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"When something becomes a measurement, it stops becoming a good target." In a world obsessed with digital efficiency and quick fixes, the real currency of a meaningful life remains independent thought, presence, and intentional effort. In this episode of Gratitude Through Hard Times, host Chris Schembra sits down with Nishat Mehta, CEO of Lexitas. A Harvard-trained mathematician and computer scientist turned executive, Nishat breaks down how leaders can navigate rapid change by embracing dialectical thinking—holding two seemingly competing truths at the same time. From balancing bottom-line performance with human empathy to shifting parenting styles as children grow, Nishat shares key insights on Goodhart's Law, confident humility, and finding extraordinary meaning in ordinary moments. 10 Memorable Quotes: "The new normal is that it will just keep changing. It really comes down to the ability to accept the change yourself and then lead the change amongst those around you." — Nishat Mehta "Confident humility balances high confidence in one's ability to figure things out with low ego regarding one's own current knowledge." — Chris Schembra "When something becomes a measurement, it stops becoming a good target." — Nishat Mehta "Always find the extraordinary in the ordinary." — Chris Schembra "Everything is on a spectrum... the edges of the spectrum are rarely the right answer." — Nishat Mehta "Efficiency increases consumption if you're not careful." — Chris Schembra "The journey is far more important than the destination... if we are constantly changing, it's because we are succeeding at the things we needed to do before." — Nishat Mehta "We must step out of the tyranny of the 'or' and into the genius of the 'and'." — Chris Schembra "Great work is something you're supposed to do, but recognizing what might seem ordinary is worthy of extraordinary gratitude." — Nishat Mehta "Gratitude doesn't change your circumstances; it changes your capacity to carry them." — Chris Schembra 10 Key Takeaways: Practicing Confident Humility: Balancing low ego regarding current knowledge with high confidence in a team's adaptability to solve complex, unfamiliar problems. Overcoming Goodhart's Law: Why single metrics distort business outcomes, and how pairing competing targets (like quantity vs. quality) maintains operational integrity. The "Genius of the 'And'": Replacing rigid "either/or" tradeoffs with dialectical thinking that unites radical acceptance with proactive change. Navigating Shifts in Family & Parenting: Transitioning from physical, hands-on care for young children to emotional, social mentorship as they grow into independence. Understanding Jevons Paradox in Modern Work: Recognizing how efficiency gains from AI can be swallowed by routine busyness unless leaders intentionally safeguard time for human connection. Harnessing "Collective Effervescence": Leveraging physical experiences, shared sports, and community gatherings to foster genuine human harmony and connection. Lowering Barriers to Access Justice: Applying technology and low-level AI guidance to make legal support more affordable, equitable, and fair. Change Management as a Core Leadership Skill: Shifting focus from day-to-day tactical execution toward building organizational consensus and motivating teams through continuous evolution. Finding Extraordinary Meaning in Ordinary Moments: Cultivating daily awareness to appreciate quiet family routines and ordinary acts of care that are often taken for granted. Embracing the Business Journey Over Destinations: Accepting that strategic goals evolve naturally as progress is made, making the growth process the ultimate measure of success. About our Guest: Nishat Mehta is the Chief Executive Officer of Lexitas, a leading national provider of technology-enabled litigation services and an Apax portfolio company based in Houston, Texas. He stepped into the CEO seat on January 1, 2025, having originally joined the company as President and Chief Operating Officer in March 2024. Under his leadership, Lexitas is launching AI-enabled deposition analysis, expanding its eLaw case-tracking platform, and continuing to serve law firms, insurance companies, and corporations across all 50 states. Nishat brings more than two decades of leadership across data, analytics, and enterprise technology. He most recently served as President of Global Products and Solutions and Chief Product Officer at Circana (formed from the merger of IRI and The NPD Group), where he ran the firm's global media, analytics, e-commerce, software, and consulting divisions. Prior to Circana, he led the customer communications team at 84.51° (Kroger's data-science and personalization arm), directed strategic partnerships at dunnhumby, and spent 15 years at MicroStrategy. He also serves on the Board of Directors of The E.W. Scripps Company (NASDAQ: SSP) and on the Board of Advisors of Adelaide Metrics. Nishat holds a Bachelor's degree in Applied Mathematics and a Master's degree in Computer Science, both from Harvard University. He lives in New York with his wife, Shalini, and their three children. His guiding quote, from John Wooden: ability may get you to the top, but it takes character to keep you there.
Dr. Ben Skutnik, PhD, CSCS*D, NSCA-CPT*D, is an Assistant Professor of Kinesiology and Human Performance Lab Director at Butler University and Head of Nutrition Coaching at Power Athlete. Dr. Ben Skutnik joins the show to separate fact from fad in aerobic training, debunking zone 2 hype and explaining why VO2 max matters but isn't everything. Expect to learn why reading scientific literature is its own skill, how uncontrolled variables quietly derail hypertrophy and menstrual-cycle training research, why treating people as fragile backfires, why anecdote in human performance often outruns the research, how "closing the loop" with clients beats chasing mechanisms, what the Norwegian 4x4 method gets right (and what most zone 2 research gets wrong about training zones), and how to actually build VO2 max with the critical power model, and much more. Connect with Ben: Power Athlete Coach Bio LinkedIn Google Scholar Episodes you'll enjoy next: #400 — Build Aerobic Fitness Before Mitochondrial Peptides: AMPK, VO2 Max & Recovery — Dr. Dwayne Jackson: Listen here #374 — Zone 2 Cardio: What the Science Really Says (Pros, Cons & Better Options) with Kristi Storoschuk: Listen here Episode Timestamps: 1:46 — Why Ben was recommended for this conversation on cardiovascular and aerobic programming 7:31 — Reading scientific literature is a skill set of its own 14:05 — Hypertrophy training to failure and uncontrolled variables in research 16:27 — Debunking myths about training around the menstrual cycle 18:20 — Human resilience and the danger of treating people as fragile 22:09 — Why consistency beats trying to "hack" optimization 22:40 — Nutrition fear-mongering and overly restrictive diets 27:25 — Changing one variable at a time vs. the "kitchen sink" approach 29:55 — Why anecdotal evidence in human performance is often ahead of the research 34:48 — The ethics of placebo and "closing the loop" with clients 37:34 — The psychology of performance: the NFL kicker example 43:13 — The Norwegian 4x4 method and zone 2 training myths 46:40 — Why most zone 2 research relies on the wrong zone model 52:53 — HYROX, CrossFit, and Goodhart's Law in training 1:05:18 — Is VO2 max overrated? Performance vs. health tradeoffs 1:15:25 — Building VO2 max: interval protocols and the critical power model 1:26:02 — Life-changing results: taking deconditioned clients from VO2 max 18 to 35 1:30:09 — Where to find Ben Skutnik and his work at Butler University and Power Athlete Get the Daily Fitness Insider newsletter (free): https://www.miketnelson.com/newsletter
MOPs & MOEs is proudly sponsored by Teamworks — the performance operations platform trusted by elite military units and professional spaces organizations worldwide. Teamworks brings your scheduling, communications, athlete monitoring, and readiness data into one unified system — so your leaders stay informed, your people stay connected, and your unit stays ready. No more scattered spreadsheets or missed messages. Just one platform built for organizations where performance is the mission. Learn more at teamworkstactical.comWe are also supported by TrainHeroic — the coaching and programming platform built for strength and conditioning coaches who train serious athletes. Whether you're programming for a military unit, a tactical team, or individual athletes, TrainHeroic gives you the tools to build and deliver professional training programs, track athlete progress, and communicate directly with your people — all through one app. Your athletes get world-class programming on their phone; you get the visibility to actually coach them. Start your free trial at trainheroic.comWhy Gamification Is Ruining Military Human Performance — C. Thi Nguyen, philosopherDrew and Alex have been talking about Goodhart's Law, McNamara's Fallacy, and the tyranny of metrics for a while now. This week they bring in a philosopher who gave them the language to finally explain why — C. Thi Nguyen from the University of Utah, whose work on games and gamification is the missing piece of that conversation.What we get into:Value capture in military human performance — when a unit starts chasing PT scores instead of physical readiness, when a strength coach starts optimizing athlete data instead of athlete outcomes, when a commander starts managing the metric instead of the mission. Value capture is the mechanism behind all of it. It's what happens when a simplified number colonizes your decision-making and you slowly forget what you were actually trying to accomplish.The quantified self problem — HRV, sleep scores, readiness metrics, wearables. All genuinely useful. All capable of becoming the thing you optimize instead of the thing they were supposed to measure. The difference between a tool that informs your judgment and a tool that replaces it is one of the most important distinctions in human performance right now.Why gamification is different from games — games are voluntary, finite, and you walk away from them. Gamification takes the reward loop of a game and attaches it to things that used to have richer value. The PT test was never supposed to be the game. It was supposed to tell you something about readiness. The moment it became a scoreboard, it stopped doing that.The promotion system as a game — what happens to an organization when the people who are best at optimizing the visible metrics rise to the top, and whether military culture has the tools to value what the metrics miss.Echo chambers and epistemic cowardice — why Nguyen's work applies directly to military organizations where rank creates enormous pressure to smooth out disagreement, and what that does to the quality of decision-making over time.Mentioned in this episode:Games: Agency as Art — C. Thi NguyenGoodhart's Law and the Core Four — see previous episodeThe Score: How to Stop Playing Somebody Else's Game — C. Thi NguyenLong and Strong — the Mops and Moes training program on TrainHeroic All Access Bundle — all of our training programs and rehab material in one placeViews expressed are those of the speakers and do not represent any official organization.
A CMO Confidential Interview with Kristin Wozniak, Chief Growth Officer and Data Officer at Cosmo5, formerly SVP Analytics and Strategy at Cossette Media. Kristin discusses why she believes many leaders have developed an unhealthy obsession with data, how "the need for certainty" stifles innovation, and Goodhart's law, which states "when a measure becomes a target, it's no longer a good measure." Key topics include:- The risk of constantly wanting more data and dashboards- The need for "buddy metrics"- Why you should link KPI's, compensation, and business results- The concepts of "AI Brain Fry" and "Hiding Behind the Data"Chapters
Ben Lindbergh and Meg Rowley banter about MLB franchise appreciation, Stat Blast about what the resurgence of three-inning saves and the expanding distribution of saves say about pitcher usage, and learn about a new baseball injury. Then (43:28) they bring on Lindsay Imber of Close Call Sports and the official scorer behind the MLB Scoring Changes account to explain the “fourth-out” rule that confused fans and players this week and to pick nits with the MLB rulebook. Finally (1:30:50), Ben brings back former major leaguer John Poff to read from his new book, reflect on his use of “greenies” and players’ political differences, and share a new fundraising initiative for the Standing Rock Reservation, followed (2:13:45) by a few updates. Audio intro: The Spaghettis, “Effectively Wild Theme” Audio interstitial: The Shirey Brothers, “Effectively Wild Theme” Audio outro: Ted O., “Effectively Wild Theme” Link to Yankees “capital infusion” Link to story about Yankees valuation Link to story about Lakers sale Link to impending Padres sale Link to FIFA takeover attempt Link to Infantino wiki Link to Littleton save Link to save rule Link to pope hat giveaway Link to Latz stats Link to Atlanta’s three-out savers Link to data on three-out saves Link to data on save-getters by season Link to multi-inning RP outings data Link to Herfindahl–Hirschman index wiki Link to annual HHI data Link to HHI graph Link to Inverse Participation Ratio Link to THT on three-inning saves Link to SB Nation on three-inning saves Link to Medium post on three-inning saves Link to WSJ closers story Link to Ben on saves slipping in 2017 Link to Betteridge’s law wiki Link to parakeet bounceback news Link to Goodhart’s law wiki Link to league SP splits Link to league RP splits Link to FG post on Goodhart’s law Link to other FG post on Goodhart’s law Link to Arrigheti report Link to more on Arrigheti Link to Morton’s neuroma wiki Link to Morton’s neuroma causes Link to Mayo Clinic on Morton’s neuroma Link to more on Morton’s neuroma Link to even more on Morton’s neuroma Link to Ben on the 2015 ALDS game Link to Ben on confused counts Link to @ScoringChanges Link to @ScoringChanges fourth-out tweet Link to @CloseCallSports Link to Close Call Sports website Link to Close Call Sports YouTube channel Link to Lindsay wiki Link to Lindsay’s fourth-out video Link to Lindsay’s Sale video Link to Lindsay’s Wheeler video Link to fourth out wiki Link to fourth-out story 1 Link to fourth-out story 2 Link to SABR on fourth outs Link to The Athletic fourth-out explainer Link to Bois on balks Link to Sam on balks Link to xkcd comic Link to Schneider quote Link to 2016 Schneider game Link to Strike Four Link to Poff Stat Blast Link to first Poff appearance Link to second Poff appearance Link to Halls of Fame discussion Link to Poff’s first Hall of Fame Link to Poff’s second Hall of Fame Link to Poff’s book Link to Carlin routine Link to baseball exceptionalism wiki Link to greenies info Link to Ben on PEDs and the ball Link to “Buffalo Bill ‘s” poem Link to Standing Rock wiki Link to Gilbert Kills Pretty Enemy art Link to John’s fundraiser Link to Scherzer’s piano-playing Link to Kendrick cameo story Link to Skenes velo graph Link to Skenes game story Link to Liquid I.V. powder Link to Rocker pickle-juice game Link to Stanton yoga report Link to Foulke wife tweet Link to Foulke firing report Sponsor Us on Patreon Give a Gift Subscription Email Us: podcast@fangraphs.com Effectively Wild Subreddit Effectively Wild Wiki Apple Podcasts Feed Spotify Feed YouTube Playlist Facebook Group Bluesky Account Twitter Account Get Our Merch! var SERVER_DATA = Object.assign(SERVER_DATA || {}); Source
bto - beyond the obvious 2.0 - der neue Ökonomie-Podcast von Dr. Daniel Stelter
Unter dem Titel The Great Demographic Reversal (dt. Die große demografische Wende) veröffentlichten die Ökonomen Charles Goodhart und Manoj Pradhan im Jahr 2020 ein vielbeachtetes Buch. Im Februar 2021 waren die beiden zu Gast im bto-Podcast. Die These aus ihrem Buch war provokant: Die Zeit der niedrigen Inflation ist historisch beendet, weil zwei einmalige Angebotsschocks – der Eintritt Chinas und Osteuropas in den Welthandel plus der Anstieg der weiblichen Erwerbsbeteiligung – auslaufen. Die Demografie kehrt sich um, das Arbeitsangebot schrumpft, die Abhängigenquote verschlechtert sich. Inflation, Zinsen und Löhne müssen strukturell höher sein.Sieben Jahre später haben Goodhart und Pradhan ein Nachfolgebuch vorgelegt: The Unanchored Central Banker. Die These wurde verschärft: Die Fiskalpolitik ist so unsustainable geworden, dass Geldpolitik in ihrer bekannten Form nicht mehr funktionieren kann. Notenbanken werden gezwungen sein, häufiger in Bondmärkte einzugreifen. Und Goodhart, geboren 1936, sagt in seinem aktuellen Interview: „Von nun an wird die demografische Entwicklung das Leben immer schlimmer und schlimmer machen. (a. d. eng./Red.)“ Inzwischen liegen zwei aktuelle deutsche Publikationen vor – beide aus dem Juli 2026 –, die Goodharts These direkt bestätigen: der ifo Schnelldienst (70 Prozent für Rente und Gesundheit) und eine Kronberger-Kreis-Studie (Tragfähigkeit der Eurozone durch demografischen Druck ernsthaft gefährdet). Zeit für ein bto REFRESH.Hinweis ABSTURZ – So retten wir Deutschland: das neue Buch von Daniel Stelter. Jetzt überall, wo es Bücher gibt. Auch bestellbar bei Thalia, Amazon, geniallokal.HörerserviceBuch The Great Demographic Reversal – Ageing Societies, Waning Inequality, and an Inflation Revival (2020) von Charles Goodhart und Manoj Pradhan: https://tinyurl.com/ynfa5tzk Buch The Unanchored Central Banker — Fiscal Dominance and the Future of Monetary Policy (2026) von Charles Goodhart und Manoj Pradhan: https://tinyurl.com/8rue2rc3 Pressemitteilung Mehr als zwei Drittel aller Sozialausgaben fließen in Rente und Gesundheit ( 2.7.2026) des ifo Instituts München: https://tinyurl.com/3j43cs9s Studie Ist der Euro zukunftsfest? (Juli 2026) des Kronberger Kreis (Feld/Fuest/Haucap/Hey/Wieland/Wigger): https://tinyurl.com/386864bz beyond the obvious – Neue Analysen, Kommentare und Einschätzungen zur Wirtschafts- und Finanzlage finden Sie unter think-bto.com.Newsletter – Den monatlichen bto-Newsletter abonnieren Sie hier.Redaktionskontakt – Wir freuen uns über Ihre Meinungen, Anregungen und Kritik unter podcast@think-bto.com.Handelsblatt – Ein exklusives Angebot für alle „bto – beyond the obvious – featured by Handelsblatt”-Hörer*innen: Testen Sie Handelsblatt Premium 4 Wochen lang für 1 Euro und bleiben Sie zur aktuellen Wirtschafts- und Finanzlage informiert. Mehr erfahren Sie unter: https://handelsblatt.com/mehrperspektiven Werbepartner – Weitere Informationen zu den Angeboten unserer aktuellen Werbepartner finden Sie hier. Hosted on Acast. See acast.com/privacy for more information.
Does moving faster with AI mean you get to skip the laws that governed every software project before it? Milan Milanovic argues the opposite, the old laws of software engineering now apply twice as hard.In this episode, Milan Milanovic, CTO and author of Laws of Software Engineering, returns to unpack why the laws that have quietly governed software projects for decades are more relevant than ever in the AI era. He walks through Gall's Law and why AI lets teams generate complex systems on unvalidated assumptions faster than ever, Conway's Law and how it now runs twice, shaping both human organizations and agent topologies, and Goodhart's Law and the trap of “tokenmaxxing” as a metric. Milan also covers Hofstadter's Law and the 90-90 rule, explaining why the last 10% of a project still takes as long with AI in the loop, and the Dunning-Kruger effect, where vibe coders overestimate their skills while senior engineers underestimate theirs. The conversation closes with his advice for juniors entering the field, his top five must-read books, and how he personally uses AI for research and planning rather than implementation.Key topics discussed:Gall's Law: the trap of generating complex systems with AIConway's Law now runs twice, on humans and on AI agent topologyWhy software architects should help design your org chartGoodhart's Law and the danger of “tokenmaxxing” as a metricThe 90-90 rule: why AI's last 10% still costs half the projectDunning-Kruger effect on vibe coders and expertsWhy less code beats more, even with AI writing it for freeTimestamps:(00:00) Trailer & Intro(03:02) What Inspired Milan to Write Laws of Software Engineering?(05:32) How Did a Book on Software Laws Reach Such a Global Audience?(06:49) How Should You Read the Laws of Software Engineering Book?(08:51) Why Does the Book Cover People and Planning, Not Just Technical Laws?(11:07) What Is Gall's Law in Software Engineering?(14:46) How Can You Apply Gall's Law When Using AI?(17:34) What Is Conway's Law in Software Engineering?(19:34) How Should Big Corporations Build New Products Without Structural Reorganization?(21:26) How Does Conway's Law Apply to Small AI-Powered Product Teams?(23:25) What Is Goodhart's Law in Software Engineering?(25:52) What Are the Best Examples of Counterbalance Metrics for Leaders Today?(28:06) What Kind of Outcomes Should You Actually Measure?(31:01) What Is Hofstadter's Law in Software Engineering?(33:35) How Does Hofstadter's Law Apply in the Age of AI?(36:41) What Is the Dunning-Kruger Effect?(40:10) How Does the Dunning-Kruger Effect Apply to Experienced Engineers Learning AI?(42:46) Are Any of These Laws Becoming Less Relevant Because of AI?(44:32) What Does the Lindy Effect Say About the Fate of Software Developers?(47:49) What Is the Best Career Advice for Junior Developers Entering the AI Landscape?(50:34) What Are the Top Five Must-Read Books for Software Engineers?(55:46) How Can Software Engineers Apply These Laws in Their Daily Work?(57:26) 3 Tech Lead Wisdom_____Milan Milanovic's BioMilan Milanović is the CTO and the author of Laws of Software Engineering. He holds a PhD in Computer Science, has more than 20 years of experience across .NET, Azure, and mobile development, and is a Microsoft MVP. He runs Tech World With Milan, a software engineering newsletter and community followed by more than 400,000 engineers. He writes about architecture, engineering leadership, and how AI is changing the way software gets built.Follow Milan:LinkedIn – linkedin.com/in/milanmilanovicTwitter / X – @milan_milanovicPersonal website – milan.milanovic.org Book's website - lawsofsoftwareengineering.com Newsletter - newsletter.techworld-with-milan.comLike this episode?Show notes & transcript: techleadjournal.dev/episodes/266.Follow @techleadjournal on LinkedIn and Instagram.Buy me a coffee or become a patron.
Jared is joined by Tim Cook who is Senior Lecturer in Physiotherapy at the University of Chichester, Advanced Physiotherapy Practitioner in the NHS, and doctoral researcher to discuss their new paper with Prof. Jeremy Lewis in the Journal of Manual & Manipulative Therapy: "Rethinking strength testing in rotator cuff-related shoulder pain: a clinical tradition that lacks muscle." Tim explains what pushed him to question routine strength testing in busy clinical practice, despite guidelines recommending it, and the pair dig into whether weakness is a cause of shoulder pain, a consequence of it, or something else altogether. They look at what subacromial anaesthetic injection studies reveal about pain versus true force output, and Tim reframes the classic "weak and painful shoulder" as the "flexible but painful shoulder," drawing parallels to other oversimplified clinical narratives like posture and core stability. They also make the case for when strength testing can still earn its keep; patient engagement, expectation violation, building confidence, and sporting or return-to-performance populations such as ACL limb symmetry index, before turning to Goodhart's Law and the risk of chasing the test rather than the outcome, and why isolated isometric tests may not translate to real-world, endurance-based shoulder tasks. Tim closes out with an update on his PhD: a reliability RCT on strength and pain-free assessment currently in press, and an upcoming scoping review on the proposed mechanisms of exercise for RCRSP. Key resources Cook, T., Lewis, J., & Powell, J. (2026). Rethinking strength testing in rotator cuff-related shoulder pain: a clinical tradition that lacks muscle. Journal of Manual & Manipulative Therapy, 34(3), 193–198. https://doi.org/10.1080/10669817.2026.2663623 Register for the complete shoulder online course Melbourne workshop details Connect with Jared: Jared on Instagram: @shoulder_physio Jared on X: @jaredpowell12 Tim on Instagram: @Timcookphysio See our Disclaimer here: The Shoulder Physio - Disclaimer
MOPs & MOEs is proudly sponsored by Teamworks — the performance operations platform trusted by elite military units and professional sports organizations worldwide. Teamworks brings your scheduling, communications, athlete monitoring, and readiness data into one unified system — so your leaders stay informed, your people stay connected, and your unit stays ready. No more scattered spreadsheets or missed messages. Just one platform built for organizations where performance is the mission. Learn more at teamworkstactical.comWe are also supported by TrainHeroic — the coaching and programming platform built for strength and conditioning coaches who train serious athletes. Whether you're programming for a military unit, a tactical team, or individual athletes, TrainHeroic gives you the tools to build and deliver professional training programs, track athlete progress, and communicate directly with your people — all through one app. Your athletes get world-class programming on their phone; you get the visibility to actually coach them. Start your free trial at trainheroic.comThe Core Four — Four Cognitive Traps Killing Military Human PerformanceDrew and Alex gave a standing-room-only breakout at the H2F Symposium on this. Now they have time to actually unpack it. Four heuristics that show up everywhere in military human performance — and in every human-run organization. You can't escape them, but you can at least recognize them. What we get into:Goodhart's Law — when a measure becomes a target, it ceases to be a good measure. The AFT was designed to signal readiness. The moment it became an incentivized metric, people started optimizing the score instead of the fitness. Same thing happened in Vietnam with body counts. The cure: use multiple metrics, reward more than test scores, and acknowledge what the assessment was actually supposed to tell you.McNamara Fallacy — only what can be quantified matters. The first step is measuring what's easy. The second is disregarding what can't be measured. The third is assuming the unmeasurable isn't important. The fourth is deciding it doesn't exist. Sound familiar? Social cohesion, trust, culture, the music playing in the gym — none of these show up on a dashboard. All of them matter enormously. Every minute a coach spends measuring is a minute they spend not coaching.Hammond's Corollary — what gets measured gets managed, but what most needs managing isn't always measurable. The soldier with a perfect AFT score who can't pick up a ruck without throwing out his back. Fitness versus capability. Athleticism versus fitness. You know it when you see it — and just because you can't put it on a graph doesn't mean it doesn't exist.Nazareth syndrome — no man is a prophet in his own home. The one place Jesus got the most skepticism was Nazareth, because they knew him as the carpenter. The embedded human performance team dealing with real constraints gets ignored. The outside consultant with slick branding and a two-day seminar gets the senior leader's full attention. You can't eliminate this one — but you can use it to your advantage.Mentioned in this episode:Drew's article — The Death of Periodization at mopsandmoes.comChris Frankel — assessment framework: camera, map, crystal ball, engineVF Ridgway, 1956 — the actual origin of "what gets measured gets managed"Justice Potter Stewart, 1964 — the porn quote. You know it when you see it.The Tyranny of Metrics — Jerry MullerLong and Strong — the Mops and Moes training program on TrainHeroicViews expressed are those of the speakers and do not represent any official organization.
No vídeo de hoje, discutimos a recente decisão de política monetária do Fed, marcada por grande incerteza sob o comando de Kevin Ward. Com as taxas de juros inalteradas, a surpresa é que a métrica oficial de inflação está sob revisão, trazendo à tona a Lei de Goodhart sobre a manipulação de indicadores públicos. Analisamos o fim do "forward guidance", a forte abertura da curva de juros americana com o Tesouro de 30 anos atingindo as máximas e como esse aperto financeiro global impacta as taxas no Brasil. Além disso, comentamos sobre a forte correção nas ações das gigantes de tecnologia e inteligência artificial, como Meta e Nvidia. Entenda o que esse novo cenário significa para os mercados.00:00 - A decisão do Fed e a incerteza00:52 - Métrica oficial de inflação sob revisão02:18 - Lei de Goodhart e manipulação de indicadores03:30 - Fim do forward guidance e nova força-tarefa04:49 - Reação do mercado e alta dos juros longos07:43 - O impacto dessa decisão de juros no Brasil09:11 - Forte correção nas ações de inteligência artificial
In June 1968, Richard and Shirley Robison took their four children to the family's secluded summer cottage near Good Hart, Michigan. Twenty-seven days later, a caretaker entered the locked home and found all six members of the family dead.Investigators built a strong circumstantial case against Richard's employee, Joseph Scolaro, pointing to missing business funds, an unexplained gap in his movements and links to weapons connected to the crime.But prosecutors never brought the case to trial, and Scolaro died before a jury could hear the evidence.In this episode of End of the Road in Michigan, we examine the crime scene, the delayed discovery, the prime suspect and the question that still divides investigators and true-crime researchers: Was the Good Hart case solved in everything but a courtroom?The End of the Road in Michigan is a production of Thumbwind PublicationsThis episode includes AI-generated content.
Tue, 28 Jul 2026 21:30:00 GMT http://relay.fm/focused/261 http://relay.fm/focused/261 David Sparks and Mike Schmitz David & Mike discuss Goodhart's Law, why effort matters more than outcomes, and how to maintain a healthy relationship with the numbers you use to keep score. David & Mike discuss Goodhart's Law, why effort matters more than outcomes, and how to maintain a healthy relationship with the numbers you use to keep score. clean 2813 David & Mike discuss Goodhart's Law, why effort matters more than outcomes, and how to maintain a healthy relationship with the numbers you use to keep score. Links and Show Notes: Deep Focus: Extended ad-free episodes with bonus deep dive content. Video for this episode Mike's Amplify Cohort Focused #260: Quarterly Planning and Riding Roller Coasters Focused #259: Nobody's Perfect, with Kathy Campbell The Score by C. Thi Nguyen Happy Scale Insta360 Luna Ultra Macstock Conference Insta360 Mic Pro Insta360 Mic Air Ugmonk Analog Field Notes Apple: The First 50 Years by David Pogue Mac Power Users #839: Fifty Years of Apple with David Pogue Women of Walt Disney Imagineering by Ginger Zee Focused #257: I Go By Vibes, with Stephen Robles Read Between The Lynes On B.S. by Harry Frankfurt
Tue, 28 Jul 2026 21:30:00 GMT http://relay.fm/focused/261 http://relay.fm/focused/261 Metrics of Success 261 David Sparks and Mike Schmitz David & Mike discuss Goodhart's Law, why effort matters more than outcomes, and how to maintain a healthy relationship with the numbers you use to keep score. David & Mike discuss Goodhart's Law, why effort matters more than outcomes, and how to maintain a healthy relationship with the numbers you use to keep score. clean 2813 David & Mike discuss Goodhart's Law, why effort matters more than outcomes, and how to maintain a healthy relationship with the numbers you use to keep score. Links and Show Notes: Deep Focus: Extended ad-free episodes with bonus deep dive content. Video for this episode Mike's Amplify Cohort Focused #260: Quarterly Planning and Riding Roller Coasters Focused #259: Nobody's Perfect, with Kathy Campbell The Score by C. Thi Nguyen Happy Scale Insta360 Luna Ultra Macstock Conference Insta360 Mic Pro Insta360 Mic Air Ugmonk Analog Field Notes Apple: The First 50 Years by David Pogue Mac Power Users #839: Fifty Years of Apple with David Pogue Women of Walt Disney Imagineering by Ginger Zee Focused #257: I Go By Vibes, with Stephen Robles Read Between The Lynes On B.S. by Harry Frankfurt Don't
It's Flashback Friday! This episode originally hit the main feed in August, 2019. It's time for a new episode of “Shootin' The Shiznit.” Brian Tramel sits down with Joel Goodhart for Episode 251. Joel talks about Rasslin Radio, TWA, bus trips, Blue Meanie, Dennis Coralluzzo,, Todd Gordon, Paul Heyman and much more! Check out our LINK OF ALL LINKS to watch the show and listen to our podcasts! https://linktr.ee/STSPOD Do you want these shows as soon as they are recorded? Join Patreon!! Subscribe now ! https://www.patreon.com/shootintheshiznit Vitality Chiropractic in Jonesboro and Newport, Arkansas, is a trusted haven for individuals seeking comprehensive chiropractic care. With a dedicated team of professionals, they prioritize spinal health and overall well-being. If you're looking for personalized and effective chiropractic services, reach out to them at (870) 523-2225 to experience their commitment to enhancing your health and vitality. Meal prep in Northeast Arkansas! 15% Off with our code STSPODCLUB at bare870.com. That's 15% off and use our code STSPODCLUB Go to bare870.com Trust Bare for your meal prep needs in Northeast Arkansas. Eat Better. Live Better. Paypal LINK ! https://py.pl/15aeX0 Link of all links: https://linktr.ee/STSPOD Search “Shooting The Shiznit” to LIKE the STSPOD FB page !! Sponsored by Spunklube is the perfect blend of water and silicone. It is an all purpose personal lubricant that can be used for any occasion. You will love the natural feeling and look of it. It is safe for sensitive skin. Go to spunklube DOT com and tell them shootin the shiznit sent you ! Follow them on Twitter @SpunkLube Have you used the UBER Eats app? If not, you can download it & get $7 off your first order by using this code: eats-briant24790ue Did you love this week's episode?? Was it worth a $1 ? $2? $100?? Donate to STS by using the Cash app and sending $$$$ to: $BTSTS In partnership with Championship Wrestling on CW30! Every Saturday at Noon on YouTube. Follow them on Twitter: @cw30wrestling Do you wanna be a pro wrestler ? Go to championshipwrestlingmemphis.com and apply for classes that start soon !! LIVE MEMPHIS WRESTLING: EVENTS: https://tinyurl.com/Upcoming-Live-Event
Matt and Michael open with the Texas heat and the strange feeling of being between seasons. Not just summer tipping toward fall, but the inner seasons of thought and spirit where old ideas fade and new ones haven't arrived. What do you do when you don't know what to do? They explore anxiety and faith, the discipline of presence, and how focusing on relationships unlocked unexpected reconnections. Then things get deeper. They wrestle with truth as conforming to reality, the fear of God as respect for overwhelming power, and why rights only work when they come from God rather than government. The conversation turns to communism and why it always ends in genocide, why markets and elections work because they let reality speak, and why everything good is good because of love. Michael calls it what it is. Two guys stumbling through ideas, pushing on things, trying to find the meat on the bone. Cheers y'all
What if your "normal" blood test is hiding a real deficiency? In this solo episode, Nick Urban reveals how your body defends its blood first, so the reserves behind it can quietly drain while the number looks perfect. Nick Urban, founder of Outliyr, bioharmonizer, and performance coach, breaks down the exact filters he uses to decide what earns a spot in his supplement stack: reading a label like a manufacturer, sorting every supplement by tier and job, and testing what actually works for you. Meet the host Nick is the founder of Outliyr and host of the High Performance Longevity podcast. A bioharmonizer and performance coach, he blends modern science with ancestral wisdom to decode what actually moves the needle on energy, healthspan, and performance. Thank you to our partners Outliyr Biohacker's Peak Performance Shop: get exclusive discounts on cutting-edge health, wellness, & performance gear Ultimate Health Optimization Deals: a database of of all the current best biohacking deals on technology, supplements, systems and more Latest Summits, Conferences, Masterclasses, and Health Optimization Events: join me at the top events around the world FREE Outliyr Nootropics Mini-Course: gain mental clarity, energy, motivation, and focus Key takeaways Blood is the last place most deficiencies show because the body defends it first Only 0.33% of your body's magnesium sits in blood serum Iron absorbs about twice as well every other day because one dose blunts the next by ~40% Magnesium oxide is about 4% absorbed; look for glycinate, malate, taurate, or citrate In a proprietary blend, the ingredient printed last is the smallest amount The FDA tested 46 supplements from Amazon and eBay and all 46 had undeclared drugs A one-time genetic test can show how you'll respond to supplements and peptides Episode highlights 02:01 Why "normal" bloodwork can mislead you 03:52 Magnesium: you measured a puddle, not the reservoir 06:53 Goodhart's Law for your health 08:36 The 3 tiers: essential, conditionally essential, non-essential 10:07 The 4 jobs every supplement does 14:25 Reading a label like a manufacturer 17:19 Proprietary blends & pixie dusting 22:25 The 7-question context lens 25:10 Build your stack for free with Outliyr Links Watch it on YouTube: https://youtu.be/8UC1VSAtzjg Full episode show notes: https://outliyr.com/269 Connect with Nick on social media Instagram Twitter (X) YouTube LinkedIn Easy ways to support Subscribe Leave an Apple Podcast review Suggest a guest Do you have questions, thoughts, or feedback for us? Let me know in the show notes above and one of us will get back to you! Be an Outliyr, Nick
In this mini-episode, we discuss Goodhart's Law: when a measure becomes a target, it ceases to become a good measure.Get our Intro to Mechanics audio course, normally $79, FREE:https://bjjmentalmodels.com/freeintro⬆️ LEVEL UP with BJJ Mental Models Premium!The world's LARGEST library of jiu-jitsu audio lessons, our complete podcast network, online coaching, and much more! Your first week is free:https://bjjmentalmodels.comNeed more BJJ Mental Models?Get the legendary BJJMM newsletter:https://bjjmentalmodels.com/newsletterLearn more mental models in our online database:https://bjjmentalmodels.com/databaseFollow us on social:https://instagram.com/bjjmentalmodelshttps://threads.com/@bjjmentalmodelshttps://bjjmentalmodels.bsky.socialhttps://youtube.com/@bjjmentalmodels
Matt and Michael explore language itself — how the same sentence can carry seven different meanings depending on which word you emphasize. From Christopher Walken's acting advice to watching a toddler learn to speak, they examine how we communicate far more than dictionary definitions. This spirals into the central claim of John's Gospel: Jesus Christ is the Word. What does it mean that the creative power of the universe is linguistic? And why do we so often use language to hide rather than reveal? They move through imposter syndrome, scripted relationships, and the death of authentic communication in a transactional culture. The conversation lands on forgiveness and repentance as the antidote to false representation, and asks whether our deepest divide isn't political but spiritual. Cheers y'all
Just as crews began repairing sections of M-119, also known as The Tunnel of Trees, damaged by April flooding, heavy rains wreaked havoc on more sections of the popular tourist route in Michigan's northern Lower Peninsula.This week's edition of the Talking Michigan Transportation podcast features conversations with State Sen. John Damoose (Harbor Springs), who represents the area, and Bill Wahl, acting North Region engineer for the Michigan Department of Transportation. (MDOT).MDOT is already investing $300,000 to stabilize the slope beneath a section of M-119 near West Stutsmanville Road, between Harbor Springs and Good Hart in Emmet County, damaged in the spring flooding. Now, engineers are assessing other sections of the roadway washed out by heavy rains on Monday, June 29, as well as sections of M-66 and M-88 in Antrim County. Wahl provides a status report on each of the washouts and short- and long-term planning for both.Sen. Damoose talks about his conversations with business owners and other constituents affected by the road closures and the ongoing challenges to fund sustainable infrastructure in Michigan.
This week, we're joined again by Jake Luigi! Jake is a jiu-jitsu analyst who made his name studying professional match footage, and he runs OutlierDB, an analytics database for grappling. In this episode, Jake lays out a three-part framework for managing risk: name the worst-case scenario, watch for the key indicators that it's coming, and only attack hard once those indicators are in your favor. Topics include: risk-reward analysis, points of control, grip fighting, terminal positions, and Goodhart's Law.Subscribe to Less Impressed More Involved on YouTube:https://www.youtube.com/@LIMIBJJCheck out OutlierDB:https://outlierdb.comMental models discussed in this episode:Return on Investmenthttps://bjjmentalmodels.com/return-on-investmentBuilding Your Triggershttps://bjjmentalmodels.com/building-your-triggersDouble Troublehttps://bjjmentalmodels.com/double-troubleCognitive Loadhttps://bjjmentalmodels.com/cognitive-loadConstraints-Led Approachhttps://bjjmentalmodels.com/constraints-led-approachAvailability Heuristichttps://bjjmentalmodels.com/availability-heuristicPoints of Controlhttps://bjjmentalmodels.com/points-of-controlTerminal Positionshttps://bjjmentalmodels.com/terminal-positionsInvariantshttps://bjjmentalmodels.com/invariantsGrips Dictate Positionhttps://bjjmentalmodels.com/grips-dictate-positionKuzushihttps://bjjmentalmodels.com/kuzushiRotational Controlhttps://bjjmentalmodels.com/rotational-controlGoodhart's Lawhttps://bjjmentalmodels.com/goodharts-law⬆️ LEVEL UP with BJJ Mental Models Premium!The world's LARGEST library of jiu-jitsu audio lessons, our complete podcast network, online coaching, and much more! Your first week is free:https://bjjmentalmodels.comNeed more BJJ Mental Models?Get the legendary BJJMM newsletter:https://bjjmentalmodels.com/newsletterLearn more mental models in our online database:https://bjjmentalmodels.com/databaseFollow us on social:https://instagram.com/bjjmentalmodelshttps://threads.com/@bjjmentalmodelshttps://bjjmentalmodels.bsky.socialhttps://youtube.com/@bjjmentalmodelsMusic by Enterprize:https://enterprize.bandcamp.com
Timestamps00:00 Claude Fable gets blocked03:26 Does the ban actually help Anthropic?08:49 AI becomes a national security issue09:28 Why countries want sovereign AI13:30 NVIDIA, Taiwan, and the AI supply chain17:04 Who should fund AI infrastructure?18:28 Are AI models actually profitable?21:27 What went wrong with GPT-4.5?24:20 Can India build its own frontier model?28:27 RL environments and the new data bottleneck30:02 Is this good or bad news for India?32:41 What this means for frontier AI labs33:36 Recursive self-improvement begins35:23 Do AI products have switching costs?40:46 Are we already in AI takeoff?42:32 Why hasn't AI caused runaway growth yet?44:42 Where do AI use cases stop?50:16 What does AI safety actually mean?52:51 Why would AI become dangerous?56:08 What safety teams are working on59:32 Does RLHF solve alignment?1:00:26 Reward hacking and Goodhart's law1:03:40 Why safety research needs compute1:07:38 What should normal AI users do?1:08:03 Gradual disempowerment1:11:27 Are we already seeing warning signs?1:13:48 Are LLMs the path to AGI?1:15:23 Timelines for automated AI researchers1:18:20 Can independent researchers contribute?1:22:45 ClosingIn this episode, I sit down with Raghav Toshniwal to unpack why AI is no longer just a product race, but increasingly a geopolitical one.We discuss the Claude Fable controversy, why countries are thinking about sovereign AI, what the US-China AI race means for India, and how compute, chips, NVIDIA, Taiwan, and frontier labs all fit into the bigger picture.We also get into recursive self-improvement, whether we are already in an AI takeoff, and why AI safety researchers worry about alignment, reward hacking, and gradual disempowerment.Topics covered: The AI arms race between the US and China Sovereign AI and why countries want their own models India's position in the frontier model race NVIDIA, Taiwan, TSMC and the compute bottleneck OpenAI, Anthropic and recursive self-improvement AI safety, alignment and existential risk Whether automated AI researchers change everythingMore on RaghavPodcast Socials :Instagram : https://www.instagram.com/decentmakeoverTwitter : https://twitter.com/decentmakeovrLinkedin : https://www.linkedin.com/in/ryan-dsouza-74542b295/PODCAST INFO:Podcast website: https://anchor.fm/ryandsouzaApple Podcasts: https://apple.co/3NQhg6SSpotify: https://spoti.fi/3qJ3tWJAmazon Music: https://amzn.to/3P66j2BGoogle Podcasts: https://bit.ly/3am7rQcGaana: https://bit.ly/3ANS4v1RSS: https://anchor.fm/s/609210d4/podcast/rss
These engagement failures, and how to fix them, map directly onto the Octalysis Core Drives. Get the free Core Drives in the Wild guide: professorgame.com/WildCD Episode Summary Rob breaks down why Amazon shut down KiroRank, the internal leaderboard that scored staff on raw AI usage on its Kiro developer platform. He shows how stacking Core Drive 2 (Development & Accomplishment) and Core Drive 5 (Social Influence & Relatedness) produced flawless compliance toward the wrong target, a textbook case of Goodhart's law: once a measure becomes a target, it stops being a good measure. Drawing on the Octalysis Strategy Dashboard and Toyota's Five Whys, he lays out the one question to ask before you measure anything. Listeners learn to measure outcomes instead of activity, and how to keep a proxy metric from quietly getting gamed. About the Host Rob Alvarez is Head of Engagement Strategy, Europe at The Octalysis Group (TOG), a leading gamification and behavioral design consultancy. A globally recognized gamification strategist and TEDx speaker, he founded and hosts Professor Game, the #1 gamification podcast, and has interviewed hundreds of global experts. He designs evidence-based engagement systems that drive motivation, loyalty, and results, and teaches LEGO® SERIOUS PLAY® and gamification at top institutions including IE Business School, EFMD, and EBS University across Europe, the Americas, and Asia. Key Takeaways Amazon shut down KiroRank, its internal leaderboard scoring staff on AI usage on the Kiro developer platform, after employees set autonomous AI agents on needless tasks just to climb the ranks and inflated the company's compute costs. Goodhart's law explains the failure: when a measure becomes a target, it stops being a good measure. You get what you measure, not what you want, so raw AI usage climbed while productivity went unmeasured. KiroRank stacked Core Drive 2 (Development & Accomplishment) through a progress bar and ranking, and Core Drive 5 (Social Influence & Relatedness) through public status, producing flawless compliance toward the wrong outcome. The more powerful and expensive the tool being measured, the more a gamed metric costs you, which is why Amazon paid in real compute money rather than a rounding error. The Octalysis Strategy Dashboard starts with business metrics by asking what outcome you actually want, using Toyota's Five Whys to move from "increase AI usage" to a result worth hitting, like productivity per employee. Engagement is the value created for users and the business, not click counts or usage volume, which is why most dashboards measure activity when they should measure the outcome. Topics Covered 0:00 - The $200 billion AI paradox 0:27 - Goodhart's law and gamed metrics 1:49 - The two Core Drives Amazon stacked 2:39 - Flawless compliance, the wrong target 3:38 - Amazon's KiroRank AI leaderboard 5:11 - Measure the right thing, not usage 5:38 - The Octalysis Strategy Dashboard 6:12 - Toyota's Five Whys for metrics 7:21 - When proxy metrics are unavoidable 7:58 - Measure the outcome, not the activity 8:33 - Get the Core Drives in the Wild guide Mentioned in This Episode Goodhart's law The Financial Times report on Amazon's KiroRank leaderboard Amazon's Kiro developer platform The Five Whys (Toyota / lean operations) A previous Professor Game episode on AI use and academic testing Free Resources and Get in Touch Core Drives in the Wild: Professor Game Free Guide Get Daily Value on Your Email Let's chat about your gamification project YouTube LinkedIn Instagram Facebook Start Your Community on Skool for Free Ask a question
Last 4 days before regular tickets sell out at AI Engineer World's Fair - this is the single biggest gathering of AI Engineers, Founders, Leaders, and Researchers in the world. Attendees get >$5000 worth of sponsor credits and talk tracks are looking FANTASTIC. Join us!The AI scaling debate always focuses on the question of “how do we get more GPUs?” but the better question may be: how do we make the most of ones we already have.The fact that a frontier lab like xAI could be running at sub-10% MFU (Model FLOPs Utilization) is just a hint at what the real problem may be.For context, older frontier-scale training runs were already much higher than 10%. GPT-3 was around 21% MFU. Gopher was around 32%. Megatron-Turing NLG was around 30%. PaLM reached around 46%. And our guest Anjney says best-in-class MFU today is closer to 60–70%.It's not necessarily that xAI is uniquely incompetent (it's clear they have talented folks) but rather the priorities may be flipped in the GPU arms race.While GPU access is a bottleneck, simply increasing CapEx won't automatically translate to better models as frontier AI is increasingly a systems problem: scheduling, utilization, networking, kernels, frameworks, data pipelines, parallelism, cluster reliability, and the thousand small decisions that determine whether your theoretical FLOPs become real training progress.From building Discord's developer platform and backing frontier AI companies like Anthropic, Mistral, Black Forest Labs, and Periodic Labs to now building AMP's independent compute grid, Anjney Midha has spent years close to the real bottlenecks of AI scaling. In this episode, Anjney joins swyx at Periodic Labs to unpack why the AI race is not just about buying more GPUs, why 95% utilization would have been considered an outage at Google, and why the next era of AI infrastructure has to be more aligned, more efficient, and more responsible.We go deep on AMP's vision for a compute grid that makes FLOPs flow like megawatts, the difference between full-stack AI labs and horizontal pooling, why AI data centers need community buy-in, and how compute markets could evolve into something closer to an independent system operator. Anjney also explains why DeepMind's unpublished research points to a market failure, why end-of-life prediction remains one of the most important AI applications he has thought about for fourteen years, and why “output maxing” may become a new discipline for frontier systems.We also discuss Anthropic's culture, why “luck favors the prepared mind” in coding models, how Claude cracked coding, why too much capital too early can make AI labs fragile, what Periodic Labs is trying to do with science and superconductors, why great researchers can become great CEOs, and why Silicon Valley is both deeply missionary and deeply mercenary.We discuss:* Why 95% utilization was considered an outage at Google* Why AI infrastructure waste compounds at frontier-lab scale* Why “move fast and break things” does not work for AI data centers* How data center backlash, power grids, and community incentives shape AI scaling* AMP's vision for making FLOPs flow like megawatts* Why compute needs an independent system operator* How interruptible demand and dynamic prioritization worked inside Google* Why DeepMind research hoarding creates negative externalities* AMP's 1.2GW base-load ambition and the need for 6GW of spike capacity* Why end-of-life prediction could become one of AI's most important healthcare applications* Frontier Systems, output maxing, and full-stack alignment* Why APIs and abstraction layers become lossy as organizations scale* Superconductors, standards, and the dream of lossless systems* SF Compute, open protocols, and the future of compute marketplaces* Why non-NVIDIA chips can still benefit from NVIDIA's reference architecture* Trust boundaries and why chip startups need visibility into future model architectures* Why VCs often underestimate researchers as CEOs* Scientists as star athletes of the mind* Why great CEOs need to be confrontational up and down the stack* Why leading the frontier matters more than “winning”* How Anthropic cracked coding* Why culture is fragile, not a permanent moat* Why hardship was a feature, not a bug, for Anthropic* Why Anthropic's P0 was coding from day one* Periodic Labs, physics as the constraint, and technical reality* Silicon Valley mercenaries, missionary teams, and what happens after a breakthroughAnjney Midha* LinkedIn: https://www.linkedin.com/in/anjney* X: https://x.com/AnjneyMidhaAMP PBC* Website: https://amppublic.com/* X: https://x.com/amppublicTimestamps00:00:00 Introduction00:00:09 Why AI Compute Is Being Wasted00:03:17 Responsible Infrastructure and Data Center Backlash00:06:07 AMP Grid: Making FLOPs Flow Like Megawatts00:12:41 Foundry, Frontier Labs, and Research Hoarding00:14:42 Gigawatt-Scale Compute and End-of-Life Prediction00:24:08 Frontier Systems, Output Maxing, and Alignment00:27:38 Compute Markets, SF Compute, and Non-NVIDIA Chips00:32:57 Trust Boundaries, Co-Design, and Researcher CEOs00:38:17 AI Coachella and First-Principles Thinking00:42:43 Leading vs Winning in Frontier AI00:45:54 How Anthropic Cracked Coding00:48:25 Culture, Hardship, and Anthropic's P000:54:03 Periodic Labs, Physics, and Silicon Valley Mercenaries00:56:26 Rishi Valley, Singapore, and Money as a Measure00:58:47 Closing ThoughtsTranscriptIntroduction: Anjney Midha, AMP, and Compute WasteSwyx [00:00:00]: We're in Periodic Labs with Anjney Midha, CEO, founder of AMP. Welcome.Compute Utilization: Node Allocation, MFU, and AlignmentAnjney [00:00:09]: Thanks for having me. At Google, there are two types of utilization usually, right? That you're measuring in these clusters. One is node allocation, and then the other's MFU. Node utilization is usually like what percentage of cards in the data center are just, used, and that, if it's not at, 95%-Swyx [00:00:29]: There is no excuseAnjney [00:00:29]: There's no excuse, right? I think 95% at Google, which is where my co-founder, Seb, came from, he built the Borg, PBorg/GQM scheduler at Google, and there I think 95% was considered an outage, so 96% node utilization is, should be standard. And most single-tenant clusters are not running at that. So that's one. And then MFU should be, I would say the best in class today is somewhere between 60 and 70%. I think this is a leadership question, right? Fundamentally it's an alignment question, which is are the people who are funding the cluster and then deploying the cluster actually aligned? And sometimes theoretically they are, but in practice the number of people in the chain, the supply chain between, the capital and all the way to whoever's managing the cluster and then whoever's measuring what the output is, are just so many, degrees of separation away that, the, The Have you ever heard the radian metaphor, which is at the beginning of an arc, if you have two arcs that are two lines that are just off by a few degrees, that-Swyx [00:01:33]: It spreads outAnjney [00:01:34]: It spreads out, right? Or at scale. And I think what's happening is a lot of cluster implementations and infrastructure, a lot of frontier labs and other teams, that's what's happening, is they're, they initialize the plan, which is kind of like North Star with a team that wants to do good, but then they're, required to scale so fast instead of iteratively that the wastage just compounds really fast at scale. And so I think we know the answer, which is just do iterative bring ups. If you spend time with people who've been in the semiconductor industry or the DSN industry for a long time, this is not new, and I don't think AI should be an excuse. Sure. Something What is new? Okay. We have a lot of new capabilities, but that doesn't mean just abandon common sense. Common sense should always be in fashion. ? AI scaling doesn't change the in fact, if anything, AI scaling should be putting a premium on the value of common sense and infrastructure because the margin of error now is so much lower and the costs of wastage are so much higher. And the cost of wastage, by the way, is not just economic. I'm, obviously I'm, I'm an investor, or I'm an investor by background. Over the last few years now we're running an AI infrastructure business called, AMP. And I think that it's okay to say this time is different on the capabilities front. We are genuinely getting capabilities at, of the, of a kind we haven't had before. That doesn't give you an excuse to say this time is different for everything, especially infrastructure. So look, I love the hacker mindset and the hustler mindset. Now, that's great for the startup mindset, but you remember this moment where Zuck went from saying, “Move fast, break things” to, move-Responsible Infrastructure and Data Center BacklashSwyx [00:03:10]: Fast and stable infrastructureAnjney [00:03:11]: Move fast with stable infrastructure. I think now we need to move fast with, responsible infrastructure. People are going to ask where the impact is. There was a really In our class yesterday, Scott Nolan, who's the founder of General Matter, came by at Stanford to speak about energy bottlenecks. And he had a phenomenal idea. He said, “if you look at the marginal unit economics of compute per hour,” he goes, “let's call it, $4 an hour. If you're having to bring up a new data center in a new community, why not just say we're going to charge 4.50 an hour, and that marginal impact or that marginal increase, we just literally take that and give it to the local community as cash?” I can tell you as a customer of that compute, I would love that. I'd be happy to pay an additional 50 cents per hour at scale.Swyx [00:03:57]: Wow. Yeah.Anjney [00:03:58]: Because if that means the public benefit is so clear to the communities that the data centers are coming up in, I'm going to feel like that compute is much more reliable. Up to 20% of all data centers this year in the US, my understanding is are at risk.Swyx [00:04:13]: Of community backlash?Anjney [00:04:14]: Correct. Of not getting the community support they need to get brought up.Swyx [00:04:19]: Wow. That's a huge number.Anjney [00:04:20]: Yeah. Now, we, I think we should dig into what that number is. I think it's a little bit of overstated. These things can get over-reported, but it-Swyx [00:04:27]: They don't just care about jobs. They care about all the other stuff around it, right? They care about power grid, they care about environments-Anjney [00:04:33]: Power grid, permitting, and so on. And imagine I think if you said there's a new AI deal. If we're bringing up a data center in your community, we're actually going to reduce the cost of your electricity bill. Okay, now we're talking. Right? The community's going, “Okay. Now this is a deal. I feel like a partner in this.” Right now that's not happening. There will be audits, there will be investigations, and when the, when the regulators come, I don't know when it's going to be, the folks who are moving fast and breaking things in the name of AI progress better be prepared. That's certainly not how we're procuring compute. Or we're, we're trying as much as we can to work with partners who have long-term track records. Many of whom, by the way, are not, AI providers. I think this whole idea of neoclouds being somehow this new category is a lot of marketing speak. There are really good, reliable, trusted data center providers in America who've been around 20 plus years. I love those folks. They know how to Sure. Are they sponsoring happy hours at NeurIPS? No. Are they legibly listed in Build? No. Are they hanging out in my, in, situational awareness parties? No. But they're adults. I trust them.Swyx [00:05:44]: They can run LAN. They can run power.Anjney [00:05:45]: They can run LAN, power, and shell. They have credit histories. We sit down, we have a conversations. Many of them live in Silicon Valley. They've, they've had to deal with the boom and bust cycles of the internet, and I love those folks. They are stable infrastructure partners and thinkers. And I think there's a lot of short-term thinking going on in the compute layer, and it's going to catch up to us. It's not going to be good.AMP Grid: Making FLOPs Flow Like MegawattsSwyx [00:06:07]: You talk about aligning incentives, and, I would think that aligning incentives means you have the full stack in one company, which is xAI and OpenAI, right? So you as a standalone infrastructure layer, why are you somehow more aligned to your portfolio companies than people who just own the whole thing?Anjney [00:06:28]: In systems design, right, there's, there's two regimes of, architecture, right? You have integration, and then you have pooling and utilization, right? So the Or rather, the way to increase utilization often is you can do systems integration where you collapse a lot of process into one node, or you can pull out a process from a node and share that amongst various That resource amongst several different nodes. And so we see the AMP grid, which is, the, what, the system we're building here, which is basically a compute grid. We're trying to do for compute what the electric grid-Swyx [00:07:02]: PowerAnjney [00:07:02]: Yeah, what the power grid did for electricity. It-- this is a pooling and utilization layer across clouds, And so we're actually the opposite of a full stack integration like approach.Swyx [00:07:12]: Super horizontal.Anjney [00:07:13]: Where it's much more horizontal and it's, it's multi-cloud, it's multi-silicon. The goal is to try to make FLOPs flow like megawatts, and that is very hard to do today for many reasons. There's stranded pools of compute all over the place and there's no fungibility. And so right now we do it at the level of scheduling, and we often do it at the economic layer. But as we start to announce what we're working on, it's extraordinary like how many folks are coming out of the woodworks and saying, “Hey, I'm actually working on a way to make compute fungible at this part of the stack and that part of the stack.” And as a grid, we'd like all of these folks to participate on the grid. There's, people often ask me, “Andra, are you a new cloud?” And I go, “No, actually neoclouds are suppliers.” sometimes they'll ask, “Are you a venture capital firm?” I go, “No, actually they are, they are demand like sort of off-takers of the grid.” We see ourselves as what's called an independent system operator. So if you study the history of the electric grid, once it became legible to a lot of factories and industrial sort of participants that, hey, actually it turns out pooling is a good idea. We should pool our generators instead of all having a generator running at half capacity in our backyard. There was a need for an independent entity who could coordinate all these parties. Transmission line, power generation, facilities, transmission lines, factories, and that neutral coordination mechanism is very critical. In order-- If you study like the history of grids, the most enduring ones were those that never owned their own assets. They were ones that had, or often started with long-term anchors who are uncorrelated sources of demand, a steel factory, a shoe mill or whatever in a particular town who weren't competitive, where the steel factory want to spike up at night, the shoe mill wanted to spike up during the day. So then you pool and you share, right? So each of you is guaranteed some base load, but then you kind of schedule your spikes to drive a peak utilization across the town. The gold standard, so to speak, historically, has been these utility companies like PJM Interconnect in the northeast of America, where they, over many years became this what's called an ISO, an independent system operator of the grid. So that's how we see ourselves. Economically, that's what we are. From a technical perspective, we started at the scheduling layer because Seb and Mihai, who, run engineering here, built that at-Swyx [00:09:28]: Did your schedulingAnjney [00:09:28]: They did that at Google. And, -Swyx [00:09:32]: And you have infra shops from Discord as well.Anjney [00:09:35]: I have some.Swyx [00:09:35]: I don't know, I don't know if Discord is like the primary identity, but what-whatever, I'm just kind of-Anjney [00:09:39]: No, D-Discord was-Swyx [00:09:40]: Choosing a well-known name.Anjney [00:09:42]: Well, I So I was running the developer platform there. The internal infrastructure I was not responsible for. That was actually a guy by the name of Mark Smith, who was extraordinary. And yes, Discord did pool So Discord is actually a counter example. I had the chance to learn a lot about fully, full stack infra there because-Swyx [00:09:56]: It's the same thing, yeahAnjney [00:09:57]: It's the, it's the other architecture which is, Discord built its own WebRTC vo-voice and video infra. So like Discord did not use-Swyx [00:10:08]: For the calls, yeah.Anjney [00:10:09]: Yeah, did not For communication, Discord did not use third party infra. It was all built in-house. And then the way you maximize utilization was you pool demand from the world's 200 million plus monthly active gamers, right? And so that's, that's how those stacks were constructed. Again, in systems design, the two concepts that keep coming up over and over again are abstraction and composition, right? And-Swyx [00:10:31]: Bundling and unbundlingAnjney [00:10:33]: Bundling and unbundling, abstraction, composition, like verticalization and-Swyx [00:10:36]: HorizontalAnjney [00:10:36]: Horizontalization. So in that sense, AMP is an independent system operator of the grid. We pool demand, we pool supply from a number of partners we trust At about 1.3 gigawatt scale over four years. And then we pool demand from some of the world's best, research labs and so on. We're sitting at one, periodic labs who need extraordinary long-term demand. And the idea is that, each of them is guaranteed base load on the grid, but they can spike up and down flexibly on, for compute, with much shorter timelines as needed. That was roughly the design of the program I came up with at a16z called Oxygen. The same-- That was the same design of the GQM, BorgX, Borg GQM implementation at Google that Mihai and Seb had built. Which was that how do you allow, teams inside of Google, on the internal infrastructure to be guaranteed capacity, for their base workloads? But when they need to spike up on research, how could they ensure that was sufficiently there? And of course, the big innovation that was not discovered, but kind of implemented in the space, this infra space maybe three, four years ago at Google was the idea of interruptible demand, right? Where you just queue up a bunch of jobs and through this like sort of credit system, there can be a bidding mechanism.Swyx [00:11:53]: Like priorities.Anjney [00:11:54]: It's a dynamic prioritization Basically. And jobs can get interrupted based on somebody else who's saying, “what? I have 10 tokens, 10 credits I want to spend on this job.” Another like team lead, research lead is “Genie 3 or whatever is only worth five, credits, and NanoBanana2 is worth 10 credits,” and so the NanoBanana job gets priority. That's a, that's a made up example.Swyx [00:12:15]: It's very real. Brain Marketplace was real. And, we've, we've covered this on the pod with David Luan, who was-Anjney [00:12:20]: Oh, great. OkaySwyx [00:12:20]: Was there. And the criticism is that, well, actually sometimes you need central command to go all in on a thing. And actually sometimes capitalism via credits doesn't work. Not, this is not a criticism of AMP. I'm just saying, this is a thing that has been tried, internally within Google, and it led to Google missing GPT.Foundry, Frontier Labs, and Research HoardingAnjney [00:12:41]: Like, we structured ourself essentially very similarly to Google. We are structured as a holdings company. So, Alphabet holdings is Alphabet holdings, and then they've got these subsidiaries called Google and-Swyx [00:12:51]: Other betsAnjney [00:12:52]: Other bets and so on. We've got, AMP holdings, and we've got our infrastructure business, and then we've got a capital business called Foundry that incubates new frontier AI labs or invests in them as venture capital, like Periodic. We put a few hundred million dollars into Anthropic from our fund earlier this year. So wherever we feel like teams are making progress, especially researchers and so on who've pushed the frontier inside of existing labs like DeepMind, I find, there comes a point where they feel misaligned with the dictatorship of Alphabet holdings. And at that point, sometimes the dictatorship doesn't want them anymore. And they're “Thank you. You've done your job here. You've kind of helped us through the zero to one phase, and for whatever reason, we're going to deprioritize your amazing, omni model or whatever it is, and instead we're going to prioritize coding.” And, I think that's a tragedy, but I get it. They're Sergey and team are running their own business there. But that doesn't mean we the rest of us should sit around waiting for that progress to get unlocked for the rest of the world and humanity. If you think about how much extraordinary research has happened inside of DeepMind over the last 10 years, I, Demis and Sergey and those guys did such a great job. But at the end of the day, so much of that has never seen the light of day?Swyx [00:14:00]: Or they're like papers only, but they never actually shipped it to production or-Anjney [00:14:03]: What's worse is the paper is actually not even being published anymore ‘cause there's a six-month embargo inside of DeepMind, right? We've heard about this where a paper comes out, and then I think there's a six-month embargo window where if anybody on the business team says, “This could be interesting” It's embargoed for life.Swyx [00:14:18]: Exactly. So the stuff that gets published is the stuff that's not good enough.Anjney [00:14:21]: There's an adverse selection problem, basically. Yeah. At this point-Swyx [00:14:25]: It's, it's a common complaint at NeurIPS, by the way, that's “Well, why would I look at the papers that are the trash of GDM?”Anjney [00:14:31]: Again, I think it's a tragedy. I get it. They're running their business, but the rest of the I think there's negative externalities of research being hoarded, and so that'there's a market failure. And somebody needs to unlock that research, and we can't do it on our own. We only have 1.2 gigawatts of compute. That's nothing. That's about $40 billion of cloud spend. We're going to need a lot-Gigawatt-Scale Compute and End-of-Life PredictionSwyx [00:14:51]: By the way, is that's a new number. I haven't, haven't come across that gigawatt number. That's huge.Anjney [00:14:56]: Yeah. And to be clear, we haven't secured all of it. That's how much demand we have started to secure. I think publicly we haven't actually confirmed how much we have for this year. In order-Swyx [00:15:04]: Where do you want to get to?Anjney [00:15:06]: I think the steady state would be that we have a base load pool Of 1.2 gigawatts at all times Of base load capacity. For spike capacity, right now my estimate is we need roughly six gigawatts over the next four years for all our teams to feel like they were able to keep moving the frontier, whatever they're working on, whether it's, like superconductor discovery over here. There's a new investment we're working on right now, which is in the end of life prediction space in healthcare. It's extraordinary how much you can, you can give this was actually my graduate school work. I went to grad school for bioinformatics at Stanford Med. And I know we-Swyx [00:15:40]: Econ, MCS, bio.Anjney [00:15:41]: So my-- I was this really weird cat where, I was never satisfied with my major options. So at one point I was an econ major, then I was a CS major, then I was a MCS major called mathematical computational science, and they decided they were going to end that major. So I took all that coursework, and I applied it to grad school, my graduate degree in bioinformatics, which was the master's program, and then I thought I was going to do a PhD. I never ended up doing it. I dropped out and went to work at Kleiner. But I was lucky enough to apprentice with this professor at, Stanford Med. His name is Nigam Shah, and he was working on end of life prediction. Stanford is one of the only research facilities in America that has a longitudinal patient data set that's larger at scale. I think it's at least 12 million patient lives. The only larger data set is at the VA, the Veterans Affairs, of America. And to do research, like do any deep learning and so on that data set, it was called the STRIDE data set at that time, you had to be a Stanford Med School affiliate, which is why I went and enrolled in the bioinformatics department. End of deep learning was early. Nigam Shah had the visibility-- the vision to see that, you could do end of life prediction to help palliative care. In America, the, over 30% of all Medicare, Medicaid spend, at least at that time, was spent on end of life care. And what's we grew up in Asia, so we all-- Yeah, at least I won't speak for you, but I have A very different relationship with death than I find folks who grew up in America do. In America, spiritually and culturally, especially in Western societies where Christianity, the Christian tradition sort of frames death as this terminal point, there's often a judgment day and so on. The way we view death is with a finality. In Indian culture, in Hindu culture, death is one-Swyx [00:17:35]: Also, he's Buddhist as well.Anjney [00:17:36]: You're Buddhist, yeah. So it's one, it's one step in a journey of many lives, right? And so, I grew up in this city called Chennai in the south of India, and when people die, you dance on the street. There's like a procession where your body is carried to be cremated and your family, like celebrates and there's drums and so on. It's this huge thing. And, It's because the idea is that you're going to be reincarnated. You've been liberated from the responsibilities of this life, and now you're onto your next. It's a new It's like going off to a new college or whatever, right? And so it was so alien to me when I got here as an undergrad- That the medical system works backwards from that assumption that we have to view death as this terminal thing and delay it, postpone it's a bad thing. And so at the time, clinical decision support in the United States was this very primitive field. Even to this day, physicians in the United States often will tell you when you have a terminal disease, this is your, we've diagnosed you, which is great. Our ability to diagnose you is extraordinary. You have somewhere between six months to six years to live. What do you do with that information? The error bars are so high that then you In times of uncertainty, we default to culture, and when the culture is let's-- this is a bad thing, I've got to prolong my life, then you start doing things like And just to, just sort of from a systems perspective, what's going on there is Physicians often feel like they need to provide such high error bars because there's always some uncertainty in end of life diagnosis, and if you provide the wrong Diagnosis or recommendation to your patient, you can be sued for medical malpractice. And then your license can be taken away. It can be catastrophic for your career. In contrast, if in countries where that's not the case, what you often observe is that patients, physicians are quite prescriptive with their recommendation. They say, “Hey, this is your condition. The literature says that you probably have this much time on Earth left. My expert opinion is that you are an outlier or whatever.” And they try to be more prescriptive, and that empowers a patient, right? ‘Cause then a patient can say, “I trust my doctor. They said on average, I have six months to live, but if I do these things, I may have a shot because of my particular predispositions or my genetic history or whatever.” And that empowers you to go about your life in a actually more scientific way than leaning on religion, culture, spirituality, and so on. In contrast, here, because of that medical malpractice sort of thing looming over your head, a physician never gives you a clear recommendation. So instead you say, “Okay, Doc, well, let's try it all.” And then you start a whole regime of drugs and therapies, and then you often spend weeks and weeks in the hospital, and that deteriorates your quality of life. And when that deteriorates your quality of life, you instead of spending your last few days doing the things you love with your family, you're spending it on a hospital bed. And that ends up being thirty percent of Medicare and Medicaid. So it's worse for the patients. The doctors feel terrible. The American taxpayer is paying a huge amount of money. And so this is why Nigam Shah, who was this professor at Stanford, said, “Anjney, if there's “ I kind of sat down with him. I was this young, I'd, I was twenty-one, and I was “I want to work on a big problem.” He's “The big problem is end of life care.” And so we tried to do deep learning to say, to-- So we started trying to run deep learning on these tried patient data sets to say, “Could you have an AI system make a recommendation that is orders of magnitude more precise about how much time you have left once you've been diagnosed with a terminal condition than a human?” And then if we can get that precision to be high enough, then you can empower the patient. And it turns out the tech works. Like it's-- Once you get the data set, like RL works. Honestly, even regression models work. You don't need to get that fancy. At the time, we were just trying, doing like very simple neural nets.Swyx [00:21:54]: Simple solutions, yeah.Anjney [00:21:54]: Today, what we can do with RL is extraordinary. The problem remains then and now is regulatory, because you actually can't shift the burden of the wrong clinical diagnoses from the physician to the AI system. And so at that time, I got quite disillusioned ten years ago for, twelve years ago where, ‘cause I felt I just didn't have the resources to influence regulation. Today, I'm very lucky. I'm in a different place. I've, I'm a lot older, and so I've been spending a lot of time on my next incubation, which is how can we unlock the, patient empowerment by training AI models to do end of life prediction much, with much more precision and ac-Swyx [00:22:37]: Oh, wow. You're still focused on this the whole time.Anjney [00:22:40]: The-- I haven't been able to get, this out of my mind a single day for the last fourteen years. This is the hill I want, I would like to die on. There's two, I would say. What? I actually, I'd prefer not to die.Swyx [00:22:51]: Yeah, exactly.Anjney [00:22:52]: But I think two bipartisan issues, I think two issues that should be bipartisan in America are how do we empower patients to make the right clinical decisions at the end of their life, such that we're reducing the taxpayer burden with science? It's just good old science, and AI can help here. And the second is, net positive data centers, ‘cause I think that's the biggest critical bottleneck on training and good enough AI models to help people at the end of their life. So there's sort of two sides of the, of the same scaling bottleneck curve, but those two, we formed AMP as a public benefit corporation. My wife and I, who you've met, you've met Viv. Her passion is education. Her family is a long line of educators and so on, and, of physicists. And so this class is my attempt to stop being the black sheep of the family and be a, an educator. But if I'm not educating, the thing I would be doing is working, on these two problems, whether on the political spectrum or as a researcher back at, in some lab. And my hope is if anyone's listening to this podcast, if they're passionate about either of those two topics, I'd love to hear from them. We'll, we'll we can share the contact in the show notes, but, we're looking for people to join both of those missions on the, on the political side as well as on the medical side, on the research side.Frontier Systems, Output Maxing, and AlignmentSwyx [00:24:08]: You said, this is a discipline that you want to form. You call it's called variously called Frontier System. It's variously called One Person Frontier Lab. What is the ideal name or shape of this? Like the, what is the mission?Anjney [00:24:24]: Of the class?Swyx [00:24:26]: Of the discipline that you're, exploring, right? I The class is called Frontier Systems. But like for me, maybe one phrase is you're, you're just anti-waste, right? Which is wasting GPUs, wasting in human and Medicare. But is there, is there a broader theme that I'm, that maybe you can encapsulate more succinctly?Anjney [00:24:45]: Yeah. The, from an engineering perspective, it's very simple. It's output maxing. It's the, it's the department of output maxing.Swyx [00:24:51]: Making the most of what we have.Anjney [00:24:52]: Exactly. I'm a huge believer in optimal outcomes. I think both in America and other countries, we are losing our appreciation for nuance, and this is the thing of And AI is the same case, right? Oh, the bitter lesson holds. Okay, fine. But that doesn't mean you just like throw 500 GB300, 500,000 GB300s at your suboptimal model scaling and you waste a bunch of compute. It also doesn't mean that, the most optimal is to have like 50 different architectures where there isn't enough standardization. One of the reasons Anthropic has had extraordinary sort of velocity is ‘cause they picked the transform architecture and said, “This is simple. Let's double down on it,” right? And now luckily there's enough investment going to the space that we can afford other architectures, but at the time, investment was just too fragmented into other architectures, so that arguably unlocked scaling. So I think there's a philosophy. I think we all owe it to ourselves to do output maxing with a new capability called AI on a global level. I think if I was starting a new department at Stanford, depending on how fuzzy or technical I wanted to be, I'd probably call it the Department of Alignment. Like-Swyx [00:25:59]: It's an overloaded termAnjney [00:26:01]: But it is, But alignment really Is a hard problem. And I think when you unlock it, full stack alignment is super hard in any organization and in any system. Like in a, in a venture capital firm, if you can have full stack alignment between your limited partners and your, the founders who are creating the value and ultimately the public that owns the IPO stock, that is a gift that keeps giving. And when you study the history of these systems, when they start off, they usually start out small scale where the feedback loop is actually so tight that there's alignment. And then the more you try to scale, the more division of labor happens, the more specialization happens, and at each step you add abstractions. And wherever there's an API interface, there's like loss. There's communication loss. And so I think a really cool thing would be for us to figure out is there a way for us to have our cake and eat it too as an engineering discipline? Is there a way to actually scale up and scale out Without losing any alignment, without lossy transmission?Swyx [00:27:01]: You mean standards?Anjney [00:27:02]: So standards is one way. The other way is you just have net new capabilities. So like what we're trying to do here is discover new superconductors. A room temperature superconductor would be a lossless transmission mechanism for energy. We would have flying cars. We are right within a few years of having a new room temperature superconductor. So I think those are the two. You either have to standardize On protocols or API specs that allow lossless communication, or you can come up with a whole new capability that unlocks so much abundance, the standardization doesn't matter ‘cause you just unlock net new capacity. This, the, so this is what I spend my days thinking about these days.Compute Markets, SF Compute, and Non-NVIDIA ChipsSwyx [00:27:38]: No, I think every infra person at, who wants scale and wants to output max does eventually end up thinking about this. We don't have time to go into it, but we have done an episode with SF Compute-Anjney [00:27:50]: Oh, coolSwyx [00:27:50]: That is trying to standardize The futures contract for compute. I don't, I don't know how that's going by the way, but like at some point this will be public.Anjney [00:27:57]: Oh, I think Evan is awesome and SF Compute is the kind of effort that I hope we can accelerate because what often happens is these exchanges are very hard to get, they, it's hard to bootstrap them, right? Because they often require-- There's many inefficiencies between parties. There's trust boundary inefficiencies in infrastructure because you don't trust, one part of the stack doesn't trust another part of the stack to give them visibility. There's capital markets inefficiencies, there's operational efficiencies. So if you can inject like a single shock to the system of a ton of compute demand or supply, then you can accelerate, these new flywheels. And so my hope is one day, or soon, if SF Compute needs extra like has excess capacity, they just hook it up to the grid and they get flooded with demand from us. And on the other side, if they have a ton of demand but they don't have supply, they just again hook up to the grid and it's a two-way protocol where they can just hook up to our capacity. And I don't think we're too far from that. Today our working implementation of it is mostly through a group of labs, universities, and a few sort of trusted parties who are, who all feel like they're in alignment to borrow an over sort of used word. But our hope is to just have it be an open protocol that anyone can hook up to on-Swyx [00:29:20]: Hook up for demand or hook up for supply? In primarily demand, it sounds like. Like you-Anjney [00:29:25]: No, bothSwyx [00:29:26]: You would want to offer demand.Anjney [00:29:27]: Both. Yeah. Unfortunately, what's happened in the last six weeks is, we thought we'd have a bunch of excess capacity by the end of this year. It's all gone.Swyx [00:29:37]: It's exploding.Anjney [00:29:38]: It, yeah. It's all gone. And so I have, my text messages are full of friends, we know many of these people, these are founders who've raised billions of dollars in San Francisco going, “Oh, any chance you have like 50 nodes in the next few weeks?”Swyx [00:29:51]: What is the scope for, non-Nvidia, right? You have Lisa Su coming and, Rainer Pope as well. And so There is a lot of demand for, more performance Alternative architectures and all that. At the same time, this hurts your standardization.Anjney [00:30:11]: I don't think so. So actually Rainer's a great example, right? Rainer is a CEO and founder of, MatX. I actually had him by for office hours in the class earlier today, and there was an insight he brought up that I hadn't considered before, which is when they decided to pick the standard For their data center, they picked the NVIDIA reference architecture. So the MatX chips Just plug in to any site that has an NVIDIA bring up planned. And, the-Swyx [00:30:42]: It's just software then. It's, it's not the-Anjney [00:30:44]: A-Swyx [00:30:44]: Hardware.Anjney [00:30:46]: Well, from an input and IO perspective It's the same footprint as an NVIDIA rack.Swyx [00:30:52]: That makes sense.Anjney [00:30:53]: Where they have done, innovated a bunch from what I can tell is on systems co-design. Which is where a lot of the gains are to be had. And so he picked He was “Anjney, we, there's just so much work to do when you're building a new chip company.”Swyx [00:31:08]: Can't fight every front.Anjney [00:31:08]: You just can't fight on every front. So my question to him was, “Well, you're working on this new chip. Their tape-out is next year. What, who are you going to partner with to host the chips?” And he said, “Whoever will host them. That's just not, that's not my focus.” And I said, “But how did you “ you decided back to our earlier systems design question, he decided that, he didn't want to be a full, fully integrated chip provider. The bottleneck they're focused on is the logic die, and they, he feels they can crank out a ton of performance gains through co-design there. But then that means you delegate, to our question earlier, it, you he's the data center provider is a different part of the stack, and so then he's dependent on that part of the ecosystem to host his chips to get the performance gains to the customer. So now you have another abstraction, and you might have loss. So I asked him, “How do you prevent loss?” And back to your point, he said, “I just picked the NVIDIA standard ‘cause I didn't want to Like I wanted to piggyback off of an existing protocol.” And that, what's great about NVIDIA is that reference architecture is known.Swyx [00:32:15]: Open.Anjney [00:32:15]: It's open. They've published it. So Jensen's actually enabled someone like Rainer to build a chip company like MatX, and I don't see them as competitive. The compute demand is so high. Like, I don't I think NVIDIA's not able to meet the demands of production, so we just need more chips. And I think it's very smart what MatX has done, which is say, “We're just going to we're not going to innovate on the data center design ‘cause actually, thank you, Jensen, you've done all the hard work. Where we can innovate is somewhere else.” And I think that's, that's very healthy. I think that's how we unblock new bottlenecks. And my view is these, the, chip teams like MatX, who have arrived at the insight that co-design is the way, The primary bottleneck for them is trust boundary. To do co-design well, you need visibility into the next model generation as soon as possible ‘cause it takes two years to tape out. So if by the time I bring my chip to market, your model architecture's changed, I'm host. Now, when he was inside Google, he was sitting next to the Gemini team. He was on Palm or whatever.Trust Boundaries, Co-Design, and Researcher CEOsSwyx [00:33:19]: His co-founder was the, was one, was one of the Palm guys, I think.Anjney [00:33:23]: Yes. Yes, exactly. So when you're inside the trust boundary of Google, then your systems co-design loop is super tight. When you leave as a founder, one of the biggest risks you take is now you're outside the trust boundary. And so what I love doing is helping chip teams who can help us unlock more capacity for the independent ecosystem access to trust. Because when I If I've been, involved with a lab from day one, and I was lucky enough to work with Anthropic, and then I'm on the board of Mistral and helped Black Forest Labs get started. I think at this point I'm on six or seven different teams.Swyx [00:33:57]: Only six? I feel like my mental number was going to be 13, but yeah, it's-Anjney [00:34:02]: No, I go deep with one at a time.Swyx [00:34:04]: You're founding CEO of Arena.Anjney [00:34:07]: Nah, that was an, that was an-Swyx [00:34:08]: Administrative CEOAnjney [00:34:09]: It was an administrative five-month gig where Whalen and Anastasios were graduating from their PhDs, and they didn't need a product team. So I helped recruit the head of engineering product and design. But Anastasios has always been the CEO of that company. I played a pinch-hitting I'm an intern. I was CEO intern For five months. -Swyx [00:34:33]: I interviewed him, and he's he's very well-spoken. I think he's a debate, former debate, champion. But also very quantitative and mathematical, which is-Anjney [00:34:41]: He-Swyx [00:34:41]: Such a unicorn.Anjney [00:34:43]: See, what's amazing about him? If you look at his output, he's an output maxer. By the time he was graduating from his PhD, which he only graduated last year, he had published more work with a citation count than, people twice his age. But at the same time, he'd already started a project called LLM Arena that was being used by millions of people As a side project. And time and time again, what I've realized is venture capitalists suck at seeing human beings as, dynamic agents where-Swyx [00:35:14]: They want to put you in a boxAnjney [00:35:15]: They want to put you in a box.Swyx [00:35:15]: This is your thing.Anjney [00:35:16]: So the first time I got introduced to Anastasios, somebody had told me “Oh, he's amazing, but he's a researcher.” I was “what? What do you mean he's a researcher?” That's what-Swyx [00:35:28]: Like he's not a CEO, not a founder.Anjney [00:35:29]: Not a CEO, exactly. I was “Are you crazy? Do you Have you met Dario?” Dario's a scientist. He's gone from zero to, what will soon be a trillion-dollar company in four years. Being a CEO, nominally speaking, is not that hard. Being a good CEO is hard. Being a great CEO actually requires a level of performance that scientists who have already published at the top of their field have accomplished. It is super hard to be a competitive scientist. To publish in academia over the last 20, 30 years, to make it to the top of your discipline at a place like Berkeley, you are a star athlete. Like, you are an athlete of the mind, and you perform at the highest levels. And to get there, whether you're, Anastasios or Whalen at Berkeley, or you are Robin, who-Swyx [00:36:23]: BFL, yeahAnjney [00:36:24]: With Black Forest, who created Stable Diffusion, or if you're, like Guillaume at Meta, who created Llama before he started Mistral. The amount of human leadership you have to demonstrate to get the resources, like get the trust of the organization, publish it, put it up. I would just fund researchers all day Right? If who have contributed already to the field. If they've, if they've put SOTA out there, they're, they're star athletes already. If they haven't done SOTA Look, they can still be good CEOs, but then I find the failure mode is that they just don't want to be CEOs, they primarily want to publish, and that's okay, too. One of the things we do with the AMP Grid is we donate excess compute. We have two nonprofits, like university labs. We carved out like a couple thousand H100s. But I do think there's extraordinary research being done on university campuses. My father-in-law's a physicist. He's a professor. Extraordinary work in physics, and we need that. But if you want to be a CEO, what you need to be willing To do is be super confrontational, outside of science. Like within the scientific community, some of the best researchers are very confrontational about their convictions, right? This architecture is right. To be a great CEO, you basically have to be willing to be confrontational up and down the stack.Swyx [00:37:41]: To your own team.Anjney [00:37:42]: To your own team-Swyx [00:37:43]: To customersAnjney [00:37:43]: Hiring, recruiting customers. Well, I would say, Yeah, pretty much to everyone Everybody. Of course-Swyx [00:37:50]: I see, I feel a little bit of that in my own work, but yeah, I can't imagine the stakes that Dario has had to go through. It's, it's pretty insane.Anjney [00:37:56]: No, I don't think the stakes are that different From how you're feeling it, right? Stakes are personal scaling vectors, right? The stakes that seem so low to you, like having this podcast where you can talk to somebody and just have a you're an extraordinary communicator, right? Like already in this conversation, you've pulled more out of me than most people, and I've been on 12 podcasts in the last two weeks.AI Coachella and First-Principles ThinkingSwyx [00:38:17]: I think I, we've just seen each other enough that there's some base trust.Anjney [00:38:20]: There's base trust.Swyx [00:38:20]: And I think, and I know that you, that I've done my homework and like I know that trust is a big deal for you, so.Anjney [00:38:27]: I think trust is about consistency, and you and I have seen each other In the community for years, right? Like, I remember the first time we met was at NeurIPS in New Orleans. I don't know if you remember that, luncheon.Swyx [00:38:38]: Oh my God.Anjney [00:38:39]: Reiko had set up this Reiko's amazing, and he set up this luncheon and-Swyx [00:38:43]: Yeah, I was “Who's this Discord guy?” I'm “Okay.” But-Anjney [00:38:45]: No, you weren't-Swyx [00:38:46]: You were just “You made some investments.”Anjney [00:38:47]: You were much less polite. You were “Who's this VC?” You're like-Swyx [00:38:51]: No, I Was I? Oh my God.Anjney [00:38:53]: It was-Swyx [00:38:53]: I'm so sorryAnjney [00:38:53]: It was visible on your face.Swyx [00:38:54]: I'm so sorry. But you weren't, you weren't The introduction was bad. I was I didn't know who you were.Anjney [00:39:00]: The, see, this is the thing about context, right? Like, but then I think I heard your accent. And I was “Are you-”Swyx [00:39:06]: Singapore, yeahAnjney [00:39:06]: “Are you Singaporean?” And you're “Yeah.” And I said, “I went to high school, JC, in Singapore.” And then the ice broke. But This is the there are in the scientific community, sometimes the stakes are very high for people who haven't had the emotional, what is called EQ Coaching and mentorship, right? Which is like to have scientific impact, you often need to be a extraordinary emotional, like emotionally in tune person with the folks you're trying to influence. And so what comes so naturally to you is actually a super high stakes thing to other people. And so I wouldn't assume that Dario's more stressed out than you. These things are you'd be surprised how similar and small sometimes the problems are to you That some of the world's biggest, leaders are facing. And that's what I've learned from this class. The guest speakers are Sam, Satya, Jensen.Swyx [00:40:01]: AI Coachella.Anjney [00:40:02]: Yeah. It's AI Coachella, right? So we got to get all the headliners, and they're I'm very lucky that some of these people have either mentored me over the years or I've done business with them. And when you, take the performative stuff out and any assumptions you may have about these people that you read in the press or on Twitter, We're all just humans. We're all trying to get along. And what's so special about this moment is AI is forcing, like scaling, the bitter lesson is forcing a lot of people to revise their assumptions for how the world works and go back to first principles or go and educate themselves. So the kind of people I was, I won't name who this person is, but I was at an event last week in Texas and, ran to somebody who said, “Anjney, I came across the class. What do you think about real time action prediction models?” And I was, don't know how happy it made me feel when they asked me that question. I know they've done the work. They've challenged themselves. I'm, they didn't ask me, “What do you think of world models?” They said, “What do you think of n-”Swyx [00:41:04]: Real time action predictionAnjney [00:41:05]: “action, real time action prediction models?” World models, don't get me wrong, are cool and everything, but you and I both know that is a layer of abstraction that is sometimes not usefully precise enough. Right? Ours-Swyx [00:41:16]: There's like four different kinds of world models.Anjney [00:41:17]: Yes, exactly.Swyx [00:41:18]: We've done the part with general intuition, by the way, which is very focused on, -Anjney [00:41:22]: Oh, cool. Yes. I love Pim. Pim is great. And this is what I love about people who've done that level of work. They realize they're not in competition with people who the rest of the world thinks they're in competition with.Swyx [00:41:34]: Because they're not in the category, they're in the specific thing they're trying to do.Anjney [00:41:37]: They're focused on their mission, and they have a systems understanding of the bottleneck they're trying to solve. And when somebody else says, “I'm working on real time, action prediction models too,” Pim goes, “Oh, I love that person. I want, I can learn from them.” But the minute they're “Oh, that person's a world model person,” it's “like which type of world model person?” But mostly they're just trying to figure out if it's a waste of their time, because we don't have enough time. So, Pim, for example, is super, loves this other company I work with we've talked about called Black Forest Labs. And he's mentioned to me multiple times that he's so, He thinks what Flux is doing is really cool. Andy Blattman came by and spoke in the class. And what I find over and over again is for people who do the work, who can be usefully precise enough about like what is actually going on in the world of frontier research, The sense of camaraderie is still well and alive, but it gets lost sometimes when you have to like abstract The technical complexities in, business terms And then the VCs are “How are you different from that world model?” I'm going to say Where do I even start to explain this stuff? And then the misalignment creeps in.Leading vs. Winning in Frontier AISwyx [00:42:43]: This is good. Yeah, I think, people listening get a sense of, what it is like to operate at a real level, like yourself, rather than at, the journalist level, where you have to sort of put everyone in, a rough category and create a narrative of competition, and who's winning today, who's behind.Anjney [00:42:58]: It-- this idea of winning is so Weird to me.Swyx [00:43:03]: You do want to win. You want you want competitiveness.Anjney [00:43:06]: No, I think you want to lead.Swyx [00:43:07]: You want SOTA.Anjney [00:43:07]: No, I think you want to lead. Yes, so you want to push the frontier. You want to push the SOTA. You want to do something that hasn't been done before. You want to capture value, but you don't want to capture so much value that, people think you're unaligned with your mission or trying to do what's best for the world. You want to capture enough value that you can keep innovating, right? And I think that people want to lead, they don't really This idea of winning and losing, again, I love Jensen. He's a, he's a leader. The mindset that he talked about on Dwarkesh's podcast, right? He's “I didn't wake up with a loser mindset.” I think that was awesome, right? Because he's, he's an engineer. Dwarkesh has done the work. So there's at least-- even though the, to me, it was very obvious they're talking about the same thing, they just passed each other. They just had to basically, Jensen has this, five-layer cake abstraction of how the industry works. And Dwarkesh had, I think from that podcast, had more of, a pre-training, mid-training, post-training systems loop concept.Swyx [00:44:04]: It's just a factor of who he talks to, right? Again, it's very clear.Anjney [00:44:06]: It's the systems It's the abstraction, the mental models, the It's the whole-- Dude, so much of the problem in the world is reasoning by analogy. And then the assumptions that are held invisibly.Swyx [00:44:19]: Yeah, I've, I've said, this is actually the best time in human history for first principles thinkers. Because everything you think will happen is actually now coming true.Anjney [00:44:28]: Correct. And the venture capital community is, notorious for this, where people look-- In times of uncertainty, they, cling to axioms that ended up being true from the previous era, and they kind of like proclaim them with confidence as if they're truths, but they're not. And it's very important to see the distinction between a heuristic and an axiom. An axiom can be proven-Swyx [00:44:55]: Like from internal consistency point of viewAnjney [00:44:56]: With internal consistency. A heuristic is a way you kind of a shortcut. And my God, the number of people I have had to put up with over the last few years who proclaim-- use heuristics As axioms to judge people, to judge which companies are going to succeed or the number of people who are “Oh, yeah, Anthropic, they're just training models right now,” but this one continue.Swyx [00:45:22]: Because that's a B2B SaaS?Anjney [00:45:23]: Yeah, the, like Which over the fullness of time, if you squint at it, maybe. But the way you arrive there is so important that you can-- you just, you can dismiss people. Here's what happened, right? What happened is Anthropic basically achieved takeoff in October of last year. That training run-Swyx [00:45:41]: Whatever, three seven?Anjney [00:45:42]: I forget the numbers now, but whatever that checkpoint was-Swyx [00:45:45]: We saw the cognition.Anjney [00:45:46]: Yeah. Right? You probably-- The, to those of us in the community, especially once post-training was done and it was released in December-Swyx [00:45:52]: Yeah. Can I sneak a sneaky question in there? I don't know if you have a perspective, maybe you don't, I just The number one question is how did Anthropic crack coding, right? Because Claude One, Claude Two, okay, like it was part of it, but it wasn't a big deal. And the leading hypothesis, it's a lucky dice roll that was then compounded, right? Like it was like Mildly better, but then they saw it and they were “Okay, let's really invest.”How Anthropic Cracked CodingAnjney [00:46:17]: I had this very annoying teacher. I went to this boarding school called Rishi Valley in India, which is like this, bird preserve. It's like three hundred and fifty acres of bird preserve in rural India, and there was no technology for seven years. There was this teacher, I won't name them, but they would have this-- I hated it every time he said this to me. He was “Luck fa-favors the prepared mind,” which is like a common saying, but the way he delivered it, always grated me, ‘cause he was always I was always one of those kids who got, a good grade without trying very hard. ‘Cause like high middle school is not that hard if you, if you're generally, paying attention and so on. And there was this one time where I-- But then I would get an eighty percent grade, and he would keep pushing me to say “The reason you didn't get the ninety-five plus percent is because you're not that lucky.” And I would say, “What do you mean?” ‘Cause I would think that I deserved that grade, and I would sometimes argue with him. And he'd say, “You didn't have a prepared mind. If you want to get lucky again “ There was basically one time where I got like ninety-five or ninety-six on this, on this subject, and I, now that I felt entitled. I was “Okay, I'm going to keep doing this,” and I didn't. And then he was “Luck favors a prepared mind. You got lucky last time, but you got to stay prepared.” And I didn't understand what he meant. Now, as I'm older, I'm okay, these adults actually knew a thing or two. Anthropic has been the most prepared company for four years. And so then when the right, context data comes in, the right developers start sending in, the right context diffs, Sure, you could say you got lucky, but if you ask me, they're pr-pretty damn prepared with paranoia for like four years. And you have to remember, it was so hard for them to get going early on that they had to do so much more with so much less that you just have to be prepared to be so efficient.Swyx [00:48:06]: Yes. There's numbers on their burn compared to OpenAI. I've, I've written about it, but they are so much more efficient in their, in their tech stack.Anjney [00:48:14]: It's not even It's not funny.Swyx [00:48:14]: Not even close.Anjney [00:48:15]: Yeah. But it's so clear, right? Like how to output max for the world. They have been prepared, and you could call that luck, but Luck favors the prepared mind.Culture, Hardship, and Anthropic's P0Swyx [00:48:25]: This is one of those things that I was going over some of your old lectures and, you were data, people think it's a moat and actually it's culture and actually it's team Actually. And I, it's-- there's different levels of moats, and this is the ultimate one that determines everything else. Which you can then compoundAnjney [00:48:43]: You're saying culture is the ultimate moat? Yeah. But the thing about culture is it's very fragile. So moats, I don't think they're-- there's very few moats I found that are actually moats. They're-- It's, it's a nice concept, but in reality, you have to replenish your culture. Ben Horowitz was, the speaker in CS153 on Tuesday, and I asked him this question about the culture bottleneck in teams because, there are several AI teams-Swyx [00:49:09]: His book, Hard Things About Hard ThingsAnjney [00:49:11]: Hard Thing About Hard Things. But more concretely, there are so many AI labs today that have all the cash they need, they have all the compute they need, and they're still not able to ship anything SOTA. And then you start seeing people leave and so on, and my diagnosis, it's, is it's the culture. And so I asked him, Ben, they're-- He's been one of the most aggressive investors in AI labs. He goes back to this thing which resonates in my mind a lot. It-- When I used to work at a16z, I would, book a conference room, and right outside the conference room, which is closest to the toilet ‘cause it was the fastest way for me to go use the bathroom between Zoom meetings-Swyx [00:49:45]: Oh my God, I'll put maxing my toilet optimization. Okay, never mind.Anjney [00:49:48]: It was not healthy in hindsight, but maybe this is TMI. But anyway, outside that conference on the wall was this quote that was printed that said, “Culture is not a set of beliefs, it's a set of actions.” And it's by Bushido, is this, Japanese philosopher. And if you stop taking the actions that demonstrate the mission alignment to what you've said to your team and to your-- the world matters to you, then your culture starts to fray. So it's not actually a moat, I would say. It's a very brittle, fragile thing that requires daily tending to like a garden. But if you figure out the system to keep that garden tended, which I think ultimately comes down to knowing yourself ‘cause you most naturally, if you're authentic and so on, you'll naturally make trade-offs that seem effortless to you, but that reinforce your culture. And then That becomes this very hard thing for other people to catch up to. And at Anthropic, from day one, there was this mission like-- missionary like zeal and belief that, hey, these capabilities will scale. These systems are stochastic, not deterministic. There will be error bars, and until we crack interpretability, there's risk. And at some point, people will go-- stop using Claude just for coding. They'll use it in some mission-critical context where there's-- it'll throw off a bug, and then people are going to come blame them, and they want to be on the right side of history where they said, “Yes, this is a powerful technology. We think it's going to change the world, And we want to be very measured and scientific about the fact that, ‘Hey, guys, these are stats models, statistical models.' That's how statistics works.” ultimately, when you're training neural nets, it is just a statistical system. And I think that Belief that safety is important and that it might seem toy-like in the early days, and sometimes, you could say, “Anjney, they totally over-exaggerated the risk,” like two years ago when they said, “Let's not launch Claude One,” or whatever. Well, okay, maybe in hindsight, but hindsight is twenty/twenty. And at the time, they didn't know how that model would be used, and to them it felt existential if somebody came and said, “You weren't responsible. It-- This wrote a bug.” The liability associated with that is massive. So how do you prevent against that? Well, day in, day out, you say safety. And when you start deviating from that, you have the team hold you accountable, you have the world hold you accountable, and I think that becomes a moat over time. At some point, that moat will get challenged and so on, and then it become fragile. I hope it endures because that's the beauty of having founders run the show, ‘cause they can make really hard trade-offs to do mission alignment. The hardest part is in the earliest days when you don't have a group of people who are going through difficulty, stress, crisis together, then your culture doesn't get defined sharply enough, and that's what I'm worried about right now, is there's so much money going to these labs. There's no hardship. There's no-Swyx [00:52:50]: To anyone who knowsAnjney [00:52:51]: There's no to anyone who knows. And that, in hindsight, was a feature, not a bug for Anthropic. The number of people who said no, the number of people who said, “Sorry, we're all doing investors in OpenAI,” that is competitive difference. It forces you to really understand, what is the hill you want to die on at the expense of everything else. What's the P zero? And there, P zero from day one was coding. The reason, the mechanism system there was if we crack coding, Then we will crack AGI. Our mission is AGI. We want to get there safely. If we focus on codin
Are you concerned about AI threats to your privacy? If you aren't, Darren's got a few reasons why you should be! Images of fingerprints and keys, wifi signals and more could leave you vulnerable to bad actors using AI. Adam wonders why the order status screens at McDonald's and other fast food restaurants often have inaccurate information. The problem leading to this is due to a problem known as Goodhart's Law.
Created by St. Louisan Lindsey Goodhart, “Closet Space” is a sapphic blind dating show that fosters a welcoming space for the LGBTQ community through its live studio audience. Goodhart reflects on how the show has sparked genuine connections and the importance of establishing dedicated queer spaces in Missouri, both on and off the stage.
The damage from your Q1 goal doesn't show up until Q3, on someone else's dashboard, after the person who flagged it got fired.Part 2 of the Outcome Trap series. Brian and Om argue why you can't see the trap from inside it: second-order effects land too late to trace, the people who spot trouble get removed, and the truth fractures across team dashboards until nobody owns the whole picture. By the end you'll have questions to ask before any number you set quietly destroys the business.Listen or watch as we discuss and debate:Why Goodhart's Law turns every new leading indicator into another surface to gameHow Sears split into 40 competing units and imploded while every department hit its OKRsThe Wells Fargo whistleblower fired for 'tardiness' eight days after calling the ethics hotlineWhy Deming's 1986 warning to eliminate numerical goals got ignored for forty yearsTwo questions to ask before setting any targetIf you've ever been in a company where every conceivable metric was green while the business slowly bleed out, this podcast is for you!.#OKRs #Deming #GoodhartsLawW. Edwards Deming (Out of the Crisis, The New Economics), Goodhart's Law, Peter Senge The Fifth Discipline, The People's Republic of Walmart, Sears (Eddie Lampert), Wells Fargo (Bill Bado), Frances Haugen Facebook testimony, Careless People by Sarah Wynn-WilliamsLINKSYouTube: https://youtu.be/BuWgxH8VpRISpotify: https://open.spotify.com/show/362QvYORmtZRKAeTAE57v3Apple: https://podcasts.apple.com/us/podcast/agile-podcast/id1568557596INTRO MUSICToronto Is My BeatBy Whitewolf (Source: https://ccmixter.org/files/whitewolf225/60181)CC BY 4.0 DEED (https://creativecommons.org/licenses/by/4.0/deed.en)
Award winning jazz saxophonist and broadcaster Soweto Kinch and writer and director of new film Köln 75, Ido Fluk, join Tom to explore the importance of Keith Jarrett's seminal performance at the Cologne Opera House in 1975, and its subsequent album, which became the bestselling solo album in jazz history.Sex Education and Rivals writer Sophie Goodhart on her award-winning comedy-drama Alice and Steve, starring Nicola Walker and Jemaine Clement. It's about best friends turned enemies, after Steve starts dating Alice's 26-year-old daughter.Cultural historian Thomas W. Laqueur talks about depictions of dogs in art, as he publishes his new book The Dog's Gaze.Critic Clarisse Loughrey talks about how small screen directors and creators on YouTube have made the leap to Hollywood's big leagues, with films like Obsession and Backrooms breaking box office records and driving Gen Z to the cinemas.Presenter: Tom Sutcliffe Presenter: Claire Bartleet
You've invested in the dashboards. You've declared data a top priority. So why does transformation still feel out of reach? In this episode, Brandon Laws sits down with Dr. Sebastian Wernicke, author of Data Inspired: Building an Organizational Culture of Inquiry for Lasting Transformation, to unpack one of business's most frustrating paradoxes: companies that succeed at data... and still don't change. Sebastian challenges the conventional wisdom around data-driven organizations, reveals why human psychology is working against your data strategy, and introduces a more powerful mindset: becoming data inspired. From Goodhart's Law to Netflix's bold decision-making model, this conversation is loaded with ideas that will fundamentally change how you think about data, leadership, and organizational transformation. Don't miss it. Key Timestamps [00:00] — Welcome & Introduction to Data Inspired [00:39] — The bold opening argument: data initiatives don't fail; they succeed at keeping organizations the same. Sebastian unpacks the difference between getting modest value from data and achieving true transformation, and why only ~10% of companies ever get there. [07:38] — Are organizations paralyzed by too much data? Sebastian explains why collecting more data is often a way of avoiding the harder, more courageous work of challenging your own assumptions. [09:39] — What "data-driven" actually means in practice and why it's harder than it sounds. Sebastian introduces the "data deficit theory" and draws on 50 years of psychological research showing that data often hardens our existing beliefs rather than changing them. [13:45] — The Charles Barkley moment: what a legendary NBA star's skepticism about data analysts gets right and wrong about using data in sports and business. [16:03] — How data is collected and used in modern organizations, and why the real challenge isn't gathering data; it's organizing it. (Yes, there's a "data swamp" warning here.) [18:00] — Why the classic 8-step decision-making model is a myth. Sebastian explains what monkey brain research and animal herds reveal about how decisions are actually made and what that means for how you introduce data into the process. [22:36] — Goodhart's Law and the GE cautionary tale: the dangerous difference between steering metrics and success metrics, and what happens when leaders confuse the two. [25:38] — The decision-making spectrum: from fully automated machine-learning decisions to pure gut instinct and how Netflix found the sweet spot between data and human judgment. [30:17] — AI vs. machine learning: why we're wired to trust the type of automated decision-making that's actually less reliable and what that means for your organization right now. [33:34] — Data fatigue is real. Sebastian introduces two archetypes, the Dashboard Director and the Data Diver, and explains why you need both to build a truly innovative organization. [36:31] — A peek into Part 5: The Toolbox, with practical checklists, workshop formats, and tried-and-tested methods developed over 20 years of real-world data projects. [39:03] — Closing wisdom: why copying what successful companies did is a trap, and what it really takes to lead transformative change with data, including the courage to slow down before you speed up. A QUICK GLIMPSE INTO OUR PODCAST Podcast: Transform Your Workplace, sponsored by Xenium HR Host: Brandon Laws In Brandon's own words: "The Transform Your Workplace podcast is your go-to source for the latest workplace trends, big ideas, and time-tested methods straight from the mouths of industry experts and respected thought-leaders." About Xenium HR Xenium HR is on a mission to transform workplaces by providing expert outsourced HR and payroll services for small and medium-sized businesses. With a people-first approach, Xenium helps organizations create thriving work environments where employees feel valued and supported. From navigating compliance to enhancing workplace culture, Xenium offers tailored solutions that empower growth and simplify HR. Whether managing employee relations, payroll processing, or implementing impactful training programs, Xenium is the trusted partner businesses rely on to elevate their workplace experience. Discover how Xenium can transform your workplace: Learn more Connect with Brandon Laws: LinkedIn | Instagram | About Connect with Xenium HR: Website | LinkedIn | Facebook | Twitter | Instagram | YouTube
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If you've ever wondered whether your endurance base could carry you into gravel or mountain bike racing — or whether your FTP is really the thing holding you back — this episode is a timely reality check. Dave Schell is the founder of Kaizen Endurance, based in Boulder, Colorado, and has spent 15 years coaching cyclists and endurance athletes through some of the most demanding events on the calendar — Unbound 200, Leadville, ultra-distance gravel and mountain bike. Before that, he spent seven years at Training Peaks as coach education manager, so he understands both the science and the real-world application better than most. We talk about why FTP is overrated as a race predictor, why skill and technique will give you more free speed than another training block, how to actually prepare your body for eight to ten hours in the saddle, the mental game of ultra-distance events, and why consistency remains the most unsexy and most powerful tool any athlete has. There's a lot in here that applies well beyond gravel. 5 KEY POINTS FTP is overrated for long events — after eight hours everyone regresses to the same sustainable pace. Durability and fat oxidation decide the result. Skill delivers free speed — technique improvements will outperform another fitness block for most athletes, most of the time. Race your race bike — training on the road and racing gravel leaves your body unprepared for the physical demands, regardless of fitness level. Recovery is where adaptation happens — most athletes need permission to rest, not encouragement to go harder. Consistency is the only secret — the work never changes, you just keep doing it week after week. 3 TAKEAWAYS Sign up for something that scares you — if there's no real possibility of failure, you'll wing it. The fear is what gets you out the door. Context beats data — RPE and athlete feedback tell you more than power numbers alone. Data without context is just noise. Extreme moderation wins — train at the right load, not the highest load. The athletes who stay consistent are the ones who progress. KILLER QUOTE
The Taproot Therapy Podcast - https://www.GetTherapyBirmingham.com
American psychiatry has built a sociological armor around itself that protects it from reform. The armor has two parts. Reverence and complexity. Together they form the most effective institutional defense system in American professional life. And the apparatus, in 2026, has evolved its most refined defensive move yet, the DSM-6 roadmap, which absorbs the entire body of structural critique against the field by publishing thoughtful documents acknowledging the critique is correct, while channeling an entire generation of reform energy into bureaucratic processes that will conclude, eventually, with the publication of a new manual that incorporates the language of the critique without changing what the manual does. Why the apparatus persists despite forty years of evidence it is failing. How residency capture, modality capture, and credentialing capture work together to produce a workforce whose tolerance for the mystery of the work has been systematically lowered. What would have to change. And why none of the obvious answers are actually answers. This episode covers: Of Two Minds. Tanya Luhrmann's anthropology of American psychiatric residency. How young doctors who enter training wanting to think across biological and psychological registers get formed, by the reward structure of training itself, into single-register practitioners. Why this is happening right now to the residents who started in 2025, and why the AI replacement is going to be welcomed by the field that has been preparing for it for a generation. How Aaron Beck got eaten. The careful, curious clinician who let his data change his mind. The three properties of cognitive therapy that made it perfectly compatible with the emerging managed care apparatus. Why Beck himself was not the version of Beck that got reproduced in the training programs. The selection pressure that captures every modality with the same properties, regardless of the founder's intent. The ABA parallel. Ivar Lovaas, the 1987 study, the autism insurance mandates, the BACB explosion. Why Applied Behavior Analysis became mandatory standard of care despite extensive evidence of harm from the autistic community. Henny Kupferstein on PTSD outcomes. The Autistic Self Advocacy Network. Private equity acquisition of ABA chains and what the moral crumple zone looks like at scale. Measurement as the real religion. The PHQ-9 and GAD-7 as Pfizer-funded screening instruments that became, by capture and convenience, the definitions of depression and anxiety in American clinical practice. Campbell's Law. Goodhart's Law. Theodore Porter on quantification as defense against weak internal authority. The IAPT case study from England, Layard's economic argument, David Clark's CBT rollout, Michael Scott's outcome research, Farhad Dalal's cognitive-behavioral tsunami. Why the entire international model of measurement-based care produces excellent statistics and very little durable change. The critics the apparatus could not absorb. Robert Whitaker on long-term outcomes and Anatomy of an Epidemic. Joanna Moncrieff and the 2022 serotonin meta-analysis that should have ended the chemical imbalance theory and didn't. Lisa Cosgrove on DSM-5-TR financial conflicts of interest. Why each of them produced exactly the kind of evidence that should have triggered structural reform, and why the apparatus dismissed each of them through credentialing arguments that were really about boundary policing. The DSM-6 trap. The closure-of-the-trap argument. Why the DSM-6 roadmap, which concedes the entire structural critique, is the apparatus's most sophisticated defensive move yet. Why being invited to participate in the DSM-6 working groups is the mechanism by which the next decade of reform energy gets neutralized. Why the manual is downstream of the apparatus and reforming the manual cannot reform the apparatus. Enshittification of care. Cory Doctorow's framework applied to American mental health. The four constraints that should have prevented it. How each was eliminated. Madeleine Clare Elish on moral crumple zones. Why clinicians absorb the moral and financial cost of an apparatus they did not design. The diploma mill. The accreditation conflict of interest. Why MSW programs, counseling programs, and PsyD programs have doubled their output without any accountability for what they produce. The accountability inversion. The structural fix. Why schools and boards should be liable for the clinicians they produce. Why the field needs both rigorous selection and rigorous accountability, and how the current system has neither. What would change if the field stopped being a diploma mill. Why this is not a return to Freud's priest class. Disagreement was the wisdom. Why the productive conflict between schools of thought was where psychology was actually thinking, and why the DSM-III atheoretical move killed the conversation that produced wisdom. Neither side wins. Why the cold machine and the warm ghost both need each other. Why the answer is not to defeat the apparatus but to stop mistaking it for the work. The coda. The Machines Will Start to Dream. The actual ending of the series. Why you do not need a conspiracy theory for any of this. The cold machines are nothing, the warm ghost is everything. The microcosm is the macrocosm because the systems are human. The AI threat as reality splitting, where the simulated layer becomes thick enough that the substrate underneath stops being accessible. Freud's permanent problem. Bureaucracy as the most successful avoidance technology humans have ever invented. The disbelief at the root. The question of whether you are more scared of yourself than of not seeing life clearly. The wager that even if humans always refuse, professional psychology should stop being the most refined refusal in the culture. About the host: Joel Blackstock is a Licensed Independent Clinical Social Worker and Clinical Supervisor, the Clinical Director of Taproot Therapy Collective in Hoover, Alabama, and the author of work on Brainspotting, Emotional Transformation Therapy, qEEG neurofeedback, somatic and depth approaches to trauma. Find more at gettherapybirmingham.com. This is the final episode of a nine-part series. #PsychotherapyOnTheCouch #AmericanConfession #DSMReform #DSM6 #DSMCritique #DiagnosticAndStatisticalManual #APA #AmericanPsychiatricAssociation #PsychiatryReform #MentalHealthReform #PsychotherapyReform #TanyaLuhrmann #OfTwoMinds #PsychiatricResidency #AaronBeck #CognitiveTherapy #CBT #CognitiveBehavioralTherapy #ABA #AppliedBehaviorAnalysis #IvarLovaas #BACB #AutismRights #AutisticSelfAdvocacy #ASAN #HennyKupferstein #PHQ9 #GAD7 #MeasurementBasedCare #CampbellsLaw #GoodhartsLaw #TheodoreporPorter #TrustInNumbers #IAPT #RichardLayard #DavidClark #MichaelScott #FarhadDalal #CognitiveBehaviouralTsunami #RobertWhitaker #AnatomyOfAnEpidemic #MadInAmerica #JoannaMoncrieff #SerotoninHypothesis #ChemicalImbalance #SSRIs #Antidepressants #LisaCosgrove #PsychiatryUnderTheInfluence #ConflictOfInterest #PharmaInfluence #BigPharma #Enshittification #CoryDoctorow #RotEconomy #EdZitron #MoralCrumpleZone #MadeleineCElish #InsuranceMentalHealth #GhostNetworks #MentalHealthParity #DiplomaMill #SocialWorkEducation #MSWPrograms #PsyD #CounselingEducation #CACREP #CSWE #APAAccreditation #LicensingBoards #ClinicalSupervision #AccountabilityInversion #PsychotherapyTraining #PsychiatricTraining #PsychologyHistory #PsychiatryHistory #FreudCivilizationDiscontents #JungianTherapy #DepthPsychology #SomaticTherapy #TraumaTherapy #ComplexTrauma #AITherapy #AIReplacingTherapists #ChatGPTTherapy #FutureOfTherapy #PsychotherapyPodcast #PsychiatryPodcast #PsychologyPodcast #MentalHealthPodcast #ClinicalSocialWork #JoelBlackstock #LICSW #TaprootTherapy #BirminghamAlabama #AlabamaTherapy #HooverAlabama #ColdMachinesWarmGhosts #TheMostSacredThingWeHave #TheMachinesWillStartToDream #WarmGhost #ReverenceAndComplexity #ProfessionalCapture #InstitutionalCapture #RegulatoryCapture #EvidenceBasedPractice #EvidenceBasedCritique #BiologicalPsychiatry #PsychiatryEpistemology
Most founders set out to build something that matters: a company that's aligned with their mission, now and forever. But what if the very systems we use to build ‘real' companies are the thing that corrupts them?In this episode, Yaniv Bernstein is joined by Silicon Valley legend Eric Ries author of the era-defining 'The Lean Startup', founder of the Long-Term Stock Exchange, and now the author of a provocative new book, 'Incorruptible: Why Good Companies Go Bad and How Great Companies Stay Great'.Eric makes the case that corruption is a structural problem, rather than a failing of the people themselves. He walks Yaniv through the ‘financial gravity' that pulls good companies away from their founders' purpose, and the governance ‘fortresses' that a small handful of outlier companies (from Costco to Novo Nordisk to Anthropic) have used to stay great.In this episode, you will:Understand ‘financial gravity' - the force that degrades values, corrupts economic decisions, and reduces long-term outcomesLearn the legend of Sol Price (the father of modern retail behind Costco), and why treating margins as a liability rather than a virtue can be a source of enduring strengthExplore the ‘industrial foundation' model behind century-old giants like Novo Nordisk and Zeiss, and why companies with this structure are roughly 6x more likely to survive to year 50Hear how Anthropic's Long-Term Benefit Trust shaped its trajectory, and why in the age of AI, trustworthiness is the single most valuable corporate assetTimestamps00:00 Coming Up...01:06 On Today's Show: Eric Ries, author of The Lean Startup and Incorruptible02:41 From 'The Lean Startup' to 'Incorruptible': Why Governance Matters04:13 The Two Mysteries05:45 Case Study: Sol Price and FedMart08:58 The Shareholder Primacy Trap14:04 Costco's Governance Fortress16:50 Founder Control vs VCs19:33 Why Markets Punish Your Mission21:30 Novo Nordisk's Foundation Model26:35 Anthropic and AI Trust29:37 Governance in Action Today31:54 Doing the Right Thing33:27 Goodhart's Law and Customer Service Metrics38:36 Why 'Harder Is Easier'39:02 Costco's Hotdog Promise41:55 Mission Lock Structures43:32 Pitching Investors, Leverage and the Fundraising Decision Tree49:33 HBO's Silicon Valley and 'Minimum Viable Product'53:57 Closing Thoughts & Book PlugResources mentioned in this episode'Incorruptible: Why Good Companies Go Bad... and How Great Companies Stay Great' by Eric Ries: https://www.incorruptible.co/'The Lean Startup' by Eric Ries: https://theleanstartup.com/bookLong-Term Stock Exchange (LTSE): https://ltse.com/'Skin in the Game' by Nassim Nicholas Taleb: https://www.amazon.com/Skin-Game-Hidden-Asymmetries-Daily/dp/0425284646Mark Cuban's Cost Plus Drugs: https://costplusdrugs.com/The PactHonor the Startup Podcast Pact! If you have listened to TSP and gotten value from it, please:Follow, rate, and review us in your listening appSecure your official TSP merchandise at https://shop.tsp.show/Follow us here on YouTube for full-video episodes: https://www.youtube.com/channel/UCNjm1MTdjysRRV07fSf0yGgGive us a public shout-out on LinkedIn or anywhere you have a social media followingKey linksThis episode of the Startup Podcast is sponsored by .tech domains. Forget weird prefixes and creative misspellings; the availability for .tech domains is simply way better than .com. For a clean name that highlights your tech credentials, get a .tech domain at your favorite registrar.The Startup Podcast website: https://www.tsp.show/episodes/Learn more about Chris and YanivWork 1:1 with Chris: http://chrissaad.com/advisory/Follow Chris on Linkedin: https://www.linkedin.com/in/chrissaad/Follow Yaniv on Linkedin: https://www.linkedin.com/in/ybernstein/Producer: Justin McArthur https://www.linkedin.com/in/justin-mcarthurAssistant Producer: Steph Hefferan https://www.linkedin.com/in/steph-heff/Intro Voice: Jeremiah Owyang https://web-strategist.com/
Somewhere in the last forty years, quantification stopped being one tool of economic governance among others and became the whole operating system. Inside the firm, shareholder value crowded out almost every other account of what a company was supposed to be for. In macroeconomic debate, GDP figures got promoted from diagnostic instrument to final verdict on whether things were going well (never mind what was happening to the people who couldn't afford the rent). Public agencies and universities were quietly retooled around audit regimes and key performance indicators imported from the private sector. The labor process itself now runs through dashboards that watch workers in real time and convert what they do into figures someone in a different building can rank against last quarter's. Whatever the explicit politics of the moment, almost every institution we pass through has been redesigned to produce numbers, and to be evaluated and disciplined by them.Goodhart's Law, the 1975 observation by economist Charles Goodhart that "when a measure becomes a target, it ceases to be a good measure," was originally a narrow point about central banks losing their grip on whatever indicator they picked to control. In the half-century since, it has quietly become a more acute diagnostic of late-capitalist life. If our institutions are now built to hit numbers, regardless of whether they're still doing the things those numbers were supposed to track, what exactly are those institutions for anymore? Who benefits from rule by metric, and who gets to decide which metric counts? Once the dashboard has been built into the architecture of political economy itself, what would it even look like to push back against it?Grab a drink and join us as we ask what our institutions were supposed to be for, before they all became scoreboards.Full episode notes available at this link:https://hotelbarpodcast.com/podcast/goodhartslaw---------------------SUBSCRIBE to the podcast now to automatically download new episodes!SUPPORT Hotel Bar Sessions podcast on Patreon here! (Or by contributing one-time donations here!)BOOKMARK the Hotel Bar Sessions website here for detailed show notes and reading lists, and contact any of our co-hosts here.Hotel Bar Sessions is also on Facebook, YouTube, BlueSky, Instagram, and TikTok. Like, follow, share, duet, whatever... just make sure your friends know about us! ★ Support this podcast on Patreon ★
I came up with at least one way to tell the difference between making a fair profit and profiteering. If someone makes more money when the patients or members they serve are worse off, yeah, call that profiteering. For a full transcript of this episode, click here. If you enjoy this podcast, be sure to subscribe to the free weekly newsletter to be a member of the Relentless Tribe. For more on what is fair profit versus what is profiteering, I would recommend you go back and listen to the episodes on mission and margin with Ben Schwartz, MD, MBA (EP481) and then with Mick Connors, MD (EP495). But it's probably not an accident that I have started an episode about Medicare Advantage in this fashion. To this end, I am very much looking forward to hearing what's up with Medicare Advantage from the one and only Betsy Seals, who is back for her third appearance on Relentless Health Value. And her advice in a nutshell is this: Don't profiteer. There are ample ways to make a fair profit. Just go back to basics and do it the right way. I wanna kind of tick through the list of things that I think about when I think about Medicare Advantage and just how it is relevant to absolutely everybody. The first thing I think about when I think about Medicare Advantage—and this is very obvious—is what Medicare Advantage plans do or don't do are our tax dollars at work or not at work, as the case may be. Along these same lines, the second thing: How does this impact our elders, our family, our friends, our grandparents? These are our senior citizens, getting the care or not getting the care that they may need. Those two are obvious. Now let's talk about a few less obvious things. Here's the third point that I think about as I listen to conversations about Medicare Advantage: cost shifting. Right? It is a well-known fact how big, vertically integrated carriers—and when I say big, vertically integrated carriers, I mean ones that have a Medicare Advantage line of business—when negotiating with big, consolidated health systems, the release valve of those negotiations is commercial rates. These are the rates that the self-insured employers are paying. So, the carrier says, "Look, gimme the best Medicare Advantage rates. I want the best Medicare Advantage rates because I, the carrier, am paying for those." Savings from those lower rates accrues to the Medicare Advantage plan and its shareholders or investors or executives, right? So, the carrier with the Medicare Advantage plan is like, "Look, go as low as we can go on the Medicare Advantage rates, but it's okay, health system, if you make up the difference with the ASO commercial book of business." Because right … ASO means administrative services only. It's not the carrier who's paying those commercial rates at the end of the day. So, the carrier uses its full book of business to negotiate lower rates for itself while, at the same time, cost shifting to commercial members. In fact, there was some research that was cited. It was episode 436 with Elizabeth Mitchell, and I quoted Luke Prettol. But there was research that puts this markup at 4.7% above what employers would otherwise pay if they had an ASO that did not have a Medicare Advantage Plan. So, yeah … number three big thing that I think about when listening to MA insights like the ones that Betsy drops today, I think about will this accelerate or ameliorate or really have anything to do with what is going on around those negotiating tables with ASOs and health systems? Because let's not forget, health systems account for about 50% of most self-insured employers' total health spend. The fourth thing that I think about: Will MA carriers underpay independent practices, especially primary care practices? Will it pay indies less? And then if it pays 'em a lot less, would ultimately manage to put them out of business, ultimately raising the total cost of care for everybody. But if we're thinking about this strictly from Medicare Advantage financial perspective, a really great move here, these are big, vertically integrated companies, don't forget. Many of them own provider organizations. This is why the FTC tends to frown on vertical integration. So, will these Medicare Advantage organizations who own provider organizations pay the provider organizations they own more? By the way, it's the same thing that's going on on the pharmacy side of the house when a PBM pays pharmacies that they own more. Here's a LinkedIn post by Stanley Warren about this topic. And there are a lot of obvious, maybe less obvious reasons for why paying providers the carrier itself owns more is a great short-term move. One of them is intracompany eliminations. Listen to the episode with Preston Alexander (EP482). But here's another reason: Rate increases paid by the government for Medicare Advantage plans are based on fee-for-service benchmarks. So, if fee-for-service rates go up, then the Medicare Advantage plans can negotiate more money for themselves. If the MA plans own the providers that are charging said FFS rates, then this is, I don't know, a great strategy, especially given the lobbying budget that some of these entities have. So, look … on today's show, I get the distinct opportunity to speak with Betsy Seals, my guest today, as I mentioned earlier; and we go through her advice for MA plans and what they need to get busy with and ensure, make a fair profit, go back to basics, and do it the right way. That's her bottom-line advice. Don't be putting your hands in the cookie jar. Sooner or later, you're gonna get caught. Focus on the members that you're really good at serving. And lastly, when it comes to STARS or other quality measures, lift them the right way—like, actually through better member health and actually better member experience, not some engineered mechanism by which one can check a box that honestly doesn't deserve to get checked. Because now we're back to the beginning and you're gonna get caught with your hand in the cookie jar, and it's profiteering. Let's just get real about that. If somebody's checking boxes that they don't deserve to check, member health is not improving. Betsy Seals, my guest today, as I have said at least three times, co-founded Rebellis Group, which is a Medicare Advantage consultancy. She became CEO of its parent company, Alerion Advisors. Now she is a board member, and also she works with start-ups in our industry. This podcast is sponsored by Aventria Health Group with an assist today from Payerset to help us with the financial support that we need to stay on the air. And with that, here is my conversation with Betsy Seals. Also mentioned in this episode are Alerion Advisors; Rebellis Group; Benjamin Schwartz, MD, MBA; Mick Connors, MD; Elizabeth Mitchell; Luke Prettol; Luke Trocchio; LoVasco; Stanley Warren; Preston Alexander; Aventria Health Group; Payerset; Eric Bricker, MD; Scott Conard, MD; Bob Herman; and Vivian Ho, PhD. For a list of healthcare industry acronyms and terms that may be unfamiliar to you, click here. You can learn more by visiting the Rebellis Group blog and by connecting with Betsy on LinkedIn. You can also email her at bseals@rebellisgroup.com. Betsy Seals is the co-founder of Rebellis Group, former CEO of Rebellis Group and Alerion Advisors, and a current board member of the Alerion Advisors family of companies. With over 25 years of experience across Medicare and Medicaid programs, Betsy is a nationally recognized leader known for her regulatory expertise, strategic vision, and ability to deliver measurable results. Betsy's work spans mergers and acquisitions, compliance, enterprise strategy, sales and marketing, supplemental benefits, and innovative benefit design that optimizes health plan performance and improves health outcomes. Betsy brings a strong blend of executive leadership, business acumen, and deep regulatory knowledge, with a focus on driving operational excellence and meaningful member impact. 00:00 Introduction to this episode. 00:43 Past episodes on profiteering: EP481 with Benjamin Schwartz, MD, MBA, and EP495 with Mick Connors, MD. 01:25 How Medicare Advantage is relevant to everyone. 06:15 A preview of today's conversation. 07:49 The "state of the state" of Medicare Advantage plans. 08:49 Video by Eric Bricker, MD, on the financial performance of the U.S. healthcare system. 09:32 Does Medicare Advantage's losses matter to the patients? 10:29 A recap of Betsy's insights so far. 11:19 The underlying strategic through line that needs to be considered. 13:04 The impact of Goodhart's Law. 14:12 What the players that are succeeding right now are doing. 14:22 The first pillar of a back-to-basics strategy: Don't get caught with your hand in the cookie jar. 16:07 EP463 with Betsy Seals. 16:50 Why short-term strategies don't work. 18:26 Stats report on prior authorizations serving the beneficiary. 19:32 EP482 with Preston Alexander. 19:38 Why prior authorization needs change. 21:28 The better strategy to use. 21:43 EP462 with Scott Conard, MD. 23:17 The second pillar of a back-to-basics strategy: Focus on the beneficiaries you actually serve well. 24:37 What it looks like to implement this focus on the beneficiaries you serve well. 25:29 How special needs plans play into this. 27:43 The third pillar of a back-to-basics strategy: Think about how STARS in clinical programs improve health. 30:04 The ethical component to implementing a Medicare Advantage program. 31:04 Betsy's advice for independent practices dealing with prior authorizations. 33:37 STAT article by Bob Herman about the effectiveness of Medicare Advantage lobbying on policy. 34:08 Betsy's final notes for all players impacted by what's currently happening. @betsyseals discusses the impact of #medicareadvantage news on our #healthcarepodcast. #healthcare #podcast #financialhealth #commercialpayermarketplace #digitalhealth #healthcareleadership #healthcaretransformation #healthcareinnovation Recent past interviews: Click a guest's name for their latest RHV episode! Patrick Nelli; Lee Lewis; Stacey Richter with 15 experts (EP507); Jerry DiMaso; Dr Ahilan Sivaganesan; Ryan Jacobs; Stacey Richter (INBW46); Ryan Wells, Dr Leo Spector, and Adam Stavisky
Peter Merel: AI Alignment Is the Agile Coach's Next Frontier — Using Throughput Accounting and Pull-Based Transformation to Prove Value Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes. "Our jobs ARE about alignment. Alignment is how do we get all of the people and all of the tools to work together for mutual benefit." - Peter Merel Peter Merel brings a provocative perspective on the biggest challenge facing agile professionals today: AI and agile alignment. With AI rapidly advancing, Peter observes that everyone in the agile community is afraid for their jobs — but argues this fear is misplaced. The real challenge isn't replacement; it's alignment. How do we get biological and electronic entities to work together for mutual benefit? Peter's answer begins with pull-based transformation — building a thin steel thread from business through to DevOps, proving it works with a small group, then growing it. He connects this to Goldratt's throughput accounting, arguing that throughput (operating expense plus net profit) is the only metric immune to Goodhart's Law. From throughput, Peter derives three flows: value flow (throughput itself), workflow (the first derivative — what increases value flow), and learning flow (the second derivative — what improves workflow). He then introduces the pirate metrics (AARRR) — acquisition, activation, retention, referral, and revenue — as market constraints that can be analyzed through Theory of Constraints. Peter's frustration is that 25 years after Agile began, most business stakeholders still can't identify their market bottleneck. Without that knowledge, he argues, priorities are meaningless. The path forward for agile coaches? Bring scientific rigor to transformation, measure what matters, and prove value before scaling. In this episode, we refer to FAST Agile, Joe Justice's work with Tesla and WikiSpeed, and the connection between throughput accounting and agile transformation metrics. Self-reflection Question: Can you identify the single biggest market constraint limiting your organization's throughput right now — and if not, how confident are you that your current priorities are the right ones? [The Scrum Master Toolbox Podcast Recommends]
People will let AI be their therapist but not their partner, their assistant but not their manager. Gillian Katz of Hannah Grey VC joins Phillip and Brian to unpack the firm's newest Cultural Vibrations journal and the qualitative study behind it: a read on how people are actually negotiating AI's role in their lives, domain by domain, role by role; from anthropology to sommelier frameworks to Goodhart's Law. You Can Manage AI, but AI Can't Manage You Key Takeaways: People accept AI in almost every domain, but reject it in specific roles within them. Naming a cultural signal may be what stops it from moving. Qualitative research captures what dashboard culture flattens. The next frontier isn't the technology, it's the governance around it. Key Quotes: [00:11:04] "No one wants to be managed by a machine, but they're okay to sort of put control over one." — Gillian Katz [00:27:08] "It's exactly like the way you wish every person interacted. But if you did actually have that experience time and time again, you would be so frustrated." — Gillian Katz, on AI sycophancy [00:29:22] "We give people the benefit of the doubt, but we expect a hundred percent accuracy from AI." — Gillian Katz [00:40:56] "If you only use AI to go build your business, you're gonna lose the discernment that's required to actually use AI well in the first place." — Brian Lange In-Show Mentions: Learn more at hannahgrey.com Read the latest issue of Cultural Vibrations, featuring Brian Lange Associated Links: Check out Future Commerce on YouTube Check out Future Commerce Plus for exclusive content and save on merch and print Subscribe to Insiders and The Senses to read more about what we are witnessing in the commerce world Listen to our other episodes of Future Commerce Have any questions or comments about the show? Let us know on futurecommerce.com, or reach out to us on Twitter, Facebook, Instagram, or LinkedIn. We love hearing from our listeners! Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Ross Goodhart is Co-CEO and a Founder of Jupiter—the new standard in dandruff care,delivering clean, elevated, and seriously effective dandruff care products. He was born andraised in Hawaii, and received his B.B.A from the University of Michigan School of Business in2002, with an emphasis in Finance, Accounting and Entrepreneurship.From 2002 to 2015, Ross worked in investment banking at Peter J. Solomon Company andthen private equity at Siguler Guff & Company, managing funds focused on consumer andtech investments in emerging markets.See omnystudio.com/listener for privacy information.
Agenturen und Freelancer: Stunden tracken vs. Wert-basiert abrechnenStunden schreiben, Tickets buchen, Angebote schätzen und am Ende trotzdem das Gefühl haben, am eigentlichen Problem vorbeizuarbeiten. Kommt dir bekannt vor? Dann ist diese Episode genau dein Ding. Denn wir gehen einer Frage nach, die viele in Agenturen, im Freelancing und in der Softwareentwicklung beschäftigt. Was passiert, wenn wir nicht mehr primär Zeit verkaufen, sondern Wert? Und warum wird genau diese Frage durch KI, Automatisierung und immer schnellere Delivery plötzlich noch viel relevanter?In dieser Episode sprechen wir mit Christoph, Geschäftsführer der E Commerce Agentur Pixolith, über Agenturgeschäft, Billable Hours, Value Based Pricing, Angebotsphasen, Vertrauen in Kundenprojekten und die Psychologie hinter Preisfindung. Wir schauen auf konkrete Beispiele aus dem E Commerce, auf B2B und B2C Shops, auf Shopware, Shopify, Updates, Migrationen und die Frage, wie sich Wert überhaupt greifbar machen lässt.Außerdem diskutieren wir, warum Scrum diese Abrechnungsfrage nicht löst, wo Goodhart's Law plötzlich sehr praktisch wird und weshalb KI nicht nur Code beschleunigt, sondern auch Beratung, Vertrieb und Delivery verändert.Wenn du verstehen willst, wie Agenturen kalkulieren, warum Stundensätze oft falsche Anreize setzen und wo Value Based Working wirklich funktioniert, bekommst du hier reichlich Stoff zum Mitdenken.Bonus mit Augenzwinkern: Selbst ein Klopapier Shop kann zum strategischen Lehrstück für Pricing, Vertrauen und Softwareprojekte werden.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:
In this episode Raghunath and Kaustubha ask a question that cuts to the heart of any serious spiritual practice: is my practice actually changing me. Goodhart's Law states that when a measure becomes a target, it ceases to be a good measure. The classic example: British colonial officials in India offered a bounty on cobra skins to reduce the cobra population, only to find that enterprising citizens began breeding cobras to collect the bounties. The measure designed to solve the problem made it worse. The Srimad Bhagavatam, an ancient Sanskrit text on Bhakti-yoga, offers a startling example through the story of the Brahmanas — learned priests who had checked every box, performed every ritual, and met every external standard yet remained spiritually shallow, while there wives, simple village women who had done none of those things, had quietly surpassed them in the spiritual depth. ******************************************************************** LOVE THE PODCAST? WE ARE COMMUNITY SUPPORTED AND WOULD LOVE FOR YOU TO JOIN! Go to https://www.wisdomofthesages.com WATCH ON YOUTUBE: https://youtube.com/@WisdomoftheSages LISTEN ON ITUNES: https://podcasts/apple.com/us/podcast/wisdom-of-the-sages/id1493055485 CONNECT ON FACEBOOK: https://facebook.com/wisdomofthesages108 *********************************************************************
In this episode Raghunath and Kaustubha ask a question that cuts to the heart of any serious spiritual practice: is my practice actually changing me. Goodhart's Law states that when a measure becomes a target, it ceases to be a good measure. The classic example: British colonial officials in India offered a bounty on cobra skins to reduce the cobra population, only to find that enterprising citizens began breeding cobras to collect the bounties. The measure designed to solve the problem made it worse. The Srimad Bhagavatam, an ancient Sanskrit text on Bhakti-yoga, offers a startling example through the story of the Brahmanas — learned priests who had checked every box, performed every ritual, and met every external standard yet remained spiritually shallow, while there wives, simple village women who had done none of those things, had quietly surpassed them in the spiritual depth. ******************************************************************** LOVE THE PODCAST? WE ARE COMMUNITY SUPPORTED AND WOULD LOVE FOR YOU TO JOIN! Go to https://www.wisdomofthesages.com WATCH ON YOUTUBE: https://youtube.com/@WisdomoftheSages LISTEN ON ITUNES: https://podcasts/apple.com/us/podcast/wisdom-of-the-sages/id1493055485 CONNECT ON FACEBOOK: https://facebook.com/wisdomofthesages108 *********************************************************************
Why do smart marketing teams keep optimizing for the wrong things?In Part 1 of this Sharp Cut series, we explored Goodhart's Law — when a measure becomes a target, it stops being a good measure.But the real problem doesn't start on the marketing dashboard.It starts two floors above it.In this episode of The Sharp Cut, Marc Binkley and Vassilis Douros trace the incentive problem all the way from the boardroom to the media buy, showing how the pressure to maximize shareholder value, hit revenue targets, and prove short-term ROI cascades through the organization — eventually shaping how marketing is measured.Drawing on insights from seven past Sleeping Barber guests, including Roger Martin, Peter Field, Avinash Kaushik, Dale Harrison, Herman Simon, Augustine Fou, and Koen Pauwels, this episode breaks down why marketing metrics often drift away from real business outcomes.We explore:Why shareholder value maximization may distort strategic decision-makingThe difference between revenue growth and real competitive growthHow efficiency metrics like ROI and ROAS can mislead organizationsWhy marketing dashboards are often 90% activity and only 10% outcomesWhy CPM may be one of the most dangerous metrics in media planningHow platform data quietly shapes the decisions marketers makeWhen incentives reward the wrong signals, even brilliant organizations can optimize themselves into decline.TakeawaysGoodheart's Law illustrates how metrics can become targets, leading to poor decision-making.Shareholder value maximization is a flawed approach that can harm long-term business health.Revenue growth does not equate to market growth; understanding this distinction is crucial.Short-term metrics can mislead organizations into making detrimental decisions.Effective marketing requires a balance between efficiency and effectiveness.Dashboards often reflect activity rather than meaningful outcomes, leading to misinterpretation of success.CPM is a dangerous metric that can create a false sense of accountability.Data reporting without context can lead to 'data puking' and poor decision-making.Organizations must evaluate whether their primary metrics truly reflect business health.Good measurement practices should focus on long-term outcomes rather than short-term gains.Chapters00:00 - Introduction to the Incentive Series01:00 - Understanding Goodheart's Law and Its Implications03:02 - The Shareholder Value Maximization Trap04:56 - Revenue vs. Growth: A Misunderstanding09:04 - The Dangers of Short-Term Metrics12:08 - The Role of Dashboards in Marketing Decisions14:59 - The Need for Better Measurement Practices
Alan Rozenshtein, research director at Lawfare, spoke with Cullen O'Keefe, research director at the Institute for Law & AI, and Kevin Frazier, AI Innovation and Law Fellow at the University of Texas at Austin School of Law and senior editor at Lawfare, about their paper, "Automated Compliance and the Regulation of AI" (and associated Lawfare article), which argues that AI systems can automate many regulatory compliance tasks, loosening the trade-off between safety and innovation in AI policy.The conversation covered the disproportionate burden of compliance costs on startups versus large firms; the limitations of compute thresholds as a proxy for targeting AI regulation; how AI can automate tasks like transparency reporting, model evaluations, and incident disclosure; the Goodhart's Law objection to automated compliance; the paper's proposal for "automatability triggers" that condition regulation on the availability of cheap compliance tools; analogies to sunrise clauses in other areas of law; incentive problems in developing compliance-automating AI; the speculative future of automated compliance meeting automated governance; and how co-authoring the paper shifted each author's views on the AI regulation debate.Find Scaling Laws on the Lawfare website, and subscribe to never miss an episode.To receive ad-free podcasts, become a Lawfare Material Supporter at www.patreon.com/lawfare. You can also support Lawfare by making a one-time donation at https://givebutter.com/lawfare-institute.Support this show http://supporter.acast.com/lawfare. Hosted on Acast. See acast.com/privacy for more information.
In 2004, Wells Fargo's internal audit flagged a problem: employees felt they couldn't hit sales targets without gaming the system.The scandal broke 12 years later.Two million fake accounts.Thousands fired.Billions in fines.No one set out to commit fraud.They optimized for the metric.In this Sharp Cut, we break down Goodhart's Law — when a measure becomes a target, it ceases to be a good measure — and show how the same pattern is operating inside marketing departments right now.We examine:Why CTR has near-zero correlation with brand growth (Nielsen, LinkedIn, Tracksuit data)How short-term ROAS creates long-term decline (Binet & Field)Why agency compensation structures reward activity over effectivenessThe MQL trap in B2BThe “cheap CPM” illusion and the cost of dull mediaAnd then we offer a prescription:How to redesign your metrics so they can't be gamed.How to pair opposing indicators.How to measure mental vs physical availability.How to ensure your dashboard actually changes decisions.This is not a rant about bad marketers.It's a structural critique of broken incentive systems.Because marketing doesn't drift by accident.It drifts because incentives are misaligned.Episode 1 of a three part series.Key Takeaways:Incentives can lead to unintended consequences in marketing.Goodhart's Law highlights the dangers of misaligned metrics.Wells Fargo's scandal exemplifies the risks of poor incentive structures.Digital advertising metrics often fail to correlate with brand outcomes.Short-term ROAS focus can deplete future demand.Agency compensation models may incentivize spending over effectiveness.MQL culture can overwhelm sales with low-quality leads.Cheap impressions may not translate to real engagement.Marketers should audit metrics for potential gaming.Effective measurement requires aligning metrics with business goals.Chapters:00:00 - Introduction 02:47 - The Wells Fargo Scandal: A Case Study05:50 - Understanding Goodhart's Law09:00 - The Metrics Trap: Digital Advertising Insights12:01 - The Short-Term ROAS Trap14:54 - Agency Compensation and MQL Culture17:58 - The Importance of Metrics and Accountability20:59 - Recap and Final Thoughts
Alan Rozenshtein, research director at Lawfare, spoke with Cullen O'Keefe, research director at the Institute for Law & AI, and Kevin Frazier, AI Innovation and Law Fellow at the University of Texas at Austin School of Law and senior editor at Lawfare, about their paper, "Automated Compliance and the Regulation of AI" (and associated Lawfare article), which argues that AI systems can automate many regulatory compliance tasks, loosening the trade-off between safety and innovation in AI policy.The conversation covered the disproportionate burden of compliance costs on startups versus large firms; the limitations of compute thresholds as a proxy for targeting AI regulation; how AI can automate tasks like transparency reporting, model evaluations, and incident disclosure; the Goodhart's Law objection to automated compliance; the paper's proposal for "automatability triggers" that condition regulation on the availability of cheap compliance tools; analogies to sunrise clauses in other areas of law; incentive problems in developing compliance-automating AI; the speculative future of automated compliance meeting automated governance; and how co-authoring the paper shifted each author's views on the AI regulation debate. Hosted on Acast. See acast.com/privacy for more information.
Britain is failing the Bus Stop Test – David Goodhart reveals why mass immigration and elite dominance are destroying Britain's high-trust society. Join the Community: https://andrewgoldheretics.com SPONSORS: Organise your life: https://akiflow.pro/Heretics Earn up to 4 per cent on gold, paid in gold: https://www.monetary-metals.com/heretics/ Cut your wireless bill to 15 bucks a month at https://mintmobile.com/heretics In this explosive Heretics interview, David Goodhart – author of The Road to Somewhere and creator of the Anywheres vs Somewheres framework – explains how rapid demographic change, declining English ethnicity, eroded solidarity, and over-dominance of mobile educated elites are fracturing Britain. From the famous "Bus Stop Test" failing in many neighbourhoods to the collapse of welfare willingness, family breakdown, fertility crisis, and the shift toward majority-minority towns, Goodhart delivers unfiltered insights on integration failures, cultural transformation, populism, and the urgent need for balance and stability. #MassImmigration #BusStopTest #DavidGoodhart Join the 30k heretics on my mailing list: https://andrewgoldheretics.com Check out my new documentary channel: https://youtube.com/@andrewgoldinvestigates Andrew on X: https://twitter.com/andrewgold_ok Insta: https://www.instagram.com/andrewgold_ok Heretics YouTube channel: https://www.youtube.com/@andrewgoldheretics Chapters: 00:00 David Goodhart's Background 04:30 Solidarity vs Diversity – The Original Essay 09:20 English Ethnicity Decline & Majority Interests 14:50 The Bus Stop Test & Failed Integration 20:00 Changing Public Norms & Way of Life 25:30 Argentina's Immigration Lesson 31:00 Universalism Errors & Group Identity 36:30 Asymmetrical Multiculturalism Exposed 41:30 Anywheres vs Somewheres – The Core Divide 47:00 Education, Populism & Backlash 52:30 De-industrialisation & Immigration Effects 58:00 Stability & Predictability for Ordinary People 1:03:00 Bradford Model vs Mixed Communities 1:08:30 Books Overview: Head Heart & The Dilemma 1:14:00 Fertility Collapse & Family Policy Solutions 1:21:30 A Heretic David admires Learn more about your ad choices. Visit megaphone.fm/adchoices
MOPs & MOEs is powered by TrainHeroic, the best coaching app on the planet. Click here to get 14 days FREE and a consult with the coaches at TrainHeroic to help you get your coaching business rolling on TrainHeroic. MOPs & MOEs delivers our training through TrainHeroic and you can get your first 7 days of training with us FREE by clicking here.To continue the conversation, join our Discord! We have experts standing by to answer your questions.We were recently invited to give the keynote presentation for the 2026 Fort Benning Human Performance Symposium. In the process of putting our talk together, we solidified our "core fore" concepts that help us filter through everything going on in the military human performance space. This led us to our main argument, which is that we should aim for "data informed" but not "data driven" to avoid falling into some common traps.Several people who couldn't attend the symposium asked if there was a way to listen to the talk, so we thought we'd just publish it as a podcast episode. Key topics we cover include: Nazareth Syndrome, Goodhart's Law, Mcnamara's Fallacy, and Hammond's Corollary (yes it's named after Drew). From there we dive into the challenge of defining "lethality" and what data can and can't do to measure it.Special shout out to SGT Donovan Saulsberry whose incredible voice you'll hear when he introduces us. Apparently he's the unofficial (or maybe official?) voice of Fort Benning. Let us know whether we should hire him to record a new intro for our podcast...
Ben Lindbergh and Meg Rowley banter about Ha-Seong Kim and the perils of slipping on ice, the contrasting retirement comments of Mookie Betts and Rich Hill, more on the Kyle Tucker and Bo Bichette signings, Goodhart’s law and baseball stats, the late Wilbur Wood, and the latest trends in Hall of Fame voting. Audio intro: Benny and a Million Shetland Ponies, “Effectively Wild Theme (Pedantic)” Audio outro: Sam Chess, “Effectively Wild Theme” Link to Kim story Link to MLBTR on Kim Link to Mateo signing Link to team SS projection Link to Cena/Mookie episode Link to MLB.com on Mookie Link to HUAL on Cena’s retirement Link to Mookie’s other stream Link to MLBTR on Hill Link to “Retire” meme Link to Hill on EW Link to team LF projection Link to team CF projection Link to Dombrowski comment Link to MLBTR on Jays offer Link to Weaver’s Dodgers stat Link to Clemens post Link to barrels definition Link to Andrews post Link to Goodhart’s law wiki Link to Wood obit Link to Wood’s IP lead Link to Sardell EW appearance Link to Sardell’s penultimate projections Link to Sardell’s final projections Link to Jay’s results preview Link to Jay on EW Link to BBWAA announcement Link to Jay on the results Link to Jay’s Jones profile Link to Jones battery report Link to Jay’s Beltrán profile Link to Felix leap stat Link to Pettitte PEDs story Link to Vizquel persuasion post Link to Sam’s ballot Link to Pollis on the results Link to MLBTR on Robert Sponsor Us on Patreon Give a Gift Subscription Email Us: podcast@fangraphs.com Effectively Wild Subreddit Effectively Wild Wiki Apple Podcasts Feed Spotify Feed YouTube Playlist Facebook Group Bluesky Account Twitter Account Get Our Merch! var SERVER_DATA = Object.assign(SERVER_DATA || {}); Source
Patrick McKenzie (patio11) sits down with Intercom co-founder Des Traynor to examine customer support through the lens of Conway's Law, Goodhart's Law, and several decades of accumulated organizational scar tissue. They discuss how AI agents are democratizing white-glove service, why modern LLMs have retrained user expectations around “chatbots” very quickly, and the surprisingly liberating effect of talking to something that will never judge you for missing a loan payment.–Full transcript available here: www.complexsystemspodcast.com/des-traynor/–Sponsor: MongoDB Tired of database limitations and architectures that break when you scale? MongoDB is the database built for developers, by developers: ACID compliant, Enterprise-ready, and fluent in AI. Start building faster at mongodb.com/build–Timestamps:(00:00) Intro(00:29) Intercom and its evolution(00:51) Challenges in customer service systems(02:54) Scaling customer support in startups(04:53) Organizational inefficiencies and customer experience(06:53) Metrics and their impact on customer support(12:40) Human capital issues in customer support(15:53) AI's role in customer support(17:01) Future of customer support roles(20:09) Sponsor: MongoDB(20:53) Future of customer support roles (continued)(26:19) AI and customer interaction(26:55) The myth of artisanal customer support(27:45) Fin Guidance: Evolution and user behavior(29:10) Fin's impact on customer support efficiency(33:30) Expanding Fin's capabilities beyond support(42:50) AI in government and other sectors(49:20) The future of AI connectivity and integration
This is a Vintage Selection from 2005Ever wonder how great restaurant wine lists actually come together — and why some completely miss the mark?The Guys tell stories of their “glamorous” lives being restaurateurs that (surprisingly) involves more plumbers than they ever expected. In this episode, The Restaurant Guys are joined by Chris Goodhart, wine buyer for Keith McNally's restaurants in New York City, to talk about what really goes into building a wine program that serves both adventurous drinkers and everyday guests.Chris shares stories from the floor, how he balances budgets with taste, and the quiet pressures behind the scenes when a bottle selection can make or break a dining experience.The guys also dig into a fascinating moment in time: the impending smoking ban, how it changed drinking culture, and what restaurants had to rethink overnight — from bar traffic to wine styles that suddenly tasted different without smoke in the room.It's thoughtful, practical, and full of the kind of perspective you only get from people who live inside restaurants.Timestamps00:00 — Setting the stage: running restaurants in 200509:18 — What's it like to run a wine program15:00 — Building wine lists for various venues20:00 — Chris' opinion of The Michelin Guide in NYC26:20 — How to take the pretentiousness out of the wine experience32:40 — Corks and Screw Tops 35:27 — How Smoking Bans Change the Way People DrinkBioChris Goodhart is a veteran New York City wine buyer, working for Keith McNally's restaurant group, known for building thoughtful, guest-friendly wine programs that balance discovery, value, and hospitality.Info Keith McNally's Balthazar, etc.https://balthazarny.com/Become a Restaurant Guys' Regular!https://www.buzzsprout.com/2401692/subscribeMagyar Bankhttps://www.magbank.com/Withum Accounting https://www.withum.com/restaurantOur Places Stage Left Steakhttps://www.stageleft.com/ Catherine Lombardi Restauranthttps://www.catherinelombardi.com/ Stage Left Wineshophttps://www.stageleftwineshop.com/ To hear more about food, wine and the finer things in life:https://www.instagram.com/restaurantguyspodcast/https://www.facebook.com/restaurantguysReach Out to The Guys!TheGuys@restaurantguyspodcast.com**Become a Restaurant Guys Regular and get two bonus episodes per month, bonus content and Regulars Only events.**Click Below!https://www.buzzsprout.com/2401692/subscribe
Russ Fradin sold his first company for $300M. He's back in the arena with Larridin, helping companies measure just how successful their AI actually is.In this episode, Russ sits down with a16z General Partner Alex Rampell to reveal why the measurement infrastructure that unlocked internet advertising's trillion-dollar boom is exactly what's missing from AI, why your most productive employees are hiding their AI usage from management, and the uncomfortable truth that companies desperately buying AI tools have no idea whether anyone's actually using them. The same playbook that built comScore into a billion-dollar measurement empire now determines which AI companies survive the coming shakeout.Timecodes: 0:00 — Introduction 2:15 — Early Career, Ad Tech, and Web 1.03:09 — Attribution Problems in Ad Tech & AI4:30 — Building Measurement Infrastructure6:49 — Software Eating Labor: Productivity Shifts8:51 — The Challenge of Measuring AI ROI14:54 — The Productivity Baseline Problem18:46 — Defining and Measuring Productivity21:27 — Goodhart's Law & the Pitfalls of Metrics22:41 — The Harvey Example: Usage vs. Value25:18 — Surveys vs. Behavioral Data28:38 — Interdepartmental Responsiveness & Real-World Metrics31:00 — Enterprise AI Adoption: What the Data Shows33:59 — Employee Anxiety & Training Gaps38:31 — The Nexus Product & Safe AI Usage42:08 — The Future of Work: Job Loss or Job Creation?44:40 — The Competitive Advantage of AI53:45 — The Product Marketing Problem in AI55:00 — The Importance of Specific Use CasesResources:Follow Russ Fradin on X: https://x.com/rfradinFollow Alex Rampell on X: https://x.com/arampell Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Podcast on SpotifyListen to the a16z Podcast on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
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