersive, more data-driven and more economically important. For more ideas and insights on Canada's economy, innovation, and competitiveness, visit  RBC Thought Leadership Primary keywords: Ross Video; David Ross; John Stackhouse; Disruptors podcast; Ottawa technology company; Canadian tech company; live production technology; sports broadcast technology; stadium technology; robotic cameras; spidercam; sports graphics; NFL first down line; MLB All-Star Game; Olympic broadcast technology; concert production technology; newsroom automation; data visualization in sports; live event infrastructure; sports media innovation Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Benjamin and Chance give their opinions on the new Pride wallpaper and watch band lineup for 2026, as well as talk about a new watch face supposedly coming with watchOS 27. Also, in our seemingly-recurring segment on Mac desktop supply constraints, Apple stopped selling the $599 Mac mini altogether this week. Also, the company shares some curious tidbits about its future strategy in its first quarterly earnings call to feature incoming CEO John Ternus.  And in Happy Hour Plus, Apple loves to talk about the customer satisfaction numbers for its products, but we give our personal takes on how the product lines stack up. Subscribe at 9to5mac.com/join. Sponsored by Bartender: Organize and control your Mac's menu bar so it stays clean and uncluttered. Visit macbartender.com/happyhour and use code HAPPYHOUR to save 10% on Bartender 6. Sponsored by Shopify: See less carts go abandoned and more sales. Sign up for a $1 per month trial at shopify.com/happyhour. Sponsored by IM8: Go to IM8HEALTH.com/happyhour and use code happyhour to get a free welcome kit, five free travel sachets, and 10% off your order.  Hosts Chance Miller @ChanceHMiller on Twitter @ChanceHMiller on Instagram @ChanceHMiller on Threads Benjamin Mayo @bzamayo on Twitter @bzamayo@mastodon.social @bzamayo on Threads Subscribe, Rate, and Review Apple Podcasts Overcast Spotify 9to5Mac Happy Hour Plus Subscribe to 9to5Mac Happy Hour Plus! Support Benjamin and Chance directly with Happy Hour Plus! 9to5Mac Happy Hour Plus includes:  Ad-free versions of every episode  Pre- and post-show content Bonus episodes Join for $5 per month or $50 a year at 9to5mac.com/join.  Feedback Submit #Ask9to5Mac questions on Twitter, Mastodon, or Threads Email us feedback and questions to happyhour@9to5mac.com Links iOS 26.5's new Pride wallpaper revealed, plus Apple Watch face Apple unveils Pride Edition Sport Loop for Apple Watch, order today iOS 26.5 adds beautiful wallpapers for your iPhone, here's what's new Here's the next Apple Watch face coming in watchOS 26.5 and how to customize it How Will John Ternus Run Apple as CEO? With More Investments, Fewer Buybacks - Bloomberg Apple discontinues base Mac mini, now starts at $799 with 512GB storage Apple's most powerful Mac Studio loses its last remaining RAM upgrade option John Ternus joins Apple's Q2 2026 earnings call, touts ‘incredible roadmap ahead' Apple says iPhone 17 lineup is officially the ‘most popular' in its history Tim Cook says iPhone 17 demand is 'off the charts', but supply constraints impacted sales Apple's R&D spending hits new record as AI investment ramps up Apple considers Intel and Samsung to diversify chip manufacturing away from TSMC iOS 27 will let you choose between Gemini, Claude, and more for AI features: report

Bernstein joins the AMD bandwagon after shares surge on earnings. Apple's R&D spend rises as it tries to catch up in the AI race. Plus, former FDIC chair Shiela Bair talks Warsh, banks, and new children's book, "How Not To Lose A Million Dollars." Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Watch the show on television by downloading the e360tv channel app to your Roku, LG or AmazonFireTV. You can also see it on YouTube.Devin: What is your superpower?Omar: Ability to combine undying persistence with visionary thinking.When most people think of healthy food, the first things that come to mind are bland flavors and unfamiliar ingredients. Omar Atia, Founder and CEO of ZeroCarb LYFE, is changing that perception. His growing food company delivers indulgent, crave-worthy foods that are also healthy—creating a game-changing option for consumers seeking low-carb, high-protein alternatives.Leveraging his experience with major food brands like Kraft and Procter & Gamble, Omar has transformed a simple kitchen-table idea into a thriving enterprise with products now available online and even on Target shelves. His mission? To create “tasty and healthy” products, including protein-based pizza crusts, chips, and tortillas, that improve quality of life.“Our protein-based pizza actually brings people's blood sugar down instead of raising it,” Omar explained in today's episode. For those living with diabetes or athletes looking for sustained energy, ZeroCarb LYFE provides an alternative to traditional comfort foods.What began as a partnership during the pandemic has now scaled into a trusted brand with over 70,000 customers. Omar emphasized how e-commerce played a pivotal role early on, noting that having a direct connection with customers allowed him to test, iterate, and refine his products using real-time feedback.Critically, ZeroCarb LYFE is building a movement around a core insight: indulgence doesn't have to mean sacrificing health. “People currently think about healthy food as something that doesn't taste great. What we want to do is bring a very different version of that,” Omar explained.In support of scaling his vision, ZeroCarb LYFE has launched a regulated crowdfunding campaign via Wefunder, inviting customers and fans alike to become co-owners. Omar sees this community-driven approach as an opportunity to “hockey-stick” growth while allowing supporters to share in the company's success.With today's growing recognition of protein's importance in human diets, ZeroCarb LYFE is more than a food brand—it's a reimagining of how we approach healthy eating. If you're intrigued, consider checking out ZeroCarb LYFE to see how this company is reshaping the way we snack and dine, one protein-packed bite at a time.tl;dr:Omar Atia shares his mission to create indulgent, healthy protein-rich foods with ZeroCarb LYFE.E-commerce allowed early product testing, feedback, and scaling to over 70,000 customers since 2019.ZeroCarb LYFE offers products like protein-based pizza crusts, chips, and tortillas for healthier eating.By engaging customers via a Wefunder campaign, ZeroCarb LYFE invites everyone to be co-owners.Omar attributes his success to persistence, visionary thinking, and applying consumer insights effectively.How to Develop Persistence and Vision As a SuperpowerOmar's superpower is his ability to combine undying persistence with visionary thinking. He said, “I just constantly believe that if you put in the effort and keep moving toward the goal you genuinely believe in, you will accomplish it.” This blend of determination and big-picture perspective— “seeing systems at a global scale,” as he described it—allows Omar to not only create comprehensive solutions but also inspire others to work toward transformative change.At the start of ZeroCarb LYFE, Omar envisioned not just a product but a platform. He and his team began with protein-based pizza crusts but built a broader concept around creating indulgent, healthy foods across categories. He brought a systems-level perspective to the operation, demonstrating how the brand could impact restaurants, e-commerce, and retail simultaneously. His vision and persistence turned ZeroCarb LYFE from a single product into a scalable, category-defining company.Actionable Tips to Develop the SuperpowerSet clear long-term goals to guide your efforts, even amid immediate challenges.Regularly zoom out to see the “big picture” and assess your decisions within a systems perspective.Continuously iterate your solutions based on customer feedback and market testing.Surround yourself with a team of capable people who align with your mission.Stay persistent and learn to filter useful signals from distracting noise in your journey.By following Omar's example and advice, you can make persistence and vision a skill. With practice and effort, you could make it a superpower that enables you to do more good in the world.Remember, however, that research into success suggests that building on your own superpowers is more important than creating new ones or overcoming weaknesses. You do you!Guest ProfileOmar Atia (he/him):Founder and CEO, ZeroCarb LYFEAbout ZeroCarb LYFE: ZeroCarb LYFE is a food company focused on transforming health through food by making protein-forward, lower-carb, clean-ingredient products that are convenient, familiar, and transparent. The company was built to help consumers eat better without needing to become food experts, and it operates through a multi-channel model spanning direct-to-consumer, retail, and foodservice.Website: zerocarblyfe.comCompany Facebook Page: facebook.com/zerocarblyfeOther URL: wefunder.com/zerocarb.lyfeBiographical Information: Omar Atia is Founder and CEO of ZeroCarb LYFE. He is a Purdue University graduate with a Chemical Engineering degree and a Master's in Industrial/Mechanical, and he built his career inside major CPG companies including Procter & Gamble, Kraft Foods, ConAgra Foods, Dean Foods, and Mead Johnson Nutrition, where he worked across R&D and operations. After leaving corporate in 2013 to launch a consulting business that grew teams in the U.S. and Dubai, he began advising startups and contributing hands-on operational and product expertise. In 2019, he discovered the product concept that became ZeroCarb LYFE, recognized its ability to scale beyond a single recipe, and built the business into a growing CPG platform centered on ingredient transparency, health transformation through food, and operational control.LinkedIn Profile: linkedin.com/in/omaratiaSupport Our SponsorsOur generous sponsors make our work possible, serving impact investors, social entrepreneurs, community builders and diverse founders. Today's advertisers include SorbiForce, High Desert Gear and Climatize. Learn more about advertising with us here.Max-Impact Members(We're grateful for every one of these community champions who make this work possible.)Brian Christie, Brainsy | Cameron Neil, Lend For Good | Carol Fineagan, Independent Consultant | Hiten Sonpal, RISE Robotics | John Berlet, CORE Tax Deeds, LLC. | Justin Starbird, The Aebli Group | Lory Moore, Lory Moore Law | Marcia Brinton, High Desert Gear | Mark Grimes, Networked Enterprise Development | Matthew Mead, Hempitecture | Michael Pratt, Qnetic | Mike Babbit | Coledger Solutions | Mike Green, Envirosult | Nick Degnan, Unlimit Ventures | Dr. Nicole Paulk, Siren Biotechnology | Paul Lovejoy, Stakeholder Enterprise | Pearl Wright, Global Changemaker | Scott Thorpe, Philanthropist | Sharon Samjitsingh, Health Care Originals | Add Your Name HereUpcoming SuperCrowd Event CalendarIf a location is not noted, the events below are virtual.SuperCrowd Impact Member Networking Session: Impact (and, of course, Max-Impact) Members of the SuperCrowd are invited to a private networking session on May 19th at 8:00 PM ET/5:00 PM PT. Mark your calendar. We'll send private emails to Impact Members with registration details. Upgrade to Impact Membership today!SuperCrowdHour, May 20, 2026, at 12:00 PM Eastern. Devin Thorpe will lead a session on “How to File Your Form C-AR Yourself for Free!” Designed for founders and issuers navigating regulated investment crowdfunding, this practical session will walk attendees through the annual Form C-AR filing process and show how to complete it independently—without unnecessary legal or filing expenses. Devin will explain what information is required, common mistakes to avoid, important deadlines to remember, and how staying compliant helps build trust with investors while protecting your raise. Whether you've recently closed an offering or are preparing for your first annual report, this SuperCrowdHour will provide a clear, cost-effective roadmap to filing your Form C-AR with confidence. Register here: https://thesupercrowd.com/20may26SuperCrowd26 featuring PurposeBuilt100™: This August 25–27, founders, investors, and ecosystem leaders will gather for a three-day, broadcast-quality global experience focused on disciplined capital formation, regulated investment crowdfunding, and purpose-driven growth. We're bringing together leading voices in impact investing, compliance, digital marketing, and circular economy innovation to deliver practical frameworks, real-world case studies, and actionable strategies. The event culminates in the PurposeBuilt100™ Showcase, recognizing 100 of the fastest-growing purpose-driven companies in the U.S. Register now to secure your seat and get all the details. August 25–27, streaming worldwide.Share the application for the PurposeBuilt100™: Purpose-driven founders deserve recognition. The PurposeBuilt100™ application window is now open—celebrating the fastest-growing companies building profit with purpose. If you know a founder creating real impact and real growth, please share this opportunity. Applications are free and confidential. Explore the program and apply today: PurposeBuilt100.com.Superpowers for Good Live Pitch on e360tv — June 3, 2026. Purpose-driven founders raising capital through Regulation Crowdfunding are invited to apply by May 6, 2026, for a chance to pitch live to a national audience of investors and impact champions.Community Event CalendarSuccessful Funding with Karl Dakin, Tuesdays at 10:00 AM ET - Click on Events.Earthstock Summit, Ojai, CA, May 29-31: The Earthstock Regenerative Summit in Ojai brings together leaders and community members for panels, workshops, films, music, and hands-on projects focused on regenerative agriculture, ecological design, resilience, health, and sustainable living.Save the Date! October 20th and 21st will be the Crowdfunding Professional Association Regulated Investment Crowdfunding Summit for 2026. This is the event of the year for everyone in the crowdfunding ecosystem.If you would like to submit an event for us to share with the 10,000+ changemakers, investors and entrepreneurs who are members of the SuperCrowd, click here.Manage the volume of emails you receive from us by clicking here.We share educational information—not investment advice. Some links may generate compensation. See our full disclosure.We use AI to help us write compelling recaps of each episode. Get full access to Superpowers for Good at www.superpowers4good.com/subscribe

Inbal Shani (CPO and Head of R&D @ Twilio) deconstructs the transformation of the R&D org at Twilio! We explore the shift from a GM-led model to a unified platform strategy and “why structure must always follow strategy.” Inbal shares her framework for moving from output-focused metrics to input goals, prioritizing “time-to-value,” and the nuances of measuring AI products. We discuss using "R&D roadshows" as a strategic company transformation tool and why engineering leaders must master product positioning. We also dive into mental models for future-proofing your business, from "working backwards" to solve customer problems, to embedding systems thinking into the DNA of your engineering team, and critical questions to identify and optimize decisions around your company's moat.    ABOUT INBAL SHANI As Chief Product Officer, Inbal leads Twilio's R&D organization, encompassing product, engineering, and R&D operations. She is dedicated to driving platform-wide innovation, empowering customers, and delivering transformative, customer-focused solutions.   Unblocked: The context engine your coding agents are missing. Give your coding agents the context your best engineers have. Your agents can read code, but they don't know how your team works. Rules and MCPs give access to information but not understanding. That's why you still have to tell them where to look and what to look for. Unblocked gives your agents the history, conventions, and decisions behind your code so they generate mergeable output without the back and forth. It automatically surfaces the right context for every task, so agents stay on track without the set up tax or the correction loops. getunblocked.com/elc   SHOW NOTES: Catalysts for Twilio's R&D transformation and the shift away from organizational silos (2:49) Strategy Drives Structure: The lightbulb moment at a strategy offsite that demanded structural change to execute vision (5:14) Why structure must follow strategy and creating a "change-constant" culture (7:23) Implementing the “working backwards” methodology and the internal power of the PRFAQ (13:52) Tactical ways to filter customer signals and find real unmet problems versus feature requests (16:35) Shifting from output-focused goals to input goals and prioritizing "Time to Value" (18:35) Using weekly product reviews to align metrics with qualitative customer feedback (21:34) Measuring AI Products: Why AI products require behavior-based measurement instead of traditional binary testing (23:24) Building security by design with layered protection for AI-generated code environments (26:09) Mental models for future-proofing your business by acting as a "fortune teller" for needs (28:45) The R&D Roadshow: Enabling the entire company on new ways of working through storytelling (32:28) Why engineering leaders must master product positioning to bridge the gap to market (38:33) Relatable storytelling: Explaining Twilio's value to your parents to sharpen your pitch (41:47) Rapid Fire Questions (43:14)   LINKS AND RESOURCES How Minds Change: The Surprising Science of Belief, Opinion, and Persuasion - In this lively journey through human psychology, bestselling author and creator of the You Are Not So Smart podcast David McRaney investigates how minds change--and how to change minds.   This episode wouldn't have been possible without the help of our incredible production team: Patrick Gallagher - Producer & Co-Host Jerry Li - Co-Host Noah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/ Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/ Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Listen to a recap of the top stories of the day from 9to5Mac. 9to5Mac Daily is available on iTunes and Apple's Podcasts app, Stitcher, TuneIn, Google Play, or through our dedicated RSS feed for Overcast and other podcast players. Sponsored by Bitwarden: Make your life easier with Bitwarden, featuring a secure, open source password manager with end-to-end encryption and seamless autofill across all your devices. New episodes of 9to5Mac Daily are recorded every weekday. Subscribe to our podcast in Apple Podcast or your favorite podcast player to guarantee new episodes are delivered as soon as they're available. Stories discussed in this episode: Apple reports Q2 2026 earnings Apple says supply constraints for Mac mini and Mac Studio to persist for several months Apple says iPhone 17 lineup is officially the ‘most popular' in its history John Ternus joins Apple's Q2 2026 earnings call, touts ‘incredible roadmap ahead' Apple's R&D spending hits new record as AI investment ramps up Listen & Subscribe: Apple Podcasts Overcast RSS Spotify TuneIn Google Podcasts Subscribe to support Chance directly with 9to5Mac Daily Plus and unlock: Ad-free versions of every episode Bonus content Catch up on 9to5Mac Daily episodes! Share your thoughts! Drop us a line at happyhour@9to5mac.com. You can also rate us in Apple Podcasts or recommend us in Overcast to help more people discover the show.

Twelve official definitions for R&D. Zero agreement. The US government publishes at least a dozen distinct official definitions across agencies, accounting standards, tax authorities, and international bodies. Not one agrees with the others on where research ends and development begins. Trillions of dollars flow through R&D budgets every year. Boards approve them. Investors evaluate them. Governments subsidize them. Analysts benchmark them. And the term at the center of all of it has no settled definition. A company can gut its research investment without triggering a single alarm on its income statement. Researchers who gained rare access to confidential federal R&D data found exactly this: when companies face financial pressure, they cut research while leaving development essentially untouched, and the combined number barely moves. Every benchmark, every board conversation, every investment thesis built around the R&D line may be built on sand. Innovation, ideas made real, requires both. Research is how you find the idea. Development is how you make it real. Strip out the research and you're not innovating, you're iterating on what already exists. Strip out the development and you're just experimenting. The problem is that nobody in the room knows which one they're actually funding, because the definition that would tell them doesn't exist. Someone needs to draw the line. This episode is about why nobody has, and the definition I think should replace the chaos. By the end, I'm going to put that definition in front of you and ask you to push back on it. Not to agree. To tell me where it breaks. How We Got Here Four institutions took a run at defining R&D. Each one got it right for their own purposes. None of them got it right for yours. Frascati: Built for Governments In June 1963, OECD economists met at a villa in Frascati, Italy, south of Rome, and produced what became the international standard for measuring R&D across nations. Now in its seventh edition. The Frascati Manual divides R&D into three tiers: basic research (theoretical work with no application in view), applied research (original investigation toward a specific practical objective), and experimental development (using existing knowledge to produce new products or processes). To qualify, an activity must be novel, creative, uncertain in outcome, systematic, and transferable. Used by governments across roughly 75 countries. Solid for what it was designed to do: let nations compare R&D investment on consistent terms. What Frascati cannot tell you: whether a specific company's spending is creating competitive advantage. It counts the type of activity. It doesn't assess what the activity produces for the organization doing the spending. A company can satisfy every Frascati criterion investigating something every competitor already knows. The knowledge is new to them. That is enough. The accountants drew a different line, for a different reason, with a different consequence. FASB: Built for Accountants In October 1974, the Financial Accounting Standards Board issued Statement No. 2, Accounting for Research and Development Costs, now codified as Topic 730. Every public company filing under US GAAP operates under it. The rule: all R&D costs expensed as incurred. Research, development, basic, applied: one line on the income statement. Their definition: research is a planned search aimed at discovery of new knowledge. Development is the translation of research findings into a plan or design for a new product. The rationale is explicit in the original standard. Future benefits from R&D are, in FASB's language, "at best uncertain." Expense everything immediately. The standard solved the problem it was asked to solve, which was accounting treatment: when to recognize the cost, not whether the cost was strategically sound. The consequence: sustaining engineering, feature maintenance, and incremental product updates all land on the same line as genuine exploratory research. Nobody looking at the income statement from outside can see the difference. The number is technically accurate and analytically opaque. Abraham Briloff, the late accounting professor at Baruch College, put it plainly: "Accounting statements are like bikinis. What they show is interesting, but what they conceal is significant." He was talking about financial reporting broadly. He could have been writing specifically about the R&D line. Researchers at Duke and London Business School spent years tracking corporate scientific output and found that it declined steadily across industries even as headline R&D spending kept rising. The combined number was hiding a substitution. Nobody on the outside could see it. Outside the United States, a different standard governs, and it creates a comparison problem most analysts never account for. IFRS: Built for International Investors IAS 38 governs R&D under IFRS, and its treatment differs from FASB in one significant way. Research costs are always expensed, same as FASB. But development costs can be capitalized as an asset on the balance sheet once a company can demonstrate technical feasibility, intent to complete, ability to use or sell the result, likely future economic benefit, adequate resources, and reliable cost measurement. A European company that capitalizes its development phase carries those costs as an asset: lower expenses in the period, higher total assets. An identical US company expensing everything under FASB takes the full hit immediately: higher expenses, lower assets. Same underlying investment. Incomparable financial pictures. Run the standard industry benchmark, R&D as a percentage of revenue, and you may conclude the US company is investing more aggressively. You may be comparing the same dollar invested under two different accounting regimes. Roughly 169 jurisdictions use IFRS. The United States does not. India uses an adapted version. Japan maintains its own standards board. The benchmark the industry trusts most is meaningless for cross-border comparison, and almost nobody says so. Section 174: Built for Tax Authorities The Internal Revenue Code adds another layer. Section 174 governs the deductibility of what the US tax authority calls "research or experimental expenditures," and the definition is not the same as FASB Topic 730. A company's R&D for tax purposes and its R&D for financial reporting can cover different activities and produce different numbers. The Tax Cuts and Jobs Act of 2017 tightened this further: domestic R&D expenses that were previously deductible immediately now must be amortized over five years, international over fifteen. The definition of what qualifies shifted when the timing rules changed. Within one country, one company, three definitional regimes apply simultaneously: Frascati for any government reporting, FASB for the income statement, and Section 174 for taxes. A single dollar of R&D spending can be classified three different ways depending on who's asking. The Gap None of Them Fill Four frameworks, built by four institutions, for four different purposes. Not one was built for the question that actually matters. Is this investment creating new knowledge that gives us a capability nobody else can easily replicate? The gap between them is where innovation decisions actually live. The National Science Foundation recognized the problem clearly enough that it publishes a separate annotated document just to catalog the competing definitions, because they're too inconsistent to assume any two readers are using the same one. That gap isn't an oversight. It's a structural consequence of four institutions doing their own jobs well. The question practitioners need answered was nobody's institutional job. You've been in the room. The R&D number is on the slide. Nobody asks what's inside it, because the accounting standard doesn't require an answer, and the room has learned not to expect one. So it went unanswered. Until now. A Better Definition for R&D Research is work directed at creating new knowledge where the outcome is genuinely uncertain and the knowledge cannot be readily obtained from existing sources. Development is the translation of that knowledge into products, services, or processes that meaningfully advance an organization's capability in ways competitors cannot easily replicate. Four elements define it: Genuinely uncertain outcome. If you know what you're going to get before the work starts, it's engineering execution, not research. The uncertainty doesn't have to be total. Most applied research has a likely direction. But there has to be real doubt about whether the approach works, whether the knowledge emerges. Cannot be obtained from existing sources. This is the one nobody puts in writing. If the knowledge is already in the literature, available from a consulting engagement, or present in a competitor's published work, finding it again isn't research. Generating new knowledge and capturing existing knowledge are different activities. Only one belongs here. This criterion alone would reclassify a significant portion of what companies currently call R&D. Advances capability competitors cannot easily replicate. Development only qualifies when it translates research into something that genuinely moves the organization forward competitively. Sustaining engineering doesn't pass it. Feature parity doesn't. Competitive catch-up doesn't. All real work, none of it development under this definition. Agnostic to accounting jurisdiction. This definition doesn't tell you how to expense or capitalize anything. That's already governed by whichever standard applies. What it does is establish what genuinely belongs in each category, regardless of where the company files. That makes it usable across FASB and IFRS companies without translation. There is a simpler way to put it. For any project in your R&D budget, ask two questions. First: are we creating new knowledge, or executing against something we already know? If you're executing, it's not research. Second: does this translate into a capability competitors cannot easily replicate? If not, it's not development either. It's product engineering, valuable and necessary, but a different budget category entirely. Three buckets: Research, Development, and Product Engineering. That taxonomy, applied honestly across a typical portfolio, would reclassify a significant share of what most companies are currently reporting as R&D. The Call I'm not asking FASB to rewrite Topic 730. What I am asking: that the people who actually make innovation decisions start applying a definition built for the question they're trying to answer. If you run an R&D function: apply this definition to your current portfolio. Not to change the accounting. To see what's actually in the category and what isn't. The gap between what your budget calls R&D and what this definition calls R&D will tell you something worth knowing. If you sit on a board: ask what portion of the R&D line is directed at new knowledge creation versus sustaining existing products. If no one in the room can answer, you're governing a number you don't understand. And if you think the definition is wrong, tell me. Where should the line be drawn differently? What element doesn't hold? What did I miss? That's not a polite invitation. That's the actual point of this episode. Definitions become standards when enough serious people apply them consistently and make the case until the institutions catch up. The four frameworks we inherited were each built by an institution serving its own purpose. This one is built for the people making the decisions. The most consequential line in any company's budget is the one separating what builds the future from what protects the present. Nobody drew it clearly. It's past time someone did. The idea was never the hard part. It never is. The call is. If this episode shifted something for you, subscribe wherever you listen to podcasts. On YouTube, hit subscribe and the bell so you don't miss the next one. And if you want to go deeper every Monday, Studio Notes is free at philmckinney.com. Until next time. See the pattern. Make the call. The Innovators Studio | philmckinney.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

Sarah Keohane Williamson, CEO of FCLT Global and coauthor of The CEO's Guide to the Investment Galaxy, offers a disciplined primer for executives operating at the intersection of corporate strategy and capital markets. Drawing from her background in investment banking, government, consulting, and asset management, she explains why "investors are not a single audience," how their incentives shape corporate outcomes, and what leaders must do differently to secure durable capital and strategic flexibility. Williamson pushes back on conventional wisdom about investor relations, replacing it with practical routines and priorities. She emphasizes a consulting-rooted discipline, "Start with the answer", as a communications principle, and translates it into a concrete playbook for CEOs who cannot afford ambiguity when describing long-term bets. She underscores that "quarterly calls are important, but they're often dominated by the sell side," and CEOs should deliberately allocate their limited time toward building trust with long-term owners and anchor shareholders. Key takeaways include: Map the owners. "Who actually owns your company? Who makes the decisions about those shares?" Owner types—retail, index funds, active managers, hedge funds—differ in incentives and time horizons, and executives should treat that map as a strategic input. Build an investor strategy like a customer strategy. Decide which kinds of capital the company needs, why, and how to attract and retain those investors. Use a long-term roadmap. Make risky investments intelligible by explaining milestones that link short-term actions to enduring value, and "don't be afraid to update the roadmap when the assumptions change." Translate investor signals into operational choices. Avoid reflexive short-term fixes, like cutting R&D to meet a quarter, without measuring the long-term cost. Treat disclosure and dialogue as governance tools. Clarity about ownership, voting, and incentives reduces misalignment and reputational risk. Reframe consultancy input for execution. "The hard part is not the analysis, the hard part is making it happen inside the organization." This episode equips CEOs, CFOs, and board members with a practical framework for raising capital, defending strategic bets, and managing shareholder composition. It reframes investor engagement from a compliance exercise into a core discipline of strategy and governance.

What happens when innovation shifts from a strategic advantage to a matter of survival? In this episode of the Innovation Storytellers Show, I sat down with Itai Green, Co-Founder and CEO of Global Innovation & Strategy and author of Innovation or Elimination. With more than two decades of experience connecting global corporations with startups, Itai brings a direct and unfiltered perspective on why so many organizations struggle to stay relevant and what it actually takes to change that trajectory. Our conversation moves beyond theory and into the real mechanics of innovation. Itai challenges the long-standing belief that companies can rely solely on internal R&D, arguing that speed, collaboration, and openness now define success. He explains why open innovation is no longer optional, how corporate mindset often becomes the biggest barrier, and why leaders must rethink everything from decision-making speed to how they measure return on investment.  Along the way, he shares candid insights on the cultural tension between startups and enterprises, the risks of ego-driven leadership, and why many innovation efforts fail before they even begin. We also explore the practical side of making innovation work at scale. From running effective pilots and selecting the right startup partners to understanding when to build internally versus collaborate externally, Itai offers a clear view into what separates companies that evolve from those that quietly fade away. His perspective on timing, trust, and execution highlights a deeper truth: that innovation is less about ideas and more about how organizations choose to act on them. So where does this leave today's leaders, especially in a world shaped by rapid advances in AI and constant disruption? And as Itai suggests, if innovation is now the price of survival, how prepared are most organizations to pay it?  

R&D teams are starting to advance AI capabilities faster than they can translate them into measurable business value, creating mounting friction between scientific progress and operational reality. In this episode, Aziz Nazha, Global Head of AI Innovations Institute at Incyte Pharmaceuticals, examines how culture, talent, infrastructure, and expectation‑setting determine whether AI meaningfully improves drug discovery and development. He highlights the practical shifts required — from redesigning workflows to disciplined upskilling and targeted validation cycles — to ensure AI adoption accelerates cycle times rather than getting stalled by organizational bottlenecks. This episode is sponsored by Deloitte. Learn how brands like Deloitte work with Emerj and other Emerj Media options at go.emerj.com/partner

Every public company's R&D number is a lie hiding in plain sight. Not because anyone falsified it. Because the number was never built to tell the truth. It was built to satisfy an accounting standard written in 1974. And for fifty years, boards, analysts, and CEOs have been making billion-dollar innovation decisions based on a number designed by accountants to solve a different problem entirely. Here's what makes this genuinely strange. The real number exists. The government has been collecting it from every major US company for decades. It would answer the question every innovation leader and investor actually needs answered. And it is locked away by federal law. Confidential. Never published. Never seen by the people who need it most. It's sitting in a federal database right now. And there's a way to estimate it for any public company, without asking anyone's permission. I know it exists because I spent years building it from the inside. Why the R&D Signal Was Blurry When I was running innovation at HP, we discovered this problem firsthand. We had a connection between R&D investment and gross margin that held up across decades of HP history. Better than anything Wall Street was using. But the signal was blurry. None of us could figure out why. The answer came from a question someone on the team asked almost as an aside. What if R&D isn't one thing? Research and Development Are Not the Same Thing Think about what actually lives inside a typical R&D budget. There's a team somewhere investigating whether a new approach could enable a capability that doesn't exist yet. No product defined. No spec written. Asking whether something is even possible. And there's a team building the next version of a product that ships in eighteen months. Spec locked. Timeline set. Engineering executing against a defined target. Both show up on the same line in the budget. Both get called R&D. Both count equally toward the number that gets reviewed every quarter. They are not the same thing. One is Research. The other is Development. Research is the work you do when you don't yet know what you're building. The output is understanding. New knowledge that might enable future products nobody has designed yet. You can't know exactly what you'll find. If you already knew, it wouldn't be research. Development is the work you do when you know exactly what you're building. The spec exists. The product is defined. The question isn't what to make. It's whether it can be made, on time, at cost, at quality. One creates the future. The other delivers the present. And for fifty years, every public company in America has been required to report them as one indistinguishable number. When we split the HP data along that line, Research on one side and Development on the other, the signal sharpened immediately. Research spend, measured against gross margin three to five years later, was a meaningfully stronger predictor than the combined number had ever been. The blur hadn't been in the gross margin data. It had been in the R&D number itself. Two fundamentally different things, averaged together, producing a number that looked precise and predicted almost nothing. But splitting R from D at the company level was only the beginning. The model was still lying to us. Just more quietly. Why Company-Level R&D Splits Still Mislead Even with the split, something was still soft. HP wasn't one business. It was dozens. Printers, PCs, servers, software, each running on different timelines, different technology cycles, different competitive dynamics. What if the R/D split meant something different depending on where it was applied? We pushed it to the product line level. Then further, to the platform level within product lines. Printers were the clearest example. HP's printer business wasn't one story. There were platforms built on established technology. Mature ink systems, proven print head chemistry, products that had been shipping for years. And there were platforms built on genuinely new core technology. New chemistry. New mechanisms. New approaches to fundamental problems that nobody had solved yet. Research investment by platform told a completely different story than Research investment by product category. The Research going into new technology platforms had a completely different relationship to future margin than Research going into mature platforms. Different time horizons. Different risk profiles. Different margin implications years down the road. Laptops told the same story. A traditional consumer laptop line and a high-performance portable workstation weren't the same investment. One was Development-heavy. Defined product, known market, engineering executing against spec. The other had genuine Research behind it. Unsolved thermal problems, new form factor constraints, and materials questions that hadn't been answered yet. When a single R&D assumption is applied across all of that, treating every dollar the same regardless of what it actually does, the signal disappears into the average. Peanut butter across the portfolio. The model only got honest when it got specific. Research by platform and Development by platform, matched against the margin performance of those specific platforms years later. Which platforms were building future margin? Which ones were running on margin that past Research had already bought? We could see it because we were inside the company. The question is whether anyone on the outside could ever see the same thing. The R&D Data the Government Collects and Won't Release Outside the internal budget process, everyone sees the same thing: a single line on the income statement. The US government recognized decades ago that the combined R&D number was analytically useless. So they built a system to collect the real one. The National Science Foundation runs a survey called the Business Enterprise Research and Development survey. The BERD survey. Every year, roughly 47,500 US companies are required to report their R&D spending broken into three categories: basic research, applied research, and experimental development. The split that every board and every investor needs to see. Mandatory. Collected. Verified. And then locked away. The firm-level data is confidential under federal law. The NSF publishes only industry-level aggregates. So every company fills out this survey and reports its real R/D split to the government. That data sits in a federal database. And the boards, investors, and analysts who need it most cannot access it. Researchers at Northwestern and Boston University were given rare access to that confidential data. What they found is striking. When companies face financial pressure and cut R&D, they don't cut Development. They cut Research. Almost entirely. Development barely moves. Every earnings squeeze. Every activist campaign. Every cost optimization program. Systematically targeting the one part of R&D that builds future margin. And because the combined number barely moves, nobody on the outside sees it happening. That's not a coincidence. That's the accounting standard doing exactly what it was designed to do: produce one clean number for the income statement. It was never asked to protect the future. How to Estimate the Research-to-Development Split Without Inside Access So what can actually be done without access to the locked data? More than most people realize. Step 1. Find the industry baseline. The aggregate BERD data is public at the sector level. Ask an AI tool for the Research-to-Development ratio for the relevant industry. That's the benchmark. Everything else gets measured against it. A company spending 8% of its R&D on Research in an industry where the average is 25% is telling you something the combined number never would. Step 2. Look at the gross margin trend compared to peers. Gross margin over time is the most honest external signal of Research health. A company with a declining margin relative to peers, while reporting flat or growing R&D spend, is almost certainly shifting the mix toward Development. The math works in the other direction, too. An AI tool can pull this comparison for any public company in minutes. This is exactly the signal that was invisible at HP until it was too late. Step 3. Look at patent trends compared to peers over time. Patents are an imperfect but useful directional indicator. Not because more patents always means more Research. It doesn't. But a sustained decline in patent output relative to peers, alongside flat R&D spend, suggests the investment is maintaining existing products rather than creating new knowledge. Combined with the gross margin trend, it starts to triangulate where the split actually sits. None of these three steps requires access to an internal budget. All of them can be done in an afternoon with public data and an AI tool. Together, they produce a working picture of the R/D split that the income statement was never designed to reveal. What the R&D Split Revealed at HP That No One Outside Could See When Hurd took over in 2005, HP was spending $3.5 billion on R&D. Roughly 4% of revenue. By 2009, his last full year as CEO, that had dropped to $2.8 billion. Revenue had grown significantly over that period, so the percentage had fallen further still, to under 2.5%. Both the dollar amount and the ratio were declining simultaneously while the company got larger. Wall Street tracked the combined number. The board reviewed it. Nobody raised a structural alarm. The Research component within that total was well below the industry average for comparable technology companies. Not slightly. Significantly. The margin consequences arrived years later. They always do. What Happens When the Definition of Research Doesn't Exist The R/D split gave us a real predictive signal. We ran with it. The conversations were sharper. But the team kept pulling on a thread that nobody expected. When we looked closely at what was actually being called Research, project by project and budget line by budget line, things that didn't feel the same kept appearing. Work aimed at fundamental discovery. Work aimed at solving a specific defined problem using entirely new methods. Both labeled Research. Up close, they behaved differently, predicted different things, and when budgets got tight, got treated very differently. So we went looking for the agreed definition. The official standard that would tell exactly where to draw the lines inside Research. It didn't exist. Not the way we needed it to. And without it, everything we'd built was sitting on sand. How do you build a predictive model on a definition that doesn't exist? That's the next episode. If this helped you see something you might have missed, subscribe wherever you listen to podcasts. On YouTube, hit subscribe and the bell so you don't miss the next episode. And if you want to go deeper every Monday, join us at Studio Notes — free, at philmckinney.com. Until next time. See the pattern. Make the call.