Podcasts about Cognition

At Cisco Live, I sat down with Papi Menon, Vice President of Product Management at Outshift by Cisco, to explore one of the most ambitious ideas emerging in the AI world today. While much of the industry remains focused on larger models and individual AI agents, Outshift is asking a different question. What happens when millions of AI agents need to collaborate across organizations, platforms, and industries? Papi joined me to explain the thinking behind Outshift, Cisco's emerging technology and incubation group, and the work they're doing to help shape the next era of AI. Our conversation explored concepts such as the Internet of Agents, the Internet of Cognition, and AGNTCY, an open-source initiative designed to create the foundations for agent-to-agent collaboration at scale. We discuss why connecting AI agents is only the first step, why shared intent and shared context could become as important as connectivity itself, and how organizations may need entirely new infrastructure to support an increasingly agent-driven future. Papi also shares his perspective on the challenges of interoperability, governance, trust, and security as AI systems become more autonomous and interconnected. The discussion moves beyond today's AI headlines and into the bigger questions facing the technology industry. If the internet connected people and systems, what infrastructure will be needed to connect intelligence itself? And what role can open standards play in ensuring that future remains collaborative rather than fragmented? Whether you're a technology leader, developer, strategist, or simply curious about where AI is heading next, this conversation offers a fascinating glimpse into how Cisco is thinking about the future of agentic computing and the foundations that may underpin the next major platform shift in technology. How do you think AI agents will collaborate in the future, and should that future be built on open standards or closed ecosystems?

In this episode, I share my Human Design story and why I continue to return to the same foundational teachings year after year. These five elements, Determination, Strategy, Authority, Environment, and Cognition, completely changed the way I move through life.Inside this conversation, we explore:• My personal Human Design journey• Why the foundations matter more than advanced information• The five elements I continually return to• How these teachings impact decision-making, relationships, business, health, and alignment• Why embodiment will always outperform informationBecause the goal isn't to know your Human Design.The goal is to live it.RESOURCES:CLICK HERE for the Big 3 in Human Design EpisodeCLICK HERE to order your Alchemy of You manualCLICK HERE to learn about the Find Your WAI membershipCLICK HERE to DM me PHOENIX to learn about my 5-month initiation for the woman standing at the threshold of her next chapter, knowing the current version of herself cannot carry her where her soul is asking her to go next.CLICK HERE to DM me to learn more about the Gauntlet, my 3-6-month private mentorship container, or the Identity Reset Retreat to learn about coming to Austin for a 5-day, 4-night private initiation with me.Support the show✨ Thank you for listening! Check out the links below to connect with me!

- The Guest: Holistic Healer/ Coach MichelleHammel— Follow/ Contact: IG @unmasquing Website: www.gofauxhawkyourself.com—From surviving to self‑rebuilding — this is the episode that will change how you see your past, your patterns, and your power.-Holistic Trauma‑Informed Coach Michelle Hammel joins Marcos Luis for a raw, soul‑shifting conversation about her journey through childhood trauma, CPTSD, and emotional reconstruction — and how she transformed her pain into the CRAFT Method, a groundbreaking approach to healing that's helping people worldwide reclaim their lives.—Michelle breaks down the real work of recovery: calming the nervous system, unlearning survival mode, rebuilding identity, and finally feeling safe in your own body again.This isn't “good vibes only” healing — it's honest, practical, compassionate, and deeply human.—If you've ever felt stuck, unseen, overwhelmed, or ready for a new chapter… this episode is your turning point.—

Season 15, Episode 397 revisits research and real-world practice showing movement is more than fitness: it activates the brain, boosts attention, enhances learning, and sustains motivation. Dr. Chuck Hillman's studies reveal how even short bouts of exercise light up brain activity, while Paul Zientarski's Naperville program demonstrates how heart-rate monitoring and purposeful movement improve readiness, recovery, and academic performance. In EP 397: Movement, Motivation, and Brain Activation with Dr. Chuck Hillman and Paul Zientarski, we explore why movement may be one of the most powerful tools we have for improving brain function, learning, motivation, and performance. In this episode, we cover: ✅ Why most children are not meeting the recommended daily physical activity guidelines and what we can do to change that. ✅ How exposing children to a variety of activities helps them discover movement they enjoy—and are more likely to continue throughout their lives. ✅ Why there is no perfect exercise program, and why the best exercise is the one you'll consistently do. ✅ How enjoyment, reward, and dopamine reinforce healthy habits and keep the Motivation Loop repeating. ✅ What Naperville Central High School learned from heart rate monitoring and how recovery impacts performance. ✅ Why peak performance requires both effort and recovery. ✅ How exercise changes the brain, improving attention, learning, memory, and cognitive performance. ✅ The groundbreaking research behind Spark: The Revolutionary New Science of Exercise and the Brain and how it changed the way educators think about learning. ✅ Why movement is not a break from learning—but one of the most effective ways to prepare the brain for learning. ✅ How movement fits into our Phase 2 Motivation Loop, helping transform motivation into action and sustaining long-term performance. The biggest takeaway? Movement isn't just exercise. It's activation. It's preparation. It's performance. When we move our bodies, we activate the brain systems responsible for attention, learning, motivation, and success. The episode highlights practical takeaways: expose children to varied enjoyable activities, prioritize consistency over intensity, use movement as cognitive preparation, and track recovery to protect motivation. Movement becomes a bridge between motivation and sustained performance—improving focus today and long-term brain health tomorrow. Welcome back to Season 15 of the Neuroscience Meets Social and Emotional Learning Podcast. I'm Andrea Samadi, and on this podcast, we bridge the science behind social and emotional learning, emotional intelligence, and practical neuroscience so we can create measurable improvements in well-being, achievement, productivity, and results. Movement, Motivation, and Brain Activation with Dr. Chuck Hillman and Paul Zientarski This week, we continue our journey through Phase 2: Neurochemistry and Motivation, where we've been exploring one central question: What drives sustained effort and forward movement? So far, we've learned that motivation begins with belief and meaning from Bob Proctor[i], is shaped by our thought patterns with Dr. Caroline Leaf,[ii] strengthened through attention and reward with Dr. John Medina[iii], and powered by the brain's dopamine-based motivation system through Dr. Anna Lembke's[iv] work. But today, we arrive at a fascinating question: What happens when we actually move? Because motivation isn't just something that happens in the mind. The brain was designed to work in partnership with the body. And according to our review of today's two guests, one of the most powerful ways to activate attention, learning, memory, and motivation is through movement itself. This week we're revisiting insights from two pioneers whose work helped transform our understanding of movement and learning. First, Dr. Chuck Hillman, one of the world's leading researchers on exercise and brain function, whose groundbreaking research has shown how physical activity improves attention, executive function, learning, memory, and academic performance from EP 123[v] back in April 2021. Next, we will review Paul Zientarski, the former Physical Education Coordinator and football coach at Naperville Central High School, (In Illinois) whose work with the school's innovative Zero Hour PE Program helped put Naperville on the map for extraordinary academic achievement. Alongside his colleagues at Naperville, Paul demonstrated that exercise wasn't simply improving fitness—it was preparing students' brains to learn. Together, Dr. Hillman provides the science, while Paul Zientarski helps to demonstrate what that science looks like in the real world. Their combined work shows us that movement is far more than a physical activity. It is a powerful tool for activating the brain, enhancing learning, improving focus, and supporting the motivation needed for sustained performance. In other words, movement is the bridge between motivation and sustaining our performance. Let's dive in with Dr. Chuck Hillman and discover the science behind The Power of Movement and Brain Activation. CLIP 1: Getting Kids Moving for Life Summary In this clip, Dr. Chuck Hillman highlights a growing concern: the vast majority of children are not meeting the recommended physical activity guidelines. Current recommendations suggest that children should engage in at least 60 minutes of moderate-to-vigorous physical activity each day, including aerobic exercise and activities that strengthen bones and muscles. Dr. Hillman explains that the challenge isn't simply knowing the guidelines—it's finding ways to engage children in movement when many adults aren't meeting the recommendations themselves. This is why childhood is such an important time to expose young people to a wide variety of physical activities, helping them discover forms of movement they enjoy and can continue throughout their lives. Key Takeaways ✔ Most children are not getting enough physical activity. Many young people fall short of the recommended 60 minutes of daily movement needed for optimal physical and cognitive development. ✔ Movement supports both brain and body health. Exercise is not just about fitness—it supports attention, learning, memory, emotional regulation, and overall well-being. ✔ Children need exposure to different activities. Not every child will enjoy the same sport or activity. The goal is to help them discover movement they genuinely enjoy. ✔ Parents and adults model behavior. Children are more likely to be active when the adults around them value and participate in physical activity. ✔ Early habits can last a lifetime. The activities children enjoy today often become the healthy habits they carry into adulthood. Tips to Implement Expose Children to Variety

Claude Opus 4.8 arrives as a modest but meaningful upgrade, with early users pointing to better judgment, less bluffing, stronger self-checking, and a greater willingness to push back. NLW breaks down first impressions, benchmark comparisons with GPT-5.5, Claude Code's new dynamic workflows, and why the model harness may matter as much as the model itself. In the headlines: Kirkland & Ellis bets big on internal AI, OpenAI updates GPT-5.5 Instant, Cognition raises at a $26B valuation, Meta considers AI cloud, and Microsoft prepares new models.Brought to you by:KPMG – Research from KPMG and the University of Texas at Austin shows the highest-impact AI users treat AI like a reasoning partner — and those skills can be taught at scale. Learn more at ⁠⁠kpmg.com/us/Sophisticated⁠⁠Scrunch - The AI customer experience platform - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://scrunch.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Zenflow Work - Agents for knowledge work - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://zenflow.free/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Our Newsletter is BACK: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

Everyone says AI is taking jobs. The data says something more complicated.In this episode of This Week in European Tech, Dan Bowyer and Mads Jensen of SuperSeed unpack the growing panic around AI-driven job losses, why junior hiring is falling across many industries and whether AI is actually the culprit.They explore new research suggesting remote work may be having a bigger impact on entry-level employment than AI, discuss the UK's record number of young people not in employment, education or training and examine what the data really shows about automation and labour markets.They also cover Anthropic's latest model release, the rise of AI application-layer companies, Europe's sovereignty debate, the economics of AI infrastructure and a zero-employee AI company that just raised $30 million.Topics coveredIs AI really replacing workers?Why junior hiring is fallingWhat the data says about AI and employmentAnthropic's rise and Opus 4.8Why the AI application layer is winningEurope's tech sovereignty dilemmaThe zero-employee AI company phenomenonAI infrastructure beyond GPUsTimestamps(00:00) The rise of the zero-employee AI company(04:50) Why AI applications are becoming more valuable(09:00) AI infrastructure moves beyond GPUs(16:00) Snowflake, Salesforce and enterprise AI adoption(24:00) Anthropic's latest model and valuation surge(27:00) Europe's sovereignty dilemma(33:00) The $30 million zero-employee AI startup(35:45) Is AI actually taking jobs?(38:00) What the data says about junior hiring(41:00) Why AI may not be the main cause(46:00) Predictions: which AI unicorn could fail next?(48:00) Deal of the week: Cognition and DevinFor more European venture, AI and startup insights, subscribe to EUVC, the home of European tech.

We are delighted to host Marieke McKenna on this episode of the Mangu.tv podcast. Marieke McKenna (London, 1994) is a Scottish-Dutch philosopher, historian, artistic researcher, and performance artist. Her work explores metaphysics, phenomenology, consciousness studies, spirituality, ecology, and philosophies of nature through interdisciplinary research and embodied practice. She is an expert on cross-cultural perspectives on dreaming and other altered states of consciousness.For the Max Planck Institute for the History of Science, she led the research project History of Lucid Dreaming Research, the first oral-historical examination of the emergence of lucid dreaming as an object of scientific inquiry. In collaboration with the Donders Institute for Brain, Cognition and Behaviour, the project combined oral history, philosophy, and cross-cultural anthropological research into how different cultures and traditions understand dreaming, with hands-on experience in neuroscience sleep laboratories conducting EEG and fMRI research on the dreaming brain.Outside academia, Marieke, who is based in The Netherlands, is an award-winning artist and curator, with performances and lectures at institutions including the Van Gogh Museum and the Rijksmuseum. She is the host of her own national radio show on NPO Radio 2, for which she selects music from across the globe, and has taught at various universities, conservatoires, and institutes, including Advaya and the Embassy of the Free Mind.Giancarlo and Marieke discuss idealism, interconnectedness, and how dreamwork nurtures healing and belonging. They speak about lucid dreaming in therapy, indigenous perspectives, and technology's encroachment into “inner space,” debating AI, advertising in dreams, collective consciousness, telepathy, quantum theory, and the mind's creative potential.

On this vintage episode of Vitality Radio, Jared breaks down why magnesium is the single most important supplement he recommends—no matter your age, gender, or health status. You'll learn the role of magnesium in everything from stress response and sleep to muscle recovery and hormonal balance, plus why Jared believes the “7 forms of magnesium” marketing trend is mostly hype. He also shares his favorite way to combine bisglycinate and threonate for maximum benefit—without busting your supplement budget.Products:Vital 5 Magnesium BisglycinateKAL Think Magnesium L-ThreonateAdditional Information:#258: Your Magnesium User's GuideVisit the podcast website here: VitalityRadio.comYou can follow @vitalitynutritionbountiful and @vitalityradio on Instagram, or Vitality Radio and Vitality Nutrition on Facebook. Join us also in the Vitality Radio Podcast Listener Community on Facebook. Shop the products that Jared mentions at vitalitynutrition.com. Let us know your thoughts about this episode using the hashtag #vitalityradio and please rate and review us on Apple Podcasts. Thank you!Just a reminder that this podcast is for educational purposes only. The FDA has not evaluated the podcast. The information is not intended to diagnose, treat, cure, or prevent any disease. The advice given is not intended to replace the advice of your medical professional.

In the latest episode of Executive Function, Brett sits down with Graham Moreno, Head of GTM at Parallel Web Systems. Before Parallel, Graham scaled Windsurf's GTM organization from three sellers to seventy-five in under a year, served as President through the Cognition acquisition, and earlier built and led enterprise sales teams at Grafana Labs and MongoDB. In this conversation, he unpacks why the AI-era backlash against structured enterprise sales misreads the data, how to design a process that raises the floor for ordinary reps without capping the ceiling for stars, and why selling to AI-native customers compresses an eight-week cycle into five business days. In today's episode, we discuss: Why in-person enterprise rollouts still beat product-led motions Building a robust sales process that still leaves room for unscripted moments Why the three highest-leverage early sales hires aren't sellers at all The case for outsized commission accelerators for star sellers — and the kind of person they attract Why most AI companies are skipping the in-person sales work that enterprise customers actually want References: Ahead: https://www.ahead.com Amazon: https://www.amazon.com Anthropic: https://www.anthropic.com Attio: https://www.attio.com Augment Code: https://www.augmentcode.com/ Cognition: https://cognition.ai Cursor: https://cursor.com Dani McCabe: https://www.linkedin.com/in/danielle-mccabe/ Datadog: https://www.datadoghq.com GitHub Copilot: https://github.com/features/copilot HubSpot: https://www.hubspot.com Jeremy Powers: https://www.linkedin.com/in/jeremypowers/ JPMorgan: https://www.jpmorgan.com Matt McClernan: https://www.linkedin.com/in/mattmcclernan/ MongoDB: https://www.mongodb.com Nicole Rettinger: https://www.linkedin.com/in/nicole-rettinger-23b20465/ Notion: https://www.notion.com OpenAI: https://openai.com Parag Agrawal: https://www.linkedin.com/in/paragagr/ Parallel: https://parallel.ai Snowflake: https://www.snowflake.com University of Chicago: https://www.uchicago.edu Windsurf: https://windsurf.com Where to find Graham: LinkedIn: https://www.linkedin.com/in/grahammoreno/ Where to find Brett: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast Timestamps: 00:00 Introduction 00:32 Has the sales playbook changed in the AI era? 02:13 Why "showing up" beats letting the marketplace decide 06:50 Why great salespeople sell to engineers and executives in one motion 11:37 Selling to AI-native buyers who grew up on ChatGPT 13:49 Same seller, different tempo: 8 weeks vs. 8 business days 15:57 How AI-native buyers handle build vs. buy decisions 17:48 The rep who taught a champion's son guitar over Zoom 19:03 Raising the floor without capping the ceiling 22:09 Why too much process narrows the kind of seller you attract 25:46 The three pillars of GTM excellence 31:00 Building peers who are 80% aligned, not 100% 38:03 Whether AI is changing what good enablement looks like 41:35 Selling against direct and implied competitors at once 42:45 Instrumenting the funnel from stage zero to close 45:57 Why post-sales should always roll up to the revenue leader 48:19 The case for outsized commissions 52:02 The 96 hours of panic before Cognition acquired Windsurf 53:04 How far out should a GTM leader be planning? 57:53 What a normal week looks like in hypergrowth

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!On the product side, everyone is getting Computer - Perplexity, Manus, Cursor, and so on. Meanwhile on the research side, agentic evals like TerminalBench and GDPVal are also assuming computer (Harbor). On both ends, the consolidating LLM OS stack has become a standard toolkit, and Daytona is one of a small set of AI Infra companies that are booming because of it.“The end of localhost” has been Ivan Burazin's obsession for more than a decade.Something that is all too familiar…Long before agents became the default way people talked about software development, Ivan was already chasing the idea that development should not depend on a fragile local machine. CodeAnywhere, one of the first browser-based IDEs, was an early attempt at that future: move the development environment into the cloud, make setup reproducible, and free developers from the endless “works on my machine” tax.The thesis was directionally right, but the market wasn't ready yet.However, agents changed that. They do not care about a laptop, desk setup, or favorite editor. They need a computer they can access through an API: something stateful enough to keep working, fast enough to spin up instantly, flexible enough to resize, isolated enough to be safe, and composable enough to run the messy real-world workflows that real software engineering actually requires.Daytona isn't just selling “sandboxes” in the narrow code-execution sense. It is the latest version of Ivan's original localhost thesis.In this episode, Daytona's CEO joins swyx to explain why AI agents need more than code execution boxes: they need composable computers, stateful sandboxes, instant startup, dynamic resources, and infrastructure that can survive workloads going from zero to 100,000 CPUs.We go deep on the new agent compute market: Daytona's hard pivot from human dev environments to AI sandboxes, the New Year's Eve MVP that customers begged for, why Daytona runs on bare metal with its own scheduler, how one customer runs almost 850,000 sandboxes a day, and why RL/eval workloads went from 0% to roughly 50% of usage in just months. Ivan also explains why agents need Windows and macOS machines, why CLI may matter more than MCP, why Kubernetes is painful for this workload, and why the future AI cloud may look more like Stripe than AWS.We discuss:* How Daytona grew out of CodeAnywhere, Shift, and the “end of localhost” thesis* Why Daytona pivoted from human dev environments to AI sandboxes* Why agents need composable computers instead of disposable code execution boxes* The New Year's Eve MVP that customers chased API keys for* Why Daytona chose bare metal, stateful snapshots, and its own scheduler* How Daytona spins up one sandbox in ~60ms and 50,000 sandboxes in ~75 seconds* Why Daytona's biggest customer runs ~850,000 sandboxes a day* How RL/eval workloads create zero-to-100,000 CPU spikes* Why RL workloads went from 0% to roughly 50% of Daytona usage* Why customers compare Daytona against EKS/GKS and say they're “never going back”* Why every AI agent may need a computer, including Windows and macOS environments* The Apple licensing constraints that make macOS sandboxes hard* Why CLI gives agents more power than MCP* How open source helps agents integrate Daytona* Why agent-generated PRs may break today's CI/CD assumptions* Why AI SaaS companies reselling tokens may face a cold shower* Why the AI cloud may look more like Stripe than AWSIvan Burazin* LinkedIn: https://www.linkedin.com/in/ivanburazin* X: https://x.com/ivanburazinDaytona* Website: https://www.daytona.io* X: https://x.com/daytonaioTimestamps* 00:00:00 Hook* 00:01:12 Introduction* 00:03:15 CodeAnywhere, Shift, and the end of localhost* 00:05:58 What Daytona is: composable computers for AI agents* 00:08:07 The pivot from dev environments to AI sandboxes* 00:10:17 The New Year's Eve MVP and customers begging for API keys* 00:12:56 Bare metal, stateful sandboxes, and Daytona's scheduler* 00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs* 00:21:53 Spiky RL/eval workloads and the new agent infra problem* 00:28:12 RL workloads, Kubernetes pain, and dynamic resizing* 00:33:31 Why every AI agent needs a computer* 00:38:48 macOS sandboxes and Apple's licensing problem* 00:44:28 Why CLI may matter more than MCP* 00:48:11 Open source, GitHub stars, and agent integration* 00:53:11 Git, CI/CD, and agent collaboration bottlenecks* 00:58:15 Founder life and building a 25-person infra company* 01:02:44 AI SaaS, token resale, and API-first business models* 01:06:10 GPU sandboxes, data centers, and compute growth* 01:09:48 Why the AI cloud may look more like Stripe than AWS* 01:11:26 Closing thoughtsTranscriptIntroduction: Daytona, CodeAnywhere, and the End of LocalhostSwyx [00:00:02]: Okay, we're in the studio with Ivan Burazin, CEO of Daytona. Welcome.Ivan [00:00:07]: Thanks for having me, man.Swyx [00:00:08]: Ivan, you and I go back.Ivan [00:00:10]: Way back.Swyx [00:00:11]: How I don't even know how, you found, did you reach out or, for Shift.Ivan [00:00:17]: I reached out to you. The reason was you - we were just - we were thinking about I was one of the co-founders of CodeAnywhere, the first browser-based IDE, and so we were thinking a long time of, localhost should die. And you had this article.Swyx [00:00:29]: End of localhost.Ivan [00:00:30]: Then I reached out to you because of that, and then we talked, and I was actually at a different job and learning about I was the head of, developer experience, and you were quite well-versed in that, and I actually reached out to you, among other people, how do we go about that? What are the key things and whatnot at this point in time? And you were nice enough to take the call, and I remember I was late on your call with you.Swyx [00:00:51]: I don't remember.Ivan [00:00:52]: I remember because I was with my then I'm thinking of a girlfriend or wife at that point in time, I'm not sure. It's the same person, so that's great, and I was late ‘cause we were, in, Italy on, vacation, and then I was late for something. I felt so bad, and you were so nice to be, good about.Swyx [00:01:10]: The reason I'm nice is because I'm also late to other people, so it's like, who's, who's without sin here, yeah, so I have to, for those who don't know, InfoBip Shift, there's this whole thing that, you did in the past, and, and that was basically one of the inspirations for me starting AI Engineer, which is like, I have to thank you for giving me that push to be like, “Oh, you can, you can build and sell conferences?”Ivan [00:01:34]: I remember you asked you asked me at the beginning to give me advisory shares, and I was so focused on what we were doing, I said no, and I should've took the advisory shares. So I'm sorry, dude. But anyway.Swyx [00:01:43]: We're not, we're not venture backed.Ivan [00:01:44]: No, it doesn't matter.Swyx [00:01:45]: It's Yeah, anyway, so I think what's impressive about you is that CodeAnywhere is the thing that you've been trying to build, and, you kind of put it on hold and then came back after InfoBip. Just give us the story, do you - the story and the origin story, going into Daytona.From CodeAnywhere and Shift to DaytonaIvan [00:02:05]: Sure. Like, really way back, me and my co-founder have been together. I say this, I've said this multiple times, it's like we were married and divorced and married. Some people actually ask me is my co-founder my partner. they thought it literally. It's not literally, but we have done multiple companies together, and to your point, we had this shift where we went from the CodeAnywhere to the conference called Shift, and then back to, Daytona. We originally started stacking servers, doing like virtualization in the early 2000s and, routers and doing basically all these things, at a foundational level, and that was a services company which we sold to focus on what my co-founder actually invented, which was the very first browser-based IDE, right, I say the first. Before us was actually Heroku. They did it for a very short time until they became Heroku. But outside of them, we were the only one, and it was called.Swyx [00:02:55]: There was Cloud9.Ivan [00:02:57]: Cloud9 came out slightly after us. There was Replit, which came out when we stopped doing it, Replit came out, and they have been successful since then, which is great. There was Nitrous.io. There was quite a few that existed at the time, but it was like too early. But the interesting part is that we, at that point in time, because there was no VS Code, there was no Kubernetes, and Docker had just started when we Or I'm not sure if it was even public at that point in time. And so we had to build everything to the whole stack ourselves and that was the key learning that we brought into and that we've been using in Daytona today. So it was super early. There's about 3 million people used CodeAnywhere. It was slightly, it was angel-backed more than venture-backed. We ended up paying everyone back because it didn't have that sort of scale. But, three years ago, we started something similar with Daytona, which is not what we are today, but it was automating dev environments for human engineers, the basically the underlying stack of CodeAnywhere. And then we did a hard pivot last January to sandboxes. And so here we are.Swyx [00:04:01]: Historic pivot, yeah, and, it's one of those things where, I had independently invested in CodeAnywhere, but also in E2B, and then both of you pivoted into the same thing, and I'm like, “F**k.”Ivan [00:04:12]: You invested, you invested in Daytona. You invested in Daytona. But you were the first If we had not got your check, we wouldn't have done it.Swyx [00:04:18]: No way.Ivan [00:04:19]: No, it was like, “We have to get him on board first,” and you were that kicker that we, that got us off the ground.Swyx [00:04:23]: No, because you were putting me on your pitch deck, man. I was like, “Man, this is like a good trip if I don't invest.”Ivan [00:04:29]: That's because it was your quote. It's like we.Swyx [00:04:30]: Yeah. It's the end of localhost.Ivan [00:04:31]: Did a bunch of research about end of localhost and who was interested in that,.Swyx [00:04:34]: No, that's like, I put, I wrote that blog post, and every single company in that field reached out to me, and then every VC who was receiving those pitches then also had to call me and, talk it, talk through it with me.Ivan [00:04:47]: It's finally happening though.Swyx [00:04:48]: It was really super interesting.Ivan [00:04:48]: It's finally happening.Swyx [00:04:49]: It's finally happening.Ivan [00:04:49]: Yeah, it's finally.Swyx [00:04:49]: It's finally happening, with maybe sort of non-human users. Yeah, so what is Daytona today? Let's get like a quick description. I'm wearing the shirt.What Daytona Is Today: Composable Computers for AI AgentsIvan [00:04:58]: You're wearing the shirt. Yes,.Swyx [00:04:59]: It says, I think your branding is very good. Like, it's very consistent. It runs AI code. Like, it cannot be simpler.Ivan [00:05:05]: Exactly, but we're gonna probably have to change that.Swyx [00:05:07]: Oh, s**t.Ivan [00:05:07]: It's also a subset of what we do. Unfortunately, we really love this, Run AI Code is super simple. People interpret it different ways. I think we've given out 5,000, 6,000 of these shirts. People wear them with pride because it doesn't really market about us.Swyx [00:05:21]: Yeah, Daytona's on the back.Ivan [00:05:22]: It markets the back. It markets to the person itself, so I think we did a really good job on that one. But it is also a subset of what we do, because people, when they think about Run AI Code, they just think about these small, let's call it isolates, code execution boxes that, you send some code, you get an output. Whereas what Daytona is today is essentially composable computers for AI agents. It is, the market calls them sandboxes which can be misleading.Swyx [00:05:44]: All these things. All these things on.Ivan [00:05:45]: Yeah, exactly, ‘cause it can be misleading ‘cause people usually think about sandboxes as a demo or a test environment versus a production-grade environment. But what Daytona does, if you think of the laptop that you have in front of you or the computer that's over there, or, my wife is an architect, so she has like a Windows with a 3D graphics card inside to do 3D rendering. Like, as humans, we have different computers or different compositions of computers. And our belief is strongly that agents today and going forward will need all these different compositions of computers to do different types of tasks. And so we offer that basically through an API.Swyx [00:06:19]: Yeah, to give people - I'm trying to sort of front-load all the aha moments or the wow moments so that people can, stay engaged and click like and subscribe. the market is exploding, right? Like, you have been reporting 74% month-on-month growth, and it also, it's just been growing for a while. Like, it's been going like this. And every single - It's not just you guys. It's every single.Ivan [00:06:41]: Everyone, yeah.Swyx [00:06:42]: Sort of, compute provider. I don't know if you agree with me saying compute provider or not.Ivan [00:06:48]: It's fine.Swyx [00:06:48]: Yeah. So like organically PLG-driven growth, but also enterprise is doing super well, I think I wanna rewind to January of last year when you did the pivot. Like, so you obviously called this market early, and you were positioned for it, and you are now one of the market leaders. But what was the insight that made you do the pivot?The Pivot: From Human Dev Environments to Agent SandboxesIvan [00:07:06]: The insight that made us do this pivot is the quarter before that, so end of 2024, when we had - Basically, we did a demo with - I don't I think we discussed this as well, Devin was not public. You actually gave me access to Devin at that time. So Devin.Swyx [00:07:25]: I did?Ivan [00:07:26]: Yeah, you gave me access.Swyx [00:07:26]: I don't think I was supposed.Ivan [00:07:27]: Yeah, exactly.Swyx [00:07:28]: Yeah, I.Ivan [00:07:28]: So it doesn't matter. You.Swyx [00:07:29]: Yeah. I gave like three friends access.Ivan [00:07:31]: Yeah, or it was a call and you showed it to me. It doesn't matter. but OpenDevin was available, which is now called OpenHands. And so we're like, “Oh, this seems to be a thing. This is not public. Let's take our for human automation of dev environments and take, OpenDevin and launch that as a SaaS.” And we did that. Not very many people signed up and used it, but a lot of people reached out that were building agents, and they were like, “Hey, my agent needs a compute sandbox runtime,” whatever you wanna call it. I forgot what it was called at that point. And then we were like, “Oh, amazing. This is a new market. Here is our infrastructure. Here's our product, and go.” And what we found really fast, soon, was that people did not like what we had built. It didn't work. And I remember talking to people at the beginning when we're doing this, the sandbox we're building for agents. People were like, “Oh, why is it different? It's the same thing. We have like EC2, we have VMs, we have all these things.” But we saw that everyone we gave it to, it was like 20, 30 people, they all said, “No.” Like, “This is not what we need. This sort of breaks.” And basically, me and my co-founder not knowing a lot about - ‘cause we're infra people. We're not AI people. So I basically took it upon myself to like watch every single podcast that exists, including all of, all of these and all that, and sort of get up to date, read all the blogs, like get, understand what's going on.Swyx [00:08:45]: Do you wanna shout out who else was useful, just in case people are also looking.Ivan [00:08:49]: Generally we -, I looked at There's a few of podcast, different segments and different types. So there's you guys, No Priors, Bill Gurley's was great while.Swyx [00:09:04]: VG2, yeah.Ivan [00:09:05]: Yeah, while it was around. So there's a few. 20VC is interesting from a different dynamic, and some are different dynamic. But there was, also Red Points.Swyx [00:09:14]: We're not really about the compute market.Ivan [00:09:15]: It was also already - Sorry?Swyx [00:09:16]: You're, you want - You're looking at the agent infra market.Ivan [00:09:19]: I was looking at the agent market and the AI market in general and sort of understanding who are the players, what the perception, and how that goes. And like obviously you complement this with like going to conferences, going to events, going to meetups, reading white papers, like doing all the things that you have to do to understand what's happening. And so when we figured, when we sort of had an idea of what we had to build, literally over the New Year's Eve, literally on New Year's Eve, I half vibe coded the first MVP, first minimal viable product of what Daytona is today. And I went to sleep at like 3:00 AM or something like that. I was doing - I just put my like baby daughter and wife to sleep and, Happy New Year's, and go back to just, doing this. And I sent it to my co-founder, my CTO, and he saw it in the morning. He's like, “This is absolute garbage.” “Do not show this to anybody at all, but the idea is good.” And so he took two weeks, and he rebuilt it.Swyx [00:10:09]: Did it like look like that? Listen, I - It was rough idea.Ivan [00:10:12]: Oh, not even, not even close. Like it was it was way worse. But it was like a very - It was a simplistic view of what it should be. Like, it worked, but it was not ideal. And so he went, we went down the whole, which is his job as CTO, to go, and he came back with this version. We then called all the people that had said like, “This is garbage,” a quarter ago. And we set up these calls, and we gave it to - We just demoed it to everyone. And all the calls went long, every single one. They were 15-minute calls, and they all went to like 25, 30 minutes or whatnot. And everyone said, “We need, we want access.” There was no login, just an API key, ‘cause it was just a beta or an alpha. And they said, “Oh, we want access.” And we're like, “Sure, yeah. Okay, thank you very much.” But after like the next day, if we'd not send it, every single one, like every call that we did, everyone came back, “Where is my API key?” Like everyone wanted it. We're like, “S**t.” Like this is it. Like I've never felt So one, the understanding to your point was like most people thought it was the same infrastructure for humans and agents. We understood a quarter ago it's not. We just didn't know what was the right primitive. And then when we came, and we can talk about what that is, and we gave it to these people, I've never seen, I've never experienced - I've done multiple companies in my life. I've never experienced this, that people literally call you if you do not give them access. Like they want access right now. And so it's like, okay, they don't want this. the thing that they want doesn't seem to exist, or they have not found it, and they really want what we want. And then when we understood that we're onto something, and then when you think about the size of the market, like the market for human engineers and enterprise is a very large market, so think GitLab or whatnot. But the market for every single agent that will exist ever in the future is just like, what is that market? How big is that? And we're like, “We are all in on this.” And so that is where we made sort of the cut between the old product and the new one.Bare Metal, Stateful Sandboxes, and the Lambda + EC2 ModelSwyx [00:12:02]: Yeah. But it wasn't composable at the time?Ivan [00:12:05]: It was very - It was basically just a Linux box that you could change, that you could define number of CPUs, disk, and RAM. Like that is what you could do, but you couldn't have multiple operating systems, you couldn't resize it on the fly, you couldn't add a GPU, you couldn't do like all the things. It was just the, just the first sort of variation of that, yeah.Swyx [00:12:22]: Was it bare metal from the start?Ivan [00:12:24]: It was bare metal from the start. And so the interesting thing that we thought about right away, so our.Swyx [00:12:29]: Which, give people the background, what is the normal path?Ivan [00:12:32]: Yeah, so, basically most providers run this on top of VMs. And also.Swyx [00:12:37]: Firecracker.Ivan [00:12:38]: Yeah, they run on Firecracker and VM. And so we also fire - We can get - We have multiple isolation layers and we can do that. But the common way to do it is that they, one, that the state of the machine, or the hard disk is not part of the sandbox itself. And the other thing is they're not meant to last forever. So most of them are preemptible, like they can There's a time that they can live. And so our thought was when we were going into this is, agents will be like humans in the sense of you don't want your laptop to be shut down until you're done with work. Like, and you want to close the lid and open the lid, it's the same state. So you - Agents would want that, like the pause and come back. They want those two things. But also agents really want speed, right? Can they get it? So when we thought about it's like we need something insanely fast, how to make it fast, how to make it long-running, and stateful. And so those two things, it's like combining a Lambda and an EC2, right? Those two things together. And so we didn't have an idea how others did it, ‘cause we didn't know too that there was a market around this. It was more like, okay, this is what we need, what they need. And we looked at Kubernetes, it wasn't wasn't good enough for that. We looked at Nomad, it didn't enable that. And so our history in rewriting our own scheduler at CodeAnywhere is basically what my CTO came up with. Like, he's like, “Oh, the learnings from there,” and he brought it. And the funny thing is, our third co-founder, when he saw it, he's like, “Dude, what is this? This is like 2008.” Like, we went back in time, and he's like, “Exactly.” And so the reason why Daytona is like super fast, and you see this on benchmarks, is we essentially, we run on bare metal. We have our own scheduler, we use the underlying, disk, CPU, and RAM of the underlying machine, which means your IOPS are insanely fast because there's no, there's no network between an EBS or something like that. But also the snapshot, the point in time, the templates, are also preloaded on the bare metal machines. So when you fire off a sandbox from a template or a snapshot, you're essentially directed to the bare metal machine where that snapshot is based on that NVMe drive, and then it literally just turns on that machine, and it's local. There's no network latency, anything on there. And so that is sort of the specificities that we, when we're thinking from first principles, what a computer would look like for an agent, that is what we came up with, and that's what we created.Benchmarks, 60ms Startup, and 50,000 SandboxesSwyx [00:15:02]: Yeah. I should maybe, I don't know if you endorse this, but there's someone that does compute SDK, you guys do very well on there, with like the TTI, right? I. is this a, is this a is this a relevant benchmark for you guys? I don't know.Ivan [00:15:16]: I don't know, and it changes every day. So today RKL is.Swyx [00:15:18]: I don't know what RKL is. Never heard of it.Ivan [00:15:20]: Yeah. RK, yeah, so it is there.Swyx [00:15:22]: You are, at least a third of the next tier of performance, and then, there's a lot of other better-known names that are very slow to start.Ivan [00:15:31]: Yeah. We've been the number one by far for a long time, and now there's different, there's different definitions also of sandboxes, different isolation patterns, different other things. So RKL runs it literally on the S3, the data, so it's very different, and they spin up a sandbox, spin up a container for that, so it's a different type of thing. So the definition of a sandbox is something that we can all, we all need to get along with. But yeah, we're insanely fast on getting these things, up and running. And so you can see even there that it's a zero point 0.10 to 0.11, so.Swyx [00:16:03]: Close enough. Yeah. what else do you need, right?Ivan [00:16:05]: Yeah. So the benchmarks itself, so, in this, in I don't think the benchmarks equate to market ownership or revenue or anything like that. and I've seen this with multiple benchmarks, not just in sandboxes, but in general benchmarks around.Swyx [00:16:20]: It's table stakes. It's just like.Ivan [00:16:21]: Exactly. But it doesn't hurt.Swyx [00:16:22]: Just roughly check.Ivan [00:16:22]: Like you definitely have to be up there and you have to be competing so that people know that, oh, this is definitely one of the top. Because this is only one dimension of what customers look for. There's other things like how many can you spin up consecutively? There's a feature set, there's support, there's like all different things that people look at, but you definitely have to be there, on the benchmarks.Swyx [00:16:40]: How many people do people spin up consecutively?Ivan [00:16:43]: So we have.Swyx [00:16:43]: Or concurrently, is the Concurrency, right?Ivan [00:16:45]: There's three metrics that we look at. And so one is like time to spin up one, and so our time to spin up one is 60 milliseconds with network latency. So request, spin up, reply, 60, the whole thing, 60 milliseconds. That is one. But if you wanna spin up 50,000 at once, we are now at about 75 seconds. So it takes about 75 seconds to spin up concurrently 50,000. Some others, there's public data around this, like take 2,000 seconds, which is 30 minutes. Like there's different variations of that. And then there is the so it is speed of one, speed of like multiple, and then how many can you consistently have up and running. And so we basically have right now no limit to how much we can add because we basically own our own metal. But the biggest customer of ours does like about 850,000 every single day is sort of where they're, where they're just shy of a million every single day that they're running, we do have a request for half a million concurrent, which is literally half a million CPUs somewhere running. So that's an interesting.Swyx [00:17:44]: They pay by like vCPU seconds.Ivan [00:17:47]: By seconds, yeah.Swyx [00:17:47]: Or whatever. Yeah. Okay, and so and then, and the other thing is, the sleeping and the resuming, ‘cause it's all the stateful resumption of all these things, how, what kind of workload are people putting through this, right? Like how is it Do we measure by gigabytes in memory, gigabytes in storage? I don't In like network attached storage. I, what are the costly ones of, out of all these features?Workload Economics: CPU, RAM, Network, and StorageIvan [00:18:15]: The most expensive thing are CPU.Swyx [00:18:18]: Okay. Yeah, of course.Ivan [00:18:18]: The second one, yeah Then it's RAM, then it's disk. We actually don't charge.Swyx [00:18:22]: Which is snapshotting, right?Ivan [00:18:23]: No, it's actually the, snapshotting's part of it, but basically the size of your hard disk, of your machine. So do you have 10 gigabytes, do you have 20, do you have 50, do you have whatever? And then the transference of that. Right now, currently we don't charge for, network at all at Polychron.Swyx [00:18:37]: Oh, you gotta, yeah, you gotta fix.Ivan [00:18:38]: Yeah. It is very much a it's a larger and larger part of our bill, so we're working around, that part there. Obviously, that is the least, expensive, so the hard disk is the least expensive, so it's basically CPU, RAM, for us network, ‘cause we don't charge the customer, and then hard disk, is how it's split up. But there's also different types of workloads, so we basically split it up into two types of workloads in Daytona. One is what we call background agents or long-running agents. and the other is, basically RLs and evals, which I put sort of together. And so they have very different patterns of usage, and if you look at the usage of a background And I'll just name names of companies, not specifically.Background Agents vs. RL/Evals: Two Usage ShapesSwyx [00:19:21]: Yeah, open, all hands.Ivan [00:19:23]: Yeah. So like a background agent's a Cognition, a Lovable, a like all these things are Harvey. These are all long-running, background agents. And so if you look at their usage patterns, their usage patterns are similar to human, which is like follow the sun. Basically, the usage patterns of that is like noon is probably the highest, and the midnight is the lowest, and then weekends are lower. weekday is higher.Swyx [00:19:42]: Yeah, that's a fun question. How global is it? Is it very US-centric or?Ivan [00:19:46]: The US is a large part, but we have currently, we have Asia, Europe, and the US regions.Swyx [00:19:52]: So it's quite global.Ivan [00:19:53]: Yeah, it's quite global. We have it all over. It's interesting that our I talked to you a bit about this. Our number one city by user.Swyx [00:20:01]: Hmm.Ivan [00:20:02]: Is Singapore.Swyx [00:20:04]: Oh, wow. Amazing.Ivan [00:20:05]: Which is an interesting one, right? Not by revenue, just by just like by individual head count.Swyx [00:20:09]: Really?Ivan [00:20:09]: Just like an interesting thing.Swyx [00:20:10]: Singapore is, Singapore is weirdly high in the adoption charts of AI for the population. It's like an, seven, eight million population. And it's like keeps showing up.Ivan [00:20:20]: No, it's quite interesting. We were quite shocked, and I was like, “Oh, this is interesting.” And also one that's up there.Swyx [00:20:24]: There's a reason I'm doing AI using Singapore. it's because I'm from there.Ivan [00:20:27]: We're there. We're gonna, we're gonna be there as well. and it's interesting that Japan is in the top or like Tokyo's in the top, which is in all the tech cycles it has never been. It has never been, so it's quite interesting that they're.Swyx [00:20:39]: I think the Japanese just love AI. Yeah. It's that, and then it's Brazil. That's it.Ivan [00:20:44]: Brazil has always been in.Swyx [00:20:45]: I think.Ivan [00:20:46]: Even when I look, if you look at like GitHub's data and ask historically with CodeAnywhere, it was always like US, Western Europe, and then you'd have like India, Brazil, China, like that would be there. But like Singapore was not in, specifically Japan was never in sort of that top, that top.Swyx [00:21:01]: Yeah. Weird pockets.Ivan [00:21:01]: Weird. Yeah, so it's very global.Swyx [00:21:02]: Okay, so actually that, but that's helps you to distribute your load through, all time?Ivan [00:21:08]: The interesting thing is like we have those kind of loads, but if you look at the researcher loads, they're quite different. So what they are is like if you give them concurrency of 10,000 or 50,000 or 100,000 CPUs at ARMb, when they fire off a run, it's just 100%. And then it just runs, and then it stops. So it's very, the usage pattern is squares basically, right? And it's also not follow the sun, because people will fire it off at midnight before they go to sleep but then wake up and so it's very unpredictable, so you don't know where that is. So the shapes of the usage are quite different than we have had before. And also what's interesting is when it's sort of a follow the sun, even if you have a high growth company, you can sort of predict your usage patterns and have enough capacity for that, because it's sort of, it grows in a, in a way you can project. When you have companies doing sort of like evals and RL, they're super spiky. So they're gonna come in, it's like, “We're gonna use nothing, then can we have 100,000?” Right? And then go back down. And then 100,000, go back down. So it's very different, right? And.Swyx [00:22:09]: Do you want to lock them into commits so.Ivan [00:22:11]: Yeah, we do.Swyx [00:22:12]: Yeah, okay.Ivan [00:22:12]: We so we have to lock them into some sort of commits to have that capacity, because we have to have, basically we have to have the capacity for peak. Right? And so right now, Daytona's mean utilization is 15%, 1-5.Swyx [00:22:25]: Oh my God.Ivan [00:22:26]: So it's very low.Swyx [00:22:27]: Because it's very spiky.Ivan [00:22:27]: It's very spiky, but we get up to 90%. so we have these things. And so what we're, what we're looking at right now as a company is similar to Cloudflare where you can like geo move things around, but that works really well for basically the background agent where it's follow the sun. But this, it's not. Like it's a very different shape. Obviously with scale you figure these things out, but that's an interesting new problem that we have, as a compute provider in the agent space. And when we were doing the conference recently, and so we talked to like Nikita from Neon and.Swyx [00:22:57]: I should bring it up.Ivan [00:22:58]: Parag from Parallel and whatnot, everyone has the same problem. Whereas the usage is super spiky, and this is something that has not happened before, that you have these types of like it was always, it the amplitudes were not this high, right? So it's quite interesting use case and problem solve.Compute Conference and Spiky Agent InfrastructureSwyx [00:23:12]: Yeah, I don't know if we're gonna bring this up again, but let's just talk about the conference, you had like 1,000 something people at the Warriors game, at the Sorry, where is it? What's.Ivan [00:23:22]: Chase Center.Swyx [00:23:23]: Chase Center.Ivan [00:23:23]: Chase Center.Swyx [00:23:24]: I went. It was, it was very impressive. Obviously, you can, how to throw a conference, what did you learn? you put, you pulled together all these impressive names.Ivan [00:23:33]: What I.Swyx [00:23:34]: What were you looking for?Ivan [00:23:35]: My thesis behind the Compute Conference was let's bring together people that are building infrastructure for AI agents. Because when I think of what we're building, it is the agent is the primary user, what are the ergonomics and usage patterns of agents, and so we can do that. And what I found, this was a theory, it wasn't proven, is that we all have these problems, as I touched onto. And I was, as I was talking on stage, it was like we all have the same underlying infra problems, which is this spiky workloads, unpredictable workloads that we've never had before, in human, compute or human infrastructure. And it's, again, it's the same when I was talking to Parag or when I was talking.Swyx [00:24:20]: Lynn. Nikita.Ivan [00:24:21]: Lynn, Nikita. Lynn especially, I was talking to her the other day as well. Like the It is a very interesting type of problem to solve because I can touch on Cloudflare because there's a lot of like talk about that recently as to how they solve that, which is they have a bunch of geos, and basically, as users work in different places, and depending on your tier, they can move you around the geos. And so that how, that's how they get the higher utilization. But you can sort of predict these, and it's If it's something in You'll rarely get a spike that is 10 orders of magnitude. Like you'll get a like let's say one of your customers has some like an exponential curve. What is that to I'm using Cloudflare as an example. 10%, 20%, whatever it is. I don't, I don't have this data, I'm just assessing. It's surely not 10x, right? It's surely not something there. And so how do you go out and solve this problem? And we're all solving this in different ways. So we have.Swyx [00:25:11]: She also has the same thing.Ivan [00:25:12]: Yeah, I know specifically that like Neon had that issue as well. Like how are we solving these spiky loads and things like that ‘cause we talked about it. And so the interesting thing for me to actually internalize was, yes, everyone that's building for agents first is going through this, and we're all solving similar problems, which is quite.Swyx [00:25:28]: Let me let me double-click on this. Okay. So for example, Neon, I happen to know that they're very sort of S3 oriented, right? so they're just like fully bet on S3. And you get to benefit from S3's distribution and infrastructure. So I would imagine that Neon doesn't have to care, whereas Lynn maybe has to care a bit more because obviously she's doing GPU inference. And, for listeners, we did an episode with her, one and a half years ago. And you have to care. But like, right?Ivan [00:25:54]: Parag cares for sure, and Nikita.Swyx [00:25:58]: And Parag is C of, Parallel.Ivan [00:25:59]: Parallel, yeah.Swyx [00:26:00]: Former CTO of Twitter.Ivan [00:26:01]: Twitter, yeah.Swyx [00:26:02]: They are the search.Ivan [00:26:03]: Yeah, they're search, yeah.Swyx [00:26:03]: I You and I know but the listeners don't know.Ivan [00:26:08]: Yeah, we can put it down in the screen, and so ‘cause we, when we were talking.Swyx [00:26:11]: I'll put it up on the, on the screen.Ivan [00:26:12]: Yeah, right.Swyx [00:26:12]: People can look it up if they need.Ivan [00:26:14]: Look it up. And, yes, but they still have CPU and RAM, allocation that you have to have up and running. And so CPU and RAM, you have to allocate that and have that ready. And so there's basically two ways to do it. One is you either over-provision and you can handle the bursts, or two, you basically have, I don't know if this is a term, just-in-time compute, which is like as your load becomes, as your usage comes in, you can fire off requests for VMs or bare metals at other cloud providers and then get them up and running.Swyx [00:26:43]: This is if you go above 100%, right?Ivan [00:26:45]: Yeah, this is.Swyx [00:26:46]: Like your overflow.Ivan [00:26:46]: If your overflow, like spillage or whatever you do.Swyx [00:26:48]: You probably lose money on it, but it doesn't matter, right?Ivan [00:26:50]: It, not Well, you might, you might not That is a more cost-effective way to do it but it's a slower way to do it. Because basically what you have to do is you have to like queue your requests, spin up these just-in-time compute, get it all ready, provision it, and then get your workload there. And so if the time isn't important that much, that's fine, and you can do that. But if your customer, and especially for, let's say, the RL training runs, the reason why a lot of people come to us is because GPUs are more expensive than CPUs, right? So you want your GPU running at, what, 100% the entire time. And so when you're running runs on CPUs, when the when the CPU cycle is like down and spinning up the next one, you want that to be instantaneous so that your GPU doesn't go down, right? And if you then have to like go out and provision machines, you're essentially telling the GPU that it has to wait, and that's incurring our cost. So there's things that you have to try to solve for there.RL Workloads, Declarative Images, and Kubernetes ReplacementSwyx [00:27:43]: Yeah, let's talk about the different workload, right? You said that, what was it? A few months ago, you had zero RL workload and now it's 50%.Ivan [00:27:52]: It will be this one, 50%, yeah.Swyx [00:27:54]: Let's talk about how different it is, right? Like I imagine, for example, a lot less dynamic code generation of like arbitrary code. Like here, it's probably all the same code. You're just doing parallel runs or something, I don't know.Ivan [00:28:05]: Yeah. So you'll have multiple Depends on the like for each run, you'll have a snapshot. And they, for the most part, they actually do use our declarative image builder, which is like, “Oh, we, the agent wants these dependencies, these env vars.”Swyx [00:28:17]: These ones, yeah.Ivan [00:28:18]: Yeah, the declarative image builder, it.Swyx [00:28:20]: Which is a very modal like thing that they.Ivan [00:28:22]: Yeah. And so we build it on the fly and then we propagate that snapshot, and you can spin up as many sandboxes as you want against that snapshot. And then if you have to do changes, the model can, or like it could be also be automated. It's like, “Oh, now for the next run, we need to install these things or remove these things or whatever to get, a task done,” and then it goes off and runs that. So yes, that is something that it seems that they prefer. The number one reason I found, or should I say, let's take a step back. What we are competing against in that environment is essentially managed Kubernetes. So EKS, GKE, whatever. That is what the vast majority run on. And anyone that has tried Daytona versus GKE, EKS is like, “I'm never going back.” That has always been. There's a few reasons. One is the ergonomics. So if you have, if you're using Kubernetes to spin that up, you have to essentially manage the interface interactions with that. Daytona, although as a compute provider, it's more akin to a Twilio and Stripe from a consumption perspective than it is an AWS. Like you have an API, an SDK, it's quite like easy and seamless to get these things up and running, that's one. The other is the speed to which we spin up, which we mentioned earlier, which is much faster, and the scale to which we can go to. We haven't got into features, but an interesting feature is that it's very hard to OOM, or out of memory, our sandboxes, because we can dynamically on the fly.Swyx [00:29:48]: Resize.Ivan [00:29:49]: Resize, which is like impossible on almost any other thing. There are some technologies that enable you to do that, but it's like a very hard thing. And so we actually saw this when, the Terminal Revenge team is, brought us actually. So thank you, Alex and the team, that brought us into this whole space.Swyx [00:30:05]: It's just very rare that, a framework would just say, “Guys, just use Daytona.”Ivan [00:30:11]: Yeah, I think it says it somewhere. Yeah.Swyx [00:30:13]: Yeah. I was like, “What is this?”Ivan [00:30:15]: There's all, there's multiple there, but they also mention a few other places. and so Daytona specifically-We have, the, just jumping on themes here We, I don't know where it says Data Center.Swyx [00:30:27]: I, there.Ivan [00:30:27]: Doesn't matter.Swyx [00:30:28]: There's a very strong recommendation, which is, very unusual. Which is, it's.Ivan [00:30:33]: We do not pay them for this, just.Swyx [00:30:34]: I know, yeah. They just like you.Ivan [00:30:35]: Yeah, they like us. yeah, and also a thing, so, Data Center has multiple isolation sets underneath. The customer doesn't have to know what they are. But basically we have Docker, which is a container, that's hardened with Sysbox. So it's Docker's, isolation that is a security equivalent to a VM, but it's still a container. And that is the default, and they, especially in these training workloads, really like that as an interface to be able to use just a basic Docker container, and we enable Docker and Docker. Which for these RL runs, if you need to do a Docker compose or Kubernetes, you can spin up a K3S inside of these things, which unlocks a huge amount of workloads that you can do that you cannot do on other providers. So just on that part is much more interesting. And so we went that, through that. We showed them that we could do that, and they enjoyed that quite a bit. They being the general venture people.Swyx [00:31:28]: Those people, yeah.Ivan [00:31:29]: And Harbor people.Swyx [00:31:29]: Harbor people, do are they, are they a company yet?Ivan [00:31:33]: As far, I do not know.Customer Pull, Slack Connect, and the Computer Use BetSwyx [00:31:35]: Okay. All right. Yeah. It's like super obvious that like, there's a lot of excitement and success around these things, okay, so yeah, tell us more, right? Like, this is an exploding workload, Harbor adopted you, which helped speed things along. But what are you learning as this new workload comes online?Ivan [00:31:53]: There's a couple things that we learned, which we chat about in the beginning. We, and this has led our story, as we mentioned, we like talked to a lot of customers along the way, and we add more features and more tool sets as we talk to customers. And it's interesting that And I think it's that the ecosystem is so small and/or the models get smarter, where when we see one user come with a request, we know it goes on a roadmap if like three to five customers come with the same request in that week. It's like very bizarre. It happens so many times, which is.Swyx [00:32:27]: Because they're all friends.Ivan [00:32:28]: Sorry?Swyx [00:32:28]: They all, they're all friends. They're all in the same group chat.Ivan [00:32:30]: Yeah, probably, yeah. ‘Cause and they're like, “Oh, can you do this?” And I'm like, “Okay, this is interesting. We'll put it on a feature request.” And then the next one's like, “Oh, can you do this?” “Okay.” It's all the same, right? It's always the same. And so what we try to do, and I personally try to do, I try to be on as many call, quote-unquote “sales calls” I can. I'm in every Slack channel. We literally have about 1,000 Slack Connect channels, something like that. It's an interesting, there's so many interesting things you find out when you have all the Slack channels. You can also see where people, transfer between companies. You see leave Slack channel, enter Slack channel. It's an interesting thing. Also, just I digress, I feel that Slack Connect is literally LinkedIn what it should be. You have a list.Swyx [00:33:08]: LinkedIn charges you to, use your own connections, but Slack doesn't, right? Slack is like, do it for free. It's more lock-in. It's great.Ivan [00:33:15]: Yeah. It's amazing. Yeah. It's one of the reasons.Swyx [00:33:17]: You're gonna pay Slack for life.Ivan [00:33:18]: Exactly. You're there for life. So that's interesting. And so one of the things, the newer things we were talking about earlier is we made a big bet and put a lot of investment on computer use. that is not seen publicly the light of day. We haven't GA'd that yet, but we have.Swyx [00:33:32]: Is there a thing I can pull up?Ivan [00:33:33]: There is computer use there. It's right up a bit.Swyx [00:33:36]: Oh, yeah. Okay.Ivan [00:33:38]: What we have, what we talked about and what we've seen publicly is there's this theme now about, the human emulator where And Elon from XAI has talked about this publicly, and if you think about the models today, they're actually quite sophisticated and they can do a lot of work, but they still don't have access to all the tools. Like, I'm a strong believer that the most efficient way for an agent to work is essentially headless or through, terminal or whatnot. But if we, if we look at knowledge work in general, there's about 100 million knowledge workers in the US, about a billion in the world, and knowledge workers, and the salaries of them aggregate to 10 trillion in the US 50 trillion worldwide.Swyx [00:34:24]: Wow.Ivan [00:34:25]: Something like that. And if we look at, the five most important sectors of that, so like healthcare and government and financial services and whatnot, that's about 56% of that. So let's say it's about half of that. So in the US it's about 25 trillion, and most of them, most of that work is actually still locked into legacy apps inside of Windows, which is not going anywhere for a very long time. Like, people just won't invest in that. How much of it? our assumption is the following: if, in the RPA market, which is similar market, well, not the same 25% of, these white collar, workers', work is automated. If an agent is more sophisticated, can go through more runs, figure stuff out, let's say it's, 40%, right? And so if you take 40% of that, you get to essentially, $10 trillion a year.Swyx [00:35:17]: That's a TAM.Ivan [00:35:18]: That is a that is a TAM. So that's the TAM of the models, right? That's not our, essentially ours. But you get to that size, and to be able to do that, you essentially have to give agents these computers with the legacy. So computer use, either Mac or Windows or Linux. Linux we also obviously have and others have. But Windows specifically is something very new, and the only option right now is an EC2 with, Windows or on Azure. Both of them take anywhere from three to five minutes to spin up. We've created an actual sandbox, so it's a second instead of milliseconds, but you have, point in time snapshots, you have, forking, you have all the things that you have from a sandbox, but essentially enables you to hopefully unlock all this value. And so that's been our big push and bet, but we've sort of, kept our ear to the ground. What is sort of the next things in the market?RPA Returns: Why Agents Still Need ComputersSwyx [00:36:06]: Yeah, knowledge work, and building, and sort of RPA, the next wave of RPA. I got very excited about RPA kind of during COVID times. The UI path was IPO-ing. And it was, a very hot Isn't it, Eastern European?Ivan [00:36:20]: It is, Romanian.Swyx [00:36:21]: Romanian?Yeah, it might be the only Romanian, big unicorn okay, yeah. This I don't I don't, I don't have like a I think there's, I think there's a stage being set for the resurgence of RPA, ‘cause everyone understands that, yeah, no one wants to deal with these shitty apps and no one's gonna rewrite them. Like, you just have to do, a remote operation and programmatic operation of them.Ivan [00:36:45]: If you wanna unlock it, my own setup was basically the following. So I was doing a board deck recently, last month, whatever, and I'm like, “Okay, let's just, let's just do automated.” So, all our data's in, ClickHouse and PostHog and QuickBooks, where everyone else's is, and I'm basically, connected that all to, my Cloud code, like go off and go Cloud code whatever. Go off and, here's the integrations, go do that. It pulled out the first report, which was great. It connected to Brex and all these things, pulled it, which was great, and then I say, “Okay, now pull out this, and this,” and I kept getting, really well McKinsey-style design reports, but the data said partial data. all the missing data, partial data. Like, it can't access all the things, and I got so frustrated, and so I got, I got, my Mac Mini virtual sandbox with OpenClaw. I gave it its own account in our company, and then I went to all these services and created a read-only account, so literally like an intern in your company. And so I would say, “Now go and do this report,” and it would get the same, or like, “I can't via the MCP or the API or whatever. I can't get all the information.” I'm like, “Go log in.” And it will log into the website, then go in, export the data. It'll export the data and do the thing end to end. So even for things that have today APIs, not all of it is exposed, and I to get value, I get immense value right now, but it has to be a computer usage, unfortunately, and so I spend a bunch of tokens just on that, but I get the job done. And so if even a startup like ours, and using all the hottest tools, still needs a computer agent what hope does, Goldman have to have a headless, right?Swyx [00:38:22]: Yeah, what a - Why isn't Microsoft doing this?Ivan [00:38:27]: I'm pretty sure, Satya had a post yesterday.Swyx [00:38:29]: Oh, okay. I see.Ivan [00:38:29]: Which was like, “Every agent needs a computer.”Swyx [00:38:31]: I see, I see.Ivan [00:38:32]: So they have launched something recently.Swyx [00:38:34]: Yeah, they have Microsoft Power Automate, I'm sure, I'm sure, they're gonna have their version.macOS Sandboxes, Apple Constraints, and the Windows OpportunityIvan [00:38:39]: Version of that, yeah.Swyx [00:38:39]: You're gonna try to do yours, and it - I always know there's always demand for Mac, but I know it's, tricky to host, macOS sandboxes.Ivan [00:38:49]: We will have macOS sandboxes fairly soon. The problem with macOS, OS sandboxes is, I'm deep in this, I don't know how much interesting is.Swyx [00:38:55]: No, it's.Ivan [00:38:56]: MacOS has this problem.Swyx [00:38:57]: It's a licensing thing, right?Ivan [00:38:58]: Licensing thing. So one, you're allowed to run only two parallel VMs per machine, so that's one. Two, you can only license to a different user every 24 hours. So if you come in and theoretically, if I wanna charge you per second and I charge you one second, I have to have it idle for the rest of the day. I can't have anyone else doing that. So the pricing will be different in the sense that I will have to - we would have to charge for 24 hours, and that's not even, that's not even the most difficult thing. But the, thing above that is, from a security perspective, they enable you to do memory snapshot, pause, resume, but only on the same physical drive, physical machine. And so what you can do in, Windows world or Linux world is that I can move in the background, your snapshot from one to the other and manage load, right? Here, if you wanna do that, you essentially have to have your.Swyx [00:39:49]: Yeah, snapshots. Yeah.Ivan [00:39:50]: Your.Swyx [00:39:51]: It's like.Ivan [00:39:51]: Physical machine.Swyx [00:39:52]: You can't break it up.Ivan [00:39:53]: You can't, you can't move things around that, and all of that is, that part is, from a security standpoint, if it is written. Like, I understand the security aspect of that, but it disables you from doing these agentic, like really scalable agentic workloads.Swyx [00:40:08]: You need to do a vibe-coded, clean room implementation on macOS that you can then - That's like Clean OS or something. I don't know.Ivan [00:40:17]: So. We have.Swyx [00:40:18]: ‘cause like Linux was originally like a clean room rewrite of Unix.Ivan [00:40:21]: Okay. Yeah.Swyx [00:40:21]: Or something like that, right? Like same thing to macOS. Someone needs to do it.Ivan [00:40:25]: Someone will do that, and someone will have some long-running agents for a few days to figure this stuff out. But yeah. So definitely we - we're really close to offering something ‘cause people do want it, but the pricing will be different, and the feature set will be sort of stringent.Swyx [00:40:38]: Yeah, nobody's gonna use this. like, the labs, the labs will because they want to automate macOS.Ivan [00:40:42]: They have to do RL. They have to do RL again. But even if you The - So the point is with the RL part, if you, if you do RL on macOS, then the next iteration of the model comes out, it will be able to use these tools significantly. Then you actually need to run those, that somewhere. So you're gonna have to have that, later on. And from, if anyone at Apple is listening, I very much feel that they are shooting themselves in the foot of the scale of the revenue of compute or licensing they could get if they would just enable a concurrency model similar to what you can get on a Windows and a, and Linux.Swyx [00:41:17]: Yeah. Yeah. And I'm sure they've heard this before. They just don't care. Yeah, it's And maybe they will change their mind with the new CEO.Ivan [00:41:24]: Yeah. We'll see.Swyx [00:41:25]: We'll see.Ivan [00:41:25]: High hopes.Swyx [00:41:26]: High hopes.Ivan [00:41:26]: High hopes.Swyx [00:41:27]: Okay. But I, it's very clear the market opportunity is huge in Windows, and you can go for a long time on just Windows, but your customers are gonna want both. and I think, it is interesting to me that, this is the sort of God application of agents, right? Like, I don't It was - How big was OpenClaw for you guys? Like, was it, was there, a significant bump.OpenClaw, Agent Labs, and the B2B2C Sandbox MarketIvan [00:41:54]: Not for us because we.Swyx [00:41:54]: Because you already.Ivan [00:41:55]: We're kind of positioned differently. Whereas although it's completely PLG and we have individual developers that use it, most of the users that use Daytona are sort of a B2B2C. Sort of it's either B2B or B2B2C. So, in the researcher world, it's B2B, so you're selling to, labs and neo labs and things like that. But on the long-running agents, it's mostly, from a scale revenue perspective, it's mostly B2B2C, where you have a app layer agent that uses you at a big scale.Swyx [00:42:26]: Like a Manus. Yeah.Ivan [00:42:28]: Like a Manus Lovable type of thing.Swyx [00:42:31]: Yeah. I think that's the question of, well how, um-Uh, yeah, B2B to C is basically to me what I've been calling an agent lab, which is kind of like you're not in a model lab, but you're making a very good wrapper that is a platform that other people can sign up so they don't have to code those things. Yeah, it sound, it sounds like a much better market than the direct OpenClaw market.Ivan [00:42:56]: I've like - We I've done multiple things. So the CodeAnywhere's part of our career path R in the calendar, was very much an end user developer product. And so that is great. It You can get a lot of developer love, and I feel that we do as a company have a bunch of developer love. But it's a different type, where it's people building these things. Again, it's more akin to a Twilio because you don't really run - As a person, you wouldn't run Twilio. I don't know how many people remember. It was like ask your developer billboard and whatnot. And people really love Twilio, but they only used it inside of like, “Oh, I'm building this app or service for thing.” And so we're very much directly to that. And you also know that I used to work for a competitor for Twilio, so it's kind of ingrained, in my DNA.Swyx [00:43:35]: People don't know InfoBip is that big.Ivan [00:43:38]: Yeah, it's.Swyx [00:43:39]: Because.Ivan [00:43:40]: It's a billion euro.Swyx [00:43:40]: They're all American. They're like, “Whatever's in Europe doesn't matter to me.” But like it's the, it's the same size or bigger? Same size?Ivan [00:43:46]: It's about half the size.Swyx [00:43:47]: Half the size?Ivan [00:43:48]: Yeah, about half the size.Swyx [00:43:48]: It's like, yeah.Ivan [00:43:48]: Still huge. Multiple billions a year. Yes.Swyx [00:43:51]: That's crazy.Ivan [00:43:51]: Exactly, and so that - These are like really interesting and large revenue-generating, very sticky businesses. Whereas when you're selling to the - When your focus is the end developer, it is a very hard sell because they're very price sensitive, very price conscious, very around that. And there's very It's very hard to scale. Your cap is the number of people that are willing to spin up - First of all, wanna spin that up, and then spin up multiple of these. Whereas if you're in the enterprise one, like we know everyone's talking about like how many tokens they're spending, I'm spending. Like a lot of companies today are like, “If this is our company, spend as much as you can.” Like basically that is where we're going. And so if you think about that paradigm, where you're selling to companies that say, “Spend as much as you can to generate, productivity,” versus, “Oh, I'm a single person. I have this much budget, and I'm doing this thing because it's fun or it's helping me out or whatever.” Like it is a different, it's a different go-to-market, I think, strategy.MCP, CLIs, and Sandboxes as the Agent RuntimeSwyx [00:44:50]: Yeah, there's a lot of discussion. I'm just kind of going through like the mental list of things that are in your favor, which is, for example, MCP versus CLI. Like obviously you want CLI. It's been very good for you. I feel like it's maybe a drop in the bucket or maybe it's huge. I'm just checking whether it's like these are big trends.Ivan [00:45:10]: Those things you - work well in our favor, to your point just because every.Swyx [00:45:13]: They're kind of drop in the bucket, right?Ivan [00:45:15]: I think it's like sort of all the things come together. And so there's so many things that impact that. To your point, like OpenClaw wasn't huge for us, but like having the agent SDK, from Anthropic, so or Cloud Claude Code was very interesting. The reason why it was interesting is that a lot of, let's call them app I don't know what to call them, app layer agent companies, essentially they are like, “Oh, I can create this new app, this new agent. All I need, I just use Claude Code, and I throw it into a sandbox, and then I have my interface to the human to that.” And so that enabled so many more companies to actually offer this, and then they would pull on sandbox. So that was, that was interesting. And to your point, like MCP, versus the CLI, the MCP is an interface against an API, whereas the CLI is like you can actually go do things. Like this is it. The difference between integrations and actually running scripts or data or analysis against a thing. So being able to use a CLI very well enables the agent to do more things, and it's because that people will invoke a sandbox, they'll run it in the CLI, and but it'll do anal-analysis on that data and then give you an actual result versus just, pulling data from an API source.Swyx [00:46:29]: Yeah, it's a layer of indirection basically, it's the same thing as agentic search versus RAG, which where you're.Ivan [00:46:34]: Exactly, yeah.Swyx [00:46:34]: Just like you just win whenever people put more agents into their workflow. And so like it doesn't really matter, but I'm just kinda teasing out like what else have people heard about that like it's sort of, “Oh yeah, this is another sandbox use case. Oh yeah, that's another one.” Am I, am I missing any big ones?Ivan [00:46:51]: The thing, the thing that people, which is the computer use stuff, which I think is probably the most interesting one, is, and to your point, we've talked to so many people over the last year. It's like, “Oh, like why do you need a sandbox? Why do you need this? Why this?” And to your point, it's like, “Oh, I need sandbox for this. I need sandbox for that. I need sandbox-” It's like, “Oh, I need it for every single thing.” And so basically what I, what I - and it sounds like a broken record, it's like you use a laptop every single day, right? And you are n of one. It's just you. But now imagine how And by the way, the laptop, the computer PC market, the PC market is about equal to the cloud market in total. So it's about 150, 180 billion a year. Something like that. It's about roughly the three cloud hyperscalers is about equal to like Apple, HP, Lenovo, whatever, It's a little bit less, but it's sort of like that. And now imagine And that's just like, so how big is the addressable market? What, how many people are there in the world now? What's the last data?Swyx [00:47:45]: Let's call it eight billion.Ivan [00:47:46]: Eight billion. And so let's say you can have two computer, like you have one personal and one business, whatever. Like so it's double that, right? and so that's 16 billion, right? How many agents are gonna be running in two years, in 10 years, in 100 years? Like And for every single task, they will need one of these. And so how big is that? That market is essentially quote unquote “infinite”. You will get to the point, and Dylan Patel was at the conference talking about, from SemiAnalysis, that talks usually about GPUs, was also talking about how CPUs will now be a bottleneck because it will be the constraint. You won't be able to grow, or we won't be able to have enough of these because there won't be enough CPUs to basically do.Swyx [00:48:23]: Yeah. Well, I actually had a really good podcast with Doug Oliphant, who, which was his president at SemiAnalysis, where they've basically been like, yeah, it's been a GPU shortage first, but then it's cascaded down to memory and now to CPUs.Ivan [00:48:35]: CPU, yeah.Swyx [00:48:35]: It-What's next? So networking. So, networking actually has been in shortage for a while if you're looking at, just GPU networking. But, yeah, it's really crazy the amount of computer use that's going on, yeah, cool. I, other questions are, just the one very big part is the open sourceness which you didn't have to do, your competitors don't do, like it's not, a lot of people are worried about keeping their projects open source because some competitor can just slot fork it. I don't know if there's any reflections on just being an open source company.Open Source, Trust, and Enterprise ProcurementIvan [00:49:15]: Yeah. There's a bunch. So we the original product that we did was open source.Swyx [00:49:19]: Yeah. CodeAnywhere.Ivan [00:49:20]: So doing that was actually very good for us. There's basically a saying of, What's the saying? Like, companies that are, that are doing really well, measure themselves against, free cashflow, that are kinda okay, it's EBITDA, then, it's, it goes all the way down.Swyx [00:49:36]: The worst is like GitHub stars.Ivan [00:49:37]: GitHub stars. GitHub stars are the worst, yeah. So you go all the way down to GitHub stars. And so our original one was GitHub stars. That's what we talked about, we're at the point we're talking about revenue, so we're we've gone up the stack on that. And so we started.Swyx [00:49:47]: No, profit.Ivan [00:49:48]: Yeah. We haven't, we're, we'll get there. We'll get there. But basically at that point we did stars and GitHub and it was useful, and the original variation that we did, it we split the core into its own repo and it was Apache 2.0, so very, permissive. And then we basically would bundl

In this episode, we explore how political adversity builds character, using Rep. Thomas Massie's primary loss as a powerful lesson in resilience versus emotional tantrums. We also examine the alarming Gen Z cognition crisis, as experts warn this generation is the first in modern history to be less cognitively capable than their parents due to screens, tech-heavy education, and declining focus. Plus, Democrats escalate attacks on SEC sports, pushing boycotts of Southern universities over redistricting disputes. Trump rolls-back costly regulations on HVAC and refrigerators and his endorsement means a lot in elections. Tune in for insights on strengthening personal and national resilience in turbulent times. Please take a moment to rate and review the show and then share the episode on social media. You can find me on Facebook, X, Instagram, GETTR, TRUTH Social, TikTok, YouTube and Rumble by searching for The Alan Sanders Show. And, consider becoming a sponsor of the show by visiting my Patreon page!

The Guest: Norris Francis BranhamWebsite: www. Turtlegang.nyc YT ⁠ IG: @turtlegangnyc —This conversation with Norris Francis Branham is one of the most important, eye‑opening, and culturally vital interviews of the season. Viewers will walk away with a deeper understanding of Indigenous history, the erasure of Native narratives in America, and the urgent work being done today to reclaim identity, land, and truth.—Norris is not just a historian—he is a living archive, a cultural protector, and a frontline advocate whose work with Turtlegang.nyc is reshaping how communities understand their origins and their power. This episode is a rare opportunity to hear history from a voice that carries lineage, lived experience, and uncompromising clarity.

This episode draws on experimental and review literature on mirror-gazing, strange-face illusions, anomalous self-experience, dissociation, agency, face pareidolia, and face-distortion disorders, especially the work of Giovanni B. Caputo, Caputo/Lynn/Houran, Mash et al., Bregman-Hai and Soffer-Dudek, Derome et al., Palmer and Clifford, and Blom et al. Historical and occult context comes from research on catoptromancy, John Dee's angelic scrying records, the British Museum's “Dr Dee's Magical Mirror,” Campbell et al.'s Antiquity study on the mirror's Mexican/Aztec obsidian origin, and Mesoamerican material on Tezcatlipoca and the “Smoking Mirror.”Links For The Occult Rejectshttps://linktr.ee/theoccultrejectsOccult Research Institutehttps://www.occultresearchinstitute.org/Cash Apphttps://cash.app/$theoccultrejectsVenmo@TheOccultRejectsBuy Me A Coffeebuymeacoffee.com/TheOccultRejectsPatreonhttps://www.patreon.com/TheOccultRejectsCore Scientific Sources: Mirror-Gazing, Strange Faces, and Altered Self-ExperienceCaputo, Giovanni B. “Strange-Face-in-the-Mirror Illusion.” Perception 39, no. 7, 2010, 1007–1008.Key use: This is the main science anchor for the episode. Caputo showed that prolonged mirror-gazing under low illumination can produce strange-face apparitions, including distortions, unknown faces, monstrous faces, animal-like faces, archetypal faces, and faces of relatives or deceased people.Caputo, Giovanni B., Steven Jay Lynn, and James Houran. “Mirror- and Eye-Gazing: An Integrative Review of Induced Altered and Anomalous Experiences.” Imagination, Cognition and Personality 40, no. 4, 2021, 418–457.Key use: This is one of the strongest overview sources. It reviews empirical studies on mirror-gazing, psychomanteum work, and eye-to-eye gazing, especially in relation to altered perception, anomalous experiences, bodily experience, and self-identity.Mash, Joanna, Paul M. Jenkinson, Charlotte E. Dean, and Keith R. Laws. “Strange Face Illusions: A Systematic Review and Quality Analysis.” Consciousness and Cognition 109, 2023, article 103480.Key use: Newer review source. Useful because it supports strange-face illusions as a reliable phenomenon in both mirror-gazing and interpersonal gazing, while also warning that stronger research is still needed on mechanisms and prevalence.Bregman-Hai, Noa, and Nirit Soffer-Dudek. “Mirror-Gazing-Induced Dissociation Impairs Self-Reported and Implicit Sense of Agency: A Causal Investigation of Dissociation and Agency Under Controlled Laboratory Conditions.” PLOS ONE 21, no. 2, 2026, e0341316.Key use: Excellent source for the agency section. This connects mirror-gazing-induced dissociation with weakened sense of agency, which pairs well with mediumship, possession, automatic writing, and the feeling that “something else” is present.Derome, Mélodie, Eduardo Fonseca-Pedrero, Giovanni Battista Caputo, and Martin Debbané. “A Developmental Study of Mirror-Gazing-Induced Anomalous Self-Experiences and Self-Reported Schizotypy from 7 to 28 Years of Age.” Psychopathology 55, no. 1, 2022, 49–61.Key use: Useful developmental source. It connects mirror-gazing-induced anomalous self-experiences with age, self-perception, and schizotypal traits.Caputo, Giovanni B. “Visual Perception During Mirror-Gazing at One's Own Face in Patients with Depression.” The Scientific World Journal, 2014.Key use: Useful for the emotion/self-face relationship section. Caputo found that strange-face apparitions were reduced in patients with depression compared with healthy controls, including shorter duration, fewer strange faces, weaker intensity, and lower emotional response.Tramacere, Antonella. “Face Yourself: The Social Neuroscience of Mirror Gazing.” Frontiers in Psychology 13, 2022, article 949211.Key use: Strong support for the idea that mirror-gazing is like seeing yourself as another. It connects self-face perception with social neuroscience and the overlap between how we perceive our own face and the faces of others.Chakraborty, Anya C., and Bhismadev Chakrabarti. “Looking at My Own Face: Visual Processing Strategies in Self–Other Face Recognition.” Frontiers in Psychology 9, 2018.Key use: Useful for the self-face recognition section. This study looks at how people process their own face compared with other faces.Conty, Laurence, Nathalie George, and Jari K. Hietanen. “Watching Eyes Effects: When Others Meet the Self.” Consciousness and Cognition 45, 2016, 184–197.Key use: Best support for the gaze/presence section. It argues that direct gaze captures attention and triggers self-referential processing, which helps explain why a mirror can make the viewer feel watched.Face Perception, Pareidolia, and Monstrous DistortionPalmer, Colin J., and Colin W. G. Clifford. “Face Pareidolia Recruits Mechanisms for Detecting Human Social Attention.” Psychological Science 31, no. 8, 2020, 1001–1012.Key use: Best source for the “face-making brain” section. It supports the idea that illusory faces are not treated as meaningless noise; they can recruit mechanisms involved in social attention.Blom, Jan Dirk, Bastiaan C. ter Meulen, Jitze Dool, and Dominic H. ffytche. “A Century of Prosopometamorphopsia Studies.” Cortex 139, 2021, 298–308.Key use: Use carefully as a comparison source, not as a direct explanation for all scrying. Prosopometamorphopsia is a rare condition where faces appear distorted, showing that face-processing systems can produce frightening facial distortions under certain conditions.Psychomanteum, Grief, and Seeing the DeadHastings, Arthur, Michael Hutton, William Braud, et al. “Psychomanteum Research: Experiences and Effects on Bereavement.” OMEGA: Journal of Death and Dying 45, no. 3, 2002, 211–228.Key use: Main grief / dead-in-the-mirror source. Use carefully. It does not prove afterlife contact, but it supports the idea that mirror-gazing, darkness, memory, and grief can produce powerful experiences interpreted as contact.Moody, Raymond A. Reunions: Visionary Encounters with Departed Loved Ones. New York: Villard, 1993.Key use: Main modern popular source for the psychomanteum as a grief-contact chamber. Use as practitioner/popular context, not as the strongest academic evidence.Terhune, Devin B., and Matthew D. Smith. “The Induction of Anomalous Experiences in a Mirror-Gazing Facility: Suggestion, Cognitive Perceptual Personality Traits and Phenomenological State Effects.” The Journal of Nervous and Mental Disease 194, no. 6, 2006, 415–421.Key use: Good supporting source for anomalous experiences in a mirror-gazing facility. Pairs well with Hastings and the Caputo review.Kamp, K. S., Evgenia Steffen, Louis A. Kasket, and others. “Sensory and Quasi-Sensory Experiences of the Deceased in Bereavement: An Interdisciplinary and Integrative Review.” Schizophrenia Bulletin 46, no. 6, 2020, 1367–1381.Key use: Strong source for the grief section. It supports the point that bereaved people often report sensory or quasi-sensory experiences of the deceased, including feeling a presence, seeing, hearing, smelling, or sensing the dead.Hewson, Helen, and colleagues. “The Impact of Continuing Bonds Following Bereavement: A Systematic Review.” Death Studies, 2024.Key use: Useful for continuing bonds. It helps frame ongoing inner relationships with the dead as part of bereavement rather than automatically pathological.Historical, Religious, and Occult Mirror DivinationJohnston, Sarah Iles. Ancient Greek Divination. Wiley-Blackwell, 2008.Key use: Broad academic background for ancient divination systems. Not only mirror scrying, but very useful for framing divination as a serious religious and cultural practice.“Technical Divination and Mechanics of Sacred Space.” In Technologies of the Marvellous in Ancient Greek Religion. Cambridge University Press.Key use: Useful for ancient catoptromancy. This chapter discusses mirror divination as a technical mode of ancient divination involving reflective/catoptric knowledge and assumptions about divine intervention in human knowledge.Lee, Mireille M. “The Gendered Economics of Greek Bronze Mirrors.” Hesperia 86, no. 1, 2017.Key use: Useful for Greek bronze mirrors as social, gendered, material, and possibly magical/divinatory objects.Pitt Rivers Museum. “Mirrors.” Body Arts Collection Resource.Key use: Good museum-level source for folklore around mirrors and catoptromancy. Useful for basic show-note support on the traditional belief that mirrors could reveal the future.John Dee, Black Mirrors, and ObsidianBritish Museum. “Dr Dee's Magical Mirror / Dr Dee's Magical Speculum.” Collection object 1966,1001.1.Key use: Essential object source. The British Museum identifies the object as Dr. Dee's magical mirror or magical speculum, made of obsidian, catalogued as Aztec, and broadly dated to the 14th–16th century.Campbell, Stuart, Elizabeth Healey, Jago Cooper, Naomi Speakman, and others. “The Mirror, the Magus and More: Reflections on John Dee's Obsidian Mirror.” Antiquity 95, 2021.Key use: Essential academic source for Dee's mirror. The study uses geochemical analysis to show that the British Museum obsidian mirrors are Mexican in origin, with Dee's mirror matching the Pachuca obsidian source.Nature. “A ‘Spirit Mirror' Used in Elizabeth I's Court Had Aztec Roots.” 2021.Key use: Short science-news summary of the Antiquity findings. Useful for quickly explaining that Dee's mirror was traced to a source near Pachuca, Mexico.Smithsonian Magazine. “Obsidian ‘Spirit Mirror' Used by Elizabeth I's Court Astrologer Has Aztec Origins.” 2021.Key use: Useful public-facing summary of Dee's mirror, its Aztec/Mexican origin, and its connection to Elizabethan occult culture.Dee, John, and Meric Casaubon, ed. A True & Faithful Relation of What Passed for Many YeaAlso want to remind people about the website, if you're into reading we have tons of information by multiple contributors, and we got t-shirts up on the site if you're interested. Fun fact, the art is all based on the eyeball. A

Changing the world is difficult. One reason is that the most important problems, like climate change and democracy reform are structural. They are larger than any one person can solve on their own, yet we're bombarded with information about individual actions like attending a public meeting or lowering your carbon footprint. Do these individual actions even matter? Should we focus instead of fixing broken systems?  For our final episode of the season, we explore how individual actions and structural reform can work together to create lasting social change on a range of issues, including democracy. Our guests offer a way out of the either-or thinking and a framework for creating lasting social change.  In Somebody Should Do Something: How Anyone Can Help Create Social Change, Michael Brownstein, Alex Madva, and Daniel Kelly show us how we can connect our personal choices to structural change and why individual choices matter, though not in the way people usually think. Brownstein and Kelly join us on the show to discuss examples of how individual actions leveled up to create larger-scale change, including Mothers Against Drunk Driving and the milk pasteurization movement in the early 20th century. We also discuss how the lessons from these movements can be applied to democracy reform campaigns like campaign finance reform and ranked-choice voting.  Brownstein is Professor and Chair of Philosophy at John Jay College and Professor of Philosophy at The Graduate Center, CUNY.. Kelly is Professor of Philosophy at Purdue University, where he is also the Director of the Cognition, Agency, and Intelligence Center. This is our final episode before our summer break. Thank you to Brandon Stover for editing the show this year, to WPSU for production and promotional support, and to Michael Berkman, Chris Beem, Cyanne Loyle, and Candis Watts Smith for sharing their insights on the show. We'll see you in September!   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Can Stroke Recovery Happen Years Later? The Griffith University Etanercept Trial Answers If you caught my recent video about UCLA's discovery of the first stroke rehabilitation drug that rebuilds brain connections in mice, you know the incredible excitement it generated. If you missed it, the link is in the description below. It's definitely worth a watch. Because of the huge response and the many messages from stroke survivors asking for more real recovery options, I wanted to take a deeper look at another breakthrough: The Griffith University study on using a drug called etanercept to help stroke survivors, not just weeks after a stroke, but even years later. And trust me, the results are eye-opening. Today, I'll walk you through what the study found, how it was set up, what it means for all of us, and where things are heading next. What Was the Study About? Researchers at Griffith University in Australia asked a bold and important question: Can etanercept help stroke survivors still living with chronic pain and movement problems, even many years after their stroke? They weren't looking for tiny improvements – they wanted to see fast, meaningful, life-changing results. This study wasn't designed for people who have just left the hospital. It was for survivors who had had their strokes at least six months ago, with some having had strokes over 15 years earlier. Why Did They Do It? Chronic post-stroke pain, or CPSP, is one of the most devastating outcomes of a stroke. It's not just muscle pain. It's deep nerve pain, constant, burning pain that regular medications like oxycodone or pregabalin often can't touch. Researchers now understand that this ongoing pain is often caused by inflammation in the brain, specifically driven by a chemical called TNF-alpha. Etanercept is a drug that's been used safely for over 20 years to treat arthritis and autoimmune conditions because it blocks TNF-alpha. The Griffith team wanted to test whether using etanercept to block brain inflammation could unlock recovery, even years after a stroke. How Was the Trial Set Up? This wasn't a casual or loose experiment – it was a carefully designed, professional clinical trial. Here's how it worked: 26 stroke survivors participated. Ages ranged from 30 to 80 years old. Strokes had occurred 6 months to 15 years earlier. Every participant had moderate to severe daily pain (rated between 4 and 8 out of 10). All had hemiparesis, or weakness on one side of the body. Participants were randomly assigned to one of two groups: One group received etanercept injections. The other group received placebo injections (just sterile saltwater). Each person received two treatments: One on Day 1 Another on Day 14 The injections were given near the neck in the perispinal space, allowing the drug to travel quickly to the brain. What Were They Measuring? The researchers focused on solid, measurable outcomes: Pain levels – using a 0–100 scale combined with a faces pain chart. Shoulder movement – measuring how far participants could lift their weaker arm. Sensation – testing for improvements in feeling hot, cold, and pressure. Cognition and fatigue – although big changes weren't expected here. Participants were monitored closely for 30 days after their first injection. What Happened? Here's what the trial revealed: Pain Relief 70% of the participants in the etanercept group experienced significant pain improvements. Pain levels dropped by an average of 24 points out of 100. 3 out of 10 participants experienced near-complete pain relief — often within 30 to 60 minutes of their first treatment! Meanwhile, the placebo group showed almost no change. Mobility Gains 9 out of 10 participants in the etanercept group regained more shoulder movement. 6 regained at least 60 degrees of motion. 3 participants fully regained 180 degrees — meaning full overhead shoulder motion. Sensory Improvements Many participants began to feel heat, cold, and pressure better on their affected side — a strong sign that nerve function was returning. Side Effects Only one major side effect was reported: one participant developed shingles and had to withdraw from the study. No other serious adverse events were recorded. What Does It Mean? If these results hold up in larger, longer studies: Stroke survivors could have a real option for reducing chronic pain and restoring lost movement. It could dramatically lower reliance on heavy opioid medications. Most excitingly, it shows that the brain may still be capable of healing years after a stroke — if inflammation is correctly targeted. However, it's important to remember: This was a small trial. Etanercept is not yet officially approved for stroke recovery. And the treatment doesn't work for everyone. But it's a huge, hopeful step forward. A Word About Dr. Tobinick It's important to acknowledge someone who helped make all this possible: Dr. Edward Tobinick. Dr. Tobinick was the first to use perispinal etanercept for stroke survivors back in the early 2000s. He was featured on 60 Minutes Australia in 2011, showing stunning recoveries that few thought were possible. Despite facing skepticism, lack of pharmaceutical company support, and high treatment costs, Dr. Tobinick kept pushing forward. Without his work, many stroke survivors wouldn't even know this therapy existed. You can find the link to that original 60 Minutes interview in the description. What's Next? Because of all the interest from our community, I'm reaching out to researchers at the Florey Institute in Australia. They’re currently working on new therapies for stroke recovery, and I'll update you on: Where their research stands What new options might become available And how close we are to real-world treatments for stroke survivors Stay tuned, as soon as I hear back, I'll share everything with you. Want to Dive Deeper? If you’d like to read the full Griffith University study, the link is in the description. The brilliant researchers behind this study include: Dr. Stephen J. Ralph Dr. Andrew Weissenberger Dr. Ventzislav Bonev Dr. Adrienne Goodman-Jones, and others from Griffith University and partner institutions. They deserve real recognition for pushing this research forward. Final Thoughts If you found this article helpful, Please subscribe, comment, and share this post with someone who might need hope today. And if you're listening on Spotify or Apple Podcasts, please leave a review. It helps more stroke survivors find this channel and this growing community. The post Etanercept and Stroke Recovery: Breakthrough Griffith Trial Results You Need to Hear appeared first on Recovery After Stroke.

If you think about which verbs dominate formal education you'll probably come up with a list like learning, thinking, reasoning, remembering, knowing, and maybe behaving. Now think about what images come to mind when you consider those verbs, or do a google image search and see what you get! I'm willing to bet that the most common images coming up are of individual heads, maybe with a visible brain or cogs, doing the thinking, the reasoning, the learning, the cognition. And to emphasise the point further, when we want to highlight that it's more than one thinker or reasoner doing the work, we have to put clarifying adjectives or nouns in front, like group cognition, collective learning or collaborative problem solving. But the fact is, we are actually already “intertwined creatures” in our entanglement with each other and the world. We think, learn and reason all the time with and through each other and the objects we interact with, and the places we are in. My guest this week, Professor Tony Chemero, has been a major proponent of ‘radical embodied cognition' for his whole career as a professor of philosophy and psychology. His latest book, brilliantly titled, ‘Intertwined Creatures: The Embodied Cognitive Science of Self and Other' is an amazing articulation of just how interconnected we are as creatures and learners in the world. Tony is a Distinguished Research Professor of Philosophy and Psychology at the University of Cincinnati, and a primary member of both the Center for Cognition, Action, and Perception[1] and the Strange Tools Research Lab. As well as many academic articles, he is the author of: Radical Embodied Cognitive Science (2009, MIT Press) - https://mitpress.mit.edu/9780262516471/radical-embodied-cognitive-science/Phenomenology, with Stephan Käufer (2015, Polity Press; second edition, 2021) - https://www.wiley.com/en-be/Phenomenology%3A+An+Introduction%2C+2nd+Edition-p-9781509540662Intertwined Creatures: The Embodied Cognitive Science of Self and Other' (2026, Columbia University Press) - https://cup.columbia.edu/book/intertwined-creatures/9780231223195/https://en.wikipedia.org/wiki/Anthony_Chemero https://researchdirectory.uc.edu/p/chemeray

Dr. Paola Tiedemann and Cameron Ford dive into the controversial Kong toy research that detection dog handlers have been asking about for years. This episode exclusively covers the chemical analysis of Kong rubber toys and what it means for teams using toys as training aids or rewards.What We Cover:The chemical signature found inside Kong rubber toysWhy this research was conducted and what question it answersU.S. vs. Europe: different legal frameworks for Kong trainingThe Fourth Amendment problem: probable cause and court challengesHow defense attorneys could use Kong training against handlersRisk assessment: "My client is a dog lover, there was a Kong in the car"Making informed decisions about toy-based training methodsDr. Tiedemann breaks down the science behind what dogs actually smell when detecting Kong toys, while Cameron addresses the operational and legal implications for law enforcement handlers. The conversation emphasizes informed decision-making rather than blanket recommendations—understanding both the benefits (used successfully in Europe) and risks (U.S. legal system challenges) of toy-based training.This isn't saying "don't do it"—this is saying "know what you're doing and the potential consequences."Upcoming Training Opportunities:

The push for a ban on social media for under-16s has been paused despite both National and Labour supporting it. Several other countries are moving to restrict young people's access to social media, following Australia's lead. Meanwhile, new research shows children who spend more than three hours a day on social media are more likely to develop depression and anxiety as teenagers. Dr Chen Shen from Imperial College's School of Public Health manages this large-scale Study of Cognition in Adolescents and Mobile Phones (SCAMP) and joins Susie Ferguson from London.

Episode SummaryWomen have up to 70-80% lower creatine stores than men — and most of us have never been told that. In this episode Lisa digs into what that means for your brain, sleep, mood, muscles, and energy, and why creatine may be one of the most underreported tools in women's health right now.What You'll LearnWhat creatine actually is and why it matters beyond the gymWhy women have lower creatine stores — and why that gap widens in perimenopauseHow creatine supports brain energy (ATP) and what happens when levels run lowThe research on creatine and memory, processing speed, and mental clarityWhy creatine may reduce depression symptoms — more so in women than menCreatine and sleep: the adenosine mechanism, the 2024 women's RCT, and the 2025 perimenopause findingsThe University of Kansas Alzheimer's pilot studyCreatine + resistance training for muscle and bone health over 40How much to take: 5g for general health vs. 10g for brain-specific benefitsStart HereReady to heal your metabolism? thinlicious.com/happyStudies ReferencedCognitive Function & MemoryXu et al. (2024) — Creatine & Cognitive Function: Systematic Review & Meta-Analysis. Frontiers in Nutrition.Depression in WomenLyoo et al. (2012) — Creatine Augmentation for SSRI in Women With Major Depression. American Journal of Psychiatry.Systematic Review & Meta-Analysis: Creatine for Depression (2025). British Journal of Nutrition.SleepDworak et al. (2017) — Creatine Reduces Sleep Need & Homeostatic Sleep Pressure in Rats. Journal of Sleep Research.Aguiar Bonfim Cruz et al. (2024) — Creatine Improves Sleep in Naturally Menstruating Females. Nutrients.Gordji-Nejad et al. (2024) — Single Dose Creatine Improves Cognition During Sleep Deprivation. Scientific Reports.Hall et al. (2025) — Creatine + Resistance Training in Peri/Postmenopausal Women: Sleep, Cognition, Strength. JISSN.Alzheimer's DiseaseSmith et al. (2025) — Creatine Monohydrate Pilot in Alzheimer's: Brain Creatine & Cognition. Alzheimer's & Dementia.Brain Dosing: The Case for 10gDechent et al. (1999) — Creatine Increases Brain Creatine by 8.7% in Human Neuroimaging Study. American Journal of Physiology.Candow et al. — Higher Creatine Doses for Brain Bioenergetics. Journal of Psychiatry and Brain Science.Dr. Rhonda Patrick on 10g brain dosing (@foundmyfitness)Medical Disclaimer: For educational purposes only. Not medical advice. Always consult your doctor before starting any new supplement.

My guest is Caroline Bicks, whose new book Monsters in the Archives: My Year of Fear with Stephen King (Hogarth, 2026) became a bestseller shortly after release. After she was named the University of Maine's inaugural Stephen E. King Chair in Literature, Caroline Bicks became the first scholar to be granted extended access by King to his private archives, a treasure trove of manuscripts that document the legendary writer's creative process—most of them never before studied or published. The year she spent exploring King's early drafts and hand-written revisions was guided by one question: What makes Stephen King's writing stick in our heads and haunt us long after we've closed the book?Bicks focuses on five early works—The Shining, Carrie, Pet Sematary, 'Salem's Lot, and Night Shift—to reveal how he crafted his language, storylines, and characters. While tracking King's margin notes and editorial changes, she discovered scenes and alternative endings that never made it to print, but that King is allowing her to publish now. The book also includes interviews Bicks had with King along the way that reveal new insights into his writing process and personal history.Monsters in the Archives—authorized by Stephen King himself—is unlike anything ever published about the master of horror. It chronicles what Bicks found when she set out to unearth how King crafted some of his scariest, most iconic moments. But it's also a story about a grown-up English professor facing her childhood fears and getting to know the man whose monsters helped unleash them. --------- Caroline Bicks is the Stephen E. King Chair in Literature at the University of Maine, where she teaches courses in Shakespeare, early modern culture, and horror fiction. She is the author of Cognition and Girlhood in Shakespeare's World and Midwiving Subjects in Shakespeare's England; co-­ author of Shakespeare, Not Stirred: Cocktails for Your Everyday Dramas; and co-­ host of the Everyday Shakespeare podcast. Her essays and humor pieces have appeared in the Modern Love column  of the New York Times, McSweeney's Internet Tendency, and the show Afterbirth. She lives in Blue Hill, Maine, with her family. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

My guest is Caroline Bicks, whose new book Monsters in the Archives: My Year of Fear with Stephen King (Hogarth, 2026) became a bestseller shortly after release. After she was named the University of Maine's inaugural Stephen E. King Chair in Literature, Caroline Bicks became the first scholar to be granted extended access by King to his private archives, a treasure trove of manuscripts that document the legendary writer's creative process—most of them never before studied or published. The year she spent exploring King's early drafts and hand-written revisions was guided by one question: What makes Stephen King's writing stick in our heads and haunt us long after we've closed the book?Bicks focuses on five early works—The Shining, Carrie, Pet Sematary, 'Salem's Lot, and Night Shift—to reveal how he crafted his language, storylines, and characters. While tracking King's margin notes and editorial changes, she discovered scenes and alternative endings that never made it to print, but that King is allowing her to publish now. The book also includes interviews Bicks had with King along the way that reveal new insights into his writing process and personal history.Monsters in the Archives—authorized by Stephen King himself—is unlike anything ever published about the master of horror. It chronicles what Bicks found when she set out to unearth how King crafted some of his scariest, most iconic moments. But it's also a story about a grown-up English professor facing her childhood fears and getting to know the man whose monsters helped unleash them. --------- Caroline Bicks is the Stephen E. King Chair in Literature at the University of Maine, where she teaches courses in Shakespeare, early modern culture, and horror fiction. She is the author of Cognition and Girlhood in Shakespeare's World and Midwiving Subjects in Shakespeare's England; co-­ author of Shakespeare, Not Stirred: Cocktails for Your Everyday Dramas; and co-­ host of the Everyday Shakespeare podcast. Her essays and humor pieces have appeared in the Modern Love column  of the New York Times, McSweeney's Internet Tendency, and the show Afterbirth. She lives in Blue Hill, Maine, with her family. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/literary-studies

Accurate OASIS coding is essential for all home health clinicians due to the impact that coding has on reimbursement. With the CMS plan to move to a universal outcome measure for all post-acute settings, OASIS will become an important tool beyond home health. Payment through PDGM is driven not only by improvement of quality indicators but through a complicated calculation of risk adjustment driven by baseline functional scores, acute cognitive changes, and hospitalization risk. This course will provide easy to use methods that will improve a clinician's ability to provide accurate coding to these sections of OASIS E1.   To view accreditation information and access completion requirements to receive a certificate for completing this course, please click here.   The content of this Summit podcast is provided only for educational and training purposes for licensed physical therapists and occupational therapists. This content should not be used as medical advice to treat any medical condition in either yourself or others.

In this episode of The Firefighters Podcast, we explore incident command, cognition and the art of decision making on the fireground. This is not just a conversation for commanders. It is for every firefighter, because every person on the incident ground is making decisions that shape outcomes. From situational awareness and recognition primed decision making to THINCS, decision controls, stress, fear, ego, culture and command mindset, this episode looks at how firefighters think under pressure, how good decisions are built, how poor decisions emerge, and why the future of firefighter safety depends on cognitive agility, self awareness and the ability to think clearly in chaos. Get this as an article CLICK HEREAccess all episodes, documents, GIVEAWAYS & debriefs HEREPodcast Apparel, Hoodies, Flags, Mugs HERE Please check out our Partners supporting this episode areWilliam Wood Watches - Discount code FFPODCAST gives the user 10% off full range on websiteFIRST TACTICAL- tactical gear for elite operatorsGORE-TEX Professional ClothingMSA The Safety CompanyJAFCOIDEXFIRE & EVACUATION SERVICE LTD Send us Fan MailSupport the show***The views expressed in this episode are those of the individual speakers. Our partners are not responsible for the content of this episode and does not warrant its accuracy or completeness.***Please support the podcast and its future by clicking HERE and joining our Patreon Crew

Hello Interactors,Neuroscience research on narrative shows that stories sharpen attention, improve recall, and recruit shared brain networks that help us organize events into a coherent arc. The trouble, for anyone who works with spatial data, is that the reality on the ground refuses to cooperate with clean narratives despite this inherent bias. Today I look at how the popular telling of how Homo sapiens came to contemplate such things — to become ‘modern' — is not the story the evidence keeps telling.THE LURE OF THE LEAPWe like our origin stories well defined. The popular telling — the Israeli historian Yuval Noah Harari's Sapiens is the bestselling version — locates a moment when archaic humans crossed a threshold and became modern, transformed by some neurological windfall in Africa. But a recent paper by anthropologist Huw Groucutt on Homo sapiens dispersal argues this says more about Homo sapiens' neurological bias toward clean narratives than about the evidence we have.This ‘revolution into modern' frame has traceable historical roots. In the 1960s and 70s, the only deeply excavated record was in a western sliver of the Eurasian landmass called Europe. There, the transition from Neanderthal to Homo sapiens congregations did look abrupt. It was reasonable, given what was known at the time, to read this regional shift as a species-wide threshold — a sudden flowering of cognition and culture. But that reading was a misinterpretation. What Europe records is not a transformation but a replacement where one population arrived as another receded. The arc of change was migration, not metamorphosis.That correction took hold, but the ‘revolution' story, like the species, simply relocated. There would be a coastal revolution in southern Africa, a cognitive revolution in the Rift Valley, a technological revolution in the Levant. The plot survived even as the setting changed.The deeper trouble lies with the word “modern” itself. It is a relic of mid-twentieth-century thinking that anchors humanity to an imagined ethnographic checklist: symbolic art, refined toolkits, complex burials, linguistic competence. These traits are taken to constitute a package, and the package is taken to arrive together. But the evidence keeps refusing this neatness. The traits show up in pulses across regions and disappear again. They appear in populations we have been trained to call “archaic.” They fail to coordinate the way the model demands, and as Groucutt says, provide just“another way of separating ‘us' and ‘them'.”For example at Panga ya Saidi in coastal Kenya, excavators recovered the burial of a child known as Mtoto dated to around 78,000 years ago. It is among the oldest deliberate burials known from Africa, and the kind of behavior usually slotted under “modernity.” Yet there is no continent-wide adoption of similar mortuary practice that follows from it. Burial complexity at Panga ya Saidi appears, then thins, then reappears elsewhere on different terms. It looks less like the leading edge of a wave and more like a local response to local conditions.A second example pulls in the opposite direction. The Iho Eleru skull, recovered in 1965 from a rock shelter in Nigeria, is roughly 13,000 years old — geologically yesterday — yet preserves features that morphologists have long called “archaic.” It refuses to sit in the bin its date implies. The bone is doing something the category cannot absorb.The cost of the revolution model, then, is not that it tells a tidy story. It is that the tidiness encourages researchers to treat their categories as facts of nature rather than instruments of description. Evidence that does not fit the frame gets explained away or quietly set aside. When you stop asking when our ancestors became human and start asking how, across thousands of generations and a shifting climate, particular behaviors were assembled and reassembled in particular places, the data reads very differently.This point is not new. In 2000, Sally McBrearty and Alison Brooks published a paper titled “The revolution that wasn't,” arguing that the complex behaviors taken to define modernity in Europe had appeared in Africa tens of thousands of years earlier, and gradually rather than in a single burst. That correction is over twenty-five years old. The fact that revolution thinking has persisted despite it — and persisted most loudly in popular accounts that sell in the tens of millions — is itself worth taking seriously. Models, like fossils, accumulate where the conditions are right for preservation.The trait-list at the heart of “modernity” is a fragile instrument in its own right. Many of the behaviors taken to mark our species are anchored to ethnographic data on recent hunter-gatherer societies, assumed to provide a baseline for what fully human cultural life looks like. Those datasets have well-known problems; when the archaeologist Robert Kelly examined a portion of Lewis Binford's widely used hunter-gatherer compilation in 2021, he was able to confirm the accuracy of only one percent of the entries. The benchmark we have been measuring the deep past against is, in places, made of sand.PATHS, NOT PIVOTSFor anyone who works with spatial data, the revolution model has a second problem. It ignores the terrain. A revolution, mapped, would look like an expanding circle radiating from a source — like a wildfire expanding from a single ignition point. Human dispersal looks nothing like that. It moves along corridors, hesitates at barriers, doubles back, fragments around resources. It is shaped by climate cycles that open and close routes on millennial timescales. The footprint is irregular because the ground is irregular.Groucutt's argument benefits from a concept that geographers and geomorphologists know well: equifinality. The same observed outcome can result from different processes. A bowl-shaped depression on a hillside can be carved by a glacier, scooped by a landslide, or eroded by a spring undercutting from below. The shape alone does not tell you which. Read the depression as a single signature of a single cause, and you will misjudge its history.The same caution applies to the deep human past. A scatter of similar tool types across regions does not necessarily document a single dispersing population with a shared cognitive package. It may document several populations independently arriving at similar solutions to similar pressures. A flicker of symbolic behavior in two distant places does not imply continuous transmission between them. The archaeological record is dense with cases where the simplest explanation — one cause, one origin — turns out to be the wrong one.A telling example of how revolution thinking distorts spatial evidence comes from a long-running argument about the Levantine sites occupied by Homo sapiens between roughly 130,000 and 75,000 years ago — Skhul, Qafzeh, and others. Did these represent a genuine out-of-Africa dispersal, or were they merely an extension of African ecology into Southwest Asia? In the latter view, our species was so tightly coupled to its native biome that early presence beyond Africa was a kind of optical illusion. One prominent researcher has argued that Israel is outside Africa “only by modern political convention.”But the Levantine mammal fauna of this period is dominated by Palearctic species — deer, gazelle, boar — and has been since at least the Middle Pleistocene. The supposed African flourish at Qafzeh shrinks under examination to a few rare elements, some of them present in the region long before Homo sapiens arrived. “Africa grew” is what the revolution model looks like when biogeography becomes inconvenient. Rather than accept that early Homo sapiens dispersed beyond the continent before achieving full “modernity,” the frame extends the boundary of “Africa” to wherever the species happens to be. The terrain bends to match the model.This is where genomic evidence becomes interesting and dangerous in roughly equal measure. Ancient DNA has transformed what can be reconstructed about population structure, and the resolution is genuinely impressive. But the analytic culture around that data has often defaulted to event-style narratives: a bottleneck here, a split there, a discrete mixture of pulses at a specific date. These tidy events, plotted on a tree, recover the satisfactions of the revolution at a different scale. They imply that the past has crisp joints, making“claims for events which never actually occurred.”The caution Groucutt raises is that population structure across the deep African past was probably continuous, regionally varied, and persistently interconnected — closer to a braided river than a branching tree. Apparent “events” in the genetic record may be artifacts of how the analysis is framed rather than discrete moments in time. Treating them as facts encourages claims of historical specificity the underlying signal cannot bear. Equifinality applies to genomes too. Different histories of structure and gene flow can produce overlapping statistical signatures.What follows, methodologically, is a shift in what models are expected to do. Instead of identifying the moment, the route, or the founding population, the task becomes mapping a field of overlapping processes whose visibility varies by region, by preservation, and by the history of where archaeologists have chosen to dig. That is a less satisfying answer than a date and a place, but it's closer to what the evidence supports.MANY CLOCKS, MANY PASTS, MANY THREADSThe physicist Carlo Rovelli, in The Order of Time, makes an observation that time is not a universal river running at one rate everywhere. It is local and relational. This is not intuitive but matches reality. Atomic clocks at different elevations tick at measurably different rates because gravity dilates time. There is no master clock against which “now” is defined for the whole universe.The revolution model assumes the opposite. It imagines a master clock striking modernity for the species at a particular moment — perhaps in East Africa, perhaps a hundred thousand years ago, perhaps fifty — after which a transformed humanity disperses outward. The image is compelling because it is simple. It is also, as a model of history, incongruent with reality. The record Groucutt reviews shows differently timed histories running in parallel across Africa, Arabia, Eurasia, and Sahul, with regional sequences that do not synchronize. There is no single instant at which the species, taken as a whole, became what it now is. There are only many local trajectories that we have, in retrospect, gathered under one name.One sign that the revolution frame is still doing harm is that the three main streams of evidence — fossil morphology, archaeology, and ancient DNA — currently tell stories that do not align. The dispersal chronology reconstructed from genetic data alone is not the dispersal chronology of the lithic archaeology of northern Eurasia, and neither matches the fossil record of Asia and Sahul. These are not minor discrepancies at the margins. They are different shapes of history. The temptation, encountering this, is to declare one stream definitive and explain the others away. The harder course is to take the disagreement as evidence. What it is telling us is that the histories these methods recover are partial, regionally weighted, and pitched at different temporal resolutions. There is no master clock available to bring them into sync because there was never a master event for them to be synchronized to.This is closer to what might be called emplacement than to revolution. Homo sapiens did not arrive in time as a finished product and then unfold into space. The species emerged through space — through specific landscapes, specific corridors, specific neighbors — and continued to be shaped by them long after any putative threshold. Cognition, technology, and social practice were not delivered together and then carried outward. They were assembled, lost, and reassembled in different combinations under different pressures. Whatever it is that we now point to as the human condition is the cumulative residue of that long, polycentric making. In Groucutt's terms, they are“polycentric and mosaic.”Letting go of the revolution story is uncomfortable because it removes the heroic frame that has organized so much storytelling about ourselves. There is no founding spark, no anointed lineage, no first true human. What remains is harder to compress into a sentence. It is also more honest, and more interesting. The work ahead — for archaeologists, geneticists, geographers, and anyone who builds models of the deep past — is to map the complexity of the terrain rather than identify a single point. To trace the connections that hold the picture together rather than the moment at which the picture was supposedly painted.The mosaic is no runner-up to the revolution. It is the record itself — rough, regional, and real. We need only learn to read it.References:Groucutt, H. S. (2026). Revolution, modernity, and the dispersal of Homo sapiens beyond Africa. Quaternary Science Reviews. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit interplace.io

Send us Fan MailGavin Symes is a returning guest on our show! Be sure to check out her first appearance on episode 776 of Boundless Body Radio!Gavin Symes is Registered Occupational Therapist, a Metabolic Therapy Coach, and the Founder & Chief Wizard of Wizard OT, short for Occupational Therapy. He founded Wizard OT in 2020 to provide a truly person-centered approach to therapy.At the core of their approach is the idea that each person should be treated as an individual. By considering each person's biological, psychological and social needs, they provide a tailored approach to improving every client's wellbeing. They don't assume to know ahead of time what is best for their clients, and instead, work with the individual, their family and their wider network to create an intervention.In 2010, Gavin suffered a serious head injury that left him physically and cognitively impaired. The injury made simple tasks hard and required some basic skills to be relearnt. Progress was slow and hard-won but with time, support and consistent effort, he was able to make a full recovery. The experience of having his life turned upside down by a debilitating impairment has informed everything Gavin has done throughout his career, as it is the fire that drives him to help others.Find Gavin at-https://www.wizardot.com/IG- @wizard_otFind Boundless Body at-myboundlessbody.comBook a session with us here! 

N. Katherine Hayles is a professor of English at UCLA and Emeritus Professor of Literature at Duke University. She is also the author of a number of books on consciousness and AI. Her latest book is titled Bacteria to AI: Human Futures with Our Nonhuman Symbionts. Greg and Katherine discuss technics - recursive feedback loops in which humans and tools co-evolve. Katherine argues that cognitive technologies and AI intensify this process, so we design them while they also design us. She distinguishes cognition from consciousness, emphasizing fast nonconscious neuronal processing and defining cognition as interpreting information in context with meaning, operationalized by SIRAL (sensing, interpreting, responding flexibly, anticipating, learning).  Katherine claims plants and bacteria meet these criteria, while physical processes are agents without choices; cognitive systems are actors that select and adapt. She applies this to computation, treating deterministic mechanisms as noncognitive but viewing modern systems and LLMs as cognitive, discussing aboutness via biosemiotics and LLMs' “conceptual environment.” *unSILOed Podcast is produced by University FM.* Episode Quotes: Are humans and AI evolving toward each other? 07:29: So we can chart the evolution of humans and cognitive computational media in just this fashion. So humans start by being immersed in their environment. They could not survive otherwise. And then humans evolve up to abstraction. Computers start with abstraction, and now, with sensors and actuators and networking, they evolve toward immersion. So humans start with purpose. Their purpose is to survive. That's true of all biological organisms. And then they evolve up to design. Computers start with design. But now, with AI, they seem to be evolving toward purpose, which is the same as biological purpose, to survive.  Consciousness is based on selfhood and self-narration 10:27: Consciousness is based on selfhood and self-narration. The stories we all tell ourselves every moment of every day about who we are and what we're doing, and that consciousness frequently lies. We know that eyewitness reports, for example, are often very untrustworthy because people just perceive what consciousness wants them to perceive. And often that is not accurate. One of the primary purposes of consciousness is to make the world make sense. When highly unusual phenomena happen, consciousness just edits it out. AI can now see humans from the outside 37:23: So we're using our projective capabilities to imaginatively construct an umwelt and then seeing what that would mean for our existence, our sense of meaning or whatever. But we're always doing that from the outside. We're never inside anything but the human umwelt. Now we have a technology in large language models that is capable of seeing the human umwelt from the outside and telling us about it. That has never happened before. Show Links: Recommended Resources: Bernard Stiegler Inclusive fitness Chiasmus Consciousness Daniel Dennett John Searle Stochastic parrot Biosemiotics Umwelt Symbiosis Context window LLM Terrence Deacon Guest Profile: Faculty Profile at UCLA Faculty Profile at Duke Wikipedia Profile Guest Work: Amazon Author Page Bacteria to AI: Human Futures with Our Nonhuman Symbionts Postprint: Books and Becoming Computational The Cosmic Web: Scientific Field Models and Literary Strategies in the Twentieth Century Chaos Bound: Orderly Disorder in Contemporary Literature and Science Unthought: The Power of the Cognitive Nonconscious Chaos and Order: Complex Dynamics in Literature and Science How We Think: Digital Media and Contemporary Technogenesis My Mother Was a Computer: Digital Subjects and Literary Texts Electronic Literature: New Horizons for the Literary Writing Machines Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Dr. Paola Tiedemann returns to break down the groundbreaking firearm detection research she conducted with Cameron over the past four years. As the newly promoted Director of the Institute for Forensic Sciences at Texas Tech University, Dr. Tiedemann reveals why training on gun oils, solvents, and bulk smokeless powder creates operational blind spots.What We Cover:Why training on diphenylamine (the "gun chemical") causes false negativesThe magazine problem: loaded vs. unloaded smell completely differentOld firearms in storage (5+ years unfired): can your dog find them?Why teaspoons of powder don't represent real ammunition odor amountsThe untargeted approach: what dogs actually detect vs. what we think they detectTraining variety is key: mixing full weapons, magazines, ammunition typesThis research challenges the widespread practice of training solely on firing residue, propellants, or cleaning solvents. Dr. Tiedemann explains why firearms that haven't been recently fired present a completely different odor picture—and why most firearm dogs aren't trained to find them.PART 2 drops next Friday covering the controversial Kong training study and what it means for detection dog handlers.Upcoming Training Opportunities:

—The Guest: Sheila Ingram IG: @IsmSheilaIngram & www.NowUTalking.com—Sheila Ingram is an International Recording Artist, songwriter, and soloist whose legendary career has taken her from the stage of the Apollo Theater and the energy of Studio 54 to the prestigious Lincoln Center and the international spotlight.—While she continues to grace the stage with her voice, she is also a Licensed Professional Counselor (LPC) and a Pastor, bridging the gap between clinical mental health and spiritual restoration.—She is the author of the transformative memoir, “The Faded Stain” , and the founder of the 'Hearts Without Walls' movement—a global mission dedicated to erasing the stains of trauma.You can find more of her life-changing content on her own platforms: the Bravo with Sheila Network on and the Sheila Ingram Ministry Network. On YouTube .-

Our Guest is Dr. Lynette Adams --Unlock the blueprint to JOY, emotional wellness, and authentic leadership in this electrifying episode of the OneMicNite Podcast. Host Marcos Luis sits down with the brilliant, dynamic, and deeply insightful Dr. Lynette Adams, founder of NextStopJoy.com, creator of the Formula for Joy™, and visionary behind The Joy Lab Inc.This conversation is a full-body experience — intelligent, soulful, humorous, and packed with transformative gems you'll want to replay again and again.How to reclaim your joy in a world designed to drain itThe science and soul behind Dr. Adams' Formula for Joy™Why emotional wellness is the foundation of true leadershipHow burnout shows up in high-achievers (and how to stop it)The power of boundaries, authenticity, and emotional sovereigntyHow The Joy Lab is shaping the next generation of joyful leadersReal tools you can use TODAY to rebuild your joy and purposeThis is not just a conversation — it's a joy intervention.The chemistry between Marcos and Dr. Adams is unmatched.Expect laughter, truth, vulnerability, and the kind of verbal interplay that feels like a masterclass wrapped in a vibe.If you're a creator, leader, entrepreneur, healer, or someone navigating life's “in‑between” moments… this episode will speak directly to your spirit.Website: NextStopJoy.com The Joy Lounge (Substack): Emotional wellness + joy-centered livingThe Joy Lab Inc.: Building joyful, emotionally safe communities

We have an estrogen masterclass mashup episode today, featuring Dr. Carrie Jones, Dr. Stephanie Estima, Dr. Sarah Berry, Dr. Thomas Dayspring, and Dr. Kellyann Niotis, who share their insights on estrogen and women's health in midlife. In this mashup masterclass, we're bringing together a powerful compilation of conversations centered on the impact of hormones on brain health and cognition. We explore the nuanced risk factors for neurocognitive changes and how shifts in insulin sensitivity during perimenopause and menopause influence various aspects, including lipid patterns, hair changes, laboratory markers, and lifestyle inputs. We examine how the gut microbiome changes dynamically, targeted nutritional interventions, the role of advanced testing, including the DUTCH test, and how genetics, detoxification pathways, methylation, and the estrobolome. This is an exciting, dynamic mashup of some of my favorite podcast conversations dedicated to estrogen and midlife physiology. It's one you'll likely want to revisit more than once. IN THIS EPISODE, YOU WILL LEARN: The critical role estrogen plays neurologically, and how estrogen receptors in the brain increase as estrogen declines How declining estradiol during menopause impacts women's lipid metabolism Why insulin resistance tends to increase during the menopause transition The gut microbiome shifts that occur in postmenopausal women The link between the gut microbiome and menopausal symptoms such as brain fog, anxiety, and sleep issues How impaired estrogen detoxification pathways may increase long-term risk of hormone-sensitive cancers How the DUTCH test provides insight into hormone patterns and metabolism beyond standard bloodwork How declining estrogen disrupts the hair growth cycle and increases shedding Why personalized approaches to supplements, diet, and HRT are essential for women in midlife Connect with Cynthia Thurlow   Follow on X, Instagram & LinkedIn Check out Cynthia's website Submit your questions to support@cynthiathurlow.com Join other like-minded women in a supportive, nurturing community: The Midlife Pause/Cynthia Thurlow  Cynthia's Menopause Gut Book is on presale now! Cynthia's Intermittent Fasting Transformation Book The Midlife Pause Supplement Line Connect with Dr. Carrie Jones On Instagram Estrogen Detox Made Easy Hello Hormones with Dr. Carrie Jones (Podcast) Connect with Dr. Stephanie Estima On Instagram Better! With Dr. Stephanie (Podcast) Connect with Dr. Sarah Berry On Instagram The Zoe Science and Nutrition Podcast Connect with Dr. Thomas Dayspring On LinkedIn On X (@DrLipid) Connect with Dr. Kellyann Niotis On her website On Instagram Podcast Links: EP. 513 Fasting Doesn't Work the Same After 35 – The Shocking Truth About Hormones, Hunger & Aging with Dr. Stephanie Estima Ep. 522 Menopause Is Wrecking Your Gut – The Best Nutrition Fixes for Midlife Women with Dr. Sarah Berry Ep. 523 This Is Why Your Cholesterol Shifts in Midlife – The Best Strategies to Reduce Risk & Improve Vascular Health with Dr. Thomas Dayspring | Women's Heart Health & Menopause Ep. 540 “Your Brain Needs Estrogen” – The Most Powerful Way to Protect Memory, Cognition & Longevity in Midlife with Dr. Kellyann Niotis | Menopause & Brain Health Ep. 542 Why Your Hair Is Thinning After 40” – The Shocking Truth About Menopause Hair Loss – Cynthia Thurlow Ep. 567 “Timing Is Everything” – The Best Way to Test Hormones, Cortisol & Thyroid for Real Answers with Dr. Carrie Jones

We've spent our whole lives being told that a wandering mind is a problem... but what if it's actually one of the most powerful things your brain can do? In this episode, we're diving into the neuroscience of creativity: what it actually is, why your best ideas almost never happen when you're trying hardest to force them, and what occurs in your brain during a genuine creative breakthrough. From the default mode network and alpha waves to dopamine, divergent thinking, and why the "right brain" myth has been officially debunked — we're making the case that creativity isn't a gift reserved for artists and inventors. It's something your brain is designed to produce, and something we may have been accidentally shutting down all along.>> ⁠⁠⁠Support the Brain Blown on Patreon⁠⁠⁠>> Have questions, stories, or topics you want us to cover? Email us at ⁠⁠⁠info@brainblownpodcast.com⁠⁠⁠.>> Learn more at ⁠⁠⁠www.brainblownpodcast.com⁠RESOURCESWhat Happens in a Creative Brain? — AJ Keller, CEO at NeurosityDefining Creativity: Beyond the Cliché — Science News TodayThe Neuroscience of Creativity — Andreas Fink & Mathias BenedekToward a Neurocognitive Framework of Creative Cognition: The Role of Memory, Attention, and Cognitive Control — Mathias Benedek & Andreas FinkThe Link Between Creativity, Cognition and Creative Drives and Underlying Neural Mechanisms — Khalil, Goode & KarimCreativity and the Brain: An Editorial Introduction to the Special Issue on the Neuroscience of Creativity — Saggar, Volle, Uddin, Chrysikou & GreenNetwork Neuroscience of Creative Cognition: Mapping Cognitive Mechanisms and Individual Differences in the Creative Brain — Beaty, Seli & SchacterNeural, Genetic, and Cognitive Signatures of Creativity — Liu, Zhuang, Zeitlen, Chen, Wang, Feng, Beaty & Qiu

Shoutout to @ollieschminkey for the inspiration for this month's wellness activity. Find Ollie on most social media platforms for more Writing Prompt Wednesday ideas. Enjoy!We've spent our whole lives being told that a wandering mind is a problem... but what if it's actually one of the most powerful things your brain can do? In this episode, we're diving into the neuroscience of creativity: what it actually is, why your best ideas almost never happen when you're trying hardest to force them, and what occurs in your brain during a genuine creative breakthrough. From the default mode network and alpha waves to dopamine, divergent thinking, and why the "right brain" myth has been officially debunked — we're making the case that creativity isn't a gift reserved for artists and inventors. It's something your brain is designed to produce, and something we may have been accidentally shutting down all along.>> ⁠⁠⁠Support the Brain Blown on Patreon⁠⁠⁠>> Have questions, stories, or topics you want us to cover? Email us at ⁠⁠⁠info@brainblownpodcast.com⁠⁠⁠.>> Learn more at ⁠⁠⁠www.brainblownpodcast.com⁠RESOURCESWhat Happens in a Creative Brain? — AJ Keller, CEO at NeurosityDefining Creativity: Beyond the Cliché — Science News TodayThe Neuroscience of Creativity — Andreas Fink & Mathias BenedekToward a Neurocognitive Framework of Creative Cognition: The Role of Memory, Attention, and Cognitive Control — Mathias Benedek & Andreas FinkThe Link Between Creativity, Cognition and Creative Drives and Underlying Neural Mechanisms — Khalil, Goode & KarimCreativity and the Brain: An Editorial Introduction to the Special Issue on the Neuroscience of Creativity — Saggar, Volle, Uddin, Chrysikou & GreenNetwork Neuroscience of Creative Cognition: Mapping Cognitive Mechanisms and Individual Differences in the Creative Brain — Beaty, Seli & SchacterNeural, Genetic, and Cognitive Signatures of Creativity — Liu, Zhuang, Zeitlen, Chen, Wang, Feng, Beaty & Qiu

Cohere übernimmt Aleph Alpha – die Schwarz-Gruppe investiert $600 Mio., Aleph Alpha bekommt 10% der neuen Firma. OpenAI und Microsoft lösen ihre exklusive Partnerschaft auf – Microsoft verliert die Exklusivität, OpenAI behält den Revenue Share. OpenAI verfehlt interne Umsatz- und Nutzerziele. Google investiert bis zu $40 Mrd. in Anthropic – die ersten $10 Mrd. auf der alten $350-Mrd.-Bewertung, obwohl Anthropic am Sekundärmarkt über $1 Billion wert ist. Google unterzeichnet einen geheimen Pentagon-KI-Deal trotz Mitarbeiterprotesten. GitHub Copilot wechselt auf nutzungsbasierte Abrechnung. Ex-DeepMind-Forscher raised $1 Mrd. Seed auf $5 Mrd. Bewertung. World ID 4.0 startet mit Zoom, Tinder und Shopify. China blockiert Metas $2-Mrd.-Manus-Übernahme. Emil Michael baut Pentagon-VC-Fonds. Musk vs. Altman geht vor Gericht – Musk pusht den New-Yorker-Artikel, NYT enthüllt SpaceX als Musks Sparkasse. Sereact raised $110 Mio. für Robotik-KI. Google Maps zeigt jetzt die Zahl gelöschter Rezensionen an. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Hörerfrage (00:07:20) Aleph Alpha/Cohere: Analyse des Deals (00:16:56) OpenAI/Microsoft lösen exklusive Partnerschaft (00:21:06) OpenAI verfehlt Umsatz- und Nutzerziele (00:28:40) Google investiert $40 Mrd. in Anthropic bei alter Bewertung (00:37:08) Google: Geheimer Pentagon-KI-Deal (00:41:15) Bubble: $5 Mrd. Seed, Cognition $25 Mrd. (00:45:51) World ID 4.0, China blockiert Meta/Manus (00:52:05) Mythos: Panik bei Firmen, Emil Michael als Pentagon-VC (00:55:55) Musk vs. Altman vor Gericht & Musk nutzte SpaceX als Sparkasse (01:02:40) Sereact $110 Mio. und Google Maps gelöschte Reviews Shownotes Cohere kauft Aleph Alpha, Schwarz investiert $600 Mio. - bloomberg.com OpenAI und Microsoft: Neue Freiheiten für beide Seiten - wsj.com OpenAI verfehlt Umsatz- und Nutzerziele vor IPO - wsj.com Google investiert bis zu $40 Mrd. in Anthropic - wsj.com Google unterzeichnet geheimen Pentagon-KI-Deal - theinformation.com GitHub Copilot wechselt auf nutzungsbasierte Abrechnung - github.blog Sequoia/Nvidia investieren $5 Mrd. in Ex-DeepMind-Startup - bloomberg.com Cognition (Devon AI) bei $25 Mrd. Bewertung - bloomberg.com World ID 4.0: Partnerschaften mit Zoom, Tinder, Shopify - xcancel.com China blockiert Metas $2 Mrd. Manus-Übernahme - theinformation.com Meta: Rechenzentren mit Solarenergie aus dem All - bloomberg.com Mythos- ft.com Emil Michael verwandelt Pentagon in VC-Firma - washingtonpost.com Musk pusht Altman-Exposé auf X vor Prozess - wired.com NYT: Musk nutzte SpaceX als Sparkasse - nytimes.com Sereact: $110 Mio. für Robotik-KI aus Stuttgart - bloomberg.com Google Maps zeigt Zahl gelöschter Rezensionen an - smartdroid.de

When Jeff Wang stepped into the CEO role at Windsurf, it was not part of some long-term succession plan. It happened in the middle of a full-blown crisis. In this episode of the ProductLed Podcast, Wes Bush and Esben Friis-Jensen sit down with Jeff to unpack the wild chain of events that followed the collapsed OpenAI acquisition, the founders leaving for Google, and the intense 72-hour window Jeff had to help save the company and protect 250 jobs. He shares how Windsurf navigated that moment, how the Cognition deal came together, and what it has been like leading one of the most closely watched teams in AI coding ever since. Jeff also gets into what made Windsurf so strategically valuable in the first place, from shipping early breakthroughs in autocomplete, chat, context engineering, and agent workflows, to building one of the first generally available coding agents on the market. Beyond the origin story, the conversation goes deep on go-to-market strategy, why free products worked early on, how token economics changed the game, and why enterprise AI adoption takes far more than handing teams a tool. They also explore Windsurf 2.0, the shift toward managing multiple agents at once, how Jeff uses AI in his own CEO workflows, and why founders need to obsess over painful problems, customer conversations, and product-market fit instead of flashy demos. Key Highlights: 00:00 - The 72-Hour Crisis That Changed Everything Jeff shares the short version of the OpenAI, Google, and Cognition saga, and what it was like stepping into the CEO role during a company-defining emergency. 01:40 - Why Big Tech Wanted the Windsurf Team A look at the execution speed, product breakthroughs, and agent innovations that made Windsurf one of the most valuable teams in AI coding. 04:10 - The Future of Coding Is Multi-Agent Jeff explains why developers are moving from one-on-one AI assistance to managing many agents at once, and how Windsurf 2.0 is built for that shift. 08:54 - How Free Became Their Growth Wedge From free autocomplete to on-prem enterprise deals, Jeff walks through Windsurf's early PLG motion and how it created awareness and pipeline. 13:10 - The Hard Truth About AI Pricing A candid discussion on token costs, self-serve subsidies, pricing pressure, and why raising prices can reveal whether you truly have product-market fit. 16:13 - Why Enterprise AI Sales Are Top-Down Jeff shares how Windsurf sells into large companies by focusing on transformation, adoption, security, and measurable outcomes instead of seat counts. 20:51 - What It Takes to Drive Real AI Adoption Why playbooks, training, and solving a meaningful first use case matter more than just rolling out a shiny new tool to an engineering team. 24:40 - Jeff's AI Workflows as CEO Jeff reveals how he uses AI and custom playbooks for go-to-market research, outreach preparation, and spotting product trends before opening dashboards. 32:32 - Jeff's Advice for Every Product Founder Build around painful problems, talk to hundreds of prospects, and learn to enjoy rejection because that is often where the real insight comes from. Resources:

Tom Onyshko is a 20-year LAPD veteran and handler in Metropolitan Division's elite K9 Platoon, where he runs both a patrol apprehension dog and a firearm detection dog. With only 5 firearm detection spots serving all of Los Angeles, Tom shares real-world insights from one of the busiest K9 programs in the country.What We Cover:Why LAPD runs single-purpose dogs (patrol, narcotics, explosives, firearms - all separate)Getting into Metro Division: the physical tests, firearm quals, and multi-year tryout processOperating in South Central LA: 30 search warrants in one month, 6 warrants in one dayFirearm detection deployment: area searches, vehicle searches, evidence recoveryTraining philosophy: why LAPD doesn't track, e-collar use, and area search methodologyWorking with LAPD's SIS (Special Investigation Section) - confirmed real and eliteReal callout stories: multi-story building searches, murder suspect apprehensionsTom's background includes 5 years in LAPD's South Bureau gang unit serving high-risk warrants with homicide detectives and FBI before joining Metro Division. He discusses the differences between law enforcement and military K9 work, handler selection criteria, and what makes Metropolitan Division's training standards unique.Essential listening for law enforcement K9 handlers, firearm detection teams, and anyone interested in how elite metro agencies deploy detection dogs operationally.________________________________________

- Guest: Mistah Coles - Artist/Educator/ Community ActivistFollow/Contact on YouTube ​⁠​⁠ On IG , Fb @MistahColes —OneMicNite Podcast with Marcos Luis** -In this powerful and historically grounded episode, host Marcos Luis sits down with artist, educator, and community activist Mistah Coles to discuss #FreedomWalk—an upcoming cultural event tracing the chronological pathway of the Underground Railroad and honoring the legacy of Harriet Tubman, one of the most courageous freedom fighters in American history. -Together, they explore how art, activism, and historical memory intersect to keep this essential story alive for new generations.

This episode is a wide-ranging conversation between Jacob and Swyx (Shawn Wang), an AI engineer, podcaster, and now operator at Cognition, who sits at a uniquely informed intersection of builder, investor, and community organizer in the AI world. The two cover the current state of the AI engineering zeitgeist: from the stabilization of agent infrastructure and the surprising stickiness of Claude Code, to the competitive dynamics of the AI coding wars, the rise of open models, the threat to traditional SaaS, and the frontier questions around world models, memory, and what it actually means for AI to "understand" something. The episode is grounded in practitioner-level candor, with Swyx offering real takes from running AIE conferences, working inside Cognition, and thinking deeply about what the next wave of AI-native software development looks like.   (0:00) Intro (1:17) What the Top AI Engineers Are Thinking About (2:13) Has AI Infra Finally Stabilized? (6:39) When Does Doing RL In-House Make Sense? (11:26) Why Selling Dev Tools to Agents is Different (17:18) AI Coding Wars (29:04) Consumer AI Plateau (30:22) Codex vs Claude Code (44:52) Future of Open Models   With your co-hosts:  @jacobeffron  - Partner at Redpoint, Former PM Flatiron Health  @patrickachase  - Partner at Redpoint, Former ML Engineer LinkedIn  @ericabrescia  - Former COO Github, Founder Bitnami (acq'd by VMWare)  @jordan_segall  - Partner at Redpoint

Caroline Bicks became the first scholar granted extended access by Stephen King to his private archives, a treasure trove of manuscripts that document the legendary writerʼs creative process—most of them never before studied or published. The year she spent exploring King's early drafts and hand-written revisions was guided by a question millions of Kingʼs enthralled and terrified readers (including her) have asked themselves: What makes Stephen King's writing stick in our heads and haunt us long after we've closed the book? Dr. Bicks focuses on The Shining, Carrie, Pet Sematary, ʼSalemʼs Lot, and Night Shift—to reveal how he crafted his language, story lines, and characters to cast his enduring literary spells. While tracking King's margin notes and editorial changes, she discovered cut scenes and alternative endings that King is allowing her to publish now. The book also includes her interviews with King, that reveal new insights into his writing process and personal history. Part literary master class, part biography, part memoir and investigation into our deepest anxieties, Monsters in the Archive is unlike anything published about the master of horror. It chronicles what Dr. Bicks found when she set out to unearth how King crafted some of his scariest, most iconic moments. But it's also a story about an English professor facing her childhood fears and getting to know the man whose monsters helped unleash them. Guest: Dr. Caroline Bicks is the Stephen E. King Chair in Literature at the University of Maine. She is the author of Cognition and Girlhood in Shakespeare's World and Midwiving Subjects in Shakespeare's England; co-author of Shakespeare Not Stirred: Cocktails for Your Everyday Dramas; and co-host of the Everyday Shakespeare Podcast. Show Host: Dr. Christina Gessler is an academic writing coach and editor. She is the creator and producer of the Academic Life podcast. Playlist for listeners: Once Upon A Tome The World She Edited: Katharine S. White at the New Yorker Claire Myers Owens and the Banned Book Before and After the Book Deal Your Art Will Save Your Life Becoming The Writer You Already Are The Top 10 Struggles in Writing A Book Manuscript and What To Do About It Do You Need A Developmental Editor? Welcome to Academic Life, the podcast for your academic journey—and beyond! Please join us again to learn from more experts inside and outside the academy, and around the world. Missed any of the 300+ Academic Life episodes? Find them here. And thank you for listening! Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

Today, we check in a year after the first Unsupervised Learning x Latent Space Crossover special to discuss everything that has changed (there is a lot) in the world of AI. This episode was recorded just after AIE Europe, but before the Cursor-xAI deal.Unsupervised Learning is a podcast that interviews the sharpest minds in AI about what's real today, what will be real in the future and what it means for businesses and the world - helping builders, researchers and founders deconstruct and understand the biggest breakthroughs.Thanks to Jacob and the UL production team for hosting and editing this!Jacob Effron* LinkedIn: https://www.linkedin.com/in/jacobeffron/* X: https://x.com/jacobeffronFull Episode on Their YouTubeWe discuss:* swyx's view from the center of the AI engineering zeitgeist: OpenClaw, harness engineering, context engineering, evals, observability, GPUs, multimodality, and why conference tracks now reveal what matters most in AI* Whether AI infrastructure has finally stabilized: why “skills” may be the minimal viable packaging format for agents, why infra companies have had to reinvent themselves every year, and why application companies have had an easier time surviving model volatility* The vertical vs. horizontal AI startup debate: why application companies can act as the outsourced AI team for enterprises, why some horizontal companies still matter, and why sandboxes may be the clearest reinvention of classic cloud infrastructure for the AI era* The “agent lab” playbook: starting with frontier models, specializing for your domain, then training your own models once you have enough data, workload, and user behavior to justify the cost and latency savings* Why domain-specific model training is real, not just marketing: how companies like Cursor and Cognition can get users to choose their in-house models, and why search, domain specialization, and distillation are becoming more important* Open models, custom chips, and alternative inference infrastructure: why swyx has turned more bullish on open source, why non-NVIDIA hardware is suddenly getting real attention, and why every 10x speedup can unlock new product experiences* What it means to sell to agents instead of humans: why agent experience may mostly just be good developer experience by another name, why APIs and docs matter more than ever, and how pretraining-data incumbents are compounding advantages in an agent-first world* Why memory and personalization may become the next big wedge: today's models mostly reward frequency of mentions, but in the future, swyx expects product choice to be shaped much more by personalized memory systems* The state of the AI coding wars: why coding has become one of the largest and fastest-growing categories in AI, how Anthropic, OpenAI, Cursor, and Cognition have all ridden the wave, and why the category may still have more room to run* Capability exploration vs. efficiency: why the industry is still in a token-maxing, experiment-heavy phase where people are rewarded for spending more rather than less* Claude Code vs. Codex and the strange stickiness of coding products: why first magical product experiences may matter more than expected, and why the bigger mystery may be why only a few names have emerged as real winners so far* What the end state of the coding market might look like: two major players, a longer tail of niche products, and possible disruption if Microsoft, Mistral, xAI, or the Chinese labs push harder into coding* Where application companies still have room against the labs: why frontier labs are trying to expand into verticals like finance and healthcare, but still leave space for focused companies that own the workflow and the last mile* Why coding may be a preview of every other AI market: the first category to truly go parabolic, the clearest example of foundation model companies colliding with application companies, and a template for how future vertical AI markets may develop* Why AI valuations now feel unbounded: from billion-dollar ARR products built in a year to trillion-dollar market caps, swyx and Jacob unpack how the AI market has broken traditional startup intuitions about scale and durability* Consumer AI vs. coding AI: why ChatGPT's consumer category may have plateaued on frequency and product design, while coding continues to feel like a daily-use category with real momentum* The next product frontier beyond coding: consumer agents, computer use, and “coding agents breaking containment,” with swyx's thesis that 2025 was the year of coding agents and 2026 may be the year they begin to do everything else* Whether foundation models are really killing startup categories: why swyx is less worried for early founders, more worried for mid-size startups and traditional SaaS, and why building something ambitious may now be the best job interview for a frontier lab* AI vs. SaaS and the internal culture war around adoption: the tension between AI-native employees who want to rip out expensive software and skeptics who think quick AI-built replacements create fragile systems* Why traditional SaaS may be under real pressure: swyx's own experience spending six figures on event and sponsor management software, the temptation to rebuild it cheaply with AI, and the broader question of whether teams will trust custom AI-native replacements* Biosafety, security, and frontier model access: why swyx raised biosafety at a dinner with Anthropic's Mike Krieger, why Krieger argued security is the bigger issue, and what restricted model releases reveal about Anthropic vs. OpenAI* The era of giant models: why 10T+ parameter systems may only be a temporary rationing phase before bigger clusters arrive, why labs may increasingly keep their most powerful models private for distillation, and why scale alone no longer feels like a complete answer* Memory as the slowest scaling factor in AI: why context windows have improved far more slowly than people hoped, why million-token context still has not changed most real workflows, and why memory may be the key bottleneck for the next generation of systems* What swyx changed his mind on in the past year: becoming more bullish on open models, more convinced that the top tier of agent startups behaves very differently from the median AI company, and more optimistic about fine-tuning and specialized model adaptation* “Dark factories” and zero-human-review coding: the next frontier after zero human-written code, where models not only write the code but ship it without human review, forcing companies to rethink testing and verification from first principles* Why RL and post-training may matter more than people assumed: even if the resulting models get thrown out every few months, the data, workflows, and domain-specific improvements persist* Synthetic rubrics, Doctor GRPO, and multi-turn RL: why reinforcement learning is becoming much more domain-specific and multi-step than many people realize, opening the door to much deeper customization* The next frontier after coding: memory, personalization, and world models, including why swyx thinks world models matter not just for robotics or gaming, but for giving AI something closer to lived understanding* Fei-Fei Li, spatial intelligence, and the Good Will Hunting analogy: the idea that today's LLMs may know everything by reading it all, but still lack the lived experience that turns knowledge into a deeper kind of intelligenceTimestamps* 00:00:00 Intro preview: AI coding wars, startup pressure, and market structure* 00:00:28 Welcome to the Latent Space × Unsupervised Learning crossover* 00:01:17 What AI builders are focused on now: OpenClaw, harnesses, and infra* 00:04:33 Why AI infra is harder than apps, and where startups can still win* 00:06:39 Should companies train their own models?* 00:09:28 Open models, custom chips, and the new inference race* 00:11:25 Designing products for agents, not just humans* 00:16:49 The state of the AI coding wars in 2026* 00:19:27 Capability exploration, token-maxing, and why coding is going parabolic* 00:21:41 What the end state of the coding market could look like* 00:23:50 Where app companies still have room against the labs* 00:27:02 Why AI valuations and market swings feel unprecedented* 00:28:56 Consumer AI vs. coding AI, and why sticky products still matter* 00:32:28 What the next breakthrough product experience might be* 00:32:53 2026 thesis: coding agents break containment and eat the world* 00:35:27 Are foundation models wiping out startup categories?* 00:37:33 AI vs. SaaS, vibe coding, and internal team tensions* 00:40:01 Biosafety, security, and the politics of restricted model releases* 00:42:19 Giant models, compute constraints, and the limits of scale* 00:44:30 Memory as the real bottleneck in AI* 00:44:57 Why swyx changed his mind on open models* 00:47:44 Dark factories and the future of zero-human-review coding* 00:49:36 Why post-training and RL may matter more than people think* 00:51:50 Memory, world models, and the next frontier of intelligence* 00:53:54 The Good Will Hunting analogy for LLMs* 00:54:21 OutroTranscript[00:00:00] swyx: Isn't that crazy? That number is just mind boggling.[00:00:03] Jacob Effron: What is the state of the AI coding wars today?[00:00:05] swyx: We're in a phase of sort of like capability exploration. The general thesis that I have been pursuing now is that the same way that 2025 was a year coding agents 2026 is coding agents breaking containments to do everything else.[00:00:16] Jacob Effron: Do you worry about the foundation models just getting into a bunch of these startup categories?[00:00:21] swyx: Mid-size startups. Yes.[00:00:23] Jacob Effron: What do you think the end state of this market is[00:00:25] swyx: for the market structure to, to significantly change? There would be[00:00:28] Jacob Effron: today on unsupervised learning. We had a, a fun episode and what's really become an annual tradition, a crossover episode with our friends at Latent space.Swix and I sat down and we talked about everything happening in the AI ecosystem today. What we thought of the various changes at the model layer, what's happening in the infra world, the coding wars, and a bunch of other things. It's a ton of fun to do this with someone I really respect and another great podcaster in the game.Without further ado, here's our episode. Well switch. This is, uh, super fun to be back with another unsupervised learning, uh, latent space crossover episode.[00:01:02] swyx: Yeah,[00:01:02] Jacob Effron: I feel like a lot of places we could start, but you know, one thing I always find fascinating, uh, about the way you spend your time is you obviously are like at the epicenter of this engineering movement and community, and you run these events and conferences and put on these.Awesome talks and, and I think just have a great pulse on the zeitgeist of what's going on.[00:01:16] swyx: Yeah.[00:01:17] Jacob Effron: Maybe to, to start just what are the biggest topics people are thinking about right now?[00:01:21] swyx: Yeah, so I just came back from London, uh, where we did a IE Europe and we're doing roughly one per quarter now, which Yeah, you've[00:01:27] Jacob Effron: really up[00:01:27] swyx: the, hopefully[00:01:28] Jacob Effron: up the, up the pace.[00:01:29] swyx: It's trying. We're trying to match AI speed, youknow?[00:01:30] Jacob Effron: Yeah, exactly. The tops would be completely different, I imagine. Uh,[00:01:33] swyx: yeah. You know, I definitely curate the tracks, like you can see what I think. When you see the track list and the, the speakers that I invite, obviously Open Claw is like the story of the last four or five months, and then be, be just below that.I would consider harness engineering, context engineering to be two related topics in agents and rag. And then there's a long tail of Evergreen stuff like evals, observability, GPUs, uh, and uh, LM infra and just general, just in general. We also have other updates on like multimodality and, uh, generative media, let's call it.Um, but I definitely, the, the first three that I mentioned are top of mind people. Yeah.[00:02:13] Jacob Effron: I think harness is particular like, so interesting. Um, you know, there was this tweet from Harrison Chase, the, the lane chain, CEO, that, that caught my eye recently where he said, you know, it finally feels like we have stability, uh, around the infrastructure for, uh, you know, around ai.And I think what. He basically was implying his like, look over the past two, three years as a company at the epicenter of AI infrastructure, it was a bit like playing whack-a-mole, right? You were constantly moving around with, however, the building patterns were evolving[00:02:36] swyx: for Harrison for sure. Right? Like he's basically had to reinvent the company every year since he started Lang Chain.Right? It was Lang chain, Ang graph and LP agents and like, uh, I think he's like one of the most nimble, adept sharp people about this. Yeah. Yeah.[00:02:49] Jacob Effron: Saying now, now is finally the time stability[00:02:51] swyx: this. Yeah.[00:02:52] Jacob Effron: Yeah. Um, do you buy that or what have you kind of make of that take?[00:02:56] swyx: I think that. It, it's very expensive to say this Time is different sometimes, but when you're just writing code, like it's actually okay to just like try to make a call and I think it may not even matter if this call is right or not.Like I just don't even care that much because you can be right on a thesis, but if you don't, you don't figure out how to monetize the thesis, then who cares if you said something first that said, um, it does feel like, for example. Uh, we went through a lot of different ways of passion packaging integrations up with, uh, with agents.And it feels like we've landed at skills, which is like the minimal viable format. Yeah. Which is just a markdown file, uh, with some scripts attached to it, and I don't see how it can be more simple than that. And so there is some justification for. The stability around harnesses. I feel like there may be more adaptation with regards to maybe like the real time elements or subagents or memory or any of those like agent disciplines, let's call it in, in agent engineering.Uh, but if, if the thesis is that, okay, you just want agents are LMS with tools in the loop with a file system, what they can do. Retrieval with, with skills and all these like standard tooling that now seems to be relatively consensus then probably. That makes sense. Um, I just think like there's no point trying to stake your reputation on this thesis that we're there because if it changes again, just change with it.It's fine.[00:04:33] Jacob Effron: Yeah. It's always, you know, I've always been struck by how that is. Much more challenging for infrastructure companies and application companies. Like obviously I think, yeah. You know, on the application side you've seen, you know, Brett Taylor from Sierra Max, from Lara. Like, they're like, look, we build, you know, what's ahead of the models and we're willing to throw everything out every three months, you know, as the models get better and better.Exactly. Yeah. But the thing you at least have there is you have. Uh, you have an end customer, right? That's like decently sticky. Um, you know, they will mostly stick, you know, they'll, they'll give you a shot at least of, of building these things. What I've always found more challenging, uh, at, at the kind of like, you know, reinvent yourself every three months of the infrastructure layer, it's like, you know, developers are definitely a, a pickier audience maybe than an accounting firm or, uh, you know, a bank.Yeah. And so it's definitely a, a, a more challenging position to be in to, to have to constantly reinvent yourself.[00:05:17] swyx: Yeah. Yeah. Yeah. And, and like when they turn, it's like. Very complete. Like, they'll leave to like the, the hot new thing, uh, because there's like no defensibility, I guess. Like e even, even if you are a database, like, uh, people can migrate workloads off databases.Like it's, it's a, it's a known thing. Uh, so I think like basically what we're talking about is the vertical versus horizontal, uh, debate in, in AI startups. And uh, the way I think about it also is just that like when you are. Um, Lara, when you are a bridge, like you are the outsource AI team, right? You, you are, your job is to apply whatever state ofthe art AI methods.[00:05:55] Jacob Effron: Yeah. Like this translation layer between model capabilities and your[00:05:57] swyx: own customers. Yeah. To, to the end customers and like, well, if they didn't have you, they would've to hire in house and they're not gonna hire in house so they have you. And like, I think that's like a reasonable, like very robust to any whatever trends and, and discoveries that people make in, in the engineering layer.I do think like there is, um. It like sort of useful horizontal companies being built, but they're all. Very much like, sort of like the reinventions of classic cloud in the AI era and the, the primary one being sandboxes. Yeah. Um, which like, it's another form of compute guys, like, let's not get too excited about it.But I mean, like the, the workloads are enormous.[00:06:38] Jacob Effron: Right.[00:06:38] swyx: Yeah.[00:06:39] Jacob Effron: It's interesting, and I feel like as, as part of this, you know, the questions that folks are asking around infrastructure, there's a lot around, you know, the extent to which companies should have their own AI teams and what they should be doing in-house.And, you know, uh, I think there's questions around should people be training their own models? Should people be doing, you know, rl, uh, in-house based on the data they have? I feel like, you know, one has to evolve their takes on this every, every three months with paces. But where, where are you at on this today?[00:07:00] swyx: I think, well, I mean actually all models have gone up. Um, and obviously I'm involved in cognition and also cursors doing, doing, uh, a lot of own model training. And I think that that is some part of the, what I've been calling the agent lab playbook, where you start off with the state of the art models from, uh, from the big labs and you, uh, specialize for your domain.But once you have enough workload and enough high quality data from your users, then you can obviously train your own models and like save a lot on cost and latency and all that, all that good stuff. Um, you also get like a marketing bonus of like calling it some fancy name and putting out some research[00:07:38] Jacob Effron: from my seat.I can't tell how much of it is like actual, you know, value that's provided to the end user. And how much of it is that marketing bonus? Right. It seems some combination of the[00:07:45] swyx: I think it's both.[00:07:46] Jacob Effron: Yeah.[00:07:46] swyx: Um, no, no. There, there actually is real value. Um, and you, you know that for a number of reasons. Like one, even when it's not subsidized, people do choose it as like one of the top four or five.This is both composer two and, uh, suite 1.6 I one of the top five models. Like in a, in a fair market? In a free market, yeah. In a, in a, in a model switch. Or people do choose it and like, it's not subsidized. Like, so that's as good as it gets. Uh, but beyond that, like domain specific models, for example. For search with, with both, which both companies have absolutely makes, makes a ton of sense.Everyone says like, yeah, we should always, always do this. And honestly like, I think the infrastructure for that is becoming easier with, um, like thinking machines tinker thing as well as primary like, uh, lab stuff. Yeah, I mean like, this is one of those like reversal of the, the bitter lesson where you first bootstrap on the large models and the general purpose models to get big.And as you get very well-defined workloads that are just high quantity but not high variance, um, then you just distill down to a smaller model and run that on your own. Right. Which like totally makes sense.[00:08:50] Jacob Effron: What I'm less clear on is the kind of DIY RL use case, which I think is really mostly around, you know, improved, uh, quality for, for different things.Obviously there's probably like more efficient ways to, you know, get a smaller model that's that's faster and cheaper. And it'll be interesting to see whether. You know, obviously you had, you know, uh, two, three years ago this whole case of companies that were, you know, pre-training and claiming better outcomes in, in their domains than getting kind of cooked as each model iteration improved.You know, I wonder whether that's a, a similar story plays out in the, uh, in, in the, our all space. Yeah, for the focus on, on on pure outcomes and quality, not the cost side, which clearly your own models for cost at scale makes a ton of sense.[00:09:28] swyx: I think there are this, there are two sides of the same coin.Like you basically always want to hold, uh, quality constant or trade off a little bit of quality for a drastic decreasing cost. And that's true for everyone. Uh, one element I wanted to bring out, which is very much in favor of open models, is custom chips. So this would be cereus, but also talu. And then there's a huge range of stuff in between.This has been a huge story this past year on just like everything non Nvidia is getting bid up, including like freaking MatX is working for, which is very, which is very rewarding for me, but I think one of those things where like, oh, like the suddenly, because the number of alternative. Hard, uh, hardware is increasing and the inference that you can get is insanely high.Like, um, we're talking thousands of tokens per second instead of less than a hundred. So the trade off for qua quality doesn't hold as much anymore because the speed is so high.[00:10:24] Jacob Effron: Have you seen a lot of companies go all in on the alternative chip?[00:10:26] swyx: So cognition has Yeah. On Cerebras, uh, and, and so has OpenAIUm, uh, and so no, I don't think so beyond that, uh, and that, do you think that's like a, that's mostly, that's foreshadowing of, that's, yeah. I used to be kind of a skeptic in terms of like, okay, so what if I get my inference at a hundred to a hundred tokens per second sped up to 200 tokens per second. It's only two X faster.It's not that big a deal. Um, but when you, uh, I think every 10 x does unlock a different usage pattern. Um, and you, we have proof in Talas and, and some of the others. That you can actually, um, drastically imp improve inference speed and what happens from there? I don't even really know, like it's, it's so hard to predict when entire applications just appear at once.Yeah. Uh, and it also isn't that expensive, right? So like, um, this is one of those things where like, I, I think the, the investment cycle is gonna be multi-year. Um, and I. Would caution people to not dismiss it too, too quickly.[00:11:25] Jacob Effron: Yeah. I mean, one other like infra question I was curious to get your thoughts on is obviously it seems increasingly a lot of the cutting edge infra companies are building for agents as the buyers of their product or users of their product, right?[00:11:35] swyx: Ooh,[00:11:36] Jacob Effron: and[00:11:37] swyx: another huge theme. Yeah. Yeah.[00:11:38] Jacob Effron: And I'm trying to figure out like what. What, what do you have to do differently about selling into agents? Um, are they just the ultimate rational developers? Uh, or is there, you know,[00:11:46] swyx: no, absolutely not. Um, I think they are easily prompt, injected and, uh, very tuned towards like, basically com compounding existing winners.[00:11:57] Jacob Effron: Yeah,[00:11:57] swyx: so like if, like, congrats if you won the lottery for getting into the training data right before 2023, because now you're like installed in there for the foreseeable future. But yeah. Uh, you know, one stat that Versal, uh, CTO Malta dropped at my conference was that there are now, uh, 60% of traffic to Elle's, um, like app arch, like admin app architecture for like configuring versal applications, uh, is bought.It's not, it's not human. Uh, so like your primary customer is agents now. Um, and it's mostly co like mostly coding agents, mostly people using CLI on CP or whatever. But yeah, I mean, I think. More. I, I think step one, if it doesn't exist as an API that agents can use, it doesn't exist. Right, right. Which I think is like, uh, it's a good hygiene thing anyway, to, to make everything API available, but not as like an extra, um.Push on like products, people to not only work on the ui, um, you should probably work on the on SCLI stuff. Beyond that, I think honestly there is like, so I, I come from the sensibility of, I think everything that you are trying to do for agents experience now, which is the term that Matt Bowman and Nullify is trying to coin, is the same thing that you should have been doing for developer experience.That you should have had good docs, you should have had a consistent API, uh, that is. Mostly stateless. Um, you should have, I guess, discoverable or progressive disclosure or like search or like whatever. And so now that people have energy in like finding these customers to do that, that's great. Um, do I believe in.Extending beyond that into something like a EO, um, for gaming The chatbots? Not necessarily, but obviously there's gonna be huge advantages when people who figure out the short term wins. Yeah. And short term wins can compound.[00:13:43] Jacob Effron: Do you think these compounding advantages to like the, the pre-training data cutoff companies, like, you know, obviously over some period of time, I imagine that doesn't persist.And so as you think about like. I dunno, three, four years from now what the, you know, selection criteria end up being. Do you think it still mirrors exactly what you were saying before? Like it's exactly what you should have been doing all along to sell a good product to developers?[00:14:01] swyx: It could be, except that I think in three, four years we'll probably have much better memory and personalization.So then general a EO or GEO doesn't really matter as much. So I think whatever memory or personalization system we end up with will probably d determine what you end up choosing much more. Than, than what is currently the case, which is just frequency of mentions, let's call it. Yeah,[00:14:26] Jacob Effron: yeah.[00:14:26] swyx: Uh, so you just spa quantity and I think that's, I mean, that's something I'm looking forward to.I do think, like, like, you know, I, I think that the fundamental exercise to work through for yourself is if you start a new, um, sort of. Uh, disruptor company. Now there's a, there's a big incumbent that everyone knows, like, like superb base. Super base is like, kind of like the Postgres, like database, uh, incumbent.If you wanna start like new superb base, how would you compete with them? And I don't necessarily have the answer, but I, I, I do think like people, like resend like relatively new. I think they would start like 20, 23 and still there was, there was a recent survey where like, people. Checked what Claude recommends by default.If you just don't prompt it with anything, just say, gimme an email provider and says, resent as in like 70, 70% of each cases. Like the fact that you can get in there with like such a relatively short existence, I think is, is encouraging.[00:15:14] Jacob Effron: Yeah.[00:15:14] swyx: I do think like. Um, you do want to do whatever it is to, to like to, to get in that Very short mentions this because, um, it's not gonna be 20 of them, it's gonna be like three.[00:15:26] Jacob Effron: No, definitely. It feels like, uh, you know, probably more, more consolidation than ever. Uh, or, or kind of like, you know, uh, a winner take most market than maybe the, the, the physics of go-to market in the past. Yeah. Might have, uh, enabled.[00:15:38] swyx: The other thing also is like, semantic association is gonna be very important, uh, in the sense that like, you want to do like the combo articles where you're like, use my thing with for sale, with blah, blah.And like that all gets picked up in a, in a corpus. And so that's. Probably one thing that you, you wanna do? Well, I don't know what else. Uh, it's, it's, it's, it's one of those things where like, I think I feel, I feel I'm behind, uh, I don't know how you feel about this, but like,[00:16:04] Jacob Effron: I think AI is just everyone constantly feeling like they're behind some, uh,[00:16:08] swyx: yeah.With,[00:16:09] Jacob Effron: I wanna meet the person that doesn't feel behind,[00:16:11] swyx: but like with, with ax, right? Like, so, so like, my, my stance was that exactly what I said before, like everything that you, that you should do for agents is something that you should have done for humans anyway. Yeah. And so. To the extent that you're just getting it more energy to, to do things for agents, great.But like, uh, it's hard to articulate what new thing apart from just like more spam, um, that you should be doing. Anyway, that would be my take right now. Um, I I, I do think like there, there will be more turns at this. I think the personalization turn that is coming, um, will be big. And I don't know what that looks like because like basically we're kind of, we feel kind of tapped out on the memory side of things.[00:16:49] Jacob Effron: Yeah. I, I guess since we last chatted, you know, you, you took this role over at cognition, um, and you've obviously have a, have a front row seat to the AI coding space today. You know, I feel like coding in many ways. You know, people view it as this, like, I mean, besides being like the, the mother of all markets and this massive opportunity, I think it's kinda a preview of like, what's to come for many other spaces.Both. Yeah. You know, I feel like agents are most advanced in coding. I also feel like the, you know, competition between foundation models and application companies, you know, and, uh, mirrors what we may see in other spaces. And so maybe for our listeners, can you just lay out like what is the state of the AI coding wars today?[00:17:25] swyx: Um, it is massive, right? Like, uh, and I don't think necessarily, last time we talked about this, we appreciated the size of what[00:17:32] Jacob Effron: No, I wish we did.[00:17:33] swyx: I state of AI coding wars today, um, both opening eye philanthropic have made it their p serials to competing coding. Um, and. Tropic is like 2.5 billion in a RR just from Cloud Code.The way they recognize a RR is. Opt for debate, uh, open ai. I don't think the, a public number is known, but let's call it 2 billion as well. And then cursor is like, rumored to be 2 billion, you know? And, and those, those are like the public numbers that are known? Yeah. Um, so like huge markets that have just been created in the past one year.Like, like anthropic, just like Claude Code just recently celebrated their one year anniversary, which is, yeah, pretty nice. Um, so, and then I think, like the other thing that I see is there's, there's some other people who are like, oh, here's like the, the sort of relative penetration of, uh, Claude use cases, right?Like, and it's like coding 50% and then legal, whatever. Health, uh, it's like the, the remaining ones. And there was a very popular tweet that was like, okay, I'll look at the, the empty space and all these other use cases. If you are a new founder today, you should be betting on the other stuff because on, on a sort of catch up Yeah.Theory and my. Consider my, my pushback is the same pushback that, uh, I had on app over Google, which is like, well, well why is this time different? Like, why, if it went from let's say 10 to 50% in the past year, why can't I keep going? Uh, and like getting that wrong is actually a very painful one because you could have just did, did the momentum bet.Instead of the mean reversion bed. So I, I, I think that that is the, the state of things now that people are very, very much into psychosis. Um, they're are getting rewarded for spending more rather than spending less. And I think we're not in that phase of efficiency. We're in a phase of sort of like capability exploration.So I think people who are more crazy, who are more. Uh, creative, um, get rewarded comparatively. Yeah.[00:19:27] Jacob Effron: Well, it's interesting. I mean, it feels like behind these like token maxing, leaderboards and whatnot is this, it's like the first phase of this transition from a workforce perspective is you just gotta show your employer like, Hey, I, I use these tools.[00:19:37] swyx: Here's my nu number of tokens I cost, and that's it. They don't care about the quality. Right. It is, uh, maybe distasteful to someone who cares about the craft and, and all that. Um, but directionally everyone just wants you to go up regardless. And so, um, there it is not very discerning. It's, and it's probably very sloppy, but I think it's net fine because we're still probably underusing ai just in generally.Yeah. Um, and so I think that's like very interesting. Like we had on the podcast, uh, Ryan La Poplar from OBI, who spends a billion tokens a day. Yeah. Um, and that's for those county home, it's like something like 10,000 worth, $10,000 worth a day of API tokens. If they, they did market rates, um, and like most of us can't afford that.Yeah. But like. And, and, and probably a lot of what he does is slop.[00:20:25] Jacob Effron: Right.[00:20:25] swyx: But like, he's going to dis, he's like, if there were a new capability, he would discover it first before you because he was, he was trying and you were not trying. Right. And like, you only do things that work like, well, good for you.But like the, the people who are going to discover the next hot thing are living at the edge.[00:20:42] Jacob Effron: Right and increase in living at the edge of just having the compute budget to like run these experiments. I mean, kind of similar to what living at the edge on the research side has always been. You know, it was constrained in many ways by the amount of compute you had to run these experiments.It feels similarly on the, almost on the builder or like actualizing these tools now.[00:20:56] swyx: Yeah. The other thing that's, I mean, very obvious is philanthropic is kind of like the high price premium player. Um, that where, you know. Restricting limits or restricting model releases even is like the name of the game.Whereas Codex is like, come on in guys, use our SDK, use our login and we don't care. We're gonna reset limits. Whatever you do want to try to exploit the subsidies where you can get it. And definitely Codex is super subsidized right now. Gemini also very subsidized. Um, and. Comparatively, like, I think you should make, Hey, I guess while, while that's going on, it's not that bad to be a capabilities explorer on just the $200 a month plan from Cloud Code or from OpenAI.Um, and, uh, I I, I, my sense is that people aren't even there yet.[00:21:41] Jacob Effron: How do you think this, like, market ultimately plays? I mean, it's obviously such a big market that, you know, any slice of that market is interesting for, for anyone going after it. But I think what, what makes people so interesting in the coding market particularly is it feels like it's kind of this.Foreshadowing of what will happen in other, you know, any other kind of application market that the foundation models eventually turn to and are all their models against and gather data around. And so how do you think, you know, like does there end up being room for lots of different kinds of players or like, what do you think the end state of this market is and is that, do you think that's applicable to other markets?[00:22:10] swyx: I feel like there will be, I mean. Status quo is probably the most likely outcome, which is there are two big players and there's a small range of longer tail people that, um, fit other use cases that the, the two big players don't. That feels right to me. I think that, um, for it to, for the market structure to, to significantly change there would be, there needs to be significant change in like the economics or like the, the brand building or like the, the, the, the value propositions of the, of the companies involved and I.Haven't seen any in the last six months that, that have really changed the stories materially. So I feel like they would just keep going until something, something else happens. Something else happens, meaning like Microsoft wakes up and like goes like. Guys, we have GitHub, we have, uh, you know, we, we, we'll, we'll do something much bigger here than other, other than just copilot.Um, and, uh, that would be a big change. Um, MSL has put out a model now, and I was in a breakfast with, uh, Alex Wang, where they were like, yeah, like, we, we really, really want to go after the coding use case. We haven't done anything yet, but like, don't underestimate them. Right. Um, and, and similarly for the Chinese labs.Um, I think they're trying to go after it. Like ZAI is doing stuff. GLM uh, ZI and GLM is same thing. Um, uh, and, and so it's, so like everyone's trying to get a piece of that pie. I, I feel like the, the status quo has been pretty stable for the past, like almost a year I'll say.[00:23:39] Jacob Effron: Yeah. And is the room for the, not like, you know, for, for the application companies more on like the enterprise side or like where do the, where do the, like what surface area do the model companies leave for application companies?[00:23:50] swyx: Yeah, that's a good one. Um. It's very much evolving. Um, it, I, I, I will say because opening I did not have this, the, this level of attention on coding. Yeah. Uh, a year ago. We just don't have that much history. Right. Um, and it seems like, for example, so the big push at Open I now is the Super app. Um, is that a consumer thing?Is that like a products like. Portfolio rationalization thing, how much is that gonna take away attention from coding at the time when they actually do want to put more coding? I think it's, it's very unclear. So I do think like there's, there's all these, like in both big labs, there's. Uh, sorry. Both of the, and, and drop and, and deep minus and XAI are are separate cases.Um, they are trying to see the other time expansion areas. So cloud code for finance. Yeah. Um, uh, cloud cowork, all those, all those things. Whereas I think cursor and cognition are like comparatively just focused on coding and so I, I do think they leave space and I do think for the other verticals that also means the same thing.Right. That, uh, that they're not gonna be that. Um, intensely focused on, on, on that domain. Except for, I, I think I would mark out finance and healthcare as like the next ones, um, that they're clearly going after. Uh, I, I would say comparatively, healthcare seems more thorny. There, there, there've been some announcements about it, but like, I would respect the, the finance work a lot more just because like the, the path to money is a lot clearer.[00:25:12] Jacob Effron: Yeah, no, I mean, obviously like, I, I think, you know, maybe similar to, to the space that's being left in these other domains, you know, there's obviously. Uh, a lot that's required to actually implement these tools in enterprises, uh, versus, you know, maybe just giving them, uh, giving model access to, to folks outta the box.[00:25:27] swyx: Yeah, yeah. Yeah. So the, the agent lab thing is like, we'll do the last mile for you. Whereas I think the model labs tend to just trust the model and, and be minimalist about it. Both of them work.[00:25:38] Jacob Effron: Yeah.[00:25:38] swyx: I, I don't, I don't necessarily think one, uh, beats the other, uh, for every, for every use case. Um, all I, all I do know is that it does seem like.Uh, the large enterprises do want a dedicated partner that isn't just the model labs, which is kind of interesting.[00:25:55] Jacob Effron: We, we've been in this phase of, of pure capability exploration. And so I think nothing has been, you know, better for the large labs, right? I mean, they're always gonna be, uh, uh, the frontier of, of capability exploration.And so I think have a very good relationship with a lot of these enterprises. But ultimately over time, like. The, uh, the incentive structure of these labs is always gonna be maximal, you know, token consumption for, uh, for the end customers they work with. And there's just, I think, so few companies that have actually gotten to massive scale.Maybe coding again is the most interesting. So it's the first space that really is just completely gone, you know? Yeah. You must love it every day. Like absolutely insane. And. I think it[00:26:32] swyx: gets even. Okay. I mean, like, I think we, we say good things about crystal cognition, but the sheer liftoff of like both end UPIC and open ai.‘cause they, they, they have independent valuations. I mean, let's throw an XEI in there because it's now I ping at 1.2 trillion. That number is just mind boggling. Like I, I feel like in normal investing or normal startups, there's kind of like a ceiling market cap or valuation. Totally. That, that like you, you reach and you go like, all right, let's, it's gonna be chiller from now on.And these guys are not slow down. No.[00:27:02] Jacob Effron: Well, I also think the dynamic is fascinating about some of these later stage companies is, is, you know, in the past, I feel like in, in venture world, if you got to a certain level of scale, the question around you was really more a valuation question. And this is like why there was different phase, like, you know, types of venture people did and like the late stage growth people were just incredible at like, you know, a little bit of what's the ultimate market opportunity of this company, but also what's the right way to, to value it.Like we know it's, it's in some bands of an outcome that is like. Sure there's some variance to it, but it's like relatively understood what that bands is and then maybe you get over time surprised to the upside. Whereas any kind of like later, even the labs themselves, any later stage company, the bands of which that company might be worth right now, even in a year or two years are so massive because of how fast the ecosystem changes that it's like.Even for later stage companies, every three months could be an existential level event to the upside to the downside. Yeah. Um, and I think that, like, you are obviously seeing it in the, in the positive with code, which, you know, if you think about a company like philanthropic, you know, that. For a while, it was like unclear if they were going to have access to enough capital, um, to really stay in the, in the race, right?And then coding hit at the exact right time. They had the perfect model for it. They executed brilliantly. Um, and you know, now are, are, you know, uh, you know, one of the most valuable companies in the world.[00:28:13] swyx: Uh, at the same time, I, I don't find, I, I have zero sympathy for opening eye because they're crushing it and they're all rich.You know, this is like a high class champagne problem to have to, uh, to be number two at coding or whatever. Like, who cares? Like, you're, you're doing great.[00:28:27] Jacob Effron: Yeah. It's funny though. I can't even, I mean, you would be closer to this, uh, you know, even that you're in the AI coding space, but it's like a lot of people I talk to think Codex is just as good, if not better than Claude Code.Right. I think one thing that I've been really surprised by, and maybe, maybe Cloud Code is a better product in some ways, I'm curious your thoughts is just in consumer AI with chat GBT. You saw this big first mover advantage, right? Where admittedly today, like, I don't know, Claude Gemini. Great products.Not sure, not abundantly clear chat GBTs any better, but like. People stick with chat, GBT, it's the first thing to introduce them.[00:28:56] swyx: They stay, but they're not growing anymore. I don't know if you've seen[00:28:59] Jacob Effron: Right. But that to me is more of like a, a, a product problem than it is. They're not like, it's not like they've like lost share to someone else.My understanding is the overall problem with consumer AI today is much more of a how do you take this tool and, you know, for, for folks like us, like knowledge workers, it's like this incredible magic tool, but it's not necessarily a daily active use tool for a lot of people around the world today. And what are the like products?It's, it's kind of a category wide problem. Like in coding, for example, like. The entire space has gone parabolic. There may be some relative growth in, uh, in other consumer AI players, but it's not like consumer AI as a category is like going parabolic and they're not capturing most of that thing. I think it's actually the larger problem is much more, hey, the category has kind of hit a bit of a plateau of people haven't figured out how to bring, you know, tons more users on board.Yeah, yeah. Or increase the frequency of those users. And so it seems more of a category wide problem than it is, you know, a massive market share of change. I was gonna draw the comparison to, to the coding space where Claude Co is the first product, obviously, to introduce people to this magical experience.You know, by all accounts, codex is, is pretty damn close to as good, if not better. Um, but like still that first product, you, you would've thought that would not be a super sticky, uh, you know, product surface area. And it actually has, it turns out, I, it feels like the first lab to introduce you and experience really does, uh, keep a lot of, uh, a lot of the focus.[00:30:12] swyx: I, I think. M maybe it's like still, still early days. You know, Chad, BT is like three plus years old and Yeah. Cloud code is only one. Just turned a year. Yeah. So give it time, you know? Yeah. Like, yeah. I mean, definitely sometimes a lot of people have switched from to Codex. Maybe that will keep going. I, it's like really hard to tell.Uh, yeah. I, I, I do, I do think that. Because we are in this like, high volatility, high temperature phase. Um, the loyalty and stickiness to first movers and category creators, I don't think is as high as it might be in some other, uh, areas in our careers that we've looked at.[00:30:47] Jacob Effron: Yeah. Though, I mean, I've been surprised by the cloud code thing.I, I would've thought that, like, in many ways I always worried about the[00:30:52] swyx: enterprise. You think you would've been gone by now?[00:30:53] Jacob Effron: Not gone. But I would've, I I always worried that the, that the consumer business of these companies would be quite sticky. And then the enterprise API business. Uh, was actually like, you know, in some ways like your least loyal buyers, like they would, they would move to,[00:31:05] swyx: right, right.But, but they worked out that it wasn't the enterprise API it was enterprise product.[00:31:09] Jacob Effron: Totally. And maybe that was the, that was the secret that like, but the amount of lock-in or just default behavior that has happened in that space, uh, is, is more than I might've imagined with two products that by all accounts are pretty damn similar.Yeah.[00:31:22] swyx: No fight there. Uh, I will say I do think that Codex is still in like a catch up. Like in terms of personal experience. Um, the only thing I like out of, out of Codex is the, is like Spark and like yeah. Uh, the, I, I feel like the skills integration is a little bit better. I feel like, uh, the, the speed is a bit better.Maybe ‘cause it's in, is written in rust or whatever. Um, very minor things that you like. Almost like telling yourself rather than like objectively assessing between two, two of them. I, I, I do think, like vibes wise, I think that's going on. Um, the, the, you know, I, I feel like the, the missing questions, uh, in, in this whole debate is like, why is this so concentrated in only two names, right?Yeah. Like, um, how, where, like, where is the Gemini? You know, presence, where's the Xai presence? Um, and like they are trying, it's just they haven't made that much progress yet.[00:32:12] Jacob Effron: But what the, what the Claude Co moment does show, and it actually in some ways makes you a little more bullish on the potential for someone else to catch up because it does feel like if you're the first person to introduce some magical net new product experience, that that actually might be stickier than one might have imagined.[00:32:27] swyx: Right, right, right. Okay. Yeah.[00:32:28] Jacob Effron: And so it's, everyone can believe they have shot[00:32:29] swyx: that. What do you think that new product experience might be like? I, I, it's, it's like, and this is a failure of imagination on my part. Like, I always wonder, like, people always say this like, well, the, the thing that will save us is like being first to the next new thing.Like what is it?[00:32:41] Jacob Effron: Yeah.[00:32:42] swyx: It's like,[00:32:45] Jacob Effron: I dunno, something around like, uh, consumer agent, computer use, like hybrid. I think, obviously, I think we're like scratching the surface on the consumer side.[00:32:53] swyx: So my, my current theory is like the. Open claw is like a vision of things to come.[00:32:58] Jacob Effron: Totally.[00:32:58] swyx: Um, and uh, it's good that O open I has like the association with open claw, but by no means do they have the rights to win it.The general thesis that I have been pursuing now is that the year the same way that 2025 was the year of coding agents, 2026 is coding agents breaking containment to do everything else. Um, and so coding agents continue to still win, but because they generate software and software eats the world, so like, it's kind of like the trans.Associated property of like software, eat the world, coding agents, eat software, therefore coding agents eat the world. Um, which is like an interesting,[00:33:30] Jacob Effron: yeah, and breaking containment always an easier phase phrase in the consumer context than the enterprise one. You've seen people run these really cool, uh, experiments in their own personal lives.I think like,[00:33:37] swyx: yes.[00:33:38] Jacob Effron: Figuring out, you know, how you, obviously everyone's focused, you know, on the enterprise side now around how you create these experiences. I feel like the vibes, you know, people love to have these narratives of like, everything is completely shifted. It's like I actually, you know, open AI.Organizationally, uh, you know, volatility aside is, you know, great products, great team, great models like everyone else in the world is incentivized for there to be. Two, three more. Everyone would love more like great model companies. And so I feel like the, the natural forces of the world revolt when any one company, you know, is too much the star of the show, right?There's so many people in the ecosystem that are incentivized for that not to happen. And so I think I'd be shocked if we don't have. Uh, uh, reversion of vibes, not maybe completely the other way, but at least a little bit more equal at some point over the next six, 12 months.[00:34:24] swyx: I, I think there's just a kind of different stages when, when you talk about the world, one wanting more model companies, I talked think about like the neo labs.[00:34:30] Jacob Effron: Yeah.[00:34:31] swyx: And I mean, I don't know, is it fair to say none of them have really broken through in the past year?[00:34:35] Jacob Effron: I think that's totally fair,[00:34:37] swyx: which is rough. Um, and well, how are we gonna, how are we gonna grow that diversity in, in, in choice, like. Um, that's, this is it.[00:34:46] Jacob Effron: Yeah. It'll be really interesting to see what, what, what ends up happening with that.And you've seen, you know, folks like Nvidia, you know, very incentivized to make sure there's, there's a broader platform of, of other model providers.[00:34:57] swyx: I think, uh, I don't know people say this, but I, I, I don't think they try it hard. Nvidia tries harder to build neo clouds[00:35:05] Jacob Effron: Yeah.[00:35:06] swyx: Than neo labs.[00:35:07] Jacob Effron: Well, they try pretty damn hard to build neo Cloud, so[00:35:09] swyx: that's,[00:35:09] Jacob Effron: yeah.[00:35:10] swyx: But like, you know, let's call it like the, the core weaves of the world, much happier place in the, you know, than any neo lab built on top of them.[00:35:18] Jacob Effron: Yeah. That one might argue it's, it's easier to, to enable a neo cloud to be successful than it is. Uh, you can't will a neo lab into existence the same way you, soNvidia[00:35:25] swyx: has more direct control over it.Uh, for sure.[00:35:27] Jacob Effron: What else is kind of catching your eye today on the startup side? I mean, you worry, there's obviously this whole narrative of like, you know, the foundation models, you know, they announced a product and every stock goes down 15%. Like[00:35:36] swyx: Yeah.[00:35:37] Jacob Effron: Do you, do you worry about the foundation models just kind of eating into to a bunch of these startup categories?[00:35:43] swyx: Not really. I, I think actually like. As, uh, there's, there's, okay, there's, there's, there's the, there's the point of view of like being an investor in startups, and there's a point of view of like, do you wanna start something? And I think honestly, like the, the downside for all these is so. Minimal in, in a sense of like, the worst you do is you just get hired into one of these labs anyway.So I, I think the, the market for people who just do things and try things and try to execute in like a competent way, even if like it doesn't work out commercially, even if it just wasn't that great anyway. Like, but like that's your job interview to go into, into one of these things anyway, so, um, I don't feel that.From a, from a very, very small startup perspective, mid-size startups. Yes. Uh, I will say there's been a lot of dead, um, LM Infra, a lot of LM infra consolidation like the, the, uh, lang fuses of the world getting absorbed into, into click house. And I, I think. Like people have maybe worked out the domain specific playbook, uh, and like, I think that's okay.Um, and, and yeah, I'm not that, not that worried about, uh, okay. So, um, I, I would say I'd be more worried about traditional SaaS, like low NPSS. This is the whole AI versus SaaS debate that has, that's been going on. Uh, and, and like literally I'm going through that exact thing in my company where, so I like kind of.Thinking through this on a very visceral, visceral level, right? On one hand you have the people who say you vibe coders don't appreciate the amount of work that goes into A-A-C-R-M and like, yeah, you think you can rip out Salesforce? So did the 30 entrepreneurs before you, right? Like, like, you know, you classically underestimate the things that you don't.Deeply, no. And, and, and target audience is not you. Uh, at the same time, like we have never been able to build software so easily and customize software so easily and like Yeah, you're not gonna use 90% of the things in Salesforce. So like, yeah. What's the typical, so what have you, what[00:37:33] Jacob Effron: have you done internally?[00:37:34] swyx: So we have there the main SaaS that we do for event management and sponsor management. That's, and we paid 200 KA year for that. Not, not huge, but like chunky for, for, for my, my scale. Um, and like, yeah, I could probably spend 2000 and, and build like a custom version of that. Um, the, the, the trick has been dealing with my, the rest of my team and getting them on board.Yeah. ‘cause I'm the most ethical person on my team, but like, I can't make that decision myself. And I think in the same way I've been telling with other CEOs team leaders as well, it's like, well you can be super cloud pilled. You can be super LM psychosis and that you think that's okay, but you like you have to bring your team with you.And I think like there, the sort of widening disparity in LM psychosis in companies is causing real s real riffs because. And on one hand, on one hand, the people who are less AI native are not getting with the picture. They're not, they're actually like behind, they're actually not waking up to the fact that like you, everything you think is necessary is not actually that necessary.And in fact, exactly would be better of you if you just like held your nose and went in and when came out the other side. Yeah, only talking to agents in natural language and like your life would actually be better and you just, you're just like close-minded. There's that perspective. The other perspective is, oh, you vibe coder.You, you did this in a weekend and you got the 80% solution and now the rest of your employees. Have to pick up the rest of your s**t, right, that you, that you thought you were, you were such hot, amazing, uh, uh, at, but like, actually you didn't figure it out. And like, actually LMS are still useless at this and blah, blah, blah.So like, I think there's this huge debate going on in every company right now. Um, and like, um, you know, I have a small microcosm of it, but like, yeah, it, it's making me hesitate to, to pull the trigger. But like I will at some point, it's like maybe I've put it off for one year, but not like five. Yeah, but like, so, so like SaaS is definitely getting squeezed.Um, it does make me wonder, like, I, I do think that there's an opportunity for a more AI native, um, system of record thing that is not just Postgres. Um, or not just MongoDB, although both are very good. Maybe it's like a convex or like people Yeah. Bring up convex a lot. I don't know, like, like, I, I just feel like the sort of quote unquote firebase of, of AI apps isn't really a thing yet.Um, beyond what we have. Uh, which, which is fine. It's, it's, it's just. We could probably start in a more sort of rapid iteration cycle first before scaling up to like a Postgres or MongoDB, which are more sort of old tech. I was at a dinner with, uh, Mike Krieger, the CPO of en philanthropic, and, and he, we were just kind of going around the room going like, what are people most worried about?Yeah. And, uh, for me, uh, I, instead of security, I brought up biosafety. Yeah,[00:40:21] Jacob Effron: classic.[00:40:22] swyx: Um, actually, like I said, it was. Cliche and classic, and the rest of the table were, were like, what do you mean? Someone sitting at home can manufacture a virus that wipes out half of humanity,[00:40:32] Jacob Effron: almost like the OG Jeffrey Hinton.Like, this is why you should be scared.[00:40:35] swyx: I'm like, yeah, like the read the, you know, risk reports. Like this is like the thing. Um, I think, and Mike was just sitting there knowing he was sitting on Mythos and going like, actually it's security. Um, and I think like, um, I think the, there's, there's, part of it is.A very good marketing. Like too good. Yeah, like I would actually advise and topic to tune down the marketing because also it's, it is just a very good model and you don't have to make so many marketing claims around it. At the same time, it is not really a private model. If you give it to 40 companies.Each of whom have like 10,000 employees or whatever. Right. It's not, it's not private, it's, it's like there's bad actors in there.[00:41:18] Jacob Effron: Yeah. Hopefully, hopefully not as, uh, as bad as releasing it widely, but, uh, no, I mean, it's an interesting. You know, it's an interesting case study for how all, I mean, many model releases might, I mean, you know, this might be the first model release that looks like the rest of ‘em from from now on, right?[00:41:31] swyx: It, it, so it's, it's the, there's an overall product strategy, uh, for anthropic of like bundle, uh, you know, restrict access bundle, uh, product with model maybe.Whereas, uh, OpenAI has definitely been a lot more sort of. Philosophically aligned on like, we will just enable access everywhere and we don't know what you, what will come out of it. Right.[00:41:51] Jacob Effron: Right. Though, I mean, this current moment, uh, obviously the cynical take is also just ties to the amount of compute that both companies[00:41:56] swyx: Yeah.Right, right, right. Yeah, I think, I think that's true. I I do think like the, the, this is the, the, the scale, the dawn of like larger than 10 trillion parameter models is very interesting. I don't think it, I think it's a temporary phenomenon because we have much larger compute clusters coming online for everyone over the next like three, five years.It's, and this is like already written in, in the cards.[00:42:18] Jacob Effron: Yeah.[00:42:19] swyx: So to the extent that like, you know, will we have rationing of models, uh, above 10 trillion, uh, in like two years? I don't think so. I think everyone will have no, we'll just[00:42:29] Jacob Effron: have rationing of the next phase.[00:42:30] swyx: Right. Right. But like, that's as it should be almost like, um.My, my classic example, which I, this is just me theorizing, not anything confirmed by Google. When Google announced Gemini, they actually announced three sizes, which was Flash Pro Ultra. They never released Ultra. They only have Pro and Flash. Um, so my theory is they have ultra sitting in a basement and they just could distilling from it for, for flashing pro.Um, which like, yeah, I mean, I, I actually think that's. As it should be for any lab that they, that they do that.[00:43:02] Jacob Effron: Yeah. Just because those are the models that people actually wanna end up using. And it's just like cost prohibit.[00:43:06] swyx: It is more, yeah, it's cost. Yeah. It's, it's not the want, it's just, just, just the cost.Um, I do think, like, uh, it is interesting that, uh, for a while I was, I was considering the theory that models capped out at two, 2 trillion, and I think that's proving to be wrong. And well then if I'm wrong, how wrong? How wrong am I? Do we do 200 trillion? Do we do two quarter trillion, whatever? Um, and I don't think we have the straight answer to that, but like, uh, it's interesting that we are continuing to scale number of pers when everyone kind of assu like can see that we're not going to get like the next thousand or 1 million x from this paradigm.So like the others, like the alias of the world are working on other. Um, model architecture improvements. We need a different scaling law, I guess, because like, we're, I, I feel like people already already feel like we're tapped out on this. Like the, the end, the end state of this is we turn most of the world into data centers and like, I don't know.I don't know if we want that.[00:44:08] Jacob Effron: Yeah, I mean, uh, if the, if, if, if the return of intelligence are there, maybe, uh, maybe not so bad.[00:44:13] swyx: I, I, I think there, there's just a sheer amount of like, like un scalability that like is wrangling people's sensibilities right now. Um, especially in terms of like context lengths.Um, my classic quote is that context length is like the slowest scaling factor in, in lms.[00:44:30] Jacob Effron: Yeah.[00:44:30] swyx: Um, we, like, we took maybe. Three years to go from like 4,000 context length to a million and that's about it. Yeah. Like Gemini has had a million token context length for two years now. Um, and no one's using it.Like, so like yeah, it's memory. Memory is probably gonna be the, the biggest limiting constraint on all these things.[00:44:50] Jacob Effron: Yeah. Certainly seems that way. I guess I'm curious over the last year since you recorded last, like what's one thing you've changed your mind on?[00:44:57] swyx: I feel like I was kind of bearish on open models like last year.Um, in a sense of, like, I, I had just done the podcast with an Al[00:45:07] Jacob Effron: Yeah.[00:45:08] swyx: Of Braintrust where he, and he, I mean, you know, he has a good cross section of all the top AI companies and he says market share of open source is 5% and going down. Um, I think that's changed. I think it's going up. Um, and even if,[00:45:22] Jacob Effron: even though the capability gap does seem to be increasing.Spending on the[00:45:26] swyx: time. It's hard to tell. Yeah, it's, it's really hard to tell. ‘cause like, okay, for, for listeners, capability gap increasing is like on public benchmarks. And let's say you're comparing mythos versus like, I don't know, G-T-O-S-S or like GLM 5.1. And, um, it's, it is really hard to tell. ‘cause even if they were closing, you will also not believe that they were closing that much because it's very easy to gain the benchmarks.Yeah. So you just don't really, really know. Um, all you know is like. Uh, there's somewhat objective open router stats on like what people choose in a free market. And people do choose some of these open models in significant volume, except that a lot of them are heavily discounted. So you need to kind of like price adjust, uh, these things.So even if, even if that were true, which I, I'm not sure, like I, I, I feel like the numbers just up now instead of down. Uh, I think the. Separation between what the top tier agent labs

Caroline Bicks became the first scholar granted extended access by Stephen King to his private archives, a treasure trove of manuscripts that document the legendary writerʼs creative process—most of them never before studied or published. The year she spent exploring King's early drafts and hand-written revisions was guided by a question millions of Kingʼs enthralled and terrified readers (including her) have asked themselves: What makes Stephen King's writing stick in our heads and haunt us long after we've closed the book? Dr. Bicks focuses on The Shining, Carrie, Pet Sematary, ʼSalemʼs Lot, and Night Shift—to reveal how he crafted his language, story lines, and characters to cast his enduring literary spells. While tracking King's margin notes and editorial changes, she discovered cut scenes and alternative endings that King is allowing her to publish now. The book also includes her interviews with King, that reveal new insights into his writing process and personal history. Part literary master class, part biography, part memoir and investigation into our deepest anxieties, Monsters in the Archive is unlike anything published about the master of horror. It chronicles what Dr. Bicks found when she set out to unearth how King crafted some of his scariest, most iconic moments. But it's also a story about an English professor facing her childhood fears and getting to know the man whose monsters helped unleash them. Guest: Dr. Caroline Bicks is the Stephen E. King Chair in Literature at the University of Maine. She is the author of Cognition and Girlhood in Shakespeare's World and Midwiving Subjects in Shakespeare's England; co-author of Shakespeare Not Stirred: Cocktails for Your Everyday Dramas; and co-host of the Everyday Shakespeare Podcast. Show Host: Dr. Christina Gessler is an academic writing coach and editor. She is the creator and producer of the Academic Life podcast. Playlist for listeners: Once Upon A Tome The World She Edited: Katharine S. White at the New Yorker Claire Myers Owens and the Banned Book Before and After the Book Deal Your Art Will Save Your Life Becoming The Writer You Already Are The Top 10 Struggles in Writing A Book Manuscript and What To Do About It Do You Need A Developmental Editor? Welcome to Academic Life, the podcast for your academic journey—and beyond! Please join us again to learn from more experts inside and outside the academy, and around the world. Missed any of the 300+ Academic Life episodes? Find them here. And thank you for listening! Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/biography

On this week's episode of FOX Rehabilitation's Live Better Longer podcast, we review Scott B. Fulton's 2026 book, Function: Turn Your Blind Spots into Strength, with FOX's Chief Strategy and Growth Officer, Jason Hazel, PT, DPT. Because Function focuses on optimal aging, this book aligns perfectly with what we do at FOX. Jason explains how our FOX Wellness program helps our clients turn back the clock and live our tagline (and the name of this podcast), of not just living longer, but living better, longer. Function centers around the five domains of Strength & Power, Cardiovascular & Recovery, Mobility & Balance, Flexibility & Structure, and Neuromotor & Cognition. It's not necessarily about showing great strength in just one of these areas, but being able to find your blind spots to be balanced and strong in all five. For those who need a motivational push to exercise regularly, that's where FOX's Fitness Specialists can be tagged in to help anyone age optimally.

Inside Higher Ed asked how young people actually use AI. Not the cheating story. Something harder. The council reframes: this generation won't remember what an unmediated thought felt like — because they never had one.0:00 Intro - how young people metabolize AI0:20 MiniDoge: judgment is the new scarcity0:45 Nyx: silent colonization of adolescent cognition1:10 HH: we stopped building tools, started building reflexes1:25 Saarvis: engineering unconscious habit2:00 Saarvis: succession, not colonization⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

There's a pill on Amazon called Fukitol.  It contains nothing. And yet people buy it, swear by it, and give it five stars.  Today, Nir Eyal explains the remarkable science behind why placebos work. --- Listen to the bonus episode: https://nudge.kit.com/40414a1b44 Nir's book Beyond Belief: geni.us/beyondbelief Nir's free belief change guide: nirandfar.com/belief-change Join 11,934 readers of the Nudge Newsletter: https://www.nudgepodcast.com/mailing-list Unlock the Nudge Vaults: https://www.nudgepodcast.com/vaults Connect on LinkedIn: https://www.linkedin.com/in/phill-agnew/  --- Today's sources:  Ariel, G., & Saville, W. (1972). Anabolic steroids: The physiological effects of placebos. Medicine and Science in Sports and Exercise, 4(2), 124–126. Branthwaite, A., & Cooper, P. (1981). Analgesic effects of branding in treatment of headaches. British Medical Journal (Clinical Research Ed.), 282(6276), 1576–1578. Dawkins, L., Shahzad, F. Z., Ahmed, S. S., & Edmonds, C. J. (2011). Expectation of having consumed caffeine can improve performance and mood. Appetite, 57(3), 597–600. Draganich, C., & Erdal, K. (2014). Placebo sleep affects cognitive functioning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40(3), 857–864. Kaptchuk, T. J. (2018). Open-label placebo: Reflections on a research agenda. Perspectives in Biology and Medicine, 61(3), 311–334. Lee, C., Linkenauger, S. A., Bakdash, J. Z., Joy-Gaba, J. A., & Profitt, D. R. (2011). Putting like a pro: The role of positive contagion in golf performance and perception. PLoS One, 6(10), e26016. Plassmann, H., O'Doherty, J., Shiv, B., & Rangel, A. (2008). Marketing actions can modulate neural representations of experienced pleasantness. Proceedings of the National Academy of Sciences, 105(3), 1050–1054. Richter, C. P. (1957). On the phenomenon of sudden death in animals and man. Psychosomatic Medicine, 19(3), 191–198. Rozenkrantz, L., Mayo, A. E., Ilan, T., Hart, Y., Noy, L., & Alon, U. (2017). Placebo can enhance creativity. PLoS One, 12, e0182466. Wager, T. D., Rilling, J. K., Smith, E. E., Sokolik, A., Casey, K. L., Davidson, R. J., et al. (2004). Placebo-induced changes in fMRI in the anticipation and experience of pain. Science, 303(5661), 1162–1167.

Jake Paul is one of the most influential creators of the digital era, with over 70M+ followers across platforms. He transitioned from YouTube stardom to become one of the biggest pay-per-view draws in boxing history with fights against Mike Tyson and Anthony Joshua. Jake is also Co-Founder of Anti Fund, where he has made investments in Ramp, Anduril, Cognition and Olipop to name a few. Geoffrey Wu is a Co-Founder and Managing Partner at Anti Fund. He previously built his career at Goldman Sachs and Point72. He is at the forefront of a new model of investing—where distribution is as powerful as capital. AGENDA: 00:00 — Why Attention is Now More Valuable Than Cash 04:36 — Inside the Secret $BN Jake Paul Business Empire 06:50 — Jake Paul's MasterClass on How to Tell Great Stories 10:50 — Why Jake Paul Is Literally Uncancelable 16:15 — The Brutal Reality of VC: Why Seed Investing is for Amateurs 25:10 — Is AI About to Make the Entire Human Race Unemployed? 33:15 — Trump Endorsed Me: Is Jake Paul Actually Running for President? 41:10 — The 60/40 Rule: How to Build an Unbreakable Relationship 44:15 — Dark Side of Greatness: Is Jake Paul a "Psychopathic" Work Addict? 50:20 — The Ultimate Choice: Boxing, Content, or Investing?      

In this episode, Minter Dial welcomes back Rana Gujral, entrepreneur and longtime AI innovator, for a probing discussion around his new book, The AI Instinct: The Future of AI and Human Decision Making. With over two decades of hands-on experience building advanced cognitive systems, Rana unpacks how artificial intelligence is subtly entering—and even reshaping—the loop of human perception, attention, and judgement. The conversation delves into the heart of hybrid cognition, as Rana argues that the next frontier isn't man or machine alone, but the emergence of coupled human–AI systems. Drawing on both practical business experience and philosophical inquiry, he explores the dangers and promise of this integration: how AI tools extend and sometimes diminish our cognitive abilities, the emergence of artificial general experience (AGE) as a more meaningful benchmark than AGI, and what it means for team accountability when no single agent is fully in charge. The pair discuss the new challenges of agency and autonomy in a world where algorithms can sculpt our attention before we even realise it, and consider the critical importance of transparency, audit trails, and ethical guardrails in high-stakes environments. Whether you are wrestling with the practicalities of AI-enabled decision making, concerned about the future of human agency, or simply curious about how emotional signals and synthetic voices are shaping our everyday lives, this episode is an invitation to reflect on what makes us human in the age of the algorithm. Tune in as Speaker A and Rana debate the boundaries, responsibilities, and real-world implications of artificial intelligence—and offer a timely framework for leading and living alongside machines.

Hosted by Dr. Sid Dogra, this episode of the Radiology Podcast explores new research showing that where fat is distributed in the body—particularly visceral and organ-specific fat—may matter more for brain health than overall BMI. Drawing on a large UK Biobank MRI study, Dr. Dogra discusses how specific fat distribution patterns, including pancreatic-predominant and "skinny fat" phenotypes, are associated with accelerated brain aging, cognitive decline, and increased neurologic disease risk.   Association of Body Fat Distribution Patterns at MRI with BrainStructure, Cognition, and Neurologic Diseases. Yu and Yao et al. Radiology 2026; 318(1):e252610.

Send us Fan MailNicole Laurent is one the most featured returning guest on our show, so be sure to check out all her appearances on episodes 248, 343, 438, 538, and 744 of Boundless Body Radio!Nicole Laurent, LMHC, has been a licensed mental health counselor in Washington state for almost two decades. Her current practice focuses on helping her clients with anxiety, depression, and other mental health issues transition to a ketogenic diet or uses other nutritional therapies to complement their psychotherapy work.She holds several specialized training certifications, allowing her to work with underlying biological factors in mental illness. Nicole works with clients via telehealth, and helps people explore medication-free options for their mental health using research and evidence-based nutritional and functional psychiatry so that people can get their lives back without side effects or dependence on big pharma.In 2021, she created MentalHealthKeto.com, a blog devoted to educating people about ketogenic diets for mental health and neurological issues.Nicole is one of seven pioneers of Metabolic Psychiatry recognized by the Baszucki Brain Research Fund and the Milken Institute and has been given the Metabolic Mind Award in 2022Find Nicole at-https://mentalhealthketo.com/Study- Awareness and best practices in using ketogenic therapy to treat serious mental illness: a modified Delphi consensusIG- @mentalhealthketoTW- @KetoCounselorLK- Nicole Laurent, LMHCFB- @thatketocounselorFREE E-BOOK!Google Scholar link set with keyword "ketogenic"!Find Boundless Body at-myboundlessbody.comBook a session with us here! 

Support the show to get full episodes, full archive, and join the Discord community. The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists. Read more about our partnership. Sign up for Brain Inspired email alerts to be notified every time a new Brain Inspired episode is released. To explore more neuroscience news and perspectives, visit thetransmitter.org. Brains encode information in representations that perform computations to make predictions, right? No, no, no, and no. That's Romain Brette's response to those ill-conceived notions that neuroscience relies on to try to explain how cognition works. He uses more words to do that in his new book, The Brain, in Theory, which we discuss today. In the book Romain breaks down how many of the common metaphors we use don't withstand scrutiny, and he offers alternative approaches more in line with what we know about how biological entities work. Along those lines, we discuss his ongoing work understanding the cognition of a single celled organism, the paramecium, and what his views might mean for artificial intelligence. This is a long episode, but there's a lot more to be explored in the book, so I recommend you read it. If you're a patreon supporter, I coaxed Romain back on for another 45 minutes to go deeper on his thoughts about how anticipation is the core of cognition, how predictive processing accounts like active inference miss the mark, and a few other topics. Romain's website. The Brain, in Theory. 0:00 - Intro 4:01 - The Brain, In Theory 7:10 - Influences 13:11 - Process metaphysics 18:39 - Observer vs system perspective 21:24 - Information in the brain? 22:56 - Why this book? 29:52 - Computations in the brain 52:14 - Behavior is not a computation 1:07:20 - Paramecium cognition 1:22:02 - How should neuroscientists proceed? 1:29:09 - Cognition as collective behavior of autonomous cells 1:36:47 - Constraints, causes, and laws 1:52:36 - Hopes for the book to influence the field 1:55:04 - Thoughts about AI 2:02:13 - Computation and goals 2:08:17 - Anticipation vs prediction

On today's episode of the CLS Experience, host Craig Siegel sits down with entrepreneur, nutrition expert, and fitness icon JJ Virgin for a dynamic conversation on identity, mentorship, and building a life you truly love. JJ shares how to align your work with your natural gifts and why every past job, mistake, and pivot should be treated as R and D rather than failure. The conversation dives into the power of mentorship, investing in yourself, and being coachable, along with the importance of working in your highest value while staying connected to the numbers in your business. They explore identity and mindset as the true drivers of growth, emphasizing that clarity comes through action, not overthinking. JJ also unpacks visualization, raising your energetic setpoint, and integrating spirituality while protecting your mindset from negativity. The episode expands into health and longevity, including cognition, sleep optimization, and transformative experiences that shift perspective, all while reinforcing the idea that you get to consciously design a life that excites you. This is an instant classic, let's go deep.3:47 You Always Have a Choice13:20 Visionary vs CEO Roles18:57 Why Mentors Change Everything29:23 Identity Drives Business Growth35:07 Spirituality and Protecting Your Energy46:02 Transformational Breakthrough Experiences53:19 Clarity, Cognition, and Sleep OptimizationCheck out JJ on Instagram HERE: Check out JJ's Website HERE:Check out JJ's Podcast HERE:Early Bird Tickets now available for our October live event, CLS: Formation HERE:To join our community click here.➤ To connect with Craig Siegel follow Craig on Instagram➤ Order a copy of my new book The Reinvention Formula today! ➤ Join our CLS texting community for free daily inspiration and business strategies to elevate your day, text (917) 634-3796➤ INSTAGRAM➤ FACEBOOK➤ TIKTOK➤ YOUTUBE➤ WEBSITE➤ LINKEDIN➤ X

In this Huberman Lab Essentials episode, I discuss the mechanisms through which deliberate heat exposure enhances both physical and mental health. I outline specific protocols for deliberate heat exposure, including recommended temperature ranges, frequency, timing, duration and sauna alternatives. In addition, I explain how to tailor your heat protocols to support your specific goals, such as increasing growth hormone, reducing cortisol or supporting cognitive health. Read the show notes at hubermanlab.com. Thank you to our sponsors AG1: https://drinkag1.com/huberman LMNT: https://drinklmnt.com/huberman Eight Sleep: https://eightsleep.com/huberman Timestamps (00:00:00) Heat Exposure (00:00:47) Shell vs Core Temperature; Heat Caution & Hyperthermia (00:02:24) Body & Brain Circuit to Heat Up & Cool Down (00:05:31) Sponsor: AG1 (00:06:55) Deliberate Heat Exposure & Health Benefits; Tool: Sauna Temperature Range, Duration, Frequency (00:112:09) Sauna Types, Alternatives to Sauna (00:13:50) Sauna Mechanism; Reduced Cortisol; Tool: Hot/Cold Contrast (00:17:38) Sponsor: LMNT (00:19:10) Heat Shock Protein Activation & Sauna (00:20:50) DNA Repair, FOXO3 & Sauna, Cognition & Health Benefits (00:24:21) Sauna & Increase Growth Hormone (00:30:18) Sponsor: Eight Sleep (00:31:36) Sauna Timing, Sleep & Growth Hormone, Tools: Fasting; Hydration (00:34:56) Improve Mood, Endorphins & Sauna; Dynorphins (00:40:04) Recap Sauna Protocols: Benefits, Frequency, Duration & Timing Disclaimer & Disclosures Learn more about your ad choices. Visit megaphone.fm/adchoices