Podcasts about Quirk

In this episode, Dr. Sheila Quirk-Bailey, the recently retired president of Illinois Central College, talks with host H.M. Kuneyl about her career journey from corporate education into community college leadership, emphasizing how her Illinois-based communication and business training, paired with a practitioner-focused doctorate from Maryland, shaped her student success-driven philosophy. Among other topics, she and Kuneyl discuss the value of working across institutional silos, knowing one's "why," and pursuing leadership roles with purpose rather than for prestige.

Happy 2026! We're kicking off the new year by previewing the highly-anticipated season 4 of The Traitors with a cast draft. Our friend Jenna Quirk is back to join us in choosing our teams and giving our thoughts on how our faithful and traitors will fair this season. We can't wait to find out!Don't forget to follow us @letschatreality and subscribe wherever you get your podcasts. Thanks for listening!

Dan Nathan and Guy Adami host Steph Guild, Chief Investment Officer at Robinhood. Steph discusses her 2026 market outlook, reflecting on tech sector growth, AI developments, and S&P 500 predictions. She emphasizes her cautious approach for the coming year, focusing on diversification and value investing. After the break, Steve Quirk, Chief Brokerage Officer at Robinhood joins the pod. Steve talks about Robinhood's latest offerings, including prediction markets and event contracts, highlighting their rapid growth and retail investor interest. The episode also explores new AI tools like Cortex for customer portfolio management and Robinhood's new social platform aimed at fostering community and idea sharing among investors. —FOLLOW USYouTube: @RiskReversalMediaInstagram: @riskreversalmediaTwitter: @RiskReversalLinkedIn: RiskReversal Media

Campbell Quirk is a Minor League Strength and Conditioning Coach in the St. Louis Cardinals organization. He has been with the organization for four seasons, having worked two in Low-A and two in High-A. He was named the 2025 MiLB Strength and Conditioning Coach of the Year in the Midwest League. Campbell is also a former college baseball player. Topics covered in this episode:-His journey and best professional baseball story-Being named the Midwest League SCOTY and finding success-Advice for others-Continuing education resourcesQuotes:-"I think, overall, Minor League Baseball has done a great job making sure the facilities are kept above par" (14:29)-"Showing the athlete you care is crucial because at the end of the day it's their journey" (18:57)-"The best day of my life was becoming a dad and having to learn how to look after someone else" (27:56)If you would like to learn more from Campbell, you can connect with him on social media:Instagram:@quirky26

Anyone like Stoney?

Lisa and Emma share their experiences from the IMA's recent Insight forums, highlighting the positive atmosphere and the encouraging conversations around the role of Insight in driving organisational change. They address the initial concerns surrounding the term "activist" and how, upon deeper understanding, it resonated positively with attendees.We also explore the current state of the Insight industry, acknowledging the opportunities and challenges posed by technological advancements, particularly AI. That's the backdrop against which we Insight activists will need to reframe our mindset and play our new roles.As we look ahead to the new year, James encourages listeners to reflect on their Insight teams and their capability and consider how they can enhance their value to their organisations. We discuss upcoming Insight forums in January and February, focusing on Insight leaders' priorities for managing and transforming their teams, and Insight professionals' priorities for self-development. And we say thank you! Thank you to all our listeners for tuning in throughout Season 11 and hope you will join us again in early 2026 for the upcoming Season 12. We wish everyone a happy holiday season and encourage you all to embrace the opportunities for Insight growth and transformation in the year ahead.Please listen to find out more! Topics DiscussedThe Insight professionals pyramid (01.56)The importance of activism in Insight (5.05)Knowledge stewardship in the age of AI (12.03)Valuing Insights and commercial impact (17.23)January and February forum themes (25.39) This is episode 82 of the Transforming Insight podcast. If you have the ambition to transform your Insight team and the role it plays in your organisation, please tune in to future episodes. We will explore the 42 secrets of successful corporate Insight teams; and the need for Insight leaders to write their own playbooks; we will also talk to senior corporate Insight leaders, delve into books that have inspired us, and discuss new best practice research carried out with the IMA's corporate members.You won't want to miss this! So please subscribe - and thank you for listening.  About James Wycherley, the author of Transforming InsightJames Wycherley was Director of Customer Insight and Analytics at Barclays Bank from 2005 to 2015 when he became Chief Executive of the Insight Management Academy (IMA). He published his first book, Transforming Insight, in 2020, and his second, The Insight Leader's Playbook, in 2025, and he hosts the Insight forums and the Transforming Insight podcast.An entertaining keynote speaker, he has presented over 50 times at Quirk's events, a global record, and has provided thought leadership in the UK, USA, Europe, Canada, Australia, India and the Middle East.The Insight Management Academy is the world's leading authority on transforming corporate Insight teams, and its vision is to inspire and support every Insight leader to transform the impact of Insight in their organisation. Resources:If you would like more information on any of the ideas discussed in this episode of the Transforming Insight podcast, please visit www.insight-management.org DisclaimerThe Transforming Insight podcast is published by the Insight Management Academy and produced by Zorbiant.All rights reserved.   

On this episode, Dr. Sadaf welcomes Dr. Michelle Quirk to discuss everything you need to know about fitness, training, and mindful movement in midlife. As a pediatrician, run coach and the founder of Mindful Marathon, Dr. Quirk shares her expertise as we run down the best fitness practices for midlife. Learn why over-exercising can elevate stress hormones, as well as how we can address pelvic floor concerns, build mental grit, and eventually (yes, of course) engance our emotional and physical intimacy!Disclaimer: Anything discussed on the show should not be taken as official medical advice. If you have any concerns about your health, please speak to your medical provider. If you have any questions about your religion, please ask your friendly neighborhood religious leader. It's the Muslim Sex Podcast because I just happen to be a Muslim woman who talks about sex.To learn more about Dr. Sadaf's practice and to become a patient visit DrSadaf.comLike and subscribe to our YouTube channel where you can watch all episodes of the podcast!Feel free to leave a review on Apple Podcasts and share the show!Follow us on Social Media...Instagram: DrSadafobgynTikTok: DrSadafobgyn

This week on Tri Verse, we dive into My Hero Academia, Bleach, and how music has been shaping our vibe lately

This week on Own It we're talking to Meryl Draper from Quirk Creative. She is truly global, leading a firm that has offices in New York, Guadalajara, and Paris, where she is based now.  Meryl spent time at iconic agencies and with impressive brands through the years including Ogilvy & Mather, MSL Group, Cisco, Blackboard, IBM and more. After co-founding Quirk Creative in 2015, she has guided the agency to Adweek's Fastest Growing companies list. She's also been recognized on AdWeek's Creative 100, and The Drum's 50 under 30.   She is impressive. So is her firm. And we loved her perspective on the industry and how we can help produce more women-led agencies … and women owned agencies … as we all continue to grow.  You can find links to Meryl Draper's LinkedIn Profile and Quirk Creative's agency website in our show notes at untilyouownit.com.  If you're enjoying Own It, please find it on your favorite podcast app and drop us a rating and review. Those help more people discover the show and join our community.  Also, if you're a female or non-binary agency owner, or you want to own an agency someday, join our growing community at that same address … untilyouownit.com.

Christmas comes early this year, some immigrants build a terrible house, a bad way to meet women, too fat for fudge, a Maddox discovery about the self-insert Power Puff Girls episode, video games in Quirk Chungus World, a toilet with no seat, Indian airlines and wheelchairs, and Epstein becomes Yakub; all that and more this week on The Dick Show!

K&K Full Show - Restaurant Nightmare's and Kennedy's Quirk 11-13-25 full 3514 Thu, 13 Nov 2025 15:20:23 +0000 j2s3xOi4BeBIJO6I6WLavrF4G6dJ3IjZ society & culture Karson & Kennedy society & culture K&K Full Show - Restaurant Nightmare's and Kennedy's Quirk 11-13-25 Karson & Kennedy are honest and open about the most intimate details of their personal lives. The show is fast paced and will have you laughing until it hurts one minute and then wiping tears away from your eyes the next. Some of K&K’s most popular features are Can’t Beat Kennedy, What Did Barrett Say, and The Dirty on the 30! 2024 © 2021 Audacy, Inc. Society & Culture False https://player.amperwavep

Kennedy's Quirk full 385 Thu, 13 Nov 2025 14:05:16 +0000 MMq5p7TF4nocd3s6XRa8Wxowm9bU9MOe latest,wwbx,society & culture Karson & Kennedy latest,wwbx,society & culture Kennedy's Quirk Karson & Kennedy are honest and open about the most intimate details of their personal lives. The show is fast paced and will have you laughing until it hurts one minute and then wiping tears away from your eyes the next. Some of K&K’s most popular features are Can’t Beat Kennedy, What Did Barrett Say, and The Dirty on the 30! 2024 © 2021 Audacy, Inc. Society & Culture False https://player.amperwavepodcasting.com?feed-link=https%3A%2F%2Frss.amperwave.

Ten years ago, Steve Wills and I became rather obsessed by this book, and it helped contextualise some of our thinking on Insight farming – the critical habit of discussing new research and analysis findings, checking facts and figures that seem at odds with existing learning, investigating contradictions, and building picture summaries drawn from multiple sources. It's not just the summaries themselves that are critical, it's the discussions between Insight people along the way.But as we think about Insight roles of the future, I think one of the behavioural roles that will give us great opportunities to expand our remit is that of the knowledge steward, where we are not only farming the collective insight produced by the Insight team, but checking, challenging, combining and curating the knowledge produced by many others around the organisation. Please listen to find out more! Topics DiscussedA framework for Insight management (2.02)Black Box Thinking: the differences between industries (4.39)The relevance for Insight teams (8.43)The importance of discussing insight (10.53)Learning from John Cleese (11.42)The emerging need for knowledge stewards (15.42)Thank you for 20,000 downloads! (22.57) This is episode 81 of the Transforming Insight podcast. If you have the ambition to transform your Insight team and the role it plays in your organisation, please tune in to future episodes. Not only will we explore the 42 secrets of successful corporate Insight teams as outlined in the Transforming Insight book, and the 9Ps of The Insight Leader's Playbook, we will also talk to senior corporate Insight leaders, delve into books that have inspired us, and discuss new best practice research carried out with the IMA's corporate members.You won't want to miss this! So please subscribe - and thank you for listening.  About James Wycherley, the author of Transforming InsightJames Wycherley was Director of Customer Insight and Analytics at Barclays Bank from 2005 to 2015 when he became Chief Executive of the Insight Management Academy (IMA). He published his first book, Transforming Insight, in 2020, and his second, The Insight Leader's Playbook, in 2025, and he hosts the Insight forums and the Transforming Insight podcast.An entertaining keynote speaker, he has presented over 50 times at Quirk's events, a global record, and has provided thought leadership in the UK, USA, Europe, Canada, Australia, India and the Middle East.The Insight Management Academy is the world's leading authority on transforming corporate Insight teams, and its vision is to inspire and support every Insight leader to transform the impact of Insight in their organisation. Resources:If you would like more information on any of the ideas discussed in this episode of the Transforming Insight podcast, please visit www.insight-management.org DisclaimerThe Transforming Insight podcast is published by the Insight Management Academy and produced by Zorbiant.All rights reserved.   

Today we tackle the perennial question: “Will my MRI change what I should do?” Short answer: rarely. Imaging can explain quirks (like why a funky position eases symptoms), but it doesn't replace learning safe, aggravation-free squats & hip hinges that you already do in daily life. We show how to run “hedged experiments,” record your reps to spot errors fast, and why belts/braces create false security. Plus: sleeping pain (win the day, not the night), DOMS vs relief tools, decompression & inversion (relief, not strength), deadlift progressions, why most adults don't strength train (and why that's your edge), SIJ vs lumbar myths, and smart upper-body work that spares your back.Start here → https://backinshapeprogram.com/start/Highlights:

"Hey Casual Anime Fanatics! Send us a text and let us know what you would like us to talk about next!In this episode of THAT ANIME PODCAST,  The Casual Anime Fanatics discuss My Hero Academia, Season 8 (the Final Season), Episode 4, titled "Quirk: Explosion!!" and Episode 5, titled, "History's Greatest Villain". Welcome to the official podcast for Casual Anime Fanatics! We deliver fresh, entertaining episodes every week, exploring everything from classic favorites to hidden gems in the anime universe. Whether you're a long-time fan or just starting your anime adventure, THAT ANIME PODCAST is your go-to source for casual and insightful anime discussions.Enjoying the show? We'd love your support! If you like what you hear, consider leaving us a 5-star review on Apple Podcasts and/or Spotify. Your reviews help us reach even more anime enthusiasts just like you!Episode 4 Synopsis (According to Crunchyroll): The fierce battle with Bakugo reminds All For One of his and his brother Yoichi's origins. Episode 5 Synopsis (According to Crunchyroll): The balance of power shifts between Izuku and Shigaraki when the latter steals a Quirk from One For All. Stay connected with us:Instagram: @thatanimepodcastDiscord: Join our communityTune in, laugh with us, and let's celebrate all things anime together!

In this edition of Frikkity Frak, We Do Talk Back, we discuss the episode "Quirk: Explosion!!" and connect it to ROmans 12 verse 4-5. Please rate, subscribe, and review this podcast, tell your friends, and if you have any questions, please contact us at frikkityfraktalkback@gmail.com or any of our social media accounts with any questions about this episode or any and all spiritual, nerdy, or general questions.Intro 00:00E163 00:31Romans 12v4-5 05:08Ratings 11:35Outro 14:00@FrikkityF on Twitter@FrikkityFrak on Instagram@FrikkityFrak on Facebook#FaithandFandom #anime #animereview #podcast #MyHeroAcademia #MHA #Bakugo

I hope you agree that these are all critical aspects to consider if we want to lead or play a part in the transformation of our Insight teams. By documenting and developing our ideas for each, we can clarify our ambitions, sketch out our designs, consider helpful habits, who we will need to talk to, and how we are going to review progress.But is that enough?I think the truth is that every Insight leader or aspiring leader who wants to achieve sustainable transformation for their Insight team and its impact on their organisation will also need to work on themselves and how they can personally become more transformational leaders.The 9th P of the Insight leader's playbook is for Personal plan. Please listen to find out more! Topics DiscussedWhy do we need a personal plan? (0.43)Reading about personal effectiveness (3.29)Developing our ambition (6.56)Designing our week (8.47)Using the Insight leadership framework (13.31)Adopting helpful habits (18.43)  This is episode 80 of the Transforming Insight podcast. If you have the ambition to transform your Insight team and the role it plays in your organisation, please tune in to future episodes. Not only will we explore the secrets of successful corporate Insight teams and their leaders, as outlined in James Wycherley's books, Transforming Insight and The Insight Leader's Playbook, we will also talk to senior corporate Insight leaders, delve into books that have inspired us, and discuss new best practice research carried out with the IMA's corporate members.You won't want to miss this! So please subscribe - and thank you for listening.  About James Wycherley, the author of Transforming InsightJames Wycherley was Director of Customer Insight and Analytics at Barclays Bank from 2005 to 2015 when he became Chief Executive of the Insight Management Academy (IMA). He published his first book, Transforming Insight, in 2020, and his second, The Insight Leader's Playbook, in 2025, and he hosts the Insight forums and the Transforming Insight podcast.An entertaining keynote speaker, he has presented over 50 times at Quirk's events, a global record, and has provided thought leadership in the UK, USA, Europe, Canada, Australia, India and the Middle East.The Insight Management Academy is the world's leading authority on transforming corporate Insight teams, and its vision is to inspire and support every Insight leader to transform the impact of Insight in their organisation. Resources:If you would like more information on any of the ideas discussed in this episode of the Transforming Insight podcast, please visit www.insight-management.org DisclaimerThe Transforming Insight podcast is published by the Insight Management Academy and produced by Zorbiant.All rights reserved.   

It's our annual check-in with Becks Quirk, while Kris is recovering from Women's Week in P-town! Becks and Tara talk about what's changed in the last year, where they're at, what keeps them motivated, as well as Becks's reflections after Banned Books Week. Official Recommendations From Becks: Every Step She Takes by Alison Cochrun In keeping with the annual nature of her visits, Becks's official recommendation this week is another Alison Cochrun audiobook, Every Step She Takes. A bout of turbulence provokes Sadie into coming out and sharing her deepest secrets with her seat companion, Mal, only to learn they're both joining the same tour of Camino de Santiago in Portugal. Becks is a sucker for physical journeys that exhaust the body and mind, leading to revelations and change. From Tara: Ladies in Hating by Alexandra Vasti Tara's official recommendation this week is Ladies in Hating by Alexandra Vasti. Lady Georgiana Cleeve is the author of many popular novels, which helps her support herself and her mother. When Lady Darling's books are equally popular and have details that are eerily similar to Georgiana's own books, Georgiana knows she has to unmask Lady Darling. She's in for the shock of her life when Lady Darling ends up being none other than Georgiana's teenage crush. Tara loved the emotional journeys in this one and how it plays with conventions from gothic novels. Works/People Discussed Banned Books Week Fun Home: A Family Tragicomic by Alison Bechdel (recommended in QR 064) Gender Queer: A Memoir by Maia Kobabe (recommended in QR 057) Flamer by Mike Curato They Came from Below by L Dreamer After All: A Sapphic Romance (Latitude & Longing Book 3) by Bryce Oakley Olive Oil and White Bread by Georgia Beers The Shape of You by Georgia Beers RuPaul's Drag Race UK (BBC Three, BBC One) Hades 2 (Supergiant Games) Iceberg by Gun Brooke Course of Action by Gun Brooke Hope in the Dark by Rebecca Solnit Meditations in an Emergency - Essays by Rebecca Solnit The Fortune Hunter's Guide to Love by Emma-Claire Sunday Heather Rose Jones Support & follow the show Buy us a Ko-fi Facebook Instagram Threads Bluesky TikTok YouTube Get all our links on Linktr.ee

Hey y'all! Join us as we discuss the My Hero Academia episode "Revival Party", including multiple angry rants, Republican makeup, and a Science Corner about Curious' Quirk. Want more? Visit our website, myheroanalysis.com. Thanks for listening!⁠⁠⁠⁠⁠⁠Fight Genocide Worldwide Master Document⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠Indivisible: A Practical Guide to Democracy on the Brink⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ACLU Know Your Rights⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bother Your Representatives

Some of the IMA's most successful Insight leaders have gone one step further, and estimated the value that the Insight team itself has added each year, ultimately expressing this as a return on Insight investment.In today's episode, James talks to two senior UK Insight leaders, Danny Russell (ex Sky and O2) and Suzanne Lugthart (formerly with ebay, ITV and Rightmove). They have just launched a new initiative called Bigger Than Biscuits that aims to catalogue some great examples of how Insight, research and analysis have added value to business decisions.Has your research led to a successful advertising campaign? Has your analysis led to cost savings or increased revenue by relocating stores or the way products are displayed in them? Has your joined-up insight helped reshape company strategy and unlocked hidden growth opportunities?If it has, your work could be an invaluable addition to this initiative and help other Insight professionals make the case for greater investment in Insight. Please listen to find out more! Topics DiscussedIntroducing Bigger Than Biscuits (2.56)Are CMOs spending less on market research? (6.59)Insight as a cost or investment? (10.20)Think commercially and keep a value log (14.02)It's all about estimation (17.42)The ECB's Hundred £1bn valuation (22.33)It starts with prioritisation (25.43)How to get involved (27.21)A challenge for all listeners (30.30) Highlights“It's the stuff that feeds the innovation pipeline. It's the stuff that feeds the creative platform development. It's all the work we do. I mean it's even stuff like customer experience research. It's anything that can help us diagnose a problem in a business so they can go and do something that will put it all right.” (04:46)“So those stories are there, but people just need the courage to let us work with them to get them out there. The point is that we have to assume that these very important stakeholders around the business let's call it nicely don't really understand and let's be realistic and say they don't really care about the incredible work that we're doing or the stresses that we're under, or the budgetary constraints, etc.” (17.19)“We'd go along and say, okay, you want our time and our budget, so tell me about this thing you're working on, how much money is it going to make and how much are we going to make a difference? And some of them hadn't really thought through and it was good for them to go back, and then they felt they were in this competition for our time and attention and suddenly you feel really quite elevated.” (24.46)“It's about being loud and proud, and if people are still too nervous about that, then just get onto LinkedIn and give us a follow on Bigger Than Biscuits, because that will at least mean they start to see the sort of work that we're doing. .” (29.41) This is episode 79 of the Transforming Insight podcast. If you have the ambition to transform your Insight team and the role it plays in your organisation, please tune in to future episodes. Not only will we explore the secrets of successful corporate Insight teams and their leaders, as outlined in James Wycherley's books, Transforming Insight and The Insight Leader's Playbook, we will also talk to senior corporate Insight leaders, delve into books that have inspired us, and discuss new best practice research carried out with the IMA's corporate members.You won't want to miss this! So please subscribe - and thank you for listening.  About James Wycherley, the author of Transforming InsightJames Wycherley was Director of Customer Insight and Analytics at Barclays Bank from 2005 to 2015 when he became Chief Executive of the Insight Management Academy (IMA). He published his first book, Transforming Insight, in 2020, and his second, The Insight Leader's Playbook, in 2025, and he hosts the Insight forums and the Transforming Insight podcast.An entertaining keynote speaker, he has presented over 50 times at Quirk's events, a global record, and has provided thought leadership in the UK, USA, Europe, Canada, Australia, India and the Middle East.The Insight Management Academy is the world's leading authority on transforming corporate Insight teams, and its vision is to inspire and support every Insight leader to transform the impact of Insight in their organisation. Resources:If you would like more information on any of the ideas discussed in this episode of the Transforming Insight podcast, please visit www.insight-management.org DisclaimerThe Transforming Insight podcast is published by the Insight Management Academy and produced by Zorbiant.All rights reserved.   

Management guru Peter Drucker said many years ago that “what gets measured gets managed” which many have adapted to ‘what gets measured gets done'.Similarly, the second habit of Stephen Covey's 7 Habits of Highly Effective People, was that we should all ‘begin with the end in mind.' That's why I decided to make the 8th P of the Insight Leader's Playbook, P for Performance.Please could I ask you to reflect on a simple question:Q _ Has your Insight team had a good year so far?I hope you think it has! But what I'm really interested in is the thought process you just went through.How did you evaluate whether your Insight team had enjoyed a good year?What were the measures you considered? The types of evidence? Did you think about the outputs you can measure in this year, or the foundations you have laid for the next? Please listen to find out more! Topics DiscussedThe types of evidence we collect (2.19)Insight team assessment ladder (6.40)Investing in Insight assets (8.07)An Insight team's critical assets (12.18)Knowledge as a critical asset (13.37) This is episode 78 of the Transforming Insight podcast.  If you have the ambition to transform your Insight team and the role it plays in your organisation, please tune in to future episodes. Not only will we explore the 42 secrets of successful corporate Insight teams as outlined in the Transforming Insight book, we will also talk to senior corporate Insight leaders, delve into books that have inspired us, and discuss new best practice research carried out with the IMA's corporate members.You won't want to miss this! So please subscribe - and thank you for listening.  About James Wycherley, the author of Transforming InsightJames Wycherley was Director of Customer Insight and Analytics at Barclays Bank from 2005 to 2015 when he became Chief Executive of the Insight Management Academy (IMA). He published his first book, Transforming Insight, in 2020, and his second, The Insight Leader's Playbook, in 2025, and he hosts the Insight forums and the Transforming Insight podcast.An entertaining keynote speaker, he has presented over 50 times at Quirk's events, a global record, and has provided thought leadership in the UK, USA, Europe, Canada, Australia, India and the Middle East.The Insight Management Academy is the world's leading authority on transforming corporate Insight teams, and its vision is to inspire and support every Insight leader to transform the impact of Insight in their organisation. Resources:If you would like more information on any of the ideas discussed in this episode of the Transforming Insight podcast, please visit www.insight-management.org DisclaimerThe Transforming Insight podcast is published by the Insight Management Academy and produced by Zorbiant.All rights reserved.   

What if you could combine your passion for helping people with the freedom to serve the clients you love most? That's exactly what Anne Quirk discovered when she launched True Self Speech Therapy in Providence, Rhode Island.Anne is a speech-language pathologist specializing in neurological conditions, voice disorders, and gender-affirming voice therapy. After years in nonprofits and hospitals, she began craving more balance, control, and the chance to serve her ideal clients.With encouragement from colleagues, Anne took the leap into private practice. Now she helps stroke survivors communicate again, supports transgender clients in finding affirming voices, and has built a thriving caseload through word-of-mouth referrals.In this episode, you'll hear how Anne has embraced the flexibility of private practice to shape a career that truly reflects her values. By expanding her team and creating resources like her newsletter, News for the Affirming SLP, she's found ways to support both her clients and her family—while stepping fully into her true self.In this episode, we discuss:The leap from healthcare burnout to starting her own practiceHow gender-affirming voice therapy helped her practice grow in its first yearBalancing private practice with raising three kidsAnd why hiring a team changed everythingAnne's journey is proof that private practice is possible at any stage of your career—and that specializing in the clients you're most passionate about can help you stand out, attract referrals, and make a bigger impact.If you're ready to build a private practice that reflects your true self—just like Anne has learned more about our Grow Your Private Practice Program – where our other coaches can help you set up systems as you shift from clinician to CEO. To learn more, please visit www.GrowYourPrivatePractice.com.Whether you want to start a private practice or grow your existing private practice, I can help you get the freedom, flexibility, fulfillment, and financial abundance that you deserve. Visit my website www.independentclinician.com to learn more.Resources Mentioned:Follow Anne on:Instagram: https://www.instagram.com/trueselfspeech/Facebook: https://www.facebook.com/trueselfspeechLinkedIn: https://www.linkedin.com/in/ashak/Check out her website: www.trueselfspeech.comLearn more about growing your practice: www.GrowYourPrivatePractice.comWhere We Can Connect: Follow the Podcast:

So the 7th P of the Insight Leader's Playbook is for Process.Process might not always sound the most exciting topic but the way we approach can often make the difference between a productive, effective, efficient and successful Insight team, and a group that is doing its best to survive another spin around the corporate hamster wheel.To quote James Clear from his bestseller, Atomic Habits:“We do not rise to the level of our goals, we fall to the level of our systems.”That's true for organisations and for individuals, and it's also true for Insight teams. Please listen to find out more! Topics DiscussedThe critical importance of process (01:19)Project-orientated processes (03:10) Enabling processes (04:52) Buying back our time (06:22)An Insight team's operating manual (8:22)Navigating the adoption gap at HMRC (12:21)Key points to consider (18:31) This is episode 77 of the Transforming Insight podcast. If you have the ambition to transform your Insight team and the role it plays in your organisation, please tune in to future episodes. Not only will we explore the 42 secrets of successful corporate Insight teams as outlined in the Transforming Insight book, we will also talk to senior corporate Insight leaders, delve into books that have inspired us, and discuss new best practice research carried out with the IMA's corporate members.You won't want to miss this! So please subscribe - and thank you for listening.   About James Wycherley, the author of Transforming InsightJames Wycherley was Director of Customer Insight and Analytics at Barclays Bank from 2005 to 2015 when he became Chief Executive of the Insight Management Academy (IMA). He published his first book, Transforming Insight, in 2020, and his second, The Insight Leader's Playbook, in 2025, and he hosts the Insight forums and the Transforming Insight podcast.An entertaining keynote speaker, he has presented over 50 times at Quirk's events, a global record, and has provided thought leadership in the UK, USA, Europe, Canada, Australia, India and the Middle East.The Insight Management Academy is the world's leading authority on transforming corporate Insight teams, and its vision is to inspire and support every Insight leader to transform the impact of Insight in their organisation. Resources:If you would like more information on any of the ideas discussed in this episode of the Transforming Insight podcast, please visit www.insight-management.org DisclaimerThe Transforming Insight podcast is published by the Insight Management Academy and produced by Zorbiant.All rights reserved.   

WEDNESDAY HR 1 Russ almost meltsdown down when he can't find the remote. What can set your day all wrong? Ryan's keyboard is gross. Do you keep your shoe's in boxes? See omnystudio.com/listener for privacy information.

WEDNESDAY HR 1 Russ almost meltsdown down when he can't find the remote. What can set your day all wrong? Ryan's keyboard is gross. Do you keep your shoe's in boxes?

In this episode of Beyond Measure, Eric Scheer and Mike Deinlein explore one of the toughest challenges facing modern businesses: aligning marketing and customer experience (CX). While marketing sets the promise, CX delivers on it, but too often these teams operate in silos, leading to brand misalignment, customer disappointment, and lost growth opportunities. Reprising their Quirk's NYC presentation and building off of their Quirk's magazine article, Eric and Mike discuss why these disconnects happen, how data silos create a “multiverse” of competing realities, and why organizations need better tools to bring marketing and CX closer together. They highlight how democratizing data and creating a unified source of truth can help teams collaborate more effectively and avoid costly missteps. The conversation also dives into real-world case studies, including Southwest Airlines and Marriott Hotels, to illustrate how companies can use data-driven frameworks to assess brand strength, close gaps between marketing promises and CX delivery, and allocate resources more strategically. If you want to break down silos, improve customer retention, and ensure your brand delivers what it promises, this episode offers actionable ideas you can take back to your organization. For more information on how Burke can help move your business forward, visit our website. Thanks for listening! Please subscribe to be notified of future episodes of Burke's BeyondMeasure podcast.

About our guest:Kimberley Quirk is a lead practitioner at Mast Cell 360, blending deep clinical insight with a nurturing approach and her own lived experience through healing. She works closely with clients to map out practical, step-by-step plans that honor each person's unique pace and sensitivities.Free haywire mast cell quiz:https://mastcell360.com/mystery-quiz/MastCell360 website:https://mastcell360.com/home/ Instagram:https://www.instagram.com/mastcell360/?hl=enStephanie's links:Tiktok: https://www.tiktok.com/@drstephpeacockInstgram: https://www.instagram.com/drstephpeacock/Work with me: https://stephaniepeacock.com/ Subscribe to my newsletter: https://stephanies-newsletter-c410d1.beehiiv.com/subscribe

Get ready for a fun and inspiring conversation with Lauren Quirk, Founder of Travel With Character®! Lauren is proof that theme park travel can be just as chic, seamless, and story-driven as a luxury FIT trip. From hand-picking resorts and tickets to crafting VIP tours and personalized itineraries, she and her team sprinkle creativity, precision, and a little magic into every detail. With her bubbly personality and entertainment background, Lauren shares how she went from Broadway stages to building a thriving agency. She talks about why she recently transitioned from independent contractors to an employee model, why charging planning fees is a must, and how she blends personality-packed social media with polished systems to create unforgettable client experiences. Ready to see theme park travel in a whole new light? This episode is packed with ideas, laughs, and a dose of Lauren's contagious enthusiasm. About Lauren Quirk: Lauren is the Founder of Travel With Character®, a backstage production team for designing luxury family vacations. Over the past six years she has assembled a small team of creative women who guide today's discerning travelers who value personalized and attentive service over DIY booking methods. Lauren's education and background are in the performing arts, with a history on the off-Broadway stages of New York, and LA productions like "Grease Live! On Fox", and Comedy Central sketches. She now translates this creativity and storytelling into her travel business, crafting unique experiences for every client, helping them to embrace their own "main character energy." Lauren's innovative approach to travel industry marketing has not gone unnoticed. She has been a Travel Weekly Gold Magellan award recipient and a finalist in TravelAge West's Trendsetter Awards, celebrating her social media content. Her unique marketing signature includes creating comedic musical pieces related to travel, engaging audiences and inviting them into a world of exploration and enjoyment. @travelwithcharacter travelwithcharacter.com Today we will cover: (03:00) The story behind Travel With Character® and Lauren's journey from Broadway to travel (06:45) Why Lauren transitioned from independent contractors to employees for stronger brand consistency (12:20) Best tools and software for planning Disney and theme park vacations (18:45) Charging planning fees and demonstrating the value of professional expertise (23:35) Offering concierge-level Disney services including Lightning Lanes and dining reservations (28:35) Streamlining workflows to deliver a polished and seamless client experience (31:45) How to collect client testimonials and Google reviews that build trust (41:05) Expanding from theme park travel to international destinations (44:50) Creating engaging social media content with musical parodies for marketing (47:15) Lauren's top advice for travel advisors: confidence, systems, and preparation JOIN THE NICHE COMMUNITY VISIT THE TEMPLATE SHOP EXPLORE THE PROGRAMS FOLLOW ALONG ON INSTAGRAM @TiqueHQ Thanks to Our Tique Talks Sponsors: Moxie & Fourth - Get $30 off the List Launch Kit with code PODCAST

What happens when a major US insurance tech player sets its sights on Europe? This week, Robin Merttens speaks with Chris Quirk, VP and General Manager at Insurity, to unpack the drivers behind the company's international expansion and what it signals for the wider market. Chris brings a unique cross-market perspective, working with carriers, MGAs and brokers on both sides of the Atlantic. He shares how Insurity is navigating the challenges of scaling legacy systems into modern, cloud-native platforms and why interoperability and open ecosystems are now critical in a post-consolidation world. Key themes include: Why the US market is looking to London and Europe for growth The role of private equity and M&A in shaping cross-border expansion Common pitfalls when exporting US tech to the UK and how to avoid them How Insurity is balancing tried-and-tested functionality with modern scalability The growing importance of structured data in preparing for agentic AI Strategic partnerships with Viper, Coherent and OIP InsurTech What a truly “distributed” insurance ecosystem could look like in the near future Chris also offers insight into where Insurity is investing over the next 12 months and why the industry may be underestimating just how much technology has matured. If you like what you're hearing, please leave us a review on whichever platform you use or contact Robin Merttens on LinkedIn. You can also contact Christopher Quirk on LinkedIn to start a conversation! Sign up to the InsTech newsletter for a fresh view on the world every Wednesday morning. Continuing Professional Development This InsTech Podcast Episode is accredited by the Chartered Insurance Institute (CII). By listening, you can claim up to 0.5 hours towards your CPD scheme. By the end of this podcast, you should be able to meet the following Learning Objectives: Explain the role of structured data and cloud-native systems in enabling AI adoption in insurance workflows. Define the concept of an “open ecosystem” in insurance technology and its advantages over closed platform models. Identify key industry partnerships that support seamless data exchange and workflow integration across markets. If your organisation is a member of InsTech and you would like to receive a quarterly summary of the CPD hours you have earned, visit the Episode 371 page of the InsTech website or email cpd@instech.co to let us know you have listened to this podcast. To help us measure the impact of the learning, we would be grateful if you would take a minute to complete a quick feedback survey.

Boston's North and South stations serve as critical transit hubs but remain disconnected despite a century of failed attempts to link them.• North Station serves 40,000 daily commuters from northern suburbs and connects to Amtrak's Downeaster route• South Station handles 60,000 daily commuters and serves as terminus for multiple Amtrak routes including Acela• Transferring between stations requires a 16-25 minute journey using multiple subway lines• Five major attempts to connect the stations have failed since the 1930s due to funding issues and political obstacles• The Big Dig highway project complicated future connection possibilities by placing a tunnel between the stations• Current estimated costs for connecting the stations range from $12-21 billion• Engineering challenges include tunneling under existing infrastructure, electrifying commuter rail, and working in a dense urban environment• Advocates continue pushing for the connection citing climate goals and regional transit equityIf you want to support the show, the best ways to do so are via our Patreon, Buy Me a Coffee, or check out our merch store!Send us a textSupport the show

The Treasure Valley's still baking in this heat, so host Lindsay Van Allen is cooling off with a sweet throwback to the delicious and chaotic world of dirty sodas. In this re-run, Deseret News writer Meg Walter explains how the Utah drive-thru phenomenon made waves in the Boise summer drink scene. We're doing our annual survey to learn more about our listeners. We'd be grateful if you took the survey at citycast.fm/survey—it's only 7 minutes long. You'll be doing us a big favor. Plus, anyone who takes the survey will be eligible to win a $250 Visa gift card–and City Cast Boise swag. Learn more about the sponsor of this August 14th episode: Babbel - Get up to 60% off at Babbel.com/CITYCAST Interested in advertising with City Cast Boise? Find more info HERE.

It's over. The bubbles have settled. The Fruit War is done.This week, we break down the final results of the Seltzer Bracket in all their sticky, citrus-stained glory. 44 flavors entered. Only one remained standing — and it might surprise you. Was it a refreshing underdog? A nostalgic juggernaut? Or a mango-tinted fever dream from the depths of the convenience store fridge?Tony and Griff walk you through each region of the “Flavor Personality Party”:

Today's Sports Daily covers “Quarterback” season 2 on Netflix drops today, the Mavericks DID donate (yay) to the flood victims, NBA Summer League under way, and an NFL scheduling quirk to keep an eye on this season.Music written by Bill Conti & Allee Willis (Casablanca Records/Universal Music Group) 

A general discussion show about everything from video games, bad actors on YouTube, to why Matthew should avoid My Hero Academia: Vigilantes. Show your thanks to Major Spoilers for this episode by becoming a Major Spoilers Patron at http://patreon.com/MajorSpoilers. It will help ensure the Major Spoilers Podcast continues far into the future! Join our Discord server and chat with fellow Spoilerites! (https://discord.gg/jWF9BbF) CLOSE Contact us at podcast@majorspoilers.com A big Thank You goes out to everyone who downloads, subscribes, listens, and supports this show. We really appreciate you taking the time to listen to our ramblings each week. Tell your friends!

A general discussion show about everything from video games, bad actors on YouTube, to why Matthew should avoid My Hero Academia: Vigilantes. Show your thanks to Major Spoilers for this episode by becoming a Major Spoilers Patron at http://patreon.com/MajorSpoilers. It will help ensure the Major Spoilers Podcast continues far into the future! Join our Discord server and chat with fellow Spoilerites! (https://discord.gg/jWF9BbF) CLOSE Contact us at podcast@majorspoilers.com A big Thank You goes out to everyone who downloads, subscribes, listens, and supports this show. We really appreciate you taking the time to listen to our ramblings each week. Tell your friends!

A general discussion show about everything from video games, bad actors on YouTube, to why Matthew should avoid My Hero Academia: Vigilantes. Show your thanks to Major Spoilers for this episode by becoming a Major Spoilers Patron at http://patreon.com/MajorSpoilers. It will help ensure the Major Spoilers Podcast continues far into the future! Join our Discord server and chat with fellow Spoilerites! (https://discord.gg/jWF9BbF) CLOSE Contact us at podcast@majorspoilers.com A big Thank You goes out to everyone who downloads, subscribes, listens, and supports this show. We really appreciate you taking the time to listen to our ramblings each week. Tell your friends!

Jim Quirk is a journalist and podcaster on his YouTube channel called 'The Quirk Zone". It's a great channel that talks about the latest news in the UFO and Paranormal world. A former newspaper journalist, Jim is quick to get out about his opinions on the state of ufology and where the story is going. He also believes that contactees are soon going to be the faces of 'Disclosure'.Become a supporter of this podcast: https://www.spreaker.com/podcast/spaced-out-radio--1657874/support.

"First day, worst day, every day — that's what we're built for." — Patrick Quirk, President & GM, Opengear At Cisco Live 2025 in San Diego, Patrick Quirk, President and General Manager of Opengear, joined Technology Reseller News publisher Doug Green to unveil a major innovation in network resilience: Opengear's new Foundational Support platform. Designed to meet the growing demands of increasingly complex, high-density network environments, the SLA-backed solution debuts as part of Opengear's commitment to full-lifecycle customer support. “It's not just about selling equipment,” said Quirk. “It's about walking with the customer through every stage of the network's lifecycle — from deployment to daily operations to disaster recovery.” A long-standing Cisco partner, Opengear has evolved from traditional console servers to a critical infrastructure provider, helping companies maintain uptime in an era where milliseconds matter — especially amid today's AI-driven network traffic spikes. “Outages aren't just inconvenient,” Quirk noted. “They're expensive. We're seeing potential losses of $21,000 per minute during downtime.” Opengear's edge? Out-of-band management. Unlike in-band VLAN control planes, which can be compromised during incidents or overloaded by data traffic, out-of-band infrastructure operates on a completely separate path. This architecture allows for immediate network visibility and control during even the worst disruptions, such as fiber cuts or cyberattacks. Supporting both operational and compliance objectives, Opengear enables organizations to maintain certifications like ISO, SOC 2, and NIST. “We're the wrapper around your network,” said Quirk, emphasizing the company's ability to enforce security and governance alongside performance. The conversation also spotlighted Opengear's recent AI-focused global research, which found a “lens gap” between network engineers and the C-suite. While both groups recognize AI's potential, engineers view it as a productivity tool, whereas executives prioritize compliance and customer value. “There's alignment,” Quirk said. “It just needs more conversation.” At Cisco Live, Opengear is exhibiting at booth 4324 and hosting sessions on topics like agentic AI and network strategy. More details are available at opengear.com.

We dig into weird rules that you created for yourselves, and people need to lean how to eat pie. Plus, WTF: Steve's Edition, featured a marriage that was doomed from the start when things got stab-y, and Tom Cruise's best role was a cameo in a fat suit. See omnystudio.com/listener for privacy information.

When are the weird things we do more than they seem to be? In this episode we explore how being curious about behaviors rather than taking them at face value, can help to identify when someone might be struggling.

In this insightful clip from Market Mondays, hosts Rashad Bilal and Troy Millings sit down with Steve Quirk, a prominent leader from Robinhood, to dive deep into smart margin strategies, the evolving behavior of young retail investors, and the exciting future of AI in investing.Steve delivers valuable advice for those considering investing with margin, urging a methodical and opportunistic approach. He shares why having a solid plan is crucial, and why sticking to that plan—especially in volatile markets—often leads to more successful outcomes. The conversation highlights how younger investors, despite smaller account sizes compared to traditional brokerage customers, are showing impressive sophistication and adapting to changing markets by leveraging broad-based ETFs, like the S&P 500.Transitioning to technology, Steve unveils Robinhood's ongoing integration of artificial intelligence, including the upcoming “Robinhood Cortex.” This tool utilizes AI to keep investors instantly informed of market-moving news related to their portfolio or watchlist, with future versions offering strategic, real-time investment advice tailored to each user's market outlook.To round out the discussion, the conversation turns to how current political and economic decisions—from tariffs to regulatory changes—are impacting the market, and why transparency and clarity in policy communication are more necessary than ever.Whether you're new to investing or looking to refine your trading strategies, this clip is packed with practical tips, industry trends, and future-facing insights you won't want to miss!✨ *Key Topics Covered:* Margin trading advice and risk management Emerging trends among young investors Shifting from individual stocks to ETFs during market volatility Robinhood's integration of artificial intelligence (AI) The future of real-time, AI-powered investment tools How political and regulatory uncertainty affects markets The importance of clear communication in policy changes

Have you ever wondered if you're a "real runner" because you take walk breaks? Dr. Michelle Quirk, pediatrician and running coach, sets the record straight in this episode, sharing why walk intervals are not only acceptable but beneficial for all runners. Her thoughtful advice for beginners—remember your "why," start slow, and focus on effort rather than pace—offers a refreshing foundation for anyone lacing up for the first time.The conversation with Dr. Quirk delves into practical wisdom that both novice and experienced runners will appreciate. She explains how to distinguish between normal muscle soreness and potential injuries through timing, location, and relieving factors—information that could save you from sidelining yourself unnecessarily or pushing through a genuine problem. For parents, Michelle offers sage advice on introducing children to running in a healthy, sustainable way that keeps the focus on fun rather than competition.Following our expert interview, we're treated to emotional firsthand accounts from three Boston Marathon finishers, including two who completed their Six Star journey at this iconic race. Their descriptions of the overwhelming crowd support and the profound feeling of turning onto Boylston Street for the final stretch will give you goosebumps. As one runner put it: "If you ever want to feel like a rock star, run the last half mile of the Boston Marathon."The episode wraps with our comprehensive race report, highlighting achievements from runners across the country and around the world—from PR-breaking performances at the London Marathon to the quirky "Reverse London Midnight Marathon" where participants run the course backward starting at 12:01 AM. Whether you're training for your first 5K or your sixth major marathon, this episode offers both practical guidance and the inspirational stories that make our running community so special. Join us as we continue to rise, run, and celebrate each step of the journey together!Dr. Michelle Quirks LinksMindful Marathon Web siteMindful Marathon InstagramMindful Marathon FacebookMindful Marathon You TubeRise and Run LinksRise and Run Podcast Facebook PageRise and Run Podcast InstagramRise and Run Podcast Website and ShopRise and Run PatreonRunningwithalysha Alysha's RunSend us a textSupport the showRise and Run Podcast is supported by our audience. When you make a purchase through one of our affiliate links, we may earn a commission. As an Amazon Associate we earn from qualifying purchases.Sponsor LinksMagic Bound Travel Stoked Metabolic CoachingRise and Run Podcast Cruise Interest Form with Magic Bound Travel Affiliate LinksRise and Run Amazon Affiliate Web Page Kawaiian Pizza ApparelGoGuarded

The boys discuss going home for the holidays, the history of the USS Texas and husky pant sizes.

We are calling for the world's best AI Engineer talks for AI Architects, /r/localLlama, Model Context Protocol (MCP), GraphRAG, AI in Action, Evals, Agent Reliability, Reasoning and RL, Retrieval/Search/RecSys , Security, Infrastructure, Generative Media, AI Design & Novel AI UX, AI Product Management, Autonomy, Robotics, and Embodied Agents, Computer-Using Agents (CUA), SWE Agents, Vibe Coding, Voice, Sales/Support Agents at AIEWF 2025! Fill out the 2025 State of AI Eng survey for $250 in Amazon cards and see you from Jun 3-5 in SF!Coreweave's now-successful IPO has led to a lot of questions about the GPU Neocloud market, which Dylan Patel has written extensively about on SemiAnalysis. Understanding markets requires an interesting mix of technical and financial expertise, so this will be a different kind of episode than our usual LS domain.When we first published $2 H100s: How the GPU Rental Bubble Burst, we got 2 kinds of reactions on Hacker News:* “Ah, now the AI bubble is imploding!”* “Duh, this is how it works in every GPU cycle, are you new here?”We don't think either reaction is quite right. Specifically, it is not normal for the prices of one of the world's most important resources right now to swing from $1 to $8 per hour based on drastically inelastic demand AND supply curves - from 3 year lock-in contracts to stupendously competitive over-ordering dynamics for NVIDIA allocations — especially with increasing baseline compute needed for even the simplest academic ML research and for new AI startups getting off the ground.We're fortunate today to have Evan Conrad, CEO of SFCompute, one of the most exciting GPU marketplace startups, talk us through his theory of the economics of GPU markets, and why he thinks CoreWeave and Modal are well positioned, but Digital Ocean and Together are not.However, more broadly, the entire point of SFC is creating liquidity between GPU owners and consumers and making it broadly tradable, even programmable:As we explore, these are the primitives that you can then use to create your own, high quality, custom GPU availability for your time and money budget, similar to how Amazon Spot Instances automated the selective buying of unused compute.The ultimate end state of where all this is going is GPU that trade like other perishable, staple commodities of the world - oil, soybeans, milk. Because the contracts and markets are so well established, the price swings also are not nearly as drastic, and people can also start hedging and managing the risk of one of the biggest costs of their business, just like we have risk-managed commodities risks of all other sorts for centuries. As a former derivatives trader, you can bet that swyx doubleclicked on that…Show Notes* SF Compute* Evan Conrad* Ethan Anderson* John Phamous* The Curve talk* CoreWeave* Andromeda ClusterFull Video PodLike and subscribe!Timestamps* [00:00:05] Introductions* [00:00:12] Introduction of guest Evan Conrad from SF Compute* [00:00:12] CoreWeave Business Model Discussion* [00:05:37] CoreWeave as a Real Estate Business* [00:08:59] Interest Rate Risk and GPU Market Strategy Framework* [00:16:33] Why Together and DigitalOcean will lose money on their clusters* [00:20:37] SF Compute's AI Lab Origins* [00:25:49] Utilization Rates and Benefits of SF Compute Market Model* [00:30:00] H100 GPU Glut, Supply Chain Issues, and Future Demand Forecast* [00:34:00] P2P GPU networks* [00:36:50] Customer stories* [00:38:23] VC-Provided GPU Clusters and Credit Risk Arbitrage* [00:41:58] Market Pricing Dynamics and Preemptible GPU Pricing Model* [00:48:00] Future Plans for Financialization?* [00:52:59] Cluster auditing and quality control* [00:58:00] Futures Contracts for GPUs* [01:01:20] Branding and Aesthetic Choices Behind SF Compute* [01:06:30] Lessons from Previous Startups* [01:09:07] Hiring at SF ComputeTranscriptAlessio [00:00:05]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:12]: Hey, and today we're so excited to be finally in the studio with Evan Conrad from SF Compute. Welcome. I've been fortunate enough to be your friend before you were famous, and also we've hung out at various social things. So it's really cool to see that SF Compute is coming into its own thing, and it's a significant presence, at least in the San Francisco community, which of course, it's in the name, so you couldn't help but be. Evan: Indeed, indeed. I think we have a long way to go, but yeah, thanks. Swyx: Of course, yeah. One way I was thinking about kicking on this conversation is we will likely release this right after CoreWeave IPO. And I was watching, I was looking, doing some research on you. You did a talk at The Curve. I think I may have been viewer number 70. It was a great talk. More people should go see it, Evan Conrad at The Curve. But we have like three orders of magnitude more people. And I just wanted to, to highlight, like, what is your analysis of what CoreWeave did that went so right for them? Evan: Sell locked-in long-term contracts and don't really do much short-term at all. I think like a lot of people had this assumption that GPUs would work a lot like CPUs and the like standard business model of any sort of CPU cloud is you buy commodity hardware, then you lay on services that are mostly software, and that gives you high margins and pretty much all your value comes from those services. Not really the underlying. Compute in any capacity and because it's commodity hardware and it's not actually that expensive, most of that can be sort of on-demand compute. And while you do want locked-in contracts for folks, it's mostly just a sort of de-risk situation. It helps you plan revenue because you don't know if people are going to scale up or down. But fundamentally, people are like buying hourly and that's how your business is structured and you make 50 percent margins or higher. This like doesn't really work in GPUs. And the reason why it doesn't work is because you end up with like super price sensitive customers. And that isn't because necessarily it's just way more expensive, though that's totally the case. So in a CPU cloud, you might have like, you know, let's say if you had a million dollars of hardware in GPUs, you have a billion dollars of hardware. And so your customers are buying at much higher volumes than you otherwise expect. And it's also smaller customers who are buying at higher amounts of volume. So relative to what they're spending in general. But in GPUs in particular, your customer cares about the scaling law. So if you take like Gusto, for example, or Rippling or an HR service like this, when they're buying from an AWS or a GCP, they're buying CPUs and they're running web servers, those web servers, they kind of buy up to the capacity that they need, they buy enough, like CPUs, and then they don't buy any more, like, they don't buy any more at all. Yeah, you have a chart that goes like this and then flat. Correct. And it's like a complete flat. It's not even like an incremental tiny amount. It's not like you could just like turn on some more nodes. Yeah. And then suddenly, you know, they would make an incremental amount of money more, like Gusto isn't going to make like, you know, 5% more money, they're gonna make zero, like literally zero money from every incremental GPU or CPU after a certain point. This is not the case for anyone who is training models. And it's not the case for anyone who's doing test time inference or like inference that has scales at test time. Because like you, your scaling laws mean that you may have some diminishing returns, but there's always returns. Adding GPUs always means your model does actually get. And that actually does translate into revenue for you. And then for test time inference, you actually can just like run the inference longer and get a better performance. Or maybe you can run more customers faster and then charge for that. It actually does translate into revenue. Every incremental GPU translates to revenue. And what that means from the customer's perspective is you've got like a flat budget and you're trying to max the amount of GPUs you have for that budget. And it's very distinctly different than like where Augusto or Rippling might think, where they think, oh, we need this amount of CPUs. How do we, you know, reduce that? How do we reduce our amount of money that we're spending on this to get the same amount of CPUs? What that translates to is customers who are spending in really high volume, but also customers who are super price sensitive, who don't give a s**t. Can I swear on this? Can I swear? Yeah. Who don't give a s**t at all about your software. Because a 10% difference in a billion dollars of hardware is like $100 million of value for you. So if you have a 10% margin increase because you have great software, on your billion, the customers are that price sensitive. They will immediately switch off if they can. Because why wouldn't you? You would just take that $100 million. You'd spend $50 million on hiring a software engineering team to replicate anything that you possibly did. So that means that the best way to make money in GPUs was to do basically exactly what CoreWeave did, which is go out and sign only long-term contracts, pretty much ignore the bottom end of the market completely, and then maximize your long-term contracts. With customers who don't have credit risk, who won't sue you, or are unlikely to sue you for frivolous reasons. And then because they don't have credit risk and they won't sue you for frivolous reasons, you can go back to your lender and you can say, look, this is a really low risk situation for us to do. You should give me prime, prime interest rate. You should give me the lowest cost of capital you possibly can. And when you do that, you just make tons of money. The problem that I think lots of people are going to talk about with CoreWeave is it doesn't really look like a cloud platform. It doesn't really look like a cloud provider financially. It also doesn't really look like a software company financially.Swyx [00:05:37]: It's a bank.Evan [00:05:38]: It's a bank. It's a real estate company. And it's very hard to not be that. The problem of that that people have tricked themselves into is thinking that CoreWeave is a bad business. I don't think CoreWeave is explicitly a bad business. There's a bunch of people, there's kind of like two versions of the CoreWeave take at the moment. There's, oh my God, CoreWeave, amazing. CoreWeave is this great new cloud provider competitive with the hyperscalers. And to some extent, this is true from a structural perspective. Like, they are indeed a real sort of thing against the cloud providers in this particular category. And the other take is, oh my gosh, CoreWeave is this horrible business and so on and blah, blah, blah. And I think it's just like a set of perception or perspective. If you think CoreWeave's business is supposed to look like the traditional cloud providers, you're going to be really upset to learn that GPUs don't look like that at all. And in fact, for the hyperscalers, it doesn't look like this either. My intuition is that the hyperscalers are probably going to lose a lot of money, and they know they're going to lose a lot of money on reselling NVIDIA GPUs, at least. Hyperscalers, but I want to, Microsoft, AWS, Google. Correct, yeah. The Microsoft, AWS, and Google. Does Google resell? I mean, Google has TPUs. Google has TPUs, but I think you can also get H100s and so on. But there are like two ways they can make money. One is by selling to small customers who aren't actually buying in any serious volume. They're testing around, they're playing around. And if they get big, they're immediately going to do one of two things. They're going to ask you for a discount. Because they're not going to pay your crazy sort of margin that you have locked into your business. Because for CPUs, you need that. They're going to pay your massive per hour price. And so they want you to sign a long-term contract. And so that's your other way that you can make money, is you can basically do exactly what CoreWeave does, which is have them pay as much as possible upfront and lock in the contract for a long time. Or you can have small customers. But the problem is that for a hyperscaler, the GPUs to... To sell on the low margins relative to what your other business, your CPUs are, is a worse business than what you are currently doing. Because you could have spent the same money on those GPUs. And you could have trained model and you could have made a model on top of it and then turn that into a product and had high margins from your product. Or you could have taken that same money and you could have competed with NVIDIA. And you could have cut into their margin instead. But just simply reselling NVIDIA GPUs doesn't work like your CPU business. Where you're able to capture high margins from big customers and so on. And then they never leave you because your customers aren't actually price sensitive. And so they won't switch off if your prices are a little higher. You actually had a really nice chart, again, on that talk of this two by two. Sure. Of like where you want to be. And you also had some hot takes on who's making money and who isn't. Swyx: So CoreUv locked up long-term contracts. Get that. Yes. Maybe share your mental framework. Just verbally describe it because we're trying to help the audio listeners as well. Sure. People can look up the chart if they want to. Evan: Sure. Okay. So this is a graph of interest rates. And on the y-axis, it's a probability you're able to sell your GPUs from zero to one. And on the x-axis, it's how much they'll depreciate in cost from zero to one. And then you had ISO cost curves or ISO interest rate curves. Yeah. So they kind of shape in a sort of concave fashion. Yeah. The lowest interest rates enable the most aggressive. form of this cost curve. And the higher interest rates go, the more you have to push out to the top right. Yeah. And then you had some analysis of where every player sits in this, including CoreUv, but also Together and Modal and all these other guys. I thought that was super insightful. So I just wanted to elaborate. Basically, it's like a graph of risk and the genres of places where you can be and what the risk is associated with that. The optimal thing for you to do, if you can, is to lock in long-term contracts that are paid all up front or in with a situation in which you trust the other party to pay you over time. So if you're, you know, selling to Microsoft or something or OpenAI. Which are together 77% of the revenue of CoreUv. Yeah. So if you're doing that, that's a great business to be in because your interest rate that you can pitch for is really low because no one thinks Microsoft is going to default. And like maybe OpenAI will default, but the backing by Microsoft kind of doesn't. And I think there's enough, like, generally, it looks like OpenAI is winning that you can make it's just a much better case than if you're selling to the pre-seed startup that just raised $30 million or something pre-revenue. It's like way easier to make the case that the OpenAI is not going to default than the pre-seed startup. And so the optimal place to be is selling to the maximally low risk customer for as long as possible. And then you never have to worry about depreciation and you make lots of money. The less. Good. Good place to be is you could sell long-term contracts to people who might default on you. And then if you're not bringing it to the present, so you're not like saying, hey, you have to pay us all up front, then you're in this like more risky territory. So is it top left of the chart? If I have the chart right, maybe. Large contracts paid over time. Yeah. Large contracts paid over time is like top left. So it's more risky, but you could still probably get away with it. And then the other opportunity is that you could sell short-term contracts for really high prices. And so lots of people tried that too, because this is actually closer to the original business model that people thought would work in cloud providers for CPUs. It works for CPUs, but it doesn't really work for GPUs. And I don't think people were trying this because they were thinking about the risk associated with it. I think a lot of people are just come from a software background, have not really thought about like cogs or margins or inventory risk or things that you have to worry about in the physical world. And I think they were just like copy pasting the same business model onto CPUs. And also, I remember fundraising like a few years ago. And I know based on. Like what we knew other people were saying who were in a very similar business to us versus what we were saying. And we know that our pitch was way worse at the time, because in the beginning of SF Compute, we looked very similar to pretty much every other GPU cloud, not on purpose, but sort of accidentally. And I know that the correct pitch to give to an investor was we will look like a traditional CPU cloud with high margins and we'll sell to everyone. And that is a bad business model because your customers are price sensitive. And so what happens is if you. Sell at high prices, which is the price that you would need to sell it in order to de-risk your loss on the depreciation curve, and specifically what I mean by that is like, let's say you're selling it like $5 an hour and you're paying $1.50 an hour for the GPU under the hood. It's a little bit different than that, but you know, nice numbers, $5 an hour, $1.50 an hour. Great. Excellent. Well, you're charging a really high price per GPU hour because over time the price will go down and you'll get competed out. And what you need is to make sure that you never go under, or if you do go under your underlying cost. You've made so much money in the first part of it that the later end of it, like doesn't matter because from the whole structure of the deal, you've made money. The problem is that just, you think that you're going to be able to retain your customers with software. And actually what happens is your customers are super price sensitive and push you down and push you down and push you down and push you down, um, that they don't care about your software at all. And then the other problem that you have is you have, um, really big players like the hyperscalers who are looking to win the market and they have way more money than you, and they can push down on margin. Much better than you can. And so if they have to, and they don't, they don't necessarily all the time, um, I think they actually keep pride of higher margin, but if they needed to, they could totally just like wreck your margin at any point, um, and push you down, which meant that that quadrant over there where you're charging a high price, um, and just to make up for the risk completely got destroyed, like did not work at all for many places because of the price sensitivity, because people could just shove you down instead that pushed everybody up to the top right-hand corner of that, which is selling short-term. Contracts for low prices paid over time, which is the worst place to be in, um, the worst financial place to be in because it has the highest interest rate, um, which means that your, um, your costs go up at the same time, your, uh, your incoming cash goes down and squeezes your margins and squeezes your margins. The nice thing for like a core weave is that most of their business is over on the, on the other sides of those quadrants that the ones that survive. The only remaining question I have with core weave, and I promise I get to ask if I can compute, and I promise this is relevant to SOF Compute in general, because the framework is important, right? Sure. To understand the company. So why didn't NVIDIA or Microsoft, both of which have more money than core weave, do core weave, right? Why didn't they do core weave? Why have this middleman when either NVIDIA or Microsoft have more money than God, and they could have done an internal core weave, which is effectively like a self-funding vehicle, like a financial instrument. Why does there have to be a third party? Your question is like... Why didn't Microsoft, or why didn't NVIDIA just do core weave? Why didn't they just set up their own cloud provider? I think, and I don't know, and so correct me if I'm wrong, and lots of people will have different opinions here, or I mean, not opinions, they'll have actual facts that differ from my facts. Those aren't opinions. Those are actually indeed differences of reality, is that NVIDIA doesn't want to compete with their customers. They make a large amount of money by selling to existing clouds. If they launched their own core weave, then it would be a lot more money. It'd make it much harder for them to sell to the hyperscalers, and so they have a complex relationship with there. So not great for them. Second is that, at least for a while, I think they were dealing with antitrust concerns or fears that if they're going through, if they own too much layers of the stack, I could imagine that could be a problem for them. I don't know if that's actually true, but that's where my mind would go, I guess. Mostly, I think it's the first one. It's that they would be competing directly with their primary customers. Then Microsoft could have done it, right? That's the other question. Yeah, so Microsoft didn't do it. And my guess is that... NVIDIA doesn't want Microsoft to do it, and so they would limit the capacity because from NVIDIA's perspective, both they don't want to necessarily launch their own cloud provider because it's competing with their customers, but also they don't want only one customer or only a few customers. It's really bad for NVIDIA if you have customer concentration, and Microsoft and Google and Amazon, like Oracle, to buy up your entire supply, and then you have four or five customers or so who pretty much get to set prices. Monopsony. Yeah, monopsony. And so the optimal thing for you is a diverse set of customers who all are willing to pay at whatever price, because if you don't, somebody else will. And so it's really optimal for NVIDIA to have lots of other customers who are all competing against each other. Great. Just wanted to establish that. It's unintuitive for people who have never thought about it, and you think about it all day long. Yeah. Swyx: The last thing I'll call out from the talk, which is kind of cool, and then I promise we'll get to SF Compute, is why will DigitalOcean and Together lose money on their clusters? Why will DigitalOcean and Together lose money on their clusters?Evan [00:16:33]: I'm going to start by clarifying that all of these businesses are excellent and fantastic. That Together and DigitalOcean and Lambda, I think, are wonderful businesses who build excellent products. But my general intuition is that if you try to couple the software and the hardware together, you're going to lose money. That if you go out and you buy a long-term contract from someone and then you layer on services, or you buy the hardware yourself and you spin it up and you get a bunch of debt, you're going to run into the same problem that everybody else did, the same problem we did, same problem the hyperscalers did. And that's exactly what the hyperscalers are doing, which is you cannot add software and make high margins like a cloud provider can. You can pitch that into investors and it will totally make sense, and it's like the correct play in CPUs, but there isn't software you could make to make this occur. If you're spending a billion dollars on hardware, you need to make a billion dollars of software. There isn't a billion dollars of software that you can realistically make, and if you do, you're going to look like SAP. And that's not a knock on SAP. SAP makes a f**k ton of money, right? Right. Right. Right. Right. There aren't that many pieces of software that you could make, that you can realistically sell, like a billion dollars of software, and you're probably not going to do it to price-sensitive customers who are spending their entire budget already on compute. They don't have any more money to give you. It's a very hard proposition to do. And so many parties have been trying to do this, like, buy their own compute, because that's what a traditional cloud does. It doesn't really work for them. You know that meme where there's, like, the Grim Reaper? And he's, like, knocking on the door, and then he keeps knocking on the next door? We have just seen door after door after door of the Grim Reeker comes by, and the economic realities of the compute market come knocking. And so the thing we encourage folks to do is if you are thinking about buying a big GPU cluster and you are going to layer on software on top, don't. There are so many dead bodies in the wake there. We would recommend not doing that. And we, as SF Compute, our entire business is structured to help you not do that. It's helped disintegrate these. The GPU clouds are fantastic real estate businesses. If you treat them like real estate businesses, you will make a lot of money. The cloud services you can make on that, all the software you want to make on that, you can do that fantastically. If you don't own the underlying hardware, if you mix these businesses together, you get shot in the head. But if you combine, if you split them, and that's what the market does, it helps you split them, it allows you to buy, like, layer on services, but just buy from the market, you can make lots of money. So companies like Modal, who don't own the underlying compute, like they don't own it, lots of money, fantastic product. And then companies like Corbeave, who are functionally like really, really good real estate businesses, lots of money, fantastic product. But if you combine them, you die. That's the economic reality of compute. I think it also splits into trading versus inference, which are different kinds of workloads. Yeah. And then, yeah, one comment about the price sensitivity thing before we leave this. This topic, I want to credit Martin Casado for coining or naming this thing, which is like, you know, you said, you said this thing about like, you don't have room for a 10% margin on GPUs for software. Yep. And Martin actually played it out further. It's his first one I ever saw doing this at large enough runs. So let's say GPT-4 and O1 both had a total trading cost of like a $500 billion is the rough estimate. When you get the $5 billion runs, when you get the $50 billion runs, it is actually makes sense to build your own. You're going to have to get into chips, like for OpenEI to get into chip design, which is so funny. I would make an ASIC for this run. Yeah, maybe. I think a caveat of that that is not super well thought about is that only works if you're really confident. It only works if you really know which chip you're going to do. If you don't, then it's a little harder. So it makes in my head, it makes more sense for inference where you've already established it. But for training there's so much like experimentation. Any generality, yeah. Yeah. The generality is much more useful. Yeah. In some sense, you know, Google's like six generations into the CPUs. Yeah. Yeah. Okay, cool. Maybe we should go into SF Compute now. Sure. Yeah.Alessio [00:20:37]: Yeah. So you kind of talked about the different providers. Why did you decide to go with this approach and maybe talk a bit about how the market dynamics have evolved since you started a company?Evan [00:20:47]: So originally we were not doing this at all. We were definitely like forced into this to some extent. And SF Compute started because we wanted to go train models for music and audio in general. We were going to do a sort of generic audio model at some points, and then we were going to do a music model at some points. It was an early company. We didn't really spec down on a particular thing. But yeah, we were going to do a music model and audio model. First thing that you do when you start any AI lab is you go out and you buy a big cluster. The thing we had seen everybody else do was they went out and they raised a really big round and then they would get stuck. Because if you raise the amount of money that you need to train a model initially, like, you know, the $50 million pre-seed, pre-revenue, your valuation is so high or you get diluted so much that you can't raise the next round. And that's a very big ask to make. And also, I don't know, I felt like we just felt like we couldn't do it. We probably could have in retrospect, but I think one, we didn't really feel like we could do it. Two, it felt like if we did, we would have been stuck later on. We didn't want to raise the big round. And so instead, we thought, surely by now, we would be able to just go out. To any provider and buy like a traditional CPU cloud would sell offer you and just buy like on demand or buy like a month or so on. And this worked for like small incremental things. And I think this is where we were basing it off. We just like assumed we could go to like Lambda or something and like buy thousands of at the time A100s. And this just like was not at all the case. So we started doing all the sales calls with people and we said, OK, well, can we just get like month to month? Can we get like one month of compute or so on? Everyone told us at the time, no. You need to have a year long contract or longer or you're out of luck. Sorry. And at the time, we were just like pissed off. Like, why won't nobody sell us a month at a time? Nowadays, we totally understand why, because it's the same economic reason. Because if you if they had sold us the month to month or so on and we canceled or so on, they would have massive risk on that. And so the optimal thing to do was to only to just completely abandon the section of the market. We didn't like that. So our plan was we were going to buy a year long contract anyway. We would use a month. And then we would. At least the other 11 months. And we were locked in for a year, but we only had to pay on every individual month. And so we did this. But then immediately we said, oh, s**t, now we have a cloud provider, not a like training models company, not an AI lab, because every 30 days we owed about five hundred thousand dollars or so and we had about five hundred thousand dollars in the bank. So that meant that every single month, if we did not sell out our cluster, we would just go bankrupt. So that's what we did for the first year of the company. And when you're in that position. You try to think how in the world you get out of that position, what that transition to is, OK, well, we tend to be pretty good at like selling this cluster every month because we haven't died yet. And so what we should do is we should go basically be like this broker for other people and we will be more like a GPU real estate or like a GPU realtor. And so we started doing that for a while where we would go to other people who had who was trying to sell like a year long contract with somebody and we'd go to another person who like maybe this person wanted six months and somebody else on six months or something and we'd like combine all these people. Together to make the deal happen and we'd organize these like one off bespoke deals that looked like basically it ended up with us taking a bunch of customers, us signing with a vendor, taking some cut and then us operating the cluster for people typically with bare metal. And so we were doing this, but this was definitely like a oh, s**t, oh, s**t, oh, s**t. How do we get out of our current situation and less of a like a strategic plan of any sort? But while we were doing this, since like the beginning of the company, we had been thinking about how to buy GPU clusters, how to sell them effectively, because we'd seen every part of it. And what we ended up with was like a book of everybody who's trying to buy and everyone is trying to sell because we were these like GPU brokers. And so that turned into what is today SF Compute, which is a compute market, which we think we are the functionally the most liquid GPU market of any capacity. Honestly, I think we're the only thing that actually is like a real market that there's like bids and asks and there's like a like a trading engine that combines everything. And so. I think we're the only place where you can do things that a market should be able to do. Like you can go on SF Compute today and you get thousands of H100s for an hour if you want. And that's because there is a price for thousands of GPUs for an hour. That is like not a thing you can reasonably do on kind of any other cloud provider because nobody should realistically sell you thousands of GPUs for an hour. They should sell it to you for a year or so on. But one of the nice things about a market is that you can buy the year on SF Compute. But then if you need to sell. Back, you can sell back as well. And that opens up all these little pockets of liquidity where somebody who's just trying to buy for a little bit of time, some burst capacity. So people don't normally buy for an hour. That's not like actually a realistic thing, but it's like the range somebody who wants, who is like us, who needed to buy for a month can actually buy for a month. They can like place the order and there is actually a price for that. And it typically comes from somebody else who's selling back. Somebody who bought a longer term contract and is like they bought for some period of time, their code doesn't work, and now they need to like sell off a little bit.Alessio [00:25:49]: What are the utilization rates at which a market? What are the utilization rates at which a market? Like this works, what do you see the usual GPU utilization rate and like at what point does the market get saturated?Evan [00:26:00]: Assuming there are not like hardware problems or software problems, the utilization rate is like near 100 percent because the price dips until the utilization is 100 percent. So the price actually has to dip quite a lot in order for the utilization not to be. That's not always the case because you just have logistical problems like you get a cluster and parts of the InfiniBand fabric are broken. And there's like some issue with some switch somewhere and so you have to take some portion of the cluster offline or, you know, stuff like this, like there's just underlying physical realities of the clusters, but nominally we have better utilization than basically anybody because, but that's on utilization of the cluster, like that doesn't necessarily translate into, I mean, I actually do think we have much better overall money made for our underlying vendors than kind of anybody else. We work with the other GPU clouds and the basic pitch to the other GPU clouds is one. So we can sell your broker so we can we can find you the long term contracts that are at the prices that you want, but meanwhile, your cluster is idle and for that we can increase your utilization and get you more money because we can sell that idle cluster for you and then the moment we find the longer, the bigger customer and they come on, you can kick off those people and then go to the other ones. You get kind of the mix of like sell your cluster at whatever price you can get on the market and then sell your cluster at the big price that you want to do for long term contract, which is your ideal business model. And then the benefit of the whole thing being on the market. Is you can pitch your customer that they can cancel their long term contract, which is not a thing that you can reasonably do if you are just the GPU cloud, if you're just the GPU cloud, you can never cancel your contract, because that introduces so much risk that you would otherwise, like not get your cheap cost of capital or whatever. But if you're selling it through the market, or you're selling it with us, then you can say, hey, look, you can cancel for a fee. And that fee is the difference between the price of the market and then the price that they paid at, which means that they canceled and you have the ability to offer that flexibility. But you don't. You don't have to take the risk of it. The money's already there and like you got paid, but it's just being sold to somebody else. One of our top pieces from last year was talking about the H100 glut from all the long term contracts that were not being fully utilized and being put under the market. You have on here dollar a dollar per hour contracts as well as it goes up to two. Actually, I think you were involved. You were obliquely quoted in that article. I think you remember. I remember because this was hidden. Well, we hid your name, but then you were like, yeah, it's us. Yeah. Could you talk about the supply and demand of H100s? Was that just a normal cycle? Was that like a super cycle because of all the VC funding that went in in 2003? What was that like? GPU prices have come down. Yeah, GPU prices have come down. And there's some part that has normal depreciation cycle. Some part of that is just there were a lot of startups that bought GPUs and never used them. And now they're lending it out and therefore you exist. There's a lot of like various theories as to why. This happened. I dislike all of them because they're all kind of like they're often said with really high confidence. And I think just the market's much more complicated than that. Of course. And so everything I'm going to say is like very hedged. But there was a series of like places where a bunch of the orders were placed and people were pitching to their customers and their investors and just the broader market that they would arrive on time. And that is not how the world works. And because there was such a really quick build out of things, you would end up with bottlenecks in the supply chain somewhere that has nothing to do with necessarily the chip. It's like the InfiniBand cables or the NICs or like whatever. Or you need a bunch of like generators or you don't have data center space or like there's always some bottleneck somewhere else. And so a lot of the clusters didn't come online within the period of time. But then all the bottlenecks got sorted out and then they all came online all at the same time. So I think you saw a short. There was a shortage because supply chain hard. And then you saw a increase or like a glut because supply chain eventually figure itself out. And specifically people overordered in order to get the allocation that they wanted. Then they got the allocations and then they went under. Yeah, whatever. Right. There was just a lot of shenanigans. A caveat of this is every time you see somebody like overordered, there is this assumption that the problem was like the demand went down. I don't think that's the case at all. And so I want to clarify that. It definitely seems like a shortage. Like there's more demand for GPUs than there ever was. It's just that there was also more supply. So at the moment, I think there is still functionally a glut. But the difference that I think is happening is mostly the test time inference stuff that you just need way more chips for that than you did before. And so whenever you make a statement about the current market, people sort of take your words and then they assume that you're making a statement about the future market. And so if you say there's a glut now, people will continue to think there's a glut. But I think what is happening at the moment. My general prediction is that like by the winter, we will be back towards shortage. But then also, this very much depends on the rollout of future chips. And that comes with its own. I think I'm trying to give you like a good here's Evan's forecast. Okay. But I don't know if my forecast is right. You don't have to. Nobody is going to hold you to it. But like I think people want to know what's true and what's not. And there's a lot of vague speculations from people who are not that close to the market actually. And you are. I think I'm a closer. Close to the market, but also a vague speculator. Like I think there are a lot of really highly confident speculators and I am indeed a vague speculator. I think I have more information than a lot of other people. And this makes me more vague of a spectator because I feel less certain or less confident than I think a lot of other people do. The thing I do feel reasonably confident about saying is that the test time inference is probably going to quite significantly expand the amount of compute that was used for inference. So a caveat. This is like pretty much all the inference demand is in a few companies. A good example is like lots of bio and pharma was using H100s training sort of the bio models of sorts. And they would come along and they would buy, you know, thousands of H100s for training and then just like not a lot of stuff for inference. Not in any, not relative to like an opening iron anthropic or something because they like don't have a consumer product. Their inference event, if they can do it right. There's really like only one inference event that matters. And obviously I think they're going to run into it. And Batch and they're not going to literally just run one inference event. But like the one that produces the drug is the important one. Right. And I'm dumb and I don't know anything about biology, so I could be completely wrong here. But my understanding is that's kind of the gist. I can check that for you. You can check that for me. Check that for me. But my understanding is like the one that produces the sequence that is the drug that, you know, cures cancer or whatever. That's the important deal. But like a lot of models look like this where they're sort of more enterprising use cases or they're so prior to something that looks like test time inference. You got lots and lots of demand for training and then pretty much entirely fell off for inference. And I think like we looked at like Open Router, for example, the entirety of Open Router that was not anthropic or like Gemini or OpenAI or something. It was like 10 H100 nodes or something like that. It's just like not that much. It's like not that many GPUs actually to service that entire demand. But that's like a really sizable portion of the sort of open source market. But the actual amount of compute needed for it was not that much. But if you imagine like what an OpenAI needs for like GPT-4, it's like tremendously big. But that's because it's a consumer product that has almost all the inference demand. Yeah, that's a message we've had. Roughly open source AI compared to closed AI is like 5%. Yeah, it's like super small. Super small. It's super small. Super small. But test time inference changes that quite significantly. So I will... I will expect that to increase our overall demand. But my question on whether or not that actually affects your compute price is entirely based on how quickly do we roll out the next chips. The way that you burst is different for test time.Alessio [00:34:01]: Any thoughts on the third part of the market, which is the more peer-to-peer distributed, some are like crypto-enabled, like Hyperbolic, Prime Intellect, and all of that. Where do those fit? Like, do you see a lot of people will want to participate in a peer-to-peer market? Or just because of the capital requirements at the end of the day, it doesn't really matter?Evan [00:34:20]: I'm like wildly skeptical of these, to be frankly. The dream is like steady at home, right? I got this $15.90. Nobody has $15.90. $14.90 sitting at home. I can rent it out. Yeah. Like, I just don't really think this is going to ever be more efficient than a fully interconnected cluster with InfiniBand or, you know, whatever the sort of next spec might be. Like, I could be completely wrong. But speaking of... I mean, like, SpeedoLite is really hard to beat. And regardless of whatever you're using, you just like can't get around that physical limitation. And so you could like imagine a decentralized market that still has a lot of places where there's like co-location. But then you would get something that looks like SF Compute. And so that's what we do. That's why we take our general take is like on SF Compute, you're not buying from like random people. You're buying from the other GPU clouds, functionally. You're buying from data centers that are the same genre of people that you would work with already. And you can specify, oh, I want all these nodes to be co-located. And I don't think you're really going to get around that. And I think I buy crypto for the purposes of like transferring money. Like the financial system is like quite painful and so on. I can understand the uses of it to sort of incentivize an initial market or try to get around the cold start problem. We've been able to get around the cold start problem just fine. So it didn't actually need that at all. What I do think is totally possible is you could launch a token and then you could like subsidize the crypto. You could compute prices for a bit, but like maybe that will help you. I think that's what Nuus is doing. Yeah, I think there's lots of people who are trying to do things like this, but at some point that runs out. So I would, I think generally agree. I think the only thread in that model is very fine grained mixture of experts that can be like algorithms can shift to adapt to hardware realities. And the hardware reality is like, okay, it's annoying to do large co-located clusters. Then we'll just redesign attention or whatever in our architecture to distribute it more. There was a little bit buzz of block attention last year that Strong Compute made a big push on. But I think like, you know, in a world where we have 200 experts in MOE model, it starts to be a little bit better. Like, I don't disagree with this. I can imagine the world in which you have like, in which you've redesigned it to be more parallelizable, like across space.Evan [00:36:43]: But assuming without that, your hardware limitation is your speed of light limitation. And that's a very hard one to get around.Alessio [00:36:50]: Any customers or like stories that you want to shout out of like maybe things that wouldn't have been economically viable like others? I know there's some sensitivity on that.Evan [00:37:00]: My favorites are grad students, are folks who are trying to do things that would normally otherwise require the scale of a big lab. And the grad students are like the worst pilots. They're like the worst possible customer for the traditional GPU clouds because they will immediately turn if you sell them a thing because they're going to graduate and they're not going to go anywhere. They're not going to like, that project isn't continuing to spend lots of money. Like sometimes it does, but not if you're like working with the university or you're working with the lab of some sort. But a lot of times it's just like the ability for us to offer like big burst capacity, I think is lovely and wonderful. And it's like one of my favorite things to do because all those folks look like we did. And I have a special place in my heart for that. I have a special place in my heart for young hackers and young grad students and researchers who are trying to do the same genre of thing that we are doing. For the same reason, I have a special place in my heart for like the startups, the people who are just actively trying to compete on the same scale, but can't afford it time-wise, but can afford it spike-wise. Yeah, I liked your example of like, I have a grant of 100K and it's expiring. I got to spend it on that. That's really beautiful. Yeah. Interesting. Has there been interesting work coming out of that? Anything you want to mention? Yeah. So from like a startup perspective, like Standard Intelligence and Find, P-H-I-N-D. We've had them on the pod.Swyx [00:38:23]: Yeah. Yeah.Evan [00:38:23]: That was great. And then from grad students' perspective, we worked a lot with like the Schmidt Futures grantees of various sorts. My fear is if I talk about their research, I will be completely wrong to a sort of almost insulting degree because I am very dumb. But yeah. I think one thing that's maybe also relevant startups and GPUs-wise. Yeah. Is there was a brief moment where it kind of made sense that VCs provided GPU clusters. And obviously you worked at AI Grants, which set up Andromeda, which is supposedly a $100 million cluster. Yeah. I can explain why that's the case or why anybody would think that would be smart. Because I remember before any of that happened, we were asking for it to happen. Yeah. And the general reason is credit risk. Again, it's a bank. Yeah. I have lower risk than you due to credit transformation. I take your risk onto my balance sheet. Correct. Exactly. If you wanted to go for a while, if you wanted to go set up a GPU cluster, you had to be the one that actually bought the hardware and racked it and stacked it, like co-located it somewhere with someone. Functionally, it was like on your balance sheet, which means you had to get a loan. And you cannot get a loan for like $50 million as a startup. Like not really. You can get like venture debt and stuff, but like it's like very, very difficult to get a loan of any serious price for that. But it's like not that difficult to get a loan for $50 million. If you already have a fund or you already have like a million dollars under your assets somewhere or like you personally can like do a personal guarantee for it or something like this. If you have a lot of money, it is way easier for you to get a loan than if you don't have a lot of money. And so the hack of a VC or some capital partner offering equity for compute is always some arbitrage on the credit risk. That's amazing. Yeah. That's a hack. You should do that. I don't think people should do it right now. I think the market has like, I think it made sense at the time and it was helpful and useful for the people who did it at the time. But I think it was a one-time arbitrage because now there are lots of other sources that can do it. And also I think like it made sense when no one else was doing it and you were the only person who was doing it. But now it's like it's an arbitrage that gets competed down. Sure. So it's like super effective. I wouldn't totally recommend it. Like it's great that Andromeda did it. But the marginal increase of somebody else doing it is like not super helpful. I don't think that many people have followed in their footsteps. I think maybe Andreessen did it. Yeah. That's it. I think just because pretty much all the value like flows through Andromeda. What? That cannot be true. How many companies are in the air, Grant? Like 50? My understanding of Andromeda is it works with all the NFTG companies or like several of the NFTG companies. But I might be wrong about that. Again, you know, something something. Nat, don't kill me. I could be completely wrong. But the but you know, I think Andromeda was like an excellent idea to do at the right time in which it occurred. Perfect. His timing is impeccable. Timing. Yeah. Nat and Daniel are like, I mean, there's lots of people who are like... Sears? Yeah. Sears. Like S-E-E-R. Oh, Sears. Like Sears of the Valley. Yeah. They for years and years before any of the like ChatGPT moment or anything, they had fully understood what was going to happen. Like way, way before. Like. AI Grant is like, like five years old, six years old or something like that. Seven years old. When I, when it like first launched or something. Depends where you start. The nonprofit version. Yeah. The nonprofit version was like, like happening for a while, I think. It's going on for quite a bit of time. And then like Nat and Daniel are like the early investors in a lot of the sort of early AI labs of various sorts. They've been doing this for a bit.Alessio [00:41:58]: I was looking at your pricing yesterday. We're kind of talking about it before. And there's this weird thing where one week is more expensive of both one day and one month. Yeah. What are like some of the market pricing dynamics? What are things that like this to somebody that is not in the business? This looks really weird. But I'm curious, like if you have an explanation for it, if that looks normal to you. Yeah.Evan [00:42:18]: So the simple answer is preemptible pricing is cheaper than non-preemptible pricing. And the same economic principle is the reason why that's the case right now. That's not entirely true on SF Compute. SF Compute doesn't really have the concept of preemptible. Instead, what it has is very short reservations. So, you know, you go to a traditional cloud provider and you can say, hey, I want to reserve contract for a year. We will let you do a reserve contract for one hour, which is the part of SFC. But what you can do is you can just buy every single hour continuously. And you're reserving just for that hour. And then the next hour you reserve just for that next hour. And this is obviously like a built in. This is like an automation that you can do. But what you're seeing when you see the cheap price is you're seeing somebody who's buying the next hour, but maybe not necessarily buying an hour after that. So if the price goes up. Up too much. They might not get that next hour. And the underlying part of this of where that's coming from the market is you can imagine like day old milk or like milk that's about to be old. It might drop its price until it's expired because nobody wants to buy the milk that's in the past. Or maybe you can't legally sell it. Compute is the same way. No, you can't sell a block of compute that is not that is in the past. And so what you should do in the market and what people do do is they take. They take a block. A block of compute. And then they drop it and drop it and drop it and drop into a floor price right before it's about to expire. And they keep dropping it until it clears. And so anything that is idle drops until some point. So if you go and use on the website and you set that that chart to like a week from now, what you'll see is much more normal looking sort of curves. But if you say, oh, I want to start right now, that immediate instant, here's the compute that I want right now is the is functionally the preemptible price. It's where most people are getting the best compute or like the best compute prices from. The caveat of that is you can do really fun stuff on SFC if you want. So because it's not actually preemptible, it's it's reserved, but only reserved for an hour, which means that the optimal way to use as of compute is to just buy on the market price, but set a limit price that is much higher. So you can set a limit price for like four dollars and say, oh, if the market ever happens to spike up to four dollars, then don't buy. I don't want to buy that at that price for that price. I don't want to buy that at that price for that price for an hour. But otherwise, just buy at the cheapest price. And if you're comfortable with that of the volatility of it, you're actually going to get like really good prices, like close to a dollar an hour or so on, sometimes down to like 80 cents or whatever. You said four, though. Yeah. So that's the thing. You want to lower the limit. So four is your max price. Four is like where you basically want to like pull the plug and say don't do it because the actual average price is not or like the, you know, the preemptible price doesn't actually look like that. So what you're doing when you're saying four is always, always, always give me this compute. Like continue to buy every hour. Don't preempt me. Don't kick me off. And I want this compute and just buy at the preemptible price, but never kick me off. The only times in which you get kicked off is if there is a big price spike. And, you know, let's say one day out of the year, there's like a four dollar an hour price because of some weird fluke or something. If there are other periods of time, you're actually getting a much lower price than you. It makes sense. Your your average cost that you're actually paying is way better. And your trade off here is you don't literally know what price you're going to get. So it's volatile. But your actual average historically has been like everyone who's done this has gotten wildly better prices. And this is like one of the clever things you can do with the market. If you're willing to make those trade offs, you can get a lot of really good prices. You can also do other things like you can only buy at night, for example. So the price goes down at night. And so you can say, oh, I want to only buy, you know, if the price is lower than 90 cents. And so if you have some long running job, you can make it only run on 90 cents and then you recover back and so on. Yeah. So what you can kind of create as like a spot inst is what other the CPU world has. Yes. But you've created a system where you can kind of manufacture the exact profile that you want. Exactly. That is not just whatever the hyperscalers offer you, which is usually just one thing. Correct. SF Compute is like the power tool. The underlying primitives of like hourly compute is there. Correct. Yeah, it's pretty interesting. I've often asked OpenAI. So like, you know, all these guys. Cloud as well. They do batch APIs. So it's half off of whatever your thing is. Yeah. And the only contract is we'll return in 24 hours. Sure. Right. And I was like, 24 hours is good. But sometimes I want one hour. I want four hours. I want something. And so based off of SF Compute's system, you can actually kind of create that kind of guarantee. Totally. That would be like, you know, not 24, but within eight hours, within four hours, like the work half of a workday. Yes. I can return your results to you. And then I can return it to you. And if your latency requirements are like that low, actually it's fine. Yes. Correct. Yeah. You can carve out that. You can financially engineer that on SFC. Yeah. Yeah. I mean, I think to me that unlocks a lot of agent use cases that I want, which is like, yeah, I worked in a background, but I don't want you to take a day. Yeah. Correct. Take a couple hours or something. Yeah. This touches a lot of my like background because I used to be a derivatives trader. Yeah. And this is a forward market. Yeah. A futures forward market, whatever you call it. Not a future. Very explicitly not a future. Not yet a futures. Yes. But I don't know if you have any other points to talk about. So you recognize that you are a, you know, a marketplace and you've hired, I met Alex Epstein at your launch event and you're like, you're, you're building out the financialization of GPUs. Yeah. So part of that's legal. Mm-hmm. Totally. Part of that is like listing on an exchange. Yep. Maybe you're the exchange. I don't know how that works, but just like, talk to me about that. Like from the legal, the standardization, the like, where is this all headed? You know, is this like a full listed on the Chicago Mercantile Exchange or whatever? What we're trying to do is create an underlying spot market that gives you an index price that you can use. And then with that index price, you can create a cash settled future. And with a cash settled future, you can go back to the data centers and you can say, lock in your price now and de-risk your entire position, which lets you get cheaper cost of capital and so on. And that we think will improve the entire industry because the marginal cost of compute is the risk. It's risk as shown by that graph and basically every part of this conversation. It's risk that causes the price to be all sorts of funky. And we think a future is the correct solution to this. So that's the eventual goal. Right now you have to make the underlying spot market in order to make this occur. And then to make the spot market work, you actually have to solve a lot of technology problems. You really cannot make a spot market work if you don't run the clusters, if you don't have control over them, if you don't know how to audit them, because these are super computers, not soybeans. They have to work. In a way that like, it's just a lot simpler to deliver a soybean than it is to deliver it. I don't know. Talk to the soybean guys. Sure. You know? Yeah. But you have to have a delivery mechanism. Your delivery mechanism, like somebody somewhere has to actually get the compute at some point and it actually has to work. And it is really complicated. And so that is the other part of our business that we go and we build a bare metal infrastructure stack that goes. And then also we do auditing of all the clusters. You sort of de-risk the technical perspective and that allows you to eventually de-risk the financial perspective. And that is kind of the pitch of SF Compute. Yeah. I'll double click on the auditing on the clusters. This is something I've had conversations with Vitae on. He started Rika and I think he had a blog post which kind of shone the light a little bit on how unreliable some clusters are versus others. Correct. Yeah. And sometimes you kind of have to season them and age them a little bit to find the bad cards. You have to burn them in. Yeah. So what do you do to audit them? There's like a burn-in process, a suite of tests, and then active checking and passive checking. Burn-in process is where you typically run LINPACK. LINPACK is this thing that like a bunch of linear algebra equations that you're stress testing the GPUs. This is a proprietary thing that you wrote? No, no, no. LINPACK is like the most common form of burn-in. If you just type in burn-in, typically when people say burn-in, they literally just mean LINPACK. It's like an NVIDIA reference version of this. Again, NVIDIA could run this before they ship, but now the customers have to do it. It's annoying. You're not just checking for the GPU itself. You're checking like the whole component, all the hardware. And it's a lot of work. It's an integration test. It's an integration test. Yeah. So what you're doing when you're running LINPACK or burn-in in general is you're stress testing the GPUs for some period of time, 48 hours, for example, maybe seven days or so on. And you're just trying to kill all the dead GPUs or any components in the system that are broken. And we've had experiences where we ran LINPACK on a cluster and it rounds out, sort of comes offline when you run LINPACK. This is a pretty good sign that maybe there is a problem with this cluster. Yeah. So LINPACK is like the most common sort of standard test. But then beyond that, what you do is we have like a series of performance tests that replicate a much more realistic environment as well that we run just assuming if LINPACK works at all, then you run the next set of tests. And then while the GPUs are in operation, you're also going through and you're doing active tests and passive tests. Passive tests are things that are running in the background while somebody else is running, while like some other workload is running. And active tests are during like idle periods. You're running some sort of check that would otherwise sort of interrupt something. And then the active tests will take something offline, basically. Or a passive check might mark it to get taken offline later and so on. And then the thing that we are working on that we have working partially but not entirely is automated refunds, which is basically like, is the case that the hardware breaks so much. And there's only so much that we can do and it is the effect of pretty much the entire industry. So a pretty common thing that I think happens to kind of everybody in the space is a customer comes online, they experience your cluster, and your cluster has the same problem that like any cluster has, or it's I mean, a different problem every time, but they experience one of the problems of HPC. And then their experience is bad. And you have to like negotiate a refund or some other thing like this. It's always case by case. And like, yeah, a lot of people just eat the cost. Correct. So one of the nice things about a market that we can do as we get bigger and have been doing as we can bigger is we can immediately give you something else. And then also we can automatically refund you. And you're still gonna experience it like the hardware problems aren't going away until the underlying vendors fix things. But honestly, I don't think that's likely because you're always pushing the limits of HPC. This is the case of trying to build a supercomputer. that's one of the nice things that we can do is we can switch you out for somebody else somewhere, and then automatically refund you or prorate or whatever the correct move is. One of the things that you say in this conversation with me was like, you know, you know, a provider is good when they guarantee automatic refunds. Which doesn't happen. But yeah, that's, that's in our contact with all the underlying cloud providers. You built it in already. Yeah. So we have a quite strict SLA that we pass on to you. The reason why

What is your weird quirk? We have a listener who reached out with this, according to his wife, “shocking” quirk!

The locally-owned fast food restaurant — a cult favorite since 1951— is famous for its deep-fried tacos coated in a bright orange powdered cheese. For generations of Kansas City residents, there's really nowhere else like “the Tub.”

Beauty is in the eye of the beholder—or in the case of music, the ear…what's pleasant to one person is nothing but noise to someone else… This is where it's good to have some patience…there are some forms of art whose beauty isn't obvious at first…you need to stick with it…and after you've given it a chance and decided that it's not for you, fine… But what about those times when something happens—suddenly or slowly and either on your own or with the prompting of someone else—and you realize that the weird music you're listening to is actually pretty good?... This is the payoff…yeah, you really had to work for it—but it was worth it…with me so far?.. “Beauty” doesn't mean “perfect”—at least in the technical sense…sometimes imperfection makes something more beautiful…or at least more interesting… This brings me to the topic of singing voices…this is a very subjective area…how many times have you said, “Listen to that guy!... I can't stand his voice!...how did he ever get a record deal?... I mean, listen to him!” But then others hear the same thing and go, “wow…that's really different…really expressive…it's full of character and emotion…what a bold move giving this dude a chance to real millions of people… I love this guy!”… These are the kind of singers we're about to review: guys with some of the most unusual voices in the history of alt-rock. Learn more about your ad choices. Visit megaphone.fm/adchoices

For a very long time—too long—women were locked in very defined roles when it came to rock'n'roll…girls were expected to look pretty and do little more than sing…okay, maybe shake a tambourine or something…but that was about it… And when it came to singing, “Just stick with conventional stuff, dear…don't get any crazy ideas in your head…this is a woman's role in rock, and you should stick to it…that's a nice little lady”… But then along came punk rock in the 1970s…punk did many things for rock—including knocking down a lot of heretofore inviolable gender roles…the central tenet of punk was that anyone should have the right to say anything in any matter they want regardless of who they are…that included women and their right to self-expression… The result was fantastic. Freed from all the old expectations, women were free to reinvent themselves as musicians in a million different ways leading to a wonderful array of female performers… Some of my favourites are the ones who decided to spit in the face of virtually every rock'n'roll convention—women who (before punk came along and liberated everyone from the tyranny of “the way things ought to be”) developed styles that were different and unique and utterly unlike anything the world had ever heard before… Yes, some of them were an acquired taste and took a little getting used to…but once people figured out what they were trying to do and what they were all about, it was inevitable that they would become addicted, enchanted, and inspired…  We're going to look at ten of these women…I call them “The Queens of Quirk” Learn more about your ad choices. Visit megaphone.fm/adchoices

Today's a Flashback Friday and a 10th episode! This one is from 230, published last  December 5, 2011. Join Jason Hartman as he and author of “Quirk”, Hannah Holmes explore human personality types and how they affect who we become, whether extroverted, conscientious, agreeable, or even neurotic or obnoxious. Is it possible that our hard-wired brain chemistry can even determine our political opinions and economic views? Research has shown that mice have personalities, and somewhere out there, perhaps in your own basement, is a mouse just like you. Hannah Holmes has led an adventurous life since graduating from the University of Southern Maine. She was an editor at the New York-based Garbage Magazine in the late 1980s, after which she returned to Maine to start a freelance writing career. She was a contributor in a variety of magazines. In the late 1990s, Hannah was recruited by the Discovery Channel Online for an experiment called live internet reporting. This grand experiment led her to distant and uncomfortable parts of the world, from hunting dinosaurs in Mongolia's Gobi desert, to the Montserrat Volcano Observatory, where fine volcanic ash ruined her computer and left her hair like a ball of jute twine. She also piloted the Alvin submarine around “black smokers” a mile and a half under the ocean. It was a glorious era until Discovery.com's plug was pulled. Hannah then went on to author several books, “The Secret Life of Dust,” “Suburban Safari: A Year on the Lawn,” and her recent book, “Quirk,” about the many fascinating personality types. Hannah's blog can be found at www.HannahHolmes.net.   Follow Jason on TWITTER, INSTAGRAM & LINKEDIN Twitter.com/JasonHartmanROI Instagram.com/jasonhartman1/ Linkedin.com/in/jasonhartmaninvestor/ Call our Investment Counselors at: 1-800-HARTMAN (US) or visit: https://www.jasonhartman.com/ Free Class:  Easily get up to $250,000 in funding for real estate, business or anything else: http://JasonHartman.com/Fund CYA Protect Your Assets, Save Taxes & Estate Planning: http://JasonHartman.com/Protect Get wholesale real estate deals for investment or build a great business – Free Course: https://www.jasonhartman.com/deals Special Offer from Ron LeGrand: https://JasonHartman.com/Ron Free Mini-Book on Pandemic Investing: https://www.PandemicInvesting.com  

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