Podcast appearances and mentions of ed catmull

Bob Baxley is a design leader who has shaped products used by billions at Apple, Pinterest, Yahoo, and ThoughtSpot. During his eight years at Apple, he led design for the online store and the App Store, and witnessed the iPhone's transformative launch while working under Steve Jobs. A student of history turned software craftsman, Bob discovered his calling after exploring photography, filmmaking, and music, ultimately recognizing software as the most powerful creative medium of our time. Bob champions the moral obligation designers have to reduce frustration in people's daily digital interactions.What you'll learn:• Why design should report to engineering, not product• The “Beatles principle”—why the best products come from teams of 4 to 6, not 40 to 60• How to create design tenets vs. principles (with real examples)• The counterintuitive reason to delay drawing or prototyping as long as possible• Why software is fundamentally a medium, like film or music (not just a tool)• Why Bob “bounced off the culture” at Pinterest, and lessons from failure• The lunar landing story that teaches us about championing radical ideas• How to evaluate if a company truly values design before joining• The moral obligation of software makers to build great products—This entire episode is brought to you by Stripe—helping companies of all sizes grow revenue.—Where to find Bob Baxley:• Instagram: https://www.instagram.com/baxley/• LinkedIn: https://www.linkedin.com/in/bbaxley/• Website: http://www.bobbaxley.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Bob Baxley(03:52) Apple's lasting culture(06:15) Navigating unique company cultures(13:19) Finding a company that truly values your role(15:46) What is design?(17:17) How to help founders understand the value of design(23:08) How to align product managers and designers(26:31) Design reporting to engineering(30:54) Integrating engineers early in the design process(33:43) The maker mindset(35:14) Challenging the assumption that design is time-intensive(38:04) Design tenets vs. design principles(45:25) The moral obligation of great design(51:48) Understanding software as a medium(01:01:20) Reducing ambiguity for product teams(01:07:04) Giving designers space for creativity(01:08:48) The "primal mark" concept(01:12:05) AI prototyping tools: benefits and risks(01:17:00) AI as a life coach(01:21:22) Life lessons from the Apollo program(01:28:24) Lightning round and final thoughts—Referenced:• Steve Jobs: https://en.wikipedia.org/wiki/Steve_Jobs• Walt Disney: https://en.wikipedia.org/wiki/Walt_Disney• Pinterest: https://www.pinterest.com/• X: https://x.com/• Uber: https://www.uber.com/• Airbnb: https://www.airbnb.com/• Slack: https://slack.com/• Ed Catmull on X: https://x.com/edcatmull• John Lasseter on X: https://x.com/johnlasseter5• Apple patented a pizza box, for pizzas: https://www.theverge.com/2017/5/16/15646154/apple-pizza-box-patent-come-on• Humane: https://en.wikipedia.org/wiki/Humane_Inc.• Jony Ive: https://en.wikipedia.org/wiki/Jony_Ive• Tony Fadell on LinkedIn: https://www.linkedin.com/in/tonyfadell/• Hiroki Asai on LinkedIn: https://www.linkedin.com/in/hiroki-asai-a44137110/• Tim Cook on X: https://x.com/tim_cook• ThoughtSpot: https://www.thoughtspot.com/• Ben Silbermann on LinkedIn: https://www.linkedin.com/in/silbermann/• Ajeet Singh on LinkedIn: https://www.linkedin.com/in/ajeetsinghmann/• Honeywell: https://www.honeywell.com• IDEO: https://www.ideo.com/• Nutanix: https://www.nutanix.com/• Lego: https://www.lego.com/• Leica: https://leica-camera.com/• Porsche: https://www.porsche.com/• Patagonia: https://www.patagonia.com• Brian Eno's website: https://www.brian-eno.net/• Scenius: why creatives are stronger together: https://thecreativelife.net/scenius/• The Beatles website: https://www.thebeatles.com/• Disneyland: https://disneyland.disney.go.com/destinations/disneyland/• Tomorrowland: https://disneyland.disney.go.com/destinations/disneyland/tomorrowland/• Unconventional product lessons from Binance, N26, Google, more | Mayur Kamat (CPO at N26, ex-Binance Head of Product): https://www.lennysnewsletter.com/p/unorthodox-product-lessons-from-n26-and-more• Larry Page: https://en.wikipedia.org/wiki/Larry_Page• Sergey Brin: https://en.wikipedia.org/wiki/Sergey_Brin• Design Principles: https://principles.design/• Tableau: https://www.tableau.com/• Figma: https://www.figma.com/• Target self-checkout: https://corporate.target.com/press/fact-sheet/2024/03/checkout-improvements• Everyone's an engineer now: Inside v0's mission to create a hundred million builders | Guillermo Rauch (founder and CEO of Vercel, creators of v0 and Next.js): https://www.lennysnewsletter.com/p/everyones-an-engineer-now-guillermo-rauch• eBay: https://www.ebay.com/• Williams Sonoma: https://www.williams-sonoma.com/• Amazon: https://www.amazon.com/• Monument to a Dead Child | Raw Data: https://podcasts.apple.com/ca/podcast/monument-to-a-dead-child/id1042137974• Toast: https://pos.toasttab.com/• The Primal Mark: How the Beginning Shapes the End in the Development of Creative Ideas: https://www.gsb.stanford.edu/faculty-research/publications/primal-mark-how-beginning-shapes-end-development-creative-ideas• The Plant: https://pixar.fandom.com/wiki/The_Plant• Microsoft CPO: If you aren't prototyping with AI you're doing it wrong | Aparna Chennapragada: https://www.lennysnewsletter.com/p/microsoft-cpo-on-ai• How have I been complicit in creating the conditions I say I don't want? | Jerry Colonna (CEO of Reboot, executive coach, former VC): https://www.lennysnewsletter.com/p/jerry-colonna• Joff Redfern on LinkedIn: https://www.linkedin.com/in/mejoff/• John C. Houbolt: https://www.nasa.gov/centers-and-facilities/langley/john-c-houbolt/• The Apollo program: https://www.nasa.gov/the-apollo-program/• Archive clip: JFK at Rice University, Sept. 12, 1962—“We choose to go to the moon”: https://www.youtube.com/watch?v=QXqlziZV63k• Alan Shepard: https://www.nasa.gov/former-astronaut-alan-shepard/• Blue Origin: https://www.blueorigin.com/• Yuri Gagarin: https://en.wikipedia.org/wiki/Yuri_Gagarin• Wernher von Braun: https://en.wikipedia.org/wiki/Wernher_von_Braun• Yuri Kondratyuk: https://en.wikipedia.org/wiki/Yuri_Kondratyuk• John Houbolt's memo: https://space.stackexchange.com/questions/2823/text-of-john-houbolts-letter-proposing-lunar-orbit-rendezvous-for-apollo• Severance on AppleTV+: https://tv.apple.com/us/show/severance/umc.cmc.1srk2goyh2q2zdxcx605w8vtx• Lawrence of Arabia on Prime Video: https://www.amazon.com/Lawrence-Arabia-Peter-OToole/dp/B0088OINTU• Leica M6: https://leica-camera.com/en-US/photography/cameras/m/m6• Habitica: https://habitica.com/static/home• Andor on Disney+: https://www.disneyplus.com/browse/entity-faba988a-a9f5-45f2-a074-0775a7d6f67a• Edward Tufte quote: https://quotefancy.com/quote/1449650/Edward-Tufte-Good-design-is-clear-thinking-made-visible-bad-design-is-stupidity-made• Ansel Adams quote: https://www.brainyquote.com/quotes/ansel_adams_106035• It Takes a Village to Determine the Origins of an African Proverb: https://www.npr.org/sections/goatsandsoda/2016/07/30/487925796/it-takes-a-village-to-determine-the-origins-of-an-african-proverb• Henry Modisett on LinkedIn: https://www.linkedin.com/in/henrymodisett/• Perplexity: https://www.perplexity.ai/• Golden State Warriors: https://www.nba.com/warriors/• Steph Curry: https://www.espn.com/nba/player/_/id/3975/stephen-curry—Recommended books:• From Counterculture to Cyberculture: Stewart Brand, the Whole Earth Network, and the Rise of Digital Utopianism: https://www.amazon.com/Counterculture-Cyberculture-Stewart-Network-Utopianism/dp/0226817423• Hare Brain, Tortoise Mind: How Intelligence Increases When You Think Less: https://www.amazon.com/Hare-Brain-Tortoise-Mind-Intelligence/dp/0060955414• The Elements of Typographic Style: https://www.amazon.com/Elements-Typographic-Style-Robert-Bringhurst/dp/0881791326• Zen and the Art of Motorcycle Maintenance: An Inquiry into Values: https://www.amazon.com/Zen-Art-Motorcycle-Maintenance-Inquiry/dp/0060589469• Time and the Art of Living: https://www.amazon.com/Time-Art-Living-Robert-Grudin/dp/0062503553/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.lennysnewsletter.com/subscribe

In this episode of Don't Waste the Chaos, Kerri Roberts discusses the essentials of business strategy, emphasizing that strategy is not merely a plan but a series of intentional choices that shape how businesses grow, adapt, and lead. With her signature clarity and practical insight, Kerri challenges the outdated notion that strategy is a one-time planning event. Instead, she presents strategy as a dynamic, living framework for decision-making that drives alignment, momentum, and long-term success. From identifying the right customer to simplifying execution, Kerri shares how leaders can use data, speed, and culture to bring strategy to life inside their organizations. She offers practical tools and reflective questions to help founders, executives, and team leaders clarify their direction and create alignment across people and performance. Tune in to hear: -Why strategy is a set of choices, not just a written plan-How clarity about your customer leads to stronger decisions-Why simplicity outperforms complexity in strategic planning-How to use the right data to make better decisions-Why fast execution beats perfect planning ResourcesThe Advantage by Patrick Lencioni https://amzn.to/4deF7LBTraction: Get a Grip on Your Business by Gino Wickman  https://amzn.to/4mdE5UgThe Lean Startup by Eric Ries https://amzn.to/4kbq47HCreativity, Inc. by Ed Catmull https://amzn.to/4iYbPlS The 21 Irrefutable Laws of Leadership by John Maxwell https://amzn.to/4jUQHhHThe Effective Executive by Peter Drucker https://amzn.to/4mkCL2b Salt & Light AdvisorsLearn more about simplifying executionhttps://www.saltandlightadvisors.com/ Join our weekly newsletter:• HR and operations insights for business professionals: https://www.saltandlightadvisors.com/contact Magic Mind Save $40 off your first order at magicmind.com/KERRIROBERTS ProducifyX For Recruitment Assistance, Tell them Kerri sent you  Connect on IG:https://www.instagram.com/saltandlightadvisorshttps://www.instagram.com/kerrimroberts Check out Don't Waste the Chaos on YouTube:https://youtube.com/@dontwastethechaospodcast Visit our websites:www.kerrimroberts.comwww.saltandlightadvisors.com

Diz o Gary Vaynerchuk que todos os negócios deveriam transformar-se em empresas de media. Se é assim, convém estarmos atentos ao que fazem essas empresas para atrair e envolver as suas audiências. Por exemplo, num canal de televisão, uma ferramenta central para esse envolvimento é a interação em tempo real dos telespectadores com o que vêem, através de plataformas digitais.Estamos a falar, neste caso, de ferramentas construídas para grandes audiências, muito diferentes das que podem ser úteis à típica empresa B2B. Mas, como a natureza humana não muda, os mecanismos que geram o envolvimento são os mesmos. Que mecanismos são esses? E como podem ser usados por empresas de todos os tamanhos?Para nos ajudar a responder, ninguém melhor do que os convidados deste episódio: a Carolina Rosa e o Pedro Centieiro, da Magycal, empresa que concebe e implementa plataformas com as quais interagem, diariamente, milhares de telespectadores.Ouça o episódio e descubra:Como fazer a sua audiência passar da atenção à açãoComo criar comunidades digitais verdadeiramente ativasComo usar os dados para gerar engagement em tempo realComo fugir à dependência exclusiva das redes sociais no seu marketingO ingrediente que permite mesmo a uma pequena empresa chegar com facilidade a grandes clientesSobre os convidados:Perfil da Carolina Rosa no LinkedInPerfil do Pedro Centieiro no LinkedInSite da MagycalPerfil da Magycal no LinkedInPerfil da Magycal no InstagramPerfil da Magycal no FacebookProjeto Viva Ronaldo Pessoa mencionada:Gary Vaynerchuk Empresas, marcas e instituições mencionadas:Universidade Nova de LisboaWaze BetanoCanal Panda PortugalTVI RealityOPTO SICCNN PortugalSIC NotíciasBig BrotherSport TV Livros recomendados:Ed Catmull e Amy Wallace - CriatividadeBJ Fogg e Dean Eckles - Mobile PersuasionGreat Mondays - Josh LevineThe Coming Wave - Mustafa SuleymanThe Design of Everyday Things - Donald Norman Para continuar a acompanhar-nos vá ao site da Hamlet e fique em dia com a comunicação de marketing B2B no nosso blog e ao subscrever a Newsletter B2B da Hamlet.Siga-nos também no LinkedIn, Instagram e Facebook.

To celebrate two decades of Under the Influence, Terry O'Reilly shares the five most influential books in his life; former news anchor Elysia Bryan-Baynes recommends three books about leaving your home country to live and work abroad; Montreal musician Lubalin on aliens, existentialism and song-writing fuel; and what makes iconic television personality Jeanne Beker feel the most Canadian on this episode of The Next Chapter.Books discussed on this week's show include:To Kill A Mockingbird by Harper LeeFifth Business by Robertson DaviesTaken at the Flood by John GuntherTicket To Ride by Larry KaneCreativity, Inc. by Ed CatmullThe Three-Body Problem by Liu CixinWe Meant Well by Erum Shazia HasanTo Tell the Truth: My Life as a Foreign Correspondent by Lewis M. SimonsThe War We Won Apart by Nahlah AyedHeart on my Sleeve by Jeanne BekerYoko by David Sheff

Show notes / PDF & Infographic / Free Audiobook / Unleash Your Inner Artist with Rick Rubin's The Creative Act: A Way of Being. Key figures and topics: Creativity, Art, Intuition, Inspiration, Process, Mindfulness, intuition, Process, self-discovery, Creativity, Art, Kanye West, Mandy Moore, Ed Sheeran, Johnny Cash, Rick Rubin, Jay Z, NYU, Lady Gaga, , The Creative Act: A Way of Being, Sterling K. Brown, Chris Sullivan, Def Jam Records, StoryShots, Ego, story shorts, Red Hot Chili Peppers, In the book summary of I Will Teach You to Be Rich by Ramit Sethi, the focus is on developing a personal financial strategy that empowers individuals to take control of their financial futures. The book emphasizes the importance of defining what being rich means to you personally, and encourages conscious spending by tracking expenses and making informed financial decisions. Sethi advocates for prioritizing spending on things you truly love while cutting back on less important expenses. The summary highlights key strategies for financial success, including starting early to leverage compound interest and breaking free from excuses that hinder financial growth. Sethi recommends focusing on macro financial decisions rather than getting caught up in minor cost-cutting measures. The book outlines 12 critical financial strategies, such as automating your money system, maintaining a good credit score, getting employer 401k matches, and negotiating raises. A central theme of the book is creating an automatic money management system that simplifies financial planning and helps individuals stick to long-term financial goals. By setting up multiple accounts for different purposes - including checking, savings, credit cards, retirement, and investment accounts - individuals can more effectively manage their finances. Sethi's approach is practical and non-judgmental, focusing on creating sustainable financial habits that align with personal values and long-term objectives. Key takeaways: Define what being 'rich' means to you personally and practice conscious spending by prioritizing expenses that truly matter to you Start investing and building wealth early to leverage compound interest and establish smart money habits over time Avoid making excuses that prevent financial progress; take action even if your initial steps aren't perfect Focus on macro financial strategies like automating your money system, maintaining a good credit score, and contributing to retirement accounts rather than getting stuck on minor expenses Set up an automatic money management system with multiple accounts (checking, savings, credit card, retirement, investment) to simplify finances and ensure consistent financial habits Prioritize five to ten key financial actions that yield significant results, such as negotiating raises, cutting major expenses, and using credit cards strategically Adopt a mindset of financial growth by being willing to start small and consistently improve your money management skills Challenge conventional financial wisdom by making informed, personalized decisions about spending and investing based on your individual goals (00:00) Introduction to The Creative Act: A Way of Being by Rick Rubin (01:05) About Rick Rubin (01:50) Everyone Is a Creator (02:55) Living Life as an Artist (03:53) Look Inward (04:52) Trust Your Intuition (06:17) Don't Let Doubt Hinder Your Creativity (07:25) Breaking Through the Creative Block (08:21) 10 Small Steps for Shaking up Your Process (10:53) 4 Steps to Bringing Creative Work to Life (12:17) Finding Fulfillment Through Experimentation and Completion (13:48) Final Summary and Review (15:00) Rating Related Book Summaries Creativity Inc. by Ed Catmull and Amy Wallace Discipline is Destiny by Ryan Holiday The War of Art by Steven Pressfield The Good Life by Robert Waldinger The Practice by Seth Godin Never Finished by David Goggins Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of 'The Wisdom Of' Show, host Simon Bowen speaks with Ed Catmull, co-founder of Pixar Animation Studios and former president of Walt Disney Animation Studios and Disneytoon Studios. With five Academy Awards® including an Oscar for Lifetime Achievement and the prestigious Turing Award for his work in computer graphics, Ed shares profound insights on creative leadership, innovation, and building world-class organizations. From pioneering 3D animation to leading the creation of beloved films that have grossed over $14 billion worldwide, Ed's journey offers valuable lessons on fostering creativity, navigating change, and building sustainable success.Ready to unlock your leadership potential and drive real change? Join Simon's exclusive masterclass on The Models Method. Learn how to articulate your unique value and create scalable impact: https://thesimonbowen.com/masterclassEpisode Breakdown00:00 Introduction and Ed's pioneering journey in animation05:18 Merging art and science: The power of interdisciplinary thinking12:36 Company culture and collective ownership beyond shares18:52 The inversion of business values: Product, People, Profit25:44 Navigating change and innovation in fast-evolving industries33:29 Pixar's 5-step decision-making framework for creative excellence38:22 Truth-finding mechanisms in organizations45:36 The CEO's role in facilitating collaborative genius52:12 Shifting from achievement to effectiveness: "Is this working?"58:43 Future implications and conclusionsKey InsightsWhy combining seemingly incongruous disciplines (science, art, math) creates richer innovationHow most businesses conflate collective ownership with shares or control, missing true ownershipThe dangerous mismatch between stated values and actual priorities in business decision-makingWhy understanding the accelerating rate of change is fundamental to business survivalThe 5-step framework Pixar uses to make all critical creative decisionsWhy most CEOs incorrectly believe they have effective error detection mechanismsHow shifting focus from "What am I achieving?" to "Is this working?" transforms leadershipThe CEO's role in fostering collaboration rather than providing all the answersWhy judging the creation, not the creator, is essential for innovationAbout Ed CatmullEd Catmull is a pioneer in computer graphics and animation who co-founded Pixar Animation Studios. Under his leadership, Pixar produced groundbreaking animated films including Toy Story, Finding Nemo, The Incredibles, and many more. After Disney acquired Pixar in 2006, Ed served as President of both Pixar and Walt Disney Animation Studios, overseeing hits like Frozen, Tangled, and Wreck-It Ralph.His numerous accolades include five Academy Awards®, the Turing Award from the Association for Computing Machinery, and the prestigious Gordon E. Sawyer Award for lifetime contributions to computer graphics in film. Ed's book "Creativity, Inc.: Overcoming the Unseen Forces That Stand in the Way of True Inspiration" is considered essential reading on creative leadership.With a Ph.D. in computer science and an initial passion for animation that led him through physics to pioneering computer graphics, Ed's career exemplifies the power of combining art and science to create revolutionary innovation.Connect with Ed CatmullLinkedIn: https://www.linkedin.com/in/edwincatmull/X:...

Go to www.LearningLeader.com for full show notes This is brought to you by Insight Global. If you need to hire 1 person, hire a team of people, or transform your business through Talent or Technical Services, Insight Global's team of 30,000 people around the world have the hustle and grit to deliver. www.InsightGlobal.com/LearningLeader Chris Beresford-Hill is the Worldwide Chief Creative Officer at BBDO. Previously he spent 2 years as North America President and CCO of Ogilvy, where he helped bring the agency and its clients a new level of relevance. He brought Workday to the Super Bowl, led the team that brought in the Verizon account, and one of the biggest Super Bowl campaigns ever, “Can't B Broken,” featuring Beyonce, and created the most celebrated Super Bowl campaign of 2024, the social & influencer lead "Michael CeraVe," for CeraVe. Chris and his teams have won every award for creativity and effectiveness many times over. He has been included in ADWEEK Best Creatives, the ADWEEK 100, and Business Insider's Most Creative People in Advertising. Notes: Cold Emails: Be specific in your praise and specific in your ask. The lame "Can I pick your brain" type emails get deleted and ignored because they aren't specific. You never need permission to take responsibility. Chris learned this from Ed Catmull's book Creativity Inc.… And he's embodied this his entire career. The people who build huge careers take ownership of their own and regularly solve problems and improve their clients' and colleagues' lives. Chris has done this since his early days as an intern. At any level taking on responsibility yourself, unasked, makes you stand out. Competence combined with insane follow-through. For some clients, it takes 50 ideas to get to the one that will work. Creating a culture where the team can share all of their bad ideas safely to get to the one great one. The creative process: Brain dump everything. Purge your brain of everything it has. When you think you're done, you're not. There's more. You have to get it all out. "A lot of creative people aren't fully aware of the process or the structure, they just feel it (Rick Rubin). "When you can see it lift off the page, you feel a sense of mastery over it." Chris's first Super Bowl commercial -- Emerald Nuts. He won it because he was both funny and added the fact that the product provided energy. Most people only covered one part, Chris did both. Push your edges - Chris is like Lionel Messi. He's always walking around in the office, asking questions, looking for ideas, being curious. Then he sees an opportunity and goes for it 100%. Chris has a standing reservation every week at the same restaurant where he meets with a mentor, mentee, or peer to deepen the important relationships in his life. That would be a good idea for us all to do. Chris was pen-pals with Dave Matthews for 8 years.  Chris saw that they recorded at Bearsville studios and wrote a letter to Dave there. He also said, "Show up with gifts." He gave Dave a Beatles Bootlegged album. A leader takes what comes and then turns it into an opportunity. The formula is Competence + Insane Follow-Through. How to build relationships: Meet with people in person. Get drunk with them. Do hard work with them. Go through something bad with them. Laugh with them. I got hired from my internship by cold calling Mark Cuban to get him to approve of using his name in an ad. The best ideas are often bad in their first moments, or massively wrong, and then someone flips it or unlocks it. You have to stay on things and play around. I made my first ad by going through a garbage can to learn how to write a script and sending a bunch of Budweiser scripts to my boss. The art of finding an idea on the edge of possible, and the value of going over your skis when on the cusp of greatness - having a stomach for it. I've told a lie to keep things moving on every great campaign I was part of. I learned the best lesson in leadership when we lost our biggest account (Accenture).  I put Danny Meyer's mentality into practice, and we took that moment to put the business and clients second and play for each other. Culture carried us. Culture is built by the stories we tell and the behaviors we highlight.  

Join host Madhavi Ravanan in the latest episode of the 10x Growth Strategies podcast featuring Anoop Bhaskaran. Anoop shares his fascinating professional journey, his passion for reading, and delves deep into Ed Catmull's 'Creativity Inc.' Discover the intriguing backstories of Pixar's creation, the visionary leadership of Steve Jobs, and the impactful concept of the 'Brain Trust'. A conversation filled with inspiring quotes, personal anecdotes, and actionable insights for both professional and personal growth. Don't miss this engaging episode packed with the secrets behind Pixar's stupendous success! Topics 00:00 Introduction and Guest Welcome 00:44 Anoop's Background and Career Journey 01:42 Anoop's Reading Habits 05:20 Discussion on 'Creativity Inc.' 07:04 The Pixar Story and Key Figures 21:25 The Brain Trust Concept 26:07 Favorite Quotes and Closing Thoughts

Dr Alvy Ray Smith is the co-founder of Pixar, a computer scientist and pioneer in the field of computer graphics and to celebrate 39 years to the day that Pixar was officially founded, I wanted to release my interview with Alvy from Series 3.After starting his career in academia, Alvy had an epiphany following a serious skiing accident. He decided to move to California to combine his two passions - art and computers - in a place where he felt something good was about to happen. Alvy was always a pioneer. From creating his first computer graphic in 1965, Alvy became an original member of the Computer Graphics Lab at the New York Institute of Technology, he witnessed the birth of the personal computer at Xerox PARC, and he was the first director of computer graphics at George Lucas's Lucasfilm. It was there that Alvy gathered some of the smartest people he knew to develop computer graphics software, including early renderer technology. He and colleague Ed Catmull then spun out to co-found the famous Pixar, soon followed by the hiring of Lucasfilm colleague John Lasseter, and Steve Jobs as an investor. It was at Pixar that Toy Story would be made - the very first, entirely computer-animated, feature film. In 2006, Pixar was sold to Disney for $7.4 billion.In this interview, Alvy recounts his career from the early days at Xerox PARC to how Pixar got started. We discuss the Pixar journey in detail, as well as his latest book – A Biography of the Pixel  (you can buy here)- including how innovation is born from three strands: An idea, chaos and a tyrant. And how Steve jobs was both the saviour and the tyrant in the incredible Pixar story.A true pioneer, this is one of my favourite conversations.Enjoy!-----NB This episode was first released in Series 3.Let us know what you think of this episode and please rate, review and share - it means the world to me and helps others to find it too.Danielle Twitter / Instagram / Substack Newsletter / YouTubeAll my podcast episodes are edited with Descript - try it for FREE hereAlvy Ray Smith on Twitter @alvyray / website Buy Alvy Ray Smith's book A Biography of the Pixel here. -----This episode was hosted by me - Danielle Newnham, a recovering founder, author and writer who has been interviewing tech founders and innovators for ten years - and produced by Jolin Cheng. Image of Alvy Ray by Christopher Michel.

Welcome to the Country Creatives Summer Series! In this encore episode, Caleb and Reece revisit one of their favourite conversations of the year: their book review episode. This thoughtful and inspiring discussion dives into two books that profoundly shaped their creative journeys in 2024. Reece shares his experience with Rick Rubin's The Creative Act: A Way of Being, a philosophical exploration of creativity as an intrinsic part of life. Caleb reflects on Ed Catmull's Creativity, Inc., a practical and insightful guide to balancing creativity and business, drawn from Catmull's experience as co-founder of Pixar. With personal anecdotes and lessons learned, Caleb and Reece unpack how these books have influenced their perspectives as creatives and professionals. Whether you're an artist, entrepreneur, or simply someone looking to deepen your creative practice, this episode offers inspiration, practical wisdom, and a few laughs along the way. Tune in, soak up the insights, and enjoy this replay as you kick back this summer!

No guests today! Instead, I'll be taking questions from the Building Better Games discord and answering them. I cover10 questions including the challenges with AAA dev, the rise of co-dev, and what production careers look like. Enjoy! Question #1 : How do you ensure that you (the royal you) are making a game that will be fun for players, not just fun for its designers to make? (Or maybe in this context - what are the ways in which production can support product management and ensure the sprint-to-sprint goals align with what internal player advocates are asking for?) Question #2 : What would you say needs to happen to make the big players more competitive / successful again? Question #3: Do you think there's an observable trend towards an increased amount of codevelopment as a way to mitigate costs/risk? What issues do you see this posing for coherent design and production if there is an increasing reliance on external development partners?   Question #4: There are clear signs when certain aspects of a game are lacking - incoherent design, low quality assets, buggy software. What are the player-facing symptoms of a game that is lacking in production or leadership competencies?    Question #5: Production organizations at larger game studios often suffer from issues of structure, such as a substantial number of producers, senior producers, and even lead producers all rolling directly up into an overburdened production director, because there doesn't seem to be an understood space for a “producer manager” between frontline production and executive/director-level production leadership. What is the rationale for this gap when manager is a well-understood conceit in other gamedev disciplines (e.g. designers will have design managers reporting to a design director, artists will have department managers reporting to a director, engineers have managers between them and directors, etc.)?  Is it just that production is typically not a large enough organization to merit managers? That producers are seen as organized and not in need of more traditional personnel management? Question #6: How can you become better at your role as a producer when you aren't at your job? Or in other words, how can you get better at what you do aside from getting more experience?   Question #7: For mid- and senior level producers: What does a career development track look like? Often it seems like the only future for a highly competent producer is executive producer (a stretch for many and not a realistic path for most) or production director, which itself is a rarified commodity at larger developers. What are the progression opportunities an IC producer should be considering? Question #8: As the only Production guy on my team (and 1 of 3 "operations people"), how would you deal with getting questions and answers when you have nobody around to rubber ducky with?   Question #9: When talking about the past, how can you learn to abstract experiences and look past the specifics? Are there any resources you recommend for learning how to tell stories so that you're not bogged down in the details of history?   Question #10: How are game developers selecting and setting up test groups to see their players are enjoying the game and it's a good market fit? Are there aspects of this process that could see refining and improving? Or common pitfalls other developers tend to see in this process? LinkedIn for Steve Bromley (https://www.linkedin.com/in/stevebromley/) and Graham McAllister (https://www.linkedin.com/in/grahammcallister/) Steve Bromley's Book: https://gamesuserresearch.com/book/ Graham McAllister URL: https://grahammcallister.com/ Steve Bromley URL: https://gamesuserresearch.com/ Agile Game Development: Build, Play, Repeat by Clinton Keith: https://www.amazon.com/Agile-Game-Development-Addison-Wesley-Signature-dp-0136527817/dp/0136527817/ Lean from the Trenches by Henrik Kniberg: https://www.amazon.com/Lean-Trenches-Managing-Large-Scale-Projects/dp/1934356859/ Creativity, Inc. by Ed Catmull and Amy Wallace: https://www.amazon.com/Creativity-Inc-Expanded-Overcoming-Inspiration/dp/0593594649/ Turn the Ship Around by L. David Marquet: https://www.amazon.com/Turn-Ship-Around-Building-Breaking/dp/0241250943 Our discord community is live! Join here to engage with leaders and producers in game dev looking to make our industry a better place that makes better games: https://discord.gg/ySCPS5aMcQ   If you're interested in an online course on becoming a better game producer, head here: https://www.buildingbettergames.gg/succeeding-in-game-production   Subscribe to our newsletter for more game development tips and resources: https://www.buildingbettergames.gg/newsletter   Ben's LinkedIn: https://www.linkedin.com/in/benjamin-carcich/ YouTube Channel: https://www.youtube.com/@buildingbettergames Spotify Podcast: https://open.spotify.com/show/6QD5yIbFdJXvccO8Z5aXpm   Help us create more amazing content! Join us on Patreon today: https://www.patreon.com/BBGOfficial  

The key to a life well-lived is prioritization, but people rarely explain how to do it effectively.   In Managing Priorities: How to Create Better Plans and Make Smarter Decisions (Rosenfeld Media, 2024), Harry Max provides a useful guide.  He explains how learning to prioritize is helpful in life as well as at work. He explains how he - and his clients - feel a sense of freedom, as though a weight is lifted, when it's clear what is most important and they are able to focus on those things. In this relatable approach, Max acknowledges that avoidance behavior is natural, and clarifies the need to understand the costs of not prioritizing intentionally. Drawing on methods used at Apple, DreamWorks, NASA, Adobe, Google, Microsoft, and beyond, Harry Max presents a practical method that you can apply either for single large decisions or for ongoing efforts.  In the book he introduces the "daily boot", a way to start the day by clearing out the fog of competing efforts, and his DEGAP® method: Decide, Engage, Gather, Arrange, Prioritize.  Max demystifies common prioritization frameworks by providing guidance on how and when to use them, either together or separately. These include the Eisenhower Matrix, the Analytic Hierarchy Process, Paired Comparison, and Stack Ranking among others.  Mentioned resources: The New How by Nilofer Merchant The Crux: How Leaders Become Strategists by Richard P. Rumelt The Kano model by Noriaki Kano. It's not a prioritization framework per se, but a valuable resource for understanding what is important as it relates to customer satisfaction.  Author recommended reading: Wiring the Winning Organization by Gene Kim and Steven J. Spear Creativity, Inc by Ed Catmull and Amy Wallace Hosted by Meghan Cochran Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

The key to a life well-lived is prioritization, but people rarely explain how to do it effectively.   In Managing Priorities: How to Create Better Plans and Make Smarter Decisions (Rosenfeld Media, 2024), Harry Max provides a useful guide.  He explains how learning to prioritize is helpful in life as well as at work. He explains how he - and his clients - feel a sense of freedom, as though a weight is lifted, when it's clear what is most important and they are able to focus on those things. In this relatable approach, Max acknowledges that avoidance behavior is natural, and clarifies the need to understand the costs of not prioritizing intentionally. Drawing on methods used at Apple, DreamWorks, NASA, Adobe, Google, Microsoft, and beyond, Harry Max presents a practical method that you can apply either for single large decisions or for ongoing efforts.  In the book he introduces the "daily boot", a way to start the day by clearing out the fog of competing efforts, and his DEGAP® method: Decide, Engage, Gather, Arrange, Prioritize.  Max demystifies common prioritization frameworks by providing guidance on how and when to use them, either together or separately. These include the Eisenhower Matrix, the Analytic Hierarchy Process, Paired Comparison, and Stack Ranking among others.  Mentioned resources: The New How by Nilofer Merchant The Crux: How Leaders Become Strategists by Richard P. Rumelt The Kano model by Noriaki Kano. It's not a prioritization framework per se, but a valuable resource for understanding what is important as it relates to customer satisfaction.  Author recommended reading: Wiring the Winning Organization by Gene Kim and Steven J. Spear Creativity, Inc by Ed Catmull and Amy Wallace Hosted by Meghan Cochran Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/psychology

The key to a life well-lived is prioritization, but people rarely explain how to do it effectively.   In Managing Priorities: How to Create Better Plans and Make Smarter Decisions (Rosenfeld Media, 2024), Harry Max provides a useful guide.  He explains how learning to prioritize is helpful in life as well as at work. He explains how he - and his clients - feel a sense of freedom, as though a weight is lifted, when it's clear what is most important and they are able to focus on those things. In this relatable approach, Max acknowledges that avoidance behavior is natural, and clarifies the need to understand the costs of not prioritizing intentionally. Drawing on methods used at Apple, DreamWorks, NASA, Adobe, Google, Microsoft, and beyond, Harry Max presents a practical method that you can apply either for single large decisions or for ongoing efforts.  In the book he introduces the "daily boot", a way to start the day by clearing out the fog of competing efforts, and his DEGAP® method: Decide, Engage, Gather, Arrange, Prioritize.  Max demystifies common prioritization frameworks by providing guidance on how and when to use them, either together or separately. These include the Eisenhower Matrix, the Analytic Hierarchy Process, Paired Comparison, and Stack Ranking among others.  Mentioned resources: The New How by Nilofer Merchant The Crux: How Leaders Become Strategists by Richard P. Rumelt The Kano model by Noriaki Kano. It's not a prioritization framework per se, but a valuable resource for understanding what is important as it relates to customer satisfaction.  Author recommended reading: Wiring the Winning Organization by Gene Kim and Steven J. Spear Creativity, Inc by Ed Catmull and Amy Wallace Hosted by Meghan Cochran Learn more about your ad choices. Visit megaphone.fm/adchoices

Unlocking Team Success: Insights from 'Culture Code' by Daniel CoyleChapter 1:Summary of Culture Code"The Culture Code: The Secrets of Highly Successful Groups" by Daniel Coyle explores the dynamics of successful group cultures and what makes them thrive. Coyle identifies three key skills that contribute to creating a strong culture:1. Build Safety: Successful groups foster a sense of belonging and psychological safety where members feel valued, secure, and free to share ideas without fear of judgment. This is cultivated through openness, support, and mutual respect, promoting trust and collaboration.2. Share Vulnerability: High-performing teams engage in sharing vulnerability to strengthen bonds among members. This involves being open about mistakes and weaknesses, which fosters a culture of honesty and encourages others to do the same, leading to increased creativity and problem-solving.3. Establish Purpose: Successful groups have a clear shared purpose that inspires and motivates members. This common goal creates alignment and a sense of direction, empowering individuals to contribute meaningfully to the group's objectives.Coyle illustrates these principles through various real-world examples from diverse settings, such as sports teams, businesses, and schools. He emphasizes that cultivating a strong culture is an ongoing process that requires continuous effort and engagement from all members. The book offers practical insights and actionable strategies for leaders and team members seeking to enhance their group's culture and effectiveness.Chapter 2:The Theme of Culture Code"Culture Code: The Secrets of Highly Successful Groups" by Daniel Coyle explores how group dynamics contribute to the success of organizations. While the book doesn't follow a traditional narrative structure with characters and plot points, it emphasizes key concepts through real-world examples and case studies across various fields, such as sports teams, businesses, and educational environments. Here's an overview of some key concepts and themes: Key Plot Points and Examples1. Safety: The first drive of a successful culture is creating a safe environment. Coyle discusses how groups that make members feel safe foster openness and psychological safety. Examples include the U.S. Navy SEALs, where trust is critical for operations.2. Vulnerability: Successful groups demonstrate a willingness to be vulnerable. Coyle illustrates this through case studies, such as the practices of Pixar, where sharing and discussing weaknesses leads to innovation and creativity.3. Purpose: Groups with a clear, compelling purpose are more cohesive. Coyle highlights organizations that align their mission with the personal values of their members, creating intrinsic motivation.4. Belonging: The sense of belonging is crucial for group cohesion. The author provides examples from the sports world, including how coaches create cultures where all team members feel they are valued contributors, regardless of their role.5. Storytelling: Coyle emphasizes storytelling as a tool for sharing culture. Successful groups often have a set of shared stories that reinforce their values and vision, which helps in stitching the fabric of the group. Character DevelopmentWhile "Culture Code" doesn't have characters in the traditional sense, it portrays leaders and organizations as central figures in developing culture. Key "characters" or archetypes include:- Leaders and Coaches: Individuals like John Wooden or Ed Catmull (of Pixar) serve as models for how effective leaders build a culture of safety, belonging, and vulnerability.- Team Members: The individuals within those groups are often depicted as learners and contributors who grow and evolve as part of the cultural framework established by their...

Welcome to Strategy Skills episode 509, an interview with a renowned innovation cultivator, speaker, and founder of Creativity Partners, Jamie Woolf.   In this episode, Jamie shared her remarkable experience working at Pixar under Ed Catmull's leadership and explained its unique culture that combines art and technology to drive innovation. She explained the concept of “constructive disruption” at work and how leaders can build healthy, innovative, and creative workplaces where everyone feels free to speak up, be heard, and support employees in bringing their authentic selves to work.   Jamie Woolf was the first Director of Culture at Pixar Animation Studios where she was recruited to be a force for “constructive disruption.” Jamie has 30+ years of experience practicing organizational psychology and draws on her impactful work with organizations like Google, Dreamworks, University of California, and Pixar Animation. Visit Jamie's blog here: https://www.creativity-partners.com/blog    Here are some free gifts for you: Overall Approach Used in Well-Managed Strategy Studies free download: www.firmsconsulting.com/OverallApproach   McKinsey & BCG winning resume free download: www.firmsconsulting.com/resumepdf   Enjoying this episode? Get access to sample advanced training episodes here: www.firmsconsulting.com/promo  

Disney legend Duncan Wardle shares keys for tapping into your creative side. — YOU'LL LEARN — 1) What blocks our creativity 2) How to hone your ideas with a “naive expert” 3) The trick to surfacing your best ideas Subscribe or visit AwesomeAtYourJob.com/ep1017 for clickable versions of the links below. — ABOUT DUNCAN — As Head of Innovation and Creativity at Disney, Duncan and his team helped Imagineering, Lucasfilm, Marvel, Pixar, and Disney Parks to innovate, creating magical new storylines and experiences.He now brings his extensive Disney expertise to audiences around the world using a unique approach to Design Thinking, helping people capture unlikely connections, leading to fresh thinking and disruptive ideas.Delivering a series of keynotes, workshops and ideation forums, his unique Innovation toolkit helps companies embed a culture of innovation into everyone's DNA.Duncan is a multiple TED speaker and contributor to Fast Company, Forbes & the Harvard Business Review. He teaches innovation Master Classes at Yale, Harvard, and Edinburgh University. • Book: The Imagination Emporium: Creative Recipes for Innovation • Website: DuncanWardle.com — RESOURCES MENTIONED IN THE SHOW — • Book: Creativity, Inc.: Overcoming the Unseen Forces That Stand in the Way of True Inspiration by Ed Catmull and Amy Wallace • Book: Virgin by Design by Nick Carson — THANK YOU SPONSORS! — • CleanMyMac. Use the promo code BEAWESOME for 10% off on any CleanMyMac subscription plan. • Lingoda. Visit try.lingoda.com/awesome and use the promo code 50AWESOME for up to 50% off until December 21! • Jenni Kayne. Use the code AWESOME15 to get 15% off your order!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Cooper Roberts is an editor and director who just released his feature debut, All You Need is Blood—a delightfully gory tribute to Amblin-esque coming-of-age movies and zombie films. Cooper's background includes experience in advertising and music videos, and most notably, he was nominated for a Grammy Award in 2016 for co-directing the music video for Jack White's band, The Dead Weather, and their song “I Feel Love.”All You Need is Blood is a movie I really want to shout from the rooftops for more people to see. It's a love letter to childhood dreams of filmmaking and zombie movies and is full of charm and blood in equal measure. The effects are also awesome, and the movie is hilarious—I urge you to see it and tell your friends. All You Need is Blood is available to stream on the KINO app, which you can download today.In this conversation with Cooper we dig into the 6 plus year journey of bringing AYNIB to fruition, the challenges of relying on practical effects on indie movies and why casting is one of the most important elements of directing.Here are some key takeaways from this conversation with Cooper Roberts.80% of Directing is CastingCooper cited a famous quote, commonly attributed to Elia Kazan, that directing is 80% casting. For this reason, he paid close attention to the casting process and took his time finding the right people, which tremendously helped bring the characters—and therefore the film—to life. Cooper noted that casting actors who naturally embody the character makes directing them much smoother and more intuitive. Casting actors outside their usual genres can also yield surprising performances, as audiences respond well to seeing familiar faces in unexpected roles, as was the case with Mina Suvari playing a comedic role, which brought a fresh, unexpected dimension to the film.Never underestimate good old cold outreach.With few industry connections, Cooper turned to IMDb Pro to cold-email indie producers. Out of a hundred emails, he connected with several promising candidates and eventually found a committed team. A lot of would-be filmmakers wait to be discovered or think it's the responsibility of an agent or manager to get their movies moving forward, but it's all on you. Even if you don't have representation, just reach out to people. Cold outreach might seem daunting, but when executed well, it can be highly effective in finding partners and funding resources.Build a ‘Brain Trust' for Script FeedbackWhile writing the script for All You Need is Blood, Cooper sought to create his own ‘brain trust' of script consultants and friends for feedback during the writing process, which he modeled after Pixar. Although he didn't take all the notes, he found the input invaluable, as even a "bad" note could highlight a weak spot in the story. He noted Stephen King's advice from On Writing—if multiple people give similar feedback on a section, it's worth reevaluating. It is very easy to fall in love with your own voice and be blind to glaring issues in your script because you're just too close to it to be objective. This is why it's crucial to have trusted advisors and confidantes who can help you mold your projects. For more on Brain Trusts, I highly recommend Creativity Inc. by Ed Catmull, which outlines how Pixar was founded and how they operate to this day with a large emphasis on storytelling. Also, shoutout to script consultant Carson Reeves—Cooper and I both worked with him, and I can tell you he's great. Check out Carson at https://scriptshadow.net.Show NotesMovies Mentioned:Dead Alive (Braindead)Toy Story 3Toy Story 4American...

Mitch Warner reveals how we end up sabotaging ourselves and how you can overcome these obstacles to strengthen relationships and your leadership as a whole. — YOU'LL LEARN — 1) How “the box” limits your perspective and opportunities 2) The tell-tale signs self-deception 3) How to make people feel safe to share their perspectives Subscribe or visit AwesomeAtYourJob.com/ep1001 for clickable versions of the links below. — ABOUT MITCH — Mitch Warner is a bestselling author and Arbinger managing partner with a background in healthcare and organizational turnaround. Mitch is the co-author of Arbinger's latest bestseller, The Outward Mindset. He writes frequently on the practical effects of mindset at the individual and organizational levels as well as the role of leadership in transforming organizational culture and results. He is an expert on mindset and culture change, leadership, strategy, performance management, organizational turnaround, and conflict resolution.Mitch is a sought-after speaker to organizations across a range of industries, bringing his practical experience to bear for leaders of corporations, governments, and organizations across the globe. Specific clients include NASA, Citrix, Aflac, the U.S. Army and Air Force, the Treasury Executive Institute, and Intermountain Healthcare. Mitch carries his first-hand perspective as a proven leader into his speeches and facilitation, dynamically bringing Arbinger's concepts and tools to life through his powerful stories and hands-on experience. His audiences leave inspired to improve and equipped with a practical roadmap to effect immediate change.• Book: Leadership and Self-Deception, Fourth Edition: The Secret to Transforming Relationships and Unleashing Results by The Arbinger Institute • Website: Arbinger.com — RESOURCES MENTIONED IN THE SHOW — • Book: Creativity, Inc.: Overcoming the Unseen Forces That Stand in the Way of True Inspiration by Ed Catmull and Amy Wallace • Book: Insanely Simple: The Obsession That Drives Apple's Success by Ken Segall — THANK YOU SPONSORS! — • Jenni Kayne. Use the code AWESOME15 to get 15% off your order!• LinkedIn Jobs. Post your job for free at LinkedIn.com/BeAwesomeSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, Nicolai Tangen is joined by Ed Catmull, co-founder and formerpresident of Pixar, and author of the highly acclaimed book "Creativity, Inc." Pixarhas set the gold standard in animation with groundbreaking films like Toy Story,Finding Nemo, and The Incredibles, earning 27 Academy Awards.Ed delves into his journey from a technical expert to a cultural leader, sharing hisphilosophy on creativity, leadership, and the unique culture at Pixar. Discover thesecrets behind Pixar´s Brain Trust. The former Pixar president also reflects on hisexperiences working with Steve Jobs and the profound lessons learned along theway. Tune in to gain valuable insights into managing talent, building creativecultures, and the future of animation and AI.In Good Company is hosted by Nicolai Tangen, CEO of Norges Bank InvestmentManagement. New episode out every Wednesday.The production team for this episode includes PLAN-B´s Pål Huuse and NiklasFigenschau Johansen. Background research was conducted by Kristian Haga. Watch the episode on YouTube: Norges Bank Investment Management - YouTubeWant to learn more about the fund? The fund | Norges Bank Investment Management (nbim.no)Follow Nicolai Tangen on LinkedIn: Nicolai Tangen | LinkedInFollow NBIM on LinkedIn: Norges Bank Investment Management: Administrator for bedriftsside | LinkedInFollow NBIM on Instagram: Explore Norges Bank Investment Management on Instagram Hosted on Acast. See acast.com/privacy for more information.

We talk a lot about the importance of emergence—of being more comfortable with being uncomfortable. However, it's hard to practice what you preach… especially for a podcast with a tight schedule. Normally, when one of two hosts is out of commission, you don't record. But when this recently happened to us, we asked “How might we?” and took a big ol' step into the unknown. We're glad we did, because this week's guest is Dr. Jason Fox, a self-proclaimed wizard-philosopher, best-selling author, and senior leadership advisor to Fortune 500 companies around the world. In classic wizard-philosopher fashion, he and Sam throw out the script for a far-reaching conversation about the importance of rituals, the roles we play when we're at work, and how embracing uncertainty is where the magic truly happens. Learn more about Jason: On his website On LinkedIn Read How to Lead A Quest or The Game Changer Follow us on LinkedIn and Instagram for more org design nerdery! Got an idea for future episodes or a thorny workplace question you need answered? Shoot us a message to podcast@theready.com. Mentioned references: Game Frame, book by Aaron Dignan Brave New Work, book by Aaron Dignan James Carse, author of Finite and Infinite Games Rodney's "I am CEO vs I hold the role of CEO": AWWTR Ep. 14 Lands of Lorecraft, series of articles by Venkatesh Rao Jevons Paradox "rivalrous dynamics" "multipolar traps" "operating rhythm": BNW Ep. 118 Creativity, Inc., book by Ed Catmull and Amy Wallace basilisk "GTD": BNW Ep. 39 with David Allen John Keats and "negative capability" Antifragile, book by Nassim Taleb "Metacrisis" The Ministry for the Future, book by Kim Stanley Robinson Children of Time, series by Adrian Tchaikovsky The Expanse, series by James S.A. Corey The Culture, series by Iain M. Banks

In this episode of the Agile Uprising podcast, host Andy Cleff explores the virtues of humility and vulnerability in leadership. He emphasizes the importance of self-awareness and the courage to admit "I don't know" as foundational to effective leadership and personal growth. Drawing on insights from thought leaders like Dr. Brené Brown, Cleff discusses how vulnerability fosters innovation, creativity, and psychological safety within teams. He shares real-world examples from leaders like Satya Nadella, Ed Catmull, and Jacinda Ardern to illustrate how embracing vulnerability can lead to trust, collaboration, and positive change. Tune in for practical tips on integrating these virtues into your leadership style. (Part of series ) Show Links   About the Agile Uprising If you enjoyed this episode, please give us a review, a rating, or leave comments on iTunes, Stitcher or your podcasting platform of choice. It really helps others find us.  Much thanks to the artist  from  who provided us our outro music free-of-charge!  If you like what you heard,     to find more music you might enjoy! If you'd like to join the discussion and share your stories,  please jump into the fray at our  We at the Agile Uprising are committed to being totally free.  However, if you'd like to contribute and help us defray hosting and production costs we do have a .  Who knows, you might even get some surprises in the mail!

In today's episode, I interview director Rebecca King and actress/producer Nina Yndis about their film "Elsa" about a tense love triangle between a Norwegian civilian woman, her adoring neighbor and a German soldier during World War II. The film will be showing this August at the Flickers Rhode Island International Film Festival.Listen to hear about where Rebecca and Nina got the inspiration for the story, what challenges they faced making the film, and if they worried about sharing the name of their film with a certain animated character.Books recommended in this episode include:Creativity, Inc. by Ed Catmull and Amy WallaceThe Book of Wilding: A Practical Guide to Rewilding, Big and Small by Isabella TreeDrive Your Plow Over the Bones of the Dead by Olga Tokarczuk and Antonia Lloyd-JonesNei og atter nei by Nina Lykke (No, A Hundred Times No)Food for Life: The New Science of Eating Well by Tim Spector (the audio got cut off a bit when Nina mentioned this book so you won't actually hear it mentioned in the episode)Films mentioned in this episode include:“Elsa” directed by Rebecca KingHigh and Low directed by Akira KurosawaRatatouille directed by Brad BirdParasite directed by Bong Joon-hoBurning directed by Lee Chang-dongCabaret directed by Bob FossePulp Fiction directed by Quentin TarantinoJoker directed by Todd PhillipsToy Story directed by John LasseterBabe directed by Chris NoonanFrozen directed by Jennifer Lee and Chris BuckBean directed by Mel SmithGrease directed by Randal KleiserFollow the film on Instagram @womenlikeelsa. Rebecca is @rebeccajking_ and Nina is @ninayndis.

Step into the world of marketing strategy and innovation with John Huntinghouse as we unpack their experience navigating the challenges of an uber-regulated industry. John, who comes from this challenging sector, shares his refreshing approach to building and leading successful teams, offering practical insights that defy industry norms. We discuss how he overcomes regulatory hurdles to foster creativity and collaboration within his teams, challenging conventional thinking along the way. John shares his journey of finding a brand voice and reveals intentional strategies and mentorship moments that helped him break free from conformity and authentically amplify his brand's message. This discussion not only highlights the importance of authenticity in marketing but also provides actionable advice for marketers aiming to navigate similar regulatory landscapes. Tune in to discover firsthand experiences, valuable lessons, and actionable strategies that promise to inspire and empower your marketing efforts.Learn more about John:John Huntinghouse is an award winning, VP of Marketing for TAB Bank. John has over 16 years of executive marketing experience, an M.B.A. from the University of Utah and has taught over 500 students at various colleges and universities and currently teaches at Weber State University. He loves spending time with his wife Kara, and his three kids Maya, Mason & Aubrey. He is a lover of all things pasta, the 49ers & anything that Ed Catmull writes. John's Links:LinkedIn: https://www.linkedin.com/in/johnhuntinghouse/Connect with Veronica on Instagram: https://www.instagram.com/vromney/If you're serious about advancing your career in marketing and you're looking for some personal insights into how then I invite you to schedule a free Pathway to Promotion call with me: https://pathwaycall.com/If you found value in today's episode, I would appreciate it if you could leave a rating and review.

Looking for a new way to solve problems? Join us as we talk with Hal Gregersen, author of the book, "Questions Are the Answer: A Breakthrough Approach to Your Most Vexing Problems at Work and in Life".  It's based on interviews with leaders like Pixar founder Ed Catmull and Salesforce CEO Marc Benioff. Hal is well-known as an innovation and leadership guru who is a Senior Lecturer at the MIT Sloan School of Management. His website is https://halgregersen.com/   Shopify is the all-in-one commerce platform that makes it simple for anyone to start, run and grow your own successful business. With Shopify, you'll create an online store, discover new customers, and grow the following that keeps them coming back. Shopify makes getting paid simple, by instantly accepting every type of payment. With Shopify's single dashboard, you can manage orders, shipping and payments from anywhere. Sign up for a one-dollar-per-month trial period at Shopify.com/nobody.

Inside Out 2 has dropped in theaters.  So it's time to stop and rank all 28 Pixar films! Today's Sponsor:  Factor Go to https://factormeals.com/seanchandler50 and use code seanchandler50 to get 50% off your delicious meals delivered right to your front door plus 20% off your next month! EXTENDED CUT ON PATREON There's an extended cut of this video on Patreon with 7 extra minutes and ADs turned off: Learn more here: https://www.patreon.com/seanchandler

The podcast for project managers by project managers. In this second part of our conversation about Decoding Megaprojects with Bent Flyvbjerg, we explore the idea of "Pixar Planning," a method inspired by Pixar Studios' approach to making movies. Next, we tackle the concept of Modularity, and the significance of standardized, modular approaches in driving efficiency and reducing the frequency and severity of project failures. Table of Contents 01:22 … Pixar Planning06:33 … Iteration10:37 … Modularity12:46 … Modular vs. Bespoke16:20 … Kevin and Kyle18:04 … Examples from Shipping Containers22:26 … Advice from Bent28:26 … Contact Bent29:22 … Closing BENT FLYVBJERG:  So, my advice to anybody working in any field is start thinking about how you modularize what you're doing.  Don't ever do bespoke projects.  Only if it's absolutely unavoidable should you ever do bespoke projects.  You should always do projects that have an element of standardization and modularity.  And the larger you can make that element of standardization and modularity, the more successful your projects will be.  So that's the direction of travel for the whole project industry, no matter what type of project you're working in.  And every one of us who's working in this industry can make a huge contribution by constantly thinking, how do we make what we do more modular and more standardized? WENDY GROUNDS:  You're listening to Manage This, the podcast by project managers for project managers.  I'm Wendy Grounds, and as always, I'm joined in the studio by the one and only Bill Yates.  This is Episode 2 of our conversation with Bent Flyvbjerg.  We are thrilled that he generously extended his time with us, and we are eager to share our conversation with you today. Before we dive in today's episode, we want to remind you to check out our website, Velociteach.com, where you can easily subscribe to the show so you never miss out on the latest insights and discussions.  And you can also earn PDUs, your Professional Development Units, by listening to our podcast. Pixar Planning BILL YATES: We're going to jump right back in where we left off. Just a quick review. The first two things we talked about were: thinking from right to left; and thinking slow and acting fast. Bent, I want to shift to a third key concept. You know, where we've seen some of their amazing movies, and Pixar Studio follows this same idea “think slow, act fast” when they take their approach to making movies.  Some of the great stories that I've read through “Creativity, Inc.,” written by Ed Catmull.  As you and I were just talking before we even started recording this, such a great book, such a great leader Ed Catmull is.  When I read the book back in 2016, I didn't latch on to what you found in this and through your research, which is this concept of Pixar planning.  So this idea of Pixar planning, I know you go into it deep.  What is it that makes that unique, and how can we apply these same concepts to our projects that Pixar does when they're developing their movies? BENT FLYVBJERG:  So Pixar planning is not a concept that Ed Catmull came up with.  This is what we call it because we think that their method is so important and ingenious that it deserves a name, you know.  And it deserves the name “Pixar Planning” because Pixar is the organization who came up with this.  And what surprised us was how much Gehry's method and the Pixar method, which was spearheaded by Ed Catmull, who was the CEO of Pixar then, he later became also CEO of Disney Animation and Pixar at the same time, and he's now retired.  So he and his team pioneered this.  And when I read Ed's book back in 2016 also, I was so excited because – and I started asking my students at Oxford to read the book. And at first they were like, what?  We don't work in the movie industry, and certainly not animated movies.  Like why would we want to read about animated movies?  You know,

How do brains picture things internally, and how might you and I imagine differently? How have recent discoveries completely changed the debate and the way we understand internal experience? What does this have to do with Disney's Fantasia, or Pixar's aphantasia? Strap in for some very wild surprises today about our internal experiences, with guest Ed Catmull, founder of Pixar Studios. 

Elyse Klaidman, after 22 years at Pixar leading creative and educational programs, is now the CEO of X IN A BOX, where she leverages her background as an artist, educator and leader.Prior to co-founding X IN A BOX in 2020, Tony DeRose led the computer graphics research group for much of his 23 years at Pixar. He is passionate about project based learning and has been very active in the Maker Movement.Brit Cruise, Chief Learning Officer at X IN A BOX and creator of educational content and products, launched his career with the YouTube channel Art of the Problem, leading to working at Khan Academy and partnerships with NASA, Google, and Disney/Pixar.Dennis Henderson, VP of Education and Strategy at X IN A BOX, leverages his role as Executive Director of Manchester Youth Development Center to drive social justice through project-based education, promoting social mobility and economic opportunities.Recommended Resources:

The team at "This is Working with Dan Roth" is excited to share this preview of Building One, the newest addition to the LinkedIn Podcast Network. Building One is hosted by Tomer Cohen, LinkedIn's Chief Product Officer. In this series of engaging one-on-one conversations with accomplished product leaders, you'll trace the professional journeys of today's top builders, gain insights into the intricacies of product development, and glimpse the stories behind the tech world's most impactful products. The episode we're bringing you today features an insightful conversation with Pixar co-founder, Ed Catmull. Tomer hears from Ed about how he built a successful creative team by hiring with a growth mindset and how to lead such a team with careful process and without ego. Follow Building One on Apple Podcasts, Spotify, or wherever you listen. Then, find the conversation on LinkedIn.

Mastering the Art of Idea Generation: Insights from Stanford's D SchoolJeremy Utley is the Director of Executive Education at Stanford's d.school, author of "IDEAFLOW: The Only Business Metric That Matters," and an incredible innovator. In today's fast-paced business landscape, where creativity and innovation are paramount, Jeremy offers unique insights and strategies to supercharge the innovation process.Jeremy opened my eyes to new ways of thinking and new ways of creating. This episode was powerful. We spoke about idea generation, AI and how we can leverage it in both business and everyday life. Please share this episode with your loved ones.You can purchase your copy of IDEAFLOW here - https://www.jeremyutley.design/ideaflowCheckout Jeremy's website here - https://www.jeremyutley.designListen to the episode mentioned on Jeremy's Podcast with Ed Catmull here - https://podcasts.apple.com/nz/podcast/s3e01-wizard-of-awe-peek-behind-the-pixar-curtain/id1586707064?i=1000628222318Read the HBR study on AI here - https://hbr.org------------------------Most people are downloading this FREE guide to level up their Personal Mastery - https://www.jjlaughlin.com/offers/2wBnEQEH/checkoutIf you would like to help James continue to bring on world-class guests, please consider making a small recurring donation to cover the back end, admin and editing costs. For many years, James has dedicated countless hours to the show and would LOVE to continue bringing you global thought leaders.Thank you for your support. It is greatly appreciated.With much gratitude.Full Transcript, Quote Cards, and a Show Summary are available here:https://www.jjlaughlin.com/blog-----Website: https://www.jjlaughlin.comYouTube: https://www.youtube.com/channel/UC6GETJbxpgulYcYc6QAKLHAFacebook: https://www.facebook.com/JamesLaughlinOfficialInstagram: https://www.instagram.com/jameslaughlinofficial/Apple Podcast: https://podcasts.apple.com/nz/podcast/life-on-purpose-with-james-laughlin/id1547874035Spotify: https://open.spotify.com/show/3WBElxcvhCHtJWBac3nOlF?si=hotcGzHVRACeAx4GvybVOQLinkedIn: https://www.linkedin.com/in/jameslaughlincoaching/James Laughlin is a High Performance Leadership Coach, Former 7-Time World Champion, Host of the Lead On Purpose Podcast and an Executive Coach to high performers and leaders. James is based in Christchurch, New Zealand.Support the show

Speaker CFPs and Sponsor Guides are now available for AIE World's Fair — join us on June 25-27 for the biggest AI Engineer conference of 2024!Soumith Chintala needs no introduction in the ML world — his insights are incredibly accessible across Twitter, LinkedIn, podcasts, and conference talks (in this pod we'll assume you'll have caught up on the History of PyTorch pod from last year and cover different topics). He's well known as the creator of PyTorch, but he's more broadly the Engineering Lead on AI Infra, PyTorch, and Generative AI at Meta.Soumith was one of the earliest supporters of Latent Space (and more recently AI News), and we were overjoyed to catch up with him on his latest SF visit for a braindump of the latest AI topics, reactions to some of our past guests, and why Open Source AI is personally so important to him.Life in the GPU-Rich LaneBack in January, Zuck went on Instagram to announce their GPU wealth: by the end of 2024, Meta will have 350k H100s. By adding all their GPU clusters, you'd get to 600k H100-equivalents of compute. At FP16 precision, that's ~1,200,000 PFLOPS. If we used George Hotz's (previous guest!) "Person of Compute" measure, Meta now has 60k humans of compute in their clusters. Occasionally we get glimpses into the GPU-rich life; on a recent ThursdAI chat, swyx prompted PaLM tech lead Yi Tay to write down what he missed most from Google, and he commented that UL2 20B was trained by accidentally leaving the training job running for a month, because hardware failures are so rare in Google.Meta AI's Epic LLM RunBefore Llama broke the internet, Meta released an open source LLM in May 2022, OPT-175B, which was notable for how “open” it was - right down to the logbook! They used only 16 NVIDIA V100 GPUs and Soumith agrees that, with hindsight, it was likely under-trained for its parameter size.In Feb 2023 (pre Latent Space pod), Llama was released, with a 7B version trained on 1T tokens alongside 65B and 33B versions trained on 1.4T tokens. The Llama authors included Guillaume Lample and Timothée Lacroix, who went on to start Mistral.July 2023 was Llama2 time (which we covered!): 3 model sizes, 7B, 13B, and 70B, all trained on 2T tokens. The three models accounted for a grand total of 3,311,616 GPU hours for all pre-training work. CodeLlama followed shortly after, a fine-tune of Llama2 specifically focused on code generation use cases. The family had models in the 7B, 13B, 34B, and 70B size, all trained with 500B extra tokens of code and code-related data, except for 70B which is trained on 1T.All of this on top of other open sourced models like Segment Anything (one of our early hits!), Detectron, Detectron 2, DensePose, and Seamless, and in one year, Meta transformed from a company people made fun of for its “metaverse” investments to one of the key players in the AI landscape and its stock has almost tripled since (about $830B in market value created in the past year).Why Open Source AIThe obvious question is why Meta would spend hundreds of millions on its AI efforts and then release them for free. Zuck has addressed this in public statements:But for Soumith, the motivation is even more personal:“I'm irrationally interested in open source. I think open source has that fundamental way to distribute opportunity in a way that is very powerful. Like, I grew up in India… And knowledge was very centralized, but I saw that evolution of knowledge slowly getting decentralized. And that ended up helping me learn quicker and faster for like zero dollars. And I think that was a strong reason why I ended up where I am. So like that, like the open source side of things, I always push regardless of like what I get paid for, like I think I would do that as a passion project on the side……I think at a fundamental level, the most beneficial value of open source is that you make the distribution to be very wide. It's just available with no friction and people can do transformative things in a way that's very accessible. Maybe it's open source, but it has a commercial license and I'm a student in India. I don't care about the license. I just don't even understand the license. But like the fact that I can use it and do something with it is very transformative to me……Like, okay, I again always go back to like I'm a student in India with no money. What is my accessibility to any of these closed source models? At some scale I have to pay money. That makes it a non-starter and stuff. And there's also the control issue: I strongly believe if you want human aligned AI, you want all humans to give feedback. And you want all humans to have access to that technology in the first place. And I actually have seen, living in New York, whenever I come to Silicon Valley, I see a different cultural bubble.We like the way Soumith put it last year: Closed AI “rate-limits against people's imaginations and needs”!What It Takes For Open Source AI to WinHowever Soumith doesn't think Open Source will simply win by popular demand. There is a tremendous coordination problem with the decentralized nature of the open source AI development right now: nobody is collecting the valuable human feedback in the way that OpenAI or Midjourney are doing.“Open source in general always has a coordination problem. If there's a vertically integrated provider with more resources, they will just be better coordinated than open source. And so now open source has to figure out how to have coordinated benefits. And the reason you want coordinated benefits is because these models are getting better based on human feedback. And if you see with open source models, like if you go to the /r/localllama subreddit, like there's so many variations of models that are being produced from, say, Nous research. I mean, like there's like so many variations built by so many people. And one common theme is they're all using these fine-tuning or human preferences datasets that are very limited and they're not sufficiently diverse. And you look at the other side, say front-ends like Oobabooga or like Hugging Chat or Ollama, they don't really have feedback buttons. All the people using all these front-ends, they probably want to give feedback, but there's no way for them to give feedback… So we're just losing all of this feedback. Maybe open source models are being as used as GPT is at this point in like all kinds of, in a very fragmented way, like in aggregate all the open source models together are probably being used as much as GPT is, maybe close to that. But the amount of feedback that is driving back into the open source ecosystem is like negligible, maybe less than 1% of like the usage. So I think like some, like the blueprint here I think is you'd want someone to create a sinkhole for the feedback… I think if we do that, if that actually happens, I think that probably has a real chance of the open source models having a runaway effect against OpenAI, I think like there's a clear chance we can take at truly winning open source.”If you're working on solving open source coordination, please get in touch!Show Notes* Soumith Chintala Twitter* History of PyTorch episode on Gradient Podcast* The Llama Ecosystem* Apple's MLX* Neural ODEs (Ordinary Differential Equations)* AlphaGo* LMSys arena* Dan Pink's "Drive"* Robotics projects:* Dobb-E* OK Robot* Yann LeCun* Yangqing Jia of Lepton AI* Ed Catmull* George Hotz on Latent Space* Chris Lattner on Latent Space* Guillaume Lample* Yannic Kilcher of OpenAssistant* LMSys* Alex Atallah of OpenRouter* Carlo Sferrazza's 3D tactile research* Alex Wiltschko of Osmo* Tangent by Alex Wiltschko* Lerrel Pinto - RoboticsTimestamps* [00:00:00] Introductions* [00:00:51] Extrinsic vs Intrinsic Success* [00:02:40] Importance of Open Source and Its Impact* [00:03:46] PyTorch vs TinyGrad* [00:08:33] Why PyTorch is the Switzerland of frameworks* [00:10:27] Modular's Mojo + PyTorch?* [00:13:32] PyTorch vs Apple's MLX* [00:16:27] FAIR / PyTorch Alumni* [00:18:50] How can AI inference providers differentiate?* [00:21:41] How to build good benchmarks and learnings from AnyScale's* [00:25:28] Most interesting unexplored ideas* [00:28:18] What people get wrong about synthetic data* [00:35:57] Meta AI's evolution* [00:38:42] How do you allocate 600,000 GPUs?* [00:42:05] Even the GPU Rich are GPU Poor* [00:47:31] Meta's MTIA silicon* [00:50:09] Why we need open source* [00:59:00] Open source's coordination problem for feedback gathering* [01:08:59] Beyond text generation* [01:15:37] Osmo and the Future of Smell Recognition TechnologyTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO in residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:15]: Hey, and today we have in the studio Soumith Chintala, welcome.Soumith [00:00:17]: Thanks for having me.Swyx [00:00:18]: On one of your rare visits from New York where you live. You got your start in computer vision at NYU with Yann LeCun. That was a very fortuitous start. I was actually listening to your interview on the Gradient podcast. So if people want to know more about the history of Soumith, history of PyTorch, they can go to that podcast. We won't spend that much time there, but I just was marveling at your luck, or I don't know if it's your luck or your drive to find AI early and then find the right quality mentor because I guess Yan really sort of introduced you to that world.Soumith [00:00:51]: Yeah, I think you're talking about extrinsic success, right? A lot of people just have drive to do things that they think is fun, and a lot of those things might or might not be extrinsically perceived as good and successful. I think I just happened to like something that is now one of the coolest things in the world or whatever. But if I happen, the first thing I tried to become was a 3D VFX artist, and I was really interested in doing that, but I turned out to be very bad at it. So I ended up not doing that further. But even if I was good at that, whatever, and I ended up going down that path, I probably would have been equally happy. It's just like maybe like the perception of, oh, is this person successful or not might be different. I think like after a baseline, like your happiness is probably more correlated with your intrinsic stuff.Swyx [00:01:44]: Yes. I think Dan Pink has this book on drive that I often refer to about the power of intrinsic motivation versus extrinsic and how long extrinsic lasts. It's not very long at all. But anyway, now you are an investor in Runway, so in a way you're working on VFX. Yes.Soumith [00:02:01]: I mean, in a very convoluted way.Swyx [00:02:03]: It reminds me of Ed Catmull. I don't know if you guys know, but he actually tried to become an animator in his early years and failed or didn't get accepted by Disney and then went and created Pixar and then got bought by Disney and created Toy Story. So you joined Facebook in 2014 and eventually became a creator and maintainer of PyTorch. And there's this long story there you can refer to on the gradient. I think maybe people don't know that you also involved in more sort of hardware and cluster decision affair. And we can dive into more details there because we're all about hardware this month. Yeah. And then finally, I don't know what else, like what else should people know about you on a personal side or professional side?Soumith [00:02:40]: I think open source is definitely a big passion of mine and probably forms a little bit of my identity at this point. I'm irrationally interested in open source. I think open source has that fundamental way to distribute opportunity in a way that is very powerful. Like, I grew up in India. I didn't have internet for a while. In college, actually, I didn't have internet except for GPRS or whatever. And knowledge was very centralized, but I saw that evolution of knowledge slowly getting decentralized. And that ended up helping me learn quicker and faster for zero dollars. And I think that was a strong reason why I ended up where I am. So the open source side of things, I always push regardless of what I get paid for, like I think I would do that as a passion project on the side.Swyx [00:03:35]: Yeah, that's wonderful. Well, we'll talk about the challenges as well that open source has, open models versus closed models. Maybe you want to touch a little bit on PyTorch before we move on to the sort of Meta AI in general.PyTorch vs Tinygrad tradeoffsAlessio [00:03:46]: Yeah, we kind of touched on PyTorch in a lot of episodes. So we had George Hotz from TinyGrad. He called PyTorch a CISC and TinyGrad a RISC. I would love to get your thoughts on PyTorch design direction as far as, I know you talk a lot about kind of having a happy path to start with and then making complexity hidden away but then available to the end user. One of the things that George mentioned is I think you have like 250 primitive operators in PyTorch, I think TinyGrad is four. So how do you think about some of the learnings that maybe he's going to run into that you already had in the past seven, eight years almost of running PyTorch?Soumith [00:04:24]: Yeah, I think there's different models here, but I think it's two different models that people generally start with. Either they go like, I have a grand vision and I'm going to build a giant system that achieves this grand vision and maybe one is super feature complete or whatever. Or other people say they will get incrementally ambitious, right? And they say, oh, we'll start with something simple and then we'll slowly layer out complexity in a way that optimally applies Huffman coding or whatever. Like where the density of users are and what they're using, I would want to keep it in the easy, happy path and where the more niche advanced use cases, I'll still want people to try them, but they need to take additional frictional steps. George, I think just like we started with PyTorch, George started with the incrementally ambitious thing. I remember TinyGrad used to be, like we would be limited to a thousand lines of code and I think now it's at 5,000. So I think there is no real magic to which why PyTorch has the kind of complexity. I think it's probably partly necessitated and partly because we built with the technology available under us at that time, PyTorch is like 190,000 lines of code or something at this point. I think if you had to rewrite it, we would probably think about ways to rewrite it in a vastly simplified way for sure. But a lot of that complexity comes from the fact that in a very simple, explainable way, you have memory hierarchies. You have CPU has three levels of caches and then you have DRAM and SSD and then you have network. Similarly, GPU has several levels of memory and then you have different levels of network hierarchies, NVLink plus InfiniBand or Rocky or something like that, right? And the way the flops are available on your hardware, they are available in a certain way and your computation is in a certain way and you have to retrofit your computation onto both the memory hierarchy and like the flops available. When you're doing this, it is actually a fairly hard mathematical problem to do this setup, like you find the optimal thing. And finding the optimal thing is, what is optimal depends on the input variables themselves. So like, okay, what is the shape of your input tensors and what is the operation you're trying to do and various things like that. Finding that optimal configuration and writing it down in code is not the same for every input configuration you have. Like for example, just as the shape of the tensors change, let's say you have three input tensors into a Sparstar product or something like that. The shape of each of these input tensors will vastly change how you do this optimally placing this operation onto the hardware in a way that will get you maximal throughput. So a lot of our complexity comes from writing out hundreds of configurations for each single PyTorch operator and templatizing these things and symbolically generating the final CUDA code or CPU code. There's no way to avoid it because mathematically we haven't found symbolic ways to do this that also keep compile time near zero. You can write a very simple framework, but then you also should be willing to eat the long compile time. So if searching for that optimal performance at runtime, but that's the trade off. There's no, like, I don't think unless we have great breakthroughs George's vision is achievable, he should be thinking about a narrower problem such as I'm only going to make this for work for self-driving car connets or I'm only going to make this work for LLM transformers of the llama style. Like if you start narrowing the problem down, you can make a vastly simpler framework. But if you don't, if you need the generality to power all of the AI research that is happening and keep zero compile time and in all these other factors, I think it's not easy to avoid the complexity.Pytorch vs MojoAlessio [00:08:33]: That's interesting. And we kind of touched on this with Chris Lattner when he was on the podcast. If you think about frameworks, they have the model target. They have the hardware target. They have different things to think about. He mentioned when he was at Google, TensorFlow trying to be optimized to make TPUs go brr, you know, and go as fast. I think George is trying to make especially AMD stack be better than ROCm. How come PyTorch has been such as Switzerland versus just making Meta hardware go brr?Soumith [00:09:00]: First, Meta is not in the business of selling hardware. Meta is not in the business of cloud compute. The way Meta thinks about funding PyTorch is we're funding it because it's net good for Meta to fund PyTorch because PyTorch has become a standard and a big open source project. And generally it gives us a timeline edge. It gives us leverage and all that within our own work. So why is PyTorch more of a Switzerland rather than being opinionated? I think the way we think about it is not in terms of Switzerland or not. We actually the way we articulate it to all hardware vendors and software vendors and all who come to us being we want to build a backend in core for PyTorch and ship it by default is we just only look at our user side of things. Like if users are using a particular piece of hardware, then we want to support it. We very much don't want to king make the hardware side of things. So as the MacBooks have GPUs and as that stuff started getting increasingly interesting, we pushed Apple to push some engineers and work on the NPS support and we spend significant time from Meta funded engineers on that as well because a lot of people are using the Apple GPUs and there's demand. So we kind of mostly look at it from the demand side. We never look at it from like oh which hardware should we start taking opinions on.Swyx [00:10:27]: Is there a future in which, because Mojo or Modular Mojo is kind of a superset of Python, is there a future in which PyTorch might use Mojo features optionally?Soumith [00:10:36]: I think it depends on how well integrated it is into the Python ecosystem. So if Mojo is like a pip install and it's readily available and users feel like they can use Mojo so smoothly within their workflows in a way that just is low friction, we would definitely look into that. Like in the same way PyTorch now depends on Triton, OpenAI Triton, and we never had a conversation that was like huh, that's like a dependency. Should we just build a Triton of our own or should we use Triton? It almost doesn't, like those conversations don't really come up for us. The conversations are more well does Triton have 10,000 dependencies and is it hard to install? We almost don't look at these things from a strategic leverage point of view. We look at these things from a user experience point of view, like is it easy to install? Is it smoothly integrated and does it give enough benefits for us to start depending on it? If so, yeah, we should consider it. That's how we think about it.Swyx [00:11:37]: You're inclusive by default as long as it meets the minimum bar of, yeah, but like maybe I phrased it wrongly. Maybe it's more like what problems would you look to solve that you have right now?Soumith [00:11:48]: I think it depends on what problems Mojo will be useful at.Swyx [00:11:52]: Mainly a performance pitch, some amount of cross compiling pitch.Soumith [00:11:56]: Yeah, I think the performance pitch for Mojo was like, we're going to be performant even if you have a lot of custom stuff, you're going to write arbitrary custom things and we will be performant. And that value proposition is not clear to us from the PyTorch side to consider it for PyTorch. So PyTorch, it's actually not 250 operators, it's like a thousand operators. PyTorch exposes about a thousand operators and people kind of write their ideas in the thousand operators of PyTorch. Mojo is like, well, maybe it's okay to completely sidestep those thousand operators of PyTorch and just write it in a more natural form. Just write raw Python, write for loops or whatever, right? So from the consideration of how do we intersect PyTorch with Mojo, I can see one use case where you have custom stuff for some parts of your program, but mostly it's PyTorch. And so we can probably figure out how to make it easier for say Torch.compile to smoothly also consume Mojo subgraphs and like, you know, the interoperability being actually usable, that I think is valuable. But Mojo as a fundamental front end would be replacing PyTorch, not augmenting PyTorch. So in that sense, I don't see a synergy in more deeply integrating Mojo.Pytorch vs MLXSwyx [00:13:21]: So call out to Mojo whenever they have written something in Mojo and there's some performance related thing going on. And then since you mentioned Apple, what should people think of PyTorch versus MLX?Soumith [00:13:32]: I mean, MLX is early and I know the folks well, Ani used to work at FAIR and I used to chat with him all the time. He used to be based out of New York as well. The way I think about MLX is that MLX is specialized for Apple right now. It has a happy path because it's defined its product in a narrow way. At some point MLX either says we will only be supporting Apple and we will just focus on enabling, you know, there's a framework if you use your MacBook, but once you like go server side or whatever, that's not my problem and I don't care. For MLS, it enters like the server side set of things as well. Like one of these two things will happen, right? If the first thing will happen, like MLX's overall addressable market will be small, but it probably do well within that addressable market. If it enters the second phase, they're going to run into all the same complexities that we have to deal with. They will not have any magic wand and they will have more complex work to do. They probably wouldn't be able to move as fast.Swyx [00:14:44]: Like having to deal with distributed compute?Soumith [00:14:48]: Distributed, NVIDIA and AMD GPUs, like just like having a generalization of the concept of a backend, how they treat compilation with plus overheads. Right now they're deeply assumed like the whole NPS graph thing. So they need to think about all these additional things if they end up expanding onto the server side and they'll probably build something like PyTorch as well, right? Like eventually that's where it will land. And I think there they will kind of fail on the lack of differentiation. Like it wouldn't be obvious to people why they would want to use it.Swyx [00:15:24]: I mean, there are some cloud companies offering M1 and M2 chips on servers. I feel like it might be interesting for Apple to pursue that market, but it's not their core strength.Soumith [00:15:33]: Yeah. If Apple can figure out their interconnect story, maybe, like then it can become a thing.Swyx [00:15:40]: Honestly, that's more interesting than the cars. Yes.Soumith [00:15:43]: I think the moat that NVIDIA has right now, I feel is that they have the interconnect that no one else has, like AMD GPUs are pretty good. I'm sure there's various silicon that is not bad at all, but the interconnect, like NVLink is uniquely awesome. I'm sure the other hardware providers are working on it, but-Swyx [00:16:04]: I feel like when you say it's uniquely awesome, you have some appreciation of it that the rest of us don't. I mean, the rest of us just like, you know, we hear marketing lines, but what do you mean when you say NVIDIA is very good at networking? Obviously they made the acquisition maybe like 15 years ago.Soumith [00:16:15]: Just the bandwidth it offers and the latency it offers. I mean, TPUs also have a good interconnect, but you can't buy them. So you have to go to Google to use it.PyTorch MafiaAlessio [00:16:27]: Who are some of the other FAIR PyTorch alumni that are building cool companies? I know you have Fireworks AI, Lightning AI, Lepton, and Yangqing, you knew since college when he was building Coffee?Soumith [00:16:40]: Yeah, so Yangqing and I used to be framework rivals, PyTorch, I mean, we were all a very small close-knit community back then. Caffe, Torch, Theano, Chainer, Keras, various frameworks. I mean, it used to be more like 20 frameworks. I can't remember all the names. CCV by Liu Liu, who is also based out of SF. And I would actually like, you know, one of the ways it was interesting is you went into the framework guts and saw if someone wrote their own convolution kernel or they were just copying someone else's. There were four or five convolution kernels that were unique and interesting. There was one from this guy out of Russia, I forgot the name, but I remembered who was awesome enough to have written their own kernel. And at some point there, I built out these benchmarks called ConNet benchmarks. They're just benchmarking all the convolution kernels that are available at that time. It hilariously became big enough that at that time AI was getting important, but not important enough that industrial strength players came in to do these kinds of benchmarking and standardization. Like we have MLPerf today. So a lot of the startups were using ConNet benchmarks in their pitch decks as like, oh, you know, on ConNet benchmarks, this is how we fare, so you should fund us. I remember Nirvana actually was at the top of the pack because Scott Gray wrote amazingly fast convolution kernels at that time. Very interesting, but separate times. But to answer your question, Alessio, I think mainly Lepton, Fireworks are the two most obvious ones, but I'm sure the fingerprints are a lot wider. They're just people who worked within the PyTorch Cafe2 cohort of things and now end up at various other places.Swyx [00:18:50]: I think as a, both as an investor and a people looking to build on top of their services, it's a uncomfortable slash like, I don't know what I don't know pitch. Because I've met Yang Tsing and I've met Lin Chao. Yeah, I've met these folks and they're like, you know, we are deep in the PyTorch ecosystem and we serve billions of inferences a day or whatever at Facebook and now we can do it for you. And I'm like, okay, that's great. Like, what should I be wary of or cautious of when these things happen? Because I'm like, obviously this experience is extremely powerful and valuable. I just don't know what I don't know. Like, what should people know about like these sort of new inference as a service companies?Soumith [00:19:32]: I think at that point you would be investing in them for their expertise of one kind. So if they've been at a large company, but they've been doing amazing work, you would be thinking about it as what these people bring to the table is that they're really good at like GPU programming or understanding the complexity of serving models once it hits a certain scale. You know, various expertise like from the infra and AI and GPUs point of view. What you would obviously want to figure out is whether their understanding of the external markets is clear, whether they know and understand how to think about running a business, understanding how to be disciplined about making money or, you know, various things like that.Swyx [00:20:23]: Maybe I'll put it like, actually I will de-emphasize the investing bit and just more as a potential customer. Oh, okay. Like, it's more okay, you know, you have PyTorch gods, of course. Like, what else should I know?Soumith [00:20:37]: I mean, I would not care about who's building something. If I'm trying to be a customer, I would care about whether...Swyx [00:20:44]: Benchmarks.Soumith [00:20:44]: Yeah, I use it and it's usability and reliability and speed, right?Swyx [00:20:51]: Quality as well.Soumith [00:20:51]: Yeah, if someone from some random unknown place came to me and say, user stuff is great. Like, and I have the bandwidth, I probably will give it a shot. And if it turns out to be great, like I'll just use it.Benchmark dramaSwyx [00:21:07]: Okay, great. And then maybe one more thing about benchmarks, since we already brought it up and you brought up Confident Benchmarks. There was some recent drama around AnyScale. AnyScale released their own benchmarks and obviously they look great on their own benchmarks, but maybe didn't give the other... I feel there are two lines of criticism. One, which is they didn't test some apples for apples on the kind of endpoints that the other providers, that they are competitors with, on their benchmarks and that is due diligence baseline. And then the second would be more just optimizing for the right thing. You had some commentary on it. I'll just kind of let you riff.Soumith [00:21:41]: Yeah, I mean, in summary, basically my criticism of that was AnyScale built these benchmarks for end users to just understand what they should pick, right? And that's a very good thing to do. I think what they didn't do a good job of is give that end user a full understanding of what they should pick. Like they just gave them a very narrow slice of understanding. I think they just gave them latency numbers and that's not sufficient, right? You need to understand your total cost of ownership at some reasonable scale. Not oh, one API call is one cent, but a thousand API calls are 10 cents. Like people can misprice to cheat on those benchmarks. So you want to understand, okay, like how much is it going to cost me if I actually subscribe to you and do like a million API calls a month or something? And then you want to understand the latency and reliability, not just from one call you made, but an aggregate of calls you've made over several various times of the day and times of the week. And the nature of the workloads, is it just some generic single paragraph that you're sending that is cashable? Or is it like testing of real world workload? I think that kind of rigor, like in presenting that benchmark wasn't there. It was a much more narrow sliver of what should have been a good benchmark. That was my main criticism. And I'm pretty sure if before they released it, they showed it to their other stakeholders who would be caring about this benchmark because they are present in it, they would have easily just pointed out these gaps. And I think they didn't do that and they just released it. So I think those were the two main criticisms. I think they were fair and Robert took it well.Swyx [00:23:40]: And he took it very well. And we'll have him on at some point and we'll discuss it. But I think it's important for, I think the market being maturing enough that people start caring and competing on these kinds of things means that we need to establish what best practice is because otherwise everyone's going to play dirty.Soumith [00:23:55]: Yeah, absolutely. My view of the LLM inference market in general is that it's the laundromat model. Like the margins are going to drive down towards the bare minimum. It's going to be all kinds of arbitrage between how much you can get the hardware for and then how much you sell the API and how much latency your customers are willing to let go. You need to figure out how to squeeze your margins. Like what is your unique thing here? Like I think Together and Fireworks and all these people are trying to build some faster CUDA kernels and faster, you know, hardware kernels in general. But those modes only last for a month or two. These ideas quickly propagate.Swyx [00:24:38]: Even if they're not published?Soumith [00:24:39]: Even if they're not published, the idea space is small. So even if they're not published, the discovery rate is going to be pretty high. It's not like we're talking about a combinatorial thing that is really large. You're talking about Llama style LLM models. And we're going to beat those to death on a few different hardware SKUs, right? Like it's not even we have a huge diversity of hardware you're going to aim to run it on. Now when you have such a narrow problem and you have a lot of people working on it, the rate at which these ideas are going to get figured out is going to be pretty rapid.Swyx [00:25:15]: Is it a standard bag of tricks? Like the standard one that I know of is, you know, fusing operators and-Soumith [00:25:22]: Yeah, it's the standard bag of tricks on figuring out how to improve your memory bandwidth and all that, yeah.Alessio [00:25:28]: Any ideas instead of things that are not being beaten to death that people should be paying more attention to?Novel PyTorch ApplicationsSwyx [00:25:34]: One thing I was like, you know, you have a thousand operators, right? Like what's the most interesting usage of PyTorch that you're seeing maybe outside of this little bubble?Soumith [00:25:41]: So PyTorch, it's very interesting and scary at the same time, but basically it's used in a lot of exotic ways, like from the ML angle, what kind of models are being built? And you get all the way from state-based models and all of these things to stuff nth order differentiable models, like neural ODEs and stuff like that. I think there's one set of interestingness factor from the ML side of things. And then there's the other set of interesting factor from the applications point of view. It's used in Mars Rover simulations, to drug discovery, to Tesla cars. And there's a huge diversity of applications in which it is used. So in terms of the most interesting application side of things, I think I'm scared at how many interesting things that are also very critical and really important it is used in. I think the scariest was when I went to visit CERN at some point and they said they were using PyTorch and they were using GANs at the same time for particle physics research. And I was scared more about the fact that they were using GANs than they were using PyTorch, because at that time I was a researcher focusing on GANs. But the diversity is probably the most interesting. How many different things it is being used in. I think that's the most interesting to me from the applications perspective. From the models perspective, I think I've seen a lot of them. Like the really interesting ones to me are where we're starting to combine search and symbolic stuff with differentiable models, like the whole AlphaGo style models is one example. And then I think we're attempting to do it for LLMs as well, with various reward models and search. I mean, I don't think PyTorch is being used in this, but the whole alpha geometry thing was interesting because again, it's an example of combining the symbolic models with the gradient based ones. But there are stuff like alpha geometry that PyTorch is used at, especially when you intersect biology and chemistry with ML. In those areas, you want stronger guarantees on the output. So yeah, maybe from the ML side, those things to me are very interesting right now.Swyx [00:28:03]: Yeah. People are very excited about the alpha geometry thing. And it's kind of like, for me, it's theoretical. It's great. You can solve some Olympia questions. I'm not sure how to make that bridge over into the real world applications, but I'm sure people smarter than me will figure it out.Synthetic Data vs Symbolic ModelsSoumith [00:28:18]: Let me give you an example of it. You know how the whole thing about synthetic data will be the next rage in LLMs is a thing?Swyx [00:28:27]: Already is a rage.Soumith [00:28:28]: Which I think is fairly misplaced in how people perceive it. People think synthetic data is some kind of magic wand that you wave and it's going to be amazing. Synthetic data is useful in neural networks right now because we as humans have figured out a bunch of symbolic models of the world or made up certain symbolic models because of human innate biases. So we've figured out how to ground particle physics in a 30 parameter model. And it's just very hard to compute as in it takes a lot of flops to compute, but it only has 30 parameters or so. I mean, I'm not a physics expert, but it's a very low rank model. We built mathematics as a field that basically is very low rank. Language, a deep understanding of language, like the whole syntactic parse trees and just understanding how language can be broken down and into a formal symbolism is something that we figured out. So we basically as humans have accumulated all this knowledge on these subjects, either synthetic, we created those subjects in our heads, or we grounded some real world phenomenon into a set of symbols. But we haven't figured out how to teach neural networks symbolic world models directly. The only way we have to teach them is generating a bunch of inputs and outputs and gradient dissenting over them. So in areas where we have the symbolic models and we need to teach all the knowledge we have that is better encoded in the symbolic models, what we're doing is we're generating a bunch of synthetic data, a bunch of input output pairs, and then giving that to the neural network and asking it to learn the same thing that we already have a better low rank model of in gradient descent in a much more over-parameterized way. Outside of this, like where we don't have good symbolic models, like synthetic data obviously doesn't make any sense. So synthetic data is not a magic wand where it'll work in all cases in every case or whatever. It's just where we as humans already have good symbolic models off. We need to impart that knowledge to neural networks and we figured out the synthetic data is a vehicle to impart this knowledge to. So, but people, because maybe they don't know enough about synthetic data as a notion, but they hear, you know, the next wave of data revolution is synthetic data. They think it's some kind of magic where we just create a bunch of random data somehow. They don't think about how, and then they think that's just a revolution. And I think that's maybe a gap in understanding most people have in this hype cycle.Swyx [00:31:23]: Yeah, well, it's a relatively new concept, so. Oh, there's two more that I'll put in front of you and then you can see what you respond. One is, you know, I have this joke that it's, you know, it's only synthetic data if it's from the Mistral region of France, otherwise it's just a sparkling distillation, which is what news research is doing. Like they're distilling GPT-4 by creating synthetic data from GPT-4, creating mock textbooks inspired by Phi 2 and then fine tuning open source models like Llama. And so I don't know, I mean, I think that's, should we call that synthetic data? Should we call it something else? I don't know.Soumith [00:31:57]: Yeah, I mean, the outputs of LLMs, are they synthetic data? They probably are, but I think it depends on the goal you have. If your goal is you're creating synthetic data with the goal of trying to distill GPT-4's superiority into another model, I guess you can call it synthetic data, but it also feels like disingenuous because your goal is I need to copy the behavior of GPT-4 and-Swyx [00:32:25]: It's also not just behavior, but data set. So I've often thought of this as data set washing. Like you need one model at the top of the chain, you know, unnamed French company that has that, you know, makes a model that has all the data in it that we don't know where it's from, but it's open source, hey, and then we distill from that and it's great. To be fair, they also use larger models as judges for preference ranking, right? So that is, I think, a very, very accepted use of synthetic.Soumith [00:32:53]: Correct. I think it's a very interesting time where we don't really have good social models of what is acceptable depending on how many bits of information you use from someone else, right? It's like, okay, you use one bit. Is that okay? Yeah, let's accept it to be okay. Okay, what about if you use 20 bits? Is that okay? I don't know. What if you use 200 bits? I don't think we as society have ever been in this conundrum where we have to be like, where is the boundary of copyright or where is the boundary of socially accepted understanding of copying someone else? We haven't been tested this mathematically before,Swyx [00:33:38]: in my opinion. Whether it's transformative use. Yes. So yeah, I think this New York Times opening eye case is gonna go to the Supreme Court and we'll have to decide it because I think we never had to deal with it before. And then finally, for synthetic data, the thing that I'm personally exploring is solving this great stark paradigm difference between rag and fine tuning, where you can kind of create synthetic data off of your retrieved documents and then fine tune on that. That's kind of synthetic. All you need is variation or diversity of samples for you to fine tune on. And then you can fine tune new knowledge into your model. I don't know if you've seen that as a direction for synthetic data.Soumith [00:34:13]: I think you're basically trying to, what you're doing is you're saying, well, language, I know how to parametrize language to an extent. And I need to teach my model variations of this input data so that it's resilient or invariant to language uses of that data.Swyx [00:34:32]: Yeah, it doesn't overfit on the wrong source documents.Soumith [00:34:33]: So I think that's 100% synthetic. You understand, the key is you create variations of your documents and you know how to do that because you have a symbolic model or like some implicit symbolic model of language.Swyx [00:34:48]: Okay.Alessio [00:34:49]: Do you think the issue with symbolic models is just the architecture of the language models that we're building? I think maybe the thing that people grasp is the inability of transformers to deal with numbers because of the tokenizer. Is it a fundamental issue there too? And do you see alternative architectures that will be better with symbolic understanding?Soumith [00:35:09]: I am not sure if it's a fundamental issue or not. I think we just don't understand transformers enough. I don't even mean transformers as an architecture. I mean the use of transformers today, like combining the tokenizer and transformers and the dynamics of training, when you show math heavy questions versus not. I don't have a good calibration of whether I know the answer or not. I, you know, there's common criticisms that are, you know, transformers will just fail at X. But then when you scale them up to sufficient scale, they actually don't fail at that X. I think there's this entire subfield where they're trying to figure out these answers called like the science of deep learning or something. So we'll get to know more. I don't know the answer.Meta AI and Llama 2/3Swyx [00:35:57]: Got it. Let's touch a little bit on just Meta AI and you know, stuff that's going on there. Maybe, I don't know how deeply you're personally involved in it, but you're our first guest with Meta AI, which is really fantastic. And Llama 1 was, you know, you are such a believer in open source. Llama 1 was more or less the real breakthrough in open source AI. The most interesting thing for us covering on this, in this podcast was the death of Chinchilla, as people say. Any interesting insights there around the scaling models for open source models or smaller models or whatever that design decision was when you guys were doing it?Soumith [00:36:31]: So Llama 1 was Guillaume Lample and team. There was OPT before, which I think I'm also very proud of because we bridged the gap in understanding of how complex it is to train these models to the world. Like until then, no one really in gory detail published.Swyx [00:36:50]: The logs.Soumith [00:36:51]: Yeah. Like, why is it complex? And everyone says, oh, it's complex. But no one really talked about why it's complex. I think OPT was cool.Swyx [00:37:02]: I met Susan and she's very, very outspoken. Yeah.Soumith [00:37:05]: We probably, I think, didn't train it for long enough, right? That's kind of obvious in retrospect.Swyx [00:37:12]: For a 175B. Yeah. You trained it according to Chinchilla at the time or?Soumith [00:37:17]: I can't remember the details, but I think it's a commonly held belief at this point that if we trained OPT longer, it would actually end up being better. Llama 1, I think, was Guillaume Lample and team Guillaume is fantastic and went on to build Mistral. I wasn't too involved in that side of things. So I don't know what you're asking me, which is how did they think about scaling loss and all of that? Llama 2, I was more closely involved in. I helped them a reasonable amount with their infrastructure needs and stuff. And Llama 2, I think, was more like, let's get to the evolution. At that point, we kind of understood what we were missing from the industry's understanding of LLMs. And we needed more data and we needed more to train the models for longer. And we made, I think, a few tweaks to the architecture and we scaled up more. And that was Llama 2. I think Llama 2, you can think of it as after Guillaume left, the team kind of rebuilt their muscle around Llama 2. And Hugo, I think, who's the first author is fantastic. And I think he did play a reasonable big role in Llama 1 as well.Soumith [00:38:35]: And he overlaps between Llama 1 and 2. So in Llama 3, obviously, hopefully, it'll be awesome.Alessio [00:38:42]: Just one question on Llama 2, and then we'll try and fish Llama 3 spoilers out of you. In the Llama 2 paper, the loss curves of the 34 and 70B parameter, they still seem kind of steep. Like they could go lower. How, from an infrastructure level, how do you allocate resources? Could they have just gone longer or were you just, hey, this is all the GPUs that we can burn and let's just move on to Llama 3 and then make that one better?Soumith [00:39:07]: Instead of answering specifically about that Llama 2 situation or whatever, I'll tell you how we think about things. Generally, we're, I mean, Mark really is some numbers, right?Swyx [00:39:20]: So let's cite those things again. All I remember is like 600K GPUs.Soumith [00:39:24]: That is by the end of this year and 600K H100 equivalents. With 250K H100s, including all of our other GPU or accelerator stuff, it would be 600-and-something-K aggregate capacity.Swyx [00:39:38]: That's a lot of GPUs.Soumith [00:39:39]: We'll talk about that separately. But the way we think about it is we have a train of models, right? Llama 1, 2, 3, 4. And we have a bunch of GPUs. I don't think we're short of GPUs. Like-Swyx [00:39:54]: Yeah, no, I wouldn't say so. Yeah, so it's all a matter of time.Soumith [00:39:56]: I think time is the biggest bottleneck. It's like, when do you stop training the previous one and when do you start training the next one? And how do you make those decisions? The data, do you have net new data, better clean data for the next one in a way that it's not worth really focusing on the previous one? It's just a standard iterative product. You're like, when is the iPhone 1? When do you start working on iPhone 2? Where is the iPhone? And so on, right? So mostly the considerations are time and generation, rather than GPUs, in my opinion.Alessio [00:40:31]: So one of the things with the scaling loss, like Chinchilla is optimal to balance training and inference costs. I think at Meta's scale, you would rather pay a lot more maybe at training and then save on inference. How do you think about that from infrastructure perspective? I think in your tweet, you say you can try and guess on like how we're using these GPUs. Can you just give people a bit of understanding? It's like, because I've already seen a lot of VCs say, Llama 3 has been trained on 600,000 GPUs and that's obviously not true, I'm sure. How do you allocate between the research, FAIR and the Llama training, the inference on Instagram suggestions that get me to scroll, like AI-generated stickers on WhatsApp and all of that?Soumith [00:41:11]: Yeah, we haven't talked about any of this publicly, but as a broad stroke, it's like how we would allocate resources of any other kinds at any company. You run a VC portfolio, how do you allocate your investments between different companies or whatever? You kind of make various trade-offs and you kind of decide, should I invest in this project or this other project, or how much should I invest in this project? It's very much a zero sum of trade-offs. And it also comes into play, how are your clusters configured, like overall, what you can fit of what size and what cluster and so on. So broadly, there's no magic sauce here. I mean, I think the details would add more spice, but also wouldn't add more understanding. It's just gonna be like, oh, okay, I mean, this looks like they just think about this as I would normally do.Alessio [00:42:05]: So even the GPU rich run through the same struggles of having to decide where to allocate things.Soumith [00:42:11]: Yeah, I mean, at some point I forgot who said it, but you kind of fit your models to the amount of compute you have. If you don't have enough compute, you figure out how to make do with smaller models. But no one as of today, I think would feel like they have enough compute. I don't think I've heard any company within the AI space be like, oh yeah, like we feel like we have sufficient compute and we couldn't have done better. So that conversation, I don't think I've heard from any of my friends at other companies.EleutherSwyx [00:42:47]: Stella from Eleuther sometimes says that because she has a lot of donated compute. She's trying to put it to interesting uses, but for some reason she's decided to stop making large models.Soumith [00:42:57]: I mean, that's a cool, high conviction opinion that might pay out.Swyx [00:43:01]: Why?Soumith [00:43:02]: I mean, she's taking a path that most people don't care to take about in this climate and she probably will have very differentiated ideas. I mean, think about the correlation of ideas in AI right now. It's so bad, right? So everyone's fighting for the same pie. In some weird sense, that's partly why I don't really directly work on LLMs. I used to do image models and stuff and I actually stopped doing GANs because GANs were getting so hot that I didn't have any calibration of whether my work would be useful or not because, oh yeah, someone else did the same thing you did. It's like, there's so much to do, I don't understand why I need to fight for the same pie. So I think Stella's decision is very smart.Making BetsAlessio [00:43:53]: And how do you reconcile that with how we started the discussion about intrinsic versus extrinsic kind of like accomplishment or success? How should people think about that especially when they're doing a PhD or early in their career? I think in Europe, I walked through a lot of the posters and whatnot, there seems to be mode collapse in a way in the research, a lot of people working on the same things. Is it worth for a PhD to not take a bet on something that is maybe not as interesting just because of funding and visibility and whatnot? Or yeah, what suggestions would you give?Soumith [00:44:28]: I think there's a baseline level of compatibility you need to have with the field. Basically, you need to figure out if you will get paid enough to eat, right? Like whatever reasonable normal lifestyle you want to have as a baseline. So you at least have to pick a problem within the neighborhood of fundable. Like you wouldn't wanna be doing something so obscure that people are like, I don't know, like you can work on it.Swyx [00:44:59]: Would a limit on fundability, I'm just observing something like three months of compute, right? That's the top line, that's the like max that you can spend on any one project.Soumith [00:45:09]: But like, I think that's very ill specified, like how much compute, right? I think that the notion of fundability is broader. It's more like, hey, are these family of models within the acceptable set of, you're not crazy or something, right? Even something like neural or DS, which is a very boundary pushing thing or states-based models or whatever. Like all of these things I think are still in fundable territory. When you're talking about, I'm gonna do one of the neuromorphic models and then apply image classification to them or something, then it becomes a bit questionable. Again, it depends on your motivation. Maybe if you're a neuroscientist, it actually is feasible. But if you're an AI engineer, like the audience of these podcasts, then it's more questionable. The way I think about it is, you need to figure out how you can be in the baseline level of fundability just so that you can just live. And then after that, really focus on intrinsic motivation and depends on your strengths, like how you can play to your strengths and your interests at the same time. Like I try to look at a bunch of ideas that are interesting to me, but also try to play to my strengths. I'm not gonna go work on theoretical ML. I'm interested in it, but when I want to work on something like that, I try to partner with someone who is actually a good theoretical ML person and see if I actually have any value to provide. And if they think I do, then I come in. So I think you'd want to find that intersection of ideas you like, and that also play to your strengths. And I'd go from there. Everything else, like actually finding extrinsic success and all of that, I think is the way I think about it is like somewhat immaterial. When you're talking about building ecosystems and stuff, slightly different considerations come into play, but that's a different conversation.Swyx [00:47:06]: We're gonna pivot a little bit to just talking about open source AI. But one more thing I wanted to establish for Meta is this 600K number, just kind of rounding out the discussion, that's for all Meta. So including your own inference needs, right? It's not just about training.Soumith [00:47:19]: It's gonna be the number in our data centers for all of Meta, yeah.Swyx [00:47:23]: Yeah, so there's a decent amount of workload serving Facebook and Instagram and whatever. And then is there interest in like your own hardware?MTIASoumith [00:47:31]: We already talked about our own hardware. It's called MTIA. Our own silicon, I think we've even showed the standard photograph of you holding the chip that doesn't work. Like as in the chip that you basically just get like-Swyx [00:47:51]: As a test, right?Soumith [00:47:52]: Yeah, a test chip or whatever. So we are working on our silicon and we'll probably talk more about it when the time is right, but-Swyx [00:48:00]: Like what gaps do you have that the market doesn't offer?Soumith [00:48:04]: Okay, I mean, this is easy to answer. So basically, remember how I told you about there's this memory hierarchy and like sweet spots and all of that? Fundamentally, when you build a hardware, you make it general enough that a wide set of customers and a wide set of workloads can use it effectively while trying to get the maximum level of performance they can. The more specialized you make the chip, the more hardware efficient it's going to be, the more power efficient it's gonna be, the more easier it's going to be to find the software, like the kernel's right to just map that one or two workloads to that hardware and so on. So it's pretty well understood across the industry that if you have a sufficiently large volume, enough workload, you can specialize it and get some efficiency gains, like power gains and so on. So the way you can think about everyone building, every large company building silicon, I think a bunch of the other large companies are building their own silicon as well, is they, each large company has a sufficient enough set of verticalized workloads that can be specialized that have a pattern to them that say a more generic accelerator like an NVIDIA or an AMD GPU does not exploit. So there is some level of power efficiency that you're leaving on the table by not exploiting that. And you have sufficient scale and you have sufficient forecasted stability that those workloads will exist in the same form, that it's worth spending the time to build out a chip to exploit that sweet spot. Like obviously something like this is only useful if you hit a certain scale and that your forecasted prediction of those kind of workloads being in the same kind of specializable exploitable way is true. So yeah, that's why we're building our own chips.Swyx [00:50:08]: Awesome.Open Source AIAlessio [00:50:09]: Yeah, I know we've been talking a lot on a lot of different topics and going back to open source, you had a very good tweet. You said that a single company's closed source effort rate limits against people's imaginations and needs. How do you think about all the impact that some of the Meta AI work in open source has been doing and maybe directions of the whole open source AI space?Soumith [00:50:32]: Yeah, in general, I think first, I think it's worth talking about this in terms of open and not just open source, because like with the whole notion of model weights, no one even knows what source means for these things. But just for the discussion, when I say open source, you can assume it's just I'm talking about open. And then there's the whole notion of licensing and all that, commercial, non-commercial, commercial with clauses and all that. I think at a fundamental level, the most benefited value of open source is that you make the distribution to be very wide. It's just available with no friction and people can do transformative things in a way that's very accessible. Maybe it's open source, but it has a commercial license and I'm a student in India. I don't care about the license. I just don't even understand the license. But like the fact that I can use it and do something with it is very transformative to me. Like I got this thing in a very accessible way. And then it's various degrees, right? And then if it's open source, but it's actually a commercial license, then a lot of companies are gonna benefit from gaining value that they didn't previously have, that they maybe had to pay a closed source company for it. So open source is just a very interesting tool that you can use in various ways. So there's, again, two kinds of open source. One is some large company doing a lot of work and then open sourcing it. And that kind of effort is not really feasible by say a band of volunteers doing it the same way. So there's both a capital and operational expenditure that the large company just decided to ignore and give it away to the world for some benefits of some kind. They're not as tangible as direct revenue. So in that part, Meta has been doing incredibly good things. They fund a huge amount of the PyTorch development. They've open sourced Llama and those family of models and several other fairly transformative projects. FICE is one, Segment Anything, Detectron, Detectron 2. Dense Pose. I mean, it's-Swyx [00:52:52]: Seamless. Yeah, seamless.Soumith [00:52:53]: Like it's just the list is so long that we're not gonna cover. So I think Meta comes into that category where we spend a lot of CapEx and OpEx and we have a high talent density of great AI people and we open our stuff. And the thesis for that, I remember when FAIR was started, the common thing was like, wait, why would Meta wanna start a open AI lab? Like what exactly is a benefit from a commercial perspective? And for then the thesis was very simple. It was AI is currently rate limiting Meta's ability to do things. Our ability to build various product integrations, moderation, various other factors. Like AI was the limiting factor and we just wanted AI to advance more and we didn't care if the IP of the AI was uniquely in our possession or not. However the field advances, that accelerates Meta's ability to build a better product. So we just built an open AI lab and we said, if this helps accelerate the progress of AI, that's strictly great for us. But very easy, rational, right? Still the same to a large extent with the Llama stuff. And it's the same values, but the argument, it's a bit more nuanced. And then there's a second kind of open source, which is, oh, we built this project, nights and weekends and we're very smart people and we open sourced it and then we built a community around it. This is the Linux kernel and various software projects like that. So I think about open source, like both of these things being beneficial and both of these things being different. They're different and beneficial in their own ways. The second one is really useful when there's an active arbitrage to be done. If someone's not really looking at a particular space because it's not commercially viable or whatever, like a band of volunteers can just coordinate online and do something and then make that happen. And that's great.Open Source LLMsI wanna cover a little bit about open source LLMs maybe. So open source LLMs have been very interesting because I think we were trending towards an increase in open source in AI from 2010 all the way to 2017 or something. Like where more and more pressure within the community was to open source their stuff so that their methods and stuff get adopted. And then the LLMs revolution kind of took the opposite effect OpenAI stopped open sourcing their stuff and DeepMind kind of didn't, like all the other cloud and all these other providers, they didn't open source their stuff. And it was not good in the sense that first science done in isolation probably will just form its own bubble where people believe their own b******t or whatever. So there's that problem. And then there was the other problem which was the accessibility part. Like, okay, I again always go back to I'm a student in India with no money. What is my accessibility to any of these closers models? At some scale I have to pay money. That makes it a non-starter and stuff. And there's also the control thing. I strongly believe if you want human aligned stuff, you want all humans to give feedback. And you want all humans to have access to that technology in the first place. And I actually have seen, living in New York, whenever I come to Silicon Valley, I see a different cultural bubble. Like all the friends I hang out with talk about some random thing like Dyson Spheres or whatever, that's a thing. And most of the world doesn't know or care about any of this stuff. It's definitely a bubble and bubbles can form very easily. And when you make a lot of decisions because you're in a bubble, they're probably not globally optimal decisions. So I think open source, the distribution of open source powers a certain kind of non-falsifiability that I think is very important. I think on the open source models, like it's going great in the fact that LoRa I think came out of the necessity of open source models needing to be fine-tunable in some way. Yeah, and I think DPO also came out of the academic open source side of things. So do any of the closed source labs, did any of them already have LoRa or DPO internally? Maybe, but that does not advance humanity in any way. It advances some companies probability of doing the winner takes all that I talked about earlier in the podcast.Open Source and TrustI don't know, it just feels fundamentally good. Like when people try to, you know, people are like, well, what are the ways in which it is not okay? I find most of these arguments, and this might be a little controversial, but I find a lot of arguments based on whether closed source models are safer or open source models are safer very much related to what kind of culture they grew up in, what kind of society they grew up in. If they grew up in a society that they trusted, then I think they take the closed source argument. And if they grew up in a society that they couldn't trust, where the norm was that you didn't trust your government, obviously it's corrupt or whatever, then I think the open source argument is what they take. I think there's a deep connection to like people's innate biases from their childhood and their trust in society and governmental aspects that push them towards one opinion or the other. And I'm definitely in the camp of open source is definitely going to actually have better outcomes for society. Closed source to me just means that centralization of power, which, you know, is really hard to trust. So I think it's going well

Ed Catmull, co-founder of Pixar, shares emotional moments from his own premieres, particularly highlighting Toy Story 3 and Coco. He expresses the challenge of following the success of Coco, pondering where to go next creatively. The discussion delves into the emotional resonance of Coco, emphasizing the film's respect for ancestral connections and the power of music to evoke profound emotions. Catmull also reveals Pixar's innovative approach to film development, highlighting the importance of allowing directors to explore multiple ideas before choosing the one they are truly passionate about.LISTEN TO THE FULL EPISODE!Sign up for the Greatness newsletter!

Ed Catmull is a computer scientist – and a force of creativity. He helped bring to life beloved, generation-defining movies like Toy Story, Finding Nemo, Ratatouille, and more.  Ricky Mulvey caught up with Catmull to discuss:  Being in the “business of exponential change”  AI's potential upheaval of the animation industry How technology and story advance each other Tickers discussed: DIS Host: Ricky Mulvey Guest: Ed Catmull Producer: Mary Long Engineer: Rick Engdahl Learn more about your ad choices. Visit megaphone.fm/adchoices

Ever wondered how visuals can transform your role as a product owner? Join Brian as he sits down with visual storyteller Stuart Young to unravel the power of visualization in product ownership. Join them on a journey to discover the art and science behind being a successful product owner. Overview Ever wondered how to elevate your product ownership game? In this episode, we delve into the world of visual storytelling with Stuart Young. Join Brian and Stuart as they discuss the diverse tools, such as story mapping and the product disposition canvas, that can bring your product visions to life. From storytelling techniques to the neurodiversity lens, we explore the art and science of communication that transcends traditional boundaries. Listen in to uncover the impactful ways visuals can shape your product strategy. Learn how being more visual can sharpen your skills, foster collaboration, and create a more inclusive and successful product development journey. Listen Now to Discover: [00:23] - Today welcomes Stuart Young, a Certified Scrum Trainer and visual storyteller to discuss storytelling through the product lens and more. [03:32] - Stuart discusses drawing large-scale pictures at conferences and recommends Visual Meetings and Visual Leaders by David Sibbit. [06:54] - Stuart emphasizes the impact of visual storytelling on individuals, highlighting the universal language and information retention through visuals. [08:46] - The benefits of visual representation in capturing the flow of ideas and aiding memory. [10:26] - The importance of varied methods for engaging different learning styles. [11:41] - Stuart discusses the value of visualization tools such as roadmaps, post-it notes, and story mapping to provide clarity and a clear narrative. [12:14] - The importance of blending Stuart references Pixar and Ed Catmull's book Creativity, Inc., discussing the importance of blending exciting elements, like storyboarding, in motivating teams and creating a compelling narrative. [15:13] - Stuart emphasizes the importance of authentic storytelling, even if it doesn't always have a happy ending, he references TEDxHogeschoolUtrecht - Steve Denning - “Leadership Storytelling" for further inspiration. [15:25] - Brian recommends Simon Sinek's TED talk on "Start With Why" as an example of effective storytelling despite not being visually polished. [16:09] - Stuart praises Henrik Kniberg's impactful video on product ownership, acknowledging the simplicity of the drawings but highlighting the potency of storytelling. He recommends the Sketchnote Handbook by Mike Rhodes for those interested in delving further into storytelling. [17:08] - The Agile Mentors Podcast is brought to you by Mountain Goat Software and their Certified Scrum Training Classes. For more information, click on the Mountain Goat Software Certified Scrum and Agile Training Schedule. [18:38] - Stuart highlights the significance of visual elements in crafting compelling visions and underscores the value of utilizing available templates, from sources like the Gamestorming book. [20:06] - Stuart discusses the role of visualization in making the intangible tangible, particularly in the tech space. [21:50] - Brian emphasizes the imprecision of words. He also discusses the value of showing rather than just telling, especially in product requirements, to enhance understanding and avoid delays caused by miscommunications. [23:34] - Stuart reflects on how visual communication can enhance inclusivity. He shares, “For people with reading and writing difficulties, pictures and symbols are better. The worst, the most abstract form, of course, is the word.” [25:22] - The role of a visual storyteller as a "human cursor" connecting diverse conceptual thinkers. Stuart recounts an illustration experience, emphasizing the challenge of visualizing details without clear specifications and underscoring the mantra of "process over art" in product ownership. [28:06] - Stuart underscores the product owner's role in leveraging the unique skills of team members to converge on a shared understanding of what "good" looks like. [29:19] - Brian references the episode of the show they did on Navigating Neurodiversity and the importance of understanding and accommodating different communication styles within a team. He highlights the need for product owners to be aware of the preferences of their team members and adjust communication methods accordingly. [30:54] - Stuart introduces the product disposition canvas and shares a personal revelation. [32:54] - Brian acknowledges the potential superpowers that come with neurodiversity, sharing his own experience of a late-in-life ADHD diagnosis and the benefits of leveraging the unique qualities each team member brings to a team. [33:36] - Stuart reflects on the importance of recognizing individual strengths and blind spots, emphasizing that everyone has a valuable contribution. [34:20] - Stuart encourages recognizing individual strengths for collective success. [35:23] - Listeners can connect with Stuart on LinkedIn and at Agile Nuggets | Agile Tips [37:38] - Please share this episode with others if you found it useful. Send feedback and suggestions for future episodes to podcast@mountaingoatsoftware.com. And don’t forget to subscribe to the Agile Mentors Podcast on Apple Podcasts so you never miss an episode. [38:21] - If this topic was impactful to you and you want to continue the discussion, join the Agile Mentors Community where we have a topic discussion for each podcast episode. You can get a free year-long membership in the community just by taking any class with Mountain Goat Software. References and resources mentioned in the show: Stuart Young on LinkedIn Agile Nuggets | Agile Tips | Cprime Learning Scrum in Under 10 Minutes #76: Navigating Neurodiversity for High-Performing Teams with Susan Fitzell David Sibbet Visual Meetings by David Sibbet Visual Leaders by David Sibbet Creativity, Inc. Sketchnote Handbook by Mike Rohde TEDxHogeschoolUtrecht - Steve Denning - “Leadership Storytelling" Simon Sinek: How Great Leaders Inspire Action | TED Talk Agile Product Ownership in a Nutshell by Henrik Kniberg Gamestorming: A Playbook for Innovators, Rulebreakers, and Changemakers Subscribe to the Agile Mentors Podcast on Apple Podcasts Certified ScrumMaster Training and Scrum Certification Certified Scrum Product Owner Training Advanced Certified Scrum Product Owner® Advanced Certified ScrumMaster® Mountain Goat Software Certified Scrum and Agile Training Schedule Join the Agile Mentors Community Want to get involved? This show is designed for you, and we’d love your input. Enjoyed what you heard today? Please leave a rating and a review. It really helps, and we read every single one. Got an Agile subject you’d like us to discuss or a question that needs an answer? Share your thoughts with us at podcast@mountaingoatsoftware.com This episode’s presenters are: Brian Milner is SVP of coaching and training at Mountain Goat Software. He's passionate about making a difference in people's day-to-day work, influenced by his own experience of transitioning to Scrum and seeing improvements in work/life balance, honesty, respect, and the quality of work. Stuart Young, a Certified Scrum Trainer and Visual Storyteller, merges Agile methodologies and design thinking to empower individuals and teams. As a thought leader, he champions Visual Storytelling for engaging stakeholders, addressing customer needs, and expediting learning. Through workshops, Stuart encourages teams to embrace visual methodologies to achieve business success.

“We don't want our time to be spread thin like peanut butter on a slice of toast. You will have greater impact when you concentrate your efforts on work that is closely tied to winning—however you define it.” Are you working in a frustration factory? If so, it's important to recognize that not all friction is created equal. Some is good, to slow down decision-making in crucial moments, and some is bad, getting in the way of progress. You'll need to tap into your inner “grease” and “gunk” sides to address both. In the introduction to their book, The Friction Project, coauthors Huggy Rao and Bob Sutton share a quote from Ed Catmull, former president of Pixar. He believes that if Pixar followed overreaching executives' advice to wring maximum efficiency and scale out of the organization, it would “kill the goose that lays the golden eggs.” "The goal isn't efficiency, it is to make something good or even great,” Catmull says. “We iterate seven to nine times, with friction in the process.” More About Huggy: Huggy Rao is the Atholl McBean professor of Organizational Behavior at the Stanford Graduate School of Business and a fellow of the Center for Advanced Study in Behavioral Science, the Sociological Research Association, and the Academy of Management. He has written for Harvard Business Review, Business Week, and the Wall Street Journal. He is the author of Market Rebels and coauthor of the bestselling book Scaling Up Excellence. Today we're talking about his new book, also coauthored with Bob Sutton, The Friction Project: How Smart Leaders Make the Right Things Easier and the Wrong Things Harder.

Ed Catmull discusses the essential elements of successful animation films, emphasizing the importance of avoiding a stagnant approach and recognizing that the industry is ever-evolving. He underscores the dynamic nature of filmmaking, where stability is an illusion, and the key is to adapt to changing technology, people, and audience expectations. Catmull delves into the structure of films and series, highlighting the need for thoughtful planning, especially in series with a well-defined endpoint, ensuring a satisfying conclusion.LISTEN TO THE FULL EPISODE!Sign up for the Greatness newsletter!

From the Author of “Brave Together” and Co-Founder of BraveCore, a leadership + culture consultancy, we hear about bravery- what it is, how it can be learned, and why co-creating is the only way to a better future. With first-hand anecdotes from some of the greatest corporate leaders of the 21st century, including Apple's Steve Jobs and Pixar's Ed Catmull- you won't want to miss Episode 24, Season 4. ABOUT THE SERIES: Future of XYZ is a bi-weekly interview series that explores big questions about where we are as a world and where we're going. Presented in collaboration with Rhode Island PBS. FOR MORE INFORMATION: Follow @futureofxyz on Instagram, and visit www.future-of.XYZ or www.ripbs.org/XYZ for show links and more.

There's no perfect process for achieving your goals. Accepting that the rules you play by need to be constantly tweaked, hacked or reinvented will open you up to new ways of innovating. Instilling this attitude throughout your organization will help you be boldly differential in your experimentation.Ed Catmull literally wrote the book on creating a dynamic and sustainable creative culture. Drawing on his experience as co-founder of celebrated animation studio Pixar, and president of Walt Disney Animation Studios, Ed shares his hard-won insights from his career as a pioneering technologist, animator and storyteller.Read a transcript of this episode: https://mastersofscale.com/Subscribe to the Masters of Scale weekly newsletter: https://mastersofscale.com/subscribeSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Chapter 1 Understand the idea behind Creativity Inc"Creativity Inc: Overcoming the Unseen Forces That Stand in the Way of True Inspiration" is a book written by Ed Catmull, co-founder of Pixar Animation Studios and former president of Pixar and Disney Animation. It was published in 2014.The book explores the culture and management principles that have made Pixar successful in the world of animation. Catmull shares insights and personal experiences about how to foster creativity, overcome challenges, and maintain a creative environment within a large organization. He provides a behind-the-scenes look at the creation of some of the most successful animated films like "Toy Story", "Finding Nemo", and "The Incredibles".Catmull discusses the importance of creating a culture where people are encouraged to take risks, share ideas, and fail on their way to success. He offers guidance on managing creative teams, dealing with unexpected obstacles, and promoting open communication. Ultimately, the book aims to inspire individuals and organizations to embrace creativity and create an environment that nurtures innovation.Chapter 2 Is Creativity Inc Worth the Hype?Yes, Creativity Inc by Ed Catmull is widely regarded as a good book. The book details Catmull's experiences and insights as the co-founder of Pixar Animation Studios, and it offers valuable lessons on creativity, leadership, and managing a successful organization. Many readers praise the book for its engaging storytelling, practical advice, and inspirational messages. Additionally, Creativity Inc has received positive reviews from a variety of sources, further affirming its quality.Chapter 3 Overview of Creativity Inc "Creativity Inc" by Ed Catmull is a book that explores the principles and practices that have made Pixar Animation Studios successful in fostering a creative and innovative culture. The book follows Catmull's journey from his early career in computer graphics to becoming one of the co-founders of Pixar. He shares the challenges, failures, and triumphs that shaped Pixar's growth and success.One of the key points in the book is the emphasis on creating a safe and open environment where creativity can flourish. Catmull explains how Pixar has developed a culture that encourages constructive criticism, experimentation, and collaboration. He believes that nurturing talent and fostering a supportive atmosphere is crucial to unleashing creativity and innovation.Catmull also discusses the importance of embracing failure and learning from it. He shares numerous examples where mistakes yielded valuable lessons that ultimately led to breakthroughs in storytelling and animation techniques. He argues that failures should be embraced as an essential part of the creative process and should not be feared or punished.Furthermore, the book highlights the significance of cultivating a diverse workforce. Catmull stresses the importance of hiring people with different perspectives, backgrounds, and skill sets, as this diversity of thought leads to more creative solutions and a richer creative output.In addition to these principles, Catmull shares practical advice on managing creative teams and providing effective leadership. He discusses the challenges of balancing creativity with the realities of budgets and schedules, and offers insights into how to navigate the tension between artistic integrity and commercial success.Overall, "Creativity Inc" is a book that not only gives an inside look into the evolution and success of Pixar Animation Studios but also provides valuable lessons and principles for nurturing creativity and innovation in any organization.Chapter 4 Creativity Inc Writer's Background The author of the book "Creativity Inc." is Ed Catmull. The book, which explores the principles

Every once in a while there's a generational thinker that emerges from the most unlikely of places. Someone capable of straddling the complexities of new industries without losing their grasp on historical and cultural perspectives. A person willing to forge new paths in new ways toward a brighter future for all. Ed Catmull is one such generational thinker. As co-founder of Pixar and later as President of Walt Disney Animation Studios, Ed played a key role in shaping a unique company culture of collaboration and creativity. He's a mastermind of innovation, a pioneer of groundbreaking technology, and a leader when it comes to using great storytelling to forge a better world. If you've ever been captivated by the beloved films Toy Story, Finding Nemo, The Incredibles, Up, and WALL-E, you have Ed to thank for that. Today we discuss the leadership and management principles that built Pixar's unique and successful studio. More specifically, we talk about the insights that fueled Ed's career, the workplace practices he leverages to build creative teams, and his personal philosophy of embracing failure as a path to growth. We also dive into his fascinating life journey, one that included both personal and professional relationships with George Lucas and Steve Jobs. There is so much to be learned from Ed's story, including some wild industry insights during his stewardship at Pixar and Disney, where he navigated through the ups and downs of the entertainment industry, all while delivering blockbuster after blockbuster, garnering eighteen Academy Awards along the way. This is the stuff of legend. If you're a creator or manager of any kind, or if you're simply looking to glean wisdom from one of the most fascinating and accomplished people alive, then you're in for a treat. I hope you enjoy this one as much as I did. Show notes + MORE Watch on YouTube Newsletter Sign-Up Today's Sponsors: Seed: Seed.com/RICHROLL On: On.com Ag1: drinkAG1.com/RICHROLL Faherty Brand: FahertyBrand.com/RICHROLL Athletic Brewing: AthleticBrewing.com/RICHROLL Indeed: Indeed.com/RICHROLL Plant Power Meal Planner: https://meals.richroll.com Peace + Plants, Rich

http://www.lewishowes.com/mindset2023 - Order a copy of my new book The Greatness Mindset today!Ed Catmull, a prominent figure at Pixar, shares his valuable insights and wisdom drawn from his extensive experience in the creative industry. The episode serves as a source of guidance and inspiration for those currently navigating the challenges and opportunities in the world of creativity.LISTEN TO THE FULL EPISODE!Sign up for the Greatness newsletter: http://www.greatness.com/newsletter

Have you ever wondered what kind of communication is necessary in order to be able to break through to the next level to have real innovation? What does it take? Well, today's guest is the absolutely perfect person to answer that question. I'm not sure there's anyone who could answer it better who's alive today. This is Ed Catmull. He's the co-founder of Pixar that went on to lead both Pixar and Disney's animation studios in what we could described as the second golden era of animation. You know the names of these movies, Toy Story, A Bug's Life, Monsters, Inc., Finding Nemo, The Incredibles, Cars, Ratatouille, and on and on and on. He not only helped to create new industry, he also created a new standard within animation the world over. By the end of this episode, you will have insights into how to actually have conversations that produce not just efficiency or productivity, but innovation, invention, breakthrough, and creativity. Learn more about Ed here: https://www.prhspeakers.com/speaker/ed-catmull Join my weekly newsletter at GregMcKeown.com/1mw Learn more about my books and courses at GregMcKeown.com Learn more about your ad choices. Visit megaphone.fm/adchoices

Welcome to our second Design Better episode on the creative process. You may not know Ed Catmull's name, but there's almost no doubt you're familiar with his work. As the co-founder of Pixar, he's responsible for helping to create movies ranging from the original Toy Story on through The Incredibles, Wall-E, Moana, and Inside Out.  Ed has a background in computer science, and as someone who pioneered many of the computer graphics and digital animation techniques that we now take for granted, he has a unique perspective on the intersection of technology and creativity. We chat with Ed about his transition from creating things himself, to leading creative teams; the elements of a sustainable creative culture, and how to give people feedback so they'll actually listen to you. Ed also collaborated with Steve Jobs longer than probably anyone else who knew him—for over 30 years—and we hear some stories that haven't been told anywhere else.  One more quick thing before we go: we have some amazing guests lined up for our upcoming AMAs, like Judy Wert Debbie Millman, which are filling up quickly. Go to our events page and you can register for free. Show notes: https://designbetterpodcast.com/p/ed-catmull-the-journey-from-lucasfilm#details Bio Dr. Ed Catmull is co-founder of Pixar Animation Studios and the former president of Pixar, Walt Disney Animation Studios, and Disneytoon Studios. For over twenty-five years, Pixar has dominated the world of animation, producing #1 box office hits that include iconic works such as Toy Story, Frozen, Cars, and The Incredibles. Pixar's works have grossed more than $14 billion at the worldwide box office, and won twenty-three Academy Awards®, 10 Golden Globes Awards, and 11 Grammys, among countless other achievements. Dr. Ed Catmull's book Creativity, Inc.—co-written with journalist Amy Wallace and years in the making—is a distillation of the ideas and management principles he has used to develop a creative culture. A book for managers who want to encourage a growth mindset and lead their employees to new heights, it also grants readers an all-access trip into the nerve center of Pixar Animation Studios—into the meetings, postmortems, and “Braintrust” sessions where some of the most successful films in history have been made. Dr. Catmull has been honored with five Academy Awards®, including an Oscar of Lifetime Achievement for his technical contributions and leadership in the field of computer graphics for the motion picture industry. He also has been awarded the Turing Award by the world's largest society of computing professionals, the Association for Computing Machinery, for his work on three-dimensional computer graphics. Please visit the links below to help support our show: Methodical Coffee: Roasted, blended, brewed, served and perfected by verified coffee nerds

In today's episode, I have the absolute honor of having a conversation with Ed Catmull. He's the co-founder of Pixar. All of the hits that you think of when you think of Pixar's Studio and how it also went on to revolutionize Disney's Studio is the work of Ed Catmull and his immediate team. He's marking the 10th anniversary of a book he wrote called Creativity, Inc. which gives you a firsthand account of how people really work together and communicate together in order to produce brilliant creative outcomes. He also worked with Steve Jobs longer than any other person, more than 25 years in all, and saw the transformation of his leadership from a visionary iconoclast into someone capable of transforming not just Pixar, not just Disney, but also Apple. This is part one of a conversation that I've been looking forward to and enjoyed immensely. Learn more about Ed here: https://www.prhspeakers.com/speaker/ed-catmull Join my weekly newsletter at GregMcKeown.com/1mw Learn more about my books and courses at GregMcKeown.com Learn more about your ad choices. Visit megaphone.fm/adchoices

The Summit of Greatness is back! Buy your tickets today – summitofgreatness.com – This episode is PART ONE of a powerful two-part interview series with Pixar Founder, Ed Catmull. Lewis and Ed dive into the strategies Pixar implements to strategically stimulate different aspects of the human mind to evoke intended emotions, with a focus on character development as a pivotal element in fostering a deep connection between the audience's minds and the story. For over twenty-five years, Pixar has dominated the world of animation, producing #1 box office hits that include iconic works such as Toy Story, Frozen, Cars, and The Incredibles. Pixar's works have grossed more than $14 billion at the worldwide box office, and won twenty-three Academy Awards®, 10 Golden Globes Awards, and 11 Grammys, among countless other achievements.In this episode you will learn,Pixar's insider techniques that tap into the human mind and create emotional connections with audiences.The firsthand principles and ideas discussed in "Creativity, Inc." that inspired a generationHow to strategically stimulate different aspects of the human mind in order to evoke an intended emotionHow character development plays a crucial role in Pixar films, and fosters a deep connection between the audience's minds and the storyThe magic formula revealing how Pixar masterfully balances humor, emotion, and suspense in their narratives, captivating audiences and maintaining engagement throughout the film.For more information go to www.lewishowes.com/1473For more Greatness text PODCAST to +1 (614) 350-3960Want more School of Greatness episodes like this one?Bruce Lipton on Manifestation: https://link.chtbl.com/1312-podJoe Dispenza on the Law of Attraction: https://link.chtbl.com/1312-pod

As they dreamed up iconic characters like Buzz and Woody, Pixar reinvented how animated movies are made. But first, they had to build a culture to make this magic possible. Pixar's co-founder and longtime president Ed Catmull and Oscar-winning Chief Creative Officer Pete Docter talk with Adam about how to spark and sustain creative collaboration. They also reveal the secret to great storytelling, discuss how to maintain and evolve a vision, and reflect on the lessons learned from working closely with the ever-enigmatic Steve Jobs. Transcripts for ReThinking are available at go.ted.com/RWAGscripts

Looking for a new way to solve problems? Join us as we talk with Hal Gregersen, author of the book, "Questions Are the Answer: A Breakthrough Approach to Your Most Vexing Problems at Work and in Life".  It's based on interviews with leaders like Pixar founder Ed Catmull and Salesforce CEO Marc Benioff. Hal is well-known as an innovation and leadership guru who is a Senior Lecturer at the MIT Sloan School of Management. His website is https://halgregersen.com/

It's 2006 and with Pixar part of Disney, Ed Catmull and John Lasseter now face the task of fixing Disney Animation Studios. And they need to fix it fast, because DreamWorks is winning at the box office.But while they strive to turn Disney Animation from has-been to hero, Hollywood's other big studios are gearing up to take a bite out of the animated movie pie and that's only going to dial up the competitive pressure on DreamWorks and Disney-Pixar.Binge all episodes early and ad-free with Wondery+. Join Wondery+ for exclusives, binges, early access, and ad free listening. Available in the Wondery App https://wondery.app.link/businesswars.Support us by supporting our sponsor!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

It's 1985 and Steve Jobs is fuming after being fired from Apple. He wants a second act, and he thinks he's found one in Lucasfilm's unwanted computer division.But when Jobs buys it and founds Pixar, it soon becomes clear that visions aren't aligned. Jobs wants to create the next-generation of personal computers. But Pixar's leaders Ed Catmull and John Lasseter have different plans. They want Pixar to make animated movies using computers.But to make that happen, they need to persuade Jobs to burn through his fortune, and convince Hollywood that a computer animated film can hold its own against the hand-drawn creations of Disney.Binge all episodes early and ad-free with Wondery+. Join Wondery+ for exclusives, binges, early access, and ad free listening. Available in the Wondery App https://wondery.app.link/businesswars.Support us by supporting our sponsor!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.