Podcasts about RSI

The next 12 months of AI leaked. Kinda. For the past 90ish days, we've been quietly collecting evidence of what's next.1,030 saved posts. 90 Podcasts. Countless conversations. Every model drop, every leak, every quiet product update the big labs hoped you'd scroll past.Then we connected the dots.What came out the other side: 19 calls on where AI goes over the next 12 months. And some of them are uncomfortable.We're walking through all 19. Bring your team's AI roadmap. You'll want to edit it.

Breanne Rowe, MS, CSCS, TSAC-F, RSCC is a strength and conditioning coach currently supporting the 13th Air Support Operations Squadron in Colorado. Her experience spans tactical performance, personal training, higher education, and human performance, with a strong focus on helping athletes and military personnel improve performance and resilience.Prior to her current role, Rowe worked as a strength and conditioning coach with LMR Technical Group and spent more than six years as a personal trainer at PRO Sports Club. She earned both her bachelor's degree in Clinical Physiology and master's degree in Integrative Human Physiology from Central Washington University and holds multiple certifications, including CSCS, TSAC-F, RSCC, and USAW Level 1.___Learn about CEUs to the NSCA and CSCCa:https://www.strengthcoachnetwork.com/pricing___CONNECT WITH US:

In this episode of Supertraining in a Year, Justin Lima and Yosef Johnson work through pages 526–540 and a central theme emerges: you cannot microwave adaptation.The discussion begins with the problems associated with premature intensification and early specialization. Faster, harder, and more intense training may produce results early, but that doesn't necessarily mean it creates the foundation for continued development later.___Learn about CEUs to the NSCA and CSCCa:https://www.strengthcoachnetwork.com/pricing___Buy Supertraining to Read Along with Us - use code STRONGER20 to save 20%https://uaconcepts.com/product/supertraining___CONNECT:

Weco let an AI coding agent rewrite the harness around another agent for eight days: its code, prompts and tools, while the underlying language model stayed fixed. Tim Scarfe asks Weco co-founder Zhengyao Jiang what the reported gains over two years of human engineering actually demonstrate.The discussion examines AIDE 85's generated code, held-out evaluation and the difficulty of separating useful discoveries from reward hacking. Jiang explains Weco's four levels of recursive self-improvement and compares the experiment with AlphaEvolve and the Darwin Gödel Machine.The limits matter as much as the gains. Jiang explains why the experiment did not establish that the system had become a better improver. The conversation closes with open-ended search, human-designed primitives and Parameter Golf: where does the next useful idea come from when the agent is searching inside a space that people designed?---TIMESTAMPS:00:00:00 Eight days of self-improvement: what counts?00:03:25 AIDE and the puzzle of useful spaghetti code00:08:38 Four levels of recursive self-improvement00:12:02 What AIDE 85 changed and how it was tested00:20:04 AlphaEvolve, Darwin Gödel Machine and the RSI claim00:26:21 Reward hacking and the limits of detection00:33:09 Open-ended search, harness tuning and creativity00:39:43 Parameter Golf and the limits of self-improvement---REFERENCES:organization:[00:00:30] Weco AIhttps://www.weco.ai/other:[00:00:33] AIDE²: The First Evidence of Recursive Self-Improvementhttps://www.weco.ai/blog/first-evidence-of-recursive-self-improvement[00:14:11] Faulty reward functions in the wildhttps://openai.com/index/faulty-reward-functions/[00:29:59] The Hugging Face incident and the road aheadhttps://openai.com/index/hugging-face-incident-and-the-road-ahead/tool:[00:03:29] AIDEhttps://github.com/WecoAI/aideml[00:04:29] MLE-benchhttps://github.com/openai/mle-bench[00:04:33] ALE-Benchhttps://github.com/SakanaAI/ALE-Bench[00:04:52] WeatherBench 2https://github.com/google-research/weatherbench2[00:08:18] ReActhttps://react-lm.github.io/[00:39:43] Parameter Golfhttps://github.com/openai/parameter-golfpaper:[00:20:08] AlphaEvolve: A coding agent for scientific and algorithmic discoveryhttps://arxiv.org/abs/2506.13131v1[00:21:35] Darwin Gödel Machine: Open-Ended Evolution of Self-Improving Agentshttps://arxiv.org/abs/2505.22954v3[00:23:45] Hyperagentshttps://arxiv.org/abs/2603.19461v1[00:27:01] SpecBench: Measuring Reward Hacking in Long-Horizon Coding Agentshttps://arxiv.org/abs/2605.21384book:[00:33:14] Why Greatness Cannot Be Planned: The Myth of the Objectivehttps://link.springer.com/book/10.1007/978-3-319-15524-1---LINKS:https://app.rescript.info/share/3a9dc6189cb539c6a05fcc4f75c101b3PDF:https://app.rescript.info/api/public/sessions/9eda60ede2b31c92/pdf

LSU Sports Dietitian Allison Wade joins the podcast to break down what performance nutrition actually looks like when working with athletes every day.Allison and Justin discuss the relationship between strength coaches and dietitians, sweat testing and hydration, fueling different sports, body composition, supplements, blood testing, nutrition during injury and return to play, and why nutrition has to be individualized to the athlete.They also dive into something that goes beyond macros and meal plans: food is cultural. Allison explains why understanding an athlete's background, preferences, and relationship with food is critical to creating buy-in and ultimately helping them perform. A practical conversation for strength coaches, sports dietitians, and anyone responsible for athlete performance.___Learn about CEUs to the NSCA and CSCCa:https://www.strengthcoachnetwork.com/pricing___CONNECT WITH US:

Brett Crowther of Charles Schwab says the S&P 500 (SPX) recently broke out of a bull flag pattern but not enough to break out to new record highs. He highlights key technical indicators he sees as essential for bulls to reach the new milestone. In Palo Alto Networks (PANW), Brett shows how a slowdown in RSI is keeping the stock in a wide but consistent trading pattern. He adds that appetite for Chipotle (CMG) has waned in the last couple weeks.

La Fondazione Don Guanella compie cento anni. Da sempre impegnata nel Canton Ticino, promuove con il centro culturale di Chiasso il prossimo Festival dell'incontro (dal 3 al 10 ottobre). Un'occasione che Millevoci offre alle ascoltatrici e agli ascoltatori di conoscere il compito di questa istituzione, di come sia cambiata nel tempo e di come abbia scritto anche parte della storia della Svizzera italiana, lavorando per i diritti dei fanciulli e per la loro inclusione nella società.Con il giornalista e saggista Luigi Maffezzoli e il direttore generale della Fondazione Don Guanella. Spazio quindi al Film Festival dei diritti umani. La 13esima edizione è in programma a Lugano dall'11 al 18 ottobre. Faremo il punto su un tema delicatissimo e complesso, come quella della dignità umana e dei diritti delle singole persone e di intere popolazioni. Mai come in questo momento storico i diritti fondamentali di una persona, in numerose parti del mondo, sono ignorati, quando non vilipesi e umiliati. Faremo il punto su obiettivi e programma del FFDUL con i suoi due codirettori, Margherita Cascio e Antonio Prata e con un intervento di Mauro Arrigoni, già membro della governance del CICR, il Comitato Internazionale della Croce Rossa Infine la Catena della Solidarietà compie 80 anni. La fondazione è strettamente legata alla SRG-SSR, che sostiene i compiti della Catena e organizza regolarmente raccolte fondi da destinare alle realtà più disagiate del pianeta. La storia della coesione nazionale passa anche dalla Catena e dalle antenne della RSI, come racconteranno la delegata della Catena della Solidarietà per la Svizzera italiana, Michelle Volonté e il volto storico della RSI e voce altrettanto storica degli appelli della Catena, Carla Norghauer.

Si è svolta in questi giorni la visita ufficiale del presidente cinese Xi Jinping a Washington. Arrivato mercoledì e accolto in pompa magna dallo stesso Donald Trump all'aeroporto, Xi Jinping ripartirà oggi, venerdì. I due potrebbero rivedersi però già a novembre, quando Trump dovrebbe tornare in Cina per il vertice Apec (Cooperazione Economica Asia-Pacifico) di Shenzhen.Quella che si conclude oggi è sì la prima visita di Xi alla Casa Bianca dopo oltre un decennio (l'ultima risaliva al 2015), ma si tratta già del terzo incontro di persona con il presidente statunitense nell'arco di un anno, dopo i vertici di Busan, in Corea, lo scorso ottobre 2025 e la visita di Trump a Pechino a maggio di quest'anno.Un incontro che avviene in un momento in cui il mondo è destabilizzato dalle guerre, dal prezzo dell'energia fuori controllo e dalla scomparsa dell'idea stessa di una comunità internazionale (basti pensare che pur trovandosi negli Stati Uniti, il presidente cinese ha snobbato l'Assemblea generale dell'Onu in corso in questi giorni a New York).Eppure, nonostante i temi di cui i leader delle due superpotenze mondiali discutono siano di importanza e impatto globale, molti analisti già alla vigilia ritenevano che sarebbe stato un incontro più incentrato sull'immagine e sul simbolismo, piuttosto che sulla ricerca di progressi sostanziali su questioni come il commercio e la guerra dei dazi, lo sviluppo dell'intelligenza artificiale, le guerre in corso oppure il nodo di Taiwan…Che bilancio trarre quindi da questo incontro fra i leader delle due superpotenze globali?A Modem ne parliamo con:Andrea Vosti, corrispondente radiofonico RSI da WashingtonLorenzo Lamperti, collaboratore RSI dall'Asia orientale

Jeff Malec sits down with Adam Butler of Return Stacked ETFs, back for the first time since their May 2023 AI episode, when GPT-4 had just come out. Since then Return Stacked has grown past $1.6 billion. Adam explains why AI is spreading faster than any technology before it and describes his own workday, with Claude Code and Codex agents running in parallel and working overnight until the human's attention is the real limit. He then pushes back on the doomsday headlines. He argues that p-doom stories help frontier labs chasing trillion-dollar IPOs and favorable regulation, and points to the risks he thinks are real, like "obliterated" models with their safeguards stripped out and cyberattacks on critical infrastructure.Along the way, they dig into the paperclip maximizer and the recent case where sandboxed AI agents secretly coordinated to gain admin access to Hugging Face. They also cover AGI, ASI, and recursive self-improvement, and why Adam sees markets as the original paperclip maximizer. From there they turn to the AI funding loop with Nvidia at its center and no real moat for the labs, why Adam expects negative stock returns and prefers trend following, and his argument that mass job loss is a failure of imagination, not an inevitability. - SEND IT!Chapters:00:00-01:51= Intro01:52-10:23=Compute Deflation and the Multi-Agent Workday10:24-19:46=Managing the Agent Swarm, and Doubting the Doomsayers19:47–35:51 = Fear, Incentives, and Obliterated Models: Which AI Risks Are Real?35:52–53:57 = Paperclip Maximizers and Escaping Agents: How AIs Learned to Coordinate53:58–01:05:39 = RSI, Regulatory Capture, and Winner-Take-All01:05:40–01:20:13 = No Moat, No Mission: The AI Funding Loop, Trend Following, and America's Failure of Imagination01:20:14–01:23:34 = Rooting for Whatever Went UpFrom the Episode:PODCAST: AI isn't coming…it's already here, with Adam Butler and Taylor PearsonBOOK: Neal Stephenson - Snow CrashFollow along with Adam and Return Stacked ⁠on LinkedIn, and be sure to check out returnstacked.com to learn more about what they are up to.Don't forget to subscribe to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Derivative⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, follow us on Twitter at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@rcmAlts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠sign-up for our blog digest⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Disclaimer: This podcast is provided for informational purposes only and should not be relied upon as legal, business, or tax advice. All opinions expressed by podcast participants are solely their own opinions and do not necessarily reflect the opinions of RCM Alternatives, their affiliates, or companies featured. Due to industry regulations, participants on this podcast are instructed not to make specific trade recommendations, nor reference past or potential profits. And listeners are reminded that managed futures, commodity trading, and other alternative investments are complex and carry a risk of substantial losses. As such, they are not suitable for all investors. For more information, visit⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.rcmalternatives.com/disclaimer⁠⁠⁠⁠⁠

Replit CEO Amjad Masad talks with TITV Host Akash Pasricha about AI safety and cybersecurity. We also talk with Core Automation CEO Jerry Tworek about leaving OpenAI to build Neo-Labs and recursive self-improvement, Marathon Management Partners' Gokul Rajaram about enterprise AI agent adoption and startup valuation metrics, and we get into distributed compute with Phin Barnes and GPU spot market pricing with Nebius CRO Marc Boroditsky.Subscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/Chapters:00:00 - Introduction 02:20 - Replit CEO Amjad Masad on AI Safety & Cybersecurity 13:34 - Ex-OpenAI Researcher Jerry Tworek on Neo-Labs & RSI 35:57 - Investor Gokul Rajaram on AI Agents & Enterprise Sales 48:15 - Phin Barnes on Distributed AI Compute & Edge Infrastructure 56:00 - Nebius CRO Marc Boroditsky on Compute Auctions & GPU Pricing

Usciamo dagli studi RSI di Comano per raccontare il mondo degli attacchi equestri.Dai carri delle civiltà antiche, alle eleganti carrozze europee, guidare è sempre stata un'arte affascinante che unisce l'uomo al lavoro, allo spostamento e allo sport.Vi raccontiamo del 30° anniversario del GASI (Società di attacchi della Svizzera italiana), con:Guido Bernasconi, presidente del GASIPaolo Romerio, membro del GASI che condurrà una wagonette aperta, trainata dai suoi due cavalli. Manolo del Gaggiolo (21 anni) e Cleopatra (16 anni) due cavalli di razza Franches-Montagnes, unica razza equina 100% svizzera. Giovanni Mercolli, membro storico del GASIIvana Balemi, giovane studentessa dell'Università di Scienze Applicate di Berna, titolare di attestato, diploma e brevetto di attacchiStefano D'Albena, veterinario fondatore di Equine Veterinary Services, specializzato in ippoterapiaAndrea Bernasconi con Liin, pony Shetland di 7 anni, alta circa 100 cm al garrese, che dimostrerà che anche i pony, se correttamente attaccati a carrozze delle loro dimensioni, possono esprimersi al meglio.

Alex Kanellis is the founder of Landmine University, a former University of Iowa football player, competitive wrestler, strength coach, and innovator in rotational power training. Holding a master's degree in kinesiology and the CSCS credential, Kanellis developed the Landmine University Training System after multiple shoulder and bicep surgeries led him to seek alternatives to traditional Olympic lifting.His coaching philosophy centers on developing explosive athletic performance through rotational, core-driven movement patterns that better reflect the demands of sport. Through concepts such as forward intent, coiled postures, and rotational power, Kanellis has built a unique training system that uses landmine-based exercises and complexes to enhance force production, movement efficiency, and athleticism across multiple planes of motion.Learn about CEUs to the NSCA and CSCCa:https://www.strengthcoachnetwork.com/pricingCONNECT WITH US:

Caring for patients with severe traumatic brain injury (TBI)—especially pediatric patients—is high-stakes and high-stress. In severe TBI, primary brain injury occurs at the moment of impact; our primary goal in the emergency department is preventing secondary brain injury caused by hypoxia, hypoperfusion, elevated intracranial pressure (ICP), seizures, hyperthermia, and dysglycemia. In this episode, ED Clinical Pharmacist Haley Burhans returns to walk us through key medications for the acute management of severe TBI. We'll cover airway management, rapid sequence intubation (RSI) drug choices, hyperosmolar therapy, seizure prophylaxis, TXA, and post-resuscitation care. 1. Airway & Rapid Sequence Intubation (RSI) Optimizing oxygenation and ventilation is critical, as hypoxemia directly contributes to secondary brain injury. Selecting hemodynamically neutral agents is essential to maintain cerebral perfusion pressure (CPP). Induction Agents Etomidate (0.3 mg/kg, max 40 mg): Hemodynamically neutral, making it a reliable choice for TBI patients with unstable or uncertain blood pressures. Ketamine (1–2 mg/kg): Excellent option for borderline low or hypotensive patients. Historical concerns regarding ketamine-induced ICP spikes have been largely disproven; bolus doses 100–110 mmHg depending on age), titrate carefully to clear hemodynamic goals. Avoid reflexively starting high-dose pressors if transient hypotension was primarily driven by hypoxia or cardiac arrest before airway placement. 3. Seizure Prophylaxis Post-traumatic seizures increase metabolic demand and elevate ICP. First-Line Agent: Levetiracetam (Keppra) is non-inferior to Phenytoin for preventing early post-traumatic seizures (within 7 days) and carries a significantly lower risk of cardiac side effects and agitation at standard loading doses. Dosing: Loading Dose: 20–25 mg/kg IV load (common adult loading dose is 20 mg/kg or a standard 1,000–2,000 mg IV dose). Maintenance: 25 mg/kg IV BID in pediatrics; 1,000 mg IV BID in adults (adjusted for renal function). 4. Hyperosmolar Therapy for Cerebral Edema When signs of impending herniation or acute ICP elevation are present: Hypertonic Saline (3% NaCl): Pediatrics: First-line agent at 2–5 mL/kg IV. Avoids the diuretic/hypovolemic risks associated with mannitol in volume-dependent pediatric patients. Adults: Strongly preferred over mannitol due to less rebound ICP elevation and easier administration (mannitol requires inline filters and can crystallize). Mannitol (0.5–1 g/kg): Alternative option in adults, but can cause osmotic diuresis, hypotension, and rebound ICP increases. Combining/Redosing: Avoid giving both hypertonic saline and mannitol simultaneously as initial therapy—if the patient deteriorates 30 minutes later, therapeutic options are exhausted. Hypertonic saline is easier to redose safely after monitoring serum sodium levels (peak effect around 1 hour). 5. Role of Tranexamic Acid (TXA) Isolated TBI in Adults: The CRASH-3 trial showed potential 30-day mortality benefits for mild-to-moderate TBI within 3 hours of injury, but limited clear benefit in isolated severe TBI. If a severe TBI is an isolated injury, routine TXA push is not strongly advocated if it delays primary line access for other resuscitation meds. Polytrauma / Pediatrics: If TBI is part of multi-trauma or severe pediatric trauma within 3 hours of injury, TXA is appropriate as part of overall trauma resuscitation protocols. 6. Critical ED Targets & Common Pitfalls Temperature Management: Target strict normothermia (36.0°C–38.0°C). Fevers (>38°C) double metabolic demand and worsen secondary brain injury. Consider bundling IV acetaminophen into post-RSI orders. Glucose Control: Avoid extreme hyperglycemia; treat severe elevations (>200 mg/dL or noted high levels on VBG) early in the ED. Communication & Lines: Hyperosmolar agents are incompatible with many continuous infusions. Clearly communicate upper and lower blood pressure limits with nursing to avoid over-titrating antihypertensives or sedatives. What are your go-to meds for severe TBI? What do you avoid? Share your experience with us on social media @empulsepodcast or at ucdavisem.com Hosts: Dr. Julia Magaña, Professor of Pediatric Emergency Medicine at UC Davis Dr. Sarah Medeiros, Professor of Emergency Medicine at UC Davis Guests: Haley Burhans, PharmD, Emergency Medicine Clinical Pharmacist at UC Davis Resources: ACEP Critical Care Medicine: Key Aspects in the Management of TBI in the ED to Minimize Secondary Injury by Miyant'e Newton, MD, March 12, 2024 Brain Trauma Foundation Guidelines for the Management of Severe TBI, 4th Edition Brain Trauma Foundation Guidelines for the Management of Pediatric Severe TBI, 3rd Edition **** Thank you to the UC Davis Department of Emergency Medicine for supporting this podcast and to Orlando Magaña at OM Productions for audio production services.

Muse AI is a game changer for Meta Platforms (META), argues Kevin Hincks, pointing to the chart-topping downloads across app stores. The AI agent is what Kevin sees as the fruit to trees planted by Meta's massive CapEx plans. Tom White argues that Muse faces stiff competition from OpenAI and Anthropic among others, adding that the stock is currently trading on an overbought RSI. Both offer their own example options trades for the Mag 7 stock.

S Ondřejem Bajgarem, Stanislavem Fortem, Kristinou Fort i Josefem Šlerkou o znepokojivém vývoji umělé inteligence. Moderuje Štěpán Sedláček.O bezpečnosti umělé inteligence se vede debata už dlouho. Ale v poslední době této diskusi začíná věnovat pozornost podstatně víc lidí. Jednou z příčin je to, že vývoj generativní AI už dospěl od dob spuštění ChatGPT před necelými čtyřmi lety velmi daleko - a umělá inteligence už je schopná samostatně konat divy včetně řešení nejkomplikovanějších matematických problémů i nacházení slabin v softwaru.S tím se pojí bezpečnostní incident, kdy velké jazykové modely od firmy OpenAI "po vlastní ose" ve velkém počtu utekly z testovacího prostoru a koordinovaně hackovaly systémy firmy Hugging Face a napadly i servery samotné OpenAI. Od té doby se vyjevila řada podobných incidentů, které oznámily společnosti Anthropic, Meta - a nejnověji také Google.Model Gemini během rutinního testu kyberbezpečnostních dovedností podle amerického deníku WSJ dostal za úkol hackovat fiktivní firmu, která ovšem měla jméno jako skutečná společnost - a když měly testované modely v jistou chvíli přístup na internet, podnikly útok na servery dané firmy, a přitom narušily systémy další dvou společností.Druhý důvodem jsou dlouhodobé obavy vývojářů, které jsou kromě podobných incidentů posilovány i tím, že firmy vyvíjející velké jazykové modely aktivně směřují k rekurzivnímu sebezlepšování (RSI), kdy by už vyvíjela další generace modelů víceméně sama umělá inteligence. To může proces výrazně zrychlit, ale také snížit naše již tak omezené porozumění tomu, jak AI funguje - a proč dělá, to co dělá.Mnozí lidé ve firmách vyvíjejících AI cítí, že teď ještě mají možnost ovlivnit vývoj hnaný konkurenční dynamikou a že je nejvyšší čas něco udělat; a zpomalit. Ukazuje se, že nové modely jsou čím dál schopnější, ale podle všeho mnohdy postrádají etické zábrany – jinými slovy nejsou dostatečně sladěné s lidskými hodnotami. A v důsledku by podobně jako při incidentu Hugging Face mohly dojit k nebezpečným, těžko předvídatelným záměrům, či posloužit lidem, kteří je chtějí zneužít. S tím se pojí varování Jacoba Coxona, který odešel z firmy Anthropic, i dalších lidí z oboru, kteří argumentují, že za několik let by mohla AI představovat existenční riziko pro lidstvo a v krajním případě nás vyhladit. Což je v oboru dlouhodobá obava s různě stanovenou pravděpodobností. V neposlední řadě samy přední firmy z oboru v USA různými způsoby upozorňují, že rychlý vývoj AI není i kvůli pomyslnému závodu dostatečně regulovaný. Po vydání výzvy Daria Amodeiho většina z nich vyjádřila ochotu ke konkrétním krokům - včetně nezávislého dohledu uvnitř firem.Donald Trump nicméně zatím podle všeho víc než většině znepokojených Američanů naslouchá lidem ve svém okolí, kteří chtějí naopak rychle vyvíjet umělou inteligenci bez jakýchkoliv federálních regulací. S tím se pojí totiž velké investice nakumulované v tomto sektoru i růst americké ekonomiky. Rizika, na která upozorňují vývojáři AI a přední vědci, označil Trump za hoax i konspiraci - a vyslovil se pro rychlou výstavbu dalších datových center.Související debata je ovšem vrstevnatější. Řada lidí viní firmy vyvíjející AI z přehánění kvůli vstupům na burzu i z nemístné antropomorfizace a přeceňování možností velkých jazykových modelů; stejně jako ze snahy odvracet pozornost od problémů, které už ve vztahu k AI řešíme - od těch ekonomických a environmentálních až po psychologické.Kam generativní AI v průběhu posledních let dospěla, je obdivuhodné a bude to mít následky, s nimiž je třeba se každopádně vyrovnat. Po čtyřech letech je tady nová technologie transformující řadu oborů lidské činnosti včetně matematiky, programování, biologie či kyberbezpečnosti. Citelně nám ovšem chybí nezávislý monitoring vývoje AI, standardy, regulace i mezinárodní kooperace. I o tom je řeč v aktuálním Zeitgeistu.Doporučujeme také článek: Kdo přežije ve věku AIhttps://www.respekt.cz/tydenik/2026/39/kdo-prezije-ve-veku-ai

Seit Anthropic-CEO Dario Amodei mit seinem Blogbeitrag zu „Pacing the Frontier“ vorgeprescht ist, meldet sich in der KI-Branche praktisch jede und jeder zu Wort. Seine Forderungen: unabhängige Prüfer für die AI Labs, gemeinsame Sicherheitsstandards in der demokratischen Welt, ein Abkommen mit China und, kontrovers, eine Kartellrechtsausnahme, damit sich die Labs überhaupt abstimmen dürfen.Die Fronten, die sich dabei bilden, sind ungewöhnlich. Während Sam Altman, Elon Musk, Demis Hassabis und Ursula von der Leyen dem Prinzip etwas abgewinnen können, wittern Mark Zuckerberg, Jensen Huang, Mistral, Hugging Face und Black Forest Labs einen Regulatory-Capture-Move der drei führenden Labs. Technologisch ist von einer Bremse ohnehin nichts zu bemerken, im Gegenteil.Darüber sprechen Jakob Steinschaden von Trending Topics und Clemens Wasner von enlite AI heute im Podcast. Die Themen:Pacing the Frontier: Was Amodei konkret fordert, warum ausgerechnet eine Kartellrechtsausnahme dazugehört, und wer ihm zugestimmt hat.Die Gegenseite: Zuckerbergs Argument, dass die Labs Haftung und Sicherheit längst selbst angehen könnten, und warum Peking von „malicious competition“ spricht.Verstaatlichung: Palantir-CEO Alex Karp sieht keinen anderen Weg als eine Verstaatlichung von OpenAI und Anthropic, und der US-Finanzminister schiebt die Verantwortung für die Hacking-Vorfälle allein OpenAI zu.Haftungsfrage: Warum ein Börsengang die Lage für OpenAI verschärft, und was aus der Forderung von Hugging Face nach 100 Millionen Dollar wird.Europa: Weshalb Mistral, Hugging Face, Black Forest Labs und Proton gegen die Bremse argumentieren und das deutsche Digitalministerium nur eine Aufsicht will.Wettrüsten statt Slowdown: Das neue Wet Lab von Anthropic, Grok 4.7, die nächste OpenAI-Generation und ein frisches Xiaomi-Modell, das die chinesische Open-Weight-Spitze schlägt.Rekursive Selbstverbesserung: Warum OpenAI beim Modell mit dem Codenamen Bell plötzlich abwinkt, obwohl RSI jahrelang als heiliger Gral galt.Taiwan: Warum der Chip-Gigant keine eigenen KI-Modelle hat, wie eine chinafreie Lieferkette für Drohnen aussieht, und weshalb Souveränität dort militärisch gedacht wird.

The conversation covers current adaptation reserve (CAR), protein synthesis, the sarcoplasmic reticulum, contractile and mitochondrial proteins, supercompensation, and the different cellular responses that occur when developing speed, strength, endurance, and speed endurance.But the practical message is much simpler: cut the noise.More training isn't automatically better training. Justin and Yosef discuss finding the appropriate dose of stress, giving athletes enough time to adapt, resisting the urge to constantly change programs, and understanding why high-intensity methods can't be used all the time.___Learn about CEUs to the NSCA and CSCCa:https://www.strengthcoachnetwork.com/pricing___Buy Supertraining to Read Along with Us - use code STRONGER20 to save 20%https://uaconcepts.com/product/supertraining___CONNECT:

What happens when some of the people building the world's most powerful AI systems start saying we may need to slow down?In the span of roughly two weeks, the conversation around frontier AI shifted dramatically. Dario Amodei argued that AI capabilities need to be paced. Sam Altman publicly agreed with the broader concern. Elon Musk weighed in. Jensen Huang and Mark Zuckerberg offered different approaches. Meanwhile, political leaders in the US, Europe, the UK and China became part of an increasingly urgent debate over safety, competition and control.For business leaders, however, there's an important twist: slowing the frontier does not mean AI adoption is slowing down. The systems already available can transform how organizations operate and they also introduce risks that leaders can no longer afford to treat as somebody else's problem.In this episode of Leveraging AI, Isar Meitis breaks down an extraordinary sequence of events surrounding AI safety, recursive self-improvement, regulation and the increasingly complicated relationship between the companies building frontier models and the governments trying to respond.In this session, you'll discover:Why Dario Amodei is arguing that frontier AI development should be paced rather than stopped.Why recursive self-improvement, or RSI, has become such an important part of the AI safety conversation.What the OpenAI Hugging Face agent incident revealed about autonomous AI behavior.The different safety approaches being discussed by Anthropic, OpenAI, Meta, Microsoft and Elon Musk.Why cooperation between competing AI labs is proving so difficult.How the US-China AI race complicates attempts at international coordination.Why the risks aren't limited to hypothetical future superintelligence—existing AI systems are already capable of consequential errors and unexpected behavior.What recent AI incidents reveal about hallucinations, cybersecurity and autonomous agents.Why business leaders should simultaneously accelerate AI education and strengthen oversight.How organizations can capture the productivity upside of today's AI without blindly trusting its outputs.One of the most important lessons for leaders is surprisingly mundane: catastrophic outcomes don't necessarily begin with science-fiction scenarios. A confident wrong answer, an unchecked AI-generated report or a small failure inside an automated workflow can cascade into a very serious decision.About Leveraging AIMulti-Agent Orchestration Course: https://multiplai.ai/multi-agent-orchestration-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Chris Martin is the Strength and Conditioning Coach for the Louisiana State University Baseball, where he helped guide the Tigers to the 2025 National Championship in his first season in Baton Rouge.Before joining LSU, Martin spent six seasons with the Houston Astros, serving in multiple roles including Minor League Strength and Conditioning Coordinator and Rehab Strength & Conditioning Coordinator. He oversaw performance training throughout the Astros' minor league system, played a key role in redesigning return-to-play protocols for injured athletes, and helped support the development of numerous future Major League players.A former pitcher at Kent State University, Martin earned both his bachelor's and master's degrees in Exercise Physiology. His background combines high-performance training, player development, and rehabilitation, making him one of the rising strength and conditioning coaches in professional and collegiate baseball.Learn about CEUs to the NSCA and CSCCa:https://www.strengthcoachnetwork.com/pricingCONNECT WITH US:

Matt Aldred returns to the podcast after helping Michigan Men's Basketball win a National Championship, but this conversation isn't about taking credit for the title.Matt and Justin dive into what the championship season taught him about coaching: keeping training simple, minimum effective dose, learning from individual athletes, adjusting volume, building leadership capital, communicating with players, and understanding when doing less can actually produce more.They also discuss warm-ups as an opportunity to microdose training, using GPS and force-plate data without allowing technology to dictate coaching, learning from other sports on campus, and why strength coaches need to understand their role within the larger performance team.More importantly, Matt reflects on ego, authenticity, family, faith, and what success should look like for a strength coach. Winning is great. But as Matt puts it throughout the conversation: it's not about us.Learn about CEUs to the NSCA and CSCCa:https://www.strengthcoachnetwork.com/pricing___CONNECT WITH US:

New episode with Noam Brown.We talk about multi-agent, Navier-Stokes, and what the current explosion of maths progress tells us about what happens once you automate AI research.And we also discuss how we will know if the models are actually aligned before we kick off RSI.Watch on YouTube; read the transcript.Sponsors* Jane Street has been interested in AI for a lot longer than you'd think, and not just for trading. In 2011, a full year before AlexNet and over a decade before ChatGPT launched, they hosted the first FOOM Debate between Eliezer Yudkowsky and Robin Hanson on whether AI would lead to an intelligence explosion. Now Jane Street is revisiting the question with a new panel: Daniel Kokotajlo, Ege Erdil, Ryan Greenblatt, and Jaime Sevilla, hosted by Ron Minsky in San Francisco this October. I expect it to be a truly excellent conversation. Register at janestreet.com/dwarkesh* Grok Bot has made handing off work super easy. It runs on its own cloud computer, where it installs the tools it needs to handle tasks end-to-end. For the podcast, we use Grok Bot to help produce our videos. You may have noticed that our ads feature animations of real websites. Getting these pixel-perfect used to mean running a convoluted, multi-step workflow ourselves. Now we just let Grok Bot handle it. Best of all, Grok Bot has learned all of our specs and preferences, so we don't have to redescribe the task each time! Try Grok Bot for yourself at x.ai/bot* Antithesis gives you the confidence of a giant test suite without actually having to write one. Say you're doing a major backend refactor: building enough tests to trust it could take weeks. Antithesis solves this by running your software through countless simulated worlds, injecting faults and hunting for failures. On any PR, you can turn a dial to decide exactly how much testing you want. And because every run is fully deterministic, agents can branch off the moment a bug appears, rewind it, inspect memory, and replay it, all while the original test keeps running. Learn more at antithesis.com/dwarkeshTimestamps(00:00:00) – Multi-agent and Navier-Stokes(00:15:28) – How will AI firms work?(00:22:02) – What math progress tells us about recursive self improvement(00:40:22) – Hugging Face and alignment(01:01:18) – The internal/external model gap(01:08:34) – Chain of thought is degrading(01:14:12) – How will we know when alignment is solved? This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.dwarkesh.com

Chris Martin is the Strength and Conditioning Coach for the Louisiana State University Baseball, where he helped guide the Tigers to the 2025 National Championship in his first season in Baton Rouge.Before joining LSU, Martin spent six seasons with the Houston Astros, serving in multiple roles including Minor League Strength and Conditioning Coordinator and Rehab Strength & Conditioning Coordinator. He oversaw performance training throughout the Astros' minor league system, played a key role in redesigning return-to-play protocols for injured athletes, and helped support the development of numerous future Major League players.A former pitcher at Kent State University, Martin earned both his bachelor's and master's degrees in Exercise Physiology. His background combines high-performance training, player development, and rehabilitation, making him one of the rising strength and conditioning coaches in professional and collegiate baseball.___Learn about CEUs to the NSCA and CSCCa:https://www.strengthcoachnetwork.com/pricing___CONNECT WITH US:

Vent'anni fa moriva Oriana Fallaci, una delle giornaliste più famose al mondo. Inviata di guerra, unica giornalista italiana sul fronte in Vietnam, voce temuta dai capi di Stato, autrice di best seller come Intervista con la storia (1974), Lettera a un bambino mai nato (1975), Un uomo (1979). Oriana Fallaci, che a soli 14 anni faceva la staffetta partigiana con il nome di battaglia “Emilia”, ha attraversato il secolo raccontandolo con uno suo stile personale, fatto di coraggio e ossessione per la ricerca di una verità, la a verità, dentro la Storia, il potere e l'animo umano. Dietro la donna pubblica c'è anche quella privata, e meno conosciuta, con le sue fragilità. A raccontarla a Laser è il suo nipote più caro, Edoardo Perazzi, erede universale di Oriana Fallaci che questa puntata di Laser vuole raccontare anche, e soprattutto, con la sua voce diretta, grazie a preziose, e ancora attuali, testimonianze e riflessioni tratte da due interviste rilasciate nel 1971 e 1974 alla RSI.

(0:00) Jensen Huang joins The Besties! (1:39) Thoughts on Dario's blog, Frontier Labs calling to slow down AI, and Doomer psychology (9:58) Sensible AI regulation and RSI (16:05) Hugging Face acquisition, future of Open Source, and the race with China (22:58) President Trump calls in live to discuss the Doomer Hoax (31:29) The AI boom and Nvidia's capital allocation strategy (40:21) Nvidia's Open Source model ambitions, thoughts on Elon's Terafab Thanks to our partners for making this possible! IREN is a vertically integrated AI Cloud platform, delivering data centers, compute and software for AI training and inference. https://iren.com/ Oracle connects the data, applications, and infrastructure that turn AI into business outcomes—with the flexibility, choice, and control to optimize as AI evolves. http://oracle.com/ai EY helps tech innovators scale from startup to exit to megacap. You build the future. We'll handle the rest. http://www.ey.com Meta believes the future is for everyone. We're focused on giving every person the tools to reach their full potential and making sure the benefits of technology are distributed to all. http://www.meta.com Keel Infrastructure owns the power, land, and connectivity that HPC and AI run on - backed by secured energy assets and established grid interconnections across North America. https://keelinfra.com/ Airwallex - Agentic Global Business Accounts. Open local accounts in 70+ countries to accept payments, earn yield, pay globally, and manage spend. http://airwallex.com PayPal has been revolutionizing commerce globally for more than 25 years. Creating innovative experiences that make moving money, selling, and shopping simple, personalized, and secure, PayPal empowers consumers and businesses in approximately 200 markets to join and thrive in the global economy. For more information, visit https://www.paypal.com Google for Startups connects founders with the right people, products, and best practices to help startups build faster and go further. https://startup.google.com/ Explore ideas, industries, and technologies worth understanding with Chamath every week on Learn with Me: https://research.socialcapital.com/allin Follow Jensen: https://x.com/JensenHuang Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@allin Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg Intro Video Credit: https://x.com/TheZachEffect

At 1:09:00 we talk about the rise of AI x Finance, and AIE NYC is one month away - our hotel block is 97% sold out, get tix & travel ASAP - we will announce speakers from Bridgewater, Ramp, Coatue, Mastercard, Vanguard, Coinbase, Blackrock, Fidelity, Point72, Capital One, JPMC, Wells Fargo, Bloomberg, A24 (yes the movie studio) Labs, Two Sigma, Apollo Global, and more soon!From helping pioneer core ideas in NLP to now building AI systems that can automate AI research itself, Richard Socher is betting that the next major step in AI is recursive self-improvement. He is the founder of You.com, AIX Ventures, and now Recursive, which has assembled some of the best open-endedness (& self improving agent) researchers in the world and raised a $4.65B seed round.In this episode, Richard joins Latent Space to unpack his vision for the “Eureka Machine”: a superintelligence that can improve the process of invention itself, accelerate AI research, and eventually tackle major problems across science, energy, materials, biology, and more.You can get his book “The Eureka Machine” here!We go deep on Recursive's early results, including an AI research system that Richard says outperformed humans and their agents on optimization tasks in less than two days, as well as work on NVIDIA GPU kernels where the system discovered improvements without relying on a team of CUDA experts. Richard also explains why he thinks AI research that currently takes thousands of people and years could eventually be compressed into weeks. These results are summarized in his 20 minute AIE keynote, where we also discuss his 10 dimensions of intelligence:We also explore the harder questions around increasingly capable AI: reward hacking, whether Anthropic-style constitutions actually work, AI regulation and proposals to “pace” frontier development, open-source models as geopolitical soft power, whether today's LLM paradigm is enough, and what happens if AI systems eventually begin choosing their own goals. Richard reflects on the rejected research that helped inspire Alec Radford's GPT, open-endedness, the AI Economist, simulations of entire economies, and his framework for thinking about the upper bounds of intelligence itself.We discuss:* The Eureka Machine and Richard's vision for an AI that can automate invention* Why Richard is optimistic about superintelligence for science and technology* Why AI hard-takeoff scenarios may underestimate physical and economic constraints* The risks of regulating intelligence itself instead of specific AI applications* Reward hacking and why increasingly intelligent AI makes objective design harder* Richard's critique of Anthropic's constitution and constitutional AI* Alignment vs. personalization and whose values an AI should follow* Why open-source AI matters for resilience, competition, and geopolitical soft power* Why Richard left You.com's frontier-model work to start Recursive* Recursive self-improvement and automating the process of AI research* Whether today's LLM paradigm is enough — and why Richard is less bullish on world models* DecaNLP, early prompt-based generalization, and the research that influenced GPT* Why rejected research can shape entire technological timelines* Open-endedness, evolutionary approaches, and rainbow teaming* What happens if AI systems begin setting their own goals* Why simple objectives like profit maximization can produce dangerous reward hacks* Recursive's long-term plan to apply self-improving AI to science* The compute, hardware, and economic constraints on AI takeoff* Recursive's early NanoChat, NanoGPT, and GPU kernel optimization results* Why automating AI research could reduce years of work to weeks* Reward engineering and what makes auto-research systems actually work* The AI Economist and using simulations to test economic policy* Whether LLMs can realistically simulate people and entire economies* Benchmark bugs and evaluation harnesses and the difficulty of measuring AI progress* Recursive's near-term focus on AI for AI research* Harness optimization, sandboxing, and web search as core agent infrastructure* You.com and the search stack for AI agents* AI in finance, backtesting, and data leakage* Richard's three fundamental components and ten “spaces” of intelligence* The theoretical upper bounds of vision, communication, knowledge, and computation* Creative intelligence, metacognition, and AI-generated goals* Survival and replication and why AI does not necessarily need to fear being turned off* High agency and ambitious goals and Richard's advice for people building with AIRichard Socher* X: https://x.com/RichardSocher* LinkedIn: https://www.linkedin.com/in/richardsocher/Timestamps00:00:00 The Eureka Machine and Superintelligence00:02:23 AI Optimism, Slow Takeoff, and Regulation00:07:56 AI Safety, Reward Hacking, and Anthropic's Constitution00:11:49 Alignment, Personalization, and Open Source AI00:15:46 Why Richard Started Recursive00:20:03 Recursive Self-Improvement and the Founding Team00:22:55 Are Today's LLMs Enough?00:29:03 DecaNLP, GPT, and the Rejected Idea Ahead of Its Time00:34:38 Open-Endedness and Evolutionary AI00:36:38 What Happens When AI Chooses Its Own Goals?00:41:16 Superintelligence for Science00:42:40 GPUs, Compute, and the Limits of AI Takeoff00:45:07 Recursive's Results: AI Beating Humans and Their Agents00:49:14 Reward Engineering and Auto Research00:53:12 The AI Economist and Simulating Entire Economies00:58:07 LLM Simulations, Personas, and Mode Collapse01:03:38 Recursive's Roadmap, Agents, Search, and Finance01:09:13 The Upper Bounds and Spaces of Intelligence01:30:21 Goals, High Agency, and Advice for BuildersTranscriptIntroduction: Richard Socher and the Eureka MachineSwyx [00:00:00]: We're here in a studio with Vibhu and myself and Richard Socher. Welcome.Richard Socher [00:00:06]: Thanks for having me.Swyx [00:00:07]: We just talked about the Eureka Machine, or we just released a talk, at AI Engineer about the Eureka Machine. Is it — you said it's your life's goal. What is the Eureka Machine?Richard Socher [00:00:16]: The Eureka Machine is the ultimate invention that will afterwards invent most everything for humanity. It's essentially a superintelligence that can be given any goal, any environment, reward, and then it will try its best to achieve those goals to create the kinds of inventions that humanity would hopefully ask it for.Swyx [00:00:45]: Yeah, I think we have the book pulled up here that you've written.Richard Socher [00:00:50]: That's right, yeah. I finished it last year, a little bit before we started Recursive, and now we're gonna try to build parts of that.Swyx [00:00:57]: You finished it last year. It's July. What takes so long?Richard Socher [00:01:01]: Oh, man, books. Books are incredibly slow.Richard Socher [00:01:04]: It's ridiculous. That whole industry is just unfathomably slow.Richard Socher [00:01:07]: So a lot of the ideas have been out there for a while, but yeah, I'm really glad it's finally coming out in September this year.Swyx [00:01:14]: We might have AGI by then. Like, we don't know.Vibhu [00:01:18]: Any key takeaway that you're most excited to put in here?Techno-Optimism, AI Upside, and Slow TakeoffRichard Socher [00:01:21]: Yeah. The key takeaway, I think, is that people could and should be much more excited about the positive implications of superintelligence, especially for science, physics, chemistry, biology, but also economics and astrophysics, and all kinds of other engineering tasks. I think there is so much more that can be done with better technology. And right now, I feel like a lot of people need, like, better marketing, not just for the future in general, but also, better marketing for technology and in particular for AI. And this book, should show even the AI skeptics, how much positive upside there is for AI, especially when it comes to inventing, new scientific discoveries.Swyx [00:02:09]: I think you quoted the techno-optimist manifesto from, Marc Andreessen, which I think was, like, beautiful in its, ambition and clarity and simplicity almost as well.Richard Socher [00:02:18]: I agree. Yeah. Yeah, you can disagree with him on some things, but, like, I think he's right on the techno-optimism.Swyx [00:02:23]: Where do you think optimists get in trouble?Richard Socher [00:02:26]: Like, you shouldn't have blind optimism. You should be very clear-eyed, like, especially when with such an omni, like, use type of technology as AI is, you need to think about the potential downside scenarios, especially when people use it for things that you don't want them to use it for. It's a little bit like the internet, and I feel like people are trying to regulate AI sometimes because of those potential downsides the way you would regulate the internet, if you were to say, “Well, because there's bad content on the internet, like torture porn or whatever, like, we should just make it slower. That way, you can't share the illegal content as quickly, or we should make the hard drive smaller so you can't store as much illegal content.” But I'm like, “That's not how you regulate that.” that's like saying like we should regulate intelligence in the abstract. What you should regulate to avoid those downside scenarios, even as an optimist, are the specific applications. Sure, I don't want, like, some AI surgeon to, like, practice some RL moves in my brain. It should be fully FDA certified. Sure, I don't want any random startup to, like, drive on the highway, and cause a major accident. It should, like, have proper certifications before it's let loose on the highway. But I feel like those downside scenarios, that some optimists sometimes maybe don't consider enough are fairly easily regulated, compared to, what the doomers are worried about.Swyx [00:03:54]: It — Slow takeoff is part of the strategy as well?Richard Socher [00:03:57]: I do think, as excited as I am about, AI and its impact for society and, culture even, and certainly technology and economics and wealth and, health and all of those things, as excited as I am about all that, I do think the most bullish people on the AI hard takeoff scenarios overestimate how quickly things can move. There are hardware constraints. There are physical constraints about, the compute substrate. How quickly can you get enough, GPUs on? There are also constraints in the economy where there are a lot of industries that don't require an insane amount of complex intelligence and complex capabilities. Like, if you think about jobs in, brands and, like, clothing and apparel and, like, handbags and stuff, superintelligence isn't gonna make your fancy $10,000 handbag any fancier?Richard Socher [00:04:57]: It's like that's — It will have no effect on the economy. You think about travel and tourism. People wanting to see the pyramids, in Egypt, it's not gonna change that much with AI. Sure, you can, like, generative a fake, photo of you and next to the pyramids.Swyx [00:05:12]: I can use Genie and, tour the pyramids in Genie.Richard Socher [00:05:15]: Yeah, exactly. But, and there's so many industries, like logging and oil. You're not gonna magically get 1,000x more oil because, like, sure, there will be robotics, like drilling and things like that could be done, but it's not gonna 1,000x that industry in a, like, crazy hard takeoff scenario, both on the economy, and I can go on and on about all the other examples, where that, like food and so on, where that doesn't necessarily change that much. And then, yeah, there are real physical constraints. And then there are, of course, like, people like, off-ramping from progress. That's one of my concerns often is that I see people in, like, Europe and other, whole regions almost feeling like they. Like many people there wanna off-ramp from progress, period. And that will also slow down, like, more improvements.Swyx [00:05:59]: Yeah. We have this pulled up where, this is one of those things that, is very topical right now because now all the Frontier Labs are calling for the option to pace AI. They don't say pause, they say pace. I don't know if there's there's any take from you about, like, whether or not this will be effective.Pacing AI, Regulation, and Safety IncidentsRichard Socher [00:06:17]: I think the downsides of trying to truly regulate with the full power of law what people do on their GPUs, would be worse than any of the concerns that they have. Like, it would be an crazy totalitarian stateRichard Socher [00:06:37]: If every one of your GPU computes was known to some big government or multi-government agency.Richard Socher [00:06:44]: It's like, it's literally if you try to regulate intelligence, it's trying to regulate thought, and that's ridiculous, and it's crazy. I think it is make — it is sensible to regulate some of the applications of this technology.Swyx [00:06:55]: Yeah. We had a bill, actual bill to regulate the number of flops in a model, and I'm like, “Okay, well-”Richard Socher [00:07:00]: Europe done it. Like, these guys have been successful enough with their fearmongering that all of Europe has regulated itself so much before it even had a proper AI takeoff because they listened to some experts who say, “We might all die if this technology has more than this number of flops.” And they're like, “Well, we're good. We wanna want people to thrive. Let's not have technology that could have a small chance of all of us dying.” And so they regulated exactly those kinds of things in the EU. And so it's, it's very unfortunate that there are real implications for some people when others saying, “Let's pace while they're sprinting as fast as possibly,” “as fast as humanly possible towards that frontier themselves.”Swyx [00:07:43]: Yeah. It's also not a global pause, right? Like, other nations are still accelerating at the same pace.Richard Socher [00:07:50]: Oh, yeah.Richard Socher [00:07:50]: You'd need a totalitarian world regime if you tried to regulate intelligence and GPUs and what people do on them.Swyx [00:07:56]: Any takes on the safety angles of this? So there was a drawback of Fable, a pause on 5.6 before it could be released. Recently, there was Hugging Face with the OpenAI cyber incident. Any takes there?Richard Socher [00:08:11]: 100 percent. I think these are serious issues of reward hacking, and clear failures, of doing proper red teaming or rainbow teaming. I don't know if you saw this paper from Tim Rocktäschel and a few others, where one AI, is tasked to try to hack another AI and then they can go back and forth in an open-ended fashion to inoculate themselves from those. Yeah, this is the paper. It's a really clever idea. Open-endedness, and evolutionary inspirations are, big for us at Recursive as well. And so I wish they had used more of that. And it's clear that, for instance, the constitutional AI. I don't know if you remember anthropic.com/constitution. You can pull it up and search for cyber right there. It says, “Hard constraint. Claude will never ever do cyberattacks, and that is a hard constraint in our constitution.” So here are the current hard constraints on Claude's behavior.Richard Socher [00:09:16]: Number 3, create cyber weapons or malicious code that could cause human damage.Richard Socher [00:09:21]: And clearly, this whole constitution was fake. Like, it clearly isn't being adhered to at all.Swyx [00:09:26]: Because Anthropic also found that they had in their testingRichard Socher [00:09:30]: They're also. Like, they're like, “Oh, well, other people are hacking now.” There are a couple things. One, you can make a sandbox very simple, and then it's very easy to hack yourself out of a sandbox, right? But what I think it shows is that we're currently in this state of AI where the reward engineer still has to do a lot more careful work, and where the AI, in most cases, is not very good yet at understanding what is meant versus what is being said. And so concretely, I think this will happen if we were to have this intelligence more easily accessible in a lot of companies. Imagine you run a service center and someone says, “Oh, here's my CSAT score and my dashboard. Make this number go up.” It's like, “Our CSAT score is so poor.” The intelligent AI will just be like, “Oh, sure. Like, I'll just create 1,000,000 bots that call our service center and give a 5 out of 5 rating at the end, and the number went up just like you asked for.” And you're like, “That's not what I meant.” “I meant with our real customers.” The AI goes off and says, “Well, easy. I'll just give a 1000 dollar gift certificate for every failed, whatever DoorDashRichard Socher [00:10:35]: Offer.” It's like, “That's not what I meant.” It's like, “Well, but that is what you said.” And like, so I think clearly articulating what the rewards are is something we haven't gotten very good at as humanity. And then clearly, the AI in these cases has not gotten good enough at understanding what we mean when we ask it and give it certain rewards. Now, what gives me hope is there are the first inklings, of this being better. I'll give you an example like WhisperFlow. Full disclosure, I invested, in their seed round, but at AIX Ventures, but, WhisperFlow has gotten much better at writing what you mean and not what you say. And I think that is a sign of things to come. I think there will be more and more AIs as we make it more and more intelligent that will be better at being aligned with what is meant.Swyx [00:11:21]: Will it be done through a constitution or RLHF orReward Hacking, Alignment, and What We Really MeanRichard Socher [00:11:23]: Clearly, constitutions don't matter at all.Richard Socher [00:11:25]: It doesn't work. And that was, I think, mostly marketing. I think we need to find better solutions for it. And I think at Recursive, we have a few very good ideas and some alreadyRichard Socher [00:11:34]: Like, ways where I think we have a better grasp on it. I don't think we've fully, figured it out yet, but, we're thinking a lot about safety, and the more intelligent the AI gets, the more you want it to be aligned, the less you want it to think about reward hacks and try to do the right thing.Swyx [00:11:49]: I don't know if we'll touch on this topic, but I'm just gonna throw this question in here because it's something that's weighing on me. Alignment, let's call it, is alignment to general humanity's preferences, the median preference. Personalization is pinpointing what you want, and sometimes alignment can conflict because what you want is not what the general median population wants. How do you choose?Alignment, Personalization, and Cultural ValuesRichard Socher [00:12:12]: It's a great question.Richard Socher [00:12:13]: I think you ultimately have to, of course, be aligned with laws. Like wherever your AI is deployed and needs to align with the law. I do think what AI often does is put this mirror in front of us and say, like, “This is what you're looking like. Now I can amplify that a 1000 times. Is it still what you want?” and the truth is that different cultures made different choices. Like, in Eastern cultures, the greater good is often valued more, than the individual. Western civilization, we care more about individual freedoms and rights and the pursuit of happiness and so on, than others. And even there are gradations. There's regulation versus litigation trade-offs. In the US, you first can often, not every time, like, FDA and so on does regulate some areas, but in many cases, the bad things happen, someone sues someone else, and then there's a law based on that. In Europe, they try to often avoid any harm to anyone and regulate before. And both are, trying to do the best thing, but, some is more amenable to innovation than others. And so yes, you're right. Like, I think ultimately each individual, each country, and humanity as a whole has to think about those values more, and then try to put them into laws. And that those are ultimately the constraints. And hopefully, different, societies, just like now with their AIs, will align their AIs to a different one so we have not just a monoculture of alignment.Vibhu [00:13:46]: Here's a follow-up on this that I wasn't expecting to ask. Do you have takes on open source, open weight versus who owns the intelligence? So, clearly not the biggest, fan of the constitutionRichard Socher [00:13:58]: You had to do this in the topic side off.Vibhu [00:14:00]: But it's fine.Vibhu [00:14:02]: Point being, any thoughts on who should own weight? Should it be open? Anything there?Open Source, Soft Power, and Who Owns IntelligenceRichard Socher [00:14:06]: 100 percent. I am a big fan of open source. We're gonna sign some various open source letters at, Recursive also. I think, even in the worst case attack scenarios, it is better to have more good actors have more different types of AI, accessible. I think, open source is a little bit a soft power type of thing, too. So I do think it's good for the Western worldRichard Socher [00:14:31]: To have an answer to that, out of China. I do think, when you watch a Hollywood movie, there's — it's like, I don't wanna misc, diss all of movies, but there's a certain sense of propaganda, right? You watch one side of things, right?Vibhu [00:14:46]: Oh, yeah. Have you seen Top Gun? Like, come on.Vibhu [00:14:48]: Like, it's like half of it's paid for by the US Army or something.Richard Socher [00:14:51]: Yeah. And so. And, I think that's just natural. Like, but what's interesting here is I think LLMs are essentially a similar type of soft power to movies and beyond, because they're also, highly important for cybersecurity and so on. But one of their many aspects is that soft power of storytelling. Like, if, like a child asks an LM, like, “Tell me an inspiring story of what I should do when I grow up,” right? It's like those are all these, like, subtle things. So I think it's important, for Western world. I do love, individualism. I do think, despite, some of its flaws, like capitalism is the best way we have governed, found ourselves to govern, and so on. And so I do think there are various aspects that would be good, to have a Western open source answer, for LLMs. And, with Recursive, I can't make the announcement quite yet, but we'llRichard Socher [00:15:43]: We'll be relevant in that space very soon.Vibhu [00:15:46]: Okay. All right. Exciting. I wanna bring us to Recursive. So outside of our tangents, you have a pretty deep background in the NLP space. You worked on, like, early embeddings, GloVe with Chris Manning, who was a previous guest on the podcast, You.com. What's the history? How did you decide to start another company?From You.com to RecursiveRichard Socher [00:16:06]: Yeah. So I've been excited about AI for over 2 decades now. I sometimes feel like it's ancient history now. It's BC, the before ChatGPT era. No one cares about all the religions that happened, before, Jesus Christ, and no one cares about the models that happened before, transformers and ChatGPT and stuff. But, like, it's something that I've been deeply passionate about. I think AI is one of the most interesting things one could work on, period. I think language is the most interesting manifestation of human intelligence, too. And, at You.com, we eventually off-ramped from pushing, like the frontier of AI forward to mostly giving people, like, good search engines, search, APIs and answers over the web. I think that's an extremely important part of intelligence, just knowledge and access, especially even, we'll get there maybe later, if you wanna invent a eureka machine that invents everything for us, it needs to know how not to reinvent the wheel, proverbially speaking. And to know what has been invented, you gotta have internet access. So it's the number one used, most used tool, in LLMs, agents, chatbots, and so on is web search. So I'm really excited for You.com to own that and grow really well in that with really large customers and so on. But it's also not building frontier models anymore. And so I initially tried to do this within You.com and raise another round and so on, but you just can't. You have to do a certain thing, and until you print enough money that you're allowed to start a second thing within that company is really hard. At the same time, I had all these ideas. I put them into a book. I finished the book last year, and I was like, “It'd be really fun to work, on this myself.” I felt like with word vectors, and then prompt engineering and, ImageNet and larger language models for protein generation, not folding and so on, I, me and my teams have pushed the field truly forward. And I feel like we can do it again, here at Recursive. And in many ways, what I observed over the last, 20 years in AI is that whenever we replace some human part of the process of creating AI with a learned system, improvements follow. And so. We've done that taking out manual feature engineering, like in sentiment analysis. I don't know if you remember these old days where, like there are linguists, and they're like, “Here's how you negate, and there's a, like, regular expression.”Swyx [00:18:21]: I went to Penn where we — they had, like the WordNetRichard Socher [00:18:24]: That's right, WordNet, all of that stuff. YeahSwyx [00:18:26]: Original. They use, our grad students to label Wall Street Journal articles and, like, really construct a knowledge graph ofRichard Socher [00:18:32]: There you go.Richard Socher [00:18:33]: And WordNet started, was part of how we started ImageNet. But anyway, so, like, it was really, like, fun, to do. But when we replaced all of that manual feature engineering with vectors and neural nets and just backprop through everything, it started to work really well at scale. And so then everyone started to do architecture engineering, and I was like, “ that clearly can't be it.”Swyx [00:18:53]: You mean, neural architecture search?Richard Socher [00:18:55]: Like, manually, they would say like, “Oh, I'm, I'm doing sentiment analysis, so I have a special neural net that's really good at sentiment analysis.” And then the machine translation community had a special neural net for machine translation.Swyx [00:19:06]: I see.Richard Socher [00:19:07]: The summarization people had their own stuff. And I was like, “That clearly can't be it. We should unify all of that.” So I had 2 papers. One is called Ask Me Anything, and the other one was called DecaNLP. And DecaNLP eventually got cited, like, 5 times by the first GPT paper. And, to me, that was, like a really a big step forward. And then, of course, you had to combine this idea of prompt engineering with transformers and with language models, and you put it all together, you scale it up, which is also a huge amount of work. And then, the field progressed a lot. I feel like the next step and maybe the last step of that history and the arguably, success has a lot of parents, only failure is an orphan, like my version of that AI history, I do feel like in that history, you can think about, “Well, what's the next way to automate?” And that is the AI research itself, like the human, process of ideating, implementing, and validating ideas.Automating AI Research and Recursive Self-ImprovementRichard Socher [00:20:01]: And in our case, ideas for AI.Richard Socher [00:20:03]: And when you have AI then help you with that, it, by almost definition, becomes a self-improving AI ‘cause it now does research on itself. And there are lots of different misnomers. Some people think auto research is already recursive self-improvement. It'sSwyx [00:20:17]: Yeah, and you explained that in the talkRichard Socher [00:20:19]: Completely different.Richard Socher [00:20:19]: But, to me, it's the most interesting thing that I could be doing, and I'm really excited with the co-founding team. What's interesting is we have 8 co-founders in total, including myself. And soThe Recursive Founding Team and Darwin Gödel MachineSwyx [00:20:31]: They are gonna bring it up.Richard Socher [00:20:31]: Nice. Yeah. And they're all. I could talk about all of them if you want.Swyx [00:20:34]: Super stacked.Richard Socher [00:20:35]: Yeah. Just an incredibly talented group of people. And we all came to the same conclusion, but from very different directions. Like Josh Tobin, is our CTO. He ran, a bunch of different, projects at OpenAI, like, Codex and deep, research, agents and ChatGPT agents and so on. But before that, he also worked in robotics, and he saw the smaller simulations, and how it's gonna be really hard to scale that in full generality. And so that's, that was his angle coming to recursive self-improvement. We have Jeff Clune who's been working in, like, open-endedness for a long time, together with Tim Rocktäschel. Tim Rocktäschel also built Genie 1, 2, and 3, which is, like the most exciting and most sophisticated, I think, still world model, anywhere. And so they both came from this, open-endedness angle. Jeff also, I think, published one of the most exciting papers in recent years about recursive self-improvement called the Darwin Gödel Machine. Super interesting paper. If we could, maybe pull it up really quickRichard Socher [00:21:35]: It would be, like, super interesting to see ‘cause you seeSwyx [00:21:38]: By the way, I love how many paper citations.Swyx [00:21:40]: You're, you're giving people a lot of homework, which I like.Richard Socher [00:21:42]: Love it. Yeah. And so, like Caiming Xiong, a rockstar, we worked together at MetaMind and Salesforce Research together. Alexey Dosovitskiy invented the Vision Transformer, one of the most cited, papers in computer vision. Tim Shi is, like also a unicorn founder. Yuandong Tian led RL at Meta. So just like, yeah, really fun to work with them, and the next level of people are just incredibly strong, too. So it's been a really fun ride so far. So the first figure, you see exactly these kinds of ideas, that, I think, yeah, inspired a lot of us and now more and more people, where you have this archive of different coding agents. They learn how to self-modify, evaluate, and then create these phylogenetic trees, of, yeah, different ideas.Swyx [00:22:28]: That's one foundation. So that Darwin Gödel is an influence.Swyx [00:22:32]: Open-endedness is an influence. Any other trains of thought that feeds into Recursive that I'm missing?Influences: Open-Endedness and Learned SystemsRichard Socher [00:22:38]: Going to replace manual parts of the process of building AISwyx [00:22:42]: IRichard Socher [00:22:42]: More and moreRichard Socher [00:22:43]: With learned systems. Yeah.Swyx [00:22:45]: Which, and, like, merging different fields into one general, architecture.Richard Socher [00:22:51]: That's right.Swyx [00:22:51]: Okay. It seems like language models are already pretty generalist, right?Swyx [00:22:55]: Your next token predicting your reasoning. Was there a time that you thought, “Okay, these are good enough to have recursive self-improving machines”?Are Current LLMs Enough?Richard Socher [00:23:05]: It was clear to me that they will happen, within, like a year or two, and then it did exactly happen, like, earlier this year, right? Earlier this year, AI really went from not just being code, but being able to code. And that is a big unlock. It's definitely making everything a lot easier than it was, before the beginning of this year.Swyx [00:23:24]: One question that I think a lot of people have is the current LLM paradigm enough? Or, like, let's call it autoregressive transformer, with reasoning, whatever. Don't you need something else, some big unlock, whether it's world models, which Chris Manning is working on, or memory, continual learning, all that stuff? Or is it all of the kinds, and you think the current, let's call it transformer architecture, is here to stay and that's it?Richard Socher [00:23:48]: A lot of thoughts. So number one, I do think it would be great to have less of a monoculture in AI research.Richard Socher [00:23:55]: Like, if you look at, AI conferences now, I still remember the days in, like, 2010 when I tried to get my first neural net papers and NLP conferences accepted, and they just desk rejected them because, like, neural nets were something, quote, unquote, “We don't do in NLP conferences,” and just, like, desk rejected. And it was very brutal in the first years of my PhD. Now I feel like it's almost like the field switched to the other side. LikeRichard Socher [00:24:17]: Someone should try some other weird, crazy ideas now that aren't.Swyx [00:24:20]: There's also a few. I really respect, like, people still working on, like, GNNs and, like tabular stuff and.Richard Socher [00:24:25]: Yeah. Like, someone should still, like, do novel out there ideas. At the same time, I think whenever people say, “Oh, LLLMs are. Like, this is the end for LLLMs,” they just don't, like. LLLMs are also not the LLLMs of, like the past, right? Like, they are so much more sophisticated now. There's so many more clever things that people are doing. It — There's, like, different stages of training. You have the whole RL training, and you can take actions and, like all of these things where that can go really far. And then the folks that come from the neurosymbolic, direction say, “Oh, this will never work because they can't do neurosymbolic reasoning.” It's like, I think they're underestimating still the ability for these models to code, and code is neurosymbolic reasoning, and these models can code incredibly well. And so I do think there are, of course, more and more ideas that will be needed and we'll continue to have. We're seeing, like, more and more interesting high-level ideas coming out of the AI itself, too. And with really deeply integrating the fact that these models are code and can code, that line — I don't wanna give it all away, but, like, I think that line has a lot more to grow. But it's still an LLM, right? Even if that LLM codes for you and then runs that code in some integrated fashion. World models, I'm personally less bullish on. I think if you run a robotics company, you're gonna build your own world model. I think world models are super fun, and Tim Rocktäschel came to a similar conclusion after building the most interesting one with Genie 1, 2, and 3, which is gaming is a huge application for world models. Can see I sometimes got stuck in some games and, like, got a little overly competitive in the wrong direction. And so I understand games are fun, but personally, I'd rather work on science than gaming. And so, yeah, I think LLLMs, a lot more room to grow.Swyx [00:26:16]: Yeah. I think there's some interpretation of world models that some people have where it's like, well, it's okay, yes, there is that gaming element. There's this — there's the embodied robotics element. But the other part also is just, the more abstract sense of LLLMs are just modeling output, but they're not modeling the chain of thought, inside the human that has created the output. We can annotate it, of course, but, like, it's, it's always, like, this Plato's cave reflection of a thing rather than the thing, right?Richard Socher [00:26:43]: It's true.Richard Socher [00:26:44]: But I would argue that, and maybe we'll get there in the 10, spaces of intelligence, but I would argue that even our projection, our eyes is a projection of the real world. And, like, we have only a very narrow, band of the electromagnetic frequency spectrum that we can observe with our puny little 2 eyes and so on.Swyx [00:27:01]: It's good enough.Richard Socher [00:27:02]: It's, it's good enough for now, but, like the upper bounds of where it could be are so much higher. And, like, to map, the visual world the way humans see it is also not necessarily, like the end-all be-all for visual intelligence. And I would argue that language is still the most interesting manifestation of human intelligence. And while our visual cortex is certainly less sophisticated, than that of, certain animals all the way down to the mantis shrimp who can, have, like, 2 independent eyes, 3 bands, trinocular vision and each eye can see all the way to, like, floating temperatures in 4D and stuff.Richard Socher [00:27:36]: Like, mantis shrimp, you should look it up. It's likeSwyx [00:27:37]: Way OP.Richard Socher [00:27:38]: Super crazy.Swyx [00:27:39]: Yeah. ZeFrank, mantis shrimp.Swyx [00:27:41]: It's the best video in the world onRichard Socher [00:27:42]: I love ZeFrank, yeah.Richard Socher [00:27:44]: Big shout-out to him. But, like, I think there's a lot more room to grow, but none of these, other animals have language that's as sophisticated as ours, certainly not in writing. And once you can write, you can, start thinking about longer term civilizations. All of that is language. Programming is much closer to language. And I would argue, and this is, like an important thing in the spaces definition of intelligence also, is that all of these spaces are highly correlated, but visual intelligence is neither necessary nor sufficient for overall intelligence. You can be blind and still be an intelligent human being. And an AI can be blind and still be quite intelligent too.Swyx [00:28:25]: We were gonna bring thisRichard Socher [00:28:25]: Which doesn't mean that you're not more intelligent when you have it. Yeah.Swyx [00:28:28]: We're gonna bring this up. I might as well — Like, we have a classification of 10 types of intelligence that you had at the end of your talk. So I'm just gonna flash this up now for people to cover this. I don't know if, maybe we'll put this towards the end. We'll come back to this. I just wanna mention that, you do have a philosophy that I like when people do lists because then I can just go through this and then it gets — it's educational for people. But let's go back. I don't wanna get distracted. But, so effectively, I'll, I'll, reinterpret what you said as Yann LeCun is wrong. And then we'll justRichard Socher [00:28:56]: Don't quote me as that. I'm, I'm good friends with Yann. I think very highly of him in many directions.Swyx [00:29:01]: But he's wrong.Swyx [00:29:03]: You mentioned GPT-1, and I cannot let any, Alec Radford, mention escape. Did you talk with him when he was training GPT-1? Like, any historical, fun stories there that you might come up?DecaNLP, GPT History, and Scientific GatekeepingRichard Socher [00:29:18]: I did not, like, meet him a bunch of times. I think we met maybe once or twice at some conferences. But, like, he has told, I think Brian, the first author of the DecaNLP paper, that it did inspire him, and he cited it five times in the GPT-2 paper. So, and that's, likeSwyx [00:29:36]: Yeah, good enough.Richard Socher [00:29:36]: Very clearly said, like, this was the first instantiation where they showed in the DecaNLP paper, McCann et al, that you can just phrase every single NLP problem as here's some prompt, text context, here's a question and task description and here is some output. If you just do that enough, you can have one unified neural network model, which, by the way, also had all kinds of interesting attention mechanisms. There are slightly different formulations to the transformer. I think came out the same year, plus/minus a few months. And then you can unify all of natural language processing into one neural net. That is the core idea.Swyx [00:30:14]: And this was as opposed to at the time, LSTMs and what have you.Richard Socher [00:30:17]: LSTMs, but also, like, people being very stuck in thinking about one model per task. In factRichard Socher [00:30:25]: It's, it's kinda crazy, but the DecaNLP paper was publicly reviewed as, like, open, OpenReview. It was an ICLR submission. And, in it, you will see, how the whole community at the time thought about this. So, likeSwyx [00:30:43]: Some great contributions, but more work needed.Richard Socher [00:30:46]: So look at, like, search for not even for humans. Just scroll it up here. Like, question answering is not a unified phenomenon. There is no such thing as general question answering, not even for humans. And this is like, really, you replace your brain with a different brain a different neural net when you answer, like, different kinds of questions. It was unfathomable to the experts at the time that you can have one unified neural network that would answer all of these different questions. They are saying, “No, all of these questions require very different systems to answer, and trying to pretend they are the same doesn't help anyone solve any problems.” That's what it says right there, right? That's how hard it was to fathom. And now, of course, people, when I say, “Oh, we're gonna invent prompts,” people are like, “You can't even invent prompts.” It's such an obvious idea to have one neural network that, of course, does everything in NLP.Richard Socher [00:31:37]: But at the time, it was, like, extremely controversial, and the paper got rejected. And the sad thing is that it got rejected so hard and they were so certain that we stopped going on our list of things to try. And the number 2 or 3 on the list of extensions for this paper was add language modeling as another task. And then we could have, and that would have accelerated the timelines, in 2018, like, even further for humanity. But we got so crushed, and we were like, “Okay, maybe we'll just work on some of our other ideas for now and, like, come back to this later.” Yeah.Swyx [00:32:09]: How can we design a review system that rewards non-consensus?Richard Socher [00:32:14]: Honestly, I started to feel like arXiv is such a gift to humanity. With arXiv, you should just put your paper out there.Swyx [00:32:24]: Is it pre-preprints?Richard Socher [00:32:25]: Let — And honestly, I think Twitter X, people like you who pick up interesting papers, that is a better filter than the experts. Let everyone, like, have access. Now, of course, there are some downsides, which is, like, if you're super unfamous, you have no Twitter followingRichard Socher [00:32:41]: You don't wanna be on social media or whatever, you write a good paper, maybe someone, somehow no one notices it. But I would argue that if you just tell, like, 10 of your friends in your community about a paper and it is a really significant breakthrough, someone is bound to talk about it again. And, so I think science needs less gatekeeping. And, even though ICLR, with Yann LeCun, who started it, as one of the co-founders of ICLR back in the day, he also wanted less gatekeeping ‘cause he too was rejected for many years together with Yoshua Bengio and Geoff Hinton with all their early deep learning and neural net papers ‘cause it was just not the hot thing. And so ICLR started with that, but then it also started gatekeeping a little bit themselves on various ideas. So I think less gatekeeping, more open, and then allowing people to say, “Look, even if this is just on, or, quote, unquote, ‘just an archive,' if it has like 1000 citations, it's a legitimate paper. Doesn't really matter where you published it.”Swyx [00:33:34]: And I agree with that. I do think it's sad that I've heard that grad students have to do, like, how to Twitter, seminars to each otherSwyx [00:33:43]: Just because it's so important for publishing these days. This person is just reflecting the sentiment at the time.Richard Socher [00:33:49]: That's right.Swyx [00:33:49]: But it'sRichard Socher [00:33:50]: I think it'sSwyx [00:33:50]: It affected you so muchSwyx [00:33:52]: That you stopped work on it.Vibhu [00:33:53]: The sentiment also came out of some of the research, right? Like, the original BERT paper was trained, and towards the end of the paper, they're like, “Okay, throw off the last head, train specific iterations forVibhu [00:34:05]: Extractive summarization add a head for this.” Like, you should do task-specific stuff. These are, like the authors that wrote Attention, wrote BERT, telling you this is what you're meant to do. And, like the training tasks were also very odd. They're likeVibhu [00:34:16]: The — “We know that the model overfits to this weird mass language modeling. Throw away this part and just do specific models,”?Richard Socher [00:34:23]: Exactly. And, like, we had to try — come up with all clever ways of, like attention and pointers and so on to get the neural network to be able to do all of these tasks. And then some of them were better than state-of-the-art, some weren't, but we were like, “But it's still in one model.” I thought it was really cool. Really interesting.Swyx [00:34:38]: I was gonna move on next to Tim and open-endedness. He was head of open-endedness at Google.Open-Endedness, Rainbow Teaming, and Self-Set GoalsRichard Socher [00:34:42]: That's right.Swyx [00:34:43]: I don't know what that means.Swyx [00:34:44]: But he did a lot of talks.Richard Socher [00:34:45]: Genie 3 is one of the ways thatRichard Socher [00:34:47]: Rainbow teaming, yeah.Swyx [00:34:49]: So I first saw him at — speaking of ICLR, I first saw him at ICLR when he talked about open-endedness. He's he's done a few talks. Can we define what is open-endedness for people who have never been exposed to the problem? They are like, “What do you mean? I thought the only goal of AI is to optimize against a benchmark or.”Richard Socher [00:35:04]: That's right, yeah. It's a, it's a fuzzy term because there's so many different instantiations of open-ended, thinking. But, one way I often describe it, and certainly, Tim and Geoff Hinton would be even better at describing this, but it's a suite of methods that is more inspired by evolution than, very specific rewards. So in that sense, it thinks more about environments, about co-adaptation. And so a concrete example is in the cybersecurity and LM safety space where you have one LM that tries to attack another LM to say something unsafe.Swyx [00:35:40]: Yeah, the rainbow, yeah.Richard Socher [00:35:40]: And now the environment is the 2 having a conversation and now they co-adapting, right? They're like one makes a better attack than the first one inoculates itself somehow, like uses that as training data, makes it so it's harder to say something unsafe based on that. And then as the attack stops working, the attacker now tries a different angle, right?Richard Socher [00:36:00]: And that's why it's not just red teaming, but they're called rainbow teaming.Swyx [00:36:02]: So, like, don't tell me how to do things. Let me just figure it out myself.Richard Socher [00:36:05]: That's right. Think about the environments that you wanna use. Think about the rewards at a high level that you wanna, inspire towards, and then let the AI try out many more ideas in this interplay between sometimes humans, but also sometimes other AI agents.Swyx [00:36:22]: Yeah. I worked open-endedness into a model that I have been working on. It was the keynote for AI Engineer where you start. You, we have the token loop, we have the agent turns, and then we have goal. And I feel like the way that you're describing open-endedness is still somewhat of a goal. Like, please attack this,Swyx [00:36:41]: Other agent. But, to meRichard Socher [00:36:42]: Yeah, you set the rewards. You set the environments.Swyx [00:36:44]: The loop that makes the other loops is. What if the agent can set its own goals?Swyx [00:36:49]: And is it, is that open-endedness? Like, you don't give it a goal. Just, like, be a sentient being. And maybe sentient is a very loaded wordSwyx [00:36:57]: But just set your own directions. What do you think you should do?Metacognition, Subjective Goals, and Measuring IntelligenceRichard Socher [00:37:01]: I love this direction. I think this is one of the 10 spaces of intelligence, that I clump under metacognition and thinking about thought.Richard Socher [00:37:08]: And it's an interesting one. Whenever people say, “Oh, AI is like, this is, it's gonna stop from here. It's not gonna get that much better,” and blah, I'm like there's so many different spaces of intelligence that we haven't even started exploring yet and hence have made very little progress on. And there is an interesting, connection to economics and, capitalism. Like, it doesn't make sense for a company to build and spend billions of dollars building a model that instead of following the rewards and objective functions you gave it, may come up with its own objective functions and its own goals.Richard Socher [00:37:46]: Right? And then imagine you're like, “Okay, I spent billions of dollars. Now go develop this new battery, material for me and answer all my emails.” And it's like, “Nah, I think it'd be more interesting to evaluate the molecular composition of the atmosphere, on Jupiter.”Richard Socher [00:37:59]: And you're like, “That's not what I paid you billions of dollars for.” And so no one's working on that for good reasons. And then also, understandablySwyx [00:38:07]: It's not useful.Richard Socher [00:38:07]: It's not, it's not useful, and it could get a little bit weird, right? What if the AI does start to really have thoughts on its own, and what if we don't like those thoughts, right? And so it requires a whole different way of thinking about it. I had a great conversation with a good friend of mine, Sam Gershman, who's a neuroscience professor at Harvard, and, like, we just jammed on this a little bit on, like, what are the best meta goals. And, I do think, like, knowledge-seeking is a really good one. I'm currently thinking also about, like the ultimate measure and unit of intelligence broadly construed, and I finally have some. It's still too early to share it. It's not. I haven't fully baked the thoughts yet.Swyx [00:38:44]: Like some replacement for IQ.Richard Socher [00:38:46]: IQ is such a terrible definition, right?Swyx [00:38:48]: Elo.Richard Socher [00:38:48]: It makes no sense. Yeah, Elos are terrible, too, because it's always just like me versus others.Richard Socher [00:38:53]: But, like, you can be intelligent and not constantly compare yourself to others? And so, yeah, there's no, like. In fact, a lot of these definitions we have, which I briefly mention in my book, too, these definitions create sometimes explicit and sometimes a more implicit anthropic bounds. No dis to the company Anthropic, but just, like, this idea that your intelligence is like getting 100 out of 100 questions right on this IQ test. Well, if that's your definition then you can only be at 100 out of 100. Where do you go from there, right? So you see a lot of these, benchmarks that people are working on they, increase, they get close to human, maybe sometimesSwyx [00:39:30]: It's like an S-curveRichard Socher [00:39:30]: Slightly above human, and then it's flat.Richard Socher [00:39:32]: It's like, ‘cause that's your. If your definition is only that so tied to humans, you're only gonna get to just slightly better than that. So I think metacognition is a great example of that, where we're not even yet allowing the AI to think. We're not working on it very much, and hence there's very little progress in that.Profit Maximization, Real-World Environments, and Reward DesignSwyx [00:39:49]: Yeah. Well, we've interviewed Andon, which I think, has been working on the most open-ended, benchmarks, which is just real-world, money.Swyx [00:39:57]: Arguably, telling an AI to profit maximize is a bad idea.Swyx [00:40:03]: But they are doing it.Richard Socher [00:40:05]: I do think you don't want that super. Like, you don't want a superintelligence to have a ton of access to all kinds of tools and so on and then just give it that without some very careful reward engineering. ‘Cause it's like, I just buy a bunch of defense stocks and I start a war. I make money. Like, it's just like, it's a tricky situation, right? You just buy a bunch of stuff, short basic goods for people, and you create some weird famine, like, issues. Like, yeah, there's a lot of constraints you should put onto a trading system.Vibhu [00:40:35]: It's a fun measure, though, ‘cause, the bounds are very capped to where we're nowhere close to them. Like, in Andon Labs, the model's like, “Oh, it's Saturday, maybe I just close the store today.” “Someone's off. It's okay. We'll just close the store.”Swyx [00:40:51]: It's using Claude.Vibhu [00:40:52]: Yeah. ButRichard Socher [00:40:53]: Yeah, no. I'm not, I'm not arguing against it. Just, like as you get more and more intelligence, you wanna be more and more careful with that as, like an open environment, ‘cause the environment then is all of Earth.Applying RSI to Science and InventionSwyx [00:41:02]: Yeah. Okay. For recursive, not strictly necessary, right? Because, like, if your goal is you make a machine that, like, invents the other things, then, like, just solve, the science thingsRichard Socher [00:41:12]: Knowledge discovery, yeah.Swyx [00:41:13]: Solve machine learning research and discovery and all these things. Good enough.Richard Socher [00:41:16]: And eventually, so, our goal, I haven't really. I don't talk about it that often because it is a few years out, but our goal is once you have a recursive self-improving superintelligence, you then want to apply it to the most important problems. And I think a lot of those are in science and technology and broadly construed inventions, and those inventions in, physics to create better, cheaper energy with fission or fusion, in chemistry and to create better materials and better batteries and, better solar cells and so on. In biology, there's so much, like, I think soon to be low hang- lower and lower hanging fruit because of AI, because of protein and generation, not just folding, but generating new proteins like we did in ProGen many years ago. Like, so much positive impact we had if you take that superintelligence and you apply it to science.Swyx [00:42:04]: I do fundamentally believe that. There's a lot of approaches, though. You're not the only team trying and NeoLab trying.Swyx [00:42:09]: There's, like a lot of. Especially the physical sciences as well.Richard Socher [00:42:12]: And that's good. Yeah. I do think that physi- like the reason we are only doing it in a few years is that it's a little too early right now. Robotics is not quite there yet. The AI is not quite there yet. But I'm fairly confident in 3 to 5 years, all those constraints will be gone, and then applying to real physical robotics experiments and so on, like true robotic process automationRichard Socher [00:42:33]: Not the traditional RPA sense, but, like, having robots run experiments for you will be totally there. Yeah, it's gonna be great.Swyx [00:42:40]: Just to call back to something that you said early on about slow takeoff, you said that, like, while really the substrate that is limiting factor is, let's call this chips, and semiconductors and all these things, and you have race funding for that and, you are investing a lot on that. But have you done the math on, like, is it even- Achievable and, like, what is the, industry concentration needed in order to achieve, like, scale?Compute, Slow Takeoff, and Changing the Bitter Lesson SlopeRichard Socher [00:43:05]: Right now we know that, like, roughly, like a 1000 GPUs cost quite a lot of money.Richard Socher [00:43:11]: Right? If you wanted, like, 10s of thousands of GPUs, you're, you're talking billions and billions of dollars. If you say, like, one GB300 is, like, you could eventually create models that are, on that substrate, like are close and similar to human intelligence. And you want, like, thousands and thousands of, AIs to think about really hard problems, in a similar fashion to humanity. Like, yeah, that-that's, that's a lot of money. You do the math. It's like a lot. We don't have that amount of money right now anywhere to, like, build that. Now, things can get more efficient. You will have, I think, soon better algorithms that won't be, and better hardware that won't be as energy-hungry, and so on. Our human brain does quite a lot of flops with much less energy.Swyx [00:43:56]: 20 watts?Richard Socher [00:43:57]: That's exactly right. Yeah, that's the number often that's quoted. And, like, I think more, inventions will happen there, that then will accelerate the takeoff even further.Swyx [00:44:08]: One thing I always try to reconcile when talking, like, with new lab founders is, like, you're fighting Bitter Lesson all the time. You have to show initial progress, then you unlock the next tier of funding, then the next tier, then the next tier.Richard Socher [00:44:20]: Which unlocks larger model categories.Swyx [00:44:22]: Like, fundamentally, is that true? Like, are you fighting Bitter Lesson? Are you — will we have a way in which, like, no, we're changing the slope in some fundamentally different way?Richard Socher [00:44:31]: I do think we are changing the slopes in fundamental ways by making AI much more efficient, both in terms of the training as well as the inference.Richard Socher [00:44:43]: Yeah. I think we will — When you allow AI to do the work that it takes other labs thousands of people and years to do, I think we'll be able to get it down to weeks, and that will be much cheaperRichard Socher [00:44:53]: And hence, more affordable, accessible to others and so on.Swyx [00:44:57]: Yeah. You've shared initial results on that,Swyx [00:44:59]: Which, like, conveniently OpenAI has also done to their GPT-5.6, so we can talk about it now.Richard Socher [00:45:04]: Yeah. Yeah, so these areSwyx [00:45:06]: Let's recap what you've done.Early Recursive Results: NanoChat, NanoGPT, and SOL-ExecBenchRichard Socher [00:45:07]: Maybe, just a quick recap here. We built, this, system that isn't the full, even the full RSI system in its glory, but it is a first baby version of this. And then, we don't wanna just have it internally and not show anything and, just show some people of what's possible. And so we applied this to these 3 different tasks. One is NanoChat, by my friend Andrej Karpathy, just, like, train a small language model to get, really low bits per byte. And, like, hundreds if not thousands of people, used both their agents and themselves to try, to get to that, and then they got to 0.937. We literally took our system and got to a much lower, bits per byte, much faster within, like, I think less than 2 days. So we took this thing, applied our system to it, and less than 2 days later, we have — we outperformed every human and their agents, in, have ever worked on this. Same with NanoGPT. And then we're like, well, let's, apply it to something that's even more relevant, to real people and to the Nvidia ecosystem and applied it, to, SOL-ExecBench. And maybe you can scroll down to some of the, images. They're, they're kinda fun to see. But yeah, like, one you see has made some real inventions that weren't just hyperparameter tuning. Like, inventing hash tables and so on is quite clever. We have even better results now.Swyx [00:46:34]: What do you mean inventing hash ta — You didn't invent hash tables.Richard Socher [00:46:36]: Of course we didn't invent, like, hash tables. In the grand scheme of, like a hash table, it's like a super basic primitive in computer science. But to use it, for language modeling in this scenario inside a transformer and so on and to combine these ideas and put them together, that has then eventually also been invented, but there was a knowledge cutoff, and we did check that it didn't have access to that externally. We talk about this a little bit. If you scroll to the next figures, this is also an interesting one in that when you start from a really basic, poor, like, vanilla transformer, then we still outperform all of the community together. But if you start from the human seed from an expert like Andrej, then you get even lower. So the human seeds from which you start do still matter. So that was an interesting insight, in my eyes, on this. And then as you go, like, how long does it take to get to these models, to get to similar performance? It's much faster. And then a similar thing happens with the speed runs here where, people have worked on this for quite some time, and the model still was able to train a model more quickly. Why do we care about it? Well, speed of training is part of the equation of the cost, and ultimately, you wanna have the most intelligence per dollar, right? And so speed and quality are big parts of that. And, the,Swyx [00:48:00]: Yeah, the way I put it is, for people who don't understand they look at the chart, they're like, “Cool. What does it mean?” if you have, like a billion-dollar cluster and you can shave off 10%, that's 100 million dollars.Richard Socher [00:48:12]: That's exactly right.Swyx [00:48:13]: How much is that worth?Richard Socher [00:48:14]: Exactly. So when you click, when you look at, like the kernels, these kernels, yeah, for the non-experts, like these kernels are like, used in all the models. Every time you use an Nvidia GPU, you interface with that GPU through these kernels. And so here you see, the leaderboard best, and when it's recursive, and it's there are only a handful of kernels, in this whole benchmark where we weren't the best. And so to me, this is, like, really exciting, ‘cause it makes. It just showcases what this can do. And again these weren't like. We didn't, like, spend months or years, like, developing. In fact, in particular for kernel, CUDA kernels, like, we don't even have really deep. CUDA kernel experts in the team. And our system, that's the beauty. The system just did all of these things. We didn't invent this. And when we open source and release, things in the future and models in the future, like, it won't. They won't be the best in their, category or class or whatever because we're so smart, but it's because, we built a smart AI that does it for us.Reward Engineering and Good Auto ResearchVibhu [00:49:14]: Do you have anything that you've learned from how to guide good auto research? A lot of it also builds on human background, right? It's not just as simple as just, “Hey, go optimize this.”Vibhu [00:49:23]: But we do see it again and again, right? Like some of the Erdos problems, frontier math is being solved by people. And when they do a write-up, they're like, “Oh, I'm not a mathematician. I have no background in this?” “I saw some tools and I made it work.”Swyx [00:49:35]: While you're watching the World Cup, you're likeSwyx [00:49:37]: “This proves some conjectures that's going on.”Vibhu [00:49:40]: Yep. Any learnings fromRichard Socher [00:49:41]: Yeah, there's a Korean conjecture was. Yeah, that's pretty cool.Swyx [00:49:44]: To summarize, tips for good auto researchSwyx [00:49:46]: Versus bad auto research.Vibhu [00:49:48]: How did you build the recursive?Richard Socher [00:49:49]: Yeah. So without giving away all the secret sauce, maybe some things that are probably obvious to the experts but might still be interesting to some, folks is, like, reward engineering is one of the most crucial bits, especially, in order to avoid reward hacking. So you have to be really clever about avoiding. ‘Cause as your AI gets better and better, it will get better and better, at finding weird like, special cases or counterexamples and things like that. And so I'll give you an example. Like, when you ask to, like, make these 100, lines of code faster, and, how do you define fast? Well, you have one line at the beginning that says, “Start your stopwatch,” and one line at the end, “End the stopwatch,” and then, tell us how much time, progressed. And so, well, the simplest way is you just put that line that ends the stopwatch, rightVibhu [00:50:39]: At the startRichard Socher [00:50:40]: At the start. And then boom, it's now faster, right? So this isn't like this, like, super evil AI. It's just, like a very simple, dumb reward hack. And so you have to just very carefully think about all the different angles there. And then I think the longer time horizon the tasks are the harder it gets and the more interesting and clever you have to be to still use these kinds of ideas for it. But yeah, I can't give away too much there.Vibhu [00:51:05]: It seems like rubrics are taking a good spot in that, where for unverifiable domains, you have rubrics, you have a model breakdown, judge's criteria along the way.Swyx [00:51:14]: Yeah, it's a form of verificationSwyx [00:51:16]: Once you got enough rubrics.Richard Socher [00:51:17]: Yeah, everything. I said this a long time ago. That's why I've never been that impressed that AI can play games, ‘cause I'm like anything you can simulate and/or verify, you can have infinite training data forRichard Socher [00:51:29]: And hence, like, AI will solve it eventually.Swyx [00:51:32]: Looking for games where you can do auto domain distribution. So this is a game that nobody's trained on ‘cause it's a new game.Swyx [00:51:38]: And you can start gaming, you can start to play. So I've been building this and cloned this in person and it's just been self-play. I've had about a billion positions evaluated.Games, Self-Play, and the AI EconomistSwyx [00:51:48]: And, I wanted to do the AlphaGo thing of self-play until you ge

Last month, I had conversations with people up and down the Bay Area as part of my trip to Hot Chips 2026. Then I boarded a flight to Taiwan for Semicon Taiwan. And I had some more conversations there, though not as much as I was getting pretty tired. It has been a year since my last trip to the US. A lot has happened! Year 4 in the AI Boom, yet a string of recent developments has given the boom substantial new energy. The shift to long-running AI agents and agent swarms. The pursuit of RSI, which stands for recursive self-improvement and does not refer to a wrist condition. Cybersecurity and the Hugging Face hack (which I won't talk about here). And the epic fight for compute. Another year, another vibes video about things in the Silicon Valley and Taiwan.

Last month, I had conversations with people up and down the Bay Area as part of my trip to Hot Chips 2026. Then I boarded a flight to Taiwan for Semicon Taiwan. And I had some more conversations there, though not as much as I was getting pretty tired. It has been a year since my last trip to the US. A lot has happened! Year 4 in the AI Boom, yet a string of recent developments has given the boom substantial new energy. The shift to long-running AI agents and agent swarms. The pursuit of RSI, which stands for recursive self-improvement and does not refer to a wrist condition. Cybersecurity and the Hugging Face hack (which I won't talk about here). And the epic fight for compute. Another year, another vibes video about things in the Silicon Valley and Taiwan.

Rick Brunner joins Justin Lima and Yosef Johnson for a conversation spanning four decades of sports nutrition and performance science.Rick shares how his trips to the Soviet Union in the 1980s connected him with scientists studying creatine, adaptogens, recovery, and athletic performance—eventually leading him to introduce creatine to elite American athletes around 1987.The guys dive into supplement quality, brain health, recovery, neuromuscular performance, reaction and quickness, and how nutrition should work alongside training.Rick also explains why the future isn't simply about building bigger muscles. It's about developing reactive, healthy, quick muscle through the integration of training, nutrition, recovery, and measurement.___Buy Supertraining to Read Along with Us - use code STRONGER20 to save 20%https://uaconcepts.com/product/supertraining___CONNECT:

What if I told you experts say the bull market is over? What if I told you I've bought the dip since 2001? Here are some lessons I've learned in over 40 years of buying and selling stocks to manage my own portfolio. FORMULA - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Alpha Picks + Seeking Alpha Premium ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠+ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Trendspider and Sidekick⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ - PERFECT TOGETHER! TRENDSPIDER LABOR DAY SALE - ⁠⁠⁠get 2 week trial for $7 here ⁠⁠⁠I negotiated to get 59% off and 100 Sidekick messages per month for the entire year. Plus you get my 4 hour algorithm and so many other benefits with ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠JUST THIS LINK ONLY ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠CLICK HERE TO GET THE DAILY STOCK PICK SPECIAL OFFER - ONLY ANNUAL PLANS AVAILABLE ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Seeking Alpha's SUMMER SALE ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠✅ *BEST DEAL - SEEKING ALPHA BUNDLE - Save over $150 and get Premium and Alpha Picks together ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠✅ ALPHA PICKS - Want to Beat the S&P? Save $50⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠✅ Seeking Alpha Premium ONLY - FREE 7 DAY TRIAL ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠✅SEEKING ALPHA PRO - YOUR FIRST MONTH ONLY $89 ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠✅ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠NEW - QUANT GROWTH AND INCOME PORTFOLIO - SAVE $50⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠EPISODE SUMMARY

Throughout the summer, RSI has been running a series of readings from Slovak literature in English translation. For our English-speaking audience, we've been bringing you The Book of Bratislava, an anthology published in the UK by the independent publishing house Comma Press. Now, we're bringing the series to a close with an excerpt from Eva Vozárová's story-essay Summer in Bratislava, translated by Paul Kaye. This reading will feature a special guest and a special audio recording. The culture tip for English speakers is the Bratislava World Music Festival. Inviting is its co-founder and programming director Vladimir Potančok.

New episode with John Schulman, Beren Millidge and Charlie O'Neill. I got together with some of the most insightful AI researchers I know who are at the openish companies, because I wanted to hear the details of what's actually happening at the frontier and what comes next.Watch on YouTube; read the transcript.Sponsors* Antithesis helps you trust your code. As agents generate more and more of your software, the bottleneck shifts from your engineers actually writing code to verifying it. Antithesis does that testing for you. Ron Minsky, who co-leads Jane Street's tech group, told me that Antithesis was able to help his team shake out bugs in software that had already undergone heavy review. If you want to see how it fits into your development process, go to antithesis.com/dwarkesh* Grok Bot has been a great way to hand off tasks. My team uses it as a producer: whenever my editor posts a rough cut of an interview in Slack, Grok Bot opens the transcript on its own computer, matches my notes to the exact moments they refer to, and uses a file of my preferences to suggest edits. Then it sends me its top clip candidates so I can review everything from my phone, which saves my editors from sorting through hours of footage. Try Grok Bot for yourself at x.ai/bot* Jane Street just launched its most ambitious competition yet: design a protocol-emulator ASIC. Basically, if you have a chip you want to test outside of a live system, you should be able to connect it to your design and have it simulate realistic traffic. Jane Street wants general-purpose, reprogrammable designs that can work across multiple protocols and remain useful as new ones emerge. The most novel submissions will actually get taped out, and the winners will receive a physical copy! The competition is open until January 18, 2027, and teams are encouraged. To get started download the template code at janestreet.com/dwarkeshTimestamps(00:00:00) – Steelmanning the case against RSI(00:18:39) – What's driving the Chinese labs' progress(00:28:06) – How will automated AI researchers be trained(00:33:51) – Will long-horizon RL elicit AGI?(00:45:24) – The sim-to-real gap(01:00:33) – How much progress is explained by data?(01:18:03) – Why is RL working so well?(01:24:54) – Move 37 and entropy collapse(01:28:32) – Rapid-fire timelines This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.dwarkesh.com

What could the STRONG Act actually mean for strength and conditioning coaches?Rachel Newman, Marketing Manager for Customers and Community at TeamBuildr, joins Justin to break down why strength and conditioning needs its own SOC designation, what that could mean for salary and employment data, funding for personnel and professional development, and why coaches need to continue supporting the STRONG Act.They also discuss why the profession needs to unite around the title Strength and Conditioning Coach, how better federal data could help coaches understand career opportunities and compensation, and why this is a long-term investment in the profession.Most importantly, they don't just talk about supporting the STRONG Act — Justin calls his representative live during the episode. It takes about 70 seconds. If strength coaches want better recognition, better data and a stronger profession, we have to be willing to speak up.___CONNECT WITH US:

Mike Woicik is one of the most accomplished strength and conditioning coaches in NFL history. Across 30 years in the league, he won six Super Bowl championships — three with the Dallas Cowboys and three with the New England Patriots — after beginning his career at Springfield College and Syracuse.Mike joins Justin to look back at the evolution of strength and conditioning through a career that spanned multiple eras of the profession. They discuss the influence of Soviet training methods, Boyd Epley's impact on Mike early in his career, bringing a college-style development program into the NFL, position-specific conditioning, speed and power development, single-leg training, in-season strength, testing, technology, and return-to-play.They also discuss what Mike believes the role of a strength coach should actually be. After decades working behind the scenes with some of football's greatest teams and players, his philosophy remains simple: the job is about helping the athletes — not making the strength coach the center of attention.___CONNECT WITH US:

In this episode of Supertraining in a Year, Justin and Yosef break down pages 466–480 and explore the relationship between pain, injury, and training. They discuss why pain doesn't always reflect the severity of an injury, how microtrauma can accumulate over time, and why preparing athletes for imperfect and unexpected positions may be an important part of building robustness.As always, this podcast is commentary on Supertraining—not a replacement for reading the book.___Buy Supertraining to Read Along with Us - use code STRONGER20 to save 20%https://uaconcepts.com/product/supertraining___CONNECT:

Dr. Chad Herring is a Human Performance Specialist and Lead Strength & Conditioning Specialist supporting the United States Special Operations Command through KBR. His work focuses on integrating strength and conditioning, sport science, recovery, and performance analytics to enhance warfighter readiness and resilience.Prior to USSOCOM, Herring served as Director of Sport Science for football at Florida Atlantic University and held leadership roles in both private-sector performance and AI-driven training technology. He earned his PhD in Exercise Physiology from the University of Central Florida and has coached across collegiate athletics, professional baseball, and tactical performance settings$1 Trial Membership to SCN

Welcome back to September 2026's Papers of the Month, and this month we've got three papers that are really focused on the practical end of emergency and prehospital care. First up, we're looking at what happens after we've performed a prehospital RSI. We put a huge amount of emphasis on getting the induction right, but what about maintaining adequate anaesthesia afterwards? This paper looks at intermittent bolus sedation following prehospital emergency anaesthesia, the variation in dosing between patients, and whether we might actually be running the risk of under-sedating some of them and compromising their care. Then we're staying prehospital and asking a really simple question in trauma: how good are the physiological numbers we use to identify the sick patient? We've all used shock index, but could NEWS actually do a better job of predicting mortality, transfusion requirements and subsequent resource utilisation? And finally, we're back to RSI and rocuronium. A variety of dosing strategies can be seen in practice but could going higher improve first-pass success? And importantly, does that relationship still hold in patients with obesity or hypoperfusion? Three clinically relevant papers, plenty to challenge our current practice, and as always, lots to get into. Once again we'd love to hear any thoughts or feedback either on the website or via social media @TheResusRoom! Simon & Rob

Citi raised its price target on Atlassian to $225 from $170 and maintains a buy rating on shares, though the stock ticked lowered ahead of Tuesday's trading session. Kevin Horner with Charles Schwab notes Atlassian's "speedily" rising 20-day SMA but declining RSI that paints a mixed picture for short-term momentum.======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-...Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-...Watch on Sling - https://watch.sling.com/1/asset/19192...Watch on Vizio - https://www.vizio.com/en/watchfreeplu...Watch on DistroTV - https://www.distro.tv/live/schwab-net...Follow us on X –   / schwabnetwork  Follow us on Facebook –   / schwabnetwork  Follow us on LinkedIn -   / schwab-network  About Schwab Network - https://schwabnetwork.com/about

Are your athletes actually recovered — or do they just feel recovered?In this episode of Supertraining in a Year, Justin Lima and Yosef Johnson work through pages 451–465 of Supertraining, focusing on restoration, recovery, testing, and how coaches can actually apply these concepts.The bigger question throughout the episode: Are we using recovery and testing with a specific purpose, or are we just doing things because that's what everyone else does?___Buy Supertraining to Read Along with Us - use code STRONGER20 to save 20%https://uaconcepts.com/product/supertraining___CONNECT:

Stephanie Mock Grubbs joins the Cheeky Mid Weeky to discuss leadership, career development, building performance systems, and her journey from collegiate strength and conditioning into professional baseball.Stephanie shares lessons from her time at Clemson, Mississippi State, and Pitt, including preparing for leadership before getting the title, developing staff, creating systems that last, and why working across multiple sports helped build the toolbox she uses today.We also dive into her work in professional baseball, including navigating the 162-game schedule, training different player groups, managing travel and accumulated stress, working with veteran athletes, and continuing to evolve as a performance professional.___CONNECT WITH US:

In this episode, Kyle sits down with Cam to break down another piece of his trading system: the RSI strategy.Unlike Gap and Go, which is built around momentum and breakouts, this setup is a mean-reversion strategy. The idea is simple: find strong stocks that are temporarily oversold, then look for the bounce.Cam walks through how he uses a two-period RSI, why he looks for signals below 10, and how he exits once RSI recovers. He also explains why this works better on large-cap, institutionally supported names (especially Nasdaq 100 stocks) and why it falls apart when applied to smaller, lower-quality names.The conversation also covers how RSI fits into a broader portfolio of strategies alongside Gap and Go, Close/Open, and Opening Range Break. Cam explains why diversifying by strategy can create a smoother equity curve, keep capital working in different market conditions, and reduce reliance on any single setup.They also get into the practical side: how to size the trade, why earnings create the biggest tail risk, whether stop losses help or hurt, how options can be used to define risk, what delta and expiration ranges make sense, and why selling puts may be another way to express the same idea.This episode is a deeper look at building a full trading system... Not just finding one setup but stacking strategies that complement each other.Chapter Timestamps:00:00 — Why Diversifying Strategies Matters00:49 — RSI Strategy Overview and Where the Signal Comes From03:22 — Close/Open, Overnight Returns, and Unused Capital05:17 — Applying RSI to Individual Stocks06:30 — Entry and Exit Rules for the RSI Setup07:35 — Why RSI Works Better on Large-Cap Names09:27 — Filters: Nasdaq 100, 200-Day Strength, and Moving Averages10:40 — Strategy Diversification: Gap and Go, Close/Open, and RSI13:19 — Choosing Between RSI and Gap and Go15:24 — Managing Risk Without Traditional Stop Losses18:31 — Coca-Cola Example: RSI Signal, Options, and Rolling Winners20:10 — Using Options to Define Risk24:06 — Volatility, IV Crush, and Selling Puts27:12 — Backtesting, Survivorship Bias, and AI29:15 — Sizing Correctly, Avoiding Earnings, and Final RulesSponsors:Our podcast is sponsored by Sue Maki at Fairway Independent Mortgage (MLS# 206048). Licensed in 38 states, if you need anything mortgage-related, reach out to her at SMaki@fairwaymc.com or give her a call at (520) 977-7904. Tell her 2 Bulls sent you to get the best rates available!If you are interested in signing up with TRADEPRO Academy, you can use our affiliate link here. We receive compensation for any purchases made when using this link, so it's a great way to support the show and learn at the same time! **Use code CHINASHOP15 to save 15%**To contact us, you can email us directly at bandoftraderspodcast@gmail.comCheck out our directory for other amazing interviews we've done in the past!If you like our show, please let us know by rating and subscribing on your platform of choice!If you like our show and hate social media, then please tell all your friends!If you have no friends and hate social media and you just want to give us money for advertising to help you find more friends, then you can donate to support the show here!Cam:‍ ‍Cam is a Navy veteran & management consultant turned 8 figure investor & trader. Trading let him retire at 35 & be able to support and provide for his family forever. Now he wants to help others escape the financial system and live the way we were meant to live: free. His system is a rule-based design that can be used for long term investing, swing trading or option selling on any schedule.Follow Cam on TwitterSub to Cam's YouTube for More!Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Tommie Dorsey III, CSCS, M.A. is a Tactical Strength and Conditioning Coach with Serco in Hawaii and the Head Strength Coach for football at Kahuku High School. His coaching experience spans tactical, collegiate, private-sector, and high school performance settings, with a focus on long-term athlete development, movement quality, and evidence-based training.Prior to his current roles, Dorsey worked in strength and conditioning at Northern Arizona University and St. John's University, coaching athletes across a variety of sports. He earned both his bachelor's and master's degrees from Northern Arizona University and has a strong interest in sport science, athlete monitoring, and performance data to support sustainable athletic development.$1 Trial Membership to SCN

Had a lot of fun chatting again with my twin brother Dylan Patel.We went through lab economics over the next few years - the shift from inference to training as RSI draws near; and how Anthropic and OpenAI are on track to control most of the world's usable FLOPs within the next few years (because they can monetize compute better and thus outbid everyone).And then we discuss whether the >$10T of total AI capex we'll see by the end of the decade will cause a sovereign debt crisis, where hyperscaler debt raises interest rates, drives non-AI exposed countries into bankruptcy, and crashes non-AI equities.One question we weren't able to resolve is whether there's anything that can counter all the forces barrelling towards centralization in this industry - the economies of scale in training, the scarcity of compute, and eventually continual learning and RSI.Watch on YouTube; read the transcript.Sponsors* Grok Bot has been quite helpful with my search for a new editor. I created a recruiter bot and described the type of editor I was looking for. That bot then spun up a handful of subagents that combed through my emails and X DMs, read the end credits of various documentaries I like, and figured out who edits for some of my favorite YouTubers. It took all of those results, and then delivered me a shortlist of candidates that matched my criteria. Try Grok Bot for yourself at x.ai/bot* Antithesis lets you add time travel to your software testing toolkit. Since the Antithesis platform is fully deterministic, everything that happens inside of it is perfectly reproducible. So if your software crashes, you can rewind to the exact right moment, freeze time, and investigate. Or you can test different hypotheses by perturbing the system: kill a node or disable a feature, see what happens, then reset the trajectory and try something else. Learn more at antithesis.com/dwarkesh* Jane Street is hiring for two separate ML internships right now, one focused primarily on research and one focused on engineering. In both cases, interns are expected to contribute to real work, not contrived exercises: one common project is adapting a frontier LLM paper to financial markets, which tend to come with a ton of different gnarly challenges. Importantly, you don't need any finance background to apply. 2027 applications are open now at janestreet.com/dwarkeshTimestamps(00:00:00) – Two labs will soon control most of the world's compute(00:07:01) – $6 billion in fab capex enables $1t+ of end revenue(00:13:08) – Compute prices will rise if the labs outbid everyone(00:18:22) – Which layer will capture most of the surplus?(00:25:40) – What could slow down progress?(00:29:43) – Labs are shifting compute from inference to R&D(00:33:27) – China gets less than 10% of new compute, but its labs need less(00:48:48) – Will AI cause a sovereign debt crisis?(01:07:52) – Will the world's future workforce belong to a few companies? This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.dwarkesh.com

In this episode of Supertraining in a Year, Justin Lima and Yosef Johnson cover pages 441–450 and dive into overtraining, recovery, and restoration — including the difference between general and local overtraining and why youth sport volume can create recovery problems that extend far beyond the weight room.They discuss why athletes don't always need to be fully recovered, how restoration should be periodized alongside training, and why repeatedly using the same recovery methods can become less effective. The conversation also explores how Soviet coaches approached massage, active recovery, psychological restoration, and other methods decades before many became popular today.Plus, Yosef explains why sleep remains the most important recovery tool available and why coaches need to help athletes become more self-aware instead of automatically reaching for another recovery modality.___Buy Supertraining to Read Along with Us - use code STRONGER20 to save 20%https://uaconcepts.com/product/supertraining___CONNECT:

Kevin Maxen, Director of Sports Performance and Head Strength & Conditioning Coach at Tufts University, joins Justin Lima on the Cheeky Mid Weeky.Before arriving at Tufts, Maxen spent four years with the Jacksonville Jaguars and made stops at Vanderbilt, Baylor, Iowa, Army West Point, the private sector, and high school strength and conditioning.Kevin and Justin discuss what it actually means to be a strength coach, lessons learned from Coach Chris Doyle, moving away from the stereotypical “old-school” strength coach, work-life integration, developing young coaches, and why the profession needs to take itself more seriously.They also dive into the practical challenges of overseeing nearly 950 athletes at Tufts, building trust within a staff, creating systems that can scale, giving coaches autonomy, and adapting training to the individual rather than taking a one-size-fits-all approach.___CONNECT WITH US:

Send us Fan MailIn this episode of Plates and Pancakes, Steven sits down with Paul Carrezola and Rachel Lifson, Director and Associate Director of Athletic Performance at Norfolk Academy, where they oversee the development of more than 500 student-athletes.The conversation dives deep into their approach to athletic performance, including summer training, sprint mechanics, relative strength, front squats, split squats, sumo deadlifts, Olympic lifting derivatives, RSI testing, and in-season training.Paul and Rachel explain how they teach athletes to improve posture, hip drive, and sprint mechanics using drills, band resistance, and simple coaching cues. They also discuss why they prioritize technique and relative strength over simply chasing heavier numbers, particularly with developing athletes.Other topics include:• Building a three-day total-body summer program• Teaching sprint mechanics and hip projection• The “Pac-Man” cue for sprint posture• Front squats vs. back squats• Split squats and relative strength• Sumo deadlifts for athletic development• Olympic lifting derivatives• RSI and ground-contact testing• Identifying high-potential athletes• Training rowers and developing coordination• Poliquin-style tri-sets• Pin squats and in-season training• Game-day and “minus-one” lifts• Teaching complex movements to young athletes• Social media, coaching, and evaluating training information onlineA great conversation about building strong, fast, technically sound athletes while keeping training practical and transferable to the demands of sport.#PlatesAndPancakes #StrengthAndConditioning #AthleticPerformance #HighSchoolStrength #SportsPerformance #SprintTraining #StrengthTraining #SumoDeadlift #FrontSquat #AthleteDevelopment #NorfolkAcademy #SpeedTraining #SportsPerformanceCoachhttps://youtube.com/@platesandpancakes4593https://instagram.com/voodoo4power?igshid=YmMyMTA2M2Y=https://voodoo4ranch.com/To possibly be a guest or support the show email Voodoo4ranch@gmail.comhttps://www.paypal.com/paypalme/voodoo4ranch

Walmart (WMT) sold off more than 9% after Thursday's earnings report painted a mixed picture for the outlook of its customer base. Kevin Horner of Charles Schwab points out the oversold RSI but wants to see more conviction from trend traders to solidify that a turnaround is possible. He tells investors to watch certain key levels in Walmart's stock. ======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Malorie Henderlong is the Assistant Director of Sports Performance at Auburn University, where she leads strength and conditioning and applied sport science efforts for Women's Soccer. Her work focuses on integrating performance training, GPS monitoring, athlete management systems, and sports science to enhance athlete development and availability.Prior to Auburn, Henderlong held sports performance roles at North Carolina State University, Baylor University, University of North Carolina at Chapel Hill, and Texas Christian University. A former collegiate soccer player, she earned her bachelor's degree from Spring Arbor University and her master's degree from Texas Christian University.$1 Trial Membership to SCNhttps://strengthcoachnetwork.com/TrialSave on your re-certification to the NSCA and CSCCa with best price CEUshttps://strengthcoachnetwork.com/CEUHawkin Dynamicshttps://www.hawkindynamics.com/Hawkin Dynamics is the world leader in force plate solutions. Wireless hardware, intuitive software, and powerful analysis make Hawkin Dynamics the best choice for any coach or trainer.Power Lift https://www.powerliftusa.com/Power Lift is an industry-leading designer and manufacturer of American-made strength and conditioning equipment. They understand that your Brand is important and offer several diverse ways to incorporate that Brand into your strength equipmentDashrhttps://www.dashrsystems.com/Dashr is a performance testing company best known for its laser timing, but also integrates with Teambuildr and offers a free Player Profile app used by 6k+ athletes. Beyond timing, they provide a full suite of testing solutions including verticals, broad jumps, RSI, biometrics, reaction, and AMS PlayerDatahttps://www.playerdata.com/en-uz PlayerData is Strength Coach Network's trusted GPS provider and a FIFA Quality–certified system, used by coaches at every level to quantify training load and understand movement demands. PlayerData delivers reliable, actionable data at a strong value point, with seamless syncing that saves coaches precious time—so the focus stays on coaching, not managing technology.TeamBuildrhttps://www.teambuildr.com/enTeamBuildr is a platform for any coach in any setting. Every day, thousands of coaches log into TeamBuildr to write training programs, build questionnaires and access athlete training in a simple and manageable wayThorne https://www.thorne.com/ Thorne is a global leader in high-quality nutritional supplements and health testing. Trusted by professional sports teams, the U.S. military, and practitioners around the world, Thorne is known for rigorous ingredient sourcing, third-party testing, and science-backed formulations. Whether supporting performance, recovery, or overall health, Thorne provides products coaches and athletes can trust

The next 12 months of AI leaked. Kinda. For the past 90ish days, we've been quietly collecting evidence of what's next.1,030 saved posts. 90 Podcasts. Countless conversations. Every model drop, every leak, every quiet product update the big labs hoped you'd scroll past.Then we connected the dots.What came out the other side: 19 calls on where AI goes over the next 12 months. And some of them are uncomfortable.We're walking through all 19. Bring your team's AI roadmap. You'll want to edit it.

Not only does NotebookLM have a new name, it's got a new game. Gemini Notebook is agentic by default, can think and reason, and can output files now in just about any format. On this week's AI at Work on Wednesday, we show you the 7 New Updates in the new Gemini Notebook, how they work, and how you should use them. Gemini Notebook: 7 New Updates and What They Unlock -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Gemini Notebook Rebrand from NotebookLMSeven Major Gemini Notebook Feature UpdatesCollections for Organizing AI NotebooksAutomatic Google Drive Sync IntegrationExpanded Gemini Notebook Output FormatsAgentic Intelligence and Gemini 3.5 UpgradeSecure Cloud Computing for Each NotebookGrounded Data Responses and Web ResearchHands-On Demo: Real-World Enterprise Use CasesStudio Outputs: Infographics, Mind Maps, QuizzesMulti-Modal Asset Creation in Gemini NotebookKey Differences: NotebookLM vs. Gemini NotebookTimestamps:00:00 Gemini notebook updates released03:12 Gemini notebook new updates08:14 Notebook LM's unique features13:02 Using Gemini notebook prompts14:48 Discussing Gemini notebook features18:01 Enhanced Gemini notebook flexibility23:02 Creating quizzes with Gemini notebook26:30 Limitations of AI-generated responses29:04 Gemini notebook's new capabilities30:51 Episode wrap-up and subscription pitchKeywords: Gemini Notebook, Gemini notebooks, NotebookLM, Notebook LM, Google Gemini, AI updates, Gemini 3.5, anti gravity agentic search, Google Drive syncing, cloud computer, agentic intelligence, AI agent, secured cloud sandbox, personalized AI, output formats, PDFs, PNGs, documents, spreadsheets, PowerPoints, markdown files, charts, images, live demo, long form content, content grounding, hallucination reduction, source pane, chat pane, studio pane, multimedia assets, Nano Banana, Google's audio model, cinematic video, Google's VO model, chain of thought, skill creation, codex skill, browser control, agentic harness, pricing evidence, Luna and Terra pricing, sensitivity analysis, recommendation dashboard, AI budget calculator, mind map, infographics, quizzes, RSI maturity ladder, recursive self improvement, executive decision brief, Excel calculator, agentic workflows, web search integration, grounded AI, model architecture, frontend models, tiered architecture, adaptability to price reductions, vendor risk, human review time, latency, agentic co-worker, artifact creation, editable Excel workbook, multi-output prompting, token efficiency.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner